Abstract: Methods comprising: evaporating a catalyst source to produce a catalyst gas; condensing the catalyst gas to produce a catalyst vapor comprising catalyst droplets suspended in a gas phase; and contacting the catalyst vapor with a hydrocarbon gas to catalyze a decomposition reaction of the hydrocarbon gas into hydrogen gas and carbon. And, systems comprising: a catalyst source evaporator that provides a first stream to a reactor; a hydrocarbon source that provides a second stream to the reactor; a cooling column coupled to the reactor via a third stream comprising hydrogen, catalyst liquid, solid carbon, optionally catalyst gas, and optionally unreacted hydrocarbon gas such that the cooling column receives the third stream from the reactor; and wherein the cooling column has effluent streams that include (a) a fourth stream that comprises hydrogen and optionally catalyst gas and (b) a fifth stream that comprises catalyst liquid.

Abstract: One embodiment of the present invention provides a system that recommends activities. During operation, the system receives a piece of content obtained from text or converted to text from speech. The system then analyzes the received content to identify any activity type, indication of willingness to participate in any type of activities, and at least one piece of temporal information, which can be implicitly and/or explicitly stated in the content, and/or one piece of location information associated with the activity type. The system further recommends one or more activities, venues, and/or services that afford or support activities for a user based on the information extracted from the content.

Abstract: One embodiment provides a method for automated design of a physical system. The method can include obtaining design requirements associated with the physical system and iteratively performing, by a computer, a reinforcement learning (RL) process and a nonlinear optimization process to generate a design solution. The RL process can generate a topology represented as a model of the physical system using a modeling language. The generated topology can specify a number of components and connections among the components. The nonlinear optimization process can determine parameters of the components in the topology based on the model and a loss function. The method can further include outputting the design solution of the physical system based on the generated topology and the determined parameters of the components, thereby facilitating construction of the physical system.

Abstract: System and methods for relational time-series learning are provided. Unlike traditional time series forecasting techniques, which assume either complete time series independence or complete dependence, the disclosed system and method allow time series forecasting that can be performed on multivariate time series represented as vertices in graphs with arbitrary structures and predicting a future classification for data items represented by one of nodes in the graph. The system and methods also utilize non-relational, relational, temporal data for classification, and allow using fast and parallel classification techniques with linear speedups. The system and methods are well-suited for processing data in a streaming or online setting and naturally handle training data with skewed or unbalanced class labels.

Abstract: Embodiments described herein provide a system for facilitating efficient troubleshooting for a product. During operation, the system can identify an artificial-intelligence- (AI-) based dialog model operating based on a structured representation, which can indicate sequential troubleshooting steps to be performed by a user. The system can provide a machine utterance of the dialog model corresponding to a troubleshooting step to the user. The system can then search the structured representation for a semantic match for a user utterance obtained in accordance with the dialog model from the user. If the semantic match indicates an anticipated option associated with the machine utterance, the system can traverse a current branch of the structured representation using the dialog model based on the anticipated option. Otherwise, if the semantic match indicates an option on a different branch, the system can jump to the option on the different branch for subsequent traversal.

Abstract: One embodiment provides a method and a system for reconstructing symbols transmitted over a high frequency (HF) communication channel. During operation, the system can receive, at a receiver, a radio frequency (RF) signal carrying an input data frame and transmitted over the HF communication channel. The input data frame includes a number of known symbols followed by a number of unknown symbols. The system can determine a set of channel parameters associated with the HF communication channel based on the received RF signal and the known symbols and reconstruct, using a machine-learning technique, the unknown symbols based on the determined channel parameters and the received RF signal.

Abstract: A transferable hybrid method for prognostics of engineering systems based on fundamental degradation modes is provided. The method includes developing a degradation model that represents degradation modes shared in different domains of application through the integration of physics and machine learning. The system measures sensor signals and data processing provides for extracting health indicators correlated with the fundamental degradation modes from sensors data. For the integration of physics and machine learning, the degradation mode is separated into different phases. Before the accelerated degradation phase of a system, the method is looking out to detect when the accelerated phase begins. When accelerated phase is active, the system applies a machine-learning model to provide information on the accelerated degradation phase, and evolves the degradation towards a failure threshold in a simulation of the updated physics-based model to predict the degradation progression.

Abstract: One embodiment provides a system which facilitates construction of an ensemble of neural network-based classifiers that optimize a diversity metric. During operation, the system defines a diversity metric based on pairwise angles between decision boundaries of three or more affine classifiers. The system includes the diversity metric as a regularization term in a loss function optimization for designing a pair of mutually orthogonal affine classifiers of the three or more affine classifiers. The system trains one or more neural networks such that parameters of the one or more neural networks are consistent with parameters of the affine classifiers to obtain an ensemble of neural network-based classifiers which optimize the diversity metric. The system predicts an outcome for a testing data object based on the obtained ensemble of neural-network based classifiers which optimize the diversity metric.

Abstract: A system determines an input video and a first annotated image from the input video which identifies an object of interest. The system initiates a tracker based on the first annotated image and the input video. The tracker generates, based on the first annotated image and the input video, information including: a sliding window for false positives; a first set of unlabeled images from the input video; and at least two images with corresponding labeled states. A semi-supervised classifier classifies, based on the information, the first set of unlabeled images from the input video. If a first unlabeled image is classified as a false positive, the system reinitiates the tracker based on a second annotated image occurring in a frame prior to a frame with the false positive. The system generates an output video comprising the input video displayed with tracking on the object of interest.

Abstract: One embodiment provides a method and system which facilitates optimizing a pair of affine classifiers based on a diversity metric. During operation, the system defines a diversity metric based on an angle between decision boundaries of a pair of affine classifiers. The system includes the diversity metric as a regularization term in a loss function optimization for designing the pair of affine classifiers, wherein the designed pair of affine classifiers are mutually orthogonal. The system predicts an outcome for a testing data object based on the designed pair of mutually orthogonal affine classifiers.

Abstract: Embodiments described herein provide a design architecture for co-designing a controller and a watermarking signal for a cyber-physical system. During operation, the architecture can determine, in conjunction with each other, respective values of a first set of parameters indicating operations of the controller and a second set of parameters representing the watermarking signal. Here, the watermarking signal is combinable with a control signal from the controller for monitoring an output signal of the cyber-physical system for detecting malicious data at different time instances. Subsequently, the architecture can determine a state manager for determining the states of the cyber-physical system from the monitored output signal based on the first and second sets of parameters. The architecture can also determine a detector capable of identifying presence of an attack from the states of the cyber-physical system at a plurality of time instances using the watermarking signal.

Abstract: A system and method provide a combination of a modular message structure, a priority-based message packing scheme, and a data packet queue management system to optimize the information content of a transmitted message in, for example, the Ocean of Things (OoT) environment. The modular message structure starts with a header that provides critical information and reference points for time and location. The rest of the message is composed of modular data packets, each of which has a data ID section that the message decoder uses for reference when reconstructing the message contents, an optional size section that specifies the length of the following data section if it can contain data of variable length, and a data section that can be compressed in a manner unique to that data type.

Abstract: A system and method are provided wherein encrypted data is stored in modular lockers on a first storage volume associated with a high-power microprocessor deployed in a system. Each encrypted locker maps, for example, to a specific time segment (e.g., one day) which simplifies mounting of the encrypted volume for data access and reduces the locker size for external access of data while encrypted (e.g., via Wifi). New data in a second storage volume associated with and generated by a low-power microprocessor associated with the system gets transferred to the encrypted data store during wake cycles of the high-power microprocessor. To manage space on the first storage volume, time stamps associated with each encrypted locker allows simple removal of files older than a specified time period by removing of any encrypted lockers older than that threshold.

Abstract: A method and system for implementing a task scheduler are provided in a resource constrained computation system that uses meta data provided for each task (e.g. data analysis algorithm or sensor sampling protocol) to determine which tasks should be run in a particular wake cycle, the order in which the tasks are run, and how the tasks are distributed across the available compute resources. When a task successfully completes, it's time of execution is logged in order to provide a reference for when that task should be run again. Task meta data is formatted in a manner to allow for simple integration of new tasks into the processing architecture.

Abstract: Mass vaccination of one or more animals may be performed topically using viscosified fluids and a spray delivery device. When applied to one or more animals, the viscosified fluids may adhere a medicament upon a topical surface of the one or more animals. The spray delivery device may comprise at least a spray component, a fluid reservoir component, a plunger, an electronics component, and a battery.

Abstract: A nondestructive method for detecting damage in parts and/or characterizing effective material properties may include: exposing a material to one or more nondestructive stimuli; measuring a response of the material to the stimuli; selecting at least one of a specific length scale or a specific time scale; and analyzing the measurement of the response with a scale-aware single- or multi-physics model to identify anomalies in the measurements as compared to an expected response of the material to the stimuli, wherein the scale-aware single- or multi-physics model is based on the at least one of the specific length scale or the specific time scale.

Abstract: A method systematically developing reduced-order (e.g., upscaled or coarse-grained, lumped- or distributed-parameter) multi-physics models for simulating additive manufacturing may include: describing governing equations of an additive manufacturing process; refactoring the governing equations into (1) constitutive laws with unknown coefficients and (2) conservation laws; discretizing the governing equations; and training the unknown coefficients of the constitutive laws with simulated data and/or experimental data relating to the additive manufacturing process where the conservation laws are enforced in the training regardless of a granularity of the constitutive laws, thereby yielding a reduced-order set of governing equations.

Abstract: A system is provided which mixes static scene and live annotations for labeled dataset collection. A first recording device obtains a 3D mesh of a scene with physical objects. The first recording device marks, while in a first mode, first annotations for a physical object displayed in the 3D mesh. The system switches to a second mode. The system displays, on the first recording device while in the second mode, the 3D mesh including a first projection indicating a 2D bounding area corresponding to the marked first annotations. The first recording device marks, while in the second mode, second annotations for the physical object or another physical object displayed in the 3D mesh. The system switches to the first mode. The first recording device displays, while in the first mode, the 3D mesh including a second projection indicating a 2D bounding area corresponding to the marked second annotations.

Abstract: A system and method provide extractions of regions of interest from images hand annotated by reviewers by lifting annotations from images, filtering out text labels, reconstructing continuous closed boundaries, and marking the contained region.

Abstract: A method and system are provided for an improved semantic segmentation using a multi-stream late fusion using pretrained encoders to encode disparate channels independently while also integrating selected image features at a more abstract level in order to provide improved localization and image classification.