Deep roots: Improving CNN efficiency with hierarchical filter groups

We propose a new method for creating computationally efficient and compact convolutional neural networks (CNNs) using a novel sparse connection structure that resembles a tree root. This allows a significant reduction in computational cost and number of parameters compared to state-of-the-art deep CNNs, without compromising accuracy, by exploiting the sparsity of inter-layer filter dependencies. We validate our approach by using it to train more efficient variants of state-of-the-art CNN architectures, evaluated on the CIFAR10 and ILSVRC datasets. Our results show similar or higher accuracy than the baseline architectures with much less computation, as measured by CPU and GPU timings. For example, for ResNet 50, our model has 40% fewer parameters, 45% fewer floating point operations, and is 31% (12%) faster on a CPU (GPU). For the deeper ResNet 200 our model has 25% fewer floating point operations and 44% fewer parameters, while maintaining state-of-the-art accuracy. For GoogLeNet, our model has 7% fewer parameters and is 21% (16%) faster on a CPU (GPU).Microsoft Research PhD Scholarshi

Evidence for multiple impurity bands in sodium-doped silicon MOSFETs

We report measurements of the temperature-dependent conductivity in a silicon metal-oxide-semiconductor field-effect transistor that contains sodium impurities in the oxide layer. We explain the variation of conductivity in terms of Coulomb interactions that are partially screened by the proximity of the metal gate. The study of the conductivity exponential prefactor and the localization length as a function of gate voltage have allowed us to determine the electronic density of states and has provided arguments for the presence of two distinct bands and a soft gap at low temperature.Comment: 4 pages; 5 figures; Published in PRB Rapid-Communication

IgG anti-apolipoprotein A-1 antibodies in patients with systemic lupus erythematosus are associated with disease activity and corticosteroid therapy: an observational study.

IgG anti-apolipoprotein A-1 (IgG anti-apoA-1) antibodies are present in patients with systemic lupus erythematosus (SLE) and may link inflammatory disease activity and the increased risk of developing atherosclerosis and cardiovascular disease (CVD) in these patients. We carried out a rigorous analysis of the associations between IgG anti-apoA-1 levels and disease activity, drug therapy, serology, damage, mortality and CVD events in a large British SLE cohort

Refining Architectures of Deep Convolutional Neural Networks

© 2016 IEEE. Deep Convolutional Neural Networks (CNNs) have recently evinced immense success for various image recognition tasks [11, 27]. However, a question of paramount importance is somewhat unanswered in deep learning research - is the selected CNN optimal for the dataset in terms of accuracy and model size? In this paper, we intend to answer this question and introduce a novel strategy that alters the architecture of a given CNN for a specified dataset, to potentially enhance the original accuracy while possibly reducing the model size. We use two operations for architecture refinement, viz. stretching and symmetrical splitting. Stretching increases the number of hidden units (nodes) in a given CNN layer, while a symmetrical split of say K between two layers separates the input and output channels into K equal groups, and connects only the corresponding input-output channel groups. Our procedure starts with a pre-trained CNN for a given dataset, and optimally decides the stretch and split factors across the network to refine the architecture. We empirically demonstrate the necessity of the two operations. We evaluate our approach on two natural scenes attributes datasets, SUN Attributes [16] and CAMIT-NSAD [20], with architectures of GoogleNet and VGG-11, that are quite contrasting in their construction. We justify our choice of datasets, and show that they are interestingly distinct from each other, and together pose a challenge to our architectural refinement algorithm. Our results substantiate the usefulness of the proposed method

Emission reduction via supply chain coordination

This paper examines the environmental impact of potential coordination on supply chains. A decentralized two-node supply chain is studied, in which one node is a buyer ordering from a second node, who is a supplier operating under the lot-for-lot policy. The supplier is allowed to use a quantity discount to manipulate the buyer's decision reducing both his individual cost and system's operational costs. This results in decreasing the frequency of deliveries. We demonstrate that environmentally friendly policies could be also cost saving. The crucial factor about the environmental benefits is the total distance travelled rather than the vehicle loads. We establish the magnitude of the environmental benefits using numerical examples under specific operational parameters. Complete and incomplete information cases are investigated, where the buyer and the supplier make their decisions to optimize their own business operations

Uncertainty and Quality rating in Analytical Vulnerability Assessment

Fragility curves represent a major component of seismic risk and vulnerability assessment of buildings and infrastructure facilities. A recently conducted extensive literature review under the framework of developing the “GEM Guide for Selecting of Existing Analytical Fragility Curves and Compilation of the Database”, shows that there is a wealth of existing analytically derived fragility curves that can be used for future applications. However, the main challenge in using these curves is how to identify and, if necessary, combine suitable fragility curves from a pool of curves with different characteristics and, often unknown, reliability. The present article introduces a rating system that has been developed following detail review and critique of the various methodologies on the derivation of analytical fragility curves that have been generated in the past two decades. The main scope is to provide guidance, either in choosing suitable and robust existing fragility curves or in generating new fragility curves. The quality rating system rates the quality of a curve according to the effect that various parameters, simulation procedures and assumptions on the reliability of fragility curve. It also assists and steers potential analysts towards a better identification and quantification of expected uncertainties throughout the process

Approximating Fractional Time Quantum Evolution

An algorithm is presented for approximating arbitrary powers of a black box unitary operation, $\mathcal{U}^t$, where $t$ is a real number, and $\mathcal{U}$ is a black box implementing an unknown unitary. The complexity of this algorithm is calculated in terms of the number of calls to the black box, the errors in the approximation, and a certain `gap' parameter. For general $\mathcal{U}$ and large $t$, one should apply $\mathcal{U}$ a total of $\lfloor t \rfloor$ times followed by our procedure for approximating the fractional power $\mathcal{U}^{t-\lfloor t \rfloor}$. An example is also given where for large integers $t$ this method is more efficient than direct application of $t$ copies of $\mathcal{U}$. Further applications and related algorithms are also discussed.Comment: 13 pages, 2 figure

Offenders' Crime Narratives across Different Types of Crimes

The current study explores the roles offenders see themselves playing during an offence and their relationship to different crime types. One hundred and twenty incarcerated offenders indicated the narrative roles they acted out whilst committing a specific crime they remembered well. The data were subjected to Smallest Space Analysis (SSA) and four themes were identified: Hero, Professional, Revenger and Victim in line with the recent theoretical framework posited for Narrative Offence Roles (Youngs & Canter, 2012). Further analysis showed that different subsets of crimes were more like to be associated with different narrative offence roles. Hero and Professional were found to be associated with property offences (theft, burglary and shoplifting), drug offences and robbery and Revenger and Victim were found to be associated with violence, sexual offences and murder. The theoretical implications for understanding crime on the basis of offenders' narrative roles as well as practical implications are discussed