bluuebunny/crossref_metadata_2025 huggingface dataset:
Practical multiple scattering for rough surfaces
Authors: Lee Joo Ho , Jarabo Adrian , Jeon Daniel S. , Gutierrez Diego , Kim Min H.
Published: November 2018

Microfacet theory concisely models light transport over rough surfaces. Specular reflection is the result of single mirror reflections on each facet, while exact computation of multiple scattering is either neglected, or modeled using costly importance sampling techniques. Practical but accurate simulation of multiple scattering in microfacet theory thus remains an open challenge. In this work, we revisit the traditional V-groove cavity model and derive an analytical, cost-effective solution for multiple scattering in rough surfaces. Our
kaleidoscopic
model is made up of both real and virtual V-grooves, and allows us to calculate higher-order scattering in the microfacets in an analytical fashion. We then extend our model to include nonsymmetric grooves, allowing for additional degrees of freedom on the surface geometry, improving multiple reflections at grazing angles with backward compatibility to traditional normal distribution functions. We validate the accuracy of our model against ground-truth Monte Carlo simulations, and demonstrate its flexibility on anisotropic and textured materials. Our model is analytical, does not introduce significant cost and variance, can be seamless integrated in any rendering engine, preserves reciprocity and energy conservation, and is suitable for bidirectional methods.
Practical SVBRDF acquisition of 3D objects with unstructured flash photography
Authors: Nam Giljoo , Lee Joo Ho , Gutierrez Diego , Kim Min H.
Published: November 2018

Capturing spatially-varying bidirectional reflectance distribution functions (SVBRDFs) of 3D objects with just a single, hand-held camera (such as an off-the-shelf smartphone or a DSLR camera) is a difficult, open problem. Previous works are either limited to planar geometry, or rely on previously scanned 3D geometry, thus limiting their practicality. There are several technical challenges that need to be overcome: First, the built-in flash of a camera is almost colocated with the lens, and at a fixed position; this severely hampers sampling procedures in the light-view space. Moreover, the near-field flash lights the object partially and unevenly. In terms of geometry, existing multiview stereo techniques assume diffuse reflectance only, which leads to overly smoothed 3D reconstructions, as we show in this paper. We present a simple yet powerful framework that removes the need for expensive, dedicated hardware, enabling practical acquisition of SVBRDF information from real-world, 3D objects with a single, off-the-shelf camera with a built-in flash. In addition, by removing the diffuse reflection assumption and leveraging instead such SVBRDF information, our method outputs high-quality 3D geometry reconstructions, including more accurate high-frequency details than state-of-the-art multiview stereo techniques. We formulate the
joint
reconstruction of SVBRDFs, shading normals, and 3D geometry as a multi-stage, iterative inverse-rendering reconstruction pipeline. Our method is also directly applicable to any existing multiview 3D reconstruction technique. We present results of captured objects with complex geometry and reflectance; we also validate our method numerically against other existing approaches that rely on dedicated hardware, additional sources of information, or both.
Aerobatics control of flying creatures via self-regulated learning
Authors: Won Jungdam , Park Jungnam , Lee Jehee
Published: November 2018

Flying creatures in animated films often perform highly dynamic aerobatic maneuvers, which require their extreme of exercise capacity and skillful control. Designing physics-based controllers (a.k.a., control policies) for aerobatic maneuvers is very challenging because dynamic states remain in unstable equilibrium most of the time during aerobatics. Recently, Deep Reinforcement Learning (DRL) has shown its potential in constructing physics-based controllers. In this paper, we present a new concept,
Self-Regulated Learning (SRL)
, which is combined with DRL to address the aerobatics control problem. The key idea of SRL is to allow the agent to take control over its own learning using an additional self-regulation policy. The policy allows the agent to regulate its goals according to the capability of the current control policy. The control and self-regulation policies are learned jointly along the progress of learning. Self-regulated learning can be viewed as building its own curriculum and seeking compromise on the goals. The effectiveness of our method is demonstrated with physically-simulated creatures performing aerobatic skills of sharp turning, rapid winding, rolling, soaring, and diving.
Semantic object reconstruction via casual handheld scanning
Authors: Hu Ruizhen , Wen Cheng , Van Kaick Oliver , Chen Luanmin , Lin Di , Cohen-Or Daniel , Huang Hui
Published: November 2018

We introduce a learning-based method to reconstruct objects acquired in a casual handheld scanning setting with a depth camera. Our method is based on two core components. First, a deep network that provides a semantic segmentation and labeling of the frames of an input RGBD sequence. Second, an alignment and reconstruction method that employs the semantic labeling to reconstruct the acquired object from the frames. We demonstrate that the use of a semantic labeling improves the reconstructions of the objects, when compared to methods that use only the depth information of the frames. Moreover, since training a deep network requires a large amount of labeled data, a key contribution of our work is an active self-learning framework to simplify the creation of the training data. Specifically, we iteratively predict the labeling of frames with the neural network, reconstruct the object from the labeled frames, and evaluate the confidence of the labeling, to incrementally train the neural network while requiring only a small amount of user-provided annotations. We show that this method enables the creation of data for training a neural network with high accuracy, while requiring only little manual effort.
Deep part induction from articulated object pairs
Authors: Yi Li , Huang Haibin , Liu Difan , Kalogerakis Evangelos , Su Hao , Guibas Leonidas
Published: November 2018

Object functionality is often expressed through part articulation - as when the two rigid parts of a scissor pivot against each other to perform the cutting function. Such articulations are often similar across objects within the same functional category. In this paper we explore how the observation of different articulation states provides evidence for part structure and motion of 3D objects. Our method takes as input a pair of unsegmented shapes representing two different articulation states of two functionally related objects, and induces their common parts along with their underlying rigid motion. This is a challenging setting, as we assume no prior shape structure, no prior shape category information, no consistent shape orientation, the articulation states may belong to objects of different geometry, plus we allow inputs to be noisy and partial scans, or point clouds lifted from RGB images. Our method learns a neural network architecture with three modules that respectively propose correspondences, estimate 3D deformation flows, and perform segmentation. To achieve optimal performance, our architecture alternates between correspondence, deformation flow, and segmentation prediction iteratively in an ICP-like fashion. Our results demonstrate that our method significantly outperforms state-of-the-art techniques in the task of discovering articulated parts of objects. In addition, our part induction is object-class agnostic and successfully generalizes to new and unseen objects.
DESIA
Authors: Wang Ziqi , Song Peng , Pauly Mark
Published: November 2018

Interlocking assemblies have a long history in the design of puzzles, furniture, architecture, and other complex geometric structures. The key defining property of interlocking assemblies is that all component parts are immobilized by their geometric arrangement, preventing the assembly from falling apart. Computer graphics research has recently contributed design tools that allow creating new interlocking assemblies. However, these tools focus on specific kinds of assemblies and explore only a limited space of interlocking configurations, which restricts their applicability for design.

In this paper, we propose a new general framework for designing interlocking assemblies. The core idea is to represent part relationships with a family of base
Directional Blocking Graphs
and leverage efficient graph analysis tools to compute an interlocking arrangement of parts. This avoids the exponential complexity of brute-force search. Our algorithm iteratively constructs the geometry of assembly components, taking advantage of all existing blocking relations for constructing successive parts. As a result, our approach supports a wider range of assembly forms compared to previous methods and provides significantly more design flexibility. We show that our framework facilitates efficient design of complex interlocking assemblies, including new solutions that cannot be achieved by state of the art approaches.
Continuous and orientation-preserving correspondences via functional maps
Authors: Ren Jing , Poulenard Adrien , Wonka Peter , Ovsjanikov Maks
Published: November 2018

We propose a method for efficiently computing orientation-preserving and approximately continuous correspondences between non-rigid shapes, using the functional maps framework. We first show how orientation preservation can be formulated directly in the functional (spectral) domain without using landmark or region correspondences and without relying on external symmetry information. This allows us to obtain functional maps that promote orientation preservation, even when using descriptors, that are invariant to orientation changes. We then show how higher quality, approximately continuous and bijective pointwise correspondences can be obtained from initial functional maps by introducing a novel refinement technique that aims to simultaneously improve the maps both in the spectral and spatial domains. This leads to a general pipeline for computing correspondences between shapes that results in high-quality maps, while admitting an efficient optimization scheme. We show through extensive evaluation that our approach improves upon state-of-the-art results on challenging isometric and non-isometric correspondence benchmarks according to both measures of continuity and coverage as well as producing semantically meaningful correspondences as measured by the distance to ground truth maps.
P
aparazzi
Authors: Liu Hsueh-Ti Derek , Tao Michael , Jacobson Alec
Published: November 2018

The image processing pipeline boasts a wide variety of complex filters and effects. Translating an individual effect to operate on 3D surface geometry inevitably results in a bespoke algorithm. Instead, we propose a general-purpose back-end optimization that allows users to edit an input 3D surface by simply selecting an off-the-shelf image processing filter. We achieve this by constructing a differentiable triangle mesh renderer, with which we can
back propagate
changes in the image domain to the 3D mesh vertex positions. The given image processing technique is applied to the entire shape via stochastic snapshots of the shape: hence, we call our method
Paparazzi.
We provide simple yet important design considerations to construct the
Paparazzi
renderer and optimization algorithms. The power of this rendering-based surface editing is demonstrated via the variety of image processing filters we apply. Each application uses an off-the-shelf implementation of an image processing method without requiring modification to the core
Paparazzi
algorithm.
Learning to dress
Authors: Clegg Alexander , Yu Wenhao , Tan Jie , Liu C. Karen , Turk Greg
Published: November 2018

Creating animation of a character putting on clothing is challenging due to the complex interactions between the character and the simulated garment. We take a model-free deep reinforcement learning (deepRL) approach to automatically discovering robust dressing control policies represented by neural networks. While deepRL has demonstrated several successes in learning complex motor skills, the data-demanding nature of the learning algorithms is at odds with the computationally costly cloth simulation required by the dressing task. This paper is the first to demonstrate that, with an appropriately designed input state space and a reward function, it is possible to incorporate cloth simulation in the deepRL framework to learn a robust dressing control policy. We introduce a salient representation of haptic information to guide the dressing process and utilize it in the reward function to provide learning signals during training. In order to learn a prolonged sequence of motion involving a diverse set of manipulation skills, such as grasping the edge of the shirt or pulling on a sleeve, we find it necessary to separate the dressing task into several subtasks and learn a control policy for each subtask. We introduce a
policy sequencing
algorithm that matches the distribution of output states from one task to the input distribution for the next task in the sequence. We have used this approach to produce character controllers for several dressing tasks: putting on a t-shirt, putting on a jacket, and robot-assisted dressing of a sleeve.
Adaptive O-CNN
Authors: Wang Peng-Shuai , Sun Chun-Yu , Liu Yang , Tong Xin
Published: November 2018

We present an Adaptive Octree-based Convolutional Neural Network (Adaptive O-CNN) for efficient 3D shape encoding and decoding. Different from volumetric-based or octree-based CNN methods that represent a 3D shape with voxels in the same resolution, our method represents a 3D shape adaptively with octants at different levels and models the 3D shape within each octant with a planar patch. Based on this adaptive patch-based representation, we propose an Adaptive O-CNN encoder and decoder for encoding and decoding 3D shapes. The Adaptive O-CNN encoder takes the planar patch normal and displacement as input and performs 3D convolutions only at the octants at each level, while the Adaptive O-CNN decoder infers the shape occupancy and subdivision status of octants at each level and estimates the best plane normal and displacement for each leaf octant. As a general framework for 3D shape analysis and generation, the Adaptive O-CNN not only reduces the memory and computational cost, but also offers better shape generation capability than the existing 3D-CNN approaches. We validate Adaptive O-CNN in terms of efficiency and effectiveness on different shape analysis and generation tasks, including shape classification, 3D autoencoding, shape prediction from a single image, and shape completion for noisy and incomplete point clouds.
Robust flow-guided neural prediction for sketch-based freeform surface modeling
Authors: Li Changjian , Pan Hao , Liu Yang , Tong Xin , Sheffer Alla , Wang Wenping
Published: November 2018

Sketching provides an intuitive user interface for communicating free form shapes. While human observers can easily envision the shapes they intend to communicate, replicating this process algorithmically requires resolving numerous ambiguities. Existing sketch-based modeling methods resolve these ambiguities by either relying on expensive user annotations or by restricting the modeled shapes to specific narrow categories. We present an approach for modeling generic freeform 3D surfaces from sparse, expressive 2D sketches that overcomes both limitations by incorporating convolution neural networks (CNN) into the sketch processing workflow.

Given a 2D sketch of a 3D surface, we use CNNs to infer the depth and normal maps representing the surface. To combat ambiguity we introduce an intermediate CNN layer that models the dense curvature direction, or flow, field of the surface, and produce an additional output confidence map along with depth and normal. The flow field guides our subsequent surface reconstruction for improved regularity; the confidence map trained unsupervised measures ambiguity and provides a robust estimator for data fitting. To reduce ambiguities in input sketches users can refine their input by providing optional depth values at sparse points and curvature hints for strokes. Our CNN is trained on a large dataset generated by rendering sketches of various 3D shapes using non-photo-realistic line rendering (NPR) method that mimics human sketching of free-form shapes. We use the CNN model to process both single- and multi-view sketches. Using our multi-view framework users progressively complete the shape by sketching in different views, generating complete closed shapes. For each new view, the modeling is assisted by partial sketches and depth cues provided by surfaces generated in earlier views. The partial surfaces are fused into a complete shape using predicted confidence levels as weights.

We validate our approach, compare it with previous methods and alternative structures, and evaluate its performance with various modeling tasks. The results demonstrate our method is a new approach for efficiently modeling freeform shapes with succinct but expressive 2D sketches.
Efficient palette-based decomposition and recoloring of images via RGBXY-space geometry
Authors: Tan Jianchao , Echevarria Jose , Gingold Yotam
Published: November 2018

We introduce an extremely scalable and efficient yet simple palette-based image decomposition algorithm. Given an RGB image and set of palette colors, our algorithm decomposes the image into a set of additive mixing layers, each of which corresponds to a palette color applied with varying weight. Our approach is based on the geometry of images in RGBXY-space. This new geometric approach is orders of magnitude more efficient than previous work and requires no numerical optimization. We provide an implementation of the algorithm in 48 lines of Python code. We demonstrate a real-time layer decomposition tool in which users can interactively edit the palette to adjust the layers. After preprocessing, our algorithm can decompose 6 MP images into layers in 20 milliseconds.
Scalable appearance filtering for complex lighting effects
Authors: Gamboa Luis E. , Guertin Jean-Philippe , Nowrouzezahrai Derek
Published: November 2018

Realistic rendering with materials that exhibit high-frequency spatial variation remains a challenge, as eliminating spatial and temporal aliasing requires prohibitively high sampling rates. Recent work has made the problem more tractable, however existing methods remain prohibitively expensive when using large environmental lights and/or (correctly filtered) global illumination. We present an appearance model with explicit high-frequency micro-normal variation, and a filtering approach that scales to multi-dimensional shading integrals. By combining a novel and compact half-vector histogram scheme with a directional basis expansion, we accurately compute the integral of filtered high-frequency reflectance over large lights with angularly varying emission. Our approach is scalable, rendering images indistinguishable from ground truth at over 10× the speed of the state-of-the-art and with only 15% the memory footprint. When filtering appearance with global illumination, we outperform the state-of-the-art by ~30×.
Construction and fabrication of reversible shape transforms
Authors: Li Shuhua , Mahdavi-Amiri Ali , Hu Ruizhen , Liu Han , Zou Changqing , Van Kaick Oliver , Liu Xiuping , Huang Hui , Zhang Hao
Published: November 2018

We study a new and elegant instance of geometric dissection of 2D shapes:
reversible hinged
dissection, which corresponds to a
dual transform
between two shapes where one of them can be dissected in its interior and then inverted
inside-out
, with hinges on the shape boundary, to reproduce the other shape, and vice versa. We call such a transform
reversible inside-out transform
or RIOT. Since it is rare for two shapes to possess even a rough RIOT, let alone an exact one, we develop both a RIOT construction algorithm and a quick filtering mechanism to pick, from a shape collection, potential shape pairs that are likely to possess the transform. Our construction algorithm is
fully automatic.
It computes an
approximate
RIOT between two given input 2D shapes, whose boundaries can undergo slight deformations, while the filtering scheme picks good inputs for the construction. Furthermore, we add properly designed hinges and connectors to the shape pieces and fabricate them using a 3D printer so that they can be played as an assembly puzzle. With many interesting and fun RIOT pairs constructed from shapes found online, we demonstrate that our method significantly expands the range of shapes to be considered for RIOT, a seemingly impossible shape transform, and offers a practical way to construct and physically realize these transforms.
FrankenGAN
Authors: Kelly Tom , Guerrero Paul , Steed Anthony , Wonka Peter , Mitra Niloy J.
Published: November 2018

Coarse building mass models are now routinely generated at scales ranging from individual buildings to whole cities. Such models can be abstracted from raw measurements, generated procedurally, or created manually. However, these models typically lack any meaningful geometric or texture details, making them unsuitable for direct display. We introduce the problem of automatically and realistically decorating such models by adding semantically consistent geometric details and textures. Building on the recent success of generative adversarial networks (GANs), we propose F
ranken
GAN, a cascade of GANs that creates plausible details across multiple scales over large neighborhoods. The various GANs are synchronized to produce consistent style distributions over buildings and neighborhoods. We provide the user with direct control over the variability of the output. We allow him/her to interactively specify the style via images and manipulate style-adapted sliders to control style variability. We test our system on several large-scale examples. The generated outputs are qualitatively evaluated via a set of perceptual studies and are found to be realistic, semantically plausible, and consistent in style.
Deep incremental learning for efficient high-fidelity face tracking
Authors: Wu Chenglei , Shiratori Takaaki , Sheikh Yaser
Published: November 2018

In this paper, we present an incremental learning framework for efficient and accurate facial performance tracking. Our approach is to alternate the modeling step, which takes tracked meshes and texture maps to train our deep learning-based statistical model, and the tracking step, which takes predictions of geometry and texture our model infers from measured images and optimize the predicted geometry by minimizing image, geometry and facial landmark errors. Our
Geo-Tex VAE
model extends the convolutional variational autoencoder for face tracking, and jointly learns and represents deformations and variations in geometry and texture from tracked meshes and texture maps. To accurately model variations in facial geometry and texture, we introduce the
decomposition
layer in the Geo-Tex VAE architecture which decomposes the facial deformation into global and local components. We train the global deformation with a fully-connected network and the local deformations with convolutional layers. Despite running this model on each frame independently - thereby enabling a high amount of parallelization - we validate that our framework achieves sub-millimeter accuracy on synthetic data and outperforms existing methods. We also qualitatively demonstrate high-fidelity, long-duration facial performance tracking on several actors.
Differentiable Monte Carlo ray tracing through edge sampling
Authors: Li Tzu-Mao , Aittala Miika , Durand Frédo , Lehtinen Jaakko
Published: November 2018

Gradient-based methods are becoming increasingly important for computer graphics, machine learning, and computer vision. The ability to compute gradients is crucial to optimization, inverse problems, and deep learning. In rendering, the gradient is required with respect to variables such as camera parameters, light sources, scene geometry, or material appearance. However, computing the gradient of rendering is challenging because the rendering integral includes visibility terms that are not differentiable. Previous work on differentiable rendering has focused on approximate solutions. They often do not handle secondary effects such as shadows or global illumination, or they do not provide the gradient with respect to variables other than pixel coordinates.

We introduce a general-purpose differentiable ray tracer, which, to our knowledge, is the first comprehensive solution that is able to compute derivatives of scalar functions over a rendered image with respect to arbitrary scene parameters such as camera pose, scene geometry, materials, and lighting parameters. The key to our method is a novel edge sampling algorithm that directly samples the Dirac delta functions introduced by the derivatives of the discontinuous integrand. We also develop efficient importance sampling methods based on spatial hierarchies. Our method can generate gradients in times running from seconds to minutes depending on scene complexity and desired precision.

We interface our differentiable ray tracer with the deep learning library PyTorch and show prototype applications in inverse rendering and the generation of adversarial examples for neural networks.
Smart Animal Agriculture: Application of Real-Time Sensors to Improve Animal Well-Being and Production
Authors: Halachmi Ilan , Guarino Marcella , Bewley Jeffrey , Pastell Matti
Published: November 2018

Consumption of animal products such as meat, milk, and eggs in first-world countries has leveled off, but it is rising precipitously in developing countries. Agriculture will have to increase its output to meet demand, opening the door to increased automation and technological innovation; intensified, sustainable farming; and precision livestock farming (PLF) applications. Early indicators of medical problems, which use sensors to alert cattle farmers early concerning individual animals that need special care, are proliferating. Wearable technologies dominate the market. In less-value-per-animal systems like sheep, goat, pig, poultry, and fish, one sensor, like a camera or robot per herd/flock/school, rather than one sensor per animal, will become common. PLF sensors generate huge amounts of data, and many actors benefit from PLF data. No standards currently exist for sharing sensor-generated data, limiting the use of commercial sensors. Technologies providing accurate data can enhance a well-managed farm. Development of methods to turn the data into actionable solutions is critical.
New Approaches for Genome Assembly and Scaffolding
Authors: Rice Edward S. , Green Richard E.
Published: November 2018

Affordable, high-throughput DNA sequencing has accelerated the pace of genome assembly over the past decade. Genome assemblies from high-throughput, short-read sequencing, however, are often not as contiguous as the first generation of genome assemblies. Whereas early genome assembly projects were often aided by clone maps or other mapping data, many current assembly projects forego these scaffolding data and only assemble genomes into smaller segments. Recently, new technologies have been invented that allow chromosome-scale assembly at a lower cost and faster speed than traditional methods. Here, we give an overview of the problem of chromosome-scale assembly and traditional methods for tackling this problem. We then review new technologies for chromosome-scale assembly and recent genome projects that used these technologies to create highly contiguous genome assemblies at low cost.
Functional Genomics for Cancer Research: Applications In Vivo and In Vitro
Authors: O'Loughlin Thomas A. , Gilbert Luke A.
Published: November 2018

Functional genomics holds great promise for the dissection of cancer biology. The elucidation of genetic cooperation and molecular details that govern oncogenesis, metastasis, and response to therapy is made possible by robust technologies for perturbing gene function coupled to quantitative analysis of cancer phenotypes resulting from genetic or epigenetic perturbations. Multiplexed genetic perturbations enable the dissection of cooperative genetic lesions as well as the identification of synthetic lethal gene pairs that hold particular promise for constructing innovative cancer therapies. Lastly, functional genomics strategies enable the highly multiplexed in vivo analysis of genes that govern tumorigenesis as well as of the complex multicellular biology of a tumor, such as immune response and metastasis phenotypes. In this review, we discuss both historical and emerging functional genomics approaches and their impact on the cancer research landscape.
MiT/TFE Family of Transcription Factors, Lysosomes, and Cancer
Authors: Perera Rushika M. , Di Malta Chiara , Ballabio Andrea
Published: November 2018

Cancer cells have an increased demand for energy sources to support accelerated rates of growth. When nutrients become limiting, cancer cells may switch to nonconventional energy sources that are mobilized through nutrient scavenging pathways involving autophagy and the lysosome. Thus, several cancers are highly reliant on constitutive activation of these pathways to degrade and recycle cellular materials. Here, we focus on the MiT/TFE family of transcription factors, which control transcriptional programs for autophagy and lysosome biogenesis and have emerged as regulators of energy metabolism in cancer. These new findings complement earlier reports that chromosomal translocations and amplifications involving the MiT/TFE genes contribute to the etiology and pathophysiology of renal cell carcinoma, melanoma, and sarcoma, suggesting pleiotropic roles for these factors in a wider array of cancers. Understanding the interplay between the oncogenic and stress-adaptive roles of MiT/TFE factors could shed light on fundamental mechanisms of cellular homeostasis and point to new strategies for cancer treatment.
Resistance to PARP Inhibitors: Lessons from Preclinical Models of BRCA-Associated Cancer
Authors: Gogola Ewa , Rottenberg Sven , Jonkers Jos
Published: November 2018

Inhibitors of poly(ADP-ribose) polymerase (PARP) have recently entered the clinic for the treatment of homologous recombination–deficient cancers. Despite the success of this approach, resistance to PARP inhibitors (PARPis) is a clinical hurdle, and it is poorly understood how cancer cells escape the deadly effects of PARPis without restoring BRCA1/2 function. By synergizing the advantages of next-generation sequencing with functional genetic screens in tractable model systems, novel mechanisms providing useful insights into DNA damage response (DDR) have been identified. BRCA1/2 models not only are tools to explore therapy escape mechanisms but also yield basic knowledge about DDR pathways and PARPis’ mechanism of action. Moreover, alterations that render cells resistant to targeted therapies may cause new synthetic dependencies that can be exploited to combat resistant disease.
The Politics of Rulemaking in the United States
Authors: Yackee Susan Webb
Published: November 2018

Rulemaking is a critical part of American government and governance. This article reviews the political underpinnings of modern rulemaking. Specifically, it highlights the process and impact of agency regulations, as well as the key tools used by the legislature, elected executive, and courts to oversee the rulemaking process. The article also reviews who participates in the rulemaking process, as well as who influences regulatory content. Finally, new directions in regulatory policymaking are explored, including data collection advancements, as well as the potential role for guidance documents as replacements for more traditionally issued notice and comment regulations.
Study on Material Properties of Magnesium Oxide Carbonized Prestressed Pipe Piles
Authors: Zhang Henian , Cao Miao , Chen Kaixiang , Shen Chen , Xi Peisheng , Zhang Fang , Wang Shaohan
Published: November 2018

Traditional PHC pipe pile in foundation engineering consumes high energy and has insufficient durability. A magnesium oxide carbonization test block is a new type of environmental protection block which bases on activated magnesium oxide cementation technology. The use of CO2 carbonation technology allows reactive magnesia to react to form basic magnesium carbonate to increase the compressive strength and durability of the block. Three kinds of different magnesium oxide powders were subjected to pressure test and determined the key technical parameters, such as optimal raw materials, sample preparation methods, carbonization environment and technology, and optimized design of pipe pile concrete material system.
Are Sports-Related Factors Correlated to the Prevalence and Initiation of Illicit Drug Misuse in Adolescence? Prospective Study in Older Adolescents
Authors: Zubak Zoran , Terzic Admir , Zenic Natasa , Ostojic Ljerka , Zubak Ivana , Jelicic Mario , Pojskic Haris
Published: November 2018

Sport participation is considered as a factor of potential influence on illicit drug misuse (IDM) in adolescence, but there is an evident lack of studies which prospectively investigated this problem. This study aimed to prospectively investigate the sports-related factors related to IDM and the initiation of IDM among older adolescents. The participants were 436 adolescents (202 females; 16 years old at study baseline). They were tested at baseline and follow-up (two years later). The predictors included variables associated with different facets of sports participation and success in sports. The criteria were (i) baseline IDM, (ii) follow-up IDM, and (iii) initiation of IDM between baseline and follow-up. Crude and adjusted (controlled for parental conflict, age, socioeconomic status, and gender) logistic regressions were applied to establish correlations between predictors and criteria. There were higher odds for baseline IDM in adolescents who quit individual sports (OR: 4.2, 95% CI: 1.3-13.9), who had better competitive sports achievements (OR: 1.8, 95% CI: 1.0-3.3), and those involved in sports for a longer time (OR: 1.6, 95% CI: 1.0-2.5). The IDM at follow-up was more prevalent in adolescents who were involved in sports for a longer time (OR: 1.7, 95% CI: 1.1-2.6). Initiation of drug use was predicted by longer experience in sports (OR: 1.8, 95% CI: 1.1-3.1). Sports-related factors were more negatively than positively related to illicit drug use. Most probably, the transition from junior to senior level in sports put specific stress on those adolescents who were highly committed to sports until that time, but who then had to question their own sports abilities and future potential in sports. Sport-authorities should be informed on established results and specific public-health efforts aimed at preventing IDM in athletic adolescents are urgently needed.
NLRP3 Deficiency Alleviates Severe Acute Pancreatitis and Pancreatitis-Associated Lung Injury in a Mouse Model
Authors: Fu Qiang , Zhai Zhensheng , Wang Yuzhu , Xu Lixia , Jia Pengchong , Xia Peng , Liu Chuanjiang , Zhang Xu , Qin Tao , Zhang Hongwei
Published: November 2018

The rapid production and release of a large number of inflammatory cytokines can cause excessive local and systemic inflammation in severe acute pancreatitis (SAP) and multiple organ dysfunction syndrome (MODS), especially pancreatitis-associated acute lung injury (P-ALI), which is the main cause of early death in patients with SAP. The NLRP3 inflammasome plays an important role in the maturation of IL-1β and the inflammatory cascade. Here, we established a model of SAP using wild-type (NLRP3+/+) and NLRP3 knockout (NLRP3-/-) mice by intraperitoneal injections of caerulein (Cae) and lipopolysaccharide (LPS). Pathological injury to the pancreas and lungs, the inflammatory response, and neutrophil infiltration were significantly mitigated in NLRP3-/- mice. Furthermore, INF-39, an NLRP3 inflammasome inhibitor, could reduce the severity of SAP and P-ALI in a dose-dependent manner. Our results suggested that SAP and P-ALI were alleviated by NLRP3 deficiency in mice, and thus, reducing NLRP3 expression may mitigate SAP-associated inflammation and P-ALI.
Variation in Expression of Inflammation-Related Signaling Molecules with Profibrotic and Antifibrotic Effects in Cutaneous and Oral Mucosa Scars
Authors: Bucur Mihai , Dinca Octavian , Vladan Cristian , Popp Cristiana , Nichita Luciana , Cioplea Mirela , Stînga Patricia , Mustatea Petronel , Zurac Sabina , Ionescu Ecaterina
Published: November 2018

Wound healing is a complex biologic process evolving in three phases: inflammation, proliferation, and tissue remodeling controlled by numerous growth factors and cytokines. Oral mucosa wounds heal with significantly less important scars with less numerous macrophages and mast cells and more numerous myofibroblasts than cutaneous counterparts. We analyzed 32 cutaneous and 32 oral mucosa scars for TGFbeta1, TGFbeta2, TGFbeta3, TNFalpha, PDGF BB and FGF1 expression in mesenchymal cells, endothelial cells, macrophages, and multinucleated giant cells. We identified differences in the expression of profibrotic and antifibrotic factors in oral mucosa and skin scars; TGFbeta2 was positive in cutaneous multinucleated giant cells, TNFalpha was positive in cutaneous macrophages, and both were negative in oral mucosa while TGFbeta3 was positive in oral macrophages and mostly negative in cutaneous ones. PDGF BB and FGF1 were positive in oral endothelial cells and oral macrophages and negative in macrophages with opposite positivity pattern in cutaneous scars. Based on these findings, macrophage seems to be the key player in modulating pro- and antifibrotic processes in wound regeneration.
Study of the Magnetic Properties of Haematite Based on Spectroscopy and the IPSO-ELM Neural Network
Authors: Mao Yachun , Liu Chongmin , Xiao Dong , Wang Jichun , Le Ba Tuan
Published: November 2018

The detection of the magnetic properties of haematite plays an important role in the adjustment of the beneficiation process of haematite and the improvement of metal recovery. The existing methods for measuring the magnetic properties of iron ore either have large errors or take a long time. Therefore, it is very necessary to find a method that can quickly and accurately detect the magnetic properties of haematite. This paper presents a method to detect the magnetic properties of haematite based on the extreme learning machine based on the improved particle swarm optimization (IPSO-ELM) algorithm and spectroscopy. The improved particle swarm optimization algorithm is used to optimize the input weights, hidden layer deviations, and hidden layer nodes of the ELM network. Introducing the linear decreasing inertia weight for the particle swarm algorithm, taking into account the norm of the output weight in the particle update process and using the variation idea to change the length of the particle give the IPSO-ELM better stability and generalization ability. The experimental results show that the IPSO-ELM prediction model has a good prediction performance and has better generalization ability than that of the ELM and PSO-ELM prediction models. Compared with traditional chemical analysis methods and manual methods, this method has great advantages in terms of economy, speed, and accuracy.
Prenatal Sonographic Image of Sirenomelia with Anencephaly and Craniorachischisis Totalis
Authors: Sugiura Takako , Sato Yuka , Nakanami Naoyuki , Tsukimori Kiyomi
Published: November 2018

Sirenomelia is a rare congenital malformation characterized by varying degrees of fusion of the lower extremities. It is commonly associated with severe urogenital and gastrointestinal malformations; however, the association of sirenomelia with anencephaly and rachischisis totalis is extremely rare. To our knowledge, the prenatal sonographic images of this association have not been previously published. Here, we present prenatal sonographic images of this association, detected during the 17th week of gestation through combined two-dimensional, four-dimensional, and color Doppler ultrasound. Two-dimensional ultrasound images showed anencephaly, spina bifida, and possible fusion of the lower limbs. Three-dimensional HDlive rendering images confirmed the final diagnosis of sirenomelia with anencephaly and rachischisis totalis. The patient opted to undergo medical termination of pregnancy and delivered a fetus with fused lower limbs, anencephaly, and rachischisis totalis confirming thein uteroimaging findings. Awareness of these rare associations will help avoid misdiagnoses and facilitate prenatal counselling. This case highlights the importance of a thorough ultrasound examination.
Modeling of Energy Efficiency for Residential Buildings Using Artificial Neuronal Networks
Authors: Álvarez José Antonio , Rabuñal Juan Ramón , García-Vidaurrázaga Dolores , Alvarellos Alberto , Pazos Alejandro
Published: November 2018

Increasing the energy efficiency of buildings is a strategic objective in the European Union, and it is the main reason why numerous studies have been carried out to evaluate and reduce energy consumption in the residential sector. The process of evaluation and qualification of the energy efficiency in existing buildings should contain an analysis of the thermal behavior of the building envelope. To determine this thermal behavior and its representative parameters, we usually have to use destructive auscultation techniques in order to determine the composition of the different layers of the envelope. In this work, we present a nondestructive, fast, and cheap technique based on artificial neural network (ANN) models that predict the energy performance of a house, given some of its characteristics. The models were created using a dataset of buildings of different typologies and uses, located in the northern area of Spain. In this dataset, the models are able to predict the U‐opaque value of a building with a correlation coefficient of 0.967 with the real U‐opaque measured value for the same building.
Heterozygous NPR2 Mutation in Two Family Members with Short Stature and Skeletal Dysplasia
Authors: Jacob Marianne , Menon Surabhi , Botti Christina , Marshall Ian
Published: November 2018

Endochondral ossification at the level of the growth plate, an essential process involved in longitudinal growth, is regulated by hormonal and local factors including C-type natriuretic peptide and its receptor, natriuretic peptide receptor B. Biallelic loss-of-function mutations in the NPR2 gene, which encodes this receptor, cause acromesomelic dysplasia, Maroteaux type (AMDM), a skeletal dysplasia characterized by severe short stature and disproportionate shortening of limbs. Heterozygous NPR2 mutations have been reported in patients previously classified with idiopathic short stature (ISS). We report the presentation of a 7-year-old girl and her mother with short stature, both of whom were identified with the same NPR2 mutation, and who demonstrated clinical and radiological features consistent with a skeletal dysplasia. We also report the patient’s response to recombinant human growth hormone (rhGH) over a 2-year period. We encourage clinicians who evaluate children with ISS to consider genetic testing, particularly when the presentation is associated with features suggestive of a skeletal dysplasia.
Silent Tumefactive Demyelinating Lesions and Radiologically Isolated Syndrome
Authors: Ekmekci Ozgul , Eraslan Cenk
Published: November 2018

Demyelinating lesions larger than 2 cm in diameter, with or without edema, are known as tumefactive demyelinating lesions (TDLs). They constitute a rare inflammatory demyelinating disorder of the central nervous system. TDLs are typically characterized by headaches, cortical symptoms such as aphasia, hemiparesis, hemisensory deficits, seizures, and changes in consciousness. TDLs may occur in patients with or without an established diagnosis of MS or may occur as the initial demyelinating event. They may also be observed during follow-up in patients with MS, neuromyelitis optica, acute disseminated encephalomyelitis, or other autoimmune diseases. Differential diagnosis includes brain tumors, abscess, granulomatous diseases, and vasculitis. In some cases, it may be very difficult to distinguish TDLs from a tumor, such that biopsy might be needed. However, no cases of asymptomatic TDLs have yet been reported in the literature. Hence, in this report, we present a case involving an asymptomatic TDL detected incidentally during magnetic resonance imaging in an 18-year-old man. The patient did not develop any symptoms during the 1-year follow-up period. During follow-up, the patient was diagnosed with a radiologically isolated syndrome. TDLs have not previously been identified as radiologically isolated syndrome. Thus, reporting similar cases in the future will help in further understanding this phenomenon.
Delamination Localization in Sandwich Skin Using Lamb Waves by Finite Element Method
Authors: Nissabouri Salah , El allami Mhammed , Boutyour El Hassan , Errkik Ahmed
Published: November 2018

In this work we model by finite element method (FEM) the Lamb waves’ propagation and their interactions with symmetric and asymmetric delamination in sandwich skin. The simulations were carried out using ABAQUS CAE by exciting the fundamental A0 Lamb mode in the frequency 300 kHz. The delamination was then estimated by analysing the signal picked up at two sensors using two technics: Two-Dimensional Fast Fourier Transform (2D-FFT) to identify the propagating and converted modes, and wavelet transform (WT) to measure the arrival times. The results showed that the mode A0 is sensible to symmetric and asymmetric delamination. Besides, based on signal changes with the delamination edges, a localization method is proposed to estimate the position and the length of the delamination. In the last section an experimental FEM verification is provided to validate the proposed method.