Language-based Abstractions for Dynamical Systems

Ordinary differential equations (ODEs) are the primary means to modelling dynamical systems in many natural and engineering sciences. The number of equations required to describe a system with high heterogeneity limits our capability of effectively performing analyses. This has motivated a large body of research, across many disciplines, into abstraction techniques that provide smaller ODE systems while preserving the original dynamics in some appropriate sense. In this paper we give an overview of a recently proposed computer-science perspective to this problem, where ODE reduction is recast to finding an appropriate equivalence relation over ODE variables, akin to classical models of computation based on labelled transition systems.Comment: In Proceedings QAPL 2017, arXiv:1707.0366

Partial level density of the n-quasiparticle excitations in the nuclei of the 39< A <201 region

Level density and radiative strength functions are obtained from the analysis of two-step cascades intensities following the thermal neutrons capture. The data on level density are approximated by the sum of the partial level densities corresponding to n quasiparticles excitation. The most probable values of the collective enhancement factor of the level density are found together with the thresholds of the next Cooper nucleons pair breaking. These data allow one to calculate the level density of practically any nucleus in given spin window in the framework of model concepts, taking into account all known nuclear excitation types. The presence of an approximation results discrepancy with theoretical statements specifies the necessity of rather essentially developing the level density models. It also indicates the possibilities to obtain the essentially new information on nucleon correlation functions of the excited nucleus from the experiment.Comment: 29 pages, 8 figures, 2 table

From metaphysical principles to dynamical laws

My thesis in this paper is: the modern concept of laws of motion—qua dynamical laws—emerges in 18th-century mechanics. The driving factor for it was the need to extend mechanics beyond the centroid theories of the late-1600s. The enabling result behind it was the rise of differential equations. In consequence, by the mid-1700s we see a deep shift in the form and status of laws of motion. The shift is among the critical inflection points where early modern mechanics turns into classical mechanics as we know it. Previously, laws of motion had been channels for truth and reference into mechanics. By 1750, the laws lose these features. Instead, now they just assert equalities between functions; and serve just to entail (differential) equations of motion for particular mechanical setups. This creates two philosophical problems. First, it’s unclear what counts as evidence for the laws of motion in the Enlightenment. Second, it’s a mystery whether these laws retain any notion of causality. That subverts the early-modern dictum that physics is a science of causes

Finding footing in a postmodern conception of law

Copyright @ 2010 Pace UniversityThe following jurisprudence paper examines the implications of postmodern thought upon our conception of law. In this paper I argue that, despite the absolute, all-consuming moral relativism towards which postmodernism seems to lead in its most extreme form, its acceptance in fact in no way undermines the possibility of finding solid ground for our legal principles. This paper contends that moral objectivity can be found in the individual experience of suffering generated by these very subjective concoctions. Subjective concoctions or not, they are real in that they imbue a sense of value into conditions, and may thus serve as foundational principles for law. While our value systems are stripped of all claim to objective authority, ultimately, all postmodernism does is force us to set aside our larger concepts of “justice,” and instead root our legal conceptions at this far more fundamental level of human experience

Distributed Computing with Adaptive Heuristics

We use ideas from distributed computing to study dynamic environments in which computational nodes, or decision makers, follow adaptive heuristics (Hart 2005), i.e., simple and unsophisticated rules of behavior, e.g., repeatedly "best replying" to others' actions, and minimizing "regret", that have been extensively studied in game theory and economics. We explore when convergence of such simple dynamics to an equilibrium is guaranteed in asynchronous computational environments, where nodes can act at any time. Our research agenda, distributed computing with adaptive heuristics, lies on the borderline of computer science (including distributed computing and learning) and game theory (including game dynamics and adaptive heuristics). We exhibit a general non-termination result for a broad class of heuristics with bounded recall---that is, simple rules of behavior that depend only on recent history of interaction between nodes. We consider implications of our result across a wide variety of interesting and timely applications: game theory, circuit design, social networks, routing and congestion control. We also study the computational and communication complexity of asynchronous dynamics and present some basic observations regarding the effects of asynchrony on no-regret dynamics. We believe that our work opens a new avenue for research in both distributed computing and game theory.Comment: 36 pages, four figures. Expands both technical results and discussion of v1. Revised version will appear in the proceedings of Innovations in Computer Science 201