nils-herrmann/clean_scirepeval huggingface dataset:
A broadband dual-polarization base station antenna element with a coupling feed

This paper presents a broadband, dual-polarized base station antenna element by reasonably designing two orthogonal symmetrical dipole, four loading cylinders, balun, feed patches, specific shape reflector and plastic fasteners. Coupling feed is adopted to avoid the direct connection between the feed cables and the dipoles. The antenna element matches well in the frequency range of 1.7-2.7 GHz and the return loss (RL) S11<;-15 dB and the isolation S21<; -30 dB. Low cross-polarization, high front-back ratio (>25dB) and stable half-power beam width (HPBW) with 65±5° are also achieved. The proposed antenna element covers the whole long term evolution (LTE) band and is backward compatible with 3G and 2G bands.
Lexical Chaining for Measuring Discourse Coherence Quality in Test-taker Essays

This paper presents an investigation of lexical chaining (Morris and Hirst, 1991) for measuring discourse coherence quality in test-taker essays. We hypothesize that attributes of lexical chains, as well as interactions between lexical chains and explicit discourse elements, can be harnessed for representing coherence. Our experiments reveal that performance achieved by our new lexical chain features is better than that of previous discourse features used for this task, and that the best system performance is achieved when combining lexical chaining features with complementary discourse features, such as those provided by a discourse parser based on rhetorical structure theory, and features that reflect errors in grammar, word usage, and mechanics.
Psychiatric morbidity and domestic violence: a survey of married women in Lahore

A number of studies have looked at the prevalence rates of psychiatric disorders in the community in Pakistan over the last two decades. However, a very little information is available on psychiatric morbidity in primary health care. We therefore decided to measure prevalence of psychiatric disorders and their correlates among women from primary health care facilities in Lahore. We interviewed 650 women in primary health care settings in Lahore. We used a semi-structured interview and questionnaires to collect information during face-to-face interviews. Nearly two-third of the women (64.3%) in our study were diagnosed to have a psychiatric problem, while one-third (30.4%) suffered with Major Depressive Disorder. Stressful life events, verbal violence and battering were positively correlated with psychiatric morbidity and social support, using reasoning to resolve conflicts and education were negatively correlated with psychiatric morbidity. The prevalence of psychiatric disorders is in line with the prevalence figures found in community studies. Domestic violence is an important correlate which can be the focus of interventions.
Unravelling the biology of SCLC: implications for therapy

Small-cell lung cancer (SCLC) is an aggressive malignancy associated with a poor prognosis. First-line treatment has remained unchanged for decades, and a paucity of effective treatment options exists for recurrent disease. Nonetheless, advances in our understanding of SCLC biology have led to the development of novel experimental therapies. Poly [ADP-ribose] polymerase (PARP) inhibitors have shown promise in preclinical models, and are under clinical investigation in combination with cytotoxic therapies and inhibitors of cell-cycle checkpoints.Preclinical data indicate that targeting of histone-lysine N-methyltransferase EZH2, a regulator of chromatin remodelling implicated in acquired therapeutic resistance, might augment and prolong chemotherapy responses. High expression of the inhibitory Notch ligand Delta-like protein 3 (DLL3) in most SCLCs has been linked to expression of Achaete-scute homologue 1 (ASCL1; also known as ASH-1), a key transcription factor driving SCLC oncogenesis; encouraging preclinical and clinical activity has been demonstrated for an anti-DLL3-antibody–drug conjugate. The immune microenvironment of SCLC seems to be distinct from that of other solid tumours, with few tumour-infiltrating lymphocytes and low levels of the immune-checkpoint protein programmed cell death 1 ligand 1 (PD-L1). Nonetheless, immunotherapy with immune-checkpoint inhibitors holds promise for patients with this disease, independent of PD-L1 status. Herein, we review the progress made in uncovering aspects of the biology of SCLC and its microenvironment that are defining new therapeutic strategies and offering renewed hope for patients.
Automatic Feature Learning for MOOC Forum Thread Classification

Discussion thread classification plays an important role for Massive Open Online Courses (MOOCs) forum. Most existing methods in this filed focus on extracting text features (e.g. key words) from the content of discussions using NLP methods. However, diversity of languages used in MOOC forums results in poor expansibility of these methods. To tackle this problem, in this paper, we artificially design 23 language independent features related to structure, popularity and underlying social network of thread. Furthermore, a hybrid model which combine Gradient Boosting Decision Tree (GBDT) with Linear Regression (LR) (GBDT + LR) is employed to reduce the traditional cost of feature learning for discussion threads classification manually. Experiments are carried out on the datasets contributed by Coursera with nearly 100, 000 discussion threads of 60 courses taught in 4 different languages. Results demonstrate that our method can significantly improve the performance of discussion threads classification. It is worth drawing that the average AUC of our model is 0.832, outperforming baseline by 15%.
Effects of occupational exposure in pesticide plant on workers' serum and erythrocyte cholinesterase activity.

The determination of cholinesterase activity has been commonly applied in the biomonitoring of exposure to organophosphates and carbamates and in the diagnosis of poisoning with anticholinesterase compounds. One of the groups who are at risk of pesticide intoxication are the workers engaged in the production of these chemicals.



The aim of this study was to assess the effect of pesticides on erythrocyte and serum cholinesterase activity in workers occupationally exposed to these chemicals.



The subjects were 63 workers at a pesticide plant. Blood samples were collected before they were employed (phase I) and after 3 months of working in the plant (phase II). Cholinesterase level in erythrocytes (EChE) was determined using the modified Ellman method, and serum cholinesterase (SChE) by butyrylthiocholine substrate assay.



The mean EChE levels were 48+/-11 IU/g Hb in phase I and 37+/-17 IU/g Hb in phase II (paired t-test, mean=-29; 95% CI=-43-14), p<0.001). The mean SChE level was 9569+/-2496 IU/l in phase I, and 7970+/-2067 IU/l in phase II (paired t-test, mean=1599; 95% CI=1140-2058, p<0.001). There was a significant increase in ALT level (p < 0.001) and a decrease in serum albumin level (p<0.001).



In view of the significant decrease in EChE and SChE levels among pesticide workers, it seems that routine assessment of cholinesterase level in workers employed in such occupations and people handling pesticides should be made obligatory.
Design and simulation of curved sensors of PVDF for fetal heart rate monitoring

This paper proposed a highly sensitive micro-force sensor using curved PVDF for fetal heart rate monitoring long-termly. Based on the finite element method, numerical simulations were conducted to compare the straight and curved PVDF films in the aspects of the sensitivity for the dynamic excitation. The results showed that the peak voltages of the sensors varied remarkably with the curvature of the PVDF film. The maximum magnitude of the peak voltage response occurred at a certain value of the curvature. In the experiments, the voltage curves of the sensors were also recorded by an oscilloscope to study the effects of the mass on the surface of the sensors and validate the linearity and sensitivity of the sensors. The results showed that the sensitivity of the sensors up to about 60mV/N, which met the needs of fetal heart rate monitoring.
Rationalization of the pKa Values of Alcohols and Thiols Using Atomic Charge Descriptors and Its Application to the Prediction of Amino Acid pKa's

In a first step toward the development of an efficient and accurate protocol to estimate amino acids' pKa's in proteins, we present in this work how to reproduce the pKa's of alcohol and thiol based residues (namely tyrosine, serine, and cysteine) in aqueous solution from the knowledge of the experimental pKa's of phenols, alcohols, and thiols. Our protocol is based on the linear relationship between computed atomic charges of the anionic form of the molecules (being either phenolates, alkoxides, or thiolates) and their respective experimental pKa values. It is tested with different environment approaches (gas phase or continuum solvent-based approaches), with five distinct atomic charge models (Mulliken, Löwdin, NPA, Merz-Kollman, and CHelpG), and with nine different DFT functionals combined with 16 different basis sets. Moreover, the capability of semiempirical methods (AM1, RM1, PM3, and PM6) to also predict pKa's of thiols, phenols, and alcohols is analyzed. From our benchmarks, the best combination to reproduce experimental pKa's is to compute NPA atomic charge using the CPCM model at the B3LYP/3-21G and M062X/6-311G levels for alcohols (R(2) = 0.995) and thiols (R(2) = 0.986), respectively. The applicability of the suggested protocol is tested with tyrosine and cysteine amino acids, and precise pKa predictions are obtained. The stability of the amino acid pKa's with respect to geometrical changes is also tested by MM-MD and DFT-MD calculations. Considering its strong accuracy and its high computational efficiency, these pKa prediction calculations using atomic charges indicate a promising method for predicting amino acids' pKa in a protein environment.
A Statistical Traffic Model for On-Chip Interconnection Networks

Network traffic modeling is a critical first step towards understanding and unraveling network power/performancerelated issues. Extensive prior research in the area of classic networks such as the Internet, Ethernet, and wireless LANs transporting TCP/IP, HTTP, and FTP traffic among others, has demonstrated how traffic models and model-based synthetic traffic generators can facilitate understanding of traffic characteristics and drive early-stage simulation to explore a large network design space. Though on-chip networks (a.k.a networks-on-chip (NoCs)) are becoming the de-facto scalable communication fabric in many-core systems-on-a-chip (SoCs) and chip multiprocessors (CMPs), no on-chip network traffic model that captures both spatial and temporal variations of traffic has been demonstrated yet. As available on-chip resources increase with technology scaling, enabling a myriad of new network architectures, NoCs need to be designed from the application’s perspective. In this paper we propose such an empirically-derived network on-chip traffic model for homogeneous NoCs. Our comprehensive model is based on three statistical parameters described with a 3-tuple, and captures the spatio-temporal characteristics of NoC traffic accurately with less than 5% error when compared to actual NoC application traces gathered from fullsystem simulations of three different chip platforms. We illustrate two potential uses of our traffic model: how it allows us to characterize and gain insights on NoC traffic patterns, and how it can be used to generate synthetic traffic traces that can drive NoC design-space exploration.
Deep Learning with Topological Signatures

Inferring topological and geometrical information from data can offer an alternative perspective on machine learning problems. Methods from topological data analysis, e.g., persistent homology, enable us to obtain such information, typically in the form of summary representations of topological features. However, such topological signatures often come with an unusual structure (e.g., multisets of intervals) that is highly impractical for most machine learning techniques. While many strategies have been proposed to map these topological signatures into machine learning compatible representations, they suffer from being agnostic to the target learning task. In contrast, we propose a technique that enables us to input topological signatures to deep neural networks and learn a task-optimal representation during training. Our approach is realized as a novel input layer with favorable theoretical properties. Classification experiments on 2D object shapes and social network graphs demonstrate the versatility of the approach and, in case of the latter, we even outperform the state-of-the-art by a large margin.
A generalized theoretical deterministic particle swarm model

A number of theoretical studies of particle swarm optimization (PSO) have been done to gain a better understanding of the dynamics of the algorithm and the behavior of the particles under different conditions. These theoretical analyses have been performed for both the deterministic PSO model and more recently for the stochastic model. However, all current theoretical analyses of the PSO algorithm were based on the stagnation assumption, in some form or another. The analysis done under the stagnation assumption is one where the personal best and neighborhood best positions are assumed to be non-changing. While analysis under the stagnation assumption is very informative, it could never provide a complete description of a PSO’s behavior. Furthermore, the assumption implicitly removes the notion of a social network structure from the analysis. This paper presents a generalization to the theoretical deterministic PSO model. Under the generalized model, conditions for particle convergence to a point are derived. The model used in this paper greatly weakens the stagnation assumption, by instead assuming that each particle’s personal best and neighborhood best can occupy an arbitrarily large number of unique positions. It was found that the conditions derived in previous theoretical deterministic PSO research could be obtained as a specialization of the new generalized model proposed. Empirical results are presented to support the theoretical findings.
Fast Moreau envelope computation I: numerical algorithms

The present article summarizes the state of the art algorithms to compute the discrete Moreau envelope, and presents a new linear-time algorithm, named NEP for NonExpansive Proximal mapping. Numerical comparisons between the NEP and two existing algorithms: The Linear-time Legendre Transform (LLT) and the Parabolic Envelope (PE) algorithms are performed. Worst-case time complexity, convergence results, and examples are included. The fast Moreau envelope algorithms first factor the Moreau envelope as several one-dimensional transforms and then reduce the brute force quadratic worst-case time complexity to linear time by using either the equivalence with Fast Legendre Transform algorithms, the computation of a lower envelope of parabolas, or, in the convex case, the non expansiveness of the proximal mapping.
Scaling Up Stochastic Dual Coordinate Ascent

Stochastic Dual Coordinate Ascent (SDCA) has recently emerged as a state-of-the-art method for solving large-scale supervised learning problems formulated as minimization of convex loss functions. It performs iterative, random-coordinate updates to maximize the dual objective. Due to the sequential nature of the iterations, it is typically implemented as a single-threaded algorithm limited to in-memory datasets. In this paper, we introduce an asynchronous parallel version of the algorithm, analyze its convergence properties, and propose a solution for primal-dual synchronization required to achieve convergence in practice. In addition, we describe a method for scaling the algorithm to out-of-memory datasets via multi-threaded deserialization of block-compressed data. This approach yields sufficient pseudo-randomness to provide the same convergence rate as random-order in-memory access. Empirical evaluation demonstrates the efficiency of the proposed methods and their ability to fully utilize computational resources and scale to out-of-memory datasets.
Nonverbal expression of psychological states in psychiatric patients

Nonverbal behavior, especially facial expression, appears as one of the most important means for communicating affective states. Studies on groups of psychiatric patients and control subjects are reported in which nonverbal behavior is analyzed from videotaped dialogues. Using a quantitative approach, results on facial behavior, speech, and gaze are described, which shed light on the expressive and communicative functions of nonverbal behavior. From longitudinal observations on depressed patients it emerged that individual-specific associations have to be taken into account for the relationship between expressive behavior and mood changes. The predominance of facial behavior in the speaker role of an individual found in patients and control groups points to the integrated communicative function of the verbal and nonverbal elements. However, recovered schizophrenic patients exhibited a dissociation of these elements. Implications for our understanding of nonverbal communications are discussed.
An Overview of Localization Methods for Multi-Agent Systems

Localization of mult i-agent systems is a fundamental requirement for multi-agent systems to operate and cooperate properly. The problem of localization can be divided into two categories; one in which a -priori informat ion is available and the second where the global position is to be asce rtained without a-priori informat ion. This paper gives a comprehensive survey of localization techniques that exist in the literature for both the categories with the objectives of knowing the current state-of-the-art, helping in selecting the proper approach in a given scenario and promoting research in this area. A detailed description of methods that exist in the literature are provided in considerable detail. Then these methods are compared, and their weaknesses and strengths are discussed. Finally, some future research recommendations are drawn out of this survey.
A linear constant current LED driver without off-chip inductor and capacitor

A linear constant current light-emitting diode (LED) driver without off-chip inductor and capacitor is presented in this paper. The proposed driver consists of one resistor, one diode bridge and one controller integrated circuits (IC). The controller IC employs four LED strings and sequences their conduction to shape the output current in proportion to the input voltage. The controller IC was designed and implemented by using HHNEC 1μm 700V process. The experimental results show that high PF of 97.93% and efficiency of 90.41% under the input voltage of 220V|rms utility voltage were obtained.
Hepatotoxicity by combination treatment of temozolomide, artesunate and Chinese herbs in a glioblastoma multiforme patient: case report review of the literature

Glioblastoma multiforme (GBM) represents an aggressive tumor type with poor prognosis. The majority of GBM patients cannot be cured. There is high willingness among patients for the compassionate use of non-approved medications, which might occasionally lead to profound toxicity. A 65-year-old patient with glioblastoma multiforme (GBM) has been treated with radiochemotherapy including temozolomide (TMZ) after surgery. The treatment outcome was evaluated as stable disease with a tendency to slow tumor progression. In addition to standard medication (ondansetron, valproic acid, levetiracetam, lorazepam, clobazam), the patient took the antimalarial drug artesunate (ART) and a decoction of Chinese herbs (Coptis chinensis, Siegesbeckia orientalis, Artemisia scoparia, Dictamnus dasycarpus). In consequence, the clinical status deteriorated. Elevated liver enzymes were noted with peak values of 238 U/L (GPT/ALAT), 226 U/L (GOT/ASAT), and 347 U/L (γ-GT), respectively. After cessation of ART and Chinese herbs, the values returned back to normal and the patient felt well again. In the literature, hepatotoxicity is well documented for TMZ, but is very rare for ART. Among the Chinese herbs used, Dictamnus dasycarpus has been reported to induce liver injury. Additional medication included valproic acid and levetiracetam, which are also reported to exert hepatotoxicity. While all drugs alone may bear a minor risk for hepatotoxicity, the combination treatment might have caused increased liver enzyme activities. It can be speculated that the combination of these drugs caused liver injury. We conclude that the compassionate use of ART and Chinese herbs is not recommended during standard radiochemotherapy with TMZ for GBM.
Real time video surveillance system using motion detection

In present scenario, the security concerns have grown tremendously. The security of restricted areas such as borders or buffer zones is of utmost importance; in particular with the worldwide increase of military conflicts, illegal immigrants, and terrorism over the past decade. Monitoring such areas rely currently on technology and man power, however automatic monitoring has been advancing in order to avoid potential human errors that can be caused by different reasons. The purpose of this project is to design a surveillance system which would detect motion in a live video feed and record the video feed only at the moment where the motion was detected also to track moving object based on background subtraction using video surveillance. The moving object is identified using the image subtraction method.
Learning Convolutional Text Representations for Visual Question Answering

Visual question answering (VQA) is a recently proposed artificial intelligence task that requires a deep understanding of both images and texts. In deep learning, images are typically modeled through convolutional neural networks (CNNs) while texts are typically modeled through recurrent neural networks (RNNs). In this work, we perform a detailed analysis on the natural language questions in VQA, which raises a different need for text representations as compared to other natural language processing tasks. Based on the analysis, we propose to rely on CNNs for learning text representations. By exploring various properties of CNNs specialized for text data, we present our “CNN Inception + Gate” model for text feature extraction in VQA. The experimental results show that simply replacing RNNs with our CNN-based model improves question representations and thus the overall accuracy of VQA models. In addition, our model has much fewer parameters and the computation is much faster. We also prove that the text representation requirement in VQA is more complicated and comprehensive than that in conventional natural language processing tasks. Shallow models like the fastText model, which can obtain comparable results with deep learning models in simple tasks like text classification, have poor performances in VQA.
Rhombencephalitis by Listeria Monocytogenes in an Immunocompetent Child

A previously healthy 21-month-old girl who presented to the emergency department with 4 days of fever and progressive lethargy, initially computed tomography (CT) scan was normal and the cerebrospinal fluid (CSF) was lymphocyte predominant pleocytosis. Despite medical treatment, she showed a progressive neurological worsening. A new CSF culture showed L. monocytogenes growing and a brain MRI demonstrated rhombencephalitis and secondary obstructive hydrocephalus. Meningoencephalitis by Listeria monocytogenes is a rare entity in immunocompetent toddlers and older children, rhombencephalitis is an unusual form of presentation, which affects the brain stem and protuberance parenchyma. With this case, we review the clinical presentation and management of this unusual infection.