nils-herrmann/clean_scirepeval huggingface dataset:
Haplogroup Prediction from Y-STR Values Using an Allele-Frequency Approach

A new approach to predicting the Y-chromosome haplogroup from a set of Y-STR marker values is presented and compared to other approaches. The method has been implemented in an Excel-based program, where an arbitrary number of STR markers may be input and a “goodness of fit” score for 10 haplogroups (E3a, E3b, G, I1a, I1b, I1c, J2, N3, R1a, and R1b) is returned. This method has been applied to 101 R1b haplotypes and 50 I1a haplotypes (all having 37 STR markers available), and the distribution of results is presented. In the case of I1a, the results are compared with the predictions of another method.
SympGraph: a framework for mining clinical notes through symptom relation graphs

As an integral part of Electronic Health Records (EHRs), clinical notes pose special challenges for analyzing EHRs due to their unstructured nature. In this paper, we present a general mining framework SympGraph for modeling and analyzing symptom relationships in clinical notes.
 A SympGraph has symptoms as nodes and co-occurrence relations between symptoms as edges, and can be constructed automatically through extracting symptoms over sequences of clinical notes for a large number of patients. We present an important clinical application of SympGraph: symptom expansion, which can expand a given set of symptoms to other related symptoms by analyzing the underlying SympGraph structure. We further propose a matrix update algorithm which provides a significant computational saving for dynamic updates to the graph. Comprehensive evaluation on 1 million longitudinal clinical notes over 13K patients shows that static symptom expansion can successfully expand a set of known symptoms to a disease with high agreement rate with physician input (average precision 0.46), a 31% improvement over baseline co-occurrence based methods. The experimental results also show that the expanded symptoms can serve as useful features for improving AUC measure for disease diagnosis prediction, thus confirming the potential clinical value of our work.
A Unified Approach to the Change of Resolution: Space and Gray-Level

Multiple resolution analysis of images is a current trend in computer vision. In most cases, only spatial resolution has been considered. However, image resolution has an additional aspect: gray level, or color, resolution. Color resolution has traditionally been considered in the area of computer graphics. By defining a suitable measure for the comparison of images, changes in resolution can be treated with the same tools as changes in color resolution. A gray tone image, for example, can be compared to a halftone image having only two colors (black and white), but of higher spatial resolution. An important application can be in pyramids, one of the most commonly used multiple (spatial) resolution schemes, where this approach provides a tool to change the color resolution as well. Increasing color resolution while reducing spatial resolution to retain more image details and prevent aliasing is an example of the possibility to find optimal combinations of resolution reduction, spatial and color, to best fit an application.
Optimizing and Visualizing Deep Learning for Benign/Malignant Classification in Breast Tumors

Breast cancer has the highest incidence and second highest mortality rate for women in the US. Our study aims to utilize deep learning for benign/malignant classification of mammogram tumors using a subset of cases from the Digital Database of Screening Mammography (DDSM). Though it was a small dataset from the view of Deep Learning (∼ 1000 patients), we show that currently state of the art architectures of deep learning can find a robust signal, even when trained from scratch. Using convolutional neural networks (CNNs), we are able to achieve an accuracy of 85% and an ROC AUC of 0.91, while leading hand-crafted feature based methods are only able to achieve an accuracy of 71%. We investigate an amalgamation of architectures to show that our best result is reached with an ensemble of the lightweight GoogLe Nets tasked with interpreting both the coronal caudal view and the mediolateral oblique view, simply averaging the probability scores of both views to make the final prediction. In addition, we have created a novel method to visualize what features the neural network detects for the benign/malignant classification, and have correlated those features with well known radiological features, such as spiculation. Our algorithm significantly improves existing classification methods for mammography lesions and identifies features that correlate with established clinical markers.
Trust Based Recommender System for Semantic Web

This paper proposes the design of a recommender system that uses knowledge stored in the form of ontologies. The interactions amongst the peer agents for generating recommendations are based on the trust network that exists between them. Recommendations about a product given by peer agents are in the form of Intuitionistic Fuzzy Sets specified using degree of membership, non membership and uncertainty. In literature, the recom-mender systems use databases to generate recommendations. The presented design uses ontologies, a knowledge representation technique for creating annotated content for Semantic Web. Seeing the potential and popularity of ontologies among researchers , we believe that ontologies will be build and maintained in numerous knowledge domains for the Semantic Web and future applications. The presented recommender system uses temporal on-tologies that absorb the effect of changes in the on-tologies due to the dynamic nature of domains, in addition to the benefits of ontologies. A case study of tourism recommender system is chosen to generate the recommendations for the selection of destination , travel agents and the flight schedule. A comparison of the generated recommendations with the manual recommendations by peers establishes the validity of the presented recommender system.
3D face landmark labelling

Most 3D face processing systems require feature detection and localisation, for example to crop, register, analyse or recognise faces. The three features often used in the literature are the tip of the nose, and the two inner corner of the eyes. Failure to localise these landmarks can cause the system to fail and they become very difficult to detect under large pose variations or when occlusion is present. In this paper, we present a proof-of-concept for a face labelling system capable of overcoming this problem, as a larger number of landmarks are employed. A set of points containing hand-placed landmarks is used as input data. The aim here is to retrieve the landmark's labels when some part of the face is missing. By using graph matching techniques to reduce the number of candidates, and translation and unit-quaternion clustering to determine a final correspondence, we evaluate the accuracy at which landmarks can be retrieved under changes in expression, orientation and in the presence of occlusions.
Highly stretchable and tough hydrogels

Hydrogels are used as scaffolds for tissue engineering, vehicles for drug delivery, actuators for optics and fluidics, and model extracellular matrices for biological studies. The scope of hydrogel applications, however, is often severely limited by their mechanical behaviour. Most hydrogels do not exhibit high stretchability; for example, an alginate hydrogel ruptures when stretched to about 1.2 times its original length. Some synthetic elastic hydrogels have achieved stretches in the range 10–20, but these values are markedly reduced in samples containing notches. Most hydrogels are brittle, with fracture energies of about 10 J m−2 (ref. 8), as compared with ∼1,000 J m−2 for cartilage and ∼10,000 J m−2 for natural rubbers. Intense efforts are devoted to synthesizing hydrogels with improved mechanical properties; certain synthetic gels have reached fracture energies of 100–1,000 J m−2 (refs 11, 14, 17). Here we report the synthesis of hydrogels from polymers forming ionically and covalently crosslinked networks. Although such gels contain ∼90% water, they can be stretched beyond 20 times their initial length, and have fracture energies of ∼9,000 J m−2. Even for samples containing notches, a stretch of 17 is demonstrated. We attribute the gels’ toughness to the synergy of two mechanisms: crack bridging by the network of covalent crosslinks, and hysteresis by unzipping the network of ionic crosslinks. Furthermore, the network of covalent crosslinks preserves the memory of the initial state, so that much of the large deformation is removed on unloading. The unzipped ionic crosslinks cause internal damage, which heals by re-zipping. These gels may serve as model systems to explore mechanisms of deformation and energy dissipation, and expand the scope of hydrogel applications.
A constant approximation algorithm for the one-warehouse multi-retailer problem

Deterministic inventory theory provides streamlined optimization models that attempt to capture tradeoffs in managing the flow of goods through a supply chain. We will consider a well-studied inventory model, called the one-warehouse multi-retailer problem (OWMR). and give the first approximation algorithm with constant performance guarantee; more specifically, we give a 2.398-approximation algorithm. Our results are based on an LP-rounding approach, and hence not only provide good algorithmic results, but show strong integrality gaps for these linear programs. Furthermore, we extend this result to obtain a constant performance guarantee for a capacitated variant of this model.
The Numerics of GANs

In this paper, we analyze the numerics of common algorithms for training Generative Adversarial Networks (GANs). Using the formalism of smooth two-player games we analyze the associated gradient vector field of GAN training objectives. Our findings suggest that the convergence of current algorithms suffers due to two factors: i) presence of eigenvalues of the Jacobian of the gradient vector field with zero real-part, and ii) eigenvalues with big imaginary part. Using these findings, we design a new algorithm that overcomes some of these limitations and has better convergence properties. Experimentally, we demonstrate its superiority on training common GAN architectures and show convergence on GAN architectures that are known to be notoriously hard to train.
Differentiable Parameterization of Catmull-Clark Subdivision Surfaces

Subdivision-based representations are recognized as important tools for the generation of high-quality surfaces for Computer Graphics. In this paper we describe two parameterizations of Catmull-Clark subdivision surfaces that allow a variety of algorithms designed for other types of parametric surfaces (i.e., B-splines) to be directly applied to subdivision surfaces. In contrast with the natural parameterization of subdivision surfaces characterized by diverging first order derivatives around extraordinary vertices of valence higher than four, the derivatives associated with our proposed methods are defined everywhere on the surface. This is especially important for Computer-Aided Design (CAD) applications that seek to address the limitations of NURBS-based representations through the more flexible subdivision framework.
The Gambler's Ruin Problem, Genetic Algorithms, and the Sizing of Populations

This paper presents a model to predict the convergence quality of genetic algorithms based on the size of the population. The model is based on an analogy between selection in GAs and one-dimensional random walks. Using the solution to a classic random walk problemthe gambler's ruinthe model naturally incorporates previous knowledge about the initial supply of building blocks (BBs) and correct selection of the best BB over its competitors. The result is an equation that relates the size of the population with the desired quality of the solution, as well as the problem size and difficulty. The accuracy of the model is verified with experiments using additively decomposable functions of varying difficulty. The paper demonstrates how to adjust the model to account for noise present in the fitness evaluation and for different tournament sizes.
SOCIAL PSYCHOLOGY OF INTERGROUP RELATIONS

INDIVIDUAL PROCESSES IN INTERGROUP BEHAVIOR ................................ 3 From Individual to Group Impressions ...................................................................... 3 GROUP MEMBERSHIP AND INTERGROUP BEHAVIOR .................................. 7 The Scope and Range of Ethnocentrism .................................................................... 8 The Development of Ethnocentrism .......................................................................... 9 Intergroup Conflict and Competition ........................................................................ 12 Interpersonal a dintergroup behavior ........................................................................ 13 Intergroup conflict and group cohesion ........................................................................ 15 Power and status in intergroup behavior ...................................................................... 16 Social Categorization and Intergroup Behavior ........................................................ 20 Social categorization: cognitions, values, and groups ...................................................... 20 Social categorization andintergroup discrimination ...................................................... 23 Social identity and social comparison .......................................................................... 24 THE REDUCTION OF INTERGROUP DISCRIMINATION ................................ 27 Intergroup Cooperation and Superordinate Goals " 28 Intergroup Contact. ..................................................................................................... 28 Multigroup Membership and "lndividualizat~’on" of the Outgroup .......................... 29 SUMMARY .................................................................................................................... 30
Authentication Using Graphical Passwords: Basic Results

Access to computer systems is most often based on the use of alphanumeric passwords. However, users have difficulty remembering a password that is long and random-appearing. Instead, they create short, simple, and insecure passwords. Graphical passwords have been designed to try to make passwords more memorable and easier for people to use and, therefore, more secure. Using a graphical password, users click on images rather than type alphanumeric characters. We have designed a new and more secure graphical password system, called PassPoints. In this paper we describe the PassPoints system, its security characteristics, and the empirical study we carried out comparing PassPoints to alphanumeric passwords. In the empirical study participants learned either an alphanumeric or graphical password and subsequently carried out three longitudinal trials to input their passwords over a period of five weeks. The results show that the graphical group took longer and made more errors in learning the password, but that the difference was largely a consequence of just a few graphical participants who had difficulty learning to use graphical passwords. In the longitudinal trials the two groups performed similarly on memory of their password, but the graphical group took more time to input a password.
Data-Driven Intelligent Transportation Systems: A Survey

For the last two decades, intelligent transportation systems (ITS) have emerged as an efficient way of improving the performance of transportation systems, enhancing travel security, and providing more choices to travelers. A significant change in ITS in recent years is that much more data are collected from a variety of sources and can be processed into various forms for different stakeholders. The availability of a large amount of data can potentially lead to a revolution in ITS development, changing an ITS from a conventional technology-driven system into a more powerful multifunctional data-driven intelligent transportation system (D2ITS) : a system that is vision, multisource, and learning algorithm driven to optimize its performance. Furthermore, D2ITS is trending to become a privacy-aware people-centric more intelligent system. In this paper, we provide a survey on the development of D2ITS, discussing the functionality of its key components and some deployment issues associated with D2ITS Future research directions for the development of D2ITS is also presented.
System-level fault-tolerance in large-scale parallel machines with buffered coscheduling

As the number of processors for multiteraflop systems grows to tens of thousands, with proposed petaflops systems likely to contain hundreds of thousands of processors, the assumption of fully reliable hardware has been abandoned. Although the mean time between failures for the individual components can be very high, the large total component count will inevitably lead to frequent failures. It is therefore of paramount importance to develop new software solutions to deal with the unavoidable reality of hardware faults. We will first describe the nature of the failures of current large-scale machines, and extrapolate these results to future machines. Based on this preliminary analysis we will present a new technology that we are currently developing, buffered coscheduling, which seeks to implement fault tolerance at the operating system level. Major design goals include dynamic reallocation of resources to allow continuing execution in the presence of hardware failures, very high scalability, high efficiency (low overhead), and transparency - requiring no changes to user applications. Preliminary results show that this is attainable with current hardware.
Soft pneumatic actuator with adjustable stiffness layers for Multi-DoF Actuation

The soft pneumatic actuators (SPAs) are a solution toward the highly customizable and light actuators with the versatility of actuation modes, and an inherent compliance. Such flexibility allows SPAs to be considered as alternative actuators for wearable rehabilitative devices and search and rescue robots. The actuator material and air-chamber design dictate the actuator's mechanical performance. Therefore, each actuator design with a single pressure source produces a highly customized motion but only a single degree of freedom (DoF). We present a novel design and fabrication method for a SPA with different modes of actuation using integrated adjustable stiffness layers (ASLs). Unlike the most SPA designs where one independent chamber is needed for each mode of actuation, here we have a single chamber that drives three different modes of actuation by activating different combinations of ASLs. Adapting customized micro heaters and thermistors for modulating the temperature and stiffness of ASLs, we considerably broaden the work space of the SPA actuator. Here, a thorough characterization of the materials and the modeling of the actuator are presented. We propose a design methodology for developing application specific actuators with multi-DoFs that are light and compact.
Prevalence and molecular identification of the sinus worm Skrjabingylus petrowi (Nematoda: Metastrongyloidea) from Martes spp. in Germany

The nematodes of the genus Skrjabingylus (family Metastrongylidae) can parasitise the nasal and frontal sinus cavities of different carnivore species. Until recently, Skrjabingylus petrowi Bageanov & Petrov, 1941, has mainly been described in pine martens (Martes martes Linnaeus, 1758) and sables (Martes zibellina Linnaeus, 1758) sampled in the European part of the former Soviet Union. Newer finds in the stone marten (Martes foina Erxleben, 1777) and from different parts of Europe suggest, however, that the species might have a broader host-species range than previously assumed and be geographically more widespread as well. Since most S. petrowi records have resulted from chance discoveries rather than systematic surveys, very little is known about the prevalence of S. petrowi in marten populations. Here, we report results of a 20-year extensive survey of fresh marten skulls, where we tested 1.059 marten carcasses originating from 248 localities in Germany for the presence of S. petrowi. We identified an infestation in only four M. martes individuals and one M. foina, despite using a reliable identification method. Based on the spicule lengths of the male nematodes, the parasites were identified as S. petrowi and genetic barcoding confirmed the identification of the samples. In a phylogenetic analysis, S. petrowi and Skrjabingylus nasicola (Leuckart, 1842), formed a sister clade to all the other members of the family Metastrongylidae. The low prevalence of S. petrowi is possibly due to its parasitising in the two marten species that are either not very common (M. martes) or predominantly live in urban habitat (M. foina).
Semantic Role Labeling of Emotions in Tweets

Past work on emotion processing has focused solely on detecting emotions, and ignored questions such as ‘who is feeling the emotion (the experiencer)?’ and ‘towards whom is the emotion directed (the stimulus)?’. We automatically compile a large dataset of tweets pertaining to the 2012 US presidential elections, and annotate it not only for emotion but also for the experiencer and the stimulus. We then develop a classifier for detecting emotion that obtains an accuracy of 56.84 on an eight-way classification task. Finally, we show how the stimulus identification task can also be framed as a classification task, obtaining an F-score of 58.30.
On compressing social networks

Motivated by structural properties of the Web graph that support efficient data structures for in memory adjacency queries, we study the extent to which a large network can be compressed. Boldi and Vigna (WWW 2004), showed that Web graphs can be compressed down to three bits of storage per edge; we study the compressibility of social networks where again adjacency queries are a fundamental primitive. To this end, we propose simple combinatorial formulations that encapsulate efficient compressibility of graphs. We show that some of the problems are NP-hard yet admit effective heuristics, some of which can exploit properties of social networks such as link reciprocity. Our extensive experiments show that social networks and the Web graph exhibit vastly different compressibility characteristics.