Abstract: A system includes an integrated circuit configured to communicating data in a channel. A channel matrix for the channel including a plurality of columns is received. A preprocessing step is performed, using a preprocessing unit, to compute a plurality of preprocessed column values corresponding to respective columns. An update step is performed, using an update unit, to update an estimation vector using a plurality of outer-loop iterations of an outer loop. Each outer-loop iteration updates the estimation vector using the plurality of preprocessed column values. An access link process is performed using the estimation vector.

Abstract: An integrated circuit (IC) includes a downlink unit including an input to receive a first plurality of frequency domain (FD) symbols associated with data symbols for a plurality of users, and an iteration unit to perform a plurality of iterations based on adjustment values. Each iteration includes generating a second plurality of FD symbols by performing a precoding process based on the first plurality of FD symbols, generating a third plurality of time domain (TD) symbols by performing a first modulation process based on the second plurality of FD symbols, generating a fourth plurality of TD symbols by performing a dynamic range reduction process based on absolute values of the third plurality of TD symbols, and updating the adjustment values. The downlink unit further includes a decision unit configured to generate transmit TD symbols for transmission through a channel to the plurality of users.

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 system includes a memory and an integrated circuit coupled to the memory. The integrated circuit is configured to communicate data in a channel characterized as a space having at least a frequency dimension. Anchor locations within the space correspond to respective regions of the space. The integrated circuit is further configured to determine a first inverse of a first matrix that corresponds to a first channel matrix for a first anchor location of the anchor locations. The first anchor location corresponds to a first region of the regions. The integrated circuit is further configured to perform an access link process for a second location other than the first anchor location but within the first region, the access link process using the first inverse determined for the first anchor location.

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: An apparatus relating generally to matrix inversion is disclosed. This apparatus includes a matrix inversion module coupled to receive matrix information and to provide an approximation of an inversion of the matrix information. The matrix inversion module comprises a decomposition block coupled to receive the matrix information and to decompose the matrix information into diagonal matrix information and off diagonal matrix information, and an expansion block. The expansion block is coupled to receive the diagonal matrix information and the off diagonal matrix information, and to invert a matrix sum of the diagonal matrix information and the off diagonal matrix information by generation of a portion of a series expansion.

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.

Abstract: A mechanism for facilitating personalized learning. A computer receives graded response data including grades that have been assigned to answers provided by learners in response to a set of questions. Output data is computed based on the graded response data using a latent factor model. The output data includes at least: an association matrix that defines a set of K concepts implicit in the set of questions, wherein K is smaller than the number of questions, wherein, for each of the K concepts, the association matrix defines the concept by specifying strengths of association between the concept and the questions; and a learner knowledge matrix including, for each learner and each of the K concepts, an extent of the learner's knowledge of the concept. The computer may display a visual representation of the association strengths in the association matrix and/or the extents in the learner knowledge matrix.

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: Compressive sensing (CS)-based spatial-multiplexing cameras (SMCs) sample a scene through a series of coded projections using a spatial light modulator and a few optical sensor elements. A co-designed video CS sensing matrix and recovery algorithm provides an efficiently computable low-resolution video preview. The scene's optical flow is estimated from the preview and fed into a convex-optimization algorithm to recover the high-resolution video.