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arXiv 2017年

作者： Tran, Dat Thanh Magris, Martin Kanniainen, Juho Gabbouj, Moncef Iosifidis, Alexandros Laboratory of Signal Processing Tampere University of Technology Tampere Finland Laboratory of Industrial and Information Management Tampere University of Technology Tampere Finland Department of Engineering Electrical & Computer Engineering Aarhus University Aarhus Denmark

Nowadays, with the availability of massive amount of trade data collected, the dynamics of the financial markets pose both a challenge and an opportunity for high-frequency traders. In order to take advantage of the rapid, subtle movement of assets in High-Frequency Trading (HFT), an automatic algorithm to analyze and detect patterns of price change based on transaction records must be available. The multichannel, time-series representation of financial data naturally suggests tensor-based learning algorithms. In this work, we investigate the effectiveness of two multilinear methods for the mid-price prediction problem against other existing methods. The experiments in a large-scale dataset which contains more than 4 million limit orders show that by utilizing tensor representation, multilinear models outperform vector-based approaches and other competing ones. Copyright © 2017, The Authors. All rights reserved.

关键词： Tensors

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Temporal attention augmented bilinear network for financial time-series data analysis

arXiv

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arXiv 2017年

作者： Tran, Dat Thanh Iosifidis, Alexandros Kanniainen, Juho Gabbouj, Moncef Laboratory of Signal Processing Tampere University of Technology Tampere Finland Department of Engineering Electrical & Computer Engineering Aarhus University Aarhus Denmark Laboratory of Industrial and Information Management Tampere University of Technology Tampere Finland

Financial time-series forecasting has long been a challenging problem because of the inherently noisy and stochastic nature of the market. In the High-Frequency Trading (HFT), forecasting for trading purposes is even a more challenging task since an automated inference system is required to be both accurate and fast. In this paper, we propose a neural network layer architecture that incorporates the idea of bilinear projection as well as an attention mechanism that enables the layer to detect and focus on crucial temporal information. The resulting network is highly interpretable, given its ability to highlight the importance and contribution of each temporal instance, thus allowing further analysis on the time instances of interest. Our experiments in a large-scale Limit Order Book (LOB) dataset show that a two-hidden-layer network utilizing our proposed layer outperforms by a large margin all existing state-of-the-art results coming from much deeper architectures while requiring far fewer computations. Copyright © 2017, The Authors. All rights reserved.

关键词： Time series

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Tensor representation in high-frequency financial data for price change prediction

Tensor representation in high-frequency financial data for p...

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IEEE Symposium Series on Computational Intelligence (SSCI)

作者： Dat Thanh Tran Martin Magris Juho Kanniainen Moncef Gabbouj Alexandros Iosifidis Laboratory of Signal Processing Tampere University of Technology Tampere Finland Laboratory of Industrial and Information Management Tampere University of Technology Tampere Finland Department of Engineering Electrical & Computer Engineering Aarhus University Aarhus Denmark

Nowadays, with the availability of massive amount of trade data collected, the dynamics of the financial markets pose both a challenge and an opportunity for high frequency traders. In order to take advantage of the rapid, subtle movement of assets in High Frequency Trading (HFT), an automatic algorithm to analyze and detect patterns of price change based on transaction records must be available. The multichannel, time-series representation of financial data naturally suggests tensor-based learning algorithms. In this work, we investigate the effectiveness of two multilinear methods for the mid-price prediction problem against other existing methods. The experiments in a large scale dataset which contains more than 4 millions limit orders show that by utilizing tensor representation, multilinear models outperform vector-based approaches and other competing ones.

关键词： Tensile stress Mathematical model Recurrent neural networks Feature extraction Heuristic algorithms Prediction algorithms

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Benchmark dataset for mid-price forecasting of limit order book data with machine learning methods

arXiv

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arXiv 2017年

作者： Ntakaris, Adamantios Magris, Martin Kanniainen, Juho Gabbouj, Moncef Iosifidis, Alexandros Laboratory of Signal Processing Tampere University of Technology Korkeakoulunkatu 1 Tampere Finland Laboratory of Industrial and Information Management Tampere University of Technology Korkeakoulunkatu 8 Tampere Finland Department of Engineering Electrical and Computer Engineering Aarhus University Inge Lehmanns Gade 10 Aarhus Denmark

Managing the prediction of metrics in high-frequency financial markets is a challenging task. An efficient way is by monitoring the dynamics of a limit order book to identify the information edge. This paper describes the first publicly available benchmark dataset of high-frequency limit order markets for mid-price prediction. We extracted normalized data representations of time series data for five stocks from the NASDAQ Nordic stock market for a time period of ten consecutive days, leading to a dataset of ≈4,000,000 time series samples in total. A day-based anchored cross-validation experimental protocol is also provided that can be used as a benchmark for comparing the performance of state-of-the-art methodologies. Performance of baseline approaches are also provided to facilitate experimental comparisons. We expect that such a large-scale dataset can serve as a testbed for devising novel solutions of expert systems for high-frequency limit order book data analysis. Copyright © 2017, The Authors. All rights reserved.

关键词： Regression analysis

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Biological Modeling Challenges in a Multiphysics Approach

Biological Modeling Challenges in a Multiphysics Approach

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IEEE Pulsed Power Conference

作者： Jessy Mathault Hamza Landari Frederic Tessier Paul Fortier Amine Miled LABioTRON Bioengineering Research Laboratory Electrical and Computer Engineering Department Universite Laval Radiocommunications and Signal Processing Laboratory (LRTS) Electrical and Computer Engineering Department Universite Laval

ISBN: (纸本)9781509063901

Modeling biological behaviour requires a multidisciplinary approach and very large computational resources. In this paper, we present an overview of our research work with regards to biological neuron network (NN) modeling, electrochemical reaction emulation for neurotransmitters and finite element modeling (FEM) of a very low pressure micropump for neurotransmitter concentration modulations. Three different approaches have been explored, namely MATLAB simulations of biological NN, MATLAB simulations of electro-chemical reactions and the FEM approach. Our objective through biological NN modeling is to investigate the need to develop a dedicated chip for a NN implementation or to use a strictly software approach. The objective of the electrochemical emulator is to provide a tool which can help us to understand how to decompose cyclic voltammetry curves into more conventional and easy to interpret graphs. However, although the three approaches appear independent, they are in fact all related as we investigate the computer performances needed for biological modeling.

关键词： Modeling Biological Neurotransmitters Finite element method interdisciplinary approach Biological Models simulating Research work MATLAB Cyclic voltammetry chemical reactions

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Notice of Violation of IEEE Publication Principles: Application of LDPC codes on PUF error correction based on code-offset construction

Notice of Violation of IEEE Publication Principles: Applicat...

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International Conference on ASIC

作者： Kai Sun Yingjie Lao Weiqiang Liu Xiaohu You Chuan Zhang National Mobile Communications Research Laboratory Southeast University Nanjing China Lab of Efficient Architectures for Digital-communication and Signal-processing (LEADS) Department of Electrical and Computer Engineering Clemson University Clemson SC USA College of Electronic and Information Engineering NUAA Nanjing China

Notice of Violation of IEEE Publication Principles "Application of LDPC Codes on PUF Error Correction Based on Code-offset Construction" by Kai Sun, Yingjie Lao, Weiqiang Liu, Xiaohu You, and Chuan Zhang Proceedings of the IEEE 12th International Conference on ASIC (ASICON), October 2017, pp. 867-870 After careful and considered review of the content and authorship of this paper by a duly constituted expert committee, this paper has been found to be in violation of IEEE's Publication Principles. This paper copies original text from the papers cited below. The original text was copied with references to the original author(s) and/or paper titles, but did not delineate sufficiently to identify the reused material. The first author, Kai Sun, was solely responsible for the misconduct, which was done without the knowledge of the other authors "On Error Correction for Physical Unclonable Functions" by Sven Puchinger, Sven Muelich, Martin Bossert, Matthias Hiller, Georg Sigl in the Proceedings of the International ITG Conference on Systems, Communications and Coding, February 2015 "A New Error Correction Scheme for Physical Unclonable Functions" by Sven Muelich, Martin Bossert in the Proceedings of the International ITG Conference on Systems, Communications and Coding, February 2017 As a newly emerging technology, physical unclonable functions (PUFs) have drawn a lot of attentions. For their cryptographic applications, error correction is an indispensable component to fight against environmental factors which influence PUFs' performance. Recently, BCH codes have been applied to the error correction part to enhance the stability of an cryptographic system using PUFs. In this paper, low-density parity-check (LDPC) codes are first used to correct errors caused by environmental factors and shown to perform better than BCH codes in some conditions. In addition, normalized minsum algorithm has been considered to balance the performance and complexity of LDPC decoding.

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Phase-processing For Voice Activity Detection: A Statistical Approach

Phase-Processing For Voice Activity Detection: A Statistical...

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European signal processing Conference

作者： Johannes Stahl Pejman Mowlaee Josef Kulmer Signal Processing and Speech Communication Laboratory Department of Electrical Engineering Graz University of Technology

ISBN: (纸本)9781509018918

Conventional voice activity detectors (VAD) mostly rely on the magnitude of the complex valued DFT spectral coefficients. In this paper, the circular variance of the Discrete Fourier transform (DFT) coefficients is investigated in terms of its ability to represent speech activity in noise. To this end we model the circular variance as a random variable with different underlying distributions for the speech and the noise class. Based on this, we derive a binary hypothesis test relying only on the circular variance estimated from the noisy speech. The experimental results show a reasonable VAD performance justifying that amplitude-independent information can characterize speech in a convenient way.

关键词： Voice activity detection Phase spectrum Circular variance speech enhancement phase spectrum speech enhancement Activity Detection speech discrete Fourier transform Noise voice activity Random variable Voice activity detectors Variance

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DC MUX PUF: A Highly Reliable Feed-Back MUX PUF Based on Measuring Duty Cycle

DC MUX PUF: A Highly Reliable Feed-Back MUX PUF Based on Mea...

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IEEE International Conference on ASIC

作者： Yunhao Xu Yingjie Lao Weiqiang Liu Xiaohu You Chuan Zhang National Mobile Communications Research Laboratory Southeast University Nanjing China Lab of Efficient Architectures for Digital-communication and Signal-processing (LEADS) Department of Electrical and Computer Engineering Clemson University Clemson SC USA College of Electronic and Information Engineering Nanjing Uni. Aero. & Astro Nanjing China

Ring oscillator physical unclonable function (RO PUF) and multiplexer physical unclonable function (MUX PUF) are the two mostly used structures that make use of gate delay, but their reliability is not satisfactory enough. In this paper, a new structure, duty cycle multiplexer physical unclonable function (DC MUX PUF) is proposed to improve the reliability. A feed-back structure is added to original MUX PUF and decision method is also changed by measuring duty cycle. Then a simplified model is built to calculate the reliability of DC MUX PUF, a primary Monte Carlo simulation with this model is accomplished. The reliability of DC MUX PUF and the original MUX PUF are compared, and it is suggested that DC MUX PUF performs better than original MUX PUF considering noise variation. Then temperature variation is considered, and simulated by HSPICE. It is suggested that reliability of them are both above 95% when temperature varies. Uniqueness and randomness are all more than 90%. Then the complexity of two kinds of PUF are also taken into consideration. When challenge length becomes long, complexity of this design is relatively lower than original MUX PUF.

关键词： Physical unclonable function feed-back MUX PUF Monte Carlo model reliability

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Guest Editorial Biological Applications of Information Theory in Honor of Claude Shannon's Centennial-Part 1

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IEEE Transactions on Molecular, Biological, and Multi-Scale Communications 2016年第1期2卷 1-4页

作者： Dimitrov, Alexander G. Fekri, Faramarz Lazar, Aurel A. Moser, Stefan M. Thomas, Peter J. Department of Mathematics and Statistics Washington State University Vancouver VancouverWA98686-9600 United States School of Electrical and Computer Engineering Georgia Institute of Technology AtlantaGA30332-0250 United States Department of Electrical Engineering Columbia University New YorkNY10027 United States Signal and Information Processing Laboratory Department of Information Technology and Electrical Engineering ETH Zurich Zürich8092 Switzerland Department of Electrical and Computer Engineering National Chiao Tung University Hsinchu300 Taiwan Department of Mathematics Applied Mathematics and Statistics Case Western Reserve University ClevelandOH44106 United States

Claude Shannon, born on April 30, 1916, pioneered the mathematical theory of communication in his 1948 paper in the Bell System Technical Journal. Information theory has since provided the foundation for the digital revolution in communications technology. In addition, it has provided a powerful framework for investigating the fundamental limitations of naturally occurring communications, particularly in biological systems. This two-part special issue of the IEEE Transactions on Molecular, Biological, and Multi-Scale Communications honors Shannon's centennial by highlighting recent progress in biological and bio-inspired information theory. Part I includes papers on applications of multivariate mutual information to data clustering, statistical thermodynamics of nonequilibrium chemical processes, information processing in neuronal systems, biochemical signal transduction systems, DNA sequencing, and the swarming of insects. © 2017 IEEE.

关键词： Information theory

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Coherent oscillations of driven rf SQUID metamaterials

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Physical Review E 2017年第5期95卷 050201(R)-050201(R)页

作者： Melissa Trepanier Daimeng Zhang Oleg Mukhanov V. P. Koshelets Philipp Jung Susanne Butz Edward Ott Thomas M. Antonsen Alexey V. Ustinov Steven M. Anlage Department of Physics CNAM University of Maryland College Park Maryland 20742 USA Department of Electrical and Computer Engineering University of Maryland College Park Maryland 20742 USA Hypres Inc. 175 Clearbrook Road Elmsford New York 10523 USA Laboratory of Superconducting Devices for Signal Detection and Processing Kotelnikov Institute of Radio Engineering and Electronics Moscow 125009 Russia Physikalisches Institut Karlsruhe Institute of Technology D-76049 Karlsruhe Germany Russian Quantum Center National University of Science and Technology MISIS Moscow 119049 Russia

Through experiments and numerical simulations we explore the behavior of rf SQUID (radio frequency superconducting quantum interference device) metamaterials, which show extreme tunability and nonlinearity. The emergent electromagnetic properties of this metamaterial are sensitive to the degree of coherent response of the driven interacting SQUIDs. Coherence suffers in the presence of disorder, which is experimentally found to be mainly due to a dc flux gradient. We demonstrate methods to recover the coherence, specifically by varying the coupling between the SQUID meta-atoms and increasing the temperature or the amplitude of the applied rf flux.

关键词： Collective dynamics Coupled oscillators Josephson effect Magnetic susceptibility Optical & microwave phenomena Synchronization Metamaterials

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