nils-herrmann/clean_scirepeval huggingface dataset:
Evaluating the Ability of NPP-VIIRS Nighttime Light Data to Estimate the Gross Domestic Product and the Electric Power Consumption of China at Multiple Scales: A Comparison with DMSP-OLS Data

The nighttime light data records artificial light on the Earth’s surface and can be used to estimate the spatial distribution of the gross domestic product (GDP) and the electric power consumption (EPC). In early 2013, the first global NPP-VIIRS nighttime light data were released by the Earth Observation Group of National Oceanic and Atmospheric Administration’s National Geophysical Data Center (NOAA/NGDC). As new-generation data, NPP-VIIRS data have a higher spatial resolution and a wider radiometric detection range than the traditional DMSP-OLS nighttime light data. This study aims to investigate the potential of NPP-VIIRS data in modeling GDP and EPC at multiple scales through a case study of China. A series of preprocessing procedures are proposed to reduce the background noise of original data and to generate corrected NPP-VIIRS nighttime light images. Subsequently, linear regression is used to fit the correlation between the total nighttime light (TNL) (which is extracted from corrected NPP-VIIRS data and DMSP-OLS data) and the OPEN ACCESS Remote Sens. 2014, 6 1706 GDP and EPC (which is from the country’s statistical data) at provincialand prefectural-level divisions of mainland China. The result of the linear regression shows that R 2 values of TNL from NPP-VIIRS with GDP and EPC at multiple scales are all higher than those from DMSP-OLS data. This study reveals that the NPP-VIIRS data can be a powerful tool for modeling socioeconomic indicators; such as GDP and EPC.
A hybrid user model for news story classification

We present an intelligent agent designed to compile a daily news program for individual users. Based on feedback from the user, the system automatically adapts to the user’s preferences and interests. In this paper we focus on the system's user modeling component. First, we motivate the use of a multi-strategy machine learning approach that allows for the induction of user models that consist of separate models for long-term and short-term interests. Second, we investigate the utility of explicitly modeling information that the system has already presented to the user. This allows us to address an important issue that has thus far received virtually no attention in the Information Retrieval community: the fact that a user's information need changes as a direct result of interaction with information. We evaluate the proposed algorithms on user data collected with a prototype of our system, and assess the individual performance contributions of both
Robust solutions for network design under transportation cost and demand uncertainty

In many applications, the network design problem (NDP) faces significant uncertainty in transportation costs and demand, as it can be difficult to estimate current (and future values) of these quantities. In this paper, we present a robust optimization-based formulation for the NDP under transportation cost and demand uncertainty. We show that solving an approximation to this robust formulation of the NDP can be done efficiently for a network with single origin and destination per commodity and general uncertainty in transportation costs and demand that are independent of each other. For a network with path constraints, we propose an efficient column generation procedure to solve the linear programming relaxation. We also present computational results that show that the approximate robust solution found provides significant savings in the worst case while incurring only minor sub-optimality for specific instances of the uncertainty.
Learning partial differential equations via data discovery and sparse optimization.

We investigate the problem of learning an evolution equation directly from some given data. This work develops a learning algorithm to identify the terms in the underlying partial differential equations and to approximate the coefficients of the terms only using data. The algorithm uses sparse optimization in order to perform feature selection and parameter estimation. The features are data driven in the sense that they are constructed using nonlinear algebraic equations on the spatial derivatives of the data. Several numerical experiments show the proposed method's robustness to data noise and size, its ability to capture the true features of the data, and its capability of performing additional analytics. Examples include shock equations, pattern formation, fluid flow and turbulence, and oscillatory convection.
Object grasping using a 1 DOF variable stiffness gripper actuated by a hybrid variable stiffness actuator

For tasks requiring robot-environment interaction, compliant motion is important to ensure stable contact and operational safety. The compliant actuators such as a series elastic actuator and a variable stiffness actuator (VSA) are expected to be one of the promising solutions to provide a compliant motion. In this paper, we propose a gripper actuated by the hybrid variable stiffness actuator developed in our previous work to show how the VSA can improve the ability of grasping. The gripper, which is equipped with two symmetric 4-bar linkages, can conduct the position and stiffness control simultaneously, and the grasping force can be calculated by the estimated torque of the HVSA. The HVSA-actuated gripper is able to grasp fragile objects with compliant motion and the relatively heavy object with stiff motion. Furthermore, the features of the variable stiffness provide operational safety by adjusting the stiffness according to the task. In this study, the relationships between the joint torque and the grasping force, and between the joint stiffness and the Cartesian stiffness are analyzed to control the grasping force. From a series of experiments, it is shown that the gripper can grasp fragile objects such as an egg and a wine glass, as well as relatively heavy object without any soft cover and force/torque sensor.
A learning-based approach to text image retrieval: using CNN features and improved similarity metrics

Text content can have different visual presentation ways with roughly similar characters. While conventional text image retrieval depends on complex model of OCR-based text recognition and text similarity detection, this paper proposes a new learning-based approach to text image retrieval with the purpose of finding out the original or similar text through a query text image. Firstly, features of text images are extracted by the CNN network to obtain the deep visual representations. Then, the dimension of CNN features is reduced by PCA method to improve the efficiency of similarity detection. Based on that, an improved similarity metrics with article theme relevance filtering is proposed to improve the retrieval accuracy. In experimental procedure, we collect a group of academic papers both including English and Chinese as the text database, and cut them into pieces of text image. A text image with changed text content is used as the query image, experimental results show that the proposed approach has good ability to retrieve the original text content.
A Novel Antenna-in-Package With LTCC Technology for W-Band Application

In this letter, a novel antenna-in-package (AiP) technology at W-band has been proposed. This technology is presented for solving the special case that the metallic package should be used to accommodate high mechanical strength. By taking advantages of the multilayer low temperature co-fired ceramic (LTCC) technology, the radiation efficiency of the antenna can be maintained. Meanwhile, high mechanical strength and shielding performance are achieved. A prototype of AiP has been designed. The prototype constitutes integrated LTCC antenna, low-loss feeder, and metallic package with a tapered horn aperture. This LTCC feeder is realized by laminated waveguide (LWG). An LWG cavity that is buried in LTCC is employed to broaden the antenna impedance bandwidth. Electromagnetic (EM) simulations and measurements of antenna performances agree well over the whole frequency range of interest. The proposed prototype achieves a -10-dB impedance bandwidth of 10 GHz from 88 to 98 GHz and a peak gain of 12.3 dBi at 89 GHz.
Exploration of Large Networks with Covariates via Fast and Universal Latent Space Model Fitting

Latent space models are effective tools for statistical modeling and exploration of network data. These models can effectively model real world network characteristics such as degree heterogeneity, transitivity, homophily, etc. Due to their close connection to generalized linear models, it is also natural to incorporate covariate information in them. The current paper presents two universal fitting algorithms for networks with edge covariates: one based on nuclear norm penalization and the other based on projected gradient descent. Both algorithms are motivated by maximizing likelihood for a special class of inner-product models while working simultaneously for a wide range of different latent space models, such as distance models, which allow latent vectors to affect edge formation in flexible ways. These fitting methods, especially the one based on projected gradient descent, are fast and scalable to large networks. We obtain their rates of convergence for both inner-product models and beyond. The effectiveness of the modeling approach and fitting algorithms is demonstrated on five real world network datasets for different statistical tasks, including community detection with and without edge covariates, and network assisted learning.
Current evidence on risk factors for knee osteoarthritis in older adults: a systematic review and meta-analysis.

Osteoarthritis (OA) is a leading cause of pain and disability and leads to a reduced quality of life. The aim was to determine the current evidence on risk factors for onset of knee pain/OA in those aged 50 and over. A systematic review and meta-analysis was conducted of cohort studies for risk factors for the onset of knee pain. Two authors screened abstracts and papers and completed data extraction. Where possible, pooled odds ratios (OR) were calculated via random effects meta-analysis and population attributable fractions (PAFs) derived. 6554 papers were identified and after screening 46 studies were included. The main factors associated with onset of knee pain were being overweight (pooled OR 1.98, 95% confidence intervals (CI) 1.57-2.20), obesity (pooled OR 2.66 95% CI 2.15-3.28), female gender (pooled OR 1.68, 95% CI 1.37-2.07), previous knee injury (pooled OR 2.83, 95% CI 1.91-4.19). Hand OA (pooled OR 1.30, 95% CI 0.90-1.87) was found to be non-significant. Smoking was found not to be a statistically significant risk or protective factor (pooled OR 0.92, 95% CI 0.83-1.01). PAFs indicated that in patients with new onset of knee pain 5.1% of cases were due to previous knee injury and 24.6% related to being overweight or obese. Clinicians can use the identified risk factors to identify and manage patients at risk of developing or increasing knee pain. Obesity in particular needs to be a major target for prevention of development of knee pain. More research is needed into a number of potential risk factors.
Information security behavior: Recognizing the influencers

With the wide spread use of Internet, comes an increase in Information Security threats. To protect against these threats, technology alone have been found not enough as it can be misused by users and become vulnerable to various threats, thus, losing its usefulness. This is evident as users tend to use weak passwords, open email attachments without checking and do not set correct security settings. However, especially with the continuously evolving threat landscape, one cannot assume that users are always motivated to learn about Information Security and practice it. Actually, there are situations of an aware user who knows how to protect himself but, simply, chooses not to, because they do not care, usability problems or because they do not consider themselves as targets. Thus, understanding human security behavior is vital for ensuring an efficient Information Security environment that cannot depend on technology only. Although a number of psychological theories and models, such as Protection Motivation Theory and Technology Acceptance Model, were used in the literature to interpret these behaviors, they tend to assess users' intensions rather than actual behavior. The aim of this paper is to understand and assess these behaviors from a holistic view by finding the significant factors that influence them and how to best assist users to protect themselves. To accomplish this, a systematic literature review was conducted where relevant literature was sought in a number of academic digital databases. As a result, several key behavioral influencers were identified to be essential to consider when educating and directing users' security behavior. Further to that, a number of Information Security awareness approaches were proposed that may transform the user from being ill-informed into a security-minded user who is able to make an informed decision.
Why Cancer and Inflammation?

Central to the development of cancer are genetic changes that endow these "cancer cells" with many of the hallmarks of cancer, such as self-sufficient growth and resistance to anti-growth and pro-death signals. However, while the genetic changes that occur within cancer cells themselves, such as activated oncogenes or dysfunctional tumor suppressors, are responsible for many aspects of cancer development, they are not sufficient. Tumor promotion and progression are dependent on ancillary processes provided by cells of the tumor environment but that are not necessarily cancerous themselves. Inflammation has long been associated with the development of cancer. This review will discuss the reflexive relationship between cancer and inflammation with particular focus on how considering the role of inflammation in physiologic processes such as the maintenance of tissue homeostasis and repair may provide a logical framework for understanding the connection between the inflammatory response and cancer.
Fitness determinants of success in men's and women's football.

In this study, we examined gender and age differences in physical performance in football. Thirty-four elite female and 34 elite male players (age 17 +/- 1.6 to 24 +/- 3.4 years) from a professional football club were divided into four groups (n=17 each) according to gender and competitive level (senior males, senior females, junior males, and junior females). Players were tested for specific endurance (Yo-YoIR1), sprint over 15 m (Sprint-15 m), vertical jump without (CMJ) or with (ACMJ) arm swing, agility (Agility-15 m), and ball dribbling over 15 m (Ball-15 m). The Yo-YoIR1 and Agility-15m performances showed both a gender and competitive level difference (P < 0.001). Senior and junior males covered 97 and 153% more distance during the Yo-YoIR1 than senior and junior females, respectively (P < 0.001). Gender but not age differences were found for Sprint-15 m performance (P < 0.001). No difference in vertical jump and Ball-15 m performances were found between senior and junior males (P > 0.05). More marked gender differences were evident in endurance than in anaerobic performance in female players. These results show major fitness differences by gender for a given competitive level in football players. It is suggested that training and talent identification should focus on football-specific endurance and agility as fitness traits in post-adolescent players of both sexes.
Sentiment-enhanced multidimensional analysis of online social networks: Perception of the mediterranean refugees crisis

We propose an analytical framework able to investigate discussions about polarized topics in online social networks from many different angles. The framework supports the analysis of social networks along several dimensions: time, space and sentiment. We show that the proposed analytical framework and the methodology can be used to mine knowledge about the perception of complex social phenomena. We selected the refugee crisis discussions over Twitter as a case study. This difficult and controversial topic is an increasingly important issue for the EU. The raw stream of tweets is enriched with space information (user and mentioned locations), and sentiment (positive vs. negative) w.r.t. refugees. Our study shows differences in positive and negative sentiment in EU countries, in particular in UK, and by matching events, locations and perception, it underlines opinion dynamics and common prejudices regarding the refugees.
A microstrip-line-fed tri-band monopole antenna for WLAN/WiMAX applications

A tri-band microstrip-line-fed patch antenna for wireless local area network (WLAN) and worldwide interoperability of microwave access (WIMAX) application is presented. The proposed antenna consists of an asymmetric V-shaped patch and a hook-like patch in the middle, there is a 60° angle between each branch and the ground plane in order to radiate more energy into the free space so that we can improve the far field performance of the antenna. The asymmetric V-shaped patch excites the 5.2 GHz-band by the left branch and 3.5 GHz-band by the right one due to the different length between them. Furthermore, the 2.4 GHz-band is excited by adding the middle patch, the length is about half guided wavelength at 2.45 GHz, the the middle patch was bent into a hook-liked structure to realize a compact design, the overall size of the antenna is 24 * 30 * 0.8mm3. Key parameters were studied and it has been found that the three bands can be tuned by different branches, respectively, it has minor effect on other bands when one band is tuned, according to the numerical results, a method of tuning the three operating bands has been summarized. The proposed antenna has been fabricated and measured, the -10-dB (S11 <; -10 dB) bands are 2.4 GHz-2.56 GHz, 3.35 GHz-3.95 GHz, 5.05 GHz-5.85 GHz, which can cover the WLAN (2.4-2.484 GHz/5.15-5.35 GHz/5.725-5.825 GHz) bands and WiMAX (3.4-3.69 GHz/5.25-5.85 GHz) bands, it has a monopole-liked radiation characteristic and the peak gain is good according to the far field results. Hence, with the compact simple structure, minor effect on adjacent bands when tuning one band characteristic, and the summarized tuning method, such design can be a good candidate for wireless applications.
General Spectral Camera Lens Simulation

We present a camera lens simulation model capable of producing advanced photographic phenomena in a general spectral Monte Carlo image rendering system. Our approach incorporates insights from geometrical diffraction theory, from optical engineering and from glass science. We show how to efficiently simulate all five monochromatic aberrations, spherical and coma aberration, astigmatism, field curvature and distortion. We also consider chromatic aberration, lateral colour and aperture diffraction. The inclusion of Fresnel reflection generates correct lens flares and we present an optimized sampling method for path generation.
Exposure Fusion: A Simple and Practical Alternative to High Dynamic Range Photography

We propose a technique for fusing a bracketed exposure sequence into a high quality image, without converting to High dynamic range (HDR) first. Skipping the physically based HDR assembly step simplifies the acquisition pipeline. This avoids camera response curve calibration and is computationally efficient. It also allows for including flash images in the sequence. Our technique blends multiple exposures, guided by simple quality measures like saturation and contrast. This is done in a multiresolution fashion to account for the brightness variation in the sequence. The resulting image quality is comparable to existing tone mapping operators.
Adverse health effects of non-medical cannabis use

For over two decades, cannabis, commonly known as marijuana, has been the most widely used illicit drug by young people in high-income countries, and has recently become popular on a global scale. Epidemiological research during the past 10 years suggests that regular use of cannabis during adolescence and into adulthood can have adverse effects. Epidemiological, clinical, and laboratory studies have established an association between cannabis use and adverse outcomes. We focus on adverse health effects of greatest potential public health interest-that is, those that are most likely to occur and to affect a large number of cannabis users. The most probable adverse effects include a dependence syndrome, increased risk of motor vehicle crashes, impaired respiratory function, cardiovascular disease, and adverse effects of regular use on adolescent psychosocial development and mental health.
Bioinformatics analysis of the structural and evolutionary characteristics for toll-like receptor 15

Toll-like receptors (TLRs) play important role in the innate immune system. TLR15 is reported to have a unique role in defense against pathogens, but its structural and evolution characterizations are still poorly understood. In this study, we identified 57 completed TLR15 genes from avian and reptilian genomes. TLR15 clustered into an individual clade and was closely related to family 1 on the phylogenetic tree. Unlike the TLRs in family 1 with the broken asparagine ladders in the middle, TLR15 ectodomain had an intact asparagine ladder that is critical to maintain the overall shape of ectodomain. The conservation analysis found that TLR15 ectodomain had a highly evolutionarily conserved region on the convex surface of LRR11 module, which is probably involved in TLR15 activation process. Furthermore, the protein-protein docking analysis indicated that TLR15 TIR domains have the potential to form homodimers, the predicted interaction interface of TIR dimer was formed mainly by residues from the BB-loops and αC-helixes. Although TLR15 mainly underwent purifying selection, we detected 27 sites under positive selection for TLR15, 24 of which are located on its ectodomain. Our observations suggest the structural features of TLR15 which may be relevant to its function, but which requires further experimental validation.
Hybrid mining approach in the design of credit scoring models

Unrepresentative data samples are likely to reduce the utility of data classifiers in practical application. This study presents a hybrid mining approach in the design of an effective credit scoring model, based on clustering and neural network techniques. We used clustering techniques to preprocess the input samples with the objective of indicating unrepresentative samples into isolated and inconsistent clusters, and used neural networks to construct the credit scoring model. The clustering stage involved a class-wise classification process. A self-organizing map clustering algorithm was used to automatically determine the number of clusters and the starting points of each cluster. Then, the K-means clustering algorithm was used to generate clusters of samples belonging to new classes and eliminate the unrepresentative samples from each class. In the neural network stage, samples with new class labels were used in the design of the credit scoring model. The proposed method demonstrates by two real world credit data sets that the hybrid mining approach can be used to build effective credit scoring models. q 2005