bluuebunny/crossref_metadata_2025 huggingface dataset:
An Open-Source Monitor-Independent Movement Summary for Accelerometer Data Processing
Authors: John Dinesh , Tang Qu , Albinali Fahd , Intille Stephen
Published: November 2019

Background: Physical behavior researchers using motion sensors often use acceleration summaries to visualize, clean, and interpret data. Such output is dependent on device specifications (e.g., dynamic range, sampling rate) and/or are proprietary, which invalidate cross-study comparison of findings when using different devices. This limits flexibility in selecting devices to measure physical activity, sedentary behavior, and sleep. Purpose: Develop an open-source, universal acceleration summary metric that accounts for discrepancies in raw data among research and consumer devices. Methods: We used signal processing techniques to generate a Monitor-Independent Movement Summary unit (MIMS-unit) optimized to capture normal human motion. Methodological steps included raw signal harmonization to eliminate inter-device variability (e.g., dynamic g-range, sampling rate), bandpass filtering (0.2–5.0 Hz) to eliminate non-human movement, and signal aggregation to reduce data to simplify visualization and summarization. We examined the consistency of MIMS-units using orbital shaker testing on eight accelerometers with varying dynamic range (±2 to ±8 g) and sampling rates (20–100 Hz), and human data (N = 60) from an ActiGraph GT9X. Results: During shaker testing, MIMS-units yielded lower between-device coefficient of variations than proprietary ActiGraph and ENMO acceleration summaries. Unlike the widely used ActiGraph activity counts, MIMS-units were sensitive in detecting subtle wrist movements during sedentary behaviors. Conclusions: Open-source MIMS-units may provide a means to summarize high-resolution raw data in a device-independent manner, thereby increasing standardization of data cleaning and analytical procedures to estimate selected attributes of physical behavior across studies.
Gait Retraining With Real-Time Visual Feedback to Treat Patellofemoral Pain in Adult Recreational Runners: A Critically Appraised Topic
Authors: Fyock Marcie , Cortes Nelson , Hulse Alex , Martin Joel
Published: November 2019

Clinical Scenario: Patellofemoral pain (PFP) is a common knee injury in recreational adult runners, possibly caused by faulty mechanics. One possible approach to reduce this pain is to retrain the runner’s gait. Current research suggests that no definitive gold standard treatment for PFP exists. Gait retraining utilizing visual feedback may reduce PFP in both the short and long term. Clinical Question: In adult runners diagnosed with PFP, does gait retraining with real-time visual feedback lead to a decrease in pain? Summary of Key Findings: A literature search was performed; 3 relevant studies utilizing gait retraining with visual feedback, pain level as an outcome measure, and follow-up measures of at least 1 month after the intervention were included. All the included studies reported a decrease in short- and long-term pain for participants following visual feedback gait retraining. In addition, biomechanical measures related to PFP, including peak hip adduction angle and the angle of contralateral pelvic drop, improved after the completion of the intervention. Clinical Bottom Line: There is level 2 evidence supporting the implementation of 8 sessions over 2 weeks of visual feedback gait retraining as a means of treating patients diagnosed with PFP. Based on current available evidence, clinicians should identify faulty mechanics of patients and implement a protocol of increasing real-time visual feedback over the first 4 sessions and decreasing visual feedback over the final 4 sessions. Strength of Recommendation: Level 2.
Mystery oil spill threatens marine sanctuary in Brazil
Authors: Escobar Herton
Published: November 2019

Months after its release, crude oil has reached Abrolhos Bank, a biodiversity hot spot in the southern Atlantic Ocean.
Immigrants from the Middle East shaped Rome
Authors: Wade Lizzie
Published: November 2019

Genetic history of the city finds little European DNA at the height of the imperial period.
Unearthing the reality of slavery
Authors: Wade Lizzie
Published: November 2019

Excavations on a Caribbean island reveal the lives of enslaved Africans, and how their labor built today's world.
Structures of a dimodular nonribosomal peptide synthetase reveal conformational flexibility
Authors: Reimer Janice M. , Eivaskhani Maximilian , Harb Ingrid , Guarné Alba , Weigt Martin , Schmeing T. Martin
Published: November 2019

Moving modules drive biosynthesis

Modular biosynthesis of small molecules—where enzyme units can be swapped in and out of assembly line complexes to produce desired products—is a distant goal in the lab despite a huge diversity of modular systems in nature. Part of the challenge is in understanding how modules interact and hand off intermediates. Reimer
et al.
determined crystal structures of portions of a nonribosomal peptide synthetase, including a full dimodule. Module positioning differed between these structures even when the same intermediate was attached to the enzyme. The authors used small-angle x-ray scattering to confirm that large conformational changes are possible during biosynthesis and handoff between modules.

Science
, this issue p.
eaaw4388
Beyond vetting donors
Authors: Thorp H. Holden
Published: November 2019

Jeffrey Epstein, the Sackler family, the Koch brothers—these major private donors have reignited talk about how research and education get funded. Increasingly, universities are spending substantial time not only deciding whether to accept gifts but defending them after the fact or renouncing them and returning the money. Although the lessons of Jeffrey Epstein are clear—the Massachusetts Institute of Technology didn't stop its Media Lab from taking his gifts when it was obvious who he was—it seems likely that these kinds of episodes are going to happen more frequently in the future. Blame the intense competition among institutions at a time when money to support university endeavors has gotten tighter. Unfortunately, there's been more talk about this problem than action to solve it.
In Vivo
Pharmacodynamic Evaluation of Omadacycline against Staphylococcus aureus in the Neutropenic Mouse Pneumonia Model
Authors: Lepak Alexander J. , Zhao Miao , Marchillo Karen , VanHecker Jamie , Andes David R.
Published: November 2019

Omadacycline is an effective therapy for community-acquired bacterial pneumonia (CABP). Given its potent activity against methicillin-susceptible
Staphylococcus aureus
(MSSA) and methicillin-resistant
S. aureus
(MRSA), we sought to determine the pharmacodynamic activity and target pharmacokinetic/pharmacodynamic (PK/PD) exposures associated with a therapeutic effect in the neutropenic mouse pneumonia model against 10 MSSA/MRSA strains.
A case of Guillain-Barre syndrome with pregnancy who delivered in ICU: a rare outcome of rare co-occurrence
Authors: Jain Rahul , Rathi Pankaj Satyanarayan , Telang Kapil , Zaidi Alia
Published: November 2019

Guillain-Barre syndrome (GBS) in pregnancy is a rare co-occurrence and it is generally accepted that it carries a high maternal risk. We report a 40-year-old housewife who developed GBS with respiratory failure during the third trimester (31 weeks of gestation). She had preterm vaginal delivery on ventilator support at 34 weeks of gestation in neurology intensive care unit. She managed successfully with ventilatory support, intravenous immunoglobulin and other supportive measures.
Inadvertent arterial placement of central venous catheter: salvage using endovascular treatment
Authors: Shaw Manish , Chandrashekhara Sheragaru Hanumanthappa , Sharma Arun , Kumar Sanjeev
Published: November 2019

The frequency of placing a central venous catheter (CVC) has increased and it is often performed in emergency situation for venous access. During such an emergency and placing without imaging guidance, sometimes inadvertent placement of CVC in subclavian artery (SCA) can occur. We hereby describe an unusual case of successful endovascular management of inadvertently inserted CVC in SCA by covered stent graft placement along with proper clinical context to manage a case of misplaced venous catheter in left SCA.
Effect of motivational interviewing on gestational weight gain and fetal growth in pregnant women with type 2 diabetes
Authors: Ásbjörnsdóttir Björg , Vestgaard Marianne , Ringholm Lene , Andersen Lise Lotte Torvin , Jensen Dorte Møller , Damm Peter , Mathiesen Elisabeth R
Published: November 2019

Objective

To study how lifestyle coaching with motivational interviewing to improve adherence to healthy eating affects gestational weight gain and fetal growth in pregnant women with type 2 diabetes in a real-world setting.

Research design and methods

A cohort study including a prospective intervention cohort of consecutive, singleton pregnant, Danish-speaking women with type 2 diabetes included between August 2015 and February 2018 and a historical reference cohort included between February 2013 and August 2015. The intervention consisted of a motivational interviewing to improve adherence to healthy eating in addition to routine care. The reference cohort received routine care only. The main outcomes were gestational weight gain and large for gestational age (LGA) infants.

Results

Ninety-seven women were included in the intervention cohort and 92 in the reference cohort. Pre-pregnancy body mass index (32.8±6.9 kg/m2vs 32.4±7.4 kg/m2, p=0.70), gestational weight gain (9.2±5.8 kg vs 10.2±5.8 kg, p=0.25), HbA1c in early pregnancy (6.7%±1.1% vs 6.5%±1.3% (50±12 mmol/mol vs 48±14 mmol/mol), p=0.32) and late pregnancy (5.9%±0.5% vs 6.0%±0.6% (41±6 mmol/mol vs 42±7 mmol/mol), p=0.34) were comparable in the two cohorts. LGA infants occurred in 20% vs 31%, p=0.07, respectively, and after adjustment for maternal characteristics 14% vs 27% delivered LGA infants (p=0.04). Birth weight z-score was 0.24±1.36 vs 0.61±1.38, p=0.06.

Conclusions

Motivational interviewing to improve adherence to healthy eating in addition to routine care in pregnant women with type 2 diabetes tended to reduce fetal overgrowth without major effect on gestational weight gain. Further studies investigating the cost-benefit of enhancing motivation are needed.

Trial registration number

NCT02883127.
The Stunting Tool for Early Prevention: development and external validation of a novel tool to predict risk of stunting in children at 3 years of age
Authors: Hanieh Sarah , Braat Sabine , Simpson Julie A , Ha Tran Thi Thu , Tran Thach D , Tuan Tran , Fisher Jane , Biggs Beverley-Ann
Published: November 2019

Introduction

Globally, an estimated 151 million children under 5 years of age still suffer from the adverse effects of stunting. We sought to develop and externally validate an early life predictive model that could be applied in infancy to accurately predict risk of stunting in preschool children.

Methods

We conducted two separate prospective cohort studies in Vietnam that intensively monitored children from early pregnancy until 3 years of age. They included 1168 and 475 live-born infants for model development and validation, respectively. Logistic regression on child stunting at 3 years of age was performed for model development, and the predicted probabilities for stunting were used to evaluate the performance of this model in the validation data set.

Results

Stunting prevalence was 16.9% (172 of 1015) in the development data set and 16.4% (70 of 426) in the validation data set. Key predictors included in the final model were paternal and maternal height, maternal weekly weight gain during pregnancy, infant sex, gestational age at birth, and infant weight and length at 6 months of age. The area under the receiver operating characteristic curve in the validation data set was 0.85 (95% Confidence Interval, 0.80–0.90).

Conclusion

This tool applied to infants at 6 months of age provided valid prediction of risk of stunting at 3 years of age using a readily available set of parental and infant measures. Further research is required to examine the impact of preventive measures introduced at 6 months of age on those identified as being at risk of growth faltering at 3 years of age.
Impact of initial structural heterogeneity on long-term swelling behavior of MX80 bentonite pellet/powder mixtures
Authors: Molinero Guerra Agustín , Mokni Nadia , Cui Yu-Jun , Delage Pierre , Tang Anh Minh , Aimedieu Patrick , Bernier Frédéric , Bornert Michel
Published: November 2019

To better understand results of SEALEX in situ tests carried out at Tournemire Underground Research Laboratory, the hydromechanical behavior of a pellet/powder MX80 bentonite mixtures prepared at a dry density of 1.49 Mg/m3 were investigated by means of microfocus X-ray computed tomography (μ-CT) observations and laboratory small scale infiltration tests. Radial and axial swelling pressures as well as relative humidity were monitored while wetting. Two configurations were considered: for the first, a pellet/powder mixture was prepared following a specific protocol to minimize initial structural heterogeneity; the second one was specially designed to study a strong heterogeneous mixture distribution. μ-CT observations performed on the two samples during hydration revealed an apparently homogeneous sample for the first mixture after 100 days of hydration. For the second specimen, several voids were still observed after 40 days of hydration. A comparison was made between the in situ and mock-up tests. It was observed that the evolutions of radial and axial swelling pressures depend on the initial heterogeneous distribution of the mixture. This heterogeneity is due to the different dry density values at the vicinity of the different sensors. The final values of axial swelling pressures were different for both configurations for the same global dry density.
Wind driven natural ventilation through multiple gridirons of a pile-supported wharf to prevent atmospheric corrosion in the marine environment
Authors: Zhou Yubao , Zhou Yujue , Zheng Yonglai , Zhang Qin , Wu Zhuorui , Liu Don
Published: November 2019

Salt fog originating from the offshore zone can easily accumulate in the semi-enclosed space (called the gridiron space) formed by the beams and slabs of a pile-supported wharf. Such accumulation can lead to the premature failure of the durability of the wharf’s substructure. Previous works have mainly focused on material-level measures to eliminate the adverse effects of salt fog accumulation, such as the use of anti-corrosion concrete and anti-corrosion coatings, which could result in increased construction costs. In fact, salt fog can be extracted from a gridiron by natural ventilation in some cases, so it is important to examine the flow characteristics of the gridiron space. In this work, a numerical simulation was conducted with natural ventilation and auxiliary ventilation groups. The critical value of the shield ratio (defined as the height ratio of edge stringer to air inlet) that led to the weakest turbulence of airflow inside the gridiron was studied in the natural ventilation group. In addition, the effect of various configurations of vents was discussed; in accordance with the results, design recommendations were proposed to enhance the durability of a pile-supported wharf.
Impacts of multitype interactions on epidemic spreading in temporal networks
Authors: Dong NingNing , Han YueXing , Li Qing , Wang Bing
Published: November 2019

Individuals have often been found to interact with each other with different intensity in a dynamical way due to their various types in real networks, which plays a fundamental role in dynamical process such as epidemic spreading. To understand the relationship between the network structure and the spreading process, we propose a kind of temporal network model which contains diverse types of individuals. Furthermore, we also assume that the transmission rate is also related to the individuals’ types. Theoretical analysis and numerical results show that the epidemic threshold is affected by several factors, such as parameters described network structure and the ratio of intra-transmission rate to inter-transmission rate. Finally, we investigate immunization strategies for the network model and propose an immunization strategy by considering the mutual effect of individual’s degree connected with the same type and those with different types. By comparing a kind of immunization strategies, we find that the proposed immunization strategy is able to suppress the outbreak with less observation time and that it is able to suppress the outbreak almost as efficient as the target immunization strategy with appropriate observation time.
An Efficient Framework for Network Code based Multimedia Content Distribution in Hybrid P2P Network
Authors: Anandaraj M. , Ganeshkumar P. , Selvaraj K. , Vijayakumar K. P.
Published: November 2019

Most of the existing peer-to-peer (P2P) content distribution schemes carry out a random or rarest piece first content dissemination procedure to avoid duplicate transmission of the same pieces of data and rare pieces of data occurring in the network. This problem is solved using P2P content distribution based on network coding scheme. Network coding scheme uses random linear combination of coded pieces. Hence, the above-stated problem is solved easily and simply. Our proposed mechanism uses network coding mechanism in which several contents having the same message are grouped into different groups and coding operation is performed only within the same group. The interested peers are also divided into several groups with each group having the responsibility to spread one set of contents of messages. The coding system is designed to assure the property that any subset of the messages can be utilized to decode the original content as long as the size of the subset is suitably large. To meet this condition, dynamic smart network coding (DSNC) scheme is defined which assures the preferred property, then peers are connected in the same group to send the corresponding message, and connect peers in different groups to disseminate messages for carrying out decoding operation. Moreover, the proposed system is readily expanded to support topology change to get better system performance further in terms of reliability, link stress and throughput. The simulation results prove that the proposed system can attain 20–25% higher throughput than existing systems, good reliability, link failure and robustness to peer churn.
Realizable ranks of joins and intersections of subgroups in free groups
Authors: Soroko Ignat
Published: November 2019

The famous Hanna Neumann Conjecture (now the Friedman–Mineyev theorem) gives an upper bound for the ranks of the intersection of arbitrary subgroups [Formula: see text] and [Formula: see text] of a non-abelian free group. It is an interesting question to “quantify” this bound with respect to the rank of [Formula: see text], the subgroup generated by [Formula: see text] and [Formula: see text]. We describe a set of realizable values [Formula: see text] for arbitrary [Formula: see text], [Formula: see text], and conjecture that this locus is complete. We study the combinatorial structure of the topological pushout of the core graphs for [Formula: see text] and [Formula: see text] with the help of graphs introduced by Dicks in the context of his Amalgamated Graph Conjecture. This allows us to show that certain conditions on ranks of [Formula: see text], [Formula: see text] are not realizable, thus resolving the remaining open case [Formula: see text] of Guzman’s “Group-Theoretic Conjecture” in the affirmative. This in turn implies the validity of the corresponding “Geometric Conjecture” on hyperbolic 3-manifolds with a 6-free fundamental group. Finally, we prove the main conjecture describing the locus of realizable values for the case when [Formula: see text].
On bicomplex Fourier–Wigner transforms
Authors: El Gourari A. , Ghanmi A. , Zine K.
Published: November 2019

We consider the [Formula: see text]d and [Formula: see text]d bicomplex analogues of the classical Fourier–Wigner transform. Their basic properties, including Moyal’s identity and characterization of their ranges giving rise to new bicomplex–polyanalytic functional spaces are discussed. Details concerning a special window function are developed explicitly. An orthogonal basis for the space of bicomplex-valued square integrable functions on the bicomplex numbers is constructed by means of a specific class of bicomplex Hermite functions.
Mitsuba 2
Authors: Nimier-David Merlin , Vicini Delio , Zeltner Tizian , Jakob Wenzel
Published: November 2019

Modern rendering systems are confronted with a dauntingly large and growing set of requirements: in their pursuit of realism, physically based techniques must increasingly account for intricate properties of light, such as its spectral composition or polarization. To reduce prohibitive rendering times, vectorized renderers exploit coherence via instruction-level parallelism on CPUs and GPUs. Differentiable rendering algorithms propagate derivatives through a simulation to optimize an objective function, e.g., to reconstruct a scene from reference images. Catering to such diverse use cases is challenging and has led to numerous purpose-built systems---partly, because retrofitting features of this complexity onto an existing renderer involves an error-prone and infeasibly intrusive transformation of elementary data structures, interfaces between components, and their implementations (in other words, everything).

We propose Mitsuba 2, a versatile renderer that is intrinsically retargetable to various applications including the ones listed above. Mitsuba 2 is implemented in modern C++ and leverages template metaprogramming to replace types and instrument the control flow of components such as BSDFs, volumes, emitters, and rendering algorithms. At compile time, it automatically transforms arithmetic, data structures, and function dispatch, turning generic algorithms into a variety of efficient implementations without the tedium of manual redesign. Possible transformations include changing the representation of color, generating a "wide" renderer that operates on bundles of light paths, just-in-time compilation to create computational kernels that run on the GPU, and forward/reverse-mode automatic differentiation. Transformations can be chained, which further enriches the space of algorithms derived from a single generic implementation.

We demonstrate the effectiveness and simplicity of our approach on several applications that would be very challenging to create without assistance: a rendering algorithm based on coherent MCMC exploration, a caustic design method for gradient-index optics, and a technique for reconstructing heterogeneous media in the presence of multiple scattering.
Learning predict-and-simulate policies from unorganized human motion data
Authors: Park Soohwan , Ryu Hoseok , Lee Seyoung , Lee Sunmin , Lee Jehee
Published: November 2019

The goal of this research is to create physically simulated biped characters equipped with a rich repertoire of motor skills. The user can control the characters interactively by modulating their control objectives. The characters can interact physically with each other and with the environment. We present a novel network-based algorithm that learns control policies from unorganized, minimally-labeled human motion data. The network architecture for interactive character animation incorporates an RNN-based motion generator into a DRL-based controller for physics simulation and control. The motion generator guides forward dynamics simulation by feeding a sequence of future motion frames to track. The rich future prediction facilitates policy learning from large training data sets. We will demonstrate the effectiveness of our approach with biped characters that learn a variety of dynamic motor skills from large, unorganized data and react to unexpected perturbation beyond the scope of the training data.
Repairing man-made meshes via visual driven global optimization with minimum intrusion
Authors: Chu Lei , Pan Hao , Liu Yang , Wang Wenping
Published: November 2019

3D mesh models created by human users and shared through online platforms and datasets flourish recently. While the creators generally have spent large efforts in modeling the visually appealing shapes with both large scale structures and intricate details, a majority of the meshes are unfortunately flawed in terms of having duplicate faces, mis-oriented regions, disconnected patches, etc., due to multiple factors involving both human errors and software inconsistencies. All these artifacts have severely limited the possible low-level and high-level processing tasks that can be applied to the rich datasets. In this work, we present a novel approach to fix these man-made meshes such that the outputs are guaranteed to be oriented manifold meshes that preserve the original structures, big and small, as much as possible. Our key observation is that the models all visually look meaningful, which leads to our strategy of repairing the flaws while always preserving the visual quality. We apply local refinements and removals only where necessary to achieve minimal intrusion of the original meshes, and global adjustments through robust optimization to ensure the outputs are valid manifold meshes with optimal connections. We test the approach on large-scale 3D datasets, and obtain quality meshes that are more readily usable for further geometry processing tasks.
Reparameterizing discontinuous integrands for differentiable rendering
Authors: Loubet Guillaume , Holzschuch Nicolas , Jakob Wenzel
Published: November 2019

Differentiable rendering has recently opened the door to a number of challenging inverse problems involving photorealistic images, such as computational material design and scattering-aware reconstruction of geometry and materials from photographs. Differentiable rendering algorithms strive to estimate partial derivatives of pixels in a rendered image with respect to scene parameters, which is difficult because visibility changes are inherently non-differentiable.

We propose a new technique for differentiating path-traced images with respect to scene parameters that affect visibility, including the position of cameras, light sources, and vertices in triangle meshes. Our algorithm computes the gradients of illumination integrals by applying changes of variables that remove or strongly reduce the dependence of the position of discontinuities on differentiable scene parameters. The underlying parameterization is created on the fly for each integral and enables accurate gradient estimates using standard Monte Carlo sampling in conjunction with automatic differentiation. Importantly, our approach does not rely on sampling silhouette edges, which has been a bottleneck in previous work and tends to produce high-variance gradients when important edges are found with insufficient probability in scenes with complex visibility and high-resolution geometry. We show that our method only requires a few samples to produce gradients with low bias and variance for challenging cases such as glossy reflections and shadows. Finally, we use our differentiable path tracer to reconstruct the 3D geometry and materials of several real-world objects from a set of reference photographs.
Learning an intrinsic garment space for interactive authoring of garment animation
Authors: Wang Tuanfeng Y. , Shao Tianjia , Fu Kai , Mitra Niloy J.
Published: November 2019

Authoring dynamic garment shapes for character animation on body motion is one of the fundamental steps in the CG industry. Established workflows are either time and labor consuming (i.e., manual editing on dense frames with controllers), or lack keyframe-level control (i.e., physically-based simulation). Not surprisingly, garment authoring remains a bottleneck in many production pipelines. Instead, we present a deep-learning-based approach for semi-automatic authoring of garment animation, wherein the user provides the desired garment shape in a selection of keyframes, while our system infers a latent representation for its motion-independent
intrinsic parameters
(e.g., gravity, cloth materials, etc.). Given new character motions, the latent representation allows to automatically generate a plausible garment animation at interactive rates. Having factored out character motion, the learned intrinsic garment space enables smooth transition between keyframes on a new motion sequence. Technically, we learn an intrinsic garment space with an motion-driven autoencoder network, where the encoder maps the garment shapes to the intrinsic space under the condition of body motions, while the decoder acts as a
differentiable simulator
to generate garment shapes according to changes in character body motion and intrinsic parameters. We evaluate our approach qualitatively and quantitatively on common garment types. Experiments demonstrate our system can significantly improve current garment authoring workflows via an interactive user interface. Compared with the standard CG pipeline, our system significantly reduces the ratio of required keyframes from 20% to 1 -- 2%.
Hierarchical and view-invariant light field segmentation by maximizing entropy rate on 4D ray graphs
Authors: Li Rui , Heidrich Wolfgang
Published: November 2019

Image segmentation is an important first step of many image processing, computer graphics, and computer vision pipelines. Unfortunately, it remains difficult to automatically and robustly segment cluttered scenes, or scenes in which multiple objects have similar color and texture. In these scenarios, light fields offer much richer cues that can be used efficiently to drastically improve the quality and robustness of segmentations.

In this paper we introduce a new light field segmentation method that respects texture appearance, depth consistency, as well as occlusion, and creates well-shaped segments that are robust under view point changes. Furthermore, our segmentation is hierarchical, i.e. with a single optimization, a whole hierarchy of segmentations with different numbers of regions is available. All this is achieved with a submodular objective function that allows for efficient greedy optimization. Finally, we introduce a new tree-array type data structure, i.e. a disjoint tree, to efficiently perform submodular optimization on very large graphs. This approach is of interest beyond our specific application of light field segmentation.

We demonstrate the efficacy of our method on a number of synthetic and real data sets, and show how the obtained segmentations can be used for applications in image processing and graphics.
ZoomOut
Authors: Melzi Simone , Ren Jing , Rodolà Emanuele , Sharma Abhishek , Wonka Peter , Ovsjanikov Maks
Published: November 2019

We present a simple and efficient method for refining maps or correspondences by iterative upsampling in the spectral domain that can be implemented in a few lines of code. Our main observation is that high quality maps can be obtained even if the input correspondences are noisy or are encoded by a small number of coefficients in a spectral basis. We show how this approach can be used in conjunction with existing initialization techniques across a range of application scenarios, including symmetry detection, map refinement across complete shapes, non-rigid partial shape matching and function transfer. In each application we demonstrate an improvement with respect to both the quality of the results and the computational speed compared to the best competing methods, with up to two orders of magnitude speed-up in some applications. We also demonstrate that our method is both robust to noisy input and is scalable with respect to shape complexity. Finally, we present a theoretical justification for our approach, shedding light on structural properties of functional maps.
Learning generative models for rendering specular microgeometry
Authors: Kuznetsov Alexandr , Hašan Miloš , Xu Zexiang , Yan Ling-Qi , Walter Bruce , Kalantari Nima Khademi , Marschner Steve , Ramamoorthi Ravi
Published: November 2019

Rendering specular material appearance is a core problem of computer graphics. While smooth analytical material models are widely used, the high-frequency structure of real specular highlights requires considering discrete, finite microgeometry. Instead of explicit modeling and simulation of the surface microstructure (which was explored in previous work), we propose a novel direction: learning the high-frequency directional patterns from synthetic or measured examples, by training a generative adversarial network (GAN). A key challenge in applying GAN synthesis to spatially varying BRDFs is evaluating the reflectance for a single location and direction without the cost of evaluating the whole hemisphere. We resolve this using a novel method for partial evaluation of the generator network. We are also able to control large-scale spatial texture using a conditional GAN approach. The benefits of our approach include the ability to synthesize spatially large results without repetition, support for learning from measured data, and evaluation performance independent of the complexity of the dataset synthesis or measurement.
StructureNet
Authors: Mo Kaichun , Guerrero Paul , Yi Li , Su Hao , Wonka Peter , Mitra Niloy J. , Guibas Leonidas J.
Published: November 2019

The ability to generate novel, diverse, and realistic 3D shapes along with associated part semantics and structure is central to many applications requiring high-quality 3D assets or large volumes of realistic training data. A key challenge towards this goal is how to accommodate diverse shape variations, including both continuous deformations of parts as well as structural or discrete alterations which add to, remove from, or modify the shape constituents and compositional structure. Such object structure can typically be organized into a hierarchy of constituent object parts and relationships, represented as a hierarchy of
n
-ary graphs. We introduce StructureNet, a hierarchical graph network which (i) can directly encode shapes represented as such
n
-ary graphs, (ii) can be robustly trained on large and complex shape families, and (iii) be used to generate a great diversity of realistic structured shape geometries. Technically, we accomplish this by drawing inspiration from recent advances in graph neural networks to propose an order-invariant encoding of
n
-ary graphs, considering jointly both part geometry and inter-part relations during network training. We extensively evaluate the quality of the learned latent spaces for various shape families and show significant advantages over baseline and competing methods. The learned latent spaces enable several structure-aware geometry processing applications, including shape generation and interpolation, shape editing, or shape structure discovery directly from un-annotated images, point clouds, or partial scans.
Modeling curved folding with freeform deformations
Authors: Rabinovich Michael , Hoffmann Tim , Sorkine-Hornung Olga
Published: November 2019

We present a computational framework for interactive design and exploration of curved folded surfaces. In current practice, such surfaces are typically created manually using physical paper, and hence our objective is to lay the foundations for the digitalization of curved folded surface design. Our main contribution is a discrete binary characterization for folds between discrete developable surfaces, accompanied by an algorithm to simultaneously fold creases and smoothly bend planar sheets. We complement our algorithm with essential building blocks for curved folding deformations: objectives to control dihedral angles and mountain-valley assignments. We apply our machinery to build the first interactive freeform editing tool capable of modeling bending and folding of complicated crease patterns.
GradNet
Authors: Guo Jie , Li Mengtian , Li Quewei , Qiang Yuting , Hu Bingyang , Guo Yanwen , Yan Ling-Qi
Published: November 2019

Monte Carlo (MC) methods for light transport simulation are flexible and general but typically suffer from high variance and slow convergence. Gradientdomain rendering alleviates this problem by additionally generating image gradients and reformulating rendering as a screened Poisson image reconstruction problem. To improve the quality and performance of the reconstruction, we propose a novel and practical deep learning based approach in this paper. The core of our approach is a multi-branch auto-encoder, termed GradNet, which end-to-end learns a mapping from a noisy input image and its corresponding image gradients to a high-quality image with low variance. Once trained, our network is fast to evaluate and does not require manual parameter tweaking. Due to the difficulty in preparing ground-truth images for training, we design and train our network in a completely unsupervised manner by learning directly from the input data. This is the first solution incorporating unsupervised deep learning into the gradient-domain rendering framework. The loss function is defined as an energy function including a data fidelity term and a gradient fidelity term. To further reduce the noise of the reconstructed image, the loss function is reinforced by adding a regularizer constructed from selected rendering-specific features. We demonstrate that our method improves the reconstruction quality for a diverse set of scenes, and reconstructing a high-resolution image takes far less than one second on a recent GPU.
Wirtinger holography for near-eye displays
Authors: Chakravarthula Praneeth , Peng Yifan , Kollin Joel , Fuchs Henry , Heide Felix
Published: November 2019

Near-eye displays using holographic projection are emerging as an exciting display approach for virtual and augmented reality at high-resolution without complex optical setups --- shifting optical complexity to computation. While precise phase modulation hardware is becoming available, phase retrieval algorithms are still in their infancy, and holographic display approaches resort to heuristic encoding methods or iterative methods relying on various relaxations.

In this work, we depart from such existing approximations and solve the phase retrieval problem for a hologram of a scene at a single depth at a given time by revisiting complex Wirtinger derivatives, also extending our framework to render 3D volumetric scenes. Using Wirtinger derivatives allows us to pose the phase retrieval problem as a quadratic problem which can be minimized with first-order optimization methods. The proposed Wirtinger Holography is flexible and facilitates the use of different loss functions, including learned perceptual losses parametrized by deep neural networks, as well as stochastic optimization methods. We validate this framework by demonstrating holographic reconstructions with an order of magnitude lower error, both in simulation and on an experimental hardware prototype.
QuadMixer
Authors: Nuvoli Stefano , Hernandez Alex , Esperança Claudio , Scateni Riccardo , Cignoni Paolo , Pietroni Nico
Published: November 2019

We propose QuadMixer, a novel interactive technique to compose quad mesh components preserving the majority of the original layouts. Quad Layout is a crucial property for many applications since it conveys important information that would otherwise be destroyed by techniques that aim only at preserving shape.

Our technique keeps untouched all the quads in the patches which are not involved in the blending. We first perform robust boolean operations on the corresponding triangle meshes. Then we use this result to identify and build new surface patches for small regions neighboring the intersection curves. These blending patches are carefully quadrangulated respecting boundary constraints and stitched back to the untouched parts of the original models. The resulting mesh preserves the designed edge flow that, by construction, is captured and incorporated to the new quads as much as possible. We present our technique in an interactive tool to show its usability and robustness.
Multi-theme generative adversarial terrain amplification
Authors: Zhao Yiwei , Liu Han , Borovikov Igor , Beirami Ahmad , Sanjabi Maziar , Zaman Kazi
Published: November 2019

Achieving highly detailed terrain models spanning vast areas is crucial to modern computer graphics. The pipeline for obtaining such terrains is via amplification of a low-resolution terrain to refine the details given a desired theme, which is a time-consuming and labor-intensive process. Recently, data-driven methods, such as the sparse construction tree, have provided a promising direction to equip the artist with better control over the theme.

These methods learn to amplify terrain details by using an exemplar of high-resolution detailed terrains to transfer the theme. In this paper, we propose Generative Adversarial Terrain Amplification (GATA) that achieves better local/global coherence compared to the existing data-driven methods while providing even more ways to control the theme. GATA is comprised of two key ingredients. Thefi rst one is a novel embedding of themes into vectors of real numbers to achieve a single tool for multi-theme amplification. The theme component can leverage existing LIDAR data to generate similar terrain features. It can also generate newfi ctional themes by tuning the embedding vector or even encoding a new example terrain into an embedding. The second one is an adversarially trained model that, conditioned on an embedding and a low-resolution terrain, generates a high-resolution terrain adhering to the desired theme. The proposed integral approach reduces the need for unnecessary manual adjustments, can speed up the development, and brings the model quality to a new level. Our implementation of the proposed method has proved successful in large-scale terrain authoring for an open-world game.
Biomimetic eye modeling & deep neuromuscular oculomotor control
Authors: Nakada Masaki , Lakshmipathy Arjun , Chen Honglin , Ling Nina , Zhou Tao , Terzopoulos Demetri
Published: November 2019

We present a novel, biomimetic model of the eye for realistic virtual human animation. We also introduce a deep learning approach to oculomotor control that is compatible with our biomechanical eye model. Our eye model consists of the following functional components: (i) submodels of the 6 extraocular muscles that actuate realistic eye movements, (ii) an iris submodel, actuated by pupillary muscles, that accommodates to incoming light intensity, (iii) a corneal submodel and a deformable, ciliary-muscle-actuated lens submodel, which refract incoming light rays for focal accommodation, and (iv) a retina with a multitude of photoreceptors arranged in a biomimetic, foveated distribution. The light intensity captured by the photoreceptors is computed using ray tracing from the photoreceptor positions through the finite aperture pupil into the 3D virtual environment, and the visual information from the retina is output via an optic nerve vector. Our oculomotor control system includes a foveation controller implemented as a locally-connected, irregular Deep Neural Network (DNN), or "LiNet", that conforms to the nonuniform retinal photoreceptor distribution, and a neuromuscular motor controller implemented as a fully-connected DNN, plus auxiliary Shallow Neural Networks (SNNs) that control the accommodation of the pupil and lens. The DNNs are trained offline through deep learning from data synthesized by the eye model itself. Once trained, the oculomotor control system operates robustly and efficiently online. It innervates the intraocular muscles to perform illumination and focal accommodation and the extraocular muscles to produce natural eye movements in order to foveate and pursue moving visual targets. We additionally demonstrate the operation of our eye model (binocularly) within our recently introduced sensorimotor control framework involving an anatomically-accurate biomechanical human musculoskeletal model.
Language-based colorization of scene sketches
Authors: Zou Changqing , Mo Haoran , Gao Chengying , Du Ruofei , Fu Hongbo
Published: November 2019

Being natural, touchless, and fun-embracing, language-based inputs have been demonstrated effective for various tasks from image generation to literacy education for children. This paper for the first time presents a language-based system for interactive colorization of scene sketches, based on semantic comprehension. The proposed system is built upon deep neural networks trained on a large-scale repository of scene sketches and cartoonstyle color images with text descriptions. Given a scene sketch, our system allows users, via language-based instructions, to interactively localize and colorize specific foreground object instances to meet various colorization requirements in a progressive way. We demonstrate the effectiveness of our approach via comprehensive experimental results including alternative studies, comparison with the state-of-the-art methods, and generalization user studies. Given the unique characteristics of language-based inputs, we envision a combination of our interface with a traditional scribble-based interface for a practical multimodal colorization system, benefiting various applications. The dataset and source code can be found at https://github.com/SketchyScene/SketchySceneColorization.
Bounded distortion tetrahedral metric interpolation
Authors: Aharon Ido , Chen Renjie , Zorin Denis , Weber Ofir
Published: November 2019

We present a method for volumetric shape interpolation with unique shape preserving features. The input to our algorithm are two or more 3-manifolds, immersed into R
3
and discretized as tetrahedral meshes with shared connectivity. The output is a continuum of shapes that naturally blends the input shapes, while striving to preserve the geometric character of the input. The basis of our approach relies on the fact that the space of metrics with bounded isometric and angular distortion is convex [Chien et al. 2016b]. We show that for high dimensional manifolds, the bounded distortion metrics form a positive semidefinite cone product space. Our method can be seen as a generalization of the bounded distortion interpolation technique of [Chen et al. 2013] from planar shapes immersed in R
2
to solids in R
3
. The convexity of the space implies that a linear blend of the (squared) edge lengths of the input tetrahedral meshes is a simple yet powerful-and-natural choice. Linearly blending flat metrics results in a new metric which is, in general, not flat, and cannot be immersed into three-dimensional space. Nonetheless, the amount of curvature that is introduced in the process tends to be very low in practical settings. We further design an extremely robust nonconvex optimization procedure that efficiently flattens the metric. The flattening procedure strives to preserve the low distortion exhibited in the blended metric while guaranteeing the validity of the metric, resulting in a locally injective map with bounded distortion. Our method leads to volumetric interpolation with superb quality, demonstrating significant improvement over the state-of-the-art and qualitative properties which were obtained so far only in interpolating manifolds of lower dimensions.