We introduce VennFan, a method for generating $n$-set Venn diagrams based on the polar coordinate projection of trigonometric boundaries, resulting in Venn diagrams that resemble a set of fan blades. Unlike most classical constructions, our method emphasizes readability and customizability by using shaped sinusoids and amplitude scaling. We describe both sine- and cosine-based variants of VennFan and propose an automatic label placement heuristic tailored to these fan-like layouts. VennFan is available as a Python package (https://pypi.org/project/vennfan/).
In this paper, we proposed a generative model that learns to synthesize the 4D facial expression with the neutral landmark. Existing works mainly focus on the generation of sequences guided by expression labels, speech, etc, while they are not robust to the change of different identities. Our LM-4DGAN utilizes neutral landmarks to guide the facial expression generation while adding an identity discriminator and a landmark autoencoder to the basic WGAN for achieving better identity robustness. Furthermore, we add a cross-attention mechanism to the existing displacement decoder which is suitable for the given identity.
Angeliki Flokou, Panagiotis Theodorou, Dimitris A. Niakas
et al.
Background: Health literacy (HL) is a key determinant of health outcomes and equity. The European Health Literacy Survey 2019 (HLS19) introduced three domain-specific instruments—HLS19-NAV, HLS19-COM-P-Q11, and HLS19-VAC. We present the translation, cultural adaptation, field testing, and descriptive pilot evaluation of their Greek versions (HLS19-NAV-GR, HLS19-COM-GR, HLS19-VAC-GR). Methods: Dual forward/back-translation and expert review (11 health professionals/academics) produced the final versions. A purposive, quota-guided field test (N = 71) approximated population distributions by sex, age, education, and geographical region. Test–retest stability (n = 16; ~12 days) was summarized primarily with intraclass correlation ICC (2,1), with Pearson/Spearman correlations reported secondarily. Internal consistency was assessed using ordinal alpha computed from polychoric (polytomous) and tetrachoric (dichotomous) correlations. We report item- and scale-level descriptive statistics for both the original polytomous (four-category, 1–4) responses and a dichotomous difficulty–ease scheme (1–2 vs. 3–4). Given the non-probability sampling in this pilot, the results are descriptive, not statistically representative. Results: Instruments were well accepted, requiring only minor revisions. Scales demonstrated high short-term stability and good internal consistency; inter-scale correlations were moderate, interpreted as associations among related but distinct constructs. Item distributions skewed toward Easy/Very Easy; several HLS19-VAC-GR items showed a clear ceiling, suggesting the need to consider harder items or a larger item pool in future validation. By scale, scores followed the descending order NAV, COM, and VAC. Distributions and ranking patterns broadly mirrored population-level findings from other countries. Conclusions: The adapted HLS19-NAV/COM/VAC-GR instruments are linguistically and culturally appropriate and prepared for large-scale validation, while items NAV9, COM4, and the VAC ceiling are flagged for further assessment.
Many Material Point Method implementations favor explicit time integration. However large time steps are often desirable for special reasons - for example, for partitioned coupling with another large-step solver, or for imposing constraints, projections, or multiphysics solves. We present a simple, plug-and-play algorithm that advances MPM with a large time step using substeps, effectively wrapping an explicit MPM integrator into a pseudo-implicit one.
Tanveer Khan Ibne Shafiq, Kamruzzaman Shaikh, Ferdous Rahman
et al.
Abstract Introduction The COVID-19 pandemic incurred numerous impediments on day-to-day emergency medical services including Opioid Substitution Therapy (OST) for People Who Inject Drugs (PWID). To prevent treatment cessation and lost to follow-up, we tried to implement an alternate mitigating intervention like telehealth. Methodology This research was conducted on a cohort of OST clients during the COVID-19 pandemic ( from 1st April 2020 to 31st March 2021) in Narayanganj, a port city adjacent to the capital Dhaka and one of the most COVID-affected districts, with a high PWID and HIV burden. The participants were male, female and transgender women who were all ex-PWID and were under OST services. A telehealth intervention model was designed and implemented in the OST clinic at Narayangonj. Quantitative data were collected during pre and post-intervention of telehealth services. Results A total of 297 OST clients of Narayangonj were provided with telehealth services from April 2020 to March 2021. The participants were predominantly male (98.7%), 37.7% were between 30–39 years of age. 39.4% of the telephone calls were related to COVID-19-related symptoms followed by 21.7% for opioid withdrawal, 12.5% for COVID-19 & vaccine-related information, 11.3% for chronic diseases like diabetes, hypertension and asthma, 9.3% for Skin and Soft Tissue Infection (SSTI), and 5.8% for methadone-related effects. There was an improvement in treatment retention (14.4% to 87%), loss to follow-up (20% to 8%), and overdose-related death (1.3% to 0%) from pre to post intervention of telehealth services. Conclusion From our experience, we found that the telehealth intervention is beneficial for the OST clients and thus ensures treatment continuity and retention, both of which serve as crucial success indicators of the OST programme. Using simply the mobile phone, this intervention can reduce structural and logistical needs like clinic spaces and fewer human resources, ensuring cost-effectiveness and value for money.
We present an approach using deep reinforcement learning (DRL) to directly generate motion matching queries for long-term tasks, particularly targeting the reaching of specific locations. By integrating motion matching and DRL, our method demonstrates the rapid learning of policies for target location tasks within minutes on a standard desktop, employing a simple reward design. Additionally, we propose a unique hit reward and obstacle curriculum scheme to enhance policy learning in environments with moving obstacles.
In this work, we present a data-driven framework for generating diverse in-betweening motions for kinematic characters. Our approach injects dynamic conditions and explicit motion controls into the procedure of motion transitions. Notably, this integration enables a finer-grained spatial-temporal control by allowing users to impart additional conditions, such as duration, path, style, etc., into the in-betweening process. We demonstrate that our in-betweening approach can synthesize both locomotion and unstructured motions, enabling rich, versatile, and high-quality animation generation.
In this paper, we introduce a new 3D hex mesh visual analysis system that emphasizes poor-quality areas with an aggregated glyph, highlights overlapping elements, and provides detailed boundary error inspection in three forms. By supporting multi-level analysis through multiple views, our system effectively evaluates various mesh models and compares the performance of mesh generation and optimization algorithms for hexahedral meshes.
We present a method for converting denoising neural networks from spatial into spatio-temporal ones by modifying the network architecture and loss function. We insert Robust Average blocks at arbitrary depths in the network graph. Each block performs latent space interpolation with trainable weights and works on the sequence of image representations from the preceding spatial components of the network. The temporal connections are kept live during training by forcing the network to predict a denoised frame from subsets of the input sequence. Using temporal coherence for denoising improves image quality and reduces temporal flickering independent of scene or image complexity.
We present a method for transferring the style from a set of images to a 3D object. The texture appearance of an asset is optimized with a differentiable renderer in a pipeline based on losses using pretrained deep neural networks. More specifically, we utilize a nearest-neighbor feature matching loss with CLIP-ResNet50 to extract the style from images. We show that a CLIP- based style loss provides a different appearance over a VGG-based loss by focusing more on texture over geometric shapes. Additionally, we extend the loss to support multiple images and enable loss-based control over the color palette combined with automatic color palette extraction from style images.
A simple motion amplification algorithm suitable for real-time applications on mobile devices, including smartphones, is presented. It is based on motion enhancement by moving average differencing (MEMAD), a temporal high-pass filter for video streams. MEMAD can amplify small moving objects or subtle motion in larger objects. It is computationally sufficiently simple to be implemented in real time on smartphones. In the specific implementation as an Android phone app, MEMAD is demonstrated on examples chosen such as to motivate applications in the engineering, biological, and medical sciences.
Theodore Kim, Holly Rushmeier, Julie Dorsey
et al.
Current computer graphics research practices contain racial biases that have resulted in investigations into "skin" and "hair" that focus on the hegemonic visual features of Europeans and East Asians. To broaden our research horizons to encompass all of humanity, we propose a variety of improvements to quantitative measures and qualitative practices, and pose novel, open research problems.
This paper extends an existing visualization, the Parallel Coordinates Plot (PCP), specifically its polar coordinate representation, the $\textit{Polar Parallel Coordinates Plot (P2CP)}$. With the additional incorporation of techniques borrowed from Hive Plot network visualizations, we demonstrate improved capabilities to explore multidimensional data in flatland, with a particular emphasis on the unique ability to represent 3-dimensional data. To demonstrate these techniques on P2CPs, we consider toy data, the Iris dataset, and socioeconomic data for counties in the United States. We conclude with an exploration of Covid-19 data from counties in the contiguous United States.
Approximating data points in three or higher dimension space based on cubic B-spline curve is presented. Representations for planar curves, are merged and extended to the higher dimension. The curve is fitted to the order of data points, or uniform parameter values are assumed for the points. Tangents are assumed at the data points, corresponding to the property used in cardinal splines, for shape preserving and visually pleasing fit. Control points of piecewise continuous cubic bezier curves, meeting the boundary conditions of cardinal spline segments, are used for b-spline curve in corresponding coordinate planes. Approximation using error computed in the least square sense, based on a fraction of data points, is also presented.
We propose a modification to Perlin noise which use computable hash functions instead of textures as lookup tables. We implemented the FNV1, Jenkins and Murmur hashes on Shader Model 4.0 Graphics Processing Units for noise generation. Modified versions of the FNV1 and Jenkins hashes provide very close performance compared to a texture based Perlin noise implementation. Our noise modification enables noise function evaluation without any texture fetches, trading computational power for memory bandwidth.
In a previous paper [11] we introduced a weighted binary average of two 2D point-normal pairs, termed circle average, and investigated subdivision schemes based on it. These schemes refine point-normal pairs in 2D, and converge to limit curves and limit normals. Such a scheme has the disadvantage that the limit normals are not the normals of the limit curve. In this paper we solve this problem by proposing a new averaging method, and obtaining a new family of algorithms based on it. We demonstrate their new editing capabilities and apply this subdivision technique to smooth a precomputed feasible polygonal point robot path.
GeneVis is a web-based tool to visualize complementary data sets of different disciplines within the field of genetics. It overlays gene-cluster information, gene-interaction data and gene-disease association data by means of web-based interactive graph visualizations. This allows an intuitive and quick assessment of possible relations between the different datasets. By starting from a high-level graph abstraction based on gene clusters, which can be selected for detailed inspection at the gene-interaction level in a separate window, GeneVis circumvents the common visual clutter problem when using gene datasets with a high number of gene entries.
This paper introduces a novel technique for smooth and efficient zooming and panning based on dynamical systems in hyperbolic space. Unlike the technique of van Wijk and Nuij, the animations produced by our technique are smooth at the endpoints and when interrupted by a change of target. To analyze the results of our technique, we introduce world/screen diagrams, a novel technique for visualizing zooming and panning animations.
El artículo presenta la llegada del nuevo milenio, un número cada vez mayor de empresarios se unieron a la aplicación del diseño sostenible que comenzó a replantearse en las empresas y el rol que juegan con el desarrollo del medio ambiente, el planeta y en la sociedad. Podemos decir que el diseño sostenible busca generar soluciones a través de servicios y estilos de vida, pero no exclusivamente a través de objetos. Con el fin de introducir una definición elaborada de diseño sostenible es necesario mencionar los sistemas sostenibles, que básicamente, se refieren a cualquier tipo de red o servicio social que puede existir y replicarse. Además de sistemas sostenibles hay otros principios dentro del diseño sostenible. Por último, cualquier tipo de resultado obtenido para satisfacer la necesidad debe ser sostenible a largo plazo entendiéndose como un proceso que permita una comunidad lograr un resultado a través de estrategias de diseño.
A new method is presented, allowing for the generation of 3D terrain and texture from coherent noise. The method is significantly faster than prevailing fractal brownian motion approaches, while producing results of equivalent quality. The algorithm is derived through a systematic approach that generalizes to an arbitrary number of spatial dimensions and gradient smoothness. The results are compared, in terms of performance and quality, to fundamental and efficient gradient noise methods widely used in the domain of fast terrain generation: Perlin noise and OpenSimplex noise. Finally, to objectively quantify the degree of realism of the results, a fractal analysis of the generated landscapes is performed and compared to real terrain data.