nils-herrmann/clean_scirepeval huggingface dataset:
Chapter 19 Sorting Networks

It is natural to ask if one can perform a computational task considerably faster by using a different architecture (i.e., a different computational model). The answer to this question is a resounding yes. A cute example is the Macaroni sort. We are given a set S = {s 1 ,. .. , S n } of n real numbers in the range (say) [1, 2]. We get a lot of Macaroni (this are longish and very narrow tubes of pasta), and cut the ith piece to be of length s i , for i = 1,. .. , n. Next, take all these pieces of pasta in your hand, make them stand up vertically, with their bottom end lying on a horizontal surface. Next, lower your handle till it hit the first (i.e., tallest) piece of pasta. Take it out, measure it height, write down its number, and continue in this fashion till you have extracted all the pieces of pasta. Clearly, this is a sorting algorithm that works in linear time. But we know that sorting takes Ω(n log n) time. Thus, this algorithm is much faster than the standard sorting algorithms. This faster algorithm was achieved by changing the computation model. We allowed new " strange " operations (cutting a piece of pasta into a certain length, picking the longest one in constant time, and measuring the length of a pasta piece in constant time). Using these operations we can sort in linear time. If this was all we can do with this approach, that would have only been a curiosity. However, interestingly enough, there are natural computation models which are considerably stronger than the standard model of computation. Indeed, consider the task of computing the output of the circuit on the right (here, the input is boolean values on the input wires on the left, and the output is the single output on the right). Clearly, this can be solved by ordering the gates in the " right " order (this can be done by topological sorting), and then computing the value of the gates one by one in this order, in such a x This work is licensed under the Creative Commons Attribution-Noncommercial 3.0 License. To view a copy of this license, visit
Data Mining: Document Classification using Naive Bayes Classifier

In data mining, classification is the way to splits the data into several dependent and independent regions and each region refer as a class. There are different kinds of classifier uses to accomplish classification task. Moreover classification is bounded in case of classifying of text documents. The motives of the work which a present in the article is to evaluate multiclass document classification and to learn achieve accuracy of classification in the case of text documents. Naive Bayes approach is used to deal with the problem of document classification via a deceptively simplistic model. The Naive Bayes approach is applied in Flat (linear) and hierarchical manner for improving the efficiency of classification model. It has been found that Hierarchical Classification technique is more effective than Flat classification. It also performs better in case of multi-label document classification. In contrast to retrospect we observe significant increase in the generation of data each day. And hence with the advent of smarter technologies, data is required to be classified and sorted before framing out decisions from it. There are so many techniques available for classifying documents into various categories or labels. Data mining is the process of non-trivial extraction of novel, implicit, and
Bio-Source of di-n-butyl phthalate production by filamentous fungi

Although DBP (di-n-butyl phthalate) is commonly encountered as an artificially-synthesized plasticizer with potential to impair fertility, we confirm that it can also be biosynthesized as microbial secondary metabolites from naturally occurring filamentous fungi strains cultured either in an artificial medium or natural water. Using the excreted crude enzyme from the fungi for catalyzing a variety of substrates, we found that the fungal generation of DBP was largely through shikimic acid pathway, which was assembled by phthalic acid with butyl alcohol through esterification. The DBP production ability of the fungi was primarily influenced by fungal spore density and incubation temperature. This study indicates an important alternative natural waterborne source of DBP in addition to artificial synthesis, which implied fungal contribution must be highlighted for future source control and risk management of DBP.
Sexual differentiation of human behavior: Effects of prenatal and pubertal organizational hormones

A key question concerns the extent to which sexual differentiation of human behavior is influenced by sex hormones present during sensitive periods of development (organizational effects), as occurs in other mammalian species. The most important sensitive period has been considered to be prenatal, but there is increasing attention to puberty as another organizational period, with the possibility of decreasing sensitivity to sex hormones across the pubertal transition. In this paper, we review evidence that sex hormones present during the prenatal and pubertal periods produce permanent changes to behavior. There is good evidence that exposure to high levels of androgens during prenatal development results in masculinization of activity and occupational interests, sexual orientation, and some spatial abilities; prenatal androgens have a smaller effect on gender identity, and there is insufficient information about androgen effects on sex-linked behavior problems. There is little good evidence regarding long-lasting behavioral effects of pubertal hormones, but there is some suggestion that they influence gender identity and perhaps some sex-linked forms of psychopathology, and there are many opportunities to study this issue.
Network-Aware Operator Placement for Stream-Processing Systems

To use their pool of resources efficiently, distributed stream-processing systems push query operators to nodes within the network. Currently, these operators, ranging from simple filters to custom business logic, are placed manually at intermediate nodes along the transmission path to meet application-specific performance goals. Determining placement locations is challenging because network and node conditions change over time and because streams may interact with each other, opening venues for reuse and repositioning of operators. This paper describes a stream-based overlay network (SBON), a layer between a stream-processing system and the physical network that manages operator placement for stream-processing systems. Our design is based on a cost space, an abstract representation of the network and on-going streams, which permits decentralized, large-scale multi-query optimization decisions. We present an evaluation of the SBON approach through simulation, experiments on PlanetLab, and an integration with Borealis, an existing stream-processing engine. Our results show that an SBON consistently improves network utilization, provides low stream latency, and enables dynamic optimization at low engineering cost.
A Cross Entropy based Optimization Algorithm with Global Convergence Guarantees

The cross entropy (CE) method is a model based search method to solve optimization problems where the objective function has minimal structure. The Monte-Carlo version of the CE method employs the naive sample averaging technique which is inefficient, both computationally and space wise. We provide a novel stochastic approximation version of the CE method, where the sample averaging is replaced with incremental geometric averaging. This approach can save considerable computational and storage costs. Our algorithm is incremental in nature and possesses additional attractive features such as accuracy, stability, robustness and convergence to the global optimum for a particular class of objective functions. We evaluate the algorithm on a variety of global optimization benchmark problems and the results obtained corroborate our theoretical findings.
To Fall Or Not To Fall: A Visual Approach to Physical Stability Prediction

Understanding physical phenomena is a key competence that enables humans and animals to act and interact under uncertain perception in previously unseen environments containing novel object and their configurations. Developmental psychology has shown that such skills are acquired by infants from observations at a very early stage. In this paper, we contrast a more traditional approach of taking a modelbased route with explicit 3D representations and physical simulation by an end-to-end approach that directly predicts stability and related quantities from appearance. We ask the question if and to what extent and quality such a skill can directly be acquired in a data-driven way— bypassing the need for an explicit simulation. We present a learning-based approach based on simulated data that predicts stability of towers comprised of wooden blocks under different conditions and quantities related to the potential fall of the towers. The evaluation is carried out on synthetic data and compared to human judgments on the same stimuli.
The Overclaiming Questionnaire : A good way to measure faking ? q

We evaluated the validity of the Overclaiming Questionnaire (OCQ) as a measure of job applicants’ faking of personality tests. We assessed whether the OCQ (a) converged with an established measure of applicant faking, Residualized Individual Change Scores (RICSs); (b) predicted admission of faking and faking tendencies (Faking Frequency, Minimizing Weaknesses, Exaggerating Strengths, and Complete Misrepresentation); and, (c) predicted the aforementioned measures as strongly as RICSs did. First, 261 participants were instructed to respond honesty to an extraversion measure. Next, in a mock job application, they filled out the extraversion measure again, as well as the OCQ. The OCQ only weakly predicted RICSs (r = .17), Faking Admission (r = .18), and Faking Frequency (r = .15), and it failed to correlate significantly with Minimizing Weaknesses, Exaggerating Strengths, and Complete Misrepresentation. Moreover, the OCQ performed significantly worse than RICS in predicting Faking Admission, Faking Frequency, Minimizing Weaknesses, Exaggerating Strengths, and Complete Misrepresentation. We urge caution in using the current version of the OCQ to measure faking, but speculate that the innovative approach taken in the OCQ might be more effectively exploited if the OCQ content were tailored to the specific job that applicants are being tested for.
Negative Pressure Pulmonary Edema Associated with Anterior Cervical Spine Surgery

We report a very rare case of negative pressure pulmonary edema (NPPE) that occurred immediately after anterior cervical discectomy and fusion (ACDF). The patient was a 25-year-old man who sustained a facet fracture-dislocation of C5 during a traffic accident. After ACDF, he developed NPPE and needed mechanical ventilation. Fortunately, he recovered fully within 24 hours. NPPE is a rare postoperative complication that may occur after cervical spine surgery. The aims of this report are to present information regarding the diagnosis and emergent treatment of NPPE, and to review the previous literature regarding this serious complication.
A low-cost, fast, and accurate microwave imaging system for breast cancer detection

In this paper we present a prototype of a Microwave Imaging (MI) system for breast cancer detection. Our system is based on low-cost off-the-shelf microwave components, custom-made antennas, and a small form-factor processing system with an embedded Field-Programmable Gate Array (FPGA) for accelerating the execution of the imaging algorithm. We show that our system can compete with a vector network analyzer in terms of accuracy, and it is more than 20x faster than a high-performance server at image reconstruction.
Recognizing and Preventing Overexposure to Methylmercury from Fish and Seafood Consumption: Information for Physicians

Fish is a valuable source of nutrition, and many people would benefit from eating fish regularly. But some people eat a lot of fish, every day or several meals per week, and thus can run a significant risk of overexposure to methylmercury. Current advice regarding methylmercury from fish consumption is targeted to protect the developing brain and nervous system but adverse health effects are increasingly associated with adult chronic low-level methylmercury exposure. Manifestations of methylmercury poisoning are variable and may be difficult to detect unless one considers this specific diagnosis and does an appropriate test (blood or hair analysis). We provide information to physicians to recognize and prevent overexposure to methylmercury from fish and seafood consumption. Physicians are urged to ask patients if they eat fish: how often, how much, and what kinds. People who eat fish frequently (once a week or more often) and pregnant women are advised to choose low mercury fish.
Fair medium access in 802.11 based wireless ad-hoc networks

The Medium Access Control (MAC) protocol through which mobile stations can share a common broadcast channel is essential in an ad-hoc network. Due to the existence of hidden terminal problem, partially-connected network topology and lack of central administration, existing popular MAC protocols like IEEE 802.11 Distributed Foundation Wireless Medium Access Control (DFWMAC)  may lead to "capture" effects which means that some stations grab the shared channel and other stations suffer from starvation. This is also known as the "fairness problem". This paper reviews some related work in the literature and proposes a general approach to address the problem. This paper borrows the idea of fair queueing from wireline networks and defines the "fairness index" for ad-hoc network to quantify the fairness, so that the goal of achieving fairness becomes equivalent to minimizing the fairness index. Then this paper proposes a different backoff scheme for IEEE 802.11 DFWMAC, instead of the original binary exponential backoff scheme. Simulation results show that the new backoff scheme can achieve far better fairness without loss of simplicity.
Bose–Einstein condensation of excitons in bilayer electron systems

An exciton is the particle-like entity that forms when an electron is bound to a positively charged ‘hole’. An ordered electronic state in which excitons condense into a single quantum state was proposed as a theoretical possibility many years ago. We review recent studies of semiconductor bilayer systems that provide clear evidence for this phenomenon and explain why exciton condensation in the quantum Hall regime, where these experiments were performed, is as likely to occur in electron–electron bilayers as in electron–hole bilayers. In current quantum Hall excitonic condensates, disorder induces mobile vortices that flow in response to a supercurrent and limit the extremely large bilayer counterflow conductivity.
Long-Term Effects of Inhaled Budesonide for Bronchopulmonary Dysplasia.

The long-term effects on neurodevelopment of the use of inhaled glucocorticoids in extremely preterm infants for the prevention or treatment of bronchopulmonary dysplasia are uncertain.



We randomly assigned 863 infants (gestational age, 23 weeks 0 days to 27 weeks 6 days) to receive early (within 24 hours after birth) inhaled budesonide or placebo. The prespecified secondary long-term outcome was neurodevelopmental disability among survivors, defined as a composite of cerebral palsy, cognitive delay (a Mental Development Index score of <85 [1 SD below the mean of 100] on the Bayley Scales of Infant Development, Second Edition, with higher scores on the scale indicating better performance), deafness, or blindness at a corrected age of 18 to 22 months.



Adequate data on the prespecified composite long-term outcome were available for 629 infants. Of these infants, 148 (48.1%) of 308 infants assigned to budesonide had neurodevelopmental disability, as compared with 165 (51.4%) of 321 infants assigned to placebo (relative risk, adjusted for gestational age, 0.93; 95% confidence interval [CI], 0.80 to 1.09; P=0.40). There was no significant difference in any of the individual components of the prespecified outcome. There were more deaths in the budesonide group than in the placebo group (82 [19.9%] of 413 infants vs. 58 [14.5%] of 400 infants for whom vital status was available; relative risk, 1.37; 95% CI, 1.01 to 1.86; P=0.04).



Among surviving extremely preterm infants, the rate of neurodevelopmental disability at 2 years did not differ significantly between infants who received early inhaled budesonide for the prevention of bronchopulmonary dysplasia and those who received placebo, but the mortality rate was higher among those who received budesonide. (Funded by the European Union and Chiesi Farmaceutici; ClinicalTrials.gov number, NCT01035190 .).
Crosstalk between Inflammation and ROCK/MLCK Signaling Pathways in Gastrointestinal Disorders with Intestinal Hyperpermeability

The barrier function of the intestine is essential for maintaining the normal homeostasis of the gut and mucosal immune system. Abnormalities in intestinal barrier function expressed by increased intestinal permeability have long been observed in various gastrointestinal disorders such as Crohn's disease (CD), ulcerative colitis (UC), celiac disease, and irritable bowel syndrome (IBS). Imbalance of metabolizing junction proteins and mucosal inflammation contributes to intestinal hyperpermeability. Emerging studies exploring in vitro and in vivo model system demonstrate that Rho-associated coiled-coil containing protein kinase- (ROCK-) and myosin light chain kinase- (MLCK-) mediated pathways are involved in the regulation of intestinal permeability. With this perspective, we aim to summarize the current state of knowledge regarding the role of inflammation and ROCK-/MLCK-mediated pathways leading to intestinal hyperpermeability in gastrointestinal disorders. In the near future, it may be possible to specifically target these specific pathways to develop novel therapies for gastrointestinal disorders associated with increased gut permeability.
A 3D printed helical antenna with integrated lens

A novel antenna configuration comprising a helical antenna with an integrated lens is demonstrated in this work. The antenna is manufactured by a unique combination of 3D printing of plastic material (ABS) and inkjet printing of silver nano-particle based metallic ink. The integration of lens enhances the gain by around 7 dB giving a peak gain of about 16.4 dBi at 9.4 GHz. The helical antenna operates in the end-fire mode and radiates a left-hand circularly polarized (LHCP) pattern. The 3-dB axial ratio (AR) bandwidth of the antenna with lens is 3.2 %. Due to integration of lens and fully printed processing, this antenna configuration offers high gain performance and requires low cost for manufacturing.
Olfaction: anatomy, physiology, and disease.

The olfactory system is an essential part of human physiology, with a rich evolutionary history. Although humans are less dependent on chemosensory input than are other mammals (Niimura 2009,  Genomics 4:107-118), olfactory function still plays a critical role in health and behavior. The detection of hazards in the environment, generating feelings of pleasure, promoting adequate nutrition, influencing sexuality, and maintenance of mood are described roles of the olfactory system, while other novel functions are being elucidated. A growing body of evidence has implicated a role for olfaction in such diverse physiologic processes as kin recognition and mating (Jacob et al. 2002a,  Horth 2007, Genomics 90:159-175; Havlicek and Roberts 2009, Psychoneuroendocrinology 34:497-512), pheromone detection (Jacob et al. 200b, Horm. Behav. 42:274-283; Wyart et al. 2007, 1265), mother-infant bonding (Doucet et al. 2009, , food preferences (Mennella et al. 2001, Pediatrics 107:E88), central nervous system physiology (Welge-Lüssen 2009, B-ENT 5:129-132), and even longevity (Murphy 2009, JAMA 288:2307-2312). The olfactory system, although phylogenetically ancient, has historically received less attention than other special senses, perhaps due to challenges related to its study in humans. In this article, we review the anatomic pathways of olfaction, from peripheral nasal airflow leading to odorant detection, to epithelial recognition of these odorants and related signal transduction, and finally to central processing. Olfactory dysfunction, which can be defined as conductive, sensorineural, or central (typically related to neurodegenerative disorders), is a clinically significant problem, with a high burden on quality of life that is likely to grow in prevalence due to demographic shifts and increased environmental exposures.
Drug-drug Interaction Extraction via Recurrent Neural Network with Multiple Attention Layers

Drug-drug interaction (DDI) is a vital information when physicians and pharmacists prepare for the combined use of two or more drugs. Thus, several DDI databases are constructed to avoid mistakenly medicine administering. In recent years, automatically extracting DDIs from biomedical text has drawn researchers’ attention. However, the existing work need either complex feature engineering or NLP tools, both of which are insufficient for sentence comprehension. Inspired by the deep learning approaches in natural language processing, we propose a recurrent neural network model with multiple attention layers for DDI classification. We evaluate our model on 2013 SemEval DDIExtraction dataset. The experiments show that our model classifies most of the drug pairs into correct DDI categories, which outperforms the existing NLP or deep learning method.
Temporal MDS Plots for Analysis of Multivariate Data

Multivariate time series data can be found in many application domains. Examples include data from computer networks, healthcare, social networks, or financial markets. Often, patterns in such data evolve over time among multiple dimensions and are hard to detect. Dimensionality reduction methods such as PCA and MDS allow analysis and visualization of multivariate data, but per se do not provide means to explore multivariate patterns over time. We propose Temporal Multidimensional Scaling (TMDS), a novel visualization technique that computes temporal one-dimensional MDS plots for multivariate data which evolve over time. Using a sliding window approach, MDS is computed for each data window separately, and the results are plotted sequentially along the time axis, taking care of plot alignment. Our TMDS plots enable visual identification of patterns based on multidimensional similarity of the data evolving over time. We demonstrate the usefulness of our approach in the field of network security and show in two case studies how users can iteratively explore the data to identify previously unknown, temporally evolving patterns.