nils-herrmann/clean_scirepeval huggingface dataset:
Gesture recognition system

To obtain a gesture recognition system which can easily be mastered, is highly reliable, and has a relatively small penalty to an erroneous input to a control mode.  This gesture recognition system is provided with a gesture signal generating means which generates the gesture signal corresponding to the existence and spatial position of a prescribed hand gesture by detecting the presence and spatial position of the hand gesture, an on-screen hand icon outputting and displaying means which outputs and displays an on-screen hand icon 26 to and at the position of an on-screen 12 corresponding to the spatial position at which the hand gesture is detected based on the output of the gesture signal generating means, and machine control signal generating means which detects the simultaneous generation of the on-screen machine control icons 28-42 of an on-screen hand icon 16 when the icon 16 is moved on the icons 28-42 in accordance with the movement of the hand gesture and generates the corresponding machine control signal in accordance with the simultaneous generation of the icons 28-42.
Example-based head tracking

We want to estimate the pose of human heads. This estimation involves a nonlinear mapping from the input image to an output parametric description. We characterize the mapping through examples from a training set, outputting the pose of the nearest example neighbor of the input. This is vector quantization, with the modification that we store an output parameter code with each quantized input code. For efficient indexing, we use a tree-structured vector quantizer (TSVQ). We make design choices based on the example application of monitoring an automobile driver's face. The reliance on stored data over computation power allows the system to be simple; efficient organization of the data allows it to be fast. We incorporate tracking in position and scale within the same vector quantization framework with virtually no cost in added computation. We show reasonable experimental results for a real-time prototype running on an inexpensive workstation.
Orientation Histograms for Hand Gesture Recognition

We present a method to recognize hand gestures, based on a pattern recognition technique developed by McConnell [16] employing histograms of local orientation. We use the orientation histogram as a feature vector for gesture class cation and interpolation. This method is simple and fast to compute, and o ers some robustness to scene illumination changes. We have implemented a real-time version, which can distinguish a small vocabulary of about 10 di erent hand gestures. All the computation occurs on a workstation; special hardware is used only to digitize the image. A user can operate a computer graphic crane under hand gesture control, or play a game. We discuss limitations of this method. For moving or \dynamic gestures", the histogram of the spatio-temporal gradients of image intensity form the analogous feature vector and may be useful for dynamic gesture recognition.
Bayesian decision theory, the maximum local mass estimate, and color constancy

Vision algorithms are often developed in a Bayesian framework. Two estimators are commonly used: maximum a posteriori (MAP), and minimum mean squared error (MMSE). We argue that neither is appropriate for perception problems. The MAP estimator makes insufficient use of structure in the posterior probability. The squared error penalty of the MMSE estimator does not reflect typical penalties. We describe a new estimator, which we call maximum local mass (MLM) [10, 26, 65], which integrates the local probability density. The MLM method is sensitive to local structure of the posterior probability, which MAP is not. The new method uses an optimality criterion that is appropriate for perception tasks: it finds the most probable approximately correct answer. For the case of low observation noise, we provide an efficient approximation. We apply this new estimator to color constancy. An unknown illuminant falls on surfaces of unknown colors. We seek to estimate both the illuminant spectrum and the surface spectra from photosensor responses which depend on the product of the unknown spectra. In simulations, we show that the MLM method performs better than the MAP estimator, and better than two standard color constancy algorithms. The MLM method may prove useful in other vision problems as well.<<ETX>>
Convex Transduction with the Normalized Cut

We discuss approaches to transduction based on graph cut cost functions. More specifically, we focus on the normalized cut, which is the cost function of choice in many clustering applications, notably in image segmentation. Since optimizing the normalized cut cost is an NP-complete problem, much of the research attention so far has gone to relaxing the problem of normalized cut clustering to tractable problems, producing so far a spectral relaxation and a more recently a tighter but computationally much tougher semi-definite programming (SDP) relaxation. In this paper we deliver two main contributions: first, we show how an alternative SDP relaxation yields a much more tractable optimization problem, and we show how scalability and speed can further be increased by making a principled approximation. Second, we show how it is possible to efficiently optimize the normalized cut cost in a transduction setting using our newly proposed approaches. Positive empirical results are reported.
Discovering Transcriptional Modules from Motif, Chip-Chip and Microarray Data

We present a method for inference of transcriptional modules from heterogeneous data sources. It allows identifying the responsible set of regulators in combination with their corresponding DNA recognition sites (motifs) and target genes. Our approach distinguishes itself from previous work in literature because it fully exploits the knowledge of three independently acquired data sources: ChIP-chip data; motif information as obtained by phylogenetic shadowing; and gene expression profiles obtained using microarray experiments. Moreover, these three data sources are dealt with in a new and fully integrated manner. By avoiding approaches that take the different data sources into account sequentially or iteratively, the transparency of the method and the interpretability of the results are ensured. Using our method on biological data demonstrated the biological relevance of the inference.
Eigenproblems in Pattern Recognition

The task of studying the properties of conﬂgurations of points embeddedin a metric space has long been a central task in pattern recognition, buthas acquired even greater importance after the recent introduction of kernel-based learning methods. These methods work by virtually embedding generaltypes of data in a vector space, and then analyzing the properties of theresulting data cloud. While a number of techniques for this task have beendeveloped in ﬂelds as diverse as multivariate statistics, neural networks, andsignal processing, many of them show an underlying unity. In this chapterwe describe a large class of pattern analysis methods based on the use ofgeneralized eigenproblems, which reduce to solving the equation Aw =
A statistical framework for genomic data fusion

During the past decade, the new focus on genomics has highlighted a particular challenge: to integrate the different views of the genome that are provided by various types of experimental data.



This paper describes a computational framework for integrating and drawing inferences from a collection of genome-wide measurements. Each dataset is represented via a kernel function, which defines generalized similarity relationships between pairs of entities, such as genes or proteins. The kernel representation is both flexible and efficient, and can be applied to many different types of data. Furthermore, kernel functions derived from different types of data can be combined in a straightforward fashion. Recent advances in the theory of kernel methods have provided efficient algorithms to perform such combinations in a way that minimizes a statistical loss function. These methods exploit semidefinite programming techniques to reduce the problem of finding optimizing kernel combinations to a convex optimization problem. Computational experiments performed using yeast genome-wide datasets, including amino acid sequences, hydropathy profiles, gene expression data and known protein-protein interactions, demonstrate the utility of this approach. A statistical learning algorithm trained from all of these data to recognize particular classes of proteins--membrane proteins and ribosomal proteins--performs significantly better than the same algorithm trained on any single type of data.


AVAILABILITY
Supplementary data at
A Framework for Genomic Data Fusion and its Application to Membrane Protein Prediction

During the past decade, the new focus on genomics has highlighted a particular challenge: to integrate the different views of the genome that are provided by various types of experimental data. This paper describes a computational framework for integrating and drawing inferences from a collection of genome-wide measurements. Each data set is represented via a kernel function, which defines generalized similarity relationships between pairs of entities, such as genes or proteins. The kernel representation is both flexible and efficient, and can be applied to many different types of data. Furthermore, kernel functions derived from different types of data can be combined in a straightforward fashion—recent advances in the theory of kernel methods have provided efficient algorithms to perform such combinations in an optimal way. These methods formulate the problem of optimal kernel combination as a convex optimization problem that can be solved with semi-definite programming techniques. In this paper, we demonstrate the utility of these techniques by investigating the problem of predicting membrane proteins from heterogeneous data, including amino acid sequences, hydropathy profiles, gene expression data and known protein-protein interactions. A statistical learning algorithm trained from all of these data performs significantly better than the same algorithm trained on any single type of data and better than existing algorithms for membrane protein classification.
DISCOVERING REGULATORY MODULES FROM HETEROGENEOUS INFORMATION SOURCES

We present a method for inference of transcriptional modules from heterogeneous data sources. It allows identifying the responsible set of regulators in combination with their corresponding DNA recognition sites (motifs) and target genes. Our approach distinguishes itself from previous work in literature because it fully exploits the knowledge of three independently acquired data sources: ChIPchip data; motif information as obtained by phylogenetic shadowing; and gene expression profiles obtained using microarray experiments. Moreover, these three data sources are dealt with in a new and fully integrated manner. By avoiding approaches that take the different data sources into account sequentially or iteratively, the transparancy of the method and the interpretability of the results are ensured. Using our method on biological data demonstrated the biological relevance of the inference.
Learning from General Label Constraints

Most machine learning algorithms are designed either for supervised or for unsupervised learning, notably classification and clustering. Practical problems in bioinformatics and in vision however show that this setting often is an oversimplification of reality. While label information is of course invaluable in most cases, it would be a huge waste to ignore the information on the cluster structure that is present in an (often much larger) unlabeled sample set. Several recent contributions deal with this topic: given partially labeled data, exploit all information available. In this paper, we present an elegant and efficient algorithm that allows to deal with very general types of label constraints in class learning problems. The approach is based on spectral clustering, and leads to an efficient algorithm based on the simple eigenvalue problem.
Kernel Methods for Exploratory Pattern Analysis: A Demonstration on Text Data

Kernel Methods are a class of algorithms for pattern analysis with a number of convenient features. They can deal in a uniform way with a multitude of data types and can be used to detect many types of relations in data. Importantly for applications, they have a modular structure, in that any kernel function can be used with any kernel-based algorithm. This means that customized solutions can be easily developed from a standard library of kernels and algorithms. This paper demonstrates a case study in which many algorithms and kernels are mixed and matched, for a cross-language text analysis task. All the software is available online.
Convex Tuning of the Soft Margin Parameter

In order to deal with known limitations of the hard margin support vector machine (SVM) for binary classification — such as overfitting and the fact that some data sets are not linearly separable —, a soft margin approach has been proposed in literature [2, 4, 5]. The soft margin SVM allows training data to be misclassified to a certain extent, by introducing slack variables and penalizing the cost function with an error term, i.e., the 1-norm or 2-norm of the corresponding slack vector. A regularization parameter C trades off the importance of maximizing the margin versus minimizing the error. While the 2-norm soft margin algorithm itself is well understood, and a generalization bound is known [4, 5], no computationally tractable method for tuning the soft margin parameter C has been proposed so far. In this report we present a convex way to optimize C for the 2-norm soft margin SVM, by maximizing this generalization bound. The resulting problem is a quadratically constrained quadratic programming (QCQP) problem, which can be solved in polynomial time O(l) with l the number of training samples.
Convex Methods for Transduction

The 2-class transduction problem, as formulated by Vapnik , involves finding a separating hyperplane for a labelled data set that is also maximally distant from a given set of unlabelled test points. In this form, the problem has exponential computational complexity in the size of the working set. So far it has been attacked by means of integer programming techniques [2] that do not scale to reasonable problem sizes, or by local search procedures . In this paper we present a relaxation of this task based on semi-definite programming (SDP), resulting in a convex optimization problem that has polynomial complexity in the size of the data set. The results are very encouraging for mid sized data sets, however the cost is still too high for large scale problems, due to the high dimensional search space. To this end, we restrict the feasible region by introducing an approximation based on solving an eigenproblem. With this approximation, the computational cost of the algorithm is such that problems with more than 1000 points can be treated.
On the Regularization of Canonical Correlation Analysis

By elucidating a parallel between canonical correlation analysis (CCA) and least squares regression (LSR), we show how regularization of CCA can be performed and interpreted in the same spirit as the regularization applied in ridge regression (RR). Furthermore, the results presented may have an impact on the practical use of regularized CCA (RCCA). More specifically, a relevant cross validation cost function for training the regularization parameter, naturally follows from the derivations.
Efficiently Learning the Metric with Side-Information

A crucial problem in machine learning is to choose an appropriate representation of data, in a way that emphasizes the relations we are interested in. In many cases this amounts to finding a suitable metric in the data space. In the supervised case, Linear Discriminant Analysis (LDA) can be used to find an appropriate subspace in which the data structure is apparent. Other ways to learn a suitable metric are found in  and [11]. However recently significant attention has been devoted to the problem of learning a metric in the semi-supervised case. In particular the work by Xing et al.  has demonstrated how semi-definite programming (SDP) can be used to directly learn a distance measure that satisfies constraints in the form of side-information. They obtain a significant increase in clustering performance with the new representation. The approach is very interesting, however, the computational complexity of the method severely limits its applicability to real machine learning tasks. In this paper we present an alternative solution for dealing with the problem of incorporating side-information. This side-information specifies pairs of examples belonging to the same class. The approach is based on LDA, and is solved by the efficient eigenproblem. The performance reached is very similar, but the complexity is only O(d 3) instead of O(d 6) where d is the dimensionality of the data. We also show how our method can be extended to deal with more general types of side-information.
Maximally entangled mixed states of two qubits

In this paper we investigate how much entanglement in a mixed two-qubit system can be created by global unitary transformations. The class of states for which no more entanglement can be created by global unitary operations is clearly a generalization of the class of Bell states to mixed states, and gives strict bounds on how the degree of mixing of a state limits its entanglement. This question is of considerable interest as entanglement is the magic ingredient of quantum information theory and experiments always deal with mixed states. Recently, Ishizaka and Hiroshima @1# independently considered the same question. They proposed a class of states and conjectured that the entanglement of formation @2# and the negativity @3# of these states could not be increased by any global unitary operation. Here we rigorously prove their conjecture and furthermore prove that the states they proposed are the only ones having the property of maximal entanglement. Closely related to the issue of generalized Bell states is the question of characterizing the set of separable density matrices @5#, as the entangled states closest to the maximally mixed state necessarily have to belong to the proposed class of maximal entangled mixed states. We can thus give a complete characterization of all nearly entangled states lying on the boundary of the sphere of separable states surrounding the maximally mixed state. As a by-product this gives an alternative derivation of the well-known result of Zyczkowski et al. @3# that all states for which the inequality Tr(r 2 )< 1
Negativity and Concurrence of mixed 2X2 states

We consider two measures of entanglement of mixed bipartite states of dimension 2X2: concurrence and negativity. We first prove the conjecture of Eisert and Plenio that concurrence can never be smaller than negativity. We then characterise all states for which concurrence equals negativity and also those states for which the difference between concurrence and negativity is maximal (keeping either the concurrence fixed, or the participation ratio R=1/trace(rho^2)).
Deterministic and Statistical Methods in Machine Learning, First International Workshop, Sheffield, UK, September 7-10, 2004, Revised Lectures

Object Recognition via Local Patch Labelling.- Multi Channel Sequence Processing.- Bayesian Kernel Learning Methods for Parametric Accelerated Life Survival Analysis.- Extensions of the Informative Vector Machine.- Efficient Communication by Breathing.- Guiding Local Regression Using Visualisation.- Transformations of Gaussian Process Priors.- Kernel Based Learning  Regularization Networks and RBF Networks.- Redundant Bit Vectors for Quickly Searching High-Dimensional Regions.- Bayesian Independent Component Analysis with Prior Constraints: An Application  Ensemble Algorithms for Feature Selection.- Can Gaussian Process Regression Be Made Robust Against Model Mismatch?.- Understanding Gaussian Process Regression Using the Equivalent Kernel.- Integrating Binding Site Predictions Using Non-linear Classification Methods.- Support Vector Machine to Synthesise Kernels.- Appropriate Kernel Functions for Support Vector Machine Learning with Sequences of Symbolic Data.- Variational Bayes Estimation of Mixing Coefficients.- A Comparison of Condition Numbers for the Full Rank Least Squares Problem.- SVM Based Learning System for Information Extraction.
On the statistical physics of radial basis function networks

Techniques from statistical physics have been applied successfully in recent years to the analysis of the generalization performance of neural networks. However, most of the analysis to date has been for perceptron-like networks or simple generalizations thereof such as committee machines, and none of the networks studied are used to any significant extent in practice. This letter presents results obtained in applying techniques from statistical physics to a popular class of neural networks that has been used successfully in many practical applications: the Gaussian radial basis function networks. We obtain expressions for the learning curves exhibited by these networks in the high-temperature limit for both realizable and unrealizable target rules.
Reducing the variability in cDNA microarray image processing by Bayesian inference

Gene expression levels are obtained from microarray experiments through the extraction of pixel intensities from a scanned image of the slide. It is widely acknowledged that variabilities can occur in expression levels extracted from the same images by different users with the same software packages. These inconsistencies arise due to differences in the refinement of the placement of the microarray 'grids'. We introduce a novel automated approach to the refinement of grid placements that is based upon the use of Bayesian inference for determining the size, shape and positioning of the microarray 'spots', capturing uncertainty that can be passed to downstream analysis.



Our experiments demonstrate that variability between users can be significantly reduced using the approach. The automated nature of the approach also saves hours of researchers' time normally spent in refining the grid placement.
Bayesian processing of microarray images

Gene expression measurements quantify the level of mRNA produced from each gene. Two principal methods exist for producing slides for extracting these levels: photolithography and spotted arrays. One difficulty with the spotted array format is determining the size and location of the spots on the array. In this paper we present a Bayesian approach to processing images produced by these arrays that seeks posterior distributions over the size and positions of the spots. This enables us to estimate expression ratios and their variances. Exact inference for the model we specify is intractable; we develop an approximate inference technique, which combines importance sampling with variational inference. Our technique has already been shown to be more consistent than both manual processing and another automated technique [N. D. Lawrence, et al., "Reducing the Variability in cDNA Microarray Image Processing by Inference"]. Here we present large-scale results for twenty-four microarray slides each representing 5760 genes and show the dramatic effects of incorporating variance in our downstream analysis. Software based on this algorithm is available for academic use.