pinecone/core-2020-05-10-deduplication huggingface dataset:
Predicting protein function and protein-ligand interaction with the 3D neighborhood kernel

Kernels for structured data have gained a lot of attention in a world with an ever increasing amount of complex data, generated from domains such as biology, chemistry, or engineering. However, while many applications involve spatial aspects, up to now only few kernel methods have been designed to take 3D information into account. We introduce a novel kernel called the 3D Neighborhood Kernel. As a first step, we focus on 3D structures of proteins and ligands, in which the atoms are represented as points in 3D space. By comparing the Euclidean distances between selected sets of atoms, the kernel can select spatial features that are important for determining functions of proteins or interactions with other molecules. We evaluate the kernel on a number of benchmark datasets and show that it obtains a competitive performance w.r.t. the state-of-the-art methods. While we apply this kernel to proteins and ligands, it is applicable to any kind of 3D data where objects follow a common schema, such as RNA, cars, or standardized equipment.status: publishe
Further applications of the Container Method

Recently, Balogh--Morris--Samotij and Saxton--Thomason proved that
hypergraphs satisfying some natural conditions have only few independent sets.
Their main results already have several applications. However, the methods of
proving these theorems are even more far reaching. The general idea is to
describe some family of events, whose cardinality a priori could be large, only
with a few certificates. Here, we show some applications of the methods,
including counting $C_4$-free graphs, considering the size of a maximum
$C_4$-free subgraph of a random graph and counting metric spaces with a given
number of points. Additionally, we discuss some connections with the
Szemer\'edi Regularity Lemma.Comment: This is a survey style paper written for the IMA volume "Recent
  Trends in Combinatorics" for the 2014-2015 Annual Thematic Program Discrete
  Structures: Analysis and Applications at the Institute for Mathematics and
  its Applications during Fall 201
Unimodality Problems in Ehrhart Theory

Ehrhart theory is the study of sequences recording the number of integer
points in non-negative integral dilates of rational polytopes. For a given
lattice polytope, this sequence is encoded in a finite vector called the
Ehrhart $h^*$-vector. Ehrhart $h^*$-vectors have connections to many areas of
mathematics, including commutative algebra and enumerative combinatorics. In
this survey we discuss what is known about unimodality for Ehrhart
$h^*$-vectors and highlight open questions and problems.Comment: Published in Recent Trends in Combinatorics, Beveridge, A., et al.
  (eds), Springer, 2016, pp 687-711, doi 10.1007/978-3-319-24298-9_27. This
  version updated October 2017 to correct an error in the original versio
Disproving Using the Inverse Method by Iterative Refinement of Finite Approximations

International audienceIn first-order logic, forward search using a complete strategy such as the inverse method can get stuck deriving larger and larger consequence sets when the goal query is unprovable. This is the case even in trivial theories where backward search strategies such as tableaux methods will fail finitely. We propose a general mechanism for bounding the consequence sets by means of finite approximations of infinite types. If the inverse method also implements forward subsumption and globalization, then the search space under this approximation is finite. We therefore obtain a type-directed iterative refinement algorithm for disproving queries. The method has been implemented for intuitionistic first-order logic, and we discuss its performance on a variety of problems
The Proof Certifier Checkers

International audienceDifferent theorem provers work within different formalisms and paradigms, and therefore produce various incompatible proof objects. Currently there is a big effort to establish foundational proof certificates (FPC), which would serve as a common " specification language " for all these formats. Such framework enables the uniform checking of proof objects from many different theorem provers while relying on a small and trusted kernel to do so. Checkers is an implementation of a proof checker using foundational proof certificates. By trusting a small kernel based on (focused) sequent calculus on the one hand and by supporting FPC specifications in a prolog-like language on the other hand, it can be used for checking proofs of a wide range of theorem provers. The focus of this paper is on the output of equational resolution theorem provers and for this end, we specify the paramodulation rule. We describe the architecture of Checkers and demonstrate how it can be used to check proof objects by supplying the FPC specification for a subset of the inferences used by E-prover and checking proofs using these inferences
M\=im\=a\d{m}s\=a deontic logic: proof theory and applications

Starting with the deontic principles in M\={\i}m\=a\d{m}s\=a texts we
introduce a new deontic logic. We use general proof-theoretic methods to obtain
a cut-free sequent calculus for this logic, resulting in decidability,
complexity results and neighbourhood semantics. The latter is used to analyse a
well known example of conflicting obligations from the Vedas.Comment: 16 pages, published in the proceedings of Automated Reasoning with
  Analytic Tableaux and Related Methods - 24th International Conference,
  TABLEAUX 2015, Wroc{\l}aw, Poland, September 21-24, 2015. The final
  publication is available at Springer via
  http//dx.doi.org/10.1007/978-3-319-24312-2_2
Integrating Simplex with Tableaux

International audienceWe propose an extension of a tableau-based calculus to deal with linear arithmetic. This extension consists of a smooth integration of arithmetic deductive rules to the basic tableau rules, so that there is a natural interleaving between arithmetic and regular analytic rules. The arithmetic rules rely on the general simplex algorithm to compute solutions for systems over rationals, as well as on the branch and bound method to deal with integer systems. We also describe our implementation in the framework of Zenon, an automated theorem prover that is able to deal with first order logic with equality. This implementation has been provided with a backend verifier that relies on the Coq proof assistant , and which can verify the validity of the generated arithmetic proofs. Finally, we present some experimental results over the arithmetic category of the TPTP library, and problems of program verification coming from the benchmark provided by the BWare project
Computing maximal autarkies with few and simple oracle queries

We consider the algorithmic task of computing a maximal autarky for a
clause-set F, i.e., a partial assignment which satisfies every clause of F it
touches, and where this property is destroyed by adding any non-empty set of
further assignments. We employ SAT solvers as oracles, using various
capabilities. Using the standard SAT oracle, log_2(n(F)) oracle calls suffice,
where n(F) is the number of variables, but the drawback is that (translated)
cardinality constraints are employed, which makes this approach less efficient
in practice. Using an extended SAT oracle, motivated by the capabilities of
modern SAT solvers, we show how to compute maximal autarkies with 2 n(F)^{1/2}
simpler oracle calls. This novel algorithm combines the previous two main
approaches, based on the autarky-resolution duality and on SAT translations.Comment: 18 pages; second version with editorial changes, to appear in LNCS
  for Theory and Applications of Satisfiability Testing - SAT 201
Incrementally Computing Minimal Unsatisfiable Cores of QBFs via a Clause
  Group Solver API

We consider the incremental computation of minimal unsatisfiable cores (MUCs)
of QBFs. To this end, we equipped our incremental QBF solver DepQBF with a
novel API to allow for incremental solving based on clause groups. A clause
group is a set of clauses which is incrementally added to or removed from a
previously solved QBF. Our implementation of the novel API is related to
incremental SAT solving based on selector variables and assumptions. However,
the API entirely hides selector variables and assumptions from the user, which
facilitates the integration of DepQBF in other tools. We present implementation
details and, for the first time, report on experiments related to the
computation of MUCs of QBFs using DepQBF's novel clause group API.Comment: (fixed typo), camera-ready version, 6-page tool paper, to appear in
  proceedings of SAT 2015, LNCS, Springe
Statistical laws in linguistics

Zipf's law is just one out of many universal laws proposed to describe
statistical regularities in language. Here we review and critically discuss how
these laws can be statistically interpreted, fitted, and tested (falsified).
The modern availability of large databases of written text allows for tests
with an unprecedent statistical accuracy and also a characterization of the
fluctuations around the typical behavior. We find that fluctuations are usually
much larger than expected based on simplifying statistical assumptions (e.g.,
independence and lack of correlations between observations).These
simplifications appear also in usual statistical tests so that the large
fluctuations can be erroneously interpreted as a falsification of the law.
Instead, here we argue that linguistic laws are only meaningful (falsifiable)
if accompanied by a model for which the fluctuations can be computed (e.g., a
generative model of the text). The large fluctuations we report show that the
constraints imposed by linguistic laws on the creativity process of text
generation are not as tight as one could expect.Comment: Proceedings of the Flow Machines Workshop: Creativity and
  Universality in Language, Paris, June 18 to 20, 201
Dynamics on expanding spaces: modeling the emergence of novelties

Novelties are part of our daily lives. We constantly adopt new technologies,
conceive new ideas, meet new people, experiment with new situations.
Occasionally, we as individuals, in a complicated cognitive and sometimes
fortuitous process, come up with something that is not only new to us, but to
our entire society so that what is a personal novelty can turn into an
innovation at a global level. Innovations occur throughout social, biological
and technological systems and, though we perceive them as a very natural
ingredient of our human experience, little is known about the processes
determining their emergence. Still the statistical occurrence of innovations
shows striking regularities that represent a starting point to get a deeper
insight in the whole phenomenology. This paper represents a small step in that
direction, focusing on reviewing the scientific attempts to effectively model
the emergence of the new and its regularities, with an emphasis on more recent
contributions: from the plain Simon's model tracing back to the 1950s, to the
newest model of Polya's urn with triggering of one novelty by another. What
seems to be key in the successful modelling schemes proposed so far is the idea
of looking at evolution as a path in a complex space, physical, conceptual,
biological, technological, whose structure and topology get continuously
reshaped and expanded by the occurrence of the new. Mathematically it is very
interesting to look at the consequences of the interplay between the "actual"
and the "possible" and this is the aim of this short review.Comment: 25 pages, 10 figure
On the structure of codimension 1 foliations with pseudoeffective conormal bundle.

International audienceLet $X$ a projective manifold equipped with a codimension $1$ (maybe singular) distribution whose conormal sheaf is assumed to be pseudoeffective. By a theorem of Jean-Pierre Demailly, this distribution is actually integrable and thus defines a codimension $1$ holomorphic foliation $\F$. We aim at describing the structure of such a foliation, especially in the non abundant case: It turns out that $\F$ is the pull-back of one of the "canonical foliations" on a Hilbert modular variety. This result remains valid for ''logarithmic foliated pairs''
Data analytics and optimization for assessing a ride sharing system

Ride-sharing schemes attempt to reduce road traffic by matching prospective passengers to drivers with spare seats in their cars. To be successful, such schemes require a critical mass of drivers and passengers. In current deployed implementations, the possible matches are based on heuristics, rather than real route times or distances. In some cases, the heuristics propose infeasible matches; in others, feasible matches are omitted. Poor ride matching is likely to deter participants from using the system. We develop a constraint-based model for acceptable ride matches which incorporates route plans and time windows. Through data analytics on a history of advertised schedules and agreed shared trips, we infer parameters for this model that account for 90% of agreed trips. By applying the inferred model to the advertised schedules, we demonstrate that there is an imbalance between riders and passengers. We assess the potential benefits of persuading existing drivers to switch to becoming passengers if appropriate matches can be found, by solving the inferred model with and without switching. We demonstrate that flexible participation has the potential to reduce the number of unmatched participants by up to 80%
Time series classification with representation ensembles

Time series has attracted much attention in recent years, with thousands of methods for diverse tasks such as classification, clustering, prediction, and anomaly detection. Among all these tasks, classification is likely the most prominent task, accounting for most of the applications and attention from the research community. However, in spite of the huge number of methods available, there is a significant body of empirical evidence indicating that the 1-nearest neighbor algorithm (1-NN) in the time domain is “extremely difficult to beat”. In this paper, we evaluate the use of different data representations in time series classification. Our work is motivated by methods used in related areas such as signal processing and music retrieval. In these areas, a change of representation frequently reveals features that are not apparent in the original data representation. Our approach consists of using different representations such as frequency, wavelets, and autocorrelation to transform the time series into alternative decision spaces. A classifier is then used to provide a classification for each test time series in the alternative domain. We investigate how features provided in different domains can help in time series classification. We also experiment with different ensembles to investigate if the data representations are a good source of diversity for time series classification. Our extensive experimental evaluation approaches the issue of combining sets of representations and ensemble strategies, resulting in over 300 ensemble configurations.São Paulo Research Foundation (FAPESP) (grant #2012/08923-8, #2013/26151-5, and #2015/07628-0)CNPq (grant #446330/2014-0 and #303083/2013-1)International Symposium on Advances in Intelligent Data Analysis - IDA (14. 2015 Saint Etienne
I-Louvain: An Attributed Graph Clustering Method

International audienceModularity allows to estimate the quality of a partition into communities of a graph composed of highly interconnected vertices. In this article, we introduce a complementary measure, based on inertia, and specially conceived to evaluate the quality of a partition based on real attributes describing the vertices. We propose also I-Louvain, a graph nodes clustering method which uses our criterion , combined with Newman's modularity, in order to detect communities in attributed graph where real attributes are associated with the vertices. Our experiments show that combining the relational information with the attributes allows to detect the communities more efficiently than using only one type of information. In addition, our method is more robust to data degradation
Constraint-based querying for Bayesian network exploration

Understanding the knowledge that resides in a Bayesian network can be hard, certainly when a large network is to be used for the first time, or when the network is complex or has just been updated. Tools to assist users in the analysis of Bayesian networks can help. In this paper, we introduce a novel general framework and tool for answering exploratory queries over Bayesian networks. The framework is inspired by queries from the constraint-based mining literature designed for the exploratory analysis of data. Adapted to Bayesian networks, these queries specify a set of constraints on explanations of interest, where an explanation is an assignment to a subset of variables in a network. Characteristic for the methodology is that it searches over different subsets of the explanations, corresponding to different marginalizations. A general purpose framework, based on principles of constraint programming, data mining and knowledge compilation, is used to answer all possible queries. This CP4BN framework employs a rich set of constraints and is able to emulate a range of existing queries from both the Bayesian network and the constraint-based data mining literature.status: publishe
Automatically discovering offensive patterns in soccer match data

In recent years, many professional sports clubs have adopted camera-based tracking technology that captures the location of both the players and the ball at a high frequency. Nevertheless, the valuable information that is hidden in these performance data is rarely used in their decision-making process. What is missing are the computational methods to analyze these data in great depth. This paper addresses the task of automatically discovering patterns in offensive strategies in professional soccer matches. To address this task, we propose an inductive logic programming approach that can easily deal with the relational structure of the data. An experimental study shows the utility of our approach.status: publishe
Efficient Model Selection for Regularized Classification by Exploiting Unlabeled Data

International audienceHyper-parameter tuning is a resource-intensive task when optimizing classification models. The commonly used k-fold cross validation can become intractable in large scale settings when a classifier has to learn billions of parameters. At the same time, in real-world, one often encounters multi-class classification scenarios with only a few labeled examples; model selection approaches often offer little improvement in such cases and the default values of learners are used. We propose bounds for classification on accuracy and macro measures (precision, recall, F1) that motivate efficient schemes for model selection and can benefit from the existence of unlabeled data. We demonstrate the advantages of those schemes by comparing them with k-fold cross validation and hold-out estimation in the setting of large scale classification
A first-order-logic based model for grounded language learning

Much is still unknown about how children learn language, but it is clear that they perform “grounded” language learning: they learn the grammar and vocabulary not just from examples of sentences, but from examples of sentences in a particular context. Grounded language learning has been the subject of much research. Most of this work focuses on particular aspects, such as constructing semantic parsers, or on particular types of applications. In this paper, we take a broader view that includes an aspect that has received little attention until now: learning the meaning of phrases from phrase/context pairs in which the phrase’s meaning is not explicitly represented. We propose a simple model for this task that uses first-order logic representations for contexts and meanings, including a simple incremental learning algorithm. We experimentally demonstrate that the proposed model can explain the gradual learning of simple concepts and language structure, and that it can easily be used for interpretation, generation, and translation of phrases.status: publishe
The Polylingual Labeled Topic Model

In this paper, we present the Polylingual Labeled Topic Model, a model which
combines the characteristics of the existing Polylingual Topic Model and
Labeled LDA. The model accounts for multiple languages with separate topic
distributions for each language while restricting the permitted topics of a
document to a set of predefined labels. We explore the properties of the model
in a two-language setting on a dataset from the social science domain. Our
experiments show that our model outperforms LDA and Labeled LDA in terms of
their held-out perplexity and that it produces semantically coherent topics
which are well interpretable by human subjects.Comment: Accepted for publication at KI 2015 (38th edition of the German
  Conference on Artificial Intelligence
Convex non-negative spherical factorization of multi-shell diffusion-weighted images

Christiaens D., Maes F., Sunaert S., Suetens P., ''Convex non-negative spherical factorization of multi-shell diffusion-weighted images'', Lecture notes in computer science, vol. 9349, pp. 166-173, 2015 (18th international conference on medical image computing and computer assisted intervention - MICCAI 2015, October 5-9, 2015, Munich, Germany).Diffusion-weighted imaging (DWI) allows to probe tissue microstructure non invasively and study healthy and diseased white matter (WM) in vivo. Yet, less research has focussed on modelling grey matter (GM), cerebrospinal fluid (CSF) and other tissues. Here, we introduce a fully data-driven approach to spherical deconvolution, based on convex non-negative matrix factorization. Our approach decomposes multi-shell DWI data, represented in the basis of spherical harmonics, into tissue-specific orientation distribution functions and corresponding response functions. We evaluate the proposed method in phantom simulations and in vivo brain images, and demonstrate its ability to reconstruct WM, GM and CSF, unsupervised and solely relying on DWI.status: publishe
Integrating Multimodal Priors in Predictive Models for the Functional Characterization of Alzheimer's Disease

International audienceFunctional brain imaging provides key information to characterize neurodegenerative diseases, such as Alzheimer's disease (AD). Specifically, the metabolic activity measured through fluorodeoxyglu-cose positron emission tomography (FDG-PET) and the connectivity extracted from resting-state functional magnetic resonance imaging (fMRI), are promising biomarkers that can be used for early assessment and prognosis of the disease and to understand its mechanisms. FDG-PET is the best suited functional marker so far, as it gives a reliable quantitative measure, but is invasive. On the other hand, non-invasive fMRI acquisitions do not provide a straightforward quantification of brain functional activity. To analyze populations solely based on resting-state fMRI, we propose an approach that leverages a metabolic prior learned from FDG-PET. More formally, our classification framework embeds population priors learned from another modality at the voxel-level, which can be seen as a regularization term in the analysis. Experimental results show that our PET-informed approach increases classification accuracy compared to pure fMRI approaches and highlights regions known to be impacted by the disease