Low-threshold analysis of CDMS shallow-site data

Data taken during the final shallow-site run of the first tower of the Cryogenic Dark Matter Search (CDMS II) detectors have been reanalyzed with improved sensitivity to small energy depositions. Four ~224 g germanium and two ~105 g silicon detectors were operated at the Stanford Underground Facility (SUF) between December 2001 and June 2002, yielding 118 live days of raw exposure. Three of the germanium and both silicon detectors were analyzed with a new low-threshold technique, making it possible to lower the germanium and silicon analysis thresholds down to the actual trigger thresholds of ~1 and ~2 keV, respectively. Limits on the spin-independent cross section for weakly interacting massive particles (WIMPs) to elastically scatter from nuclei based on these data exclude interesting parameter space for WIMPs with masses below 9 GeV/c^2. Under standard halo assumptions, these data partially exclude parameter space favored by interpretations of the DAMA/LIBRA and CoGeNT experiments’ data as WIMP signals, and exclude new parameter space for WIMP masses between 3 and 4 GeV/c^2

Combined limits on WIMPs from the CDMS and EDELWEISS experiments

The CDMS and EDELWEISS collaborations have combined the results of their direct searches for dark matter using cryogenic germanium detectors. The total data set represents 614  kg·days equivalent exposure. A straightforward method of combination was chosen for its simplicity before data were exchanged between experiments. The results are interpreted in terms of limits on spin-independent weakly interacting, massive particle (WIMP)-nucleon cross section. For a WIMP mass of 90  GeV/c^2, where this analysis is most sensitive, a cross section of 3.3×10^(-44)  cm^2 is excluded at 90% C.L. At higher WIMP masses, the combination improves the individual limits, by a factor 1.6 above 700  GeV/c^2. Alternative methods of combining the data provide stronger constraints for some ranges of WIMP masses and weaker constraints for others

Search for inelastic dark matter with the CDMS II experiment

Results are presented from a reanalysis of the entire five-tower data set acquired with the Cryogenic Dark Matter Search (CDMS II) experiment at the Soudan Underground Laboratory, with an exposure of 969 kg-days. The analysis window was extended to a recoil energy of 150 keV, and an improved surface-event background-rejection cut was defined to increase the sensitivity of the experiment to the inelastic dark matter (iDM) model. Three dark matter candidates were found between 25 keV and 150 keV. The probability to observe three or more background events in this energy range is 11%. Because of the occurrence of these events, the constraints on the iDM parameter space are slightly less stringent than those from our previous analysis, which used an energy window of 10–100 keV

SPACE4AI-R: a Runtime Management Tool for AI Applications Component Placement and Resource Scaling in Computing Continua

The recent migration towards Internet of Things determined the rise of a Computing Continuum paradigm where Edge and Cloud resources coordinate to support the execution of Artificial Intelligence (AI) applications, becoming the foundation of use-cases spanning from predictive maintenance to machine vision and healthcare. This generates a fragmented scenario where computing and storage power are distributed among multiple devices with highly heterogeneous capacities. The runtime management of AI applications executed in the Computing Continuum is challenging, and requires ad-hoc solutions. We propose SPACE4AI-R, which combines Random Search and Stochastic Local Search algorithms to cope with workload fluctuations by identifying the minimum-cost reconfiguration of the initial production deployment, while providing performance guarantees across heterogeneous resources including Edge devices and servers, Cloud GPU-based Virtual Machines and Function as a Service solutions. Experimental results prove the efficacy of our tool, yielding up to 60% cost reductions against a static design-time placement, with a maximum execution time under 1.5s in the most complex scenarios

A Random Greedy based Design Time Tool for AI Applications Component Placement and Resource Selection in Computing Continua

Artificial Intelligence (AI) and Deep Learning (DL) are pervasive today, with applications spanning from personal assistants to healthcare. Nowadays, the accelerated migration towards mobile computing and Internet of Things, where a huge amount of data is generated by widespread end devices, is determining the rise of the edge computing paradigm, where computing resources are distributed among devices with highly heterogeneous capacities. In this fragmented scenario, efficient component placement and resource allocation algorithms are crucial to orchestrate at best the computing continuum resources. In this paper, we propose a tool to effectively address the component placement problem for AI applications at design time. Through a randomized greedy algorithm, our approach identifies the placement of minimum cost providing performance guarantees across heterogeneous resources including edge devices, cloud GPU-based Virtual Machines and Function as a Service solutions. Finally, we compare the random greedy method with the HyperOpt framework and demonstrate that our proposed approach converges to a near-optimal solution much faster, especially in large scale systems

Monitoramento ambiental e agropecuário do território de Frederico Westphalen (1998 - 2007).

bitstream/item/34754/1/boletim-129.pd

Obstacle Avoidance Cell Discovery using mm-waves Directive Antennas in 5G Networks

With the advent of next-generation mobile devices, wireless networks must be upgraded to fill the gap between huge user data demands and scarce channel capacity. Mm-waves tech- nologies appear as the key-enabler for the future 5G networks design, exhibiting large bandwidth availability and high data rate. As counterpart, the small wave-length incurs in a harsh signal propagation that limits the transmission range. To overcome this limitation, array of antennas with a relatively high number of small elements are used to exploit beamforming techniques that greatly increase antenna directionality both at base station and user terminal. These very narrow beams are used during data transfer and tracking techniques dynamically adapt the direction according to terminal mobility. During cell discovery when initial synchronization must be acquired, however, directionality can delay the process since the best direction to point the beam is unknown. All space must be scanned using the tradeoff between beam width and transmission range. Some support to speed up the cell search process can come from the new architectures for 5G currently being investigated, where conventional wireless network and mm-waves technologies coexist. In these architecture a functional split between C-plane and U-plane allows to guarantee the continuous availability of a signaling channel through conventional wireless technologies with the opportunity to convey context information from users to network. In this paper, we investigate the use of position information provided by user terminals in order to improve the performance of the cell search process. We analyze mm-wave propagation environment and show how it is possible to take into account of position inaccuracy and reflected rays in presence of obstacle

Rituximab for people with multiple sclerosis

Objectives: This is a protocol for a Cochrane Review (intervention). The objectives are as follows:. The main objective is to assess the benefits and harms of rituximab compared to placebo or another DMT for people with multiple sclerosis. Specific comparisons include:. rituximab compared with placebo or other DMTs as first choice treatment for relapsing forms of MS; rituximab when switching from another DMT compared with placebo or other DMTs for relapsing forms of MS; rituximab compared with placebo or other DMTs as first choice treatment for progressive forms of MS; and rituximab when switching from another DMT compared with placebo or other DMTs for progressive forms of MS

Evaluating the flood damage on dairy farms: a methodological proposal

The debate on climate change arose several concerns on the impacts of floods on agriculture and, consequently, on food security. At the same time, the European Floods Directive asks Members States to implement suitable measures to mitigate flood damage on economic activities, including farms. Still, while several analyses were developed to estimate potential flood damages on crops, a gap exists for livestock productions. The purpose of this study is to develop a conceptual model for the assessment of flood economic damages on dairy farms. Results propose a static and a dynamic model of farms recovery actions to re-establish the farming activity, which takes into account all farm components, as well as their interaction. Facing the destruction caused by floods, a first reaction of farmers could be closing the activity, with repercussion on this well-being as well as on farm workers\u2019, and on the economy of rural areas. From this perspective, this study wants to provide a first methodological pathway to support farmers in restoring their activity