Efficient And Scalable Evaluation Of Continuous, Spatio-temporal Queries In Mobile Computing Environments

A variety of research exists for the processing of continuous queries in large, mobile environments. Each method tries, in its own way, to address the computational bottleneck of constantly processing so many queries. For this research, we present a two-pronged approach at addressing this problem. Firstly, we introduce an efficient and scalable system for monitoring traditional, continuous queries by leveraging the parallel processing capability of the Graphics Processing Unit. We examine a naive CPU-based solution for continuous range-monitoring queries, and we then extend this system using the GPU. Additionally, with mobile communication devices becoming commodity, location-based services will become ubiquitous. To cope with the very high intensity of location-based queries, we propose a view oriented approach of the location database, thereby reducing computation costs by exploiting computation sharing amongst queries requiring the same view. Our studies show that by exploiting the parallel processing power of the GPU, we are able to significantly scale the number of mobile objects, while maintaining an acceptable level of performance. Our second approach was to view this research problem as one belonging to the domain of data streams. Several works have convincingly argued that the two research fields of spatiotemporal data streams and the management of moving objects can naturally come together. [IlMI10, ChFr03, MoXA04] For example, the output of a GPS receiver, monitoring the position of a mobile object, is viewed as a data stream of location updates. This data stream of location updates, along with those from the plausibly many other mobile objects, is received at a centralized server, which processes the streams upon arrival, effectively updating the answers to the currently active queries in real time. iv For this second approach, we present GEDS, a scalable, Graphics Processing Unit (GPU)-based framework for the evaluation of continuous spatio-temporal queries over spatiotemporal data streams. Specifically, GEDS employs the computation sharing and parallel processing paradigms to deliver scalability in the evaluation of continuous, spatio-temporal range queries and continuous, spatio-temporal kNN queries. The GEDS framework utilizes the parallel processing capability of the GPU, a stream processor by trade, to handle the computation required in this application. Experimental evaluation shows promising performance and shows the scalability and efficacy of GEDS in spatio-temporal data streaming environments. Additional performance studies demonstrate that, even in light of the costs associated with memory transfers, the parallel processing power provided by GEDS clearly counters and outweighs any associated costs. Finally, in an effort to move beyond the analysis of specific algorithms over the GEDS framework, we take a broader approach in our analysis of GPU computing. What algorithms are appropriate for the GPU? What types of applications can benefit from the parallel and stream processing power of the GPU? And can we identify a class of algorithms that are best suited for GPU computing? To answer these questions, we develop an abstract performance model, detailing the relationship between the CPU and the GPU. From this model, we are able to extrapolate a list of attributes common to successful GPU-based applications, thereby providing insight into which algorithms and applications are best suited for the GPU and also providing an estimated theoretical speedup for said GPU-based application

High performance FPGA and GPU complex pattern matching over spatio-temporal streams

The wide and increasing availability of collected data in the form of trajectories has led to research advances in behavioral aspects of the monitored subjects (e.g., wild animals, people, and vehicles). Using trajectory data harvested by devices, such as GPS, RFID and mobile devices, complex pattern queries can be posed to select trajectories based on specific events of interest. In this paper, we present a study on FPGA- and GPU-based architectures processing complex patterns on streams of spatio-temporal data. Complex patterns are described as regular expressions over a spatial alphabet that can be implicitly or explicitly anchored to the time domain. More importantly, variables can be used to substantially enhance the flexibility and expressive power of pattern queries. Here we explore the challenges in handling several constructs of the assumed pattern query language, with a study on the trade-offs between expressiveness, scalability and matching accuracy. We show an extensive performance evaluation where FPGA and GPU setups outperform the current state-of-the-art (single-threaded) CPU-based approaches, by over three orders of magnitude for FPGAs (for expressive queries) and up to two orders of magnitude for certain datasets on GPUs (and in some cases slowdown). Unlike software-based approaches, the performance of the proposed FPGA and GPU solutions is only minimally affected by the increased pattern complexity

Multicentre observational study on multisystem inflammatory syndrome related to COVID-19 in Argentina

Background: The impact of the pediatric inflammatory multisystem syndrome temporally associated with SARS-CoV-2 (PIMS-TS) in low- and middle-income countries remains poorly understood. Our aim was to understand the characteristics and outcomes of PIMS-TS in Argentina. Methods: This observational, prospective, and retrospective multicenter study enrolled patients younger than 18 years-old manifesting PIMS-TS, Kawasaki disease (KD) or Kawasaki shock syndrome (KSS) between March 2020 and May 2021. Patients were followed-up until hospital discharge or death (one case). The primary outcome was pediatric intensive care unit (PICU) admission. Multiple logistic regression was used to identify variables predicting PICU admission. Results: Eighty-one percent, 82%, and 14% of the 176 enrolled patients fulfilled the suspect case criteria for PIMS-TS, KD, and KSS, respectively. Temporal association with SARS-CoV-2 was confirmed in 85% of the patients and 38% were admitted to the PICU. The more common clinical manifestations were fever, abdominal pain, rash, and conjunctival injection. Lymphopenia was more common among PICU-admitted patients (87% vs. 51%, p < 0.0001), who also showed a lower platelet count and higher plasmatic levels of inflammatory and cardiac markers. Mitral valve insufficiency, left ventricular wall motion alterations, pericardial effusion, and coronary artery alterations were observed in 30%, 30%, 19.8%, and 18.6% of the patients, respectively. Days to initiation of treatment, rash, lymphopenia, and low platelet count were significant independent contributions to PICU admission. Conclusion: Rates of severe outcomes of PIMS-TS in the present study agreed with those observed in high-income countries. Together with other published studies, this work helps clinicians to better understand this novel clinical entity.Fil: Vainstein, Eduardo. Gobierno de la Ciudad de Buenos Aires. Hospital General de Niños "Ricardo Gutiérrez"; ArgentinaFil: Baleani, Silvia. Gobierno de la Ciudad de Buenos Aires. Hospital General de Niños "Ricardo Gutiérrez"; ArgentinaFil: Urrutia, Luis. Gobierno de la Ciudad de Buenos Aires. Hospital de Pediatría "Juan P. Garrahan"; ArgentinaFil: Affranchino, Nicolás. Gobierno de la Ciudad de Buenos Aires. Hospital de Pediatría "Juan P. Garrahan"; ArgentinaFil: Ackerman, Judith. Gobierno de la Ciudad de Buenos Aires. Hospital General de Niños Pedro Elizalde (ex Casa Cuna); ArgentinaFil: Cazalas, Mariana. Gobierno de la Ciudad de Buenos Aires. Hospital General de Niños "Ricardo Gutiérrez"; ArgentinaFil: Goldsman, Alejandro. Gobierno de la Ciudad de Buenos Aires. Hospital General de Niños "Ricardo Gutiérrez"; ArgentinaFil: Sardella, Angela. Gobierno de la Ciudad de Buenos Aires. Hospital General de Niños "Ricardo Gutiérrez"; ArgentinaFil: Tolin, Ana Laura. Gobierno de la Provincia de Mendoza. Hospital Pediátrico Humberto Notti; ArgentinaFil: Goldaracena, Pablo. Provincia de Buenos Aires. Ministerio de Salud. Hospital de Niños "Sor María Ludovica" de La Plata; ArgentinaFil: Fabi, Mariana. Provincia de Buenos Aires. Ministerio de Salud. Hospital de Niños "Sor María Ludovica" de La Plata; ArgentinaFil: Cosentino, Mariana. Hospital Británico de Buenos Aires; ArgentinaFil: Magliola, Ricardo. Hospital Británico de Buenos Aires; ArgentinaFil: Roggiero, Gustavo. Provincia de Buenos Aires. Ministerio de Salud. Hospital Alta Complejidad en Red El Cruce Dr. Néstor Carlos Kirchner Samic; ArgentinaFil: Manso, Paula. Provincia de Buenos Aires. Ministerio de Salud. Hospital Alta Complejidad en Red El Cruce Dr. Néstor Carlos Kirchner Samic; ArgentinaFil: Triguy, Jésica. Gobierno de la Provincia de Mendoza. Hospital Pediátrico Humberto Notti; ArgentinaFil: Ballester, Celeste. Gobierno de la Provincia de Mendoza. Hospital Pediátrico Humberto Notti; ArgentinaFil: Cervetto, Vanesa. Gobierno de la Ciudad de Buenos Aires. Hospital General de Niños Pedro Elizalde (ex Casa Cuna); ArgentinaFil: Vaccarello, María. Sanatorio de la Trinidad; ArgentinaFil: De Carli, Domingo Norberto. Clínica del Niño de Quilmes; ArgentinaFil: De Carli, Maria Estela. Clínica del Niño de Quilmes; ArgentinaFil: Ciotti, Ana Laura. Hospital Nacional Profesor Alejandro Posadas; ArgentinaFil: Sicurello, María Irene. Gobierno de la Ciudad de Buenos Aires. Hospital General de Niños "Ricardo Gutiérrez"; ArgentinaFil: Rios Leiva, Cecilia. Provincia de Buenos Aires. Ministerio de Salud. Hospital Interzonal de Agudos "Eva Perón"; ArgentinaFil: Villalba, Claudia. Gobierno de la Ciudad de Buenos Aires. Hospital de Pediatría "Juan P. Garrahan"; ArgentinaFil: Hortas, María. Sanatorio de la Trinidad; ArgentinaFil: Peña, Sonia. Gobierno de la Provincia de Mendoza. Hospital Pediátrico Humberto Notti; ArgentinaFil: González, Gabriela. Gobierno de la Provincia de Mendoza. Hospital Pediátrico Humberto Notti; ArgentinaFil: Zold, Camila Lidia. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Houssay. Instituto de Fisiología y Biofísica Bernardo Houssay. Universidad de Buenos Aires. Facultad de Medicina. Instituto de Fisiología y Biofísica Bernardo Houssay; ArgentinaFil: Murer, Mario Gustavo. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Houssay. Instituto de Fisiología y Biofísica Bernardo Houssay. Universidad de Buenos Aires. Facultad de Medicina. Instituto de Fisiología y Biofísica Bernardo Houssay; ArgentinaFil: Grippo, M.. No especifíca;Fil: Vázquez, H.. No especifíca;Fil: Morós, C.. No especifíca;Fil: Di Santo, M.. No especifíca;Fil: Villa, A.. No especifíca;Fil: Lazota, P.. No especifíca;Fil: Foti, M.. No especifíca;Fil: Napoli, N.. No especifíca;Fil: Katsikas, M. M.. No especifíca;Fil: Tonello, L.. No especifíca;Fil: Peña, J.. No especifíca;Fil: Etcheverry, M.. No especifíca;Fil: Iglesias, D.. No especifíca;Fil: Alcalde, A. L.. No especifíca;Fil: Bruera, M.J.. No especifíca;Fil: Bruzzo, V.. No especifíca;Fil: Giordano, P.. No especifíca;Fil: Pena Acero, F.. No especifíca;Fil: Netri Pelandi, G.. No especifíca;Fil: Pastaro, D.. No especifíca;Fil: Bleiz, J.. No especifíca;Fil: Rodríguez, M. F.. No especifíca;Fil: Laghezza, L.. No especifíca;Fil: Molina, M. B.. No especifíca;Fil: Patynok, N.. No especifíca;Fil: Chatelain, M. S.. No especifíca;Fil: Aguilar, M. J.. No especifíca;Fil: Gamboa, J.. No especifíca;Fil: Cervan, M.. No especifíca;Fil: Ruggeri, A.. No especifíca;Fil: Marinelli, I.. No especifíca;Fil: Checcacci, E.. No especifíca;Fil: Meregalli, C.. No especifíca;Fil: Damksy Barbosa, J.. No especifíca;Fil: Fernie, L.. No especifíca;Fil: Fernández, M. J.. No especifíca;Fil: Saenz Tejeira, M.M.. No especifíca;Fil: Cereigido, C.. No especifíca;Fil: Nunell, A.. No especifíca;Fil: Villar, D.. No especifíca;Fil: Mansilla, A. D.. No especifíca;Fil: Darduin, M. D.. No especifíca

Gamma-ray radiation effects in graphene-based transistors with h-BN nanometer film substrates

Radiation effects on graphene field effect transistors (GFETs) with hexagonal boron nitride (h-BN) thin film substrates are investigated using 60Co gamma-ray radiation. This study examines the radiation response using many samples with varying h-BN film thicknesses (1.6 and 20 nm thickness) and graphene channel lengths (5 and 10 μm). These samples were exposed to a total ionizing dose of approximately 1 Mrad(Si). I-V measurements were taken at fixed time intervals between irradiations and postirradiation. Dirac point voltage and current are extracted from the I-V measurements, as well as mobility, Dirac voltage hysteresis, and the total number of GFETs that remain properly operational. The results show a decrease in Dirac voltage during irradiation, with a rise of this voltage and permanent drop in Dirac current postirradiation. 1.6 nm h-BN substrate GFETs show an increase in mobility during irradiation, which drops back to preirradiation conditions in postirradiation measurements. Hysteretic changes to the Dirac voltage are the strongest during irradiation for the 20 nm thick h-BN substrate GFETs and after irradiation for the 1.6 nm thick h-BN GFETs. Failure rates were similar for most GFET types during irradiation; however, after irradiation, GFETs with 20 nm h-BN substrates experienced substantially more failures compared to 1.6 nm h-BN substrate GFETs

Position-dependent and millimetre-range photodetection in phototransistors with micrometre-scale graphene on SiC.

The extraordinary optical and electronic properties of graphene make it a promising component of high-performance photodetectors. However, in typical graphene-based photodetectors demonstrated to date, the photoresponse only comes from specific locations near graphene over an area much smaller than the device size. For many optoelectronic device applications, it is desirable to obtain the photoresponse and positional sensitivity over a much larger area. Here, we report the spatial dependence of the photoresponse in backgated graphene field-effect transistors (GFET) on silicon carbide (SiC) substrates by scanning a focused laser beam across the GFET. The GFET shows a nonlocal photoresponse even when the SiC substrate is illuminated at distances greater than 500 µm from the graphene. The photoresponsivity and photocurrent can be varied by more than one order of magnitude depending on the illumination position. Our observations are explained with a numerical model based on charge transport of photoexcited carriers in the substrate