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: 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, apparatuses, and methods include technology that generates final frame predictions for a first plurality of frames of a video, where the first plurality of frames is associated with unlabeled data. The technology predicts an ordered list of actions for the first plurality of frames based on the final frame predictions, and temporally aligning the ordered list of actions to the final frame predictions to generate labels.

Abstract: Systems, apparatuses, and methods include technology that identifies, with a neural network, that a predetermined amount of a first action is completed at a first portion of a plurality of portions. A subset of the plurality of portions collectively represents the first action. The technology generates a first loss based on the predetermined amount of the first action being identified as being completed at the first portion. The technology updates the neural network based on the first loss.

Abstract: Systems, apparatuses, and methods include technology that extracts a plurality of features from the input data. The technology generates a confidence metric for the plurality of features. The confidence metric corresponds to a degree that at least one feature of the plurality of features is relevant for classification of the input data.

Abstract: The present disclosure provides apparatus and methods for determining the sequence of a nucleic acid. The apparatus comprises electrodes that form a tunnel gap through which the nucleic acid can pass. The electrodes comprise a reagent that is capable of selectively interacting with a nucleobase of the nucleic acid sequence. When the reagent interacts with a nucleobase, a detectable signal is produced and used to identify the nucleobase of the nucleic acid. Advantageously, the apparatus of this disclosure is specific to identifying nucleic acids.

Abstract: The present disclosure provides apparatus and methods for determining the sequence of a nucleic acid. The apparatus comprises electrodes that form a tunnel gap through which the nucleic acid can pass. The electrodes comprise a reagent that is capable of selectively interacting with a nucleobase of the nucleic acid sequence. When the reagent interacts with a nucleobase, a detectable signal is produced and used to identify the nucleobase of the nucleic acid. Advantageously, the apparatus of this disclosure is specific to identifying nucleic acids.

Abstract: Triazole-based molecules, methods of making and using the same are provided. Triazole-based molecules may be used as reading molecules and incorporated into or operatively-linked with electrodes, for example, and used in recognition tunneling systems to identify individual and/or sequences of molecules (e.g., DNA bases, carbohydrates, proteins, peptides, and/or amino-acids).

Abstract: The present disclosure provides apparatus and methods for determining the sequence of a nucleic acid. The apparatus comprises electrodes that form a tunnel gap through which the nucleic acid can pass. The electrodes comprise a reagent that is capable of selectively interacting with a nucleobase of the nucleic acid sequence. When the reagent interacts with a nucleobase, a detectable signal is produced and used to identify the nucleobase of the nucleic acid. Advantageously, the apparatus of this disclosure is specific to identifying nucleic acids.

Abstract: Embodiments disclose methods, systems and non-transitory computer readable medium for joint temporal segmentation and classification of user activities in an egocentric video.

Abstract: Triazole-based molecules, methods of making and using the same are provided. Triazole-based molecules may be used as reading molecules and incorporated into or operatively-linked with electrodes, for example, and used in recognition tunneling systems to identify individual and/or sequences of molecules (e.g., DNA bases, carbohydrates, proteins, peptides, and/or amino-acids).

Abstract: Some embodiments of the present disclosure are directed to a compound 5(6)-mercapto-1H-benzo[d]imidazole-2-carboxamide (“BIA”) which yields enhanced signals for recognition tunneling. Other embodiments are directed toward methods for producing such compounds as well as apparatuses and systems which utilize such compounds for recognition tunneling for molecule identification/sequencing (for example).