10,429 research outputs found
Quantum Coin Hedging, and a Counter Measure
A quantum board game is a multi-round protocol between a single quantum
player against the quantum board. Molina and Watrous discovered quantum
hedging. They gave an example for perfect quantum hedging: a board game with
winning probability < 1, such that the player can win with certainty at least
1-out-of-2 quantum board games played in parallel. Here we show that perfect
quantum hedging occurs in a cryptographic protocol - quantum coin flipping. For
this reason, when cryptographic protocols are composed, hedging may introduce
serious challenges into their analysis.
We also show that hedging cannot occur when playing two-outcome board games
in sequence. This is done by showing a formula for the value of sequential
two-outcome board games, which depends only on the optimal value of a single
board game; this formula applies in a more general setting, in which hedging is
only a special case
Measuring Implicit Bias Using SHAP Feature Importance and Fuzzy Cognitive Maps
In this paper, we integrate the concepts of feature importance with implicit bias in the context of pattern classification. This is done by means of a three-step methodology that involves (i) building a classifier and tuning its hyperparameters, (ii) building a Fuzzy Cognitive Map model able to quantify implicit bias, and (iii) using the SHAP feature importance to active the neural concepts when performing simulations. The results using a real case study concerning fairness research support our two-fold hypothesis. On the one hand, it is illustrated the risks of using a feature importance method as an absolute tool to measure implicit bias. On the other hand, it is concluded that the amount of bias towards protected features might differ depending on whether the features are numerically or categorically encoded
Thin Games with Symmetry and Concurrent Hyland-Ong Games
We build a cartesian closed category, called Cho, based on event structures.
It allows an interpretation of higher-order stateful concurrent programs that
is refined and precise: on the one hand it is conservative with respect to
standard Hyland-Ong games when interpreting purely functional programs as
innocent strategies, while on the other hand it is much more expressive. The
interpretation of programs constructs compositionally a representation of their
execution that exhibits causal dependencies and remembers the points of
non-deterministic branching.The construction is in two stages. First, we build
a compact closed category Tcg. It is a variant of Rideau and Winskel's category
CG, with the difference that games and strategies in Tcg are equipped with
symmetry to express that certain events are essentially the same. This is
analogous to the underlying category of AJM games enriching simple games with
an equivalence relations on plays. Building on this category, we construct the
cartesian closed category Cho as having as objects the standard arenas of
Hyland-Ong games, with strategies, represented by certain events structures,
playing on games with symmetry obtained as expanded forms of these arenas.To
illustrate and give an operational light on these constructions, we interpret
(a close variant of) Idealized Parallel Algol in Cho
The multi-program performance model: debunking current practice in multi-core simulation
Composing a representative multi-program multi-core workload is non-trivial. A multi-core processor can execute multiple independent programs concurrently, and hence, any program mix can form a potential multi-program workload. Given the very large number of possible multiprogram workloads and the limited speed of current simulation methods, it is impossible to evaluate all possible multi-program workloads. This paper presents the Multi-Program Performance Model (MPPM), a method for quickly estimating multiprogram multi-core performance based on single-core simulation runs. MPPM employs an iterative method to model the tight performance entanglement between co-executing programs on a multi-core processor with shared caches. Because MPPM involves analytical modeling, it is very fast, and it estimates multi-core performance for a very large number of multi-program workloads in a reasonable amount of time. In addition, it provides confidence bounds on its performance estimates. Using SPEC CPU2006 and up to 16 cores, we report an average performance prediction error of 2.3% and 2.9% for system throughput (STP) and average normalized turnaround time (ANTT), respectively, while being up to five orders of magnitude faster than detailed simulation. Subsequently, we demonstrate that randomly picking a limited number of multi-program workloads, as done in current pactice, can lead to incorrect design decisions in practical design and research studies, which is alleviated using MPPM. In addition, MPPM can be used to quickly identify multi-program workloads that stress multi-core performance through excessive conflict behavior in shared caches; these stress workloads can then be used for driving the design process further
Computational Physics on Graphics Processing Units
The use of graphics processing units for scientific computations is an
emerging strategy that can significantly speed up various different algorithms.
In this review, we discuss advances made in the field of computational physics,
focusing on classical molecular dynamics, and on quantum simulations for
electronic structure calculations using the density functional theory, wave
function techniques, and quantum field theory.Comment: Proceedings of the 11th International Conference, PARA 2012,
Helsinki, Finland, June 10-13, 201
Malware Detection Using Dynamic Analysis
In this research, we explore the field of dynamic analysis which has shown promis- ing results in the field of malware detection. Here, we extract dynamic software birth- marks during malware execution and apply machine learning based detection tech- niques to the resulting feature set. Specifically, we consider Hidden Markov Models and Profile Hidden Markov Models. To determine the effectiveness of this dynamic analysis approach, we compare our detection results to the results obtained by using static analysis. We show that in some cases, significantly stronger results can be obtained using our dynamic approach
Stated belief and play in normal form games
Using data on one-shot games, we investigate the assumption that players respond to
underlying expectations about their opponent�s behavior. In our laboratory experiments, subjects
play a set of 14 two-person 3x3 games, and state first order beliefs about their opponent�s
behavior. The sets of responses in the two tasks are largely inconsistent. Rather, we find
evidence that the subjects perceive the games differently when they (i) choose actions, and (ii)
state beliefs � they appear to pay more attention to the opponent�s incentives when they state
beliefs than when they play the games. On average, they fail to best respond to their own stated
beliefs in almost half of the games. The inconsistency is confirmed by estimates of a unified
statistical model that jointly uses the actions and the belief statements. There, we can control for
noise, and formulate a statistical test that rejects consistency. Effects of the belief elicitation
procedure on subsequent actions are mostly insignificant
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