bluuebunny/crossref_metadata_2025_split huggingface dataset:
Trends in cyclicity and amplitudes on amplitude‐integrated electroencephalography during transition in extremely low gestational age infants
Authors: Deshpande Poorva , Dirks Julia , Jain Amish , McNamara Patrick J. , Hahn Cecil , Shah Prakesh S. , Guerguerian Anne‐Marie 
Published: March 2023

AbstractAimThe aim of the study was to describe amplitude integrated electroencephalography (aEEG) cyclicity, background pattern, voltage margins and maturation scores in extremely low gestational age neonates (ELGANs) in the first 72 h.MethodsFifty infants with gestational age (GA) 23+0–27+6 weeks were prospectively studied. Infants with intraventricular haemorrhage ≦ Grade I and no disorders of transition (persistent pulmonary hypertension, hypotension, pulmonary haemorrhage) belonged to the ‘Uncomplicated’ group and those with intraventricular haemorrhage > Grade I and/or disorders of transition, to the ‘Complicated’ group.ResultsThirty‐six infants without opioid exposure were included: 23 with GA 25.9 (23.1–27.7) weeks in the ‘Uncomplicated’ group and 13 with GA 24.6 (23.3–27.4) weeks in the ‘Complicated’ group. Cyclicity was more common in the ‘Uncomplicated’ group [20/23 (87%) vs. 7/13 (54%), p = 0.045] with more cycles/hour [0.2 (0–0.78) vs. 0.03 (0–67), p = 0.036]. Age at appearance of cyclicity was similar [20 (7.7–40.7) hours in ‘Uncomplicated’ vs. 23.7 (5.4–60) hours in ‘Complicated’ group, p = 0.8]. In the ‘Uncomplicated’ group, maturation scores (p = 0.02), high (p < 0.0001) and low (p = 0.03) base voltage increased over time.ConclusionDuring the first 72 h, clinically stable ELGANs without neurological injury demonstrate increased cyclicity compared to those with a complicated course. Maturation score, high and low base voltage increase over time.
L‐histidine inhibits the heat‐induced gelation of actomyosin in a low ionic strength solution
Authors: Hayakawa Toru , Kubono Yu , Fujii Shuji , Wakamatsu Jun‐ichi , Kumura Haruto 
Published: March 2023

AbstractThe heat‐induced gelation of actomyosin plays a key role in meat processing. Our previous study showed that L‐histidine could affect the characteristics of a heat‐induced gel of myosin on a low ionic strength. To apply the specific effect of L‐histidine to meat processing, the heat‐induced gel properties of actomyosin in the presence of L‐histidine were investigated. Actomyosin in a low ionic strength solution containing L‐histidine did not form a gel upon heating. The dynamic rheological properties of actomyosin in low ionic strength solutions were distinct depending on the presence or absence of L‐histidine. Electron microscopy showed that, heated at 50°C, actomyosin in a low ionic strength solution containing L‐histidine remained a filamentous structure. The surface hydrophobicity of actomyosin was stable up to 50°C in a low ionic strength solution containing L‐histidine. In conclusion, L‐histidine might suppress the aggregation of actomyosin and inhibit heat‐induced gelation in a low ionic strength solution.
Intraperitoneal transfer of microRNA‐29b‐containing small extracellular vesicles can suppress peritoneal metastases of gastric cancer
Authors: Kimura Yuki , Ohzawa Hideyuki , Miyato Hideyo , Kaneko Yuki , Kuchimaru Takahiro , Takahashi Rei , Yamaguchi Hironori , Kurashina Kentaro , Saito Shin , Hosoya Yoshinori , Lefor Alan Kawarai , Sata Naohiro , Kitayama Joji 
Published: March 2023

AbstractSmall extracellular vesicles (sEV) contain various microRNAs (miRNAs) and play crucial roles in the tumor metastatic process. Although miR‐29b levels in peritoneal exosomes were markedly reduced in patients with peritoneal metastases (PM), their role has not been fully clarified. In this study, we asked whether the replacement of miR‐29b can affect the development of PM in a murine model. UE6E7T‐12, human bone marrow‐derived mesenchymal stem cells (BMSCs), were transfected with miR‐29b‐integrating recombinant lentiviral vector and sEV were isolated from culture supernatants using ultracentrifugation. The sEV contained markedly increased amounts of miR‐29b compared with negative controls. Treatment with transforming growth factor‐β1 decreased the expression of E‐cadherin and calretinin with increased expression of vimentin and fibronectin on human omental tissue‐derived mesothelial cells (HPMCs). However, the effects were totally abrogated by adding miR‐29b‐rich sEV. The sEV inhibited proliferation and migration of HPMCs by 15% (p < 0.005, n = 6) and 70% (p < 0.005, n = 6), respectively, and inhibited adhesion of NUGC‐4 and MKN45 to HPMCs by 90% (p < 0.0001, n = 5) and 77% (p < 0.0001, n = 5), respectively. MicroRNA‐29b‐rich murine sEV were similarly obtained using mouse BMSCs and examined for in vivo effects with a syngeneic murine model using YTN16P, a highly metastatic clone of gastric cancer cell. Intraperitoneal (IP) transfer of the sEV every 3 days markedly reduced the number of PM from YTN16P in the mesentery (p < 0.05, n = 6) and the omentum (p < 0.05, n = 6). Bone marrow mesenchymal stem cell‐derived sEV are a useful carrier for IP administration of miR‐29b, which can suppress the development of PM of gastric cancer.
A Second Order Cone Programming Approach for Simulating Biphasic Materials
Authors: Tang Pengbin , Coros Stelian , Thomaszewski Bernhard 
Published: March 2023

AbstractStrain limiting is a widely used approach for simulating biphasic materials such as woven textiles and biological tissue that exhibit a soft elastic regime followed by a hard deformation limit. However, existing methods are either based on slowly converging local iterations, or offer no guarantees on convergence. In this work, we propose a new approach to strain limiting based on second order cone programming (SOCP). Our work is based on the key insight that upper bounds on per‐triangle deformations lead to convex quadratic inequality constraints. Though nonlinear, these constraints can be reformulated as inclusion conditions on convex sets, leading to a second order cone programming problem—a convex optimization problem that a) is guaranteed to have a unique solution and b) allows us to leverage efficient conic programming solvers. We first cast strain limiting with anisotropic bounds on stretching as a quadratically constrained quadratic program (QCQP), then show how this QCQP can be mapped to a second order cone programming problem. We further propose a constraint reflection scheme and empirically show that it exhibits superior energy‐preservation properties compared to conventional end‐of‐step projection methods. Finally, we demonstrate our prototype implementation on a set of examples and illustrate how different deformation limits can be used to model a wide range of material behaviors.
A Second‐Order Explicit Pressure Projection Method for Eulerian Fluid Simulation
Authors: Jiang J. , Shen X. , Gong Y. , Fan Z. , Liu Y. , Xing G. , Ren X. , Zhang Y. 
Published: March 2023

AbstractIn this paper, we propose a novel second‐order explicit midpoint method to address the issue of energy loss and vorticity dissipation in Eulerian fluid simulation. The basic idea is to explicitly compute the pressure gradient at the middle time of each time step and apply it to the velocity field after advection. Theoretically, our solver can achieve higher accuracy than the first‐order solvers at similar computational cost. On the other hand, our method is twice and even faster than the implicit second‐order solvers at the cost of a small loss of accuracy. We have carried out a large number of 2D, 3D and numerical experiments to verify the effectiveness and availability of our algorithm.
Sketching Vocabulary for Crowd Motion
Authors: Mathew C.D.T. , Benes B. , Aliaga D.G. 
Published: March 2023

AbstractThis paper proposes and evaluates a sketching language to author crowd motion. It focuses on the path, speed, thickness, and density parameters of crowd motion. A sketch‐based vocabulary is proposed for each parameter and evaluated in a user study against complex crowd scenes. A sketch recognition pipeline converts the sketches into a crowd simulation. The user study results show that 1) participants at various skill levels and can draw accurate crowd motion through sketching, 2) certain sketch styles lead to a more accurate representation of crowd parameters, and 3) sketching allows to produce complex crowd motions in a few seconds. The results show that some styles although accurate actually are less preferred over less accurate ones.
Cognitive Model of Agent Exploration with Vision and Signage Understanding
Authors: Johnson Colin , Haworth Brandon 
Published: March 2023

AbstractSignage systems play an essential role in ensuring safe, stress‐free, and efficient navigation for the occupants of indoor spaces. Crowd simulations with sufficiently realistic virtual humans provide a convenient and cost‐effective approach to evaluating and optimizing signage systems. In this work, we develop an agent model which makes use of image processing on parametric saliency maps to visually identify signage and distractions in the agent's field of view. Information from identified signs is incorporated into a grid‐based representation of wayfinding familiarity, which is used to guide informed exploration of the agent's environment using a modified A* algorithm. In areas with low wayfinding familiarity, the agent follows a random exploration behaviour based on sampling a grid of previously observed locations for heuristic values based on space syntax isovist measures. The resulting agent design is evaluated in a variety of test environments and found to be able to reliably navigate towards a goal location using a combination of signage and random exploration.
Generating Upper‐Body Motion for Real‐Time Characters Making their Way through Dynamic Environments
Authors: Alvarado Eduardo , Rohmer Damien , Cani Marie‐Paule 
Published: March 2023

AbstractReal‐time character animation in dynamic environments requires the generation of plausible upper‐body movements regardless of the nature of the environment, including non‐rigid obstacles such as vegetation. We propose a flexible model for upper‐body interactions, based on the anticipation of the character's surroundings, and on antagonistic controllers to adapt the amount of muscular stiffness and response time to better deal with obstacles. Our solution relies on a hybrid method for character animation that couples a keyframe sequence with kinematic constraints and lightweight physics. The dynamic response of the character's upper‐limbs leverages antagonistic controllers, allowing us to tune tension/relaxation in the upper‐body without diverging from the reference keyframe motion. A new sight model, controlled by procedural rules, enables high‐level authoring of the way the character generates interactions by adapting its stiffness and reaction time. As results show, our real‐time method offers precise and explicit control over the character's behavior and style, while seamlessly adapting to new situations. Our model is therefore well suited for gaming applications.
Synthesizing Get‐Up Motions for Physics‐based Characters
Authors: Frezzato A. , Tangri A. , Andrews S. 
Published: March 2023

AbstractWe propose a method for synthesizing get‐up motions for physics‐based humanoid characters. Beginning from a supine or prone state, our objective is not to imitate individual motion clips, but to produce motions that match input curves describing the style of get‐up motion. Our framework uses deep reinforcement learning to learn control policies for the physics‐based character. A latent embedding of natural human poses is computed from a motion capture database, and the embedding is furthermore conditioned on the input features. We demonstrate that our approach can synthesize motions that follow the style of user authored curves, as well as curves extracted from reference motions. In the latter case, motions of the physics‐based character resemble the original motion clips. New motions can be synthesized easily by changing only a small number of controllable parameters. We also demonstrate the success of our controllers on rough and inclined terrain.
MODNet: Multi‐offset Point Cloud Denoising Network Customized for Multi‐scale Patches
Authors: Huang A. , Xie Q. , Wang Z. , Lu D. , Wei M. , Wang J. 
Published: March 2023

AbstractThe intricacy of 3D surfaces often results cutting‐edge point cloud denoising (PCD) models in surface degradation including remnant noise, wrongly‐removed geometric details. Although using multi‐scale patches to encode the geometry of a point has become the common wisdom in PCD, we find that simple aggregation of extracted multi‐scale features can not adaptively utilize the appropriate scale information according to the geometric information around noisy points. It leads to surface degradation, especially for points close to edges and points on complex curved surfaces. We raise an intriguing question – if employing multi‐scale geometric perception information to guide the network to utilize multi‐scale information, can eliminate the severe surface degradation problem? To answer it, we propose a Multi‐offset Denoising Network (MODNet) customized for multi‐scale patches. First, we extract the low‐level feature of three scales patches by patch feature encoders. Second, a multi‐scale perception module is designed to embed multi‐scale geometric information for each scale feature and regress multi‐scale weights to guide a multi‐offset denoising displacement. Third, a multi‐offset decoder regresses three scale offsets, which are guided by the multi‐scale weights to predict the final displacement by weighting them adaptively. Experiments demonstrate that our method achieves new state‐of‐the‐art performance on both synthetic and real‐scanned datasets. Our code is publicly available at https://github.com/hay-001/MODNet.
Specular Manifold Bisection Sampling for Caustics Rendering
Authors: Jhang Jia‐Wun , Chang Chun‐Fa 
Published: March 2023

AbstractWe propose Specular Manifold Bisection Sampling (SMBS), an improved version of Specular Manifold Sampling (SMS) [ZGJ20]. SMBS is inspired by the small and large mutations in Metropolis Light Transport (MLT) [VG97]. While the Jacobian Matrix of the original SMS method performs well in local convergence (the small mutation), it might fail to find a valid manifold path when the ray deviates too much from the light or bounces from a complex surface. Our proposed SMBS method adds a large mutation step to avoid such a problematic convergence to the local minimum. The results show SMBS can find valid manifold paths in fewer iterations and also find more valid manifold paths. In scenes with complex reflective or refractive surfaces, our method achieves nearly twice or more improvement when measured in manifold walk success rate (SR) and root mean square error (RMSE).
A Wide Spectral Range Sky Radiance Model
Authors: Vévoda P. , Bashford‐Rogers T. , Kolářová M. , Wilkie A. 
Published: March 2023

AbstractPre‐computed models of sky radiance are a tool to rapidly determine incident solar irradiance in applications as diverse as movie VFX, lighting simulation for architecture, experimental biology, and flight simulators. Several such models exist, but most provide data only for the visible range and, in some cases, for the near‐UV. But for accurate simulations of photovoltaic plant yield and the thermal properties of buildings, a pre‐computed reference sky model which covers the entire spectral range of terrestrial solar irradiance is needed: and this range is considerably larger than what extant models provide. We deliver this, and for a ground‐based observer provide the three components of sky dome radiance, atmospheric transmittance, and polarisation. We also discuss the additional aspects that need to be taken into consideration when including the near‐infrared in such a model. Additionally, we provide a simple standalone C++ implementation as well as an implementation with a GUI.
Contrastive Semantic‐Guided Image Smoothing Network
Authors: Wang Jie , Wang Yongzhen , Feng Yidan , Gong Lina , Yan Xuefeng , Xie Haoran , Wang Fu Lee , Wei Mingqiang 
Published: March 2023

AbstractImage smoothing is a fundamental low‐level vision task that aims to preserve salient structures of an image while removing insignificant details. Deep learning has been explored in image smoothing to deal with the complex entanglement of semantic structures and trivial details. However, current methods neglect two important facts in smoothing: 1) naive pixel‐level regression supervised by the limited number of high‐quality smoothing ground‐truth could lead to domain shift and cause generalization problems towards real‐world images; 2) texture appearance is closely related to object semantics, so that image smoothing requires awareness of semantic difference to apply adaptive smoothing strengths. To address these issues, we propose a novel Contrastive Semantic‐Guided Image Smoothing Network (CSGIS‐Net) that combines both contrastive prior and semantic prior to facilitate robust image smoothing. The supervision signal is augmented by leveraging undesired smoothing effects as negative teachers, and by incorporating segmentation tasks to encourage semantic distinctiveness. To realize the proposed network, we also enrich the original VOC dataset with texture enhancement and smoothing labels, namely VOC‐smooth, which first bridges image smoothing and semantic segmentation. Extensive experiments demonstrate that the proposed CSGIS‐Net outperforms state‐of‐the‐art algorithms by a large margin. Code and dataset are available at https://github.com/wangjie6866/CSGIS-Net.
Shape‐Guided Mixed Metro Map Layout
Authors: Batik T. , Terziadis S. , Wang Y.‐S. , Nöllenburg M. , Wu H.‐Y. 
Published: March 2023

AbstractMetro or transit maps, are schematic representations of transit networks to facilitate effective route‐finding. These maps are often advertised on a web page or pamphlet highlighting routes from source to destination stations. To visually support such route‐finding, designers often distort the layout by embedding symbolic shapes (e.g., circular routes) in order to guide readers' attention (e.g., Moscow map and Japan railway map). However, manually producing such maps is labor‐intensive and the effect of shapes remains unclear. In this paper, we propose an approach to generalize such mixed metro maps that take user‐defined shapes as an input. In this mixed design, lines that are used to approximate the shapes are arranged symbolically, while the remaining lines follow classical layout convention. A three‐step algorithm, including (1) detecting and selecting routes for shape approximation, (2) shape and layout deformation, and (3) aligning lines on a grid, is integrated to guarantee good visual quality. Our contribution lies in the definition of the mixed metro map problem and the formulation of design criteria so that the problem can be resolved systematically using the optimization paradigm. Finally, we evaluate the performance of our approach and perform a user study to test if the embedded shapes are recognizable or reduce the map quality.
Large‐Scale Worst‐Case Topology Optimization
Authors: Zhang Di , Zhai Xiaoya , Fu Xiao‐Ming , Wang Heming , Liu Ligang 
Published: March 2023

AbstractWe propose a novel topology optimization method to efficiently minimize the maximum compliance for a high‐resolution model bearing uncertain external loads. Central to this approach is a modified power method that can quickly compute the maximum eigenvalue to evaluate the worst‐case compliance, enabling our method to be suitable for large‐scale topology optimization. After obtaining the worst‐case compliance, we use the adjoint variable method to perform the sensitivity analysis for updating the density variables. By iteratively computing the worst‐case compliance, performing the sensitivity analysis, and updating the density variables, our algorithm achieves the optimized models with high efficiency. The capability and feasibility of our approach are demonstrated over various large‐scale models. Typically, for a model of size 512×170×170 and 69934 loading nodes, our method took about 50 minutes on a desktop computer with an NVIDIA GTX 1080Ti graphics card with 11 GB memory.
Eye‐Tracking‐Based Prediction of User Experience in VR Locomotion Using Machine Learning
Authors: Gao Hong , Kasneci Enkelejda 
Published: March 2023

AbstractVR locomotion is one of the most important design features of VR applications and is widely studied. When evaluating locomotion techniques, user experience is usually the first consideration, as it provides direct insights into the usability of the locomotion technique and users' thoughts about it. In the literature, user experience is typically measured with post‐hoc questionnaires or surveys, while users' behavioral (i.e., eye‐tracking) data during locomotion, which can reveal deeper subconscious thoughts of users, has rarely been considered and thus remains to be explored. To this end, we investigate the feasibility of classifying users experiencing VR locomotion into L‐UE and H‐UE (i.e., low‐ and high‐user‐experience groups) based on eye‐tracking data alone. To collect data, a user study was conducted in which participants navigated a virtual environment using five locomotion techniques and their eye‐tracking data was recorded. A standard questionnaire assessing the usability and participants' perception of the locomotion technique was used to establish the ground truth of the user experience. We trained our machine learning models on the eye‐tracking features extracted from the time‐series data using a sliding window approach. The best random forest model achieved an average accuracy of over 0.7 in 50 runs. Moreover, the SHapley Additive exPlanations (SHAP) approach uncovered the underlying relationships between eye‐tracking features and user experience, and these findings were further supported by the statistical results. Our research provides a viable tool for assessing user experience with VR locomotion, which can further drive the improvement of locomotion techniques. Moreover, our research benefits not only VR locomotion, but also VR systems whose design needs to be improved to provide a good user experience.
Implicit Neural Deformation for Sparse‐View Face Reconstruction
Authors: Li Moran , Huang Haibin , Zheng Yi , Li Mengtian , Sang Nong , Ma Chongyang 
Published: March 2023

AbstractIn this work, we present a new method for 3D face reconstruction from sparse‐view RGB images. Unlike previous methods which are built upon 3D morphable models (3DMMs) with limited details, we leverage an implicit representation to encode rich geometric features. Our overall pipeline consists of two major components, including a geometry network, which learns a deformable neural signed distance function (SDF) as the 3D face representation, and a rendering network, which learns to render on‐surface points of the neural SDF to match the input images via self‐supervised optimization. To handle in‐the‐wild sparse‐view input of the same target with different expressions at test time, we propose residual latent code to effectively expand the shape space of the learned implicit face representation as well as a novel view‐switch loss to enforce consistency among different views. Our experimental results on several benchmark datasets demonstrate that our approach outperforms alternative baselines and achieves superior face reconstruction results compared to state‐of‐the‐art methods.