nils-herrmann/clean_scirepeval huggingface dataset:
Information rates of densely sampled Gaussian data

With mean-squared error D as a goal, it is well known that one may approach the rate-distortion function R(D) of a nonbandlimited, continuous-time Gaussian source by sampling at a sufficiently high rate, applying the Karhunen-Loeve transform to sufficiently long blocks, and then independently coding the transform coefficients of each type. Motivated by the question of the efficiency of dense sensor networks for sampling, encoding and reconstructing spatial random fields, this paper studies the following three cases. In the first, we consider a centralized encoding setup with a sample-transform-quantize scheme where the quantization is assumed to be optimal. In the second, we consider a distributed setup, where a spatio-temporal source is sampled and distributively encoded to be reconstructed at a receiver. We show that with ideal distributed lossy coding, dense sensor networks can efficiently sense and convey a field, in contrast to the negative result obtained by Marco et al. for encoders based on time- and space-invariant scalar quantization and ideal Slepian-Wolf distributed lossless coding. In the third, we consider a centralized setup, with a sample-and-transform coding scheme in which ideal coding of coefficients is replaced by coding with some specified family of quantizers. It is shown that when the sampling rate is large, the operational rate-distortion function of such a scheme comes within a finite constant of that of the first case.
On the support of MSE-optimal, fixed-rate, scalar quantizers

This paper determines how the support regions of optimal and asymptotically optimal fixed-rate scalar quantizers (with respect to mean-squared error) depend on the number of quantization points N and the probability density of the variable being quantized. It shows that for asymptotic optimality it is necessary and sufficient that the support region grow fast enough that the outer (or overload) distortion decreases as o(1/N/sup 2/). Formulas are derived for the minimal support of asymptotically optimal quantizers for generalized gamma densities, including Gaussian and Laplacian. Interestingly, these turn out to be essentially the same as for the support of optimal fixed-rate uniform scalar quantizers. Heuristic arguments are then used to find closed-form estimates for the support of truly optimal quantizers for generalized gamma densities. These are found to be more accurate than the best prior estimates, as computed by numerical algorithms. They demonstrate that the support of an optimal quantizer is larger than the minimal asymptotically optimal support by a factor depending on the density but not N, and that the outer distortion of optimal quantizers decreases as 1/N/sup 3/.
Polar quantization revisited

In polar quantization, a vector (X/sub 1/,X/sub 2/) is quantized in terms of its magnitude and phase. This is natural for circularly symmetric densities such as IID Gaussian. A number of polar schemes have been proposed, analyzed and optimized. One of the best is unrestricted polar quantization (UPQ), in which the phase step size depends on the quantized magnitude. Prior analysis has shown that when optimized, its SNR is only 0.17 dB less than optimal 2-dimensional VQ. We show that UPQ can be very simply analyzed and optimized using the VQ version of Bennett's integral. Indeed, it is shown that one may optimize UPQ based solely on the criteria that it have the point density of an optimal VQ, and square rather than hexagonal cells. We also analyze and optimize power law polar quantization, in which the number of phase levels is proportional to a power of the quantized magnitude.
Robust source coding of weakly compact classes

A fixed-rate block code is said to be strongly robust for a class of sources \Lambda if its maximum distortion over \Lambda h is no larger than the maximum of the distortion-rate functions of the sources in the class at the rate of the code. It is shown that such codes exist at all positive rates whenever the class is compact with resect to the topology of weak convergence and satisfies certain additional, but not very strong, constraints on the alphabet and distortion measure. Examples of classes that satisfy these conditions are given. In addition, classes are exhibited for which there are no strongly robust codes. These help to demarcate the boundary between classes for which strongly robust codes do and do not exist. More specifically, they show that strongly robust codes exist more widely than strongly universal codes but !ess widely than weakly universal codes.
Similarity of Scenic Bilevel Images

This paper presents a study of bilevel image similarity, including new objective metrics intended to quantify similarity consistent with human perception, and a subjective experiment to obtain ground truth for judging the performance of the objective similarity metrics. The focus is on scenic bilevel images, which are complex, natural or hand-drawn images, such as landscapes or portraits. The ground truth was obtained from ratings by 77 subjects of 44 distorted versions of seven scenic images, using a modified version of the SDSCE testing methodology. Based on hypotheses about human perception of bilevel images, several new metrics are proposed that outperform existing ones in the sense of attaining significantly higher Pearson and Spearman-rank correlation coefficients with respect to the ground truth from the subjective experiment. The new metrics include adjusted percentage error, bilevel local direction, and connected components comparison. Combinations of these metrics are also proposed, which exploit their complementarity to attain even better performance. These metrics and the ground truth are then used to assess the relative severity of various kinds of distortion and the performance of several lossy bilevel compression methods.
Deterministic Annealing for Entropy-Constrained Vector Quantizer Design

Typical vector quantizer (VQ) design algorithms, such as generalized Lloyd methods, are susceptible to local minima. In addition, for entropy-constrained VQ, it is not obvious what the "right" number of codevectors is for any given source and target rate. One method that has shown promising results for solving both problems is deterministic annealing, where randomness is added to the encoding process, the uncertainty of which is gradually reduced until a deterministic coder is found. This method has been used for fixed-rate and entropy-constrained unstructured VQ, as well as for a variety of structurally constrained VQs. For the unstructured entropy-constrained case, we generalize the existing methods to include new cost functions, and we argue that, depending on the chosen cost function, some splitting criteria are more suitable than others. We give analytical advantages and disadvantages to several cost functions and several splitting criteria, then give performance evaluations by testing on synthetic and natural sources.
Asymptotic Bounds In Source-channel Coding

We consider the problem of scalar quantization in the presence of channel noise and develop lower bounds on the mean-squared error between a real-valued hput and its reproduction. Bennett's htegral is an asymptotic formula for the distortion of a scalar quantizer in terms of the source density and the distribution of quantization points (i.e. point density), when the channel is assumed to be noiseless. We look to derive a bound on distortion for the noisy channel channel case that depends on the channel statistics, the source density and the quantizer point density. The lower bounds are derived for the general scalar case and are evaluated for uniform and nonuniform (Gaussian pdfoptimized) scalar quantizers.
Distributed encoding of sensor data

In this paper, we ignore transmission issues and focus on the total number of bits to transmit to the collector to form a reconstruction of the field with a given MSE. We assume that all sensors can transmit bits to the collector without error. With this assumption, with total number of bits as the cost measure, and with the style of coding, it can be argued that sensor-to-sensor relaying offers no advantages. This problem is similar to image coding and transmission, except that the quantization, encoding and transmission are constrained to take place separately at each sensor (pixel location), in contrast to traditional image coding and transmission, wherein the entire image is available for quantization, encoding, and transmission. Due to the need to separately encode values from separate sensors, we pursue a Slepian-Wolf style coding approach.
Optimizing prediction gain in axial symmetric scans

Though most lossless image coding techniques use a raster scan to order the pixels for context-based predictive coding, other scans, such as the Hilbert or Peano scan, have been proposed as alternatives with potentially better performance. However, a general understanding of the merits of different scans has been lacking. In previous work, the authors had presented a framework in which the effect of pixel scan order on lossless compression can be quantitatively analyzed, so that comparisons of different scans can be made. Assuming a quantized-Gaussian and isotropic image model with contexts consisting of previously scanned adjacent pixels in a distance constrained neighborhood, it was found that the raster scan is better than the Hilbert scan. In this paper we further develop our arguments and show that for a large class of scans, which we call axial symmetric scans, the raster scan is indeed optimal. We would like to note that many common scans including the Hilbert scan fall under the class of axial symmetric scans.
Joint and tandem source-channel coding with complexity and delay constraints

Two common source-channel coding strategies, joint and tandem, are compared on the basis of distortion versus complexity and distortion versus delay by analyzing specific representatives of each when transmitting analog data samples across a binary symmetric channel. Channel-optimized transform coding is the joint source-channel strategy; transform coding plus Reed-Solomon coding is the tandem strategy. For each strategy, formulas for the mean-squared error, computational complexity, and delay are found and used to minimize distortion subject to constraints on complexity and delay, for source data modeled as Gauss-Markov. The results of such optimizations suggest there is a complexity threshold such that when the number of operations per data sample available for encoding and decoding is greater than this threshold, tandem coding is better, and when less, channel-optimized transform coding is better. Similarly, the results suggest there is also a delay threshold such that tandem coding is better than joint coding when the permissible encoding and decoding delay is greater than this threshold.
Coding for channels with cost constraints

We address the problem of finite-state code construction for the costly channel. Adler et al. developed powerful state-splitting algorithm for use in the construction of finite-state codes for hard-constrained channels. We extend the state-splitting algorithm to the costly channel. We present several examples of costly channels related to magnetic recording, the telegraph channel, and shaping gain modulation. We design a number of synchronous and asynchronous codes, some of which come very close to achieving capacity.
Upper bounds on the capacity of the digital magnetic recording channel

Upper bounds are derived for the channel capacity of a model for a digital magnetic disk storage device. The model is similar to that of C.A. French and J.K. Wolf (1988) in that it contains a linear filter to represent the Lorentzian step response and two additive Gaussian noise processes to represent the electronic and media noises. It differs in that both the lineal bit density and the track density are parameters that may be varied, as opposed to just track density. Using typical media parameters, it is estimated that, for this model, the channel capacity (in bits per unit area) may be increased up to a factor of approximately 20 by appropriately increasing the lineal and track densities.
Reduced storage VQ via secondary quantization

This paper introduces methods for reducing the table storage required for encoding and decoding with unstructured vector quantization (UVQ) or tree-structured vector quantization (TSVQ). Specifically, a low-storage secondary quantizer is used to compress the code vectors (and test vectors) of the primary quantizer. The relative advantages of uniform and nonuniform secondary quantization are investigated. A Linde-Buzo-Gray (LBG) like algorithm that optimizes the primary UVQ codebook for a given secondary codebook and another that jointly optimizes both primary and secondary codebooks are presented. In comparison to conventional methods, it is found that significant storage reduction is possible (typically a factor of two to three) with little loss of signal-to-noise ratio (SNR). Moreover, when reducing dimension is considered as another method of reducing storage, it is found that the best strategy is a combination of both. The method of secondary quantization is also applied to TSVQ to reduce the table storage required for both encoding and decoding. It is shown that by exploiting the correlation among the test vectors in the tree, both encoder and decoder storage can be significantly reduced with little loss of SNR--by a factor of about four (or two) relative to the conventional method of storing test vectors (or test hyperplanes).
The validity of the additive noise model for uniform scalar quantizers

A uniform scalar quantizer with small step size, large support, and midpoint reconstruction levels is frequently modeled as adding orthogonal noise to the quantizer input. This paper rigorously demonstrates the asymptotic validity of this model when the input probability density function (pdf) is continuous and satisfies several other mild conditions. Specifically, as step size decreases, the correlation between input and quantization error becomes negligible relative to the mean-squared error (MSE). The model is even valid when the input density is discontinuous at the origin, but discontinuities elsewhere can prevent the correlation from being negligible. Though this invalidates the additive model, an asymptotic formula for the correlation is found in terms of the step size and the heights and positions of the discontinuities. For a finite support input density, such as uniform, it is shown that the support of the uniform quantizer can be matched to that of the density in ways that make the correlation approach a variety of limits. The derivations in this paper are based on an analysis of the asymptotic convergence of cell centroids to cell midpoints. This convergence is fast enough that the centroids and midpoints induce the same asymptotic MSE, but not fast enough to induce the same correlations.
Indecomposable finite state channels and primative approximation

The class of discrete stationary channels with memory that can be arbitrarily well approximated by indecomposable finite state channels is investigated. The \bar{d} concept of channel distance is used to quantify the notion of one channel approximating another, and it is shown that the class of interest equals the class of channels approximable by nonanticipating primitive channels. The latter class includes all nonanticipating finite memory channels and all nonanticipating \bar{d} -continuous, conditional almost block independent (CABI) channels. In addition, it is shown that for a large class of finite state channels, \bar{d} continuity and CABI imply indecomposability.
Some simple parametric lossless image compressors

This paper proposes lossless image compressors that are simpler than existing ones and yet still work well. The compressors process images in raster-scan order, and to code a pixel first estimate that pixel's value by using a linear function of already-coded pixels. Next the compressors estimate the uncertainty in the first estimate by using a nonlinear function of already-coded pixels. Finally, based on these estimates, they select a discretized Laplacian with which an arithmetic coder represents the pixel. Alternatively, the compressors may select Golomb codewords based on the estimates, and thus directly represent the pixels. These compressors' rates come within 6 to 8% of CALIC, a highly-effective image compressor. Another benefit is that a simple theoretical motivation exists for the chosen uncertainty estimators.
A new subjective procedure for evaluation and development of texture similarity metrics

In order to facilitate the development of objective texture similarity metrics and to evaluate their performance, one needs a large texture database accurately labeled with perceived similarities between images. We propose ViSiProG, a new Visual Similarity by Progressive Grouping procedure for conducting subjective experiments that organizes a texture database into clusters of visually similar images. The grouping is based on visual blending, and greatly simplifies pairwise labeling. ViSiProG collects subjective data in an efficient and effectivemanner, so that a relatively large database of textures can be accommodated. Experimental results and comparisons with structural texture similarity metrics demonstrate both the effectiveness of the proposed subjective testing procedure and the performance of the metrics.
Frog-in-the-box index codes with maximum likelihood decoding for robust scalar quantization

The performance of frog-in-the box (FIB) code index assignments is numerically investigated when maximum likelihood (ML) decoding is used in place of optimal MMSE decoding. Specifically, the reproduction levels are chosen to be those that would be used for a noiseless channel, and the decoder simply maps the channel output to a level whose index codeword is closest to the channel output.
Fusion of diverse denoising systems for robust automatic speech recognition

We present a framework for combining different denoising front-ends for robust speech enhancement for recognition in noisy conditions. This is contrasted against results of optimally fusing diverse parameter settings for a single denoising algorithm. All frontends in the latter case exploit the same denoising algorithm, which combines harmonic decomposition, with noise estimation and spectral subtraction. The set of associated parameters involved in these steps are dependent on the noise conditions. Rather than explicitly tuning them, we suggest a strategy that tries to account for the trade-off between average word error rate and diversity to find an optimal subset of these parameter settings. We present the results on Aurora4 database and also compare against traditional speech enhancement methods e.g. Wiener filtering and spectral subtraction.
Predicting interruptions in dyadic spoken interactions

Interruptions occur frequently in spontaneous conversations, and they are often associated with changes in the flow of conversation. Predicting interruption is essential in the design of natural human-machine spoken dialog interface. The modeling can bring insights into the dynamics of human-human conversation. This work utilizes Hidden Condition Random Field (HCRF) to predict occurrences of interruption in dyadic spoken interactions by modeling both speakers' behaviors before a turn change takes place. Our prediction model, using both the foreground speaker's acoustic cues and the listener's gestural cues, achieves an F-measure of 0.54, accuracy of 70.68%, and unweighted accuracy of 66.05% on a multimodal database of dyadic interactions. The experimental results also show that listener's behaviors provides an indication of his/her intention of interruption.
Robust ECG Biometrics by Fusing Temporal and Cepstral Information

The use of vital signs as a biometric is a potentially viable approach in a variety of application scenarios such as security and personalized health care. In this paper, a novel robust Electrocardiogram (ECG) biometric algorithm based on both temporal and cepstral information is proposed. First, in the time domain, after pre-processing and normalization, each heartbeat of the ECG signal is modeled by Hermite polynomial expansion (HPE) and support vector machine (SVM). Second, in the homomorphic domain, cepstral features are extracted from the ECG signals and modeled by Gaussian mixture modeling (GMM). In the GMM framework, heteroscedastic linear discriminant analysis and GMM super vector kernel is used to perform feature dimension reduction and discriminative modeling, respectively. Finally, fusion of both temporal and cepstral system outcomes at the score level is used to improve the overall performance. Experiment results show that the proposed hybrid approach achieves 98.3% accuracy and 0.5% equal error rate on the MIT-BIH Normal Sinus Rhythm Database.
Spoken language synthesis: experiments in synthesis of spontaneous monologues

While TTS technology has come a long way, there is an ongoing need for bringing improved "naturalness" to synthesized speech. One predominant aspect of natural, spontaneous speech is the variability in it along several dimensions - in terms of vocabulary, prosodic features, paralinguistic elements and discourse markers. Such variability is typically carefully avoided or minimized in conventional text to speech for the sake of high intelligibility. However, in applications requiring immersive anthropomorphic human-machine interfaces, including those with computer-generated avatars, there is a great desire to mimic human-like synthesized speech output. In this paper we investigate methods and the usefulness of incorporating certain features characterizing fluent natural speech for increasing "naturalness" in synthesized speech. We propose a data driven approach for modeling both speaker-independent and speaker-dependent spontaneous speech features at the lexical and acoustic levels (so-called, VoiceFonts). This method has the potential to create unique, custom speaking styles of a target speaker. A simple limited domain synthesizer was built based on this idea using data from a classroom lecture and was used to synthesize 28 target utterances. Results from preliminary listening experiments by 19 volunteers showed that such an approach indeed improves naturalness, without significant loss in intelligibility, beyond the limitations of the underlying waveform. synthesis. For example, subjects could correctly identify natural speech with a probability of 0.6 and confused the clips synthesized in this work with natural speech with a probability of 0.27 in a 4-way choice listening test.
Limited domain synthesis of expressive military speech for animated characters

Text-to-speech synthesis can play an important role in interactive education and training applications, as voices for animated agents. Such agents need high-quality voices capable of expressing intent and emotion. This paper presents preliminary results in an effort aimed at synthesizing expressive military speech for training applications. Such speech has acoustic and prosodic characteristics that can differ markedly from ordinary conversational speech. A limited domain synthesis approach is used employing samples of expressive speech, classified according to speaking style. The resulting synthesizer was tested both in isolation and in the context of a virtual reality training scenario with animated characters.