Philanthropic Engagement with Community Youth Violence Prevention Initiatives

Around the country, many communities are employing a new approach to prevent youth violence. Pulling together leaders across disciplines, shaped by local champions of change, communities are engaging in innovative and data-driven multidisciplinary efforts that engage all key community entities in order to stem the tide of youth violence and restore hope and opportunity so that every child in their midst can be safe in backyards and schoolyards, sidewalks and hallways on every street and neighborhood. Some of these comprehensive community-wide efforts have been seeded by federal initiatives including the Promise and Choice Neighborhood initiatives, Safe Streets, Strong Communities, Defending Childhood and the National Forum on Youth Violence Prevention. This paper will highlight the work of the National Forum and opportunities for philanthropic engagement with this work

Multichannel high resolution NMF for modelling convolutive mixtures of non-stationary signals in the time-frequency domain

Several probabilistic models involving latent components have been proposed for modeling time-frequency (TF) representations of audio signals such as spectrograms, notably in the nonnegative matrix factorization (NMF) literature. Among them, the recent high-resolution NMF (HR-NMF) model is able to take both phases and local correlations in each frequency band into account, and its potential has been illustrated in applications such as source separation and audio inpainting. In this paper, HR-NMF is extended to multichannel signals and to convolutive mixtures. The new model can represent a variety of stationary and non-stationary signals, including autoregressive moving average (ARMA) processes and mixtures of damped sinusoids. A fast variational expectation-maximization (EM) algorithm is proposed to estimate the enhanced model. This algorithm is applied to piano signals, and proves capable of accurately modeling reverberation, restoring missing observations, and separating pure tones with close frequencies

Political and Social Significance of Islam in the Middle East

While its operation is difficult to specify, and illusive in its character, anyone who lives in the area constantly feels the effect of its influence. We are to determine this morning, as far as one can in a single lecture, something of the character and place of this spiritual influence in the social and political life of the Middle East

MULTILINEAR SINGULAR VALUE DECOMPOSITION FOR STRUCTURED TENSORS

International audienceThe Higher-Order SVD (HOSVD) is a generalization of the Singular Value Decompo- sition (SVD) to higher-order tensors (i.e. arrays with more than two indices) and plays an important role in various domains. Unfortunately, this decomposition is computationally demanding. Indeed, the HOSVD of a third-order tensor involves the computation of the SVD of three matrices, which are referred to as "modes", or "matrix unfoldings". In this paper, we present fast algorithms for computing the full and the rank-truncated HOSVD of third-order structured (symmetric, Toeplitz and Hankel) tensors. These algorithms are derived by considering two specific ways to unfold a structured tensor, leading to structured matrix unfoldings whose SVD can be efficiently computed1

ADAPTIVE MULTILINEAR SVD FOR STRUCTURED TENSORS

International audienceThe Higher-Order SVD (HOSVD) is a generalization of the SVD to higher-order tensors (ie. arrays with more than two indexes) and plays an important role in various domains. Unfortunately, the computational cost of this decomposition is very high since the basic HOSVD algorithm involves the computation of the SVD of three highly redundant block-Hankel matrices, called modes. In this paper, we present an ultra-fast way of computing the HOSVD of a third-order structured tensor. The key result of this work lies in the fact it is possible to reduce the basic HOSVD algorithm to the computation of the SVD of three non-redundant Hankel matrices whose columns are multiplied by a given weighting function. Next, we exploit an FFT-based implementation of the orthogonal iteration algorithm in an adaptive way. Even though for a square (I ×I ×I) tensor the complexity of the basic full-HOSVD is O(I4) and O(rI3) for its r-truncated version, our approach reaches a linear complexity of O(rI log2(I))