nils-herrmann/clean_scirepeval huggingface dataset:
Genome-wide scan demonstrates significant linkage for male sexual orientation.

Findings from family and twin studies support a genetic contribution to the development of sexual orientation in men. However, previous studies have yielded conflicting evidence for linkage to chromosome Xq28.



We conducted a genome-wide linkage scan on 409 independent pairs of homosexual brothers (908 analyzed individuals in 384 families), by far the largest study of its kind to date.



We identified two regions of linkage: the pericentromeric region on chromosome 8 (maximum two-point LOD = 4.08, maximum multipoint LOD = 2.59), which overlaps with the second strongest region from a previous separate linkage scan of 155 brother pairs; and Xq28 (maximum two-point LOD = 2.99, maximum multipoint LOD = 2.76), which was also implicated in prior research.



Results, especially in the context of past studies, support the existence of genes on pericentromeric chromosome 8 and chromosome Xq28 influencing development of male sexual orientation.
Multi-scale volumes for deep object detection and localization

This study aims to analyze the benefits of improved multiscale reasoning for object detection and localization with deep convolutional neural networks. To that end, an efficient and general object detection framework which operates on scale volumes of a deep feature pyramid is proposed. In contrast to the proposed approach, most current state-of-the-art object detectors operate on a single-scale in training, while testing involves independent evaluation across scales. One benefit of the proposed approach is in better capturing of multi-scale contextual information, resulting in significant gains in both detection performance and localization quality of objects on the PASCAL VOC dataset and a multi-view highway vehicles dataset. The joint detection and localization scale-specific models are shown to especially benefit detection of challenging object categories which exhibit large scale variation as well as detection of small objects.
Interactive Tree Of Life v2: online annotation and display of phylogenetic trees made easy

Interactive Tree Of Life (http://itol.embl.de) is a web-based tool for the display, manipulation and annotation of phylogenetic trees. It is freely available and open to everyone. In addition to classical tree viewer functions, iTOL offers many novel ways of annotating trees with various additional data. Current version introduces numerous new features and greatly expands the number of supported data set types. Trees can be interactively manipulated and edited. A free personal account system is available, providing management and sharing of trees in user defined workspaces and projects. Export to various bitmap and vector graphics formats is supported. Batch access interface is available for programmatic access or inclusion of interactive trees into other web services.
Median Filtering Forensics Based on Convolutional Neural Networks

Median filtering detection has recently drawn much attention in image editing and image anti-forensic techniques. Current image median filtering forensics algorithms mainly extract features manually. To deal with the challenge of detecting median filtering from small-size and compressed image blocks, by taking into account of the properties of median filtering, we propose a median filtering detection method based on convolutional neural networks (CNNs), which can automatically learn and obtain features directly from the image. To our best knowledge, this is the first work of applying CNNs in median filtering image forensics. Unlike conventional CNN models, the first layer of our CNN framework is a filter layer that accepts an image as the input and outputs its median filtering residual (MFR). Then, via alternating convolutional layers and pooling layers to learn hierarchical representations, we obtain multiple features for further classification. We test the proposed method on several experiments. The results show that the proposed method achieves significant performance improvements, especially in the cut-and-paste forgery detection.
Meta-analysis of the heritability of human traits based on fifty years of twin studies

Despite a century of research on complex traits in humans, the relative importance and specific nature of the influences of genes and environment on human traits remain controversial. We report a meta-analysis of twin correlations and reported variance components for 17,804 traits from 2,748 publications including 14,558,903 partly dependent twin pairs, virtually all published twin studies of complex traits. Estimates of heritability cluster strongly within functional domains, and across all traits the reported heritability is 49%. For a majority (69%) of traits, the observed twin correlations are consistent with a simple and parsimonious model where twin resemblance is solely due to additive genetic variation. The data are inconsistent with substantial influences from shared environment or non-additive genetic variation. This study provides the most comprehensive analysis of the causes of individual differences in human traits thus far and will guide future gene-mapping efforts. All the results can be visualized using the MaTCH webtool.
DBMiner: A System for Mining Knowledge in Large Relational Databases

A data mining system, DBMiner, has been developed for interactive mining of multiple-level knowledge in large relational databases. The system implements a wide spectrum of data mining functions, including generalization, characterization, association, classi cation, and prediction. By incorporating several interesting data mining techniques, including attributeoriented induction, statistical analysis, progressive deepening for mining multiple-level knowledge, and meta-rule guided mining, the system provides a userfriendly, interactive data mining environment with good performance.
The Consensus Molecular Subtypes of Colorectal Cancer

Colorectal cancer (CRC) is a frequently lethal disease with heterogeneous outcomes and drug responses. To resolve inconsistencies among the reported gene expression–based CRC classifications and facilitate clinical translation, we formed an international consortium dedicated to large-scale data sharing and analytics across expert groups. We show marked interconnectivity between six independent classification systems coalescing into four consensus molecular subtypes (CMSs) with distinguishing features: CMS1 (microsatellite instability immune, 14%), hypermutated, microsatellite unstable and strong immune activation; CMS2 (canonical, 37%), epithelial, marked WNT and MYC signaling activation; CMS3 (metabolic, 13%), epithelial and evident metabolic dysregulation; and CMS4 (mesenchymal, 23%), prominent transforming growth factor–β activation, stromal invasion and angiogenesis. Samples with mixed features (13%) possibly represent a transition phenotype or intratumoral heterogeneity. We consider the CMS groups the most robust classification system currently available for CRC—with clear biological interpretability—and the basis for future clinical stratification and subtype-based targeted interventions.
The Semantic Web: ESWC 2017 Satellite Events

This work addresses the problem of underwater archaeological surveys from the point of view of knowledge. We propose an approach based on underwater photogrammetry guided by a representation of knowledge used, as structured by ontologies. Survey data feed into to ontologies and photogrammetry in order to produce graphical results. This paper focuses on the use of ontologies during the exploitation of 3D results. JAVA software dedicated to photogram‐ metry and archaeological survey has been mapped onto an OWL formalism. The use of procedural attachment in a dual representation (JAVA OWL) of the involved concepts allows us to access computational facilities directly from OWL. As SWRL The use of rules illustrates very well such ‘double formalism’ as well as the use of computational capabilities of ‘rules logical expression’. We present an application that is able to read the ontology populated with a photo‐ grammetric survey data. Once the ontology is read, it is possible to produce a 3D representation of the individuals and observing graphically the results of logical spatial queries on the ontology. This work is done on a very important underwater archaeological site in Malta named Xlendi, probably the most ancient shipwreck of the central Mediterranean Sea.
Trailer-like leader following trajectory planning

In this paper a trajectory planner for n autonomous vehicles following a common leader is presented, with the planning being accomplished in real time and in a three dimensional setting. The trajectory planner is designed such that n follower vehicles behave as n distinct points of a unique two dimensional trailer attached to a single leader vehicle. We prove that for a wide range of initial conditions the trailer reference frame converges to a unique solution, thus guaranteeing that each follower can plan its trajectory independently from its peers, thereby reducing the need for communications among vehicles. Additionally, convergence to a fixed formation of n+1 vehicles with respect to the trailer reference frame is also guaranteed. Finally, we present bounds on the planned velocity and acceleration, which provide conditions for the feasibility of the planned trajectory. An experimental validation of the planner's behavior is presented with quadrotor vehicles, demonstrating the richness of the planned trajectories.
Survey of Medicinal Plants Used to Treat Malaria by Sidama People of Boricha District, Sidama Zone, South Region of Ethiopia

In Ethiopia, malaria control has been complicated due to resistance of the parasite to the current drugs. Thus, new drugs are required against drug-resistant Plasmodium strains. Historically, many of the present antimalarial drugs were discovered from plants. This study was, therefore, conducted to document antimalarial plants utilized by Sidama people of Boricha District, Sidama Zone, South Region of Ethiopia. An ethnobotanical survey was carried out from September 2011 to February 2012. Data were collected through semistructured interview and field and market observations. Relative frequency of citation (RFC) was calculated and preference ranking exercises were conducted to estimate the importance of the reported medicinal plants in Boricha District. A total of 42 antimalarial plants belonging to 27 families were recorded in the study area. Leaf was the dominant plant part (59.0%) used in the preparation of remedies and oral (97.4%) was the major route of administration. Ajuga integrifolia scored the highest RFC value (0.80). The results of this study revealed the existence of rich knowledge on the use of medicinal plants in the study area to treat malaria. Thus, an attempt should be made to conserve and evaluate the claimed antimalarial medicinal plants with priority given to those that scored the highest RFC values.
A human study of patch maintainability

Identifying and fixing defects is a crucial and expensive part of the software lifecycle. Measuring the quality of bug-fixing patches is a difficult task that affects both functional correctness and the future maintainability of the code base. Recent research interest in automatic patch generation makes a systematic understanding of patch maintainability and understandability even more critical. 
 We present a human study involving over 150 participants, 32 real-world defects, and 40 distinct patches. In the study, humans perform tasks that demonstrate their understanding of the control flow, state, and maintainability aspects of code patches. As a baseline we use both human-written patches that were later reverted and also patches that have stood the test of time to ground our results. To address any potential lack of readability with machine-generated patches, we propose a system wherein such patches are augmented with synthesized, human-readable documentation that summarizes their effects and context. Our results show that machine-generated patches are slightly less maintainable than human-written ones, but that trend reverses when machine patches are augmented with our synthesized documentation. Finally, we examine the relationship between code features (such as the ratio of variable uses to assignments) with participants' abilities to complete the study tasks and thus explain a portion of the broad concept of patch quality.
The role of human ventral visual cortex in motion perception

Visual motion perception is fundamental to many aspects of visual perception. Visual motion perception has long been associated with the dorsal (parietal) pathway and the involvement of the ventral 'form' (temporal) visual pathway has not been considered critical for normal motion perception. Here, we evaluated this view by examining whether circumscribed damage to ventral visual cortex impaired motion perception. The perception of motion in basic, non-form tasks (motion coherence and motion detection) and complex structure-from-motion, for a wide range of motion speeds, all centrally displayed, was assessed in five patients with a circumscribed lesion to either the right or left ventral visual pathway. Patients with a right, but not with a left, ventral visual lesion displayed widespread impairments in central motion perception even for non-form motion, for both slow and for fast speeds, and this held true independent of the integrity of areas MT/V5, V3A or parietal regions. In contrast with the traditional view in which only the dorsal visual stream is critical for motion perception, these novel findings implicate a more distributed circuit in which the integrity of the right ventral visual pathway is also necessary even for the perception of non-form motion.
Evaluation of skyline algorithms in PostgreSQL

In this paper, we present our work on evaluating the skyline algorithms BNL, SFS, and a variant of LESS in PostgreSQL. It is well known that the performance of skyline queries is sensitive to a number of parameters. From extensive experiments on skyline implementations we have discovered several rules, which are remarkably simple and useful, but hard to obtain from theoretical investigation. Our findings are beneficial for developing heuristics for the skyline query optimization, and in the meantime, provide some insight for a deeper understanding of the skyline query characteristics.
A Survey of Coverage Based Testing Tools

Test coverage is sometimes used as a way to measure how thoroughly software is tested. Coverage is used by software developers and sometimes by vendors to indicate their confidence in the readiness of their software. This survey studies and compares 17 coverage-based testing tools focusing on, but not restricted to coverage measurement. We also survey additional features, including program prioritization for testing, assistance in debugging, automatic generation of test cases, and customization of test reports. Such features make tools more useful and practical, especially for large-scale, real-life commercial software applications. Our initial motivations were both to understand the available test coverage tools and to compare them to a tool that we have developed, called eXVantage1 (a tool suite that includes code coverage testing, debugging, performance profiling, and reporting). Our study shows that each tool has its unique features tailored to its application domains. Therefore this study can be used to pick the right coverage testing tools depending on various requirements.
HDR image construction from multi-exposed stereo LDR images

In this paper, we present an algorithm that generates high dynamic range (HDR) images from multi-exposed low dynamic range (LDR) stereo images. The vast majority of cameras in the market only capture a limited dynamic range of a scene. Our algorithm first computes the disparity map between the stereo images. The disparity map is used to compute the camera response function which in turn results in the scene radiance maps. A refinement step for the disparity map is then applied to eliminate edge artifacts in the final HDR image. Existing methods generate HDR images of good quality for still or slow motion scenes, but give defects when the motion is fast. Our algorithm can deal with images taken during fast motion scenes and tolerate saturation and radiometric changes better than other stereo matching algorithms.
Round-Based Public Transit Routing

We study the problem of computing all Pareto-optimal journeys in a dynamic public transit network for two criteria: arrival time and number of transfers. Existing algorithms consider this as a graph problem, and solve it using variants of Dijkstra’s algorithm. Unfortunately, this leads to either high query times or suboptimal solutions. We take a different approach. We introduce RAPTOR, our novel round-based public transit router. Unlike previous algorithms, it is not Dijkstrabased, looks at each route (such as a bus line) in the network at most once per round, and can be made even faster with simple pruning rules and parallelization using multiple cores. Because it does not rely on preprocessing, RAPTOR works in fully dynamic scenarios. Moreover, it can be easily extended to handle flexible departure times or arbitrary additional criteria, such as fare zones. When run on London’s complex public transportation network, RAPTOR computes all Paretooptimal journeys between two random locations an order of magnitude faster than previous approaches, which easily enables interactive applications.
QuickCheck: a lightweight tool for random testing of Haskell programs

Quick Check is a tool which aids the Haskell programmer in formulating and testing properties of programs. Properties are described as Haskell functions, and can be automatically tested on random input, but it is also possible to define custom test data generators. We present a number of case studies, in which the tool was successfully used, and also point out some pitfalls to avoid. Random testing is especially suitable for functional programs because properties can be stated at a fine grain. When a function is built from separately tested components, then random testing suffices to obtain good coverage of the definition under test.
Modeling and control of a saucer type Coandä effect UAV

The concept of utilizing the Coandia effect to produce stable flight in saucer type Aerial Vehicles (AV) was first conceived in 1935 by Henri Marie Coanda. Coanda's proposed AV design remained a relatively unexplored curiosity for decades until the recent surge in popularity of small Unmanned Aerial Vehicles (UAV) revitalized interest in the topic. Coandä effect UAV offer some distinct advantages over standard multirotors due to their robust frame design, which offers a unique mix of crash resistance, flight safety, and flight performance making them ideal for Human-Robot Interaction (HRI) applications. While much work has been performed in the area of characterization for Coanda surfaces and improved mechanical design of saucer type UAV, little work has been found in the literature discussing stability and attitude control of the platform. This work seeks to contribute to the study of these drones by introducing an approximate servo mapping for desired control effort of a particular prototype, and an experimental control law implemented within the framework of a modified commercially available flight stack.
Blind Channel Equalization Using Variational Autoencoders

A new maximum likelihood estimation approach for blind channel equalization, using variational autoencoders (VAEs), is introduced. Significant and consistent improvements in the error rate of the reconstructed symbols, compared to constant modulus equalizers, are demonstrated. In fact, for the channels that were examined, the performance of the new VAE blind channel equalizer was close to the performance of a nonblind adaptive linear minimum mean square error equalizer. The new equalization method enables a significantly lower latency channel acquisition compared to the constant modulus algorithm (CMA). The VAE uses a convolutional neural network with two layers and a very small number of free parameters. Although the computational complexity of the new equalizer is higher compared to CMA, it is still reasonable, and the number of free parameters to estimate is small.
Stochastic flash ADC considering comparator metastability implemented in FPGA

This paper deals with the implementation of a stochastic flash ADC with the presence of comparator metastability, in a field-programmable gate array. Stochastic flash ADC exploits comparator threshold variation and can be implemented with simple and highly digital structures. We show that such designs is also prone to comparator metastability, therefore we propose an averaging scheme as a simple means to handle the situation. Experimental results from a prototype system based on an FPGA is given which shows the effectiveness of the averaging technique, resulting in a maximum measured SNDR of 22.24 dB with a sampling rate of 98 kHz.
Adaptive Morphology: A Design Principle for Multimodal and Multifunctional Robots

Morphology plays an important role in behavioral and locomotion strategies of living and artificial systems. There is biological evidence that adaptive morphological changes can not only extend dynamic performances by reducing tradeoffs during locomotion but also provide new functionalities. In this article, we show that adaptive morphology is an emerging design principle in robotics that benefits from a new generation of soft, variable-stiffness, and functional materials and structures. When moving within a given environment or when transitioning between different substrates, adaptive morphology allows accommodation of opposing dynamic requirements (e.g., maneuverability, stability, efficiency, and speed). Adaptive morphology is also a viable solution to endow robots with additional functionalities, such as transportability, protection, and variable gearing. We identify important research and technological questions, such as variable-stiffness structures, in silico design tools, and adaptive control systems to fully leverage adaptive morphology in robotic systems.
Neural Arithmetic Expression Calculator

This paper presents a pure neural solver for arithmetic expression calculation (AEC) problem. Previous work utilizes the powerful capabilities of deep neural networks and attempts to build an end-to-end model to solve this problem. However, most of these methods can only deal with the additive operations. It is still a challenging problem to solve the complex expression calculation problem, which includes the adding, subtracting, multiplying, dividing and bracketing operations. In this work, we regard the arithmetic expression calculation as a hierarchical reinforcement learning problem. An arithmetic operation is decomposed into a series of sub-tasks, and each sub-task is dealt with by a skill module. The skill module could be a basic module performing elementary operations, or interactive module performing complex operations by invoking other skill models. With curriculum learning, our model can deal with a complex arithmetic expression calculation with the deep hierarchical structure of skill models. Experiments show that our model significantly outperforms the previous models for arithmetic expression calculation.
Multi-channel BiLSTM-CRF Model for Emerging Named Entity Recognition in Social Media

In this paper, we present our multichannel neural architecture for recognizing emerging named entity in social media messages, which we applied in the Novel and Emerging Named Entity Recognition shared task at the EMNLP 2017 Workshop on Noisy User-generated Text (W-NUT). We propose a novel approach, which incorporates comprehensive word representations with multichannel information and Conditional Random Fields (CRF) into a traditional Bidirectional Long Short-Term Memory (BiLSTM) neural network without using any additional hand-crafted features such as gazetteers. In comparison with other systems participating in the shared task, our system won the 3rd place in terms of the average of two evaluation metrics.