A1 Functional advantages of cell-type heterogeneity in neural circuits Tatyana O. Sharpee A2 Mesoscopic modeling of propagating waves in visual cortex Alain Destexhe A3 Dynamics and biomarkers of mental disorders Mits...
A1 Functional advantages of cell-type heterogeneity in neural circuits Tatyana O. Sharpee A2 Mesoscopic modeling of propagating waves in visual cortex Alain Destexhe A3 Dynamics and biomarkers of mental disorders Mitsuo Kawato F1 Precise recruitment of spiking output at theta frequencies requires dendritic h-channels in multi-compartment models of oriens-lacunosum/moleculare hippocampal interneurons Vladislav Sekulić, Frances K. Skinner F2 Kernel methods in reconstruction of current sources from extracellular potentials for single cells and the whole brains Daniel K. Wójcik, Chaitanya Chintaluri, Dorottya Cserpán, Zoltán Somogyvári F3 The synchronized periods depend on intracellular transcriptional repression mechanisms in circadian clocks. Jae Kyoung Kim, Zachary P. Kilpatrick, Matthew R. Bennett, Kresimir Josić O1 Assessing irregularity and coordination of spiking-bursting rhythms in central pattern generators Irene Elices, David Arroyo, Rafael Levi, Francisco B. Rodriguez, Pablo Varona O2 Regulation of top-down processing by cortically-projecting parvalbumin positive neurons in basal forebrain Eunjin Hwang, Bowon Kim, Hio-Been Han, Tae Kim, James T. McKenna, Ritchie E. Brown, Robert W. McCarley, Jee Hyun Choi O3 Modeling auditory stream segregation, build-up and bistability James Rankin, Pamela Osborn Popp, John Rinzel O4 Strong competition between tonotopic neural ensembles explains pitch-related dynamics of auditory cortex evoked fields Alejandro Tabas, André Rupp, Emili Balaguer-Ballester O5 A simple model of retinal response to multi-electrode stimulation Matias I. Maturana, David B. Grayden, Shaun L. Cloherty, Tatiana Kameneva, Michael R. Ibbotson, Hamish Meffin O6 Noise correlations in V4 area correlate with behavioral performance in visual discrimination task Veronika Koren, Timm Lochmann, Valentin Dragoi, Klaus Obermayer O7 Input-location dependent gain modulation in cerebellar nucleus neurons Maria Psarrou, Maria Schilstra, Neil Davey, Benjamin Torben-Ni
This Letter presents a search for new resonances with mass larger than 250 GeV, decaying to a Z boson and a photon. The dataset consists of an integrated luminosity of 3.2 fb−1 of pp collisions collected at s=13 TeV w...
详细信息
Dependability analysis is an important step in designing and analyzing safety computer systems and protection *** multi-processor and virtual machine increases the system faults' complexity,diversity and dynamic,i...
详细信息
Dependability analysis is an important step in designing and analyzing safety computer systems and protection *** multi-processor and virtual machine increases the system faults' complexity,diversity and dynamic,in particular for software-induced failures,with an impact on the overall ***,it is very different for safety system to operate successfully at any active phase,since there is a huge difference in failure rate between hardware-induced and softwareinduced *** handle these difficulties and achieve accurate dependability evaluation,consistently reflecting the construct it measures,a new formalism derived from dynamic fault graphs(DFG) is developed in this *** exploits the concept of system event as fault state sequences to represent dynamic behaviors,which allows us to execute probabilistic measures at each timestamp when change *** approach automatically combines the reliability analysis with the system *** this paper,we describe how to use the proposed methodology drives to the overall system dependability analysis through the phases of modeling,structural discovery and probability analysis,which is also discussed using an example of a virtual computing system.
Deterministic multithreading (DMT) system is well-known to eliminate the harmful program behaviors caused by nondeterminism, i.e., always proceeding the program execution into the same thread schedule for the same giv...
详细信息
Deterministic multithreading (DMT) system is well-known to eliminate the harmful program behaviors caused by nondeterminism, i.e., always proceeding the program execution into the same thread schedule for the same given input. To achieve this goal, two kinds of schedules are enforced by existing DMT systems. 1) A mem-based schedule ensures the determinism with the total order of the shared memory accesses, and 2) A sync-based schedule makes it by only enforcing the total order of the synchronization operations. Mem-schedule achieves full determinism but suffers from prohibitive overhead; while sync-schedule mitigates this overhead but cannot ensure the determinism for the race schedules, i.e., part determinism. Much recent research is devoted to the hybrid schedule combining the determinism of mem-schedule and efficiency of sync-schedule. However, they suffer from the practicability and scalability problems due to the defects of their technical characteristics, such as trace collection in advance and huge schedule memoization. To address the above problem, this paper proposes esDMT, an efficient and scalable DMT system using a new technique of memory isolation. It can improve the efficiency by proceeding the execution of each thread in parallel within its private virtual memory, and defers the determinism guarantee by updating private memory into shared memory in a deterministic order according to deterministic lock algorithm, thus further reducing the overhead of inter-thread waiting. In contrast to the previous hybrid work avoiding the nondeterminism of race schedules offline based on the enormous historical records, our key insight is to eliminate the nondeterminism of race schedules online at runtime. Our experimental results on PARSEC benchmarks show that esDMT eliminates the nondeterminism successfully, almost gains the same performance as the sync-schedule (with
MapReduce is a popular programming model and an associated implementation for parallel processing big data in the distributed environment. Since large scaled MapReduce data centers usually provide services to many use...
详细信息
ISBN:
(纸本)9781479966226
MapReduce is a popular programming model and an associated implementation for parallel processing big data in the distributed environment. Since large scaled MapReduce data centers usually provide services to many users, it is an essential problem to preserve the privacy between different applications in the same network. In this paper, we propose SDPMN, a framework that using software defined network (SDN) to distinguish the network between each application, which is a manageable and scalable method. We design this framework based on the existing SDN structure and Hardtop networks. Since the rule space of each SDN device is limited, we also propose the rule placement optimization for this framework to maximize the hardware supported isolated application networks. We state this problem in a general MapReduce network and design a heuristic algorithm to find the solution. From the simulation based evaluation, with our algorithm, the given network can support more privacy preserving application networks with SDN switches.
A search for heavy pseudoscalar (A) and scalar (H) Higgs bosons decaying into a top quark pair (tt¯) has been performed with 20.3 fb−1 of proton-proton collision data collected by the ATLAS experiment at the Lar...
详细信息
A search for heavy pseudoscalar (A) and scalar (H) Higgs bosons decaying into a top quark pair (tt¯) has been performed with 20.3 fb−1 of proton-proton collision data collected by the ATLAS experiment at the Large Hadron Collider at a center-of-mass energy s=8 TeV. Interference effects between the signal process and standard model tt¯ production, which are expected to distort the signal shape from a single peak to a peak-dip structure, are taken into account. No significant deviation from the standard model prediction is observed in the tt¯ invariant mass spectrum in final states with an electron or muon, large missing transverse momentum, and at least four jets. The results are interpreted within the context of a type-II two-Higgs-doublet model. Exclusion limits on the signal strength are derived as a function of the mass mA/H and the ratio of the vacuum expectation values of the two Higgs fields, tanβ, for mA/H>500 GeV.
Several extensions of the standard model predict associated production of dark-matter particles with a Higgs boson. Such processes are searched for in final states with missing transverse momentum and a Higgs boson de...
详细信息
Several extensions of the standard model predict associated production of dark-matter particles with a Higgs boson. Such processes are searched for in final states with missing transverse momentum and a Higgs boson decaying to a bb¯ pair with the ATLAS detector using 36.1 fb−1 of pp collisions at a center-of-mass energy of 13 TeV at the LHC. The observed data are in agreement with the standard model predictions and limits are placed on the associated production of dark-matter particles and a Higgs boson.
A search for the dimuon decay of the Higgs boson was performed using data corresponding to an integrated luminosity of 36.1 fb−1 collected with the ATLAS detector in pp collisions at s=13 TeV at the Large Hadron Col...
详细信息
A search for the dimuon decay of the Higgs boson was performed using data corresponding to an integrated luminosity of 36.1 fb−1 collected with the ATLAS detector in pp collisions at s=13 TeV at the Large Hadron Collider. No significant excess is observed above the expected background. The observed (expected) upper limit on the cross section times branching ratio is 3.0 (3.1) times the Standard Model prediction at the 95% confidence level for a Higgs boson mass of 125 GeV. When combined with the pp collision data at s=7 TeV and s=8 TeV, the observed (expected) upper limit is 2.8 (2.9) times the Standard Model prediction.
With the rapid growth of data, near-duplicate documents bearing high similarity are abundant. Elimination of near-duplicates can reduce storage cost and improve the quality of search indexes in data mining. A challeng...
详细信息
ISBN:
(纸本)9781479957125
With the rapid growth of data, near-duplicate documents bearing high similarity are abundant. Elimination of near-duplicates can reduce storage cost and improve the quality of search indexes in data mining. A challenging problem is to find near-duplicate records in large-scale collections efficiently. There have already been several efforts on implementing near-duplicate detection on different architectures. In this paper, a new implementation, using a special hash function namely simhash, is proposed to identify near-duplicate documents on CUDA enabled devices. Two mechanisms are designed to achieve higher performance, including swapping and dynamic allocating. Experimental results show that our parallel implementation outperforms the serial CPU version, achieving up to 18 times.
暂无评论