nils-herrmann/clean_scirepeval huggingface dataset:
Challenges in a future IP/Ethernet-based in-car network for real-time applications

In current vehicles, a large number of control units are connected by several automotive specific communication buses, facilitating innovative distributed applications. At the same time, computers and entertainment devices use IP and commodity communications technology like Ethernet to connect to the Internet, allowing for innovative solutions and maintaining fast innovation cycles. Today, one can see first applications of Ethernet for in-vehicle communication in contemporary cars. In next generation vehicles, many innovative applications could benefit from the increased bandwidth Ethernet can offer. Therefore, a examination of Ethernet usage for additional in-vehicle communication use cases is needed. In this paper, we show simulation results of promising use cases for in-car Ethernet, while looking at different realistic topologies, types of traffic, and configurations.
MPNET: An End-to-End Deep Neural Network for Object Detection in Surveillance Video

Object detection is one of the most important topics in computer vision task and has obtained impressive performance thanks to the use of deep convolutional neural network. For object detection, especially in still image, it has achieved excellent performance during past two years, such as the series of R-CNN which plays a vital role in improving performance. However, with the number of surveillance videos increasing, the current methods may not meet the growing demand. In this paper, we propose a new framework named moving-object proposals generation and prediction framework (MPGP) to reduce the searching space and generate some accurate proposals which can reduce computational cost. In addition, we explore the relation of moving regions in feature map of different layers and predict candidates according to the results of previous frames. Last but not least, we utilize spatial-temporal information to strengthen the detection score and further adjust the location of the bounding boxes. Our MPGP framework can be applied to different region-based networks. Experiments on CUHK data set, XJTU data set, and AVSS data set, show that our approach outperforms the state-of-the-art approaches.
Streaming and Exploration of Dynamically Changing Dense 3D Reconstructions in Immersive Virtual Reality

We introduce a novel framework that enables large-scale dense 3D scene reconstruction, data streaming over the network and immersive exploration of the reconstructed environment using virtual reality. The system is operated by two remote entities, where one entity – for instance an autonomous aerial vehicle – captures and reconstructs the environment as well as transmits the data to another entity – such as human observer – that can immersivly explore the 3D scene, decoupled from the view of the capturing entity. The performance evaluation revealed the framework’s capabilities to perform RGB-D data capturing, dense 3D reconstruction, streaming and dynamic scene updating in real time for indoor environments up to a size of 100m2, using either a state-of-the-art mobile computer or a workstation. Thereby, our work provides a foundation for enabling immersive exploration of remotely captured and incrementally reconstructed dense 3D scenes, which has not shown before and opens up new research aspects in future.
Pedestrian detection and tracking with night vision

This paper presents a method for pedestrian detection and tracking using a single night-vision video camera installed on the vehicle. To deal with the nonrigid nature of human appearance on the road, a two-step detection/tracking method is proposed. The detection phase is performed by a support vector machine (SVM) with size-normalized pedestrian candidates and the tracking phase is a combination of Kalman filter prediction and mean shift tracking. The detection phase is further strengthened by information obtained by a road-detection module that provides key information for pedestrian validation. Experimental comparisons (e.g., grayscale SVM recognition versus binary SVM recognition and entire-body detection versus upper-body detection) have been carried out to illustrate the feasibility of our approach.
5 Mbps optical wireless communication with error correction coding for underwater sensor nodes

One issue with underwater sensors is how to efficiently transfer large amounts of data collected by the node to an interrogating platform such as an underwater vehicle. It is often impractical to make a physical connection between the node and the vehicle which suggests an acoustic or optical wireless solution. For large amounts of data, the high bandwidth of underwater optical wireless is an advantage. A small, low-cost platform to demonstrate the potential of an optical wireless communications interface for underwater sensor nodes is demonstrated. To enhance the reliability and robustness of the optical wireless communication digital signal processing and error correction techniques are used. The system was tested in 3 and 7.7 meter tanks at 5 Mbps with the turbidity of the water controlled by the addition of Maalox.
3D Semantic Mapping Based on Convolutional Neural Networks

As an important part of environmental perception, maps guarantee the accuracy of intelligent robots in navigation, localization and path planning. The traditional 3D maps mainly focus on the spatial structure of the objects, which lacks the semantic information. A method is proposed in the paper, this method combines convolutional neural networks (CNNs) and Simultaneous Localization and Mapping (SLAM) to create global dense 3D semantic maps for indoor scenes. The deep neural network that includes convolution and deconvolution is designed to predict semantic category of every pixel. RGB-D camera is used to obtain scene information, accomplish localization and build 3D maps simultaneously. the semantic information is integrated into the 3D scene, we present an octree map method to replace traditional point clouds method, which can reduce the error from pose estimation and single frame labeling. By this method, the accuracy of semantic information is greatly improved.
ISUOG consensus statement: what constitutes a fetal echocardiogram?

In 2006 the International Society of Ultrasound in Obstetrics and Gynecology (ISUOG) published practice guidelines for the sonographic screening of congenital heart disease (CHD) during the second trimester of pregnancy1. This document described two levels for screening low-risk fetuses for heart anomalies. Firstly, a ‘basic’ scan should be performed by analyzing a four-chamber view of the fetal heart. Secondly, an ‘extended-basic’ scan further examines the size and relationships of both arterial outflow tracts. The term ‘fetal echocardiogram’ was also mentioned as a more detailed sonographic evaluation to be performed by specialists in the prenatal diagnosis of CHD. Although common indications and imaging techniques were briefly discussed, it was thought important to further explain how this advanced diagnostic procedure differs from the basic and extended-basic cardiac screening examinations. A Fetal Echocardiography Task Force was subsequently asked to develop a standard description of ‘what constitutes a fetal echocardiogram’. Our original goal was to develop guidelines for a detailed examination of the fetal heart that were based on the literature and a consensus opinion of an expert panel. However, we soon realized that this project was quite different from developing minimum practice guidelines for fetal cardiac screening. There are several imaging modalities that can be used to evaluate fetal heart anomalies, ranging from M-mode techniques and color Doppler sonography to the use of four-dimensional (4D) ultrasonography with spatiotemporal image correlation (STIC). Consequently, our original efforts to develop a minimum practice guideline for fetal echocardiography evolved into a consensus statement that covers the following topics:
Social media analytics and research test-bed (SMART dashboard)

We developed a social media analytics and research testbed (SMART) dashboard for monitoring Twitter messages and tracking the diffusion of information in different cities. SMART dashboard is an online geo-targeted search and analytics tool, including an automatic data processing procedure to help researchers to 1) search tweets in different cities; 2) filter noise (such as removing redundant retweets and using machine learning methods to improve precision); 3) analyze social media data from a spatiotemporal perspective, and 4) visualize social media data in various ways (such as weekly and monthly trends, top URLs, top retweets, top mentions, or top hashtags). By monitoring social messages in geo-targeted cities, we hope that SMART dashboard can assist researchers investigate and monitor various topics, such as flu outbreaks, drug abuse, and Ebola epidemics at the municipal level.
Contributions of the ventromedial prefrontal cortex to goal-directed action selection.

In this article, it will be argued that one of the key contributions of the ventromedial prefrontal cortex (vmPFC) to goal-directed action selection lies both in retrieving the value of goals that are the putative outcomes of the decision process and in establishing a relative preference ranking for these goals by taking into account the value of each of the different goals under consideration in a given decision-making scenario. These goal-value signals are then suggested to be used as an input into the on-line computation of action values mediated by brain regions outside of the vmPFC, such as parts of the parietal cortex, supplementary motor cortex, and dorsal striatum. Collectively, these areas can be considered to be constituent elements of a multistage decision process whereby the values of different goals must first be represented and ranked before the value of different courses of action available for the pursuit of those goals can be computed.
The ND2DB Attack: Database Content Extraction Using Timing Attacks on the Indexing Algorithms

In this paper we present a new attack technique that allows extraction of selected database content relying merely on the attacker’s ability to perform database transactions (INSERTs or UPDATEs) that are usually available to any anonymous database user. Our attack technique uses a side-channel timing attack in the realm of database indexing algorithms and data structures. We prove that by exploiting the inherent characteristics of the most commonly used indexing data structures and algorithms in today’s commercial database management systems it is possible to extract privacy-sensitive data from a database. In particular we prove, both in theory and practice that it is feasible to do so if the B-tree data structure is used and the attacker is able to insert records with chosen data that is used as the search key of one of the table’s indexes. We present experimental results of a successful attack implementation against MySQL and provide conclusions and ideas for further research.
La reconstruction du nid et les coordinations interindividuelles chezBellicositermes natalensis etCubitermes sp. la théorie de la stigmergie: Essai d'interprétation du comportement des termites constructeurs

~.ECONSTRUCTION PAR LES Cubi termes sp . . . . . . . . . . . . . . . . . . . . . . . . . . . . 43 I . La r e c o n s t r u c t i o n p r o p r e m e n t dire, p. 43. 2. Le c o m p o r t e m e n t en fonc t ion du groupe , p. 46. 3. Les condui tes individuel les , p. 49. 4. Les ac t iv i t4s au t r e s que la r econs t ruc t i on , p. 51. I I [ .  PAR Bell icosi termes natalensis .  L a phase d ' incoordinat ion, p. 57. A. La s i tua t ion s t i m u l a n t e , p. 57 ; B. Les act iv i t~s incoordonn~es , p. 59 ; C. La local isa t ion des d~p6ts de t e r re , p. 60. 2. L a phase de coordinat ion, p. 61. A. La densi t6 cr i t ique des bou le t t e s de te r re et l ' o r i en ta t ion du c o m p o r t e m e n t , p. 61 ; B. La s t i g m e r g i e et les s t i m u l a t i o n s s imul tan6es , p. 62 ; C. N a t u r e des s t imu l i a g i s s a n t s u r les c o n s t r u c t e u r s : les odeurs fo rmes , p. 66 ; D. La t endance l ' un i t4 , p. 67 ; E, R61e de la m~moi re d a n s ia cons t ruc t ion , p. 67 ; F. Essa i de s y n t h ~ s e , p. 68. V. COMPORTEMENT DES TERMITES COMPARE A CELUI D'ANIMAUX SOLITAIRES. 73 VI .
The Adaptive Markets Hypothesis Market efficiency from an evolutionary perspective

30TH ANNIVERSARY ISSUE 2004  The 30th anniversary of The Journal of Portfolio Management is a milestone in the rich intellectual history of modern finance, firmly establishing the relevance of quantitative models and scientific inquiry in the practice of financial management. One of the most enduring ideas from this intellectual history is the Efficient Markets Hypothesis (EMH), a deceptively simple notion that has become a lightning rod for its disciples and the proponents of behavioral economics and finance. In its purest form, the EMH obviates active portfolio management, calling into question the very motivation for portfolio research. It is only fitting that we revisit this groundbreaking idea after three very successful decades of this Journal. In this article, I review the current state of the controversy surrounding the EMH and propose a new perspective that reconciles the two opposing schools of thought. The proposed reconciliation, which I call the Adaptive Markets Hypothesis (AMH), is based on an evolutionary approach to economic interactions, as well as some recent research in the cognitive neurosciences that has been transforming and revitalizing the intersection of psychology and economics. Although some of these ideas have not yet been fully articulated within a rigorous quantitative framework, long time students of the EMH and seasoned practitioners will no doubt recognize immediately the possibilities generated by this new perspective. Only time will tell whether its potential will be fulfilled. I begin with a brief review of the classic version of the EMH, and then summarize the most significant criticisms leveled against it by psychologists and behavioral economists. I argue that the sources of this controversy can ANDREW W. LO is Harris & Harris Group Professor at the MIT Sloan School of Management, and chief scientific officer at AlphaSimplex Group, LLC, in Cambridge, MA. The Adaptive Markets Hypothesis
Rethinking Privacy Decisions: Pre-Existing Attitudes, Pre-Existing Emotional States, and a Situational Privacy Calculus

As a potential explanation to measured inconsistencies between stated privacy concerns and actual disclosing behavior, denoted as the „privacy paradox“, scholars have proposed a systematic distinction between situational privacy considerations and pre-existing, superordinate factors that shape the decisive situation without being directly connected to the situation itself. Deploying an experimental approach, we explored the dynamics of two types of such pre-existing factors, namely (1) pre-existing attitudes (such as general privacy concerns and general institutional trust) and (2) pre-existing emotional states (such as an individual’s current mood) in shaping situation-specific risk and benefit considerations (i.e., a situational privacy calculus). Compared to a negative emotional state, individuals in a positive emotional state were found to perceive lowered situation-specific privacy risks, even if the sources of this state were unrelated to the decisive situation at hand. Moreover, results indicated that pre-existing attitudes may be partially or even fully overridden by situational risk and benefit considerations. Adopting a differentiated view on privacy decision-making, these findings imply that the privacy paradox could be driven by a gap between pre-existing cognitive and affective factors on the one side, and situation-specific considerations and decisions on the other. Implications for researchers and practitioners are discussed.
MicroRNA Expression Changes during Interferon-Beta Treatment in the Peripheral Blood of Multiple Sclerosis Patients

MicroRNAs (miRNAs) are small non-coding RNA molecules acting as post-transcriptional regulators of gene expression. They are involved in many biological processes, and their dysregulation is implicated in various diseases, including multiple sclerosis (MS). Interferon-beta (IFN-beta) is widely used as a first-line immunomodulatory treatment of MS patients. Here, we present the first longitudinal study on the miRNA expression changes in response to IFN-beta therapy. Peripheral blood mononuclear cells (PBMC) were obtained before treatment initiation as well as after two days, four days, and one month, from patients with clinically isolated syndrome (CIS) and patients with relapsing-remitting MS (RRMS). We measured the expression of 651 mature miRNAs and about 19,000 mRNAs in parallel using real-time PCR arrays and Affymetrix microarrays. We observed that the up-regulation of IFN-beta-responsive genes is accompanied by a down-regulation of several miRNAs, including members of the mir-29 family. These differentially expressed miRNAs were found to be associated with apoptotic processes and IFN feedback loops. A network of miRNA-mRNA target interactions was constructed by integrating the information from different databases. Our results suggest that miRNA-mediated regulation plays an important role in the mechanisms of action of IFN-beta, not only in the treatment of MS but also in normal immune responses. miRNA expression levels in the blood may serve as a biomarker of the biological effects of IFN-beta therapy that may predict individual disease activity and progression.
Role of the gut microbiota in immunity and inflammatory disease

The mammalian intestine is colonized by trillions of microorganisms, most of which are bacteria that have co-evolved with the host in a symbiotic relationship. The collection of microbial populations that reside on and in the host is commonly referred to as the microbiota. A principal function of the microbiota is to protect the intestine against colonization by exogenous pathogens and potentially harmful indigenous microorganisms via several mechanisms, which include direct competition for limited nutrients and the modulation of host immune responses. Conversely, pathogens have developed strategies to promote their replication in the presence of competing microbiota. Breakdown of the normal microbial community increases the risk of pathogen infection, the overgrowth of harmful pathobionts and inflammatory disease. Understanding the interaction of the microbiota with pathogens and the host might provide new insights into the pathogenesis of disease, as well as novel avenues for preventing and treating intestinal and systemic disorders.
Achievements in Space Robotics Expanding the Horizons of Service and Exploration BY

O uter space is an ultimate field for the application of robotics technology. As outer space is a harsh environment with extreme temperatures, vacuum, radiation, gravity, and great distances, human access is very difficult and hazardous and is therefore limited. To assist human activities in space for constructing and maintaining space modules and structures, robotic manipulators have been playing essential roles in orbital operations. Moreover, expanding the horizons of exploration beyond the areas of human access, robots that land and travel on planetary surfaces have been greatly contributing to our knowledge of the solar system. New challenges are expected in the future. This article consists of three parts. In the first part, the achievements of orbital robotics technology in the last decade are reviewed, highlighting the Engineering Test Satellite (ETS-VII) and Orbital Express flight demonstrations. In the second part, some of the selected topics of planetary robotics from the field robotics research point of view are described. Recent achievements in the author’s laboratory are added as an illustrative example. Finally, technological challenges to asteroid robotics are discussed. When designing a robot to explore the surface of an asteroid, microgravity raises an interesting problem of how to stick and move on the surface. Some ideas to address these questions are introduced.  Ideas to assemble space structures by a flying robot in orbit or to conduct servicing missions to existing satellites have been discussed since the 1980s.  1 is an illustration describing the robotic assembling of a space structure, which was published in a National Aeronautics and Space Administration (NASA) report in 1983 . One attractive scenario was to build a space station using teleoperated or autonomous free-flying robots. In reality, the current International Space Station (ISS) has not been constructed by such free-flying robots but by many hours of human extravehicular activities (EVA) with the assistance of Shuttle Remote Manipulator System (SRMS, Canadarm) and Space Station Remote Manipulator System (SSRMS, Canadarm2). Another important scenario for an orbital free-flying space robot is to retrieve and dock with an existing satellite in orbit and then conduct servicing tasks. Such tasks include replacing components, resupplying expendables, refueling propellants and repairing, rescuing, and reorbiting the satellite. These servicing scenarios have been receiving the researcher’s attention because of their potential for flexible operation of satellites, including the extension of mission life and the orbital transfer at the end of life.
Effects of social media usage and social media multitasking on the academic performance of university students

In today's society, social media have become an almost indispensable part of daily life, particularly among university students, who are generally heavy social media users. Social media multitasking has also been increasingly prevalent. Little, however, is known about how social media usage and social media multitasking influence the academic performance of university students. This study examined whether and how these two behaviors predict academic performance among university students. From a sample of 348 undergraduate students at a comprehensive university in Hong Kong, this study found that using social media for academic purposes was not a significant predictor of academic performance as measured by cumulative grade point average, whereas using social media for nonacademic purposes (video gaming in particular) and social media multitasking significantly negatively predicted academic performance.
On-line consistent ranking on e-recruitment: seeking the truth behind a well-formed CV

In this work we present a novel approach for evaluating job applicants in online recruitment systems, using machine learning algorithms to solve the candidate ranking problem and performing semantic matching techniques. An application of our approach is implemented in the form of a prototype system, whose functionality is showcased and evaluated in a real-world recruitment scenario. The proposed system extracts a set of objective criteria from the applicants’ LinkedIn profile, and compares them semantically to the job’s prerequisites. It also infers their personality characteristics using linguistic analysis on their blog posts. Our system was found to perform consistently compared to human recruiters, thus it can be trusted for the automation of applicant ranking and personality mining.