1,754 research outputs found
Chimeras in a Network of Three Oscillator Populations with Varying Network Topology
We study a network of three populations of coupled phase oscillators with
identical frequencies. The populations interact nonlocally, in the sense that
all oscillators are coupled to one another, but more weakly to those in
neighboring populations than to those in their own population. Using this
system as a model system, we discuss for the first time the influence of
network topology on the existence of so called chimera states. In this context,
the network with three populations represents an interesting case because the
populations may either be connected as a triangle, or as a chain, thereby
representing the simplest discrete network of either a ring or a line segment
of oscillator populations. We introduce a special parameter that allows us to
study the effect of breaking the triangular network structure, and to vary the
network symmetry continuously such that it becomes more and more chain-like. By
showing that chimera states only exist for a bounded set of parameter values we
demonstrate that their existence depends strongly on the underlying network
structures. We conclude that chimeras exist on networks with a chain-like
character, which indicates that it might be possible to observe chimeras on a
continuous line segment of oscillators.Comment: 9 pages, 4 figure
On the Effectiveness of Vision Transformers for Zero-shot Face Anti-Spoofing
The vulnerability of face recognition systems to presentation attacks has
limited their application in security-critical scenarios. Automatic methods of
detecting such malicious attempts are essential for the safe use of facial
recognition technology. Although various methods have been suggested for
detecting such attacks, most of them over-fit the training set and fail in
generalizing to unseen attacks and environments. In this work, we use transfer
learning from the vision transformer model for the zero-shot anti-spoofing
task. The effectiveness of the proposed approach is demonstrated through
experiments in publicly available datasets. The proposed approach outperforms
the state-of-the-art methods in the zero-shot protocols in the HQ-WMCA and
SiW-M datasets by a large margin. Besides, the model achieves a significant
boost in cross-database performance as well.Comment: 8 pages, 3 figures, Accepted for Publication in IJCB202
Multi-Task Policy Search
Learning policies that generalize across multiple tasks is an important and challenging research topic in reinforcement learning and robotics. Training individual policies for every single potential task is often impractical, especially for continuous task variations, requiring more principled approaches to share and transfer knowledge among similar tasks. We present a novel approach for learning a nonlinear feedback policy that generalizes across multiple tasks. The key idea is to define a parametrized policy as a function of both the state and the task, which allows learning a single policy that generalizes across multiple known and unknown tasks. Applications of our novel approach to reinforcement and imitation learning in real-robot experiments are shown
Leveraging Staggered Tessellation for Enhanced Spatial Resolution in High-Granularity Calorimeters
We advance the concept of high-granularity calorimeters with staggered
tessellations, underscoring the effectiveness of a design incorporating
multifold staggering cycles based on hexagonal cells to enhance position
resolution. Moreover, we introduce HEXPLIT, a sub-cell re-weighting algorithm
tailored to harness staggered designs, resulting in additional performance
improvements. By combining our proposed staggered design with HEXPLIT, we
achieve an approximately twofold enhancement in position resolution for
neutrons across a wide energy range, as compared to unstaggered designs. These
findings hold the potential to elevate particle-flow performance across various
forthcoming facilities
Turing conditions for pattern forming systems on evolving manifolds
The study of pattern-forming instabilities in reaction-diffusion systems on
growing or otherwise time-dependent domains arises in a variety of settings,
including applications in developmental biology, spatial ecology, and
experimental chemistry. Analyzing such instabilities is complicated, as there
is a strong dependence of any spatially homogeneous base states on time, and
the resulting structure of the linearized perturbations used to determine the
onset of instability is inherently non-autonomous. We obtain general conditions
for the onset and structure of diffusion driven instabilities in
reaction-diffusion systems on domains which evolve in time, in terms of the
time-evolution of the Laplace-Beltrami spectrum for the domain and functions
which specify the domain evolution. Our results give sufficient conditions for
diffusive instabilities phrased in terms of differential inequalities which are
both versatile and straightforward to implement, despite the generality of the
studied problem. These conditions generalize a large number of results known in
the literature, such as the algebraic inequalities commonly used as a
sufficient criterion for the Turing instability on static domains, and
approximate asymptotic results valid for specific types of growth, or specific
domains. We demonstrate our general Turing conditions on a variety of domains
with different evolution laws, and in particular show how insight can be gained
even when the domain changes rapidly in time, or when the homogeneous state is
oscillatory, such as in the case of Turing-Hopf instabilities. Extensions to
higher-order spatial systems are also included as a way of demonstrating the
generality of the approach
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