Abstract: Deep learning models, such as diffusion models, can synthesize images from noise. Diffusion models implement a complex denoising process involving many denoising operations. It can be a challenge to understand the mechanics of diffusion models. To better understand how and when structure is formed, saliency maps and concept formation intensity can be extracted from the sampling network of a diffusion model. Using the input map and the output map of a given denoising operation in a sampling network, a noise gradient map representative of the predicted noise of a given denoising operation can be determined. The noise gradient maps from the denoising operations at different indices can be combined to generate a saliency map. A concept formation intensity value can be determined from a noise gradient map. Concept formation intensity values from the denoising operations at different indices can be plotted.

Abstract: The present invention features compounds useful in the treatment of neurological disorders. The compounds of the invention, alone or in combination with other pharmaceutically active agents, can be used for treating or preventing neurological disorders.

Abstract: A system and method of monitoring sobriety using a hand-held breath testing device that, on receipt of a user's breath, generates a breath test signal comprising substance content data and user identification data, and wirelessly transmits the breath test signal to a breath test signal receiving station.

Abstract: Machine learning models can process a video and generate outputs such as action segmentation assigning portions of the video to a particular action, or action classification assigning an action class for each frame of the video. Some machine learning models can accurately make predictions for short videos but may not be particularly suited for performing action segmentation for long duration, structured videos. An effective machine learning model may include a hybrid architecture involving a temporal convolutional network and a bi-directional graph neural network. The machine learning model can process long duration structured videos by using a temporal convolutional network as a first pass action segmentation model to generate rich, frame-wise features. The frame-wise features can be converted into a graph having forward edges and backward edges. A graph neural network can process the graph to refine a final fine-grain per-frame action prediction.

Abstract: Systems, methods and devices are disclosed for monitoring sobriety of a first user by a second user which can be discrete, wirelessly linked via a communications network and can provide various forms of alerts to the first and second users.

Abstract: The disclosure provides gene fusions, gene variants, and novel associations with disease states, as well as kits, probes, and methods of using the same.

Abstract: The disclosure provides gene fusions, gene variants, and novel associations with disease states, as well as kits, probes, and methods of using the same.

Abstract: A system and method of monitoring sobriety using a handheld breath testing device that, on receipt of a user's breath, generates a breath test signal comprising substance content data and user identification data, and wirelessly transmits the breath test signal to a breath test signal receiving station, and wherein the breath testing device further includes a fingerprint reader and a plurality of sensors.

Abstract: A system and method of monitoring sobriety using a hand-held breath testing device that, on receipt of a user's breath, generates a breath test signal comprising substance content data and user identification data, and wirelessly transmits the breath test signal to a breath test signal receiving station.

Abstract: Disclosed is a technical solution to process a video that captures actions to be performed for completing a task based on a chronological sequence of stages within the task. An example system may identify an action sequence from an instruction for the task. The system inputs the action sequence into a trained model (e.g., a recurrent neural network), which outputs the chronological sequence of stages. The RNN may be trained through self-supervised learning. The system may input the video and the chronological sequence of stages into another trained model, e.g., a temporal convolutional network. The other trained model may include hidden layers arranged before an attention layer. The hidden layers may extract features from the video and feed the features into the attention layer. The attention layer may determine attention weights of the features based on the chronological sequence of stages.

Abstract: Disclosed is a technical solution to calibrate confidence scores of classification networks. A classification network has been trained to receive an input and output a label of the input that indicates a class of the input. The classification network also outputs a confidence score of the label, which indicates a likelihood of the input falling into the class, i.e., a confidence level of the classification network that the label is correct. To calibrate the confidence of the classification network, a logit transformation function may be added into the classification network. The logic transformation function may be an entropy-based function and have learnable parameters, which may be trained by inputting calibration samples into the classification network and optimizing a negative log likelihood based on the labels generated by the classification network and ground-truth labels of the calibration samples. The trained logic transformation function can be used to compute reliable confidence scores.

Abstract: Systems, methods and devices are disclosed for monitoring sobriety of a first user by a second user which can be discrete, wirelessly linked via a communications network and can provide various forms of alerts to the first and second users.

Abstract: A method for assessing idea generation and generating recommendations regarding idea implementation includes providing to each of a plurality of assessment subjects, at a first time, a first assessment prompt. The method includes receiving a first response to the first assessment prompt from each of the plurality. The method includes providing, to each of the plurality of assessment subjects, at a second time subsequent to the first time, a second assessment prompt and receiving from each of the plurality of assessment subjects, a second response. The method includes receiving, from each of the plurality of assessment subjects, a self-evaluation of the first response and the second response. The method includes identifying, based on the analysis, at least one idea generation characteristic for each of the plurality of assessment subjects. The method includes dynamically updating a representation of a graph displaying a comparison of the idea generation characteristic across assessment subjects.

Abstract: A system and method of monitoring sobriety using a handheld breath testing device that, on receipt of a user's breath, generates a breath test signal comprising substance content data and user identification data, and wirelessly transmits the breath test signal to a breath test signal receiving station, and wherein the breath testing device further includes a fingerprint reader and a plurality of sensors.

Abstract: An apparatus is provided for deep learning. The apparatus accesses a neural network including an input layer, hidden layers, and an output layer. The apparatus adds an activation function to one or more of the hidden layers of the hidden layers and output layer. The activation function includes a tunable parameter, the value of which can be adjusted during the training of the neural network. The apparatus trains the neural network by inputting training samples into the neural network and determining internal parameters of the neural network based on the training samples. Determining the internal parameters includes determining a value of the tunable parameter based on the training samples. The apparatus may determine two different values of the tunable parameter for two different layers. The activation function may include another tunable parameter. The apparatus can determine a value for the other tunable parameter during the training of the neural network.

Abstract: An apparatus is provided to compress CNN models using a combination of filter pruning and tensor decomposition. For example, the apparatus accesses a trained CNN that includes convolutional tensors. The apparatus prunes the filters of a convolutional tensor to generate a sparse tensor. Further, the apparatus decomposes the sparse tensor to generate a low-rank approximation of the sparce tensor. The low-rank approximation of the sparse tensor includes a core tensor and principal tensors. The apparatus generates a convolutional flow that includes the core tensor and convolutional operations generated based on the principal tensors. The apparatus may replace some or all the convolutional tensors in the trained CNN with the corresponding convolutional flows. The apparatus may fine-tune the updated CNN by re-training the updated CNN. The number of epochs for re-training the updated CNN may be smaller than the number of epochs for training the CNN.

Abstract: A system and method of monitoring sobriety using a hand-held breath testing device that, on receipt of a user's breath, generates a breath test signal comprising substance content data and user identification data, and wirelessly transmits the breath test signal to a breath test signal receiving station.

Abstract: A tailgate assembly mounted along an open edge of a vehicle's load carrying bed includes a frame having a first or proximal lateral side adapted to be hingedly connected for opening in a swing open mode and a second or distal lateral side oppositely positioned relative to the first lateral side. The tailgate assembly further includes a reinforcement frame component overlaid onto the frame at the first lateral side to increase torsional stiffness at or near the first lateral side.

Abstract: A system and method of monitoring sobriety using a handheld breath testing device that, on receipt of a user's breath, generates a breath test signal comprising substance content data and user identification data, and wirelessly transmits the breath test signal to a breath test signal receiving station, and wherein the breath testing device further includes a fingerprint reader and a plurality of sensors.

Abstract: A tailgate assembly mounted along an open edge of a vehicle's load carrying bed includes a frame having a first or proximal lateral side adapted to be hingedly connected for opening in a swing open mode and a second or distal lateral side oppositely positioned relative to the first lateral side. The tailgate assembly further includes a reinforcement frame component overlaid onto the frame at the first lateral side to increase torsional stiffness at or near the first lateral side.