A two-armed bandit based scheme for accelerated decentralized learning

The two-armed bandit problem is a classical optimization problem where a decision maker sequentially pulls one of two arms attached to a gambling machine, with each pull resulting in a random reward. The reward distributions are unknown, and thus, one must balance between exploiting existing knowledge about the arms, and obtaining new information. Bandit problems are particularly fascinating because a large class of real world problems, including routing, QoS control, game playing, and resource allocation, can be solved in a decentralized manner when modeled as a system of interacting gambling machines. Although computationally intractable in many cases, Bayesian methods provide a standard for optimal decision making. This paper proposes a novel scheme for decentralized decision making based on the Goore Game in which each decision maker is inherently Bayesian in nature, yet avoids computational intractability by relying simply on updating the hyper parameters of sibling conjugate priors, and on random sampling from these posteriors. We further report theoretical results on the variance of the random rewards experienced by each individual decision maker. Based on these theoretical results, each decision maker is able to accelerate its own learning by taking advantage of the increasingly more reliable feedback that is obtained as exploration gradually turns into exploitation in bandit problem based learning. Extensive experiments demonstrate that the accelerated learning allows us to combine the benefits of conservative learning, which is high accuracy, with the benefits of hurried learning, which is fast convergence. In this manner, our scheme outperforms recently proposed Goore Game solution schemes, where one has to trade off accuracy with speed. We thus believe that our methodology opens avenues for improved performance in a number of applications of bandit based decentralized decision making

Task Switching and Single vs. Multiple Alarms for Supervisory Control of Multiple Robots

Foraging tasks, such as search and rescue or reconnaissance, in which UVs are either relatively sparse and unlikely to interfere with one another or employ automated path planning, form a broad class of applications in which multiple robots can be controlled sequen-tially in a round-robin fashion. Such human-robot systems can be described as a queuing sys-tem in which the human acts as a server while robots presenting requests for service are the jobs. The possibility of improving system performance through well-known scheduling tech-niques is an immediate consequence. Unfortunately, real human-multirobot systems are more complex often requiring operator monitoring and other ancillary tasks. Improving perfor-mance through scheduling (jobs) under these conditions requires minimizing the effort ex-pended monitoring and directing the operator’s attention to the robot offering the most gain. Two experiments investigating scheduling interventions are described. The first compared a system in which all anomalous robots were alarmed (Open-queue), one in which alarms were presented singly in the order in which they arrived (FIFO) and a Control condition without alarms. The second experiment employed failures of varying difficulty supporting an optimal shortest job first (SJF) policy. SJF, FIFO, and Open-queue conditions were compared. In both experiments performance in directed attention conditions was poorer than predicted. A possi-ble explanation based on effects of volition in task switching is propose

Efficient exploration of unknown indoor environments using a team of mobile robots

Whenever multiple robots have to solve a common task, they need to coordinate their actions to carry out the task efficiently and to avoid interferences between individual robots. This is especially the case when considering the problem of exploring an unknown environment with a team of mobile robots. To achieve efficient terrain coverage with the sensors of the robots, one first needs to identify unknown areas in the environment. Second, one has to assign target locations to the individual robots so that they gather new and relevant information about the environment with their sensors. This assignment should lead to a distribution of the robots over the environment in a way that they avoid redundant work and do not interfere with each other by, for example, blocking their paths. In this paper, we address the problem of efficiently coordinating a large team of mobile robots. To better distribute the robots over the environment and to avoid redundant work, we take into account the type of place a potential target is located in (e.g., a corridor or a room). This knowledge allows us to improve the distribution of robots over the environment compared to approaches lacking this capability. To autonomously determine the type of a place, we apply a classifier learned using the AdaBoost algorithm. The resulting classifier takes laser range data as input and is able to classify the current location with high accuracy. We additionally use a hidden Markov model to consider the spatial dependencies between nearby locations. Our approach to incorporate the information about the type of places in the assignment process has been implemented and tested in different environments. The experiments illustrate that our system effectively distributes the robots over the environment and allows them to accomplish their mission faster compared to approaches that ignore the place labels

Neutrinoless double-beta decay search with CUORE and CUORE-0 experiments

The Cryogenic Underground Observatory for Rare Events (CUORE) is an upcoming experiment designed to search for the neutrinoless double-beta decays. Observation of the process would unambiguously establish that neutrinos are Majorana particles and provide information on their absolute mass scale hierarchy. CUORE is now under construction and will consist of an array of 988 TeO2 crystal bolometers operated at 10 mK, but the first tower (CUORE-0) is already taking data. The experimental techniques used will be presented as well as the preliminary CUORE-0 results. The current status of the full-mass experiment and its expected sensitivity will then be discussed

Search for massive, long-lived particles using multitrack displaced vertices or displaced lepton pairs in pp collisions at √s = 8 TeV with the ATLAS detector

Many extensions of the Standard Model posit the existence of heavy particles with long lifetimes. This article presents the results of a search for events containing at least one long-lived particle that decays at a significant distance from its production point into two leptons or into five or more charged particles. This analysis uses a data sample of proton-proton collisions at √s=8  TeV corresponding to an integrated luminosity of 20.3  fb−1 collected in 2012 by the ATLAS detector operating at the Large Hadron Collider. No events are observed in any of the signal regions, and limits are set on model parameters within supersymmetric scenarios involving R-parity violation, split supersymmetry, and gauge mediation. In some of the search channels, the trigger and search strategy are based only on the decay products of individual long-lived particles, irrespective of the rest of the event. In these cases, the provided limits can easily be reinterpreted in different scenarios

Search for high-mass diphoton resonances in pp collisions at √s = 8 TeV with the ATLAS detector

This article describes a search for high-mass resonances decaying to a pair of photons using a sample of 20.3  fb−¹ of pp collisions at √s = 8 TeV recorded with the ATLAS detector at the Large Hadron Collider. The data are found to be in agreement with the Standard Model prediction, and limits are reported in the framework of the Randall-Sundrum model. This theory leads to the prediction of graviton states, the lightest of which could be observed at the Large Hadron Collider. A lower limit of 2.66 (1.41) TeV at 95% confidence level is set on the mass of the lightest graviton for couplings of k/M̄Pl=0.1(0.01)

Muon reconstruction performance of the ATLAS detector in proton–proton collision data at √s = 13 TeV

This article documents the performance of the ATLAS muon identification and reconstruction using the LHC dataset recorded at √s = 13 TeV in 2015. Using a large sample of J/ψ→μμ and Z→μμ decays from 3.2 fb−1 of pp collision data, measurements of the reconstruction efficiency, as well as of the momentum scale and resolution, are presented and compared to Monte Carlo simulations. The reconstruction efficiency is measured to be close to 99% over most of the covered phase space (|η| 2.2, the pT resolution for muons from Z→μμ decays is 2.9 % while the precision of the momentum scale for low-pT muons from J/ψ→μμ decays is about 0.2%

Measurement of the differential cross-section of highly boosted top quarks as a function of their transverse momentum in s =8 TeV proton-proton collisions using the ATLAS detector

The differential cross-section for pair production of top quarks with high transverse momentum is measured in 20.3  fb−1 of proton-proton collisions at a center-of-mass energy of 8 TeV. The measurement is performed for tt¯ events in the lepton+jets channel. The cross-section is reported as a function of the hadronically decaying top quark transverse momentum for values above 300 GeV. The hadronically decaying top quark is reconstructed as an anti-kt jet with radius parameter R=1.0 and identified with jet substructure techniques. The observed yield is corrected for detector effects to obtain a cross-section at particle level in a fiducial region close to the event selection. A parton-level cross-section extrapolated to the full phase space is also reported for top quarks with transverse momentum above 300 GeV. The predictions of a majority of next-to-leading-order and leading-order matrix-element Monte Carlo generators are found to agree with the measured cross-sections.- We thank CERN for the very successful operation of the LHC, as well as the support staff from our institutions without whom ATLAS could not be operated efficiently. We acknowledge the support of ANPCyT, Argentina; YerPhI, Armenia; ARC, Australia; BMWFW and FWF, Austria; ANAS, Azerbaijan; SSTC, Belarus; CNPq and FAPESP, Brazil; NSERC, NRC and CFI, Canada; CERN; CONICYT, Chile; CAS, MOST and NSFC, China; COLCIENCIAS, Colombia; MSMT CR, MPO CR and VSC CR, Czech Republic; DNRF, DNSRC and Lundbeck Foundation, Denmark; IN2P3-CNRS, CEA-DSM/IRFU, France; GNSF, Georgia; BMBF, HGF, and MPG, Germany; GSRT, Greece; RGC, Hong Kong SAR, China; ISF, I-CORE and Benoziyo Center, Israel; INFN, Italy; MEXT and JSPS, Japan; CNRST, Morocco; FOM and NWO, Netherlands; RCN, Norway; MNiSW and NCN, Poland; FCT, Portugal; MNE/IFA, Romania; MES of Russia and NRC KI, Russian Federation; JINR; MESTD, Serbia; MSSR, Slovakia; ARRS and MIZS, Slovenia; DST/NRF, South Africa; MINECO, Spain; SRC and Wallenberg Foundation, Sweden; SERI, SNSF and Cantons of Bern and Geneva, Switzerland; MOST, Taiwan; TAEK, Turkey; STFC, United Kingdom; DOE and NSF, United States of America. In addition, individual groups and members have received support from BCKDF, the Canada Council, CANARIE, CRC, Compute Canada, FQRNT, and the Ontario Innovation Trust, Canada; EPLANET, ERC, FP7, Horizon 2020 and Marie Sklodowska-Curie Actions, European Union; Investissements d'Avenir Labex and Idex, ANR, Region Auvergne and Fondation Partager le Savoir, France; DFG and AvH Foundation, Germany; Herakleitos, Thales and Aristeia programmes co-financed by EU-ESF and the Greek NSRF; BSF, GIF and Minerva, Israel; BRF, Norway; the Royal Society and Leverhulme Trust, United Kingdom. The crucial computing support from all WLCG partners is acknowledged gratefully, in particular from CERN and the ATLAS Tier-1 facilities at TRIUMF (Canada), NDGF (Denmark, Norway, Sweden), CC-IN2P3 (France), KIT/GridKA (Germany), INFN-CNAF (Italy), NL-T1 (Netherlands), PIC (Spain), ASGC (Taiwan), RAL (UK) an

Measurement of the total cross section from elastic scattering in pp collisions at s√ = 7 TeV with the ATLAS detector

A measurement of the total pp cross section at the LHC at √s = 7 TeV is presented. In a special run with high-β beam optics, an integrated luminosity of 80 µb−1 was accumulated in order to measure the differential elastic cross section as a function of the Mandelstam momentum transfer variable t. The measurement is performed with the ALFA sub-detector of ATLAS. Using a fit to the differential elastic cross section in the |t| range from 0.01 GeV2 to 0.1 GeV2 to extrapolate to |t| → 0, the total cross section, σtot(pp → X), is measured via the optical theorem to be: σtot(pp → X) = 95.35 ± 0.38 (stat.) ± 1.25 (exp.) ± 0.37 (extr.) mb, where the first error is statistical, the second accounts for all experimental systematic uncertainties and the\ud last is related to uncertainties in the extrapolation to |t| → 0. In addition, the slope of the elastic cross section at small |t| is determined to be B = 19.73 ± 0.14 (stat.) ± 0.26 (syst.) GeV−2