nils-herrmann/clean_scirepeval huggingface dataset:
Learning to cluster using local neighborhood structure

This paper introduces an approach for clustering/classification which is based on the use of local, high-order structure present in the data. For some problems, this local structure might be more relevant for classification than other measures of point similarity used by popular unsupervised and semi-supervised clustering methods. Under this approach, changes in the class label are associated to changes in the local properties of the data. Using this idea, we also pursue to learn how to cluster given examples of clustered data (including from different datasets). We make these concepts formal by presenting a probability model that captures their fundamentals and show that in this setting, learning to cluster is a well defined and tractable task. Based on probabilistic inference methods, we then present an algorithm for computing the posterior probability distribution of class labels for each data point. Experiments in the domain of spatial grouping and functional gene classification are used to illustrate and test these concepts.
Probabilistic sparse matrix factorization with an application to discovering gene functions in mouse mRNA expression data

We address the problem of sparse matrix factorization using a generative model. Our algorithm, probabilistic sparse matrix factorization (PSMF), is a probabilistic extension of a previous hard-decision algorithm for this problem (Srebro and Jaakkola 2001). PSMF allows for varying levels of sensor noise in the data, uncertainty in the hidden prototypes used to explain the data, and uncertainty as to which prototypes are selected to explain each data vector. We present experimental results demonstrating that our method can better recover functionally relevant clusterings in mRNA expression data than standard clustering techniques, and we show that by computing probabilities instead of point estimates, our method avoids converging to poor solutions.
Probabilistic Sparse Matrix Factorization

Many kinds of data can be viewed as consisting of a set of vectors, each of which is a noisy combination of a small number of noisy prototype vectors. Physically, these prototype vectors may correspond to different hidden variables that play a role in determining the measured data. For example, a gene’s expression is inuenced by the presence of transcription factor proteins, and two genes may be activated by overlapping sets of transcription factors. Consequently, the activity of each gene can be explained by the activities of a small number of transcription factors. This task can be viewed as the problem of factorizing a data matrix, while taking into account hard constraints reecting structural knowledge of the problem and probabilistic relationships between variables that are induced by known uncertainties in the problem. We present soft-decision probabilistic sparse matrix factorization (PSMF) to better account for uncertainties due to varying levels of noise in the data, varying levels of noise in the prototypes used to explain the data, and uncertainty as to which hidden prototypes are selected to explain each expression vector.
The functional landscape of mouse gene expression

Large-scale quantitative analysis of transcriptional co-expression has been used to dissect regulatory networks and to predict the functions of new genes discovered by genome sequencing in model organisms such as yeast. Although the idea that tissue-specific expression is indicative of gene function in mammals is widely accepted, it has not been objectively tested nor compared with the related but distinct strategy of correlating gene co-expression as a means to predict gene function.  We generated microarray expression data for nearly 40,000 known and predicted mRNAs in 55 mouse tissues, using custom-built oligonucleotide arrays. We show that quantitative transcriptional co-expression is a powerful predictor of gene function. Hundreds of functional categories, as defined by Gene Ontology 'Biological Processes', are associated with characteristic expression patterns across all tissues, including categories that bear no overt relationship to the tissue of origin. In contrast, simple tissue-specific restriction of expression is a poor predictor of which genes are in which functional categories. As an example, the highly conserved mouse gene PWP1 is widely expressed across different tissues but is co-expressed with many RNA-processing genes; we show that the uncharacterized yeast homolog of PWP1 is required for rRNA biogenesis.  We conclude that 'functional genomics' strategies based on quantitative transcriptional co-expression will be as fruitful in mammals as they have been in simpler organisms, and that transcriptional control of mammalian physiology is more modular than is generally appreciated. Our data and analyses provide a public resource for mammalian functional genomics.
On interacting encoders and decoders in multiuser settings

In multiuser communication systems the exchange of some number of rate-limited messages both between encoders, and between decoders, can enlarge the achievable rate region. We consider interaction between a pair of Slepian-Wolf encoders, and a pair of deterministic broadcast channel decoders. For these systems, a single one-way message is sufficient. More generally, we make connections to relay channels and consider how to quantize data for relaying.
A Segmental HMM for Speech Waveforms

We present a purely time domain approach to speech processing which identies waveform samples at the boundaries between glottal pulse periods (in voiced speech) or at the boundaries between unvoiced segments. An ecien t algorithm for inferring these boundaries is derived from a simple probabilistic generative model of speech and state of the art results are presented on pitch tracking, voiced/unvoiced detection and timescale modication.
Efficient variable length channel coding for unknown DMCs

We present a strategy for the reliable communication of a message, in a variable number of channel uses, over an unknown discrete memoryless channel (DMC). The decoder periodically tests the received sequence and, when it can decode, sends an acknowledgment to the transmitter, which then stops transmitting. By choosing the size of the codebook large enough, the rate that is reliably realized by the strategy can be made to approach arbitrarily closely the mutual information between channel input and output induced by the user-chosen input distribution. The strategy presented can be considered as a generalization to arbitrary unknown DMCs of earlier variable length coding schemes, such as digital fountain codes for binary erasure channels (BECs), and a coding strategy for binary symmetric channels (BSCs) presented by Tchamkerten and Telatar
Iterative analysis of microarray data

In the past decade, technologies have been developed that enable researchers to quantify the levels of specific DNA transcripts present in cultures and tissues. While the signal-to-noise ratios of these technologies has consistently improved over the years, the signals provide only an observable projection of hidden, complex underlying processes. A variety of interesting information processing problems aimed at uncovering these hidden processes have emerged, and in this paper we survey some of the problems being studied in our group.
Multibaseline InSAR terrain elevation estimation: a dynamic programming approach

When estimating terrain elevation via interferometric synthetic aperture radar (InSAR), phase unwrapping procedures have difficulty in dealing with rough regions or large noise. Multiple baseline is used to reduce or avoid this problem. Conventional maximum likelihood (ML) methods reconstruct terrain heights in a pointwise fashion, which does not utilize the smooth characteristics of natural terrain. We propose a new algorithm taking smoothness into account. The new approach tackles the problem in a Bayesian framework. Instead of using ML estimation, we use maximum a posteriori (MAP) estimation, where the likelihood function is defined as in the ML method and the prior is defined as a first-order Gaussian Markov random field. This MAP estimation makes the algorithm more robust to noise, and at the same time, more accurate in reconstructing rough regions. A form of 2-D dynamic programming is used to implement the MAP estimation efficiently. The new algorithm has the advantage over the ML methods in that none of the baselines must be chosen so small as to avoid phase wrapping. Specifically, both baselines can be large so that the noise in the reconstructed height can be low. The new algorithm is shown to be able to achieve lower noise than the conventional ML and least-squares methods.
Translating images by unsupervised estimation of switching filters

We propose a method for altering pixel statistics of one image according to another (source) image. Given an input or observed image (probably degraded by one or more unknown processes), and a source image exhibiting the general patch (group of pixels) properties expected in the input image (before degradation), we seek to infer the original image and the process that affected it to produce the observed image. The foundation of our approach is to transform known image patches with desired statistics to patches found in the input image using a finite set of filters or transformations. These transformations are unknown; thus they also must be estimated. We cast this problem as an approximate probabilistic inference problem and show how it can be approached using belief propagation and expectation maximization. Experimental results for joint image restoration and filter estimation are presented.
Learning Generative Models of Similarity Matrices

Recently, spectral clustering (a.k.a. normalized graph cut) techniques have become popular for their potential ability at finding irregularly-shaped clusters in data. The input to these methods is a similarity measure between every pair of data points. If the clusters are well-separated, the eigenvectors of the similarity matrix can be used to identify the clusters, essentially by identifying groups of points that are related by transitive similarity relationships. However, these techniques fail when the clusters are noisy and not well-separated, or when the scale parameter that is used to map distances between points to similarities is not set correctly. Our approach to solving these problems is to introduce a generative probability model that explicitly models noise and can be trained in a maximum-likelihood fashion to estimate the scale parameter. Exact inference is computationally intractable, but we describe tractable, approximate techniques for inference and learning. Interestingly, it turns out that greedy inference and learning in one of our models with a fixed scale parameter is equivalent to spectral clustering. We examine several data sets, and demonstrate that our method finds better clusters compared with spectral clustering.
Probabilistic Inference of Speech Signals from Phaseless Spectrograms

Many techniques for complex speech processing such as denoising and deconvolution, time/frequency warping, multiple speaker separation, and multiple microphone analysis operate on sequences of short-time power spectra (spectrograms), a representation which is often well-suited to these tasks. However, a significant problem with algorithms that manipulate spectrograms is that the output spectrogram does not include a phase component, which is needed to create a time-domain signal that has good perceptual quality. Here we describe a generative model of time-domain speech signals and their spectrograms, and show how an efficient optimizer can be used to find the maximum a posteriori speech signal, given the spectrogram. In contrast to techniques that alternate between estimating the phase and a spectrally-consistent signal, our technique directly infers the speech signal, thus jointly optimizing the phase and a spectrally-consistent signal. We compare our technique with a standard method using signal-to-noise ratios, but we also provide audio files on the web for the purpose of demonstrating the improvement in perceptual quality that our technique offers.
Epitomic analysis of appearance and shape

We present novel simple appearance and shape models that we call epitomes. The epitome of an image is its miniature, condensed version containing the essence of the textural and shape properties of the image. As opposed to previously used simple image models, such as templates or basis functions, the size of the epitome is considerably smaller than the size of the image or object it represents, but the epitome still contains most constitutive elements needed to reconstruct the image. A collection of images often shares an epitome, e.g., when images are a few consecutive frames from a video sequence, or when they are photographs of similar objects. A particular image in a collection is defined by its epitome and a smooth mapping from the epitome to the image pixels. When the epitomic representation is used within a hierarchical generative model, appropriate inference algorithms can be derived to extract the epitome from a single image or a collection of images and at the same time perform various inference tasks, such as image segmentation, motion estimation, object removal and super-resolution.
Real-time On-line Learning of Transformed Hidden Markov Models from Video

The transformed hidden Markov model is a temporal model that captures three typical causes of variability in video scene/object class, appearance variability within the class, and image motion. In our previous work, we showed that an exact EM algorithm can jointly learn the appearances of multiple objects and/or poses of an object, and track the objects or camera motion in video, starting simply from random initialization. As such, this model can serve as a basis for both video clustering and object tracking applications. However, the original algorithm requires a significant amount of computation that renders it impractical for video clustering and its off-line nature makes it unsuitable for real-time tracking applications. In this paper, we propose a new, significantly faster, on-line learning algorithm that enables real-time clustering and tracking. We demonstrate that the algorithm can extract objects using the constraints on their motion and also perform tracking while the appearance models are learned. We also demonstrate the clustering results on an example of typical unrestricted personal media the vacation video.
Extending Factor Graphs so as to Unify Directed and Undirected Graphical Models

The two most popular types of graphical model are Bayesian networks (BNs) and Markov random fields (MRFs). These types of model offer complementary properties in model construction, expressing conditional independencies, expressing arbitrary factorizations of joint distributions, and formulating messagepassing inference algorithms. We show how the notation and semantics of factor graphs (a relatively new type of graphical model) can be extended so as to combine the strengths of BNs and MRFs. Every BN or MRF can be easily converted to a factor graph that expresses the same conditional independencies, expresses the same factorization of the joint distribution, and can be used for probabilistic inference through application of a single, simple message-passing algorithm. We describe a modified "Bayes-ball" algorithm for establishing conditional independence in factor graphs, and we show that factor graphs form a strict superset of BNs and MRFs. In particular, we give an example of a commonly-used model fragment, whose independencies cannot be represented in a BN or an MRF, but can be represented in a factor graph. For readers who use chain graphs, we describe a further extension of factor graphs that enables them to represent properties of chain graphs.
Unsupervised image translation

An interesting and potentially useful vision/graphics task is to render an input image in an enhanced form or also in an unusual style; for example with increased sharpness or with some artistic qualities. In previous work [10, 5], researchers showed that by estimating the mapping from an input image to a registered (aligned) image of the same scene in a different style or resolution, the mapping could be used to render a new input image in that style or resolution. Frequently a registered pair is not available, but instead the user may have only a source image of an unrelated scene that contains the desired style. In this case, the task of inferring the output image is much more difficult since the algorithm must both infer correspondences between features in the input image and the source image, and infer the unknown mapping between the images. We describe a Bayesian technique for inferring the most likely output image. The prior on the output image P(X) is a patch-based Markov random field obtained from the source image. The likelihood of the input P(Y/spl bsol/X) is a Bayesian network that can represent different rendering styles. We describe a computationally efficient, probabilistic inference and learning algorithm for inferring the most likely output image and learning the rendering style. We also show that current techniques for image restoration or reconstruction proposed in the vision literature (e.g., image super-resolution or de-noising) and image-based nonphotorealistic rendering could be seen as special cases of our model. We demonstrate our technique using several tasks, including rendering a photograph in the artistic style of an unrelated scene, de-noising, and texture transfer.
Learning appearance and transparency manifolds of occluded objects in layers

By mapping a set of input images to points in a low-dimensional manifold or subspace, it is possible to efficiently account for a small number of degrees of freedom. For example, images of a person walking can be mapped to a one-dimensional manifold that measures the phase of the person's gait. However, when the object is moving around the frame and being occluded by other objects, standard manifold modeling techniques (e.g., principal components analysis, factor analysis, locally linear embedding) try to account for global motion and occlusion. We show how factor analysis can be incorporated into a generative model of layered, 2.5-dimensional vision, to jointly locate objects, resolve occlusion ambiguities, and learn models of the appearance manifolds of objects. We demonstrate the algorithm on a video consisting of four occluding objects, two of which are people who are walking, and occlude each other for most of the duration of the video. Whereas standard manifold modeling techniques fail to extract information about the gaits, the layered model successfully extracts a periodic representation of the gait of each person.
Advances in Algorithms for Inference and Learning in Complex Probability Models

Computer vision is currently one of the most exciting areas of artificial intelligence research, largely because it has recently become possible to record, store and process large amounts of visual data. Impressive results have been obtained by applying discriminative techniques in an ad hoc fashion to large amounts of data, e.g., using support vector machines for detecting face patterns in images. However, it is even more exciting that researchers may be on the verge of introducing computer vision systems that perform realistic scene analysis, decomposing a video into its constituent objects, lighting conditions, motion patterns, and so on. In our view, two of the main challenges in computer vision are finding efficient models of the physics of visual scenes and finding efficient algorithms for inference and learning in these models. In this paper, we advocate the use of graph-based generative probability models and their associated inference and learning algorithms for computer vision and scene analysis. We review exact techniques and various approximate, computationally efficient techniques, including iterative conditional modes, the expectation maximization algorithm, the mean field method, variational techniques, structured variational techniques, Gibbs sampling, the sum-product algorithm and “loopy” belief propagation. We describe how each technique can be applied to an illustrative example of inference and learning in models of multiple, occluding objects, and compare the performances of the techniques.
Spatial Bias Removal in Microarray Images

1  Gene expression arrays are revolutionizing the field of molecular biology. The expression levels of thousands of genes can be measured in a single experiment. Already this technology has lead to new genetic disease identification and treatment [2], new cancer treatments [1][3], and a significant number of new findings in molecular biology. Each microarray, a type of gene expression array, consists of thousands of cDNA probes settled in a grid on an immobile substrate. The amount of mRNA bound to the probes would generally reflect the amount of mRNA transcribed in the sample, which in turn would provide information about the gene expression levels and protein production in the cell. A phosphorous version of the mRNA extracted from the tissue is bound to the probes on the microarray. Laser scanners then excite the dye, causing it to fluorescence and producing an image of the probes. A major obstacle in analyzing microarray data, and the focus of this paper , is the large amount of noise introduced to the gene expression measurements. This noise stems from multiple sources, such as background leakage, cross-hybridization, uneven sample washing, and scanner biasing. Here, we introduce a spatial bias removal strategy involving a high pass Gaussian filter. The filter is applied using a two dimensional Fourier transform on the image for computational efficiency. The filtering is performed on the log of the measured intensity values, as there is strong evidence to suggest that the bias is in the form of a multiplicative factor. 2 2 The spatial bias model An underlying assumption in our spatial bias removal is that the amount of mRNA bound to one probe of the microarray is independent of the probes around it. Since the probes are generally placed on the microarray in a random permutation, and not corresponding to their corresponding genes' placement in the genome sequence, this assumption is valid whether the microarray was designed to detect complete genes or specific exons in genes. Therefore, visually, the intensity measurements should be independent, and the image obtained should look like random noise. An inclusive model of all the factors that contribute to the intensity measurement at a certain probe can be written as: observed i = true i × bias i + noise i where we have termed the multiplicative noise as bias, to be distinguished from the additive noise. The bias is often found as a spatial gradient …