bluuebunny/crossref_metadata_2025 huggingface dataset:
Exercise training and resting blood pressure: a large-scale pairwise and network meta-analysis of randomised controlled trials
Authors: Edwards Jamie J , Deenmamode Algis H P , Griffiths Megan , Arnold Oliver , Cooper Nicola J , Wiles Jonathan D , O'Driscoll Jamie M
Published: July 2023

Objective

To perform a large-scale pairwise and network meta-analysis on the effects of all relevant exercise training modes on resting blood pressure to establish optimal antihypertensive exercise prescription practices.

Design

Systematic review and network meta-analysis.

Data sources

PubMed (Medline), the Cochrane library and Web of Science were systematically searched.

Eligibility criteria

Randomised controlled trials published between 1990 and February 2023. All relevant work reporting reductions in systolic blood pressure (SBP) and/or diastolic blood pressure (DBP) following an exercise intervention of ≥2 weeks, with an eligible non-intervention control group, were included.

Results

270 randomised controlled trials were ultimately included in the final analysis, with a pooled sample size of 15 827 participants. Pairwise analyses demonstrated significant reductions in resting SBP and DBP following aerobic exercise training (−4.49/–2.53 mm Hg, p<0.001), dynamic resistance training (–4.55/–3.04 mm Hg, p<0.001), combined training (–6.04/–2.54 mm Hg, p<0.001), high-intensity interval training (–4.08/–2.50 mm Hg, p<0.001) and isometric exercise training (–8.24/–4.00 mm Hg, p<0.001). As shown in the network meta-analysis, the rank order of effectiveness based on the surface under the cumulative ranking curve (SUCRA) values for SBP were isometric exercise training (SUCRA: 98.3%), combined training (75.7%), dynamic resistance training (46.1%), aerobic exercise training (40.5%) and high-intensity interval training (39.4%). Secondary network meta-analyses revealed isometric wall squat and running as the most effective submodes for reducing SBP (90.4%) and DBP (91.3%), respectively.

Conclusion

Various exercise training modes improve resting blood pressure, particularly isometric exercise. The results of this analysis should inform future exercise guideline recommendations for the prevention and treatment of arterial hypertension.
Electronic patient-reported outcome (e-PRO) monitoring for adverse event management during cabozantinib treatment in patients with advanced renal cell carcinoma: protocol for a three-arm, randomised, multicentre phase II trial (e-PRO vs paper-PRO or usual care)
Authors: Osawa Takahiro , Fujii Yasuhisa , Kimura Go , Kitamura Hiroshi , Nagashima Yoji , Iizumi Sakura , Osaka Tsuyoshi , Tsubouchi Ryoichi , Shinohara Nobuo
Published: July 2023

Introduction

Cabozantinib monotherapy is an option for treatment of advanced renal cell carcinoma (RCC). However, cabozantinib dose modification and discontinuation due to symptomatic adverse events (AEs) remains a challenge. The use of patient-reported outcomes (PROs) may help manage symptomatic AEs, which is reported to lead to improved quality of life (QOL), avoidance of drug discontinuation and better survival. This study aims to investigate the clinical benefits of PROs in patients with RCC receiving cabozantinib and the most appropriate medium for PRO monitoring (electronic [e]-PRO or paper-PRO).

Methods and analysis

This study is being conducted at about 35 sites in Japan. Patients aged ≥18 years with unresectable or metastatic RCC initiating treatment with cabozantinib monotherapy are eligible and will be randomised to: (1) e-PRO monitoring, (2) paper-PRO monitoring or (3) usual care without PRO monitoring. Recruitment began in December 2021 (target sample size, 105). Patients start treatment with cabozantinib 60 mg once daily, and in the PRO groups, will record daily medication intake, weight, temperature, blood pressure and AEs. Endpoints include the proportion of patients with a ≥5-point deterioration on the Functional Assessment of Cancer Therapy-Kidney Cancer Symptom Index (FKSI-19; primary endpoint), progression-free survival, QOL, dose adjustments, relative dose intensity, treatment-emergent AEs and frequency of interventions for AEs outside of the scheduled visits. Patient and physician opinions of the PRO monitoring systems and patient compliance with e-PRO/paper-PRO input are also being measured.

Ethics and dissemination

The study is being conducted in compliance with the Declaration of Helsinki, the International Council for Harmonisation guidelines for Good Clinical Practice and the Clinical Trials Act. Written informed consent is being obtained from all patients, and the protocol has been approved by the Hokkaido University Hospital Certified Review Board (approval number, CRB021-005). The results will be presented at conferences and submitted to a peer-reviewed journal.

Trial registration number

jRCTs011210055.
Academic Department of Military Rehabilitation (ADMR): avoiding the pitfalls of ‘the Walker Dip’
Authors: Coppack Russell J , Ladlow P , Cassidy R P , Egginton N , Barker-Davies R , Houston A , Lunt K M , O'Sullivan O , Bennett A N
Published: July 2023

A key research theme identified during the 2021 Strategic Delivery Plan (SDP) for Defence Medical Services (DMS) Research was preventing and treating musculoskeletal injury (MSKI). MSKI is a significant burden to military populations globally, reducing both operational strength and force readiness. It is therefore essential that research is conducted to gain a greater understanding of the epidemiology, aetiology and risk factors associated with MSKI to develop targeted prevention strategies and rehabilitation interventions. The Academic Department of Military Rehabilitation (ADMR) and the recently established MSKI research-theme working group must use a combination of balance and flexibility when coordinating research priorities to ensure they align and reflect both higher level DMS and UK Defence Rehabilitation practitioner-driven priorities. This article describes the response ADMR have taken to meet and align with the requirements of the 2021 SDP.
Arrayed and entangled silicon nanowires using Au nanoparticle catalysts prepared by pulsed laser-induced dewetting
Authors: Fulton Alison Joy , Shi Yujun
Published: July 2023

The use of pulsed laser-induced dewetting (PLiD) is reported as a novel approach in the fabrication of Au nanoparticle (NP) catalytic arrays for the growth of Si nanowires (NWs) by chemical vapor deposition using SiCl4 in the presence of H2. On polished Si substrates, PLiD generates Au NP catalysts with long-range order and narrow size distributions. It has been shown that the monodispersed distribution of Au NPs provides consistent diameter control of the as-grown Si NWs. A systematic exploration of the Si NW synthesis time, temperature, and gas flow rates illustrates a level of tunability in terms of morphology, be it arrayed or entangled Si NWs, with varying experimental parameters. An investigation of the effect of growth temperature also showed that Si NWs can be synthesized at temperatures as low as 700 °C when using SiCl4 as the precursor. The use of porous Si substrates enabled direct observation of the diameter-dependent growth due to the simultaneous presence of three Au NP size distributions. Growth from the small- and medium-sized Au NP catalysts occurred first, followed by that from the large-sized Au NPs, which was only observed at extended times or high SiCl4 flow rates. The delayed onset of growth from the large-sized Au NPs is due to the longer time to achieve Si super-saturation of larger catalyst NPs. The morphology and diameter control of the as-grown Si NWs reported in this work makes this approach potentially useful toward applications such as nanoelectronics, sensors, and lithium ion battery electrodes depending on the desired morphology.
Regulation of feed intake, digestive enzyme activity, and growth in response to live feed and prepared diet during early rearing of Labeo rohita
Authors: Ali Mashooq , Zuberi Amina , Ahmad Muhammad , Younus Naima
Published: July 2023

A 35 day feeding trial in a replicate of five was conducted to evaluate the impact of partial and total replacement of live feed (LF) with nanoparticulate-prepared diet (ND) on early rearing of rohu ( Labeo rohita (Hamilton, 1822)). Larvae 3 days after hatching (DAH) were evenly distributed into three groups; T1 was reared exclusively on LF, T2 was on ND, and T3 was co-fed both LF and ND (1:1). All groups showed a feed-dependent increase in growth and the expression of genes involved in feed intake and growth with age DAH. The T3 group showed significantly higher weight gain, specific growth rate, and expression of insulin-like growth factor-1 followed by the T2 group, while the highest expression of ghrelin and growth hormone secretagogue receptor was observed in T3 followed by T1. Furthermore, leptin showed the highest expression in the T2 followed by the T1 group. The intestinal enzymes showed variable trends, with the highest activity of cellulase, amylase, and protease in the T1, T2, and T3 groups, respectively. Moreover, in all groups, cellulase increased continuously with age DAH, while amylase and protease showed a positive correlation up to 30 DAH and then declined. The results of this study could be helpful in larval nutrition programming.
Hyperbolic behavior of anisotropic Bianchi type-I cosmological model
Authors: Alam Md Khurshid , Singh S. Surendra
Published: July 2023

Locally Rotationally Symmetric (LRS) Bianchi type-I metric is examined in the presence of perfect fluid. Exact solutions of Einstein’s field equations (EFE) have been studied by taking into account a hyperbolic scale factor. We observed that the model has initial singularity. It is found that the Universe approaches isotropy at late times. Through state finder pair [Formula: see text], it is observed that at late cosmic time, the model behaves analogous to [Formula: see text]CDM model. Energy conditions of the model are studied and it is found that null energy condition (NEC), weak energy condition (WEC) and dominant energy conditions (DEC) are satisfied for our model while SEC is violated. We investigate some physically and geometrically realistic models in order to develop a viable cosmological model.
DYNAMICAL STUDY OF FEAR EFFECT IN PREY–PREDATOR MODEL WITH DISEASE IN PREDATOR
Authors: SHA AMAR , CHATTOPADHYAY JOYDEV
Published: July 2023

In predator–prey system, the impact of predation fear on the prey population has received significant attention from researchers recently. A predator–prey community with infected predator in presence of predation fear has not yet been studied. We model a predator–prey system with disease transmission in predator and predator-induced fear in prey. We consider two crucial mechanisms where fear of prey can be detrimental to the prey due to low reproduction, and disease in predator can be beneficial to the prey due to low predation risk. In this study, we investigate the simultaneous effect of disease transmission and the cost of predation fear on the system dynamics. We study the dynamics of such system around the equilibrium points by stability and bifurcation analysis. Analytical results establish the occurrence of transcritical and Hopf bifurcation into the system dynamics. Numerical results reveal that high strength of predation fear and disease transmissions can stabilize the system by excluding oscillations. We show that the increased strength of disease transmission control or extinction of the predator yet permits the prey species to recuperate. The study indicates that infectious diseases may act as a biological control to control undesirable species. In the presence of fear, the half-saturation constant plays a vital role in shrinking or expanding the oscillatory region. Also, we identify a scenario in which the disease transmission rate produces a bubbling effect around an endemic equilibrium. We validate our analytical findings through numerical simulations.
PORE FLOW FRACTAL PROPERTIES OF POROUS LiOH ABSORBENTS
Authors: CHEN YUANCHAO , CAO JIYIN , ZHOU RUI , YANG XIA , WANG LONGFEI
Published: July 2023

The flow law of carbon dioxide in the pores of the novel porous LiOH adsorbent is studied, and the intrinsic relationship between the pore structure of the absorbent and the fractal dimension is analyzed. In this paper, the fractal structure — Sierpinski–Menger sponge structure — is used to establish a three-dimensional fractal pore model to achieve different porosities and pore structures. The flow process of the working fluid is numerically simulated, and the flow law under different flow velocities (1, 0.1, and 0.01 mm/s) is analyzed. The simulation results show that the flow of gas in the Sierpinski–Menger sponge structure has fractal characteristics. The streamlines and velocity clouds of different pore structures and porosities reflect the distribution characteristics of the flow velocity in the porous media. The velocity field and pressure field distribution of sponge models of different series are consistent, which has obvious self-similarity symmetry. It provides a numerical reference for the study of the pore adsorption law of real LiOH absorbent.
A NEW SEVEN-REGION FLOW MODEL FOR DELIVERABILITY EVALUATION OF MULTIPLY-FRACTURED HORIZONTAL WELLS IN TIGHT OIL FRACTAL RESERVOIR
Authors: GUO FANRONG , CHEN ROU , YAN WEIWEI , SU YING , HU YANYAN , XIONG SHENGCHUN
Published: July 2023

Deliverability evaluation plays an important role in the reservoir exploitation. In this study, a new seven-region semi-analytical mathematical model considering the influences of fractal, imbibition and non-Darcy flow is proposed to evaluate the deliverability of multiply-fractured horizontal wells in tight oil reservoirs. The Laplace transformation, perturbation method and Stehfest numerical inversion are employed to solve the model. The reliability and accuracy of the analytical solution are verified by the field example. The sensitivity analysis of the major influencing factors on the deliverability is specifically analyzed. The numerical results indicate that the seven-region semi-analytical model can better explain the heterogeneity of fracture network, and its solution can provide an effective algorithm for the deliverability evaluation. It is found that the fractal plays a predominant influence on the productivity of tight oil reservoirs. The larger the fractal dimension and the smaller the fractal index, the higher the accumulative production rate. The imbibition also has an important effect on the deliverability of tight oil reservoirs. As the rising of wetting angle, both daily and accumulative production rates can obviously decrease. The imbibition has a positive impact on the production rate in the water-wet formations, while it has a negative impact on the production rate in the oil-wet formations. Compared with the fractal and imbibition, the threshold pressure gradient has less influence on the production of tight oil reservoirs. There exists a negative correlation between the threshold pressure gradient and the production performance. This work provides a new approach to understand the fractal tight oil reservoirs, which is of great significance for the deliverability evaluation.
A NOVEL ANALYTICAL MODEL OF THE EFFECTIVE THERMAL CONDUCTIVITY OF POROUS MATERIALS UNDER STRESS
Authors: TANG JIADI , LEI GANG , LI XIAODONG , ZHANG LING , CHENG WAN , PANG JIANGTAO , JIANG GUOSHENG
Published: July 2023

With the increasing demand for energy, heat and mass transfer through porous media has been widely studied. To achieve accuracy in studying the behavior of heat transfer, a good knowledge of the effective thermal conductivity (ETC) of porous materials is needed. Because pore structure dominates the ETC of porous materials and effective stress leads to a change in pore structure, effective stress is one of the key influencing factors affecting ETC. In this study, considering the structure of surface roughness and pore size, based on fractal theory, a novel analytical solution at the pore scale for ETC of porous materials under stress conditions is proposed. Furthermore, in this model, capillaries in porous materials saturated with multiple phases have sinusoidal periodically constricted boundaries. The derived ETC model is validated against available experimental data. Moreover, the influences of the effective stress, initial effective porosity, roughness structure characterization, and wetting phase saturation on the ETC are analyzed. Compared with previous models, the rough surfaces of porous materials and the coupling of heat conduction and mechanics are taken into consideration to make the model more reasonable. As a result, this ETC model can better reveal the mechanism of heat conduction in porous media under stress conditions.
A METHOD FOR PREDICTING UNSATURATED SOIL PERMEABILITY COEFFICIENT BASED ON CLAY CONTENT
Authors: TAO GAOLIANG , PENG YINJIE , LIU YIMING
Published: July 2023

The unsaturated permeability coefficient is of importance to study the behavior of the water seepage and contaminant transport in unsaturated soils. The direct measurement of the unsaturated permeability coefficient is time consuming and laborious. Therefore, indirect approaches are usually applied to obtain the unsaturated permeability coefficient. However, indirect methods still have some drawbacks. An indirect method is usually fitted through the soil–water characteristic curve of a special type of soil, which also requires a large workload and its application is limited. To overcome these drawbacks, a fractal model of the relative permeability coefficient of unsaturated soils has been deduced in this study based on the relation between clay contents and fractal dimensions in terms of mass. A new fractal simple prediction method of unsaturated soil absolute permeability coefficient associated with the new fractal model has also been proposed by combining the simple relation between saturated permeability coefficient and air-entry value. The method is validated with the experimental data of existing literature, and the result shows that the proposed model has a higher prediction accuracy.
EXPERIMENTAL INVESTIGATION ON FRACTAL CHARACTERIZATION OF IN-SITU FOAM IN POROUS MEDIA
Authors: CHEN HAILONG , JI BINGXIN , WANG FEI , WANG YUCHEN , ZENG FAMING , LI ZHAOMIN , JIANG QI
Published: July 2023

The in-situ foam technology has been extensively applied in the complex reservoir reconstruction since it improves the sweep efficiency by diverting the flow of injected fluids into areas with lower permeability and as a result enhances the oil recovery. The in-situ foam structure inside the pores can significantly affect the sweep efficiency, however, quantitative characterizations on foam structure are inadequate. Here, we propose a quantitative method based on fractal theory and the two-dimensional (2D) micro physical simulation experiment for the study of fractal characteristic, evolution law and sensitivity analysis. The findings demonstrate that foam confined within porous media exhibits fractal characteristics, as evidenced by the measured box-counting fractal dimensions ranging between 1.05 and 1.752 based on acquired structural images. Notably, a higher fractal dimension corresponds to a more irregular in-situ foam structure. Besides, in-situ foam in the porous media presents the “quasi check sign” evolution law, which can be divided into three time-dependent stages. Moreover, the evolution laws of in-situ foam within porous media remains consistent across varying temperatures and concentrations of foaming agents, and increasing temperature and decreasing concentration can shorten the time to reach the inflection point.
A NOVEL KOZENY–CARMAN CONSTANT MODEL FOR POROUS MEDIA EMBEDDED WITH TREE-LIKE BRANCHING NETWORKS WITH ROUGHENED SURFACES
Authors: XIAO BOQI , CHEN FENGYE , ZHANG YIDAN , LI SHAOFU , ZHANG GUOYING , LONG GONGBO , ZHOU HUAN , LI YI
Published: July 2023

Although the hydraulic features of the tree-like branching network have been widely investigated, the seepage characteristics of the networks have not been studied sufficiently. In this study, the seepage characteristics of porous media embedded with a tree-like branching network with the effects of roughness are studied based on fractal theory. Then, the Kozeny–Carman (KC) constant of the composite network is derived. The KC constant of porous media embedded with a tree-like branching network with roughened surfaces is in good agreement with the experimental data in the literature. The effects of structural parameters on seepage characteristics are also discussed. Notably, the results show that the KC constant of the composite network increases with an increasing volume porosity, and decreases with an increase in the relative roughness. Besides, the model established in this paper contains no empirical constants to ensure that each parameter has its physical significance. Thus, the proposed model can facilitate a better understanding of the seepage characteristics of fluid transport through a tree-like branching network embedded in porous media.
A study of the patterns of prescriptions for heart diseases in The Four Tantras (rGyud bZhi) based on the Tibetan medicinal properties theory
Authors: Nanjia Cairang
Published: July 2023

Objective: To reveal the internal and effective rules of prescription compatibility for treating heart diseases in the four tantras.

Method: To compare the four kinds of disease in the four tantras.

Result: Formulas for the treatment of loong(rlung) disease were mainly formulated sweet, pungent flavors with hot and greasy effects; formulas for the treatment of tripa(mkris-pa) were mainly formulated sweet, pungent and bitter flavors with cold and blunt effects; formulas for the treatment of beygen(bad-kan) were mainly formulated sweet, pungent flavors with hot and dynamic effects; and formulas for the treatment of khrag khrugs nad () were mainly formulated sweet, bitter flavors with cold and blunt effects.

Conclusion: It can be seen that the four tantras have a rigorous and regular theory of prescriptions and strong theoretical guidance, which we need to study and learn in depth, and apply in clinical practice in conjunction with the properties of the disease.
DreamFace: Progressive Generation of Animatable 3D Faces under Text Guidance
Authors: Zhang Longwen , Qiu Qiwei , Lin Hongyang , Zhang Qixuan , Shi Cheng , Yang Wei , Shi Ye , Yang Sibei , Xu Lan , Yu Jingyi
Published: July 2023

Emerging Metaverse applications demand accessible, accurate and easy-to-use tools for 3D digital human creations in order to depict different cultures and societies as if in the physical world. Recent large-scale vision-language advances pave the way for novices to conveniently customize 3D content. However, the generated CG-friendly assets still cannot represent the desired facial traits for human characteristics. In this paper, we present Dream-Face, a progressive scheme to generate personalized 3D faces under text guidance. It enables layman users to naturally customize 3D facial assets that are compatible with CG pipelines, with desired shapes, textures and fine-grained animation capabilities. From a text input to describe the facial traits, we first introduce a coarse-to-fine scheme to generate the neutral facial geometry with a unified topology. We employ a selection strategy in the CLIP embedding space to generate coarse geometry, and subsequently optimize both the detailed displacements and normals using Score Distillation Sampling (SDS) from the generic Latent Diffusion Model (LDM). Then, for neutral appearance generation, we introduce a dual-path mechanism, which combines the generic LDM with a novel texture LDM to ensure both the diversity and textural specification in the UV space. We also employ a two-stage optimization to perform SDS in both the latent and image spaces to significantly provide compact priors for fine-grained synthesis. It also enables learning the mapping from the compact latent space into physically-based textures (diffuse albedo, specular intensity, normal maps, etc.). Our generated neutral assets naturally support blendshapes-based facial animations, thanks to the unified geometric topology. We further improve the animation ability with personalized deformation characteristics. To this end, we learn the universal expression prior in a latent space with neutral asset conditioning using the cross-identity hypernetwork, we subsequently train a neural facial tracker from video input space into the pre-trained expression space for personalized fine-grained animation. Extensive qualitative and quantitative experiments validate the effectiveness and generalizability of DreamFace. Notably, DreamFace can generate realistic 3D facial assets with physically-based rendering quality and rich animation ability from video footage, even for fashion icons or exotic characters in cartoons and fiction movies.
Masonry Shell Structures with Discrete Equivalence Classes
Authors: Chen Rulin , Qiu Pengyun , Song Peng , Deng Bailin , Wang Ziqi , He Ying
Published: July 2023

This paper proposes a method to model masonry shell structures where the shell elements fall into a set of discrete equivalence classes. Such shell structure can reduce the fabrication cost and simplify the physical construction due to reuse of a few template shell elements. Given a freeform surface, our goal is to generate a small set of template shell elements that can be reused to produce a seamless and buildable structure that closely resembles the surface. The major technical challenge in this process is balancing the desire for high reusability of template elements with the need for a seamless and buildable final structure. To address the challenge, we define three error metrics to measure the seamlessness and buildability of shell structures made from discrete equivalence classes and develop a hierarchical cluster-and-optimize approach to generate a small set of template elements that produce a structure closely approximating the surface with low error metrics. We demonstrate the feasibility of our approach on various freeform surfaces and geometric patterns, and validate buildability of our results with four physical prototypes. Code and data of this paper are at https://github.com/Linsanity81/TileableShell.
A Practical Walk-on-Boundary Method for Boundary Value Problems
Authors: Sugimoto Ryusuke , Chen Terry , Jiang Yiti , Batty Christopher , Hachisuka Toshiya
Published: July 2023

We introduce the
walk-on-boundary
(WoB) method for solving boundary value problems to computer graphics. WoB is a grid-free Monte Carlo solver for certain classes of second order partial differential equations. A similar Monte Carlo solver, the walk-on-spheres (WoS) method, has been recently popularized in computer graphics due to its advantages over traditional spatial discretization-based alternatives. We show that WoB's intrinsic properties yield further advantages beyond those of WoS. Unlike WoS, WoB naturally supports various boundary conditions (Dirichlet, Neumann, Robin, and mixed) for both interior and exterior domains. WoB builds upon boundary integral formulations, and it is mathematically more similar to light transport simulation in rendering than the random walk formulation of WoS. This similarity between WoB and rendering allows us to implement WoB on top of Monte Carlo ray tracing, and to incorporate advanced rendering techniques (e.g., bidirectional estimators with multiple importance sampling, the virtual point lights method, and Markov chain Monte Carlo) into WoB. WoB does not suffer from the intrinsic bias of WoS near the boundary and can estimate solutions precisely on the boundary. Our numerical results highlight the advantages of WoB over WoS as an attractive alternative to solve boundary value problems based on Monte Carlo.
A Contact Proxy Splitting Method for Lagrangian Solid-Fluid Coupling
Authors: Xie Tianyi , Li Minchen , Yang Yin , Jiang Chenfanfu
Published: July 2023

We present a robust and efficient method for simulating Lagrangian solid-fluid coupling based on a new operator splitting strategy. We use variational formulations to approximate fluid properties and solid-fluid interactions, and introduce a unified two-way coupling formulation for SPH fluids and FEM solids using interior point barrier-based frictional contact. We split the resulting optimization problem into a fluid phase and a solid-coupling phase using a novel time-splitting approach with augmented
contact proxies
, and propose efficient custom linear solvers. Our technique accounts for fluids interaction with nonlinear hyperelastic objects of different geometries and codimensions, while maintaining an algorithmically guaranteed non-penetrating criterion. Comprehensive benchmarks and experiments demonstrate the efficacy of our method.
Attend-and-Excite: Attention-Based Semantic Guidance for Text-to-Image Diffusion Models
Authors: Chefer Hila , Alaluf Yuval , Vinker Yael , Wolf Lior , Cohen-Or Daniel
Published: July 2023

Recent text-to-image generative models have demonstrated an unparalleled ability to generate diverse and creative imagery guided by a target text prompt. While revolutionary, current state-of-the-art diffusion models may still fail in generating images that fully convey the semantics in the given text prompt. We analyze the publicly available Stable Diffusion model and assess the existence of
catastrophic neglect
, where the model fails to generate one or more of the subjects from the input prompt. Moreover, we find that in some cases the model also fails to correctly bind attributes (
e.g.
, colors) to their corresponding subjects. To help mitigate these failure cases, we introduce the concept of
Generative Semantic Nursing (GSN)
, where we seek to intervene in the generative process on the fly during inference time to improve the faithfulness of the generated images. Using an attention-based formulation of GSN, dubbed
Attend-and-Excite
, we guide the model to refine the cross-attention units to
attend
to all subject tokens in the text prompt and strengthen --- or
excite
--- their activations, encouraging the model to generate all subjects described in the text prompt. We compare our approach to alternative approaches and demonstrate that it conveys the desired concepts more faithfully across a range of text prompts. Code is available at our project page: https://attendandexcite.github.io/Attend-and-Excite/.
Eventfulness for Interactive Video Alignment
Authors: Sun Jiatian , Deng Longxiulin , Afouras Triantafyllos , Owens Andrew , Davis Abe
Published: July 2023

Humans are remarkably sensitive to the alignment of visual events with other stimuli, which makes synchronization one of the hardest tasks in video editing. A key observation of our work is that most of the alignment we do involves salient localizable events that occur sparsely in time. By learning how to recognize these events, we can greatly reduce the space of possible synchronizations that an editor or algorithm has to consider. Furthermore, by learning descriptors of these events that capture additional properties of visible motion, we can build active tools that adapt their notion of eventfulness to a given task as they are being used. Rather than learning an automatic solution to one specific problem, our goal is to make a much broader class of interactive alignment tasks significantly easier and less time-consuming. We show that a suitable visual event descriptor can be learned entirely from stochastically-generated synthetic video. We then demonstrate the usefulness of learned and adaptive eventfulness by integrating it in novel interactive tools for applications including audio-driven time warping of video and the extraction and application of sound effects across different videos.
Example-Based Procedural Modeling Using Graph Grammars
Authors: Merrell Paul
Published: July 2023

We present a method for automatically generating polygonal shapes from an example using a graph grammar. Most procedural modeling techniques use grammars with manually created rules, but our method can create them automatically from an example. Our graph grammars generate graphs that are locally similar to a given example. We disassemble the input into small pieces called primitives and then reassemble the primitives into new graphs. We organize all possible locally similar graphs into a hierarchy and find matching graphs within the hierarchy. These matches are used to create a graph grammar that can construct every locally similar graph. Our method generates graphs using the grammar and then converts them into a planar graph drawing to produce the final shape.
Word-As-Image for Semantic Typography
Authors: Iluz Shir , Vinker Yael , Hertz Amir , Berio Daniel , Cohen-Or Daniel , Shamir Ariel
Published: July 2023

A word-as-image is a semantic typography technique where a word illustration presents a visualization of the meaning of the word, while also preserving its readability. We present a method to create word-as-image illustrations automatically. This task is highly challenging as it requires semantic understanding of the word and a creative idea of where and how to depict these semantics in a visually pleasing and legible manner. We rely on the remarkable ability of recent large pretrained language-vision models to distill textual concepts visually. We target simple, concise, black-and-white designs that convey the semantics clearly. We deliberately do not change the color or texture of the letters and do not use embellishments. Our method optimizes the outline of each letter to convey the desired concept, guided by a pretrained Stable Diffusion model. We incorporate additional loss terms to ensure the legibility of the text and the preservation of the style of the font. We show high quality and engaging results on numerous examples and compare to alternative techniques. Code and demo will be available at our project page.
Image vectorization and editing via linear gradient layer decomposition
Authors: Du Zheng-Jun , Kang Liang-Fu , Tan Jianchao , Gingold Yotam , Xu Kun
Published: July 2023

A key advantage of vector graphics over raster graphics is their editability. For example, linear gradients define a spatially varying color fill with a few intuitive parameters, which are ubiquitously supported in standard vector graphics formats and libraries. By layering regions filled with linear gradients, complex appearances can be created. We propose an automatic method to convert a raster image into layered regions of linear gradients. Given an input raster image segmented into regions, our approach decomposes the resulting regions into opaque and semi-transparent linear gradient fills. Our approach is fully automatic (e.g., users do not identify a background as in previous approaches) and exhaustively considers all possible decompositions that satisfy perceptual cues. Experiments on a variety of images demonstrate that our method is robust and effective.
OctFormer: Octree-based Transformers for 3D Point Clouds
Authors: Wang Peng-Shuai
Published: July 2023

We propose octree-based transformers, named OctFormer, for 3D point cloud learning. OctFormer can not only serve as a general and effective backbone for 3D point cloud segmentation and object detection but also have linear complexity and is scalable for large-scale point clouds. The key challenge in applying transformers to point clouds is reducing the quadratic, thus overwhelming, computation complexity of attentions. To combat this issue, several works divide point clouds into non-overlapping windows and constrain attentions in each local window. However, the point number in each window varies greatly, impeding the efficient execution on GPU. Observing that attentions are robust to the shapes of local windows, we propose a novel octree attention, which leverages sorted shuffled keys of octrees to partition point clouds into local windows containing a fixed number of points while permitting shapes of windows to change freely. And we also introduce dilated octree attention to expand the receptive field further. Our octree attention can be implemented in 10 lines of code with open-sourced libraries and runs 17 times faster than other point cloud attentions when the point number exceeds 200
k.
Built upon the octree attention, OctFormer can be easily scaled up and achieves state-of-the-art performances on a series of 3D semantic segmentation and 3D object detection benchmarks, surpassing previous sparse-voxel-based CNNs and point cloud transformers in terms of both efficiency and effectiveness. Notably, on the challenging ScanNet200 dataset, OctFormer outperforms sparse-voxel-based CNNs by 7.3 in mIoU.
Our code and trained models are available at https://wang-ps.github.io/octformer.
NeRO: Neural Geometry and BRDF Reconstruction of Reflective Objects from Multiview Images
Authors: Liu Yuan , Wang Peng , Lin Cheng , Long Xiaoxiao , Wang Jiepeng , Liu Lingjie , Komura Taku , Wang Wenping
Published: July 2023

We present a neural rendering-based method called NeRO for reconstructing the geometry and the BRDF of reflective objects from multiview images captured in an unknown environment. Multiview reconstruction of reflective objects is extremely challenging because specular reflections are view-dependent and thus violate the multiview consistency, which is the cornerstone for most multiview reconstruction methods. Recent neural rendering techniques can model the interaction between environment lights and the object surfaces to fit the view-dependent reflections, thus making it possible to reconstruct reflective objects from multiview images. However, accurately modeling environment lights in the neural rendering is intractable, especially when the geometry is unknown. Most existing neural rendering methods, which can model environment lights, only consider direct lights and rely on object masks to reconstruct objects with weak specular reflections. Therefore, these methods fail to reconstruct reflective objects, especially when the object mask is not available and the object is illuminated by indirect lights. We propose a two-step approach to tackle this problem. First, by applying the split-sum approximation and the integrated directional encoding to approximate the shading effects of both direct and indirect lights, we are able to accurately reconstruct the geometry of reflective objects without any object masks. Then, with the object geometry fixed, we use more accurate sampling to recover the environment lights and the BRDF of the object. Extensive experiments demonstrate that our method is capable of accurately reconstructing the geometry and the BRDF of reflective objects from only posed RGB images without knowing the environment lights and the object masks. Codes and datasets are available at https://github.com/liuyuan-pal/NeRO.
Dictionary Fields: Learning a Neural Basis Decomposition
Authors: Chen Anpei , Xu Zexiang , Wei Xinyue , Tang Siyu , Su Hao , Geiger Andreas
Published: July 2023

We present Dictionary Fields, a novel neural representation which decomposes a signal into a product of factors, each represented by a classical or neural field representation, operating on transformed input coordinates. More specifically, we factorize a signal into a coefficient field and a basis field, and exploit periodic coordinate transformations to apply the same basis functions across multiple locations and scales. Our experiments show that Dictionary Fields lead to improvements in approximation quality, compactness, and training time when compared to previous fast reconstruction methods. Experimentally, our representation achieves better image approximation quality on 2D image regression tasks, higher geometric quality when reconstructing 3D signed distance fields, and higher compactness for radiance field reconstruction tasks. Furthermore, Dictionary Fields enable generalization to unseen images/3D scenes by sharing bases across signals during training which greatly benefits use cases such as image regression from partial observations and few-shot radiance field reconstruction.
Evolutionary Piecewise Developable Approximations
Authors: Zhao Zheng-Yu , Li Mo , Zhang Zheng , Fang Qing , Liu Ligang , Fu Xiao-Ming
Published: July 2023

We propose a novel method to compute high-quality piecewise developable approximations for triangular meshes. Central to our approach is an evolutionary genetic algorithm for optimizing the combinatorial and discontinuous fitness function, including the approximation error, the number of patches, the patch boundary length, and the penalty for small patches and narrow regions within patches. The genetic algorithm's operations (i.e., initialization, selection, mutation, and crossover) are explicitly designed to minimize the fitness function.

The main challenge is evaluating the fitness function's approximation error as it requires developable patches, which are difficult or time-consuming to obtain. Resolving the challenge is based on a critical observation: the approximation error and the mapping distortion between an input surface and its developable approximation are positively correlated empirically. To efficiently measure distortion without explicitly generating developable shapes, we creatively use conformal mapping techniques. Then, we control the mapping distortion at a relatively low level to achieve high shape similarity in the genetic algorithm.

The feasibility and effectiveness of our method are demonstrated over 240 complex examples. Compared with the state-of-the-art methods, our results have much smaller approximation errors, fewer patches, shorter patch boundaries, and fewer small patches and narrow regions.
Neural Volumetric Reconstruction for Coherent Synthetic Aperture Sonar
Authors: Reed Albert , Kim Juhyeon , Blanford Thomas , Pediredla Adithya , Brown Daniel , Jayasuriya Suren
Published: July 2023

Synthetic aperture sonar (SAS) measures a scene from multiple views in order to increase the resolution of reconstructed imagery. Image reconstruction methods for SAS coherently combine measurements to focus acoustic energy onto the scene. However, image formation is typically under-constrained due to a limited number of measurements and bandlimited hardware, which limits the capabilities of existing reconstruction methods. To help meet these challenges, we design an analysis-by-synthesis optimization that leverages recent advances in neural rendering to perform coherent SAS imaging. Our optimization enables us to incorporate physics-based constraints and scene priors into the image formation process. We validate our method on simulation and experimental results captured in both air and water. We demonstrate both quantitatively and qualitatively that our method typically produces superior reconstructions than existing approaches. We share code and data for reproducibility.
Rhizomorph: The Coordinated Function of Shoots and Roots
Authors: Li Bosheng , Klein Jonathan , Michels Dominik L. , Benes Bedrich , Pirk Sören , Pałubicki Wojtek
Published: July 2023

Computer graphics has dedicated a considerable amount of effort to generating realistic models of trees and plants. Many existing methods leverage procedural modeling algorithms - that often consider biological findings - to generate branching structures of individual trees. While the realism of tree models generated by these algorithms steadily increases, most approaches neglect to model the root system of trees. However, the root system not only adds to the visual realism of tree models but also plays an important role in the development of trees. In this paper, we advance tree modeling in the following ways: First, we define a physically-plausible soil model to simulate resource gradients, such as water and nutrients. Second, we propose a novel developmental procedural model for tree roots that enables us to emergently develop root systems that adapt to various soil types. Third, we define long-distance signaling to coordinate the development of shoots and roots. We show that our advanced procedural model of tree development enables - for the first time - the generation of trees with their root systems.
Deployable strip structures
Authors: Liu Daoming , Pellis Davide , Chiang Yu-Chou , Rist Florian , Wallner Johannes , Pottmann Helmut
Published: July 2023

We introduce the new concept of C-mesh to capture kinetic structures that can be deployed from a collapsed state. Quadrilateral C-meshes enjoy rich geometry and surprising relations with differential geometry: A structure that collapses onto a flat and straight strip corresponds to a Chebyshev net of curves on a surface of constant Gaussian curvature, while structures collapsing onto a circular strip follow surfaces which enjoy the linear-Weingarten property. Interestingly, allowing more general collapses actually leads to a smaller class of shapes. Hexagonal C-meshes have more degrees of freedom, but a local analysis suggests that there is no such direct relation to smooth surfaces. Besides theory, this paper provides tools for exploring the shape space of C-meshes and for their design. We also present an application for freeform architectural skins, namely paneling with spherical panels of constant radius, which is an important fabrication-related constraint.
Complex Wrinkle Field Evolution
Authors: Chen Zhen , Kaufman Danny , Skouras Mélina , Vouga Etienne
Published: July 2023

We propose a new approach for representing wrinkles, designed to capture complex and detailed wrinkle behavior on coarse triangle meshes, called Complex Wrinkle Fields. Complex Wrinkle Fields consist of an almost-everywhere-unit complex-valued phase function over the surface; a frequency one-form; and an amplitude scalar, with a soft compatibility condition coupling the frequency and phase. We develop algorithms for interpolating between two such wrinkle fields, for visualizing them as displacements of a Loop-subdivided refinement of the base mesh, and for making smooth local edits to the wrinkle amplitude, frequency, and/or orientation. These algorithms make it possible, for the first time, to create and edit animations of wrinkles on triangle meshes that are smooth in space, evolve smoothly through time, include singularities along with their complex interactions, and that represent frequencies far finer than the surface resolution.
Winding Numbers on Discrete Surfaces
Authors: Feng Nicole , Gillespie Mark , Crane Keenan
Published: July 2023

In the plane, the
winding number
is the number of times a curve wraps around a given point. Winding numbers are a basic component of geometric algorithms such as point-in-polygon tests, and their generalization to data with noise or topological errors has proven valuable for geometry processing tasks ranging from surface reconstruction to mesh booleans. However, standard definitions do not immediately apply on surfaces, where not all curves bound regions. We develop a meaningful generalization, starting with the well-known relationship between winding numbers and harmonic functions. By processing the derivatives of such functions, we can robustly filter out components of the input that do not bound any region. Ultimately, our algorithm yields (i) a closed, completed version of the input curves, (ii) integer labels for regions that are meaningfully bounded by these curves, and (iii) the complementary curves that do not bound any region. The main computational cost is solving a standard Poisson equation, or for surfaces with nontrivial topology, a sparse linear program. We also introduce special basis functions to represent singularities that naturally occur at endpoints of open curves.