nils-herrmann/clean_scirepeval huggingface dataset:
Noise in the nervous system

Noise — random disturbances of signals — poses a fundamental problem for information processing and affects all aspects of nervous-system function. However, the nature, amount and impact of noise in the nervous system have only recently been addressed in a quantitative manner. Experimental and computational methods have shown that multiple noise sources contribute to cellular and behavioural trial-to-trial variability. We review the sources of noise in the nervous system, from the molecular to the behavioural level, and show how noise contributes to trial-to-trial variability. We highlight how noise affects neuronal networks and the principles the nervous system applies to counter detrimental effects of noise, and briefly discuss noise's potential benefits.
Endodontic applications of cone-beam volumetric tomography.

The ability to assess an area of interest in 3 dimensions might benefit both novice and experienced clinicians alike. High-resolution limited cone-beam volumetric tomography (CBVT) has been designed for dental applications. As opposed to sliced-image data of conventional computed tomography (CT) imaging, CBVT captures a cylindrical volume of data in one acquisition and thus offers distinct advantages over conventional medical CT. These advantages include increased accuracy, higher resolution, scan-time reduction, and dose reduction. Specific endodontic applications of CBVT are being identified as the technology becomes more prevalent. CBVT has great potential to become a valuable tool in the modern endodontic practice. The objectives of this article are to briefly review cone-beam technology and its advantages over medical CT and conventional radiography, to illustrate current and future clinical applications of cone-beam technology in endodontic practice, and to discuss medicolegal considerations pertaining to the acquisition and interpretation of 3-dimensional data.
Needle: Leveraging Program Analysis to Analyze and Extract Accelerators from Whole Programs

Technology constraints have increasingly led to the adoption of specialized coprocessors, i.e. hardware accelerators. The first challenge that computer architects encounter is identifying "what to specialize in the program". We demonstrate that this requires precise enumeration of program paths based on dynamic program behavior. We hypothesize that path-based [4] accelerator offloading leads to good coverage of dynamic instructions and improve energy efficiency. Unfortunately, hot paths across programs demonstrate diverse control flow behavior. Accelerators (typically based on dataflow execution), often lack an energy-efficient, complexity effective, and high performance (eg. branch prediction) support for control flow. We have developed NEEDLE, an LLVM based compiler framework that leverages dynamic profile information to identify, merge, and offload acceleratable paths from whole applications. NEEDLE derives insight into what code coverage (and consequently energy reduction) an accelerator can achieve. We also develop a novel program abstraction for offload calledBraid, that merges common code regions across different paths to improve coverage of the accelerator while trading off the increase in dataflow size. This enables coarse grained offloading, reducing interaction with the host CPU core. To prepare the Braids and paths for acceleration, NEEDLE generates software frames. Software frames enable energy efficient speculative execution on accelerators. They are accelerator microarchitecture independent support speculative execution including memory operations. NEEDLE is automated and has been used to analyze 225K paths across 29 workloads. It filtered and ranked 154K paths for acceleration across unmodified SPEC, PARSEC and PERFECT workload suites. We target NEEDLE's offload regions toward a CGRA and demonstrate 34% performance and 20% energy improvement.
Acute and Residual Soccer Match-Related Fatigue: A Systematic Review and Meta-analysis.

Understanding soccer players' match-related fatigue and recovery profiles likely helps with developing conditioning programs that increase team performance and reduce injuries and illnesses. In order to improve match recovery (the return-to-play process and ergogenic interventions) it is also pivotal to determine if match simulation protocols and actual match-play lead to similar responses.



(1) To thoroughly describe the development of fatigue during actual soccer match play and its recovery time course in terms of physiological, neuromuscular, technical, biochemical and perceptual responses, and (2) to determine similarities of recovery responses between actual competition (11 vs. 11) and match simulations.



A first screening phase consisted of a systematic search on PubMed (MEDLINE) and SportDiscus databases until March 2016. Inclusion criteria were: longitudinal study with soccer players; match or validated protocol; duration > 45 min; and published in English.



A total of 77 eligible studies (n = 1105) were used to compute 1196 effect sizes (ES). Half-time assessments revealed small to large alterations in immunological parameters (e.g. leukocytes, ES = 1.9), a moderate decrement in insulin concentration (ES = - 0.9) and a small to moderate impairment in lower-limb muscle function (ES = - 0.5 to - 0.7) and physical performance measures (e.g. linear sprint, ES = - 0.3 to - 1.0). All the systematically analyzed fatigue-related markers were substantially altered at post-match. Hamstrings force production capacity (ES = - 0.7), physical performance (2-4%, ES = 0.3-0.5), creatine kinase (CK, ES = 0.4), well-being (ES = 0.2-0.4) and delayed onset muscle soreness (DOMS, ES = 0.6-1.3) remained substantially impaired at G + 72 h. Compared to simulation protocols, 11 vs. 11 match format (CK, ES = 1.8) induced a greater magnitude of change in muscle damage (i.e. CK, ES = 1.8 vs. 0.7), inflammatory (IL-6, ES = 2.6 vs. 1.1) and immunological markers and DOMS (ES = 1.5 vs. 0.7) than simulation protocols at post-assessments. Neuromuscular performances at post-match did not differ between protocols.



While some parameters are fully recovered (e.g. hormonal and technical), our systematic review shows that a period of 72 h post-match play is not long enough to completely restore homeostatic balance (e.g. muscle damage, physical and well-being status). The extent of the recovery period post-soccer game cannot consist of a 'one size fits all approach'. Additionally, the 'real match' (11 vs. 11 format) likely induces greater magnitudes of perceptual (DOMS) and biochemical alterations (e.g. muscle damage), while neuromuscular alterations were essentially similar. Overall, coaches must adjust the structure and content of the training sessions during the 72-h post-match intervention to effectively manage the training load within this time-frame.
signSGD: compressed optimisation for non-convex problems

Training large neural networks requires distributing learning across multiple workers, where the cost of communicating gradients can be a significant bottleneck. SIGNSGD alleviates this problem by transmitting just the sign of each minibatch stochastic gradient. We prove that it can get the best of both worlds: compressed gradients and SGD-level convergence rate. The relative `1/`2 geometry of gradients, noise and curvature informs whether SIGNSGD or SGD is theoretically better suited to a particular problem. On the practical side we find that the momentum counterpart of SIGNSGD is able to match the accuracy and convergence speed of ADAM on deep Imagenet models. We extend our theory to the distributed setting, where the parameter server uses majority vote to aggregate gradient signs from each worker enabling 1-bit compression of worker-server communication in both directions. Using a theorem by Gauss (1823) we prove that majority vote can achieve the same reduction in variance as full precision distributed SGD. Thus, there is great promise for sign-based optimisation schemes to achieve fast communication and fast convergence. Code to reproduce experiments is to be found at https://github.com/jxbz/signSGD.
Post-Quantum Cryptography on FPGA Based on Isogenies on Elliptic Curves

To the best of our knowledge, we present the first hardware implementation of isogeny-based cryptography available in the literature. Particularly, we present the first implementation of the supersingular isogeny Diffie-Hellman (SIDH) key exchange, which features quantum-resistance. We optimize this design for speed by creating a high throughput multiplier unit, taking advantage of parallelization of arithmetic in  $\mathbb {F}_{p^{2}}$ , and minimizing pipeline stalls with optimal scheduling. Consequently, our results are also faster than software libraries running affine SIDH even on Intel Haswell processors. For our implementation at 85-bit quantum security and 128-bit classical security, we generate ephemeral public keys in 1.655 million cycles for Alice and 1.490 million cycles for Bob. We generate the shared secret in an additional 1.510 million cycles for Alice and 1.312 million cycles for Bob. On a Virtex-7, these results are approximately 1.5 times faster than known software implementations running the same 512-bit SIDH. Our results and observations show that the isogeny-based schemes can be implemented with high efficiency on reconfigurable hardware.
Statistically-Motivated Second-Order Pooling

Second-order pooling, a.k.a. bilinear pooling, has proven effective for deep learning based visual recognition. However, the resulting second-order networks yield a final representation that is orders of magnitude larger than that of standard, first-order ones, making them memory-intensive and cumbersome to deploy. Here, we introduce a general, parametric compression strategy that can produce more compact representations than existing compression techniques, yet outperform both compressed and uncompressed second-order models. Our approach is motivated by a statistical analysis of the network’s activations, relying on operations that lead to a Gaussian-distributed final representation, as inherently used by first-order deep networks. As evidenced by our experiments, this lets us outperform the state-of-the-art first-order and second-order models on several benchmark recognition datasets.
CPC: assess the protein-coding potential of transcripts using sequence features and support vector machine

Recent transcriptome studies have revealed that a large number of transcripts in mammals and other organisms do not encode proteins but function as noncoding RNAs (ncRNAs) instead. As millions of transcripts are generated by large-scale cDNA and EST sequencing projects every year, there is a need for automatic methods to distinguish protein-coding RNAs from noncoding RNAs accurately and quickly. We developed a support vector machine-based classifier, named Coding Potential Calculator (CPC), to assess the protein-coding potential of a transcript based on six biologically meaningful sequence features. Tenfold cross-validation on the training dataset and further testing on several large datasets showed that CPC can discriminate coding from noncoding transcripts with high accuracy. Furthermore, CPC also runs an order-of-magnitude faster than a previous state-of-the-art tool and has higher accuracy. We developed a user-friendly web-based interface of CPC at http://cpc.cbi.pku.edu.cn. In addition to predicting the coding potential of the input transcripts, the CPC web server also graphically displays detailed sequence features and additional annotations of the transcript that may facilitate users' further investigation.
In vitro and in vivo activity of melflufen (J1)in lymphoma

Melphalan has been used in the treatment of various hematologic malignancies for almost 60 years. Today it is part of standard therapy for multiple myeloma and also as part of myeloablative regimens in association with autologous allogenic stem cell transplantation. Melflufen (melphalan flufenamide ethyl ester, previously called J1) is an optimized derivative of melphalan providing targeted delivery of active metabolites to cells expressing aminopeptidases. The activity of melflufen has compared favorably with that of melphalan in a series of in vitro and in vivo experiments performed preferentially on different solid tumor models and multiple myeloma. Melflufen is currently being evaluated in a clinical phase I/II trial in relapsed or relapsed and refractory multiple myeloma. Cytotoxicity of melflufen was assayed in lymphoma cell lines and in primary tumor cells with the Fluorometric Microculture Cytotoxicity Assay and cell cycle analyses was performed in two of the cell lines. Melflufen was also investigated in a xenograft model with subcutaneous lymphoma cells inoculated in mice. Melflufen showed activity with cytotoxic IC50-values in the submicromolar range (0.011-0.92 μM) in the cell lines, corresponding to a mean of 49-fold superiority (p < 0.001) in potency vs. melphalan. In the primary cultures melflufen yielded slightly lower IC50-values (2.7 nM to 0.55 μM) and an increased ratio vs. melphalan (range 13–455, average 108, p < 0.001). Treated cell lines exhibited a clear accumulation in the G2/M-phase of the cell cycle. Melflufen also showed significant activity and no, or minimal side effects in the xenografted animals. This study confirms previous reports of a targeting related potency superiority of melflufen compared to that of melphalan. Melflufen was active in cell lines and primary cultures of lymphoma cells, as well as in a xenograft model in mice and appears to be a candidate for further evaluation in the treatment of this group of malignant diseases.
Modifying the baricity of local anesthetics for spinal anesthesia by temperature adjustment: model calculations.

Although local anesthetics (LAs) are hyperbaric at room temperature, density drops within minutes after administration into the subarachnoid space. LAs become hypobaric and therefore may cranially ascend during spinal anesthesia in an uncontrolled manner. The authors hypothesized that temperature and density of LA solutions have a nonlinear relation that may be described by a polynomial equation, and that conversion of this equation may provide the temperature at which individual LAs are isobaric.



Density of cerebrospinal fluid was measured using a vibrating tube densitometer. Temperature-dependent density data were obtained from all LAs commonly used for spinal anesthesia, at least in triplicate at 5 degrees, 20 degrees, 30 degrees, and 37 degrees C. The hypothesis was tested by fitting the obtained data into polynomial mathematical models allowing calculations of substance-specific isobaric temperatures.



Cerebrospinal fluid at 37 degrees C had a density of 1.000646 +/- 0.000086 g/ml. Three groups of local anesthetics with similar temperature (T, degrees C)-dependent density (rho) characteristics were identified: articaine and mepivacaine, rho1(T) = 1.008-5.36 E-06 T2 (heavy LAs, isobaric at body temperature); L-bupivacaine, rho2(T) = 1.007-5.46 E-06 T2 (intermediate LA, less hypobaric than saline); bupivacaine, ropivacaine, prilocaine, and lidocaine, rho3(T) = 1.0063-5.0 E-06 T (light LAs, more hypobaric than saline). Isobaric temperatures (degrees C) were as follows: 5 mg/ml bupivacaine, 35.1; 5 mg/ml L-bupivacaine, 37.0; 5 mg/ml ropivacaine, 35.1; 20 mg/ml articaine, 39.4.



Sophisticated measurements and mathematic models now allow calculation of the ideal injection temperature of LAs and, thus, even better control of LA distribution within the cerebrospinal fluid. The given formulae allow the adaptation on subpopulations with varying cerebrospinal fluid density.
Positive Psychological Well-Being and Cardiovascular Disease: JACC Health Promotion Series.

Facets of positive psychological well-being, such as optimism, have been identified as positive health assets because they are prospectively associated with the 7 metrics of cardiovascular health (CVH) and improved outcomes related to cardiovascular disease. Connections between psychological well-being and cardiovascular conditions may be mediated through biological, behavioral, and psychosocial pathways. Individual-level interventions, such as mindfulness-based programs and positive psychological interventions, have shown promise for modifying psychological well-being. Further, workplaces are using well-being-focused interventions to promote employee CVH, and these interventions represent a potential model for expanding psychological well-being programs to communities and societies. Given the relevance of psychological well-being to promoting CVH, this review outlines clinical recommendations to assess and promote well-being in encounters with patients. Finally, a research agenda is proposed. Additional prospective observational studies are needed to understand mechanisms underlying the connection between psychological well-being and cardiovascular outcomes. Moreover, rigorous intervention trials are needed to assess whether psychological well-being-promoting programs can improve cardiovascular outcomes.
Genetic Programming using Direct Manipulation

Some of the features in Discipulus that contribute to its extraordinary performance [3, 4, 5, 6, 9] are: • Discipulus implements a Genetic Programming algorithm. This algorithm determines the appropriate functional form and optimizes the parameters of the function. It is an ideal algorithm for complex, noisy, poorly understood domains. • Discipulus performs Genetic Programming thru direct manipulation of binary machine code. This makes Discipulus about sixty to two-hundred times faster than comparable automated learning approaches . • Discipulus performs multi-run Genetic Programming, intelligently adapting its own parameters to the problem at hand. Each of these capabilities of Discipulus are discussed below.
Virtual reality in context of Industry 4.0 proposed projects at Brno University of Technology

This paper deals with virtual reality in context of Industry 4.0 projects. Due to sufficient computing power, virtual reality allows visualization of virtual objects in both professional and public spheres. With virtual reality, solving complex projects has become much easier, especially of those based on Industry 4.0 standards. Industry 4.0 projects require connection between all systems of modern machines (physical systems, embedded systems, sensors, actuators, electronic hardware, software etc.) via integrated data chains and these links are used, among other things, for monitoring operating conditions and share virtual models in the development cycle, both of which can be visualized via virtual reality. Our virtual reality lab has the CAVE and Powerwall virtual reality technologies and several examples of its application are presented.
Adaptive Self-tuning Memory in DB2

DB2 for Linux, UNIX, and Windows Version 9.1 introduces the Self-Tuning Memory Manager (STMM), which provides adaptive self tuning of both database memory heaps and cumulative database memory allocation. This technology provides state-of-the-art memory tuning combining control theory, runtime simulation modeling, cost-benefit analysis, and operating system resource analysis. In particular, the nove use of cost-benefit analysis and control theory techniques makes STMM a breakthrough technology in database memory management. The cost-benefit analysis allows STMM to tune memory between radically different memory consumers such as compiled statement cache, sort, and buffer pools. These methods allow for the fast convergence of memory settings while also providing stability in the presence of system noise. The tuning mode has been found in numerous experiments to tune memory allocation as well as expert human administrators, including OLTP, DSS, and mixed environments. We believe this is the first known use of cost-benefit analysis and control theory in database memory tuning across heterogeneous memory consumers.
Analysis of Gaze on Comic Book Panel Structure

Comics have been shown to be able to tell a story by guiding the viewers gaze patterns through a sequence of images. However, not much research has been done on how comic techniques affect these patterns. We focused this study to investigate the effect that the structure of a comics panels have on the viewers reading patterns, specifically with the time spent reading the comic and the number of times the viewer fixates on a point. We use two versions of a short comic as a stimulus, one version with four long panels and another with sixteen smaller panels. We collected data using the GazePoint eye tracker, focusing on viewing time and number of fixations, and we collected subjective information about the viewers preferences using a questionnaire. We found that no significant effect between panel structure and viewing time or number of fixations, but those viewers slightly tended to prefer the format of four long panels.
Adaptive Hierarchical Fair Competition (AHFC) Model For Parallel Evolutionary Algorithms

The HFC model for parallel evolutionary computation is inspired by the stratified competition often seen in society and biology. Subpopulations are stratified by fitness. Individuals move from low-fitness to higher-fitness subpopulations if and only if they exceed the fitness-based admission threshold of the receiving subpopulation, but not of a higher one. The HFC model implements several critical features of a competent parallel evolutionary computation model, simultaneously and naturally, allowing rapid exploitation while impeding premature convergence. The AHFC model is an adaptive version of HFC, extending it by allowing the admission thresholds of fitness levels to be determined dynamically by the evolution process itself. The effectiveness of the Adaptive HFC model is compared with the HFC model on a genetic programming-based evolutionary synthesis example.
Don't panic

A study is being done into the psychology of crowd behaviour during emergencies, and ways of ensuring safety during mass evacuations by encouraging more altruistic behaviour. Crowd emergencies have previously been understood as involving panic and selfish behaviour. The present study tests the claims that (1) co-operation and altruistic behaviour rather than panic will predominate in mass responses to emergencies, even in situations where there is a clear threat of death; and that this is the case not only because (2) everyday norms and social roles continue to exert an influence, but also because (3) the external threat can create a sense of solidarity amongst strangers. Qualitative analysis of interviews with survivors of different emergencies supports these claims. A second study of the July 7 London bombings is on-going and also supports these claims. While these findings provide support for some existing models of mass emergency evacuation, it also points to the necessity of a new theoretical approach to the phenomena, using Self-Categorization Theory. Practical applications for the future management of crowd emergencies are also considered.