Low Latency Geo-distributed Data Analytics

Low latency analytics on geographically distributed dat-asets (across datacenters, edge clusters) is an upcoming and increasingly important challenge. The dominant approach of aggregating all the data to a single data-center significantly inflates the timeliness of analytics. At the same time, running queries over geo-distributed inputs using the current intra-DC analytics frameworks also leads to high query response times because these frameworks cannot cope with the relatively low and variable capacity of WAN links. We present Iridium, a system for low latency geo-distri-buted analytics. Iridium achieves low query response times by optimizing placement of both data and tasks of the queries. The joint data and task placement op-timization, however, is intractable. Therefore, Iridium uses an online heuristic to redistribute datasets among the sites prior to queries ’ arrivals, and places the tasks to reduce network bottlenecks during the query’s ex-ecution. Finally, it also contains a knob to budget WAN usage. Evaluation across eight worldwide EC2 re-gions using production queries show that Iridium speeds up queries by 3 × − 19 × and lowers WAN usage by 15% − 64 % compared to existing baselines

Application and testing of the L neural network with the self-consistent magnetic field model of RAM-SCB

Abstract We expanded our previous work on L neural networks that used empirical magnetic field models as the underlying models by applying and extending our technique to drift shells calculated from a physics-based magnetic field model. While empirical magnetic field models represent an average, statistical magnetospheric state, the RAM-SCB model, a first-principles magnetically self-consistent code, computes magnetic fields based on fundamental equations of plasma physics. Unlike the previous L neural networks that include McIlwain L and mirror point magnetic field as part of the inputs, the new L neural network only requires solar wind conditions and the Dst index, allowing for an easier preparation of input parameters. This new neural network is compared against those previously trained networks and validated by the tracing method in the International Radiation Belt Environment Modeling (IRBEM) library. The accuracy of all L neural networks with different underlying magnetic field models is evaluated by applying the electron phase space density (PSD)-matching technique derived from the Liouville\u27s theorem to the Van Allen Probes observations. Results indicate that the uncertainty in the predicted L is statistically (75%) below 0.7 with a median value mostly below 0.2 and the median absolute deviation around 0.15, regardless of the underlying magnetic field model. We found that such an uncertainty in the calculated L value can shift the peak location of electron phase space density (PSD) profile by 0.2 RE radially but with its shape nearly preserved. Key Points L* neural network based on RAM-SCB model is developed L* calculation accuracy is estimated by PSD matching using RBSP data L* uncertainty causes a radial shift in the electron phase space density profile

StreamJIT: A Commensal Compiler for High-Performance Stream Programming

There are many domain libraries, but despite the performance benefits of compilation, domain-specific languages are comparatively rare due to the high cost of implementing an optimizing compiler. We propose commensal compilation, a new strategy for compiling embedded domain-specific languages by reusing the massive investment in modern language virtual machine platforms. Commensal compilers use the host language's front-end, use host platform APIs that enable back-end optimizations by the host platform JIT, and use an autotuner for optimization selection. The cost of implementing a commensal compiler is only the cost of implementing the domain-specific optimizations. We demonstrate the concept by implementing a commensal compiler for the stream programming language StreamJIT atop the Java platform. Our compiler achieves performance 2.8 times better than the StreamIt native code (via GCC) compiler with considerably less implementation effort.United States. Dept. of Energy. Office of Science (X-Stack Award DE-SC0008923)Intel Corporation (Science and Technology Center for Big Data)SMART3 Graduate Fellowshi

The impact of behavioural skills training on the knowledge, skills and well-being of front line staff in the intellectual disability sector: a clustered randomised control trial

© 2019 MENCAP and International Association of the Scientific Study of Intellectual and Developmental Disabilities and John Wiley & Sons Ltd Background: Staff with varying backgrounds and educational qualifications can be effectively trained to implement procedures in line with evidence-based practice. Behavioural skills training (BST) is a competency-based training model used to effectively educate a broad selection of professionals, including front line staff, in a range of work-related skills. However, BST has yet to be evaluated in a large group-based experiment. Methods: This study involved a parallel cluster randomised control trial. Six service sites, with a total of 54 participants, were randomised to the intervention condition using the ‘coin toss’ method. The intervention condition used BST to coach intellectual disability staff in reinforcement, systematic prompting, functional communication training and task analysis. Six service sites, with a total of 50 participants, were also randomised to a control condition in which generalised training in behavioural interventions was restricted. Recruited service sites were randomly assigned to the intervention condition (N = 6, n = 54) or the control condition (N = 6, n = 50) at one point in time, immediately after recruitment and before baseline testing took place. Allocations were stratified by service type (residential or day) and geographical region. One member of the research team allocated service sites using the ‘coin toss’ method, and another member, blind to the allocations, decided which experimental arm would receive the intervention and which would be designated as control. It was not possible to mask the intervention from participants, but they were recruited prior to randomisation. Results: Participants in the intervention condition demonstrated statistically significant improvements in their knowledge scores over the study period. Participants in the control condition showed no change or a statistically significant decrease in their knowledge scores. No statistically significant changes to well-being were observed for either group. There was clear evidence of knowledge maintenance, as well as skill acquisition and subsequent generalisation to the workplace environment, among participants in the intervention condition. Participants also evaluated the BST intervention positively. Conclusions: Results support BST as a method for disseminating evidence-based practice to front line staff working with adults with intellectual and developmental disabilities

Tachyon: Reliable, Memory Speed Storage for Cluster Computing Frameworks

Tachyon is a distributed file system enabling reliable data sharing at memory speed across cluster computing frameworks. While caching today improves read workloads, writes are either network or disk bound, as replication is used for fault-tolerance. Tachyon eliminates this bottleneck by pushing lineage, a well-known technique, into the storage layer. The key challenge in making a long-running lineage-based storage system is timely data recovery in case of failures. Tachyon addresses this issue by introducing a checkpointing algorithm that guarantees bounded recovery cost and resource allocation strategies for recomputation under commonly used resource schedulers. Our evaluation shows that Tachyon outperforms in-memory HDFS by 110x for writes. It also improves the end-to-end latency of a realistic workflow by 4x. Tachyon is open source and is deployed at multiple companies.National Science Foundation (U.S.) (CISE Expeditions Award CCF-1139158)Lawrence Berkeley National Laboratory (Award 7076018)United States. Defense Advanced Research Projects Agency (XData Award FA8750-12-2-0331

Dissociative recombination and electron-impact de-excitation in CH photon emission under ITER divertor-relevant plasma conditions

For understanding carbon erosion and redeposition in nuclear fusion devices, it is important to understand the transport and chemical break-up of hydrocarbon molecules in edge plasmas, often diagnosed by emission of the CH A^2\Delta - X^2\Pi Ger\"o band around 430 nm. The CH A-level can be excited either by electron-impact or by dissociative recombination (D.R.) of hydrocarbon ions. These processes were included in the 3D Monte Carlo impurity transport code ERO. A series of methane injection experiments was performed in the high-density, low-temperature linear plasma generator Pilot-PSI, and simulated emission intensity profiles were benchmarked against these experiments. It was confirmed that excitation by D.R. dominates at T_e < 1.5 eV. The results indicate that the fraction of D.R. events that lead to a CH radical in the A-level and consequent photon emission is at least 10%. Additionally, quenching of the excited CH radicals by electron impact de-excitation was included in the modeling. This quenching is shown to be significant: depending on the electron density, it reduces the effective CH emission by a factor of 1.4 at n_e=1.3*10^20 m^-3, to 2.8 at n_e=9.3*10^20 m^-3. Its inclusion significantly improved agreement between experiment and modeling

Ureterocele in children

Catedra Chirurgie, Ortopedie şi Anesteziologie Pediatrică USMF “N. Testemiţanu”, Centrul Naţional Ştiinţifico-Practic de Chirurgie Pediatrică “Natalia Gheorghiu”, Al V-lea Congres de Urologie, Dializă şi Transplant Renal din Republica Moldova cu participare internaţională (1-13 iunie 2011)Summary. The study is based on the analyses of treatment results of 27 children ages between 0-18 years with ureterocele. The authors gave proves to the basic diagnostic and treatments methods and demonstrated that the surgical treatment is associated with good immediate and distant results

Факторы риска и диагностика патологии мочевой системы у детей

The paper gives the current data available in the literature on the risk factors and etiology of various tract diseases in children. Determination of enzyme activity in plasma and urine to evaluate the extent of damage of structural and functional elements of the nephron, specify preferential localization process and judge the prognosis of the disease.Представлены современные данные литературы по вопросам факторов риска и этиологии различных заболеваний мочевой системы у детей. Определение активности ферментов в плазме и моче позволяет оценить степень повреждения структурно-функциональных элементов нефрона, уточнить преимущественную локализацию процесса и судить о прогнозе заболевания