Representation Of Motion Research Articles

Propagation of Modelling Uncertainties for Seismic Risk Assessment: The Effect of Sampling Techniques on Low-Rise Non-Ductile RC Frames

ABSTRACT Quantifying the impact of modelling uncertainty on seismic performance assessment of existing buildings is non-trivial when considering the partial information available on material properties, construction details, and the uncertainty in the capacity models. This task is further complicated when uncertainty related to ground motion representation is considered. To address this issue, record-to-record variability, uncertainties in structural model parameters, and fragility model parameters due to limited sample size are propagated herein by employing a nonlinear dynamic analysis procedure based on recorded ground motions. A one-to-one sampling approach is adopted in which each recorded ground motion is paired up with a different structural model realization. Uncertainty propagation is explored by measuring the impact of different sampling techniques, such as Monte Carlo simulation with standard random sampling and Latin Hypercube sampling (with Simulated Annealing) in the presence of three alternative nonlinear dynamic analysis procedures: Incremental Dynamic Analysis (IDA), Modified Cloud Analysis (MCA), and Cloud to IDA (a highly efficient IDA-like procedure). This is all illustrated through application to an existing reinforced-concrete school building in southern Italy. It is shown that with a small subset of records, both MCA and Cloud to IDA can provide reliable structural fragility (and risk) estimates for three considered limit states, comparable to the results of more resource-intensive schemes.

Time-domain dimension-reduction representation for stochastic ground motion utilizing filtered white noise

A method is proposed for characterizing and simulating both stationary and fully non-stationary stochastic ground motions. This method is based on discrete filtered white noise models, including single and double filtered, with the latter is introduced to suppress low-frequency components. Specifically, the proposed method expresses seismic ground motion as a linear combination of products involving orthogonal random variables and deterministic functions. Further, by defining high-dimensional orthogonal random variables as orthogonal functions of extremely low dimensional elementary random variables, efficient dimension-reduction (DR) of primitive ground motion process can be achieved. To illustrate this concept, three distinct categories of random orthogonal functions involving only one or two elementary random variables are examined, employing filtered white noise models to simulate ground motion acceleration processes, thereby demonstrating the accuracy and efficiency of the proposed method. Simultaneously, recommendations for employing the proposed method in simulations are provided based on an analysis of the impacts of various parameters on random ground motion processes. Case studies demonstrate the accuracy and robustness of the proposed method compared to Monte Carlo (MC) methods. Furthermore, case studies on fully non-stationary ground motion highlight the practical applicability of the proposed method in engineering.

Egocentric 3D Skeleton Learning in a Deep Neural Network Encodes Obese-like Motion Representations.

Obesity is a growing health concern, mainly caused by poor dietary habits. Yet, accurately tracking the diet and food intake of individuals with obesity is challenging. Although 3D motion capture technology is becoming increasingly important in healthcare, its potential for detecting early signs of obesity has not been fully explored. In this research, we used a deep LSTM network trained with individual identity (identity-trained deep LSTM network) to analyze 3D time-series skeleton data from mouse models with diet-induced obesity. First, we analyzed the data from two different viewpoints: allocentric and egocentric. Second, we trained various deep recurrent networks (e.g., RNN, GRU, LSTM) to predict the identity. Lastly, we tested whether these models effectively encode obese-like motion representations by training a support vector classifier with the latent features from the last layer. Our experimental results indicate that the optimal performance is achieved when utilizing an identity-trained deep LSTM network in conjunction with an egocentric viewpoint. This approach suggests a new way to use deep learning to spot health risks in mouse models of obesity and should be useful for detecting early signs of obesity in humans.

Candlestick Pattern Recognition in Cryptocurrency Price Time-Series Data Using Rule-Based Data Analysis Methods

In the rapidly evolving domain of cryptocurrency trading, accurate market data analysis is crucial for informed decision making. Candlestick patterns, a cornerstone of technical analysis, serve as visual representations of market sentiment and potential price movements. However, the sheer volume and complexity of cryptocurrency price time-series data presents a significant challenge to traders and analysts alike. This paper introduces an innovative rule-based methodology for recognizing candlestick patterns in cryptocurrency markets using Python. By focusing on Ethereum, Bitcoin, and Litecoin, this study demonstrates the effectiveness of the proposed methodology in identifying key candlestick patterns associated with significant market movements. The structured approach simplifies the recognition process while enhancing the precision and reliability of market analysis. Through rigorous testing, this study shows that the automated recognition of these patterns provides actionable insights for traders. This paper concludes with a discussion on the implications, limitations, and potential future research directions that contribute to the field of computational finance by offering a novel tool for automated analysis in the highly volatile cryptocurrency market.

Construction of Gender Equality in Arab News Media: A Critical Discourse Analysis

Women in Arabia are often seen as subjects who need liberation from the constraints of patriarchal culture. Based on a report released by the United Nation in 2020, Saudi Arabia has big challenges in terms of women's empowerment and gender equality. The subject of limited space for women in Arabia has been extensively examined in earlier works from social and political science perspectives. However, there has been a lack of research on this topic from a linguistic viewpoint, particularly in the field of critical discourse analysis. This study aims to examine how Arab media portrays gender equality in the context of reporting on Arab government policy concerning women's rights. This research uses a critical discourse analysis approach initiated by Fairclough (1995) and a transitivity system approach introduced by Halliday (2014). This study discovered that the Arab media perceives Saudi Arabia's policy change on women's rights in a positive light. It is depicted as a demonstration of gender equality, a representation of the women's liberation movement, and a significant advancement that will propel Arab nations forward. In this particular instance, it pertains to the advancement of gender equality, which is being achieved through the implementation of policy reforms by the Saudi Arabian government with regards to women's rights. The reporting that is carried out can be a tool to guide public opinion, in this case, what is being constructed is gender equality which is realized through the Saudi Arabian government's policy reforms regarding women's rights.

Brain representations of motion and position in the double-drift illusion.

In the 'double-drift' illusion, local motion within a window moving in the periphery of the visual field alters the window's perceived path. The illusion is strong even when the eyes track a target whose motion matches the window so that the stimulus remains stable on the retina. This implies that the illusion involves the integration of retinal signals with non-retinal eye-movement signals. To identify where in the brain this integration occurs, we measured BOLD fMRI responses in visual cortex while subjects experienced the double-drift illusion. We then used a combination of univariate and multivariate decoding analyses to identify (1) which brain areas were sensitive to the illusion and (2) whether these brain areas contained information about the illusory stimulus trajectory. We identified a number of cortical areas that responded more strongly during the illusion than a control condition that was matched for low-level stimulus properties. Only in area hMT+ was it possible to decode the illusory trajectory. We additionally performed a number of important controls that rule out possible low-level confounds. Concurrent eye tracking confirmed that subjects accurately tracked the moving target; we were unable to decode the illusion trajectory using eye position measurements recorded during fMRI scanning, ruling out explanations based on differences in oculomotor behavior. Our results provide evidence for a perceptual representation in human visual cortex that incorporates extraretinal information.

Dimension-Reduction Representation of Multivariate Mainshock-Aftershock Ground Motions Based on Measured Records

ABSTRACT This study proposes an integrated dimension-reduction representation strategy for simulating multivariate mainshock-aftershock stochastic ground motions based on measured motion records. The strategy includes a parameter identification method for modeling evolutionary power spectrum density (EPSD) of mainshock-aftershocks, a coherence function describing the spectrum correlation between mainshock and aftershock, and the EPSD function matrix establishment for mainshock-aftershock field combining with complex spatial coherence function. Furthermore, the dimension-reduction representation for simulating the ground motions considering the sequence and spatial variability simultaneously is presented with merely two elementary random variables. Numerical results effectively demonstrate the accuracy, efficiency, and engineering applicability of the proposed method.

The cognitive roots of multimodal symbolic forms with an analysis of multimodality in movies

Condillac’s (1754) “Traité des sensations” is the philosophical background of modern discussions on the relationship between perception and multimodal communication. The differences between perception and communication and the transitions between them are discussed with a focus on odor and color. It becomes clear that even at this primary level, the complex interactions of different modalities are the precondition for effective and rich communication. The second part discusses Cassirer’s “Philosophy of Symbolic Forms” as a relevant framework for multimodality studies. Basic aspects are first commented on with a focus on music and visual art. The interaction is even more complex and rich in the case of language; the difficulty of large symbolic forms is mainly due to semantic composition and only to a lesser degree to syntactic concatenation. The first must merge/blend different semantic spaces. It must allow for the plurality of levels of integration from the lexical level, the level of phrases and sentences, up to texts and discourse. The third part focuses on multimodality in film. It treats the representation of movement and action in (film) narratives, the visual perception and representation/communication of movement and action, and the integration of music, moving images, and language.

Cybersickness with passenger VR in the aircraft: Influence of turbulence and VR content

Using VR in the airplane cabin is appealing, primarily because of the enhanced entertainment value, increased privacy, and improved recreational opportunities provided by higher levels of immersion. However, VR applications in aircrafts contain the risk of passengers developing cybersickness. The particular environment of a moving aircraft in interaction with visual representation of movements in VR could lead to severe cybersickness, especially during turbulence. We had 129 participants experience VR in a full flight simulator with different content (static or dynamic VR clips) and during varying phases of flight including turbulence. The employed simulator is equipped with a cabin module, creating an economically valid environment. VR induced significant but mild symptoms of cybersickness. Nausea and dizziness symptoms were most severe during turbulence and especially with dynamic VR content being presented. More anxious participants tended to report more symptoms. In addition, there was an association with video game use and attitudes toward new technologies. While mild content and short exposure times only led to fairly low expressions of cybersickness, a long-term use of VR under turbulence could possibly become a concern. Airlines should especially address passengers’ negative attitudes toward new technologies, and VR in particular, to reduce fears and the risk of low tolerability.

Hierarchical Constraints on the Distribution of Attention in Dynamic Displays.

Human vision is remarkably good at recovering the latent hierarchical structure of dynamic scenes. Here, we explore how visual attention operates with this hierarchical motion representation. The way in which attention responds to surface physical features has been extensively explored. However, we know little about how the distribution of attention can be distorted by the latent hierarchical structure. To explore this topic, we conducted two experiments to investigate the relationship between minimal graph distance (MGD), one key factor in hierarchical representation, and attentional distribution. In Experiment 1, we constructed three hierarchical structures consisting of two moving objects with different MGDs. In Experiment 2, we generated three moving objects from one hierarchy to eliminate the influence of different structures. Attention was probed by the classic congruent-incongruent cueing paradigm. Our results show that the cueing effect is significantly smaller when the MGD between two objects is shorter, which suggests that attention is not evenly distributed across multiple moving objects but distorted by their latent hierarchical structure. As neither the latent structure nor the graph distance was part of the explicit task, our results also imply that both the construction of hierarchical representation and the attention to that representation are spontaneous and automatic.

Erroneous Compensation for Long-Latency Feedback Delays as Origin of Essential Tremor.

Essential tremor (ET), a movement disorder characterized by involuntary oscillations of the limbs during movement, remains to date not well understood. It has been recently suggested that the tremor originates from impaired delay compensation, affecting movement representation and online control. Here we tested this hypothesis directly with 24 ET patients (14 female; 10 male) and 28 neurologically intact (NI) human volunteers (17 female; 11 male) in an upper limb postural perturbation task. After maintaining their hand in a visual target, participants experienced perturbations of unpredictable direction and magnitude and were instructed to counter the perturbation and steer their hand back to the starting position. In comparison with NI volunteers, ET patients' early muscular responses (short and long-latency responses, 20-50 and 50-100 ms, respectively) were preserved or even slightly increased. However, they exhibited perturbation-dependent deficits when stopping and stabilizing their hand in the final target supporting the hypothesis that the tremor was generated by the feedback controller. We show in a computational model that errors in delay compensation accumulating over time produced the same small increase in initial feedback response followed by oscillations that scaled with the perturbation magnitude as observed in ET population. Our experimental results therefore validate the computational hypothesis that inaccurate delay compensation in long-latency pathways could be the origin of the tremor.

ER-NeRF++: Efficient region-aware Neural Radiance Fields for high-fidelity talking portrait synthesis

Despite conditional Neural Radiance Fields (NeRF) achieving great success in modeling audio-driven talking portraits, the generation quality is increasingly hampered by the lack of efficient use of space information. This paper presents ER-NeRF, a novel conditional NeRF-based architecture for talking portrait synthesis, and its variant version ER-NeRF++ to concurrently achieve fast convergence, real-time rendering, and state-of-the-art performance with small model size. Inspired by the unequal contribution of spatial regions, we propose two modules in ER-NeRF to guide the talking portrait modeling: (1) A compact and expressive Tri-Plane Hash Representation to improve the accuracy of dynamic head reconstruction by pruning empty spatial regions with three planar hash encoders. (2) A Region Attention Module for the audio–visual feature fusion, including a novel cross-modal attention mechanism to connect audio features with different spatial regions explicitly for local motion priors. Additionally, to tackle the difficulty in learning large facial motions, we propose a deformable variant ER-NeRF++ by including a Deformation Grid Transformer to enable the reuse of cross-regional spatial features for large motion representation. Compared to ER-NeRF, our ER-NeRF++ framework achieves a significant improvement in facial motion quality while maintaining the ability of fast training and real-time rendering. For the torso part, a directAdaptive Pose Encoding is introduced to simplify the pose information for a better head-torso connection. Extensive experiments demonstrate that both of our proposed frameworks can efficiently render lifelike talking portrait videos with rich realistic details, performing better in image quality and audio-lip synchronization compared to previous methods.

Intelligent event-based lip reading word classification with spiking neural networks using spatio-temporal attention features and triplet loss

Lip reading is a visual alternative to enhance the intelligence of traditional speech recognition, which can benefit from retina-like event cameras that focus on dynamic movements. Spiking Neural Networks (SNNs) are inherently well-suited to cooperate with event cameras. However, employing SNN for event-based lip reading presents significant challenges, particularly in effectively extracting spatio-temporal features from input events and distinguishing between phonetically similar elements. To address these challenges, this paper proposes a novel event-based lip-reading model that leverages the SNN framework, enriched by a designed Spatial-Temporal Attention Block (STAB) and a triplet loss. Specifically, STAB comprises both spatial and temporal attention branches, dynamically emphasizing those spatial and temporal characteristics most reflective of lip movements. STAB also includes a fusion mechanism to integrate these spatial and temporal insights, providing a comprehensive and focused representation of lip movements. In addition, we enhance the model by incorporating triplet loss into the SNN training process, further improving the model's ability to distinguish between visually similar words. Experimental results show the superior performance of our SNN and validate the effect of STAB and triplet loss. We also conduct the energy consumption analysis to affirm the model's energy efficiency.

Mechanism and Application of Attitude and Orbit Coupling Dynamics for Spacecraft Proximity Relative Motion

This paper analyzes the root causes of attitude-orbit coupling effects of spacecraft proximity relative motion in space precision collaborative tasks from three aspects: mathematical representation, physical definition, and engineering applications. At first, taking mathematical representation as the context, spacecraft proximity relative motion representations such as particle relative dynamic model, extended particle relative dynamic model, and dual-spiral-based relative dynamic model are investigated in detail. On this basis, the mechanism of attitude-orbit coupling effects originating from different mathematical representations is further investigated. Second, spiral theory–based attitude-orbit coupling relative dynamics is developed. The innovation of this work is extending the dual number representation from rigid body to flexible body, which makes it possible to describe the proximity relative motion between two rigid-flexible coupling spacecraft. Third, the application value of attitude-orbit coupling relative dynamic model in precision collaborative mission such as precision formation, rendezvous and docking, space manipulation, and on-orbit assembly is provided. Finally, simulation results verify the engineering significance of the attitude-orbit coupling relative dynamic model.

Labor linkages and flow paths of industry in China

As economic power increases and market patterns adapt, labor becomes an increasingly significant factor of production. However, there is a dearth of discourse regarding the structural changes that have occurred in the correlation of the labor force across industries, as well as a visual representation of the labor force's movement across industries. To quantify and analyze the correlation effect with greater precision, it is necessary to establish an input-output model as the foundation of analysis, comparing the changes in the total output of the economic system prior to and subsequent to the exclusion using the vertical integration algorithm. By decomposing the path structure, the average propagation distance of the labor force population's demand for each industry can be determined. By employing labor force population data from the corresponding years and China's input-output tables published by the National Bureau of Statistics of China (NBS) from 2005 to 2020, this study conducts a quantitative analysis of the correlation effect between labor force population and the trend of its transfer across 19 industries. The findings indicate that the correlation effect between labor force and population is most pronounced in the manufacturing sector. Furthermore, the construction sector faces an especially critical requirement for labor force personnel from other industries. The article culminates with a recommendation that the government enhance its macro-control endeavors to address labor market risk shocks and take an active stance in response to labor market fluctuations.

Iconicity in Chinese Sign Language and Filipino Sign Language

Sign language is primarily used as a means of communication by the deaf and hard of hearing. Iconicity is considered as its typical feature. This paper makes a preliminary comparison on lexical items between Chinese Sign Language (CSL) and Filipino Sign Language (FSL) through examining the iconic devices used by the CSL and FSL signs. The study provides some valuable evidence that the iconicity is prevalent in CSL and FSL which always use similar iconic device for the same concept due to shared embodied experience though different iconic devices are occasionally used. These iconic devices include direct (1) presentation; (2) number representation; (3) shape representation; (4) movement representation (5) size representation; (6) part-for-whole representation; (7) metonymic/metaphorical representation. The findings of the research could help to reveal the relationship between language and cognition and make some contributions to the communications among the deaf and hard of hearing in both Chian and the Philippines.

Characterization and low-order representation of the vortex meandering motion in upswept afterbody wakes

Vortex meandering is often characterized by seemingly stochastic motions of the vortex core. An elucidation of the underlying mechanisms of core movement aids not only in characterizing the meandering phenomenon but also forms a starting point to guide flow control techniques. This paper investigates the complex motion of longitudinal vortices behind an abstracted cargo aircraft fuselage. In contrast to wingtip vortices that arise in relative isolation, in this case the large aft region upsweep generates a meandering counter-rotating vortex pair that retains its coherence for long distances downstream. High-resolution spatio-temporal datasets obtained from large-eddy simulation of a cylindrical fuselage with longitudinally aligned axis and a sharp-edged base are used for this purpose. The circulation of each vortex in the pair initially increases immediately after formation and asymptotes to a near constant value downstream. Although the time-mean form of the vortices becomes nearly axisymmetric, the transient disorganized motions include time-local regions of opposite vorticity. The complicated meandering motion is decomposed using proper-orthogonal decomposition (POD) modes as primary building blocks. The first two modes constitute a mutually orthogonal |m|=1 elliptic pair, accounting for about 35% of the total energy; individually these modes account for the displacement of vortex cores along straight lines whose slopes coincide with their respective dipole axes. The wake outside of the immediate vicinity of the base displays low-rank behavior, in that the number of modes required to reproduce the flow to a given degree of accuracy diminishes rapidly. Beyond two fuselage (cylinder) diameters downstream, the two leading POD modes can reconstruct the dominant meandering motion and spatial structure in the LES data with less than 15% error using a suitable loss measure.

Action Recognition of Taekwondo Unit Actions Using Action Images Constructed with Time-Warped Motion Profiles.

Taekwondo has evolved from a traditional martial art into an official Olympic sport. This study introduces a novel action recognition model tailored for Taekwondo unit actions, utilizing joint-motion data acquired via wearable inertial measurement unit (IMU) sensors. The utilization of IMU sensor-measured motion data facilitates the capture of the intricate and rapid movements characteristic of Taekwondo techniques. The model, underpinned by a conventional convolutional neural network (CNN)-based image classification framework, synthesizes action images to represent individual Taekwondo unit actions. These action images are generated by mapping joint-motion profiles onto the RGB color space, thus encapsulating the motion dynamics of a single unit action within a solitary image. To further refine the representation of rapid movements within these images, a time-warping technique was applied, adjusting motion profiles in relation to the velocity of the action. The effectiveness of the proposed model was assessed using a dataset compiled from 40 Taekwondo experts, yielding remarkable outcomes: an accuracy of 0.998, a precision of 0.983, a recall of 0.982, and an F1 score of 0.982. These results underscore this time-warping technique's contribution to enhancing feature representation, as well as the proposed method's scalability and effectiveness in recognizing Taekwondo unit actions.

From Streamline to Pathline: Visualizing Particle Trajectories Under Changing Velocity Fields

Summary Monitoring of subsurface fluid motion is critical for optimizing hydrocarbon production and CO2 sequestration. Streamlines are frequently used to visualize fluid flow; however, they provide only an instantaneous snapshot of the velocity field and do not offer an exact representation of fluid movement under varying field conditions. In contrast, pathlines are constructed by tracking individual particles within the fluid, enabling us to trace the movement of these particles as they traverse through changing velocity fields. Pathline is the trajectory that an individual fluid particle follows in the reservoir. It can be thought of as “recording” the path of a fluid element in the flow field for a given time interval. Pathlines are distinct from streamlines which represent a snapshot of the velocity field at a given instant. The direction the path takes is determined by the streamlines at a specific instant. To start with, streamlines are traced based on the grid face fluxes of finite-difference simulation. Streamline tracing continues till the time of flight equals the current time. The endpoints of the current streamlines become the starting points for the next tracing period. Thus, our formulation incorporates changing flow fields, and the process is repeated for each time interval until the end. The proposed injection monitoring method is tested using a 3D field-scale model with complex geologic features to demonstrate its power and utility. The pathlines were compared with streamlines, as well as the water saturation distribution. Three scenarios are tested: a constant well schedule, a changing well schedule with partial shut-in, and a changing well schedule with a whole field cessation. Results indicate that the pathline provides a more accurate swept volume, consistent with saturation distribution. The robustness of our algorithm and implementation is demonstrated with a complex embedded discrete fracture model (EDFM) to visualize flow patterns in discrete facture network. Pathlines display the fluid flow across fractures and are subsequently used to explore the sweep efficiency and the well connectivity.

CT respiratory motion synthesis using joint supervised and adversarial learning

Objective. Four-dimensional computed tomography (4DCT) imaging consists in reconstructing a CT acquisition into multiple phases to track internal organ and tumor motion. It is commonly used in radiotherapy treatment planning to establish planning target volumes. However, 4DCT increases protocol complexity, may not align with patient breathing during treatment, and lead to higher radiation delivery. Approach. In this study, we propose a deep synthesis method to generate pseudo respiratory CT phases from static images for motion-aware treatment planning. The model produces patient-specific deformation vector fields (DVFs) by conditioning synthesis on external patient surface-based estimation, mimicking respiratory monitoring devices. A key methodological contribution is to encourage DVF realism through supervised DVF training while using an adversarial term jointly not only on the warped image but also on the magnitude of the DVF itself. This way, we avoid excessive smoothness typically obtained through deep unsupervised learning, and encourage correlations with the respiratory amplitude. Main results. Performance is evaluated using real 4DCT acquisitions with smaller tumor volumes than previously reported. Results demonstrate for the first time that the generated pseudo-respiratory CT phases can capture organ and tumor motion with similar accuracy to repeated 4DCT scans of the same patient. Mean inter-scans tumor center-of-mass distances and Dice similarity coefficients were 1.97 mm and 0.63, respectively, for real 4DCT phases and 2.35 mm and 0.71 for synthetic phases, and compares favorably to a state-of-the-art technique (RMSim). Significance. This study presents a deep image synthesis method that addresses the limitations of conventional 4DCT by generating pseudo-respiratory CT phases from static images. Although further studies are needed to assess the dosimetric impact of the proposed method, this approach has the potential to reduce radiation exposure in radiotherapy treatment planning while maintaining accurate motion representation. Our training and testing code can be found at https://github.com/cyiheng/Dynagan.