nils-herrmann/clean_scirepeval huggingface dataset:
A 54 Mbps (3,6)-regular FPGA LDPC decoder

Applying a joint code and decoder design methodology, we develop a high-speed (3, k)-regular LDPC code partly parallel decoder architecture, based on which a 9216-bit, rate-1/2 (3,6)-regular LDPC code decoder is implemented on an Xilinx FPGA device. When performing maximum 18 iterations for each code block decoding, this partly parallel decoder supports a maximum symbol throughput of 54 Mbps and achieves BER 10/sup -6/ at 2 dB over an AWGN channel.
Efficient VLSI Architectures for Error-Correcting Coding

This thesis is devoted to several efficient VLSI architecture design issues in errorcorrecting coding, including finite field arithmetic, (Generalized) Low-Density ParityCheck (LDPC) codes, and Reed-Solomon codes. A systematic low-complexity bit-parallel finite field multiplier design approach is proposed. This design approach is applicable to GF (2m) constructed by arbitrary irreducible polynomials. It effectively exploits the spatial correlation in the bit-parallel finite field multiplication to reduce the hardware complexity. A systematic low-complexity design approach for modified bit-parallel multiplier that is desirable for GF (2m) constructed by high-Hamming weight irreducible polynomials is also proposed. For the hardware implementation of LDPC code decoder, the finite precision analysis is performed to develop a quantization scheme considering the tradeoff between hardware complexity and LDPC code error-correcting capability. A joint (3, k)-regular LDPC code and codec design approach is proposed to develop good (3, k)-regular LDPC codes that exactly fit to high-speed partly parallel decoder and low-complexity encoder implementations. A modified joint design approach is proposed to further reduce the decoder hardware complexity for those high-rate (3, k)-regular LDPC codes applied to silicon area critical applications. To demonstrate this joint design methodology, an FPGA (Field Programmable Gate Arrays) implementation of a (3, 6)-regular LDPC partly parallel decoder is realized using Xilinx virtex-E device. This decoder can achieve upto 54Mbps symbol decoding throughput and BER of 10−6 at 2dB over AWGN channel. Generalized Low-Density (GLD) Parity-Check has been proposed as an alternative to the product code. This thesis considers the practical GLD codec VLSI design. An approach is proposed to reduce the GLD encoding complexity, which can be effectively implemented using hardware/software codesign. It has been shown that Max-Log-MAP algorithm is a promising candidate decoding scheme for practical GLD coding systems by developing several techniques to reduce the GLD decoding complexity. A Berlekamp-Massey algorithm transformation is performed for high-speed errorsand-erasures RS decoder implementation. A regular hardware architecture is presented to implement the reformulated Berlekamp-Massey algorithm, and an operation scheduling scheme is proposed to reduce the hardware complexity without loss of speed.
On the high-speed VLSI implementation of errors-and-erasures correcting reed-solomon decoders

Recently a novel algorithm transformation was proposed to reduce the critical path of Berlekamp-Massey algorithm implementation for errors-alone Reed-Solomon decoding. In this paper, we apply the same methodology to transform the Berlekamp-Massey algorithm for errors-and-erasures RS decoding. We present a regular hardware architecture to implement the reformulated Berlekamp-Massey algorithm, which can achieve high throughput. Moreover, an operation scheduling scheme is proposed to further reduce the hardware complexity without loss of throughput.
A class of efficient-encoding generalized low-density parity-check codes

In this paper, we investigate an efficient encoding approach for generalized low-density (GLD) parity check codes, a generalization of Gallager's (1962, 1963) low-density parity check (LDPC) codes. We propose a systematic approach to construct an approximate upper triangular GLD parity check matrix which defines a class of efficient-encoding GLD codes. It is shown that such GLD codes have equally good performance. By effectively exploiting structure sharing in the encoding process, we also present a hardware/software codesign for practical encoder implementation of these efficient-encoding GLD codes.
On finite precision implementation of low density parity check codes decoder

In this paper, we analyze the finite precision effects on the decoding performance of Gallager's low density parity check (LDPC) codes and develop optimal finite word lengths of variables as far as the tradeoffs between the performance and hardware complexity are concerned. We have found that 4 bits and 6 bits are adequate for representing the received data and extrinsic information, respectively. Simulation results indicate that the quantization scheme we have developed for the LDPC decoder is effective in approximating the infinite precision implementation.
VLSI implementation-oriented (3, k)-regular low-density parity-check codes

In the past few years, Gallager's low-density parity-check (LDPC) codes received a lot of attention and many efforts have been devoted to analyzing and improving their error-correcting performance. However, little consideration has been given to the LDPC decoder VLSI implementation. The straightforward fully parallel decoder architecture usually incurs too high complexity for many practical purposes and should be transformed to a partly parallel realization. Unfortunately, due to the randomness of LDPC codes, it is nearly impossible to develop an effective transformation for an arbitrarily given LDPC code. We propose a joint code and decoder design approach to construct a class of (3, k)-regular LDPC codes which exactly fit a partly parallel decoder implementation and have a very good performance. Moreover, for such LDPC codes, we propose a systematic, efficient encoding scheme by effectively exploiting the sparseness of its parity check matrix.
High-performance, low-complexity decoding of generalized low-density parity-check codes

A class of pseudo-random compound error-correcting codes, called generalized low-density (GLD) parity-check codes, has been proposed recently. As a generalization of Gallager's low-density parity-check (LDPC) codes, GLD codes are also asymptotically good in the sense of minimum distance criterion and can be effectively decoded based on iterative soft-input soft-output (SISO) decoding of individual constituent codes. The code performance and decoding complexity of GLD codes are heavily dependent on the employed SISO decoding algorithm. In this paper, we show that Max-Log-MAP is an attractive SISO decoding algorithm for GLD coding scheme, considering the trade-off between performance and complexity in the practical implementations. A normalized Max-Log-MAP is presented to improve the GLD code performance significantly compared with using conventional Max-Log-MAP. Moreover, we propose two techniques, decoding task scheduling and reduced search Max-Log-MAP, to effectively reduce the decoding complexity without any performance degradation.
Joint code and decoder design for implementation-oriented (3, k)-regular LDPC codes

Gallager's low-density parity-check (LDPC) codes have recently received a lot of attention because of their excellent performance. The decoder hardware implementation is obviously one of the most crucial issues determining the extent of LDPC applications in the real world. The straightforward fully parallel decoder architecture usually incurs too high complexity for many practical purposes and should be transformed to a partly parallel realization. We propose a joint code and decoder design approach to construct a class of (3, k)-regular LDPC codes which exactly fit to a partly parallel decoder implementation. The partly parallel decoder architecture is suitable for efficient VLSI implementation and it has been shown that the jointly developed (3, k)-regular LDPC codes have very good performance.
Systematic Design of Original and Modified Mastrovito Multipliers for General Irreducible Polynomials

This paper considers the design of bit-parallel dedicated finite field multipliers using standard basis. An explicit algorithm is proposed for efficient construction of Mastrovito product matrix, based on which we present a systematic design of Mastrovito multiplier applicable to GF(2/sup m/) generated by an arbitrary irreducible polynomial. This design effectively exploits the spatial correlation of elements in Mastrovito product matrix to reduce the complexity. Using a similar methodology, we propose a systematic design of modified Mastrovito multiplier, which is suitable for GF(2/sup m/) generated by high-Hamming weight irreducible polynomials. For both original and modified Mastrovito multipliers, the developed multiplier architectures are highly modular, which is desirable for VLSI hardware implementation. Applying the proposed algorithm and design approach, we study the Mastrovito multipliers for several special irreducible polynomials, such as trinomial and equally-spaced-polynomial, and the obtained complexity results match the best known results. Moreover, we have discovered several new special irreducible polynomials which also lead to low-complexity Mastrovito multipliers.
Systematic design approach of Mastrovito multipliers over GF(2/sup m/)

This paper considers the design of low-complexity bit-parallel dedicated finite field multiplier. A systematic design approach of Mastrovito (1988) multiplier is proposed, which is applicable to GF(2/sup m/) generated by an arbitrary irreducible polynomial. This approach extensively exploits the spatial correlation of matrix elements in Mastrovito multiplication to reduce the complexity. The developed general Mastrovito multiplier is highly modular, which is desirable for VLSI hardware implementation. Meanwhile, the presented approach can be used to develop efficient Mastrovito multipliers for several special irreducible polynomials, such as a trinomial and equally-spaced-polynomial, and further find some other special irreducible polynomials which can also lead to low-complexity multipliers.
A Computational Model which Learns to Selectively Attend in Category Learning

Shepard et al. (1961) made empirical and theoretical investigation of the difficulties of different kinds of classifications using both learning and memory tasks. As the difficulty rank mirrors the number of feature dimensions relevant to the category, later researchers took it as evidence that category learning includes learning how to selectively attend to only useful features, i.e. learning to optimally allocate the attention to those dimensions relative to the category (Rosch and Mervis, 1975). We built a recurrent neural network model that sequentially attended to individual features. Only one feature is explicitly available at one time (as in Rehder and Hoffman's eye tracking settings (Render and Hoffman, 2003)) and previous information is represented implicitly in the network. The probabilities of eye movement from one feature to the next is kept as a fixation transition table. The fixations started randomly without much bias on any particular feature or any movement. The network learned the relevant feature(s) and did the classification by sequentially attending to these features. The rank of the learning time qualitatively matched the difficulty of the categories
Transmitting and Decoding Facial Expressions

This article examines the human face as a transmitter of expression signals and the brain as a decoder of these expression signals. If the face has evolved to optimize transmission of such signals, the basic facial expressions should have minimal overlap in their information. If the brain has evolved to optimize categorization of expressions, it should be efficient with the information available from the transmitter for the task. In this article, we characterize the information underlying the recognition of the six basic facial expression signals and evaluate how efficiently each expression is decoded by the underlying brain structures.
Yarbus lives: a foveated exploration of how task influences saccadic eye movement

Qualitative analysis. Some images resulted in viewing patterns that were easily differentiated according to task (boat among flooded trees), other images less so (Gauguin's Mary). Some trials were very focused on the task at hand; others scanned more widely. Free viewing frequently resulted in longer viewing times than specific tasks. The new visualization, because it is closer to V1's representation than the traditional visualization, is a useful means to represent eye movement data.
Fusion Via a Linear Combination of Scores

We present a thorough analysis of the capabilities of the linear combination (LC) model for fusion of information retrieval systems. The LC model combines the results lists of multiple IR systems by scoring each document using a weighted sum of the scores from each of the component systems. We first present both empirical and analytical justification for the hypotheses that such a model should only be used when the systems involved have high performance, a large overlap of relevant documents, and a small overlap of nonrelevant documents. The empirical approach allows us to very accurately predict the performance of a combined system. We also derive a formula for a theoretically optimal weighting scheme for combining 2 systems. We introduce d—the difference between the average score on relevant documents and the average score on nonrelevant documents—as a performance measure which not only allows mathematical reasoning about system performance, but also allows the selection of weights which generalize well to new documents. We describe a number of experiments involving large numbers of different IR systems which support these findings.
Four Blobs: "Y" or Face?

What is the difference between processing faces and other objects such as letters? What makes humans face experts, and what makes this expertise different from other identification skills? It is well known that people are very sensitive to the configural information in faces. How does the sensitivity to face configuration compare to sensitivity to configurations of other stimuli? To investigate these issues, Nishimura et al. (2004) designed a test to contrast two types of processing using the same stimuli. They primed subjects to see four blobs as either a “Y” or as a face. Then they asked the subjects to discriminate pairs of these stimuli that differed only in small shifts in the blob locations. Although the stimuli were exactly the same, subjects were more accurate in the face condition than the “Y” condition. With Nishimura et al., we assumed that the subjects were relying on their letter recognition networks in the “Y” condition and their face recognition networks in the face condition to perform the task. We therefore trained two networks, a face recognition network and a letter recognition network that were otherwise identical in structure, and show here that the internal representations in the letter network for the blobs were less differentiated than the internal representations for the blobs in the face network. We argue that this is a natural consequence of the requirements of the two tasks.
Seeing Blobs as Faces or Letters: Modeling Effects on Discrimination

What is the difference between processing faces and other objects such as letters? What makes humans face experts, and what makes this expertise different from other identification skills? It is well known that people are very sensitive to the configural information in faces. How does the sensitivity to face configuration compare to sensitivity to configurations of other stimuli? To investigate these issues, Nishimura et al. (2004) designed a test to contrast two types of processing using the same stimuli. They primed subjects to see four blobs as either a “Y” or as a face. Then they asked the subjects to discriminate pairs of these stimuli that differed only in small shifts in the blob locations. Although the stimuli were exactly the same, subjects were more accurate in the face condition than the “Y” condition. With Nishimura et al., we assumed that the subjects were relying on their letter recognition networks in the “Y” condition and their face recognition networks in the face condition to perform the task. We therefore trained two networks, a face recognition network and a letter recognition network that wereotherwiseidenticalinstructure, andshowherethatthe internal representations in the letter network for the blobs were less differentiated than the internal representations for the blobs in the face network. We argue that this is a natural consequence of the requirements of the two tasks.
Churchland on Connectionism 1 Running head : CHURCHLAND ON CONNECTIONISM Churchland on Connectionism Aarre Laakso Psychology

Paul Churchland cemented his appointment as Ambassador of Connectionism to Philosophy with the 1986 publication of his paper " Some reductive strategies in cognitive neurobiology. " However, as Churchland tells the story in the preface to his collection of papers, A Neurocomputational Perspective, his relationship with connectionism began three years earlier, when he became acquainted with the model of the cerebellum put forward by Andras Pellionisz and Rodolfo Llinas (1979). The work of Pellionisz and Llinas foreshadows many of the arguments that Churchland makes. They argue that functions of the brain are represented in multidimensional spaces, that neural networks should therefore be treated as " geometrical objects " (p. 323), and that " the internal language of the brain is vectorial " (p. 330). The Pellionisz and Llinas paper also includes an argument for the superiority of neural network organization over von Neumann computer organization on the grounds that the network is more reliable and resistant to damage, a theme to which Churchland often returns. Over the years, Churchland has applied connectionism to several areas of philosophy, notably: philosophy of mind, epistemology, philosophy of science, and ethics. Churchland's arguments in these areas have a common structure. First, he shows that the predominant positions in the field are (a) based on an assumption that the fundamental objects of study are propositions and logical inferences, and (b) have significant internal difficulties largely attributable to that assumption. Second, he presents a re-construal of the field based on connectionism, giving a " neurocomputational perspective " on the Churchland on Connectionism 3 fundamental issues in the field. Finally, he argues that his connectionist alternative fares better than the predominant position on a number of criteria, and explores its further consequences. This is certainly not a formula, since Churchland always considers the particularities of each field, but it is a pattern. In this paper, we explicate these arguments in a little more detail, and try to give some indication of what we think Churchland has right and what he has wrong. The explication is brief, because we are attempting to cover, in just a few pages, topics about which Churchland has written hundreds of pages. The idea is simply to give enough context that the reader may understand the gist of Churchland's arguments and the role that certain problematic claims play in his larger program. In evaluating Churchland's relation to connectionism, we attempt to skirt …
A model of scan paths applied to face recognition

We develop a model of scan path generation based on the output of low level filters. The highest variance of Gabor jet filters computed over orientations are used as the object of attention. These points are held in a feature map which is inhibited as attention points are visited, creating a new attention point elsewhere. Scan paths generated this way can be used for recognition purposes where “single-shot” methods, such as PCA, would fail because the image is not registered.
SOLVING THE VISUAL EXPERTISE MYSTERY

Through brain imaging studies and studies of brain-lesioned patients with face or object recognition deflcits, the fusiform face area (FFA) has been identifled as a face-speciflc processing area. Recent work, however, illustrates that the FFA is also responsive to a wide variety of non-face objects if levels of discrimination and expertise are controlled. The mystery is why an expertise area, whose initial domain of expertise is presumably faces, would be recruited for these other domains. Here we show that features tuned for flne-level discrimination within one visually homogeneous class have high-variance responses across that class. This variability generalizes to other homogenous classes, providing a foothold for learning.
Does English Need its Pronouns ? Simulating the Effect of Pro-drop on SVO Languages

We present the results from a large set of connectionist simulations exploring the effect of subject omission (prodrop) on languages with an SVO word order. We show that pro-drop only affects the learnability of the languages if there are no cues available to tell nouns and verbs apart. We also argue that neither creoles nor Mandarin Chinese instantiate the language type which was unlearnable in the simulations.
Summer Events at UCSD The 1990 Connectionist Models Summer

This newsletter is produced and distributed by the CENTER FOR RESEARCH IN LANGUAGE, a research center at the University of California, San Diego that unites the efforts of fields such as Cognitive Science, Linguistics, Psychology, Computer Science, Sociology, and Philosophy, all who share an interest in language. We feature papers related to language and cognition (1-10 pages, sent via email) and welcome response from friends and colleagues at UCSD as well as other institutions. Please forward correspondence to:
Imaging Reveals Synaptic Targets of a Swim-Terminating Neuron in the Leech CNS

In the leech, the command-like neuron called cell Tr2 is known to stop swimming, but the connections from cell Tr2 to the swim central pattern generator have not been identified. We used fluorescence resonance energy transfer voltage-sensitive dyes to identify three neurons that are synaptic targets of cell Tr2. We then used electrophysiological techniques to show that these connections are monosynaptic, chemical, and excitatory. Two of the novel targets, cell 256 and cell 54, terminate swimming when stimulated. These neurons are likely to mediate swim cessation caused by cell Tr2 activity, and thus play the role of intermediate control cells in the leech CNS.
Principled Methods for Advising Reinforcement Learning Agents

An important issue in reinforcement learning is how to incorporate expert knowledge in a principled manner, especially as we scale up to real-world tasks. In this paper, we present a method for incorporating arbitrary advice into the reward structure of a reinforcement learning agent without altering the optimal policy. This method extends the potential-based shaping method proposed by Ng et al. (1999) to the case of shaping functions based on both states and actions. This allows for much more specific information to guide the agent - which action to choose - without requiring the agent to discover this from the rewards on states alone. We develop two qualitatively different methods for converting a potential function into advice for the agent. We also provide theoretical and experimental justifications for choosing between these advice-giving algorithms based on the properties of the potential function.