nils-herrmann/clean_scirepeval huggingface dataset:
A globally stable adaptive congestion control scheme for Internet-style networks with delay

In this paper, we develop, analyze and implement a congestion control scheme in a noncooperative game framework, where each user's cost function is composed of a pricing function proportional to the queueing delay experienced by the user, and a fairly general utility function which captures the user demand for bandwidth. Using a network model based on fluid approximations and through a realistic modeling of queues, we establish the existence of a unique equilibrium as well as its global asymptotic stability for a general network topology, where boundary effects are also taken into account. We also provide sufficient conditions for system stability when there is a bottleneck link shared by multiple users experiencing nonnegligible communication delays. In addition, we study an adaptive pricing scheme using hybrid systems concepts. Based on these theoretical foundations, we implement a window-based, end-to-end congestion control scheme, and simulate it in ns-2 network simulator on various network topologies with sizable propagation delays.
Addressing challenges in the clinical applications associated with CRISPR/Cas9 technology and ethical questions to prevent its misuse

Xiang Jin Kang, Chiong Isabella Noelle Caparas, Boon Seng Soh, Yong Fan 1 Key Laboratory for Major Obstetric Diseases of Guangdong Province, Key Laboratory of Reproduction and Genetics of Guangdong Higher Education Institutes, Center for Reproductive Medicine, The Third Affiliated Hospital of Guangzhou Medical University, Guangzhou 510150, China 2 Disease Modeling and Therapeutics Laboratory, A*STAR Institute of Molecular and Cell Biology, 61 Biopolis Drive Proteos, Singapore 138673, Singapore & Correspondence: bssoh@imcb.a-star.edu.sg (B. S. Soh), yongfan011@gzhmu.edu.cn (Y. Fan)
OCC applied to a 180W HPF single-stage LED driver based on Zeta converter in CCM

This paper presents a high power factor single-stage driver powering a high-power-LED. The driver employs a PFC based on a Zeta converter, using the One-Cycled Control (OCC) technique. A design procedure for Zeta converter is presented, which is based on the flyback converter as proposed by (Lamar, et al. ). Through simulation results of Zeta converter working as PFC, classical OCC and exponential ramp OCC approaches are compared and discussed. Showing that the ramp OCC approach results in better performance of the PFC, in relationship to conventional OCC. This paper also presents the stages of operation of the Zeta converter in the continuous conduction mode (CCM).
Lipreading with long short-term memory

Lipreading, i.e. speech recognition from visual-only recordings of a speaker's face, can be achieved with a processing pipeline based solely on neural networks, yielding significantly better accuracy than conventional methods. Feedforward and recurrent neural network layers (namely Long Short-Term Memory; LSTM) are stacked to form a single structure which is trained by back-propagating error gradients through all the layers. The performance of such a stacked network was experimentally evaluated and compared to a standard Support Vector Machine classifier using conventional computer vision features (Eigenlips and Histograms of Oriented Gradients). The evaluation was performed on data from 19 speakers of the publicly available GRID corpus. With 51 different words to classify, we report a best word accuracy on held-out evaluation speakers of 79.6% using the end-to-end neural network-based solution (11.6% improvement over the best feature-based solution evaluated).
Analyzing and Managing Role-Based Access Control Policies

Today more and more security-relevant data is stored on computer systems; security-critical business processes are mapped to their digital counterparts. This situation applies to various domains such as health care industry, digital government, and financial service institutes requiring that different security requirements must be fulfilled. Authorisation constraints can help the policy architect design and express higher-level organisational rules. Although the importance of authorisation constraints has been addressed in the literature, there does not exist a systematic way to verify and validate authorisation constraints. In this paper, we specify both non-temporal and history-based authorisation constraints in the Object Constraint Language (OCL) and first-order linear temporal logic (LTL). Based upon these specifications, we attempt to formally verify role-based access control policies with the help of a theorem prover and to validate policies with the USE system, a validation tool for OCL constraints. We also describe an authorisation engine, which supports the enforcement of authorisation constraints.
Semantic Labeling of Aerial and Satellite Imagery

Inspired by the recent success of deep convolutional neural networks (CNNs) and feature aggregation in the field of computer vision and machine learning, we propose an effective approach to semantic pixel labeling of aerial and satellite imagery using both CNN features and hand-crafted features. Both CNN and hand-crafted features are applied to dense image patches to produce per-pixel class probabilities. Conditional random fields (CRFs) are applied as a postprocessing step. The CRF infers a labeling that smooths regions while respecting the edges present in the imagery. The combination of these factors leads to a semantic labeling framework which outperforms all existing algorithms on the International Society of Photogrammetry and Remote Sensing (ISPRS) two-dimensional Semantic Labeling Challenge dataset. We advance state-of-the-art results by improving the overall accuracy to 88% on the ISPRS Semantic Labeling Contest. In this paper, we also explore the possibility of applying the proposed framework to other types of data. Our experimental results demonstrate the generalization capability of our approach and its ability to produce accurate results.
McRT-STM: a high performance software transactional memory system for a multi-core runtime

Applications need to become more concurrent to take advantage of the increased computational power provided by chip level multiprocessing. Programmers have traditionally managed this concurrency using locks (mutex based synchronization). Unfortunately, lock based synchronization often leads to deadlocks, makes fine-grained synchronization difficult, hinders composition of atomic primitives, and provides no support for error recovery. Transactions avoid many of these problems, and therefore, promise to ease concurrent programming.We describe a software transactional memory (STM) system that is part of McRT, an experimental Multi-Core RunTime. The McRT-STM implementation uses a number of novel algorithms, and supports advanced features such as nested transactions with partial aborts, conditional signaling within a transaction, and object based conflict detection for C/C++ applications. The McRT-STM exports interfaces that can be used from C/C++ programs directly or as a target for compilers translating higher level linguistic constructs.We present a detailed performance analysis of various STM design tradeoffs such as pessimistic versus optimistic concurrency, undo logging versus write buffering, and cache line based versus object based conflict detection. We also show a MCAS implementation that works on arbitrary values, coexists with the STM, and can be used as a more efficient form of transactional memory. To provide a baseline we compare the performance of the STM with that of fine-grained and coarse-grained locking using a number of concurrent data structures on a 16-processor SMP system. We also show our STM performance on a non-synthetic workload -- the Linux sendmail application.
An Improved PSFB PWM DC–DC Converter for High-Power and Frequency Applications

In the phase shifted full bridge (PSFB) pulse width modulation (PWM) converter, external snubber capacitors are connected in parallel to insulated gate bipolar transistors (IGBTs) in order to decrease turn-off losses. The zero voltage transition (ZVT) condition is not provided at light loads, thus the parallel capacitors discharge through IGBTs at turn on which causes switching losses and failure risk of the IGBTs. Capacitor discharge through IGBT restricts the use of high-value snubber capacitors, and turn-off loss of the IGBT increases at high currents. This problematic condition occurs especially at the lagging leg. In this study, a new technique enabling the use of high-value snubber capacitors with the lagging leg of the PSFB PWM converter is proposed. As advantages of the proposed technique, high-capacitive discharge current through IGBT is prevented at light loads, the turn-off switching losses of the IGBTs are decreased, and the performance of the converter is improved at high currents. The proposed PSFB PWM converter includes an auxiliary circuit, and it has a simple structure, low cost, and ease of control as well. The operation principle and detailed design procedure of the converter are presented. The theoretical analysis is verified exactly by a prototype of 75 kHz and 10 kW converter.
Bluetooth Portable Device for ECG and Patient Motion Monitoring

The cardiovascular disease does harms to person’s h ealth, and most of them are concerned with arrhythm ia which is the leading cause of death. The chances of suffering a myocardial infarc tion are great and increase up to fifteen times aft er the first occurrence. During a heart attack, heart muscle is depraved of oxygen and will literally die if the artery remains blocked. The f irst few hours are critical in saving much of the dying heart muscle and preventing perma nent heart damage. Unfortunately, the symptoms vary and the most common reason for critical delays in medical treatment is lack of early warning and patient unawareness. It is possible to detect the onset of a heart attac k and eliminate patient error. In this paper, we pr sent a portable miniature wireless device for ECG measurements. The patient will only be requi red to carry a cell phone equipped with Bluetooth. W hen the device detects a heart attack, it will alert the cell phone which in turn will automatically call for help and provide the pa tient’s location. The goal is to provide early heart attack detection so that the patient wi ll be given medical attention within the first few critical hours, thus greatly improving his or her chances of survival.
Track--Stair Interaction Analysis and Online Tipover Prediction for a Self-Reconfigurable Tracked Mobile Robot Climbing Stairs

This paper analyzes track-stair interactions and develops an online tipover prediction algorithm for a self-reconfigurable tracked mobile robot climbing stairs, which is vulnerable to tipping-over. Tipover prediction and prevention for a tracked mobile robot in stair climbing are intractable problems because of the complex track--stair interactions. Unlike the wheeled mobile robots, which are normally assumed to obey the nonholonomic constraints, slippage is unavoidable for a tracked mobile robot, especially in stair climbing. Furthermore, the track-stair interactive forces are complicated, which may take the forms of grouser-tread hooking force, track--stair edge frictional force, grouser-riser clutching force, and even their compositions. In this paper, the track--stair interactions are analyzed systematically, and tipover stability criteria are derived for a tracked mobile robot climbing stairs. An online tipover prediction algorithm is also developed, which forms an essential part for autonomous and semiautonomous stair-climbing control. The effectiveness of the proposed algorithms are verified by experiments.
Modelling neural correlates of working memory: A coordinate-based meta-analysis

Working memory subsumes the capability to memorize, retrieve and utilize information for a limited period of time which is essential to many human behaviours. Moreover, impairments of working memory functions may be found in nearly all neurological and psychiatric diseases. To examine what brain regions are commonly and differently active during various working memory tasks, we performed a coordinate-based meta-analysis over 189 fMRI experiments on healthy subjects. The main effect yielded a widespread bilateral fronto-parietal network. Further meta-analyses revealed that several regions were sensitive to specific task components, e.g. Broca's region was selectively active during verbal tasks or ventral and dorsal premotor cortex were preferentially involved in memory for object identity and location, respectively. Moreover, the lateral prefrontal cortex showed a division in a rostral and a caudal part based on differential involvement in task set and load effects. Nevertheless, a consistent but more restricted "core" network emerged from conjunctions across analyses of specific task designs and contrasts. This "core" network appears to comprise the quintessence of regions, which are necessary during working memory tasks. It may be argued that the core regions form a distributed executive network with potentially generalized functions for focussing on competing representations in the brain. The present study demonstrates that meta-analyses are a powerful tool to integrate the data of functional imaging studies on a (broader) psychological construct, probing the consistency across various paradigms as well as the differential effects of different experimental implementations.
Attention Deficit Hyperactivity Disorder: Is There an App for That? Suitability Assessment of Apps for Children and Young People With ADHD

Attention-deficit/hyperactivity disorder (ADHD) is a complex highly comorbid disorder, which can have a huge impact on those with ADHD, their family, and the community around them. ADHD is currently managed using pharmacological and nonpharmacological interventions. However, with advances in technology and an increase in the use of mobile apps, managing ADHD can be augmented using apps specifically designed for this population. However, little is known regarding the suitability and usability of currently available apps.



The aim of this study was to explore the suitability of the top 10 listed apps for children and young people with ADHD and clinicians who work with them. It is hypothesized that mobile apps designed for this population could be more suitably designed for this population.



The top 10 listed apps that are specifically targeted toward children and young people with ADHD in the United Kingdom were identified via the Google Play (n=5) and iTunes store (n=5). Interviews were then undertaken with 5 clinicians who specialize in treating this population and 5 children and young people with ADHD themselves, to explore their opinions of the 10 apps identified and what they believe the key components are for apps to be suitable for this population.



Five themes emerged from clinician and young people interviews: the accessibility of the technology, the importance of relating to apps, addressing ADHD symptoms and related difficulties, age appropriateness, and app interaction. Three additional themes emerged from the clinician interviews alone: monitoring symptoms, side effects and app effect on relationships, and the impact of common comorbid conditions. The characteristics of the apps did not appear to match well with the views of our sample.



These findings suggest that the apps may not be suitable in meeting the complex needs associated with this condition. Further research is required to explore the value of apps for children and young people with ADHD and their families and, in particular, any positive role for apps in the management of ADHD in this age group. A systematic review on how technology can be used to engage this population and how it can be used to help them would be a useful way forward. This could be the platform to begin exploring the use of apps further.
Image Completion Using Efficient Belief Propagation Via Priority Scheduling and Dynamic Pruning

In this paper, a new exemplar-based framework is presented, which treats image completion, texture synthesis, and image inpainting in a unified manner. In order to be able to avoid the occurrence of visually inconsistent results, we pose all of the above image-editing tasks in the form of a discrete global optimization problem. The objective function of this problem is always well-defined, and corresponds to the energy of a discrete Markov random field (MRF). For efficiently optimizing this MRF, a novel optimization scheme, called priority belief propagation (BP), is then proposed, which carries two very important extensions over the standard BP algorithm: ldquopriority-based message schedulingrdquo and ldquodynamic label pruning.rdquo These two extensions work in cooperation to deal with the intolerable computational cost of BP, which is caused by the huge number of labels associated with our MRF. Moreover, both of our extensions are generic, since they do not rely on the use of domain-specific prior knowledge. They can, therefore, be applied to any MRF, i.e., to a very wide class of problems in image processing and computer vision, thus managing to resolve what is currently considered as one major limitation of the BP algorithm: its inefficiency in handling MRFs with very large discrete state spaces. Experimental results on a wide variety of input images are presented, which demonstrate the effectiveness of our image-completion framework for tasks such as object removal, texture synthesis, text removal, and image inpainting.
Motivation of medical students: selection by motivation or motivation by selection

Medical schools try to implement selection procedures that will allow them to select the most motivated students for their programs. Though there is a general feeling that selection stimulates student motivation, conclusive evidence for this is lacking. The current study aims to use the perspective of Self-determination Theory (SDT) of motivation as a lens to examine how medical students' motivation differs in relation to different selection procedures. The hypotheses were that 1) selected students report higher strength and autonomous motivation than non-selected students, and 2) recently selected students report higher strength and autonomous motivation than non-selected students and students who were selected longer ago.



First- (Y1) and fourth-year (Y4) medical students in the six-year regular programme and first-year students in the four-year graduate entry programme (GE) completed questionnaires measuring motivation strength and type (autonomous-AM, controlled-CM). Scores were compared between students admitted based on selection, lottery or top pre-university GPA (top GPA) using ANCOVAs. Selected students' answers on open-ended questions were analysed using inductive thematic analysis to identify reasons for changes in motivation.



The response rate was 61.4 % (n = 357). Selected students (Y1, Y4 and GE) reported a significantly higher strength of motivation than non-selected students (Y1 and Y4 lottery and top GPA) (p < 0.01). Recently selected students (Y1 and GE) reported significantly higher strength (p < 0.01) and higher AM (p < 0.01) and CM (p < 0.05) than non-selected students (lottery and top GPA) and Y4 students who were selected three years ago. Students described that being selected enhanced their motivation as they felt autonomous, competent and that they belonged to a special group. These reported reasons are in alignment with the basic psychological needs described by Self-Determination Theory as important in enhancing autonomous motivation.



A comprehensive selection procedure, compared to less demanding admission procedures, does not seem to yield a student population which stands out in terms of autonomous motivation. The current findings indicate that selection might temporarily enhance students' motivation. The mechanism through which this occurs seems to be through feelings of autonomy, competence and relatedness inspired by selection.
Large Pages on Steroids: Small Ideas to Accelerate Big Memory Applications

Utilizing small (e.g., 4 KB) pages incurs frequent TLB misses on modern big memory applications, substantially degrading the performance of the system. Large (e.g., 1 GB) pages or direct segments can alleviate this penalty due to page table walks, but at the same time such a strategy exposes the organizational and operational details of modern DRAM-based memory systems to applications. Row-buffer conflicts caused by accesses heading to the same DRAM bank but different rows from multiple threads are regarded as the main culprits behind the very large gaps between peak and achieved main memory throughput, but hardware-based approaches in memory controllers have achieved only limited success whereas existing proposals that change memory allocators cannot be applied to large pages or direct segments. In this paper, we propose a set of application-level techniques to improve the effective main memory bandwidth. The techniques stem from the two key observations that 1) each thread of an application exclusively accesses certain datasets for a short or long period of time, and 2) superfluous memory reads originating from a cache's write allocation policy can be avoided if scatters during the data shuffling pass through intermediate cache-friendly buffers. Experiments with a contemporary x86 server show that combining large pages with the proposed address linearization, bank coloring, and write streaming techniques improves the performance of the three big memory applications of high-throughput key-value store, fast-Fourier transform, and radix sort by 37.6, 22.9, and 68.1 percent, respectively.
The Relationships among Acceleration , Agility , Sprinting Ability , Speed Dribbling Ability and Vertical Jump Ability in 14-Year-Old Soccer Players

The aim of this study was to evaluate the relationships among acceleration, agility, sprinting ability, speed dribbling ability and vertical jump ability in 14-year-old soccer players. Twenty-five young soccer players (average age 13.52 ± 0.51 years; height 158.81 ± 5.76 cm; weight 48.92 ± 6.48 kg; training age 3.72 ± 0.64 years) performed a series of physical tests: Yo-Yo Intermittent Recovery Test Level 1 (YYIRT); zigzag agility with the ball (ZAWB); without the ball (ZAWOB); sprinting ability (10-m, 20-m and 30-m); speed dribbling ability (SDA), acceleration ability (FLST) and jumping ability (counter-movement jump (CMJ), squat jump (SJ) and drop jump (DJ). The results showed that 10-m sprint was correlated with 20-m (r = 0.682), 30-m sprint (r = 0.634) and also SDA, FLST and ZAWOB (r = 0.540, r = 0.421; r = 0.533 respectively). Similarly, 20-m sprint was correlated with 30-m sprint (r = 0.491) and ZAWOB (r = 0.631). 30-m sprint was negatively correlated with CMJ (r = -0.435), while strong and moderate correlated with FLST (r = 0.742), ZAWOB (r = 0.657). In addition, CMJ strong correlated with SJ and DJ (r = 0.779, r = 0.824 respectively). CMJ, SJ and DJ were negatively correlated with ZAWOB. Furthermore, SDA strong and moderate correlated with FLST (r = 0.875), ZAWB (r = 0.718) and ZAWOB (r = 0.645). Similarly, a correlation was also found among FLST, ZAWB and ZAWOB (r = 0.421, r = 0.614 respectively). Finally, ZAWOB was strong and moderate correlated with the performance of soccer players in different field tests. In conclusion, the findings of the present study indicated that agility without the ball associated with the all physical fitness components. In addition, agility performance affects acceleration, sprint and jumping performances in young soccer players. Therefore, soccer players should focus on agility exercises in order to improve their acceleration, sprinting and jumping performance.