Higher homotopy commutativity and cohomology of finite H-spaces

We study connected mod p finite A_p-spaces admitting AC_n-space structures with n<p for an odd prime p. Our result shows that if n is greator than (p-1)/2, then the mod p Steenrod algebra acts on the mod p cohomology of such a space in a systematic way. Moreover, we consider A_p-spaces which are mod p homotopy equivalent to product spaces of odd dimensional spheres. Then we determine the largest integer n for which such a space admits an AC_n-space structure compatible with the A_p-space structure.Comment: This is the version published by Geometry & Topology Monographs on 29 January 200

An Epistemic Approach to the Formal Specification of Statistical Machine Learning

We propose an epistemic approach to formalizing statistical properties of machine learning. Specifically, we introduce a formal model for supervised learning based on a Kripke model where each possible world corresponds to a possible dataset and modal operators are interpreted as transformation and testing on datasets. Then we formalize various notions of the classification performance, robustness, and fairness of statistical classifiers by using our extension of statistical epistemic logic (StatEL). In this formalization, we show relationships among properties of classifiers, and relevance between classification performance and robustness. As far as we know, this is the first work that uses epistemic models and logical formulas to express statistical properties of machine learning, and would be a starting point to develop theories of formal specification of machine learning.Comment: Accepted in Software and Systems Modeling https://rdcu.be/b7ssR This paper is the journal version of the SEFM'19 conference paper arxiv:1907.1032

Local Distribution Obfuscation via Probability Coupling

We introduce a general model for the local obfuscation of probability distributions by probabilistic perturbation, e.g., by adding differentially private noise, and investigate its theoretical properties. Specifically, we relax a notion of distribution privacy (DistP) by generalizing it to divergence, and propose local obfuscation mechanisms that provide divergence distribution privacy. To provide f-divergence distribution privacy, we prove that probabilistic perturbation noise should be added proportionally to the Earth mover's distance between the probability distributions that we want to make indistinguishable. Furthermore, we introduce a local obfuscation mechanism, which we call a coupling mechanism, that provides divergence distribution privacy while optimizing the utility of obfuscated data by using exact/approximate auxiliary information on the input distributions we want to protect.Comment: Full version of Allerton 2019 paper (This paper extends some part of the unpublished v3 of arXiv:1812.00939, while v4 of arXiv:1812.00939 extends the other part and is published in ESORICS'19.

Formalizing Statistical Causality via Modal Logic

We propose a formal language for describing and explaining statistical causality. Concretely, we define Statistical Causality Language (StaCL) for expressing causal effects and specifying the requirements for causal inference. StaCL incorporates modal operators for interventions to express causal properties between probability distributions in different possible worlds in a Kripke model. We formalize axioms for probability distributions, interventions, and causal predicates using StaCL formulas. These axioms are expressive enough to derive the rules of Pearl's do-calculus. Finally, we demonstrate by examples that StaCL can be used to specify and explain the correctness of statistical causal inference

Sound and Relatively Complete Belief Hoare Logic for Statistical Hypothesis Testing Programs

We propose a new approach to formally describing the requirement for statistical inference and checking whether a program uses the statistical method appropriately. Specifically, we define belief Hoare logic (BHL) for formalizing and reasoning about the statistical beliefs acquired via hypothesis testing. This program logic is sound and relatively complete with respect to a Kripke model for hypothesis tests. We demonstrate by examples that BHL is useful for reasoning about practical issues in hypothesis testing. In our framework, we clarify the importance of prior beliefs in acquiring statistical beliefs through hypothesis testing, and discuss the whole picture of the justification of statistical inference inside and outside the program logic

Theme Aspect Argumentation Model for Handling Fallacies

From daily discussions to marketing ads to political statements, information manipulation is rife. It is increasingly more important that we have the right set of tools to defend ourselves from manipulative rhetoric, or fallacies. Suitable techniques to automatically identify fallacies are being investigated in natural language processing research. However, a fallacy in one context may not be a fallacy in another context, so there is also a need to explain how and why it has come to be judged a fallacy. For the explainable fallacy identification, we present a novel approach to characterising fallacies through formal constraints, as a viable alternative to more traditional fallacy classifications by informal criteria. To achieve this objective, we introduce a novel context-aware argumentation model, the theme aspect argumentation model, which can do both: the modelling of a given argumentation as it is expressed (rhetorical modelling); and a deeper semantic analysis of the rhetorical argumentation model. By identifying fallacies with formal constraints, it becomes possible to tell whether a fallacy lurks in the modelled rhetoric with a formal rigour. We present core formal constraints for the theme aspect argumentation model and then more formal constraints that improve its fallacy identification capability. We show and prove the consequences of these formal constraints. We then analyse the computational complexities of deciding the satisfiability of the constraints

INFLUENCE OF TURN RADIUS OF RUNNING ON TORSIONAL LOADING OF THE TIBIA

The purpose of this study was to investigate influence of turn radius of running on the torsional loading of the tibia. Six male subjects ran on a straightway and anti-clockwise corners with different turn radiuses (R=15m and 5m). Data were collected using two high-speed cameras and force platforms. The torsional stresses acting on the inner tibias of runners were compared among each running condition. At beginning, net torsional moments at both ends of the lower leg were calculated. Then, the tibial torsional stresses were estimated, based on equilibrium of those moments. Much larger torsional stress acted on the tibia in later portion of the stance phase of sharper cornering compared to other two running conditions. Mean value of the maximum stress in sharper cornering was also significantly larger (