ppxscal/embedding_network huggingface dataset:
Automated Assume-Guarantee Reasoning by Abstraction Refinement
Authors: Mihaela Gheorghiu Bobaru, Corina S. Păsăreanu, Dimitra Giannakopoulou
Venue: Proceedings of the 20th international conference on Computer Aided Verification (2008)

Current automated approaches for compositional model checking in the assume-guarantee style are based on learning of assumptions as deterministic automata. We propose an alternative approach based on abstraction refinement. Our new method computes the assumptions for the assume-guarantee rules as conservative and not necessarily deterministic abstractions of some of the components, and refines those abstractions using counterexamples obtained from model checking them together with the other components. Our approach also exploits the alphabets of the interfaces between components and performs iterative refinement of those alphabets as well as of the abstractions. We show experimentally that our preliminary implementation of the proposed alternative achieves similar or better performance than a previous learning-based implementation.
Fast Bayesian Haplotype Inference Via Context Tree Weighting
Authors: Pasi Rastas, Jussi Kollin, Mikko Koivisto
Venue: Proceedings of the 8th international workshop on Algorithms in Bioinformatics (2008)

We present a new, Bayesian method for inferring haplotypes for unphased genotypes. The method can be viewed as a unification of some ideas of variable-order Markov chain modelling and ensemble learning that so far have been implemented only separately in some of the state-of-the-art methods. Specifically, we make use of the Context Tree Weighting algorithm to efficiently compute the posterior probability of any given haplotype assignment; we employ a simulated annealing scheme to rapidly find several local optima of the posterior; and we sketch a full Bayesian analogue, in which a weighted sample of haplotype assignments is drawn to summarize the posterior distribution. We also show that one can minimize in linear time the average switch distance, a popular measure of phasing accuracy, to a given (weighted) sample of haplotype assignments. We demonstrate empirically that the presented method typically performs as well as the leading fast haplotype inference methods, and sometimes better. The methods are freely available in a computer program BACH (Bayesian Context-based Haplotyping)
Modeling biological systems from heterogeneous data
Authors: Alexander J. Hartemink
Venue:  (2008)

Most high-throughput biological data are inherently heterogeneous, providing information at the various levels at which organisms integrated inputs to arrive at an observable phenotype. Approaches are needed to not only analyze such heterogeneous data, but also model their complex experimental observation procedures. We first present a graphical model approach for learning dynamic cell cycle regulatory networks. Our algorithm combines evidence from gene expression data through a likelihood term and protein-DNA binding data through an informative structure prior.We next demonstrate how analysis of cell cycle measurements from a synchronized population of cells are obstructed by synchrony loss. We introduce a probabilistic model, CLOCCS, capable of characterizing multiple sources of asynchrony in synchronized cell populations. Using CLOCCS, we formulate a convex optimization deconvolution procedure that recovers single cell estimates from observed population-level measurements. Our algorithm offers a solution for monitoring individual cells rather than a population of cells losing synchrony over time. Using our deconvolution algorithm, we provide a global high resolution view of cell cycle gene expression in budding yeast, beginning from the cell cycle of an initial cell, to right across the newly created mother and daughter cell.Understanding any cellular process is incomplete without knowledge of the activity of proteins and protein complexes. We introduce PROCTOR , a statistical approach capable of learning the hidden interaction topology of protein complexes from direct protein-protein interaction data and indirect co-complexed protein interaction data. Using PROCTOR, we provide a global view of the physical interactome in budding yeast.We conclude by demonstrating how our algorithms, utilizing information from heterogeneous biological data, can provide a dynamic view of regulatory control in the budding yeast cell cycle.
Predicting protein function using sequence derived features selected by genetic algorithms
Authors: Andrew Kernytsky
Venue:  (2008)

Large scale sequencing of genomes has created a peculiar problem for biology: there is now a glut of information in the form of nucleotide sequences, but deciphering the higher level annotations buried in the nucleotides remains a problem that is unsolved to varying extents. One aspect of this problem is deducing the function of a protein from its sequence. This is an important challenge because the number of raw protein sequences far surpasses the number of well characterized proteins. We address this problem by using computational methods to predict protein function from sequence derived features. We describe a method for the prediction of protein function in terms of enzymatic activity classification (Enzyme Commission numbers) using only the protein sequence. The method begins by generating sequence derived features for a protein that range from the amino acid composition to predicted features such as secondary structure and solvent accessibility. In order to capture the local environment surrounding a key residue&mdash;a residue involved in catalysis, for example&mdash;the method searches for combinations of these features that have predictive power when they occur at the same residue. The learning algorithm may find that a particular amino acid residue is a good indicator of some protein function when another sequence derived feature indicates that the residue is predicted to be at the surface of a protein or to be in a beta sheet secondary structure element. These predictive combinations of features are detected by a genetic algorithm used as a wrapper around a neural network. By incorporating features in the environment surrounding a single residue, the method may be seen as a specialized motif detector that detects instances of these combined features that are correlated with protein function. We evaluate the performance of this method across 59 enzymatic activity classes and find that the genetic algorithm based selection of feature combinations is able to significantly increase the predictive power of the method. The ACM Portal is published by the Association for Computing Machinery. Copyright © 2010 ACM, Inc. Terms of Usage Privacy Policy Code of Ethics Contact Us Useful downloads: Adobe Acrobat QuickTime Windows Media Player Real Player
Trading off solution cost for smaller runtime in DCOP search algorithms
Authors: William Yeoh, Sven Koenig, Xiaoxun Sun
Venue: Proceedings of the 7th international joint conference on Autonomous agents and multiagent systems - Volume 3 (2008)

Distributed Constraint Optimization (DCOP) is a key technique for solving multiagent coordination problems. Unfortunately, finding minimal-cost DCOP solutions is NP-hard. We therefore propose two mechanisms that trade off the solution costs of two DCOP search algorithms (ADOPT and BnB-ADOPT) for smaller runtimes, namely the Inadmissible Heuristics Mechanism and the Relative Error Mechanism. The solution costs that result from these mechanisms are bounded by a more meaningful quantity than the solution costs that result from the existing Absolute Error Mechanism since they both result in solution costs that are larger than minimal by at most a user-specified percentage. Furthermore, the Inadmissible Heuristics Mechanism experimentally dominates both the Absolute Error Mechanism and the Relative Error Mechanism for BnB-ADOPT and is generally no worse than them for ADOPT.
Using Multiplicity Automata to Identify Transducer Relations from Membership and Equivalence Queries
Authors: Jose Oncina
Venue: Proceedings of the 9th international colloquium on Grammatical Inference: Algorithms and Applications (2008)

Multiplicity Automata are devices that implement functions from a string space to a field. Usually the real number's field is used. From a learning point of view there exists some algorithms that are able to identify any multiplicity automaton from membership and equivalence queries.In this work we realize that those algorithms can also be used if the algebraic structure of a field is relaxed to a divisive ring structure, that is, the commutativity of the product operation is dropped.Moreover, we define an algebraic structure, which is an extension of the string monoid, that allows the identification of any transduction that can be realized by finite state machines without empty-transitions.
D-Stable H8 Fault-Tolerant Control for Delta Operator Systems with Actuator Failure
Authors: Duanjin Zhang, Luohua Zhang, Steven X. Ding
Venue: Proceedings of the 2008 ISECS International Colloquium on Computing, Communication, Control, and Management - Volume 02 (2008)

The H-infinity fault-tolerant control problem of delta operator formulated linear systems with circular pole constraints is considered. Based on the delta operator bounded real lemma, a necessary and sufficient condition of D-stable H-infinity control for delta operator systems with actuator failure is presented using the matrix inequality approach. The design of H-infinity fault-tolerant controller with pole assignment is transformed into solving a Riccati inequality. The proposed results can be regarded as extensions of existing results on H-infinity control and pole assignment of fault-tolerant control systems using delta operator. A numerical example is given to show the effectiveness of the proposed method.
Continuity of Performance Metrics for Thin Feature Maps
Authors: Adam Kowalczyk
Venue: Proceedings of the 18th international conference on Algorithmic Learning Theory (2007)

We study the class of hypothesis composed of linear functionals superimposed with smooth feature maps. We show that for "typical" smooth feature map, the pointwise convergence of hypothesis implies the convergence of some standard metrics such as error rate or area under ROC curve with probability 1 in selection of the test sample from a (Lebesgue measurable) probability density. Proofs use transversality theory. The crux is to show that for every "typical", sufficiently smooth feature map into a finite dimensional vector space, the counter-image of every affine hyperplane has Lebesgue measure 0.The results extend to every real analytic, in particular polynomial, feature map if its domain is connected and the limit hypothesis is non-constant. In the process we give an elementary proof of the fundamental lemma that locus of zeros of a real analytic function on a connected domain either fills the whole space or forms a subset of measure 0.
The Influence of Asynchronous Dynamics in the Spatial Prisoner's Dilemma Game
Authors: Carlos Grilo, Luís Correia
Venue: Proceedings of the 10th international conference on Simulation of Adaptive Behavior: From Animals to Animats (2008)

We examine the influence of asynchronism in the Spatial Prisoner's Dilemma game. Previous studies reported that less cooperation is achieved with the asynchronous version of the game than with the synchronous one. Here, we show that, in general, the opposite is the most common outcome. This conclusion is only possible because a larger number of scenarios was tested, namely, different interaction topologies, a transition rule that can be tuned to emulate different levels of determinism in the choice of the next strategy to be adopted and different rates of asynchronism. The influence of stochastic and deterministic periodic updating in the outcome of the system is also compared. We found that these two update disciplines lead basically to the same result. This is an important issue in the simulation of social and biological behavior.
Higher order Proof Reconstruction from Paramodulation-Based Refutations: The Unit Equality Case
Authors: Andrea Asperti, Enrico Tassi
Venue: Proceedings of the 14th symposium on Towards Mechanized Mathematical Assistants: 6th International Conference (2007)

In this paper we address the problem of reconstructing a higher order, checkable proof object starting from a proof trace left by a first order automatic proof searching procedure, in a restricted equational framework. The automatic procedure is based on superposition rules for the unit equality case. Proof transformation techniques aimed to improve the readability of the final proof are discussed.
Single-machine scheduling with past-sequence-dependent setup times and time-dependent learning effect
Authors: Ji-Bo Wang
Venue: Computers and Industrial Engineering (2008)

This paper studies the single-machine scheduling problem with time-dependent learning effect and setup times considerations. The time-dependent learning effect means that the processing time of a job is defined by a function of the total normal processing time of the already processed jobs. The setup times are proportional to the length of the already processed jobs, i.e., the setup times are past-sequence-dependent (p-s-d). We consider the following objective functions: the makespan, the total completion time, the sum of the quadratic job completion times, the total weighted completion time and the maximum lateness. We show that the makespan minimization problem, the total completion time minimization problem and the sum of the quadratic job completion times minimization problem can be solved in polynomial time, respectively. We also show that the total weighted completion time minimization problem and the maximum lateness minimization problem can be solved in polynomial time under certain conditions.
From World-Wide-Web Mining to Worldwide Webmining: Understanding People's Diversity for Effective Knowledge Discovery
Authors: Bettina Berendt, Anett Kralisch
Venue: From Web to Social Web: Discovering and Deploying User and Content Profiles: Workshop on Web Mining, WebMine 2006, Berlin, Germany, September 18, 2006. Revised Selected and Invited Papers (2007)

Users are well-established objects of analysis in Web mining: Web usage mining investigates users' behaviour, Web content and structure mining analyze the content and link structures they generate, Web community mining transfers these questions from analyses of individuals to analyses of groups, etc. However, too often users are reduced to the digital data they have created and/or accessed, and it is (generally implicitly) assumed that "all users are alike" in the ways in which they create and access those data. We argue that to make these analyses and findings more meaningful, a shift is needed from technology to human aspects. This shift calls for a multidisciplinary approach that integrates insights from behavioural, psychological, and linguistic sciences into the field of knowledge discovery. In this paper, we introduce the concept ubiquity of people to emphasize that data and knowledge are created and accessed globally, from users who differ in language, culture, and other factors. The Web is the major medium for these activities. The paper investigates how knowledge discovery, including but not limited to Web mining, may benefit from an integration of the concept of ubiquity of people. We provide an overview of the impact of language and culture on how data and knowledge are accessed, shared, and evaluated. We describe a series of studies as an example of integrating these questions into Web (usage) mining. We conclude with a discussion of research questions that are raised by the integration of the ubiquity of people into knowledge discovery, in particular with regard to data collection, data processing, and data presentation.
Performance Evaluation of Algorithms for Soft Evidential Update in Bayesian Networks: First Results
Authors: Scott Langevin, Marco Valtorta
Venue: Proceedings of the 2nd international conference on Scalable Uncertainty Management (2008)

In this paper we analyze the performance of three algorithms for soft evidential update, in which a probability distribution represented by a Bayesian network is modified to a new distribution constrained by given marginals, and closest to the original distribution according to cross entropy. The first algorithm is a new and improved version of the big clique algorithm [1] that utilizes lazy propagation [2]. The second and third algorithm [3] are wrapper methods that convert soft evidence to virtual evidence, in which the evidence for a variable consists of a likelihood ratio. Virtual evidential update is supported in existing Bayesian inference engines, such as Hugin. To evaluate the three algorithms, we implemented BRUSE (Bayesian Reasoning Using Soft Evidence), a new Bayesian inference engine, and instrumented it. The resulting statistics are presented and discussed.
TriangleVision: A Toy Visual System
Authors: Thomas Bangert
Venue: Proceedings of the 18th international conference on Artificial Neural Networks, Part I (2008)

This paper presents a simple but fully functioning and complete artificial visual system. The triangle is the simplest object of perception and therefore the simplest visual system is one which sees only triangles. The system presented is complete is the sense that it will see any triangle presented to it as visual input in bitmap form, even triangles with illusory contours that can only be detected by inference.
Data Collection with Multiple Mobile Actors in Underwater Sensor Networks
Authors: Jincheng Wang, Deshi Li, Mi Zhou, Dipak Ghosal
Venue: Proceedings of the 2008 The 28th International Conference on Distributed Computing Systems Workshops (2008)

Underwater Sensor Networks (USNs) could provide real time in-situ monitoring to supply high spatio-temporal resolution capability. However underwater environment represents many challenges for sensor networks. It has become an urgent problem to get high temporal precision sampling data from USNs and to prolong USNs’ lifetime at the same time. In this paper, we present an underwater application model for collecting detail data from USNs with multiple mobile actors to get high temporal resolution capability. Our proposed complete strategy works in two ways: local base station collects sensors’ data with regular time resolution, and mobile actors collect data from virtual clusters with high temporal resolution. We present three algorithms to accomplish the network model: a) area partitioning and actors scattering algorithm, b) sub-region optimizing algorithm, and c) virtual cluster formation algorithm. The paper present simulations to evaluate our proposed algorithms. The results indicates that our strategy can achieve lower end-to-end delay which is one eighth of that by former methods, actors can collect high temporal resolution data through virtual clusters, local network alternates within two network states(local USN and virtual clusters) freely. At the same time, since the proposed approach does not reduce local USNs’ lifetime, it has great potential in underwater network applications.
The Role of Affect and Emotion in HCI
Authors: Russell Beale, Christian Peter
Venue: Affect and Emotion in Human-Computer Interaction: From Theory to Applications (2008)

Affect and emotion play an important role in our everyday lives: they are present whatever we do, wherever we are, wherever we go, without us being aware of them for much of the time. When it comes to interaction, be it with humans, technology, or humans via technology, we suddenly become more aware of emotion, either by seeing the other's emotional expression, or by not getting an emotional response while anticipating it. Given this, it seems only sensible to commit to affect and emotion in human-computer interaction, to investigate the underlying principles, to study the role they play, to develop methods to quantify them, and to finally build applications that make use of them. In this introductory chapter we discuss and give short accounts on present developments in the field, covering theoretical issues, user experience and design aspects, sensing issues, and report on some affective applications that have been developed.
A comparison of c++ concepts and haskell type classes
Authors: Jean-Philippe Bernardy, Patrik Jansson, Marcin Zalewski, Sibylle Schupp, Andreas Priesnitz
Venue: Proceedings of the ACM SIGPLAN workshop on Generic programming (2008)

Earlier studies have introduced a list of high-level evaluation criteria to assess how well a language supports generic programming. Since each language that meets all criteria is considered generic, those criteria are not fine-grained enough to differentiate between languages for generic programming. We refine these criteria into a taxonomy that captures differences between type classes in Haskell and concepts in C++, and discuss which differences are incidental and which ones are due to other language features. The taxonomy allows for an improved understanding of language support for generic programming, and the comparison is useful for the ongoing discussions among language designers and users of both languages.
Cooperative localization using angle of arrival measurements in non-line-of-sight environments
Authors: Bharath Ananthasubramaniam, Upamanyu Madhow
Venue: Proceedings of the first ACM international workshop on Mobile entity localization and tracking in GPS-less environments (2008)

We investigate localization of a transmitting node using angle of arrival (AoA) measurements made at a geographically dispersed network of cooperating receivers with known locations. A low-complexity sequential algorithm for updating the source location estimates under line-of-sight (LOS) environments is developed. This serves as a building block for an algorithm that suppresses outliers arising due to multipath scattering and reflection in non-line-of-sight (NLOS) scenarios. Maximal likelihood (ML) location estimation requires exhaustive testing of estimates from all possible subsets of measurements. We avoid this by utilizing a randomized algorithm that approaches the ML performance at a complexity that is only quadratic in the number of measurements. The localization error is proportional in the AoA error variance and coverage area, and can be reduced by an increase in the number of estimates with a strong LOS component.
Classification Methods for Speaker Recognition
Authors: D. E. Sturim, W. M. Campbell, D. A. Reynolds
Venue: Speaker Classification I: Fundamentals, Features, and Methods (2007)

Automatic speaker recognition systems have a foundation built on ideas and techniques from the areas of speech science for speaker characterization, pattern recognition and engineering. In this chapter we provide an overview of the features, models, and classifiers derived from these areas that are the basis for modern automatic speaker recognition systems. We describe the components of state-of-the-art automatic speaker recognition systems, discuss application considerations and provide a brief survey of accuracy for different tasks.
One-Shot Learners Using Negative Counterexamples and Nearest Positive Examples
Authors: Sanjay Jain, Efim Kinber
Venue: Proceedings of the 18th international conference on Algorithmic Learning Theory (2007)

As some cognitive research suggests, in the process of learning languages, in addition to overt explicit negative evidence, a child often receives covert explicit evidence in form of corrected or rephrased sentences. In this paper, we suggest one approach to formalization of overt and covert evidence within the framework of one-shot learners via subset and membership queries to a teacher (oracle). We compare and explore general capabilities of our models, as well as complexity advantages of learnability models of one type over models of other types, where complexity is measured in terms of number of queries. In particular, we establish that "correcting" positive examples give sometimes more power to a learner than just negative (counter)examples and access to full positive data.