nils-herrmann/clean_scirepeval huggingface dataset:
Measuring the Impact of Diabetes Through Patient Report of Treatment Satisfaction, Productivity and Symptom Experience

Diabetes is a common, debilitating chronic illness with multiple impacts. The impact on treatment satisfaction, productivity impairment and the symptom experience may be among the most important for patient-reported outcomes. This study developed and validated disease-specific, patient-reported measures for these outcomes that address limitations in currently available measures.  Data was collected from the literature, experts and patients and a conceptual model of the patient-reported impact of diabetes was created. Item pools, based on the conceptual model, were then generated. The items were administered to 991 diabetes patients via a web-based survey to perform item reduction, identify relevant factor structures and assess reliability and validity following an a-priori analysis plan.  All validation criteria and hypotheses were met resulting in three new, valid measures: a 21-item Satisfaction Measure (three sub-scales: burden, efficacy and symptoms), a 30-item Symptom Measure and a 14-item Productivity Measure assessing both life and work productivity impairments. This triad of measures captures important components of the multifaceted diabetes patient experience and can be considered as valid, viable options when choosing measures to assess patient-reported outcomes. Addressing these outcomes may assist researchers and clinicians to develop more patient-centered diabetes interventions and care.
Japanese Society for Cancer of the Colon and Rectum (JSCCR) Guidelines 2014 for treatment of colorectal cancer

Colorectal cancer is a major cause of death in Japan, where it accounts for the largest number of deaths from malignant neoplasms among women and the third largest number among men. Many new methods of treatment have been developed during recent decades. The Japanese Society for Cancer of the Colon and Rectum Guidelines 2014 for treatment of colorectal cancer (JSCCR Guidelines 2014) have been prepared as standard treatment strategies for colorectal cancer, to eliminate treatment disparities among institutions, to eliminate unnecessary treatment and insufficient treatment, and to deepen mutual understanding among health-care professionals and patients by making these guidelines available to the general public. These guidelines have been prepared as a result of consensuses reached by the JSCCR Guideline Committee on the basis of careful review of evidence retrieved by literature searches and taking into consideration the medical health insurance system and actual clinical practice in Japan. They can, therefore, be used as a guide for treating colorectal cancer in clinical practice. More specifically, they can be used as a guide to obtaining informed consent from patients and choosing the method of treatment for each patient. As a result of the discussions of the Guideline Committee, controversial issues were selected as clinical questions, and recommendations were made. Each recommendation is accompanied by a classification of the evidence and a classification of recommendation categories, on the basis of consensus reached by Guideline Committee members. Here we present the English version of the JSCCR Guidelines 2014.
Learning Linear Transformations for Fast Arbitrary Style Transfer

Given a random pair of images, an arbitrary style transfer method extracts the feel from the reference image to synthesize an output based on the look of the other content image. Recent arbitrary style transfer methods transfer second order statistics from reference image onto content image via a multiplication between content image features and a transformation matrix, which is computed from features with a pre-determined algorithm. These algorithms either require computationally expensive operations, or fail to model the feature covariance and produce artifacts in synthesized images. Generalized from these methods, in this work, we derive the form of transformation matrix theoretically and present an arbitrary style transfer approach that learns the transformation matrix with a feed-forward network. Our algorithm is highly efficient yet allows a flexible combination of multi-level styles while preserving content affinity during style transfer process. We demonstrate the effectiveness of our approach on four tasks: artistic style transfer, video and photo-realistic style transfer as well as domain adaptation, including comparisons with the stateof-the-art methods.
Teachers' pedagogical beliefs and their use of digital media in classrooms: Sharpening the focus of the 'will, skill, tool' model and integrating teachers' constructivist orientations

The ‘will, skill, tool’ model is a well-established theoretical framework that elucidates the conditions under which teachers are most likely to employ information and communication technologies (ICT) in the classroom. Past studies have shown that these three factors explain a very high degree of variance in the frequency of classroom ICT use. The present study replicates past findings using a different set of measures and hones in on possible subfactors. Furthermore, the study examines teacher affiliation for constructivist-style teaching, which is often considered to facilitate the pedagogical use of digital media. The study’s survey of 357 Swiss secondary school teachers reveals significant positive correlations between will, skill, and tool variables and the combined frequency and diversity of technology use in teaching. A multiple linear regression model was used to identify relevant subfactors. Five factors account for a total of 60% of the explained variance in the intensity of classroom ICT use. Computer and Internet applications are more often used by teachers in the classroom when: (1) teachers consider themselves to be more competent in using ICT for teaching; (2) more computers are readily available; (3) the teacher is a form teacher and responsible for the class; (4) the teacher is more convinced that computers improve student learning; and (5) the teacher more often employs constructivist forms of teaching and learning. The impact of constructivist teaching was small, however.
Patch-based Segmentation of Brain Tissues

We describe our submission to the Brain Tumor Segmentation Challenge (BraTS) at MICCAI 2013. This segmentation approach is based on similarities between multi-channel patches. After patches are extracted from several MR channels for a test case, similar patches are found in training images for which label maps are known. These labels maps are then combined to result in a segmentation map for the test case. The labelling is performed, in a leave-one-out scheme, for each case of a publicly available training set, which consists of 30 real cases (20 highgrade gliomas, 10 low-grade gliomas) and 50 synthetic cases (25 highgrade gliomas, 25 low-grade gliomas). Promising results are shown on the training set, and we believe this algorithm would perform favourably well in comparison to the state of the art on a testing set.
"You better not leave me shaming!": Conditional indirect effect analyses of anti-fat attitudes, body shame, and fat talk as a function of self-compassion in college women.

The present investigation provided a theoretically-driven analysis testing whether body shame helped account for the predicted positive associations between explicit weight bias in the form of possessing anti-fat attitudes (i.e., dislike, fear of fat, and willpower beliefs) and engaging in fat talk among 309 weight-diverse college women. We also evaluated whether self-compassion served as a protective factor in these relationships. Robust non-parametric bootstrap resampling procedures adjusted for body mass index (BMI) revealed stronger indirect and conditional indirect effects for dislike and fear of fat attitudes and weaker, marginal effects for the models inclusive of willpower beliefs. In general, the indirect effect of anti-fat attitudes on fat talk via body shame declined with increasing levels of self-compassion. Our preliminary findings may point to useful process variables to target in mitigating the impact of endorsing anti-fat prejudice on fat talk in college women and may help clarify who is at higher risk.
New Verification Strategy for Finger-Vein Recognition System

This paper proposes a new finger-vein recognition system that uses a binary robust invariant elementary feature from accelerated segment test feature points and an adaptive thresholding strategy. Subsequently, the proposed a multi-image quality assessments (MQA) are applied to conduct a second stage verification. As oppose to other studies, the region of interest is directly identified using a range of normalized feature point area, which reduces the complexity of pre-processing. This recognition structure allows an efficient feature points matching using a robust feature and rigorous verification using the MQA process. As a result, this method not only reduces the system computation time, comparisons against former relevant studies demonstrate the superiority of the proposed method.
Learning Compositional Sparse Models of Bimodal Percepts

Various perceptual domains have underlying compositional semantics that are rarely captured in current models. We suspect this is because directly learning the compositional structure has evaded these models. Yet, the compositional structure of a given domain can be grounded in a separate domain thereby simplifying its learning. To that end, we propose a new approach to modeling bimodal percepts that explicitly relates distinct projections across each modality and then jointly learns a bimodal sparse representation. The resulting model enables compositionality across these distinct projections and hence can generalize to unobserved percepts spanned by this compositional basis. For example, our model can be trained on red triangles and blue squares; yet, implicitly will also have learned red squares and blue triangles. The structure of the projections and hence the compositional basis is learned automatically for a given language model. To test our model, we have acquired a new bimodal dataset comprising images and spoken utterances of colored shapes in a tabletop setup. Our experiments demonstrate the benefits of explicitly leveraging compositionality in both quantitative and human evaluation studies.
Motivational Profiles From a Self-Determination Perspective : The Quality of Motivation Matters

The present research complements extant variable-centered research that focused on the dimensions of autonomous and controlled motivation through adoption of a person-centered approach for identifying motivational profiles. Both in high school students (Study 1) and college students (Study 2), a cluster analysis revealed 4 motivational profiles: a good quality motivation group (i.e., high autonomous, low controlled); a poor quality motivation group (i.e., low autonomous, high controlled); a low quantity motivation group (i.e., low autonomous, low controlled); and a high quantity motivation group (i.e., high autonomous, high controlled). To compare the 4 groups, the authors derived predictions from qualitative and quantitative perspectives on motivation. Findings generally favored the qualitative perspective; compared with the other groups, the good quality motivation group displayed the most optimal learning pattern and scored highest on perceived need-supportive teaching. Theoretical and practical implications of the findings are discussed.
An approximate Dirac-type theorem for k -uniform hypergraphs

A k-uniform hypergraph is hamiltonian if for some cyclic ordering of its vertex set, every k consecutive vertices form an edge. In 1952 Dirac proved that if the minimum degree in an n-vertex graph is at least n/2 then the graph is hamiltonian. We prove an approximate version of an analogous result for uniform hypergraphs: For every k ≥ 3 and γ > 0, and for all n large enough, a sufficient condition for an n-vertex k-uniform hypergraph to be hamiltonian is that each (k − 1)-element set of vertices is contained in at least (1/2 + γ)n edges. Research supported by NSF grant DMS-0300529. Research supported by KBN grant 2 P03A 015 23 and N201036 32/2546. Part of research performed at Emory University, Atlanta. Research supported by NSF grant DMS-0100784
Deep Learning for Target Classification from SAR Imagery: Data Augmentation and Translation Invariance

This report deals with translation invariance of convolutional neural networks (CNNs) for automatic target recognition (ATR) from synthetic aperture radar (SAR) imagery. In particular, the translation invariance of CNNs for SAR ATR represents the robustness against misalignment of target chips extracted from SAR images. To understand the translation invariance of the CNNs, we trained CNNs which classify the target chips from the MSTAR into the ten classes under the condition of with and without data augmentation, and then visualized the translation invariance of the CNNs. According to our results, even if we use a deep residual network, the translation invariance of the CNN without data augmentation using the aligned images such as the MSTAR target chips is not so large. A more important factor of translation invariance is the use of augmented training data. Furthermore, our CNN using augmented training data achieved a state-of-the-art classification accuracy of 99.6%. These results show an importance of domain-specific data augmentation.
Opportunistic communications in WSN using UAV

This paper studies the opportunistic routing (OR) in unmanned aerial vehicle (UAV) assisted wireless sensor networks (WSNs). We consider the scenario where a UAV collects data from randomly deployed mobile sensors that are moving with different velocities along a predefined route. Due to the dynamic topology, mobile sensors have different opportunities to communicate with the UAV. This paper proposes the All Neighbors Opportunistic Routing (ANOR) and Highest Velocity Opportunistic Routing (HVOR) protocols. In essence, ANOR forwards packets to all neighbors and HVOR forwards them to one neighbor with highest velocity. HVOR is a new OR protocol which dynamically selects route on a pre-transmission basis in multi-hop network. HVOR helps the sensor which has little opportunity to communicate with the UAV to determine which sensor, among all the sensors that are within its range, is the forwarder. The selected node forwards the packet. As a result, in each hop, the packet moves to the sensor that has higher opportunity to communicate with the UAV. In addition, we focus on various performance metrics, including Packets Delivery Ratio (PDR), Routing Overhead Ratio (ROR), Average Latency (AL) and Average Hop Count (AHC), to evaluate the proposed algorithms and compare them with a Direct Communication (DC) protocol. Through extensive simulations, we have shown that both HVOR and ANOR algorithms work better than DC. Moreover, the HVOR algorithm outperforms the other two algorithms in terms of the average overhead.
A New Large Urdu Database for Off-Line Handwriting Recognition

A new large Urdu handwriting database, which includes isolated digits, numeral strings with/without decimal points, five special symbols, 44 isolated characters, 57 Urdu words (mostly financial related), and Urdu dates in different patterns, was designed at Centre for Pattern Recognition and Machine Intelligence (CENPARMI). It is the first database for Urdu off-line handwriting recognition. It involves a large number of Urdu native speakers from different regions of the world. Moreover, the database has different formats – true color, gray level and binary. Experiments on Urdu digits recognition has been conducted with an accuracy of 98.61%. Methodologies in image pre-processing, gradient feature extraction and classification using SVM have been described, and a detailed error analysis is presented on the recognition results.
Pelvic congestion syndrome: demonstration and diagnosis by helical CT

Pelvic pain is a common gynaecological complaint, sometimes without any obvious etiology. We report a case of pelvic congestion syndrome, an often overlooked cause of pelvic pain, diagnosed by helical computed tomography. This seems to be an effective and noninvasive imaging modality. RID=""ID=""<e5>Correspondence to:</e5> J. H. Desimpelaere
Data + Intuition: A Hybrid Approach to Developing Product North Star Metrics

“You make what you measure” is a familiar mantra at datadriven companies. Accordingly, companies must be careful to choose North Star metrics that create a better product. Metrics fall into two general categories: direct count metrics such as total revenue and monthly active users, and nuanced quality metrics regarding value or other aspects of the user experience. Count metrics, when used exclusively as the North Star, might inform product decisions that harm user experience. Therefore, quality metrics play an important role in product development. We present a five-step framework for developing quality metrics using a combination of machine learning and product intuition. Machine learning ensures that the metric accurately captures user experience. Product intuition makes the metric interpretable and actionable. Through a case study of the Endorsements product at LinkedIn, we illustrate the danger of optimizing exclusively for count metrics, and showcase the successful application of our framework toward developing a quality metric. We show how the new quality metric has driven significant improvements toward creating a valuable, user-first product.
A New Experimental Approach Using Image Processing-Based Tracking for an Efficient Fault Diagnosis in Pantograph–Catenary Systems

The periodical maintenance of railway systems is very important in terms of maintaining safe and comfortable transportation. In particular, the monitoring and diagnosis of faults in the pantograph catenary system are required to provide a transmission from the catenary line to the electric energy locomotive. Surface wear that is caused by the interaction between the pantograph and catenary and nonuniform distribution on the surface of a pantograph of the contact points can cause serious accidents. In this paper, a novel approach is proposed for image processing-based monitoring and fault diagnosis in terms of the interaction and contact points between the pantograph and catenary in a moving train. For this purpose, the proposed method consists of two stages. In the first stage, the pantograph catenary interaction has been modeled; the simulation results were given a failure analysis with a variety of scenarios. In the second stage, the contact points between the pantograph and catenary were detected and implemented in real time with image processing algorithms using actual video images. The pantograph surface for a fault analysis was divided into three regions: safe, dangerous, and fault. The fault analysis of the system was presented using the number of contact points in each region. The experimental results demonstrate the effectiveness, applicability, and performance of the proposed approach.
Simulation of three-phase cascaded H-bridge Multilevel Inverter with THIPWM

This paper presents the simulation of a Three Phase Five Level Cascaded H-bridge Multilevel Inverter. Third Harmonics Injection Pulse Width Modulation (THIPWM) technique was employed through s-function approach in SIMULINK environment to minimize the complexity of circuit design. The advantages of THIPWM are revealed in term of voltage and current THD reduction compared to Sinusoidal Pulse Width Modulation (SPWM) in three phase system. Simulation results reveal that voltage and current THD of THIPWM improved as much as 36% and 28% respectively compared to SPWM's.
SIRT1 protects the heart from ER stress-induced cell death through eIF2α deacetylation

Over the past decade, endoplasmic reticulum (ER) stress has emerged as an important mechanism involved in the pathogenesis of cardiovascular diseases including heart failure. Cardiac therapy based on ER stress modulation is viewed as a promising avenue toward effective therapies for the diseased heart. Here, we tested whether sirtuin-1 (SIRT1), a NAD+-dependent deacetylase, participates in modulating ER stress response in the heart. Using cardiomyocytes and adult-inducible SIRT1 knockout mice, we demonstrate that SIRT1 inhibition or deficiency increases ER stress-induced cardiac injury, whereas activation of SIRT1 by the SIRT1-activating compound STAC-3 is protective. Analysis of the expression of markers of the three main branches of the unfolded protein response (i.e., PERK/eIF2α, ATF6 and IRE1) showed that SIRT1 protects cardiomyocytes from ER stress-induced apoptosis by attenuating PERK/eIF2α pathway activation. We also present evidence that SIRT1 physically interacts with and deacetylates eIF2α. Mass spectrometry analysis identified lysines K141 and K143 as the acetylation sites on eIF2α targeted by SIRT1. Furthermore, mutation of K143 to arginine to mimic eIF2α deacetylation confers protection against ER stress-induced apoptosis. Collectively, our findings indicate that eIF2α deacetylation on lysine K143 by SIRT1 is a novel regulatory mechanism for protecting cardiac cells from ER stress and suggest that activation of SIRT1 has potential as a therapeutic approach to protect the heart against ER stress-induced injury.
Voluntary motion support by an upper limb support system based on bioelectrical signals for heavy overhead tasks

We have developed a wearable upper limb support system (ULSS) for support during heavy overhead tasks. The purpose of this study is to develop the voluntary motion support algorithm for the ULSS, and to confirm the effectiveness of the ULSS with the developed algorithm through dynamic evaluation experiments. The algorithm estimates the motor intention of the wearer based on a bioelectrical signal (BES). The ULSS measures the BES via electrodes attached onto the triceps brachii, deltoid, and clavicle. The BES changes in synchronization with the motion of the wearer's upper limbs. The algorithm changes a control phase by comparing the BES and threshold values. The algorithm achieves voluntary motion support for dynamic tasks by changing support torques of the ULSS in synchronization with the control phase. Five healthy adult males moved heavy loads vertically overhead in the evaluation experiments. In a random instruction experiment, the volunteers moved in synchronization with random instructions, and we confirmed that the control phase changes in synchronization with the random instructions. In a motion support experiment, we confirmed that the average number of the vertical motion with the ULSS increased 2.3 times compared to the average number without the ULSS. As a result, the ULSS with the algorithm supports the motion voluntarily, and it has a positive effect on the support. In conclusion, we could develop the novel voluntary motion support algorithm of the ULSS.