bluuebunny/crossref_metadata_2025 huggingface dataset:
Early cortical maturation predicts neurodevelopment in very preterm infants
Authors: Kline Julia E , Illapani Venkata Sita Priyanka , He Lili , Altaye Mekibib , Logan John Wells , Parikh Nehal A
Published: November 2019

Objective

To evaluate the ability of four objectively defined, cortical maturation features—surface area, gyrification index, sulcal depth and curvature—from structural MRI at term-equivalent age (TEA) to independently predict cognitive and language development at 2 years corrected age in very preterm (VPT) infants.

Design

Population-based, prospective cohort study. Structural brain MRI was performed at term, between 40 and 44 weeks postmenstrual age and processed using the developing Human Connectome Project pipeline.

Setting

Multicentre study comprising four regional level III neonatal intensive care units in the Columbus, Ohio region.

Patients

110 VPT infants (gestational age (GA) ≤ 31 weeks).

Main outcome measures

Cognitive and language scores at 2 years corrected age on the Bayley Scales of Infant and Toddler Development, Third Edition.

Results

Of the 94 VPT infants with high-quality T2-weighted MRI scans, 75 infants (80%) returned for Bayley-III testing. Cortical surface area was positively correlated with cognitive and language scores in nearly every brain region. Curvature of the inner cortex was negatively correlated with Bayley scores in the frontal, parietal and temporal lobes. In multivariable regression models, adjusting for GA, sex, socioeconomic status, and injury score on MRI, regional measures of surface area and curvature independently explained more than one-third of the variance in cognitive and language scores at 2 years corrected age in our cohort.

Conclusions

We identified increased cortical curvature at TEA as a new prognostic biomarker of adverse neurodevelopment in very premature infants. When combined with cortical surface area, it enhanced prediction of cognitive and language development. Larger studies are needed to externally validate our findings.
Gender differences in the effect of diabetes mellitus and its treatment on osteoarthritic pain
Authors: Castano Betancourt Martha Cecilia , Morais Carolina Larissa , Vannucci Nunes Lipay Monica , Aragão Jordani , de Azevedo e Souza Munhoz Marcelo , Gomes Machado Eduardo , Marchi Evaldo
Published: November 2019

Objective

(1) To investigate differences in pain severity and its distribution between patients with and without diabetes mellitus (DM) in a population with advanced osteoarthritis (OA). (2) To explore the role of medication used for diabetes in these associations.

Research design and methods

This is a hospital-based cohort study of patients with advanced OA requiring total joint arthroplasty. Interviews and electronic records included: age, gender, occupation, DM (including medication and duration), analgesics used, anthropometry, joints affected by pain and disease duration. Joint pain was scored by the patients using numerical rating scale. Pain severity score was calculated by adding the number of joints affected by pain and the maximum pain score. All analyses were adjusted and/or stratified by gender, age and body mass index.

Results

In total, 489 patients with painful OA were included. From those, 139 patients had DM (30% males and 28% females, p=0.03). Pain severity, principally the number of joints affected by pain, and analgesic consumption, was higher in males with diabetes compared with males without diabetes (p=0.012 and OR=3.03; 95% CI 1.24 to 7.36, p=0.015, respectively). These associations were not significant in females (p=0.41 and p=0.66). Pain was more severe in males using insulin versus those who did not (p=0.025). Male subjects with diabetes had higher odds of hand pain or knee and hand pain compared with males without diabetes (OR=3.7, 95% CI 1.15 to 12; p=0.028 and OR=5.54; 95% CI 1.43 to 21.5, p=0.013, respectively).

Conclusions

Males with diabetes, especially those who require insulin, have more severe joint pain and consume more analgesics than males without diabetes or those who have DM and use other DM medication.
Exercise-based rehabilitation for major non-communicable diseases in low-resource settings: a scoping review
Authors: Heine Martin , Lupton-Smith Alison , Pakosh Maureen , Grace Sherry L , Derman Wayne , Hanekom Susan D
Published: November 2019

Introduction

While there is substantial evidence for the benefits of exercise-based rehabilitation in the prevention and management of non-communicable disease (NCD) in high-resource settings, it is not evident that these programmes can be effectively implemented in a low-resource setting (LRS). Correspondingly, it is unclear if similar benefits can be obtained. The objective of this scoping review was to summarise existing studies evaluating exercise-based rehabilitation, rehabilitation intervention characteristics and outcomes conducted in an LRS for patients with one (or more) of the major NCDs.

Methods

The following databases were searched from inception until October 2018: PubMed/Medline, Embase, CINAHL, Cochrane Library, PsycINFO and trial registries. Studies on exercise-based rehabilitation for patients with cardiovascular disease, diabetes, cancer or chronic respiratory disease conducted in an LRS were included. Data were extracted with respect to study design (eg, type, patient sample, context), rehabilitation characteristics (eg, delivery model, programme adaptations) and included outcome measures.

Results

The search yielded 5930 unique citations of which 60 unique studies were included. Study populations included patients with cardiovascular disease (48.3%), diabetes (28.3%), respiratory disease (21.7%) and cancer (1.7%). Adaptations included transition to predominant patient-driven home-based rehabilitation, training of non-conventional health workers, integration of rehabilitation in community health centres, or triage based on contextual or patient factors. Uptake of adapted rehabilitation models was 54%, retention 78% and adherence 89%. The majority of the outcome measures included were related to body function (65.7%).

Conclusions

The scope of evidence suggests that adapted exercise-based rehabilitation programmes can be implemented in LRS. However, this scope of evidence originated largely from lower middle-income, urban settings and has mostly been conducted in an academic context which may hamper extrapolation of evidence to other LRS. Cost-benefits, impact on activity limitations and participation restrictions, and subsequent mortality and morbidity are grossly understudied.
Quality of life trajectories in survivors of acute myocardial infarction: a national longitudinal study
Authors: Munyombwe Theresa , Hall Marlous , Dondo Tatendashe Bernadette , Alabas Oras A , Gerard Oliver , West Robert M , Pujades-Rodriguez Mar , Hall Alistair , Gale Chris P
Published: November 2019

Aim

To define trajectories of perceived health-related quality of life (HRQoL) among survivors of acute myocardial infarction (AMI) and identify factors associated with trajectories.

Methods

Data on HRQoL among 9566 survivors of AMI were collected from 77 National Health Service hospitals in England between 1 November 2011 and 24 June 2015. Longitudinal HRQoL was collected using the EuroQol five-dimension questionnaire measured at hospitalisation, 1, 6 and 12 months post-AMI. Trajectories of perceived HRQoL post-MI were determined using multilevel regression analysis and latent class growth analysis (LCGA).

Results

One or more percieved health problems in mobility, self-care, usual activities, pain/discomfort and anxiety/depression was reported by 69.1% (6607/9566) at hospitalisation and 59.7% (3011/5047) at 12 months. Reduced HRQoL was associated with women (−4.07, 95% CI −4.88 to −3.25), diabetes (−2.87, 95% CI −3.87 to −1.88), previous AMI (−1.60, 95% CI −2.72 to −0.48), previous angina (−1.72, 95% CI −2.77 to −0.67), chronic renal failure (−2.96, 95% CI −5.08 to −0.84; −3.10, 95% CI −5.72 to −0.49), chronic obstructive pulmonary disease (−3.89, 95% CI −5.07 to −2.72) and cerebrovascular disease (−2.60, 95% CI −4.24 to −0.96). LCGA identified three subgroups of HRQoL which we labelled: improvers (68.1%), non-improvers (22.1%) and dis-improvers (9.8%). Non-improvers and dis-improvers were more likely to be women, non-ST-elevation myocardial infarction (NSTEMI) and have long-term health conditions, compared with improvers.

Conclusions

Quality of life improves for the majority of survivors of AMI but is significantly worse and more likely to decline for women, NSTEMI and those with long-term health conditions. Assessing HRQoL both in hospital and postdischarge may be important in determining which patients could benefit from tailored interventions.

Trial registration

NCT01808027andNCT01819103.
Early famine exposure and adult disease risk based on a 10-year prospective study of Chinese adults
Authors: Meng Ruogu , Yu Canqing , Guo Yu , Bian Zheng , Si Jiahui , Nie Jia , Yang Ling , Chen Yiping , Du Huaidong , Zhou Liyuan , Liu Yun , Chen Junshi , Chen Zhengming , Li Liming , Lv Jun
Published: November 2019

Objective

To comprehensively examine the potential impacts of prenatal experience of the Chinese Great Famine on chronic disease risks in the middle age.

Methods

This study included 92 284 participants aged 39–51 years from China Kadoorie Biobank born around the famine period and without major chronic diseases at baseline. We categorised participants into non-famine births (born between 1 October 1956 and 30 September 1958, and 1 October 1962 and 30 September 1964) and famine births (born between 1 October 1959 and 30 September 1961). The outcomes were incident cardiovascular disease, cancer and respiratory system disease. Cox regression was used to estimate adjusted HR and 95% CI for famine exposure. Subgroup analyses were performed according to baseline characteristics.

Results

During a median 10.1 years of follow-up, we identified 4626 incident ischaemic heart disease (IHD) cases, 7332 cerebrovascular disease cases, 3111 cancer cases and 16 081 respiratory system disease cases. In the whole population, prenatal famine exposure was not statistically associated with the risks of developing any chronic diseases in adulthood. However, for urban participants, compared with non-famine births, famine births had a higher risk of cerebrovascular disease (HR 1.18; 95% CI 1.09 to 1.28); such association was not shown for rural participants (p for interaction <0.001). Also, we observed the associations of prenatal famine exposure with IHD (HR 1.15; 95% CI 1.05 to 1.26) and cerebrovascular disease (HR 1.13; 95% CI 1.05 to 1.21) in participants with lower physical activity level, but not in those with higher ones (all p for interaction=0.003).

Conclusion

Our findings indicate that prenatal exposure to the Chinese famine might be associated with an increased cardiovascular risk and such risk may be modified by adult lifestyle.
A novel method to identify influential nodes in complex networks
Authors: Yang Yuanzhi , Yu Lei , Wang Xing , Chen Siyi , Chen You , Zhou Yipeng
Published: November 2019

Identifying influential nodes in complex networks continues to be an open and vital issue, which is of great significance to the robustness and vulnerability of networks. In order to accurately identify influential nodes in complex networks and avoid the deviation in the evaluation of node influence by single measure, a novel method based on improved Technology for Order Preference by Similarity to an Ideal Solution (TOPSIS) is proposed to integrate multiple measures and identify influential nodes. Our method takes into account degree centrality (DC), closeness centrality (CC) and betweenness centrality (BC), and uses the information of the decision matrix to objectively assign weight to each measure, and takes the closeness degree from each node to be the ideal solution as the basis for comprehensive evaluation. At last, four experiments based on the Susceptible-Infected (SI) model are carried out, and the superiority of our method can be demonstrated.
A Hybrid Swarm Intelligence Algorithm for Clustering-Based Routing in Wireless Sensor Networks
Authors: Barzin Amirhossein , Sadegheih Ahmad , Zare Hassan Khademi , Honarvar Mahbooeh
Published: November 2019

Wireless sensor networks (WSNs) comprise a large number of tiny sensing nodes, which are battery-powered with limited energy. An energy-efficient routing protocol is of utmost importance to prolong the network lifetime. Clustering is the most common technique to balance energy consumption among all nodes, while minimizing traffic and overhead during the data transmission phases. In this paper, a Multi-Objective nature-inspired algorithm based on Shuffled frog-leaping algorithm and Firefly Algorithm (named MOSFA) as an adaptive application-specific clustering-based multi-hop routing protocol for WSNs is proposed. MOSFA’s multi-objective function regards different criteria (e.g., inter- and intra-cluster distances, the residual energy of nodes, distances from the sink, overlap, and load of clusters) to select appropriate cluster heads at each round. Moreover, another multi-objective function is proposed to select the forwarder nodes in the routing phase. The controllable parameters of MOSFA in both clustering and multi-hop phases can be adaptively tuned to achieve the best performance based on the network requirements according to the specific application. Simulation results demonstrate average lifetime improvements of 182%, 68%, 30%, and 28% when compared with LEACH, ERA, SIF, and FSFLA, respectively, in different network scenarios.
Understanding Users Information Needs and Collaborative Sensemaking of Microbiome Data
Authors: Otiono Jennifer , Olaosebikan Monsurat , Shaer Orit , Nov Oded , Ball Mad Price
Published: November 2019

Recent years are seeing a sharp increase in the availability of personal omic (e.g. genomes, microbiomes) data to non-experts through direct-to-consumer testing kits. While the scientific understanding of human -omic information is evolving, the interpretation of the data may impact well-being of users and relevant others, and therefore poses challenges and opportunities for CSCW research. We identify the information, interaction, and sense-making needs of microbiomic data users, within the broader context of social omics - the sharing and collaborative engagement with data and interpretation. Analyzing users' discussions on Reddit's r/HumanMicrobiome, we identified seven user needs for microbiome data: reviewing an annotated report, comparing microbiome data, tracking changes, receiving personalized actionable information, curating and securing information, documenting and sharing self experiments, and enhancing the communication between patients and health-care providers. We highlight the ways in which users interact with each other to collaboratively make sense of the data. We conclude with design implications, including tools for better communication with care providers, and for symptom-centered sharing and discussion.
SDM-NET
Authors: Gao Lin , Yang Jie , Wu Tong , Yuan Yu-Jie , Fu Hongbo , Lai Yu-Kun , Zhang Hao
Published: November 2019

We introduce SDM-NET, a deep generative neural network which produces
structured deformable meshes.
Specifically, the network is trained to generate a spatial arrangement of closed, deformable mesh parts, which respects the global part structure of a shape collection, e.g., chairs, airplanes, etc. Our key observation is that while the overall structure of a 3D shape can be complex, the shape can usually be decomposed into a set of parts, each homeomorphic to a box, and the finer-scale geometry of the part can be recovered by
deforming
the box. The architecture of SDM-NET is that of a
two-level variational autoencoder
(VAE). At the part level, a PartVAE learns a deformable model of part geometries. At the structural level, we train a Structured Parts VAE (SP-VAE), which
jointly
learns the part structure of a shape collection and the part geometries, ensuring the coherence between global shape structure and surface details. Through extensive experiments and comparisons with the state-of-the-art deep generative models of shapes, we demonstrate the superiority of SDM-NET in generating meshes with visual quality, flexible topology, and meaningful structures, benefiting shape interpolation and other subsequent modeling tasks.
Handheld mobile photography in very low light
Authors: Liba Orly , Murthy Kiran , Tsai Yun-Ta , Brooks Tim , Xue Tianfan , Karnad Nikhil , He Qiurui , Barron Jonathan T. , Sharlet Dillon , Geiss Ryan , Hasinoff Samuel W. , Pritch Yael , Levoy Marc
Published: November 2019

Taking photographs in low light using a mobile phone is challenging and rarely produces pleasing results. Aside from the physical limits imposed by read noise and photon shot noise, these cameras are typically handheld, have small apertures and sensors, use mass-produced analog electronics that cannot easily be cooled, and are commonly used to photograph subjects that move, like children and pets. In this paper we describe a system for capturing clean, sharp, colorful photographs in light as low as 0.3 lux, where human vision becomes monochromatic and indistinct. To permit handheld photography without flash illumination, we capture, align, and combine multiple frames. Our system employs "motion metering", which uses an estimate of motion magnitudes (whether due to handshake or moving objects) to identify the number of frames and the per-frame exposure times that together minimize both noise and motion blur in a captured burst. We combine these frames using robust alignment and merging techniques that are specialized for high-noise imagery. To ensure accurate colors in such low light, we employ a learning-based auto white balancing algorithm. To prevent the photographs from looking like they were shot in daylight, we use tone mapping techniques inspired by illusionistic painting: increasing contrast, crushing shadows to black, and surrounding the scene with darkness. All of these processes are performed using the limited computational resources of a mobile device. Our system can be used by novice photographers to produce shareable pictures in a few seconds based on a single shutter press, even in environments so dim that humans cannot see clearly.
Holographic near-eye displays based on overlap-add stereograms
Authors: Padmanaban Nitish , Peng Yifan , Wetzstein Gordon
Published: November 2019

Holographic near-eye displays are a key enabling technology for virtual and augmented reality (VR/AR) applications. Holographic stereograms (HS) are a method of encoding a light field into a hologram, which enables them to natively support view-dependent lighting effects. However, existing HS algorithms require the choice of a hogel size, forcing a tradeoff between spatial and angular resolution. Based on the fact that the short-time Fourier transform (STFT) connects a hologram to its observable light field, we develop the overlap-add stereogram (OLAS) as the correct method of "inverting" the light field into a hologram via the STFT. The OLAS makes more efficient use of the information contained within the light field than previous HS algorithms, exhibiting better image quality at a range of distances and hogel sizes. Most remarkably, the OLAS does not degrade spatial resolution with increasing hogel size, overcoming the spatio-angular resolution tradeoff that previous HS algorithms face. Importantly, the optimal hogel size of previous methods typically varies with the depth of every object in a scene, making the OLAS not only a hogel size-invariant method, but also nearly scene independent. We demonstrate the performance of the OLAS both in simulation and on a prototype near-eye display system, showing focusing capabilities and view-dependent effects.
Distortion-minimizing injective maps between surfaces
Authors: Schmidt Patrick , Born Janis , Campen Marcel , Kobbelt Leif
Published: November 2019

The problem of discrete surface parametrization, i.e. mapping a mesh to a planar domain, has been investigated extensively. We address the more general problem of mapping
between
surfaces. In particular, we provide a formulation that yields a map between two disk-topology meshes, which is continuous and injective by construction and which locally minimizes intrinsic distortion. A common approach is to express such a map as the composition of two maps via a simple intermediate domain such as the plane, and to independently optimize the individual maps. However, even if both individual maps are of minimal distortion, there is potentially high distortion in the composed map. In contrast to many previous works, we minimize distortion in an end-to-end manner, directly optimizing the quality of the composed map. This setting poses additional challenges due to the discrete nature of both the source and the target domain. We propose a formulation that, despite the combinatorial aspects of the problem, allows for a purely continuous optimization. Further, our approach addresses the non-smooth nature of discrete distortion measures in this context which hinders straightforward application of off-the-shelf optimization techniques. We demonstrate that, despite the challenges inherent to the more involved setting, discrete surface-to-surface maps can be optimized effectively.
A multi-scale model for coupling strands with shear-dependent liquid
Authors: Fei Yun (Raymond) , Batty Christopher , Grinspun Eitan , Zheng Changxi
Published: November 2019

We propose a framework for simulating the complex dynamics of strands interacting with compressible, shear-dependent liquids, such as oil paint, mud, cream, melted chocolate, and pasta sauce. Our framework contains three main components: the strands modeled as discrete rods, the bulk liquid represented as a continuum (material point method), and a reduced-dimensional flow of liquid on the surface of the strands with detailed elastoviscoplastic behavior. These three components are tightly coupled together. To enable discrete strands interacting with continuum-based liquid, we develop models that account for the volume change of the liquid as it passes through strands and the momentum exchange between the strands and the liquid. We also develop an extended constraint-based collision handling method that supports cohesion between strands. Furthermore, we present a principled method to preserve the total momentum of a strand and its surface flow, as well as an analytic plastic flow approach for Herschel-Bulkley fluid that enables stable semi-implicit integration at larger time steps. We explore a series of challenging scenarios, involving splashing, shaking, and agitating the liquid which causes the strands to stick together and become entangled.
Colorblind-shareable videos by synthesizing temporal-coherent polynomial coefficients
Authors: Xinghong Hu , Xueting Liu , Zhuming Zhang , Menghan Xia , Chengze Li , Tien-Tsin Wong
Published: November 2019

To share the same visual content between color vision deficiencies (CVD) and normal-vision people, attempts have been made to allocate the two visual experiences of a binocular display (wearing and not wearing glasses) to CVD and normal-vision audiences. However, existing approaches only work for still images. Although state-of-the-art temporal filtering techniques can be applied to smooth the per-frame generated content, they may fail to maintain the multiple binocular constraints needed in our applications, and even worse, sometimes introduce color inconsistency (same color regions map to different colors). In this paper, we propose to train a neural network to predict the temporal coherent polynomial coefficients in the domain of global color decomposition. This indirect formulation solves the color inconsistency problem. Our key challenge is to design a neural network to predict the temporal coherent coefficients, while maintaining all required binocular constraints. Our method is evaluated on various videos and all metrics confirm that it outperforms all existing solutions.
Orometry-based terrain analysis and synthesis
Authors: Argudo Oscar , Galin Eric , Peytavie Adrien , Paris Axel , Gain James , Guérin Eric
Published: November 2019

Mountainous digital terrains are an important element of many virtual environments and find application in games, film, simulation and training. Unfortunately, while existing synthesis methods produce locally plausible results they often fail to respect global structure. This is exacerbated by a dearth of automated metrics for assessing terrain properties at a macro level.

We address these issues by building on techniques from orometry, a field that involves the measurement of mountains and other relief features. First, we construct a sparse metric computed on the peaks and saddles of a mountain range and show that, when used for classification, this is capable of robustly distinguishing between different mountain ranges. Second, we present a synthesis method that takes a coarse elevation map as input and builds a graph of peaks and saddles respecting a given orometric distribution. This is then expanded into a fully continuous elevation function by deriving a consistent river network and shaping the valley slopes. In terms of authoring, users provide various control maps and are also able to edit, reposition, insert and remove terrain features all while retaining the characteristics of a selected mountain range.

The result is a terrain analysis and synthesis method that considers and incorporates orometric properties, and is, on the basis of our perceptual study, more visually plausible than existing terrain generation methods.
DReCon
Authors: Bergamin Kevin , Clavet Simon , Holden Daniel , Forbes James Richard
Published: November 2019

Interactive control of self-balancing, physically simulated humanoids is a long standing problem in the field of real-time character animation. While physical simulation guarantees realistic interactions in the virtual world, simulated characters can appear unnatural if they perform unusual movements in order to maintain balance. Therefore, obtaining a high level of responsiveness to user control, runtime performance, and diversity has often been overlooked in exchange for motion quality. Recent work in the field of deep reinforcement learning has shown that training physically simulated characters to follow motion capture clips can yield high quality tracking results. We propose a two-step approach for building responsive simulated character controllers from unstructured motion capture data. First, meaningful features from the data such as movement direction, heading direction, speed, and locomotion style, are interactively specified and drive a kinematic character controller implemented using motion matching. Second, reinforcement learning is used to train a simulated character controller that is general enough to track the entire distribution of motion that can be generated by the kinematic controller. Our design emphasizes responsiveness to user input, visual quality, and low runtime cost for application in video-games.
Discrete geodesic parallel coordinates
Authors: Wang Hui , Pellis Davide , Rist Florian , Pottmann Helmut , Müller Christian
Published: November 2019

Geodesic parallel coordinates are orthogonal nets on surfaces where one of the two families of parameter lines are geodesic curves. We describe a discrete version of these special surface parameterizations and show that they are very useful for specific applications, most of which are related to the design and fabrication of surfaces in architecture. With the new discrete surface model, it is easy to control strip widths between neighboring geodesics. This facilitates tasks such as cladding a surface with strips of originally straight flat material or designing geodesic gridshells and timber rib shells. It is also possible to model nearly developable surfaces. These are characterized by geodesic strips with almost constant strip widths and are used for generating shapes that can be manufactured from materials which allow for some stretching or shrinking like felt, leather, or thin wooden boards. Most importantly, we show how to constrain the strip width parameters to model a class of intrinsically symmetric surfaces. These surfaces are isometric to surfaces of revolution and can be covered with doubly-curved panels that are produced with only a few molds when working with flexible materials like metal sheets.
Modeling endpoint distribution of pointing selection tasks in virtual reality environments
Authors: Yu Difeng , Liang Hai-Ning , Lu Xueshi , Fan Kaixuan , Ens Barrett
Published: November 2019

Understanding the endpoint distribution of pointing selection tasks can reveal the underlying patterns on how users tend to acquire a target, which is one of the most essential and pervasive tasks in interactive systems. It could further aid designers to create new graphical user interfaces and interaction techniques that are optimized for accuracy, efficiency, and ease of use. Previous research has explored the modeling of endpoint distribution outside of virtual reality (VR) systems that have shown to be useful in predicting selection accuracy and guide the design of new interactive techniques. This work aims at developing an endpoint distribution of selection tasks for VR systems which has resulted in
EDModel
, a novel model that can be used to predict endpoint distribution of pointing selection tasks in VR environments. The development of EDModel is based on two users studies that have explored how factors such as target size, movement amplitude, and target depth affect the endpoint distribution. The model is built from the collected data and its generalizability is subsequently tested in complex scenarios with more relaxed conditions. Three applications of EDModel inspired by previous research are evaluated to show the broad applicability and usefulness of the model: correcting the bias in Fitts's law, predicting selection accuracy, and enhancing pointing selection techniques. Overall, EDModel can achieve high prediction accuracy and can be adapted to different types of applications in VR.
Adversarial Monte Carlo denoising with conditioned auxiliary feature modulation
Authors: Xu Bing , Zhang Junfei , Wang Rui , Xu Kun , Yang Yong-Liang , Li Chuan , Tang Rui
Published: November 2019

Denoising Monte Carlo rendering with a very low sample rate remains a major challenge in the photo-realistic rendering research. Many previous works, including regression-based and learning-based methods, have been explored to achieve better rendering quality with less computational cost. However, most of these methods rely on handcrafted optimization objectives, which lead to artifacts such as blurs and unfaithful details. In this paper, we present an adversarial approach for denoising Monte Carlo rendering. Our key insight is that generative adversarial networks can help denoiser networks to produce more realistic high-frequency details and global illumination by learning the distribution from a set of high-quality Monte Carlo path tracing images. We also adapt a novel feature modulation method to utilize auxiliary features better, including normal, albedo and depth. Compared to previous state-of-the-art methods, our approach produces a better reconstruction of the Monte Carlo integral from a few samples, performs more robustly at different sample rates, and takes only a second for megapixel images.
Real2Sim
Authors: Hahn David , Banzet Pol , Bern James M. , Coros Stelian
Published: November 2019

This paper presents a method for optimizing visco-elastic material parameters of a finite element simulation to best approximate the dynamic motion of real-world soft objects. We compute the gradient with respect to the material parameters of a least-squares error objective function using either direct sensitivity analysis or an adjoint state method. We then optimize the material parameters such that the simulated motion matches real-world observations as closely as possible. In this way, we can directly build a useful simulation model that captures the visco-elastic behaviour of the specimen of interest. We demonstrate the effectiveness of our method on various examples such as numerical coarsening, custom-designed objective functions, and of course real-world flexible elastic objects made of foam or 3D printed lattice structures, including a demo application in soft robotics.
Automatically translating image processing libraries to halide
Authors: Ahmad Maaz Bin Safeer , Ragan-Kelley Jonathan , Cheung Alvin , Kamil Shoaib
Published: November 2019

This paper presents Dexter, a new tool that automatically translates image processing functions from a low-level general-purpose language to a high-level domain-specific language (DSL), allowing them to leverage cross-platform optimizations enabled by DSLs. Rather than building a classical syntax-driven compiler to do this translation, Dexter leverages recent advances in program synthesis and program verification, along with a new domain-specific synthesis algorithm, to translate C++ image processing code to the Halide DSL, while guaranteeing semantic equivalence. This new synthesis algorithm scales and generalizes to much larger and more complex functions than prior work, including the ability to handle tiling, conditionals, and multi-stage pipelines in the original low-level code. To demonstrate the effectiveness of our approach, we evaluate Dexter using real-world image processing functions from Adobe Photoshop, a widely used multi-platform image processing program. Our results show that Dexter can translate 264 out of 353 functions in our test set, with the original implementations ranging from 20 to 150 lines of code. By leveraging Halide's advanced auto-scheduling capabilities, we get median speedups of 7.03× and 4.52× for Dexter-translated functions as compared to the original implementations on Intel and ARM architectures, respectively.
An integrated 6DoF video camera and system design
Authors: Pozo Albert Parra , Toksvig Michael , Schrager Terry Filiba , Hsu Joyce , Mathur Uday , Sorkine-Hornung Alexander , Szeliski Rick , Cabral Brian
Published: November 2019

Designing a fully integrated 360° video camera supporting 6DoF head motion parallax requires overcoming many technical hurdles, including camera placement, optical design, sensor resolution, system calibration, real-time video capture, depth reconstruction, and real-time novel view synthesis. While there is a large body of work describing various system components, such as multi-view depth estimation, our paper is the first to describe a complete, reproducible system that considers the challenges arising when designing, building, and deploying a full end-to-end 6DoF video camera and playback environment. Our system includes a computational imaging software pipeline supporting online markerless calibration, high-quality reconstruction, and real-time streaming and rendering. Most of our exposition is based on a professional 16-camera configuration, which will be commercially available to film producers. However, our software pipeline is generic and can handle a variety of camera geometries and configurations. The entire calibration and reconstruction software pipeline along with example datasets is open sourced to encourage follow-up research in high-quality 6DoF video reconstruction and rendering
1
.
Integral formulations of volumetric transmittance
Authors: Georgiev Iliyan , Misso Zackary , Hachisuka Toshiya , Nowrouzezahrai Derek , Křivánek Jaroslav , Jarosz Wojciech
Published: November 2019

Computing the light attenuation between two given points is an essential yet expensive task in volumetric light transport simulation. Existing unbiased transmittance estimators are all based on "null-scattering" random walks enabled by augmenting the media with fictitious matter. This formulation prevents the use of traditional Monte Carlo estimator variance analysis, thus the efficiency of such methods is understood from a mostly empirical perspective. In this paper, we present several novel integral formulations of volumetric transmittance in which existing estimators arise as direct Monte Carlo estimators. Breaking from physical intuition, we show that the null-scattering concept is not strictly required for unbiased transmittance estimation, but is a form of control variates for effectively reducing variance. Our formulations bring new insight into the problem and the efficiency of existing estimators. They also provide a framework for devising new types of transmittance estimators with distinct and complementary performance tradeoffs, as well as a clear recipe for applying sample stratification.
Document rectification and illumination correction using a patch-based CNN
Authors: Li Xiaoyu , Zhang Bo , Liao Jing , Sander Pedro V.
Published: November 2019

We propose a novel learning method to rectify document images with various distortion types from a single input image. As opposed to previous learning-based methods, our approach seeks to first learn the distortion flow on input image patches rather than the entire image. We then present a robust technique to stitch the patch results into the rectified document by processing in the gradient domain. Furthermore, we propose a second network to correct the uneven illumination, further improving the readability and OCR accuracy. Due to the less complex distortion present on the smaller image patches, our patch-based approach followed by stitching and illumination correction can significantly improve the overall accuracy in both the synthetic and real datasets.
DeepRemaster
Authors: Iizuka Satoshi , Simo-Serra Edgar
Published: November 2019

The remastering of vintage film comprises of a diversity of sub-tasks including super-resolution, noise removal, and contrast enhancement which aim to restore the deteriorated film medium to its original state. Additionally, due to the technical limitations of the time, most vintage film is either recorded in black and white, or has low quality colors, for which colorization becomes necessary. In this work, we propose a single framework to tackle the entire remastering task semi-interactively. Our work is based on temporal convolutional neural networks with attention mechanisms trained on videos with data-driven deterioration simulation. Our proposed source-reference attention allows the model to handle an arbitrary number of reference color images to colorize long videos without the need for segmentation while maintaining temporal consistency. Quantitative analysis shows that our framework outperforms existing approaches, and that, in contrast to existing approaches, the performance of our framework increases with longer videos and more reference color images.
The relightables
Authors: Guo Kaiwen , Lincoln Peter , Davidson Philip , Busch Jay , Yu Xueming , Whalen Matt , Harvey Geoff , Orts-Escolano Sergio , Pandey Rohit , Dourgarian Jason , Tang Danhang , Tkach Anastasia , Kowdle Adarsh , Cooper Emily , Dou Mingsong , Fanello Sean , Fyffe Graham , Rhemann Christoph , Taylor Jonathan , Debevec Paul , Izadi Shahram
Published: November 2019

We present "The Relightables", a volumetric capture system for photorealistic and high quality relightable full-body performance capture. While significant progress has been made on volumetric capture systems, focusing on 3D geometric reconstruction with high resolution textures, much less work has been done to recover photometric properties needed for relighting. Results from such systems lack high-frequency details and the subject's shading is prebaked into the texture. In contrast, a large body of work has addressed relightable acquisition for image-based approaches, which photograph the subject under a set of basis lighting conditions and recombine the images to show the subject as they would appear in a target lighting environment. However, to date, these approaches have not been adapted for use in the context of a high-resolution volumetric capture system. Our method combines this ability to realistically relight humans for arbitrary environments, with the benefits of free-viewpoint volumetric capture and new levels of geometric accuracy for dynamic performances. Our subjects are recorded inside a custom geodesic sphere outfitted with 331 custom color LED lights, an array of high-resolution cameras, and a set of custom high-resolution depth sensors. Our system innovates in multiple areas: First, we designed a novel active depth sensor to capture 12.4 MP depth maps, which we describe in detail. Second, we show how to design a hybrid geometric and machine learning reconstruction pipeline to process the high resolution input and output a volumetric video. Third, we generate temporally consistent reflectance maps for dynamic performers by leveraging the information contained in two alternating color gradient illumination images acquired at 60Hz. Multiple experiments, comparisons, and applications show that The Relightables significantly improves upon the level of realism in placing volumetrically captured human performances into arbitrary CG scenes.
Fluid carving
Authors: Flynn Sean , Egbert Parris , Holladay Seth , Morse Bryan
Published: November 2019

We present a method for intelligently resizing fluid simulation data using seam carving methods. While advances in post-processing techniques have allowed artists greater control over content late in the production process, this technology has largely remained confined to image processing. Our fluid carving system allows fluid simulation post-processing by performing content-aware non-uniform scaling on baked-out fluid simulation data. Specifically, we extend video seam carving techniques to 4-dimensional animated fluid volume data with a graph cut energy function based on mean curvature and kinetic energy. To reduce the complexity of performing graph cuts on 4D data, we provide a new graph construction formulation that greatly reduces the run-time and memory consumption, which are otherwise prohibitively expensive. We demonstrate that our system is useful for post-production fluid simulation changes and editable fluid FX libraries.
RPM-Net
Authors: Yan Zihao , Hu Ruizhen , Yan Xingguang , Chen Luanmin , Van Kaick Oliver , Zhang Hao , Huang Hui
Published: November 2019

We introduce RPM-Net, a deep learning-based approach which simultaneously infers
movable parts
and hallucinates their
motions
from a single, un-segmented, and possibly partial, 3D point cloud shape. RPM-Net is a novel Recurrent Neural Network (RNN), composed of an encoder-decoder pair with interleaved Long Short-Term Memory (LSTM) components, which together predict a temporal sequence of
pointwise displacements
for the input point cloud. At the same time, the displacements allow the network to learn movable parts, resulting in a motion-based shape segmentation. Recursive applications of RPM-Net on the obtained parts can predict finer-level part motions, resulting in a hierarchical object segmentation. Furthermore, we develop a separate network to estimate part mobilities, e.g., per-part motion parameters, from the segmented motion sequence. Both networks learn deep predictive models from a training set that exemplifies a variety of mobilities for diverse objects. We show results of simultaneous motion and part predictions from synthetic and real scans of 3D objects exhibiting a variety of part mobilities, possibly involving multiple movable parts.
Tomographic projector
Authors: Jo Youngjin , Lee Seungjae , Yoo Dongheon , Choi Suyeon , Kim Dongyeon , Lee Byoungho
Published: November 2019

Over the past century, as display evolved, people have demanded more realistic and immersive experiences in theaters. Here, we present a tomographic projector for a volumetric display system that accommodates large audiences while providing a uniform experience. The tomographic projector combines high-speed digital micromirror and three spatial light modulators to refresh projection images at 7200 Hz. With synchronization of the tomographic projector and wearable focus-tunable eyepieces, the presented system can reconstruct 60 focal planes for volumetric representation right in front of audiences. We demonstrate proof of concept of the proposed system by implementing a miniaturized theater environment. Experimentally, we show that this system has wide expressible depth range with focus cues from 25 cm to optical infinity with sufficient tolerance while preserving high resolution and contrast. We also confirm that the proposed system provides uniform experience in a wide range of viewing zone through simulation and experiment. Additionally, the tomographic projector has capability to equalize vergence state that varies in conventional stereoscopic 3D theater according to viewing position as well as interpupillary distance. This study is concluded with thorough discussion about tomographic projectors in terms of challenges and research issues.
Culturally-Embedded Visual Literacy
Authors: Wang Yuan , Li Yukun , Gui Xinning , Kou Yubo , Liu Fenglian
Published: November 2019

Social media is often analyzed as a "front stage" where social media users perform their identities and manage impressions through various forms of user-generated content: text, picture, video, as well as visual expression such as emoticon, emoji, sticker, and meme. Previous scholarship has suggested various functions of visual expression in supporting computer-mediated communication. In this study, we move beyond the utility perspective to ask what constitutes the literacy to utilize visual expression for impression management. Towards this goal, we conducted an interview study with 30 social media users in China. We found that visual literacy intersects with its cultural context. Our interviewees used text and visual expressions in sophisticated, skillful ways and tailored for different audiences in order to construct desired images. They also interacted with audience memory in order to perform their uniqueness and creativity. Lastly, we discuss culturally-embedded visual literacy and provide implications for design.
Including the Voice of Care Recipients in Community Health Feedback Loops in Rural Kenya
Authors: Okeke Fabian , Wasunna Beatrice , Amulele Mercy , Holeman Isaac , Dell Nicola
Published: November 2019

Community health programs in low-resource settings (like rural Kenya) aim to provide essential health services to vulnerable populations. However, to date, there has been limited research that explores the design of mechanisms that enable care recipients to provide feedback regarding their satisfaction with the services they receive. Such feedback has the potential to increase the motivation of community health workers (CHWs), enhance training procedures, detect fraudulent behavior, and inform key performance indicators for health programs. Our paper explores the design and deployment of a USSD-based system that allows anyone who possesses a basic mobile phone to provide feedback regarding the health services and quality of care they received from a CHW or during a hospital visit. Our system was designed through iterative fieldwork in rural Kenya that engaged with multiple stakeholder groups, including care recipients, CHWs, and high-level decision makers. After designing and testing the system, we deployed it for seven weeks in Siaya, Kenya, collecting both quantitative system usage data and qualitative data from six focus groups with 42 participants. Findings from our deployment show that 168 care recipients engaged with the system, submitting 495 reports via USSD. We discuss the broader factors impacting deployment, including the feasibility of USSD, actionability of feedback, scalability, and sustainability. Taken together, our findings suggest that USSD is a promising approach for enabling care recipients to submit feedback in a way that balances privacy, equity, and sustainability.