Abstract: The disclosed technology teaches recovering a first virtual machine or an instance with an Internet Protocol address, a first root volume and one or more data volumes that are corrupted. The first virtual machine is hosted by a first cloud server that hosts plurality of virtual machines. The disclosed technology includes instructing the first cloud server to launch a recovery virtual machine. The recovery virtual machine launches one or more new data volumes based upon captured file system images in one or more snapshots taken of corrupted data volumes of the first virtual machine prior to becoming corrupted. The recovery virtual machine detaches the corrupted data volumes and attaches the new data volumes launched to the first virtual machine. The Internet Protocol address of the first virtual machine remains unchanged.

Abstract: In one aspect, an example method includes (i) causing a content-presentation device to output for presentation reference visual content; (ii) causing a lighting device to emit light in accordance with a lighting parameter, wherein initially the light emitted in accordance with the lighting parameter does not correspond to the reference visual content; (iii) receiving first input indicating an adjustment to the lighting parameter; (iv) responsive to receiving the first input, causing the lighting device to adjust the lighting parameter, thereby causing the lighting device to adjust the emitted light; (v) receiving second input indicating that the adjusted emitted light corresponds to the reference visual content; and (vi) responsive to receiving the second input, calibrating the lighting device at least by (i) causing storage of the adjusted lighting parameter and (ii) causing the lighting device to be configured to emit light in accordance with the stored lighting parameter.

Abstract: An automated data observation system, platform, and corresponding methods and non-transitory computer readable medium include periodically obtaining metadata, automatically generating data quality metrics, obtaining predicted values for the data quality metrics, determining a first anomaly, generating a data pipeline graph, and estimating a root cause relating to the first anomaly by traversing the data pipeline graph. Obtaining predicted values for the data quality metrics includes executing a plurality of candidate machine learning models over time series data relating to the data quality metrics to generate candidate predicted values and selecting a machine learning model for a given data quality metric based on the candidate predicted values and measured values.

Abstract: Combined cycle gas turbine (CCGT) power plants have become common for generation of electric power due to their high efficiencies. There are various problem related with improving the efficiency of CCGT plants by optimizing the manipulated variables. The method and system for optimizing the operation of a combined cycle gas turbine has been provided. The system is configured to calculate an optimal value of manipulated variables (MV) with efficiency as one of the key performance parameters. The MVs from the existing CCGT automation system, i.e. a first set of manipulated variables and the manipulated variables from the optimization approach, i.e. a second set of manipulated variables are combined to determine an optimal set of manipulated variables. The method further checks for the anomalous behavior of the system and define the root cause of the identified anomaly and the operational state of the CCGT plant.

Abstract: According to one implementation of the present disclosure, a circuit includes: two or more metal wires, respective XOR gates coupled to each of the two or more top metal wires, a shift register having outputs coupled to the XOR gates, an OR gate configured to receive each of the outputs of the XOR gates, and a latch configured to receive an output of the OR gate and transmit an output signal corresponding to an alarm signal.

Abstract: A method for controlling a dual fuel engine system includes estimating a total indicated engine load, where the total indicated engine load is based on a sum of a measured engine power and a power loss estimate. The method further includes determining a total fueling amount based on an engine speed and the total indicated engine load, where the total fueling amount includes a gas fueling amount and a diesel fueling amount. The method also includes controlling the dual fuel engine system using the total fueling amount.

Abstract: Systems and methods provide techniques for more effective and efficient predictive monitoring of a software application framework. In response, embodiments of the present invention provide methods, apparatuses, systems, computing devices, and/or the like that are configured to enable effective and efficient predictive monitoring of a software application framework using incident signatures for the software application that are generated by using a natural language processing machine learning framework, a structured data processing machine learning model, a feature combination machine learning model, and a clustering machine learning model.

Abstract: A technique including capturing, by one or more cameras of a set of cameras disposed about a vehicle, one or more images, wherein a surround view system of the vehicle is configured to render a surround view image using a first hardware accelerator based on the one or more images, determining that a first hardware accelerator is unavailable, and rendering the surround view image using a second hardware accelerator based on the captured one or more images.

Abstract: Embodiments described herein provide A method for training a neural network based model. The methods include receiving a training dataset with a plurality of training samples, and those samples are encoded into representations in feature space. A positive sample is determined from the raining dataset based on a relationship between the given query and the positive sample in feature space. For a given query, a positive sample from the training dataset is selected based on a relationship between the given query and the positive sample in a feature space. One or more negative samples from the training dataset that are within a reconfigurable distance to the positive sample in the feature space are selected, and a loss is computed based on the positive sample and the one or more negative samples. The neural network is trained based on the loss.

Abstract: Embodiments described herein provide a training framework for generative NLP models that operate on previously learnt knowledge from pretrained large language models. Specifically, to train an NLP model to generate a response to a user utterance (e.g., “resolve login issue”), document embeddings of support IT documents encoded by a pretrained LLM are fed to an NLP decoder together with a training dialogue (e.g., a dialogue between the chat agent on how to “resolve login issue”). The NLP decoder can thus be trained by a causal language modeling loss computed based on the predicted next token and the ground-truth token from the training dialogue.

Abstract: Embodiments described herein provide a training framework for generative NLP models. Specifically, the training input, e.g., in the form of a sequence of tokens representing a user-agent dialogue, may be randomly masked for a few spans, which can be one or more tokens, one or more words, one or more sentences, or one or more paragraphs. These masked spans are replaced with their embeddings generated from pre-trained large language models are then used for training the NLP model.

Abstract: The disclosure provides for use of steganographically-encoded data as a basis to disambiguated a fingerprint-based channel-multimatch. A fingerprint-matching server or other entity could detect a channel-multi-match scenario by determining that query fingerprints representing media content being rendered by a content presentation device match reference fingerprints respectively representing multiple channels. In view of that detected channel-multi-match scenario, the content presentation device could then provide channel-identification information that the content presentation device extracts from a watermark that is steganographically encoded in the media content of the channel that the content presentation device is rendering, for use of the extracted channel-identification information to determine which channel the content presentation device is rendering.

Abstract: Systems, methods, models, and training data for models are discussed, for determining vehicle positioning, and in particular identifying tailgating. Simulated training images showing vehicles following other vehicles, under various conditions, are generated using a virtual environment. Models are trained to determine following distance between two vehicles. Trained models are used in detection of tailgating, based on determined distance between two vehicles. Results of tailgating are output to warn a driver, or to provide a report on driver behavior. Following distance over time is determined, and simplified following distance data is generated for use at a management device.

Abstract: Systems, methods, models, and training data for models are discussed, for determining vehicle positioning, and in particular identifying tailgating. Simulated training images showing vehicles following other vehicles, under various conditions, are generated using a virtual environment. Models are trained to determine following distance between two vehicles. Trained models are used in detection of tailgating, based on determined distance between two vehicles. Results of tailgating are output to warn a driver, or to provide a report on driver behavior. Following distance over time is determined, and simplified following distance data is generated for use at a management device.

Abstract: Systems, methods, and apparatuses of using biometric information to authenticate a first device of a user to a second device are described herein. A method includes storing, by the first device, a first key share of a private key and a first template share of a biometric template of the user. The second device stores a public key, and one or more other devices of the user store other key shares and other template shares. The first device receives a challenge message from the second device, measures biometric features of the user to obtain a measurement vector, and sends the measurement vector and the challenge message to the other devices. The first device receives partial computations, generated using a respective template share, key share, and the challenge message, from the other devices, uses them to generate a signature of the challenge message and send the signature to the second device.

Abstract: Techniques are disclosed for training a feature extraction model. A computing device can receive a training image and generate noised feature vectors using a feature extraction model characterized by first parameters and taking the training image as input. The computing device can determine the noised feature vectors by at least determining a feature vector for individual pixels in the training image and applying noise to each feature vector. The computing device can generate a reconstructed image using a reconstructor model characterized by second parameters and taking the noised feature vectors as input. The computing device can determine a reconstruction loss by comparing the training image with the reconstructed image and a noise loss using the noise applied to each feature vector. The computing device can update the first parameters based on the noise loss.

Abstract: Vapor deposition precursor recovery systems and methods are provided. Methods and systems include a precursor container housing a precursor material and a carrier gas container housing a carrier gas. Systems and methods include a condenser assembly having a condenser in fluid connection with an exhaust line, a recycled precursor container, and a cooling circuit. Systems and methods include a vaporizer having one or more inlets in fluid connection with the precursor container and the carrier gas container, and an outlet in fluid connection with a gas distributor and the condenser assembly. Systems and methods include where the condenser is maintained at an internal temperature of greater than or about 10° C. below a boiling point of the precursor material and greater than or about 10° C. above a boiling point of the carrier gas.

Abstract: A method and system for using steganographically-encoded time information as a basis to control when a client carries out a content-related action. The client processes for presentation one or more linear media streams, each defining a respective time sequence of frames of media content, and at least one of the streams being steganographically encoded with at least one watermark at a respective time-point within the linear media stream, the watermark encoding a timestamp of the respective time-point within the linear media stream according to a server clock. The client extracts the timestamp from the watermark and computes a time offset based on a difference between the extracted timestamp and a current time according to a client clock. And the client uses the computed time offset as a basis to determine when to carry out a content-related action in a given one of the one or more linear media streams.

Abstract: A method for probabilistic computing is provided. The method comprises specifying a target distribution for a computational model, wherein the target distribution is defined by a function. A number of coin flips are performed with a number of weighted coinflip devices, wherein weights for the coinflip devices are determined by the function. The number of coinflips are then converted to a random number from the target distribution according to outputs of the weighted coinflip devices, wherein a circuit uses the coin flips as inputs to randomly activate bits in a binary representation of the random number.

Abstract: In one aspect, disclosed is an example method for use by a lighting device including a microphone and a light source, the method including: (i) receiving, by the lighting device via the microphone, an audio signal; (ii) decoding and extracting, by the lighting device, a lighting device control instruction from the received audio signal; and (iii) using, by the lighting device, at least the decoded and extracted lighting device control instruction as a basis to control operation of the light source.