Enhanced study of complex systems by unveiling hidden symmetries with Dynamical Visibility

One of the great challenges in complex and chaotic dynamics is to reveal its deterministic structures. These temporal dynamical structures are sometimes a consequence of hidden symmetries. Detecting and understanding them can allow the study of complex systems even without knowing the full underlying mathematical description of the system. Here we introduce a new technique, called Dynamical Visibility, that quantifies temporal correlations of the dynamics based upon some symmetry conditions. This visibility measures the departure of the dynamics from internal symmetries. We apply this technique to well-known chaotic systems, such as the logistic map and the circle map, as well as to experimental data from a diode laser with optical feedback and external modulation. Our results show the robustness of the method in characterizing dynamics and highlighting transitions in the dynamical behavior of a complex system. We envision that this can be a useful tool in experimental data, as it can extract key features of the deterministic laws that govern the dynamics of a system, despite the lack of knowledge of those specific quantitative descriptions

Spiritual factors in Vietnamese consumers' purchasing decisions

Consumer demand is a complex and dynamic phenomenon that exerts a profound influence on economic decision-making, market trends, and overall societal well-being. In this context, comprehending the determinants of consumer demand assumes paramount significance for various stakeholders, including businesses, policymakers, and scholars. Furthermore, the acknowledgment of spiritual factors as influential determinants in shaping the values and behaviors of a population introduces a nuanced dimension, particularly within culturally diverse societies such as Vietnam. This research endeavor seeks to engage with these pivotal dimensions by elucidating the role of spiritual factors in tandem with the determinants of consumer demand, with specific reference to the distinctive cultural milieu of Vietnam. Employing a comprehensive multidimensional methodology, encompassing qualitative interviews, surveys, quantitative data analysis, historical and cultural inquiries, logical reasoning, and empirical case studies, this study strives to unravel the intricate interplay between spirituality and consumer behavior. It aims to explicate the influence of deeply ingrained spiritual beliefs, firmly entrenched within Vietnamese culture, on various spheres of decision-making. These spheres span from the observance of auspicious birth rituals and naming conventions to the orchestration of significant life events and the valuation of high-value assets such as real estate and automobiles. Moreover, this research seeks to illuminate the enduring nexus between spirituality, cultural heritage, and the choices made by consumers. It endeavors to provide insights into the multifaceted dynamics governing decision-making processes within the Vietnamese context. Additionally, this inquiry extends its purview to the exploration of the economic implications and marketing strategies predicated upon spiritual underpinnings, underscoring the substantial role played by these spiritual beliefs in shaping the consumer landscape. Ultimately, this research aspires to furnish a holistic comprehension of how spirituality is interwoven into the fabric of daily existence in Vietnam, significantly molding the values and behaviors of its citizenry

TARDYS Quantifiers: Extracting Temporal and Reversible DYnamical Symmetries

One of the great challenges in complex and chaotic dynamics is to reveal the details of its underlying determinism. This can be manifest in the form of temporal correlations or structured patterns in the dynamics of a measurable variable. These temporal dynamical structures are sometimes a consequence of hidden global symmetries. Here we identify the temporal (approximate) symmetries of a semiconductor laser with external optical feedback, based on which we define the Temporal And Reversible DYnamical Symmetry (TARDYS) quantifiers to evaluate the relevance of specific temporal correlations in a time series. We show that these symmetries are also present in other complex dynamical systems, letting us to extrapolate one system\u27s symmetries to characterize and distinguish chaotic regimes in other dynamical systems. These symmetries, natural of the dynamics of the laser with feedback, can also be used as indicators in forecasting regular-to-chaos transitions in mathematical iterative maps. We envision that this can be a useful tool in experimental data, as it can extract key features of the deterministic laws that govern the dynamics of a system, despite the lack of knowledge of those specific quantitative descriptions

Sampling-Based Trajectory (re)planning for Differentially Flat Systems: Application to a 3D Gantry Crane

In this paper, a sampling-based trajectory planning algorithm for a laboratory-scale 3D gantry crane in an environment with static obstacles and subject to bounds on the velocity and acceleration of the gantry crane system is presented. The focus is on developing a fast motion planning algorithm for differentially flat systems, where intermediate results can be stored and reused for further tasks, such as replanning. The proposed approach is based on the informed optimal rapidly exploring random tree algorithm (informed RRT*), which is utilized to build trajectory trees that are reused for replanning when the start and/or target states change. In contrast to state-of-the-art approaches, the proposed motion planning algorithm incorporates a linear quadratic minimum time (LQTM) local planner. Thus, dynamic properties such as time optimality and the smoothness of the trajectory are directly considered in the proposed algorithm. Moreover, by integrating the branch-and-bound method to perform the pruning process on the trajectory tree, the proposed algorithm can eliminate points in the tree that do not contribute to finding better solutions. This helps to curb memory consumption and reduce the computational complexity during motion (re)planning. Simulation results for a validated mathematical model of a 3D gantry crane show the feasibility of the proposed approach.Comment: Published at IFAC-PapersOnLine (13th IFAC Symposium on Robot Control

Singularity Avoidance with Application to Online Trajectory Optimization for Serial Manipulators

This work proposes a novel singularity avoidance approach for real-time trajectory optimization based on known singular configurations. The focus of this work lies on analyzing kinematically singular configurations for three robots with different kinematic structures, i.e., the Comau Racer 7-1.4, the KUKA LBR iiwa R820, and the Franka Emika Panda, and exploiting these configurations in form of tailored potential functions for singularity avoidance. Monte Carlo simulations of the proposed method and the commonly used manipulability maximization approach are performed for comparison. The numerical results show that the average computing time can be reduced and shorter trajectories in both time and path length are obtained with the proposed approachComment: 8 pages, 2 figures, Accepted for publication at IFAC World Congress 202

Binding mechanism and SERS spectra of 5-fluorouracil on gold clusters

The adsorption behaviour of the 5-fluorouracil (5FU) on small gold clusters AuN with N = 6, 8, 20 was evaluated by means of density functional theory using the PBE-D3 functional in combination with a mixed basis set, i.e. cc-pVDZ-PP for gold atoms and cc-pVTZ for non-metal elements. The binding energies between 5FU and gold clusters were determined in the range of 16–24 and 11–19 kcal/mol in gas-phase and aqueous media, respectively. The corresponding Gibbs energies were found to be around -7 to -10 kcal/mol in vacum and sigificantly reduced to -1 to -6 kcal/mol in water solution, indicating that both the association and dissociation processes are likely spontaneous. An analysis on the charge density difference tends to confirm the existence of a charge transfer from the 5FU molecule to Au atoms. Analysis of the surface-enhanced Raman scattering (SERS) spectra of 5FU adsorbed on the Au surfaces shows that the stretching vibrations of N−H and C=O bonds play a major role in the SERS phenomenon. A mechanism for the drug releasing from the gold surfaces is also proposed. The process is triggered by either the low pH in cancerous tumors or the presence of cysteine residues in protein matrices

Open-Vocabulary Affordance Detection in 3D Point Clouds

Affordance detection is a challenging problem with a wide variety of robotic applications. Traditional affordance detection methods are limited to a predefined set of affordance labels, hence potentially restricting the adaptability of intelligent robots in complex and dynamic environments. In this paper, we present the Open-Vocabulary Affordance Detection (OpenAD) method, which is capable of detecting an unbounded number of affordances in 3D point clouds. By simultaneously learning the affordance text and the point feature, OpenAD successfully exploits the semantic relationships between affordances. Therefore, our proposed method enables zero-shot detection and can be able to detect previously unseen affordances without a single annotation example. Intensive experimental results show that OpenAD works effectively on a wide range of affordance detection setups and outperforms other baselines by a large margin. Additionally, we demonstrate the practicality of the proposed OpenAD in real-world robotic applications with a fast inference speed (~100ms). Our project is available at https://openad2023.github.io.Comment: Accepted to The 2023 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2023

Factors Affecting Consumers’ Impulsive Purchasing Behavior in Circle K Convenience Stores in Hanoi, Vietnam

Impulsive purchasing behavior has been observed as one of the important studies conducted by marketers and researchers, as impulse buying has become a prevalent phenomenon in every retail format. The study was conducted to assess factors affecting consumers’ impulsive purchasing behavior in Circle K convenience stores in Hanoi, Vietnam. After reviewing a group of previous studies, the authors indicated 05 factors that affected consumers' impulsive purchasing behavior including impulsiveness, instant gratification, visual appeal, promotions and money availability. The study had selected 05 experts in the field of economics to conduct the expert interview. Moreover, the research team had also handed out the questionnaire and received 310 observations. Specifically, Impulsiveness had the strongest influence on the impulsive purchasing behavior of Circle K’s consumers in Hanoi. Keywords: factors, Impulsive purchasing behavior, Circle K convenience stores DOI: 10.7176/JESD/14-8-03 Publication date: April 30th 2023

Open-Vocabulary Affordance Detection using Knowledge Distillation and Text-Point Correlation

Affordance detection presents intricate challenges and has a wide range of robotic applications. Previous works have faced limitations such as the complexities of 3D object shapes, the wide range of potential affordances on real-world objects, and the lack of open-vocabulary support for affordance understanding. In this paper, we introduce a new open-vocabulary affordance detection method in 3D point clouds, leveraging knowledge distillation and text-point correlation. Our approach employs pre-trained 3D models through knowledge distillation to enhance feature extraction and semantic understanding in 3D point clouds. We further introduce a new text-point correlation method to learn the semantic links between point cloud features and open-vocabulary labels. The intensive experiments show that our approach outperforms previous works and adapts to new affordance labels and unseen objects. Notably, our method achieves the improvement of 7.96% mIOU score compared to the baselines. Furthermore, it offers real-time inference which is well-suitable for robotic manipulation applications.Comment: 8 page

Language-driven Scene Synthesis using Multi-conditional Diffusion Model

Scene synthesis is a challenging problem with several industrial applications. Recently, substantial efforts have been directed to synthesize the scene using human motions, room layouts, or spatial graphs as the input. However, few studies have addressed this problem from multiple modalities, especially combining text prompts. In this paper, we propose a language-driven scene synthesis task, which is a new task that integrates text prompts, human motion, and existing objects for scene synthesis. Unlike other single-condition synthesis tasks, our problem involves multiple conditions and requires a strategy for processing and encoding them into a unified space. To address the challenge, we present a multi-conditional diffusion model, which differs from the implicit unification approach of other diffusion literature by explicitly predicting the guiding points for the original data distribution. We demonstrate that our approach is theoretically supportive. The intensive experiment results illustrate that our method outperforms state-of-the-art benchmarks and enables natural scene editing applications. The source code and dataset can be accessed at https://lang-scene-synth.github.io/.Comment: Accepted to NeurIPS 202