nils-herrmann/clean_scirepeval huggingface dataset:
Biochemical aspects of overtraining in endurance sports: a review.

Top-level performances in endurance sports require several years of hard training loads. A major objective of this endurance training is to reach the most elevated metabolic adaptations the athlete will be able to support. As a consequence, overtraining is a recurrent problem that highly-trained athletes may experience during their career. Many studies have revealed that overtraining could be highlighted by various biochemical markers but a principal discrepancy in the diagnosis of overtraining stems from the fact that none of these markers may be considered as universal. In endurance sports, the metabolic aspects of training fatigue appear to be the most relevant parameters that may characterise overtraining when recovery is not sufficient, or when dietary habits do not allow an optimal replenishment of substrate stores. From the skeletal muscle functions to the overall energetic substrate availability during exercise, six metabolic schemes have been studied in relation to overtraining, each one related to a central parameter, i.e. carbohydrates, branched-chain amino acids, glutamine, polyunsaturated fatty acids, leptin, and proteins. We summarise the current knowledge on these metabolic hypotheses regarding the occurrence of overtraining in endurance sports.
Improved Line Maze Solving Algorithm for Curved and Zig-zag Track

–- Line maze solving algorithm is an algorithm used to solve a maze made of lines to be traced by a mobile robot. But it is designed only for lines with right angle intersection or turn. Meanwhile in real world, there are also curved and zig-zag turn. In this work, this algorithm is tested for curved and zig-zag track by using Arduino Uno. It turns out that line maze solving algorithm still has some deficiencies, even for the maze without curved and zig-zag line. Moreover, for the curved and zig-zag track, algorithm improvements are needed. Therefore, some of existing functions has been modified and replaced, and one new function added. When the improvements have been done, new algorithm is obtained. Then the test is done again on the mobile robot in a line maze with curved and zig-zag track. The result has proven that the new algorithm has successfully solved the maze.
VLSI design and analysis of low power 6T SRAM cell using cadence tool

CMOS SRAM cell is very less power consuming and have less read and write time. Higher cell ratios can decrease the read and write time and improve stability. PMOS transistor with less width reduces the power consumption. This paper implements 6T SRAM cell with reduced read and write time, area and power consumption. It has been noticed often that increased memory capacity increases the bit-line parasitic capacitance which in turn slows down voltage sensing and make bit-line voltage swings energy expensive. This result in slower and more energy hungry memories.. In this paper Two SRAM cell is being designed for 4 Kb of memory core with supply voltage 1.8 V. A technique of global bit line is used for reducing the power consumption and increasing the memory capacity.
Metaphor and meaning in a clinical interview.

This study examined the narrative features of 20 life histories gathered from psychiatric patients with the Occupational Performance History Interview. The aim was to identify how narrative features were present in the patient interview responses and to illustrate how such narrative features can be located. We found that the patients organized their interview responses with deep metaphors that served to "emplot," or give meaning to, the life story. This article illustrates how patients used the deep metaphors to both circumscribe and frame possible solutions to the problems in their lives. Deep metaphors are consistent, recurring images of a life story that give coherence to, and aid in, the interpretation of the events of that life. Moreover, we explored how metaphors can be located in patient life histories and their implications for occupational therapy.
Associative Compression Networks

This paper introduces Associative Compression Networks (ACNs), a new framework for variational autoencoding with neural networks. The system differs from existing variational autoencoders in that the prior distribution used to model each code is conditioned on a similar code from the dataset. In compression terms this equates to sequentially transmitting the data using an ordering determined by proximity in latent space. As the prior need only account for local, rather than global variations in the latent space, the coding cost is greatly reduced, leading to rich, informative codes, even when autoregressive decoders are used. Experimental results on MNIST, CIFAR-10, ImageNet and CelebA show that ACNs can yield improved dataset compression relative to orderagnostic generative models, with an upper bound of 73.9 nats per image on binarized MNIST. They also demonstrate that ACNs learn high-level features such as object class, writing style, pose and facial expression, which can be used to cluster and classify the data, as well as to generate diverse and convincing samples.
A versatile microreactor platform featuring a chemical-resistant microvalve array for addressable multiplex syntheses and assays

A versatile microreactor platform featuring a novel chemical-resistant microvalve array has been developed using combined silicon/polymer micromachining and a special polymer membrane transfer process. The basic valve unit in the array has a typical ‘transistor’ structure and a PDMS/parylene double-layer valve membrane. A robust multiplexing algorithm is also proposed for individual addressing of a large array using a minimal number of signal inputs. The in-channel microvalve is leakproof upon pneumatic actuation. In open status it introduces small impedance to the fluidic flow, and allows a significantly larger dynamic range of flow rates (∼ml min−1) compared with most of the microvalves reported. Equivalent electronic circuits were established by modeling the microvalves as PMOS transistors and the fluidic channels as simple resistors to provide theoretical prediction of the device fluidic behavior. The presented microvalve/reactor array showed excellent chemical compatibility in the tests with several typical aggressive chemicals including those seriously degrading PDMS-based microfluidic devices. Combined with the multiplexing strategy, this versatile array platform can find a variety of lab-on-a-chip applications such as addressable multiplex biochemical synthesis/assays, and is particularly suitable for those requiring tough chemicals, large flow rates and/or high-throughput parallel processing. As an example, the device performance was examined through the addressed synthesis of 30-mer DNA oligonucleotides followed by sequence validation using on-chip hybridization. The results showed leakage-free valve array addressing and proper synthesis in target reactors, as well as uniform flow distribution and excellent regional reaction selectivity. (Some figures in this article are in colour only in the electronic version) 0960-1317/06/081433+11$30.00
Joint Learning of Response Ranking and Next Utterance Suggestion in Human-Computer Conversation System

Conversation systems are of growing importance since they enable an easy interaction interface between humans and computers: using natural languages. To build a conversation system with adequate intelligence is challenging, and requires abundant resources including an acquisition of big data and interdisciplinary techniques, such as information retrieval and natural language processing. Along with the prosperity of Web 2.0, the massive data available greatly facilitate data-driven methods such as deep learning for human-computer conversation systems. Owing to the diversity of Web resources, a retrieval-based conversation system will come up with at least some results from the immense repository for any user inputs. Given a human issued message, i.e., query, a traditional conversation system would provide a response after adequate training and learning of how to respond. In this paper, we propose a new task for conversation systems: joint learning of response ranking featured with next utterance suggestion. We assume that the new conversation mode is more proactive and keeps user engaging. We examine the assumption in experiments. Besides, to address the joint learning task, we propose a novel Dual-LSTM Chain Model to couple response ranking and next utterance suggestion simultaneously. From the experimental results, we demonstrate the usefulness of the proposed task and the effectiveness of the proposed model.
Defining a Standard for Particle Swarm Optimization

Particle swarm optimization has become a common heuristic technique in the optimization community, with many researchers exploring the concepts, issues, and applications of the algorithm. In spite of this attention, there has as yet been no standard definition representing exactly what is involved in modern implementations of the technique. A standard is defined here which is designed to be a straightforward extension of the original algorithm while taking into account more recent developments that can be expected to improve performance on standard measures. This standard algorithm is intended for use both as a baseline for performance testing of improvements to the technique, as well as to represent PSO to the wider optimization community
High flow priapism: diagnosis and treatment in pediatric population

To present three cases of arterial high flow priapism (HFP) and propose a management algorithm for this condition. We studied three children with post-traumatic arterial HFP (two patients with perineal trauma and one with penis trauma). Spontaneous resolution was observed in all the patients. The time of resolution by a return to a completely flaccid penis was different: 14, 27 and 36 days in each case. Absence of long-term damaging effects of arterial HFP on erectile tissue combined with the possibility of spontaneous resolution associated with blunt perineal trauma are suggestive signs for the introduction of an observation period in the management algorithm of HFP. Such a period may help to avoid unnecessary surgical intervention. Thus, these cases reinforce the decision to manage these patients conservatively and avoid angiographic embolization as a first therapeutic choice.
Water-only fasting and an exclusively plant foods diet in the management of stage IIIa, low-grade follicular lymphoma.

Follicular lymphoma (FL), the second most common non-Hodgkin's lymphoma (NHL), is well characterised by a classic histological appearance and an indolent course. Current treatment protocols for FL range from close observation to immunotherapy, chemotherapy and/or radiotherapies. We report the case of a 42-year-old woman diagnosed by excisional biopsy with stage IIIa, grade 1 FL. In addition to close observation, the patient underwent a medically supervised, 21-day water-only fast after which enlarged lymph nodes were substantially reduced in size. The patient then consumed a diet of minimally processed plant foods free of added sugar, oil and salt (SOS), and has remained on the diet since leaving the residential facility. At 6 and 9-month follow-up visits, the patient's lymph nodes were non-palpable and she remained asymptomatic. This case establishes a basis for further studies evaluating water-only fasting and a plant foods, SOS-free diet as a treatment protocol for FL.
Efficient human activity recognition using hyperdimensional computing

Human activity recognition is a key task of many Internet of Things (IoT) applications to understand underlying contexts and react with the environments. Machine learning is widely exploited to identify the activities from sensor measurements, however, they are often overcomplex to run on less-powerful IoT devices. In this paper, we present an alternative approach to efficiently support the activity recognition tasks using brain-inspired hyperdimensional (HD) computing. We show how the HD computing method can be applied to the recognition problem in IoT systems while improving the accuracy and efficiency. In our evaluation conducted for three practical datasets, the proposed design achieves the speedup of the model training by up to 486x as compared to the state-of-the-art neural network training. In addition, our design improves the performance of the HD-based inference procedure by 7x on a low-power ARM processor.
Predicting High-Risk Preterm Birth Using Artificial Neural Networks

A reengineered approach to the early prediction of preterm birth is presented as a complimentary technique to the current procedure of using costly and invasive clinical testing on high-risk maternal populations. Artificial neural networks (ANNs) are employed as a screening tool for preterm birth on a heterogeneous maternal population; risk estimations use obstetrical variables available to physicians before 23 weeks gestation. The objective was to assess if ANNs have a potential use in obstetrical outcome estimations in low-risk maternal populations. The back-propagation feedforward ANN was trained and tested on cases with eight input variables describing the patient's obstetrical history; the output variables were: 1) preterm birth; 2) high-risk preterm birth; and 3) a refined high-risk preterm birth outcome excluding all cases where resuscitation was delivered in the form of free flow oxygen. Artificial training sets were created to increase the distribution of the underrepresented class to 20%. Training on the refined high-risk preterm birth model increased the network's sensitivity to 54.8%, compared to just over 20% for the nonartificially distributed preterm birth model
Holistic measurement of well-being: psychometric properties of the physical, mental and social well-being scale (PMSW-21) for adults.

A holistic approach to health requires the development of tools that would allow to measure the inner world of individuals within its physical, mental and social dimensions.



To create the Physical, Mental and Social Well-being scale (PMSW-21) that allows a holistic representation of various dimensions of well-being in such a way as they are perceived by the individuals and how affected their health.



The study was conducted on the sample of 406 inhabitants of Warsaw involving in the Social Participation in Health Reform project. The PMSW-21 scale included: headache, tiredness, abdominal pain, palpitation, joint pain, backache, sleep disturbance (physical domain), anxiety, guiltiness, helplessness, hopelessness, sadness, self-dissatisfaction, hostility (mental domain), security, communicability, protection, loneliness, rejection, sociability and appreciation (social domain). The five criterial variables of health and seven of life experiences were adopted to assess the discriminative power of the PMSW-21 scale.



The total well-being scale as well as its physical, mental and social domains showed high reliability (Cronbach a 0.81, 0.77, 0.90, 0.72, respectively). The analysis confirmed the construct validity. All the items stronger correlated with their own domain than with the others (ranges for physical: 0.41 - 0.55, mental: 0.49 - 0.80 and social: 0.31 - 0.50). The total scale demonstrate high sensitivity; it significantly differentiated almost all criterial variables. Physical domain showed high sensitivity for health as well as for negative life events variables, while the mental and social domains were more sensitive for life events.



The analysis confirmed the usefulness of PMSW-21 scale for measure the holistic well-being. The reliability of the total scale and its domains, construct validity and sensitivity for health and life determinants were at acceptable level.
Interaction between basal ganglia and limbic circuits in learning and memory processes.

Hippocampus and striatum play distinctive roles in memory processes since declarative and non-declarative memory systems may act independently. However, hippocampus and striatum can also be engaged to function in parallel as part of a dynamic system to integrate previous experience and adjust behavioral responses. In these structures the formation, storage, and retrieval of memory require a synaptic mechanism that is able to integrate multiple signals and to translate them into persistent molecular traces at both the corticostriatal and hippocampal/limbic synapses. The best cellular candidate for this complex synthesis is represented by long-term potentiation (LTP). A common feature of LTP expressed in these two memory systems is the critical requirement of convergence and coincidence of glutamatergic and dopaminergic inputs to the dendritic spines of the neurons expressing this form of synaptic plasticity. In experimental models of Parkinson's disease abnormal accumulation of α-synuclein affects these two memory systems by altering two major synaptic mechanisms underlying cognitive functions in cholinergic striatal neurons, likely implicated in basal ganglia dependent operative memory, and in the CA1 hippocampal region, playing a central function in episodic/declarative memory processes.
Accelerating last-mile web performance with popularity-based prefetching

As broadband speeds increase, latency is becoming a bottleneck for many applications—especially for Web downloads. Latency affects many aspects of Web page load time, from DNS lookups to the time to complete a three-way TCP handshake; it also contributes to the time it takes to transfer the Web objects for a page. Previous work has shown that much of this latency can occur in the last mile . Although some performance bottlenecks can be mitigated by increasing downstream throughput (e.g., by purchasing a higher service plan), in many cases, latency introduces performance bottlenecks, particularly for connections with higher throughput. To mitigate latency bottlenecks in the last mile, we have implemented a system that performs DNS prefetching and TCP connection caching to the Web sites that devices inside a home visit most frequently, a technique we call popularity-based prefetching. Many devices and applications already perform DNS prefetching and maintain persistent TCP connections, but most prefetching is predictive based on the content of the page, rather than on past site popularity. We evaluate the optimizations using a simulator that we drive from traffic traces that we collected from five homes in the BISmark testbed . We find that performing DNS prefetching and TCP connection caching for the twenty most popular sites inside the home can double DNS and connection cache hit rates.
Clustering techniques for streaming data-a survey

Nowadays many applications are generating streaming data for an example real-time surveillance, internet traffic, sensor data, health monitoring systems, communication networks, online transactions in the financial market and so on. Data Streams are temporally ordered, fast changing, massive, and potentially infinite sequence of data. Data Stream mining is a very challenging problem. This is due to the fact that data streams are of tremendous volume and flows at very high speed which makes it impossible to store and scan streaming data multiple time. Concept evolution in streaming data further magnifies the challenge of working with streaming data. Clustering is a data stream mining task which is very useful to gain insight of data and data characteristics. Clustering is also used as a pre-processing step in over all mining process for an example clustering is used for outlier detection and for building classification model. In this paper we will focus on the challenges and necessary features of data stream clustering techniques, review and compare the literature for data stream clustering by example and variable, describe some real world applications of data stream clustering, and tools for data stream clustering.
Comparison of 18F-FACBC and 11C-choline PET/CT in patients with radically treated prostate cancer and biochemical relapse: preliminary results

We assessed the rate of detection rate of recurrent prostate cancer by PET/CT using anti-3-18F-FACBC, a new synthetic amino acid, in comparison to that using 11C-choline as part of an ongoing prospective single-centre study. Included in the study were 15 patients with biochemical relapse after initial radical treatment of prostate cancer. All the patients underwent anti-3-18F-FACBC PET/CT and 11C-choline PET/CT within a 7-day period. The detection rates using the two compounds were determined and the target–to-background ratios (TBR) of each lesion are reported. No adverse reactions to anti-3-18F-FACBC PET/CT were noted. On a patient basis, 11C-choline PET/CT was positive in 3 patients and negative in 12 (detection rate 20 %), and anti-3-18F-FACBC PET/CT was positive in 6 patients and negative in 9 (detection rate 40 %). On a lesion basis, 11C-choline detected 6 lesions (4 bone, 1 lymph node, 1 local relapse), and anti-3-18F-FACBC detected 11 lesions (5 bone, 5 lymph node, 1 local relapse). All 11C-choline-positive lesions were also identified by anti-3-18F-FACBC PET/CT. The TBR of anti-3-18F-FACBC was greater than that of 11C-choline in 8/11 lesions, as were image quality and contrast. Our preliminary results indicate that anti-3-18F-FACBC may be superior to 11C-choline for the identification of disease recurrence in the setting of biochemical failure. Further studies are required to assess efficacy of anti-3-18F-FACBC in a larger series of prostate cancer patients.
A Unified View of Piecewise Linear Neural Network Verification

1: function BaB(net, domain, ) 2: global ub← inf 3: global lb← − inf 4: doms← [(global lb, domain)] 5: while global ub− global lb > do 6: ( , dom)← pick out(doms) 7: [subdom 1, . . . , subdom s]← split(dom) 8: for i = 1 . . . s do 9: dom ub← compute UB(net, subdom i) 10: dom lb← compute LB(net, subdom i) 11: if dom ub < global ub then 12: global ub← dom ub 13: prune domains(doms, global ub) 14: end if 15: if dom lb < global ub then 16: domains.append((dom lb, subdom i)) 17: end if 18: end for 19: global lb← min{lb | (lb, dom) ∈ doms} 20: end while 21: return global ub 22: end function
A MapReduce solution for associative classification of big data

Associative classifiers have proven to be very effective in classification problems. Unfortunately, the algorithms used for learning these classifiers are not able to adequately manage big data because of time complexity and memory constraints. To overcome such drawbacks, we propose a distributed association rule-based classification scheme shaped according to the MapReduce programming model. The scheme mines classification association rules (CARs) using a properly enhanced, distributed version of the well-known FP-Growth algorithm. Once CARs have been mined, the proposed scheme performs a distributed rule pruning. The set of survived CARs is used to classify unlabeled patterns. The memory usage and time complexity for each phase of the learning process are discussed, and the scheme is evaluated on seven real-world big datasets on the Hadoop framework, characterizing its scalability and achievable speedup on small computer clusters. The proposed solution for associative classifiers turns to be suitable to practically address ∗Corresponding Author: Tel: +39 05