nils-herrmann/clean_scirepeval huggingface dataset:
Approximate Linear Programming for Average-Cost Dynamic Programming

This paper extends our earlier analysis on approximate linear programming as an approach to approximating the cost-to-go function in a discounted-cost dynamic program . In this paper, we consider the average-cost criterion and a version of approximate linear programming that generates approximations to the optimal average cost and differential cost function. We demonstrate that a naive version of approximate linear programming prioritizes approximation of the optimal average cost and that this may not be well-aligned with the objective of deriving a policy with low average cost. For that, the algorithm should aim at producing a good approximation of the differential cost function. We propose a two-phase variant of approximate linear programming that allows for external control of the relative accuracy of the approximation of the differential cost function over different portions of the state space via state-relevance weights. Performance bounds suggest that the new algorithm is compatible with the objective of optimizing performance and provide guidance on appropriate choices for state-relevance weights.
Mixed H2/h∞ Control Of Flexible Structures

This paper addresses the design of full order linear dynamic output feedback controllers for flexible structures. Unstructured H∞ uncertainty models are introduced for systems in modal coordinates and in reduced order form. Then a controller is designed in order to minimize a given H2 performance function while keeping the maximum supported H∞ perturbation below some appropriate level. To solve this problem we develop an algorithm able to provide local optimal solutions to optimization problems with convex constraints and non-convex but differentiable objective functions. A controller design procedure based on a trade-off curve is proposed and a simple example is solved, providing a comparison between the proposed method and the usual minimization of an upper bound H2 to the norm. The method is applied to two different flexible structure theoretical models and the properties of the resulting controllers are shown in several simulations.
Approximate Dynamic Programming via Linear Programming

The curse of dimensionality gives rise to prohibitive computational requirements that render infeasible the exact solution of large-scale stochastic control problems. We study an efficient method based on linear programming for approximating solutions to such problems. The approach "fits" a linear combination of pre-selected basis functions to the dynamic programming cost-to-go function. We develop bounds on the approximation error and present experimental results in the domain of queueing network control, providing empirical support for the methodology.
Approximate value iteration with randomized policies

The curse of dimensionality in dynamic programming prevents, in most problems of practical interest, the exact computation of the value function. We study the fixed points of approximate value iteration, a simple algorithm that combats the curse of dimensionality by generating approximate iterates of the classical value iteration algorithm in the span of a set of prespecified basis functions. We show that, in general, the modified dynamic programming operator need not possess a fixed point, and therefore, approximate value iteration should not be expected to converge. However, by using a class of randomized policies, approximate value iteration is guaranteed to possess at least one fixed point. We finally discuss the link between approximate value iteration and temporal-difference learning (TD), and show that the existence of fixed points for approximate value iteration implies existence of stationary points for the ordinary differential equation approximated by a version of TD that incorporates "exploration".
Approximate value iteration and temporal-difference learning

In principle, a wide variety of sequential decision problems-ranging from dynamic resource allocation in communication networks to inter-temporal investment-can be formulated in terms of stochastic control and solved by the algorithms of dynamic programming. Unfortunately, due to the curse of dimensionality, the associated computational requirements become intractable in most practical contexts. The paper discusses approximate valve iteration, an algorithm that tries to alleviate the curse of dimensionality. We present potential problems associated to it and special cases in which desirable results are obtained. We also discuss the relationship between approximate value iteration and temporal-difference learning.
Output feedback control of Markov jump linear systems in continuous-time

This paper addresses the dynamic output feedback control problem of continuous-time Markovian jump linear systems. The fundamental point in the analysis is an LMI characterization, comprising all dynamical compensators that stabilize the closed-loop system in the mean square sense. The H/sub 2/ and H/sub /spl infin//-norm control problems are studied, and the H/sub 2/ and H/sub /spl infin// filtering problems are solved as a by product.
On the existence of fixed points for approximate value iteration and temporal-difference learning

Approximate value iteration is a simple algorithm that combats the curse of dimensionality in dynamic programs by approximating iterates of the classical value iteration algorithm in a spirit reminiscent of statistical regression. Each iteration of this algorithm can be viewed as an application of a modified dynamic programming operator to the current iterate. The hope is that the iterates converge to a fixed point of this operator, which will then serve as a useful approximation of the optimal value function. In this paper, we show that, in general, the modified dynamic programming operator need not possess a fixed point; therefore, approximate value iteration should not be expected to converge. We then propose a variant of approximate value iteration for which the associated operator is guaranteed to possess at least one fixed point. This variant is motivated by studies of temporal-difference (TD) learning, and existence of fixed points implies here existence of stationary points for the ordinary differential equation approximated by a version of TD that incorporates exploration.
Comparison of hearing loss compensation algorithms using speech intelligibility measures

Sensorineural hearing loss includes loss of high‐frequency sensitivity which results in decreased speech intelligibility. The loss cannot be compensated by inverting the audiogram because of the non‐linear effects of sensorineural hearing loss (frequency smearing, decreased dynamic range, decreased time‐frequency resolution). Several non‐linear compensation schemes exist (Half‐gain, POGO, NAL‐R, Fig. 6, DSL and LGOB) and this paper provides a comparison of those using the objective Perceptual Evaluation of Subjective Quality (PESQ) score and the subjective Hearing In Noise Test (HINT). The listening tests were run on 15 unaided hearing impaired listeners as well as 15 normal hearing listeners using a simulated hearing loss algorithm. These results show marked improvement in intelligibility for the compensated speech over the normal speech for both normal and hearing impaired adults.
Computational Neural Networks

Brain function remains one of the most elusive and fascinating phenomena challenging modem science (Churchland, 1986). Although a lot is already known about the neuron and its functional characteristics, when we address the information-processing capabilities of a neural assembly, called here the mesoscopic description (Freeman, 1975), more often than not we are unable to quantify function and abstract the computation. The reasons can be found in the distributed nature of the system architecture, the lack of appropriate tools and metaphors to describe the communication among neurons (the neural code) (Rao et al, 2002), and also very often due to the absence of a detailed knowledge of the function being implemented (Nicolelis, 2001). Hence, conducting studies in computational neuroscience requires a carefully planned methodology and experimental design.
A warped bandwidth expansion filter

A warped filter is presented as a new speech enhancement method to adjust formant bandwidths on a critical band scale. The warped filter enhances perceived loudness without adding signal energy by exploiting the psychoacoustic nature of the auditory system. The critical band concept in auditory theory states that when the energy in a signal remains constant, loudness increases when the energy spreads beyond a critical bandwidth. A warped filter is proposed and developed to elevate the perceived loudness of clean speech by applying nonlinear bandwidth expansion to the formant regions of vowels in accordance with the critical band scale. The filter has been inspired and motivated by the biological representation of loudness in the peripheral auditory system and the critical band concept of hearing.
Bandwidth extension of telephone speech using frame-based excitation and robust features

The standards that are still in use for telephone communications since the 1950s limit the information bandwidth to 300-3400Hz. However, in normal conversational speech, the frequency content is mainly between 0-8000Hz. This constraint degrades not only the sound quality but also the intelligibility of the transmitted signal. Instead of modifying the present telecommunication infrastructures, which would cost billions of dollars, many researchers have been studying more efficient methods to increase the quality of telephone speech. This paper develops an innovative solution to bandwidth extension, which is based upon the Linear Source Filter Model that breaks speech up into two parts: the excitation and the spectral envelope. Novel approaches are used to extend the frequency information for both parts. This algorithm particularly emphasizes low frequency reconstruction without neglecting high frequencies. Furthermore, different feature sets to model the spectral envelope are employed for better performance under noisy conditions.
Factors affecting the perception of Korean‐accented American English

This experiment examines the relative contribution of two factors, intonation and articulation errors, on the perception of foreign accent in Korean‐accented American English. Ten native speakers of Korean and ten native speakers of American English were asked to read ten English sentences. These sentences were then modified using high‐quality speech resynthesis techniques [STRAIGHT; Kawahara et al.,  (1999)] to generate four sets of stimuli. In the first two sets of stimuli, the intonation patterns of the Korean speakers and American speakers were switched with one another. The articulatory errors for each speaker were not modified. In the final two sets, the sentences from the Korean and American speakers were resynthesized without any modifications. Fifteen listeners were asked to rate all the stimuli for the degree of foreign accent. Preliminary results show that, for native speakers of American English, articulation errors may play a greater role in the perception of foreign...
Automatic detection of microchiroptera echolocation calls from field recordings using machine learning algorithms

The authors have recently presented experimental results of applying machine learning algorithms, used extensively in human automatic speech recognition research (ASR), to automatic species identification of echolocating bats [Skowronski and Harris,  2639 (2004)]. The results of those experiments demonstrated that frame‐based classification, preferred in ASR, out‐performs holistic classification typically employed in automatic echolocating species identification. The authors have extended the paradigm of machine learning algorithms to the related problem of bat call detection. A robust automatic bat call detection algorithm, to replace hand labeling, is required for two reasons: (1) for real‐time species identification in the field, and (2) because hand labeling is subjective, tedious, slow, and error‐prone. The current experiments compare various frame‐based features (log energy, pitch estimates, pitch slopes) with several models of detection (matched filters, Gaussian mixtures, d...
Disordered Speech Assessment Using Automatic Methods Based on Quantitative Measures

Speech quality assessment methods are necessary for evaluating and documenting treatment outcomes of patients suffering from degraded speech due to Parkinson's disease, stroke, or other disease processes. Subjective methods of speech quality assessment are more accurate and more robust than objective methods but are time-consuming and costly. We propose a novel objective measure of speech quality assessment that builds on traditional speech processing techniques such as dynamic time warping (DTW) and the Itakura-Saito (IS) distortion measure. Initial results show that our objective measure correlates well with the more expensive subjective methods.
Disordered speech evaluation using objective quality measures

Speech quality assessment methods are necessary for evaluating and documenting treatment outcomes of patients suffering from degraded speech due to Parkinson's disease, stroke or other disease processes. Subjective methods of speech quality assessment are more accurate and more robust than objective methods but are time-consuming and costly. We propose a novel objective measure of speech quality assessment that builds on traditional speech processing techniques such as dynamic time warping (DTW) and the Itakura-Saito (IS) distortion measure. Initial results show that our objective measure correlates well with the more expensive subjective methods.
VLSI potentiostat for amperometric measurements for electrolytic reactions

This paper describes a CMOS integrated potentiostatic control circuit. The design maintains a constant bias potential between the reference and working electrodes for an amperometric chemical sensor. A technique of converting input currents into time for amperometric measurements is proposed. Redox currents ranging from a 1 pA to 200 nA can be measured with a maximum nonlinearity of /spl plusmn/ 0.1% over this range. Analog inputs are processed and digital outputs are generated without requiring a power-hungry A/D converter. A prototype chip has been fabricated in the 0.5 /spl mu/m AMI CMOS process. Experimental results are reported showing the performance of the circuit as a chemical sensor.
A loudness enhancement technique for speech

We propose an application of the vocoder postfilter to increase perceived loudness of clean speech without increasing signal energy or degrading intelligibility. The critical band concept in auditory theory states that perceived loudness of a narrow-band signal will increase when the bandwidth of that signal increases beyond a critical band, even though the energy remains constant. Our post-filter technique applies formant bandwidth expansion to the vowel regions of speech without changing the vowel power to elevate perceived loudness. Vowels are known to contain the highest energy, have a smooth spectral envelope, long temporal sustenance, and for this reason are suitable candidates to target for a loudness enhancement technique. ISO-532B loudness analysis patterns and listening tests are provided to demonstrate a perceptual loudness improvement corresponding to a 2dB power gain.
Signal reconstruction from spiking neuron models

We describe a method for signal reconstruction from spiking neuron models such as integrate-and-fire or leaky integrate-and-fire neurons. These neural models encode a single analog signal in the timing of asynchronous digital pulses. We show that using only the output firing times of these neurons, we can recover a bandlimited input signal to within machine precision. A major application of this work is for a replacement of conventional analog-to-digital converters in some applications where simpler analog hardware is traded off more complex reconstruction on the part of the subsequent digital processor. Realistic SPICE simulations of CMOS spiking neurons show that accurate reconstruction with more than 12-bit precision can be achieved. The effects of frequency aliasing, noise, and temporal quantization are considered.
Ascertaining the importance of neurons to develop better brain-machine interfaces

In the design of brain-machine interface (BMI) algorithms, the activity of hundreds of chronically recorded neurons is used to reconstruct a variety of kinematic variables. A significant problem introduced with the use of neural ensemble inputs for model building is the explosion in the number of free parameters. Large models not only affect model generalization but also put a computational burden on computing an optimal solution especially when the goal is to implement the BMI in low-power, portable hardware. In this paper, three methods are presented to quantitatively rate the importance of neurons in neural to motor mapping, using single neuron correlation analysis, sensitivity analysis through a vector linear model, and a model-independent cellular directional tuning analysis for comparisons purpose. Although, the rankings are not identical, up to sixty percent of the top 10 ranking cells were in common. This set can then be used to determine a reduced-order model whose performance is similar to that of the ensemble. It is further shown that by pruning the initial ensemble neural input with the ranked importance of cells, a reduced sets of cells (between 40 and 80, depending upon the methods) can be found that exceed the BMI performance levels of the full ensemble.
Towards a spiking VLSI implementation of Freeman's olfactory model

We introduce the Freeman model - a nonlinear dynamic system proposed to model the operation of the cortex - and explain how its interconnected nature complicates physical realizations. We propose changing the Freeman model so that it is a spiking network in order to alleviate the implementation problems while preserving the important dynamic characteristics. We include simulation results showing that our modified spiking model demonstrates those dynamics. Finally, we show experimental results from a VLSI chip implementation of the spiking model as a proof-of-concept.
Enhanced energy redistribution speech intelligibility algorithm with real‐time implementation

Speech intelligibility enhancement is a concern for mobile platforms operating in noisy environments. Current noise‐reduction techniques, such as subspace methods and spectral subtraction, operate on speech corrupted by acoustic and transmission noise. Yet preprocessing techniques, which operate on clean speech before noise corruption, have received little attention. Previously, the authors have developed the energy redistribution algorithm , which, based on characteristics of clear speech as well as the Lombard effect, redistributes energy in time from voiced regions to unvoiced regions of speech. The algorithm is designed efficiently for real‐time implementation, and in this work the algorithm is demonstrated on a mobile platform, TIs TMS320C6713 DSK board. Furthermore, two enhancements to the algorithm are introduced: (1) a variable unvoiced gain factor and (2) a high pass filter (HPF). The variable unvoiced gain factor adjusts the amount of energy redistributed, a...
A time-to-first spike CMOS imager

A novel time-to-first-spike CMOS imager is presented, in which the timing of a single spike from each pixel encodes the illuminance of each pixel. This temporal representation of illuminance can widen the image sensor dynamic range to over 100 dB. To reduce power consumption, an asynchronous address-event readout technique is incorporated. The imager essentially implements a pixel-level A/D conversion and benefits from the continued process scaling to deep submicron levels. Results are shown from a 32/spl times/32 pixel imager fabricated in the 0.5 /spl mu/m AMI process with measured dynamic range of 104 dB in one image. A test chip with one pixel and digital control logic has been fabricated through MOSIS using the TSMC 0.18 /spl mu/m standard digital CMOS process. It has demonstrated the expected functionalities. A modified architecture is also proposed for a larger imager array.
Statistical automatic species identification of microchiroptera from echolocation calls: Lessons learned from human automatic speech recognition

Current automatic species identification of microchiroptera through echolocation search calls utilizes holistic features, such as call duration, bandwidth, and frequency extrema, which stem from expert knowledge of sonograms of individual calls. This approach parallels the early acoustic‐phonetic paradigm of human automatic speech recognition (ASR), which relied heavily on expert knowledge to account for variations in canonical linguistic units of interest. Acoustic‐phonetic ASR gave way to the statistical paradigm of ASR primarily because of the superior ability of machine learning to account for variations in the acoustic signal (noise, speaker characteristics) [Juang and Furui, Proc.  (8), 1142–1165 (2000)]. In the current work, machine learning methods from human ASR, hidden Markov models (HMM) and dynamic time warping (DTW), are considered for the problem of automatic species identification of microchiroptera from echolocation calls. The extensive history of ASR provides valuable lessons, whic...
Spike-Based Sensing and Processing (Profile)

Dr. John G. Harris co-directs the Computational NeuralEngineering Lab (CNEL) at the University of Florida, together with its founder: Dr. Jose C. Principe. CNEL seeks to advance the theory and applications of adaptive systems using mathematics and anthropomorphic principles. This work is highly multidisciplinary and of broad impact since it is geared to provide new engineering design principles. Analogies from biology are expressed in appropriate mathematical frameworks and implemented in digital algorithms or directly in analog VLSI chips. Since its inception in 1992, the CNEL has created an international reputation in the areas of adaptive filtering theory, artificial neural networks, nonlinear dynamics, neuromorphic engineering, and more recently in brain machine interfaces and information theoretic learning. 1. Incredible fault tolerance: the brain loses an average of 10,000 neurons per day without requiring any sort of explicit reconfiguration or rewiring. 2. Ultra-low power consumption: The brain consumes an average of 12 Watts, much less than a typical Pentium computer performing much less computation. Fig. 2. Experimental 32x32 pixel time-to-first-spike imager.
A comparison of three algorithms for estimating aspiration noise in dysphonic voices

Dysphonic voices are frequently characterized by increased aspiration noise. Several algorithms have been proposed to quantify the noise present in such voices. Yet, an independent analysis of these algorithms has not been reported. These algorithms differ in a number of aspects such as the theory underlying these measurements, the procedures used for estimating the noise, and the nature of their output. Three different algorithms for estimating noise in dysphonic voices were implemented in MATLAB and their output for synthetic and natural voice samples compared. These algorithms include: (1) the pitch‐predictive signal‐to‐noise ratio reported by Milenkovic [Workshop on Acoustic Voice Analysis: Proceedings (1994)], which analyzes signals in the time domain and provides a time waveform of the aspiration noise: (2) the harmonics‐to‐noise ratio reported by deKrom [ (1993)], which performs a cepstral analysis to provide the spectrum of the aspiration noise; and (3) the glottal‐...