Lifshitz transitions and zero point lattice fluctuations in sulfur hydride showing near room temperature superconductivity

Emerets's experiments on pressurized sulfur hydride have shown that H3S metal has the highest known superconducting critical temperature Tc=203K. The Emerets data show pressure induced changes of the isotope coefficient between 0.25 and 0.5, in disagreement with Eliashberg theory which predicts a nearly constant isotope coefficient. We assign the pressure dependent isotope coefficient to Lifshitz transitions induced by pressure and zero point lattice fluctuations. It is known that pressure could induce changes of the topology of the Fermi surface, called Lifshitz transitions, but were neglected in previous papers on the H$_3$S superconductivity issue. Here we propose that H3S is a multi-gap superconductor with a first condensate in the BCS regime (in the large Fermi surface with high Fermi energy) which coexists with a second condensates in the BCS-BEC crossover regime (located on a small Fermi surface spots with small Fermi energy) near the $\Gamma$ and M point. We discuss the need of Bianconi-Perali-Valletta (BPV) superconductivity theory for superconductivity in H3S. It includes both the correction of the chemical potential due to pairing and the configuration interaction between different condensates, neglected by the Eliashberg theory. Here the shape resonance in superconducting gaps, similar to Feshbach resonance in ultracold gases, gives a relevant contribution to amplify the critical temperature. Therefore this work provides some key tools needed in the search for new room temperature superconductors.Comment: 12 pages, 6 figure

Decentralized dynamic task allocation for UAVs with limited communication range

We present the Limited-range Online Routing Problem (LORP), which involves a team of Unmanned Aerial Vehicles (UAVs) with limited communication range that must autonomously coordinate to service task requests. We first show a general approach to cast this dynamic problem as a sequence of decentralized task allocation problems. Then we present two solutions both based on modeling the allocation task as a Markov Random Field to subsequently assess decisions by means of the decentralized Max-Sum algorithm. Our first solution assumes independence between requests, whereas our second solution also considers the UAVs' workloads. A thorough empirical evaluation shows that our workload-based solution consistently outperforms current state-of-the-art methods in a wide range of scenarios, lowering the average service time up to 16%. In the best-case scenario there is no gap between our decentralized solution and centralized techniques. In the worst-case scenario we manage to reduce by 25% the gap between current decentralized and centralized techniques. Thus, our solution becomes the method of choice for our problem

Automatic plant disease diagnosis using mobile capture devices, applied on a wheat use case

Disease diagnosis based on the detection of early symptoms is a usual threshold taken into account for integrated pest management strategies. Early phytosanitary treatment minimizes yield losses and increases the efficacy and efficiency of the treatments. However, the appearance of new diseases associated to new resistant crop variants complicates their early identification delaying the application of the appropriate corrective actions. The use of image based automated identification systems can leverage early detection of diseases among farmers and technicians but they perform poorly under real field conditions using mobile devices. A novel image processing algorithm based on candidate hot-spot detection in combination with statistical inference methods is proposed to tackle disease identification in wild conditions. This work analyses the performance of early identification of three European endemic wheat diseases – septoria, rust and tan spot. The analysis was done using 7 mobile devices and more than 3500 images captured in two pilot sites in Spain and Germany during 2014, 2015 and 2016. Obtained results reveal AuC (Area under the Receiver Operating Characteristic –ROC– Curve) metrics higher than 0.80 for all the analyzed diseases on the pilot tests under real conditions

Using the Spring Physical Model to Extend a Cooperative Caching Protocol for Many-Core Processors

International audienceAs the number of embedded cores grows up, the off-chip memory wall becomes an overwhelming bottleneck. As a consequence, it is more and more prevalent to efficiently exploit on-chip data storage. In a previous work, we proposed a data sliding mechanism that allows to store data onto our closest neighborhood, even under heavy stress loads. However, each cache block is allowed to migrate only one time to a neighbor's cache (e.g. 1-Chance Forwarding). In this paper, we propose an extension of our mechanism in order to expand the cooperative caching area. Our work is based on an adaptive physical model, where each cache block is considered as a mass connected to a spring. This technique constrains data migration according to the spring constant and the difference of work-loads between cores. This adaptive data sliding approach leads to a balanced spread of data on the chip and therefore improves on-chip storage. On-chip data access has been evaluated using an analytical approach. Results show that the extended data sliding increases the global cache hit rate on the chip, especially in the context of juxtaposed hot spots

Shared versus distributed memory multiprocessors

The question of whether multiprocessors should have shared or distributed memory has attracted a great deal of attention. Some researchers argue strongly for building distributed memory machines, while others argue just as strongly for programming shared memory multiprocessors. A great deal of research is underway on both types of parallel systems. Special emphasis is placed on systems with a very large number of processors for computation intensive tasks and considers research and implementation trends. It appears that the two types of systems will likely converge to a common form for large scale multiprocessors

An Approach for Actions to Prevent Suicides on Commuter and Metro Rail Systems in the United States, MTI Report 12-33

The primary goals of this report are to discuss measures to prevent suicides on commuter and metro rail systems, and to outline an approach for suicide prevention on rail systems. Based on existing literature and analysis of data obtained from the Metrolink system in Southern California, it was found that most suicides occur near station platforms and near access points to the track. Suicides occurred most frequently when relatively more trains were in operation and in areas of high population density. There do not appear to be suicide “hot spots” (e.g., linked to mental hospitals in the proximity, etc.), based on data analyzed for U.S. systems. The suicide prevention measures range from relatively inexpensive signs posting call-for-help suicide hotline information to costly platform barriers that physically prevent people from jumping onto tracks in front of trains. Other prevention measures fall within this range, such as hotlines available at high frequency suicide locations, or surveillance systems that can report possible suicide attempts and provide the opportunity for intervention tactics. Because of the relatively low number of suicides on rail systems, as compared to the overall number of suicides in general, a cost-effective strategy for preventing suicides on rail systems should be approached in a very focused manner. The prevention measures executed by the rail authorities should be focused on the suicides occurring on the rail systems themselves, while the broader problem of suicides should be left to community-based prevention efforts. Moreover, prevention measures, such as surveillance and response, could “piggyback” on surveillance and response systems used for other purposes on the rail systems to make such projects economically feasible