881 research outputs found
Minimum energy paths for conformational changes of viral capsids
In this work we study how a viral capsid can change conformation using
techniques of Large Deviations Theory for stochastic differential equations.
The viral capsid is a model of a complex system in which many units - the
proteins forming the capsomers - interact by weak forces to form a structure
with exceptional mechanical resistance. The destabilization of such a structure
is interesting both per se, since it is related either to infection or
maturation processes, and because it yields insights into the stability of
complex structures in which the constitutive elements interact by weak
attractive forces. We focus here on a simplified model of a dodecahederal viral
capsid, and assume that the capsomers are rigid plaquettes with one degree of
freedom each. We compute the most probable transition path from the closed
capsid to the final configuration using minimum energy paths, and discuss the
stability of intermediate states.Comment: 27 pages, 4 figures. New version, to appear in Physical Review
-limit of the cut functional on dense graph sequences
A sequence of graphs with diverging number of nodes is a dense graph sequence
if the number of edges grows approximately as for complete graphs. To each such
sequence a function, called graphon, can be associated, which contains
information about the asymptotic behavior of the sequence. Here we show that
the problem of subdividing a large graph in communities with a minimal amount
of cuts can be approached in terms of graphons and the -limit of the
cut functional, and discuss the resulting variational principles on some
examples. Since the limit cut functional is naturally defined on Young
measures, in many instances the partition problem can be expressed in terms of
the probability that a node belongs to one of the communities. Our approach can
be used to obtain insights into the bisection problem for large graphs, which
is known to be NP-complete.Comment: 25 pages, 5 figure
Learning New Classes from Limited Data in Image Segmentation and Object Detection
L'abstract è presente nell'allegato / the abstract is in the attachmen
The RGB-D Triathlon: Towards Agile Visual Toolboxes for Robots
Deep networks have brought significant advances in robot perception, enabling
to improve the capabilities of robots in several visual tasks, ranging from
object detection and recognition to pose estimation, semantic scene
segmentation and many others. Still, most approaches typically address visual
tasks in isolation, resulting in overspecialized models which achieve strong
performances in specific applications but work poorly in other (often related)
tasks. This is clearly sub-optimal for a robot which is often required to
perform simultaneously multiple visual recognition tasks in order to properly
act and interact with the environment. This problem is exacerbated by the
limited computational and memory resources typically available onboard to a
robotic platform. The problem of learning flexible models which can handle
multiple tasks in a lightweight manner has recently gained attention in the
computer vision community and benchmarks supporting this research have been
proposed. In this work we study this problem in the robot vision context,
proposing a new benchmark, the RGB-D Triathlon, and evaluating state of the art
algorithms in this novel challenging scenario. We also define a new evaluation
protocol, better suited to the robot vision setting. Results shed light on the
strengths and weaknesses of existing approaches and on open issues, suggesting
directions for future research.Comment: This work has been submitted to IROS/RAL 201
Preys’ exploitation of predators’ fear: when the caterpillar plays the Gruffalo
Alike the little mouse of the Gruffalo's tale, many harmless preys use intimidating deceptive signals as anti-predator strategies. For example, several caterpillars display eyespots and face-like colour patterns that are thought to mimic the face of snakes as deterrents to insectivorous birds. We develop a theoretical model to investigate the hypothesis that these defensive strategies exploit adaptive cognitive biases of birds, which make them much more likely to confound caterpillars with snakes than vice versa. By focusing on the information-processing mechanisms of decision-making, the model assumes that, during prey assessment, the bird accumulates noisy evidence supporting either the snake-escape or the caterpillar-attack motor responses, which compete against each other for execution. Competition terminates when the evidence for either one of the responses reaches a critical threshold. This model predicts a strong asymmetry and a strong negative correlation between the prey- and the predator-decision thresholds, which increase with the increasing risk of snake predation and assessment uncertainty. The threshold asymmetry causes an asymmetric distribution of false-negative and false-positive errors in the snake–caterpillar decision plane, which makes birds much more likely to be deceived by the intimidating signals of snake-mimicking caterpillars than by the alluring signals of caterpillar-mimicking snakes
A machine-learning procedure to detect network attacks
The goal of this note is to assess whether simple machine learning algorithms
can be used to determine whether and how a given network has been attacked. The
procedure is based on the -Nearest Neighbor and the Random Forest
classification schemes, using both intact and attacked Erd\H{o}s-R\'enyi,
Barabasi-Albert and Watts-Strogatz networks to train the algorithm. The types
of attacks we consider here are random failures and maximum-degree or
maximum-betweenness node deletion. Each network is characterized by a list of 4
metrics, namely the normalized reciprocal maximum degree, the global clustering
coefficient, the normalized average path length and the assortativity: a
statistical analysis shows that this list of graph metrics is indeed
significantly different in intact or damaged networks. We test the procedure by
choosing both artificial and real networks, performing the attacks and applying
the classification algorithms to the resulting graphs: the procedure discussed
here turns out to be able to distinguish between intact networks and those
attacked by the maximum-degree of maximum-betweenness deletions, but cannot
detect random failures. Our results suggest that this approach may provide a
basis for the analysis and detection of network attacks.Comment: 18 pages, 4 tables, 1 figur
Boosting Deep Open World Recognition by Clustering
While convolutional neural networks have brought significant advances in
robot vision, their ability is often limited to closed world scenarios, where
the number of semantic concepts to be recognized is determined by the available
training set. Since it is practically impossible to capture all possible
semantic concepts present in the real world in a single training set, we need
to break the closed world assumption, equipping our robot with the capability
to act in an open world. To provide such ability, a robot vision system should
be able to (i) identify whether an instance does not belong to the set of known
categories (i.e. open set recognition), and (ii) extend its knowledge to learn
new classes over time (i.e. incremental learning). In this work, we show how we
can boost the performance of deep open world recognition algorithms by means of
a new loss formulation enforcing a global to local clustering of class-specific
features. In particular, a first loss term, i.e. global clustering, forces the
network to map samples closer to the class centroid they belong to while the
second one, local clustering, shapes the representation space in such a way
that samples of the same class get closer in the representation space while
pushing away neighbours belonging to other classes. Moreover, we propose a
strategy to learn class-specific rejection thresholds, instead of heuristically
estimating a single global threshold, as in previous works. Experiments on
RGB-D Object and Core50 datasets show the effectiveness of our approach.Comment: IROS/RAL 202
Effect of strain-induced electronic topological transitions on the superconducting properties of LaSrCuO thin films
We propose a Ginzburg-Landau phenomenological model for the dependence of the
critical temperature on microscopic strain in tetragonal high-Tc cuprates. Such
a model is in agreement with the experimental results for LSCO under epitaxial
strain, as well as with the hydrostatic pressure dependence of Tc in most
cuprates. In particular, a nonmonotonic dependence of Tc on hydrostatic
pressure, as well as on in-plane or apical microstrain, is derived. From a
microscopic point of view, such results can be understood as due to the
proximity to an electronic topological transition (ETT). In the case of LSCO,
we argue that such an ETT can be driven by a strain-induced modification of the
band structure, at constant hole content, at variance with a doping-induced
ETT, as is usually assumed.Comment: EPJB, to be publishe
Multi-Phase Equilibrium of Crystalline Solids
A continuum model of crystalline solid equilibrium is presented in which the
underlying periodic lattice structure is taken explicitly into account. This
model also allows for both point and line defects in the bulk of the lattice
and at interfaces, and the kinematics of such defects is discussed in some
detail. A Gibbsian variational argument is used to derive the necessary bulk
and interfacial conditions for multi-phase equilibrium (crystal-crystal and
crystal-melt) where the allowed lattice variations involve the creation and
transport of defects in the bulk and at the phase interface. An interfacial
energy, assumed to depend on the interfacial dislocation density and the
orientation of the interface with respect to the lattices of both phases, is
also included in the analysis. Previous equilibrium results based on nonlinear
elastic models for incoherent and coherent interfaces are recovered as special
cases for when the lattice distortion is constrained to coincide with the
macroscopic deformation gradient, thereby excluding bulk dislocations. The
formulation is purely spatial and needs no recourse to a fixed reference
configuration or an elastic-plastic decomposition of the strain. Such a
decomposition can be introduced however through an incremental elastic
deformation superposed onto an already dislocated state, but leads to
additional equilibrium conditions. The presentation emphasizes the role of
{configurational forces} as they provide a natural framework for the
description and interpretation of singularities and phase transitions.Comment: 32 pages, to appear in Journal of the Mechanics and Physics of Solid
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