nils-herrmann/clean_scirepeval huggingface dataset:
Constrained Hidden Markov Models

By thinking of each state in a hidden Markov model as corresponding to some spatial region of a fictitious topology space it is possible to naturally define neighbouring states as those which are connected in that space. The transition matrix can then be constrained to allow transitions only between neighbours; this means that all valid state sequences correspond to connected paths in the topology space. I show how such constrained HMMs can learn to discover underlying structure in complex sequences of high dimensional data, and apply them to the problem of recovering mouth movements from acoustics in continuous speech.
Data-driven production models for speech processing

When difficult computations are to be performed on sensory data it is often advantageous to employ a model of the underlying process which produced the observations. Because such generative models capture information about the set of possible observations, they can help to explain complex variability naturally present in the data and are useful in separating signal from noise. In the case of neural and artificial sensory processing systems generative models are learned directly from environmental input although they are often rooted in the underlying physics of the modality involved. One effective use of learned models is made by performing model inversion or state inference on incoming observation sequences to discover the underlying state or control parameter trajectories which could have produced them. These inferred states can then be used as inputs to a pattern recognition or pattern completion module. 
In the case of human speech perception and production, the models in question are called articulatory models and relate the movements of a talker's mouth to the sequence of sounds produced. Linguistic theories and substantial psychophysical evidence argue strongly that articulatory model inversion plays an important role in speech perception and recognition in the brain. Unfortunately, despite potential engineering advantages and evidence for being part of the human strategy, such inversion of speech production models is absent in almost all artificial speech processing systems. 
This dissertation presents a series of experiments which investigate articulatory speech processing using real speech production data from a database containing simultaneous audio and mouth movement recordings. I show that it is possible to learn simple low dimensionality models which accurately capture the structure observed in such real production data. I discuss how these models can be used to learn a forward synthesis system which generates spectral sequences from articulatory movements. I also describe an inversion algorithm which estimates movements from an acoustic signal. Finally, I demonstrate the use of articulatory movements, both true and recovered, in a simple speech recognition task, showing the possibility of doing true articulatory speech recognition in artificial systems.
A Sticker-Based Model for DNA Computation

We introduce a new model of molecular computation that we call the sticker model. Like many previous proposals it makes use of DNA strands as the physical substrate in which information is represented and of separation by hybridization as a central mechanism. However, unlike previous models, the stickers model has a random access memory that requires no strand extension and uses no enzymes; also (at least in theory), its materials are reusable. The paper describes computation under the stickers model and discusses possible means for physically implementing each operation. Finally, we go on to propose a specific machine architecture for implementing the stickers model as a microprocessor-controlled parallel robotic workstation. In the course of this development a number of previous general concerns about molecular computation (Smith, 1996; Hartmanis, 1995; Linial et al., 1995) are addressed. First, it is clear that general-purpose algorithms can be implemented by DNA-based computers, potentially solving a wide class of search problems. Second, we find that there are challenging problems, for which only modest volumes of DNA should suffice. Third, we demonstrate that the formation and breaking of covalent bonds is not intrinsic to DNA-based computation. Fourth, we show that a single essential biotechnology, sequence-specific separation, suffices for constructing a general-purpose molecular computer. Concerns about errors in this separation operation and means to reduce them are addressed elsewhere (Karp et al., 1995; Roweis and Winfree, 1999). Despite these encouraging theoretical advances, we emphasize that substantial engineering challenges remain at almost all stages and that the ultimate success or failure of DNA computing will certainly depend on whether these challenges can be met in laboratory investigations.
Signal Reconstruction from Zero-CrossingsSam

We present a method for recovering (to within a constant factor) periodic , octave band-limited signals given the times of the zero-crossings. Recovery involves taking the singular-value decomposition of a size N2M matrix, where N is the number of zero-crossings within one period and M is product of the octave bandwidth and the period length. We also discuss approximate approaches which can be used to reconstruct aperiodic or very-long-period signals. Our algorithm achieves an inversion of Logan's theorem in the case where such is possible. Sampling theorems provide conditions under which continuous signals may be represented by countable sets of real numbers. In the usual setting, we agree on a regular grid of time points and provide samples of the signal amplitude at those times. The Nyquist-Shannon theorems tell us that a low-pass signal may be reconstructed exactly, so long as the times of samples are spaced at least as densely as half the period of the highest frequency. However , we could also agree upon a regular grid of amplitude levels and provide the times at which the signal crossed those levels. although there is no clear theory relating the frequency content of the signal and the number of levels required. Logan's theorem Logan, Jr., 1977] addresses a special case of this general problem of signal reconstruction from level crossings. It states that if a signal is band-limited to a single octave then the times of the zero crossings are suucient to reconstruct the signal { to within a constant factor of course. 1 For Nyquist-style sampling (uniform 1 There are some additional important technical caveats. The signal must also have
Towards articulatory speech recognition: learning smooth maps to recover articulator information

We present a novel method for recovering articulator movements from speech acoustics based on a constrained form [9] of a hidden Markov model. The model attempts to explain sequences of high dimensional data using smooth and slow trajectories in a latent variable space. The key insight is that this continuity constraint when applied to speech helps to solve the \ill-posed" problem of acoustic to articulatory mapping. By working with sequences of spectra rather than looking only at individual spectra, it is possible to choose between competing articulatory con gurations for any given spectrum by selecting the con guration \closest" to those at nearby times. We present results of applying this algorithm to recover articulator movements from acoustics using data from the Wisconsin X-ray microbeam project . We nd that the recovered traces are highly correlated with the measured articulator movements under a single linear transform. Such recovered traces have the potential to be used for speech recognition, an application we are currently investigating.
Computing with Action Potentials

Most computational engineering based loosely on biology uses continuous variables to represent neural activity. Yet most neurons communicate with action potentials. The engineering view is equivalent to using a rate-code for representing information and for computing. An increasing number of examples are being discovered in which biology may not be using rate codes. Information can be represented using the timing of action potentials, and efficiently computed with in this representation. The "analog match" problem of odour identification is a simple problem which can be efficiently solved using action potential timing and an underlying rhythm. By using adapting units to effect a fundamental change of representation of a problem, we map the recognition of words (having uniform time-warp) in connected speech into the same analog match problem. We describe the architecture and preliminary results of such a recognition system. Using the fast events of biology in conjunction with an underlying rhythm is one way to overcome the limits of an event-driven view of computation. When the intrinsic hardware is much faster than the time scale of change of inputs, this approach can greatly increase the effective computation per unit time on a given quantity of hardware.
EM Algorithms for PCA and SPCA

I present an expectation-maximization (EM) algorithm for principal component analysis (PCA). The algorithm allows a few eigenvectors and eigenvalues to be extracted from large collections of high dimensional data. It is computationally very efficient in space and time. It also naturally accommodates missing information. I also introduce a new variant of PCA called sensible principal component analysis (SPCA) which defines a proper density model in the data space. Learning for SPCA is also done with an EM algorithm. I report results on synthetic and real data showing that these EM algorithms correctly and efficiently find the leading eigenvectors of the covariance of datasets in a few iterations using up to hundreds of thousands of datapoints in thousands of dimensions.
LEARNING LEGGED LOCOMOTION OVER EXTREME TERRAIN

Currently, legged locomotion over obstacles remains a great challenge in robotics. The equations describing so-called “extreme” walks are highly complex, and the contact forces between the robot and its environment are not easily modeled. A major research goal in the field of robotics is to develop capacity for “extreme” walks while keeping computations tractable. This study was designed to create a method for making a Sony Aibo walk up a 1-inch step consistently. Project activities focused on finding ways to apply simple models for generating walks onto the step. Once the study identified a class of “extreme” walks in lower dimensions, parameters were hand-tuned in an effort to assess whether a prospective class of walks was appropriate or not.
Cooperative relative robot localization with audible acoustic sensing

We describe a method for estimating the relative poses of a team of mobile robots using only acoustic sensing. The relative distances and bearing angles of the robots are estimated using the time of arrival of audible sound signals on stereo microphones. The robots emit specially designed sound waveforms that simultaneously enable robot identification and time of arrival estimation. These acoustic observations are then combined with odometry to update a belief state describing the positions and heading angles of all the robots. To efficiently resolve the ambiguity in the heading angle of the observing robot as well as the back-front ambiguity of the observed robot, we employ a Rao-Blackwellised particle filter (RBPF) where the distribution over heading angles is represented by a discrete set of particles, and the uncertainty in the translational positions conditioned on each of these particles is described by a Gaussian. This approach combines the representational accuracy of conventional particle filters with the efficiency of Kalman filter updates in modeling the pose distribution over a number of robots. We demonstrate how the RBPF can quickly resolve uncertainties in the binaural acoustic measurements and yield a globally consistent pose estimate. Simulations as well as an experimental implementation on robots with generic sound hardware illustrate the accuracy and the convergence of the resulting pose estimates.
The University of Pennsylvania Robocup 2004 Legged Soccer Team

This paper presents the software design for a team of soccer playing robots developed at the Univ. of Pennsylvania for the 2004 Robocup competition. The software was a port and slight modification from the 2003 competition code. Lower level sensory and motor functions were first prototyped in Matlab. High level behaviors and team coordination were implemented using an embedded Perl interpreter. The robots’ walk parameters were optimized using a hill climbing routine. These developments allowed the development of the team that ultimately resulted in a fourth place finish at the international competition.
Learning nonlinear appearance manifolds for robot localization

We propose a nonlinear method for learning the low-dimensional pose of a robot from high-dimensional panoramic images. The panoramic images are assumed to lie on a nonlinear low-dimensional appearance manifold that is embedded in a high-dimensional image space. We demonstrate that the local geometry of a point and its nearest neighbors on this manifold can be used to project the point onto a low-dimensional coordinate space. Using this embedding, the unknown camera position can be estimated from a novel panoramic image. We show how the image-based position measurements can be integrated with odometry information in a Bayesian framework to yield an online estimate of a robot's position. Results from simulated data show that the proposed method outperforms other appearance-based models based upon principal components analysis and kernel density estimation.
Relevant deconvolution for acoustic source estimation

We describe a robust deconvolution algorithm for simultaneously estimating an acoustic source signal and convolutive filters associated with the acoustic room impulse responses from a pair of microphone signals. In contrast to conventional blind deconvolution techniques which rely upon a knowledge of the statistics of the source signal, our algorithm exploits the nonnegativity and sparsity structure of room impulse responses. The algorithm is formulated as a quadratic optimization problem with respect to both the source signal and filter coefficients, and proceeds by iteratively solving the optimization in two alternating steps. In the H-step, the nonnegative filter coefficients are optimally estimated within a Bayesian framework using a relevant set of regularization parameters. In the S-step, the source signal is estimated without any prior assumption on its statistical distribution. The resulting estimates converge to a relevant solution exhibiting appropriate sparseness in the filters. Simulation results indicate that the algorithm is able to precisely recover both the source signal and filter coefficients, even in the presence of large ambient noise.
Bayesian Regularization and Nonnegative Deconvolution for Time Delay Estimation

Bayesian Regularization and Nonnegative Deconvolution (BRAND) is proposed for estimating time delays of acoustic signals in reverberant environments. Sparsity of the nonnegative filter coefficients is enforced using an L1-norm regularization. A probabilistic generative model is used to simultaneously estimate the regularization parameters and filter coefficients from the signal data. Iterative update rules are derived under a Bayesian framework using the Expectation-Maximization procedure. The resulting time delay estimation algorithm is demonstrated on noisy acoustic data.
A kernel view of the dimensionality reduction of manifolds

We interpret several well-known algorithms for dimensionality reduction of manifolds as kernel methods. Isomap, graph Laplacian eigenmap, and locally linear embedding (LLE) all utilize local neighborhood information to construct a global embedding of the manifold. We show how all three algorithms can be described as kernel PCA on specially constructed Gram matrices, and illustrate the similarities and differences between the algorithms with representative examples.
Dimensionality Reduction for Sensorimotor Learning in Mobile Robotics

Mobile robotic systems with a wide variety of sensors, actuators, and onboard high-speed processors are commercially and readily available. The information processing capabilities of these system presently lack the robustness and sophistication of biological systems. One challenge is that the high-dimensional input signals from the sensors need to be converted into a smaller number of perceptually relevant features. This dimensionality reduction can be performed on static signals such as a single image or on dynamic data such as a speech spectrogram. This proceedings discusses several different models for dimensionality reduction that differ only on the constraints on the variables and parameters of the models. In particular, nonnegativity constraints are shown to give rise to distributed yet sparse representations of both static and dynamic data.
The translinear Principle: a General Framework for Implementing Chaotic oscillators

In this Letter, we propose a class of nonlinear dynamic translinear circuits as a viable tool for the rapid and systematic implementation of chaotic oscillators in analog integrated circuits. In this regard, our primary focus is on multiple-input translinear element (MITE) networks and their particular merits. We also describe, as an illustrative example, our monolithic implementation of the Lorenz equations, and report on results from a test chip fabricated in a 0.5 μm CMOS process through MOSIS. We also show that Chua's circuit can be implemented easily in our framework.
A programmable floating-gate bump circuit with variable width

We propose a new programmable bump circuit using floating-gate transistors with a simple topology. The center and the width of this bump circuit are orthogonally tunable and programmable. The input signal range is rail to rail and the power consumption does not change dramatically while varying the width. Therefore, this circuit is suitable for low power applications. We use a vector-quantizer as an example to illustrate how this circuit fits into a large scale network.
Synthesis of MITE log-domain filters with unique operating points

Practical log-domain filter circuits might have multiple operating points in regions in which the translinear element does not obey the exponential law. In this paper, a method is proposed to implement any filter by a log-domain circuit that necessarily has a unique operating point. Any state-space description of the filter is shown to have an equivalent description that can be implemented by such a circuit. This methodology is applied to the synthesis of multiple-input translinear element (MITE) filters. As an example, shifted-companion-form (SCF) filters are synthesized. Further, it is proved that the resulting filters have a unique operating point.
Synthesis of static and dynamic multiple-input translinear element networks

In this paper, we discuss the process of synthesizing static and dynamic multiple-input translinear element (MITE) networks systematically from high-level descriptions given in the time domain, in terms of static polynomial constraints and algebraic differential equations. We provide several examples, illustrating the process for both static and dynamic system constraints. Although our examples will all involve MITE networks, the early steps of the synthesis process are equally applicable to the synthesis of static and dynamic translinear-loop circuits.
A fully programmable log-domain bandpass filter using multiple-input translinear elements

In this paper a second order log-domain bandpass filter using multiple input translinear elements (MITEs) operating at a 3V supply. We enhance the capabilities of the filter by utilizing programmable MITE structures as well as programmable current sources, which are covered in this paper. The synthesized bandpass filter is implemented and fabricated using these programmable translinear devices (MITEs). Experimental results are shown from circuit fabricated on a 0.5/spl mu/m nwell CMOS process available through MOSIS.
Low voltage and performance tunable CMOS circuit design using independently driven double gate MOSFETs

Independently driven double-gate MOSFETs (DGFETs) facilitate design of analog circuits under digital logic constraints and provide in-circuit parameter adaptability through threshold voltage control. Threshold voltage tuning is achieved by biasing one of the two gates where as strong coupling of surface potentials at the two interfaces provides a low resistance feedback path. The geometry also allows a back-floating gate NVRAM structure with superior scalability and floating gate related analog applications without any read disturbance. This paper gives examples across breadth of circuits where this tunability is exploited.
Construction and transformation of multiple-input translinear element networks

We present a simple algorithmic procedure for constructing a multiple-input translinear element (MITE) network from a translinear-loop equation. We also give a number of MITE-network transformations that alter the structure of the MITE network without altering the translinear-loop equation that it embodies. The results that we establish in this paper serve as foundations for the synthesis of both static and dynamic MITE networks from high-level specifications.
The chemoreceptive neuron MOS transistors (C/spl nu/MOS): a novel floating-gate device for molecular and chemical sensing

We propose a novel chemoreceptive neuron MOS transistor (C/spl nu/MOS) with distinctive features that are crucial to molecular sensing system integration. We have fabricated C/spl nu/MOS transistors with generic molecular receptive areas and experimentally demonstrated high sensitivity from channel current changes and large dynamic range from subthreshold slope shifts in sensing polar and electrolytic liquids. Preliminary measurements have validated most of our assumptions about the performance of these devices and have indicated the feasibility in developing a micropower neuromorphic electronic interface based on existing CMOS technology.
Highly linear, wide-dynamic-range multiple-input translinear element networks

In this paper, we propose a modification to the class of circuits known as multiple input translinear element (MITE) networks. Our proposed modification leads to a MITE network that is free from certain nonidealities encountered in previous implementations. Further, the new MITE network described here readily accommodates the use of bipolar junction transistors in the input and output stages, thus implying a significantly wider dynamic range than we can achieve using subthreshold MOSFETs.
A physical compact model of DG MOSFET for mixed-signal circuit applications- part I: model description

To use double-gate (DG) MOSFET for mixed-signal circuit applications, especially for circuits in which the two gates are independently driven, such as in the case of dynamic-threshold and fixed-potential-plane operations, physical compact models that are valid for all modes of operations are necessary for accurate design and analysis. Employing physically rigorous current-voltage (I-V) relationship in subthreshold and above-threshold regions as asymptotic cases, we have constructed a model that joins the two operating regions by using carrier-screening functions. We have included consistently source/drain series resistance, low drain-field mobility, and small-geometry effects of drain-induced barrier lowering (DIBL), MOS interface mobility, velocity saturation and channel-length modulation (CLM) with validation from two-dimensional (2-D) distributed simulation. All model parameters can be extracted from large-signal I-V characteristics in dc conditions with given geometrical data. Parameter extraction methods and verification from simulation are presented in Part II.