nils-herrmann/clean_scirepeval huggingface dataset:
Tracking the evolutionary origins of dog-human cooperation: the “Canine Cooperation Hypothesis”

At present, beyond the fact that dogs can be easier socialized with humans than wolves, we know little about the motivational and cognitive effects of domestication. Despite this, it has been suggested that during domestication dogs have become socially more tolerant and attentive than wolves. These two characteristics are crucial for cooperation, and it has been argued that these changes allowed dogs to successfully live and work with humans. However, these domestication hypotheses have been put forward mainly based on dog-wolf differences reported in regard to their interactions with humans. Thus, it is possible that these differences reflect only an improved capability of dogs to accept humans as social partners instead of an increase of their general tolerance, attentiveness and cooperativeness. At the Wolf Science Center, in order to detangle these two explanations, we raise and keep dogs and wolves similarly socializing them with conspecifics and humans and then test them in interactions not just with humans but also conspecifics. When investigating attentiveness toward human and conspecific partners using different paradigms, we found that the wolves were at least as attentive as the dogs to their social partners and their actions. Based on these findings and the social ecology of wolves, we propose the Canine Cooperation Hypothesis suggesting that wolves are characterized with high social attentiveness and tolerance and are highly cooperative. This is in contrast with the implications of most domestication hypotheses about wolves. We argue, however, that these characteristics of wolves likely provided a good basis for the evolution of dog-human cooperation.
The predictor impact of Web search media on Bitcoin trading volumes

In the last decade, Web 2.0 services have been widely used as communication media. Due to the huge amount of available information, searching has become dominant in the use of Internet. Millions of users daily interact with search engines, producing valuable sources of interesting data regarding several aspects of the world. Search queries prove to be a useful source of information in financial applications, where the frequency of searches of terms related to the digital currency can be a good measure of interest in it. Bitcoin, a decentralized electronic currency, represents a radical change in financial systems, attracting a large number of users and a lot of media attention. In this work we studied the existing relationship between Bitcoin's trading volumes and the queries volumes of Google search engine. We achieved significant cross correlation values, demonstrating search volumes power to anticipate trading volumes of Bitcoin currency.
Geometric Robustness of Deep Networks: Analysis and Improvement

Deep convolutional neural networks have been shown to be vulnerable to arbitrary geometric transformations. However, there is no systematic method to measure the invariance properties of deep networks to such transformations. We propose ManiFool as a simple yet scalable algorithm to measure the invariance of deep networks. In particular, our algorithm measures the robustness of deep networks to geometric transformations in a worst-case regime as they can be problematic for sensitive applications. Our extensive experimental results show that ManiFool can be used to measure the invariance of fairly complex networks on high dimensional datasets and these values can be used for analyzing the reasons for it. Furthermore, we build on ManiFool to propose a new adversarial training scheme and we show its effectiveness on improving the invariance properties of deep neural
Multiple players tracking and identification using group detection and player number recognition in sports video

We are interested in the problem of automatically tracking and identifying players in sports video. While there are many automatic multi-target tracking methods, in sports video, it is difficult to track multiple players due to frequent occlusions, quick motion of players and camera, and camera position. We propose tracking method that associates tracklets of a same player using results of player number recognition. To deal with frequent occlusions, we detect human region by level set method and then estimates if it is occluded group region or unoccluded individual one. Moreover, we associate tracklets using the results of player number recognition at each frame by keypoints-based matching with templates from multiple viewpoints, so that final tracklets include occluded region.
Analysis of quadratic dickson based envelope detectors for IoE sensor node applications

This paper presents a study of passive Dickson based envelope detectors operating in the quadratic small signal regime, specifically intended to be used in RF front end of sensing units of IoE sensor nodes. Critical parameters such as open-circuit voltage sensitivity (OCVS), charge time, input impedance, and output noise are studied and simplified circuit models are proposed to predict the behavior of the detector, resulting in practical design intuitions. There is strong agreement between model predictions, simulation results and measurements of 15 representative test structures that were fabricated in a 130 nm RF CMOS process.
Examining the sex difference in lateralisation for processing facial emotion: Does biological sex or psychological gender identity matter?

The research examining sex differences in functional lateralisation has shown varying results. While some provide evidence for males being more strongly lateralised than females, a number have shown either no relationship or the opposite pattern of findings. In this study we consider whether psychological gender identity might clarify some of the conflicting results in this area of research. Eight five participants (39 males) aged from 18 to 49 years old were tested. We found that psychological masculinity was associated with stronger patterns of lateralisation for the processing of a range of emotional expressions. We also found an interaction between biological sex and psychological gender identity, with a positive relationship between psychological masculinity and lateralisation found for males, but a negative relationship found for females. The possible role of hormonal exposure in this relationship is discussed.
The Large Scale Machine Learning in an Artificial Society: Prediction of the Ebola Outbreak in Beijing

Ebola virus disease (EVD) distinguishes its feature as high infectivity and mortality. Thus, it is urgent for governments to draw up emergency plans against Ebola. However, it is hard to predict the possible epidemic situations in practice. Luckily, in recent years, computational experiments based on artificial society appeared, providing a new approach to study the propagation of EVD and analyze the corresponding interventions. Therefore, the rationality of artificial society is the key to the accuracy and reliability of experiment results. Individuals' behaviors along with travel mode directly affect the propagation among individuals. Firstly, artificial Beijing is reconstructed based on geodemographics and machine learning is involved to optimize individuals' behaviors. Meanwhile, Ebola course model and propagation model are built, according to the parameters in West Africa. Subsequently, propagation mechanism of EVD is analyzed, epidemic scenario is predicted, and corresponding interventions are presented. Finally, by simulating the emergency responses of Chinese government, the conclusion is finally drawn that Ebola is impossible to outbreak in large scale in the city of Beijing.
Germinal Center B Cell Dynamics.

Germinal centers (GCs) are the site of antibody diversification and affinity maturation and as such are vitally important for humoral immunity. The study of GC biology has undergone a renaissance in the past 10 years, with a succession of findings that have transformed our understanding of the cellular dynamics of affinity maturation. In this review, we discuss recent developments in the field, with special emphasis on how GC cellular and clonal dynamics shape antibody affinity and diversity during the immune response.
The adjustable nanosecond pulse generation based on highl-voltage MOSFET

A computer-controlled adjustable nanosecond pulse generator based on high-voltage MOSFET is designed in this paper, which owns stable performance and miniaturization profile of 32×30×7 cm3. The experiment results show that the pulser can generate electrical pulse with Gaussian rising time of 20 nanosecond, section-adjustable index falling time of 40–200 nanosecond, continuously adjustable repitition frequency of 0–5 kHz, quasi-continuously adjustable amplitude of 0–1 kV at 50 Ω load. And the pulser could meet the requiremen.
Introducing a ROS based planning and execution framework for human-robot interaction

Working in human populated environments requires fast and robust action selection and execution especially when deliberately trying to interact with humans. This work presents the combination of a high-level planner (ROSPlan) for action sequencing and automatically generated finite state machines (PNP) for execution. Using this combined system we are able to exploit the speed and robustness of the execution and the flexibility of the sequence generation and combine the positive aspects of both approaches.
How to Build an Embodiment Lab: Achieving Body Representation Illusions in Virtual Reality

1 Experimental Virtual Environments for Neuroscience and Technology Laboratory, Facultat de Psicologia, Universitat de Barcelona, Barcelona, Spain 2 CERMA UMR CNRS 1563, École Centrale de Nantes, Nantes, France 3 Renaissance Computing Institute, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA 4 Institut d’Investigacions Biomédiques August Pi i Sunyer (IDIBAPS), Barcelona, Spain 5 Institució Catalana Recerca i Estudis Avançats (ICREA), Barcelona, Spain
Bouveret's syndrome. An unusual localization of gallstone ileus.

This case report describes a 77-year-old male, who presented to the emergency room with symptoms of an acute proximal small bowel obstruction. Abdominal CT scan with multi-planar reconstructions led to the diagnosis of an intestinal obstruction due to impaction of a large gallstone in the second portion of the duodenum. The CT scan demonstrated a large cholecysto-duodenal fistula as the origin of the gallstone migration. Surgical treatment consisted of milking the stone down beyond the ligament of Treitz, where it was removed through a jejunal enterotomy. The postoperative course was uncomplicated. No attempt was made to repair the choledocho-duodenal fistula at the initial intervention nor subsequently, and there have been no complications due to the fistula over 36 months of follow-up observation.
Water vulnerability assessment in karst environments : a new method of defining protection areas using a multi-attribute approach and GIS tools ( EPIK method )

Groundwater resources from karst aquifers play a major role in the water supply in karst areas in the world, such as in Switzerland. Defining groundwater protection zones in karst environment is frequently not founded on a solid hydrogeological basis. Protection zones are often inadequate and as a result they may be ineffective. In order to improve this situation, the Federal Office for Environment, Forests and Landscape with the Swiss National Hydrological and Geological Survey contracted the Centre of Hydrogeology of the Neuchâtel University to develop a new groundwater protection-zones strategy in karst environment. This approach is based on the vulnerability mapping of the catchment areas of water supplies provided by springs or boreholes. Vulnerability is here defined as the intrinsic geological and hydrogeological characteristics which determine the sensitivity of groundwater to contamination by human activities. The EPIK method is a multi-attribute method for vulnerability mapping which takes into consideration the specific hydrogeological behaviour of karst aquifers. EPIK is based on a conceptual model of karst hydrological systems, which suggests considering four karst aquifer attributes: (1) Epikarst, (2) Protective cover, (3) Infiltration conditions and (4) Karst network development. Each of these four attributes is subdivided into classes which are mapped over the whole water catchment. The attributes and their classes are then weighted. Attribute maps are overlain in order to obtain a final vulnerability map. From the vulnerability map, the groundwater protection zones are defined precisely. This method was applied at several sites in Switzerland where agriculture contamination problems have frequently occurred. These applications resulted in recommend new boundaries for the karst water supplies protection-zones.
Removing Structural Noise in Handwriting Images using Deep Learning

Since handwriting recognition is very sensitive to structural noise, like superimposed objects such as straight lines and other marks, it is necessary to remove noise in a preprocessing stage before recognition. Although numerous denoising approaches have been proposed, it remains a challenge. The difficulties are due to non-locality of structural noise and hard discernment between spurious and the meaningful regions. We propose a supervised approach using deep learning to remove structural noise. Specifically, we generalize the deep autoencoder into the deep denoising autoencoder (DDAE), which consists in training a neural network with noisy and clean pairs to minimize cross-entropy error. Inspired by recurrent neural networks, we introduce feedback loop from the output to enhance the "repaired" image well in the reconstruction stage in our framework. We test the DDAE model on three handwritten image data sets, and show advantages over Wiener filter, robust Boltzmann machines and deep autoencoder.
Where to Go on Your Next Trip?: Optimizing Travel Destinations Based on User Preferences

Recommendation based on user preferences is a common task for e-commerce websites. New recommendation algorithms are often evaluated by offline comparison to baseline algorithms such as recommending random or the most popular items. Here, we investigate how these algorithms themselves perform and compare to the operational production system in large scale online experiments in a real-world application. Specifically, we focus on recommending travel destinations at Booking.com, a major online travel site, to users searching for their preferred vacation activities. To build ranking models we use multi-criteria rating data provided by previous users after their stay at a destination. We implement three methods and compare them to the current baseline in Booking.com: random, most popular, and Naive Bayes. Our general conclusion is that, in an online A/B test with live users, our Naive-Bayes based ranker increased user engagement significantly over the current online system.
Active learning for regression using greedy sampling

Regression problems are pervasive in real-world applications. Generally a substantial amount of labeled samples are needed to build a regression model with good generalization ability. However, many times it is relatively easy to collect a large number of unlabeled samples, but time-consuming or expensive to label them. Active learning for regression (ALR) is a methodology to reduce the number of labeled samples, by selecting the most beneficial ones to label, instead of random selection. This paper proposes two new ALR approaches based on greedy sampling (GS). The first approach (GSy) selects new samples to increase the diversity in the output space, and the second (iGS) selects new samples to increase the diversity in both input and output spaces. Extensive experiments on 12 UCI and CMU StatLib datasets from various domains, and on 15 subjects on EEG-based driver drowsiness estimation, verified their effectiveness and robustness.
National study of information seeking behavior of academic researchers in the United States

As new technologies and information delivery systems emerge, the way in which individuals search for information to support research, teaching, and creative activities is changing. To understand different aspects of researchers’ information-seeking behavior, this article surveyed 2,063 academic researchers in natural science, engineering, and medical science from five research universities in the United States. A Web-based, in-depth questionnaire was designed to quantify researchers’ information searching, information use, and information storage behaviors. Descriptive statistics are reported.
Integrating Fundamental and Technical Analysis of Stock Market through Multi-layer Perceptron

We use Multi-layer Perceptron and propose a hybrid model of fundamental and technical analysis by utilizing stock prices (from 2012–06 to 2017–12) and financial ratios of Technology companies listed on Nasdaq. Our model uses data discretization and feature selection preprocesses. The best results are obtained through topology optimizations using a three-hidden layer MLP. We examine the predictability of our hybrid model through a training/test split and cross-validation. It is found that the hybrid model successfully predicts the future stock movements. Our model results in the greatest average directional accuracy (65.87%) compared to the results obtained from the fundamental and technical analysis in isolation. The numerical results provide enough evidence to conclude that the market is not perfectly efficient.
Pathogenic bacteria enhance dispersal through alteration of Drosophila social communication

Pathogens and parasites can manipulate their hosts to optimize their own fitness. For instance, bacterial pathogens have been shown to affect their host plants’ volatile and non-volatile metabolites, which results in increased attraction of insect vectors to the plant, and, hence, to increased pathogen dispersal. Behavioral manipulation by parasites has also been shown for mice, snails and zebrafish as well as for insects. Here we show that infection by pathogenic bacteria alters the social communication system of Drosophila melanogaster. More specifically, infected flies and their frass emit dramatically increased amounts of fly odors, including the aggregation pheromones methyl laurate, methyl myristate, and methyl palmitate, attracting healthy flies, which in turn become infected and further enhance pathogen dispersal. Thus, olfactory cues for attraction and aggregation are vulnerable to pathogenic manipulation, and we show that the alteration of social pheromones can be beneficial to the microbe while detrimental to the insect host. Behavioral manipulation of host by pathogens has been observed in vertebrates, invertebrates, and plants. Here the authors show that in Drosophila, infection with pathogenic bacteria leads to increased pheromone release, which attracts healthy flies. This process benefits the pathogen since it enhances bacterial dispersal, but is detrimental to the host.
Towards Release Strategy Optimization for Apps in Google Play

In the appstore-centric ecosystem, app developers have an urgent requirement to optimize their release strategy to maximize user adoption of their apps. To address this problem, we introduce an approach to assisting developers to select the proper release opportunity based on the purpose of the update and current condition of the app. Before that, we propose the update interval to characterize release patterns of apps, and find significance of the updates through empirical analysis. We mined the release-history data of 17,820 apps from 33 categories in Google Play, over a period of 105 days. With 41,028 releases identified from these apps, we reveal important characteristics of update intervals and how these factors can influence update effects. We suggest developers to synthetically consider app ranking, rating trend, and update purpose in addition to the timing of releasing an app version. We propose a Multinomial Naive Bayes model to help decide an optimal release opportunity to gain better user adoption.
Periodontal conditions in patients with Marfan syndrome – a multicenter case control study

Marfan syndrome (MFS) is a disorder of the connective tissues. Alterations of the elastic fibers may manifest in different tissues especially in the skeletal, cardiovascular and ocular system. Oral manifestations like orthodontic or skeletal anomalies and fragility of the temporomandibular joint have been well described by various authors. However, no data are available regarding a possible periodontal involvement of MFS. Hence, the aim of the present study was to investigate for the first time if MFS may increase the susceptibility to periodontitis.



A comprehensive periodontal examination including documentation of probing pocket depth, gingival recession, clinical attachment level, and bleeding on probing was conducted in all patients. In addition, dental conditions were assessed by determining the Index for Decayed, Missing and Filled Teeth (DMFT) and a self-administered questionnaire was filled out by patients. For statistical analysis, the unpaired t-Test was applied (level of significance: p < 0.05). Both groups were matched concerning well known periodontal risk factors like age, gender and smoking habits.



82 participants, 51 patients with MFS (30 female and 21 male, mean age: 40.20 ± 15.35 years) and 31 sound controls (17 female and 14 male, mean age: 40.29 ± 13.94 years), were examined. All assessed periodontal and dental parameters were not significantly different between groups.



Based on our data, patients with MFS did not reveal a higher prevalence of periodontitis compared to the control group. However, Marfan patients showed a tendency to more inflammation signs, which can be explained by the crowded teeth. Therefore, a regular professional cleaning of the teeth is recommendable (i.e., 6 months intervals) in order to reduce the bacterial biofilm in the oral cavity and thus resulting in a decreased risk of systemic diseases, specifically endocarditis.
Effects of Syringe Material and Silicone Oil Lubrication on the Stability of Pharmaceutical Proteins

Currently, polymer-based prefillable syringes are being promoted to the pharmaceutical market because they provide an increased break resistance relative to traditionally used glass syringes. Despite this significant advantage, the possibility that barrel material can affect the oligomeric state of the protein drug exists. The present study was designed to compare the effect of different syringe materials and silicone oil lubrication on the protein aggregation. The stability of a recombinant fusion protein, abatacept (Orencia), and a fully human recombinant immunoglobulin G1, adalimumab (Humira), was assessed in silicone oil-free (SOF) and silicone oil-lubricated 1-mL glass syringes and polymer-based syringes in accelerated stress study. Samples were subjected to agitation stress, and soluble aggregate levels were evaluated by size-exclusion chromatography and verified with analytical ultracentrifugation. In accordance with current regulatory expectations, the amounts of subvisible particles resulting from agitation stress were estimated using resonant mass measurement and dynamic flow-imaging analyses. The amount of aggregated protein and particle counts were similar between unlubricated polymer-based and glass syringes. The most significant protein loss was observed for lubricated glass syringes. These results suggest that newly developed SOF polymer-based syringes are capable of providing biopharmaceuticals with enhanced physical stability upon shipping and handling.