Abstract: The present disclosure provides methods for quantifying target analytes in sample by providing framework for expanded multiplexing through asynchronous fingerprinting.

Abstract: A method for processing formulae includes encoding a formula by: training, with a server, a model by using a machine learning algorithm with a data set that includes a plurality of formulae; transforming, with a processor, a first formula into a tree format using the trained model; converting, with the processor, the tree format of the first formula into a plurality of lists; and encoding, with the processor, the plurality of lists into a fixed dimension vector by leveraging a stacked attention module; and generating one or more formula candidates by: obtaining, with the processor, input information; and generating, with the processor, one or more second formula candidates based on input information by using the stacked attention module with a tree beam search algorithm.

Abstract: The present disclosure is directed to compositions and methods present a universal microbial diagnostic (UMD) platform to screen for microbial organisms in a sample using a small number of random DNA probes that are agnostic to the target DNA sequences. The UMD platform can be used to direct and monitor appropriate treatments, thus minimizing the risk of antibiotic resistance, and enhancing patient care.

Abstract: Real-world data may not be sparse in a fixed basis, and current high-performance recovery algorithms are slow to converge, which limits compressive sensing (CS) to either non-real-time applications or scenarios where massive back-end computing is available. Presented herein are embodiments for improving CS by developing a new signal recovery framework that uses a deep convolutional neural network (CNN) to learn the inverse transformation from measurement signals. When trained on a set of representative images, the network learns both a representation for the signals and an inverse map approximating a greedy or convex recovery algorithm. Implementations on real data indicate that some embodiments closely approximate the solution produced by state-of-the-art CS recovery algorithms, yet are hundreds of times faster in run time.

Abstract: A mechanism for compiling a generative description of an inference task into a neural network. First, an arbitrary generative probabilistic model from the exponential family is specified (or received). The model characterizes a conditional probability distribution for measurement data given a set of latent variables. A factor graph is generated for the generative probabilistic model. Each factor node of the factor graph is expanded into a corresponding sequence of arithmetic operations, based on a specified inference task and a kind of message passing algorithm. The factor graph and the sequences of arithmetic operations specify the structure of a neural network for performance of the inference task. A learning algorithm is executed, to determine values of parameters of the neural network. The neural network is then ready for performing inference on operational measurements.

Abstract: Real-world data may not be sparse in a fixed basis, and current high-performance recovery algorithms are slow to converge, which limits compressive sensing (CS) to either non-real-time applications or scenarios where massive back-end computing is available. Presented herein are embodiments for improving CS by developing a new signal recovery framework that uses a deep convolutional neural network (CNN) to learn the inverse transformation from measurement signals. When trained on a set of representative images, the network learns both a representation for the signals and an inverse map approximating a greedy or convex recovery algorithm. Implementations on real data indicate that some embodiments closely approximate the solution produced by state-of-the-art CS recovery algorithms, yet are hundreds of times faster in run time.

Abstract: Mechanisms for automatically grading a large number of solutions provided by learners in response to an open response mathematical question. Each solution is mapped to a corresponding feature vector based on the mathematical expressions occurring in the solution. The feature vectors are clustered using a conventional clustering method, or alternatively, using a presently-disclosed Bayesian nonparametric clustering method. A representative solution is selected from each solution cluster. An instructor supplies a grade for each of the representative solutions. Grades for the remaining solutions are automatically generated based on their cluster membership and the instructor supplied grades. The Bayesian method may also automatically identify the location of an error in a given solution. The error location may be supplied to the learner as feedback. The error location may also be used to extract information from correct solutions. The extracted information may be supplied to a learner as a solution hint.

Abstract: A compressive sensing system for dynamic video acquisition. The system includes a video signal interface including a compressive imager configured to acquire compressive sensed video frame data from an object, a video processing unit including a processor and memory. The video processing unit is configured to receive the compressive sensed video frame data from the video signal interface. The memory comprises computer readable instructions that when executed by the processor cause the processor to generate a motion estimate from the compressive sensed video frame data and generate dynamical video frame data from the motion estimate and the compressive sensed video frame data. The dynamical video frame data may be output.

Abstract: The present disclosure is directed to compositions and methods present a universal microbial diagnostic (UMD) platform to screen for microbial organisms in a sample using a small number of random DNA probes that are agnostic to the target DNA sequences. The UMD platform can be used to direct and monitor appropriate treatments, thus minimizing the risk of antibiotic resistance, and enhancing patient care.

Abstract: A mechanism for compiling a generative description of an inference task into a neural network. First, an arbitrary generative probabilistic model from the exponential family is specified (or received). The model characterizes a conditional probability distribution for measurement data given a set of latent variables. A factor graph is generated for the generative probabilistic model. Each factor node of the factor graph is expanded into a corresponding sequence of arithmetic operations, based on a specified inference task and a kind of message passing algorithm. The factor graph and the sequences of arithmetic operations specify the structure of a neural network for performance of the inference task. A learning algorithm is executed, to determine values of parameters of the neural network. The neural network is then ready for performing inference on operational measurements.

Abstract: A mechanism for discerning user preferences for categories of provided content. A computer receives response data including a set of preference values that have been assigned to content items by content users. Output data is computed based on the response data using a latent factor model. The output data includes at least: an association matrix that defines K concepts associated with the content items, wherein K is smaller than the number of the content items, wherein, for each of the K concepts, the association matrix defines the concept by specifying strengths of association between the concept and the content items; and a concept-preference matrix including, for each content user and each of the K concepts, an extent to which the content user prefers the concept. The computer may display a visual representation of the association strengths in the association matrix and/or the extents in the concept-preference matrix.

Abstract: A new framework for video compressed sensing models the evolution of the image frames of a video sequence as a linear dynamical system (LDS). This reduces the video recovery problem to first estimating the model parameters of the LDS from compressive measurements, from which the image frames are then reconstructed. We exploit the low-dimensional dynamic parameters (state sequence) and high-dimensional static parameters (observation matrix) of the LDS to devise a novel compressive measurement strategy that measures only the dynamic part of the scene at each instant and accumulates measurements over time to estimate the static parameters. This enables us to lower the compressive measurement rate considerably yet obtain video recovery at a high frame rate that is in fact inversely proportional to the length of the video sequence. This property makes our framework well-suited for high-speed video capture and other applications.

Abstract: A compressive sensing system for dynamic video acquisition. The system includes a video signal interface including a compressive imager configured to acquire compressive sensed video frame data from an object, a video processing unit including a processor and memory. The video processing unit is configured to receive the compressive sensed video frame data from the video signal interface. The memory comprises computer readable instructions that when executed by the processor cause the processor to generate a motion estimate from the compressive sensed video frame data and generate dynamical video frame data from the motion estimate and the compressive sensed video frame data. The dynamical video frame data may be output.

Abstract: A compressive sensing system for dynamic video acquisition. The system includes a video signal interface including a compressive imager configured to acquire compressive sensed video frame data from an object, a video processing unit including a processor and memory. The video processing unit is configured to receive the compressive sensed video frame data from the video signal interface. The memory comprises computer readable instructions that when executed by the processor cause the processor to generate a motion estimate from the compressive sensed video frame data and generate dynamical video frame data from the motion estimate and the compressive sensed video frame data. The dynamical video frame data may be output.

Abstract: Compressive imaging apparatus employing multiple modulators in various optical schemes to generate the modulation patterns before the signal is recorded at a detector. The compressive imaging apparatus is equally valid when applying compressive imaging to structured light embodiments where the placement is shifted from the acquisition path between the subject and the detector into the illumination path between the source and the subject to be imaged.

Abstract: Mechanisms for automatically grading a large number of solutions provided by learners in response to an open response mathematical question. Each solution is mapped to a corresponding feature vector based on the mathematical expressions occurring in the solution. The feature vectors are clustered using a conventional clustering method, or alternatively, using a presently-disclosed Bayesian nonparametric clustering method. A representative solution is selected from each solution cluster. An instructor supplies a grade for each of the representative solutions. Grades for the remaining solutions are automatically generated based on their cluster membership and the instructor supplied grades. The Bayesian method may also automatically identify the location of an error in a given solution. The error location may be supplied to the learner as feedback. The error location may also be used to extract information from correct solutions.

Abstract: Compressive imaging apparatus employing multiple modulators in various optical schemes to generate the modulation patterns before the signal is recorded at a detector. The compressive imaging apparatus is equally valid when applying compressive imaging to structured light embodiments where the placement is shifted from the acquisition path between the subject and the detector into the illumination path between the source and the subject to be imaged.

Abstract: Compressive imaging apparatus employing multiple modulators in various optical schemes to generate the modulation patterns before the signal is recorded at a detector. The compressive imaging apparatus is equally valid when applying compressive imaging to structured light embodiments where the placement is shifted from the acquisition path between the subject and the detector into the illumination path between the source and the subject to be imaged.

Abstract: A mechanism is disclosed for tracing variation of concept knowledge of learners over time and evaluating content organization of learning resources used by the learners. Computational iterations are performed until a termination condition is achieved. Each of the computational iterations includes a message passing process and a parameter estimation process. The message passing process includes computing a sequence of probability distributions representing time evolution of concept knowledge of the learners for a set of concepts based on (a) learner response data acquired over time, (b) state transition parameters modeling transitions in concept knowledge resulting from interaction with the learning resources, (c) question-related parameters characterizing difficulty of the questions and strengths of association between the questions and the concepts.

Abstract: A database of questions is designed to test understanding of a set of concepts. A subset of the questions is selected for administering to one or more learners in a test. One desires for the subset to be small, to minimize testing workload for the learners and grading workload for instructors. However, to preserve the ability to accurately estimate learners' knowledge of the concepts, the questions of the subset should be appropriately chosen and not too small in number. We propose among other things a non-adaptive algorithm and an adaptive algorithm for test size reduction (TeSR) using an extended version of the Sparse Factor Analysis (SPARFA) framework. The SPARFA framework is a framework for modeling learner responses to questions. Our new TeSR algorithms find fast approximate solutions to a combinatorial optimization problem that involves minimizing the uncertainly in assessing a learner's knowledge of the concepts.