9,033 research outputs found
Neural Predictive Monitoring for Collective Adaptive Systems
Reliable bike-sharing systems can lead to numerous environmental, economic and social benefits and therefore play a central role in the effective development of smart cities. Bike-sharing models deal with spatially distributed stations and interact with an unpredictable environment, the users. Monitoring the trustworthiness of such a collective system is of paramount importance to ensure a good quality of the delivered service, but this task can become computationally demanding due to the complexity of the model under study. Neural Predictive Monitoring (NPM) [5], a neural-network learning-based approach to predictive monitoring (PM) with statistical guarantees, can be employed to preemptively detect violations of a specific requirement – e.g. a station has no more bikes available or a station is full. The computational efficiency of NPM makes PM applicable at runtime even on embedded devices with limited computational power. The goal of this paper is to demonstrate the applicability of NPM on collective adaptive systems such as bike-sharing systems. In particular, we first analyze the performance of NPM over a collective system evolving deterministically. Then, following [7], we tackle a more realistic scenario, where sensors allow only for partial observability and where the system evolves in a stochastic fashion. We evaluate the approach on multiple bike-sharing network topologies, obtaining highly accurate predictions and effective error detection rules
Measurement of angular momentum transport in turbulent flow between independently rotating cylinders
We present measurements of the angular momentum flux (torque) in
Taylor-Couette flow of water between independently rotating cylinders for all
regions of the \(\Omega_1, \Omega_2\) parameter space at high Reynolds
numbers, where \(\Omega_2\) is the inner (outer) cylinder angular
velocity. We find that the Rossby number Ro = \(\Omega_1 -
\Omega_2\)/\Omega_2 fully determines the state and torque as compared to
G(Ro = \infty) \equiv \Gi. The ratio G/\Gi is a linear function of
in four sections of the parameter space. For flows with
radially-increasing angular momentum, our measured torques greatly exceed those
of previous experiments [Ji \textit{et al.}, Nature, \textbf{444}, 343 (2006)],
but agree with the analysis of Richard and Zahn [Astron. Astrophys.,
\textbf{347}, 734 (1999)].Comment: 4 pages, 4 figures, to appear in Physical Review Letter
Multiple verification in computational modeling of bone pathologies
We introduce a model checking approach to diagnose the emerging of bone
pathologies. The implementation of a new model of bone remodeling in PRISM has
led to an interesting characterization of osteoporosis as a defective bone
remodeling dynamics with respect to other bone pathologies. Our approach allows
to derive three types of model checking-based diagnostic estimators. The first
diagnostic measure focuses on the level of bone mineral density, which is
currently used in medical practice. In addition, we have introduced a novel
diagnostic estimator which uses the full patient clinical record, here
simulated using the modeling framework. This estimator detects rapid (months)
negative changes in bone mineral density. Independently of the actual bone
mineral density, when the decrease occurs rapidly it is important to alarm the
patient and monitor him/her more closely to detect insurgence of other bone
co-morbidities. A third estimator takes into account the variance of the bone
density, which could address the investigation of metabolic syndromes, diabetes
and cancer. Our implementation could make use of different logical combinations
of these statistical estimators and could incorporate other biomarkers for
other systemic co-morbidities (for example diabetes and thalassemia). We are
delighted to report that the combination of stochastic modeling with formal
methods motivate new diagnostic framework for complex pathologies. In
particular our approach takes into consideration important properties of
biosystems such as multiscale and self-adaptiveness. The multi-diagnosis could
be further expanded, inching towards the complexity of human diseases. Finally,
we briefly introduce self-adaptiveness in formal methods which is a key
property in the regulative mechanisms of biological systems and well known in
other mathematical and engineering areas.Comment: In Proceedings CompMod 2011, arXiv:1109.104
Explicit characterization of the identity configuration in an Abelian Sandpile Model
Since the work of Creutz, identifying the group identities for the Abelian
Sandpile Model (ASM) on a given lattice is a puzzling issue: on rectangular
portions of Z^2 complex quasi-self-similar structures arise. We study the ASM
on the square lattice, in different geometries, and a variant with directed
edges. Cylinders, through their extra symmetry, allow an easy determination of
the identity, which is a homogeneous function. The directed variant on square
geometry shows a remarkable exact structure, asymptotically self-similar.Comment: 11 pages, 8 figure
Cosmological effects of the Galileon term in Scalar-Tensor Theories
We study the cosmological effects of a Galileon term in scalar-tensor
theories of gravity. The subset of scalar-tensor theories considered are
characterized by a non-minimal coupling , a kinetic term with
arbitrary sign with , a potential
, and a Galileon term . In addition to the modified dynamics, the Galileon term provides a
screening mechanism to potentially reconcile the models with General Relativity
predictions inside a Vainshtein radius. Thanks to the Galileon term, the
stability conditions, namely ghost and Laplacian instabilities, in the branch
with a negative kinetic term () are fulfilled for a large volume of the
parameter space. Solving numerically the background evolution and linear
perturbations, we derive the constraints on the cosmological parameters in
presence of a Galileon term for different combination of the cosmic microwave
background (CMB) data from Planck, baryon acoustic oscillations (BAO)
measurements from BOSS, and supernovae from the Pantheon compilation. We find
that the Galileon term alters the dynamics of all the studied cases. For a
standard kinetic term (), we find that Planck data and a compilation of
BAO data constrain the Galileon term to small values that allow screening very
inefficiently. For a negative kinetic term (), a Galileon term and a
non-zero potential lead to an efficient screening in a physically viable regime
of the theory, with a value for the Hubble constant today which alleviates the
tension between its CMB and local determinations. For a vanishing potential,
the case with and the Galileon term driving the late acceleration of the
Universe is ruled out by Planck data.Comment: 23 pages, 15 figures, 4 table
SiPM and front-end electronics development for Cherenkov light detection
The Italian Institute of Nuclear Physics (INFN) is involved in the
development of a demonstrator for a SiPM-based camera for the Cherenkov
Telescope Array (CTA) experiment, with a pixel size of 66 mm. The
camera houses about two thousands electronics channels and is both light and
compact. In this framework, a R&D program for the development of SiPMs suitable
for Cherenkov light detection (so called NUV SiPMs) is ongoing. Different
photosensors have been produced at Fondazione Bruno Kessler (FBK), with
different micro-cell dimensions and fill factors, in different geometrical
arrangements. At the same time, INFN is developing front-end electronics based
on the waveform sampling technique optimized for the new NUV SiPM. Measurements
on 11 mm, 33 mm, and 66 mm NUV SiPMs
coupled to the front-end electronics are presentedComment: In Proceedings of the 34th International Cosmic Ray Conference
(ICRC2015), The Hague, The Netherlands. All CTA contributions at
arXiv:1508.0589
Neural Simplex Architecture
We present the Neural Simplex Architecture (NSA), a new approach to runtime
assurance that provides safety guarantees for neural controllers (obtained e.g.
using reinforcement learning) of autonomous and other complex systems without
unduly sacrificing performance. NSA is inspired by the Simplex control
architecture of Sha et al., but with some significant differences. In the
traditional approach, the advanced controller (AC) is treated as a black box;
when the decision module switches control to the baseline controller (BC), the
BC remains in control forever. There is relatively little work on switching
control back to the AC, and there are no techniques for correcting the AC's
behavior after it generates a potentially unsafe control input that causes a
failover to the BC. Our NSA addresses both of these limitations. NSA not only
provides safety assurances in the presence of a possibly unsafe neural
controller, but can also improve the safety of such a controller in an online
setting via retraining, without overly degrading its performance. To
demonstrate NSA's benefits, we have conducted several significant case studies
in the continuous control domain. These include a target-seeking ground rover
navigating an obstacle field, and a neural controller for an artificial
pancreas system.Comment: 12th NASA Formal Methods Symposium (NFM 2020
Using Resonances to Control Chaotic Mixing within a Translating and Rotating Droplet
Enhancing and controlling chaotic advection or chaotic mixing within liquid
droplets is crucial for a variety of applications including digital
microfluidic devices which use microscopic ``discrete'' fluid volumes
(droplets) as microreactors. In this work, we consider the Stokes flow of a
translating spherical liquid droplet which we perturb by imposing a
time-periodic rigid-body rotation. Using the tools of dynamical systems, we
have shown in previous work that the rotation not only leads to one or more
three-dimensional chaotic mixing regions, in which mixing occurs through the
stretching and folding of material lines, but also offers the possibility of
controlling both the size and the location of chaotic mixing within the drop.
Such a control was achieved through appropriate tuning of the amplitude and
frequency of the rotation in order to use resonances between the natural
frequencies of the system and those of the external forcing. In this paper, we
study the influence of the orientation of the rotation axis on the chaotic
mixing zones as a third parameter, as well as propose an experimental set up to
implement the techniques discussed.Comment: 15 pages, 6 figure
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