nils-herrmann/clean_scirepeval huggingface dataset:
Substate tying with combined parameter training and reduction in tied-mixture HMM design

Two approaches are proposed for the design of tied-mixture hidden Markov models (TMHMM). One approach improves parameter sharing via partial tying of TMHMM states. To facilitate tying at the substate level, the state emission probabilities are constructed in two stages or, equivalently, are viewed as a "mixture of mixtures of Gaussians." This paradigm allows, and is complemented with, an optimization technique to seek the best complexity-accuracy tradeoff solution, which jointly exploits Gaussian density sharing and substate tying. Another approach to enhance model training is combined training and reduction of model parameters. The procedure starts by training a system with a large universal codebook of Gaussian densities. It then iteratively reduces the size of both the codebook and the mixing coefficient matrix, followed by parameter re-training. The additional cost in design complexity is modest. Experimental results on the ISOLET database and its E-set subset show that substate tying reduces the classification error rate by over 15%, compared to standard Gaussian sharing and whole-state tying. TMHMM design with combined training and reduction of parameters reduces the classification error rate by over 20% compared to conventional TMHMM design. When the two proposed approaches were integrated, 25% error rate reduction over TMHMM with whole-state tying was achieved.
Motion vector quantization in a rate-distortion framework

Motion compensation is formulated as quantization of the true motion field and optimized within an overall rate-distortion framework. The desired accuracy in motion estimation is achieved by designing the codebook of the motion quantizer for the target rate. We propose an iterative algorithm for the codebook design which optimizes the compression performance of the coder by directly minimizing the overall rate-distortion cost. A multistage structure is imposed on the codebook to obtain a practical motion quantization scheme, which is optimized through a joint design of the stages. Simulation results on video sequences are presented which demonstrate that RD optimized motion vector quantization outperforms conventional motion compensation and can achieve significant gains of 1.3 dB in the PSNR of the reconstructed picture. Further, the multiresolution nature of multistage motion quantization makes it applicable for scalable video coding applications.
The trials of wafer-scale integration: Although major technical problems have been overcome since WSI was first tried in the 1960s, commercial companies can't yet make it fly

The advantages and problems of wafer-scale integration (WSI) are described. The advantages of semiconductor integrated circuits are high reliability, minimized interconnections, and decreased signal delay. The problems concern heat removal, signal quality, and the need for discretionary wiring. Finally, some recent approaches to WSI fabrication are discussed.
Crosstalk evaluation, suppression and modeling in 3D through-strata-via (TSV) network

Through-strata-via (TSV) is regarded as a critical component in 3D integration that extends Moore's Law. This paper reports on TSV crosstalk performance under high speed operations using a 3D electromagnetic field solver and a SPICE simulator in both the frequency domain and time domain. Impacts of the rise time, the TSV pitch/height, the substrate resistivity and the guarding TSV termination on crosstalk noise are studied. Effects of adjacent aggressors and their switching patterns on time delay and peak noise of the victim TSV signal are evaluated. For large and dense TSV networks, crosstalk matrices of different TSV line/array arrangements are investigated at certain frequency points, detailing the coupling noise levels among these TSVs. Furthermore, the frequency dependent near-end crosstalk (NEXT) and far-end crosstalk (FEXT) are accurately modeled by SPICE tools.
Asymptotic closed-loop design for transform domain temporal prediction

Current video coders exploit temporal dependencies via prediction that consists of motion-compensated pixel copying operations. Such per-pixel temporal prediction ignores important underlying spatial correlations, as well as considerable variations in temporal correlation across frequency components. In the transform domain, however, spatial decorrelation is first achieved, allowing for the true temporal correlation at each frequency to emerge and be properly accounted for, with particular impact at high frequencies, whose lower correlation is otherwise masked by the dominant low frequencies. This paper focuses on effective design of transform domain temporal prediction that: i) fully accounts for the effects of sub-pixel interpolation filters, and ii) circumvents the challenge of catastrophic design instability due to quantization error propagation through the prediction loop. We design predictors conditioned on frequency and sub-pixel position, employing an iterative open-loop (hence stable) design procedure that, on convergence, approximates closed-loop operation. Experimental results validate the effectiveness of both the asymptotic closed-loop design procedure and the transform-domain temporal prediction paradigm, with significant and consistent performance gains over the standard.
Modeling and characterization of SIPOS emitter and quasi-SIS emitter bipolar transistors

SIPOS (semi-insulating polycrystalline Si) emitter bipolar transistors have been fabricated with a common-emitter current gain of 8000 and a figure of merit (gain divided by intrinsic base sheet resistance) of 200 (k Omega /sq)/sup -1/. The high gain is attributed to a relatively low interface recombination velocity of the emitter contact, as measured by photo-induced microwave reflectometry. The cutoff frequency is measured to be 250 MHz, the low value attributed to a large emitter contact resistance of the SIPOS emitter. The authors suggest that a new figure of merit-transconductance divided by emitter resistance-should be considered for the comparison of the high-frequency performances of high emitter efficiency bipolar transistors. A quasi-SIS semiconductor-insulator-semiconductor emitter bipolar with a poly-Si emitter and undoped SIPOS as an interfacial layer was also fabricated. By incorporating a field-enhancement factor in the SIPOS, the behavior of this transistor is successfully explained by a SIS emitter model. The ideality factor ratio in the Gummel plot is attributed to the different barrier heights of electrons and holes at the SiO/sub 2//n-Si interface.
Asymptotic closed-loop design of predictive multi-stage vector quantizers

This work considers the design of a multi-stage vector quantizer (MSVQ) for application to the motion compensated prediction error of video signals. It is well known that the design of predictive vector quantizers suffers from fundamental difficulties due to the prediction loop, which have an impact on the convergence and the stability of the design procedure. We propose an approach to predictive MSVQ design that enjoys the stability of open-loop design while ensuring ultimate optimization of the closed-loop system. The proposed design method is tested on video compression at low bit rates, where it significantly outperforms widely-used closed-loop design techniques, and achieves improvement over the H.263 standard.
A unified framework for spectral domain prediction and end-to-end distortion estimation in scalable video coding

A novel scalable coding approach is proposed for video transmission over lossy networks, which builds on two estimation-theoretic (ET) paradigms previously developed by our group: (1) an ET approach to enhancement layer prediction in scalable video coding (ET-SVC) that optimally combines all available information from both the current base layer and prior enhancement layer frames, and (2) the spectral coefficient-wise optimal recursive estimate (SCORE) of end-to-end distortion. SCORE provides the encoder with an estimate of distortion per decoder-reconstructed transform coefficient, accounting for the effects of quantization, concealment, packet loss and error propagation via the prediction loop. The current work significantly extends the scope of SCORE to encompass the setting of ET-SVC, whose prediction involves non-linear operations. This advance enables optimization of ET-SVC systems for transmission over lossy networks, thereby combining optimal prediction with optimal mode decisions at the enhancement layer. Experiments first demonstrate the estimation accuracy of SCORE in the settings of the ET-SVC coder. They then show considerable gains when SCORE is incorporated into ET-SVC to optimize encoding decisions under a wide range of packet loss and bit rates.
Adaptive Cluster-Distance Bounding for Nearest Neighbor Search in Image Databases

We consider approaches for exact similarity search in a high dimensional space of correlated features representing image datasets, based on principles of clustering and vector quantization. We develop an adaptive cluster distance bound based on separating hyperplanes, that complements our index in selectively retrieving clusters that contain data entries closest to the query. Experiments conducted on real data-sets confirm the efficiency of our approach with random disk IOs reduced by 100X, as compared with the popular vector approximation-file (VA-File) approach, when allowed (roughly) the same number of sequential disk accesses, with relatively low preprocessing storage and computational costs.
Processing material evaluation and ultra-wideband modeling of through-strata-via (TSV) in 3D integrated circuits and systems

Since the conventional planar ICs encountered many physical, technological and economic bottlenecks, 3D integration by stacking and connecting function blocks in a vertical fashion is regarded as a viable approach to alleviate such bottlenecks. Through-strata-via (TSV) is one of the most attractive 3D integration solutions, which offers a massive number of short interconnects, high bandwidth, reduced delay and power consumption, heterogeneous integration, small footprint, improved yield, and reduced volume production cost. This paper reports on electrical characterizations and wideband modeling of typical TSV structures up to 100 GHz. The analysis is conducted in both the time domain and frequency domain, unveiling the impacts of processing materials on TSV electrical performance. The accurate broadband modeling approaches and results facilitate and benefit the infant 3D CAD development.
Evaluation of convolutional codes by error types simulation

Error types simulation (ETS)-a new method for performance evaluation of convolutional codes, is presented. The key idea is to consider types of channel error sequences separately and estimate the contribution of each type to the information bit error rate (I-BER). By averaging these contributions, weighted by the corresponding probability of occurrence of the type, we obtain the I-BER curve. We show that this approach yields an accurate estimate of the entire I-BER curve, while maintaining a computational complexity that is only a very small fraction of the complexity of a Monte-Carlo simulation for a single channel condition. The ETS is shown to outperform competing methods, in terms of both accuracy of the estimate, and the total computational complexity.
Trellis-Based Approaches to Rate-Distortion Optimized Audio Encoding

Many important audio coding applications, such as streaming and playback of stored audio, involve offline compression. In such scenarios, encoding delays no longer represent a major concern. Despite this fact, most current audio encoders constrain delay by making encoding decisions on a per frame basis. This paper is concerned with delayed-decision approaches to optimize the encoding operation for the entire audio file. Trellis-based dynamic programming is used for efficient search in the parameter space. A two-layered trellis effectively optimizes the choice of quantization and coding parameters within a frame, as well as window decisions and bit distribution across frames, while minimizing a psychoacoustically relevant distortion measure under a prescribed bit-rate constraint. The bitstream thus produced is standard compatible and there is no additional decoding delay. Objective and subjective results indicate substantial gains over the reference encoder.
Recursive end-to-end distortion estimation for error-resilient adaptive predictive compression systems

Linear prediction is widely used in speech, audio and video coding systems. Predictive coders often operate over unreliable channels or networks prone to packet loss, wherein errors propagate through the prediction loop and may catastrophically degrade the reconstructed signal at the decoder. To mitigate this problem, end-to-end distortion (EED) estimation, accounting for error propagation and concealment at the decoder, has been developed for video coding, and enables optimal rate-distortion (RD) decisions at the encoder. However, this approach was limited to the video coder's simple setting of a single tap constant coefficient temporal predictor. This paper considerably generalizes the framework to account for: i) high order prediction filters, and ii) filter adaptation to local signal statistics. Specifically, we propose to simultaneously track the decoder statistics of the reconstructed signal and the prediction parameters, which enable effective estimation of the overall EED. We first demonstrate the accuracy of the EED estimate in comparison to extensive simulation of transmission through a lossy network. Finally, experimental results demonstrate how this EED estimate can be leveraged, by an encoder with short and long term linear prediction, to improve RD decisions and achieve major performance gains.
On Scalable Distributed Coding of Correlated Sources

This paper considers the problem of scalable distributed coding of correlated sources that are communicated to a central unit, a setting typically encountered in sensor networks. As communication channel conditions may vary with time, it is often desirable to guarantee a base layer of coarse information during channel fades, as well as robustness to channel link (or sensor) failures. The main contribution is twofold. First, we consider the special case of multistage distributed coding, and show that naive combination of distributed coding with multistage coding yields poor rate-distortion performance, due to underlying conflicts between the objectives of these two coding methods. An appropriate system paradigm is developed, which allows such tradeoffs to be explicitly controlled. Next, we consider the unconstrained scalable distributed coding problem. Although a standard ¿Lloyd-style¿ distributed coder design algorithm is easily generalized to encompass scalable coding, the algorithm performance is heavily dependent on initialization and will virtually always converge to a poor local minimum. We propose an effective initialization scheme for such a system, which employs a properly designed multistage distributed coder. We present iterative design techniques and derive the necessary conditions for optimality for both multistage and unconstrained scalable distributed coding systems. Simulation results show substantial gains for the proposed multistage distributed coding system over naive extensions which incorporate scalability in a multistage distributed coding system. Moreover, the proposed overall scalable distributed coder design consistently and substantially outperforms the randomly initialized ¿Lloyd-style¿ scalable distributed coder design.
A new achievable region for Gaussian multiple descriptions based on subset typicality

This paper addresses the L-channel multiple descriptions problem for a Gaussian source under mean squared error (MSE) distortion metric. We focus on particular cross-sections of the general rate-distortion region where a subset the 2L - 1 distortion constraints are not imposed. Specifically, we assume that certain descriptions are never received simultaneously at the decoder and thereby the transmitted codewords require joint typicality only within prescribed subsets. We derive a new encoding scheme and an associated rate-distortion region wherein joint typicality of codewords only within the prescribed subsets is maintained. We show that enforcing joint typicality of all the codewords entails strict suboptimality in the achievable rate-distortion region. Specifically, we consider a 3 descriptions scenario wherein descriptions 1 and 3 are never received simultaneously at the decoder and show that a strictly larger achievable region is obtained when the encoder maintains joint typicality of codewords only within the required subsets. To prove these results, we derive a lemma called the `subset typicality lemma' which plays a critical role in establishing the new achievable region.
Optimal Zero-Delay Jamming Over an Additive Noise Channel

This paper considers the problem of optimal zero-delay jamming over an additive noise channel. Building on a sequence of recent results on conditions for linearity of optimal estimation, and of optimal mappings in source-channel coding, the saddle-point solution to the jamming problem is derived for general sources and channels, without recourse to Gaussianity assumptions. The linearity conditions are shown to play a pivotal role in jamming, in the sense that the optimal jamming strategy is to effectively force both the transmitter and the receiver to default to linear mappings, i.e., the jammer ensures, whenever possible, that the transmitter and the receiver cannot benefit from non-linear strategies. This result is shown to subsume the known result for Gaussian source and channel. The conditions and general settings where such unbeatable strategy can indeed be achieved by the jammer are analyzed. Moreover, a numerical procedure is provided to approximate the optimal jamming strategy in the remaining (source-channel) cases where the jammer cannot impose linearity on the transmitter and the receiver. Next, the analysis is extended to vector sources and channels. This extension involves a new aspect of optimization: the allocation of available transmit and jamming power over source and channel components. Similar to the scalar setting, the saddle-point solution is derived using the linearity conditions in vector spaces. The optimal power allocation strategies for the jammer and the transmitter have an intuitive interpretation as the jammer allocates power according to water-filling over the channel eigenvalues, while the transmitter performs water-pouring (reverse water-filling) over the source eigenvalues.
A Look-Up-Based Universal Real-Time Transformer for Image Coding

A universal transformer for two-dimensional separable linear transforms in image coding is proposed. It is not restricted to fast transforms, and switching between transforms only requires writing the appropriate constants into an internal RAM. Look-up tables are used to reduce computational complexity. The linear transform properties are exploited to reduce the memory requirements by decomposing the input vector into smaller arrays and into bit planes. The input is pipelined into parallel data paths. An implementation example on a single gate array with existing technology achieves real-time transformation of 8 × 8 blocks, with area requirements of around 38K gates.
First-order distortion estimation for efficient video streaming at moderate to high packet loss rates

This paper reports on advances in end-to-end distortion estimation for streaming of pre-compressed video. It builds on the “first-order distortion estimate” FODE) which employs a Taylor expansion of the distortion about the operating point of zero packet loss rate (PLR), while accounting for packet loss, error propagation and error concealment at the decoder. Improved estimation can be achieved by similar expansion about operating points of non-zero PLR, but would normally involve impractical complexity to evaluate the impact of many possible packet loss patterns. Instead, we propose a new method where FODE is combined with the “recursive optimal per-pixel estimate” (ROPE) to provide accurate, low complexity estimates via expansion about an arbitrary reference PLR vector. The reference vector need not be uniform and can exhibit the expected profile of effective PLR versus packet position in the group of packets (GOP). The method further accounts for the commonly neglected error propagation across GOP boundaries due to error concealment at the decoder. The main experimental results show significant gains in estimation accuracy at medium to high packet loss rates. Some preliminary results on embedding the estimate within a simple delivery policy selection system are also provided.
Variable block-size transform image coder

A discrete cosine transform image-coding scheme based on adaptive block size is proposed and demonstrated. The scheme adaptively optimizes the block sizes according to some local activity indices of the image scenes. The optimization algorithm is achieved by applying a technique from graph theory. Nine rectangular block sizes are used, corresponding to all combinations of 8, 16, and 32, pixels per side. The approach reduces interblock correlation between coefficients and increases the energy compaction in the transform domain. Experimental results are presented and compared to results obtained by an identical coder using a fixed block size.<<ETX>>
A maximum entropy approach for optimal statistical classification

A global optimization technique is introduced for statistical classifier design to minimize the probability of classification error. The method, which is based on ideas from information theory and analogies to statistical physics, is inherently probabilistic. During the design phase, data are assigned to classes in probability, with the probability distributions chosen to maximize entropy subject to a constraint on the expected classification error. This entropy maximization problem is seen to be equivalent to a free energy minimization, motivating a deterministic annealing approach to minimize the misclassification cost. Our method is applicable to a variety of classifier structures, including nearest prototype, radial basis function, and multilayer perceptron-based classifiers. On standard benchmark examples, the method applied to nearest prototype classifier design achieves performance improvements over both the learning vector quantiser, as well as over multilayer perceptron classifiers designed by the standard backpropagation algorithm.
Dominance of interface effects in SRO-SiO/sub 2/-SRO DEIS structures for EAROMs

Current injection at silicon-rich oxide (SRO)-thermal oxide and SRO-CVD (chemical-vapor-deposited) oxide interfaces is shown to be interface-limited. I-V characteristics for both interfaces fit a model for injection enhancement that depends on the curvature of Si islands in the SRO. Results indicate that only a fraction of the Si islands contribute to the injection. The asymmetry observed in dual-electron-injector structures (DEIS) can be explained by a 2.5-times difference in the number of islands between top and bottom injecting interfaces.