Shrinkage Confidence Procedures

The possibility of improving on the usual multivariate normal confidence was first discussed in Stein (1962). Using the ideas of shrinkage, through Bayesian and empirical Bayesian arguments, domination results, both analytic and numerical, have been obtained. Here we trace some of the developments in confidence set estimation.Comment: Published in at http://dx.doi.org/10.1214/10-STS319 the Statistical Science (http://www.imstat.org/sts/) by the Institute of Mathematical Statistics (http://www.imstat.org

Exact solution of a quantum forced time-dependent harmonic oscillator

The Schrodinger equation is used to exactly evaluate the propagator, wave function, energy expectation values, uncertainty values, and coherent state for a harmonic oscillator with a time dependent frequency and an external driving time dependent force. These quantities represent the solution of the classical equation of motion for the time dependent harmonic oscillator

Live Viewfinder on Inner and Outer Displays of a Foldable Mobile Device

This publication describes techniques directed at utilizing a live viewfinder on inner and outer displays of a foldable mobile device. As foldable displays are becoming a more common feature of mobile devices, creative uses of the foldable inner display and the outer display are desirable. If a user uses the inner display as a viewfinder to take a photograph of one or more human subjects, the outer display, facing the subjects, may likewise be utilized as a viewfinder and include providing the subjects with prompts (contextual tips), e.g., to position themselves within the frame. Having viewfinders on the inner and outer displays of the foldable mobile device and providing contextual tips may enrich the communal experience of both user and subject

BindingDB in 2015: A public database for medicinal chemistry, computational chemistry and systems pharmacology.

BindingDB, www.bindingdb.org, is a publicly accessible database of experimental protein-small molecule interaction data. Its collection of over a million data entries derives primarily from scientific articles and, increasingly, US patents. BindingDB provides many ways to browse and search for data of interest, including an advanced search tool, which can cross searches of multiple query types, including text, chemical structure, protein sequence and numerical affinities. The PDB and PubMed provide links to data in BindingDB, and vice versa; and BindingDB provides links to pathway information, the ZINC catalog of available compounds, and other resources. The BindingDB website offers specialized tools that take advantage of its large data collection, including ones to generate hypotheses for the protein targets bound by a bioactive compound, and for the compounds bound by a new protein of known sequence; and virtual compound screening by maximal chemical similarity, binary kernel discrimination, and support vector machine methods. Specialized data sets are also available, such as binding data for hundreds of congeneric series of ligands, drawn from BindingDB and organized for use in validating drug design methods. BindingDB offers several forms of programmatic access, and comes with extensive background material and documentation. Here, we provide the first update of BindingDB since 2007, focusing on new and unique features and highlighting directions of importance to the field as a whole

Full Resolution Image Compression with Recurrent Neural Networks

This paper presents a set of full-resolution lossy image compression methods based on neural networks. Each of the architectures we describe can provide variable compression rates during deployment without requiring retraining of the network: each network need only be trained once. All of our architectures consist of a recurrent neural network (RNN)-based encoder and decoder, a binarizer, and a neural network for entropy coding. We compare RNN types (LSTM, associative LSTM) and introduce a new hybrid of GRU and ResNet. We also study "one-shot" versus additive reconstruction architectures and introduce a new scaled-additive framework. We compare to previous work, showing improvements of 4.3%-8.8% AUC (area under the rate-distortion curve), depending on the perceptual metric used. As far as we know, this is the first neural network architecture that is able to outperform JPEG at image compression across most bitrates on the rate-distortion curve on the Kodak dataset images, with and without the aid of entropy coding.Comment: Updated with content for CVPR and removed supplemental material to an external link for size limitation

Inflection points and industry change: Was Andy Grove right after all?

We examine whether the ‘strategic inflection points’ described by former Intel CEO Andy Grove correspond to mathematical inflection points in the product/technology life cycle. We find one sense in which they do and two senses in which they do not. This leads to a mapping of colloquial uses of inflection point, tipping point, volatility, chaos, and turbulence against the scientific definitions of these terms. The mapping should be of use to researchers and educators, and also suggests to managers that the possibility of foresight and control in technology-dependent industries is more sharply limited than generally believed. The paper highlights implications for organizational sustainability and offers possible coping mechanisms for managers and directions for educators and researchers

Improved Lossy Image Compression with Priming and Spatially Adaptive Bit Rates for Recurrent Networks

We propose a method for lossy image compression based on recurrent, convolutional neural networks that outperforms BPG (4:2:0 ), WebP, JPEG2000, and JPEG as measured by MS-SSIM. We introduce three improvements over previous research that lead to this state-of-the-art result. First, we show that training with a pixel-wise loss weighted by SSIM increases reconstruction quality according to several metrics. Second, we modify the recurrent architecture to improve spatial diffusion, which allows the network to more effectively capture and propagate image information through the network's hidden state. Finally, in addition to lossless entropy coding, we use a spatially adaptive bit allocation algorithm to more efficiently use the limited number of bits to encode visually complex image regions. We evaluate our method on the Kodak and Tecnick image sets and compare against standard codecs as well recently published methods based on deep neural networks