Miracle-dz/UniFAR-FTUs huggingface dataset:
A preliminary study of incorporating GPUs in the Hadoop framework

Fine-grained parallel processors can be employed as accelerators in MapReduce clusters to improve the completion time of MapReduce jobs or to substantially reduce the size of the clusters required to achieve a desired parallel speedup. However, significant architectural differences between conventional CPUs and accelerators pose new challenges for effective scheduling of MapReduce tasks on individual cluster nodes. In this paper, we present a Hadoop-based framework that allows employing both CPUs and GPUs for MapReduce-type applications. We base a novel framework, called Surena, on existing work that allows writing MapReduce applications for GPUs and they incorporate it in the overall Hadoop framework. In particular, we show that by using simple scheduling optimizations, Surena can fully utilize GPUs during the map phase of MapReduce jobs which is often the dominant component in the total execution time of MapReduce applications. Our performance results shows speedups of up to 21x for our framework compare to Hadoop.
An industrial case study of Coman's automated task detection algorithm: What Worked, What Didn't, and Why

Programmers need explicit tool support for software maintenance tasks, and a prerequisite for this is an understanding of where the boundaries between distinct tasks lie. Asking developers to indicate manually when they switch tasks is disruptive to their normal work flow, so researchers have sought ways to infer task boundaries automatically based on the content of the interaction histories with the IDE. Coman previously reported a fully automated algorithm that achieved 80% accuracy in a lab validation study. In this paper, we evaluate the use of this algorithm within an industrial setting. We found two problems: first, a large number of the tasks identified are in fact only sessions or subparts of a larger task; second, the demonstrable effects of interruptions are not considered. We argue that the problem of task boundary detection consists of two sub-problems: first, detecting task sessions; and second, linking task sessions. Coman's algorithm only partially addresses the first, and ignores the second.
Estimating quality of life in acute venous thrombosis.

IMPORTANCE Future funding for new treatments in venous thromboembolism will be guided by cost-utility analyses. There is little available information on the utility of acute venous thromboembolism, limiting the validity of economic analyses. OBJECTIVE To measure the quality of life in the health states relating to thromboembolism cost-utility analyses. DESIGN A prospective cohort study. SETTING A single-center, university-affiliated thrombosis clinic. PARTICIPANTS Two hundred sixteen thrombosis clinic patients with a history of lower limb deep vein thrombosis (DVT) or pulmonary embolism (PE). EXPOSURES Participants consented to take a standard gamble interview. Each participant rated the quality of life in acute DVT, acute PE, and bleeding complication health states. MAIN OUTCOMES AND MEASURES The standard gamble measured quality of life (utility value) for acute DVT, acute PE, major intracranial bleeding event, minor intracranial bleeding event, and gastrointestinal bleeding event. RESULTS Two hundred fifteen responses were included in the analysis. Twenty-six percent had experienced both PE and DVT; 54%, DVT alone; and 20%, PE alone. Forty-two percent had experienced more than 1 episode of thrombosis, and 23% had had cancer-associated thrombosis. We found the median utility for acute DVT was 0.81 (interquartile range [IQR], 0.55-0.94); acute PE, 0.75 (IQR, 0.45-0.91); major intracranial bleeding event, 0.15 (IQR, 0.00-0.65); minor intracranial bleeding event, 0.75 (IQR, 0.55-0.92); and gastrointestinal bleeding event, 0.65 (IQR, 0.15-0.86). The median length of symptoms for DVT or PE was 1 week (IQR, <1-3 weeks). CONCLUSIONS AND RELEVANCE To our knowledge, this is the largest published study on utilities in which the participants had personal experience of venous thromboembolism. We present unique information for economic analyses but have also identified future challenges for research in this area. Our summary results differ from those previously published, and we found wide variation in individual responses.
Race-Speci fi c Predictors of Mortality in Intracerebral Hemorrhage : Differential Impacts of Intraventricular Hemorrhage and Age Among Blacks and Whites

Methods and Results-—We used 2 databases, the Johns Hopkins clinical stroke database and the Nationwide Inpatient Sample (NIS). We included 226 patients with the primary diagnosis of spontaneous ICH from our stroke database between 2010 and 2013; in the NIS, 42 077 patients met inclusion criteria. Logistic regression was used to assess differences in predictors of mortality in blacks compared to whites. In our clinical stroke database, Glasgow Coma Scale (GCS; P=0.016), ICH volume (P=0.013), intraventricular haemorrhage (IVH; P=0.023), and diabetes mellitus (P=0.037) were predictors of mortality in blacks, whereas GCS (P=0.007), ICH volume (P=0.005), age (P=0.002), chronic kidney disease (P=0.003), and smoking (P=0.010) predicted mortality in whites. Among patients with IVH, blacks had over 7 times higher odds of mortality compared to whites (odds ratio [OR], 7.27; P value for interaction, 0.017) and were more likely to present with hydrocephalus (OR, 2.76; P=0.026). In the NIS, black ICH patients had higher rates of external ventricular drain (EVD) placement compared to whites (9.7% vs 5.0%; P<0.001) and were more likely to develop hydrocephalus (OR, 1.32; 95% CI, 1.20–1.46). Comparison of a race-specific ICH score to the original ICH score showed that the various ICH score components have differential relevance for ICH score performance by race.
Volcano - An Extensible and Parallel Query Evaluation System

To investigate the interactions of extensibility and parallelism in database query processing, we have developed a new dataflow query execution system called Volcano. The Volcano effort provides a rich environment for research and education in database systems design, heuristics for query optimization, parallel query execution, and resource allocation. Volcano uses a standard interface between algebra operators, allowing easy addition of new operators and operator implementations. Operations on individual items, e.g., predicates, are imported into the query processing operators using support functions. The semantics of support functions is not prescribed; any data type including complex objects and any operation can be realized. Thus, Volcano is extensible with new operators, algorithms, data types, and type-specific methods. Volcano includes two novel meta-operators. The choose-plan meta-operator supports dynamic query evaluation plans that allow delaying selected optimization decisions until run-time, e.g., for embedded queries with free variables. The exchange meta-operator supports intra-operator parallelism on partitioned datasets and both vertical and horizontal inter-operator parallelism, translating between demand-driven dataflow within processes and data-driven dataflow between processes. All operators, with the exception of the exchange operator, have been designed and implemented in a single-process environment, and parallelized using the exchange operator. Even operators not yet designed can be parallelized using this new operator if they use and provide the interator interface. Thus, the issues of data manipulation and parallelism have become orthogonal, making Volcano the first implemented query execution engine that effectively combines extensibility and parallelism. >
Quantitative analysis of vector code

In this paper we present the results of a detailed simulation study of the execution of vector programs on a single processor of a Convex C3480 machine, using a subset of the Perfect Club benchmarks. We are interested in evaluating several cost/performance tradeoffs that the machine designers made in order to assess which features of the architecture severely limit the performance attainable. We present the detailed usage of the vector functional units and a study of the kinds of resource conflicts that stall the machine. The results obtained show that the resources of the vector architecture are not efficiently used mainly due to the single bus memory architecture. Other severe limitations of the machine turn out to be the lack of chaining between vector loads and vector computations, and the lack of a second general purpose functional unit. We also present some data about the port pressure on the vector register file and we see that stalls due to port conflicts are relatively high. We also consider the slow-down introduced by spill code and find that the limited number of vector registers also limits performance. <<ETX>>
Supercontinuum Generation in Photonic Crystal Fibers by use of an Ultrashort Pulse Laser with a Cr:LiSAF Crystal

Abstract: We present the generation of two supercontinuum spectrums, in wavelength intervals of 580 to 950 nm and of 450 to 950 nm. These spectrums were obtained in two different photonic crystal fibers using an ultrashort pulse laser with low repetition of 41 MHz developed with a Cr:LiSAF crystal. With the results obtained, tunable low cost sources and applications in the field of frequency combs as calibration references for standard lasers can be developed.
Caching in the Distributed Environment

The impact of cache is well understood in the system-design domain. While the concept of cache is extensively utilized in the von Neumann architecture, the same is not true for the distributed-computing architecture. For example, consider a three-tiered Web-based business application running on a commercial RDBMS. Every time a new Web page loads, many database calls are made to fill the drop down lists on the page. Performance of the application is greatly affected by the unnecessary database calls and the network traffic between the Web server and the database server.
Competitive Replacement of Invasive Congeners May Relax Impact on Native Species: Interactions among Zebra, Quagga, and Native Unionid Mussels

Determining when and where the ecological impacts of invasive species will be most detrimental and whether the effects of multiple invaders will be superadditive, or subadditive, is critical for developing global management priorities to protect native species in advance of future invasions. Over the past century, the decline of freshwater bivalves of the family Unionidae has been greatly accelerated by the invasion of Dreissena. The purpose of this study was to evaluate the current infestation rates of unionids by zebra (Dreissena polymorpha) and quagga (D. rostriformis bugensis) mussels in the lower Great Lakes region 25 years after they nearly extirpated native unionids. In 2011–2012, we collected infestation data for over 4000 unionids from 26 species at 198 nearshore sites in lakes Erie, Ontario, and St. Clair, the Detroit River, and inland Michigan lakes and compared those results to studies from the early 1990s. We found that the frequency of unionid infestation by Dreissena recently declined, and the number of dreissenids attached to unionids in the lower Great Lakes has fallen almost ten-fold since the early 1990s. We also found that the rate of infestation depends on the dominant Dreissena species in the lake: zebra mussels infested unionids much more often and in greater numbers. Consequently, the proportion of infested unionids, as well as the number and weight of attached dreissenids were lower in waterbodies dominated by quagga mussels. This is the first large-scale systematic study that revealed how minor differences between two taxonomically and functionally related invaders may have large consequences for native communities they invade.
Improving Signal To Noise Ratio And Time Resolution For Solid-State Nanopore Measurements

Nanopores have seen broad applicability as single-molecule sensors because of their spatiotemporally localized transduction and high intrinsic gain. In this dissertation, we seek to increase the bandwidths accessible to nanopore measurements through improvements to nanopores, associated measurements electronics, and their integration. Solid-state pores, in particular, can generate signals that are often more than an order of magnitude larger than their biological counterparts. These larger signals make solid-state pores much more amenable to high-bandwidth measurements. Earlier work showed DNA translocation measurements with sub-microsecond temporal resolution using silicon nitride nanopores. In this dissertation, we further improve the temporal resolution to 100 ns by a recently developed CMOS nanopore amplifier (CNP2) with 10 MHz bandwidth capacity using silicon nitride pores thinned with electronic beam techniques to < 3 nm thickness, with pore diameter compatible for ssDNA that hugs the molecules as it translocates. Overall signal-to-noise-ratio-limited bandwidth is optimized through appropriate choice of pore size, salt and bias voltage. To further reduce Cpore, we are passivating silicon-nitride pores with thick dielectrics. We have previously reported on fused-silica based solid state membrane carrying platform which allows us to reduce Cpore to values < 1 pF. We also make use of this versatile, low-capacitance platform to suspend other thin, two-dimensional membrane such as MoS2 to take advantage of the atomic thickness of these 2D materials to increase spatial resolution. In this dissertation, we present data of improvements in DNA translocation recordings in both time resolution and signal to noise ratio (SNR) from combining our custom electronics with these low-capacitance, high-conductance ultra-thin pores. The ultra-low measurement noise allows us to observe an excess current dependent noise due to the pore itself, and the rich dynamics as DNA translocate through the nanopore. We also explore other applications beyond single nanopore such as nanopore arrays and nanoribbon-nanopore devices. Degree Type Dissertation Degree Name Doctor of Philosophy (PhD) Graduate Group Physics & Astronomy First Advisor Marija Drndic Second Advisor Eleni Katifori
FINDDD: A fast 3D descriptor to characterize textiles for robot manipulation

Most current depth sensors provide 2.5D range images in which depth values are assigned to a rectangular 2D array. In this paper we take advantage of this structured information to build an efficient shape descriptor which is about two orders of magnitude faster than competing approaches, while showing similar performance in several tasks involving deformable object recognition. Given a 2D patch surrounding a point and its associated depth values, we build the descriptor for that point, based on the cumulative distances between their normals and a discrete set of normal directions. This processing is made very efficient using integral images, even allowing to compute descriptors for every range image pixel in a few seconds. The discriminative power of our descriptor, dubbed FINDDD, is evaluated in three different scenarios: recognition of specific cloth wrinkles, instance recognition from geometry alone, and detection of reliable and informed grasping points.
Static and dynamic characterization of a >13kV SiC p-ETO device

This study addresses the transient and steady-state performance of a >13 kV SiC p-ETO. The developed SiC p-ETO is based on a 1 cm2, 15 kV SiC p-GTO with an extremely low differential resistance. Static performance of the device, including the on-state voltage drop at different temperatures and different currents is carried out in this paper. Furthermore, transient performance of the device, including the turn off energy and also the Safe Operating Area (SOA) of the device has been studied. Also, the superior performance of the p-type SiC-ETO has been exploited to design and implement a solid-state circuit breaker. The studies verify the superiority of the SiC pETO compared to other solid state devices for this application.
Kainic Acid-Induced Seizures Enhance Dentate Gyrus Inhibition by Downregulation of GABAB Receptors

Seizures cause a persistent enhancement in dentate synaptic inhibition concurrent with, and possibly compensatory for, seizure-induced hippocampal hyperexcitability. To study this phenomenon, we evoked status epilepticus in rats with systemic kainic acid (KA), and 2 weeks later assessed granule cell inhibition with paired-pulse stimulation of the perforant path (PP) in vitro. Controls demonstrated three components of paired-pulse inhibition: early inhibition (10–30 msec), intermediate facilitation (30–120 msec), and late inhibition (120 msec to 120 sec). After seizures, inhibition in all components was enhanced significantly. The GABAA antagonist bicuculline blocked only early enhanced inhibition, demonstrating that both GABAA and GABABpostsynaptic receptors contribute to seizure-induced enhanced inhibition. In controls, the GABAB antagonist CGP 35348 increased both GABAA and GABAB responses in granule cells, suggesting that CGP 35348 acts presynaptically, blocking receptors that suppress GABA release. In contrast, slices from KA-treated rats were markedly less sensitive to CGP 35348. To test the hypothesis that GABAB receptors regulating GABA release are downregulated after seizures, we measured paired-pulse suppression of recurrent IPSPs, or disinhibition, using mossy fiber stimuli. Early disinhibition (< 200 msec) was reduced after seizures, whereas late disinhibition remained intact. CGP 35348 blocked the early component of disinhibition in controls and, to a lesser extent, reduced disinhibition in KA slices. However, paired monosynaptic IPSPs recorded intracellularly showed no difference in disinhibition between groups. Our findings indicate that seizure-induced enhancement in dentate inhibition is caused, at least in part, by reduced GABAB function in the polysynaptic recurrent inhibitory circuit, resulting in reduced disinhibition and heightened GABA release.
Design and Control of IPMSM Sensorless Drive for Mechanical Rotor Position Estimation Capability

In this paper, a design and control method of interior permanent magnet synchronous machines (IPMSMs), which is controlled in sensorless position control mode based on high frequency signal injection, for absolute position estimation and control capability has been addressed. Although conventional motor designs with symmetric structure work well in sensorless speed and torque control modes, their actual mechanical rotor position is unidentifiable since their inductance profile is repeated for every electrical revolution. To realize the mechanical rotor position estimation capability, from a given template six-pole/nine-slot IPMSM with symmetric structure, an IPMSM design with asymmetric winding and asymmetric rotor geometry is proposed and constructed as a prototype. Based on the proposed design, the mechanical rotor position can be identified at the cost of slight degradation of basic motor characteristics such as torque ripples, harmonics in back EMF, and so on. The overall performances of the proposed IPMSM have been evaluated by rigorous finite element method simulations and experiments.
Range extension and distribution of the invasive Moreau’s Tropical House Gecko, Hemidactylus mabouia (Moreau de Jonnès, 1818) (Squamata: Gekkonidae), in Suriname

We document a population of Moreau’s Tropical House Gecko, Hemidactylus mabouia (Moreau de Jonnes, 1818), at a remote Amerindian village, Kwamalasamutu, Suriname, South America. This village is approximately 370 km southwest of the northern coastal region where the presence of H. mabouia was previously recorded. Numerous individuals and a gravid female provide evidence of a viable population. The morphological characters of the two female specimens examined fall within the range of characters recorded from African and Ascension Island specimens. Due to its overland inaccessibility, we believe that colonization occurred anthropogenically through air or boat transport from the coastal region.
Voltage regulation in mircogrid using adaptive controller

Due to fossil fuel depletion, power shortage has become a major problem in the world. This increases the importance of Distributed Energy Resources (DERs) among the consumers which in turn led to the expansion of microgrid. Microgrid operates in grid-connected mode as well as in islanded mode. Microgrid is connected to main grid in grid connected mode. In islanded mode, there is no such connection and hence the microgrid operates independently. Microgrid may move to islanded mode intentionally or unintentionally. Power in the grid is not constant which in turn affected the loads connected to the grid. Due to the Sun light variation the voltage output from the grid may not be constant. In three phase microgrid, fault in one phase causes loss of voltage stability in other two phases. PID Control has been used for microgrid voltage control but it does not work properly during fault conditions. So, an adaptive PI controller has been proposed to provide voltage regulation in microgrid which provides better performance under all the circumstances. The performance of the system employing adaptive PI controller is validated using the results obtained from the MATLAB Simulink.
Optimization of Steam Injection for Heavy Oil Reservoirs Using Reinforcement Learning

Steam injection rate through life cycle optimization (e.g., the constant rate for a long period of time) could lead to the sub-optimal performance of a thermal heavy oil recovery process. On the other hand, finding the optimal steam injection strategy (policy) represents a major challenge due to the complex dynamic of the physical phenomenon, i.e., nonlinear, slow, high order, time-varying, and potentially highly heterogeneous reservoirs. To address this challenge, the problem can be formulated as an optimal control problem that has typically been solved using adjoint state optimization and a model-predictive control (MPC) strategy. In contrast, this work presents a reinforcement learning (RL) approach in which the mathematical model of the dynamic process (SAGD) is assumed unknown. An agent is trained to find the optimal policy only through continuous interactions with the environment (e.g., numerical reservoir simulation model). At each time step, the agent executes an action (e.g., increase steam injection rate), receives a reward (e.g., net present value) and observes the new state (e.g., pressure distribution) of the environment. During this interaction, an action-value function is approximated; this function will offer for a given state of the environment the action that will maximize total future reward. This process continues for multiple simulations (episodes) of the dynamic process until convergence is achieved. In this implementation, the state-action-reward-state-action (SARSA) online policy learning algorithm is employed in which the action-value function is continually estimated after every time step and further used to choose the optimal action. The environment consists of a reservoir simulation model built using data from a reservoir located in northern Alberta. The model consists of one well pair (one injector and one producer) and production horizon of 250 days (one episode) is considered. The state of the environment is defined as cumulative, oil and water production, and water injection and for each time step; three possible actions are considered, i.e., increase, decrease or no change of current steam injection rate; and the reward represents the net present value (NPV). Additionally, stochastic gradient descent is used to approximate the action-value function. Results show that the optimal steam injection policy obtained using RL implementation improves NPV by at least 30% with more than 60% lower computation cost.