In an example, a method for generating responses by a Machine Learning (ML) system includes processing, by a first language model, a natural language instruction to generate an instruction representation based on a meaning of the natural language instruction; translating, by a translation module comprising an interface between the first language model and a second language model, the instruction representation into data indicating an intent of the natural language instruction, wherein the second language model is trained with domain specific knowledge; providing, by the translation module, the natural language instruction and the data indicating the intent of the natural language instruction to the second language model; and generating, by the second language model, a response based on the natural language instruction and the data indicating the intent of the natural language instruction.
In an example, a method for fine-tuning a Large Visual Language Model (LVLM) includes providing visual queries, each of the visual queries comprises at least an image and a textual query related to the image; processing, by the LVLM, the visual queries to extract visual embeddings from the visual queries, wherein the LVLM comprises a Visual Language Model (VLM), a first Large Language Model (LLM), and a linear projection layer interconnecting the VLM and the LLM; for visual queries: i) generating, by the LVLM, a response to the corresponding visual query based on the corresponding visual embedding; ii) evaluating, by a second LLM, the generated response to verify that the generated response satisfies predefined criteria; and iii) providing, by the second LLM, a feedback to the LVLM, in response to the evaluating the generated response; and fine-tuning the LVLM using aggregated feedback provided by the second LLM for the visual queries.
Techniques are described for a machine learning system configured to generate respective sample embeddings for a plurality of sample statements. The machine learning system may further be configured to generate a statement embedding for a statement. The machine learning system may further be configured to determine, based on the sample embedding and the statement embedding, respective similarity scores for the sample embeddings. The machine learning system may further be configured to select, based on the respective similarity scores for the sample embeddings, one or more sample statements from the plurality of sample statements. The machine learning system may further be configured to generate a prompt including the one or more sample statements, the statement, and at least one of respective ground-truth information or respective paraphrases for the selected one or more sample statements. The machine learning system may further be configured to provide the prompt to a machine learning model.
The invention includes compositions comprising altered immune mast (AIM) cells modified to reduce expression of at least one gene that promotes an allergic response and/or increase expression of at least one gene for preventing or reducing an allergic response, as well as methods of producing AIM cells. The invention also provides methods of preventing or reducing an allergic response or allergic disease comprising administering to a subject an AIM cell.
C12N 5/0787 - Granulocytes, e.g. basophils, eosinophils, neutrophils or mast cells
A61K 35/15 - Cells of the myeloid line, e.g. granulocytes, basophils, eosinophils, neutrophils, leucocytes, monocytes, macrophages or mast cellsMyeloid precursor cellsAntigen-presenting cells, e.g. dendritic cells
C12N 15/113 - Non-coding nucleic acids modulating the expression of genes, e.g. antisense oligonucleotides
An example Geiger mode avalanche photodiode includes a first semiconductor alloy forming a compositionally graded gain region configured to form a conduction band having free electrons, a valence band having free holes, and a bandgap between the valence band and the conduction band that varies in size across the graded gain region; a second semiconductor alloy forming an absorber region; and a semiconductor substrate.
H10F 30/225 - Individual radiation-sensitive semiconductor devices in which radiation controls the flow of current through the devices, e.g. photodetectors the devices having potential barriers, e.g. phototransistors the devices being sensitive to infrared, visible or ultraviolet radiation the devices having only one potential barrier, e.g. photodiodes the potential barrier working in avalanche mode, e.g. avalanche photodiodes
G01S 7/481 - Constructional features, e.g. arrangements of optical elements
H10F 71/00 - Manufacture or treatment of devices covered by this subclass
H10F 77/124 - Active materials comprising only Group III-V materials, e.g. GaAs
H10F 77/14 - Shape of semiconductor bodiesShapes, relative sizes or dispositions of semiconductor regions within semiconductor bodies
6.
MULTI-LEVEL AIDING SIGNAL TO SUPPORT RAPID COMMUNICATION
Disclosed herein are methods and systems that involve an aiding signal that can be used to acquire a long code in a. wireless signal, such as a ranging code in a wireless navigation signal transmitted by a satellite. A receiver may receive a first wireless signal and a first aiding signal transmitted by a first transmitter of a plurality of transmitters. The first wireless signal comprises a first, long code. The first aiding signal comprises repeating instances of a sequence of short codes, wherein the sequence has a predetermined time relationship with the first long code and is specific to the first transmitter. The receiver may use the first aiding signal to acquire the first long code by determining the sequence of short codes in the first aiding signal and using the predetermined time relationship to synchronize with the first long code.
In general, various aspects of the techniques are directed to causal analysis using large scale time series data. A computing system may convert large scale time series data to first time period records and second time period records according to a multi-scale time resolution. The computing system may implement a hierarchical machine learning model to generate embeddings that capture temporal characteristics of features of the large scale time series data. The computing system may generate a graph data structure indicating cause and effect correlations between features of the large scale time series data based on temporal dynamics captured in the cause and second time period records and/or the embeddings.
12 2 are independently selected from H, O, phenyl, allyl, alkenyl, carbonyl, and a combination thereof. In some embodiments, the synthetic compounds can bind to a spike protein of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) or to fentanyl.
Method and apparatus for processing input information using an adaptable and continually learning neural network architecture comprising an encoder, at least one adaptor and at least one reconfigurator. The encoder, at least one reconfigurator and at least one adaptor determine whether the input information is out-of-distribution or in-distribution. If the input information is in distribution, the architecture extracts features from the input information, creates hyperdimensional vectors representing the features and classifies the hyperdimensional vectors. If the input information is out of distribution, the architecture creates at least one adaptor to operate with the encoder and the at least one reconfigurator to extract features from the input information, create hyperdimensional vectors representing the features and classify the hyperdimensional vectors.
A method, apparatus, and system for developing an understanding of at least one perceived environment includes determining semantic features and respective positional information of the semantic features from received data related to images and respective depth-related content of the at least one perceived environment on the fly as changes in the received data occur, for each perceived environment, combining information of the determined semantic features with the respective positional information to determine a compact representation of the perceived environment which provides information regarding positions of the semantic features in the perceived environment and at least spatial relationships among the sematic features, for each of the at least one perceived environments, combining information from the determined intermediate representation with information stored in a foundational model to determine a respective understanding of the perceived environment, and outputting an indication of the determined respective understanding.
G06F 30/27 - Design optimisation, verification or simulation using machine learning, e.g. artificial intelligence, neural networks, support vector machines [SVM] or training a model
G06F 30/13 - Architectural design, e.g. computer-aided architectural design [CAAD] related to design of buildings, bridges, landscapes, production plants or roads
Techniques are disclosed for a soft logic block that can provide visual primitives to soft logic in coordination with a learned attention mechanism. In an example, computing system for object detection, the computing comprising processing circuitry and a storage device, wherein the processing circuitry has access to the storage device and is configured to execute a machine learning system comprising a placement neural network configured to process a patch of image data to generate local placement parameters for aligning a footprint in the patch to a template footprint; and a template comprising a backend network and the template footprint, the template configured to process a transformed footprint comprising the footprint in the patch transformed according to the local placement parameters, to generate a probability value quantifying a likelihood that a particular pattern is present in the footprint.
G06V 10/82 - Arrangements for image or video recognition or understanding using pattern recognition or machine learning using neural networks
G06T 7/246 - Analysis of motion using feature-based methods, e.g. the tracking of corners or segments
G06V 10/24 - Aligning, centring, orientation detection or correction of the image
G06V 10/764 - Arrangements for image or video recognition or understanding using pattern recognition or machine learning using classification, e.g. of video objects
An encryption module and the decryption module cooperate with an identity-based key generator of users in a communication system in order to use an identity-based and certificate-based construction using two or more encryption schemes. An encrypted message is communicated between users of the communication system such that the encrypted message received by the device of the user needs keys from each of the two or more encryption schemes to be able to decrypt the encrypted message. The validation module cooperates with a limited-time of validity certificate issued from a certificate authority platform to decrypt the encrypted message via the limited-time of validity certificate. The validation module allows the decryption module to decrypt the encrypted message with the limited-time of validity certificate corresponding to an identity of the user when the user is determined to be actually validated for a period of time specified for the limited-time of validity certificate. The certificate authority platform grants the users validation.
G06F 21/64 - Protecting data integrity, e.g. using checksums, certificates or signatures
H04L 9/06 - Arrangements for secret or secure communicationsNetwork security protocols the encryption apparatus using shift registers or memories for blockwise coding, e.g. D.E.S. systems
A computing system is configured to obtain a plurality of media files that each includes speech of one or more speakers. The computing system is further configured to process the plurality of media files to generate indexed data, wherein the indexed data includes a corresponding embedding for each speaker of the one or more speakers identified in the media file and a corresponding one or more keywords identified in the speech in the media file. The computing system is further configured to receive an indication at least one of a selection of a particular speaker from the one or more speakers or a selection of a particular keyword from a plurality of keywords. The computing system is further configured to generate one or more correlations based on the indexed data. The computing system is further configured to output an alert regarding the one or more correlations.
G06F 16/683 - Retrieval characterised by using metadata, e.g. metadata not derived from the content or metadata generated manually using metadata automatically derived from the content
A method, apparatus, and system for object detection on an edge device include projecting a hyperdimensional vector of a query request for an image received at the edge device into a hyperdimensional embedding space to identify at least one exemplar in the hyperdimensional embedding space having a predetermined measure of similarity to the query request using a network trained to: generate a respective hyperdimensional image vector and a respective hyperdimensional text vector for the image and received text descriptions of the image, generate a hyperdimensional query text vector of the query request, combine and embed respective ones of the hyperdimensional image vectors and the hyperdimensional text vectors into a hyperdimensional embedding space to generate respective exemplars, project the hyperdimensional query text vector into the hyperdimensional embedding space, and determine a similarity measure between the hyperdimensional query text vector and at least one of the respective exemplars.
A silicon-based image sensor can have i) a pixel array with one or more pixels and ii) an upconversion layer of crystals on at least one of a front side and a backside of the silicon-based image sensor. A pulse repetition frequency decoder cooperates with the upconversion layer of crystals to decode a pulse repetition frequency of a laser flash captured by one or more of the pixels of the silicon-based image sensor. The pulse repetition frequency decoder can use a known frame rate of the silicon-based image sensor and a decay time of an upconverting emission from the upconversion layer of crystals to decode the pulse repetition frequency of the laser flash.
H04N 23/11 - Cameras or camera modules comprising electronic image sensorsControl thereof for generating image signals from different wavelengths for generating image signals from visible and infrared light wavelengths
Embodiments are directed to systems, devices, and methods for perturbing a biosystem to quantify responsiveness. An example system comprises stimulation circuitry configured to output a plurality of biostimulation signals to a target of a subject, sensor circuitry configured to obtain measures of a biosignal from the subject, and processor circuitry. The processor circuitry is configured to cause the stimulation circuitry to output the plurality of biostimulation signals to perturb a biosystem of the subject, and quantify responsiveness of the biosystem to the perturbation based on the plurality of biostimulation signals and measures of the biosignal responsive to the plurality of biostimulation signals applied to the target.
In an example, a method includes obtaining data indicating a plurality of trajectories representing a behavior of a team comprising a plurality of agents; obtaining a plurality of baseline profiles, wherein each of the plurality of baseline profiles encodes at least one of a preference and/or a goal that is relevant to a task performed by the team; generating a probability distribution of each agent of the plurality of agents over the plurality of baseline profiles, wherein the probability distribution of each agent describes a behavior of the agent; updating the corresponding probability distribution of each agent of the plurality of agents; and generating, based on the updated probability distributions of the plurality of agents, reward functions that explain the observed joint actions performed by the team, wherein each of the reward functions describes the behavior of a corresponding one of the plurality of agents.
The USA, as Represented by the Secretary, Department of Health and Human Services (USA)
SRI International (USA)
Inventor
Lee, Min
Blithe, Diana
Fang, Jia-Hwa
Ruiz, Eduardo
Chen, Ken
Abstract
Disclosed herein are formulation embodiments comprising levonorgestrel butanoate (“LNGB”) particles having particle sizes that facilitate administering a higher concentration of LNGB at lower volumes. The disclosed formulation embodiments exhibit long-lasting contraceptive effects and can be administered subcutaneously, which lends to their utility in acting as self-administrable contraceptive formulations that do not result in side effects associated with other contraceptive agents.
A61K 31/565 - Compounds containing cyclopenta[a]hydrophenanthrene ring systemsDerivatives thereof, e.g. steroids not substituted in position 17 beta by a carbon atom, e.g. oestrane, oestradiol
Techniques are disclosed for storing bits of information in composite classes of valid data. An example method includes determining, by processing circuitry and based on a bitstring format, a first set of bitstrings for a first transcoder, determining, by the processing circuitry and based on the bitstring format, a second set of bitstrings for a second transcoder, wherein no bitstring of the first set of bitstrings matches a bitstring of the second set of bitstrings, and outputting, by the processing circuitry, an indication of a first bit value assigned to the first transcoder and a second bit value assigned to the second transcoder.
In general, the disclosure describes a computing system to automatically identify and classify audio input, including non-speech audio signals. The computing system may also add new classes based on only a limited number of examples of the new classes, to identify classes of sounds for which the system had not been trained.
G10L 25/51 - Speech or voice analysis techniques not restricted to a single one of groups specially adapted for particular use for comparison or discrimination
G10L 25/30 - Speech or voice analysis techniques not restricted to a single one of groups characterised by the analysis technique using neural networks
In an example, a method for training a machine learning model to transpile quantum circuits includes generating, by a quantum circuit generator, a first plurality of quantum circuits according to a general quantum circuit design language, wherein each of the first plurality of quantum circuits comprises a sequence of instructions comprising one or more gates and one or more gate operations; obtaining a second plurality of quantum circuits, wherein each of the second plurality of quantum circuits is transpiled for a target quantum device from a corresponding one of the first plurality of quantum circuits; and training, using the first plurality of quantum circuits and the second plurality of quantum circuits, a machine learning model to transpile a quantum circuit according to the general quantum circuit design language to a quantum circuit for the target quantum device.
In general, the disclosure describes sensor including an intermediate band layer including a plurality of dopant particles, wherein the intermediate band layer is configured to absorb a portion of incident electromagnetic radiation comprising a first range of wavelengths greater than 1100 nm and form optically induced minority carriers. The sensor also includes a photo-sensitive silicon substrate configured to detect the electromagnetic radiation comprising a second range of wavelengths less than or equal to 1100 nm.
In general, the disclosure describes techniques for detecting synthetic speech of a speaker. In an example, a machine learning system may be configured to generate, using a deep learning model trained to distinguish between synthetic speech and authentic speech, reference embeddings for the speaker that characterize a first set of acoustic features and a first set of phonetic features associated with the speaker. The machine learning system may further be configured to generate, using the deep learning model, a test embedding for an audio clip that characterizes a second set of acoustic features and a second set of phonetic features associated with the audio clip. The machine learning system may further be configured to compute a score based on the test embedding and the reference embeddings. The machine learning system may further be configured to output, based on the score, an indication of whether the audio clip includes synthetic speech.
A method, apparatus, and system for training a language model for enhanced consistency include selecting at least a portion of the content data of the language model, generating reasoning statements in the form of natural language relevant to the selected portion of the content data, and training the language model using the generated reasoning statements such that a logical inference of the trained language model in response to a prompt directed to the selected portion of the content data is increased as compared with the logical inference of the language model in response to the same or similar prompt before the training of the language model to enhance the consistency of the language model with respect to the selected portion of the content data. The trained language model can be used to generate a logical inference having enhanced consistency for at least a portion of content data.
G06N 3/098 - Distributed learning, e.g. federated learning
G06F 30/27 - Design optimisation, verification or simulation using machine learning, e.g. artificial intelligence, neural networks, support vector machines [SVM] or training a model
G06F 40/40 - Processing or translation of natural language
In an example, a method for a method for training a Machine Learning (ML) model using arbitrarily sized training data files, to selectively identify informative portions of one or more training data files for improving the ML model includes automatically selectively identifying, by a computing system, one or more informative portions of one or more training data files; calculating, by the computing system, gradients for the identified one or more informative portions; and updating, by the computing system, weights of a ML model using the calculated gradients.
The disclosure relates to the discovery of Neu5Gc-Lewisaas a biomarker for cancer cells and tissues and provides methods for diagnosis, treatment, and drug targeting based on the same, as well as molecules that specifically bind to Neu5Gc-Lewisa.
27.
Systems and Methods for Isolation of Application Services in a Network Environment
An example method for identifying one or more potential malicious activities in a software-defined open radio access network includes detecting, by a trusted monitoring device, a communication flow from a sender component to a receiver component via an intermediate component. The method also includes, in response to the detecting of the communication flow, generating, by the trusted monitoring device and utilizing an intermediate identifier associated with the intermediate component, a flow record based on one or more parameters associated with the communication flow. The method further includes providing, by the trusted monitoring device and based on the flow record, an indication of the one or more potential malicious activities in the software-defined open radio access network.
A computing system is configured to obtain a video that includes text elements and visual elements. The computing system is further configured to generate a plurality of text tokens representative of audio spoken in the video and a plurality of frame tokens representative of one or more frames of the video. The computing system is further configured to generate a set of features that includes a text feature, a frame feature, and a multi-modal feature, wherein the multi-modal feature is representative of multi-modal elements of the video, and wherein generating the set of features is based on the plurality of text tokens and the plurality of frame tokens. The computing system is further configured to associate the set of features with one or more labels to generate a multi-label classification of the video. The computing system is further configured to output an indication of the multi-label classification of the video.
H04N 21/44 - Processing of video elementary streams, e.g. splicing a video clip retrieved from local storage with an incoming video stream or rendering scenes according to encoded video stream scene graphs
H04N 21/439 - Processing of audio elementary streams
29.
CONFIDENCE CALIBRATION FOR SYSTEMS WITH CASCADED PREDICTIVE MODELS
In general, techniques are described that address the limitations of existing conformal prediction methods for cascaded models. In an example, a method includes receiving a first validation data set for validating performance of an upstream model of the two or more cascaded models and receiving a second validation data set for validating performance of a downstream model of the two or more cascaded models wherein the second validation data set is different than the first validation set; estimating system-level errors caused by predictions of the upstream model based on the first validation data set; estimating system-level errors caused by predictions of the downstream model based on the second validation data set; and generating a prediction confidence interval that indicates a confidence for the system based on the system-level errors caused by predictions of the upstream model and based on the system-level errors caused by predictions of the downstream model.
This disclosure is in the field of cardiac diseases. For example, the disclosure provides a new platform comprising iPSC and cardiomyocytes carrying one or more gene mutations, and models to study the effect of those gene mutations on cardiomyocytes, and on drug toxicity. This platform is identified herein as PREDICT PLATFORM.
A method, machine readable medium and system for RGBD semantic segmentation of video data includes determining semantic segmentation data and depth segmentation data for less than all classes for images of each frame of a first video, determining semantic segmentation data and depth segmentation data for images of each key frame of a second video including a synchronous combination of respective frames of the RGB video and the depth-aware video in parallel to the determination of the semantic segmentation data and the depth segmentation data for each frame of the first video, temporally and geometrically aligning respective frames of the first video and the second video, and predicting semantic segmentation data and depth segmentation data for images of a subsequent frame of the first video based on the determination of the semantic segmentation data and depth segmentation data for images of a key frame of the second video.
In an example, a system includes processing circuitry in communication with storage media. The processing circuitry is configured to execute a machine learning system including at least a first module, a second module and a third module. The machine learning system is configured to train one or more machine learning models. The first module is configured to generate augmented input data based on the streaming input data. The second module includes a machine learning model configured to perform a specific task based at least in part on the augmented input data. The third module configured to adapt a network architecture of the one or more machine learning models based on changes in the streaming input data.
A method, apparatus, and system for determining an uncertainty estimation of at least one layer of a neural network includes identifying a neural network to be analyzed, representing values of each layer of the neural network as respective variable nodes in a graphical representation of the neural network, and modeling connections among each of the layers of the neural network as different respective factors across the variable nodes in the graphical representation, the graphical representation to be used to determine the uncertainty estimation of at least one layer of the neural network. The method, apparatus, and system can further include propagating data through the graphical representation to determine the uncertainty estimation of the neural network.
Examples are directed a plurality of particles comprising transparent conductive oxide particles. Example transparent conductive oxide particles have a median diameter of about 200 nanometer (nm) to about 500 nm in the particle size distribution, and a plasma wavelength of about less than 2000 nm. Additionally, the transparent conductive oxide particles may comprise a primary component and about 2 weight percent to about 20 weight percent of a secondary component. Also disclosed are methods of producing a plurality of particles comprising transparent conductive oxide particles and compositions comprising a plurality of particles comprising transparent conductive oxide particles and a binder.
In general, the disclosure describes techniques for detecting synthetic speech in an audio clip. In an example, a computing system may include processing circuitry and memory for executing a machine learning system. The machine learning system may be configured to process an audio clip to generate a plurality of speech artifact embeddings based on a plurality of synthetic speech artifact features. The machine learning system may further be configured to compute one or more scores based on the plurality of speech artifact embeddings. The machine learning system may further be configured to determine, based on the one or more scores, whether one or more frames of the audio clip include synthetic speech. The machine learning system may further be configured to output an indication of whether the one or more frames of the audio clip include synthetic speech.
G10L 17/26 - Recognition of special voice characteristics, e.g. for use in lie detectorsRecognition of animal voices
G10L 17/02 - Preprocessing operations, e.g. segment selectionPattern representation or modelling, e.g. based on linear discriminant analysis [LDA] or principal componentsFeature selection or extraction
G10L 17/04 - Training, enrolment or model building
An example photonic integrated circuit includes a wavelength selective switch (WSS), the WSS including an input port configured to receive a plurality of optical signals. Each optical signal has a center wavelength and a signal bandwidth, and the center wavelength of each optical signal is different from the center wavelengths of the other optical signals. The WSS also includes an output port and a spectral filter configured to switch an optical signal of the plurality of optical signals to the output port based on the center wavelength of the optical signal.
H04J 14/02 - Wavelength-division multiplex systems
G02B 6/12 - Light guidesStructural details of arrangements comprising light guides and other optical elements, e.g. couplings of the optical waveguide type of the integrated circuit kind
37.
GENERATING TRAINING EXAMPLES FOR TRANSLATION OF NATURAL LANGUAGE QUERIES TO EXECUTABLE DATABASE QUERIES
In an example, a method includes, generating, by a machine learning system, one or more formal queries based on data contained in a database repository; generating, by the machine learning system, a natural language query for each formal query of the one or more formal queries to generate pairs of formal queries and corresponding natural language queries by applying a general grammar for a language of each formal query; and training, by the machine learning system, a neural network configured to translate natural language queries into formal queries using the pairs of the formal queries and corresponding natural language queries generated by the machine learning system.
Embodiments provided herein include rotatable magnets (e.g., spherical dipole magnets) disposed within sets of coils that can be used to operate the magnets as reaction/momentum “spheres” and/or as control moment gyroscopes. The coils are able to exert three-dimensional torques onto the magnet in order to effect attitude control of a satellite or other system. The coils can also optionally exert translational forces onto the magnet in order to maintain the magnet in position and avoid contact with static components. Diamagnetic materials can also be included to provide stabilizing repulsive magnetic forces to maintain the magnet in position and/or to reduce the necessary performance of the coils with respect to applying stabilizing translational forces.
A robotic arm according to various implementations includes: a tool driver configured to hold a surgical tool; a first section comprising a first end coupled to a base, a second end distal from first end; a first link that includes a motor configured to rotate at least a portion of the first section around a pitch axis; a second link coupled to the first link, the second link including a motor configured to rotate at least a portion of the first section around a roll axis; and a second section comprising: a first end coupled to the second end of the first section, a second end distal from the first end, a first link that includes a motor configured to rotate at least a portion of the second section around a roll axis, a second link coupled to the first link.
A61B 46/10 - Surgical drapes specially adapted for instruments
B25J 3/04 - Manipulators of leader-follower type, i.e. both controlling unit and controlled unit perform corresponding spatial movements involving servo mechanisms
A computing system that receives an audio waveform representing speech from an individual and produces as output a modified version of the audio waveform that maintains the speaker's speech characteristics as well as prosody for specific utterances (e.g., voice timbre, intonation, timing, intensity). The system uses a bottleneck-based autoencoder with speech spectrograms as input and output. To produce the output audio waveform, the system includes a reconstruction error-based loss function with two additional loss functions. The second loss function is speaker “real vs fake” discriminator that penalizes for the output not sounding like the speaker. The third loss function is a speech intelligibility scorer that penalizes the output for speech that is difficult for the target population to understand. The produced modified audio waveform is an enhanced speech output that delivers speech m a target accent without sacrificing the personality of the speaker.
G10L 17/02 - Preprocessing operations, e.g. segment selectionPattern representation or modelling, e.g. based on linear discriminant analysis [LDA] or principal componentsFeature selection or extraction
G10L 17/04 - Training, enrolment or model building
G10L 17/26 - Recognition of special voice characteristics, e.g. for use in lie detectorsRecognition of animal voices
41.
ANALYSIS OF INTERESTINGNESS FOR COMPETENCY-AWARE DEEP REINFORCEMENT LEARNING
In an example, a method includes, collecting interaction data comprising one or more interactions between one or more Reinforcement Learning (RL) agents and an environment; analyzing interestingness of the interaction data along one or more interestingness dimensions; determining competency of the one or more RL agents along the one or more interestingness dimensions based on the interestingness of the interaction data; and outputting an indication of the competency of the one or more RL agents.
A method, apparatus and system for adapting a language model for understanding domain-specific multimodal content include acquiring domain-specific multimodal content for at least one content domain and applying question/answer pairs to the acquired, domain-specific multimodal content for the at least one content domain to train the language model to learn tasks associated with the domain-specific multimodal content for the at least one domain. As such, the trained language model can be implemented to answer questions directed to the domain-specific multimodal content for the at least one domain.
Disclosed is a temperature-actuated optical switch comprising a. material having a ferroelectric to paraelectric transition at a Curie temperature, wherein the material has a first transmissivity to infrared light below the Curie temperature and has a second transmissivity to the infrared light above the Curie temperature, and wherein the second transmissivity is higher than the first transmissivity. Also disclosed are optical systems that comprise such a temperature-actuated optical switch and methods for using such a temperature-actuated optical switch.
G02F 1/035 - Devices or arrangements for the control of the intensity, colour, phase, polarisation or direction of light arriving from an independent light source, e.g. switching, gating or modulatingNon-linear optics for the control of the intensity, phase, polarisation or colour based on ceramics or electro-optical crystals, e.g. exhibiting Pockels or Kerr effect in an optical waveguide structure
G02F 1/05 - Devices or arrangements for the control of the intensity, colour, phase, polarisation or direction of light arriving from an independent light source, e.g. switching, gating or modulatingNon-linear optics for the control of the intensity, phase, polarisation or colour based on ceramics or electro-optical crystals, e.g. exhibiting Pockels or Kerr effect with ferro-electric properties
An image sensor with a pixel array that has two or more pixels is discussed. The defective pixel detector detects a defective pixel. A dynamic threshold range is based on amplitude levels of neighboring pixels. The defective pixel detector detects the defective pixel with the dynamic threshold range to detect and determine a defective status for the pixel under analysis based on an amplitude level of the pixel under analysis relative to the amplitude levels of neighboring pixels. The defective pixel detector applies algorithms to create candidate pixel patterns for the neighboring pixels used to create the dynamic threshold range. The value of the dynamic threshold range will change based on the amplitude levels of the neighboring pixels.
H04N 25/683 - Noise processing, e.g. detecting, correcting, reducing or removing noise applied to defects by defect estimation performed on the scene signal, e.g. real time or on the fly detection
H04N 25/63 - Noise processing, e.g. detecting, correcting, reducing or removing noise applied to dark current
In one example, a sensor comprising a vapor cell including a vapor of alkali atoms is disclosed. The sensor further comprises a system configured to direct electromagnetic (EM) radiation of one or more frequencies into the vapor cell and incident on the vapor of alkali atoms. The EM radiation of one or more frequencies is configured to prepare the alkali atoms from a first quantum state to a Rydberg state. The alkali atoms prepared in the Rydberg state comprise an orbital angular momentum quantum number that is at least the number of quanta of the one or more frequencies. The sensor further comprises a detector configured to detect a response of the alkali atoms to incident electromagnetic radiation after the alkali atoms are prepared in the Rydberg state.
In an example, a method for adapting a machine learning model includes receiving a digital representation of a three-dimensional (3D) object; learning, using a surrogate model, relationships between a plurality of points on a surface of the 3D object; and generating, using the surrogate model, one or more predictions about fluid properties along the surface of the 3D object.
In general, techniques are described for unsupervised domain adaptation of models with pseudo-label curation. In an example, a method includes generating a plurality of pseudo-labels for a dataset of unlabeled data using a source machine learning model; estimating a reliability of each pseudo-label of the plurality of pseudo-labels using one or more reliability measures; selecting a subset of the plurality of pseudo-labels having estimated reliabilities that satisfy a reliability threshold; and training, using one or more curriculum learning techniques, a target machine learning model starting with the selected subset of the plurality of pseudo-labels and the corresponding unlabeled data.
Techniques for a machine learning system configured to obtain a dataset of a plurality of sample speech clips; generate a plurality of sequence; initialize a plurality of speaker embeddings and a plurality of accent embeddings; update the plurality of speaker embeddings; update the plurality of accent embeddings; generate a plurality of augmented embeddings based on the plurality of sequence embeddings, the plurality of speaker embeddings, and the plurality of accent embeddings; and generate a plurality of synthetic speech clips based on the plurality of augmented embeddings. The machine learning system may further be configured to obtain an audio waveform; decompose the audio waveform into first magnitude spectral slices and an original phase; process the first magnitude spectral slices to map the first magnitude spectral slices to second magnitude spectral slices; and generate a modified audio waveform in part by combining the second magnitude spectral slices and the original phase.
In general, the disclosure describes techniques for estimating a relative position of a receiver in a global navigation satellite system (GNSS) using time domain recursive filtering applied to GNSS data for an additional plurality of receivers. For example, multiple receivers each obtains GNSS data that indicates the raw position information for the receiver. A system applying techniques described herein may use the GNSS data, obtained for each receiver of the additional plurality of receivers, to improve position accuracy for a particular receiver using time domain recursive filtering.
A method, apparatus, and system for providing orientation and location estimates for a query ground image include determining spatial-aware features of a ground image and applying a model to the determined spatial-aware features to determine orientation and location estimates of the ground image. The model can be trained by collecting a set of ground images, determining spatial-aware features for the ground images, collecting a set of geo-referenced images, determining spatial-aware features for the geo-referenced images, determining a similarity of the spatial-aware features of the ground images and the geo-referenced images, pairing ground images and geo-referenced images based on the determined similarity, determining a loss function that jointly evaluates orientation and location information, creating a training set including the paired ground images and geo-referenced images and the loss function, and training the neural network to determine orientation and location estimates of ground images without the use of 3D data.
Example embodiments relate to techniques and systems for optimizing the topology of antennas. A computing device initially obtains a set of parameters specifying a model and a design domain for an antenna and identifies a Figure of Merit for use when generating the antenna. The computing device then generates an initial design for the antenna based on the set of parameters and the Figure of Merit and modifies, using up to a total of the design domain, the initial design for the antenna iteratively until a final design for the antenna i s generated that maximizes performance of the antenna for the Figure of Merit. Tire computing device provides an output representing the final design for the antenna, which can be used to manufacture the antenna. The computing device can use disclosed techniques to generate different types of antenna designs that optimize one or more Figure of Merits.
G06F 3/00 - Input arrangements for transferring data to be processed into a form capable of being handled by the computerOutput arrangements for transferring data from processing unit to output unit, e.g. interface arrangements
G06F 30/27 - Design optimisation, verification or simulation using machine learning, e.g. artificial intelligence, neural networks, support vector machines [SVM] or training a model
52.
System, device, and method to provide generalized knowledge routing utilizing machine learning to a user within the system
The machine learning in the neural networks module can analyze an annotation and its metadata on the annotation made by a first user on a first computing device to make an embedding regarding the annotation and then cooperate with the persistence knowledge store to store the embedding of the machine learning's understanding of the annotation and its metadata. The delivery module can proactively push a notice regarding a potentially related embedding out to a second user on a second computing device based on a threshold amount of relatedness between one or more factors of i) a first task undertaken by the first user and a second task undertaken by the second user, ii) a role of the first user and a role of the second user, and iii) a subject matter of the embedding to a subject matter of a task undertaken by the second user.
In general, techniques are described for coordinating actions of a plurality of agents or subsystems using a machine learning system that implements a Capability Graph Network (CGN). In an example, a method includes generating a control policy model comprising a plurality of nodes and a plurality of edges interconnecting the plurality of nodes, wherein the plurality of nodes represents a plurality of agents or subsystems and the plurality of edges represent information exchange between the plurality of agents or subsystems; and encoding agent behavior control policy within the control policy model for executing to coordinate a plurality of the actions of the plurality of agents or subsystems.
In general, techniques are described for generating counterfactuals using a machine learning system that implements a generative model. In an example, a method includes receiving, by a trained generative machine learning model, an input query, wherein the generative machine learning model is trained by jointly encoding a plurality of input observations and a plurality of outcome variables based on the plurality of input observations; generating, by the trained generative machine learning model, latent representation of the input query; and transforming, by the trained generative machine learning system, the latent representation of the input query to generate a counterfactual related to the received input query, wherein the generated counterfactual meets a predefined outcome criteria.
A method, apparatus, and system for creating a script for rendering audio and/or video streams include identifying at least one prosodic speech feature in a received audio stream and/or a received language model, creating a respective prosodic speech symbol for each of the at least one identified prosodic speech features, converting the received audio stream and/or the received language model into a text stream, temporally inserting the created at least one prosodic speech symbol into the text stream, identifying in a received video stream at least one prosodic gesture of at least a portion of a body of a speaker of the received audio stream, creating at least one respective gesture symbol for each of the at least one identified prosodic gestures, and temporally inserting the created at least one gesture symbol into the text stream along with the at least one prosodic speech symbol to create a prosodic script.
G10L 15/18 - Speech classification or search using natural language modelling
G10L 15/02 - Feature extraction for speech recognitionSelection of recognition unit
G10L 15/06 - Creation of reference templatesTraining of speech recognition systems, e.g. adaptation to the characteristics of the speaker's voice
G10L 15/183 - Speech classification or search using natural language modelling using context dependencies, e.g. language models
G10L 15/25 - Speech recognition using non-acoustical features using position of the lips, movement of the lips or face analysis
G10L 25/18 - Speech or voice analysis techniques not restricted to a single one of groups characterised by the type of extracted parameters the extracted parameters being spectral information of each sub-band
56.
COMPUTATIONAL GENERATION OF CHEMICAL SYNTHESIS ROUTES AND METHODS
G16C 20/70 - Machine learning, data mining or chemometrics
G16C 10/00 - Computational theoretical chemistry, i.e. ICT specially adapted for theoretical aspects of quantum chemistry, molecular mechanics, molecular dynamics or the like
G16C 20/10 - Analysis or design of chemical reactions, syntheses or processes
A computer-implemented method for defending, preventing, and/or mitigating short message service (SMS) based activities is provided. The method includes monitoring, by a security component in a radio interface layer (RIL) of a computing device configured to access a radio network, data related to SMS interactions over the radio network. The method also includes, based on the monitoring, detecting a potential activity associated with an SMS interaction. The method additionally includes providing an indication of the potential activity.
Disclosed herein are methods and systems for object recognition utilizing reflective light blocking. Further disclosed herein are systems and methods for recognition of at least one fluorescent object being present in a scene by using a light source including at least one illuminant and a bandpass filter for each illuminant of the light source, a sensor array including at least one light sensitive sensor and at least one filter selectively blocking the reflected light originating from illuminating the scene with the light source and allowing passage of luminescence originating from illuminating the scene with the light source into the at least one color sensitive sensor, and a processing unit for identifying the at least one object based on the data detected by the sensory array and known data on luminescence properties associated with known objects.
G06V 10/143 - Sensing or illuminating at different wavelengths
G06V 10/74 - Image or video pattern matchingProximity measures in feature spaces
G06V 10/88 - Image or video recognition using optical means, e.g. reference filters, holographic masks, frequency domain filters or spatial domain filters
G06V 20/80 - Recognising image objects characterised by unique random patterns
59.
METHOD AND SYSTEM FOR DETERMINING A MEASURE OF CONCEPTUAL CONSISTENCY IN LARGE LANGUAGE MODELS
Embodiments of the present principles generally relate to methods, apparatuses and systems for determining a measure of conceptual consistency in large language models for understanding of relevant concepts. In some embodiments, a method for measuring conceptual consistency may include prompting an LLM in order to extract answers to background queries and anchor tasks. The method also includes comparing background knowledge facts for a given anchor task associated with known answers with facts extracted from the LLM to determine an LLM performance. The method also includes determining a background knowledge score and an anchor task score based on the LLM's performance. The method also includes determining a conceptual may include score for the LLM by predicting the anchor task score from the background knowledge score. The method also includes outputting an indication of the conceptual may include score.
09 - Scientific and electric apparatus and instruments
16 - Paper, cardboard and goods made from these materials
35 - Advertising and business services
36 - Financial, insurance and real estate services
40 - Treatment of materials; recycling, air and water treatment,
41 - Education, entertainment, sporting and cultural services
42 - Scientific, technological and industrial services, research and design
45 - Legal and security services; personal services for individuals.
Goods & Services
Video cameras; multi-purpose cameras; image sensors for
cameras; downloadable and recorded computer software for
video test tools; computer hardware for video test tools;
tactical robots; downloadable speech recognition software;
downloadable and recorded computer vision software, namely,
downloadable and recorded software for image- and
video-based indexing and search, object recognition, event
detection and recognition, tracking, 3D scene modeling,
computational imaging, embedded processing, video archiving
and retrieval, video text recognition and extraction;
downloadable and recorded computer software for artificial
intelligence, namely, downloadable and recorded software for
intelligent personal assistants, planning and scheduling,
robotic control, text processing and analytics, process
automation and discovery, human-machine dialog, forecasting,
intelligent user interfaces, analytic assistants, game
playing, intelligent control systems, intelligent design,
autonomous and multi-agent systems, ontologies and semantic
technologies, intelligent training systems, personalization
and recommender systems, question-answering systems,
bioinformatics, intelligent drug design, hybrid symbolic
neural reasoning and learning, expert systems, biometrics,
image and video understanding, automated theorem proving;
downloadable and recorded computer software for artificial
intelligence for conceptual prototyping; downloadable and
recorded computer software for networking and security;
downloadable and recorded computer software for cyber
security analytics; downloadable and recorded computer
software for training simulation; downloadable and recorded
software for augmented reality for operating and controlling
augmented reality helmet and binocular system for military
training, weapon shooting system for military training and
gaming, training system assisting in maintenance and repair
tasks of complex machinery, namely, vehicles, appliances,
and industrial machinery; downloadable and recorded
augmented reality software for integrating electronic data
with real world environments for the purpose of education
and training; downloadable and recorded augmented reality
software for transmitting data and electronic communications
for conceptual prototyping and collaborative design;
downloadable and recorded computer software, namely,
software development tools for use in collaborative software
development; downloadable and recorded computer software for
control systems, namely, computer software used in the field
of robotics, artificial intelligence, computer vision,
bioscience and medical systems, high-assurance systems,
training systems, defense and security, client-sponsored
research and development and commercialization programs;
downloadable and recorded computer software for robotics for
robotic programming; downloadable and recorded software for
use in biological modeling; downloadable and recorded
computer software, namely, a software platform for
pharmaceutical applications; downloadable and recorded
computer software for use in synthesis of pharmaceuticals;
downloadable and recorded software for use in analysis and
development of pharmaceuticals and medicinal chemicals;
microchips; downloadable electronic publications in the
nature of articles, journal articles, posts, technical
reports, and conference papers in the fields of science,
technology, education, healthcare, biotechnology, and
pharmaceuticals; downloadable electronic publications in the
nature of research reports featuring information and
findings in the fields of science, technology, education,
healthcare, biotechnology, and pharmaceuticals. Printed publications in the nature of articles, journal
articles, posts, technical reports, and conference papers in
the fields of science, technology, education, healthcare,
biotechnology, defense and security, and pharmaceuticals;
research reports featuring information and findings in the
fields of science, technology, education, healthcare,
biotechnology, defense and security, and pharmaceuticals;
posters. Business management and organization consultancy;
assistance, advisory services and consultancy with regard to
innovation planning (term considered too vague by the
International Bureau - Rule 13 (2) (b) of the Regulation),
business planning, business analysis, business management,
business organization, marketing and customer analysis;
public policy research services (term considered too vague
by the International Bureau - Rule 13 (2) (b) of the
Regulation); public policy consultancy services (term
considered too vague by the International Bureau - Rule 13
(2) (b) of the Regulation); research and analysis in the
fields of economics and economic policy; management
consulting and advisory services in the areas of corporate
growth strategy, innovation and growth processes,
organizational transformation, and talent management and
development strategies (term considered too vague by the
International Bureau - Rule 13 (2) (b) of the Regulation);
providing information in the fields of business innovation
process, business management and business opportunities. Financial consultancy services in the nature of advising and
consulting regarding investments and funding to support
innovation. Foundry services in the nature of manufacturing services for
others in the field of microcircuits and integrated circuits
(term considered too vague by the International Bureau -
Rule 13 (2) (b) of the Regulation); foundry services in the
nature of custom manufacturing of microcircuit emulation
services; custom manufacturing services for others for
microcircuits and integrated circuits. Research services in the field of education; consultation
services in the field of K-12 and higher education systems;
educational consultancy services, namely, providing
consultation and data analysis for educators and education
administrators, government agencies, and foundations;
providing online non-downloadable publications in the nature
of articles, journal articles, technical reports, conference
papers, and online posts in the fields of science,
technology, education, healthcare, biotechnology, defense
and security, and pharmaceuticals; providing online
non-downloadable publications in the nature of research
reports featuring information and findings in the fields of
science, technology, education, healthcare, biotechnology,
defense and security, and pharmaceuticals; consulting
services in the field of education. Scientific research and consulting services in the fields of
physical and life sciences, engineering, economics,
management, social and computer sciences; pharmaceutical
research services; pharmaceutical drug development services;
consulting services in the fields of medicinal chemistry,
biotechnology, and pharmaceutical research and development;
software as a service (SAAS) services featuring software for
speech recognition, namely, software for speech and audio
enhancement and noise cancellation, RF signal processing and
identification of signals of interest, audio segmentation
based on speech presence, speaker changes and other audio
events of interest, word recognition for multiple languages
and multiple genres, identification of speakers,
identification of language, detection of keywords and
phrases of interest, identification of speaker state and
trait, speech and language translation for multiple
languages and genres, natural language understanding, dialog
systems, topic detection from audio and text, and
information extraction and retrieval; advanced product
research in the field of artificial intelligence; innovation
consulting services, namely, advising others in the areas of
product development; design for others in the field of
computer hardware; design for others in the field of
software; consultation services in the field of development
and commercializing of new technology, technology processes
and technology services (term considered too vague by the
International Bureau - Rule 13 (2) (b) of the Regulation);
consultation services in the field of process redesign and
change management (term considered too vague by the
International Bureau - Rule 13 (2) (b) of the Regulation);
consultation services in the field of research and
development of information systems, communications,
chemical, energy, financial service and healthcare
technologies; consultation to management of technology and
technological developments; consultation services in the
field of information systems, project and technology
management, technology portfolio and strategic business
planning, and product and market assessment for the
computing, chemical, energy, engineering, pharmaceutical and
medical health care fields; review, assessment, and
consulting with regard to technology development strategy
and technology development roadmaps, namely, technology
development in field of biosciences, health and medical
research, computing and information technology, defense and
security, ocean and space technology, robotics and sensing
technology. Licensing of intellectual property rights.
Disclosed are compositions comprising an antibody conjugated to one or more molecular guidance system (MGS) peptides. Disclosed are methods of treating a subject in need thereof comprising administering to the subject in need thereof an effective amount of an antibody conjugated to one or more MGS peptides, wherein the antibody targets an intracellular target involved in the disease process. Disclosed are methods of targeting an intracellular target comprising administering an antibody conjugated to one or more MGS peptides, wherein the antibody targets an intracellular target.
C07K 14/47 - Peptides having more than 20 amino acidsGastrinsSomatostatinsMelanotropinsDerivatives thereof from animalsPeptides having more than 20 amino acidsGastrinsSomatostatinsMelanotropinsDerivatives thereof from humans from vertebrates from mammals
A61K 47/64 - Drug-peptide, drug-protein or drug-polyamino acid conjugates, i.e. the modifying agent being a peptide, protein or polyamino acid which is covalently bonded or complexed to a therapeutically active agent
C07K 16/28 - Immunoglobulins, e.g. monoclonal or polyclonal antibodies against material from animals or humans against receptors, cell surface antigens or cell surface determinants
A variety of transmission mechanisms are provided that include ‘radially inverted’ pulleys that are nested within each other or otherwise overlap in order to exert ‘inward’ forces onto a compressive belt that transmits power between the pulleys. By exerted forces ‘inward’ onto the belt, the belt can be subjected to net compression everywhere along its length. This allows the belt to be less complex and to have a lower cost than belts of transmissions that exert forces ‘outward’ from pulleys onto a belt, thus requiring expensive and technically challenging belt packs or other elements to sustain the longitudinal tensions experienced by such belts, even when such belts are operated as “push” belts. Transmissions that include such ‘radially inverted’ pulleys can exhibit reduced size and cost and increased power capacity compared to transmissions that employ ‘non-radially inverted’ pulleys.
F16H 9/20 - Gearings for conveying rotary motion with variable gear ratio, or for reversing rotary motion, by endless flexible members without members having orbital motion using belts, V-belts, or ropes engaging a pulley built-up out of relatively axially-adjustable parts in which the belt engages the opposite flanges of the pulley directly without interposed belt-supporting members using two pulleys, both built-up out of adjustable conical parts both flanges of the pulleys being adjustable
F16G 1/22 - Driving-belts consisting of several parts
F16H 37/02 - Combinations of mechanical gearings, not provided for in groups comprising essentially only toothed or friction gearings
63.
HIERARCHICAL FRAMING TRANSFORMER FOR ACTIVITY DETECTION
In general, various aspects of the techniques are directed to a hierarchical framing transformer for activity detection. A computing system comprising a memory and processing circuitry may implement the techniques. The memory may store a plurality of input vectors representative of time-series data. The processing circuitry may implement an unsupervised machine learning transformer, where the unsupervised machine learning transformer is configured to process the plurality of input vectors to obtain a sequence of time ordered segments that maintain a time order of the plurality of input vectors. The unsupervised machine learning transformer may also encode the sequence of time ordered segments to obtain a single semantic embedding vector that identifies an activity occurring over at least a portion of the time-series data represented by the plurality of input vectors, and output an indication of the activity detected based on the semantic embedding vector.
In general, the disclosure describes techniques for joint spatiotemporal Artificial Intelligence (AI) models that can encompass multiple space and time resolutions through self-supervised learning. In an example, a method includes for each of a plurality of multimodal data, generating, by a computing system, using a first machine learning model, a respective modality feature vector representative of content of the multimodal data, wherein each of the generated modality feature vectors has a different modality; processing, by the computing system, each of generated modality feature vectors with a second machine learning model comprising an encoder model to generate event data comprising a plurality of events and/or activities of interest; and analyzing, by the computing system, the event data to generate anomaly data indicative of detected anomalies in the multimodal data.
G06V 20/40 - ScenesScene-specific elements in video content
G06V 10/44 - Local feature extraction by analysis of parts of the pattern, e.g. by detecting edges, contours, loops, corners, strokes or intersectionsConnectivity analysis, e.g. of connected components
H04N 23/21 - Cameras or camera modules comprising electronic image sensorsControl thereof for generating image signals from infrared radiation only from near infrared [NIR] radiation
A robotic manipulator is provided that exhibits improved grip strength and dexterity. The manipulator includes multiple fingers, each of which includes a grip surface formed by a belt running along a. compliant member. Actuation of the fingers about axes of rotation that are not parallel to the grip surfaces can result in exertion of forces from the compliant member into an object, resulting in deformation of the compliant member, This deformation can improve the contact area and grip strength with which the object is held. The belt can be driven along the grip surface, allowing the object to also be rotated around an axis that is not parallel to the direction along the grip surface while still being held securely. Each finger may also include a compliant joint to allow the fingers to rotate passively when an object is held, orienting the grip surfaces toward the object to improve grip strength.
A method, apparatus and system for lifelong reinforcement learning include receiving features of a task, communicating the task features to a learning system, where the learning system learns or performs a task related to the features based on learning or performing similar previous tasks, determining from the features if the task has changed and if so, communicating the features of the changed task to the learning system, where the learning system learns or performs the changed task based on learning or performing similar previous tasks, automatically annotating feature characteristics of received features including differences between the features of the original task and the features of the changed task to enable the learning system to more efficiently learn or perform at least the changed task, and if the task has not changed, processing the task features of a current task by the learning system to learn or perform the current task.
Techniques are disclosed for searching audio recordings in a second language with a key phrase in a first language. For example, a system as described herein receives a first key phrase in the first language and an audio recording in the second language. The system converts the first key phrase into a second key phrase in the second language. The system processes the second key phrase to produce a second key phrase variant. The system identifies, from a graph of words in the second language generated from the audio recording, instances of the second key phrase or the second key phrase variant within the audio recording. The system displays the identified instances of the second key phrase or the second key phrase variant within the audio recording to enhance searchability of the audio recording in the second language.
G06F 16/683 - Retrieval characterised by using metadata, e.g. metadata not derived from the content or metadata generated manually using metadata automatically derived from the content
Various embodiments of the present disclosure are directed to compounds having Formula (I), Formula (II), Formula (III), and/or pharmaceutically acceptable salts thereof. The compounds can be suitable for providing antimicrobial activity and/or inhibiting lipoxygenases. In some embodiments, the compounds may be administered to a patient as part of a pharmaceutical formulation.
A computing system is configured to process a first document using an anchor rule, wherein the anchor rule identifies tokens for a domain. The computing system is further configured to identify, using the anchor rule, a first set of phrases from the first document that match the tokens. The computing system is further configured to receive a first selection from a first subset of the first set of phrases. The computing system is further configured to determine, based on the first selection, a word list, wherein the word list is a list of words ranked by rate of appearance in the first document. The computing system is further configured to process, based on the word list, a second document to extract one or more points of information from the second document.
Method and apparatus for processing data using a reconfigurable, hyperdimensional neural network architecture comprising a feature extractor and a classifier. The feature extractor comprises a neural network for encoding input information into hyperdimensional (HD) vectors and extracting at least one particular HD vector representing at least one feature within the input information, wherein the neural network comprises no more than one multiply and accumulate operator. The classifier is coupled to the feature extractor for classifying the at least one particular HD vector to produce an indicium of classification for the at least one particular HD vector and wherein the classifier does not comprise any multiply and accumulate operators.
G06V 10/82 - Arrangements for image or video recognition or understanding using pattern recognition or machine learning using neural networks
G06N 3/0442 - Recurrent networks, e.g. Hopfield networks characterised by memory or gating, e.g. long short-term memory [LSTM] or gated recurrent units [GRU]
G06V 10/764 - Arrangements for image or video recognition or understanding using pattern recognition or machine learning using classification, e.g. of video objects
71.
Systems and Methods for Training Against Adversarial Attacks
A method for creating a training playbook to train a potential target of social engineering against an attack by an adversarial actor includes generating a training playbook based on a plurality of conversational patterns from a respective plurality of dialogs between non-human synthetic persona with the adversarial actor. A given conversational pattern of the plurality of conversational patterns comprises one or more dialog interaction elements utilized by the adversarial actor during a respective dialog of the plurality of dialogs. The method also includes configuring a dialog model for a second synthetic persona to engage in a training dialog with a target trainee to train the target trainee to detect, avoid and/or mitigate a future attack by the adversarial actor. The dialog model is configured to emulate the future attack by the adversarial actor by utilizing the training playbook. The method additionally includes outputting the training playbook.
An example method for monitoring a software-defined radio access network (SD-RAN) includes receiving, by a computing device, data indicative of communications between a base station configured to provide radio access and one or more network devices. The method also includes generating, by the computing device and based on the data, a telemetry stream indicative of potential anomalous activity in the SD-RAN. The method further includes providing, by the computing device and based on the telemetry stream, an indication of the potential anomalous activity.
In an example, a system includes processing circuitry in communication with storage media, the processing circuitry configured to execute a reinforcement learning system configured to: perform, by an agent, an action within an environment; obtain one or more measurements from the environment, the one or more measurements resultant of the action performed by the agent; determine, based on the one or more measurements, a state of a domain model for the environment reached by the agent; and distribute credit, based at least in part on a reward associated with the state, across an explored state space for the domain model for the environment.
In an example, an iterative method for generating designs includes receiving, by a computing system, a plurality of symbolic rules and a plurality of design objectives for a design of a system; generating, by the computing system, a first plurality of designs for the system based on the plurality of the symbolic rules; evaluating performance of the first plurality of designs; training a machine learning model using the first plurality of designs and performance metrics; generating a second plurality of designs; evaluating, by the computing system, using a machine learning model, performance of the second plurality of designs to filter one or more designs that meet one or more of the plurality of the design objectives; evaluating performance of the filtered designs; and updating, by the computing system, the plurality of the design objectives and/or the plurality of the symbolic rules based on the evaluated performance of the filtered designs.
G06F 30/27 - Design optimisation, verification or simulation using machine learning, e.g. artificial intelligence, neural networks, support vector machines [SVM] or training a model
75.
APPARATUSES FOR REACTION SCREENING AND OPTIMIZATION, AND METHODS THEREOF
Embodiments in accordance with the present disclosure are directed to apparatuses used for reaction screening and optimization purposes. An example apparatus includes a plurality of reaction vessels, a dispensing subsystem, at least one reactor module, an analysis subsystem, an automation subsystem, and control circuitry. The dispensing subsystem delivers reagents to the plurality of reaction vessels for a plurality of reaction mixtures having varied reaction conditions. The at least one reactor module drives a plurality of reactions within the plurality of reaction vessels. The analysis subsystem analyzes compositions contained in the plurality of reaction vessels. The automation subsystem selectively moves the plurality of reaction vessels from a location proximal to the dispensing subsystem to the at least one reactor module based on experimental design parameters. And, the control circuitry identifies optimum reaction conditions for a target end product based on the analysis.
G01N 35/10 - Devices for transferring samples to, in, or from, the analysis apparatus, e.g. suction devices, injection devices
G16C 10/00 - Computational theoretical chemistry, i.e. ICT specially adapted for theoretical aspects of quantum chemistry, molecular mechanics, molecular dynamics or the like
G16C 20/10 - Analysis or design of chemical reactions, syntheses or processes
G16C 20/70 - Machine learning, data mining or chemometrics
76.
ATTRIBUTE BASED ENCRYPTION WITH BOUNDED COLLUSION RESISTANCE
An attribute based encryption system includes a key generator configured to generate a cyclic group (G) having a prime order q; generate an attribute key matrix (M); generate a master public key (MPK) based, at least in part, on the group (G) and (M); generate a master secret key (MSK) based on a first vector s and a second vector t that represent an encryption policy; generate a user secret key based, at least in part, on the MSK and on a set of one or more user attributes; and send the user secret key to a user device, wherein a ciphertext message can be successfully decrypted if a vector y associated with attributes of the user associated with the user secret key is orthogonal to each row of a set of rows of M selected according to a set of authorization attributes used to encrypt the ciphertext message.
H04L 9/06 - Arrangements for secret or secure communicationsNetwork security protocols the encryption apparatus using shift registers or memories for blockwise coding, e.g. D.E.S. systems
H04L 9/30 - Public key, i.e. encryption algorithm being computationally infeasible to invert and users' encryption keys not requiring secrecy
77.
Polarized Image Enhancement using Deep Neural Networks
Methods and systems directed to processing of a polarized image are disclosed. A method may involve determining a polarization characterization for a polarized image. The polarization characterization is indicative of polarization data associated with a plurality of polarization directions of incident light in the polarized image. The method may also involve extracting, from the polarized image, a first collection of global features and a second collection of local features. The method may further involve performing, based on the polarization characterization, a global feature fusion to fuse global features in the first collection, and a local feature fusion to fuse local features in the second collection. The method may involve compositing the polarization characterization with the fused global features and the fused local features to generate a reconstructed image. The method may also involve providing the reconstructed image to an image processing resource to perform one or more image processing tasks.
G06V 10/80 - Fusion, i.e. combining data from various sources at the sensor level, preprocessing level, feature extraction level or classification level
G06V 10/42 - Global feature extraction by analysis of the whole pattern, e.g. using frequency domain transformations or autocorrelation
G06V 10/44 - Local feature extraction by analysis of parts of the pattern, e.g. by detecting edges, contours, loops, corners, strokes or intersectionsConnectivity analysis, e.g. of connected components
G06V 10/82 - Arrangements for image or video recognition or understanding using pattern recognition or machine learning using neural networks
78.
ALL-OPTICAL RF SIMULTANEOUS I/Q PHASE READOUT IN A QUANTUM ANTENNA
GOVERNMENT OF THE UNITED STATES OF AMERICA, AS REPRESENTED BY THE SECRETARY OF COMMERCE (USA)
Inventor
Moore, Kaitlin
Christesen, Joseph
Abstract
A method includes detecting, by an atomic receiver, incident electromagnetic (EM) radiation comprising an incident EM frequency and an incident EM phase, and generating, by the atomic receiver and based on the detected incident EM radiation, a signal indicative of the incident EM phase, wherein the atomic receiver generates the signal without the use of a local oscillator.
In an example, a method for adapting a machine learning model includes receiving first input data; choosing a first set of unlabeled textual spans in the first input data, wherein the chosen first set of unlabeled textual spans is associated with a first domain; labeling the chosen first set of unlabeled textual spans to generate a labeled first set of textual spans; categorizing the labeled first set of textual spans to generate a categorized labeled first set of textual spans; receiving second input data; choosing a second set of unlabeled textual spans, wherein the second set of unlabeled textual spans is associated with a second domain; and adapting the machine learning model to the second domain based on the categorized second set of unlabeled textual spans that is generated based on the categorized labeled first set of textual spans.
In general, the disclosure describes techniques for obtaining, by a computing system, a content item and a purported source for the content item, wherein the content item may include multimodal data. The techniques may further include generating, by the computing system, a plurality of modality feature vectors representative of the multimodal data, wherein each of the generated modality feature vectors has a different, corresponding modality feature. The techniques may further include mapping, by the computing system, the generated modality feature vectors based on a statistical distribution associated with the purported source. The techniques may further include determining, by the computing system, a score based on the mapping. The techniques may further include outputting, by the computing system and based on the score, an indication of whether the content item originated from the purported source.
In an example, a method of designing a system or architecture includes, receiving a plurality of parameter values and a set of requirements for a plurality of objective functions related to a design problem; compressing the plurality of parameters to generate a latent representation; forward processing, with one or more Invertible Neural Networks (INNs), the latent representation to generate a plurality of objective values corresponding to the plurality of the objective functions; inverse processing the plurality of objective values; and generating, based on the latent representation, a plurality of solutions to the design problem that satisfy the set of requirements for the plurality of objective functions.
An example antigen test device comprises a genetically engineered diagnostic cell comprising an exogenous polynucleotide sequence including a receptor element that encodes a chimeric antigen receptor (CAR) comprising an extracellular antigen binding domain operably linked to a transmembrane domain, and an intracellular signaling domain that recognizes an antigen on a surface of a pathogen-infected cell from a sample or on a surface of a virus particle associated with a pathogen from the sample, an actuator element that encodes a transcription factor binding site, and an effector element that encodes a detectable reporter protein, wherein, in response to the antigen binding domain of the CAR binding to the antigen, the genetically engineered diagnostic cell is configured to activate and to synthesize the detectable reporter protein.
C07K 16/28 - Immunoglobulins, e.g. monoclonal or polyclonal antibodies against material from animals or humans against receptors, cell surface antigens or cell surface determinants
C12N 5/0783 - T cellsNK cellsProgenitors of T or NK cells
A variety of transmission mechanisms are provided that include split pulleys nested within each other in order to reduce the size of the transmissions, to provide infinitely variable transmission ratios that include forward and reverse ratios, or to provide some other benefits. These transmissions include “W”-shaped belts, having both contact surfaces that are directed outward from the belt and inward, toward a center-line of the belt, to contact the pulleys with respective different inward- and outward-directed contact surfaces. Accordingly, the surfaces at which the belt contacts the different pulleys may be substantially the same with respect to a hinge or other structure of the belt. This improvement may permit higher levels of power transmission, increased efficiency, and increased transmission lifetime. Additionally, variable-eccentricity variable transmissions are provided wherein the eccentricity of rotation of an inner, nested pulley varies with changes in the transmission ratio of the transmission.
F16H 9/16 - Gearings for conveying rotary motion with variable gear ratio, or for reversing rotary motion, by endless flexible members without members having orbital motion using belts, V-belts, or ropes engaging a pulley built-up out of relatively axially-adjustable parts in which the belt engages the opposite flanges of the pulley directly without interposed belt-supporting members using two pulleys, both built-up out of adjustable conical parts
F16G 1/24 - Driving-belts consisting of several parts in the form of links
F16G 3/00 - Belt fastenings, e.g. for conveyor belts
F16H 37/08 - Combinations of mechanical gearings, not provided for in groups comprising essentially only toothed or friction gearings with a plurality of driving or driven shaftsCombinations of mechanical gearings, not provided for in groups comprising essentially only toothed or friction gearings with arrangements for dividing torque between two or more intermediate shafts with differential gearing
F16H 61/28 - Generation or transmission of movements for final actuating mechanisms with at least one movement of the final actuating mechanism being caused by a non-mechanical force, e.g. power-assisted
An example radar target classification system for identifying classes of objects includes a cluster engine includes processing circuitry and configured to process radar data to determine a cluster of radar detections, the radar data being based on radio waves reflected from one or more objects over a time window. The example system includes a feature extraction engine comprising processing circuitry and configured to determine a plurality of statistical features based on the determined cluster of radar detections. The example system includes a classifier comprising processing circuitry and configured to classify a first object of the one or more objects based on the determined plurality of statistical features and to output an indication of a class of the first object.
G01S 7/41 - Details of systems according to groups , , of systems according to group using analysis of echo signal for target characterisationTarget signatureTarget cross-section
85.
GENETICALLY ENGINEERED ELECTRICALLY-STIMULATED EFFECTOR CELLS FOR IN SITU SYNTHESIS OF PROTEINS
An example genetically engineered electrically-stimulated (ES) cell comprises an exogenous polynucleotide sequence that includes an electrical-sensor element, an actuator element, and an effector element. The electrical-sensor element encodes a voltage-gated calcium ion channel (CaV), wherein the CaV is configured to transition from a closed state to an open state in response to stimulation. The actuator element encodes a transcription factor binding site that upregulates synthesis of an effector protein. The effector element encodes the effector protein, wherein, in response to the transition of the CaV to the open state, the genetically engineered ES effector cell is configured to activate and, to synthesize and secrete the effector protein.
A method for AI-driven augmented reality mentoring includes determining semantic features of objects in at least one captured scene, determining 3D positional information of the objects, combining information regarding the identified objects with respective 3D positional information to determine at least one intermediate representation, completing the determined intermediate representation using machine learning to include additional objects or positional information of the objects not identifiable from the at least one captured scene, determining at least one task to be performed and determining steps to be performed using a knowledge database, generating at least one visual representation relating to the determined steps for performing the at least one task, determining a correct position for displaying the at least one visual representation, and displaying the at least one visual representation on the see-through display in the determined correct position as an augmented overlay to the view of the at least one user.
Provided are formulations and methods to treat ultra-potent synthetic opioid overdose with high dose Naloxone formulations comprising naloxone hydrochloride or a pharmaceutically acceptable salt thereof, one or more preservatives, one or more buffering agents, a tonicity modifier, and optionally a pH modifier. Further, wherein an aqueous pharmaceutical composition comprises naloxone hydrochloride or a pharmaceutically acceptable salt thereof.
A satellite orbiting the Earth may perform orbit-aware routing by receiving a data packet, determining whether a final destination plane of the data packet is different from an orbital plane of the satellite, in response to determining that the final destination plane of the data packet is different from the orbital plane of the satellite, determining whether the satellite is able to communicate with one or more cross-plane neighboring satellites, selecting a neighboring satellite to receive the data packet based at least in part on whether the satellite is able to communicate with one or more cross-plane neighboring satellites, and forwarding the data packet to the neighboring satellite.
An example genetically engineered effector cell comprises an isolated CD4 T-cell carrying an exogenous polynucleotide sequence that includes a receptor element, an actuator element, and an effector element. The receptor element encodes a chimeric antigen receptor (CAR) comprising an extracellular antigen binding domain operably linked to a transmembrane domain, and an intracellular signaling domain, wherein the extracellular antigen binding domain recognizes a surface antigen of a target cell. The actuator element encodes a transcription factor binding site that upregulates synthesis of an effector protein. The effector element encodes the effector protein, wherein, in response to the antigen binding domain of the CAR binding to the antigen of the target cell, the genetically engineered effector cell is configured to activate and, to synthesize and secrete the effector protein.
C07K 16/30 - Immunoglobulins, e.g. monoclonal or polyclonal antibodies against material from animals or humans against receptors, cell surface antigens or cell surface determinants from tumour cells
C07K 16/28 - Immunoglobulins, e.g. monoclonal or polyclonal antibodies against material from animals or humans against receptors, cell surface antigens or cell surface determinants
90.
HARDENING A DEEP NEURAL NETWORK AGAINST ADVERSARIAL ATTACKS USING A STOCHASTIC ENSEMBLE
In general, the disclosure describes techniques for implementing an MI-based attack detector. In an example, a method includes training a neural network using training data, applying stochastic quantization to one or more layers of the neural network, generating, using the trained neural network, an ensemble of neural networks having a plurality of quantized members, wherein at least one of weights or activations of each of the plurality of quantized members have different bit precision, and combining predictions of the plurality of quantized members of the ensemble to detect one or more adversarial attacks and/or determine performance of the ensemble of neural networks.
Embodiments are directed to an apparatus comprising a support to receive a biological sample and a plurality of reagents. The plurality of reagents can include a plurality of first agents configured to bind to a pre-active peptide portion of a defensin protein in the biological sample, a plurality of second agents that exhibit a detectable signal and configured to bind to at least one of the pre-active peptide portion or respective ones of the plurality of first agents. Wherein binding between different respective ones of the plurality of first agents, the pre-active peptide portion, and the plurality of second agents on the support is configured to generate the detectable signal which is indicative of an expression level of the pre-active peptide portion in the biological sample.
G01N 33/543 - ImmunoassayBiospecific binding assayMaterials therefor with an insoluble carrier for immobilising immunochemicals
C07K 14/47 - Peptides having more than 20 amino acidsGastrinsSomatostatinsMelanotropinsDerivatives thereof from animalsPeptides having more than 20 amino acidsGastrinsSomatostatinsMelanotropinsDerivatives thereof from humans from vertebrates from mammals
An example image intensifier includes a quantum well infrared photodetector (QWIP) configured to receive photons to photoexcite carriers out of a localized quantum state; and a light emitting diode (LED), wherein the photoexcited carriers control the LED.
H01L 31/153 - SEMICONDUCTOR DEVICES NOT COVERED BY CLASS - Details thereof structurally associated with, e.g. formed in or on a common substrate with, one or more electric light sources, e.g. electroluminescent light sources, and electrically or optically coupled thereto the light source or sources being controlled by the semiconductor device sensitive to radiation, e.g. image converters, image amplifiers or image storage devices the light sources and the devices sensitive to radiation all being semiconductor devices characterised by at least one potential or surface barrier formed in, or on, a common substrate
H01L 31/111 - Devices sensitive to infrared, visible or ultraviolet radiation characterised by at least three potential barriers, e.g. photothyristor
H05B 45/10 - Controlling the intensity of the light
H05B 47/11 - Controlling the light source in response to determined parameters by determining the brightness or colour temperature of ambient light
93.
MULTILINGUAL CONTENT MODERATION USING MULTIPLE CRITERIA
A method, apparatus and system for moderating multilingual content data, for example, presented during a communication session include receiving or pulling content data that can include multilingual content, classifying, using a first machine learning system, the content data by projecting the content data into a trained embedding space to determine at least one English-language classification for the content data, and determining, using a second machine learning system, if the content data violates at least one predetermined moderation rule, wherein the second machine learning system is trained to determine from English-language classifications determined by the first machine learning system if the content data violates moderation rules. In some embodiments, the method apparatus and system can further include prohibiting a presentation of the content data related to the at least one English-language classification determined to violate the at least one predetermined moderation rule.
Repulsive force created by actuated permanent magnets is used to levitate and transport heavy loads. A bed of permanent magnets is selectively actuated to levitate an array of magnets positioned above the bed, such that the magnets in the levitated array are opposed to the actuated magnets, and of the same magnetic pole, thereby creating a repulsive force. The actuated magnets are vertically offset from magnets in the bed of permanent magnets that have not been raised, thereby imparting maximum levitation forces to the magnets in the levitated array. These systems can levitate and transport objects over level or sloped surfaces, in a straight path or along curves and corners. A bed of magnets can be attached to the floor, or to a set of moving decks that rearrange themselves in a desired path. Our systems can simulate walking or running, similar to a treadmill or virtual gaming platform.
Disclosed herein, are compositions comprising one or more a molecular guidance system (MGS) peptides and a cytotoxic agent. Also described herein, are methods of administering the compositions to patients with cancer.
A61K 47/64 - Drug-peptide, drug-protein or drug-polyamino acid conjugates, i.e. the modifying agent being a peptide, protein or polyamino acid which is covalently bonded or complexed to a therapeutically active agent
A61K 47/60 - Medicinal preparations characterised by the non-active ingredients used, e.g. carriers or inert additivesTargeting or modifying agents chemically bound to the active ingredient the non-active ingredient being chemically bound to the active ingredient, e.g. polymer-drug conjugates the non-active ingredient being a modifying agent the modifying agent being an organic macromolecular compound, e.g. an oligomeric, polymeric or dendrimeric molecule obtained otherwise than by reactions only involving carbon-to-carbon unsaturated bonds, e.g. polyureas or polyurethanes the organic macromolecular compound being a polyoxyalkylene oligomer, polymer or dendrimer, e.g. PEG, PPG, PEO or polyglycerol
A variety of bidirectional wrap spring clutch mechanisms, and improvements thereof, are provided. These mechanisms include a single wrap spring that is operable to bidirectionally couple an input shaft to an output shaft, a brake, or some other output member. Use of a single w rap spring for bidirectional clutching results in reduced size, weight, cost, and complexity. Each end of the wrap spring is coupled to a respective tang block that can be magnetically or mechanically controlled to prevent engagement of the wrap spring (by exerting forces to, e.g., expand the wrap spring) or to permit engagement of the wrap spring in one or both directions. This can include exerting magnetic forces onto the tang block(s) from a non-rotating coil or exerting axial mechanical forces, through a bearing, onto a rotating mechanism that converts the axial force into mechanical forces on the ends of the wrap spring.
F16D 13/08 - Friction clutches with a helical band or equivalent member, which may be built-up from linked parts, with more than one turn embracing a drum or the like, with or without an additional clutch actuating the end of the band
F16D 27/02 - Magnetically-actuated clutchesControl or electric circuits therefor with electromagnets incorporated in the clutch, i.e. with collecting rings
A computing system comprising a memory configured to store an artificial intelligence (AI) model and an image, and a computation engine executing one or more processors may be configured to perform the techniques for error-based explanations for AI behavior. The computation engine may execute the AI model to analyze the image to output a result. The AI model may, when analyzing the image to output the result, process, based on data indicative of the result, the image to assign an error score to each image feature extracted from the image, and obtain, based on the error scores, an error map. The AI model may next update, based on the error map and to obtain a first updated image, the image to visually indicate the error score assigned to each of the image features, and output one or more of the error scores, the error map, and the first updated image.
G06V 10/98 - Detection or correction of errors, e.g. by rescanning the pattern or by human interventionEvaluation of the quality of the acquired patterns
G06V 10/77 - Processing image or video features in feature spacesArrangements for image or video recognition or understanding using pattern recognition or machine learning using data integration or data reduction, e.g. principal component analysis [PCA] or independent component analysis [ICA] or self-organising maps [SOM]Blind source separation
An image sensor having a set of pixels making up the image sensor to capture an image. Two or more pixels in the set of pixels each have an architecture that includes multiple photodiodes configurable to form an individual pixel. A control system can cooperate with the multiple photodiodes to form the individual pixel. Each of the multiple photodiodes can have a transfer gate electrically coupled to that photodiode. A common region can hold or transfer charge at least during or after an integration time. A read gate electrically coupled to the common region and a sense node, can supply charge from the common region through the read gate to the sense node.
H04N 25/533 - Control of the integration time by using differing integration times for different sensor regions
H04N 25/583 - Control of the dynamic range involving two or more exposures acquired simultaneously with different integration times
H04N 25/771 - Pixel circuitry, e.g. memories, A/D converters, pixel amplifiers, shared circuits or shared components comprising storage means other than floating diffusion
H04N 25/778 - Pixel circuitry, e.g. memories, A/D converters, pixel amplifiers, shared circuits or shared components comprising amplifiers shared between a plurality of pixels, i.e. at least one part of the amplifier must be on the sensor array itself
Systems and methods for providing remote farm damage assessment are provided herein. In some embodiments, a system and method for providing remote farm damage assessment may include, determining a set of damage assessment locales for damage assessment; incorporating the set of damage assessment locales into a workflow; providing the workflow to a user device; receiving a first set of damage assessment images from the user device based on the workflow provided, wherein each of the first set of damage assessment images includes geolocation information and camera information; determining a damage assessment based on the first set of damage assessment images using a damage assessment machine learning model; and outputting a damage assessment indication including one or more of whether there is damage, a confidence level of assessing the damage, or a confidence level associated with the level of damage.
An example genetically engineered natural killer (NK) cell comprises an exogenous polynucleotide sequence that includes a receptor element, an actuator element, and an effector element. The receptor element encodes a chimeric antigen receptor (CAR) comprising an extracellular antigen binding domain operably linked to a transmembrane domain, and an intracellular signaling domain, wherein the extracellular antigen binding domain recognizes a surface antigen of a target cell. The actuator element encodes a transcription factor binding site that upregulates synthesis of an effector protein. The effector element encodes the effector protein operably linked to a signal peptide, wherein, in response to the antigen binding domain of the CAR binding to the antigen of the target cell, the engineered NK cell is configured to activate and, to synthesize and secrete the effector protein.
