nils-herrmann/clean_scirepeval huggingface dataset:
Hybrid Clustering Algorithm based on Mahalanobis Distance and MST

Most of the clustering algorithms are based on Euclidean distance as measure of similarity between data objects. Theses algorithms also require initial setting of parameters as a prior, for example the number of clusters. The Euclidean distance is very sensitive to scales of variables involved and independent of correlated variables. To conquer these drawbacks a hybrid clustering algorithm based on Mahalanobis distance is proposed in this paper. The reason for the hybridization is to relieve the user from setting the parameters in advance. The experimental results of the proposed algorithm have been presented for both synthetic and real datasets. General Terms Data Mining, Clustering, Pattern Recognition, Algorithms.
The impact of stretching on sports injury risk: a systematic review of the literature.

We conducted a systematic review to assess the evidence for the effectiveness of stretching as a tool to prevent injuries in sports and to make recommendations for research and prevention.



Without language limitations, we searched electronic data bases, including MEDLINE (1966-2002), Current Contents (1997-2002), Biomedical Collection (1993-1999), the Cochrane Library, and SPORTDiscus, and then identified citations from papers retrieved and contacted experts in the field. Meta-analysis was limited to randomized trials or cohort studies for interventions that included stretching. Studies were excluded that lacked controls, in which stretching could not be assessed independently, or where studies did not include subjects in sporting or fitness activities. All articles were screened initially by one author. Six of 361 identified articles compared stretching with other methods to prevent injury. Data were abstracted by one author and then reviewed independently by three others. Data quality was assessed independently by three authors using a previously standardized instrument, and reviewers met to reconcile substantive differences in interpretation. We calculated weighted pooled odds ratios based on an intention-to-treat analysis as well as subgroup analyses by quality score and study design.



Stretching was not significantly associated with a reduction in total injuries (OR = 0.93, CI 0.78-1.11) and similar findings were seen in the subgroup analyses.



There is not sufficient evidence to endorse or discontinue routine stretching before or after exercise to prevent injury among competitive or recreational athletes. Further research, especially well-conducted randomized controlled trials, is urgently needed to determine the proper role of stretching in sports.
PipeTron series - Robots for pipe inspection

Pipes are present in most of the infrastructure around us - in refineries, chemical plants, power plants, not to mention sewer, gas and water distribution networks. Inspection of these pipes is extremely important, as failures may result in catastrophic accidents with loss of lives. However, inspection of small pipes (from 3 to 6 inches) is usually neglected or performed only partially due to the lack of satisfactory tools. This paper introduces a new series of robots named PipeTron, developed especially for inspection of pipes in refineries and power plants. The mobility concept and design of each version will be described, follower by results of field deployment and considerations for future improvements.
CONCEPT LEARNING WITH ENERGY-BASED MODELS

Many hallmarks of human intelligence, such as generalizing from limited experience, abstract reasoning and planning, analogical reasoning, creative problem solving, and capacity for language and explanation are still lacking in the artificial intelligent agents. We, as others (Rosch et al. (1976); Lakoff & Johnson (1980); Lake et al. (2016)) believe what enables these abilities is the capacity to consolidate experience into concepts, which act as basic building blocks of understanding and reasoning.
Oedema and fatty degeneration of the soleus and gastrocnemius muscles on MR images in patients with achilles tendon abnormalities

The purpose of this study was to evaluate the frequency of oedema and fatty degeneration of the soleus and gastrocnemius muscles in patients with Achilles tendon abnormalities. Forty-five consecutive patients (mean 51 years; range 14–84 years) with achillodynia were examined with magnetic resonance (MR) images of the calf. The frequency of oedema and fatty degeneration in the soleus and gastrocnemius muscles was determined in patients with normal tendons, tendinopathy and in patients with a partial tear or a complete tear of the Achilles tendon. Oedema was encountered in 35% (7/20) of the patients with tendinopathy (n = 20; range 13–81 years), and in 47% (9/19) of the patients with partial tears or complete tears (n = 19; 28–78 years). Fatty degeneration was encountered in 10% (2/20) of the patients with tendinopathy, and in 32% (6/19) of the patients with tears. The prevalence of fatty degeneration was significantly more common in patients with a partial or complete tear compared with the patients with a normal Achilles tendon (p = 0.032 and p = 0.021, respectively). Oedema and fatty degeneration of the soleus and gastrocnemius muscles are common in patients with Achilles tendon abnormalities.
A Generalization of Regenerating Codes for Clustered Storage Systems

We study a generalization of the setting of regenerating codes, motivated by applications to storage systems consisting of clusters of storage nodes. There are n clusters in total, with m nodes per cluster. A data file is coded and stored across the mn nodes, with each node storing α symbols. For availability of data, we demand that the file is retrievable by downloading the entire content from any subset of k clusters. Nodes represent entities that can fail, and here we distinguish between intra-cluster and inter-cluster bandwidth-costs during node repair. Node-repair is accomplished by downloading β symbols each from any set of d other clusters. The replacement-node also downloads content from any set of ` surviving nodes in the same cluster during the repair process. We identity the optimal trade-off between storage-overhead and inter-cluster (IC) repair-bandwidth under functional repair, and also present optimal exact-repair code constructions for a class of parameters. Our results imply that it is possible to simultaneously achieve both optimal storage overhead and optimal minimum IC bandwidth, for sufficiently large values of nodes per cluster. The simultaneous optimality comes at the expense of intra-cluster bandwidth, and we obtain lower bounds on the necessary intra-cluster repair-bandwidth. Simulation results based on random linear network codes suggest optimality of the bounds on intra-cluster repair-bandwidth.
ATLAS: A scalable and high-performance scheduling algorithm for multiple memory controllers

Modern chip multiprocessor (CMP) systems employ multiple memory controllers to control access to main memory. The scheduling algorithm employed by these memory controllers has a significant effect on system throughput, so choosing an efficient scheduling algorithm is important. The scheduling algorithm also needs to be scalable — as the number of cores increases, the number of memory controllers shared by the cores should also increase to provide sufficient bandwidth to feed the cores. Unfortunately, previous memory scheduling algorithms are inefficient with respect to system throughput and/or are designed for a single memory controller and do not scale well to multiple memory controllers, requiring significant finegrained coordination among controllers. This paper proposes ATLAS (Adaptive per-Thread Least-Attained-Service memory scheduling), a fundamentally new memory scheduling technique that improves system throughput without requiring significant coordination among memory controllers. The key idea is to periodically order threads based on the service they have attained from the memory controllers so far, and prioritize those threads that have attained the least service over others in each period. The idea of favoring threads with least-attained-service is borrowed from the queueing theory literature, where, in the context of a single-server queue it is known that least-attained-service optimally schedules jobs, assuming a Pareto (or any decreasing hazard rate) workload distribution. After verifying that our workloads have this characteristic, we show that our implementation of least-attained-service thread prioritization reduces the time the cores spend stalling and significantly improves system throughput. Furthermore, since the periods over which we accumulate the attained service are long, the controllers coordinate very infrequently to form the ordering of threads, thereby making ATLAS scalable to many controllers. We evaluate ATLAS on a wide variety of multiprogrammed SPEC 2006 workloads and systems with 4–32 cores and 1–16 memory controllers, and compare its performance to five previously proposed scheduling algorithms. Averaged over 32 workloads on a 24-core system with 4 controllers, ATLAS improves instruction throughput by 10.8%, and system throughput by 8.4%, compared to PAR-BS, the best previous CMP memory scheduling algorithm. ATLAS's performance benefit increases as the number of cores increases.
Corrigendum: Differential gene and transcript expression analysis of RNA-seq experiments with TopHat and Cufflinks

Recent advances in high-throughput cDNA sequencing (RNA-seq) can reveal new genes and splice variants and quantify expression genome-wide in a single assay. The volume and complexity of data from RNA-seq experiments necessitate scalable, fast and mathematically principled analysis software. TopHat and Cufflinks are free, open-source software tools for gene discovery and comprehensive expression analysis of high-throughput mRNA sequencing (RNA-seq) data. Together, they allow biologists to identify new genes and new splice variants of known ones, as well as compare gene and transcript expression under two or more conditions. This protocol describes in detail how to use TopHat and Cufflinks to perform such analyses. It also covers several accessory tools and utilities that aid in managing data, including CummeRbund, a tool for visualizing RNA-seq analysis results. Although the procedure assumes basic informatics skills, these tools assume little to no background with RNA-seq analysis and are meant for novices and experts alike. The protocol begins with raw sequencing reads and produces a transcriptome assembly, lists of differentially expressed and regulated genes and transcripts, and publication-quality visualizations of analysis results. The protocol's execution time depends on the volume of transcriptome sequencing data and available computing resources but takes less than 1 d of computer time for typical experiments and ∼1 h of hands-on time.
Process compensated low power LO divider chain with asynchronous odd integer 50% duty cycle CML dividers

This paper illustrates the design of a process compensated bias for asynchronous CML dividers for a low power, high performance LO divide chain operating at 4Ghz of input RF frequency. The divider chain provides division by 4,8,12,16,20, and 24. It provides a differential CML level signal for the in-loop modulated transmitter, and 25% duty cycle non-overlapping rail to rail waveforms for I/Q receiver for driving passive mixer. Asynchronous dividers have been used to realize divide by 3 and 5 with 50% duty cycle, quadrature outputs. All the CML dividers use a process compensated bias to compensate for load resistor variation and tail current variation using dual analog feedback loops. Frabricated in 180nm CMOS technology, the divider chain operate over industrial temperature range (−40 to 90°C), and provide outputs in 138–960Mhz range, consuming 2.2mA from 1.8V regulated supply at the highest output frequency.
Keyword based searching in social networks

The main purpose of analyzing the social network data is to observe the behaviors and trends that are followed by people. How people interact with each other, what they usually share, what are their interests on social networks, so that analysts can focus new trends for the provision of those things which are of great interest for people so in this paper an easy approach of gathering and analyzing data through keyword based search in social networks is examined using NodeXL and data is gathered from twitter in which political trends have been analyzed. As a result it will be analyzed that, what people are focusing most in politics.
Pediatric complex regional pain syndrome: a review.

Complex regional pain syndrome (CRPS) is a chronic, intensified localized pain condition that can affect children and adolescents as well as adults, but is more common among adolescent girls. Symptoms include limb pain; allodynia; hyperalgesia; swelling and/or changes in skin color of the affected limb; dry, mottled skin; hyperhidrosis and trophic changes of the nails and hair. The exact mechanism of CRPS is unknown, although several different mechanisms have been suggested. The diagnosis is clinical, with the aid of the adult criteria for CRPS. Standard care consists of a multidisciplinary approach with the implementation of intensive physical therapy in conjunction with psychological counseling. Pharmacological treatments may aid in reducing pain in order to allow the patient to participate fully in intensive physiotherapy. The prognosis in pediatric CRPS is favorable.
Climate Change Is Increasing the Risk of the Reemergence of Malaria in Romania

The climatic modifications lead to global warming; favouring the risk of the appearance and development of diseases are considered until now tropical diseases. Another important factor is the workers' immigration, the economic crisis favouring the passive transmission of new species of culicidae from different areas. Malaria is the disease with the widest distribution in the globe. Millions of people are infected every year in Africa, India, South-East Asia, Middle East, and Central and South America, with more than 41% of the global population under the risk of infestation with malaria. The increase of the number of local cases reported in 2007-2011 indicates that the conditions can favour the high local transmission in the affected areas. In the situation presented, the establishment of the level of risk concerning the reemergence of malaria in Romania becomes a priority.
Neural Network

Self-replication is a key aspect of biological life that has been largely overlooked in Artificial Intelligence systems. Here we describe how to build and train self-replicating neural networks. The network replicates itself by learning to output its own weights. The network is designed using a loss function that can be optimized with either gradient-based or nongradient-based methods. We also describe a method we call regeneration to train the network without explicit optimization, by injecting the network with predictions of its own parameters. The best solution for a self-replicating network was found by alternating between regeneration and optimization steps. Finally, we describe a design for a self-replicating neural network that can solve an auxiliary task such as MNIST image classification. We observe that there is a trade-off between the network’s ability to classify images and its ability to replicate, but training is biased towards increasing its specialization at image classification at the expense of replication. This is analogous to the trade-off between reproduction and other tasks observed in nature. We suggest that a selfreplication mechanism for artificial intelligence is useful because it introduces the possibility of continual improvement through natural selection.
Effects of combined strength and kick coordination training on soccer kick biomechanics in amateur players.

The aim of this study was to examine the effect of a soccer (strength and technique) training program on kinematics and electromyographic (EMG) muscle activity during a instep kick. Ten amateur soccer players (aged 19.9+/-0.4 years, body mass 74.8+/-9.1 kg, height 177.4+/-6.7 cm) constituted the experimental group (EG) whereas 10 players (age 21.6+/-1.3 years, weight 71.5+/-6.7 kg, height 175.2+/-3.4 cm) served as controls (CG). The EG followed a 10-week soccer-specific training program combining strength and technique exercises. All participants performed an instep soccer kick using a two-step approach while three-dimensional data and EMG from six muscles of swinging and support legs were recorded prior to and after training. Maximum isometric leg press strength, 10-m sprint performance and maximum speed performance on a bicycle ergometer were also measured. Analysis of variance designs with repeated measures showed that the EG improved significantly (P<0.05) maximum ball speed, the linear velocity of the foot, ankle and angular velocity of all joints during the final phase of the kick. Training had insignificant effects on EMG values, apart from an increase in the averaged EMG of the vastus medialis whereas maximum isometric strength and sprint times significantly improved after training (P<0.05). The present results suggest that the application of the training programs using soccer-specific strength exercises would be particularly effective in improving of soccer kick performance.
Socioecological psychology.

Socioecological psychology investigates humans' cognitive, emotional, and behavioral adaption to physical, interpersonal, economic, and political environments. This article summarizes three types of socioecological psychology research: (a) association studies that link an aspect of social ecology (e.g., population density) with psychology (e.g., prosocial behavior), (b) process studies that clarify why there is an association between social ecology and psychology (e.g., residential mobility → anxiety → familiarity seeking), and (c) niche construction studies that illuminate how psychological states give rise to the creation and maintenance of a social ecology (e.g., familiarity seeking → dominance of national chain stores). Socioecological psychology attempts to bring the objectivist perspective to psychological science, investigating how objective social and physical environments, not just perception and construal of the environments, affect one's thinking, feeling, and behaviors, as well as how people's thinking, feeling, and behaviors give rise to social and built environments.
The End-to-End Effects of Internet Path Selection

The path taken by a packet traveling across the Internet depends on a large number of factors, including routing protocols and per-network routing policies. The impact of these factors on the end-to-end performance experienced by users is poorly understood. In this paper, we conduct a measurement-based study comparing the performance seen using the "default" path taken in the Internet with the potential performance available using some alternate path. Our study uses five distinct datasets containing measurements of "path quality", such as round-trip time, loss rate, and bandwidth, taken between pairs of geographically diverse Internet hosts. We construct the set of potential alternate paths by composing these measurements to form new synthetic paths. We find that in 30-80% of the cases, there is an alternate path with significantly superior quality. We argue that the overall result is robust and we explore two hypotheses for explaining it.
Optimization principles and application performance evaluation of a multithreaded GPU using CUDA

GPUs have recently attracted the attention of many application developers as commodity data-parallel coprocessors. The newest generations of GPU architecture provide easier programmability and increased generality while maintaining the tremendous memory bandwidth and computational power of traditional GPUs. This opportunity should redirect efforts in GPGPU research from ad hoc porting of applications to establishing principles and strategies that allow efficient mapping of computation to graphics hardware. In this work we discuss the GeForce 8800 GTX processor's organization, features, and generalized optimization strategies. Key to performance on this platform is using massive multithreading to utilize the large number of cores and hide global memory latency. To achieve this, developers face the challenge of striking the right balance between each thread's resource usage and the number of simultaneously active threads. The resources to manage include the number of registers and the amount of on-chip memory used per thread, number of threads per multiprocessor, and global memory bandwidth. We also obtain increased performance by reordering accesses to off-chip memory to combine requests to the same or contiguous memory locations and apply classical optimizations to reduce the number of executed operations. We apply these strategies across a variety of applications and domains and achieve between a 10.5X to 457X speedup in kernel codes and between 1.16X to 431X total application speedup.
Deep Multi-Output Forecasting: Learning to Accurately Predict Blood Glucose Trajectories

In many forecasting applications, it is valuable to predict not only the value of a signal at a certain time point in the future, but also the values leading up to that point. This is especially true in clinical applications, where the future state of the patient can be less important than the patient's overall trajectory. This requires multi-step forecasting, a forecasting variant where one aims to predict multiple values in the future simultaneously. Standard methods to accomplish this can propagate error from prediction to prediction, reducing quality over the long term. In light of these challenges, we propose multi-output deep architectures for multi-step forecasting in which we explicitly model the distribution of future values of the signal over a prediction horizon. We apply these techniques to the challenging and clinically relevant task of blood glucose forecasting. Through a series of experiments on a real-world dataset consisting of 550K blood glucose measurements, we demonstrate the effectiveness of our proposed approaches in capturing the underlying signal dynamics. Compared to existing shallow and deep methods, we find that our proposed approaches improve performance individually and capture complementary information, leading to a large improvement over the baseline when combined (4.87 vs. 5.31 absolute percentage error (APE)). Overall, the results suggest the efficacy of our proposed approach in predicting blood glucose level and multi-step forecasting more generally.
The Need and the Advantages of Generalized Tensor Algebra for Kronecker Structured Representations ∗

This paper presents the advantages in extending Classical T ensor Algebra (CTA), also known as Kronecker Algebra, to allow the definition of functions, i.e., functional dependencies among its operands. Such extended tensor algebra have been called Generalized Tenso r Algebra (GTA). Stochastic Automata Networks (SAN) and Superposed Generalized Stochastic Petri Ne ts (SGSPN) formalisms use such Kronecker representations. We show that SAN, which uses GTA, has the sa m application scope of SGSPN, which uses CTA. We also show that any SAN model with functions has at least one equivalent representation without functions. In fact, the use of functions, and conseq uently the GTA, is not really a “need” since there is an equivalence of formalisms, but in some cases it represe nts, in a computational cost point of view, some irrefutable “advantages”. Some modeling examples are pres ent d in order to draw comparisons between the memory needs and CPU time to the generation, and the solution of the presented models.