nils-herrmann/clean_scirepeval huggingface dataset:
GPPS: A Gaussian Process Positioning System for Cellular Networks

In this article, we present a novel approach to solving the localization problem in cellular networks. The goal is to estimate a mobile user's position, based on measurements of the signal strengths received from network base stations. Our solution works by building Gaussian process models for the distribution of signal strengths, as obtained in a series of calibration measurements. In the localization stage, the user's position can be estimated by maximizing the likelihood of received signal strengths with respect to the position. We investigate the accuracy of the proposed approach on data obtained within a large indoor cellular network.
Local Factorization of Functions

This paper is concerned with the notion of a local factorization of a function where we are mostly interested in the special case that this function is a probability distribution. We introduce the notions of local independence and of the local Kullback-Leibler divergence. We introduce a specific approximate local factorization. The number of terms required in the approximation is linear in the number of input dimensions and the approximation does not require the calculation of higher derivatives (as in a Taylor expansion) and is not limited to approximations near the mode of a function. We provide examples where we believe the approximation might be useful as in the approximate calculation of certain integrals.
Classification of rheumatoid joint inflammation based on laser imaging

We describe a classification system for a novel imaging method for arthritic finger joints. The basis of this system is a laser imaging technique which is sensitive to the optical characteristics of finger joint tissue. From the laser images acquired at baseline and follow-up, finger joints can automatically be classified according to whether the inflammatory status has improved or worsened. To perform the classification task, various linear and kernel-based systems were implemented and their performances were compared. Based on the results presented in this paper, we conclude that the laser-based imaging permits a reliable classification of pathological finger joints, making it a sensitive method for detecting arthritic changes.
Sorting it out : Machine learning and fingerprints

Machine learning concepts can find applications in many domains. We describe here one such application to the problem of fingerprint verification. In biometric verification using fingerprints, one often has to handle large archives of fingerprint images. When verifying an individual fingerprint, it is important to have a fast method at hand that selects suitable candidate images out of a large archive. We present an approach to sort a fingerprint archive according to similarity with a given fingerprint, inspired by an opto-electronic device called the wedge-ring-detector.
The RA Scanner: Prediction of Rheumatoid Joint Inflammation Based on Laser Imaging

We describe the RA scanner, a novel system for the examination of patients suffering from rheumatoid arthritis. The RA scanner is based on a novel laser-based imaging technique which is sensitive to the optical characteristics of finger joint tissue. Based on the laser images, finger joints are classified according to whether the inflammatory status has improved or worsened. To perform the classification task, various linear and kernel-based systems were implemented and their performances were compared. Special emphasis was put on measures to reliably perform parameter tuning and evaluation, since only a very small data set was available. Based on the results presented in this paper, it was concluded that the RA scanner permits a reliable classification of pathological finger joints, thus paving the way for a further development from prototype to product stage.
Transductive and Inductive Methods for Approximate Gaussian Process Regression

Gaussian process regression allows a simple analytical treatment of exact Bayesian inference and has been found to provide good performance, yet scales badly with the number of training data. In this paper we compare several approaches towards scaling Gaussian processes regression to large data sets: the subset of representers method, the reduced rank approximation, online Gaussian processes, and the Bayesian committee machine. Furthermore we provide theoretical insight into some of our experimental results. We found that subset of representers methods can give good and particularly fast predictions for data sets with high and medium noise levels. On complex low noise data sets, the Bayesian committee machine achieves significantly better accuracy, yet at a higher computational cost.
Removing redundancy and inconsistency in memory-based collaborative filtering

The application range of memory-based collaborative filtering (CF) is limited due to CF's high memory consumption and long runtime. The approach presented in this paper removes redundant and inconsistent instances (users) from the data. This paper aims to distinguish informative instances (users) from large raw user preference database and thus alleviate the memory and runtime cost of the widely used memory-based collaborative filtering (CF) algorithm. Our work shows that a satisfactory accuracy can be achieved by using only a small portion of the original data set, thereby alleviating the storage and runtime cost of the CF algorithm. In our approach, we consider instance selection as the problem of selecting informative data that increase the We begin by discussing the instance selection problem in a general sense that is to increase a posteriori probability of the optimal model by selecting informative data. We evaluate the empirical performance of our approach PF on two real-world data sets and attain very promisingpositive experimental results. The dData size and the prediction time are significantly reduced, while the prediction accuracy is on a par with almost the same as the results achieved by using the complete database.
Efficient Estimation of OOMs

A standard method to obtain stochastic models for symbolic time series is to train state-emitting hidden Markov models (SE-HMMs) with the Baum-Welch algorithm. Based on observable operator models (OOMs), in the last few months a number of novel learning algorithms for similar purposes have been developed: (1,2) two versions of an "efficiency sharpening" (ES) algorithm, which iteratively improves the statistical efficiency of a sequence of OOM estimators, (3) a constrained gradient descent ML estimator for transition-emitting HMMs (TE-HMMs). We give an overview on these algorithms and compare them with SE-HMM/EM learning on synthetic and real-life data.
Harnessing nonlinearity : predicting chaotic systems and boosting wireless communication . " ( Ref : 1091277 ) Refutation of Second Reviewer ' s Objections

• The stepsize that I use is actually not large. The stepsize 0.1 that I used must be seen in relation to the attractor's inherent timescale, which is very slow. With this stepsize, one loop around the attractor takes about 500 simulation steps, which cannot be considered a coarse approximation. When the Mackey-Glass system is simulated with a standard commercial solver for differential delay equations (namely, Matlab's dde23 function), a stepsize of about 2.0 is automatically selected, 20 times the stepsize I used. • The stepsize that I use yields an accurate simulation of the attractor. I computed estimates of the first Lyapunov exponent λ1 for both my simulation (stepsize 0.1) and the Matlab simulation function dde23. I obtained λ1 ≈ 0.0064 for my simulation and λ1 ≈ 0.0059 for the commercial simulation tool. Both values are in agreement with estimates found in the literature.
Learning observable operator models via the ES algorithm 1

Hidden Markov Models (HMMs) today are the method of choice for blackbox modelling of symbolic, stochastic time series with memory. HMMs are usually trained using the expectation-maximization (EM) algorithm. This learning algorithm is note entirely satisfactory due to slow convergence and the presence of many globally suboptimal solutions. Observable operator models (OOMs) present an alternative. At the surface OOMs appear almost like HMMs: both can be expressed in structurally identical matrix formalisms. However, the matrices and state vectors of OOMs may contain negative components, whereas the corresponding components in the world of HMMs are non-negative probabilities. This freedom in sign gives OOMs algebraic properties that radically differ from HMMs, and leads to novel learning algorithms that are fast and yield asymptotically correct model estimates. Unfortunately, the basic versions of these algorithms are statistically inefficient, which has so far precluded a widespread use of OOMs. This chapter gives, first, a tutorial introduction to OOMs, and second, introduces a novel approach to OOM estimation called efficiency sharpening (ES). The ES method is iterative. In each iteration, the model estimated in the previous round is used to construct an estimator with a better statistical efficiency than the previous one. The computational load per iteration is comparable to one EM iteration, but only 2 to 5 iterations are typically needed. The chapter gives an analytical derivation of the ES principle and describes two learing algorithms that build on this principle, a simple “poor man’s” version and a more complicated but superior version which is based on a suffix-tree representation of the training string. The quality of the latter algorithm is demonstrated on a task of learning a model of a long belletristic text, where OOM models markedly outperform HMM models in quality, requiring only a fraction of learning time.
ReservoirRiddles:Suggestions forEcho State NetworkResearch(Extended Abstract)

Echostate networks (ESNs) offer asimple learning algorithm fordynamical systems. Itworksbytraining linear readout neurons thatcombinethesignals froma random, fixed, excitable "dynamical reservoir" network. Oftenthemethod worksbeautifully, sometimes itworkspoorly - andwe donot really understand why.Thiscontribution discusses phenomena related topoorlearning performance andsuggests research directions. Thecommonthemeistounderstand thereservoir dynamics intermsofadynamical representation ofthetask's input signals. I.RESERVOIR RIDDLES... Echostate networks (ESNs), aswellastheclosely related "liquid state machines" (LSM)(1), present arecurrent neural network (RNN)learning architecture whichischaracterized by . alarge, randomly connected, recurrent "reservoir" net- workthat ispassively excited bythetask's input signal, and . trainable readout neurons that combine thedesired output fromtheexcited reservoir state. Training anESNonasupervised learning taskboils downto compute theoutput weights. Fromacomputational perspective thisisjust a linear regression, forwhichnumerous batch andadaptive online algorithms areavailable. Thissimple methodyields models that inmanyengineering tasks surpass inaccuracy othermodelling methods (2).TheESN/LSM principle - combine atarget signal fromrandom, dynamic input variations - mayalsobeeffective inbiological brains
A tutorial on training recurrent neural networks , covering BPPT , RTRL , EKF and the " echo state network " approach - Semantic Scholar

This tutorial is a worked-out version of a 5-hour course originally held at AIS in September/October 2002. It has two distinct components. First, it contains a mathematically-oriented crash course on traditional training methods for recurrent neural networks, covering back-propagation through time (BPTT), real-time recurrent learning (RTRL), and extended Kalman filtering approaches (EKF).  The remaining sections 1 and 6 – 9 are much more gentle, more detailed, and illustrated with simple examples. They are intended to be useful as a stand-alone tutorial for the echo state network (ESN) approach to recurrent neural network training.
Reservoir riddles: suggestions for echo state network research

Echo state networks (ESNs) offer a simple learning algorithm for dynamical systems. It works by training linear readout neurons that combine the signals from a random, fixed, excitable "dynamical reservoir" network. Often the method works beautifully, sometimes it works poorly - and we do not really understand why. This contribution discusses phenomena related to poor learning performance and suggests research directions. The common theme is to understand the reservoir dynamics in terms of a dynamical representation of the task's input signals.
Harnessing Nonlinearity: Predicting Chaotic Systems and Saving Energy in Wireless Communication

We present a method for learning nonlinear systems, echo state networks (ESNs). ESNs employ artificial recurrent neural networks in a way that has recently been proposed independently as a learning mechanism in biological brains. The learning method is computationally efficient and easy to use. On a benchmark task of predicting a chaotic time series, accuracy is improved by a factor of 2400 over previous techniques. The potential for engineering applications is illustrated by equalizing a communication channel, where the signal error rate is improved by two orders of magnitude.
Modulated Modules Designing Behaviors as Dynamical Systems

A recent development in behavior oriented robotics is to view behaviors in an agent as coupled dynamical subsystems When one designs such subsystems a di culty arises The desired dynamics of the subsystem is likely to become qualitatively disrupted due to the superimposed dynamics of the behavior s interaction with other behaviors and the dynamics of sen sor input A mathematical technique is presented for making subsystems to some degree immune against such qualitative disruptions By adding a suit able compensation term to the original ODE speci cation of the subsystem the dynamics of its coupling variables lead to a benign modulation instead of a potential disruption For the combined modulative e ects of shift am plitude variation and velocity variation the compensation term is given in an explicit ready to use form This is a step toward a modular design of behavior systems from interacting subsystems Zusammenfassung Ein j ungerer Ansatz in der behavior orientierten Robotik besteht darin Behaviors in einem Agenten als gekoppelte dynami sche Teilsysteme zu modellieren Wenn man solche Teilsysteme im Zuge einer Roboterkonstruktion entwirft entsteht die Schwierigkeit da die be absichtigte Dynamik des Teilsystems durch dessen Interaktion mit anderen Teilsystemen und mit sensorischem Input qualitativ ver andert wird Die vorliegende Arbeit beschreibt wie Behavior Subsysteme bis zu einem gewis sen Grad gegen solche qualitativen Ver anderungen immun gemacht werden k onnen Durch die Hinzuf ugung eines geeigneten Kompensationsterms zu den urspr unglichen Systemgleichungen werden die Auswirkungen der Kop plungsdynamik auf Modulationen beschr ankt die den qualitativen Typ des Systemverhaltens erhalten F ur einige solcher Modulationen Shift Amplitu den und Geschwindigkeitsvariation wird der Kompensationsterm in einer gebrauchsfertigen expliziten Form angegeben Dies stellt einen Schritt in Richtung eines modularen Entwurfs von Behavior Systemen aus gekoppelten Teilsystemen dar
Discrete-time, discrete-valued observable operator models: a tutorial

This tutorial gives a basic yet rigorous introduction to observable operator models (OOMs). OOMs are a recently discovered class of models of stochastic processes. They are mathematically simple in that they require only concepts from elementary linear algebra. The linear algebra nature gives rise to an efficient, consistent, unbiased, constructive learning procedure for estimating models from empirical data. The tutorial describes in detail the mathematical foundations and the practical use of OOMs for identifying and predicting discrete-time, discrete-valued processes, both for output-only and input-output systems. key words: stochastic time series, system identification, observable operator models
Experiences Using the Dynamical System Paradigm for Programming RoboCup Robots

Autonomous mobile robots in dynamic environments have to switch from the „stop and wait until I’m sure“ mentality to a continuous movement kind of style. As both the robot and objects in its surrounding move, the robot has to react on sensory input as fast as possible but without becoming instable. This is challenging also because speed increases the uncertainty of sensory input. Therefore techniques are needed to protect the behavior system from the effects of noisy input, to avoid self-induced instabilities, and to issue motor commands which are continuous over time. The mathematical framework of dynamical systems offers techniques to care for system stability and therefore is the natural choice. The Dual Dynamics architecture allows to model robot behavior as dynamical systems. We use the DD approach successfully since years to program our soccer robots. These experiences are reported in this paper.
Learning to Ground Fact Symbols in Behavior-Based Robots

A robot running a hybrid control system (its architecture comprising a deliberative and a reactive part) must permanently update its symbolic situation model to allow its ongoing deliberation to operate. Previous work has shown that this update can be improved by using, possibly among other sources, the robot's sensor information as filtered through recent activation value histories of robot behaviors. In that work, characteristic patterns in groups of behavior activation values are used to define chronicles, which allow true facts about the current situation to be hypothesized. Chronicle definitions are hand-crafted as part of the domain modeling. 
 
In this paper, we demonstrate that analogs of chronicle definitions can be learned. We use an echo state network, which is a particular type of recurrent neural network. To train it, the same activation value data are used as before in chronicle definitions. The training process is fast. The detection process is cheap to run on-line on board the robot. The new method is demonstrated on data from a robot simulator. It provides the robot programmer an alternative tool for getting recent symbolic situation fact hypotheses.
Adaptive Nonlinear System Identification with Echo State Networks

Echo state networks (ESN) are a novel approach to recurrent neural network training. An ESN consists of a large, fixed, recurrent "reservoir" network, from which the desired output is obtained by training suitable output connection weights. Determination of optimal output weights becomes a linear, uniquely solvable task of MSE minimization. This article reviews the basic ideas and describes an online adaptation scheme based on the RLS algorithm known from adaptive linear systems. As an example, a 10-th order NARMA system is adaptively identified. The known benefits of the RLS algorithms carry over from linear systems to nonlinear ones; specifically, the convergence rate and misadjustment can be determined at design time.
The''echo state''approach to analysing and training recurrent neural networks

The report introduces a constructive learning algorithm for recurrent neural networks, which modifies only the weights to output units in order to achieve the learning task. key words: recurrent neural networks, supervised learning Zusammenfassung. Der Report führt ein konstruktives Lernverfahren für rekurrente neuronale Netze ein, welches zum Erreichen des Lernzieles lediglich die Gewichte der zu den Ausgabeneuronen führenden Verbindungen modifiziert. Stichwörter: rekurrente neuronale Netze, überwachtes Lernen
Modeling and learning continuous-valued stochastic processes with OOMs

Observable operator models (OOMs) are a generalization of hidden Markov models (HMMs). They support a fast, novel learning algorithm. Standardly, OOMs model discrete-valued (\symbol") processes. This report shows how OOMs for continuous-valued processes can be de ned, and how the learning algorithm can be extended. key words: stochastic processes, observable operator models Zusammenfassung. Observable Operator Modelle (OOMs) sind eine Verallgemeinerung von Hidden Markov Modellen (HMMs). Sie sind mit einem neuartigen, schnellen Lernalgorithmus ausger ustet. Die bisher bekannten OOMs modellierten diskretwertige Prozesse. In diesem Report werden OOMs und der Lernalgorithmus auf kontinuierlichwertige Prozesse erweitert.  Stochastische Prozesse, observable operator models
GMD-Robots

The overall research goal of GMD ́s RoboCup middle-size league team is to increase both, the controlled speed of mobile robots acting as a team in dynamic environments and the speed of the development process for robot control systems. Therefore, GMD started in 1998 to develop a proprietary fast robot platform and in parallel the development of the integrated Dual Dynamics Design Environment. This team description gives some details on the current state of our hardware platform (February 2001), the control software architecture we use and the design environment we constructed to specify, simulate, run and test our robots. In addition, we point our some specific skills of our robots.
Observable Operator Models for Discrete Stochastic Time Series

A widely used class of models for stochastic systems is hidden Markov models. Systems that can be modeled by hidden Markov models are a proper subclass of linearly dependent processes, a class of stochastic systems known from mathematical investigations carried out over the past four decades. This article provides a novel, simple characterization of linearly dependent processes, called observable operator models. The mathematical properties of observable operator models lead to a constructive learning algorithm for the identification of linearly dependent processes. The core of the algorithm has a time complexity of O (N + nm3), where N is the size of training data, n is the number of distinguishable outcomes of observations, and m is model state-space dimension.
Behavior Engineering with "Dual Dynamics" Models and Design Tools

Dual Dynamics (DD) is a mathematical model of a behavior control system for mobile autonomous robots. Behaviors are specified through differential equations, forming a global dynamical system made of behavior subsystems which interact in a number of ways. DD models can be directly compiled into executable code. The article (i) explains the model, (ii) sketches the Dual Dynamics Designer (DDD) environment that we use for the design, simulation, implementation and documentation, and (iii) illustrates our approach with the example of kicking a moving ball into a goal.
From Continuous Dynamics to Symbols

This article deals with mathematical models of discrete identi able symbolic events in neural and cognitive dynamics These dynamical symbols are the sup posed correlates of identi able motor action patterns from phoneme utterances to restaurant visits In the rst main part of the article models of dynamical symbols o ered by dynamical systems theory are reviewed attractors bifurca tions spatial segregation and boundary formation and several others In the second main part transient attractors TA s are o ered as yet another mathe matical model of dynamical symbols TAs share with ordinary attractors a basic property namely local phase space contraction However a TA can disappear almost as soon as it is created which could not very rigorously be interpreted as a bifurcation induced by quickly changing control parameters Such fast bifurcation sequences standardly occur in neural and cognitive dynamics