nils-herrmann/clean_scirepeval huggingface dataset:
Prescient mode selection for robust video coding

In standard predictive video coders, intra-mode coding of macroblocks (MBs) provides packet loss resilience, at the cost of reduced compression efficiency. Conventional mode selection algorithms are "greedy" as they focus on minimization of distortion of current MB, while ignoring the effect of this selection on subsequent frames. This paper proposes an algorithm for prescient mode selection: the coding modes of MBs are chosen while taking into account the distortion of subsequent frames. The problem is formulated as one of joint selection of MB coding modes, for a group of pictures, so as to minimize the rate-distortion cost. The straightforward solution based on dynamic programming requires enormous computational complexity. We propose an iterative algorithm which obtains a locally optimal solution at feasible complexity. The total decoder distortion is computed using recursive optimal per-pixel estimate (ROPE) which accurately accounts for the effects of quantization, packet loss, error propagation, and error concealment. Simulation results show consistent improvement over our previous "greedy" ROPE-RD mode selection, and substantial gains over other (non-ROPE) mode selection schemes.
Deterministically annealed design of hidden Markov model speech recognizers

Many conventional speech recognition systems are based on the use of hidden Markov models (HMM) within the context of discriminant-based pattern classification. While the speech recognition objective is a low rate of misclassification, HMM design has been traditionally approached via maximum likelihood (ML) modeling which is, in general, mismatched with the minimum error objective and hence suboptimal. Direct minimization of the error rate is difficult because of the complex nature of the cost surface, and has only been addressed previously by discriminative design methods such as generalized probabilistic descent (GPD). While existing discriminative methods offer significant benefits, they commonly rely on local optimization via gradient descent whose performance suffers from the prevalence of shallow local minima. As an alternative, we propose the deterministic annealing (DA) design method that directly minimizes the error rate while avoiding many poor local minima of the cost. The DA is derived from fundamental principles of statistical physics and information theory. In DA, the HMM classifier's decision is randomized and its expected error rate is minimized subject to a constraint on the level of randomness which is measured by the Shannon entropy. The entropy constraint is gradually relaxed, leading in the limit of zero entropy to the design of regular nonrandom HMM classifiers. An efficient forward-backward algorithm is proposed for the DA method. Experiments on synthetic data and on a simplified recognizer for isolated English letters demonstrate that the DA design method can improve recognition error rates over both ML and GPD methods.
Constrained clustering for data assignment problems with examples of module placement

The authors introduce a method that aims to avoid local optima and improve upon conventional solutions to data assignment problems, viewing them in the context of constrained clustering. The clustering algorithm is an extension of the deterministic annealing method which incorporates clustering constraints, initially generates soft data assignments, and then reduces the fuzziness of these assignments by lowering an effective temperature. In the low temperature limit, a legal data assignment solution is obtained, which seeks to minimize the desired objective function. The method was implemented on the two-dimensional module placement problem. For the minimum squared wire length objective, the algorithm outperformed several other methods on module placement examples from the literature.<<ETX>>
Multi-stage vector quantizer design for image transmission over packet networks

The design of a source-channel system based on the multi-stage vector quantizer (MSVQ) is optimized for packet networks. Resilience to packet loss is further enhanced by a proposed interleaving scheme that ensures that a single lost packet only eliminates a subset of the vector stages. The design is optimized while taking into account compression efficiency, packet loss rate, and the interleaving technique in use. Simulation results demonstrate that the packet network-optimized MSVQ outperforms traditional MSVQ for a variety of images and channel conditions, with gains of up to 2.0 dB in PSNR. Although the formulation is given in the context of packet networks, the work is directly extendible to the broader category of erasure channels.
Multiple voltage-supplies in TSV-based three-dimensional (3D) power distribution networks

It is important to understand how to deliver power into 3D heterogeneous systems, which require different power supplies for different components (e.g., digital, analog, mixed-signal, MEMS parts). This paper reports on a study for a simplified case, where the two power supplies with different voltage levels are uniformly distributed through a TSV-based 3D system. In addition to intrinsic voltage losses along the interconnections, we investigated for the first time the power noises due to electrical couplings between the multiple power supplies. We evaluated the through-silicon-via (TSV)-based 3D power delivery networks by combining the electromagnetic (EM) and SPICE simulations. With this hybrid approach, we first partitioned the 3D system into a number of elements, extracted the RLGC parasitics for each decomposed physical element, and imported them into an assembled system-level equivalent circuit to characterize the 3D power distribution networks.
Cascaded Long Term Prediction for Enhanced Compression of Polyphonic Audio Signals

Audio compression systems exploit periodicity in signals to remove inter-frame redundancies via the long term prediction (LTP) tool. This simple tool capitalizes on the periodic component of the waveform by selecting a past segment as the basis for prediction of the current frame. However, most audio signals are polyphonic in nature, containing a mixture of several periodic components. While such polyphonic signals may themselves be periodic with overall period equaling the least common multiple of the individual component periods, the signal rarely remains sufficiently stationary over the extended period, rendering the LTP tool suboptimal. Instead of seeking a past segment that represents a “compromise” for incompatible component periods, we propose a more complex filter that predicts every periodic component of the signal from its immediate history, and this is achieved by cascading LTP filters, each corresponding to individual periodic component. We also propose a recursive “divide and conquer” technique to estimate parameters of all the LTP filters. We then demonstrate the effectiveness of cascaded LTP in two distinct settings of the ultra low delay Bluetooth Subband Codec and the MPEG Advanced Audio Coding (AAC) standard. In MPEG AAC, we specifically adapt the cascaded LTP parameter estimation to take into account the perceptual distortion criteria, and also propose a low decoder complexity variant. Objective and subjective results for all the settings validate the effectiveness of the proposal on a variety of polyphonic signals.
Cluster-oriented scheduling in pipelined data path synthesis

We present a new algorithm for cluster-oriented scheduling (COS) in pipelined data path synthesis. First, cluster structure information is extracted from the corresponding linear graph representation of a data flow graph (DFG). Then, scheduling and allocation globally interact with the cluster information under the timing constraints of pipeline latency and data initialization interval (DII). Using this technique, the costs of registers, multiplexors, and interconnection were reduced dramatically in both the FIR and elliptic filter examples.<<ETX>>
A 12-bit, 50 MS/s SiGe BiCMOS sample-and-hold residue amplifier

A 3.3 V, fully differential, switched-capacitor, gain-of-2, SiGe BiCMOS, sample-and-hold amplifier (SHA) for use as a residue amplifier in a 1.5-bit per stage pipeline A/D converter is presented. The SHA is realized by using an operational transconductance amplifier (OTA) in a closed loop negative feedback system. The SiGe BiCMOS OTA has a simulated DC gain of 88 dB with a unity gain frequency (f/sub T/) of 0.5 GHz at a phase margin of 65 degrees. The OTA has a differential output swing of 3 V and its differential input referred noise power spectral density (PSD) is 3.62 nV//spl radic/Hz. The SHA output settles to 0.01% accuracy in 9 ns, which allows realization of a 12-bit, 50 MS/s pipeline A/D converter (with an SNR /spl ap/ 72.6 dB) even without digital self-calibration. High performance is obtained by optimal use of SiGe HBTs as cascodes. In earlier work, a pipeline A/D converter fabricated in the same technology achieved 12-bit performance at 34 MS/s. The IBM 6HP technology provides 47 GHz SiGe HBTs and 250 nm CMOS.
Analog multiple descriptions: A zero-delay source-channel coding approach

This paper extends the well-known source coding problem of multiple descriptions, in its general and basic setting, to analog source-channel coding scenarios. Encoding-decoding functions that optimally map between the (possibly continuous valued) source and the channel spaces are numerically derived. The main technical tool is a non-convex optimization method, namely, deterministic annealing, which has recently been successfully used in other mapping optimization problems. The obtained functions exhibit several interesting structural properties, map multiple source intervals to the same interval in the channel space, and consistently outperform the known competing mapping techniques.
Jointly Optimized Spatial Prediction and Block Transform for Video and Image Coding

This paper proposes a novel approach to jointly optimize spatial prediction and the choice of the subsequent transform in video and image compression. Under the assumption of a separable first-order Gauss-Markov model for the image signal, it is shown that the optimal Karhunen-Loeve Transform, given available partial boundary information, is well approximated by a close relative of the discrete sine transform (DST), with basis vectors that tend to vanish at the known boundary and maximize energy at the unknown boundary. The overall intraframe coding scheme thus switches between this variant of the DST named asymmetric DST (ADST), and traditional discrete cosine transform (DCT), depending on prediction direction and boundary information. The ADST is first compared with DCT in terms of coding gain under ideal model conditions and is demonstrated to provide significantly improved compression efficiency. The proposed adaptive prediction and transform scheme is then implemented within the H.264/AVC intra-mode framework and is experimentally shown to significantly outperform the standard intra coding mode. As an added benefit, it achieves substantial reduction in blocking artifacts due to the fact that the transform now adapts to the statistics of block edges. An integer version of this ADST is also proposed.
MSVQ design for packet networks with application to LSF quantization

The design of a multi-stage vector quantizer (MSVQ) based source-channel coding system is optimized for packet networks. Resilience to packet loss is further enhanced by a proposed interleaving approach that ensures that a single lost packet only eliminates a subset of the vector stages. The design is optimized while taking into account compression efficiency, packet loss rate, and the interleaving technique in use. The new source-channel-optimized MSVQ is tested on memoryless line spectral frequency (LSF) parameter quantization in speech coders. A source-channel-optimization MSVQ is shown to yield a gain of up to 2.0 dB in SNR, for coding the LSFs, over traditional MSVQ and to substantially enhance the robustness of packetized speech transmission. Although the formulation is given in the context of packet networks, the work is directly extendible to the broader category of erasure channels.
Cascaded long term prediction for coding polyphonic audio signals

The long term prediction (LTP) tool is used in audio compression systems to exploit periodicity in signals. This tool capitalizes on the periodic component of the waveform by selecting a past segment as the basis for prediction of the current frame. However, most audio signals are polyphonic in nature, consisting of a mixture of periodic signals. This renders the LTP suboptimal, as the mixture's period equals the least common multiple of its individual component periods, which typically extends far beyond the duration over which the signal is stationary. Instead of seeking a past segment that represents a “compromise” for incompatible component periods, we propose a more complex filter that caters to the individual signal components. This proposed technique predicts every periodic component of the signal from its immediate history, and this is achieved by cascading LTP filters, each corresponding to individual periodic component. We also propose a recursive “divide and conquer” technique to estimate the parameters of all the LTP filters. For a real world evaluation, we employ this technique within the Bluetooth Sub-band Codec. Considerable gains achieved on a variety of polyphonic signals demonstrate the effectiveness of the proposal.
On Constrained Randomized Quantization

Randomized (dithered) quantization is a method capable of achieving white reconstruction error independent of the source. Dithered quantizers have traditionally been considered within their natural setting of uniform quantization. In this paper we extend conventional dithered quantization to nonuniform quantization, via a subterfage: dithering is performed in the companded domain. Closed form necessary conditions for optimality of the compressor and expander mappings are derived for both fixed and variable rate randomized quantization. Numerically, mappings are optimized by iteratively imposing these necessary conditions. The framework is extended to include an explicit constraint that deterministic or randomized quantizers yield reconstruction error that is uncorrelated with the source. Surprising theoretical results show direct and simple connection between the optimal constrained quantizers and their unconstrained counterparts. Numerical results for the Gaussian source provide strong evidence that the proposed constrained randomized quantizer outperforms the conventional dithered quantizer, as well as the constrained deterministic quantizer. Moreover, the proposed constrained quantizer renders the reconstruction error nearly white. In the second part of the paper, we investigate whether uncorrelated reconstruction error requires random coding to achieve asymptotic optimality. We show that for a Gaussian source, the optimal vector quantizer of asymptotically high dimension whose quantization error is uncorrelated with the source, is indeed random. Thus, random encoding in this setting of rate-distortion theory, is not merely a tool to characterize performance bounds, but a required property of quantizers that approach such bounds.
Enhancement of one-dimensional variable-length DPCM images corrupted by transmission errors

Methods are presented for enhancing one-dimensional variable-length differential pulse-code modulation (VLDPCM) images corrupted by transmission errors, are presented. The proposed methods take advantage of the VLDPCM code's high sensitivity to errors. Two methods are described. The first involves no increase of transmitted bit rate and consists of processing the corrupted image. The second method involves the transmission of additional information to enable an almost exact reconstruction of the original image. The amount of added redundancy can be reduced at the cost of performing more computations. In a simulation, the authors used less than 7% redundancy. Both methods achieve significant improvements which are demonstrated by the simulation results. The simulation results include original, corrupted, and enhanced images as well as the signal-to-noise ratio (SNR) versus bit-error rate (BER) curve. For BER 10/sup -3/, the SNR (signal-to-noise ratio) gains for the first and second methods are greater than 17 and 44 dB, respectively.
Jointly optimized transform domain temporal prediction and sub-pixel interpolation

Conventional pixel-domain block matching temporal (inter) prediction is suboptimal, since it ignores the underlying spatial correlation. Hence in our recent research we proposed transform domain temporal prediction (TDTP), wherein spatially decorrelated transform coefficients are individually predicted. Later we proposed extended block TDTP (EB-TDTP), which fully exploits spatial correlation around reference block boundaries. However, the transform domain temporal correlation exploited by (EB-)TDTP interferes with the frequency response of sub-pixel interpolation filters. Thus, in this paper, we propose to replace the standard sub-pixel interpolation with filters which are jointly designed with EB-TDTP based on statistics of the data, for either separable or non-separable interpolation structures. We also employ a two-loop asymptotic closed-loop (ACL) approach for statistically stable off-line design. Experiments show that our framework can achieve up to 1dB gain in PSNR over HEVC.
Deformable Registration of Biomedical Images Using 2D Hidden Markov Models

Robust registration of unimodal and multimodal images is a key task in biomedical image analysis, and is often utilized as an initial step on which subsequent analysis techniques critically depend. We propose a novel probabilistic framework, based on a variant of the 2D hidden Markov model, namely, the turbo hidden Markov model, to capture the deformation between pairs of images. The hidden Markov model is tailored to capture spatial transformations across images via state transitions, and modality-specific data costs via emission probabilities. The method is derived for the unimodal setting (where simpler matching metrics may be used) as well as the multimodal setting, where different modalities may provide very different representations for a given class of objects, necessitating the use of advanced similarity measures. We utilize a rich model with hundreds of model parameters to describe the deformation relationships across such modalities. We also introduce a local edge-adaptive constraint to allow for varying degrees of smoothness between object boundaries and homogeneous regions. The parameters of the described method are estimated in a principled manner from training data via maximum likelihood learning, and the deformation is subsequently estimated using an efficient dynamic programming algorithm. Experimental results demonstrate the improved performance of the proposed approach over the state-of-the-art deformable registration techniques, on both unimodal and multimodal biomedical data sets.
An information-theoretic framework for optimization with application to supervised learning

The article develops a unified approach for hard optimization problems involving data association, i.e. the assignment of elements viewed as "data" {x/sub i/}, to one of a set of classes, (C/sub j/), so as to minimize the resulting cost. The diverse problems which fit this description include data clustering, statistical classifier design to minimize probability of error, piecewise regression, structured vector quantization, as well as optimization problems in graph theory, e.g. graph partitioning. Whereas standard descent-based methods are susceptible to finding poor local optima of the cost, the suggested approach provides some potential for avoiding local optima, yet without the computational complexity of stochastic annealing. The approach we develop is based on ideas from information theory and statistical physics, and builds on the work of Rose, Gurewitz, and Fox (see  1992) for clustering and related problems.
Nonuniform Dithered Quantization

Dithered quantization has useful properties such as producing quantization noise independent of the source and continuous reconstruction at the decoder side. Dithered quantizers have traditionally been considered within their natural setting of uniform quantization framework. A uniformly distributed (with step size matched to the quantization interval) dither signal is added before quantization and the same dither signal is subtracted from the quantized value at the decoder side (only subtractive dithering is considered in this paper). The quantized values are entropy coded conditioned on the dither signal.
An MDCT Domain Frame-Loss Concealment Technique for MPEG Advanced Audio Coding

We propose a frame loss concealment technique for decoders compatible with MPEG advanced audio coding (AAC). The spectral information of the lost frame is estimated in the modified discrete cosine transform (MDCT) domain via efficient techniques that are tailored to individual source signal components: in noise-like spectral bins the MDCT coefficients are obtained by shaped-noise insertion, while coefficients in tone-dominant bins are estimated by frame interpolation followed by a refinement procedure so as to optimize the fit of the concealed frames with neighboring frames. Experimental results demonstrate that the proposed technique offers performance superior to techniques adopted in commercial AAC decoders.
Entropy-constrained tree-structured vector quantizer design by the minimum cross entropy principle

The authors address the variable rate tree-structured vector quantizer design problem, wherein the rate is measured by the quantizer's entropy. For this problem, tree pruning via the generalized Breiman-Friedman-Olshen-Stone (1980) algorithm obtains solutions which are optimal over the restricted solution space consisting of all pruned trees derivable from an initial tree. However, the restrictions imposed on such solutions have several implications. In addition to depending on the tree initialization, growing and pruning solutions result in tree-structured vector quantizers which use a sub-optimal encoding rule. To remedy the latter problem, they consider a "tree-constrained" version of entropy-constrained vector quantizer design. This leads to an optimal tree-structured encoding rule for the leaves. In practice, though, improvements obtained in this fashion are limited by the tree initialization, as well as by the sub-optimal encoding performed at non-leaf nodes. To address these problems, they develop a joint optimization method which is inspired by the deterministic annealing algorithm for data clustering, and which extends their previous work on tree-structured vector quantization. The method is based on the principle of minimum cross entropy, using informative priors to approximate the unstructured solution while imposing the structural constraint. As in the original deterministic annealing method, the number of distinct codevectors (and hence the tree) grows by a sequence of bifurcations in the process, which occur as solutions of a free energy minimization. Their method obtains performance gains over growing and pruning methods for variable rate quantization of Gauss-Markov and Gaussian mixture sources.<<ETX>>
Zero-Error Source-Channel Coding With Side Information

This correspondence presents a novel application of the theta function defined by Lovasz. The problem of coding for transmission of a source through a channel without error when the receiver has side information about the source is analyzed. Using properties of the Lovasz theta function, it is shown that separate source and channel coding is asymptotically suboptimal in general. By contrast, in the case of vanishingly small probability of error, separate source and channel coding is known to be asymptotically optimal. For the zero-error case, it is further shown that the joint coding gain can in fact be unbounded. Since separate coding simplifies code design and use, conditions on sources and channels for the optimality of separate coding are also derived