T. Antal, D. ben-Avraham, E. Ben-Naim, and P. L. Krapivsky 4 Program for Evolutionary Dynamics, Harvard University, Cambridge, Massachusetts, 02138 USA Physics Department, Clarkson University, Potsdam, New York 13699 USA Theoretical Division and Center for Nonlinear Studies, Los Alamos National Laboratory, Los Alamos, New Mexico 87545 USA Department of Physics, Boston University, Boston, Massac...

We employ Clarkson and Schneider’s “hyperproperties” to classify various verification problems of quantitative information flow. The results of this paper unify and extend the previous results on the hardness of checking and inferring quantitative information flow. In particular, we identify a subclass of liveness hyperproperties, which we call “k-observable hyperproperties”, that can be checke...

C. Graff, Z. Li, E. W. Clarkson, C. Huang, A. Bilgin, and M. I. Altbach Program in Applied Mathematics, University of Arizona, Tucson, AZ, United States, Department of Radiology, University of Arizona, Tucson, AZ, United States, Department of Mathematics, University of Arizona, Tucson, AZ, United States, Department of Electrical and Computer Engineering, University of Arizona, Tucson, AZ, Unite...

For most computer systems, even virtual reality systems, sensing techniques are a means of getting input directly from the user. However, wearable sensors and computers offer a unique opportunity to redirect sensing technology toward recovering more general user context. Wearable computers have the potential to “see” as the user sees, “hear” as the user hears, and experience the life of the use...

Abstract During product development, the customer or internal stakeholders initiate changes concerning components functions of a cyber-physical system (CPS). The complexity such CPS causes difficulties in evaluating effects component change. Accordingly, developers need an assistance to quantify impact change on hardware, software, functions, and production processes. Therefore, this paper focu...

Badoiu and Clarkson [1] introduced an extremely simple incremental algorithm which finds the smallest enclosing ball around points with precision in at most O( ) iteration steps. A simplified proof for this quadratic scaling is given. Based on this proof it is shown that the number of steps in fact increases only like O( ). This new bound leads to a new optimal step size of the algorithm. With ...

We present improved differential lattice decoding (DLD) for multi-antenna differential modulation by exploiting both basis reduction and sphere decoding. The extra complexity of DLD is shown to be worthwhile in terms of the obtained performance gain over Clarkson et al.’s decoding scheme. With roughly another fold of complexity of basis reduction, DLD augmented by a local search practically at...