Spectral Sparsification and Regret Minimization Beyond Matrix Multiplicative Updates

In this paper, we provide a novel construction of the linear-sized spectral sparsifiers of Batson, Spielman and Srivastava [BSS14]. While previous constructions required $\Omega(n^4)$ running time [BSS14, Zou12], our sparsification routine can be implemented in almost-quadratic running time $O(n^{2+\varepsilon})$. The fundamental conceptual novelty of our work is the leveraging of a strong connection between sparsification and a regret minimization problem over density matrices. This connection was known to provide an interpretation of the randomized sparsifiers of Spielman and Srivastava [SS11] via the application of matrix multiplicative weight updates (MWU) [CHS11, Vis14]. In this paper, we explain how matrix MWU naturally arises as an instance of the Follow-the-Regularized-Leader framework and generalize this approach to yield a larger class of updates. This new class allows us to accelerate the construction of linear-sized spectral sparsifiers, and give novel insights on the motivation behind Batson, Spielman and Srivastava [BSS14]

Unsupervised Learning with Self-Organizing Spiking Neural Networks

We present a system comprising a hybridization of self-organized map (SOM) properties with spiking neural networks (SNNs) that retain many of the features of SOMs. Networks are trained in an unsupervised manner to learn a self-organized lattice of filters via excitatory-inhibitory interactions among populations of neurons. We develop and test various inhibition strategies, such as growing with inter-neuron distance and two distinct levels of inhibition. The quality of the unsupervised learning algorithm is evaluated using examples with known labels. Several biologically-inspired classification tools are proposed and compared, including population-level confidence rating, and n-grams using spike motif algorithm. Using the optimal choice of parameters, our approach produces improvements over state-of-art spiking neural networks

Clear Speech strategies and speech perception in adverse listening conditions

The study investigated the impact of different types of clear speech on speech perception in an adverse listening condition. Tokens were extracted from spontaneous speech dialogues in which participants completed a problem-solving task in good listening conditions or while experiencing a one-sided ‘communication barrier’: a real-time vocoder or multibabble noise. These two adverse conditions induced the ‘unimpaired’ participant to produce clear speech. When tokens from these three conditions were presented in multibabble noise, listeners were quicker at processing clear tokens produced to counter the effects of multibabble noise than clear tokens produced to counteract the vocoder, or tokens produced in good communicative conditions. A clarity rating experiment using the same tokens presented in quiet showed that listeners do not distinguish between different types of clear speech. Together, these results suggest that clear speaking styles produced in different communicative conditions have acoustic-phonetic characteristics adapted to the needs of the listener, even though they may be perceived as being of similar clarity

The effect of age and hearing loss on partner-directed gaze in a communicative task

The study examined the partner-directed gaze patterns of old and young talkers in a task (DiapixUK) that involved two people (a lead talker and a follower) engaging in a spontaneous dialogue. The aim was (1) to determine whether older adults engage less in partner-directed gaze than younger adults by measuring mean gaze frequency and mean total gaze duration; and (2) examine the effect that mild hearing loss may have on older adult’s partner-directed gaze. These were tested in various communication conditions: a no barrier condition; BAB2 condition in which the lead talker and the follower spoke and heard each other in multitalker babble noise; and two barrier conditions in which the lead talker could hear clearly their follower but the follower could not hear the lead talker very clearly (i.e., the lead talker’s voice was degraded by babble (BAB1) or by a Hearing Loss simulation (HLS). 57 single-sex pairs (19 older adults with mild Hearing Loss, 17 older adults with Normal Hearing and 21 younger adults) participated in the study. We found that older adults with normal hearing produced fewer partner-directed gazes (and gazed less overall) than either the older adults with hearing loss or younger adults for the BAB1 and HLS conditions. We propose that this may be due to a decline in older adult’s attention to cues signaling how well a conversation is progressing. Older adults with hearing loss, however, may attend more to visual cues because they give greater weighting to these for understanding speech

Homogeneisation du comportement polycristallin considerer la plasticite a l'echelle des grains ou des bandes de glissement ?

International audienc

Inapproximability of maximal strip recovery

In comparative genomic, the first step of sequence analysis is usually to decompose two or more genomes into syntenic blocks that are segments of homologous chromosomes. For the reliable recovery of syntenic blocks, noise and ambiguities in the genomic maps need to be removed first. Maximal Strip Recovery (MSR) is an optimization problem proposed by Zheng, Zhu, and Sankoff for reliably recovering syntenic blocks from genomic maps in the midst of noise and ambiguities. Given $d$ genomic maps as sequences of gene markers, the objective of \msr{d} is to find $d$ subsequences, one subsequence of each genomic map, such that the total length of syntenic blocks in these subsequences is maximized. For any constant $d \ge 2$, a polynomial-time 2d-approximation for \msr{d} was previously known. In this paper, we show that for any $d \ge 2$, \msr{d} is APX-hard, even for the most basic version of the problem in which all gene markers are distinct and appear in positive orientation in each genomic map. Moreover, we provide the first explicit lower bounds on approximating \msr{d} for all $d \ge 2$. In particular, we show that \msr{d} is NP-hard to approximate within $\Omega(d/\log d)$. From the other direction, we show that the previous 2d-approximation for \msr{d} can be optimized into a polynomial-time algorithm even if $d$ is not a constant but is part of the input. We then extend our inapproximability results to several related problems including \cmsr{d}, \gapmsr{\delta}{d}, and \gapcmsr{\delta}{d}.Comment: A preliminary version of this paper appeared in two parts in the Proceedings of the 20th International Symposium on Algorithms and Computation (ISAAC 2009) and the Proceedings of the 4th International Frontiers of Algorithmics Workshop (FAW 2010

Acoustic and visual adaptations in speech produced to counter adverse listening conditions

This study investigated whether communication modality affects talkers’ speech adaptation to an interlocutor exposed to background noise. It was predicted that adaptations to lip gestures would be greater and acoustic ones reduced when communicating face-to-face. We video recorded 14 Australian-English talkers (Talker A) speaking in a face-to-face or auditory only setting with their interlocutors who were either in quiet or noise. Focusing on keyword productions, acoustic-phonetic adaptations were examined via measures of vowel intensity, pitch, keyword duration, vowel F1/F2 space and VOT, and visual adaptations via measures of vowel interlip area. The interlocutor adverse listening conditions lead Talker A to reduce speech rate, increase pitch and expand vowel space. These adaptations were not significantly reduced in the face-to-face setting although there was a trend for a smaller degree of vowel space expansion than in the auditory only setting. Visible lip gestures were more enhanced overall in the face-to-face setting, but also increased in the auditory only setting when countering the effects of noise. This study therefore showed only small effects of communication modality on speech adaptations

On k-Column Sparse Packing Programs

We consider the class of packing integer programs (PIPs) that are column sparse, i.e. there is a specified upper bound k on the number of constraints that each variable appears in. We give an (ek+o(k))-approximation algorithm for k-column sparse PIPs, improving on recent results of $k^2\cdot 2^k$ and $O(k^2)$. We also show that the integrality gap of our linear programming relaxation is at least 2k-1; it is known that k-column sparse PIPs are $\Omega(k/ \log k)$-hard to approximate. We also extend our result (at the loss of a small constant factor) to the more general case of maximizing a submodular objective over k-column sparse packing constraints.Comment: 19 pages, v3: additional detail