nils-herrmann/clean_scirepeval huggingface dataset:
Link Prediction of Social Networks Based on Weighted Proximity Measures

Question-Answering Bulletin Boards (QABB), such as Yahoo! Answers and Windows Live QnA, are gaining popularity recently. Communications on QABB connect users, and the overall connections can be regarded as a social network. If the evolution of social networks can be predicted, it is quite useful for encouraging communications among users. This paper describes an improved method for predicting links based on weighted proximity measures of social networks. The method is based on an assumption that proximities between nodes can be estimated better by using both graph proximity measures and the weights of existing links in a social network. In order to show the effectiveness of our method, the data of Yahoo! Chiebukuro (Japanese Yahoo! Answers) are used for our experiments. The results show that our method outperforms previous approaches, especially when target social networks are sufficiently dense.
Longitudinal effects of media violence on aggression and empathy among German adolescents

a r t i c l e i n f o To address the longitudinal relation between adolescents' habitual usage of media violence and aggressive behavior and empathy, N = 1237 seventh and eighth grade high school students in Germany completed measures of violent and nonviolent media usage, aggression, and empathy twice in twelve months. Cross-lagged panel analyses showed significant pathways from T1 media violence usage to higher physical aggression and lower empathy at T2. The reverse paths from T1 aggression or empathy to T2 media violence usage were nonsignificant. The links were similar for boys and girls. No links were found between exposure to nonviolent media and aggression or between violent media and relational aggression. T1 physical aggression moderated the impact of media violence usage, with stronger effects of media violence usage among the low aggression group.  Despite the rapidly growing body of research addressing the potentially harmful effects of exposure to violent media, the evidence currently available is still limited in several ways. First, there is a shortage of longitudinal research examining the associations of media violence usage and aggression over time. Such evidence is crucial for examining hypotheses about the causal directions of observed co-variations of media violence usage and aggression that cannot be established on the basis of cross-sectional research. Second, most of the available longitudinal evidence has focused on aggression as the critical outcome variable, giving comparatively little attention to other potentially harmful effects, such as a decrease in empathy with others in distress. Third, the vast majority of studies available to date were conducted in North America. However, even in the age of globalization, patterns of media violence usage and their cultural contexts may vary considerably, calling for a wider database from different countries to examine the generalizability of results to address each of these aspects. It presents findings from a longitudinal study with a large sample of early adolescents in Germany, relating habitual usage of violence in movies, TV series, and interactive video games to self-reports of physical aggression and empathy over a period of twelve months. The study focused on early adolescence as a developmental period characterized by a confluence of risk factors as a result of biological, psychological, and social changes for a range of adverse outcomes. Regular media violence usage may significantly contribute to the overall risk of aggression as one such negative outcome. Media consumption increases from childhood …
Efficacy of 2LPAPI ® , a Micro-Immunotherapy Drug, in Patients with High-Risk Papillomavirus Genital Infection

Human papillomaviruses (HPVs) are well known for being linked to the development of cervical cancers, most of them being caused by the high-risk (HR) oncogenic genotypes, mainly 16 and 18. The efficacy of 2LPAPI® (Labo’Life), a micro-immunotherapy homeopathic drug, has been evaluated in HR-HPV infected women (n = 18), in a private gynecology practice, by comparing them to an untreated control group (n = 18). Patients were 20 to 45 years old and had cytology with Atypical Squamous Cells of Undetermined Significance (ASCUS) or Low grade Superficial Intra Lesions/ Cervical Intraepithelial Neoplasia Grade I (LSIL/CINI). Patients freely chose to be treated with the drug or not. Those deciding not to take the drug remained untreated and were followed as a control group. The drug was taken at the regimen of one capsule per day during 6 months. HR-HPV and cytology were evaluated at 6 and 12 months. After 12 months, HR-HPV was cleared in 78% of the patients taking the drug versus 44% in those not taking it (p = 0.086). In patients over 25 years, HR-HPV clearance in the treated group was significantly higher (81.3%) than in the control group (20%) (p = 0.004). The difference in the regression of the lesion grades almost reached statistical significance (p = 0.053). This follow-up confirms that the micro-immunotherapy drug 2LPAPI® is a safe and effective therapeutic approach to treat HR-HPV cervical lesions in women over 25 years.
A multicenter prospective study of risk factors and treatment of unusual site thrombosis.

Unusual site deep vein thrombosis (USDVT) is an uncommon form of venous thromboembolism (VTE) with heterogeneity in pathophysiology and clinical features. While the need for anticoagulation treatment is generally accepted, there is little data on optimal USDVT treatment. The TRUST study aimed to characterize the epidemiology, treatment and outcomes of USDVT. From 2008 to 2012, 152 patients were prospectively enrolled at 4 Canadian centers. After baseline, patients were followed at 6, 12 and 24months. There were 97 (64%) cases of splanchnic, 33 (22%) cerebral, 14 (9%) jugular, 6 (4%) ovarian and 2 (1%) renal vein thrombosis. Mean age was 52.9years and 113 (74%) cases were symptomatic. Of 72 (47%) patients tested as part of clinical care, 22 (31%) were diagnosed with new thrombophilia. Of 138 patients evaluated in follow-up, 66 (48%) completed at least 6months of anticoagulation. Estrogen exposure or inflammatory conditions preceding USDVT were commonly associated with treatment discontinuation before 6months, while previous VTE was associated with continuing anticoagulation beyond 6months. During follow-up, there were 22 (16%) deaths (20 from cancer), 4 (3%) cases of recurrent VTE and no fatal bleeding events. Despite half of USDVT patients receiving <6months of anticoagulation, the rate of VTE recurrence was low and anticoagulant treatment appears safe. Thrombophilia testing was common and thrombophilia prevalence was high. Further research is needed to determine the optimal investigation and management of USDVT.
An Experimental Analysis of a Compact Graph Representation

In previous work we described a method for compactly representing graphs with small separators, which makes use of small separators, and presented preliminary experimental results. In this paper we extend the experimental results in several ways, including extensions for dynamic insertion and deletion of edges, a comparison of a variety of coding schemes, and an implementation of two applications using the representation. The results show that the representation is quite effective for a wide variety of real-world graphs, including graphs from finite-element meshes, circuits, street maps, router connectivity, and web links. In addition to significantly reducing the memory requirements, our implementation of the representation is faster than standard representations for queries. The byte codes we introduce lead to DFT times that are a factor of 2.5 faster than our previous results with gamma codes and a factor of between 1 and 9 faster than adjacency lists, while using a factor of between 3 and 6 less space.
Delay-constrained energy-efficient cluster-based multi-hop routing in wireless sensor networks

Energy efficiency is the main objective in the design of a wireless sensor network (WSN). In many applications, sensing data must be transmitted from sources to a sink in a timely manner. This paper describes an investigation of the trade-off between two objectives in WSN design: minimizing energy consumption and minimizing end-to-end delay. We first propose a new distributed clustering approach to determining the best clusterhead for each cluster by considering both energy consumption and end-to-end delay requirements. Next, we propose a new energy-cost function and a new end-to-end delay function for use in an inter-cluster routing algorithm. We present a multi-hop routing algorithm for use in disseminating sensing data from clusterheads to a sink at the minimum energy cost subject to an end-to-end delay constraint. The results of a simulation are consistent with our theoretical analysis results and show that our proposed performs much better than similar protocols in terms of energy consumption and end-to-end delay.
iVoLVER: Interactive Visual Language for Visualization Extraction and Reconstruction

We present the design and implementation of iVoLVER, a tool that allows users to create visualizations without textual programming. iVoLVER is designed to enable flexible acquisition of many types of data (text, colors, shapes, quantities, dates) from multiple source types (bitmap charts, webpages, photographs, SVGs, CSV files) and, within the same canvas, supports transformation of that data through simple widgets to construct interactive animated visuals. Aside from the tool, which is web-based and designed for pen and touch, we contribute the design of the interactive visual language and widgets for extraction, transformation, and representation of data. We demonstrate the flexibility and expressive power of the tool through a set of scenarios, and discuss some of the challenges encountered and how the tool fits within the current infovis tool landscape.
Implementing liquid biopsies into clinical decision making for cancer immunotherapy

During the last decade, novel immunotherapeutic strategies, in particular antibodies directed against immune checkpoint inhibitors, have revolutionized the treatment of different malignancies leading to an improved survival of patients. Identification of immune-related biomarkers for diagnosis, prognosis, monitoring of immune responses and selection of patients for specific cancer immunotherapies is urgently required and therefore areas of intensive research. Easily accessible samples in particular liquid biopsies (body fluids), such as blood, saliva or urine, are preferred for serial tumor biopsies.Although monitoring of immune and tumor responses prior, during and post immunotherapy has led to significant advances of patients' outcome, valid and stable prognostic biomarkers are still missing. This might be due to the limited capacity of the technologies employed, reproducibility of results as well as assay stability and validation of results. Therefore solid approaches to assess immune regulation and modulation as well as to follow up the nature of the tumor in liquid biopsies are urgently required to discover valuable and relevant biomarkers including sample preparation, timing of the collection and the type of liquid samples. This article summarizes our knowledge of the well-known liquid material in a new context as liquid biopsy and focuses on collection and assay requirements for the analysis and the technical developments that allow the implementation of different high-throughput assays to detect alterations at the genetic and immunologic level, which could be used for monitoring treatment efficiency, acquired therapy resistance mechanisms and the prognostic value of the liquid biopsies.
A Strategic Technology Planning Framework: A Case of Taiwan's Semiconductor Foundry Industry

The increasingly important role that technologies play in today's business success is well known. To ensure proper selection and development of the key technologies, a deliberate technology plan is needed. In this paper, a strategic technology planning framework is proposed. A hierarchical decision model and its sensitivity analysis are presented as two major steps of the framework to provide effective technology assessment and to generate technology scenarios. The hierarchical model links an organization's competitive goals and strategies in evaluating the technology alternativespsila overall contributions to business success; the sensitivity analysis helps to forecast and implement possible future changes in the economic environment, industry policies, and organization strategies. With the proposed framework, organizations can start to implement their technology plans synoptically and follow up with incremental adaptations as necessary. A case study on Taiwan's semiconductor foundry industry is presented to demonstrate the model in detail.
Patterns of cetacean vaginal folds yield insights into functionality

Complex foldings of the vaginal wall are unique to some cetaceans and artiodactyls and are of unknown function(s). The patterns of vaginal length and cumulative vaginal fold length were assessed in relation to body length and to each other in a phylogenetic context to derive insights into functionality. The reproductive tracts of 59 female cetaceans (20 species, 6 families) were dissected. Phylogenetically-controlled reduced major axis regressions were used to establish a scaling trend for the female genitalia of cetaceans. An unparalleled level of vaginal diversity within a mammalian order was found. Vaginal folds varied in number and size across species, and vaginal fold length was positively allometric with body length. Vaginal length was not a significant predictor of vaginal fold length. Functional hypotheses regarding the role of vaginal folds and the potential selection pressures that could lead to evolution of these structures are discussed. Vaginal folds may present physical barriers, which obscure the pathway of seawater and/or sperm travelling through the vagina. This study contributes broad insights to the evolution of reproductive morphology and aquatic adaptations and lays the foundation for future functional morphology analyses.
Patch2Vec: Globally Consistent Image Patch Representation

Many image editing applications rely on the analysis of image patches. In this paper, we present a method to analyze patches by embedding them to a vector space, in which the Euclidean distance reflects patch similarity. Inspired by Word2Vec, we term our approach Patch2Vec. However, there is a significant difference between words and patches. Words have a fairly small and well defined dictionary. Image patches, on the other hand, have no such dictionary and the number of different patch types is not well defined. The problem is aggravated by the fact that each patch might contain several objects and textures. Moreover, Patch2Vec should be universal because it must be able to map never-seen-before texture to the vector space. The mapping is learned by analyzing the distribution of all natural patches. We use Convolutional Neural Networks (CNN) to learn Patch2Vec. In particular, we train a CNN on labeled images with a triplet-loss objective function. The trained network encodes a given patch to a 128D vector. Patch2Vec is evaluated visually, qualitatively, and quantitatively. We then use several variants of an interactive single-click image segmentation algorithm to demonstrate the power of our method.
A 6.6kW SiC bidirectional on-board charger

This paper proposes a bidirectional architecture to implement an on-board charger (OBC) including an interleaved totem bridgeless PFC and Mode-Switched (MS) DC-DC converter. In the charging mode with the relay on, the MS DC-DC converter behave as a high efficiency Pulse Frequency Modulation (PFM) LLC converter since the charging mode is the most majority application scenario for the OBC. The PFC output voltage is regulated between 360 V to 400 V feeding into the DC-DC converter, so that 900 V SiC MOSFETs can be used both in the AC-DC and DC-DC stage to realize high frequency and high efficiency owing to the fast switching speed and high voltage rating with low on-resistance. The synchronous rectification (SR) is applied on the secondary side MOSFETs of the LLC converter to further improve the efficiency taking advantage of the bidirectional full-bridge topology. In the discharging mode with the relay off, the MS DC-DC stage is a two-stage converter. The reverse operation of the LLC converter operates as a DC-DC transformer (DCX) at the resonant point, and its output voltage is regulated to the bus voltage by a boost converter because the reverse gain of the LLC converter is lower than 1. A 6.6 kW SiC MS bidirectional OBC of DC-DC stage is built. In the charging mode, the peak efficiency is 96.37% at 6.6 kW with 380 V input voltage and 336 V output voltage. In the discharging mode, the peak efficiency is 96.87% at 3.3 kW with 346 V input voltage and 380 V output voltage.
An Efficient Globally Optimal Algorithm for Asymmetric Point Matching

Although the robust point matching algorithm has been demonstrated to be effective for non-rigid registration, there are several issues with the adopted deterministic annealing optimization technique. First, it is not globally optimal and regularization on the spatial transformation is needed for good matching results. Second, it tends to align the mass centers of two point sets. To address these issues, we propose a globally optimal algorithm for the robust point matching problem in the case that each model point has a counterpart in scene set. By eliminating the transformation variables, we show that the original matching problem is reduced to a concave quadratic assignment problem where the objective function has a low rank Hessian matrix. This facilitates the use of large scale global optimization techniques. We propose a modified normal rectangular branch-and-bound algorithm to solve the resulting problem where multiple rectangles are simultaneously subdivided to increase the chance of shrinking the rectangle containing the global optimal solution. In addition, we present an efficient lower bounding scheme which has a linear assignment formulation and can be efficiently solved. Extensive experiments on synthetic and real datasets demonstrate the proposed algorithm performs favorably against the state-of-the-art methods in terms of robustness to outliers, matching accuracy, and run-time.
Deep variance network: An iterative, improved CNN framework for unbalanced training datasets

Convolutional neural network (CNN) has demonstrated its superior ability to achieve amazing accuracy in computer vision field. Nevertheless, for practical domain-specific image recognition tasks, it still remains difficult to obtain massive high-quality labeled datasets due to the strong requirements for extensive, tedious manual processing. Inspired by the well-known observation that human brain can accurately recognize objects without relying on massive congeneric examples, we propose a novel deep variance network (DVN) to further enhance the generalization ability of CNN in this paper, which could still produce higher recognition accuracy even with unbalanced training datasets than original CNN. The key idea of our DVN is built upon the intrinsic exploitation of inter-class homogeneity and intra-class heterogeneity. Towards such goal, we make the first attempt to incorporate a hierarchical Bayesian model into the powerful CNN framework, which can transfer the joint feature distribution from certain object’s complete training dataset to other object’s incomplete training dataset in an iterative way. In each training cycle, the CNNresulted features are clustered into discrimination-related subspaces to guide the learning and adaptive adjustment of homogeneity and heterogeneity over unbalanced training datasets. In practice, we furnish several state-of-the-art deep networks with our proposed DVN, and conduct extensive experiments and comprehensive evaluations over CIFAR-10, MNIST, and SVHN benchmarks. The experiments have shown that, most of the furnished deep networks can benefit from our DVN, wherein they gain at most 6.9% accuracy improvement over CIFAR-10 benchmark, 52.83% error reduction over MNIST benchmark, and an improvement of 6.2% over SVHN datasets.
FOURIER TRANSFORM SPECTROSCOPY OF THE A3Π−X3Σ− TRANSITION OF OH

The OH ion is an important species in the Interstellar Medium. It has been used to infer the cosmic ray ionization rate and is an important intermediate for generation of more complex astrochemical species. OH observations are typically performed in the sub-millimeter and near-UV ranges, and rely on laboratory spectroscopy to provide transition frequencies. Observations of the A3Π−X3Σ− bands are used to both identify OH and determine the column densities along sight lines.a These A-X observations have relied on previous measurements with a grating spectrometer and photographic plates.b Here, we present data recorded at Kitt Peak using a Fourier transform spectrometer of the A-X band system. This data and other available data are combined to determine new molecular constants for the A and X electronic states. These new data are between one and two orders of magnitude more precise and should be used in support of observations in lieu of the older transition frequencies. We also intend to calculate improved line intensities in support of astronomical observations.
Generative Programming and Active Libraries Extended Abstract

We describe generative programming, an approach to generating customized programming components or systems, and active libraries, which are based on this approach. In contrast to conventional libraries, active libraries may contain metaprograms that implement domain-specific code generation, optimizations, debugging, profiling and testing. Several working examples (Blitz++, GMCL, Xroma) are presented to illustrate the potential of active libraries. We discuss relevant implementation technologies.
Amygdala-frontal connectivity during emotion regulation.

Successful control of affect partly depends on the capacity to modulate negative emotional responses through the use of cognitive strategies (i.e., reappraisal). Recent studies suggest the involvement of frontal cortical regions in the modulation of amygdala reactivity and the mediation of effective emotion regulation. However, within-subject inter-regional connectivity between amygdala and prefrontal cortex in the context of affect regulation is unknown. Here, using psychophysiological interaction analyses of functional magnetic resonance imaging data, we show that activity in specific areas of the frontal cortex (dorsolateral, dorsal medial, anterior cingulate, orbital) covaries with amygdala activity and that this functional connectivity is dependent on the reappraisal task. Moreover, strength of amygdala coupling with orbitofrontal cortex and dorsal medial prefrontal cortex predicts the extent of attenuation of negative affect following reappraisal. These findings highlight the importance of functional connectivity within limbic-frontal circuitry during emotion regulation.