Representation Of Motion Research Articles

Memorializing (violent) resistance to authoritarianism: challenges and tensions at Portugal’s Aljube Museum - resistance and freedom

ABSTRACT Museums play a critical role in constituting and communicating narratives about historical violence and connecting them to contemporary and future concerns. In this paper, we examine the memorialization of resistance movements, especially resistance to authoritarianism and state violence, as exemplified at the Aljube Museum – Resistance and Freedom in Lisbon, Portugal. We offer a methodological and theoretical discussion on storytelling and autoethnography in understanding the narratives of historical violence and war, focusing on representations of historical resistance movements and their uses of violence. We argue that the museum has avoided grappling with armed resistance as a method of effecting change and challenging authoritarianism by crafting a coherent and unitary narrative about oppression and non-violent resistance that downplays violent opposition to authoritarianism. After outlining some silences noted in the museum’s representations of resistance, we offer a counter-memory that discusses the role of non-state armed actors and shows multiple ways in which historical authoritarianism has been resisted and fought.

Expertise Advantage of Automatic Prediction in Visual Motion Representation is Domain-General: A Meta-Analysis.

The expertise advantage denotes the superior perceptual-cognitive skills exhibited by experts. Nevertheless, the extent to which the expertise advantage influences automatic prediction in visual motion representation remains unclear. This meta-analysis aims to consolidate findings from various studies on the expertise advantage of automatic prediction in visual motion representation. We identified empirical research literature that measured expertise advantage of automatic prediction from the Web of Science (Core Collection), APA PsycInfo, PubMed, and Cochrane Library databases. We identified 16 studies contributing 23 effect sizes from a pool of 1110 records. The standardized mean difference (Hedges' g) was computed for the expertise advantage. The meta-analysis results reveal that, in comparison to novices, experts demonstrate superior automatic prediction in visual motion representation (Hedges' g = 0.73, 95% CI [0.54, 0.92]). Moderator analyses suggest that, when measuring the expertise advantage of automatic prediction, the effect size of the recall task is significantly larger than that of the recognition task. Other moderating variables did not exhibit significant moderating effects. These results suggest that the expertise advantage extends to automatic prediction in visual motion representation, and that this advantage is general, stable, and widespread.

One-Dimensional Motion Representation for Standing/Sitting and Their Transitions.

In everyday life, people often stand up and sit down. Unlike young, able-bodied individuals, older adults and those with disabilities usually stand up or sit down slowly, often pausing during the transition. It is crucial to design interfaces that accommodate these movements. Additionally, in public settings, protecting personal information is essential. Addressing these considerations, this paper presents a distance-based representation scheme for the motions of standing up and sitting down. This proposed scheme identifies both standing and sitting positions, as well as the transition process between these two states. Our scheme is based solely on the variations in distance between a sensor and the surfaces of the human body during these movements. Specifically, the proposed solution relies on distance as input, allowing for the use of a proximity sensor without the need for cameras or additional wearable sensor attachments. A single microcontroller is adequate for this purpose. Our contribution highlights that using a proximity sensor broadens the applicability of the approach while ensuring that personal information remains secure. Additionally, the scheme alleviates users' mental burden, particularly regarding privacy concerns. Extensive experiments were performed on 58 subjects, including 19 people over the age of 70, to verify the effectiveness of the proposed solution, and the results are described in detail.

Dinamika Transformasi Tasawuf Era Modern: Neo-Sufisme dan Gerakan Islam Transnasional

This article examines the dynamics transformation of Sufism in the modern era. Sufism in the modern era appears running into transformation both in terms of its concept and movement. From the aspect of the concept of Sufism, modern Sufism is running into a transformation, namely neo-Sufism. Likewise, in the network aspect of the Sufism movement in the modern era, Sufism moves dynamically, from a local movement to becoming a network of transnational Islamic movements. This research is qualitative research using transnationalism theory as an analytical tool and historical studies as an approach. In this research, at least two findings were found. First, the presence of neo-Sufism is a form of transformation of classical Sufism teachings. The concept of neo-Sufism tends to be balanced (tawazun) between exoteric and esoteric aspects and can be a spiritual alternative for urban Muslim communities. Second, modern Sufism can be categorized as a representation of a transnational Islamic movement, as a non-governmental movement structured across countries in terms of thought, spiritual and political movements. The main route behind the transnational Islamic movement of Sufism is through da'wah, trade and immigrants. Keywords: Sufism, Neo-Sufism, Transnationalism, Transformative

A Pressure-Based Model of IV Fluid Therapy Kinetics.

The kinetics of intravenous (IV) fluid therapy and how it affects the movement of fluids within humans and animals is an ongoing research topic. Clinical researchers have in the past used a mathematical model adopted from pharmacokinetics that attempts to mimic these kinetics. This linear model is based on the ideas that the body tries to maintain fluid levels in various compartments at some baseline targets and that fluid movement between compartments is driven by differences between the actual volumes and the targets. Here a nonlinear pressure-based model is introduced, where the driving force of fluid movement out of the blood stream is the pressure differences, both hydrostatic and oncotic, between the capillaries and the interstitial space. This model is, like the linear model, a coarse representation of fluid movement on the whole body scale, but, unlike the linear model, it is based on some of the body's biophysical processes. The abilities of both models to fit data from experiments on both awake and anesthetized cats was analyzed. The pressure-based model fit the data better than the linear model in all but one case, and was deemed statistically significantly better in a third of the cases.

Motion-guided semantic alignment for line art animation colorization

Line art animation colorization aims to colorize a sequence of line art frames with a reference while ensuring semantic alignment across frames. Recent methods have utilized similarity metrics to maintain inter-frame semantic alignment. However, these approaches typically struggle with precise pixel-to-pixel motion alignment, resulting in issues like semantic inconsistencies and color blurring. To address these problems, we introduce an innovative Motion-Guided Colorization Network(MGC-Net), which incorporates line art motions into account for achieving coarse to fine semantic alignment. At the coarse level, our approach includes a Hierarchical Cost-feature Distillation (HCD) module. This module is designed to establish accurate pixel-to-pixel motion alignment between frames. It employs a motion distillation loss, which effectively transfers accurate color motion representation to line arts at varying hierarchical levels. Specifically, we utilize the cost features in inter-frames as a representation of motion, processed through our self-supervision operator. At the finer level, we propose the Correlation Refinement (CR) module. This module enhances semantic alignment by integrating semantic contextual dependencies between the color reference and the motion-based reference obtained from our motion-based confidence estimation block. CR module refines the final colorization process, improving clear color detail and semantic consistency across frames. Quantitative and qualitative experiments on animation datasets show that MGC-Net outperforms the state-of-the-art methods.

Representation of Popular Islamic Movements on Social Media: A Study of the Hashtag #LogIndiCloseTheDoor

This article an discusses the Ramadan podcast series Hashtag #LogIndiCloseTheDoor as an example of the Popular Islamic Movement on social media. The series was a huge hit even though it dealt with a delicate subject. It is impossible to consider this truth to be a phenomenon deserving of the widespread success of social media material. This article is a qualitative study employing Talal Asad's Islamic Anthropology method. According to the article's conclusion, the Popular Islam Movement is a group of middle-class Muslims, particularly those in their millennial years, who are using modern and popular culture as a means of self-actualization and self-study. The media portrays him in two ways: dakwahtainment and religiotainment. Ramadan's audio series #LogIndiCloseTheDoor is one of them. The Islamic pretext Raḥmatan lil 'Ālamīn (a mercy to all creation) serves as the basis for the television show. The discussion that goes along with this series focuses on inclusivism in religion as well as the commoditization and commodification of religion. These discourses were communicated and developed into a new religious paradigm by way of the power relations network that Deddy Corbuzier, Habib Ja'far Hussein, and Leonardo da Vinci constructed. This essay contends that the #LogIndiCloseTheDoor Ramadan Hashtag audio series, which combines modern and Islamic ideals, transforms the initially conservative, restrictive, and inflexible perception of Islam into one that is inclusive, tolerant, and dynamic.

Neural representations of visual motion for perception and interception

Neural representations of visual motion for perception and interception

Humanoid Robot Teleoperation Through 3-D Pose Estimation

The knowledge of how the human motions is performed helps to understand how the human body works. This paper presents a method to estimate the human limbs angles through a kinematic model depicted by Roll-Pitch-Yaw rotationmatrix and the mimic of those angles on a humanoid robot. The advantage of this model is the detailed representation of each joint movement in the coordinate axes (x, y, z). The angles estimation is made with the information provided by algorithms of artificial vision and artificial intelligence. In order to reduce the latency between the human motion capture and robot motions, a fuzzy logic controller is implemented in order to control each robot joint. The final robot limbs angles are compared with the human angles in order to obtain the final error between those measurements. This method shows a similar result on the arms posture regarding previous works.

Hematopoietic Endothelial Progenitor cells enhance motor function and cortical motor map integrity following cerebral ischemia.

Hematopoietic stem cells (HSC) are recruited to ischemic areas in the brain and contribute to improved functional outcome in animals. However, little is known regarding the mechanisms of improvement following HSC administration post cerebral ischemia. To better understand how HSC effect post-stroke improvement, we examined the effect of HSC in ameliorating motor impairment and cortical dysfunction following cerebral ischemia. Baseline motor performance of male adult rats was established on validated motor tests. Animals were assigned to one of three experimental cohorts: control, stroke, stroke + HSC. One, three and five weeks following a unilateral stroke all animals were tested on motor skills after which intracortical microstimulation was used to derive maps of forelimb movement representations within the motor cortex ipsilateral to the ischemic injury. Stroke + HSC animals significantly outperformed stroke animals on single pellet reaching at weeks 3 and 5 (28±3% and 33±3% versus 11±4% and 17±3%, respectively, p < 0.05 at both time points). Control animals scored 44±1% and 47±1%, respectively. Sunflower seed opening task was significantly improved in the stroke + HSC cohort versus the stroke cohort at week five-post stroke (79±4 and 48±5, respectively, p < 0.05). Furthermore, Stroke + HSC animals had significantly larger forelimb motor maps than animals in the stroke cohort. Overall infarct size did not significantly differ between the two stroked cohorts. These data suggest that post stroke treatment of HSC enhances the functional integrity of residual cortical tissue, which in turn supports improved behavioral outcome, despite no observed reduction in infarct size.

Contextual visual and motion salient fusion framework for action recognition in dark environments

Infrared (IR) human action recognition (AR) exhibits resilience against shifting illumination conditions, changes in appearance, and shadows. It has valuable applications in numerous areas of future sustainable and smart cities including robotics, intelligent systems, security, and transportation. However, current IR-based recognition approaches predominantly concentrate on spatial or local temporal information and often overlook the potential value of global temporal patterns. This oversight can lead to incomplete representations of body part movements and prevent accurate optimization of a network. Therefore, a contextual-motion coalescence network (CMCNet) is proposed that operates in a streamlined and end-to-end manner for robust action representation in darkness in a near-infrared (NIR) setting. Initially, data are preprocessed to separate foreground, normalized, and resized. The framework employs two parallel modules: the contextual visual features learning module (CVFLM) for local feature extraction, and the temporal optical flow learning module (TOFLM) for acquiring motion dynamics. These modules focus on action-relevant regions used shift window-based operations to ensure accurate interpretation of motion information. The coalescence block harmoniously integrates the contextual and motion features within a unified framework. Finally, the temporal decoder module discriminatively identifies the boundaries of the action sequence. This sequence of steps ensures the synergistic optimization of both CVFLM and TOFLM and thorough competent feature extraction for precise AR. Evaluations of CMCNet are carried out on publicly available datasets, InfAR and NTURGB-D, where superior performance is achieved. Our model yields the highest average precision of 89% and 85% on these datasets, respectively, representing an improvement of 2.25% (on InfAR) compared to conventional methods operating at spatial and optical flow levels which underscores its efficacy.

Energy spectrum and magnetic susceptibility of the improved Pöschl-Teller potential

This study presents an analytical representation of the internal motion of a diatomic system using the improved Pöschl-Teller potential. The ro-vibrational energy spectrum is derived by solving the radial Schrödinger time independent wave equation (STIWE) under a 2D electromagnetic potential field. Additionally, an explicit equation for magnetic susceptibility is formulated from the internal partition function. The analytical models are used to investigate physical properties of CO (X1∑+), K2 (a3∑u+), BCl (X1∑+), and NaK (c3∑+) molecules. Average percentage absolute deviations (APAD) for the ro-vibrational energy spectrum are calculated as 0.1001 %, 0.5306 %, 1.2070 %, and 0.9779 %, respectively, compared to observed data. Numerical results are consistent with existing literature. Furthermore, the study reveals that the magnetic susceptibility of the system increases with temperature and decreases with an increase in magnetic field intensity, indicating its paramagnetic nature.

Self-chemophoresis in the thin diffuse interface approximation

Self-chemophoresis is an appealing and quite successful interpretation of the motility exhibited by certain chemically active colloidal particles suspended in a solution of their ‘fuel’: the particle has a phoretic response to self-generated, rather than externally imposed, inhomogeneities in the chemical composition of the solution. The postulated mechanism of chemophoresis is the interaction of the particle (via an adsorption potential) with the chemical inhomogeneities in the surrounding medium. When the range of this interaction is much smaller than any other relevant scale in the system, the (translational and rotational) phoretic velocities can be described in terms of a phoretic coefficient and a slip fluid velocity at the surface of the particle. Using the case of a spherical particle as a simple and physically insightful example, here we exploit an integral representation of the rigid-body motion to critically re-examine this framework. The systematic analysis highlights two steps in the approximation: first the thin–layer approximation (very large particle size), and subsequently a lubrication approximation (slow variation of the adsorption potential along the tangential direction). We also discuss how these approximations could be relaxed and the effect of this on the particle's motion.

Revisiting Numerical Solutions of Weakly Bound Noble Gases' Vibrational Energy Levels Modeled by the Improved Lennard-Jones Potential.

We revisit the numerical solutions of vibrational eigenstates of weakly bound homonuclear and heteronuclear noble gas pairs by applying a Fortran program based on the Numerov method. The harmonic, Lennard-Jones (LJ), Morse, Tang-Toennies (TT), and Improved Lennard-Jones (ILJ) potential models have been implemented to represent the potential energy curves (PECs). The obtained vibrational energies spectrum was tested on the experimental data and accurate ab initio calculations at CCSD(T)/CBS level. The vibrational eigenvalues and eigenfunctions can be reproduced accurately within the ILJ potential model. Moreover, considering the calculated lifetime of van der Waals (vdW) complexes, the implementation of ILJ rather than standard LJ potential model has a significant impact on the systems dynamics by providing more representative atomic trajectories when the function is incorporated in force fields for molecular dynamics (MD) simulations. Overall, the ILJ function is the best suited potential model for the representation of vibrational motions and the determination of vibrational energy levels of weakly bound systems, both at equilibrium and non-equilibrium conditions.

Enhanced human motion detection with hybrid RDA-WOA-based RNN and multiple hypothesis tracking for occlusion handling

Human motion detection in complex scenarios poses challenges due to occlusions. This paper presents an integrated approach for accurate human motion detections by combining Adapted Canny Edge detection as a preprocessing step, backbone-modified Mask R-CNN for precise segmentation, Hybrid RDA-WOA-based RNN as a classification, and a Multiple-hypothesis model for effective occlusion handling. Adapted Canny Edge detection is utilized as an initial preprocessing step to highlight significant edges in the input image. The resulting edge map enhances object boundaries and highlights structural features, simplifying subsequent processing steps. The improved image is then passed through backbone-modified Mask R-CNN for the pixel-level segmentation of humans. Backbone-modified Mask R-CNN along with IoU, Euclidean Distance, and Z-Score recognizes moving objects in complex scenes exactly. After recognizing moving objects, the optimized Hybrid RDA-WOA-based RNN classifies humans. To handle the self-occlusion, Multiple Hypothesis Tracking (MHT) is used. Real-world situations frequently include occlusions where humans can be partially or completely hidden by objects. The proposed approach integrates a Multiple-hypothesis model into the detection pipeline to address this challenge. Moreover, the proposed human motion detection approach includes an optimized Hybrid RDA-WOA-based RNN trained with 2D representations of 3D skeletal motion. The proposed work was evaluated using the IXMAS, KTH, Weizmann, NTU RGB + D, and UCF101 Datasets. It achieved an accuracy of 98% on the IXMAS, KTH, Weizmann, and UCF101 Datasets and 97.1% on the NTU RGB + D Dataset. The simulation results unveil the superiority of the proposed methodology over the existing approaches.

Functional mapping of movement and speech using task-based electrophysiological changes in stereoelectroencephalography.

Stereoelectroencephalography (SEEG) has become the predominant method for intracranial seizure localization. When imaging, semiology, and scalp EEG findings are not in full agreement or definitively localizing, implanted SEEG recordings are used to test candidate seizure onset zones (SOZs). Discovered SOZs may then be targeted for resection, laser ablation, or neurostimulation. If an SOZ is eloquent, resection and ablation are both contraindicated, so identifying functional representation is crucial for therapeutic decision-making. The authors present a novel functional brain mapping technique that utilizes task-based electrophysiological changes in SEEG during behavioral tasks and test this in pediatric and adult patients. SEEG was recorded in 20 patients with epilepsy who ranged in age from 6 to 39 years (12 female, 18 of 20 patients < 21 years of age) and underwent implanted monitoring to identify seizure onset. Each performed 1) visually cued simple repetitive movements of the hand, foot, or tongue while electromyography was recorded; and 2) simple picture-naming or verb-generation speech tasks while audio was recorded. Broadband changes in the power spectrum of the SEEG recording were compared between behavior and rest. Electrophysiological functional mapping of movement and/or speech areas was completed in all 20 patients. Eloquent representation was identified in both cortex and white matter and generally corresponded to classically described functional anatomical organization as well as other clinical mapping results. Robust maps of brain activity were identified in healthy brain, regions of developmental or acquired structural abnormality, and SOZs. Task-based electrophysiological mapping using broadband changes in the SEEG signal reliably identifies movement and speech representation in pediatric and adult epilepsy patients.

MoConVQ: Unified Physics-Based Motion Control via Scalable Discrete Representations

In this work, we present MoConVQ, a novel unified framework for physics-based motion control leveraging scalable discrete representations. Building upon vector quantized variational autoencoders (VQ-VAE) and model-based reinforcement learning, our approach effectively learns motion embeddings from a large, unstructured dataset spanning tens of hours of motion examples. The resultant motion representation not only captures diverse motion skills but also offers a robust and intuitive interface for various applications. We demonstrate the versatility of MoConVQ through several applications: universal tracking control from various motion sources, interactive character control with latent motion representations using supervised learning, physics-based motion generation from natural language descriptions using the GPT framework, and, most interestingly, seamless integration with large language models (LLMs) with in-context learning to tackle complex and abstract tasks.

The Ecofeminism Practice: The Women's Movement in the Documentary Film “Sangihe Melawan”

Ecofeminism is an interrelated and holistic theory and practice. Ecology, in conjunction with discourses on women's freedom and radical democracy, constitutes one of the primary pillars of democratic confederalism, which is the development of the political paradigm in Indonesia. Ecofeminism also manifests in several areas, including the women's resistance movement against gold mining by PT Tambang Mas Sangihe (TMS) in Sangihe. This study aims to describe the representation of the women's movement in the documentary film "Sangihe Melawan," which was uploaded to the YouTube channel of Watchdoc Documentary on July 1, 2022. This research employs semiotics techniques. The data analysis unit in this study includes images or visuals, audio (dialogue and narration), acting, setting, type of shot, and angle, which collectively demonstrate the role of women in the concept of Social Ecofeminism. The results indicate that women are represented as the most disadvantaged parties in the exploitation of natural resources. The women's movement to protect the environment is also depicted as an effortful endeavor. The women's resistance to the expansion of gold mining in Sangihe was manifested in various actions, including awareness-raising activities, a women's rally, legal action against PT TMS through the Manado and Jakarta Administrative Courts, and other legal avenues.

Gilbert Austin’s Chironomia Revisited: Sympathy, Science, and the Representation of Movement, by Sara Newman and Sigrid Streit

<i>Gilbert Austin’s Chironomia Revisited: Sympathy, Science, and the Representation of Movement</i>, by Sara Newman and Sigrid Streit

Concurrent optogenetic motor mapping of multiple limbs in awake mice reveals cortical organization of coordinated movements.

Motor mapping allows for determining the macroscopic organization of motor circuits and corresponding motor movement representations on the cortex. Techniques such as intracortical microstimulation (ICMS) are robust, but can be time consuming and invasive, making them non-ideal for cortex-wide mapping or longitudinal studies. In contrast, optogenetic motor mapping offers a rapid and minimally invasive technique, enabling mapping with high spatiotemporal resolution. However, motor mapping has seen limited use in tracking 3-dimensonal, multi-limb movements in awake animals. This gap has left open questions regarding the underlying organizational principles of motor control of coordinated, ethologically relevant movements involving multiple limbs. Our first objective was to develop Multi-limb Optogenetic Motor Mapping (MOMM) to concurrently map motor movement representations of multiple limbs with high fidelity in awake mice. Having established MOMM, our next objective was determine whether maps of coordinated and ethologically relevant motor output were topographically organized on the cortex. We combine optogenetic stimulation with a deep learning driven pose-estimation toolbox, DeepLabCut (DLC), and 3-dimentional triangulation to concurrently map motor movements of multiple limbs in awake mice. MOMM consistently revealed cortical topographies for all mapped features within and across mice. Many motor maps overlapped and were topographically similar. Several motor movement representations extended beyond cytoarchitecturally defined somatomotor cortex. Finer articulations of the forepaw resided within gross motor movement representations of the forelimb. Moreover, many cortical sites exhibited concurrent limb coactivation when photostimulated, prompting the identification of several cortical regions harboring coordinated and ethologically relevant movements. The cortex appears to be topographically organized by motor programs, which are responsible for coordinated, multi-limbed, and behavioral-like movements.