nils-herrmann/clean_scirepeval huggingface dataset:
A subthreshold-CMOS chaotic oscillator

This paper reports on the design of a subthreshold-CMOS chaotic oscillator based on Chua's circuit. The circuit is autonomous and generates chaotic signals from three state variables in a manner analogous to a Chua circuit. Previous CMOS IC versions of the Chua circuit operate above threshold and make use of capacitors over 200 times larger than the subthreshold design. Also, this design is based on fundamentally nonlinear subthreshold building blocks permitting flexible adjustment of bifurcation parameters for investigation of complex phenomena not present in a piecewise linear model. Finally, this design incorporates all necessary elements on chip, eliminating the need for an external resistor. The combination of reduced size and flexible nonlinearity make this design suitable for a single-chip VLSI synthesis of complex circuits making use of chaotic signals, including coupled maps generating spatio-temporal chaos and systems exploiting chaos synchronization.
The silicon vocal tract

The human vocal tract has been successfully modeled as a series of uniform cross-section tubes concatenated end-to-end. Such a model has been part of the study of the human speech generation system for many years. The similarity of the equations for sound propagation in these tubes and transmission line equations has been exploited in articulatory speech synthesis systems. Each section of tube is modeled as an RLC circuit whose element values are determined by its area and length with the current and voltage corresponding to volume velocity and pressure in the tube. With the proper vocal tract configurations, the circuit has been used as part of a speech generation system. An analog VLSI circuit is described and simulated that emulates these same equations. Spectral analysis of the simulated circuit output reveals that the first three resonance frequencies are in the same range as the corresponding human vowel formants. The silicon vocal tract will form the basis of a completely integrated analog VLSI articulatory speech synthesizer. The VLSI cylindrical section analog circuit has been designed, simulated and submitted to the MOSIS silicon foundry for fabrication in a 2 /spl mu/m CMOS process.
A continuous-time analog circuit for computing time delays between signals

We describe an analog VLSI circuit that computes the time delay between an arbitrary input signal and its delayed version. Versions of this circuit will be used as the basic computational elements for human auditory localization models in both the horizontal and vertical directions. Since the circuit uses subthreshold CMOS circuitry, chip measurements show that delays ranging over five orders of magnitude can be recovered. The circuit delays the first input signal by two distinct time-constants and correlates each with the second input signal. The correlation values at the two points are then integrated and used to compute a finite-difference derivative that dynamically adjusts the time constants until an optimal delay is found. This paper describes the first continuous-time circuit for computing delays between arbitrary signals.
Automatic scale detection

Most image processing and computer vision algorithms assume knowledge of the proper spatial scale in the image. We propose an automatic technique that adaptively sets the proper spatial scale based on the size of local features in the image. We discuss an analog VLSI circuit implementation that uses this method to automatically determine the proper scale based on a dynamic system model that continuously searches scale space. Though this technique is motivated by analog circuit concepts, we show results for a digital implementation for image enhancements of mammograms. Digital implementations must discretize the scale space and perform an optimization over this finite set.
Motion-based visual behaviors for mobile robots

We investigate two specific visual motion behaviors that are appropriate for mobile robot applications because of their simplicity and low computational load. The first behavior determines the distance to a flat wall to the side of the moving robot. Such a measurement is essential for a wall-following behavior. The second behavior determines the projected amount of time until the robot collides with a stationary object assuming that the robot continues to move at a constant velocity. This time-to-contact estimate relies on the expansion of the optical flow field and can act as a collision-warning sensor to warn of impending obstacles. The numberical estimates given by both behaviors are expressed as simple ratios of easily measured visual quantities and do not require any elaborate calibration procedures. These behaviors use a 1D patch-wise correlation technique that was developed by Poggio and Ancona. We have further simplified the computation and show results on true image sequences.
Continuous-time calibration of VLSI sensors for gain and offset variations

Parameter variations cause unavoidable nonuniformities in infrared focal plane arrays and other integrated sensors. A one-time calibration procedure is normally used to counteract the effect of these variations between components. Unfortunately, many of these variations fluctuate with time--either with operating point (such as data-dependent variations) or with external conditions (such as temperature). Calibrating these sensors one-time only at the `factory' is not suitable--much more frequent calibration is required. We have developed an adaptive algorithm that continually calibrates an array of sensors that contains gain and offset variations. This paper extends the work of Ullman and Schechtman who developed an algorithm for gain adjustment. The adaptive nonlinear dynamical system can be mapped to analog VLSI or a discretized version may be efficiently implemented in digital hardware.
Unsupervised Learning with Non-Ignorable Missing Data

In this paper we explore the topic of unsupervised learning in the presence of nonignorable missing data with an unknown missing data mechanism. We discuss several classes of missing data mechanisms for categorical data and develop learning and inference methods for two specific models. We present empirical results using synthetic data which show that these algorithms can recover both the unknown selection model parameters and the underlying data model parameters to a high degree of accuracy. We also apply the algorithms to real data from the domain of collaborative filtering, and report initial results.
Mining student CVS repositories for performance indicators

Over 200 CVS repositories representing the assignments of students in a second year undergraduate computer science course have been assembled. This unique data set represents many individuals working separately on identical projects, presenting the opportunity to evaluate the effects of the work habits captured by CVS on performance. This paper outlines our experiences mining and analyzing these repositories. We extracted various quantitative measures of student behaviour and code quality, and attempted to correlate these features with grades. Despite examining 166 features, we find that grade performance cannot be accurately predicted; certainly no predictors stronger than simple lines-of-code were found.
Modeling complete distributions with incomplete observations : The velocity ellipsoid from Hipparcos data

An algorithm is developed to model the three-dimensional velocity distribution function of a sample of stars using only measurements of each star's two-dimensional tangential velocity. The algorithm works with "missing data": it reconstructs the three-dimensional distribution from data (velocity measurements) that all have one dimension that is unmeasured (the radial direction). It also accounts for covariant measurement uncertainties on the tangential velocity components. The algorithm is applied to tangential velocities measured in a kinematically unbiased sample of 11,865 stars taken from the Hipparcos catalog, chosen to lie on the main sequence and have well-measured parallaxes. The local stellar velocity distribution function of each of a set of 20 color-selected subsamples is modeled as a mixture of two three-dimensional Gaussian ellipsoids of arbitrary relative responsibility. In the fitting, one Gaussian (the "halo") is fixed at the known mean velocity and velocity variance tensor of the Galaxy halo, and the other (the "disk") is allowed to take an arbitrary mean and an arbitrary variance tensor. The mean and variance tensors (commonly known as the "velocity ellipsoid") of the disk velocity distribution are both found to be strong functions of stellar color, with long-lived populations showing larger velocity dispersion, slower mean rotation velocity, and smaller vertex deviation than short-lived populations. The local standard of rest (LSR) is inferred in the usual way, and the Sun's motion relative to the LSR is found to be (U, V, W)☉ = (10.1, 4.0, 6.7) ± (0.5, 0.8, 0.2) km s-1. Artificial data sets are made and analyzed, with the same error properties as the Hipparcos data, to demonstrate that the analysis is unbiased. The results are shown to be insensitive to the assumption that the velocity distributions are Gaussian.
A statistical learning approach to document image analysis

In the field of computer analysis of document images, the problems of physical and logical layout analysis have been approached through a variety of heuristic, rule-based, and grammar-based techniques. In this paper we investigate the effectiveness of statistical pattern recognition algorithms for solving these two problems, and report results suggesting that these more complex and powerful techniques are worth pursuing. First, we developed a new software environment for manual page image segmentation and labeling, and used it to create a dataset containing 932 page images from academic journals. Next, a physical layout analysis algorithm based on a logistic regression classifier was developed, and found to outperform existing algorithms of comparable complexity. Finally, three statistical classifiers were applied to the logical layout analysis problem, also with encouraging results.
Automatic Speech Processing by Inference in Generative Models

In this chapter, we have explored the use of inference in probabilistic generative models as a powerful signal processing tool for speech and audio. The basic paradigm explored was to design a simple model for the data we observe in which the key quantities that we would eventually like to compute appear as hidden (latent) variables. By executing probabilistic inference in such models, we automatically estimating the hidden quantities and thus perform our desired computation. In a sense, the rules of probability derive for us, automatically, the optimal signal processing algorithm for our desired outputs given our inputs under the model assumptions. Crucially, even though the generative model may be quite simple and may not capture all of the variability present in the data, the results of inference can still be extremely informative.
Nonlinear blind sensor fusion and identification

When several uncharacterized sensors measure the same unknown signal or image, we would like to simultaneously combine the measurements into an estimate of true source (fusion) and learn the properties of the individual sensors (identification). This paper presents a model in which sensors perform (time-invariant) linear filtering followed by pointwise nonlinear squashing with additive noise and shows how, given several such noisy nonlinear observations, it is possible to recover the true signal and also estimate the sensor parameters. The setup assumes that both the linear filtering and the nonlinear squashing are spatially (temporally) invariant, but does not make any prior assumptions (such as smoothness, sparsity or heavily tailed marginals) about the signal being recovered and thus is appropriate for a variety of source distributions, such as astronomical images, speech signals and hyperspectral satellite data which may violate one or more standard prior assumptions. An efficient estimation algorithm minimizes the sum of squared errors between the predicted sensor outputs and the sensor readings actually observed, using an efficient procedure, isomorphic to the backpropagation algorithm. The setup can be thought of as learning the weights and unknown common input for several one-layer neural networks given their outputs.
Hierarchical Clustering of a Mixture Model

In this paper we propose an efficient algorithm for reducing a large mixture of Gaussians into a smaller mixture while still preserving the component structure of the original model; this is achieved by clustering (grouping) the components. The method minimizes a new, easily computed distance measure between two Gaussian mixtures that can be motivated from a suitable stochastic model and the iterations of the algorithm use only the model parameters, avoiding the need for explicit resampling of datapoints. We demonstrate the method by performing hierarchical clustering of scenery images and handwritten digits.
CVS Data Extraction and Analysis: A Case Study

Version control repositories contain a wealth of detailed information about the evolution of a codebase. In this paper, we outline our experiences parsing and analyzing data from a large collection of CVS repositories created by many students working on a small set of assignments in a second year undergraduate computer science course. We believe the data to be quite unique because rather than a single team working on a single large project we have many individuals working separatly on identical projects. We describe a system for parsing repositories into a SQL database. From the database and repositories we extract various statistical measures of the code and version histories. Finally, we attempt to correlate the code and repository features with grades. Despite using 166 features derived from the code and repository histories, we nd that grade performance cannot be accurately predicted; certainly no predictors stronger than simple lines-of-code were found.
Factorial models and refiltering for speech separation and denoising

This paper proposes the combination of several ideas, some old and some new, from machine learning and speech processing. We review the max approximation to log spectrograms of mixtures, show why this motivates a “refiltering” approach to separation and denoising, and then describe how the process of inference in factorial probabilistic models performs a computation useful for deriving the masking signals needed in refiltering. A particularly simple model, factorial-max vector quantization (MAXVQ), is introduced along with a branch-and-bound technique for efficient exact inference and applied to both denoising and monaural separation. Our approach represents a return to the ideas of Ephraim, Varga and Moore but applied to auditory scene analysis rather than to speech recognition. 1. Sparsity & Redundancy in Spectrograms 1.1. The Log-MaxApproximation When two clean speech signals are mixed additively in the time domain, what is the relationship between the individual log spectrograms of the sources and the log spectrogram of the mixture? Unless the sources are highly dependent (synchronized), the spectrogram of the mixture is almost exactly the maximum of the individual spectrograms, with the maximum operating over small time-frequency regions (fig. 2). This amazing fact, first noted by Roger Moore in 1983, comes from the fact that unless e1 and e2 are both large and almost equal, log(e1 + e2) ≈ max(log e1, log e2) (fig. 1a). The sparse nature of the speech code across time and frequency is the key to the practical usefulness of this approximation: most narrow frequency bands carry substantial energy only a small fraction of the time and thus it is rare that two independent sources inject large amounts of energy into the same subband at the same time.  This is partly why humans perform so well on many monaural speech separation and denoising tasks. When we solve the cocktail party problem or recognize degraded speech, we are doing structural analysis, or a kind of “perceptual grouping” on the incoming sound. There is substantial evidence that the appropriate subparts of an audio signal for use in grouping may be narrow frequency bands over short times. To generate these parts computationally, we can perform multiband analysis – break the original speech signal y(t) into many subband signals bi(t) each lo g e2 ma
Relationship between gradient and EM steps in latent variable models

We present a close relationship between Expectation Maximization algorithm and direct optimization approaches such as gradient-based methods for parameter learning. We show that the step EM takes in the parameter space and true gradient are related by the symmetric positive definite P matrix, and provide an explicit form of this matrix for several widely used latent variable models. We then go on deriving a general form of the P matrix for the regular exponential family in terms of its natural parameters.
Adaptive Overrelaxed Bound Optimization Methods

We study a class of overrelaxed bound optimization algorithms, and their relationship to standard bound optimizers, such as Expectation-Maximization, Iterative Scaling, CCCP and Non-Negative Matrix Factorization. We provide a theoretical analysis of the convergence properties of these optimizers and identify analytic conditions under which they are expected to outperform the standard versions. Based on this analysis, we propose a novel, simple adaptive overrelaxed scheme for practical optimization and report empirical results on several synthetic and real-world data sets showing that these new adaptive methods exhibit superior performance (in certain cases by several times speedup) compared to their traditional counterparts. Our extensions are simple to implement, apply to a wide variety of algorithms, almost always give a substantial speedup, and do not require any theoretical analysis of the underlying algorithm.
Simultaneous Localization and Surveying with Multiple Agents

We apply a constrained Hidden Markov Model architecture to the problem of simultaneous localization and surveying from sensor logs of mobile agents navigating in unknown environments. We show the solution of this problem for the case of one robot and extend our model to the more interesting case of multiple agents, that interact with each other through proximity sensors. Since exact learning in this case becomes exponentially expensive, we develop an approximate method for inference using loopy belief propagation and apply it to the localization and surveying problem with multiple interacting robots. In support of our analysis, we report experimental results showing that with the same amount of data, approximate learning with the interaction signals outperforms exact learning ignoring interactions.
Non-linear CCA and PCA by Alignment of Local Models

We propose a non-linear Canonical Correlation Analysis (CCA) method which works by coordinating or aligning mixtures of linear models. In the same way that CCA extends the idea of PCA, our work extends recent methods for non-linear dimensionality reduction to the case where multiple embeddings of the same underlying low dimensional coordinates are observed, each lying on a different high dimensional manifold. We also show that a special case of our method, when applied to only a single manifold, reduces to the Laplacian Eigenmaps algorithm. As with previous alignment schemes, once the mixture models have been estimated, all of the parameters of our model can be estimated in closed form without local optima in the learning. Experimental results illustrate the viability of the approach as a non-linear extension of CCA.
An Alternate Objective Function for Markovian Fields

A new red poinsettia cultivar particularly distinguished by its earlier blooming and at a lower temperature than the plant that it most nearly resembles, namely Poinsettia V-14, U.S. Plant Pat. No. 4,384; and by its having the unique growth and habit characteristics of the Gutbier's Family of poinsettia plants, including easy multiple branching, full bracts, and small cyathia clusters.
Automatic Alignment of Local Representations

We present an automatic alignment procedure which maps the disparate internal representations learned by several local dimensionality reduction experts into a single, coherent global coordinate system for the original data space. Our algorithm can be applied to any set of experts, each of which produces a low-dimensional local representation of a high-dimensional input. Unlike recent efforts to coordinate such models by modifying their objective functions [1, 2], our algorithm is invoked after training and applies an efficient eigensolver to post-process the trained models. The post-processing has no local optima and the size of the system it must solve scales with the number of local models rather than the number of original data points, making it more efficient than model-free algorithms such as Isomap  or LLE [4].
One Microphone Source Separation

Source separation, or computational auditory scene analysis, attempts to extract individual acoustic objects from input which contains a mixture of sounds from different sources, altered by the acoustic environment. Unmixing algorithms such as ICA and its extensions recover sources by reweighting multiple observation sequences, and thus cannot operate when only a single observation signal is available. I present a technique called refiltering which recovers sources by a nonstationary reweighting ("masking") of frequency sub-bands from a single recording, and argue for the application of statistical algorithms to learning this masking function. I present results of a simple factorial HMM system which learns on recordings of single speakers and can then separate mixtures using only one observation signal by computing the masking function and then refiltering.
Modeling Systematic Variations in Pronunciation via a Language-Dependent Hidden Speaking Mode

This paper describes the research efforts of the “Hidden Speaking Mode” group participating in the 1996 summer workshop on speech recognition. The goal of this project is to model pronunciation variations that occur in conversational speech in general and, more specifically, to investigate the use of a hidden speaking mode to represent systematic variations that are correlated with the word sequence (e.g. predictable from syntactic structure). This paper describes the theoretical formulation of hidden mode modeling, as well as some results in error analysis, language modeling and pronunciation modeling.
On Applying Molecular Computation to the Data Encryption Standard

Recently, Boneh, Dunworth, and Lipton (1996) described the potential use of molecular computation in attacking the United States Data Encryption Standard (DES). Here, we provide a description of such an attack using the sticker model of molecular computation. Our analysis suggests that such an attack might be mounted on a tabletop machine using approximately a gram of DNA and might succeed even in the presence of a large number of errors.
On the Reduction of Errors in DNA Computation

In this paper, we discuss techniques for reducing errors in DNA computation. We investigate several methods for achieving acceptable overall error rates for a computation using basic operations that are error prone. We analyze a single essential biotechnology, sequence-specific separation, and show that separation errors theoretically can be reduced to tolerable levels by invoking a tradeoff between time, space, and error rates at the level of algorithm design. These tradeoffs do not depend upon improvement of the underlying biotechnology which implements the separation step. We outline several specific ways in which error reduction can be done and present numerical calculations of their performance.