Authorship Attribution Using a Neural Network Language Model

In practice, training language models for individual authors is often expensive because of limited data resources. In such cases, Neural Network Language Models (NNLMs), generally outperform the traditional non-parametric N-gram models. Here we investigate the performance of a feed-forward NNLM on an authorship attribution problem, with moderate author set size and relatively limited data. We also consider how the text topics impact performance. Compared with a well-constructed N-gram baseline method with Kneser-Ney smoothing, the proposed method achieves nearly 2:5% reduction in perplexity and increases author classification accuracy by 3:43% on average, given as few as 5 test sentences. The performance is very competitive with the state of the art in terms of accuracy and demand on test data. The source code, preprocessed datasets, a detailed description of the methodology and results are available at https://github.com/zge/authorship-attribution.Comment: Proceedings of the 30th AAAI Conference on Artificial Intelligence (AAAI'16

Effective risk governance for environmental policy making: a knowledge management perspective

Effective risk management within environmental policy making requires knowledge on natural, economic and social systems to be integrated; knowledge characterised by complexity, uncertainty and ambiguity. We describe a case study in a (UK) central government department exploring how risk governance supports and hinders this challenging integration of knowledge. Forty-five semi-structured interviews were completed over a two year period. We found that lateral knowledge transfer between teams working on different policy areas was widely viewed as a key source of knowledge. However, the process of lateral knowledge transfer was predominantly informal and unsupported by risk governance structures. We argue this made decision quality vulnerable to a loss of knowledge through staff turnover, and time and resource pressures. Our conclusion is that the predominant form of risk governance framework, with its focus on centralised decision-making and vertical knowledge transfer is insufficient to support risk-based, environmental policy making. We discuss how risk governance can better support environmental policy makers through systematic knowledge management practices

Recursive time-varying filter banks for subband image coding

Filter banks and wavelet decompositions that employ recursive filters have been considered previously and are recognized for their efficiency in partitioning the frequency spectrum. This paper presents an analysis of a new infinite impulse response (IIR) filter bank in which these computationally efficient filters may be changed adaptively in response to the input. The filter bank is presented and discussed in the context of finite-support signals with the intended application in subband image coding. In the absence of quantization errors, exact reconstruction can be achieved and by the proper choice of an adaptation scheme, it is shown that IIR time-varying filter banks can yield improvement over conventional ones

Large-scale Star Formation Triggering in the Low-mass Arp 82 System: A Nearby Example of Galaxy Downsizing Based on UV/Optical/Mid-IR Imaging

As part of our Spitzer Spirals, Bridges, and Tails project to help understand the effects of galaxy interactions on star formation, we analyze GALEX ultraviolet, SARA optical, and Spitzer infrared images of the interacting galaxy pair Arp 82 (NGC 2535/6) and compare to a numerical simulation of the interaction. We investigate the multiwavelength properties of several individual star forming complexes (clumps). Using optical and UV colors, EW(Halpha), and population synthesis models we constrain the ages of the clumps and find that the median clump age is about 12 Myr. The clumps have masses ranging from a few times 10^6 to 10^9 solar masses. In general, the clumps in the tidal features have similar ages to those in the spiral region, but are less massive. The 8 micron and 24 micron luminosities are used to estimate the far-infrared luminosities and the star formation rates of the clumps. The total clump star formation rate is 2.0+/-0.8 solar masses per year, while the entire Arp 82 system is forming stars at a rate of 4.9+/-2.0 solar masses per year. We find, for the first time, stars in the HI arc to the southeast of the NGC 2535 disk. Population synthesis models indicate that all of the observed populations have young to intermediate ages. We conclude that although the gas disks and some old stars may have formed early-on, the progenitors are late-type or low surface brightness and the evolution of these galaxies was halted until the recent encounter.Comment: Accepted for publication in the AJ, 22 Figures, 5 Table

High Order Entropy-Constrained Residual VQ for Lossless Compression of Images

High order entropy coding is a powerful technique for exploiting high order statistical dependencies. However, the exponentially high complexity associated with such a method often discourages its use. In this paper, an entropy-constrained residual vector quantization method is proposed for lossless compression of images. The method consists of first quantizing the input image using a high order entropy-constrained residual vector quantizer and then coding the residual image using a first order entropy coder. The distortion measure used in the entropy-constrained optimization is essentially the first order entropy of the residual image. Experimental results show very competitive performance

A Subband Coding Method for HDTV

This paper introduces a new HDTV coder based on motion compensation, subband coding, and high order conditional entropy coding. The proposed coder exploits the temporal and spatial statistical dependencies inherent in the HDTV signal by using intra- and inter-subband conditioning for coding both the motion coordinates and the residual signal. The new framework provides an easy way to control the system complexity and performance, and inherently supports multiresolution transmission. Experimental results show that the coder outperforms MPEG-2, while still maintaining relatively low complexity

Image coding using entropy-constrained residual vector quantization

The residual vector quantization (RVQ) structure is exploited to produce a variable length codeword RVQ. Necessary conditions for the optimality of this RVQ are presented, and a new entropy-constrained RVQ (ECRVQ) design algorithm is shown to be very effective in designing RVQ codebooks over a wide range of bit rates and vector sizes. The new EC-RVQ has several important advantages. It can outperform entropy-constrained VQ (ECVQ) in terms of peak signal-to-noise ratio (PSNR), memory, and computation requirements. It can also be used to design high rate codebooks and codebooks with relatively large vector sizes. Experimental results indicate that when the new EC-RVQ is applied to image coding, very high quality is achieved at relatively low bit rates

Conditional Entropy-Constrained Residual VQ with Application to Image Coding

This paper introduces an extension of entropy-constrained residual vector quantization (VQ) where intervector dependencies are exploited. The method, which we call conditional entropy-constrained residual VQ, employs a high-order entropy conditioning strategy that captures local information in the neighboring vectors. When applied to coding images, the proposed method is shown to achieve better rate-distortion performance than that of entropy-constrained residual vector quantization with less computational complexity and lower memory requirements. Moreover, it can be designed to support progressive transmission in a natural way. It is also shown to outperform some of the best predictive and finite-state VQ techniques reported in the literature. This is due partly to the joint optimization between the residual vector quantizer and a high-order conditional entropy coder as well as the efficiency of the multistage residual VQ structure and the dynamic nature of the prediction