Hasil untuk "physics.flu-dyn"

Zhengdi Yu, S. Zafeiriou, Tolga Birdal

We propose Dyn-HaMR, to the best of our knowledge, the first approach to reconstruct 4D global hand motion from monocular videos recorded by dynamic cameras in the wild. Reconstructing accurate 3D hand meshes from monocular videos is a crucial task for understanding human behaviour, with significant applications in augmented and virtual reality (AR/VR). However, existing methods for monocular hand reconstruction typically rely on a weak perspective camera model, which simulates hand motion within a limited camera frustum. As a result, these approaches struggle to recover the full 3D global trajectory and often produce noisy or incorrect depth estimations, particularly when the video is captured by dynamic or moving cameras, which is common in egocentric scenarios. Our DynHaMR consists of a multi-stage, multi-objective optimization pipeline, that factors in (i) simultaneous localization and mapping (SLAM) to robustly estimate relative camera motion, (ii) an interacting-hand prior for generative infilling and to refine the interaction dynamics, ensuring plausible recovery under (self-)occlusions, and (iii) hierarchical initialization through a combination of state-of-the-art hand tracking methods. Through extensive evaluations on both in-the-wild and indoor datasets, we show that our approach significantly outperforms state-of-the-art methods in terms of 4D global mesh recovery. This establishes a new benchmark for hand motion reconstruction from monocular video with moving cameras. Our project page is at https://dyn-hamr.github.io/.

Jingming Hou, Saidah Saad, Nazlia Omar

Our study focuses on Traditional Chinese Medical (TCM) named entity recognition (NER), which involves identifying and extracting specific entity names from TCM record. This task has significant implications for doctors and researchers, as it enables the automated identification of relevant TCM terms, ultimately enhancing research efficiency and accuracy. However, the current Bidirectional Encoder Representations from Transformers-Long Short Term Memory-Conditional Random Fields (BERT-LSTM-CRF) model for TCM NER is constrained by a traditional structure, limiting its capacity to fully harness the advantages provided by Bidirectional Encoder Representations from Transformers (BERT) and long short term memory (LSTM) models. Through comparative experiments, we also observed that the straightforward superimposition of models actually leads to a decrease in recognition results. To optimize the structure of the traditional BERT-BiLSTM-CRF model and obtain more effective text representations, we propose the Dyn-Att Net model, which introduces dynamic attention and a parallel structure. By integrating BERT and LSTM models with the dynamic attention mechanism, our model effectively captures semantic, contextual, and sequential relations within text sequences, resulting in high accuracy. To validate the effectiveness of our model, we compared it with nine other models in TCM dataset namely the publicly available PaddlePaddle dataset. Our Dyn-Att Net model, based on BERT, outperforms the other models, achieving an F1 score of 81.91%, accuracy of 92.06%, precision of 80.26%, and recall of 83.76%. Furthermore, its robust generalization capability is substantiated through validation on the APTNER, MSRA, and EduNER datasets. Overall, the Dyn-Att Net model not only enhances NER accuracy within the realm of traditional Chinese medicine, but also showcases considerable potential for cross-domain generalization. Moreover, the Dyn-Att Net model’s parallel architecture facilitates efficient computation, contributing to time-saving efforts in NER tasks.

Yurong Zhang, Honghao Chen, Xinyu Zhang et al.

Parameter-efficient transfer learning (PETL) is a promising task, aiming to adapt the large-scale pre-trained model to downstream tasks with a relatively modest cost. However, current PETL methods struggle in compressing computational complexity and bear a heavy inference burden due to the complete forward process. This paper presents an efficient visual recognition paradigm, called Dynamic Adapter (Dyn-Adapter), that boosts PETL efficiency by subtly disentangling features in multiple levels. Our approach is simple: first, we devise a dynamic architecture with balanced early heads for multi-level feature extraction, along with adaptive training strategy. Second, we introduce a bidirectional sparsity strategy driven by the pursuit of powerful generalization ability. These qualities enable us to fine-tune efficiently and effectively: we reduce FLOPs during inference by 50%, while maintaining or even yielding higher recognition accuracy. Extensive experiments on diverse datasets and pretrained backbones demonstrate the potential of Dyn-Adapter serving as a general efficiency booster for PETL in vision recognition tasks.

Hugo D. Pasinato

This study constitutes the second phase of a research endeavor aimed at evaluating the feasibility of employing Long Short-Term Memory (LSTM) neural networks as a replacement for Reynolds-Averaged Navier-Stokes (RANS) turbulence models. In the initial phase of this investigation (titled Modeling Turbulent Flows with LSTM Neural Networks, arXiv:2307.13784v1 [physics.flu-dyn] 25 Jul 2023), the application of an LSTM-based recurrent neural network (RNN) as an alternative to traditional RANS models was demonstrated. LSTM models were used to predict shear Reynolds stresses in both developed and developing turbulent channel flows, and these predictions were propagated through RANS simulations to obtain mean flow fields of turbulent flows. A comparative analysis was conducted, juxtaposing the LSTM results from computational fluid dynamics (CFD) simulations with outcomes from the $\kappa-\epsilon$ model and data from direct numerical simulations (DNS). These initial findings indicated promising performance of the LSTM approach. This second phase delves further into the challenges encountered and presents robust solutions. Additionally, new results are provided, demonstrating the efficacy of the LSTM model in predicting turbulent behavior in perturbed flows. While the overall study serves as a proof-of-concept for the application of LSTM networks in RANS turbulence modeling, this phase offers compelling evidence of its potential in handling more complex flow scenarios.

Shangzhan Zhang, Sida Peng, Yinji ShenTu et al.

Recently, the editing of neural radiance fields (NeRFs) has gained considerable attention, but most prior works focus on static scenes while research on the appearance editing of dynamic scenes is relatively lacking. In this paper, we propose a novel framework to edit the local appearance of dynamic NeRFs by manipulating pixels in a single frame of training video. Specifically, to locally edit the appearance of dynamic NeRFs while preserving unedited regions, we introduce a local surface representation of the edited region, which can be inserted into and rendered along with the original NeRF and warped to arbitrary other frames through a learned invertible motion representation network. By employing our method, users without professional expertise can easily add desired content to the appearance of a dynamic scene. We extensively evaluate our approach on various scenes and show that our approach achieves spatially and temporally consistent editing results. Notably, our approach is versatile and applicable to different variants of dynamic NeRF representations.

Yan Pang, Ai Shan, Zhen Wang et al.

Dynamic graph neural networks (DGNNs) have been widely used in modeling and representation learning of graph structure data. Current dynamic representation learning focuses on either discrete learning which results in temporal information loss, or continuous learning which involves heavy computation. In this study, we proposed a novel DGNN, sparse dynamic (Sparse‐Dyn). It adaptively encodes temporal information into a sequence of patches with an equal amount of temporal‐topological structure. Therefore, while avoiding using snapshots which cause information loss, it also achieves a finer time granularity, which is close to what continuous networks could provide. In addition, we also designed a lightweight module, Sparse Temporal Transformer, to compute node representations through structural neighborhoods and temporal dynamics. Since the fully connected attention conjunction is simplified, the computation cost is far lower than the current state‐of‐the‐art. Link prediction experiments are conducted on both continuous and discrete graph data sets. By comparing several state‐of‐the‐art graph embedding baselines, the experimental results demonstrate that Sparse‐Dyn has a faster inference speed while having competitive performance.

Kenza Tlales, Kheir-eddine Otmani, G. Ntoukas et al.

We present a machine learning-based mesh refinement technique for steady and unsteady flows. The clustering technique proposed by Otmani et al. arXiv:2207.02929 [physics.flu-dyn] is used to mark the viscous and turbulent regions for the flow past a cylinder at Re=40 (steady laminar flow) and Re=3900 (unsteady turbulent flow). Within this clustered region, we increase the polynomial order to show that it is possible to obtain similar levels of accuracy to a uniformly refined mesh. The method is effective as the clustering successfully identifies the two flow regions, a viscous/turbulent dominated region (including the boundary layer and wake) and an inviscid/irrotational region (a potential flow region). The data used within this framework are generated using a high-order discontinuous Galerkin solver, allowing to locally refine the polynomial order (p-refinement) in each element of the clustered region. For the steady laminar test case we are able to reduce the computational cost up to 32% and for the unsteady turbulent case up to 33%.

Aqsa Kashaf, Vyas Sekar, Y. Agarwal

Many websites rely on third parties for services (e.g., DNS, CDN, etc.). However, it also exposes them to shared risks from attacks (e.g., Mirai DDoS attack [24]) or cascading failures (e.g., GlobalSign revocation error [21]). Motivated by such incidents, we analyze the prevalence and impact of third-party dependencies, focusing on three critical infrastructure services: DNS, CDN, and certificate revocation checking by CA. We analyze both direct (e.g., Twitter uses Dyn) and indirect (e.g., Netflix uses Symantec as CA which uses Verisign for DNS) dependencies. We also take two snapshots in 2016 and 2020 to understand how the dependencies evolved. Our key findings are: (1) 89% of the Alexa top-100K websites critically depend on third-party DNS, CDN, or CA providers i.e., if these providers go down, these websites could suffer service disruption; (2) the use of third-party services is concentrated, and the top-3 providers of CDN, DNS, or CA services can affect 50%-70% of the top-100K websites; (3) indirect dependencies amplify the impact of popular CDN and DNS providers by up to 25X; and (4) some third-party dependencies and concentration increased marginally between 2016 to 2020. Based on our findings, we derive key implications for different stakeholders in the web ecosystem.

Jichao Jiao, Weilun Liu, Yaokai Mo et al.

Jinyin Chen, Haiyang Xiong, Haibin Zheng et al.

Dynamic link prediction (DLP) makes graph prediction based on historical information. Since most DLP methods are highly dependent on the training data to achieve satisfying prediction performance, the quality of the training data is crucial. Backdoor attacks induce the DLP methods to make wrong prediction by the malicious training data, i.e., generating a subgraph sequence as the trigger and embedding it to the training data. However, the vulnerability of DLP toward backdoor attacks has not been studied yet. To address the issue, we propose a novel backdoor attack framework on DLP, denoted as Dyn-Backdoor. Specifically, Dyn-Backdoor generates diverse initial-triggers by a generative adversarial network (GAN). Then partial links of the initial-triggers are selected to form a trigger set, according to the gradient information of the attack discriminator in the GAN, so as to reduce the size of triggers and improve the concealment of the attack. Experimental results show that Dyn-Backdoor launches effective backdoor attacks on several state-of-the-art DLP models with a success rate more than 90%. Additionally, we conduct a possible defense against Dyn-Backdoor to testify its resistance in defensive settings, highlighting the needs of defenses for backdoor attacks on DLP.

M. Calvo, Andrés Fernández-Martín, Aida Gutiérrez-García et al.

Prior research using static facial stimuli (photographs) has identified diagnostic face regions (i.e., functional for recognition) of emotional expressions. In the current study, we aimed to determine attentional orienting, engagement, and time course of fixation on diagnostic regions. To this end, we assessed the eye movements of observers inspecting dynamic expressions that changed from a neutral to an emotional face. A new stimulus set (KDEF-dyn) was developed, which comprises 240 video-clips of 40 human models portraying six basic emotions (happy, sad, angry, fearful, disgusted, and surprised). For validation purposes, 72 observers categorized the expressions while gaze behavior was measured (probability of first fixation, entry time, gaze duration, and number of fixations). Specific visual scanpath profiles characterized each emotional expression: The eye region was looked at earlier and longer for angry and sad faces; the mouth region, for happy faces; and the nose/cheek region, for disgusted faces; the eye and the mouth regions attracted attention in a more balanced manner for surprise and fear. These profiles reflected enhanced selective attention to expression-specific diagnostic face regions. The KDEF-dyn stimuli and the validation data will be available to the scientific community as a useful tool for research on emotional facial expression processing.

J. Estivalezes, Wojciech Aniszewski, Franck Auguste et al.

A series of benchmarks based on the physical situation of "phase inversion" between two incompressible liquids is presented. These benchmarks aim at progressing toward the direct numerical simulation of two-phase flows. Several CFD codes developed in French laboratories and using either Volume of Fluid or Level Set interface tracking methods are utilized to provide physical solutions of the benchmarks, convergence studies and code comparisons. Two typical configurations are retained, with integral scale Reynolds numbers of 1.37 10 4 and 4.33 10 5 , respectively. The physics of the problem are probed through macroscopic quantities such as potential and kinetic energies, interfa-cial area, enstrophy or volume ratio of the light fluid in the top part 1 arXiv:1906.02655v1 [physics.flu-dyn]

M. Calvo, Andrés Fernández-Martín, G. Recio et al.

Most experimental studies of facial expression processing have used static stimuli (photographs), yet facial expressions in daily life are generally dynamic. In its original photographic format, the Karolinska Directed Emotional Faces (KDEF) has been frequently utilized. In the current study, we validate a dynamic version of this database, the KDEF-dyn. To this end, we applied animation between neutral and emotional expressions (happy, sad, angry, fearful, disgusted, and surprised; 1,033-ms unfolding) to 40 KDEF models, with morphing software. Ninety-six human observers categorized the expressions of the resulting 240 video-clip stimuli, and automated face analysis assessed the evidence for 6 expressions and 20 facial action units (AUs) at 31 intensities. Low-level image properties (luminance, signal-to-noise ratio, etc.) and other purely perceptual factors (e.g., size, unfolding speed) were controlled. Human recognition performance (accuracy, efficiency, and confusions) patterns were consistent with prior research using static and other dynamic expressions. Automated assessment of expressions and AUs was sensitive to intensity manipulations. Significant correlations emerged between human observers’ categorization and automated classification. The KDEF-dyn database aims to provide a balance between experimental control and ecological validity for research on emotional facial expression processing. The stimuli and the validation data are available to the scientific community.

S. Greenstein

Nobody knows who organized the attack. The program hijacked many cameras and home devices, and redirected them to engineer a series of distributed denial of server (DDOS) attacks on a few hours apart, all on 21 October 2016. By executing this novel and rather clever hijack of many devices for a DDOS attack, the attack exposed an important vulnerability in today's internet. The attack contains one other element. It aimed at Dyn, who acts as a name resolver. Dyn enables Internet traffic by translating the site's domain name (URL) into the IP address where the server behind that domain is to be found. During the later phases of the attack, Dyn servers were unable to process users' requests, and as a result, users lost access to web domains contracting with Dyn, such as Netflix, CNBC, and Twitter. Other well-known firms also were disabled, such as Airbnb, Etsy, Play Station Network, and Wikia. This article focuses on the aftermath of this event, which did not get headlines, but illustrates an important features of the situation. Specifically, how did users react? User behavior tells us something about the challenges facing suppliers, and in this case, it tells us about a basic challenge in network security today. It will take a bit of work to appreciate the lesson, and, let me tip my hand, the news is not good. The article provides a summary of a longer study done by a group of my colleagues and myself.

Zsolt Farkas, M. Petrič, Xianghua Liu et al.

Phagocytosis of various targets, such as apoptotic cells or opsonized pathogens, by macrophages is coordinated by a complex signaling network initiated by distinct phagocytic receptors. Despite the different initial signaling pathways, each pathway ends up regulating the actin cytoskeletal network, phagosome formation and closure, and phagosome maturation leading to degradation of the engulfed particle. Herein, we describe a new phagocytic function for the nucleoside diphosphate kinase 1 (NDK‐1), the nematode counterpart of the first identified metastasis inhibitor NM23‐H1 (nonmetastatic clone number 23) nonmetastatic clone number 23 or nonmetastatic isoform 1 (NME1). We reveal by coimmunoprecipitation, Duolink proximity ligation assay, and mass spectrometry that NDK‐1/NME1 works in a complex with DYN‐1/Dynamin (Caenorhabditis elegans/human homolog proteins), which is essential for engulf ment and phagosome maturation. Time‐lapse microscopy shows that NDK‐1 is expressed on phagosomal surfaces during cell corpse clearance in the same time window as DYN‐1. Silencing of NM23‐M1 in mouse bone marrow—derived macrophages resulted in decreased phagocytosis of apoptotic thymocytes. In human macrophages, NM23‐H1 and Dynamin are corecruited at sites of phagosome formation in F‐actin—rich cups. In addition, NM23‐H1 was required for efficient phagocytosis. Together, our data demonstrate that NDK‐1/NME1 is an evolutionarily conserved element of successful phagocytosis.—Farkas, Z., Petric, M., Liu, X., Herit, F., Rajnavölgyi, É., Szondy, Z., Budai, Z., Orbán, T. I., Sándor, S., Mehta, A., Bajtay, Z., Kovács, T., Jung, S. Y., Afaq Shakir, M., Qin, J., Zhou, Z., Niedergang, F., Boissan, M., Takács‐Vellai, K. The nucleoside diphosphate kinase NDK‐1/NME1 promotes phagocytosis in concert with DYN‐1/dynamin. FASEB J. 33, 11606–11614 (2019). www.fasebj.org

Sacha Ghebali, S. Chernyshenko, M. Leschziner

Yongqiang Li, S. Shen, S. Ding et al.

M. Bustamante, Umar Hayat, P. Lynch et al.

The purpose of this note is to remove the confusion about counting of resonant wave triads for Rossby and drift waves in the context of the Charney-Hasegawa-Mima equation. In particular, we aim to point out a major error of over-counting of triads in the paper "Discrete exact and quasi-resonances of Rossby/drift waves on beta-plane with periodic boundary conditions", by Kartashov and Kartashova, arXiv:1307.8272v1 [physics.flu-dyn] (2013).

R. Maladen, Yang Ding, A. Kamor et al.

AbstractIn this uid dynamics video, we use high-speed x-ray imaging to re-veal how a small (˘ 10cm) desert dwelling lizard, the sand sh (Scincusscincus), swims within a granular medium 1 . On the surface, the lizarduses a standard diagonal gait, but once below the surface, the organ-ism no longer uses limbs for propulsion. Instead it propagates a largeamplitude single period sinusoidal traveling wave down its body andtail to propel itself at speeds up to ˇ 1:5 body-length/sec. Motivatedby these experiments we study a numerical model of the sand sh asit swims within a validated soft sphere Molecular Dynamics granularmedia simulation. We use this model as a tool to understand dynam-ics like ow elds and forces generated as the animal swims within thegranular media. The link to the video is: Video1-mpg1 formatVideo2-mpg2 format 1 Maladen, R.D., Ding, Y., Li, C., and Goldman, D.I., Undulatory Swimming in Sand:Subsurface Locomotion of the Sand sh Lizard, Science, 325, 314, 2009 1 arXiv:0910.3248v1 [physics.flu-dyn] 17 Oct 2009