nils-herrmann/clean_scirepeval huggingface dataset:
Synthetic Aperture Radar Image Processing using the Supervised Textural-Neural Network Classification Algorithm

Synthetic Aperture Radar (SAR) satellite images have proven to be a successful tool for identifying oil slicks. Natural oil seeps can be detected as elongated, radar-dark slicks in SAR images. Use of SAR images for seep detection is enhanced by a Texture Classifying Neural Network Algorithm (TCNNA), which delineates areas where layers of floating oil suppress Bragg scattering. The effect is strongly influenced by wind strength and sea state. A multi orientation Leung-Malik filter bank  is used to identify slick shapes under projection of edges. By integrating ancillary data consisting of the incidence angle, descriptors of texture and environmental variables, considerable accuracy were added to the classification ability to discriminate false targets from oil slicks and look-alike pixels. The reliability of the TCNNA is measured after processing 71 images containing oil slicks.
Machine Learning for Outcome Prediction of Acute Ischemic Stroke Post Intra-Arterial Therapy

Stroke is a major cause of death and disability. Accurately predicting stroke outcome from a set of predictive variables may identify high-risk patients and guide treatment approaches, leading to decreased morbidity. Logistic regression models allow for the identification and validation of predictive variables. However, advanced machine learning algorithms offer an alternative, in particular, for large-scale multi-institutional data, with the advantage of easily incorporating newly available data to improve prediction performance. Our aim was to design and compare different machine learning methods, capable of predicting the outcome of endovascular intervention in acute anterior circulation ischaemic stroke.



We conducted a retrospective study of a prospectively collected database of acute ischaemic stroke treated by endovascular intervention. Using SPSS®, MATLAB®, and Rapidminer®, classical statistics as well as artificial neural network and support vector algorithms were applied to design a supervised machine capable of classifying these predictors into potential good and poor outcomes. These algorithms were trained, validated and tested using randomly divided data.



We included 107 consecutive acute anterior circulation ischaemic stroke patients treated by endovascular technique. Sixty-six were male and the mean age of 65.3. All the available demographic, procedural and clinical factors were included into the models. The final confusion matrix of the neural network, demonstrated an overall congruency of ∼ 80% between the target and output classes, with favourable receiving operative characteristics. However, after optimisation, the support vector machine had a relatively better performance, with a root mean squared error of 2.064 (SD: ± 0.408).



We showed promising accuracy of outcome prediction, using supervised machine learning algorithms, with potential for incorporation of larger multicenter datasets, likely further improving prediction. Finally, we propose that a robust machine learning system can potentially optimise the selection process for endovascular versus medical treatment in the management of acute stroke.
HairNet: Single-View Hair Reconstruction Using Convolutional Neural Networks

We introduce a deep learning-based method to generate full 3D hair geometry from an unconstrained image. Our method can recover local strand details and has real-time performance. State-of-the-art hair modeling techniques rely on large hairstyle collections for nearest neighbor retrieval and then perform ad-hoc refinement. Our deep learning approach, in contrast, is highly efficient in storage and can run 1000 times faster while generating hair with 30K strands. The convolutional neural network takes the 2D orientation field of a hair image as input and generates strand features that are evenly distributed on the parameterized 2D scalp. We introduce a collision loss to synthesize more plausible hairstyles, and the visibility of each strand is also used as a weight term to improve the reconstruction accuracy. The encoder-decoder architecture of our network naturally provides a compact and continuous representation for hairstyles, which allows us to interpolate naturally between hairstyles. We use a large set of rendered synthetic hair models to train our network. Our method scales to real images because an intermediate 2D orientation field, automatically calculated from the real image, factors out the difference between synthetic and real hairs. We demonstrate the effectiveness and robustness of our method on a wide range of challenging real Internet pictures, and show reconstructed hair sequences from videos.
Building Better Quality Predictors Using "$\epsilon$-Dominance"

Despite extensive research, many methods in software quality prediction still exhibit some degree of uncertainty in their results. Rather than treating this as a problem, this paper asks if this uncertainty is a resource that can simplify software quality prediction. For example, Deb’s principle of ε-dominance states that if there exists some ε value below which it is useless or impossible to distinguish results, then it is superfluous to explore anything less than ε . We say that for “large ε problems”, the results space of learning effectively contains just a few regions. If many learners are then applied to such large ε problems, they would exhibit a “many roads lead to Rome” property; i.e., many different software quality prediction methods would generate a small set of very similar results.  The success of DART for defect prediction begs the questions: how many other domains in software quality predictors can also be radically simplified? This will be a fruitful direction for future work.
Flash Lidar Sensor Using Fiber-Coupled APDs

In contrast to scanning light detection and ranging (Lidar) system, the flash Lidar has no mechanical moving errors, and its features include light weight, small volume, and energy efficiency. This paper presents the advances of the newest research on a flash Lidar sensing instrument based on fiber-coupled avalanche photodiodes (APDs), which is named GLidar featured with easily increasable pixels of Lidar sensor, effectively reducible electrical crosstalk of adjacent pixels, and cost efficiency when compared with flash Lidar with a single APD array. A complete laser emitting system, including laser emitting and receiving system, controlling systems, electro-optic transmitting system and high precision time-interval measurement system, and different kinds of power supplies has been designed and implemented. The fiber-coupled APDs are arranged in a 5 × 5 array as a key element of the Lidar sensor. The experimental results through the multipoint distance measurement show that the greatest error among the 25 channels of the Lidar sensor is 0.08 m when the target is 13 m away, and the accuracy validation in a hallway demonstrated the accuracy of distance measurement can reach 10.8 cm.
An Introduction to Arithmetic Coding

Arithmetic coding is a data compression technique that encodes data (the data string) by creating a code string which represents a fractional value on the number line between 0 and 1. The coding algorithm is symbolwise recursive; i.e., it operates upon and encodes (decodes) one data symbol per iteration or recursion. On each recursion, the algorithm successively partitions an interval of the number line between 0 and I , and retains one of the partitions as the new interval. Thus, the algorithm successively deals with smaller intervals, and the code string, viewed as a magnitude, lies in each of the nested intervals. The data string is recovered by using magnitude comparisons on the code string to recreate how the encoder must have successively partitioned and retained each nested subinterval. Arithmetic coding differs considerably from the more familiar compression coding techniques, such as prefix (Huffman) codes. Also, it should not be confused with error control coding, whose object is to detect and correct errors in computer operations. This paper presents the key notions of arithmetic compression coding by means of simple examples.
Contemporary perspectives of core stability training for dynamic athletic performance: a survey of athletes, coaches, sports science and sports medicine practitioners

Core stability training has grown in popularity over 25 years, initially for back pain prevention or therapy. Subsequently, it developed as a mode of exercise training for health, fitness and sport. The scientific basis for traditional core stability exercise has recently been questioned and challenged, especially in relation to dynamic athletic performance. Reviews have called for clarity on what constitutes anatomy and function of the core, especially in healthy and uninjured people. Clinical research suggests that traditional core stability training is inappropriate for development of fitness for heath and sports performance. However, commonly used methods of measuring core stability in research do not reflect functional nature of core stability in uninjured, healthy and athletic populations. Recent reviews have proposed a more dynamic, whole body approach to training core stabilization, and research has begun to measure and report efficacy of these modes training. The purpose of this study was to assess extent to which these developments have informed people currently working and participating in sport.



An online survey questionnaire was developed around common themes on core stability training as defined in the current scientific literature and circulated to a sample population of people working and participating in sport. Survey results were assessed against key elements of the current scientific debate.



Perceptions on anatomy and function of the core were gathered from a representative cohort of athletes, coaches, sports science and sports medicine practitioners (n = 241), along with their views on effectiveness of various current and traditional exercise training modes. Most popular method of testing and measuring core function was subjective assessment through observation (43%), while a quarter (22%) believed there was no effective method of measurement. Perceptions of people in sport reflect the scientific debate, and practitioners have adopted a more functional approach to core stability training. There was strong support for loaded, compound exercises performed upright, compared to moderate support for traditional core stability exercises. Half of the participants (50%) in the survey, however, still support a traditional isolation core stability training.



Perceptions in applied practice on core stability training for dynamic athletic performance are aligned to a large extent to the scientific literature.
Brain stem and cortical mechanisms underlying the binaural masking level difference in humans: an auditory steady-state response study.

The behavioral binaural masking level difference (BMLD) is believed to reflect brain stem processing. However, this conflicts with transient auditory evoked potential research that indicates the auditory brain stem and middle latency responses do not demonstrate the BMLD. The objective of the present study is to investigate the brain stem and cortical mechanisms underlying the BMLD in humans using the brain stem and cortical auditory steady-state responses (ASSRs).



A 500-Hz pure tone, amplitude-modulated (AM) at 80 Hz and 7 (or 13) Hz, was used to elicit brain stem and cortical ASSRs, respectively. The masker was a 200-Hz-wide noise centered on 500 Hz. Eleven adult subjects with normal hearing were tested. Both ASSR (brain stem and cortical) and behavioral thresholds for diotic AM stimuli (when the signal and noise are in phase binaurally: SoNo) and dichotic AM stimuli (when either the signal or noise is 180 degrees out-of-phase between the two ears: SpiNo, SoNpi) were investigated. ASSR and behavioral BMLDs were obtained by subtracting the threshold for the dichotic stimuli from that for the diotic stimuli, respectively. Effects for modulation rate, signal versus noise phase changes, and behavioral versus ASSR measure on the BMLD were investigated.



Behavioral BMLDs (mean = 8.5 to 10.5 dB) obtained are consistent with results from past research. The ASSR results are similar to the pattern of results previously found for the transient auditory brain stem responses and the N1-P2 cortical auditory evoked potential, in that only the cortical ASSRs (7 or 13 Hz) demonstrate BMLDs (mean = 5.8 dB); the brain stem ASSRs (80 Hz) (mean = 1.5 dB) do not. The ASSR results differ from the previous transient N1-P2 studies, however, in that the cortical ASSRs show a BMLD only when there is a change in the signal interaural phase, but not for changes of noise interaural phase.



Results suggest that brain processes underlying the BMLD occur either in a different pathway or beyond the brain stem auditory processing underlying the 80-Hz ASSR. Results also suggest that the cortical ASSRs have somewhat different neural sources than the transient N1-P2 responses, and that they may reflect the output of neural populations that previous research has shown to be insensitive to binaural differences in noise.
Evaluation and Retraining of the Intrinsic Foot Muscles for Pain Syndromes Related to Abnormal Control of Pronation

Little clinical research exists on the contribution of the intrinsic foot muscles (IFM) to gait or on the specific clinical evaluation or retraining of these muscles. The purpose of this clinical paper is to review the potential functions of the IFM and their role in maintaining and dynamically controlling the medial longitudinal arch. Clinically applicable methods of evaluation and retraining of these muscles for the effective management of various foot and ankle pain syndromes are discussed.
DLS$@$CU: Sentence Similarity from Word Alignment and Semantic Vector Composition

We describe a set of top-performing systems at the SemEval 2015 English Semantic Textual Similarity (STS) task. Given two English sentences, each system outputs the degree of their semantic similarity. Our unsupervised system, which is based on word alignments across the two input sentences, ranked 5th among 73 submitted system runs with a mean correlation of 79.19% with human annotations. We also submitted two runs of a supervised system which uses word alignments and similarities between compositional sentence vectors as its features. Our best supervised run ranked 1st with a mean correlation of 80.15%.
Evaluation of the DSC algorithm and the BSS color scheme in dense cellular-like IEEE 802.11ax deployments

Coping with the extreme growth of the number of users is one of the main challenges for the future IEEE 802.11 networks. The high interference level, along with the conventional standardized carrier sensing approaches, will degrade the network performance. To tackle these challenges, the Dynamic Sensitivity Control (DSC) and the BSS Color scheme are considered in IEEE 802.11ax and IEEE 802.11ah, respectively. The main purpose of these schemes is to enhance the network throughput and improve the spectrum efficiency in dense networks. In this paper, we evaluate the DSC and the BSS Color scheme along with the PARTIAL-AID (PAID) feature introduced in IEEE 802.11ac, in terms of throughput and fairness. We also, exploit the performance when the aforementioned techniques are combined. The simulations show a significant gain in total throughput when these techniques are applied.
Revisiting MOSFET threshold voltage extraction methods

This article presents an up-to-date review of the several extraction methods commonly used to determine the value of the threshold voltage of MOSFETs. It includes the different methods that extract this quantity from the drain current versus gate voltage transfer characteristics measured under linear operation conditions for crystalline and non-crystalline MOSFETs. The various methods presented for the linear region are adapted to the saturation region and tested as a function of drain voltage whenever possible. The implementation of the extraction methods is discussed and tested by applying them to real state-ofthe-art devices in order to compare their performance. The validity of the different methods with respect to the presence of parasitic series resistance is also evaluated using 2-D simulations.
Teaching students global software engineering skills using distributed Scrum

In this paper we describe distributed Scrum augmented with best practices in global software engineering (GSE) as an important paradigm for teaching critical competencies in GSE. We report on a globally distributed project course between the University of Victoria, Canada and Aalto University, Finland. The project-driven course involved 16 students in Canada and 9 students in Finland, divided into three cross-site Scrum teams working on a single large project. To assess learning of GSE competencies we employed a mixed-method approach including 13 post-course interviews, pre-, post-course and iteration questionnaires, observations, recordings of Daily Scrums as well as collection of project asynchronous communication data. Our analysis indicates that the Scrum method, along with supporting collaboration practices and tools, supports the learning of important GSE competencies, such as distributed communication and teamwork, building and maintaining trust, using appropriate collaboration tools, and inter-cultural collaboration.
Roles of Regulatory RNAs for Antibiotic Resistance in Bacteria and Their Potential Value as Novel Drug Targets

The emergence of antibiotic resistance mechanisms among bacterial pathogens increases the demand for novel treatment strategies. Lately, the contribution of non-coding RNAs to antibiotic resistance and their potential value as drug targets became evident. RNA attenuator elements in mRNA leader regions couple expression of resistance genes to the presence of the cognate antibiotic. Trans-encoded small RNAs (sRNAs) modulate antibiotic tolerance by base-pairing with mRNAs encoding functions important for resistance such as metabolic enzymes, drug efflux pumps, or transport proteins. Bacteria respond with extensive changes of their sRNA repertoire to antibiotics. Each antibiotic generates a unique sRNA profile possibly causing downstream effects that may help to overcome the antibiotic challenge. In consequence, regulatory RNAs including sRNAs and their protein interaction partners such as Hfq may prove useful as targets for antimicrobial chemotherapy. Indeed, several compounds have been developed that kill bacteria by mimicking ligands for riboswitches controlling essential genes, demonstrating that regulatory RNA elements are druggable targets. Drugs acting on sRNAs are considered for combined therapies to treat infections. In this review, we address how regulatory RNAs respond to and establish resistance to antibiotics in bacteria. Approaches to target RNAs involved in intrinsic antibiotic resistance or virulence for chemotherapy will be discussed.
Reduced lung-cancer mortality with low-dose computed tomographic screening.

The aggressive and heterogeneous nature of lung cancer has thwarted efforts to reduce mortality from this cancer through the use of screening. The advent of low-dose helical computed tomography (CT) altered the landscape of lung-cancer screening, with studies indicating that low-dose CT detects many tumors at early stages. The National Lung Screening Trial (NLST) was conducted to determine whether screening with low-dose CT could reduce mortality from lung cancer.



From August 2002 through April 2004, we enrolled 53,454 persons at high risk for lung cancer at 33 U.S. medical centers. Participants were randomly assigned to undergo three annual screenings with either low-dose CT (26,722 participants) or single-view posteroanterior chest radiography (26,732). Data were collected on cases of lung cancer and deaths from lung cancer that occurred through December 31, 2009.



The rate of adherence to screening was more than 90%. The rate of positive screening tests was 24.2% with low-dose CT and 6.9% with radiography over all three rounds. A total of 96.4% of the positive screening results in the low-dose CT group and 94.5% in the radiography group were false positive results. The incidence of lung cancer was 645 cases per 100,000 person-years (1060 cancers) in the low-dose CT group, as compared with 572 cases per 100,000 person-years (941 cancers) in the radiography group (rate ratio, 1.13; 95% confidence interval [CI], 1.03 to 1.23). There were 247 deaths from lung cancer per 100,000 person-years in the low-dose CT group and 309 deaths per 100,000 person-years in the radiography group, representing a relative reduction in mortality from lung cancer with low-dose CT screening of 20.0% (95% CI, 6.8 to 26.7; P=0.004). The rate of death from any cause was reduced in the low-dose CT group, as compared with the radiography group, by 6.7% (95% CI, 1.2 to 13.6; P=0.02).



Screening with the use of low-dose CT reduces mortality from lung cancer. (Funded by the National Cancer Institute; National Lung Screening Trial ClinicalTrials.gov number, NCT00047385.).
Crossing Theory and Hierarchy Mapping

Techniques are introduced which are applicable to machine construction of digraph maps. These techniques are oriented toward reduction of the number of crossings in a map as a means of improving the readability of hierarchical structures. Permuting, psi-factoring, rotating, twirling, and absorbing are among the techniques considered. Examples are given to illustrate these techniques. When these and related procedures are fully developed for use with machine-interactive processes, they will facilitate group modeling efforts.
Modelling Sentence Generation from Sum of Word Embedding Vectors as a Mixed Integer Programming Problem

Converting a sentence to a meaningful vector representation has uses in many NLP tasks, however very few methods allow that representation to be restored to a human readable sentence. Being able to generate sentences from the vector representations demonstrates the level of information maintained by the embedding representation – in this case a simple sum of word embeddings. We introduce such a method for moving from this vector representation back to the original sentences. This is done using a two stage process, first a greedy algorithm is utilised to convert the vector to a bag of words, and second a simple probabilistic language model is used to order the words to get back the sentence. To the best of our knowledge this is the first work to demonstrate quantitatively the ability to reproduce text from a large corpus based directly on its sentence embeddings.