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EUROPEAN JOURNAL OF COMBINATORICS 2004年第3期25卷 339-354页

作者： Bilu, Y Hoory, S Hebrew Univ Jerusalem Inst Comp Sci IL-91904 Jerusalem Israel Univ Toronto Dept Comp Sci Toronto ON Canada

We propose a new family of asymptotically good binary codes, generalizing previous constructions of expander codes to t-uniform hypergraphs. We also describe an efficient decoding algorithm for these codes, that for a certain region of rates improves the known results for decoding distance of expander codes. The construction is based on hypergraphs with a certain "expansion" property called herein chomogeneity. For t-uniform t-partite Delta-regular hypergraphs, the expansion property required is roughly as follows: given t sets, A(1),..., A(t), one at each side, the number of hyper-edges with one vertex in each set is approximately what would be expected had the edges been chosen at random. We show that in an appropriate random model, almost all hypergraphs have this property, and also present an explicit construction of such hypergraphs. Having a family of such hypergraphs, and a small code C-0 subset of or equal to {0, 1}(Delta), with relative distance So and rate R-0, we construct "hypergraphs codes". These have rate greater than or equal to R-0 - (t - 1), and relative distance greater than or equal todelta(0)(t(t-1)) -o(1). When t = 2l we also suggest a decoding algorithm, and prove that the fraction of errors that it decodes correctly is at least ((2l-1)(l))(-1/l). (delta(0)/2)((l+1)/l) - o(1). In both cases, the o(1) is an additive term that tends to 0 as the length of the hypergraph code tends to infinity. (C) 2003 Elsevier Ltd. All rights reserved.

关键词： hypergraph decoding New families Codes Expander Device Component Random DISTANCE decoding algorithm

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The Ising decoder: reading out the activity of large neural ensembles

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JOURNAL OF COMPUTATIONAL NEUROSCIENCE 2012年第1期32卷 101-118页

作者： Schaub, Michael T. Schultz, Simon R. Univ London Imperial Coll Sci Technol & Med Dept Bioengn London SW7 2AZ England

The Ising model has recently received much attention for the statistical description of neural spike train data. In this paper, we propose and demonstrate its use for building decoders capable of predicting, on a millisecond timescale, the stimulus represented by a pattern of neural activity. After fitting to a training dataset, the Ising decoder can be applied "online" for instantaneous decoding of test data. While such models can be fit exactly using Boltzmann learning, this approach rapidly becomes computationally intractable as neural ensemble size increases. We show that several approaches, including the Thouless-Anderson-Palmer (TAP) mean field approach from statistical physics, and the recently developed Minimum Probability Flow Learning (MPFL) algorithm, can be used for rapid inference of model parameters in large-scale neural ensembles. Use of the Ising model for decoding, unlike other problems such as functional connectivity estimation, requires estimation of the partition function. As this involves summation over all possible responses, this step can be limiting. Mean field approaches avoid this problem by providing an analytical expression for the partition function. We demonstrate these decoding techniques by applying them to simulated neural ensemble responses from a mouse visual cortex model, finding an improvement in decoder performance for a model with heterogeneous as opposed to homogeneous neural tuning and response properties. Our results demonstrate the practicality of using the Ising model to read out, or decode, spatial patterns of activity comprised of many hundreds of neurons.

关键词： Neural coding decoding algorithm Brain machine interface Brain computer interface BCI Correlations Synchrony Visual cortex Spatial patterns

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Generalized geometric Goppa codes

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COMMUNICATIONS IN ALGEBRA 2002年第6期30卷 2763-2789页

作者： Heydtmann, AE Tech Univ Denmark Dept Math DK-2800 Kongens Lyngby Denmark

Conventional geometric Goppa codes are defined in terms of functions of an algebraic function field associated with a divisor evaluated in places of degree 1. The generalization that will be treated here allows evaluations in places of arbitrary degree. With the appropriate inner product, the dual of the code can be defined and described in terms of Weil differentials similarly to conventional geometric Goppa codes. A decoding algorithm is derived.

关键词： Dot product Codes Divisor Goppa codes Differentials decoding algorithm ALGEBRAIC FUNCTIONS

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A SEMI-SYSTOLIC DECODER FOR THE PDSC-73 ERROR-CORRECTING CODE

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DISCRETE APPLIED MATHEMATICS 1991年第1-3期33卷 109-128页

作者： KARPLUS, K KRIT, H Board of Studies in Computer Engineering University of California at Santa Cruz Santa Cruz CA 95064 USA

This paper presents a semi-systolic architecture for decoding cyclic linear error-correcting codes at high speed. The architecture implements a variant of Tanner's algorithm B, modified for simpler and faster implementation. The main features of the architecture are low computational complexity, a simple, regular arrangement of cells for easy layout, short critical paths, and a high clock rate. A prototype chip has been designed to decode a 73-bit perfect difference set code. This 4600-mu-m x 6800-mu-m chip should achieve 25MHz decoding in 2-mu-m n-well cMOS. The success of the implementation illustrates the value of using technology dependent constraints and cost measures to guide the design of algorithms and architectures.

关键词： ERROR CORRECTION PERFECT DIFFERENCE SET CODE SYSTOLIC ARCHITECTURE DECODER FOR ERROR CORRECTION CODECS decoding algorithm THRESHOLD decoding TANNER algorithm

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Exploit Network Coding Over GF(2(q)) for Multi-user Cooperative Wireless Networks

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INTERNATIONAL JOURNAL OF WIRELESS INFORMATION NETWORKS 2014年第1期21卷 1-14页

作者： Guo, Bin Liu, Yu Zhou, Chi Illinois Inst Technol Dept Elect & Comp Engn Chicago IL 60616 USA

In this paper, we present a decode-and-forward network coded (DFNC) scheme over GF(2(q)) for the multi-user cooperative communication systems. In particular, we consider a cooperative network with m users transmitting independent packets to the same destination. These users form a cooperation set to help each other by using linear network coding. We propose a coding coefficients construction method which can efficiently reduce the transmission overhead from m(q + log(2)m) to m bits compared with conventional random network coding. Furthermore, we propose a novel decoding algorithm-credit-based updating algorithm in order to improve the solvability of decoding set of equations at the destination. The proposed decoding algorithm is combined with channel decoding and is applied on symbol-level. It can fully make use of the error recovery property of network coding while conventional decoding algorithms (e.g., Gaussian elimination) overlook it. We theoretically analyze the diversity performance in terms of information outage probability, and the results show that diversity order of m + 1 can be achieved for a m-user cooperation system. Moreover, we conduct extensive simulations to show that DFNC outperforms other transmission schemes in terms of symbol error rate and achieves higher diversity order. We also demonstrate that the proposed decoding algorithm provides significant performance gain over conventional decoding algorithm.

关键词： Network coding Multi-user cooperative communication Diversity gain decoding algorithm

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Convolutional Neural Decoder for Surface Codes

IEEE TRANSACTIONS ON QUANTUM ENGINEERING

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IEEE TRANSACTIONS ON QUANTUM ENGINEERING 2024年第1期5卷 1页

作者： Jung, Hyunwoo Ali, Inayat Ha, Jeongseok Korea Adv Inst Sci & Technol Sch Elect Engn Daejeon 34141 South Korea

To perform reliable information processing in quantum computers, quantum error correction (QEC) codes are essential for the detection and correction of errors in the qubits. Among QEC codes, topological QEC codes are designed to interact between the neighboring qubits, which is a promising property for easing the implementation requirements. In addition, the locality to the qubits provides unusual tolerance to local errors. Recently, various decoding algorithms based on machine learning have been proposed to improve the decoding performance and latency of QEC codes. In this work, we propose a new decoding algorithm for surface codes, i.e., a type of topological codes, by using convolutional neural networks (CNNs) tailored for the topological lattice structure of the surface codes. In particular, the proposed algorithm takes advantage of the syndrome pattern, which is represented as a part of a rectangular lattice given to the CNN as its input. The remaining part of the rectangular lattice is filled with a carefully selected incoherent value for better logical error rate performance. In addition, we introduce how to optimize the hyperparameters in the CNN, according to the lattice structure of a given surface code. This reduces the overall decoding complexity and makes the CNN-based decoder computationally more suitable for implementation. The numerical results show that the proposed decoding algorithm effectively improves the decoding performance in terms of logical error rate as compared to the existing algorithms on various quantum error models.

关键词： Codes Qubit decoding Lattices Machine learning algorithms Convolutional neural networks Surface treatment Convolutional neural network (CNN) decoding algorithm lattice structure surface codes topological quantum error correction codes

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The use of speed-up techniques for a speech recognizer system

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INTERNATIONAL JOURNAL OF SPEECH TECHNOLOGY 2006年第3-4期9卷 95-107页

作者： Kocsor, Andras Gosztolya, Gabor Hungarian Acad Sci Res Grp Artificial Intelligence Aradi Vertanuk tere 1 H-6720 Szeged Hungary Univ Szeged H-6720 Szeged Hungary

In speech recognition, not just the accuracy of an automatic speech recognition application is important, but also its speed. However, if we want to create a real-time speech recognizer, this requirement limits the time that is spent on searching for the best hypothesis, which can even affect the recognition accuracy. Thus the applied search method plays an important role in the speech recognition task, and so does its efficiency, i. e. how quickly it finds the uttered words. To speed up this search process, various ideas are available in the literature: we can use search heuristics, multi-pass search, or apply a family of aggregation operators. In this paper we test all these methods in turn, and combine them with a set of other novel speed-up ideas. The test results confirm that all of these techniques are valuable: using combinations of them helped make the speech recognition process over 12 times faster than the basic multi-stack decoding algorithm, and almost 11 times faster than the Viterbi beam search method.

关键词： Speech recognition Multi-stack decoding algorithm Viterbi beam search Multi-pass search Clustering algorithms Aggregation operators

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Sentence-level heuristic tree search for long text generation

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COMPLEX & INTELLIGENT SYSTEMS 2024年第2期10卷 3153-3167页

作者： Chen, Zheng Liu, Zhejun Univ Elect Sci & Technol China Sch Informat & Software Engn 4 Sect 2 North Jianshe Rd Chengdu 610054 Sichuan Peoples R China

In this study, we primarily aim to address the exposure bias issue in long text generation intrinsic to statistical language models. We propose a sentence-level heuristic tree search algorithm, specially tailored for long text generation, to mitigate the problem by managing generated texts in a tree structure and curbing the compounding of biases. Our algorithm utilizes two pre-trained language models, an auto-regressive model for generating new sentences and an auto-encoder model for evaluating sentence quality. These models work in tandem to perform four critical operations: expanding the text tree with new sentences, evaluating the quality of the additions, sampling potential unfinished text fragments for further generation, and pruning leaf nodes deemed unpromising. This iterative process continues until a pre-defined number of [EOS] tokens are produced, at which point we select the highest-scoring completed text as our final output. Moreover, we pioneer two novel token-level decoding techniques-nucleus sampling with temperature and diverse beam search with sampling. These methods, integrated with our sentence-level search algorithm, aim to improve the consistency and diversity of text generation. Experimental results, both automated measures (including Jaccard similarity, Word2vec similarity, and unique word ratio) and human evaluations (assessing consistency, fluency, and rhetorical skills), conclusively demonstrate that our approach considerably enhances the quality of machine-generated long-form text. Through this research, we aim to inspire further innovations in sentence-level search-based text generation algorithms.

关键词： Text generation Heuristic search decoding algorithm

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Simultaneous decoding of cardiovascular and respiratory functional changes from pig intraneural vagus nerve signals

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JOURNAL OF NEURAL ENGINEERING 2021年第4期18卷

作者： Vallone, Fabio Ottaviani, Matteo Maria Dedola, Francesca Cutrone, Annarita Romeni, Simone Panarese, Adele Macri Bernini, Fabio Cracchiolo, Marina Strauss, Ivo Gabisonia, Khatia Gorgodze, Nikoloz Mazzoni, Alberto Recchia, Fabio A. Micera, Silvestro Scuola Super Sant Anna BioRobot Inst Pisa Italy Scuola Super Sant Anna Dept Excellence Robot & Artificial Intelligence Pisa Italy Scuola Super Sant Anna Inst Life Sci Pisa Italy Ecole Polytech Fed Lausanne Ctr Neuroprosthet Bertarelli Fdn Chair Translat Neural Engn Lausanne Switzerland Ecole Polytech Fed Lausanne Inst Bioengn Lausanne Switzerland Fdn Toscana Gabriele Monasterio Pisa Italy Temple Univ Dept Physiol Cardiovasc Res Ctr Lewis Katz Sch Med Philadelphia PA 19122 USA

Objective. Bioelectronic medicine is opening new perspectives for the treatment of some major chronic diseases through the physical modulation of autonomic nervous system activity. Being the main peripheral route for electrical signals between central nervous system and visceral organs, the vagus nerve (VN) is one of the most promising targets. Closed-loop VN stimulation (VNS) would be crucial to increase effectiveness of this approach. Therefore, the extrapolation of useful physiological information from VN electrical activity would represent an invaluable source for single-target applications. Here, we present an advanced decoding algorithm novel to VN studies and properly detecting different functional changes from VN signals. Approach. VN signals were recorded using intraneural electrodes in anaesthetized pigs during cardiovascular and respiratory challenges mimicking increases in arterial blood pressure, tidal volume and respiratory rate. We developed a decoding algorithm that combines discrete wavelet transformation, principal component analysis, and ensemble learning made of classification trees. Main results. The new decoding algorithm robustly achieved high accuracy levels in identifying different functional changes and discriminating among them. Interestingly our findings suggest that electrodes positioning plays an important role on decoding performances. We also introduced a new index for the characterization of recording and decoding performance of neural interfaces. Finally, by combining an anatomically validated hybrid neural model and discrimination analysis, we provided new evidence suggesting a functional topographical organization of VN fascicles. Significance. This study represents an important step towards the comprehension of VN signaling, paving the way for the development of effective closed-loop VNS systems.

关键词： bioelectronic medicine neuroprosthetics vagus nerve intraneural electrodes decoding algorithm hybrid modeling framework

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Recasting brain-machine interface design from a physical control system perspective

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JOURNAL OF COMPUTATIONAL NEUROSCIENCE 2015年第2期39卷 107-118页

作者： Zhang, Yin Chase, Steven M. Carnegie Mellon Univ Inst Robot Pittsburgh PA 15213 USA Carnegie Mellon Univ Ctr Neural Basis Cognit Pittsburgh PA 15213 USA Carnegie Mellon Univ Dept Biomed Engn Pittsburgh PA 15213 USA

With the goal of improving the quality of life for people suffering from various motor control disorders, brain-machine interfaces provide direct neural control of prosthetic devices by translating neural signals into control signals. These systems act by reading motor intent signals directly from the brain and using them to control, for example, the movement of a cursor on a computer screen. Over the past two decades, much attention has been devoted to the decoding problem: how should recorded neural activity be translated into the movement of the cursor? Most approaches have focused on this problem from an estimation standpoint, i.e., decoders are designed to return the best estimate of motor intent possible, under various sets of assumptions about how the recorded neural signals represent motor intent. Here we recast the decoder design problem from a physical control system perspective, and investigate how various classes of decoders lead to different types of physical systems for the subject to control. This framework leads to new interpretations of why certain types of decoders have been shown to perform better than others. These results have implications for understanding how motor neurons are recruited to perform various tasks, and may lend insight into the brain's ability to conceptualize artificial systems.

关键词： Brain-machine interfaces decoding algorithm Physical control system

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