bluuebunny/crossref_metadata_2025_split huggingface dataset:
Rank‐1 Lattices for Efficient Path Integral Estimation
Authors: Liu Hongli , Han Honglei , Jiang Min 
Published: June 2021

AbstractWe introduce rank‐1 lattices as a quasi‐random sequence to the numerical estimation of the high‐dimensional path integral. Previous attempts at utilizing rank‐1 lattices in computer graphics were very limited to low‐dimensional applications, intentionally avoiding high dimensionality due to that the lattice search is NP‐hard. We propose a novel framework that tackles this challenge, which was inspired by the rippling effect of the sample paths. Contrary to the conventional search approaches, our framework is based on recursively permuting the preliminarily selected components of the generator vector to achieve better pairwise projections and minimize the discrepancy of the path vertex coordinates in scene manifold spaces, resulting in improved rendering quality. It allows for the offline search of arbitrarily high‐dimensional lattices to finish in a reasonable amount of time while removing the need to use all lattice points in the traditional definition, which opens the gate for their use in progressive rendering. Our rank‐1 lattices successfully maintain the pixel variance at a comparable or even lower level compared to Sobol′ sampler, which offers a brand new solution to design efficient samplers for path tracing.
Learning Multiple‐Scattering Solutions for Sphere‐Tracing of Volumetric Subsurface Effects
Authors: Leonard L. , Höhlein K. , Westermann R. 
Published: June 2021

AbstractAccurate subsurface scattering solutions require the integration of optical material properties along many complicated light paths. We present a method that learns a simple geometric approximation of random paths in a homogeneous volume with translucent material. The generated representation allows determining the absorption along the path as well as a direct lighting contribution, which is representative of all scatter events along the path. A sequence of conditional variational auto‐encoders (CVAEs) is trained to model the statistical distribution of the photon paths inside a spherical region in the presence of multiple scattering events. A first CVAE learns how to sample the number of scatter events, occurring on a ray path inside the sphere, which effectively determines the probability of this ray to be absorbed. Conditioned on this, a second model predicts the exit position and direction of the light particle. Finally, a third model generates a representative sample of photon position and direction along the path, which is used to approximate the contribution of direct illumination due to in‐scattering. To accelerate the tracing of the light path through the volumetric medium toward the solid boundary, we employ a sphere‐tracing strategy that considers the light absorption and can perform a statistically accurate next‐event estimation. We demonstrate efficient learning using shallow networks of only three layers and no more than 16 nodes. In combination with a GPU shader that evaluates the CVAEs’ predictions, performance gains can be demonstrated for a variety of different scenarios. We analyze the approximation error that is introduced by the data‐driven scattering simulation and shed light on the major sources of error.
Automatic Surface Segmentation for Seamless Fabrication Using 4‐axis Milling Machines
Authors: Nuvoli S. , Tola A. , Muntoni A. , Pietroni N. , Gobbetti E. , Scateni R. 
Published: June 2021

AbstractWe introduce a novel geometry‐processing pipeline to guide the fabrication of complex shapes from a single block of material using 4‐axis CNC milling machines. This setup extends classical 3‐axis CNC machining with an extra degree of freedom to rotate the object around a fixed axis. The first step of our pipeline identifies the rotation axis that maximizes the overall fabrication accuracy. Then we identify two height‐field regions at the rotation axis's extremes used to secure the block on the rotation tool. We segment the remaining portion of the mesh into a set of height‐fields whose principal directions are orthogonal to the rotation axis. The segmentation balances the approximation quality, the boundary smoothness, and the total number of patches. Additionally, the segmentation process takes into account the object's geometric features, as well as saliency information. The output is a set of meshes ready to be processed by off‐the‐shelf software for the 3‐axis tool‐path generation. We present several results to demonstrate the quality and efficiency of our approach to a range of inputs.
LoBSTr: Real‐time Lower‐body Pose Prediction from Sparse Upper‐body Tracking Signals
Authors: Yang Dongseok , Kim Doyeon , Lee Sung‐Hee 
Published: June 2021

AbstractWith the popularization of games and VR/AR devices, there is a growing need for capturing human motion with a sparse set of tracking data. In this paper, we introduce a deep neural network (DNN) based method for real‐time prediction of the lower‐body pose only from the tracking signals of the upper‐body joints. Specifically, our Gated Recurrent Unit (GRU)‐based recurrent architecture predicts the lower‐body pose and feet contact states from a past sequence of tracking signals of the head, hands, and pelvis. A major feature of our method is that the input signal is represented by the velocity of tracking signals. We show that the velocity representation better models the correlation between the upper‐body and lower‐body motions and increases the robustness against the diverse scales and proportions of the user body than position‐orientation representations. In addition, to remove foot‐skating and floating artifacts, our network predicts feet contact state, which is used to post‐process the lower‐body pose with inverse kinematics to preserve the contact. Our network is lightweight so as to run in real‐time applications. We show the effectiveness of our method through several quantitative evaluations against other architectures and input representations with respect to wild tracking data obtained from commercial VR devices.
Quad Layouts via Constrained T‐Mesh Quantization
Authors: Lyon M. , Campen M. , Kobbelt L. 
Published: June 2021

AbstractWe present a robust and fast method for the creation of conforming quad layouts on surfaces. Our algorithm is based on the quantization of a T‐mesh, i.e. an assignment of integer lengths to the sides of a non‐conforming rectangular partition of the surface. This representation has the benefit of being able to encode an infinite number of layout connectivity options in a finite manner, which guarantees that a valid layout can always be found. We carefully construct the T‐mesh from a given seamless parametrization such that the algorithm can provide guarantees on the results' quality. In particular, the user can specify a bound on the angular deviation of layout edges from prescribed directions. We solve an integer linear program (ILP) to find a coarse quad layout adhering to that maximal deviation. Our algorithm is guaranteed to yield a conforming quad layout free of T‐junctions together with bounded angle distortion. Our results show that the presented method is fast, reliable, and achieves high quality layouts.
Two‐step Temporal Interpolation Network Using Forward Advection for Efficient Smoke Simulation
Authors: Oh Young Jin , Lee In‐Kwon 
Published: June 2021

AbstractIn this paper, we propose a two‐step temporal interpolation network using forward advection to generate smoke simulation efficiently. By converting a low frame rate smoke simulation computed with a large time step into a high frame rate smoke simulation through inference of temporal interpolation networks, the proposed method can efficiently generate smoke simulation with a high frame rate and low computational costs. The first step of the proposed method is optical flow‐based temporal interpolation using deep neural networks (DNNs) for two given smoke animation frames. In the next step, we compute temporary smoke frames with forward advection, a physical computation with a low computational cost. We then interpolate between the results of the forward advection and those of the first step to generate more accurate and enhanced interpolated results. We performed quantitative analyses of the results generated by the proposed method and previous temporal interpolation methods. Furthermore, we experimentally compared the performance of the proposed method with previous methods using DNNs for smoke simulation. We found that the results generated by the proposed method are more accurate and closer to the ground truth smoke simulation than those generated by the previous temporal interpolation methods. We also confirmed that the proposed method generates smoke simulation results more efficiently with lower computational costs than previous smoke simulation methods using DNNs.
Interactive Photo Editing on Smartphones via Intrinsic Decomposition
Authors: Shekhar Sumit , Reimann Max , Mayer Maximilian , Semmo Amir , Pasewaldt Sebastian , Döllner Jürgen , Trapp Matthias 
Published: June 2021

AbstractIntrinsic decomposition refers to the problem of estimating scene characteristics, such as albedo and shading, when one view or multiple views of a scene are provided. The inverse problem setting, where multiple unknowns are solved given a single known pixel‐value, is highly under‐constrained. When provided with correlating image and depth data, intrinsic scene decomposition can be facilitated using depth‐based priors, which nowadays is easy to acquire with high‐end smartphones by utilizing their depth sensors. In this work, we present a system for intrinsic decomposition of RGB‐D images on smartphones and the algorithmic as well as design choices therein. Unlike state‐of‐the‐art methods that assume only diffuse reflectance, we consider both diffuse and specular pixels. For this purpose, we present a novel specularity extraction algorithm based on a multi‐scale intensity decomposition and chroma inpainting. At this, the diffuse component is further decomposed into albedo and shading components. We use an inertial proximal algorithm for non‐convex optimization (iPiano) to ensure albedo sparsity. Our GPU‐based visual processing is implemented on iOS via the Metal API and enables interactive performance on an iPhone 11 Pro. Further, a qualitative evaluation shows that we are able to obtain high‐quality outputs. Furthermore, our proposed approach for specularity removal outperforms state‐of‐the‐art approaches for real‐world images, while our albedo and shading layer decomposition is faster than the prior work at a comparable output quality. Manifold applications such as recoloring, retexturing, relighting, appearance editing, and stylization are shown, each using the intrinsic layers obtained with our method and/or the corresponding depth data.
STALP: Style Transfer with Auxiliary Limited Pairing
Authors: Futschik D. , Kučera M. , Lukáč M. , Wang Z. , Shechtman E. , Sýkora D. 
Published: June 2021

AbstractWe present an approach to example‐based stylization of images that uses a single pair of a source image and its stylized counterpart. We demonstrate how to train an image translation network that can perform real‐time semantically meaningful style transfer to a set of target images with similar content as the source image. A key added value of our approach is that it considers also consistency of target images during training. Although those have no stylized counterparts, we constrain the translation to keep the statistics of neural responses compatible with those extracted from the stylized source. In contrast to concurrent techniques that use a similar input, our approach better preserves important visual characteristics of the source style and can deliver temporally stable results without the need to explicitly handle temporal consistency. We demonstrate its practical utility on various applications including video stylization, style transfer to panoramas, faces, and 3D models.
Algorithms for Microscopic Crowd Simulation: Advancements in the 2010s
Authors: van Toll W. , Pettré J. 
Published: June 2021

AbstractThe real‐time simulation of human crowds has many applications. Simulating how the people in a crowd move through an environment is an active and ever‐growing research topic. Most research focuses on microscopic (or ‘agent‐based’) crowd‐simulation methods that model the behavior of each individual person, from which collective behavior can then emerge.This state‐of‐the‐art report analyzes how the research on microscopic crowd simulation has advanced since the year 2010. We focus on the most popular research area within the microscopic paradigm, which is local navigation, and most notably collision avoidance between agents. We discuss the four most popular categories of algorithms in this area (force‐based, velocity‐based, vision‐based, and data‐driven) that have either emerged or grown in the last decade. We also analyze the conceptual and computational (dis)advantages of each category. Next, we extend the discussion to other types of behavior or navigation (such as group behavior and the combination with path planning), and we review work on evaluating the quality of simulations.Based on the observed advancements in the 2010s, we conclude by predicting how the research area of microscopic crowd simulation will evolve in the future. Overall, we expect a significant growth in the area of data‐driven and learning‐based agent navigation, and we expect an increasing number of methods that re‐group multiple ‘levels’ of behavior into one principle. Furthermore, we observe a clear need for new ways to analyze (real or simulated) crowd behavior, which is important for quantifying the realism of a simulation and for choosing the right algorithms at the right time.
Belt and Road Initiative in Central Asia: Anticipating socioecological challenges from large‐scale infrastructure in a global biodiversity hotspot
Authors: Foggin J Marc , Lechner Alex M , Emslie‐Smith Matthew , Hughes Alice C , Sternberg Troy , Dossani Rafiq 
Published: June 2021

AbstractUntil recently, China's Belt and Road Initiative (BRI) has overlooked many of the social and environmental dimensions of its projects and actions in favor of more immediate economic and sociopolitical considerations. The main focus of investments under BRI has largely been to improve transport, telecommunication, and energy infrastructures. However, in Central Asia, biodiversity is not only foundational for the livelihoods and socioeconomic wellbeing of communities, it also shapes people's culture and identities. Furthermore, ecosystem services derived from functioning landscapes bring enormous benefit for millions of people downstream through integrated and transboundary water systems. Already under pressure from climate‐induced melting of glaciers, the fate of ecologically important areas is considered in light of the potential harm arising from large‐scale linear infrastructure projects and related investments under China‐led BRI. Following review of some of the anticipated impacts of BRI on mountain environments and societies in the region, we highlight several emerging opportunities and then offer recommendations for development programs—aiming fundamentally to enhance the sustainability of BRI investments. Leveraging new opportunities to strengthen partner countries’ priority Sustainable Development Goals and enhancing their agency in the selection of collaborations and the standards to use in environmental impact and risk assessments are recommended.
Insights into the potential role of alpha1‐antitrypsin in COVID‐19 patients: Mechanisms, current update, and future perspectives
Authors: Marzouk Saber , Attia Noha , Mashal Mohamed 
Published: June 2021

AbstractIn this work, we provide an up‐to‐date summary of the available molecular‐ and cell‐related mechanisms by which alpha1‐antitrypsin (AAT) protein could be of benefit in treating COVID‐19 patients. As well, we demonstrate the current status in terms of the ongoing clinical trials using AAT in COVID‐19 patients. Finally, we touch on the potential role gene therapy and stem cell‐based gene therapy could have in such emerging and serious condition caused by the SARS‐CoV‐2 virus.
Consequences of oral antithrombotic use in patients with chronic kidney disease
Authors: Laville Solène M. , Lambert Oriane , Hamroun Aghiles , Metzger Marie , Jacquelinet Christian , Laville Maurice , Frimat Luc , Fouque Denis , Combe Christian , Ayav Carole , Pecoits‐Filho Roberto , Stengel Bénédicte , Massy Ziad A. , Liabeuf Sophie ,   the CKD‐REIN Study Collaborators
Published: June 2021

AbstractWe assessed the risks of bleeding, acute kidney injury (AKI), and kidney failure associated with the prescription of antithrombotic agents (oral anticoagulants and/or antiplatelet agents) in patients with moderate‐to‐advanced chronic kidney disease (CKD). CKD‐REIN is a prospective cohort of 3022 nephrology outpatients with CKD stages 2–5 at baseline. We used cause‐specific Cox proportional hazard models to estimate hazard ratios (HRs) for bleeding (identified through hospitalizations), AKI, and kidney failure. Prescriptions of oral antithrombotics were treated as time‐dependent variables. At baseline, 339 (11%) patients (65% men; 69 [60–76] years) were prescribed oral anticoagulants only, 1095 (36%) antiplatelets only, and 101 (3%) both type of oral antithrombotics. Over a median (interquartile range [IQR]) follow‐up period of 3.0 (IQR, 2.8–3.1) years, 152 patients experienced a bleeding event, 414 patients experienced an episode of AKI, and 270 experienced kidney failure. The adjusted HRs (95% confidence interval [95% CI]) for bleeding associated with prescriptions of antiplatelets only, oral anticoagulants only, and antiplatelet + oral anticoagulant were, respectively, 0.74 (95% CI, 0.46–1.19), 2.38 (95% CI, 1.45–3.89), and 3.96 (95% CI, 2.20–7.12). An increased risk of AKI risk was associated with the prescription of oral anticoagulants (adjusted HR, 1.90, 95% CI, 1.47–2.45) but not the prescription of antiplatelets (HR, 1.24, 95% CI, 0.98–1.56). Kidney failure was not associated with the prescription of oral antithrombotics of any type. This study confirms the high risk of AKI associated with oral anticoagulants prescription in patients with CKD and also highlights the potential aggravating effect of combining vitamin K antagonist (VKA) and antiplatelets on the risk of bleeding.
Financial inclusion, Pradhan Mantri Jan Dhan Yojna Scheme and economic growth: Evidence from Indian States
Authors: Singh Bhanu Pratap , Kumari Annu , Sharma Tanya , Malhotra Abhishek 
Published: June 2021

AbstractThe study examines the impact of financial inclusion, promoted through the Pradhan Mantri Jan Dhan Yojna (PMJDY) scheme, on the economic performance across the Indian states. Using the index of financial inclusion developed in Sarma (2008), the current study develops a three‐dimensional financial inclusion index for 25 major Indian states from 2011 to 2016 to assess the status of financial inclusion across Indian states. The impact of financial inclusion promoted through the PMJDY scheme on the economic performance of the Indian states is investigated using bootstrap corrected fixed effects estimation and inference in the dynamic panel. The study's finding suggests that most Indian states fall under the low or medium level of financial inclusion. The dynamic panel results reveal a positive and significant association between financial inclusion and economic growth across Indian states. Further, results show PMJDY scheme marginally improved the pace of economic growth but failed to improve the overall economic prosperity level across states. Poor usage of financial services and a rise in the number of dormant accounts after the PMJDY scheme's launch are the significant limitations of the PMJDY scheme's failure.
Searching for new predators of the invasive Halyomorpha halys: the role of the black garden ant Lasius niger
Authors: Bulgarini Giacomo , Castracani Cristina , Mori Alessandra , Grasso Donato A. , Maistrello Lara 
Published: June 2021

AbstractIn recent years, Halyomorpha halys (Stål) (Hemiptera: Pentatomidae, Cappaeini) has become an invasive pest in North America and Europe, where it caused extensive damage to agriculture, resulting in great economic losses. Evaluating the potential of native predators in the invaded areas, ants might represent good candidates thanks to their biology, ecology, and behavior. In Italy, H. halys proved to be the top key pest in pear orchards, where the black garden ant, Lasius niger (L.) (Hymenoptera: Formicidae, Lasiini), is the most abundant ant species. The aim of this study was to evaluate the predatory ability of L. niger on the eggs and on all the juvenile instars of H. halys under laboratory conditions. The results indicate that L. niger significantly reduces the survival of the second and third nymphal instars by 56 and 58%, respectively, but it is unable to reduce the egg hatching and the survival of the first, fourth, and fifth instars. Our preliminary results obtained in laboratory conditions suggest a possible role of the ant L. niger in controlling H. halys invasion mainly acting on the smaller and more mobile nymphal stages. The effective role of this species as potential biocontrol agents of H. halys in fruit orchards in association with other ant species as well as with other predatory insects is discussed.
Analysis in the ethical decision‐making of dental, nurse and physiotherapist students, through case‐based learning
Authors: Macpherson Ignacio , Roqué María Victoria , Martín‐Sánchez Juan Carlos , Segarra Ignacio 
Published: June 2021

AbstractIntroductionTraining in ethical competencies is perceived with special interest among the objectives of health education. The dimensions of the person such as integrity, autonomy and dignity influence the choice of interventions, but the different specialties of the health sciences conceive these dimensions with different perspectives depending on the clinical setting. These divergences can be detected during the first years of undergraduate studies, and it is important to know the professional bias and its possible causes.Materials and MethodsA procedure was developed through case‐based learning (CBL) to assess various characteristics of decision‐making during the early stages of student training. A semi‐quantitative method was designed based on the narrative responses of a case with ethical implications in the field of gender violence. The method was applied to 294 undergraduate students in nursing (95), physiotherapy (109) and dentistry (90) from the Faculty of Health Sciences of a Spanish university. A frequency analysis of the narrative responses of the students to the proposed case was carried out, using the chi‐square test to determine any association between the variables studied: gender, specialty and ethical knowledge.ResultsFour types of response categories were detected, as a result of combining the personal conversation, report to legal authority or require assistance of other teams. The most common option in dentists is conversation only, while physical therapists include the assistance of other teams. In nursing, a balance is observed between both possibilities. The results show that student responses differ significantly among specialties and also differ significantly according to test scores on ethical knowledge. However, no significant differences were found between the responses provided by men and women.ConclusionMost of the health sciences students highly valued their own capacity for dialogue and reflection to approach situations with complex ethical dimensions. We consider that case‐based learning (CBL), in combination with narrative analysis is a valid means of evaluating the professional ethical competencies of students in health sciences careers applied to a common goal.