nils-herrmann/clean_scirepeval huggingface dataset:
Accelerating LINPACK with MPI-OpenCL on Clusters of Multi-GPU Nodes

OpenCL is an open standard to write parallel applications for heterogeneous computing systems. Since its usage is restricted to a single operating system instance, programmers need to use a mix of OpenCL and MPI to program a heterogeneous cluster. In this paper, we introduce an MPI-OpenCL implementation of the LINPACK benchmark for a cluster with multi-GPU nodes. The LINPACK benchmark is one of the most widely used benchmark applications for evaluating high performance computing systems. Our implementation is based on High Performance LINPACK (HPL) and uses the blocked LU decomposition algorithm. We address that optimizations aimed at reducing the overhead of CPUs are necessary to overcome the performance gap between the CPUs and the multiple GPUs. Our LINPACK implementation achieves 93.69 Tflops (46 percent of the theoretical peak) on the target cluster with 49 nodes, each node containing two eight-core CPUs and four GPUs.
CONTINUUM LIMIT OF TOTAL VARIATION ON POINT CLOUDS

We consider point clouds obtained as random samples of a measure on a Euclidean domain. A graph representing the point cloud is obtained by assigning weights to edges based on the distance between the points they connect. Our goal is to develop mathematical tools needed to study the consistency, as the number of available data points increases, of graph-based machine learning algorithms for tasks such as clustering. In particular, we study when is the cut capacity, and more generally total variation, on these graphs a good approximation of the perimeter (total variation) in the continuum setting. We address this question in the setting of Γ-convergence. We obtain almost optimal conditions on the scaling, as number of points increases, of the size of the neighborhood over which the points are connected by an edge for the Γ-convergence to hold. Taking the limit is enabled by a new metric which allows to suitably compare functionals defined on different point clouds.
The contribution of structured activity and deliberate play to the development of expert perceptual and decision-making skill.

The developmental histories of 32 players in the Australian Football League (AFL), independently classified as either expert or less skilled in their perceptual and decision-making skills, were collected through a structured interview process and their year-on-year involvement in structured and deliberate play activities retrospectively determined. Despite being drawn from the same elite level of competition, the expert decision-makers differed from the less skilled in having accrued, during their developing years, more hours of experience in structured activities of all types, in structured activities in invasion-type sports, in invasion-type deliberate play, and in invasion activities from sports other than Australian football. Accumulated hours invested in invasion-type activities differentiated between the groups, suggesting that it is the amount of invasion-type activity that is experienced and not necessarily intent (skill development or fun) or specificity that facilitates the development of perceptual and decision-making expertise in this team sport.
Sentiment classification based on deep learning

The increase in the usage rate of the Internet and social media around the world has also led to an increase in the text-based contents. Content shared in a social media environment contains important information about the person in many ways. In revealing this information; After extracting the attributes on the text based content, it is possible by machine learning algorithms. Nowadays, in addition to the machine algorithms, the extraction of the feature from the text can be performed automatically with the help of deep learning methods. In this study, binary sentiment classification (positive-negative) operation was performed on messages shared in the Twitter environment, which is one of the most popular social media applications. The classification process is carried out with various machine learning algorithms and Convolutional Neural Networks (CNN) which is a deep learning algorithm. As a result of the classification process, the deep learning algorithm classifies the data set with higher performance than the five machine learning algorithms. We also examined the effect of the batch size on classification process.
A Pressure Test to Make 10 Molecules in 90 Days: External Evaluation of Methods to Engineer Biology.

Centralized facilities for genetic engineering, or "biofoundries", offer the potential to design organisms to address emerging needs in medicine, agriculture, industry, and defense. The field has seen rapid advances in technology, but it is difficult to gauge current capabilities or identify gaps across projects. To this end, our foundry was assessed via a timed "pressure test", in which 3 months were given to build organisms to produce 10 molecules unknown to us in advance. By applying a diversity of new approaches, we produced the desired molecule or a closely related one for six out of 10 targets during the performance period and made advances toward production of the others as well. Specifically, we increased the titers of 1-hexadecanol, pyrrolnitrin, and pacidamycin D, found novel routes to the enediyne warhead underlying powerful antimicrobials, established a cell-free system for monoterpene production, produced an intermediate toward vincristine biosynthesis, and encoded 7802 individually retrievable pathways to 540 bisindoles in a DNA pool. Pathways to tetrahydrofuran and barbamide were designed and constructed, but toxicity or analytical tools inhibited further progress. In sum, we constructed 1.2 Mb DNA, built 215 strains spanning five species ( Saccharomyces cerevisiae, Escherichia coli, Streptomyces albidoflavus, Streptomyces coelicolor, and Streptomyces albovinaceus), established two cell-free systems, and performed 690 assays developed in-house for the molecules.
An Internet accessible telepresence

The US Vice President, Al Gore, in a speech on the information superhighway, suggested that it could be used to remotely control a nuclear reactor. We do not have enough confidence in computer software, hardware, or networks to attempt this experiment, but have instead built a Internet-accessible, remote-controlled model car that provides a race driver's view via a video camera mounted on the model car. The remote user can see live video from the car, and, using a mouse, control the speed and direction of the car. The challenge was to build a car that could be controlled by novice users in narrow corridors, and that would work not only with the full motion video that the car natively provides, but also with the limited size and frame rate video available over the Internet multicast backbone. We have built a car that has been driven from a site 50 miles away over a 56-kbps IP link using $\mbox{{\tt nv}}$ format video at as little as one frame per second and at as low as $100\times 100$ pixels resolution. We also built hardware to control the car, using a slightly modified voice grade channel videophone. Our experience leads us to believe that it is now possible to put together readily available hardware and software components to build a cheap and effective telepresence.
A 32.4 ppm/°C 3.2-1.6V self-chopped relaxation oscillator with adaptive supply generation

A self-chopped relaxation oscillator with adaptive supply generation provides the stable output clock against variations in temperature and supply voltages. The frequency drift is less than ±0.1% for the supply voltage changing from 1.6 to 3.2 V and ±0.1% for a temperature range from -20 to 100°C, which is reduced by 83% with the self-chopped technique. This relaxation oscillator is implemented in a 60-nm CMOS technology with its active area equals to 0.048 mm2. It consumes 2.8 uA from a 1.6-V supply.
Tagvisor: A Privacy Advisor for Sharing Hashtags

Hashtag has emerged as a widely used concept of popular culture and campaigns, but its implications on people’s privacy have not been investigated so far. In this paper, we present the first systematic analysis of privacy issues induced by hashtags. We concentrate in particular on location, which is recognized as one of the key privacy concerns in the Internet era. By relying on a random forest model, we show that we can infer a user’s precise location from hashtags with accuracy of 70% to 76%, depending on the city. To remedy this situation, we introduce a system called Tagvisor that systematically suggests alternative hashtags if the user-selected ones constitute a threat to location privacy. Tagvisor realizes this by means of three conceptually different obfuscation techniques and a semantics-based metric for measuring the consequent utility loss. Our findings show that obfuscating as little as two hashtags already provides a near-optimal trade-off between privacy and utility in our dataset. This in particular renders Tagvisor highly time-efficient, and thus, practical in real-world settings.
Combining passive haptics with redirected walking

Two large problems faced by virtual environment designers are lack of haptic feedback and constraints imposed by limited tracker space. Passive haptic feedback has been used effectively to provide a sense of touch to users (Insko, et al., 2001). Redirected walking is a promising solution to the problem of limited tracker space (Razzaque, et al., 2001). However, these solutions to these two problems are typically mutually exclusive because their requirements conflict with one another. We introduce a method by which they can be combined to address both problems simultaneously.
Deep Perceptual Mapping for Thermal to Visible Face Recognition

Cross modal face matching between the thermal and visible spectrum is a much desired capability for night-time surveillance and security applications. Due to a very large modality gap, thermal-to-visible face recognition is one of the most challenging face matching problem. In this paper, we present an approach to bridge this modality gap by a significant margin. Our approach captures the highly non-linear relationship between the two modalities by using a deep neural network. Our model attempts to learn a non-linear mapping from visible to thermal spectrum while preserving the identity information. We show substantive performance improvement on a difficult thermal-visible face dataset (UND-X1). The presented approach improves the state-of-the-art by more than 10% in terms of Rank-1 identification and bridge the drop in performance due to the modality gap by more than 40%. The goal of training the deep network is to learn the projections that can be used to bring the two modalities together. Typically, this would mean regressing the representation from one modality towards the other. We construct a deep network comprising N +1 layers with m(k) units in the k-th layer, where k = 1,2, · · · ,N. For an input of x ∈Rd , each layer will output a non-linear projection by using the learned projection matrix W and the non-linear activation function g(·). The output of the k-th hidden layer is h(k) = g(W(k)h(k−1) + b(k)), where W(k) ∈ Rm×m(k−1) is the projection matrix to be learned in that layer, b(k) ∈Rm is a bias vector and g : Rm 7→ Rm is the non-linear activation function. Similarly, the output of the most top level hidden layer can be computed as:
Expert Series Persistent Occiput Posterior

Persistent occiput posterior (OP) is associated with increased rates of maternal and newborn morbidity. Its diagnosis by physical examination is challenging but is improved with bedside ultrasonography. Occiput posterior discovered in the active phase or early second stage of labor usually resolves spontaneously. When it does not, prophylactic manual rotation may decrease persistent OP and its associated complications. When delivery is indicated for arrest of descent in the setting of persistent OP, a pragmatic approach is suggested. Suspected fetal macrosomia, a biparietal diameter above the pelvic inlet or a maternal pelvis with android features should prompt cesarean delivery. Nonrotational operative vaginal delivery is appropriate when the maternal pelvis has a narrow anterior segment but ample room posteriorly, like with anthropoid features. When all other conditions are met and the fetal head arrests in an OP position in a patient with gynecoid pelvic features and ample room anteriorly, options include cesarean delivery, nonrotational operative vaginal delivery, and rotational procedures, either manual or with the use of rotational forceps. Recent literature suggests that maternal and fetal outcomes with rotational forceps are better than those reported in older series. Although not without significant challenges, a role remains for teaching and practicing selected rotational forceps operations in contemporary obstetrics.
Dual-Feed Dual-Polarized Patch Antenna With Low Cross Polarization and High Isolation

In this communication, a dual-feed dual-polarized microstrip antenna with low cross polarization and high isolation is experimentally studied. Two different feed mechanisms are designed to excite a dual orthogonal linearly polarized mode from a single radiating patch. One of the two modes is excited by an aperture-coupled feed, which comprises a compact resonant annular-ring slot and a T-shaped microstrip feedline; while the other is excited by a pair of meandering strips with a 180$^{\circ}$  phase differences. Both linearly polarized modes are designed to operate at 2400-MHz frequency band, and from the measured results, it is found that the isolation between the two feeding ports is less than 40 dB across a 10-dB input-impedance bandwidth of 14%. In addition, low cross polarization is observed from the radiation patterns of the two modes, especially at the broadside direction. Simulation analyses are also carried out to support the measured results.
CUTE: Constrained and Unconstrained Testing Environment

The purpose of this article is to discuss the scope and functionality of a versatile environment for testing small- and large-scale nonlinear optimization algorithms. Although many of these facilities were originally produced by the authors in conjunction with the software package LANCELOT, we believe that they will be useful in their own right and should be available to researchers for their development of optimization software. The tools can be obtained by anonymous ftp from a number of sources and may, in many cases, be installed automatically. The scope of a major collection of test problems written in the standard input format (SIF) used by the LANCELOT software package is described. Recognizing that most software was not written with the SIF in mind, we provide tools to assist in building an interface between this input format and other optimization packages. These tools provide a link between the SIF and a number of existing packages, including MINOS and OSL. Additionally, as each problem includes a specific classification that is designed to be useful in identifying particular classes of problems, facilities are provided to build and manage a database of this information. There is a Unix and C shell bias to many of the descriptions in the article, since, for the sake of simplicity, we do not illustrate everything in its fullest generality. We trust that the majority of potential users are sufficiently familiar with Unix that these examples will not lead to undue confusion.
A Multi-stage Dynamic Game-Theoretic Approach for Multi-Workflow Scheduling on Heterogeneous Virtual Machines from Multiple Infrastructure-as-a-Service Clouds

Distributed computing systems such as clouds continue to evolve to support various types of scientific applications, especially scientific workflows, with dependable, consistent, pervasive, and inexpensive access to geographically-distributed computational capabilities. Scheduling multiple workflows on distributed computing systems like Infrastructure-as-a-Service (IaaS) clouds is well recognized as a fundamental NP-complete problem that is critical to meeting various types of Quality-of-Service (QoS) requirements. In this paper, we propose a multiobjective optimization workflow scheduling approach based on dynamic game-theoretic model aiming at reducing workflow make-spans, reducing total cost, and maximizing system fairness in terms of workload distribution among heterogeneous cloud virtual machines (VMs). We conduct extensive case studies as well based on various well-known scientific workflow templates and real-world third-party commercial IaaS clouds. Experimental results clearly suggest that our proposed approach outperform traditional ones by achieving lower workflow make-spans, lower cost, and better system fairness.
Ivermectin to reduce malaria transmission II. Considerations regarding clinical development pathway

The development of ivermectin as a complementary vector control tool will require good quality evidence. This paper reviews the different eco-epidemiological contexts in which mass drug administration with ivermectin could be useful. Potential scenarios and pharmacological strategies are compared in order to help guide trial design. The rationale for a particular timing of an ivermectin-based tool and some potentially useful outcome measures are suggested.
A centralized scheduling algorithm for IEEE 802.15.4e TSCH based industrial low power wireless networks

Time-Slotted Channel Hopping (TSCH) is a part of an emerging IEEE 802.15.4e standard to enable deterministic low-power mesh networking. It promises to pave the way to the future Internet of (Important) things by offering high reliability and low latency for wireless industrial applications. Nonetheless, the standard only provides a framework but it does not mandate a specific scheduling mechanism. In this paper, we propose a centralized Adaptive MUlti-hop Scheduling method (AMUS) based on the latest TSCH MAC. AMUS first enables sequential multi-hop scheduling to provide low latency guarantee for time-critical applications. Secondly, the novel tentative cell allocation method allocates additional resources to vulnerable links such that possible MAC retransmissions can be accommodated within the same slotframe, hence significantly reducing the delay caused by interference or collisions. Last but not least, the battery power of the node can be further conserved by adopting the proposed End-of-Q notification mechanism. Preliminary simulation results have confirmed that AMUS outperforms other popular scheduling algorithms in the literature.
Effects of mind-body exercises on the physiological and psychosocial well-being of individuals with Parkinson's disease: A systematic review and meta-analysis.

The effects of mind-body exercises on individuals with chronic illnesses have attracted increasing attention. However, little effort had been made to systematically review the effects of these mind-body exercises on individuals with Parkinson's disease (PD). This review aimed to appraise the current evidence of the effects of mind-body exercises on the physiological and psychological outcomes for the PD population.



Systematic review and meta-analysis of randomized controlled trials.



Four English databases, namely, the EMBASE, Ovid Medline, Psych Info, and Cochrane Library, were searched on January 2016.



Studies involving participants with idiopathic PD were included if mind-body exercises were applied and compared with a non-exercise control to improve physiological and psychosocial well-being. The Effective Public Health Practice Project quality assessment tool was used for quality appraisal. RevMan 5.3 was employed to perform this meta-analysis. A subgroup analysis regarding the types and the dose of intervention was conducted to explore the sources of heterogeneity.



Ten studies met the inclusion criteria for quality appraisal. The overall methodological rating of these studies indicated that one study was strong; five studies were moderate; and four studies were weak. Nine articles comprising five Tai Chi, two yoga, and two dance studies were included in the meta-analysis. The results of this review showed that mind-body exercises had a large, significant beneficial effect in motor symptoms in terms of UPDRS III for people with mild to moderate PD [SMD=-0.91, 95% CI (-1.37, -0.45), p<0.05]. Significant subgroup differences were found among various types of mind-body exercises (p=0.001). Yoga demonstrated the largest and most significant beneficial effect in reducing UPDRS III scores [SMD=-2.35, 95% CI (-3.21, -1.50), p<0.01]. The pooled meta-analysis results showed that mind-body exercises had a large, significant effect in improving postural instability in terms of the Berg Balance Scale [SMD=1.48, 95% CI (0.91, 2.06), p<0.01] and Timed Up and Go test [SMD=-0.97, 95% CI (-1.46, -0.47), p<0.01] and moderate, significant effect in improving functional mobility in terms of the Six-minute Walk test [SMD=0.78, 95% CI (0.35, 1.21), p<0.05].



This review found that mind-body exercises demonstrated immediate moderate to large beneficial effects on motor symptoms, postural instability, and functional mobility among individuals with mild to moderate PD. However, the effects of mind-body exercises on psychosocial well-being had not been amply investigated, especially for yoga intervention. Future research should address the psychosocial effects of mind-body exercises on the PD population.
An architecture for real time television audience measurement

Currently, audience measurement reports of television programs are only available after a significant period of time, for example as a daily report. This paper proposes an architecture for real time measurement of television audience. Real time measurement can give channel owners and advertisers important information that can positively impact their business. We show that television viewership can be captured by set top box devices which detect the channel logo and transmit the viewership data to a server over internet. The server processes the viewership data and displays it in real time on a web-based dashboard. In addition, it has facility to display charts of hourly and location-wise viewership trends and online TRP (Television Rating Points) reports. The server infrastructure consists of in-memory database, reporting and charting libraries and J2EE based application server.
Mathematical modeling approaches in the study of glaucoma disparities among people of African and European descents.

Open angle glaucoma (OAG) is a severe ocular disease characterized by progressive and irreversible vision loss. While elevated intraocular pressure (IOP) is a well-established risk factor for OAG, the progression of OAG in many cases, despite IOP treatment, suggests that other risk factors must play significant roles in the development of the disease. For example, various structural properties of the eye, ocular blood flow properties, and systemic conditions have been identified as risk factors for OAG. Ethnicity has also been indicated as a relevant factor that affects the incidence and prevalence of OAG; in fact, OAG is the leading cause of blindness among people of African descent. Numerous clinical studies have been designed to examine the possible correlation and causation between OAG and these factors; however, these studies are met with the challenge of isolating the individual role of multiple interconnected factors. Over the last decade, various mathematical modeling approaches have been implemented in combination with clinical studies in order to provide a mechanical and hemodynamical description of the eye in relation to the entire human body and to assess the contribution of single risk factors to the development of OAG. This review provides a summary of the clinical evidence of ocular structural differences, ocular vascular differences and systemic vascular differences among people of African and European descent, describes the mathematical approaches that have been proposed to study ocular mechanics and hemodynamics while discussing how they could be used to investigate the relevance to OAG of racial disparities, and outlines possible new directions of research.