bluuebunny/crossref_metadata_2025 huggingface dataset:
Novel Synthesis and Phenotypic Analysis of Mutant Clouds for Hepatitis E Virus Genotype 1
Authors: Agarwal Shubhra , Baccam Prasith , Aggarwal Rakesh , Veerapu Naga Suresh
Published: November 2017

Many RNA viruses exist as an ensemble of genetically diverse, replicating populations known as a mutant cloud. The genetic diversity (cloud size) and composition of this mutant cloud may influence several important phenotypic features of the virus, including its replication capacity. We applied a straightforward, bacterium-free approach using error-prone PCR coupled with reverse genetics to generate infectious mutant RNA clouds with various levels of genetic diversity from a genotype 1 strain of hepatitis E virus (HEV). Cloning and sequencing of a genomic fragment encompassing 70% of open reading frame 1 (
ORF1
) or of the full genome from variants in the resultant clouds showed the occurrence of nucleotide mutations at a frequency on the order of 10
−3
per nucleotide copied and the existence of marked genetic diversity, with a high normalized Shannon entropy value. The mutant clouds showed transient replication in cell culture, while wild-type HEV did not. Cross-sectional data from these cell cultures supported the existence of differential effects of clouds of various sizes and compositions on phenotypic characteristics, such as the replication level of (+)-RNA progeny, the amounts of double-stranded RNA (a surrogate for the rate of viral replication) and ORF1 protein, and the expression of interferon-stimulated genes. Since mutant cloud size and composition influenced the viral phenotypic properties, a better understanding of this relationship may help to provide further insights into virus evolution and prediction of emerging viral diseases.

IMPORTANCE
Several biological or practical limitations currently prevent the study of phenotypic behavior of a mutant cloud
in vitro
. We developed a simple and rapid method for synthesizing mutant clouds of hepatitis E virus (HEV), a single-stranded (+)-RNA [ss(+) RNA] virus, with various and controllable levels of genetic diversity, which could then be used in a cell culture system to study the effects of cloud size and composition on viral phenotype. In a cross-sectional analysis, we demonstrated that a particular mutant cloud which had an extremely high genetic diversity had a replication rate exceeding that of wild-type HEV. This method should thus provide a useful model for understanding the phenotypic behavior of ss(+) RNA viruses.
Lactobacillus acidophilus
Metabolizes Dietary Plant Glucosides and Externalizes Their Bioactive Phytochemicals
Authors: Theilmann Mia C. , Goh Yong Jun , Nielsen Kristian Fog , Klaenhammer Todd R. , Barrangou Rodolphe , Abou Hachem Maher
Published: November 2017

Therapeutically active glycosylated phytochemicals are ubiquitous in the human diet. The human gut microbiota (HGM) modulates the bioactivities of these compounds, which consequently affect host physiology and microbiota composition. Despite a significant impact on human health, the key players and the underpinning mechanisms of this interplay remain uncharacterized. Here, we demonstrate the growth of
Lactobacillus acidophilus
on mono- and diglucosyl dietary plant glycosides (PGs) possessing small aromatic aglycones. Transcriptional analysis revealed the upregulation of host interaction genes and identified two loci that encode phosphotransferase system (PTS) transporters and phospho-β-glucosidases, which mediate the uptake and deglucosylation of these compounds, respectively. Inactivating these transport and hydrolysis genes abolished or severely reduced growth on PG, establishing the specificity of the loci to distinct groups of PGs. Following intracellular deglucosylation, the aglycones of PGs are externalized, rendering them available for absorption by the host or for further modification by other microbiota taxa. The PG utilization loci are conserved in
L. acidophilus
and closely related lactobacilli, in correlation with versatile growth on these compounds. Growth on the tested PG appeared more common among human gut lactobacilli than among counterparts from other ecologic niches. The PGs that supported the growth of
L. acidophilus
were utilized poorly or not at all by other common HGM strains, underscoring the metabolic specialization of
L. acidophilus
. These findings highlight the role of human gut
L. acidophilus
and select lactobacilli in the bioconversion of glycoconjugated phytochemicals, which is likely to have an important impact on the HGM and human host.

IMPORTANCE
Thousands of therapeutically active plant-derived compounds are widely present in berries, fruits, nuts, and beverages like tea and wine. The bioactivity and bioavailability of these compounds, which are typically glycosylated, are altered by microbial bioconversions in the human gut. Remarkably, little is known about the bioconversion of PGs by the gut microbial community, despite the significance of this metabolic facet to human health. Our work provides the first molecular insights into the metabolic routes of diet relevant and therapeutically active PGs by
Lactobacillus acidophilus
and related human gut lactobacilli. This taxonomic group is adept at metabolizing the glucoside moieties of select PG and externalizes their aglycones. The study highlights an important role of lactobacilli in the bioconversion of dietary PG and presents a framework from which to derive molecular insights into their metabolism by members of the human gut microbiota.
Direct PCR Offers a Fast and Reliable Alternative to Conventional DNA Isolation Methods for Gut Microbiomes
Authors: Videvall Elin , Strandh Maria , Engelbrecht Anel , Cloete Schalk , Cornwallis Charlie K.
Published: November 2017

The microbial communities of animals can have large impacts on their hosts, and the number of studies using high-throughput sequencing to measure gut microbiomes is rapidly increasing. However, the library preparation procedure in microbiome research is both costly and time-consuming, especially for large numbers of samples. We investigated a cheaper and faster direct PCR method designed to bypass the DNA isolation steps during 16S rRNA library preparation and compared it with a standard DNA extraction method. We used both techniques on five different gut sample types collected from 20 juvenile ostriches and sequenced samples with Illumina MiSeq. The methods were highly comparable and highly repeatable in three sample types with high microbial biomass (cecum, colon, and feces), but larger differences and low repeatability were found in the microbiomes obtained from the ileum and cloaca. These results will help microbiome researchers assess library preparation procedures and plan their studies accordingly.
Patient-centred improvement to repeat prescribing using the Always Event concept
Authors: Grosset Katherine Anne , Deary Elaine , El-Farargy Nancy
Published: November 2017

Repeat prescriptions are prescriptions issued to a patient for a second or subsequent time without requiring a consultation with a doctor. Repeat prescribing is common and an efficient system is necessary to deliver a high-quality service. Always Events can be used to drive patient-centred improvements in healthcare delivery. Our aim was to use the Always Event concept to improve our repeat prescribing system. This quality improvement project was carried out in a deprived, inner-city general practice setting in Glasgow, UK. 51 patients taking repeat medications completed short questionnaires, and the Always Event ‘Repeat prescriptions should be ready and available to collect’ was generated. We used the Plan-Do-Study-Act cycles to elucidate how our system could be improved and check if our intervention was effective. Over a 3-day period in July 2016, 269 out of 292 prescriptions (92.1%) were ready. We mapped out the repeat prescribing process and discovered that sometimes reception staff graded a request as inappropriate, for example, requested too early, and these requests were therefore not processed. Patients would then attend to collect a prescription that was not there. This was both inconvenient for the patient and time-consuming for the reception staff to investigate the reason. Our system was changed so that any request that was not being processed was recorded and the patient informed. In September 260 out of 267 (97.4%) prescriptions were ready, in November 350 out of 364 (96.2%), and in February 2017 314 out of 323 (97.2%) were ready. In conclusion, the Always Event approach allowed us to elicit important feedback from patients to identify a weakness in our repeat prescribing system, which was simple to rectify and led to an improved, more efficient service.
Synthesis of limnological data from lakes and ponds across Arctic and Boreal Canada
Authors: Dranga Stefana Aurora , Hayles Simon , Gajewski Konrad
Published: November 2017

A compilation of published and new limnological data from 1489 shallow lakes and ponds in northern Canada, sampled between 1979 and 2009, revealed significant patterns that correlated with landscape features and climate. Lakes and ponds underlain by Archean or Proterozoic bedrock had lower specific conductivity and pH. Vegetation cover had a lesser influence on these parameters. Forested landscapes tended to have higher phosphorus and nitrogen, as did younger rock types. Dissolved organic carbon was higher, but dissolved inorganic carbon was lower in forested regions. Phytoplankton biomass of the surface waters, as estimated by chlorophyll a concentrations, was positively correlated with July air temperature and nutrients, and was higher in forested relative to polar desert regions. There were no significant differences in the measured limnological variables between shallow (<2 m depth) and deep lakes (>2 m); however, all water chemistry parameters were negatively correlated with depth. Despite large variability within and among regions, spatial trends in water chemistry were associated with geology, vegetation, and climate at a continental scale.
Trinexapac-ethyl causes stimulatory effect on eucalyptus initial growth under nutritional deficiency
Authors: Bacha Allan Lopes , Martins Pedro de Figueiredo Rocha Barbosa , Alves Pedro Luis da Costa Aguiar , Paula Rinaldo Cesar de
Published: November 2017

Eucalyptus plants are sensitive to abiotic stresses in their initial growth, and nutritional deficiency is one of the most recurrent among them. Trinexapac-ethyl, which is a plant growth regulator, can positively affect eucalyptus, a response known as hormesis, possibly providing plants with greater tolerance to stress. The objective of this study was to evaluate the effect of trinexapac-ethyl at two application times, before planting (BP) or after planting (AP), in Eucalyptus urophylla under conditions of nutritional deficiency (NPK). Two experiments (one for each application time) were conducted simultaneously during 81 days after planting of eucalyptus in 15 L pots. The treatments consisted of three doses of trinexapac-ethyl (0, 30, and 60 g a.i.·ha−1) and four variations of nutrient supply: complete solution (NPK) and solutions without nitrogen (−N), without phosphorus (−P), and without potassium (−K). The variables of gas exchange, growth, and dry matter were evaluated. For both application times, trinexapac-ethyl had a positive effect on the root–shoot ratio of plants grown in −N and also positively affected some eucalyptus photosynthetic characteristics. In the AP application, the compound provided gains in height and dry matter, regardless of the nutrient supply. Under phosphorus deficiency, trinexapac-ethyl provided gains in total dry matter (BP) and leaf area (AP).
Study on sensing property of one-dimensional ring mirror-defect photonic crystal
Authors: Chen Ying , Luo Pei , Cao Huiying , Zhao Zhiyong , Zhu Qiguang
Published: November 2017

Based on the photon localization and the photonic bandgap characteristics of photonic crystals (PCs), one-dimensional (1D) ring mirror-defect photonic crystal structure is proposed. Due to the introduction of mirror structure, a defect cavity is formed in the center of the photonic crystal, and then the resonant transmission peak can be obtained in the bandgap of transmission spectrum. The transfer matrix method is used to establish the relationship model between the resonant transmission peak and the structure parameters of the photonic crystals. Using the rectangular air gate photonic crystal structure, the dynamic monitoring of the detected gas sample parameters can be achieved from the shift of the resonant transmission peak. The simulation results show that the Q-value can attain to 1739.48 and the sensitivity can attain to 1642 nm ⋅ RIU[Formula: see text], which demonstrates the effectiveness of the sensing structure. The structure can provide certain theoretical reference for air pollution monitoring and gas component analysis.
Multitime Scale Study of Bursting Activities in the Pre-Bötzinger Complex
Authors: Lü Zhuosheng , Zhao Cui , Zhang Bizhao , Duan Lixia
Published: November 2017

In this paper, we consider a single cell model of pre-Bötzinger complex, which is derived by adding an external tonic drive ([Formula: see text]) to the model developed by Park and Rubin. Using fast–slow geometric decomposition and bifurcation analysis, we study firing activities of the system and try to reveal the mechanisms underlying the bursts related to the mean level of excitatory input ([Formula: see text]) and the maximal conductance associated with the sodium ([Formula: see text]). Since a regular bursting requires at least two timescales, we consider the effects of timescale, especially of the slow timescale, on the bursting oscillations. Unlike the previous works, in this paper, we conduct our investigation by choosing different slow variables. We show how [Formula: see text] and [Formula: see text] affect bifurcations of the fast subsystem and how the bifurcations further determine firing activities of the full system with different slow variables.
Numerical Evaluation of Plastic Buckling of Short Cylinders Under Combined Loading
Authors: Guarracino Federico , Simonelli Maria Grazia
Published: November 2017

The so-called “plastic buckling paradox” originates from the fact that the Prandtl–Reuss’ flow theory of plasticity overestimates the plastic buckling load of plates and shells, whereas Hencky’s deformation theory of plasticity provides results that are more accurate. However, it has been shown that this problem can be overcome by introducing certain initial imperfection in accurate finite element (FE) simulations based on the flow theory of plasticity. 1 – 4 The present study goes deeper into the problem and reveals that in the case of short cylinders under combined loading, which have long been the object of extensive research in the elastic range, 5 a different modeling of the material behavior can also trigger a mode jumping from the initial imperfection, which may even reverse the reported predictions by the flow and deformation theories of plasticity. This fact must be taken in maximum consideration when performing nonlinear FE analyses for estimating the plastic buckling of thin and moderately thin short cylindrical shells.
Sedimentology and microfacies of a mud-rich slope succession: in the Carboniferous Bowland Basin, NW England (UK)
Authors: Newport Sarah M. , Jerrett Rhodri M. , Taylor Kevin G. , Hough Edward , Worden Richard H.
Published: November 2017

A paucity of studies on mud-rich basin slope successions has resulted in a significant gap in our sedimentological understanding in these settings. Here, macro- and micro-scale analysis of mudstone composition, texture and organic matter was undertaken on a continuous core through a mud-dominated slope succession from the Marl Hill area in the Carboniferous Bowland Basin. Six lithofacies, all dominated by turbidites and debrites, combine into three basin slope facies associations: sediment-starved slope, slope dominated by low-density turbidites and slope dominated by debrites. Variation in slope sedimentation was a function of relative sea-level change, with the sediment-starved slope occurring during maximum flooding of the contemporaneous shelf, and the transition towards a slope dominated by turbidites and then debrites occurring during normal or forced shoreline progradation towards the shelf margin. The sediment-starved slope succession is dominated by Type II kerogen, whereas the slope dominated by low-density turbidites is dominated by Type III kerogen. This study suggests that mud-dominated lower slope settings are largely active depositional sites, with consistent evidence for sediment traction. Additionally, the composition and texture of basin slope mudstones, as well as organic content, vary predictably as a function of shelf processes linked to relative sea-level change.
Fixed-Priority Scheduling for Two-Phase Mixed-Criticality Systems
Authors: Li Zheng , He Shuibing
Published: November 2017

In this article, a two-phase execution model is proposed for mixed-criticality (MC) tasks. Different from traditional MC tasks with a computation phase only, the two-phase execution model requires a memory-access phase first to fetch the instructions and data, and then computation. Theoretical foundations are first established for a schedulability test under given memory-access and computation priority assignment. Based on the established theoretical conclusions, a two-stage priority assignment algorithm, which can find the best priority assignment for both memory-access and computation phases under fixed-priority scheduling, is further developed. Extensive experiments have been conducted and the experimental results validate the effectiveness of our proposed approach.
Software Platforms for Smart Cities
Authors: Santana Eduardo Felipe Zambom , Chaves Ana Paula , Gerosa Marco Aurelio , Kon Fabio , Milojicic Dejan S.
Published: November 2017

Information and communication technologies (ICT) can be instrumental in progressing towards smarter city environments, which improve city services, sustainability, and citizens’ quality of life. Smart City software platforms can support the development and integration of Smart City applications. However, the ICT community must overcome current technological and scientific challenges before these platforms can be widely adopted. This article surveys the state of the art in software platforms for Smart Cities. We analyzed 23 projects concerning the most used enabling technologies, as well as functional and non-functional requirements, classifying them into four categories: Cyber-Physical Systems, Internet of Things, Big Data, and Cloud Computing. Based on these results, we derived a reference architecture to guide the development of next-generation software platforms for Smart Cities. Finally, we enumerated the most frequently cited open research challenges and discussed future opportunities. This survey provides important references to help application developers, city managers, system operators, end-users, and Smart City researchers make project, investment, and research decisions.
Cooperative Multi-Agent Planning
Authors: Torreño Alejandro , Onaindia Eva , Komenda Antonín , Štolba Michal
Published: November 2017

Cooperative multi-agent planning (MAP) is a relatively recent research field that combines technologies, algorithms, and techniques developed by the Artificial Intelligence Planning and Multi-Agent Systems communities. While planning has been generally treated as a single-agent task, MAP generalizes this concept by considering multiple intelligent
agents
that work cooperatively to develop a course of action that satisfies the goals of the group.

This article reviews the most relevant approaches to MAP, putting the focus on the solvers that took part in the 2015 Competition of Distributed and Multi-Agent Planning, and classifies them according to their key features and relative performance.
Reconfigurable interlocking furniture
Authors: Song Peng , Fu Chi-Wing , Jin Yueming , Xu Hongfei , Liu Ligang , Heng Pheng-Ann , Cohen-Or Daniel
Published: November 2017

Reconfigurable assemblies consist of a common set of parts that can be assembled into different forms for use in different situations. Designing these assemblies is a complex problem, since it requires a compatible decomposition of shapes with correspondence across forms, and a planning of well-matched joints to connect parts in each form. This paper presents computational methods as tools to assist the design and construction of reconfigurable assemblies, typically for furniture. There are three key contributions in this work. First, we present the compatible decomposition as a weakly-constrained dissection problem, and derive its solution based on a dynamic bipartite graph to construct parts across multiple forms; particularly, we optimize the parts reuse and preserve the geometric semantics. Second, we develop a joint connection graph to model the solution space of reconfigurable assemblies with part and joint compatibility across different forms. Third, we formulate the backward interlocking and multi-key interlocking models, with which we iteratively plan the joints consistently over multiple forms. We show the applicability of our approach by constructing reconfigurable furniture of various complexities, extend it with recursive connections to generate extensible and hierarchical structures, and fabricate a number of results using 3D printing, 2D laser cutting, and woodworking.
Learning to predict part mobility from a single static snapshot
Authors: Hu Ruizhen , Li Wenchao , Van Kaick Oliver , Shamir Ariel , Zhang Hao , Huang Hui
Published: November 2017

We introduce a method for learning a model for the
mobility
of parts in 3D objects. Our method allows not only to understand the dynamic functionalities of one or more parts in a 3D object, but also to apply the mobility functions to
static
3D models. Specifically, the learned part mobility model can
predict
mobilities for parts of a 3D object given in the form of a
single static
snapshot reflecting the spatial configuration of the object parts in 3D space, and
transfer
the mobility from relevant units in the training data. The training data consists of a set of
mobility units
of different motion types. Each unit is composed of a pair of 3D object parts (one moving and one reference part), along with usage examples consisting of a few snapshots capturing different motion states of the unit. Taking advantage of a linearity characteristic exhibited by most part motions in everyday objects, and utilizing a set of part-relation descriptors, we define a mapping from static snapshots to dynamic units. This mapping employs a
motion-dependent
snapshot-to-unit distance obtained via metric learning. We show that our learning scheme leads to accurate motion prediction from single static snapshots and allows proper motion transfer. We also demonstrate other applications such as motion-driven object detection and motion hierarchy construction.
Autonomous reconstruction of unknown indoor scenes guided by time-varying tensor fields
Authors: Xu Kai , Zheng Lintao , Yan Zihao , Yan Guohang , Zhang Eugene , Niessner Matthias , Deussen Oliver , Cohen-Or Daniel , Huang Hui
Published: November 2017

Autonomous reconstruction of unknown scenes by a mobile robot inherently poses the question of balancing between exploration efficacy and reconstruction quality. We present a
navigation-by-reconstruction
approach to address this question, where moving paths of the robot are planned to account for both
global
efficiency for fast exploration and
local
smoothness to obtain high-quality scans. An RGB-D camera, attached to the robot arm, is dictated by the desired reconstruction quality as well as the movement of the robot itself. Our key idea is to harness a time-varying tensor field to guide robot movement, and then solve for 3D camera control under the constraint of the 2D robot moving path. The tensor field is updated in real time, conforming to the progressively reconstructed scene. We show that tensor fields are well suited for guiding autonomous scanning for two reasons: first, they contain sparse and controllable singularities that allow generating a locally smooth robot path, and second, their topological structure can be used for globally efficient path routing within a partially reconstructed scene. We have conducted numerous tests with a mobile robot, and demonstrate that our method leads to a smooth exploration and high-quality reconstruction of unknown indoor scenes.
Learning a model of facial shape and expression from 4D scans
Authors: Li Tianye , Bolkart Timo , Black Michael J. , Li Hao , Romero Javier
Published: November 2017

The field of 3D face modeling has a large gap between high-end and low-end methods. At the high end, the best facial animation is indistinguishable from real humans, but this comes at the cost of extensive manual labor. At the low end, face capture from consumer depth sensors relies on 3D face models that are not expressive enough to capture the variability in natural facial shape and expression. We seek a middle ground by learning a facial model from thousands of accurately aligned 3D scans. Our FLAME model (Faces Learned with an Articulated Model and Expressions) is designed to work with existing graphics software and be easy to fit to data. FLAME uses a linear shape space trained from 3800 scans of human heads. FLAME combines this linear shape space with an articulated jaw, neck, and eyeballs, pose-dependent corrective blendshapes, and additional global expression blendshapes. The pose and expression dependent articulations are learned from 4D face sequences in the D3DFACS dataset along with additional 4D sequences. We accurately register a template mesh to the scan sequences and make the D3DFACS registrations available for research purposes. In total the model is trained from over 33, 000 scans. FLAME is low-dimensional but more expressive than the FaceWarehouse model and the Basel Face Model. We compare FLAME to these models by fitting them to static 3D scans and 4D sequences using the same optimization method. FLAME is significantly more accurate and is available for research purposes (http://flame.is.tue.mpg.de).
DS++
Authors: Dym Nadav , Maron Haggai , Lipman Yaron
Published: November 2017

Correspondence problems are often modelled as quadratic optimization problems over permutations. Common scalable methods for approximating solutions of these NP-hard problems are the spectral relaxation for non-convex energies and the doubly stochastic (DS) relaxation for convex energies. Lately, it has been demonstrated that semidefinite programming relaxations can have considerably improved accuracy at the price of a much higher computational cost.

We present a convex quadratic programming relaxation which is provably stronger than both DS and spectral relaxations, with the same scalability as the DS relaxation. The derivation of the relaxation also naturally suggests a projection method for achieving meaningful integer solutions which improves upon the standard closest-permutation projection. Our method can be easily extended to optimization over doubly stochastic matrices, injective matching, and problems with additional linear constraints. We employ recent advances in optimization of linear-assignment type problems to achieve an efficient algorithm for solving the convex relaxation.

We present experiments indicating that our method is more accurate than local minimization or competing relaxations for non-convex problems. We successfully apply our algorithm to shape matching and to the problem of ordering images in a grid, obtaining results which compare favorably with state of the art methods.

We believe our results indicate that our method should be considered the method of choice for quadratic optimization over permutations.
Material-minimizing forms and structures
Authors: Kilian Martin , Pellis Davide , Wallner Johannes , Pottmann Helmut
Published: November 2017

Three-dimensional structures in building construction and architecture are realized with conflicting goals in mind: engineering considerations and financial constraints easily are at odds with creative aims. It would therefore be very beneficial if optimization and side conditions involving statics and geometry could play a role already in early stages of design, and could be incorporated in design tools in an unobtrusive and interactive way. This paper, which is concerned with a prominent class of structures, is a substantial step towards this goal. We combine the classical work of Maxwell, Michell, and Airy with differential-geometric considerations and obtain a geometric understanding of "optimality" of surface-like lightweight structures. It turns out that total absolute curvature plays an important role. We enable the modeling of structures of minimal weight which in addition have properties relevant for building construction and design, like planar panels, dominance of axial forces over bending, and geometric alignment constraints.
Casual 3D photography
Authors: Hedman Peter , Alsisan Suhib , Szeliski Richard , Kopf Johannes
Published: November 2017

We present an algorithm that enables casual 3D photography. Given a set of input photos captured with a hand-held cell phone or DSLR camera, our algorithm reconstructs a
3D photo
, a central panoramic, textured, normal mapped, multi-layered geometric mesh representation. 3D photos can be stored compactly and are optimized for being rendered from viewpoints that are near the capture viewpoints. They can be rendered using a standard rasterization pipeline to produce perspective views with motion parallax. When viewed in VR, 3D photos provide geometrically consistent views for both eyes. Our geometric representation also allows interacting with the scene using 3D geometry-aware effects, such as adding new objects to the scene and artistic lighting effects.

Our 3D photo reconstruction algorithm starts with a standard structure from motion and multi-view stereo reconstruction of the scene. The dense stereo reconstruction is made robust to the imperfect capture conditions using a novel near envelope cost volume prior that discards erroneous near depth hypotheses. We propose a novel parallax-tolerant stitching algorithm that warps the depth maps into the central panorama and stitches two color-and-depth panoramas for the front and back scene surfaces. The two panoramas are fused into a single non-redundant, well-connected geometric mesh. We provide videos demonstrating users interactively viewing and manipulating our 3D photos.
Online generative model personalization for hand tracking
Authors: Tkach Anastasia , Tagliasacchi Andrea , Remelli Edoardo , Pauly Mark , Fitzgibbon Andrew
Published: November 2017

We present a new algorithm for real-time hand tracking on commodity depth-sensing devices. Our method does not require a user-specific calibration session, but rather learns the geometry as the user performs live in front of the camera, thus enabling seamless virtual interaction at the consumer level. The key novelty in our approach is an online optimization algorithm that jointly estimates pose and shape in each frame, and determines the uncertainty in such estimates. This knowledge allows the algorithm to integrate per-frame estimates over time, and build a personalized geometric model of the captured user. Our approach can easily be integrated in state-of-the-art continuous generative motion tracking software. We provide a detailed evaluation that shows how our approach achieves accurate motion tracking for real-time applications, while significantly simplifying the workflow of accurate hand performance capture. We also provide quantitative evaluation datasets at http://gfx.uvic.ca/datasets/handy
Deep reverse tone mapping
Authors: Endo Yuki , Kanamori Yoshihiro , Mitani Jun
Published: November 2017

Inferring a high dynamic range (HDR) image from a single low dynamic range (LDR) input is an ill-posed problem where we must compensate lost data caused by under-/over-exposure and color quantization. To tackle this, we propose the first deep-learning-based approach for fully automatic inference using convolutional neural networks. Because a naive way of directly inferring a 32-bit HDR image from an 8-bit LDR image is intractable due to the difficulty of training, we take an indirect approach; the key idea of our method is to synthesize LDR images taken with different exposures (i.e.,
bracketed images
) based on supervised learning, and then reconstruct an HDR image by merging them. By learning the relative changes of pixel values due to increased/decreased exposures using 3D deconvolutional networks, our method can reproduce not only natural tones without introducing visible noise but also the colors of saturated pixels. We demonstrate the effectiveness of our method by comparing our results not only with those of conventional methods but also with ground-truth HDR images.
Scratch iridescence
Authors: Werner Sebastian , Velinov Zdravko , Jakob Wenzel , Hullin Matthias B.
Published: November 2017

The surface of metal, glass and plastic objects is often characterized by microscopic scratches caused by manufacturing and/or wear. A closer look onto such scratches reveals iridescent colors with a complex dependency on viewing and lighting conditions. The physics behind this phenomenon is well understood; it is caused by diffraction of the incident light by surface features on the order of the optical wavelength. Existing analytic models are able to reproduce spatially unresolved microstructure such as the iridescent appearance of compact disks and similar materials. Spatially resolved scratches, on the other hand, have proven elusive due to the highly complex wave-optical light transport simulations needed to account for their appearance. In this paper, we propose a wave-optical shading model based on non-paraxial scalar diffraction theory to render this class of effects. Our model expresses surface roughness as a collection of line segments. To shade a point on the surface, the individual diffraction patterns for contributing scratch segments are computed analytically and superimposed coherently. This provides natural transitions from localized glint-like iridescence to smooth BRDFs representing the superposition of many reflections at large viewing distances. We demonstrate that our model is capable of recreating the overall appearance as well as characteristic detail effects observed on real-world examples.
Fast gaze-contingent optimal decompositions for multifocal displays
Authors: Mercier Olivier , Sulai Yusufu , Mackenzie Kevin , Zannoli Marina , Hillis James , Nowrouzezahrai Derek , Lanman Douglas
Published: November 2017

As head-mounted displays (HMDs) commonly present a single, fixed-focus display plane, a conflict can be created between the vergence and accommodation responses of the viewer. Multifocal HMDs have long been investigated as a potential solution in which multiple image planes span the viewer's accommodation range. Such displays require a scene decomposition algorithm to distribute the depiction of objects across image planes, and previous work has shown that simple decompositions can be achieved in real-time. However, recent optimal decompositions further improve image quality, particularly with complex content. Such decompositions are more computationally involved and likely require better alignment of the image planes with the viewer's eyes, which are potential barriers to practical applications.

Our goal is to enable interactive optimal decomposition algorithms capable of driving a vergence- and accommodation-tracked multifocal testbed. Ultimately, such a testbed is necessary to establish the requirements for the practical use of multifocal displays, in terms of computational demand and hardware accuracy. To this end, we present an efficient algorithm for optimal decompositions, incorporating insights from vision science. Our method is amenable to GPU implementations and achieves a three-orders-of-magnitude speedup over previous work. We further show that eye tracking can be used for adequate plane alignment with efficient image-based deformations, adjusting for both eye rotation and head movement relative to the display. We also build the first binocular multifocal testbed with integrated eye tracking and accommodation measurement, paving the way to establish practical eye tracking and rendering requirements for this promising class of display. Finally, we report preliminary results from a pilot user study utilizing our testbed, investigating the accommodation response of users to dynamic stimuli presented under optimal decomposition.
City-scale traffic animation using statistical learning and metamodel-based optimization
Authors: Li Weizi , Wolinski David , Lin Ming C.
Published: November 2017

Rapid urbanization and increasing traffic have caused severe social, economic, and environmental problems in metropolitan areas worldwide. Traffic reconstruction and visualization using existing traffic data can provide novel tools for vehicle navigation and routing, congestion analysis, and traffic management. While traditional data collection methods are becoming increasingly common (e.g. using in-road sensors), GPS devices are also becoming ubiquitous. In this paper, we address the problem of traffic reconstruction, visualization, and animation using mobile vehicle data (i.e. GPS traces). We first conduct city-scale traffic reconstruction using statistical learning on mobile vehicle data for traffic animation and visualization, and then dynamically complete missing data using metamodel-based simulation optimization in areas of insufficient data coverage. We evaluate our approach quantitatively and qualitatively, and demonstrate our results with 2D visualization of citywide traffic, as well as 2D and 3D animation of reconstructed traffic in virtual environments.
DeepToF
Authors: Marco Julio , Hernandez Quercus , Muñoz Adolfo , Dong Yue , Jarabo Adrian , Kim Min H. , Tong Xin , Gutierrez Diego
Published: November 2017

Time-of-flight (ToF) imaging has become a widespread technique for depth estimation, allowing affordable off-the-shelf cameras to provide depth maps in real time. However, multipath interference (MPI) resulting from indirect illumination significantly degrades the captured depth. Most previous works have tried to solve this problem by means of complex hardware modifications or costly computations. In this work, we avoid these approaches and propose a new technique to correct errors in depth caused by MPI, which requires no camera modifications and takes just 10 milliseconds per frame. Our observations about the nature of MPI suggest that most of its information is available in image space; this allows us to formulate the depth imaging process as a spatially-varying convolution and use a convolutional neural network to correct MPI errors. Since the input and output data present similar structure, we base our network on an autoencoder, which we train in two stages. First, we use the encoder (convolution filters) to learn a suitable basis to represent MPI-corrupted depth images; then, we train the decoder (deconvolution filters) to correct depth from synthetic scenes, generated by using a physically-based, time-resolved renderer. This approach allows us to tackle a key problem in ToF, the lack of ground-truth data, by using a large-scale captured training set with MPI-corrupted depth to train the encoder, and a smaller synthetic training set with ground truth depth to train the decoder stage of the network. We demonstrate and validate our method on both synthetic and real complex scenarios, using an off-the-shelf ToF camera, and with only the captured, incorrect depth as input.
Retinal 3D
Authors: Jang Changwon , Bang Kiseung , Moon Seokil , Kim Jonghyun , Lee Seungjae , Lee Byoungho
Published: November 2017

We introduce an augmented reality near-eye display dubbed "Retinal 3D." Key features of the proposed display system are as follows: Focus cues are provided by generating the pupil-tracked light field that can be directly projected onto the retina. Generated focus cues are valid over a large depth range since laser beams are shaped for a large depth of field (DOF). Pupil-tracked light field generation significantly reduces the needed information/computation load. Also, it provides "dynamic eye-box" which can be a break-through that overcome the drawbacks of retinal projection-type displays. For implementation, we utilized a holographic optical element (HOE) as an image combiner, which allowed high transparency with a thin structure. Compared with current augmented reality displays, the proposed system shows competitive performances of a large field of view (FOV), high transparency, high contrast, high resolution, as well as focus cues in a large depth range. Two prototypes are presented along with experimental results and assessments. Analysis on the DOF of light rays and validity of focus cue generation are presented as well. Combination of pupil tracking and advanced near-eye display technique opens new possibilities of the future augmented reality.
Learning to predict indoor illumination from a single image
Authors: Gardner Marc-André , Sunkavalli Kalyan , Yumer Ersin , Shen Xiaohui , Gambaretto Emiliano , Gagné Christian , Lalonde Jean-François
Published: November 2017

We propose an automatic method to infer high dynamic range illumination from a single, limited field-of-view, low dynamic range photograph of an indoor scene. In contrast to previous work that relies on specialized image capture, user input, and/or simple scene models, we train an end-to-end deep neural network that directly regresses a limited field-of-view photo to HDR illumination, without strong assumptions on scene geometry, material properties, or lighting. We show that this can be accomplished in a three step process: 1) we train a robust lighting classifier to automatically annotate the location of light sources in a large dataset of LDR environment maps, 2) we use these annotations to train a deep neural network that predicts the location of lights in a scene from a single limited field-of-view photo, and 3) we fine-tune this network using a small dataset of HDR environment maps to predict light intensities. This allows us to automatically recover high-quality HDR illumination estimates that significantly outperform previous state-of-the-art methods. Consequently, using our illumination estimates for applications like 3D object insertion, produces photo-realistic results that we validate via a perceptual user study.
Compact single-shot hyperspectral imaging using a prism
Authors: Baek Seung-Hwan , Kim Incheol , Gutierrez Diego , Kim Min H.
Published: November 2017

We present a novel, compact single-shot hyperspectral imaging method. It enables capturing hyperspectral images using a conventional DSLR camera equipped with just an ordinary refractive prism in front of the camera lens. Our computational imaging method reconstructs the full spectral information of a scene from dispersion over edges. Our setup requires no coded aperture mask, no slit, and no collimating optics, which are necessary for traditional hyperspectral imaging systems. It is thus very cost-effective, while still highly accurate. We tackle two main problems: First, since we do not rely on collimation, the sensor records a projection of the dispersion information, distorted by perspective. Second, available spectral cues are sparse, present only around object edges. We formulate an image formation model that can predict the perspective projection of dispersion, and a reconstruction method that can estimate the full spectral information of a scene from sparse dispersion information. Our results show that our method compares well with other state-of-the-art hyperspectral imaging systems, both in terms of spectral accuracy and spatial resolution, while being orders of magnitude cheaper than commercial imaging systems.
A Tutorial on Canonical Correlation Methods
Authors: Uurtio Viivi , Monteiro João M. , Kandola Jaz , Shawe-Taylor John , Fernandez-Reyes Delmiro , Rousu Juho
Published: November 2017

Canonical correlation analysis is a family of multivariate statistical methods for the analysis of paired sets of variables. Since its proposition, canonical correlation analysis has, for instance, been extended to extract relations between two sets of variables when the sample size is insufficient in relation to the data dimensionality, when the relations have been considered to be non-linear, and when the dimensionality is too large for human interpretation. This tutorial explains the theory of canonical correlation analysis, including its regularised, kernel, and sparse variants. Additionally, the deep and Bayesian CCA extensions are briefly reviewed. Together with the numerical examples, this overview provides a coherent compendium on the applicability of the variants of canonical correlation analysis. By bringing together techniques for solving the optimisation problems, evaluating the statistical significance and generalisability of the canonical correlation model, and interpreting the relations, we hope that this article can serve as a hands-on tool for applying canonical correlation methods in data analysis.
Access-Time-Aware Cache Algorithms
Authors: Neglia Giovanni , Carra Damiano , Feng Mingdong , Janardhan Vaishnav , Michiardi Pietro , Tsigkari Dimitra
Published: November 2017

Most of the caching algorithms are oblivious to requests’ timescale, but caching systems are capacity constrained and, in practical cases, the hit rate may be limited by the cache’s impossibility to serve requests fast enough. In particular, the hard-disk access time can be the key factor capping cache performance. In this article, we present a new cache replacement policy that takes advantage of a hierarchical caching architecture, and in particular of access-time difference between memory and disk. Our policy is optimal when requests follow the independent reference model and significantly reduces the hard-disk load, as shown also by our realistic, trace-driven evaluation. Moreover, we show that our policy can be considered in a more general context, since it can be easily adapted to minimize any retrieval cost, as far as costs add over cache misses.
On the Measurement and Prediction of Web Content Utility
Authors: Yao Yuan , Tong Hanghang , Xu Feng , Lu Jian
Published: November 2017

Nowadays, various types and large amount of content are available on the Web. Characterizing the Web content and predicting its inherent usefulness become important problems that may benefit many applications such as information filtering and content recommendation. In this article, we present a brief review of the existing measurements and the corresponding prediction methods for Web content utility. Specially, we focus on three close and widely studied tasks, i.e., content popularity prediction, content quality prediction, and scientific article impact prediction. While reviewing the existing work in each of the above three tasks, we mainly aim to answer the following two fundamental questions: how to measure the Web content utility, and how to make the predictions under the measurement. We find that while the three tasks are closely related, they bear subtle differences in terms of prediction urgency, feature extraction, and algorithm design. After that, we discuss some future directions in measuring and predicting Web content utility
Toward a Progress Indicator for Machine Learning Model Building and Data Mining Algorithm Execution
Authors: Luo Gang
Published: November 2017

For user-friendliness, many software systems offer progress indicators for long-duration tasks. A typical progress indicator continuously estimates the remaining task execution time as well as the portion of the task that has been finished. Building a machine learning model often takes a long time, but no existing machine learning software supplies a non-trivial progress indicator. Similarly, running a data mining algorithm often takes a long time, but no existing data mining software provides a nontrivial progress indicator. In this article, we consider the problem of offering progress indicators for machine learning model building and data mining algorithm execution. We discuss the goals and challenges intrinsic to this problem. Then we describe an initial framework for implementing such progress indicators and two advanced, potential uses of them, with the goal of inspiring future research on this topic