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Sparse Probit Factor Analysis for Learning Analytics
(2013)2013 IEEE International Conference on Acoustics, Speech and Signal ProcessingWe develop a new model and algorithm for machine learning-based learning analytics, which estimate a learner's knowledge of the concepts underlying a domain. Our model represents the probability that a learner provides the correct response to a question in terms of three factors: their understanding of a set of underlying concepts, the concepts involved in each question, and each question's intrinsic difficulty. We estimate these factors ...Conference Paper -
A Robust and Efficient Method to Recover Neural Events from Noisy and Corrupted Data
(2013)2013 6th International IEEE/EMBS Conference on Neural Engineering (NER)In a variety of neural data analysis problems, “neural events” such as action potentials (APs) or post-synaptic potentials (PSPs), must be recovered from noisy and possibly corrupted measurements. For instance, in calcium imaging, an AP or group of APs generate a stereotyped calcium signal with a quick rise time and slow decay. In this work, we develop a general-purpose method for: (i) learning a template waveform that signifies the ...Conference Paper -
HSPA+/LTE-A Turbo Decoder on GPU and Multicore CPU
(2013)2013 Asilomar Conference on Signals, Systems and ComputersThis paper compares two implementations of reconfigurable and high-throughput turbo decoders. The first implementation is optimized for an NVIDIA Kepler graphics processing unit (GPU), whereas the second implementation is for an Intel Ivy Bridge processor. Both implementations support max-log-MAP and log-MAP turbo decoding algorithms, various code rates, different interleaver types, and all block-lengths, as specified by HSPA; and ...Conference Paper -
Tag-aware ordinal sparse factor analysis for learning and content analytics
(2013)Proceedings of the 6th International Conference on Educational Data Mining (EDM 2013)Conference Paper -
Recovering Sparse Low-rank Blocks in Tandem Mass Spectrometry
(2013)We develop a novel sparse low-rank block (SLoB) signal recovery framework that simultaneously exploits sparsity and low-rankness to accurately identify peptides (fragments of proteins) from biological samples observed using tandem mass spectrometery (TMS). To efficiently perform SLoB-based peptide identification, we propose two novel recovery algorithms: An exact iterative method based on the alternating method of multipliers (ADMM) and ...Conference Paper -
Approximate Matrix Inversion for High-Throughput Data Detection in the Large-Scale MIMO Uplink
(2013)2013 IEEE International Symposium on Circuits and Systems (ISCAS)The high processing complexity of data detection in the large-scale multiple-input multiple-output (MIMO) uplink necessitates high-throughput VLSI implementations. In this paper, we propose - to the best of our knowledge - first matrix inversion implementation suitable for data detection in systems having hundreds of antennas at the base station (BS). The underlying idea is to carry out an approximate matrix inversion using a small number ...Conference Paper -
Full-Duplex in Large-Scale Wireless Systems
(2013)2013 Asilomar Conference on Signals, Systems and ComputersIn this paper, we investigate the combination of full-duplex wireless communication with large-scale multiple-input multiple-output (MIMO) technology, which has the potential for bidirectional wireless communication at high spectral efficiency and low power consumption. In addition, we study its application to cellular (multi-user) systems that could be extended with large antenna arrays, such as 3GPP LTE. In order to solve the fundamental ...Conference Paper -
Bayesian Pairwise Collaboration Detection in Educational Datasets
(2013)2013 IEEE Global Conference on Signal and Information ProcessingOnline education affords the opportunity to revolutionize learning by providing access to high-quality educational resources at low costs. The recent popularity of so-called MOOCs (massive open online courses) further accelerates this trend. However, these exciting advancements result in several challenges for the course instructors. Among these challenges is the detection of collaboration between learners on online tests or take-home ...Conference Paper -
Subspace Clustering with Dense Representations
(2013)2013 IEEE International Conference on Acoustics, Speech and Signal ProcessingUnions of subspaces have recently been shown to provide a compact nonlinear signal model for collections of high-dimensional data, such as large collections of images or videos. In this paper, we introduce a novel data-driven algorithm for learning unions of subspaces directly from a collection of data; our approach is based upon forming minimum l2 -norm (least-squares) representations of a signal with respect to other signals in the ...Conference Paper -
Test-size Reduction Using Sparse Factor Analysis
(2013)Proceedings of the 10th International Conference on Sampling Theory and Applications (SampTA 2013)Conference Paper