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Robust Assignment Using Redundant Robots on Transport Networks with Uncertain Travel Time
This paper considers the problem of assigning mo- bile robots to goals on transport networks with uncertain and potentially correlated information about travel times. Our aim is to produce optimal assignments, such that the average waiting time at destinations is minimized. Since noisy travel time estimates result in sub-optimal assignments, we propose a method that offers robustness to uncertainty by making use of redundant robot assignments. However, solving the redundant assignment problem optimally is strongly NP-hard. Hence, we exploit structural properties of our mathematical problem formulation to propose a polynomial-time, near-optimal solution. We demonstrate that our problem can be reduced to minimizing a supermodular cost function subject to a matroid constraint. This allows us to develop a greedy assignment algorithm, for which we derive sub-optimality bounds. We demonstrate the effectiveness of our approach with simulations on transport networks with correlated uncertain edge costs and uncertain node positions that lead to noisy travel time estimates. Comparisons to benchmark algorithms show that our method performs near-optimally and significantly better than non-redundant assignment. Finally, our findings include results on the benefit of diversity and complementarity in redundant robot coalitions; these insights contribute towards providing resilience to uncertainty through targeted composition of robot coalitions.This work was supported by ARL DCIST CRA W911NF- 17-2-0181, by the Centre for Digital Built Britain, under InnovateUK grant number RG96233, for the research project “Co-Evolving Built Environments and Mobile Autonomy for Future Transport and Mobility”, and by the Engineering and Physical Sciences Research Council (grant EP/S015493/1)
Centrality dependence of global variables in relativistic heavy ion collisions: Final data analysis in the framework of a statistical model
The global variables like the transverse energy at midrapidity, the charged
particle multiplicity at midrapidity and the total multiplicity of charged
particles are evaluated in the single-freeze-out statistical model for
different centrality bins at RHIC at and 200 GeV. Full
description of decays of hadron resonances is applied in these estimations. The
geometric parameters of the model are obtained from the fit to the final data
on the spectra. The predicted values of the global variables agree
qualitatively well with the experimental data. The centrality independence of
the total number of charged particles per participant pair has been also
reproduced.Comment: Revtex, 12 figures (included), 16 pages. This is the revised final
version accepted for publication in Physical Review C. The main difference
with the first version is that the geometric parameters of the model have
been fitted again with the use of the newer estimates of the statistical
parameters reported in Refs. [20,21] for the case of GeV.
Also because of the editorial reasons the title has been slightly change
Fair Robust Assignment Using Redundancy
We study the consideration of fairness in redundant assignment for multi-agent task allocation. It has recently been shown that redundant assignment of agents to tasks provides robustness to uncertainty in task performance. However, the question of how to fairly assign these redundant resources across tasks remains unaddressed. In this paper, we present a novel problem formulation for fair redundant task allocation, in which we cast it as the optimization of worst-case task costs. Solving this problem optimally is NP-hard. Therefore, we exploit properties of supermodularity to propose a polynomial-time, near-optimal solution. Our algorithm provides a solution set that is α times larger than the optimal set size in order to guarantee a solution cost at least as good as the optimal target cost. We derive the sub- optimality bound on this cardinality relaxation, α. Additionally, we demonstrate that our algorithm performs near-optimally without the cardinality relaxation. We show the algorithm in simulations of redundant assignments of robots to goal nodes on transport networks with uncertain travel times. Empirically, our algorithm outperforms benchmarks, scales to large problems, and provides improvements in both fairness and average utility.We gratefully acknowledge the support from ARL Grant DCIST CRA W911NF-17-2-0181, NSF Grant CNS-1521617, ARO Grant W911NF-13-1- 0350, ONR Grants N00014-20-1-2822 and ONR grant N00014-20-S-B001, and Qualcomm Research. The first author acknowledges support from the National Science Foundation Graduate Research Fellowship under Grant No. DGE-1845298
SO(2)-Equivariant Downwash Models for Close Proximity Flight
Multirotors flying in close proximity induce aerodynamic wake effects on each
other through propeller downwash. Conventional methods have fallen short of
providing adequate 3D force-based models that can be incorporated into robust
control paradigms for deploying dense formations. Thus, learning a model for
these downwash patterns presents an attractive solution. In this paper, we
present a novel learning-based approach for modelling the downwash forces that
exploits the latent geometries (i.e. symmetries) present in the problem. We
demonstrate that when trained with only 5 minutes of real-world flight data,
our geometry-aware model outperforms state-of-the-art baseline models trained
with more than 15 minutes of data. In dense real-world flights with two
vehicles, deploying our model online improves 3D trajectory tracking by nearly
36% on average (and vertical tracking by 56%)
J/Psi suppression in colliding nuclei: statistical model analysis
We consider the suppression at a high energy heavy ion collision. An
ideal gas of massive hadrons in thermal and chemical equilibrium is formed in
the central region. The finite-size gas expands longitudinally in accordance
with Bjorken law. The transverse expansion in a form of the rarefaction wave is
taken into account. We show that suppression in such an environment,
when combined with the disintegration in nuclear matter, gives correct
evaluation of NA38 and NA50 data in a broad range of initial energy densities.Comment: 14 pages, 13 figures. Accepted for publication in Phys. Rev.
On the formation of Hubble flow in Little Bangs
A dynamical appearance of scaling solutions in the relativistic hydrodynamics
applied to describe ultra-relativistic heavy-ion collisions is studied. We
consider the boost-invariant cylindrically symmetric systems and the effects of
the phase transition are taken into account by using a temperature dependent
sound velocity inferred from the lattice simulations of QCD. We find that the
transverse flow acquires the scaling form r/t within the short evolution times,
10 - 15 fm, only if the initial transverse flow originating from the
pre-equilibrium collective behavior is present at the initial stage of the
hydrodynamic evolution. The amount of such pre-equilibrium flow is correlated
with the initial pressure gradient; larger gradients require smaller initial
flow. The results of the numerical calculations support the phenomenological
parameterizations used in the Blast-Wave, Buda-Lund, and Cracow models of the
freeze-out process.Comment: 11 page
WDHA syndrome caused by pheochromocytoma: report of a case.
A case in which a pheochromocytoma secreted vasoactive intestinal peptide, causing WDHA syndrome, is reported. The patient, a 43-year-old woman, was seen because of intractable watery diarrhea, hypokalemia and weight loss. She was found to have a mass in the right adrenal area. Preoperatively, vasoactive intestinal peptide levels were elevated, and the diagnosis of WDHA syndrome was entertained. Exploratory laparotomy revealed a tumor of the right adrenal gland, measuring 15 x 15 cm, which was resected. Histologic examination revealed it to be a pheochromocytoma. Postoperatively, vasoactive intestinal peptide returned to normal. The patient had complete remission of symptoms, and has remained well since
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