nils-herrmann/clean_scirepeval huggingface dataset:
Visually Exploring Transportation Schedules

Public transportation schedules are designed by agencies to optimize service quality under multiple constraints. However, real service usually deviates from the plan. Therefore, transportation analysts need to identify, compare and explain both eventual and systemic performance issues that must be addressed so that better timetables can be created. The purely statistical tools commonly used by analysts pose many difficulties due to the large number of attributes at tripand station-level for planned and real service. Also challenging is the need for models at multiple scales to search for patterns at different times and stations, since analysts do not know exactly where or when relevant patterns might emerge and need to compute statistical summaries for multiple attributes at different granularities. To aid in this analysis, we worked in close collaboration with a transportation expert to design TR-EX, a visual exploration tool developed to identify, inspect and compare spatio-temporal patterns for planned and real transportation service. TR-EX combines two new visual encodings inspired by Marey's Train Schedule: Trips Explorer for trip-level analysis of frequency, deviation and speed; and Stops Explorer for station-level study of delay, wait time, reliability and performance deficiencies such as bunching. To tackle overplotting and to provide a robust representation for a large numbers of trips and stops at multiple scales, the system supports variable kernel bandwidths to achieve the level of detail required by users for different tasks. We justify our design decisions based on specific analysis needs of transportation analysts. We provide anecdotal evidence of the efficacy of TR-EX through a series of case studies that explore NYC subway service, which illustrate how TR-EX can be used to confirm hypotheses and derive new insights through visual exploration.
Wireless Sensor Networks and Monitoring of Environmental Parameters in Precision Agriculture

The handiness and ease of use of tele-technology like mobile phones has surged the growth of ICT in developing countries like India than ever. Mobile phones are showing overwhelming responses and have helped farmers to do the work on timely basis and stay connected with the outer farming world. But mobile phones are of no use when it comes to the real-time farm monitoring or accessing the accurate information because of the little research and application of mobile phone in agricultural field for such uses. The current demand of use of WSN in agricultural fields has revolutionized the farming experiences. In Precision Agriculture, the contribution of WSN are numerous staring from monitoring soil health, plant health to the storage of crop yield. Due to pressure of population and economic inflation, a lot of pressure is on farmers to produce more out of their fields with fewer resources. This paper gives brief insight into the relation of plant disease prediction with the help of wireless sensor networks.  Plant Disease Monitoring, Precision Agriculture, Environmental Parameters, Wireless Sensor Network (WSN)
Decision Tree Algorithms Predict the Diagnosis and Outcome of Dengue Fever in the Early Phase of Illness

Dengue is re-emerging throughout the tropical world, causing frequent recurrent epidemics. The initial clinical manifestation of dengue often is confused with other febrile states confounding both clinical management and disease surveillance. Evidence-based triage strategies that identify individuals likely to be in the early stages of dengue illness can direct patient stratification for clinical investigations, management, and virological surveillance. Here we report the identification of algorithms that differentiate dengue from other febrile illnesses in the primary care setting and predict severe disease in adults.



A total of 1,200 patients presenting in the first 72 hours of acute febrile illness were recruited and followed up for up to a 4-week period prospectively; 1,012 of these were recruited from Singapore and 188 from Vietnam. Of these, 364 were dengue RT-PCR positive; 173 had dengue fever, 171 had dengue hemorrhagic fever, and 20 had dengue shock syndrome as final diagnosis. Using a C4.5 decision tree classifier for analysis of all clinical, haematological, and virological data, we obtained a diagnostic algorithm that differentiates dengue from non-dengue febrile illness with an accuracy of 84.7%. The algorithm can be used differently in different disease prevalence to yield clinically useful positive and negative predictive values. Furthermore, an algorithm using platelet count, crossover threshold value of a real-time RT-PCR for dengue viral RNA, and presence of pre-existing anti-dengue IgG antibodies in sequential order identified cases with sensitivity and specificity of 78.2% and 80.2%, respectively, that eventually developed thrombocytopenia of 50,000 platelet/mm(3) or less, a level previously shown to be associated with haemorrhage and shock in adults with dengue fever.



This study shows a proof-of-concept that decision algorithms using simple clinical and haematological parameters can predict diagnosis and prognosis of dengue disease, a finding that could prove useful in disease management and surveillance.
Multi-Camera Calibration with One-Dimensional Object under General Motions

It is well known that in order to calibrate a single camera with a one-dimensional (1D) calibration object, the object must undertake some constrained motions, in other words, it is impossible to calibrate a single camera if the object motion is of general one. For a multi-camera setup, i.e., when the number of camera is more than one, can the cameras be calibrated by a 1D object under general motions? In this work, we prove that all cameras can indeed be calibrated and a calibration algorithm is also proposed and experimentally tested. In contrast to other multi-camera calibration method, no one calibrated "base" camera is needed. In addition, we show that for such multi-camera cases, the minimum condition of calibration and critical motions are similar to those of calibrating a single camera with 1D calibration object.
Deep neural network based speech separation for robust speech recognition

In this paper, a novel deep neural network (DNN) architecture is proposed to generate the speech features of both the target speaker and interferer for speech separation without using any prior information about the interfering speaker. DNN is adopted here to directly model the highly nonlinear relationship between speech features of the mixed signals and the two competing speakers. Experimental results on a monaural speech separation and recognition challenge task show that the proposed DNN framework enhances the separation performance in terms of different objective measures under the semi-supervised mode where the training data of the target speaker is provided while the unseen interferer in the separation stage is predicted by using multiple interfering speakers mixed with the target speaker in the training stage. Furthermore, as a preprocessing step in the testing stage for robust speech recognition, our speech separation approach can achieve significant improvements of the recognition accuracy over the baseline system with no source separation.
Robotic vertical jumping agility via series-elastic power modulation

Several arboreal mammals have the ability to rapidly and repeatedly jump vertical distances of 2m, starting from rest. We characterize this performance by ametric we call vertical jumping agility. Through basic kinetic relations, we show that this agility metric is fundamentally constrained by available actuator power. Although rapid high jumping is an important performance characteristic, the ability to control forces during stance also appears critical for sophisticated behaviors. Theanimalwith thehighest vertical jumpingagility, thegalago (Galago senegalensis), is known touse apowermodulating strategy to obtain higher peak power than that of muscle alone. Few previous robots have used serieselastic power modulation (achieved by combining series-elastic actuation with variable mechanical advantage), and because of motor power limits, the best current robot has a vertical jumping agility of only 55% of a galago. Through use of a specialized leg mechanism designed to enhance power modulation, we constructed a jumping robot that achieved 78% of the vertical jumping agility of a galago. Agile robots can explore venues of locomotion that were not previously attainable. We demonstrate this with a wall jump, where the robot leaps from the floor to a wall and then springs off thewall to reach anet height that is greater than that accessibleby a single jump.Our results show that series-elastic power modulation is an actuation strategy that enables a clade of vertically agile robots. d fom
A Vision-Based GPS-Spoofing Detection Method for Small UAVs

In recent years, consumer-class UAVs have come into public view and cyber security starts to attract the attention of researchers and hackers. The tasks of positioning, navigation and return-to-home (RTH) of UAV heavily depend on GPS. However, the signal structure of civil GPS used by UAVs is completely open and unencrypted, and the signal received by ground devices is very weak. As a result, GPS signals are vulnerable to jamming and spoofing. The development of software define radio (SDR) has made GPS-spoofing easy and costless. GPS-spoofing may cause UAVs to be out of control or even hijacked. In this paper, we propose a novel method to detect GPS-spoofing based on monocular camera and IMU sensor of UAV. Our method was demonstrated on the UAV of DJI Phantom 4.
Wideband Omnidirectional and Compact Antenna for VHF/UHF Band

A wideband omnidirectional and compact (WIBOC) antenna is proposed in this letter. The antenna is constructed with a combination of an upper metallic plane with an elliptic shape, a rectangular metallic plane placed underneath, and a discone monopole antenna used as an excitator located between these metallic planes. This antenna provides broadband characteristics and omnidirectionnal radiation patterns in the azimuth plane over the whole bandwidth with a height limited to λLF/29, where λLF is the lowest frequency of the operating band.
Skeleton-to-Response: Dialogue Generation Guided by Retrieval Memory

For dialogue response generation, traditional generative models generate responses solely from input queries. Such models rely on insufficient information for generating a specific response since a certain query could be answered in multiple ways. Consequentially, those models tend to output generic and dull responses, impeding the generation of informative utterances. Recently, researchers have attempted to fill the information gap by exploiting information retrieval techniques. When generating a response for a current query, similar dialogues retrieved from the entire training data are considered as an additional knowledge source. While this may harvest massive information, the generative models could be overwhelmed, leading to undesirable performance. In this paper, we propose a new framework which exploits retrieval results via a skeleton-then-response paradigm. At first, a skeleton is generated by revising the retrieved responses. Then, a novel generative model uses both the generated skeleton and the original query for response generation. Experimental results show that our approaches significantly improve the diversity and informativeness of the generated responses.
Mitochondrial Reactive Oxygen Species in Lipotoxic Hearts Induce Post-Translational Modifications of AKAP121, DRP1, and OPA1 That Promote Mitochondrial Fission.

Cardiac lipotoxicity, characterized by increased uptake, oxidation, and accumulation of lipid intermediates, contributes to cardiac dysfunction in obesity and diabetes mellitus. However, mechanisms linking lipid overload and mitochondrial dysfunction are incompletely understood.



To elucidate the mechanisms for mitochondrial adaptations to lipid overload in postnatal hearts in vivo.



Using a transgenic mouse model of cardiac lipotoxicity overexpressing ACSL1 (long-chain acyl-CoA synthetase 1) in cardiomyocytes, we show that modestly increased myocardial fatty acid uptake leads to mitochondrial structural remodeling with significant reduction in minimum diameter. This is associated with increased palmitoyl-carnitine oxidation and increased reactive oxygen species (ROS) generation in isolated mitochondria. Mitochondrial morphological changes and elevated ROS generation are also observed in palmitate-treated neonatal rat ventricular cardiomyocytes. Palmitate exposure to neonatal rat ventricular cardiomyocytes initially activates mitochondrial respiration, coupled with increased mitochondrial polarization and ATP synthesis. However, long-term exposure to palmitate (>8 hours) enhances ROS generation, which is accompanied by loss of the mitochondrial reticulum and a pattern suggesting increased mitochondrial fission. Mechanistically, lipid-induced changes in mitochondrial redox status increased mitochondrial fission by increased ubiquitination of AKAP121 (A-kinase anchor protein 121) leading to reduced phosphorylation of DRP1 (dynamin-related protein 1) at Ser637 and altered proteolytic processing of OPA1 (optic atrophy 1). Scavenging mitochondrial ROS restored mitochondrial morphology in vivo and in vitro.



Our results reveal a molecular mechanism by which lipid overload-induced mitochondrial ROS generation causes mitochondrial dysfunction by inducing post-translational modifications of mitochondrial proteins that regulate mitochondrial dynamics. These findings provide a novel mechanism for mitochondrial dysfunction in lipotoxic cardiomyopathy.
Biomass Characterization: Recent Progress in Understanding Biomass Recalcitrance

The ever-increasing global demand for energy and materials has a pronounced effect on worldwide economic stability, diplomacy, and technical advancement. In response, a recent key research area in biotechnology has centered on the biological conversion of lignocellulosic biomass to simple sugars. Lignocellulosic biomass, converted to fermentable sugars via enzymatic hydrolysis of cell wall polysaccharides, can be utilized to generate a variety of downstream fuels and chemicals. Ethanol, in particular, has a high potential as transportation fuel to supplement or even replace gasoline derived from petroleum feedstocks. Biological or enzymatic hydrolysis offers the potential for low-cost, highyield, and selective production of targeted chemicals and value-added coproducts at milder operating conditions than thermochemical processes such as gasification or pyrolysis. Due to the complex nature of biomass, degrading enzymes, and their interactions, there is a substantial knowledge gap with respect to the mechanism of enzymatic hydrolysis and the relationship between biomass structure and enzymatic performance. This knowledge gap has greatly contributed to the fact that biological conversion of lignocellulosic biomass has not met the target performance and cost requirements for large-scale production and market entrance. This review highlights recent advances in analytical methods to characterize the chemical and molecular features related to the ability of biomass to resist biological deconstruction, defined as biomass recalcitrance. We also briefly discuss the application of some of these methods in a variety of studies that draw attention to relationships between biomass structure, the effectiveness of enzymatic hydrolysis and biomass recalcitrance.
A study of finger lengths, semen quality and sex hormones in 360 young men from the general Danish population.

It has been suggested that finger length may correlate with function or disorders of the male reproductive system. This is based on the HOXA and HOXD genes' common embryological control of finger development and differentiation of the genital bud. The objective of this study was to explore the association between the ratio of 2nd to 4th finger length (2D:4D ratio) and testis function in a sample of young Danish men from the general population.



Semen samples and finger measurements were obtained from a total of 360 young Danish men in addition to blood samples for sex hormone analysis to describe the possible association between 2D:4D and semen and sex-hormone parameters.



A statistically significant inverse association with the 2D:4D was found only in relation to hormone levels of FSH in the group of young men with a 2D:4D >1 (P = 0.036) and a direct association with the total sperm count in the group of young men with a 2D:4D < or = 1 (P = 0.045).



The statistically significant results may be 'false positives' (type I error) rather than representing true associations. This relatively large study of young, normal Danish men shows no reliable association between 2D:4D finger ratio and testicular function. Measurements of finger lengths do not have the power to predict the testicular function of adult men.
Cholesteryl Ester Transfer Protein (CETP) Deficiency and CETP Inhibitors

Epidemiologic studies have shown that low-density lipoprotein cholesterol (LDL-C) is a strong risk factor, whilst high-density lipoprotein cholesterol (HDL-C) reduces the risk of coronary heart disease (CHD). Therefore, strategies to manage dyslipidemia in an effort to prevent or treat CHD have primarily attempted at decreasing LDL-C and raising HDL-C levels. Cholesteryl ester transfer protein (CETP) mediates the exchange of cholesteryl ester for triglycerides between HDL and VLDL and LDL. We have published the first report indicating that a group of Japanese patients who were lacking CETP had extremely high HDL-C levels, low LDL-C levels and a low incidence of CHD. Animal studies, as well as clinical and epidemiologic evidences, have suggested that inhibition of CETP provides an effective strategy to raise HDL-C and reduce LDL-C levels. Four CETP inhibitors have substantially increased HDL-C levels in dyslipidemic patients. This review will discuss the current status and future prospects of CETP inhibitors in the treatment of CHD. At present anacetrapib by Merck and evacetrapib by Eli Lilly are under development. By 100mg of anacetrapib HDL-C increased by 138%, and LDL-C decreased by 40%. Evacetrapib 500 mg also showed dramatic 132% increase of HDL-C, while LDL-C decreased by 40%. If larger, long-term, randomized, clinical end point trials could corroborate other findings in reducing atherosclerosis, CETP inhibitors could have a significant impact in the management of dyslipidemic CHD patients. Inhibition of CETP synthesis by antisense oligonucleotide or small molecules will produce more similar conditions to human CETP deficiency and may be effective in reducing atherosclerosis and cardiovascular events. We are expecting the final data of prospective clinical trials by CETP inhibitors in 2015.
2 Characteristics of low Reynolds number aerodynamics applied to flapping MAV

We describe the design of four ornithopters ranging in wing span from 10 cm to 40 cm, and in weight from 5 g to 45 g. The controllability and power supply are two major considerations, so we compare the efficiency and characteristics between different types of subsystems such as gearbox and tail shape. Our current ornithopter is radio-controlled with inbuilt visual sensing and capable of takeoff and landing. We also concentrate on its wing efficiency based on design inspired by a real insect wing and consider that aspects of insect flight such as delayed stall and wake capture are essential at such small size. Most importantly, the advance ratio, controlled either by enlarging the wing beat amplitude or raising the wing beat frequency, is the most significant factor in an ornithopter which mimics an insect.
RAIL: Risk-Averse Imitation Learning

Imitation learning algorithms learn viable policies by imitating an expert’s behavior when reward signals are not available. Generative Adversarial Imitation Learning (GAIL) is a state-of-the-art algorithm for learning policies when the expert’s behavior is available as a fixed set of trajectories.We evaluate in terms of the expert’s cost function and observe that the distribution of trajectory-costs is often more heavy-tailed for GAIL-agents than the expert at a number of benchmark continuous-control tasks. Thus, high-cost trajectories, corresponding to tail-end events of catastrophic failure, are more likely to be encountered by the GAIL-agents than the expert. This makes the reliability of GAIL-agents questionable when it comes to deployment in risk-sensitive applications like robotic surgery and autonomous driving. In this work, we aim to minimize the occurrence of tail-end events by minimizing tail risk within the GAIL framework. We quantify tail risk by the Conditional-Value-atRisk (CVaR) of trajectories and develop the Risk-Averse Imitation Learning (RAIL) algorithm. We observe that the policies learned with RAIL show lower tail-end risk than those of vanilla GAIL. Thus, the proposed RAIL algorithm appears as a potent alternative to GAIL for improved reliability in risk-sensitive applications.
Automated criminal link analysis based on domain knowledge

Link (association) analysis has been used in the criminal justice domain to search large datasets for associations between crime entities in order to facilitate crime investigations. However, link analysis still faces many challenging problems, such as information overload, high search complexity, and heavy reliance on domain knowledge. To address these challenges, this article proposes several techniques for automated, effective, and efficient link analysis. These techniques include the co-occurrence analysis, the shortest path algorithm, and a heuristic approach to identifying associations and determining their importance. We developed a prototype system called CrimeLink Explorer based on the proposed techniques. Results of a user study with 10 crime investigators from the Tucson Police Department showed that our system could help subjects conduct link analysis more efficiently than traditional single-level link analysis tools. Moreover, subjects believed that association paths found based on the heuristic approach were more accurate than those found based solely on the co-occurrence analysis and that the automated link analysis system would be of great help in crime investigations.
Prevalence and factors associated to sexual behavior in Puerto Rican adolescents.

Sexual relations with intercourse (ASR-I) and high prevalence of teen pregnancies (19.2%, in 2002) among adolescents in Puerto Rico constitute a serious biopsychosocial problem. Studying the consequences and correlates of ASR-I in community and mental health samples of adolescents is important in designing and implementing sexual health programs. Randomized representative cross-sectional samples of male and female adolescents from 11-18 years old (N = 994 from the general community, N = 550 receiving mental health services) who had engaged in ASR-I were the subjects of this study. Demographic, family, and sexual data and the DISC-IV were collected from individual interviews. Logistic regression models, bivariate odds ratios, Chi-squares, and t tests were used in the statistical analysis. The mental health sample showed higher rates of ASR-I, lifetime reports of pregnancy and lower age of ASR-I onset for females. No gender difference in the prevalence of ASR-I was observed in both samples. Older adolescents from the community sample meeting psychiatric diagnosis criteria, and with lower parental monitoring, were more likely to engage in ASR-I, whereas in the mental health sample, adolescents with lower parental monitoring and parental involvement reported significantly more ASR-I. Prevalence of ASR-I and Risky Sexual Behavior (RSB) were almost identical. Adolescents with mental health disorders initiate and engage in ASR-I earlier and more frequently regardless of gender. Older adolescents are more likely to engage in ASR-I and parent-child relationships emerged as a highly relevant predictor of adolescent sexual behavior. The high correspondence between ASR-I and RSB has important clinical implications.