9,377 research outputs found
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
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