Abstract: An embodiment of the present disclosure provides a method and an apparatus for fusing multi-sensor data. The method includes: obtaining a current vision perception result and a current first radar perception result; based on a current vision perception feature corresponding to each current vision perception target in each current vision perception result and a target vision feature fusion model, determining a fusion vision feature corresponding to each current vision perception target; based on the fusion vision feature corresponding to each current vision perception target, a current first radar perception feature corresponding to each current first radar perception target in the current first radar perception result and a pre-constructed radar vision feature fusion model, determining mutually-matched current vision perception targets and current first radar perception targets and corresponding current fusion perception features. In this way, accurate fusion on multi-sensor data can be achieved.

Abstract: A system and method for automated single cell capture and processing is described, where the system includes a deck supporting and positioning a set of sample processing elements; a gantry for actuating tools for interactions with the set of sample processing elements supported by the deck; and a base supporting various processing subsystems and a control subsystems in communication with the processing subsystems. The system can automatically execute workflows associated with single cell processing, including mRNA capture, cDNA synthesis, protein-associated assays, and library preparation, for next generation sequencing.

Abstract: Methods, systems, and computer programs are presented for determining parameters of neural networks and selecting embedding dimensions for the feature fields. One method includes an operation for initializing parameters of a neural network and weights for embedding sizes for each feature associated with the neural network. The parameters of the neural network and the weights are iteratively optimized. Each optimization iteration comprises training the neural network with current parameters of the neural network to optimize a value of the weights, and training the neural network with current values of the weights to optimize the parameters of the neural network. Further, the method includes operations for selecting embedding sizes for the features based on the optimized values of the weights, and for training the neural network based on the selected embedding sizes for the features to obtain an estimator model. A prediction is generated utilizing the estimator model.

Abstract: A user of an automated natural language system may submit an ambiguous or incomplete request, and interactive techniques may be used to obtain clarification information from the user and then determine a response for presentation to the user. A user's initial request may be processed to compute a category score for each possible category of request. The category scores may be processed to determine if clarification of the request is needed. Where clarification is needed, one or more tags may be selected to determine a clarification question to be presented to the user. For example, a tag clarification score may be computed for each tag that indicates a value of the tag in clarifying the request. After receiving the clarification information from the user, one or more categories may be selected or, where needed, additional clarification information may be obtained.

Abstract: A system and method for automated single cell capture and processing is described, where the system includes a deck supporting and positioning a set of sample processing elements; a gantry for actuating tools for interactions with the set of sample processing elements supported by the deck; and a base supporting various processing subsystems and a control subsystems in communication with the processing subsystems. The system can automatically execute workflows associated with single cell processing, including mRNA capture, cDNA synthesis, protein-associated assays, and library preparation, for next generation sequencing.

Abstract: Disclosed herein is a synthetic method, apparatus, and system for the continuous-flow synthesis of 3,4-dinitropyrazole from pyrazole in a microfluidic environment. This synthetic strategy consist of three (3) synthetic steps, including (1) N-nitration of pyrazole, (2) thermal rearrangement into 3-nitropyrazole, and (3) 4-nitration of 3-nitropyrazole. The current technique produces 3,4-dinitropyrazole in yields up to 85% in particular embodiments, in comparison to 40-50% yields demonstrated by the current state of-the-art batch process for large scale synthesis from pyrazole.

Abstract: An embodiment of a composition for target material separation includes: a body and one or more molecules coupled to the body and structured for functionalization of the composition. In embodiments, each of the one or more molecules can include one or more of: a linker region; a polymerase chain reaction (PCR) segment or oligo binding region; one or more barcode region(s); a unique molecule identifier; a preparation-facilitating segment; an active segment; and a molecular scissor or cleavage region, wherein various regions can be coupled together in order to provide functionality to the composition. The invention(s) also cover manufacturing of the composition and various applications of use, in the context of target material capture (e.g., from single cells or other biological material).

Abstract: A system and method for automated single cell capture and processing is described, where the system includes a deck supporting and positioning a set of sample processing elements; a gantry for actuating tools for interactions with the set of sample processing elements supported by the deck; and a base supporting various processing subsystems and a control subsystems in communication with the processing subsystems. The system can automatically execute workflows associated with single cell processing, including mRNA capture, cDNA synthesis, protein-associated assays, and library preparation, for next generation sequencing.

Abstract: A system and method for automated single cell capture and processing is described, where the system includes a deck supporting and positioning a set of sample processing elements; a gantry for actuating tools for interactions with the set of sample processing elements supported by the deck; and a base supporting various processing subsystems and a control subsystems in communication with the processing subsystems. The system can automatically execute workflows associated with single cell processing, including mRNA capture, cDNA synthesis, protein-associated assays, and library preparation, for next generation sequencing.

Abstract: A system and method for automated single cell capture and processing is described, where the system includes a deck supporting and positioning a set of sample processing elements; a gantry for actuating tools for interactions with the set of sample processing elements supported by the deck; and a base supporting various processing subsystems and a control subsystems in communication with the processing subsystems. The system can automatically execute workflows associated with single cell processing, including mRNA capture, cDNA synthesis, protein-associated assays, and library preparation, for next generation sequencing.

Abstract: A user of an automated natural language system may submit an ambiguous or incomplete request, and interactive techniques may be used to obtain clarification information from the user and then determine a response for presentation to the user. A user's initial request may be processed to compute a category score for each possible category of request. The category scores may be processed to determine if clarification of the request is needed. Where clarification is needed, one or more tags may be selected to determine a clarification question to be presented to the user. For example, a tag clarification score may be computed for each tag that indicates a value of the tag in clarifying the request. After receiving the clarification information from the user, one or more categories may be selected or, where needed, additional clarification information may be obtained.

Abstract: Techniques for providing a personalized neural query auto-completion pipeline are disclosed herein. In some embodiments, a computer system, in response to detecting user-entered text that has been entered by a user in a search field of a search engine, generates auto-completion candidates based on the user-entered text and a corresponding frequency level for each one of the auto-completion candidates, ranks the auto-completion candidates based on profile data of the user using a neural network model, and causes at least a portion of the plurality of auto-completion candidates to be displayed in an auto-complete user interface element of the search field within the user interface of the computing device of the user based on the ranking prior to the user-entered text being submitted by the user as part of a search query.