326 research outputs found
Heavy Rainfall Identification within the Framework of the LEXIS Project: The Italian Case Study
LEXIS (Large-scale EXecution for Industry and Society) H2020 project is currently developing an advanced system for Big Data analysis that takes advantage of interacting large-scale geographically-distributed HPC infrastructure and cloud services. More specifically, LEXIS Weather and Climate Large-Scale Pilot workflows ingest data coming from different sources, like global/regional weather models, conventional and unconventional meteorological observations, application models and socio-economic impact models, in order to provide enhanced meteorological information at the European scale. In the framework of LEXIS Weather and Climate Large-scale Pilot, CIMA Research Foundation is running a 7.5 km resolution WRF (Weather Research and Forecasting) model with European coverage, radar assimilation over the Italian area, and daily updates with 48 hours forecast. WRF data is then processed by ITHACA ERDS (Extreme Rainfall Detection System - http://erds.ithacaweb.org), an early warning system for the monitoring and forecasting of heavy rainfall events. The WRF model provides more detailed information compared to GFS (Global Forecast Systems) data, the most widely used source of rainfall forecasts, implemented in ERDS also. The entire WRF - ERDS workflow was applied to two of the most severe heavy rainfall events that affected Italy in 2020. The first case study is related to an intense rainfall event that affected Toscana during the afternoon and the evening of 4th June 2020. In this case, the Italian Civil Protection issued an orange alert for thunderstorms, on a scale from yellow (low) to orange (medium) to red (high). In several locations of the northern part of the Region more than 100 mm of rainfall were recorded in 3 hours, corresponding to an estimated return period equal to or greater than 200 years. As far as the 24-hours time interval concerns, instead, the estimated return period decreases to 10-50 years. Despite the slight underestimation, WRF model was able to properly forecast the spatial distribution of the rainfall pattern. In addition, thanks to WRF data, precise information about the locations that would be affected by the event were available in the early morning, several hours before the event affected these areas. The second case study is instead related to the heavy rainfall event that affected Palermo (Southern Italy) during the afternoon of 15th July 2020. According to SIAS (Servizio Informativo Agrometeorologico Siciliano) more than 130 mm of rain fell in about 2.5 hours, producing widespread damages due to urban flooding phenomena. The event was not properly forecasted by meteorological models operational at the time of the event, and the Italian Civil Protection did not issue an alert on that area (including Palermo). During that day, in fact, only a yellow alert for thunderstorms was issued on northern-central and western Sicily. Within LEXIS, no alert was issued using GFS data due to the severe underestimation of the amount of forecasted rainfall. Conversely, a WRF modelling experiment (three nested domain with 22.5, 7.5 and 2.5 km grid spacing, innermost over Italy) was executed, by assimilating the National radar reflectivity mosaic and in situ weather stations from the Italian Civil Protection Department, and it resulted in the prediction of a peak rainfall depth of about 35 mm in 1 hour and 55 mm in 3 hours, roughly 30 km far apart the actual affected area, thus values supportive at least a yellow alert over the Palermo area. Obtained results highlight how improved rainfall forecast, made available thanks to the use of HPC resources, significantly increases the capabilities of an operational early warning system in the extreme rainfall detection. Global-scale low-resolution rainfall forecasts like GFS one are in fact widely known as good sources of information for the identification of large-scale precipitation patterns but lack precision for local-scale applications
An Efficient Monte Carlo-based Probabilistic Time-Dependent Routing Calculation Targeting a Server-Side Car Navigation System
Incorporating speed probability distribution to the computation of the route
planning in car navigation systems guarantees more accurate and precise
responses. In this paper, we propose a novel approach for dynamically selecting
the number of samples used for the Monte Carlo simulation to solve the
Probabilistic Time-Dependent Routing (PTDR) problem, thus improving the
computation efficiency. The proposed method is used to determine in a proactive
manner the number of simulations to be done to extract the travel-time
estimation for each specific request while respecting an error threshold as
output quality level. The methodology requires a reduced effort on the
application development side. We adopted an aspect-oriented programming
language (LARA) together with a flexible dynamic autotuning library (mARGOt)
respectively to instrument the code and to take tuning decisions on the number
of samples improving the execution efficiency. Experimental results demonstrate
that the proposed adaptive approach saves a large fraction of simulations
(between 36% and 81%) with respect to a static approach while considering
different traffic situations, paths and error requirements. Given the
negligible runtime overhead of the proposed approach, it results in an
execution-time speedup between 1.5x and 5.1x. This speedup is reflected at
infrastructure-level in terms of a reduction of around 36% of the computing
resources needed to support the whole navigation pipeline
Pegasus: Performance Engineering for Software Applications Targeting HPC Systems
Developing and optimizing software applications for high performance and energy efficiency is a very challenging task, even when considering a single target machine. For instance, optimizing for multicore-based computing systems requires in-depth knowledge about programming languages, application programming interfaces, compilers, performance tuning tools, and computer architecture and organization. Many of the tasks of performance engineering methodologies require manual efforts and the use of different tools not always part of an integrated toolchain. This paper presents Pegasus, a performance engineering approach supported by a framework that consists of a source-to-source compiler, controlled and guided by strategies programmed in a Domain-Specific Language, and an autotuner. Pegasus is a holistic and versatile approach spanning various decision layers composing the software stack, and exploiting the system capabilities and workloads effectively through the use of runtime autotuning. The Pegasus approach helps developers by automating tasks regarding the efficient implementation of software applications in multicore computing systems. These tasks focus on application analysis, profiling, code transformations, and the integration of runtime autotuning. Pegasus allows developers to program their strategies or to automatically apply existing strategies to software applications in order to ensure the compliance of non-functional requirements, such as performance and energy efficiency. We show how to apply Pegasus and demonstrate its applicability and effectiveness in a complex case study, which includes tasks from a smart navigation system
A System Development Kit for Big Data Applications on FPGA-based Clusters: The EVEREST Approach
Modern big data workflows are characterized by computationally intensive
kernels. The simulated results are often combined with knowledge extracted from
AI models to ultimately support decision-making. These energy-hungry workflows
are increasingly executed in data centers with energy-efficient hardware
accelerators since FPGAs are well-suited for this task due to their inherent
parallelism. We present the H2020 project EVEREST, which has developed a system
development kit (SDK) to simplify the creation of FPGA-accelerated kernels and
manage the execution at runtime through a virtualization environment. This
paper describes the main components of the EVEREST SDK and the benefits that
can be achieved in our use cases.Comment: Accepted for presentation at DATE 2024 (multi-partner project
session
Constraining 2HDM by Present and Future Muon(g-2) Data
Constraints on the general 2HDM ("Model II") are obtained from the existing
data including limits on Higgs bosons masses from LEP I data. We
consider separately two cases: with a light scalar and with a light
pseudoscalar , assuming . The charged Higgs
contribution is also included. It is found that already the present
data improve limits obtained recently by ALEPH collaboration on
\tb for the mass of the pseudoscalar below \mr 2 GeV. The improvement in
the accuracy by factor 20 in the forthcoming E821 experiment may lead to more
stringent, than provided by ALEPH group, limits up to 30 GeV if the
mass difference between and is . Similar results should hold
for a light scalar scenario as well.Comment: 19 pages, including 5 figure
Event-Related Potential Effects of Object Recognition depend on Attention and Part-Whole Configuration
The effects of spatial attention and part-whole configuration on recognition of repeated objects were investigated with behavioral and event-related potential (ERP) measures. Short-term repetition effects were measured for probe objects as a function of whether a preceding prime object was shown as an intact image or coarsely scrambled (split into two halves) and whether or not it had been attended during the prime display. In line with previous behavioral experiments, priming effects were observed from both intact and split primes for attended objects, but only from intact (repeated sameview) objects when they were unattended. These behavioral results were reflected in ERP waveforms at occipitalâtemporal locations as more negative-going deflections for repeated items in the time window between 220 and 300 ms after probe onset (N250r).Attended intact images showed generally more enhanced repetition effects than split ones. Unattended images showed repetition effects only when presented in an intact configuration, and this finding was limited to the right-hemisphere electrodes. Repetition effects in earlier (before 200 ms) time windows were limited to attended conditions at occipito-temporal sites during the N1, a component linked to the encoding of object structure, while repetition effects at central locations during the same time window (P150) were found for attended and unattended probes but only when repeated in the same intact configuration. The data indicate that view-generalization is mediated by a combination of analytic (part-based) representations and automatic view-dependent representations
Science with the Einstein Telescope: a comparison of different designs
The Einstein Telescope (ET), the European project for a third-generation
gravitational-wave detector, has a reference configuration based on a
triangular shape consisting of three nested detectors with 10 km arms, where in
each arm there is a `xylophone' configuration made of an interferometer tuned
toward high frequencies, and an interferometer tuned toward low frequencies and
working at cryogenic temperature. Here, we examine the scientific perspectives
under possible variations of this reference design. We perform a detailed
evaluation of the science case for a single triangular geometry observatory,
and we compare it with the results obtained for a network of two L-shaped
detectors (either parallel or misaligned) located in Europe, considering
different choices of arm-length for both the triangle and the 2L geometries. We
also study how the science output changes in the absence of the low-frequency
instrument, both for the triangle and the 2L configurations. We examine a broad
class of simple `metrics' that quantify the science output, related to compact
binary coalescences, multi-messenger astronomy and stochastic backgrounds, and
we then examine the impact of different detector designs on a more specific set
of scientific objectives.Comment: 197 pages, 72 figure
Taxanes trigger cancer cell killing in vivo by inducing non-canonical T cell cytotoxicity
Although treatment with taxanes does not always lead to clinical benefit, all patients are at risk of their detrimental side effects such as peripheral neuropathy. Understanding the in vivo mode of action of taxanes can help design improved treatment regimens. Here, we demonstrate that in vivo, taxanes directly trigger T cells to selectively kill cancer cells in a non-canonical, T cell receptor-independent manner. Mechanistically, taxanes induce T cells to release cytotoxic extracellular vesicles, which lead to apoptosis specifically in tumor cells while leaving healthy epithelial cells intact. We exploit these findings to develop an effective therapeutic approach, based on transfer of T cells pre-treated with taxanes ex vivo, thereby avoiding toxicity of systemic treatment. Our study reveals a different in vivo mode of action of one of the most commonly used chemotherapies, and opens avenues to harness T cell-dependent anti-tumor effects of taxanes while avoiding systemic toxicity
Taxanes trigger cancer cell killing in vivo by inducing non-canonical T cell cytotoxicity
Although treatment with taxanes does not always lead to clinical benefit, all patients are at risk of their detrimental side effects such as peripheral neuropathy. Understanding the in vivo mode of action of taxanes can help design improved treatment regimens. Here, we demonstrate that in vivo, taxanes directly trigger T cells to selectively kill cancer cells in a non-canonical, T cell receptor-independent manner. Mechanistically, taxanes induce T cells to release cytotoxic extracellular vesicles, which lead to apoptosis specifically in tumor cells while leaving healthy epithelial cells intact. We exploit these findings to develop an effective therapeutic approach, based on transfer of T cells pre-treated with taxanes ex vivo, thereby avoiding toxicity of systemic treatment. Our study reveals a different in vivo mode of action of one of the most commonly used chemotherapies, and opens avenues to harness T cell-dependent anti-tumor effects of taxanes while avoiding systemic toxicity
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