nils-herrmann/clean_scirepeval huggingface dataset:
First results on the application of the Fynesse control architecture

A system is presented that learns to control a priori unknown nonlinear dynamical systems and enables the user to interpret, examine and correct the control strategy in every stage of learning. The application of dynamic programming methods to train a neural network for such control purposes was already quite successful. In order to shorten the long training process the system should allow the incorporation of a priori knowledge about the control strategy in terms of classical linear controllers, fuzzy control rules etc. As a solution, we propose the hybrid control architecture Fynesse: the control strategy is represented by a fuzzy relation that can be interpreted and contain a priori knowledge, whereas the more complex part of learning is solved by a neural network as before. This article reports results on the application of the Fynesse controller to a chemical plant.
Daily Prediction of the Foreign Exchange Rate Between the US Dollar and the German Mark Using Neural

In this paper we study the problem of predicting the daily foreign exchange rate (FX) between the US Dollar and the German Mark. The study can be seen as a typical example of predicting noisy time series. Our approach based on neural networks consists of searching for the optimal representation of the underlying market by selecting the optimal training patterns. Moreover we develop a trading system which is tested in several out-of-sample test periods from January 1990 to June 1995. After the encouraging results on the test sets the neural network prediction system has been embedded in a real trading system. Since July 1995 this trading system is successfully used to make online predictions at the FX market in New York.
A new method for the analysis of neural reference model control

In recent years there has been much effort to develop the theoretical aspects of neural MRAC-control, that is to find conditions under which an unknown process can be identified by an input-output model and controllers can be trained by gradient descent. On the other hand, the application of neural network techniques to real world control of nonlinear dynamical systems has been of substantial interest. Since the theoretical conditions that ensure controllability and the applicability of indirect adaptive control are hard to verify in practice, the success of controller training is mostly shown by testing relevant situations. We trained a controller for a subsystem of a spark ignition engine by dynamic backpropagation and various truncated gradient algorithms. Afterwards we related the neural MRAC-approach to pole placement and linearization techniques in order to show the successful training by pole analysis of the completely trained loop. This is a new method to verify the plausibility of the adaptation process and the trained regulator.
Application of a self-learning controller with continuous control signals based on the DOE-approach

An apparatus for generating patterned images which are variable in color contrast and spatial frequency while retaining a substantially constant, uniform level of photometric luminance. Two broad spectrum beams of luminous energy are individually projected through two orthogonally oriented polarizers and interleaved upon being split. The intensity in one beam is homogeneous while the other contains a spatially periodic pattern. The two beams created by splitting and interleaving are selectively color filtered and combined after one is translated sufficiently to reverse its phase relative to the other. A rotating polarizer in the path of the output combined beam alters the pattern color composition between one extreme where the spatially reversing colors are defined by the color filters and the opposite extreme in which the pattern disappears into a uniform composition of the two colors.
Fast Network Pruning and Feature Extraction by using the Unit-OBS Algorithm

The algorithm described in this article is based on the OBS algorithm by Hassibi, Stork and Wolff ( and ). The main disadvantage of OBS is its high complexity. OBS needs to calculate the inverse Hessian to delete only one weight (thus needing much time to prune a big net). A better algorithm should use this matrix to remove more than only one weight, because calculating the inverse Hessian takes the most time in the OBS algorithm. 
 
The algorithm, called Unit-OBS, described in this article is a method to overcome this disadvantage. This algorithm only needs to calculate the inverse Hessian once to remove one whole unit thus drastically reducing the time to prune big nets. 
 
A further advantage of Unit-OBS is that it can be used to do a feature extraction on the input data. This can be helpful on the understanding of unknown problems.
Application of sequential reinforcement learning to control dynamic systems

The article describes the structure of a neural reinforcement learning controller, based on the approach of asynchronous dynamic programming. The learning controller is applied to a well-known benchmark problem, the cart-pole system. In crucial difference to previous approaches, the goal of learning is not only to avoid failure, but moreover to stabilize the cart in the middle of the track, with the pole standing in an upright position. The aim is to learn high quality control trajectories known from conventional controller design, by providing only a minimum amount of a priori knowledge and teaching information.
Self-learning neural control of a mobile robot

Reinforcement learning is a promising paradigm for the training of intelligent controllers. The learning capabilities of a neural network based controller architecture are shown by its application to control a mobile robot in an unknown environment. Based on the multi-sensor information provided by four infrared sensors, the controller has to learn to avoid collisions, receiving only a final training signal of success or failure. The article further shows that simulation can be used to avoid the long real world training effort.
Massively Parallel Training of Multi Layer Perceptrons With Irregular Topologies

In this paper we present an approach to the training of feed forward neural networks on massively parallel SIMD-architectures. In order to cover a wide field of applications we focus our attention on the flexibility of the load balancing routines. Our approach is characterized by three important properties: 1. All four types of parallelism inherent in the training phase are used. 2. In a preprocessing step neural networks are transformed into equivalent topologies, more suited for parallel computation. 3. Each learning task can be parallelized in a number of different ways, the best of which is chosen according to estimations of the computing efficiency.
MASSIVELY PARALLEL TRAINING OF MULTI LAYERPERCEPTRONS WITH IRREGULAR

|In this paper we present an approach to the training of feed forward neural networks on massively parallel SIMD-architectures. In order to cover a wide eld of applications we focus our attention on the exibility of the load balancing routines. Our approach is characterized by three important properties: 1. All four types of parallelism inherent in the training phase are used. 2. In a preprocessing step neural networks are transformed into equivalent topologies, more suited for parallel computation. 3. Each learning task can be parallelized in a number of diierent ways, the best of which is chosen according to estimations of the computing eeciency. Following these concepts we developed PINK 2 , a massively parallel simulator kernel for the MasPar MP1216. In contrast to most known approaches, ef-cient only for special topologies, it achieves good computing performance on a broad range of diiering benchmark problems.  The vast requirements of computing power for the training of neural networks and their inherent parallelism made neural computation to an interesting and thoroughly investigated eld of application for parallel and massively parallel machines. Apart from dedicated hardware solutions for the training and recall phase of feed forward neural networks , the most promising results have been shown by SIMD-architectures with often some thousand processing elements (e.g. 1], 2], 3], 4], 5]). However, while attaining good peak values on special neu-ral networks their average performance on other network topologies is mostly comparatively poor. It depends heavily on the structure of the given learning problem, i.e. the number of networks to be trained, the number of training examples , the number and width of the network layers and the structure of interconnection between them. To put it another way: each parallelization is tacitly or explicitly tailored to a special structure of learning problems; their load balancing routines are designed for those special cases. As a result the scope of most massively parallel neural
Using Neural Reinforcement Controllers in Robotics

Reinforcement Learning is a promising paradigm for the training of intelligent controllers when only a minimum of a priori knowledge is available. This is especially true for situations, where uncertainties and nonlinearities in sensors and actuators make the application of conventional control design techniques impossible or at least a very hard task. The article describes a neural control architecture based on the asynchronous dynamic programming approach (Barto et al., 1993). Its capabilities are shown on two applications from the domain of robotics.
Learnability of Probabilistic Automata via Oracles

Efficient learnability using the state merging algorithm is known for a subclass of probabilistic automata termed μ-distinguishable. In this paper, we prove that state merging algorithms can be extended to efficiently learn a larger class of automata. In particular, we show learnability of a subclass which we call μ 2 -distinguishable Using an analog of the Myhill-Nerode theorem for probabilistic automata, we analyze μ-distinguishability and generalize it to μ p -distinguishability. By combining new results from property testing with the state merging algorithm we obtain KL-PAC learnability of the new automata class.
Online Learning on Spheres

The classical on-line learning algorithms such as the LMS (Least Mean Square) algorithm have attracted renewed interest recently because of many variants based on non-standard parametrisations that bias the algorithms in favour of certain prior knowledge of the problem. Tools such as link functions and Bregman divergences have been used in the design and analysis of such algorithms. In this paper we reconsider the development of such variants of classical stochastic gradient descent (SGD) in a purely geometric setting. The Bregman divergence is replaced by the (squared) Riemannian distance. The property of convexity of a loss function is replaced by a more general notion of compatibility with a metric. The ideas are explicated in the development and analysis of an algorithm for online learning on spheres.
Removing Loops from LDPC Codes

An angulated indicator lens secured to the top portion of a relay valve for providing maximum refraction and dispersion of the several indicating colors on the valve stem indicating the operative or inoperative position of the valve. The lens is constructed of a unitary transparent body having an interior angulated, truncated surface of approximately 45 DEG to the horizontal, with an exterior angulated, truncated upper viewing surface of approximately 54 DEG to the vertical. The transparent lens body receives ambient colored light from the stem providing the maximum refraction of light onto the first interior angulated surface and directing the color to be indicated through the lens medium and onto the second exterior angulated truncated surface for maximum indicating area. A portion of the lens body is provided which is totally non-reflective and serves to shade the reflecting of that coloration of the valve stem which is not being indicating in order to avoid misreading of the valve as the valve shifts from an opened to a closed position. Distinctive markings are also included for indicating through the lens the operative or inoperative position of the stem for the color blind.
Experimental comparison of particle filtering algorithms for acoustic source localization in a reverberant room

Traditional acoustic source localization techniques attempt to determine the current location of an acoustic source from data obtained at an array of sensors during the current time only. Recently, state-space methods have been proposed that use particle filters to perform recursive estimation of the current source location using all previous data. In this paper we present an overview of these particle filter algorithms, and formulate performance measures for determining their ability to track a moving source. We present results of experiments using reverberant data recorded in a real room, and show that steered beamforming methods have improved performance over GCC-based approaches.
Acoustic beamforming exploiting directionality of human speech sources

The paper examines the improvement that can be attained with perfect knowledge of the sound source directivity pattern and orientation in beamformer designs in the problem of speech acquisition. Data-independent beamformers are derived through formulation of a constrained optimization problem with a unity-gain constraint. Using computer simulation, these beamforming schemes are compared to the delay-and-sum (DS) beamformer and the best single sensor in a reverberant room environment. Criteria used to measure performance are: (1) the direct-to-reverberant signal ratio, to assess extent of reverberation suppression; (2) an objective measure of speech intelligibility called the speech transmission index (STI). For human-speaker source directivity, simulation results show that modest improvements to performance are obtainable.
Online Bayes Point Machines

We present a new and simple algorithm for learning large margin classifiers that works in a truly online manner. The algorithm generates a linear classifier by averaging the weights associated with several perceptron-like algorithms run in parallel in order to approximate the Bayes point. A random subsample of the incoming data stream is used to ensure diversity in the perceptron solutions. We experimentally study the algorithm's performance on online and batch learning settings. The online experiments showed that our algorithm produces a low prediction error on the training sequence and tracks the presence of concept drift. On the batch problems its performance is comparable to the maximum margin algorithm which explicitly maximises the margin.
The plenacoustic function, sampling and reconstruction

We study the spatialization of the sound field in a room, in particular the evolution of room impulse responses as a function of their spatial positions. The presented technique allows us to characterize the sound field in any arbitrary location completely if the sound field is known in a certain finite number of positions. The existing techniques usually make use of room models to recreate the sound field present at some point in the space. Our technique simply starts from the measurements of impulse responses in a finite number of positions and, with this information, the total sound field can be recreated. An analytical solution of the problem is given for different cases of spaces. Further, we determine the number and the spacing between the microphones needed to reconstruct the sound field perfectly up to a certain temporal frequency. The optimal sampling pattern for the microphone positions is given. Applications are also discussed.
SESSION L: POSTER SESSION I1 - ICASSP'03 PAPERS Spatial processing, content analysis, and other topics

In the present paper, we study the spatialization of the sound field in a room, in particular the evolution of room impulse responses as function of their spatial positions. The presented technique allows us to completely characterize the sound field in amy arbitrary location if the sound field is known in a cenain finite number of positions. The existing techniques usually make use of room models to recreate the sound field present at some pint in the space. Our technique simply starls from the measurements of impulse responses in a finite number of positions and with this information the total sound field can be recreated. An analytical solution of the problem is given for different cases of spaces. Funher, we determine the number and the spacing between the micmphones needed to perfectly reconstruct the sound field up to a certain temporal frequency. The optimal sampling pattern for the microphone positions is given. Applications are also discussed.
Particle filtering algorithms for tracking an acoustic source in a reverberant environment

Traditional acoustic source localization algorithms attempt to find the current location of the acoustic source using data collected at an array of sensors at the current time only. In the presence of strong multipath, these traditional algorithms often erroneously locate a multipath reflection rather than the true source location. A recently proposed approach that appears promising in overcoming this drawback of traditional algorithms, is a state-space approach using particle filtering. In this paper we formulate a general framework for tracking an acoustic source using particle filters. We discuss four specific algorithms that fit within this framework, and demonstrate their performance using both simulated reverberant data and data recorded in a moderately reverberant office room (with a measured reverberation time of 0.39 s). The results indicate that the proposed family of algorithms are able to accurately track a moving source in a moderately reverberant room.
Acoustic beamforming exploiting directionality of human speech sources

This paper examines the improvement that can be attained with perfect knowledge of the sound source directivity pattern and orientation in beamformer designs in the problem of speech acquisition. Data-independent beamformers are derived through formulation of a constrained optimization problem with a unity-gain constraint. Using computer simulation, these beamforming schemes are compared to the delay and sum (DS) beamformer and the best single sensor in a reverberant room environment. Criteria used to measure performance are: (1) the direct to reverberant ratio, to assess extent of reverberation suppression; and (2) an objective measure of speech intelligibility called the speech transmission index (STI). For human-speaker source directivity, simulation results show that modest improvements to performance are obtainable.
Particle Filtering Algorithms for Acoustic Source Localization

Traditional acoustic source localization algorithms attempt to find the current location of the acoustic source using data collected at an array of sensors at the current time only. In the presence of strong multipath, these traditional algorithms often erroneously locate a multipath reflection rather than the true source location. A recently proposed approach that appears promising in overcoming this drawback of traditional algorithms, is a statespace approach using particle filtering. In this paper we formulate a general framework for acoustic source localization using particle filters. We discuss four specific algorithms that fit within this framework, and demonstrate their performance using both simulated reverberant data and data recorded in a moderately reverberant office room (with a measured reverberation time of 0.39 seconds). The results indicate that the proposed family of algorithms are able to accurately track a moving source in a moderately reverberant room.
Experimental comparison of particle filtering algorithms for acoustic source localization in a reverberant room

Traditional acoustic source localization techniques attempt to determine the current location of an acoustic source from data obtained at an array of sensors during the current time only. Recently, state-space methods have been proposed that use particle filters to perform recursive estimation of the current source location using all previous data. We present an overview of these particle filter algorithms, and formulate performance measures for determining their ability to track a moving source. We present results of experiments using reverberant data recorded in a real room, and show that steered beamforming methods have improved performance over GCC-based approaches.
Fast convolutive blind speech separation via subband adaptation

We consider the problem of blind source separation (BSS) applied to speech signals. Due to reverberation, BSS in the time domain is usually expensive in terms of computations. We propose a subband BSS system based on the use of adaptive feedback de-mixing networks in an oversampled uniform DFT filter bank structure. We show that the computational cost can be significantly decreased if BSS is carried out in subbands due to the possibility of reducing the sampling rate. Experiments with real speech signals, conducted with two-input two-output BSS systems using oversampled 32-subband and fullband adaptation, indicate that separation quality and distortion are similar for both systems. However, the proposed subband system is more than 10 times faster computationally than the fullband one.
Channel equalization and the Bayes point machine

Equalizers trained with a large margin have an ability to better handle noise in unseen data and drift in the target solution. We present a method of approximating the Bayes optimal strategy which provides a large margin equalizer, the Bayes point equalizer. The method we use to estimate the Bayes point is to average N equalizers that are run on independently chosen subsets of the data. To better estimate the Bayes point we investigated two methods to create diversity amongst the N equalizers. We show experimentally that the Bayes point equalizer for appropriately large step sizes offers improvement on LMS and LMA in the presence of channel noise and training sequence errors. This allows for shorter training sequences albeit with higher computational requirements.
Loop removal from LDPC codes

It is hypothesised that small loops in the graphical representations of low-density parity-check codes are detrimental to the code's performance. We demonstrate a procedure for removing such loops from the codes. An algorithm based on the code's adjacency matrix is used to locate unwanted loops, and then certain edges within the graph are exchanged to eliminate those loops (without simultaneously creating any others). This process can be repeated until there are no loops remaining with a size less than a particular limit. A comparison of some theoretical and experimental values for this limit is given.
Particle filter beamforming for acoustic source localization in a reverberant environment

Traditional acoustic source localization uses a two-step procedure requiring intermediate time-delay estimates from pairs of microphones. An alternative single-step approach is proposed in this paper in which particle filtering is used to estimate the source location through steered beamforming. This scheme is especially attractive in speech enhancement applications, where the localization estimates are typically used to steer a beamformer at a later stage. Simulation results show that the algorithm is robust to reverberation, and is able to accurately follow the source trajectory.