Invited Lectures.- Tableau Algorithms for Description Logics.- Modality and Databases.- Local Symmetries in Propositional Logic.- Comparison.- Design and Results of TANCS-2000 Non-classical (Modal) Systems Comparison.- Consistency Testing: The RACE Experience.- Benchmark Analysis with FaCT.- MSPASS: Modal Reasoning by Translation and First-Order Resolution.- TANCS-2000 Results for DLP.- Evaluating *SAT on TANCS 2000 Benchmarks.- Research Papers.- A Labelled Tableau Calculus for Nonmonotonic (Cumulative) Consequence Relations.- A Tableau System for Godel-Dummett Logic Based on a Hypersequent Calculus.- An Analytic Calculus for Quantified Propositional Godel Logic.- A Tableau Method for Inconsistency-Adaptive Logics.- A Tableau Calculus for Integrating First-Order and Elementary Set Theory Reasoning.- Hypertableau and Path-Hypertableau Calculi for some Families of Intermediate Logics.- Variants of First-Order Modal Logics.- Complexity of Simple Dependent Bimodal Logics.- Properties of Embeddings from Int to S4.- Term-Modal Logics.- A Subset-Matching Size-Bounded Cache for Satisfiability in Modal Logics.- Dual Intuitionistic Logic Revisited.- Model Sets in a Nonconstructive Logic of Partial Terms with Definite Descriptions.- Search Space Compression in Connection Tableau Calculi Using Disjunctive Constraints.- Matrix-Based Inductive Theorem Proving.- Monotonic Preorders for Free Variable Tableaux.- The Mosaic Method for Temporal Logics.- Sequent-Like Tableau Systems with the Analytic Superformula Property for the Modal Logics KB, KDB, K5, KD5.- A Tableau Calculus for Equilibrium Entailment.- Towards Tableau-Based Decision Procedures for Non-Well-Founded Fragments of Set Theory.- Tableau Calculus for Only Knowing and Knowing At Most.- A Tableau-Like Representation Framework for Efficient Proof Reconstruction.- The Semantic Tableaux Version of the Second Incompleteness Theorem Extends Almost to Robinson's Arithmetic Q.- System Descriptions.- Redundancy-Free Lemmatization in the Automated Model-Elimination Theorem Prover AI-SETHEO.- E-SETHEO: An Automated3 Theorem Prover.

Automated Reasoning with Analytic Tableaux and Related Methods: International Conference, TABLEAUX 2000 St Andrews, Scotland, UK, July 3-7, 2000 Proceedings

We study the query answering using views (QAV) problem for tree pattern queries. Given a query and a view, the QAV problem is traditionally formulated in two ways: (i) find an equivalent rewriting of the query using only the view, or (ii) find a maximal contained rewriting using only the view. The former is appropriate for classical query optimization and was recently studied by Xu and Ozsoyoglu for tree pattern queries (TP). However, for information integration, we cannot rely on equivalent rewriting and must instead use maximal contained rewriting as shown by Halevy. Motivated by this, we study maximal contained rewriting for TP, a core subset of XPath, both in the absence and presence of a schema. In the absence of a schema, we show there are queries whose maximal contained rewriting (MCR) can only be expressed as the union of exponentially many TPs. We characterize the existence of a maximal contained rewriting and give a polynomial time algorithm for testing the existence of an MCR. We also give an algorithm for generating the MCR when one exists. We then consider QAV in the presence of a schema. We characterize the existence of a maximal contained rewriting when the schema contains no recursion or union types, and show that it consists of at most one TP. We give an efficient polynomial time algorithm for generating the maximal contained rewriting whenever it exists. Finally, we discuss QAV in the presence of recursive schemas.

/pdf/answering-tree-pattern-queries-using-views-xbvgrvmt4b.pdf

Answering tree pattern queries using views

That one-quarter of Medicare spending in the United States occurs in the last year of life is commonly interpreted as waste. But this interpretation presumes knowledge of who will die and when. Here we analyze how spending is distributed by predicted mortality, based on a machine-learning model of annual mortality risk built using Medicare claims. Death is highly unpredictable. Less than 5% of spending is accounted for by individuals with predicted mortality above 50%. The simple fact that we spend more on the sick—both on those who recover and those who die—accounts for 30 to 50% of the concentration of spending on the dead. Our results suggest that spending on the ex post dead does not necessarily mean that we spend on the ex ante “hopeless.”

/pdf/predictive-modeling-of-u-s-health-care-spending-in-late-life-2787a7zajb.pdf

Predictive modeling of U.S. health care spending in late life.

Separation logic (SL) is a widely used formalism for verifying heap manipulating programs. Existing SL solvers focus on decidable fragments for list-like structures. More complex data structures such as trees are typically unsupported in implementations, or handled by incomplete heuristics. While complete decision procedures for reasoning about trees have been proposed, these procedures suffer from high complexity, or make global assumptions about the heap that contradict the separation logic philosophy of local reasoning. In this paper, we present a fragment of classical first-order logic for local reasoning about tree-like data structures. The logic is decidable in NP and the decision procedure allows for combinations with other decidable first-order theories for reasoning about data. Such extensions are essential for proving functional correctness properties. We have implemented our decision procedure and, building on earlier work on translating SL proof obligations into classical logic, integrated it into an SL-based verification tool. We successfully used the tool to verify functional correctness of tree-based data structure implementations.

/pdf/automating-separation-logic-with-trees-and-data-1zy24sagtj.pdf

Automating Separation Logic with Trees and Data

The discrete cosine transform (DCT), introduced by Ahmed, Natarajan and Rao, has been used in many applications of digital signal processing, data compression and information hiding. There are four types of the discrete cosine transform. In simulating the discrete cosine transform, we propose a generalized discrete cosine transform with three parameters, and prove its orthogonality for some new cases. A new type of discrete cosine transform is proposed and its orthogonality is proved. Finally, we propose a generalized discrete W transform with three parameters, and prove its orthogonality for some new cases. Keywords: Discrete Fourier transform, discrete sine transform, discrete cosine transform, discrete W transform Nigerian Journal of Technological Research , vol7(1) 2012

On discrete cosine transform

We present a tool for the static analysis of XPath queries and XML Schemas. The tool introduces techniques used in the field of verification (such as binary decision diagrams) in order to efficiently solve XPath query satisfiability, containment, and equivalence, in the presence of real-world XML Schemas. The tool can be used in query optimizers, in order to prove soundness of query rewriting. It can also be used in type-checkers and optimizing compilers that need to perform all kinds of compile-time analyses involving XPath queries and XML tree constraints.

/pdf/xml-reasoning-made-practical-1dy45pzuf7.pdf

XML reasoning made practical

The Cascading Style Sheets (CSS) language constitutes a key component of web applications. It offers a series of sophisticated features to stylize web pages. Its apparent simplicity and power are however counter-balanced by the difficulty of debugging and maintaining style sheets, tasks for which developers still lack appropriate tools. In particular, significant portions of CSS code become either useless or redundant, and tend to accumulate over time. The situation becomes even worse as more complex features are added to the CSS language (e.g. CSS3 powerful selectors). A direct consequence is a waste of CPU that is required to display web pages, as well as the significant amount of useless traffic at web scale.

Style sheets are designed to operate on a set of documents (possibly generated). However, existing techniques consist in syntax validators, optimizers and runtime debuggers that operate in one particular document instance. As such, they do not provide guarantees concerning all web pages in CSS refactoring, such as preservation of the formatting. This is partly because they are essentially syntactic and do not take advantage of CSS semantics to detect redundancies.

We propose a set of automated refactoring techniques aimed at removing redundant and inaccessible declarations and rules, without affecting the layout of any document to which the style sheet is applied. We implemented a prototype that has been extensively tested with popular web sites (such as Google Sites, CNN, Apple, etc.). We show that significant size reduction can be obtained while preserving the code readability and improving maintainability.

https://hal.inria.fr/hal-01149248/document

Reasoning with style

Early identification of patients at risk of developing complications during their hospital stay is currently one of the most challenging issues in healthcare. Complications include hospital-acquired infections, admissions to intensive care units, and in-hospital mortality. Being able to accurately predict the patients' outcomes is a crucial prerequisite for tailoring the care that certain patients receive, if it is believed that they will do poorly without additional intervention. We consider the problem of complication risk prediction, such as inpatient mortality, from the electronic health records of the patients. We study the question of making predictions on the first day at the hospital, and of making updated mortality predictions day after day during the patient's stay. We develop distributed models that are scalable and interpretable. Key insights include analysing diagnoses known at admission and drugs served, which evolve during the hospital stay. We leverage a distributed architecture to learn interpretable models from training datasets of gigantic size. We test our analyses with more than one million of patients from hundreds of hospitals, and report on the lessons learned from these experiments.

/pdf/scalable-and-interpretable-predictive-models-for-electronic-oz57i0hzb7.pdf

Scalable and Interpretable Predictive Models for Electronic Health Records

Federated Learning allows distributed entities to train a common model collaboratively without sharing their own data. Although it prevents data collection and aggregation by exchanging only parameter updates, it remains vulnerable to various inference and reconstruction attacks where a malicious entity can learn private information about the participants' training data from the captured gradients. Differential Privacy is used to obtain theoretically sound privacy guarantees against such inference attacks by noising the exchanged update vectors. However, the added noise is proportional to the model size which can be very large with modern neural networks. This can result in poor model quality. In this paper, compressive sensing is used to reduce the model size and hence increase model quality without sacrificing privacy. We show experimentally, using 2 datasets, that our privacy-preserving proposal can reduce the communication costs by up to 95 % with only a negligible performance penalty compared to traditional non-private federated learning schemes.

https://hal.archives-ouvertes.fr/hal-03066941/document

Pierre Genevès

Papers

XML reasoning made practical

Reasoning with style

Scalable and Interpretable Predictive Models for Electronic Health Records

Compression Boosts Differentially Private Federated Learning

Scalable Machine Learning for Predicting At-Risk Profiles Upon Hospital Admission