28,136 research outputs found
Universal Simulation of Hamiltonians Using a Finite Set of Control Operations
Any quantum system with a non-trivial Hamiltonian is able to simulate any
other Hamiltonian evolution provided that a sufficiently large group of unitary
control operations is available. We show that there exist finite groups with
this property and present a sufficient condition in terms of group characters.
We give examples of such groups in dimension 2 and 3. Furthermore, we show that
it is possible to simulate an arbitrary bipartite interaction by a given one
using such groups acting locally on the subsystems.Comment: 18 pages, LaTeX2
Group emotion modelling and the use of middleware for virtual crowds in video-games
In this paper we discuss the use of crowd
simulation in video-games to augment their realism. Using
previous works on emotion modelling and virtual crowds we
define a game world in an urban context. To achieve that, we
explore a biologically inspired human emotion model,
investigate the formation of groups in crowds, and examine
the use of physics middleware for crowds. Furthermore, we
assess the realism and computational performance of the
proposed approach. Our system runs at interactive frame-rate
and can generate large crowds which demonstrate complex
behaviour
Better Safe Than Sorry: An Adversarial Approach to Improve Social Bot Detection
The arm race between spambots and spambot-detectors is made of several cycles
(or generations): a new wave of spambots is created (and new spam is spread),
new spambot filters are derived and old spambots mutate (or evolve) to new
species. Recently, with the diffusion of the adversarial learning approach, a
new practice is emerging: to manipulate on purpose target samples in order to
make stronger detection models. Here, we manipulate generations of Twitter
social bots, to obtain - and study - their possible future evolutions, with the
aim of eventually deriving more effective detection techniques. In detail, we
propose and experiment with a novel genetic algorithm for the synthesis of
online accounts. The algorithm allows to create synthetic evolved versions of
current state-of-the-art social bots. Results demonstrate that synthetic bots
really escape current detection techniques. However, they give all the needed
elements to improve such techniques, making possible a proactive approach for
the design of social bot detection systems.Comment: This is the pre-final version of a paper accepted @ 11th ACM
Conference on Web Science, June 30-July 3, 2019, Boston, U
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