bluuebunny/crossref_metadata_2025_split huggingface dataset:
When anxiety becomes my propeller: Mental toughness moderates the relation between academic anxiety and academic avoidance
Authors: Hasty Leslie M. , Malanchini Margherita , Shakeshaft Nicholas , Schofield Kerry , Malanchini Maddalena , Wang Zhe 
Published: July 2020

BackgroundHigh academic anxiety is associated with poor academic performance. One proposed mechanism of this association is that academic anxiety promotes learning avoidance behaviours, which in turn hinders students' opportunities to learn and grow. However, this proposition has not been thoroughly examined, particularly in afterschool learning settings. The present study aimed to address this gap.AimsFirst, we investigated whether individual differences in academic anxiety across three domains (mathematics, native language or L1, and second language learning or L2) predicted students' academic avoidance in the corresponding domain in high school. Second, given that individual differences in personality may result in employing different coping strategies to deal with academic anxiety, we examined how mental toughness (MT) moderated the relation between academic anxiety and academic avoidance.SampleTwo waves of longitudinal data that were one semester apart were available for four hundred and forty‐four high school students.MethodsStudents self‐reported their MT, academic anxiety, and academic avoidance (i.e., time spent on studying a subject afterschool) in mathematics, L1, and L2.ResultsFor students with higher MT, higher mathematics, L1, and L2 anxiety in the first semester predicted more time spent on learning the corresponding subject in the following semester, even after controlling for general anxiety, academic achievement, and initial academic avoidance.ConclusionsThese results challenge the proposition that all students with higher domain‐specific anxiety are more likely to avoid learning altogether in that domain. Rather, among students from the general school population who generally exhibit low to moderate levels of academic anxiety, higher academic anxiety is associated with more time investment in afterschool learning in mentally tough students.
Oncological outcome according to attainment of pentafecta after robot‐assisted radical cystectomy in patients with bladder cancer included in the multicentre KORARC database
Authors: Oh Jong Jin , Lee Sangchul , Ku Ja Hyeon , Kwon Tae Gyun , Kim Tae‐Hwan , Jeon Seung Hyun , Lee Sang Hyup , Nam Jong Kil , Kim Wan Seok , Jeong Byong Chang , Lee Ji Youl , Hong Sung Hoo , Rha Koon Ho , Han Woong Kyu , Ham Won Sik , Lee Young Goo , Lee Yong Seong , Park Sung Yul , Yoon Young Eun , Kang Sung Gu , Kang Seok Ho ,   Korean Robot Assisted Radical Cystectomy (KORARC) Study Group
Published: July 2020

ObjectivesTo investigate the oncological significance of a robot‐assisted radical cystectomy (RARC)‐related pentafecta in patients with bladder cancer.Patients and MethodsUsing the KORARC database, which includes data from 12 centres, data from 730 patients who underwent RARC between April 2007 and May 2019 were prospectively collected and retrospectively analysed. Pentafecta was achieved if patients met all of the following criteria: (i) negative soft tissue surgical margin; (ii) ≥16 lymph nodes removed; (iii) no major complications (Clavien–Dindo grade 3–5) within 90 days; (iv) no clinical recurrence within the first 12 months; and (v) no ureteroenteric stricture. Patients were divided into two groups according to pentafecta attainment, and a comparison of overall survival (OS) and cancer‐specific survival (CSS) using multivariate Cox proportional analysis was then carried out.ResultsOf the 730 patients included in this analysis, 208 (28.5%) attained the RARC pentafecta; the remaining 522 (71.5%) did not. The mean age of the patients was 64.67 years, 85.1% were men, 53.6% received a conduit, 37.7% received orthotopic neobladders and the total complication rate was 57.8%. Those who attained the pentafecta received more neobladders (P = 0.039), were more likely to be treated with the intracorporeal technique (P < 0.001), had longer operating times (P = 0.020) and had longer console time (P = 0.021) compared with those who did not attain the pentafecta. Over a mean of 31.1 months of follow‐up, the pentafecta attainment group had significantly higher OS and CSS rates compared with the non‐attainment group (10‐year OS 70.4% vs 58.1%, respectively [P = 0.016]; 10‐year CSS 87.8% vs 70.0%, respectively [P = 0.036]). Multivariate analysis showed that the RARC pentafecta was a significant predictor of overall mortality (hazard ratio 0.561; P = 0.038).ConclusionsPatients who attained the RARC pentafecta had significantly better survival outcomes compared with those who did not. These criteria could be used to standardize assessment of the surgical quality of RARC. In the future, a similar study using an independent cohort is warranted to confirm our results.
Lorlatinib in previously treated anaplastic lymphoma kinase‐rearranged non–small cell lung cancer: Japanese subgroup analysis of a global study
Authors: Seto Takashi , Hayashi Hidetoshi , Satouchi Miyako , Goto Yasushi , Niho Seiji , Nogami Naoyuki , Hida Toyoaki , Takahashi Toshiaki , Sakakibara‐Konishi Jun , Morise Masahiro , Nagasawa Takashi , Suzuki Mie , Ohkura Masayuki , Fukuhara Kei , Thurm Holger , Peltz Gerson , Nishio Makoto 
Published: July 2020

AbstractLorlatinib is a potent, brain‐penetrant, third‐generation anaplastic lymphoma kinase (ALK)/ROS proto‐oncogene 1 (ROS1) tyrosine kinase inhibitor (TKI) that is active against most known resistance mutations. This is an ongoing phase 1/2, multinational study (NCT01970865) investigating the efficacy, safety and pharmacokinetics of lorlatinib in ALK‐rearranged/ROS1‐rearranged advanced non–small cell lung cancer (NSCLC) with or without intracranial (IC) metastases. Because patterns of ALK TKI use in Japan differ from other regions, we present a subgroup analysis of Japanese patients. Patients were enrolled into six expansion (EXP) cohorts based on ALK/ROS1 mutation status and treatment history. The primary endpoint was the objective response rate (ORR) and the IC‐ORR based on independent central review. Secondary endpoints included pharmacokinetic evaluations. At data cutoff, 39 ALK‐rearranged/ROS1‐rearranged Japanese patients were enrolled across the six expansion cohorts; all received lorlatinib 100 mg once daily. Thirty‐one ALK‐rearranged patients previously treated with ≥1 ALK TKI (EXP2 to EXP5) were evaluable for ORR and 15 were evaluable for IC‐ORR. The ORR and the IC‐ORR for Japanese patients in EXP2‐5 were 54.8% (95% confidence interval [CI]: 36.0‐72.7) and 46.7% (95% CI: 21.3‐73.4), respectively. Among patients who had received prior alectinib only (EXP3B), the ORR was 42.9%; 95% CI: 9.9‐81.6). The most common treatment‐related adverse event (TRAE) was hypercholesterolemia (79.5%). Hypertriglyceridemia was the most common grade 3/4 TRAE (25.6%). Single‐dose and multiple‐dose pharmacokinetic profiles among Japanese patients were similar to those in non–Japanese patients. Lorlatinib showed clinically meaningful responses and IC responses among ALK‐rearranged Japanese patients with NSCLC who received ≥1 prior ALK TKI, including meaningful responses among those receiving prior alectinib only. Lorlatinib was generally well tolerated.
Relation of health‐related quality of life to near final height and body composition in adolescents with chronic endocrinopathies during transition period
Authors: Mittnacht Janna , Choukair Daniela , Breil Thomas , Klose Daniela , Inta Ioana , Bettendorf Markus 
Published: July 2020

AbstractIntroductionWe evaluated sequelae of disease and therapy in adolescents with chronic endocrinopathies using a medical and psychological workup to record health‐related quality of life (HRQoL), near final height (NFH) and body compositions during the transition period from paediatric to adult care.MethodsNear final height, weight, body mass index (BMI), grip strength (GS), hip and waist circumference (HC; WC), skin folds (SF) and HRQoL T‐scores by KIDSCREEN and DISABKIDS were assessed in 134 patients (70 females and 64 males) from May 2010 to March 2016 diagnosed with congenital adrenal hyperplasia (CAH; n = 22), multiple pituitary hormone deficiency (MPHD; n = 17), growth hormone deficiency (GHD; n = 37), Turner syndrome (TS; n = 27), SGA‐short stature (SGA; n = 20) and Klinefelter syndrome (KS; n = 11).ResultsMedian HRQoL T‐scores for KIDSCREEN (50.6‐56.5) and DISABKIDS (52.7‐58.9) ranged within references with considerable variations but without significant deficit in any diagnosis. Median‐corrected height SDS (CoH‐SDS: NFH‐SDS–TH [target height]‐SDS) was >−1, except in KS (SDS + 1.3) and in TS (SDS – 1.9; P < .0001) without correlations with HRQoL. Median BMI was below 25 kg/m2 in all patients except MPHD (26.5 kg/m2; SDS 1.5; P = .006). BMI correlated negatively in CAH females with self‐perception (rs = −.64, P = .0338), physical well‐being (rs = −.8; P = .0086), social exclusion rs = −.65; P = .031) and emotions (rs = −.7; P = .0169).ConclusionHealth‐related quality of life and body compositions were similar to those of healthy adolescents. Lower scores in HRQoL dimensions as self‐perception, physical well‐being, social exclusion and emotions were detected and correlated negatively with BMI. Treatment strategies and psychological support should consider HRQoL and adapted in specific treatment guidelines.
CPU Ray Tracing of Tree‐Based Adaptive Mesh Refinement Data
Authors: Wang Feng , Marshak Nathan , Usher Will , Burstedde Carsten , Knoll Aaron , Heister Timo , Johnson Chris R. 
Published: July 2020

AbstractAdaptive mesh refinement (AMR) techniques allow for representing a simulation's computation domain in an adaptive fashion. Although these techniques have found widespread adoption in high‐performance computing simulations, visualizing their data output interactively and without cracks or artifacts remains challenging. In this paper, we present an efficient solution for direct volume rendering and hybrid implicit isosurface ray tracing of tree‐based AMR (TB‐AMR) data. We propose a novel reconstruction strategy, Generalized Trilinear Interpolation (GTI), to interpolate across AMR level boundaries without cracks or discontinuities in the surface normal. We employ a general sparse octree structure supporting a wide range of AMR data, and use it to accelerate volume rendering, hybrid implicit isosurface rendering and value queries. We demonstrate that our approach achieves artifact‐free isosurface and volume rendering and provides higher quality output images compared to existing methods at interactive rendering rates.
DRLViz: Understanding Decisions and Memory in Deep Reinforcement Learning
Authors: Jaunet T. , Vuillemot R. , Wolf C. 
Published: July 2020

AbstractWe present DRLViz, a visual analytics interface to interpret the internal memory of an agent (e.g. a robot) trained using deep reinforcement learning. This memory is composed of large temporal vectors updated when the agent moves in an environment and is not trivial to understand due to the number of dimensions, dependencies to past vectors, spatial/temporal correlations, and co‐correlation between dimensions. It is often referred to as a black box as only inputs (images) and outputs (actions) are intelligible for humans. Using DRLViz, experts are assisted to interpret decisions using memory reduction interactions, and to investigate the role of parts of the memory when errors have been made (e.g. wrong direction). We report on DRLViz applied in the context of video games simulators (ViZDoom) for a navigation scenario with item gathering tasks. We also report on experts evaluation using DRLViz, and applicability of DRLViz to other scenarios and navigation problems beyond simulation games, as well as its contribution to black box models interpretability and explain‐ability in the field of visual analytics.
Reading Traces: Scalable Exploration in Elastic Visualizations of Cultural Heritage Data
Authors: Bludau M.‐J. , Brüggemann V. , Busch A. , Dörk M. 
Published: July 2020

AbstractThrough a design study, we develop an approach to data exploration that utilizes elastic visualizations designed to support varying degrees of detail and abstraction. Examining the notions of scalability and elasticity in interactive visualizations, we introduce a visualization of personal reading traces such as marginalia or markings inside the reference library of German realist author Theodor Fontane. To explore such a rich and extensive collection, meaningful visual forms of abstraction and detail are as important as the transitions between those states. Following a growing research interest in the role of fluid interactivity and animations between views, we are particularly interested in the potential of carefully designed transitions and consistent representations across scales. The resulting prototype addresses humanistic research questions about the interplay of distant and close reading with visualization research on continuous navigation along several granularity levels, using scrolling as one of the main interaction mechanisms. In addition to presenting the design process and resulting prototype, we present findings from a qualitative evaluation of the tool, which suggest that bridging between distant and close views can enhance exploration, but that transitions between views need to be crafted very carefully to facilitate comprehension.
VA‐TRAC: Geospatial Trajectory Analysis for Monitoring, Identification, and Verification in Fishing Vessel Operations
Authors: Storm‐Furru S. , Bruckner S. 
Published: July 2020

AbstractIn order to ensure sustainability, fishing operations are governed by many rules and regulations that restrict the use of certain techniques and equipment, specify the species and size offish that can be harvested, and regulate commercial activities based on licensing schemes. As the world's second largest exporter offish and seafood products, Norway invests a significant amount of effort into maintaining natural ecosystem dynamics by ensuring compliance with its constantly evolving science‐based regulatory body. This paper introduces VA‐TRAC, a geovisual analytics application developed in collaboration with the Norwegian Directorate of Fisheries in order to address this complex task. Our approach uses automatic methods to identify possible catch operations based on fishing vessel trajectories, embedded in an interactive web‐based visual interface used to explore the results, compare them with licensing information, and incorporate the analysts’ domain knowledge into the decision making process. We present a data and task analysis based on a close collaboration with domain experts, and the design and implementation of VA‐TRAC to address the identified requirements.
Orchard: Exploring Multivariate Heterogeneous Networks on Mobile Phones
Authors: Eichmann P. , Edge D. , Evans N. , Lee B. , Brehmer M. , White C. 
Published: July 2020

AbstractPeople are becoming increasingly sophisticated in their ability to navigate information spaces using search, hyperlinks, and visualization. But, mobile phones preclude the use of multiple coordinated views that have proven effective in the desktop environment (e.g., for business intelligence or visual analytics). In this work, we propose to model information as multivariate heterogeneous networks to enable greater analytic expression for a range of sensemaking tasks while suggesting a new, list‐based paradigm with gestural navigation of structured information spaces on mobile phones. We also present a mobile application, called Orchard, which combines ideas from both faceted search and interactive network exploration in a visual query language to allow users to collect facets of interest during exploratory navigation. Our study showed that users could collect and combine these facets with Orchard, specifying network queries and projections that would only have been possible previously using complex data tools or custom data science.
QUESTO: Interactive Construction of Objective Functions for Classification Tasks
Authors: Das Subhajit , Xu Shenyu , Gleicher Michael , Chang Remco , Endert Alex 
Published: July 2020

AbstractBuilding effective classifiers requires providing the modeling algorithms with information about the training data and modeling goals in order to create a model that makes proper tradeoffs. Machine learning algorithms allow for flexible specification of such meta‐information through the design of the objective functions that they solve. However, such objective functions are hard for users to specify as they are a specific mathematical formulation of their intents. In this paper, we present an approach that allows users to generate objective functions for classification problems through an interactive visual interface. Our approach adopts a semantic interaction design in that user interactions over data elements in the visualization are translated into objective function terms. The generated objective functions are solved by a machine learning solver that provides candidate models, which can be inspected by the user, and used to suggest refinements to the specifications. We demonstrate a visual analytics system QUESTO for users to manipulate objective functions to define domain‐specific constraints. Through a user study we show that QUESTO helps users create various objective functions that satisfy their goals.
Peax: Interactive Visual Pattern Search in Sequential Data Using Unsupervised Deep Representation Learning
Authors: Lekschas Fritz , Peterson Brant , Haehn Daniel , Ma Eric , Gehlenborg Nils , Pfister Hanspeter 
Published: July 2020

AbstractWe present Peax, a novel feature‐based technique for interactive visual pattern search in sequential data, like time series or data mapped to a genome sequence. Visually searching for patterns by similarity is often challenging because of the large search space, the visual complexity of patterns, and the user's perception of similarity. For example, in genomics, researchers try to link patterns in multivariate sequential data to cellular or pathogenic processes, but a lack of ground truth and high variance makes automatic pattern detection unreliable. We have developed a convolutional autoencoder for unsupervised representation learning of regions in sequential data that can capture more visual details of complex patterns compared to existing similarity measures. Using this learned representation as features of the sequential data, our accompanying visual query system enables interactive feedback‐driven adjustments of the pattern search to adapt to the users’ perceived similarity. Using an active learning sampling strategy, Peax collects user‐generated binary relevance feedback. This feedback is used to train a model for binary classification, to ultimately find other regions that exhibit patterns similar to the search target. We demonstrate Peax's features through a case study in genomics and report on a user study with eight domain experts to assess the usability and usefulness of Peax. Moreover, we evaluate the effectiveness of the learned feature representation for visual similarity search in two additional user studies. We find that our models retrieve significantly more similar patterns than other commonly used techniques.
VisuaLint: Sketchy In Situ Annotations of Chart Construction Errors
Authors: Hopkins Aspen K. , Correll Michael , Satyanarayan Arvind 
Published: July 2020

AbstractChart construction errors, such as truncated axes or inexpressive visual encodings, can hinder reading a visualization, or worse, imply misleading facts about the underlying data. These errors can be caught by critical readings of visualizations, but readers must have a high level of data and design literacy and must be paying close attention. To address this issue, we introduce VisuaLint: a technique for surfacing chart construction errors in situ. Inspired by the ubiquitous red wavy underline that indicates spelling mistakes, visualization elements that contain errors (e.g., axes and legends) are sketchily rendered and accompanied by a concise annotation. VisuaLint is unobtrusive — it does not interfere with reading a visualization — and its direct display establishes a close mapping between erroneous elements and the expression of error. We demonstrate five examples of VisualLint and present the results of a crowdsourced evaluation (N = 62) of its efficacy. These results contribute an empirical baseline proficiency for recognizing chart construction errors, and indicate near‐universal difficulty in error identification. We find that people more reliably identify chart construction errors after being shown examples of VisuaLint, and prefer more verbose explanations for unfamiliar or less obvious flaws.
Many At Once: Capturing Intentions to Create And Use Many Views At Once In Large Display Environments
Authors: Aurisano Jillian , Kumar Abhinav , Alsaiari Abeer , Eugenio Barbara Di , Johnson Andrew 
Published: July 2020

AbstractThis paper describes results from an observational, exploratory study of visual data exploration in a large, multi‐view, flexible canvas environment. Participants were provided with a set of data exploration sub‐tasks associated with a local crime dataset and were instructed to pose questions to a remote mediator who would respond by generating and organizing visualizations on the large display. We observed that participants frequently posed requests to cast a net around one or several subsets of the data or a set of data attributes. They accomplished this directly and by utilizing existing views in unique ways, including by requesting to copy and pivot a group of views collectively and posing a set of parallel requests on target views expressed in one command. These observed actions depart from multi‐view flexible canvas environments that typically provide interfaces in support of generating one view at a time or actions that operate on one view at a time. We describe how participants used these ‘cast‐a‐net’ requests for tasks that spanned more than one view and describe design considerations for multi‐view environments that would support the observed multi‐view generation actions.
Quantitative Evaluation of Time‐Dependent Multidimensional Projection Techniques
Authors: Vernier E. F. , Garcia R. , Silva I. P. da , Comba J. L. D. , Telea A. C. 
Published: July 2020

AbstractDimensionality reduction methods are an essential tool for multidimensional data analysis, and many interesting processes can be studied as time‐dependent multivariate datasets. There are, however, few studies and proposals that leverage on the concise power of expression of projections in the context of dynamic/temporal data. In this paper, we aim at providing an approach to assess projection techniques for dynamic data and understand the relationship between visual quality and stability. Our approach relies on an experimental setup that consists of existing techniques designed for time‐dependent data and new variations of static methods. To support the evaluation of these techniques, we provide a collection of datasets that has a wide variety of traits that encode dynamic patterns, as well as a set of spatial and temporal stability metrics that assess the quality of the layouts. We present an evaluation of 9 methods, 10 datasets, and 12 quality metrics, and elect the best‐suited methods for projecting time‐dependent multivariate data, exploring the design choices and characteristics of each method. Additional results can be found in the online benchmark repository. We designed our evaluation pipeline and benchmark specifically to be a live resource, open to all researchers who can further add their favorite datasets and techniques at any point in the future.