nils-herrmann/clean_scirepeval huggingface dataset:
Distributed Storage Codes With Repair-by-Transfer and Nonachievability of Interior Points on the Storage-Bandwidth Tradeoff

Regenerating codes are a class of recently developed codes for distributed storage that, like Reed-Solomon codes, permit data recovery from any subset of nodes within the -node network. However, regenerating codes possess in addition, the ability to repair a failed node by connecting to an arbitrary subset of nodes. It has been shown that for the case of functional repair, there is a tradeoff between the amount of data stored per node and the bandwidth required to repair a failed node. A special case of functional repair is exact repair where the replacement node is required to store data identical to that in the failed node. Exact repair is of interest as it greatly simplifies system implementation. The first result of this paper is an explicit, exact-repair code for the point on the storage-bandwidth tradeoff corresponding to the minimum possible repair bandwidth, for the case when . This code has a particularly simple graphical description, and most interestingly has the ability to carry out exact repair without any need to perform arithmetic operations. We term this ability of the code to perform repair through mere transfer of data as repair by transfer. The second result of this paper shows that the interior points on the storage-bandwidth tradeoff cannot be achieved under exact repair, thus pointing to the existence of a separate tradeoff under exact repair. Specifically, we identify a set of scenarios which we term as “helper node pooling,” and show that it is the necessity to satisfy such scenarios that overconstrains the system.
Robust video multicast under rate and channel variability with applications to wireless LANs

We consider the problem of robust multicast of video to clients having disparate bandwidths and channel loss profiles. We pose this problem as an optimization problem in a multiple description (MD) coding framework and offer a solution that jointly considers the rate-distortion characteristics of the multicast video stream and the receiver channel parameters. In this work, we confine ourselves to a single multicast stream, though extensions to more than one stream are straightforward. Our problem is particularly applicable to the wireless LAN environment where client mobility results in channels with diverse loss and bandwidth characteristics, with the sender unable to adapt dynamically to these changes due to feedback implosion in the multicast setting.
Turbo and trellis-based constructions for source coding with side information

The problem of rate-distortion efficient constructions is studied for the problem of source coding with side information (SCSI), which has assumed heightened interest. While the Wyner-Ziv theorem from information theory has prescribed rate-distortion performance bounds for the SCSI problem, the gap between theory and practice has remained large. To reduce this gap, two different frameworks are proposed based on a trellis construction and a turbo-based construction respectively. Simulation results on the Gaussian SCSI problem reveal the promise of the proposed approaches: at 1 bit per sample, 0.5 bits/sample, 0.25 bits/sample and 0.125 bits/sample, these constructions attain performance within 1.3 dB, 1.1 dB, 0.85 dB and 0.5 dB respectively of the theoretical Wyner-Ziv rate-distortion bound.
Fractional repetition codes for repair in distributed storage systems

We introduce a new class of exact Minimum-Bandwidth Regenerating (MBR) codes for distributed storage systems, characterized by a low-complexity uncoded repair process that can tolerate multiple node failures. These codes consist of the concatenation of two components: an outer MDS code followed by an inner repetition code. We refer to the inner code as a Fractional Repetition code since it consists of splitting the data of each node into several packets and storing multiple replicas of each on different nodes in the system. Our model for repair is table-based, and thus, differs from the random access model adopted in the literature. We present constructions of Fractional Repetition codes based on regular graphs and Steiner systems for a large set of system parameters. The resulting codes are guaranteed to achieve the storage capacity for random access repair. The considered model motivates a new definition of capacity for distributed storage systems, that we call Fractional Repetition capacity. We provide upper bounds on this capacity, while a precise expression remains an open problem.
Distributed Secret Dissemination Across a Network

Shamir's (n,k) threshold secret sharing is an important component of several cryptographic protocols, such as those for secure multiparty-computation and key management. These protocols typically assume the presence of direct communication links from the dealer to all participants, in which case the dealer can directly pass the shares of the secret to each participant. In this paper, we consider the problem of secret sharing when the dealer does not have direct communication links to all the participants, and instead, the dealer and the participants form a general network. Existing methods are based on separate secure message transmissions from the dealer to each participant, requiring considerable coordination and communication in the network. We present a distributed algorithm for disseminating shares over a network, which we call the “SNEAK” algorithm, requiring each node to know only the identities of its one-hop neighbors. While SNEAK imposes a stronger condition on the network by requiring the dealer to be what we call k-propagating rather than k-connected as required by the existing solutions, we show that in addition to being distributed, it achieves significant reduction in the amount of communication and the randomness required. We also derive information-theoretic lower bounds on the amount of communication for secret sharing over networks, which may be of independent interest.
Fundamental limits of DNA storage systems

Due to its longevity and enormous information density, DNA is an attractive medium for archival storage. In this work, we study the fundamental limits and tradeoffs of DNA-based storage systems under a simple model, motivated by current technological constraints on DNA synthesis and sequencing. Our model captures two key distinctive aspects of DNA storage systems: (1) the data is written onto many short DNA molecules that are stored in an unordered way and (2) the data is read by randomly sampling from this DNA pool. Under this model, we characterize the storage capacity, and show that a simple index-based coding scheme is optimal.
Distributed High Accuracy Peer-to-Peer Localization in Mobile Multipath Environments

In this paper we consider the problem of high accuracy localization of mobile nodes in a multipath-rich environment where sub-meter accuracies are required. We employ a peer to peer framework where the vehicles/nodes can get pairwise multipath-degraded ranging estimates in local neighborhoods together with a fixed number of anchor nodes. The challenge is to overcome the multipath-barrier with redundancy in order to provide the desired accuracies especially under severe multipath conditions when the fraction of received signals corrupted by multipath is dominating. We invoke a message passing analytical framework based on particle filtering and reveal its high accuracy localization promise through simulations.
Explicit and optimal codes for distributed storage

In the distributed storage coding problem we consider, data is stored across n nodes in a network, each capable of storing α symbols. It is required that the complete data can be reconstructed by downloading data from any k nodes. There is also the key additional requirement that a failed node be regenerated by connecting to any d nodes and downloading β symbols from each of them. Our goal is to minimize the repair bandwidth dβ. In this paper we provide explicit constructions for several parameter sets of interest.
Inverse halftoning using wavelets

This paper introduces a new approach to inverse halftoning using nonorthogonal wavelets. The distinct features of this wavelet-based approach are: a) edge information in the highpass wavelet images of a halftone is extracted and used to assist inverse halftoning, b) cross-scale correlations in the multiscale wavelet decomposition are used for removing background halftoning noise while preserving important edges in the wavelet lowpass image, c) experiments show that our simple wavelet-based approach outperforms the best results obtained from inverse halftoning methods published in the literature, which are iterative in nature.
Optimal DNA shotgun sequencing: Noisy reads are as good as noiseless reads

We establish the fundamental limits of DNA shotgun sequencing under noisy reads. We show a surprising result: for the i.i.d. DNA model, noisy reads are as good as noiseless reads, provided that the noise level is below a certain threshold which can be surprisingly high. As an example, for a uniformly distributed DNA sequence and a symmetric substitution noisy read channel, the threshold is as high as 19%.
JPEG optimization using an entropy-constrained quantization framework

Previous works, including adaptive quantizer selection and adaptive coefficient thresholding, have addressed the optimization of a baseline-decodable JPEG coder in a rate-distortion (R-D) sense. In this work, by developing an entropy-constrained quantization framework, we show that these previous works do not fully realize the attainable coding gain, and then formulate a computationally efficient way that attempts to fully realize this gain for baseline-JPEG-decodable systems. Interestingly, we find that the gains obtained using the previous algorithms are almost additive. The framework involves viewing a scalar-quantized system with fixed quantizers as a special type of vector quantizer (VQ), and then to use techniques akin to entropy-constrained vector quantization (ECVQ) to optimize the system. In the JPEG case, a computationally efficient algorithm can be derived, without training, by jointly performing coefficient thresholding, quantizer selection, and Huffman table customization, all compatible with the baseline JPEG syntax. Our algorithm achieves significant R-D improvement over standard JPEG (about 2 dB for typical images) with performance comparable to that of more complex "state-of-the-art" coders. For example, for the Lenna image coded at 1.0 bits per pixel, our JPEG-compatible coder achieves a PSNR of 39.6 dB, which even slightly exceeds the published performance of Shapiro's wavelet coder. Although PSNR does not guarantee subjective performance, our algorithm can be applied with a flexible range of visually-based distortion metrics.
Fundamental Data Retention Limits in SRAM Standby Experimental Results

SRAM leakage power dominates the total power of low duty-cycle applications, e.g., sensor nodes. Accordingly, leakage power reduction during data-retention in SRAM standby is often addressed by reducing the supply voltage. Each SRAM cell has a minimum supply voltage parameter called the data-retention voltage (DRV), above which the stored bit can be retained reliably. The DRV exhibits significant intra-chip variation in the deep sub-micron era. As supply voltage is lowered, leakage power reduces, but a larger fraction of SRAM cells is prone to retention failures. Use of appropriate error-correction to mitigate cell- reliability is proposed. Using this approach, the standby supply voltage is selected to minimize leakage power per useful bit. The fundamental limits on the leakage power per useful bit, while taking the DRV distribution into account, are established. Minimization of power per bit results in a supply-voltage at which a small fraction of cells fail to retain the data. For experimental DRV -distributions, a [31,26,3] Hamming code based implementation achieves a significant portion of the leakage power reduction compared to the fundamental limit. These analytical results are verified by twenty-four experimental chips manufactured in an industrial 90 nm CMOS process.
An efficient algorithm to find a jointly optimal time-frequency segmentation using time-varying filter banks

We examine the question of how to choose a time-varying filter bank representation for a signal which is optimal with respect to an additive cost function. We present in detail an efficient algorithm for the Haar filter set which finds the optimal basis, given the constraint that the time and frequency segmentations are binary. Extension to multiple dimensions is simple, and the use of arbitrary filter sets is also possible. We verify that the algorithm indeed produces a lower cost representation than any of the wavelet packet representations for compression of images using a simple rate-distortion cost.
A factor graph framework for joint source-channel decoding of images

We propose a framework for iterative joint source-channel decoding for communication with a fidelity criterion. We consider a class of source models that are used in current state-of-the-art transform image coding schemes. We construct a global graphical model that includes both the channel coding redundancy and the source model and we apply the sum-product algorithm to estimate the transmitted signal with minimum distortion. Our results show the promise of our framework for improving over existing techniques of digital communication.
Spatially adaptive statistical modeling of wavelet image coefficients and its application to denoising

This paper deals with the application to denoising of a very simple but effective "local" spatially adaptive statistical model for the wavelet image representation that was previously introduced successfully in a compression context. Motivated by the intimate connection between compression and denoising, this paper explores the significant role of the underlying statistical wavelet image model. The model used here, a simplified version of the one proposed by LoPresto, Ramchandran and Orchard (see Proc.  1997), is that of a mixture process of independent component fields having a zero-mean Gaussian distribution with unknown variances /spl sigma//sub s//sup 2/ that are slowly spatially-varying with the wavelet coefficient location s. We propose to use this model for image denoising by initially estimating the underlying variance field using a maximum likelihood (ML) rule and then applying the minimum mean squared error (MMSE) estimation procedure. In the process of variance estimation, we assume that the variance field is "locally" smooth to allow its reliable estimation, and use an adaptive window-based estimation procedure to capture the effect of edges. Despite the simplicity of our method, our denoising results compare favorably with the best reported results in the denoising literature.
Fundamental Redundancy Versus Power Trade-Off in Standby SRAM

We study the problem of reducing power during data-retention in a standby static random access memory (SRAM). For successful data-retention, the supply voltage of an SRAM cell should be greater than a critical data retention voltage (DRV). Due to circuit parameter variations, the DRV for different cells on the same chip exhibits variation with a distribution having diminishing tail. For reliable data retention, the existing low-power design uses a worst-case technique in which a standby supply voltage that is larger than the highest DRV among all cells in an SRAM is used. Instead, our approach uses aggressive voltage reduction and counters the ensuing unreliability through a fault-tolerant memory architecture. The main results of this work are as follows: (i) We establish fundamental bounds on the power reduction in terms of the DRV-distribution using techniques from information theory. For the DRV-distribution of test-chip in (Qin, H, et al., 2006), we show that 49% power reduction with respect to (w.r.t.) the worst-case is a fundamental lower bound while 40% power reduction w.r.t. the worst-case is achievable with a practical combinatorial scheme, (ii) We study the power reduction as a function of the block-length for low-latency codes since most applications using SRAM are latency constrained. We propose a reliable memory architecture based on the Hamming code for the next test-chip implementation with a predicted power reduction of 33% while accounting for coding overheads.
Turbo coded trellis-based constructions for data embedding: channel coding with side information

A host of emerging applications in multimedia and communications are connected to the problem of channel coding with side information (CCSI). In CCSI, side information about the transmission channel is present at the sender but not at the receiver. The goal of this paper is to bridge the gap between practical code constructions and the theoretical capacity bounds. Specifically, we present a new turbo-coded trellis-based code construction that appears to be very promising: for the Gaussian CCSI problem at a transmission rate of 1 bit/channel use, our proposed approach comes within 2.72 dB of the information-theoretic capacity established by M. Costa (see  1983).
Bits through ARQs: Spectrum Sharing with a Primary Packet System

We study a problem motivated by cognitive radio in which the primary is a packet system that employs ARQ feedback. A secondary system is allowed to transmit in the same frequency band provided it ensures that the primary attains a specified target rate. That is, the secondary has a certain "interference budget". The crux of the problem is that the secondary does not know how much interference it creates on the primary and therefore is ignorant of its interference budget. Absent this knowledge, we propose a scheme in which the secondary eavesdrops on the primary's ARQ and uses this knowledge to stay within its interference budget. Under certain assumptions, we show there exists an optimal rate-interference budget (RIB) tradeoff. We compare how far fixed strategies are from this RIB function as we vary the interference budget. Further, we exhibit a strategy that is optimal beyond a threshold interference budget and within 1 bit per primary packet elsewhere.
A successively refinable wavelet-based representation for content-based image retrieval

Content based retrieval of image and video data from databases is a very challenging problem, whose interest is derived from the need of future databases to support efficient access to vast amounts of visual information. Typical queries to be performed in this context check attributes of objects present in image data, such as shape, color, relative locations, etc. Therefore, the way in which image data is represented plays a fundamental role in the efficient implementation of those queries. One possibility is to take the naive approach of storing images using standard compression techniques, storing image features (such as object shape descriptors, color histograms, etc.) as explicit side information, and whenever an image is involved in the evaluation of a query decoding it to full resolution; however, many more efficient techniques (in terms of storage and computational requirements) are possible. We propose a new image coding technique which combines a wavelet image representation, embedded coding of the wavelet coefficients, and segmentation of semantically meaningful objects in the wavelet domain, to generate a bitstream in which each object is encoded independently of every other object in the image, and without explicitly storing shape boundary information. Furthermore, since the representation of each object is fully embedded, applications may, independently for each object, specify the desired target bitrate and retrieve bits from the compressed bitstream.
PRISM: A Video Coding Paradigm With Motion Estimation at the Decoder

We describe PRISM, a video coding paradigm based on the principles of lossy distributed compression (also called source coding with side information or Wyner-Ziv coding) from multiuser information theory. PRISM represents a major departure from conventional video coding architectures (e.g., the MPEGx, H.26x families) that are based on motion-compensated predictive coding, with the goal of addressing some of their architectural limitations. PRISM allows for two key architectural enhancements: (1) inbuilt robustness to "drift" between encoder and decoder and (2) the feasibility of a flexible distribution of computational complexity between encoder and decoder. Specifically, PRISM enables transfer of the computationally expensive video encoder motion-search module to the video decoder. Based on this capability, we consider an instance of PRISM corresponding to a near reversal in codec complexities with respect to today's codecs (leading to a novel light encoder and heavy decoder paradigm), in this paper. We present encouraging preliminary results on real-world video sequences, particularly in the realm of transmission losses, where PRISM exhibits the characteristic of rapid recovery, in contrast to contemporary codecs. This renders PRISM as an attractive candidate for wireless video applications.
Duality between source coding and channel coding and its extension to the side information case

We explore the information-theoretic duality between source coding with side information at the decoder and channel coding with side information at the encoder. We begin with a mathematical characterization of the functional duality between classical source and channel coding, formulating the precise conditions under which the optimal encoder for one problem is functionally identical to the optimal decoder for the other problem. We then extend this functional duality to the case of coding with side information. By invoking this duality, we are able to generalize the result of Wyner and Ziv (1976) relating to no rate loss for source coding with side information from Gaussian to more arbitrary distributions. We consider several examples corresponding to both discrete- and continuous-valued cases to illustrate our formulation. For the Gaussian cases of coding with side information, we invoke geometric arguments to provide further insights into their duality. Our geometric treatment inspires the construction and dual use of practical coset codes for a large class of emerging applications for coding with side information, such as distributed sensor networks, watermarking, and information-hiding communication systems.
A factor graph framework for semantic indexing and retrieval in video

This paper proposes a novel framework for semantic indexing and retrieval in digital video. The components of the framework are probabilistic multimedia objects (multijects) and a network of such objects (multinets). The main contribution of this paper is a novel application of a factor graph framework to model the interactions in a network of multijects (multinet) at a semantic level. Factor graphs are statistical graphical models that provide an efficient framework for exact and approximate inference via the sum-product algorithm. Incorporating the statistical interactions between the concepts using factor graphs enhances the detection probability of individual multijects and provides a unified framework for integrating multiple modalities and supports inference of unobservable concepts based on their relation with observable concepts. Our experiments reveal significant performance improvement using the inference on the factor graph models.