nils-herrmann/clean_scirepeval huggingface dataset:
Simultaneous Localization and Mapping with Sparse Extended Information Filters

In this paper we describe a scalable algorithm for the simultaneous mapping and localization (SLAM) problem. SLAM is the problem of acquiring a map of a static environment with a mobile robot. The vast majority of SLAM algorithms are based on the extended Kalman filter (EKF). In this paper we advocate an algorithm that relies on the dual of the EKF, the extended information filter (EIF). We show that when represented in the information form, map posteriors are dominated by a small number of links that tie together nearby features in the map. This insight is developed into a sparse variant of the EIF, called the sparse extended information filter (SEIF). SEIFs represent maps by graphical networks of features that are locally interconnected, where links represent relative information between pairs of nearby features, as well as information about the robot’s pose relative to the map. We show that all essential update equations in SEIFs can be executed in constant time, irrespective of the size of the map. We also provide empirical results obtained for a benchmark data set collected in an outdoor environment, and using a multi-robot mapping simulation.
Probabilistic Models for Relational Data

We introduce a graphical language for relational data called the probabilistic entityrelationship (PER) model. The model is an extension of the entity-relationship model, a common model for the abstract representation of database structure. We concentrate on the directed version of this model—the directed acyclic probabilistic entity-relationship (DAPER) model. The DAPER model is closely related to the plate model and the probabilistic relational model (PRM), existing models for relational data. The DAPER model is more expressive than either existing model, and also helps to demonstrate their similarity. In addition to describing the new language, we discuss important facets of modeling relational data, including the use of restricted relationships, self relationships, and probabilistic relationships. Many examples are provided.
The Correlated Correspondence Algorithm for Unsupervised Registration of Nonrigid Surfaces

We present an unsupervised algorithm for registering 3D surface scans of an object undergoing significant deformations. Our algorithm does not need markers, nor does it assume prior knowledge about object shape, the dynamics of its deformation, or scan alignment. The algorithm registers two meshes by optimizing a joint probabilistic model over all point-to-point correspondences between them. This model enforces preservation of local mesh geometry, as well as more global constraints that capture the preservation of geodesic distance between corresponding point pairs. The algorithm applies even when one of the meshes is an incomplete range scan; thus, it can be used to automatically fill in the remaining surfaces for this partial scan, even if those surfaces were previously only seen in a different configuration. We evaluate the algorithm on several real-world datasets, where we demonstrate good results in the presence of significant movement of articulated parts and non-rigid surface deformation. Finally, we show that the output of the algorithm can be used for compelling computer graphics tasks such as interpolation between two scans of a non-rigid object and automatic recovery of articulated object models.
Detecting and modeling doors with mobile robots

We describe a probabilistic framework for detection and modeling of doors from sensor data acquired in corridor environments with mobile robots. The framework captures shape, color, and motion properties of door and wall objects. The probabilistic model is optimized with a version of the expectation maximization algorithm, which segments the environment into door and wall objects and learns their properties. The framework allows the robot to generalize the properties of detected object instances to new object instances. We demonstrate the algorithm on real-world data acquired by a Pioneer robot equipped with a laser range finder and an omni-directional camera. Our results show that our algorithm reliably segments the environment into walls and doors, finding both doors that move and doors that do not move. We show that our approach achieves better results than models that only capture behavior, or only capture appearance.
Identifying Protein-Protein Interaction Sites on a Genome-Wide Scale

Protein interactions typically arise from a physical interaction of one or more small sites on the surface of the two proteins. Identifying these sites is very important for drug and protein design. In this paper, we propose a computational method based on probabilistic relational model that attempts to address this task using high-throughput protein interaction data and a set of short sequence motifs. We learn the model using the EM algorithm, with a branch-and-bound algorithm as an approximate inference for the E-step. Our method searches for motifs whose presence in a pair of interacting proteins can explain their observed interaction. It also tries to determine which motif pairs have high affinity, and can therefore lead to an interaction. We show that our method is more accurate than others at predicting new protein-protein interactions. More importantly, by examining solved structures of protein complexes, we find that 2/3 of the predicted active motifs correspond to actual interaction sites.
Learning associative Markov networks

Markov networks are extensively used to model complex sequential, spatial, and relational interactions in fields as diverse as image processing, natural language analysis, and bioinformatics. However, inference and learning in general Markov networks is intractable. In this paper, we focus on learning a large subclass of such models (called associative Markov networks) that are tractable or closely approximable. This subclass contains networks of discrete variables with K labels each and clique potentials that favor the same labels for all variables in the clique. Such networks capture the "guilt by association" pattern of reasoning present in many domains, in which connected ("associated") variables tend to have the same label. Our approach exploits a linear programming relaxation for the task of finding the best joint assignment in such networks, which provides an approximate quadratic program (QP) for the problem of learning a margin-maximizing Markov network. We show that for associative Markov network over binary-valued variables, this approximate QP is guaranteed to return an optimal parameterization for Markov networks of arbitrary topology. For the nonbinary case, optimality is not guaranteed, but the relaxation produces good solutions in practice. Experimental results with hypertext and newswire classification show significant advantages over standard approaches.
Recovering Articulated Object Models from 3D Range Data

We address the problem of unsupervised learning of complex articulated object models from 3D range data. We describe an algorithm whose input is a set of meshes corresponding to different configurations of an articulated object. The algorithm automatically recovers a decomposition of the object into approximately rigid parts, the location of the parts in the different object instances, and the articulated object skeleton linking the parts. Our algorithm first registers all the meshes using an unsupervised non-rigid technique described in a companion paper. It then segments the meshes using a graphical model that captures the spatial contiguity of parts. The segmentation is done using the EM algorithm, iterating between finding a decomposition of the object into rigid parts, and finding the location of the parts in the object instances. Although the graphical model is densely connected, the object decomposition step can be performed optimally and efficiently, allowing us to identify a large number of object parts while avoiding local maxima. We demonstrate the algorithm on real world datasets, recovering a 15-part articulated model of a human puppet from just 7 different puppet configurations, as well as a 4 part model of a flexing arm where significant non-rigid deformation was present.
A module map showing conditional activity of expression modules in cancer

DNA microarrays are widely used to study changes in gene expression in tumors, but such studies are typically system-specific and do not address the commonalities and variations between different types of tumor. Here we present an integrated analysis of 1,975 published microarrays spanning 22 tumor types. We describe expression profiles in different tumors in terms of the behavior of modules, sets of genes that act in concert to carry out a specific function. Using a simple unified analysis, we extract modules and characterize gene-expression profiles in tumors as a combination of activated and deactivated modules. Activation of some modules is specific to particular types of tumor; for example, a growth-inhibitory module is specifically repressed in acute lymphoblastic leukemias and may underlie the deregulated proliferation in these cancers. Other modules are shared across a diverse set of clinical conditions, suggestive of common tumor progression mechanisms. For example, the bone osteoblastic module spans a variety of tumor types and includes both secreted growth factors and their receptors. Our findings suggest that there is a single mechanism for both primary tumor proliferation and metastasis to bone. Our analysis presents multiple research directions for diagnostic, prognostic and therapeutic studies.
Adaptive Probabilistic Networks with Hidden Variables

Probabilistic networks (also known as Bayesian belief networks) allow a compact description of complex stochastic relationships among several random variables. They are used widely for uncertain reasoning in artificial intelligence. In this paper, we investigate the problem of learning probabilistic networks with known structure and hidden variables. This is an important problem, because structure is much easier to elicit from experts than numbers, and the world is rarely fully observable. We present a gradient-based algorithm and show that the gradient can be computed locally, using information that is available as a byproduct of standard inference algorithms for probabilistic networks. Our experimental results demonstrate that using prior knowledge about the structure, even with hidden variables, can significantly improve the learning rate of probabilistic networks. We extend the method to networks in which the conditional probability tables are described using a small number of parameters. Examples include noisy-OR nodes and dynamic probabilistic networks. We show how this additional structure can be exploited by our algorithm to speed up the learning even further. We also outline an extension to hybrid networks, in which some of the nodes take on values in a continuous domain.
Multi-Agent Planning in Complex Uncertain Environments

Many tasks require a team of agents to act together in a coordinated way in a complex, uncertain environment. Examples include search and rescue, control of a complex system such as a factory, or robot soccer. Such tasks involve many agents, and huge numbers of states and possible actions. Factored Markov decision processes (MDPs) provide a formal foundation for modeling such complex systems naturally and compactly. We propose a framework for multi-agent coordination and planning in factored MDPs based on the use of a factored value function — an approximate decomposition of the team value function as a sum of value functions of small subteams of agents. We show that factored value functions naturally give rise to an optimal distributed algorithm for joint action selection, whose communication structure naturally mirrors the interactions between the subteams. We present an efficient linear-programming-based algorithm for computing a factored value function for a factored MDP. We show how the use of factored value functions can form the basis for interdomain plan generalization, where we “learn” from plans constructed for some set of problems, and can provide good solutions to other unseen problems of the same type without any need for planning. We describe the application of this approach to the task of multi-agent planning in a strategic computer war game, and the application by Kok et al. for multi-agent coordination in their world-champion RoboSoccer team. Acknowledgements. Joint work with Carlos Guestrin, Ronald Parr, Chris Gearhart, Neal Kanodia, and Shobha Venkataraman.
Sfp1 is a stress- and nutrient-sensitive regulator of ribosomal protein gene expression.

Yeast cells modulate their protein synthesis capacity in response to physiological needs through the transcriptional control of ribosomal protein (RP) genes. Here we demonstrate that the transcription factor Sfp1, previously shown to play a role in the control of cell size, regulates RP gene expression in response to nutrients and stress. Under optimal growth conditions, Sfp1 is localized to the nucleus, bound to the promoters of RP genes, and helps promote RP gene expression. In response to inhibition of target of rapamycin (TOR) signaling, stress, or changes in nutrient availability, Sfp1 is released from RP gene promoters and leaves the nucleus, and RP gene transcription is down-regulated. Additionally, cells lacking Sfp1 fail to appropriately modulate RP gene expression in response to environmental cues. We conclude that Sfp1 integrates information from nutrient- and stress-responsive signaling pathways to help control RP gene expression.
Being Bayesian About Network Structure. A Bayesian Approach to Structure Discovery in Bayesian Networks

In many multivariate domains, we are interested in analyzing the dependency structure of the underlying distribution, e.g., whether two variables are in direct interaction. We can represent dependency structures using Bayesian network models. To analyze a given data set, Bayesian model selection attempts to find the most likely (MAP) model, and uses its structure to answer these questions. However, when the amount of available data is modest, there might be many models that have non-negligible posterior. Thus, we want compute the Bayesian posterior of a feature, i.e., the total posterior probability of all models that contain it. In this paper, we propose a new approach for this task. We first show how to efficiently compute a sum over the exponential number of networks that are consistent with a fixed order over network variables. This allows us to compute, for a given order, both the marginal probability of the data and the posterior of a feature. We then use this result as the basis for an algorithm that approximates the Bayesian posterior of a feature. Our approach uses a Markov Chain Monte Carlo (MCMC) method, but over orders rather than over network structures. The space of orders is smaller and more regular than the space of structures, and has much a smoother posterior “landscape”. We present empirical results on synthetic and real-life datasets that compare our approach to full model averaging (when possible), to MCMC over network structures, and to a non-Bayesian bootstrap approach.
Constructing Small Sample Spaces for De � Randomization of Algorithms

The subject of this paper is nding small sample spaces for joint distributions of n Bernoulli random variables where the probabilities of some events are prescribed The problem of recognizing whether the prescribed probabilities are consistent is NP hard It is shown however that if the probabilities are consistent then there exists a sample space that supports them whose cardinality does not exceed the number of events with prescribed probabilities It is also shown that if the probabilities are consistent with a joint distribution of n independent Bernoulli variables then a small sample space can be constructed in polynomial time This last result is useful for converting randomized algorithms into deterministic ones We demonstrate this technique by an application to the problem of nding large independent sets in sparse hypergraphs
Probabilistic discovery of overlapping cellular processes and their regulation

Many of the functions carried out by a living cell are regulated at the transcriptional level, to ensure that genes are expressed when they are needed. Thus, to understand biological processes, it is thus necessary to understand the cell's transcriptional network. In this paper, we propose a novel probabilistic model of gene regulation for the task of identifying overlapping biological processes and the regulatory mechanism controlling their activation. A key feature of our approach is that we allow genes to participate in multiple processes, thus providing a more biologically plausible model for the process of gene regulation. We present an algorithm to learn this model automatically from data, using only genome-wide measurements of gene expression as input. We compare our results to those obtained by other approaches, and show significant benefits can be gained by modeling both the organization of genes into overlapping cellular processes and the regulatory programs of these processes. Moreover, our method successfully grouped genes known to function together, recovered many regulatory relationships that are known in the literature, and suggested novel hypotheses regarding the regulatory role of previously uncharacterized proteins.
FastSLAM: An Efficient Solution to the Simultaneous Localization And Mapping Problem with Unknown Data

This article provides a comprehensive description of FastSLAM, a new family of algorithms for the simultaneous localization and mapping problem, which specifically address hard data association problems. The algorithm uses a particle filter for sampling robot paths, and extended Kalman filters for representing maps acquired by the vehicle. This article presents two variants of this algorithm, the original algorithm along with a more recent variant that provides improved performance in certain operating regimes. In addition to a mathematical derivation of the new algorithm, we present a proof of convergence and experimental results on its performance on real-world data.
Discovering molecular pathways from protein interaction and gene expression data

In this paper, we describe an approach for identifying 'pathways' from gene expression and protein interaction data. Our approach is based on the assumption that many pathways exhibit two properties: their genes exhibit a similar gene expression profile, and the protein products of the genes often interact. Our approach is based on a unified probabilistic model, which is learned from the data using the EM algorithm. We present results on two Saccharomyces cerevisiae gene expression data sets, combined with a binary protein interaction data set. Our results show that our approach is much more successful than other approaches at discovering both coherent functional groups and entire protein complexes.
Generalizing Plans to New Environments in Relational MDPs

A longstanding goal in planning research is the ability to generalize plans developed for some set of environments to a new but similar environment, with minimal or no replanning. Such generalization can both reduce planning time and allow us to tackle larger domains than the ones tractable for direct planning. In this paper, we present an approach to the generalization problem based on a new framework of relational Markov Decision Processes (RMDPs). An RMDP can model a set of similar environments by representing objects as instances of different classes. In order to generalize plans to multiple environments, we define an approximate value function specified in terms of classes of objects and, in a multiagent setting, by classes of agents. This class-based approximate value function is optimized relative to a sampled subset of environments, and computed using an efficient linear programming method. We prove that a polynomial number of sampled environments suffices to achieve performance close to the performance achievable when optimizing over the entire space. Our experimental results show that our method generalizes plans successfully to new, significantly larger, environments, with minimal loss of performance relative to environment-specific planning. We demonstrate our approach on a real strategic computer war game.
Max-Margin Markov Networks

In typical classification tasks, we seek a function which assigns a label to a single object. Kernel-based approaches, such as support vector machines (SVMs), which maximize the margin of confidence of the classifier, are the method of choice for many such tasks. Their popularity stems both from the ability to use high-dimensional feature spaces, and from their strong theoretical guarantees. However, many real-world tasks involve sequential, spatial, or structured data, where multiple labels must be assigned. Existing kernel-based methods ignore structure in the problem, assigning labels independently to each object, losing much useful information. Conversely, probabilistic graphical models, such as Markov networks, can represent correlations between labels, by exploiting problem structure, but cannot handle high-dimensional feature spaces, and lack strong theoretical generalization guarantees. In this paper, we present a new framework that combines the advantages of both approaches: Maximum margin Markov (M3) networks incorporate both kernels, which efficiently deal with high-dimensional features, and the ability to capture correlations in structured data. We present an efficient algorithm for learning M3 networks based on a compact quadratic program formulation. We provide a new theoretical bound for generalization in structured domains. Experiments on the task of handwritten character recognition and collective hypertext classification demonstrate very significant gains over previous approaches.
Learning on the Test Data: Leveraging Unseen Features

This paper addresses the problem of classification in situations where the data distribution is not homogeneous: Data instances might come from different locations or times, and therefore are sampled from related but different distributions. In particular, features may appear in some parts of the data that are rarely or never seen in others. In most situations with nonhomogeneous data, the training data is not representative of the distribution under which the classifier must operate. We propose a method, based on probabilistic graphical models, for utilizing unseen features during classification. Our method introduces, for each such unseen feature, a continuous hidden variable describing its influence on the class -- whether it tends to be associated with some label. We then use probabilistic inference over the test data to infer a distribution over the value of this hidden variable. Intuitively, we "learn" the role of this unseen feature from the test set, generalizing from those instances whose label we are fairly sure about. Our overall probabilistic model is learned from the training data. In particular, we also learn models for characterizing the role of unseen features; these models use "meta-features" of those features, such as words in the neighborhood of an unseen feature, to infer its role. We present results for this framework on the task of classifying news articles and web pages, showing significant improvements over models that do not use unseen features.