nils-herrmann/clean_scirepeval huggingface dataset:
A global optimization technique for statistical classifier design

A global optimization method is introduced that minimize the rate of misclassification. We first derive the theoretical basis for the method, on which we base the development of a novel design algorithm and demonstrate its effectiveness and superior performance in the design of practical classifiers for some of the most popular structures currently in use. The method, grounded in ideas from statistical physics and information theory, extends the deterministic annealing approach for optimization, both to incorporate structural constraints on data assignments to classes and to minimize the probability of error as the cost objective. During the design, data are assigned to classes in probability so as to minimize the expected classification error given a specified level of randomness, as measured by Shannon's entropy. The constrained optimization is equivalent to a free-energy minimization, motivating a deterministic annealing approach in which the entropy and expected misclassification cost are reduced with the temperature while enforcing the classifier's structure. In the limit, a hard classifier is obtained. This approach is applicable to a variety of classifier structures, including the widely used prototype-based, radial basis function, and multilayer perceptron classifiers. The method is compared with learning vector quantization, back propagation (BP), several radial basis function design techniques, as well as with paradigms for more directly optimizing all these structures to minimize probability of error. The annealing method achieves significant performance gains over other design methods on a number of benchmark examples from the literature, while often retaining design complexity comparable with or only moderately greater than that of strict descent methods. Substantial gains, both inside and outside the training set, are achieved for complicated examples involving high-dimensional data and large class overlap.
A Deterministic Annealing Approach for Parsimonious Design of Piecewise Regression Models

A new learning algorithm is proposed for piecewise regression modeling. It employs the technique of deterministic annealing to design space partition regression functions. While the performance of traditional space partition regression functions such as CART and MARS is limited by a simple tree-structured partition and by a hierarchical approach for design, the deterministic annealing algorithm enables the joint optimization of a more powerful piecewise structure based on a Voronoi partition. The new method is demonstrated to achieve consistent performance improvements over regular CART as well as over its extension to allow arbitrary hyperplane boundaries. Comparison tests, on several benchmark data sets from the regression literature, are provided.
A spectral approach to recursive end-to-end distortion estimation for sub-pixel motion-compensated video coding

Error resilient video coding critically relies on the accuracy of end-to-end distortion estimation. An established solution, the recursive optimal per-pixel estimate (ROPE), is based on tracking the first and second moments of the decoder reconstructed pixels. This paper is focused on an alternative estimation approach, the spectral coefficient-wise optimal recursive estimate (SCORE), whose recursion is performed in the transform domain. The SCORE formulation is extended to derive a new technique for effective end-to-end distortion estimation, which accounts for sub-pixel motion compensation. Specifically, this technique exploits properties of the transform, such as coefficient de-correlation and energy compaction, to overcome ROPE's remaining shortcoming due to the proliferation of cross-correlation terms requiring excessive complexity or relatively crude approximations. Experiments show that the accuracy of SCORE matches ROPE in the full-pixel motion compensation setting, where ROPE is known to be optimal. More importantly, in the problematic setting of sub-pixel motion compensation, SCORE substantially outperforms ROPE and yields highly accurate distortion estimation.
Encoder Assisted Frame Loss Concealment for MPEG-AAC Decoder

This paper proposes an efficient method for frame loss concealment within the advanced audio coding (AAC) decoder, which can effectively mitigate the adverse impact of transmission errors on the reconstruction quality. The lost frame information is estimated in the modified discrete cosine transform (MDCT) domain in terms of magnitude and sign of the coefficients. A computationally efficient approach that is capable of providing accurate estimation is employed for the magnitude estimate. Enhanced sign estimation is developed and implemented, which exploits extra information from encoder. Extensive subjective quality evaluation demonstrates that the proposed method achieves substantial quality improvement with minimal encoder assistance
Architecture design exploration of three-dimensional (3D) integrated DRAM

Motivated by increasingly promising three-dimensional (3D) integration technologies, this paper reports an architecture design of 3D integrated dynamic RAM (DRAM). To accommodate the potentially significant pitch mismatch between DRAM word-line/bit-line and through silicon vias (TSVs) for 3D integration, this paper presents two modestly different coarse-grained inter-sub-array 3D DRAM architecture partitioning strategies. Furthermore, to mitigate the potential yield loss induced by 3D integration, we propose an interdie inter-sub-array redundancy repair approach to improve the memory repair success rate. For the purpose of evaluation, we modified CACTI 5 to support the proposed coarse-grained 3D partitioning strategies. Estimation results show that, for the realization of a 1Gb DRAM with 8 banks and 256-bit data I/O, such 3D DRAM design strategies can effectively reduce the silicon area, access latency, and energy consumption compared with 3D packaging with wire bonding and conventional 2D design. We further developed a memory redundancy repair simulator to demonstrate the effectiveness of proposed inter-die inter-subarray redundancy repair approach.
Rate-distortion approach to databases: storage and content-based retrieval

The problem of similarity search is central to a wide range of applications in multimedia databases. The degree of similarity is often quantified by a distance measure defined over the space of extracted feature vectors. In typical applications, since the volume of data is huge, and the feature vectors are of high dimensionality, it becomes necessary to access the feature vectors from a hard storage medium during the search. Since I/O operations on hard storage devices are slow, the time complexity of the search is dominated by the I/O time. One of the most effective approaches to reduce the time complexity is compromising the search accuracy by accessing compressed feature vectors. The processing time is then approximately proportional to the rate R at which the sequence of feature vectors is compressed. In this paper we characterize the set of all achievable time-accuracy pairs.
Predictive vector quantizer design using deterministic annealing

A new approach is proposed for predictive vector quantizer (PVQ) design, which is inherently probabilistic, and is based on ideas from information theory and analogies to statistical physics. The approach effectively resolves three longstanding fundamental shortcomings of standard PVQ design. The first complication is due to the PVQ prediction loop, which has a detrimental impact on the convergence and the stability of the design procedure. The second shortcoming is due to the piecewise constant nature of the quantizer function, which makes it difficult to optimize the predictor with respect to the overall reconstruction error. Finally, a shortcoming inherited from standard VQ design is the tendency of the design algorithm to terminate at a locally, rather than the globally, optimal solution. We propose a new PVQ design approach that embeds our previous asymptotic closed-loop (ACL) approach within a deterministic annealing (DA) framework. The overall DA-ACL method profits from its two main components in a complementary way. ACL is used to overcome the first difficulty and offers the means for stable quantizer design as it provides an open-loop design platform, yet allows the PVQ design algorithm to asymptotically converge to optimization of the closed-loop performance objective. DA simultaneously mitigates or eliminates the remaining design shortcomings. Its probabilistic framework replaces hard quantization with a differentiable expected cost function that can be jointly optimized for the predictor and quantizer parameters, and its annealing schedule allows the avoidance of many poor local optima. Substantial performance gains over traditional methods have been achieved in the simulations.
The Lossy Common Information of Correlated Sources

The two most prevalent notions of common information (CI) are due to Wyner and Gács-Körner and both the notions can be stated as two different characteristic points in the lossless Gray-Wyner region. Although the information theoretic characterizations for these two CI quantities can be easily evaluated for random variables with infinite entropy (e.g., continuous random variables), their operational significance is applicable only to the lossless framework. The primary objective of this paper is to generalize these two CI notions to the lossy Gray-Wyner network, which hence extends the theoretical foundation to general sources and distortion measures. We begin by deriving a single letter characterization for the lossy generalization of Wyner's CI, defined as the minimum rate on the shared branch of the Gray-Wyner network, maintaining minimum sum transmit rate when the two decoders reconstruct the sources subject to individual distortion constraints. To demonstrate its use, we compute the CI of bivariate Gaussian random variables for the entire regime of distortions. We then similarly generalize Gács and Körner's definition to the lossy framework. The latter half of this paper focuses on studying the tradeoff between the total transmit rate and receive rate in the Gray-Wyner network. We show that this tradeoff yields a contour of points on the surface of the Gray-Wyner region, which passes through both the Wyner and Gács-Körner operating points, and thereby provides a unified framework to understand the different notions of CI. We further show that this tradeoff generalizes the two notions of CI to the excess sum transmit rate and receive rate regimes, respectively.
COMET: a set of global transformation algorithms for high-level pipeline synthesis

The authors present a new systematic approach toward global hardware synthesis of DSP ASICs from behavioral specifications. A VHDL behavioral description is the input language of the COMET system. Given the stage time, data initialization interval (DII), and area-time tradeoff weight, a VHDL structural description is generated. A set of global data flow graph transformations is performed to minimize execution time of arithmetic expressions and to maximize conditonal resource sharing. Moreover, cluster structures derived from repeated pattern identification are used to guide global scheduling and allocation. The system provides the designer with a weight parameter to control the tradeoffs between area and time. With these transformations, major improvements are achieved with fewer registers and multiplexors; leading to a production quality design.<<ETX>>
Toward optimality in scalable predictive coding

A method is proposed for efficient scalability in predictive coding, which overcomes known fundamental shortcomings of the prediction loop at enhancement layers. The compression efficiency of an enhancement-layer is substantially improved by casting the design of its prediction module within an estimation-theoretic framework, and thereby exploiting all information available at that layer for the prediction of the signal, and encoding of the prediction error. While the most immediately important application is in video compression, the method is derived in a general setting and is applicable to any scalable predictive coder. Thus, the estimation-theoretic approach is first developed for basic DPCM compression and demonstrates the power of the technique in a simple setting that only involves straightforward prediction, scalar quantization, and entropy coding. Results for the scalable compression of first-order Gauss-Markov and Laplace-Markov signals illustrate the performance. A specific estimation algorithm is then developed for standard scalable DCT-based video coding. Simulation results show consistent and substantial performance gains due to optimal estimation at the enhancement-layers.
Motion Compensation by Adaptive Vector Quantization

We propose an eecient motion compensation scheme where adaptive vector quanti-zation (AVQ) is used to optimally compress the dense motion eld. The vector quantizer codebook is designed on-line by backward adaptation based on previous reconstructed frames. It thus takes advantage of the information already available at the receiver for adapting the codebook without recourse to side information. The method adapts the codebook to local motion statistics and thus exploits interframe motion correlations. It also eliminates the need for extensive prior codebook training, as well as problems of statistical mismatch due to scene variation. Preliminary experiments on benchmark video sequences show average improvement of about 0.8dB in SNR, and improved perceptual quality of the reconstructed video. The enhanced performance is achieved at the cost of only a fractional increase in complexity as compared to standard motion compensation methods.
Lossy source coding under a maximum distortion constraint with decoder side-information

A basic problem in information theory is source coding under a distortion constraint when the decoder has side-information about the source. In this paper the problem of variable length coding of a memoryless source under a maximum distortion constraint when there is side-information solely at the decoder is focused. Using a combinatorial approach, a nonsingle-letter expression for the minimum asymptotic average rate as well as single-letter bounds on this rate is derived
On Zero-Delay Source-Channel Coding

This paper studies the zero-delay source-channel coding problem, and specifically the problem of obtaining the vector transformations that optimally map between the m-dimensional source space and k-dimensional channel space, under a given transmission power constraint and for the mean square error distortion. The functional properties of the cost are studied and the necessary conditions for the optimality of the encoder and decoder mappings are derived. An optimization algorithm that imposes these conditions iteratively, in conjunction with the noisy channel relaxation method to mitigate poor local minima, is proposed. The numerical results show strict improvement over prior methods. The numerical approach is extended to the scenario of source-channel coding with decoder side information. The resulting encoding mappings are shown to be continuous relatives of, and in fact subsume as special case, the Wyner-Ziv mappings encountered in digital distributed source coding systems. A well-known result in information theory pertains to the linearity of optimal encoding and decoding mappings in the scalar Gaussian source and channel setting, at all channel signal-to-noise ratios (CSNRs). In this paper, the linearity of optimal coding, beyond the Gaussian source and channel, is considered and the necessary and sufficient condition for linearity of optimal mappings, given a noise (or source) distribution, and a specified a total power constraint are derived. It is shown that the Gaussian source-channel pair is unique in the sense that it is the only source-channel pair for which the optimal mappings are linear at more than one CSNR values. Moreover, the asymptotic linearity of optimal mappings is shown for low CSNR if the channel is Gaussian regardless of the source and, at the other extreme, for high CSNR if the source is Gaussian, regardless of the channel. The extension to the vector settings is also considered where besides the conditions inherited from the scalar case, additional constraints must be satisfied to ensure linearity of the optimal mappings.
Quantizer design for exploiting common information in layered coding

Today's diverse data consumption devices and heterogeneous network conditions require content to be coded at different quality levels. Conventional scalable coding, which generates hierarchical layers that refine quality incrementally, introduces a performance penalty, as most sources are not successively refinable at finite delays for the distortion measure employed and the combination of rates at each layer. On the other hand encoding different copies at required quality levels is clearly wasteful in resources. We previously proposed a common information based framework with a relaxed hierarchical structure to generate common and individuals bit-streams for different quality levels, to provide the flexibility of operating at points between conventional scalable coding and independent coding. In this paper we propose a quantizer design technique for this layered coding framework, which enables extracting common information between two quality levels with negligible performance penalty. Experimental results for Laplacian sources, which are prevalent in practical multimedia systems, substantiate the effectiveness of our proposed technique.
Multiple description quantization by deterministic annealing

The design of vector quantizers for diversity-based communication over two or more channels of possibly differing capacities and failure probabilities, is considered. The crucial dependence of current design techniques on initialization, especially of index assignment, is well recognized. Instead, we propose to pursue a deterministic annealing approach which is independent of initialization, does not assume any prior knowledge of the source density, and avoids many poor local minima of the cost surface. The approach consists of iterative optimization of a random encoder at gradually decreasing levels of randomness as measured by the Shannon entropy. At the limit of zero entropy, a hard multiple description (MD) quantizer is obtained. This process is directly analogous to annealing processes in statistical physics. Via an alternative derivation, we show that it may also be interpreted as approximating the minimum rate sums among points on the convex hull of the MD achievable rate-distortion region of El Gamal and Cover, subject to constraints on the sizes of the reproduction alphabets. To illustrate the potential of our approach, we present simulation results that show substantial performance gains over existing design techniques.
A deterministic annealing approach to optimization of zero-delay source-channel codes

This paper studies optimization of zero-delay source-channel codes, and specifically the problem of obtaining globally optimal transformations that map between the source space and the channel space, under a given transmission power constraint and for the mean square error distortion. Particularly, we focus on the setting where the decoder has access to side information, whose cost surface is known to be riddled with local minima. Prior work derived the necessary conditions for optimality of the encoder and decoder mappings, along with a greedy optimization algorithm that imposes these conditions iteratively, in conjunction with the heuristic “noisy channel relaxation” method to mitigate poor local minima. While noisy channel relaxation is arguably effective in simple settings, it fails to provide accurate global optimization results in more complicated settings including the decoder with side information as considered in this paper. We propose a global optimization algorithm based on the ideas of “deterministic annealing” - a non-convex optimization method, derived from information theoretic principles with analogies to statistical physics, and successfully employed in several problems including clustering, vector quantization and regression. We present comparative numerical results that show strict superiority of the proposed algorithm over greedy optimization methods as well as over the noisy channel relaxation.
Tracking geometrical descriptors on 3-D deformable surfaces: application to the left-ventricular surface of the heart

Motion and deformation analysis of the myocardium are of utmost interest in cardiac imaging. Part of the research is devoted to the estimation of the heart function by analysis of the shape changes of the left-ventricular endocardial surface. However, most clinically used shape-based approaches are often two-dimensional (2-D) and based on the analysis of the shape at only two cardiac instants. Three-dimensional (3-D) approaches generally make restrictive hypothesis about the actual endocardium motion to be able to recover a point-to-point correspondence between two surfaces. The present work is a first step toward the automatic spatio-temporal analysis and recognition of deformable surfaces. A curvature-based and easily interpretable description of the surfaces is derived. Based on this description, shape dynamics is first globally estimated through the temporal shape spectra. Second, a regional curvature-based tracking approach is proposed assuming a smooth deformation. It combines geometrical and spatial information in order to analyze a specific endocardial region. These methods are applied both on true 3-D X-ray data and on simulated normal and abnormal left ventricles. The results are coherent and easily interpretable. Shape dynamics estimations and comparisons between deformable object sequences are now possible through these techniques. This promising framework is a suitable tool for a complete regional description of deformable surfaces.
Using an elastic deformable template for the segmentation of ultrasound cardiac radio frequency images based on the velocity information

In the context of echocardiographic imaging, we investigate the feasibility of segmentation based on the velocity information, estimated by tracking radiofrequency patterns in consecutive frames. We subsequently segment the variance of the velocity by means of a deformable template, which is constrained by an elasticity term and evolves attracted by the edges of the velocity variance. Segmentation of a simulated image and of an entire cardiac sequence proves a good performance of our method. Since the velocity information is complementary with respect to the classically used envelope one, it could be used to increase reliability of the detection of cardiac structure.
Blood Velocity Estimation Using Compressive Sensing

Duplex ultrasonography is a mode of medical ultrasonography that allows one to visualize, at the same time, the inner structure of the body (B-mode) and the blood flow at a particular point in the body (Doppler mode). This mode requires a strategy for alternating B-mode and flow emissions. Traditional strategies either halve the maximum measurable velocity or introduce gaps in the flow data. The objective of this article is to propose a completely original method based on compressive sensing for reconstructing the Doppler signal segment by segment. Our approach is based on randomly alternating B-mode and flow emissions. The influence of the different parameters on the reconstruction quality is studied in detail. The technique is evaluated and its feasability is validated in simulation and from experimental in vivo data. It is also compared to the only method from the literature, proposed by Jensen, that reconstructs blood velocity estimates from sparse data sets.