906 research outputs found
Verifying Safety Properties With the TLA+ Proof System
TLAPS, the TLA+ proof system, is a platform for the development and
mechanical verification of TLA+ proofs written in a declarative style requiring
little background beyond elementary mathematics. The language supports
hierarchical and non-linear proof construction and verification, and it is
independent of any verification tool or strategy. A Proof Manager uses backend
verifiers such as theorem provers, proof assistants, SMT solvers, and decision
procedures to check TLA+ proofs. This paper documents the first public release
of TLAPS, distributed with a BSD-like license. It handles almost all the
non-temporal part of TLA+ as well as the temporal reasoning needed to prove
standard safety properties, in particular invariance and step simulation, but
not liveness properties
Rendezvous on a Line by Location-Aware Robots Despite the Presence of Byzantine Faults
A set of mobile robots is placed at points of an infinite line. The robots
are equipped with GPS devices and they may communicate their positions on the
line to a central authority. The collection contains an unknown subset of
"spies", i.e., byzantine robots, which are indistinguishable from the
non-faulty ones. The set of the non-faulty robots need to rendezvous in the
shortest possible time in order to perform some task, while the byzantine
robots may try to delay their rendezvous for as long as possible. The problem
facing a central authority is to determine trajectories for all robots so as to
minimize the time until the non-faulty robots have rendezvoused. The
trajectories must be determined without knowledge of which robots are faulty.
Our goal is to minimize the competitive ratio between the time required to
achieve the first rendezvous of the non-faulty robots and the time required for
such a rendezvous to occur under the assumption that the faulty robots are
known at the start. We provide a bounded competitive ratio algorithm, where the
central authority is informed only of the set of initial robot positions,
without knowing which ones or how many of them are faulty. When an upper bound
on the number of byzantine robots is known to the central authority, we provide
algorithms with better competitive ratios. In some instances we are able to
show these algorithms are optimal
Revisiting the Problem of Searching on a Line
We revisit the problem of searching for a target at an unknown location on a
line when given upper and lower bounds on the distance D that separates the
initial position of the searcher from the target. Prior to this work, only
asymptotic bounds were known for the optimal competitive ratio achievable by
any search strategy in the worst case. We present the first tight bounds on the
exact optimal competitive ratio achievable, parameterized in terms of the given
bounds on D, along with an optimal search strategy that achieves this
competitive ratio. We prove that this optimal strategy is unique. We
characterize the conditions under which an optimal strategy can be computed
exactly and, when it cannot, we explain how numerical methods can be used
efficiently. In addition, we answer several related open questions, including
the maximal reach problem, and we discuss how to generalize these results to m
rays, for any m >= 2
Parallel Search with no Coordination
We consider a parallel version of a classical Bayesian search problem.
agents are looking for a treasure that is placed in one of the boxes indexed by
according to a known distribution . The aim is to minimize
the expected time until the first agent finds it. Searchers run in parallel
where at each time step each searcher can "peek" into a box. A basic family of
algorithms which are inherently robust is \emph{non-coordinating} algorithms.
Such algorithms act independently at each searcher, differing only by their
probabilistic choices. We are interested in the price incurred by employing
such algorithms when compared with the case of full coordination. We first show
that there exists a non-coordination algorithm, that knowing only the relative
likelihood of boxes according to , has expected running time of at most
, where is the expected running time of the best
fully coordinated algorithm. This result is obtained by applying a refined
version of the main algorithm suggested by Fraigniaud, Korman and Rodeh in
STOC'16, which was designed for the context of linear parallel search.We then
describe an optimal non-coordinating algorithm for the case where the
distribution is known. The running time of this algorithm is difficult to
analyse in general, but we calculate it for several examples. In the case where
is uniform over a finite set of boxes, then the algorithm just checks boxes
uniformly at random among all non-checked boxes and is essentially times
worse than the coordinating algorithm.We also show simple algorithms for Pareto
distributions over boxes. That is, in the case where for
, we suggest the following algorithm: at step choose uniformly
from the boxes unchecked in ,
where . It turns out this algorithm is asymptotically
optimal, and runs about times worse than the case of full coordination
Grouping based feature attribution in metacontrast masking
The visibility of a target can be strongly suppressed by metacontrast masking.
Still, some features of the target can be perceived within the mask. Usually,
these rare cases of feature mis-localizations are assumed to reflect errors of
the visual system. To the contrary, I will show that feature
"mis-localizations" in metacontrast masking follow rules of
motion grouping and, hence, should be viewed as part of a systematic feature
attribution process
Time-Energy Tradeoffs for Evacuation by Two Robots in the Wireless Model
Two robots stand at the origin of the infinite line and are tasked with
searching collaboratively for an exit at an unknown location on the line. They
can travel at maximum speed and can change speed or direction at any time.
The two robots can communicate with each other at any distance and at any time.
The task is completed when the last robot arrives at the exit and evacuates. We
study time-energy tradeoffs for the above evacuation problem. The evacuation
time is the time it takes the last robot to reach the exit. The energy it takes
for a robot to travel a distance at speed is measured as . The
total and makespan evacuation energies are respectively the sum and maximum of
the energy consumption of the two robots while executing the evacuation
algorithm.
Assuming that the maximum speed is , and the evacuation time is at most
, where is the distance of the exit from the origin, we study the
problem of minimizing the total energy consumption of the robots. We prove that
the problem is solvable only for . For the case , we give an
optimal algorithm, and give upper bounds on the energy for the case .
We also consider the problem of minimizing the evacuation time when the
available energy is bounded by . Surprisingly, when is a
constant, independent of the distance of the exit from the origin, we prove
that evacuation is possible in time , and this is optimal up
to a logarithmic factor. When is linear in , we give upper bounds
on the evacuation time.Comment: This is the full version of the paper with the same title which will
appear in the proceedings of the 26th International Colloquium on Structural
Information and Communication Complexity (SIROCCO'19) L'Aquila, Italy during
July 1-4, 201
Evacuating Two Robots from a Disk: A Second Cut
We present an improved algorithm for the problem of evacuating two robots
from the unit disk via an unknown exit on the boundary. Robots start at the
center of the disk, move at unit speed, and can only communicate locally. Our
algorithm improves previous results by Brandt et al. [CIAC'17] by introducing a
second detour through the interior of the disk. This allows for an improved
evacuation time of . The best known lower bound of was shown by
Czyzowicz et al. [CIAC'15].Comment: 19 pages, 5 figures. This is the full version of the paper with the
same title accepted in the 26th International Colloquium on Structural
Information and Communication Complexity (SIROCCO'19
Almost optimal asynchronous rendezvous in infinite multidimensional grids
Two anonymous mobile agents (robots) moving in an asynchronous manner have to meet in an infinite grid of dimension δ> 0, starting from two arbitrary positions at distance at most d. Since the problem is clearly infeasible in such general setting, we assume that the grid is embedded in a δ-dimensional Euclidean space and that each agent knows the Cartesian coordinates of its own initial position (but not the one of the other agent). We design an algorithm permitting the agents to meet after traversing a trajectory of length O(d δ polylog d). This bound for the case of 2d-grids subsumes the main result of [12]. The algorithm is almost optimal, since the Ω(d δ) lower bound is straightforward. Further, we apply our rendezvous method to the following network design problem. The ports of the δ-dimensional grid have to be set such that two anonymous agents starting at distance at most d from each other will always meet, moving in an asynchronous manner, after traversing a O(d δ polylog d) length trajectory. We can also apply our method to a version of the geometric rendezvous problem. Two anonymous agents move asynchronously in the δ-dimensional Euclidean space. The agents have the radii of visibility of r1 and r2, respectively. Each agent knows only its own initial position and its own radius of visibility. The agents meet when one agent is visible to the other one. We propose an algorithm designing the trajectory of each agent, so that they always meet after traveling a total distance of O( ( d)), where r = min(r1, r2) and for r ≥ 1. r)δpolylog ( d r
NF-κB: A lesson in family values
A set of mobile robots (represented as points) is distributed in the Cartesian plane. The collection contains an unknown subset of byzantine robots which are indistinguishable from the reliable ones. The reliable robots need to gather, i.e., arrive to a configuration in which at the same time, all of them occupy the same point on the plane. The robots are equipped with GPS devices and at the beginning of the gathering process they communicate the Cartesian coordinates of their respective positions to the central authority. On the basis of this information, without the knowledge of which robots are faulty, the central authority designs a trajectory for every robot. The central authority aims to provide the trajectories which result in the shortest possible gathering time of the healthy robots. The efficiency of a gathering strategy is measured by its competitive ratio, i.e., the maximal ratio between the time required for gathering achieved by the given trajectories and the optimal time required for gathering in the offline case, i.e., when the faulty robots are known to the central authority in advance. The role of the byzantine robots, controlled by the adversary, is to act so that the gathering is delayed and the resulting competitive ratio is maximized. The objective of our paper is to propose efficient algorithms when the central authority is aware of an upper bound on the number of byzantine robots. We give optimal algorithms for collections of robots known to contain at most one faulty robot. When the proportion of byzantine robots is known to be less than one half or one third, we provide algorithms with small constant competitive ratios. We also propose algorithms with bounded competitive ratio in the case where the proportion of faulty robots is arbitrary
Lack of Agreement in Pediatric Emergency Department Discharge Diagnoses from Clinical and Administrative Data Sources
Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/75409/1/j.1553-2712.2007.tb01852.x.pd
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