Modeling of Human Motor Control and Its Application in Human Interaction with Machines

Human civilization started with the invention of tools which enhanced and expanded human motor capability. With the recent development of virtual reality technology and artificial intelligence, the interaction between humans and machines has become more and more intricate. A better understanding of our motor system and the way it interacts with machines will allow us to better design intelligent devices. However, previous works in motor control modeling mostly focused on linear dynamics and had limitations in incorporating the process of learning. A musculoskeletal model based on mechanical principles and a motor control model based on Bayesian probability are proposed in this study. The probability-theoretical formulation of the problem not only facilitates the understanding of motor learning but also transforms nonlinear dynamics into linear problems. Using these models, the interactions in which both human and machine are capable of learning and adapting are formulated and analyzed. Intelligent control policies for machine imitating the human motor control are proposed. Simulation results are also presented

Robust Design in Game Theory: Bayesian Optimization Approach to Minimax Problems with Equilibrium Constraints

Modern engineering systems have become increasingly complex due to the integration of human actors and advanced artificial intelligence, both of which can be interpreted as intelligent agents. Game theory is a mathematical framework that provides an explanatory model for systems constituted of those intelligent agents. It postulates that the apparent behavior of a system is an equilibrium resulting from each agent within the system individually optimizing their own objectives. Thus, designing an intelligent system is to identify a configuration such that its equilibrium is desirable with respect to some external criteria. However, equilibria are often not unique and form sets that lack topological properties on which optimization heavily relies on, e.g., convexity, connectedness, or even compactness in some cases. The unsureness nature, i.e., uncertainty, of equilibria also appeals for another common design criterion: robustness. In this context, a robust design should reach worst-case optimality to avoid sensitivity to the eventual outcome among all possible equilibria. In this dissertation, I incorporate both the game theoretical aspect and the robustness requirement of system design using the formulation of minimax problems with equilibrium constraints. The complexity of the problem structure and the non-uniqueness of potential equilibria require a new solution strategy different from traditional gradient based methods. I propose a Bayesian approach which infers the probabilistic belief of the optimality of a design given sampled objective function values. Due to the anisotropic natural of systems of independent agents, I then revisit the original Kushner’s Wiener process prior instead of radial basis kernel prior despite their popularity for other global optimization applications. I also derive theoretical results on sample maxima and their locations, develop an effective method to decompose the search space into independent regions, and design necessary adaptations to take into account the equilibrium constraints and minimax objective. Finally, I discuss a few applications of the proposed design framework

Integrated Face Analytics Networks through Cross-Dataset Hybrid Training

Face analytics benefits many multimedia applications. It consists of a number of tasks, such as facial emotion recognition and face parsing, and most existing approaches generally treat these tasks independently, which limits their deployment in real scenarios. In this paper we propose an integrated Face Analytics Network (iFAN), which is able to perform multiple tasks jointly for face analytics with a novel carefully designed network architecture to fully facilitate the informative interaction among different tasks. The proposed integrated network explicitly models the interactions between tasks so that the correlations between tasks can be fully exploited for performance boost. In addition, to solve the bottleneck of the absence of datasets with comprehensive training data for various tasks, we propose a novel cross-dataset hybrid training strategy. It allows "plug-in and play" of multiple datasets annotated for different tasks without the requirement of a fully labeled common dataset for all the tasks. We experimentally show that the proposed iFAN achieves state-of-the-art performance on multiple face analytics tasks using a single integrated model. Specifically, iFAN achieves an overall F-score of 91.15% on the Helen dataset for face parsing, a normalized mean error of 5.81% on the MTFL dataset for facial landmark localization and an accuracy of 45.73% on the BNU dataset for emotion recognition with a single model.Comment: 10 page

Progressive3D: Progressively Local Editing for Text-to-3D Content Creation with Complex Semantic Prompts

Recent text-to-3D generation methods achieve impressive 3D content creation capacity thanks to the advances in image diffusion models and optimizing strategies. However, current methods struggle to generate correct 3D content for a complex prompt in semantics, i.e., a prompt describing multiple interacted objects binding with different attributes. In this work, we propose a general framework named Progressive3D, which decomposes the entire generation into a series of locally progressive editing steps to create precise 3D content for complex prompts, and we constrain the content change to only occur in regions determined by user-defined region prompts in each editing step. Furthermore, we propose an overlapped semantic component suppression technique to encourage the optimization process to focus more on the semantic differences between prompts. Extensive experiments demonstrate that the proposed Progressive3D framework generates precise 3D content for prompts with complex semantics and is general for various text-to-3D methods driven by different 3D representations.Comment: Project Page: https://cxh0519.github.io/projects/Progressive3D

Evidential Detection and Tracking Collaboration: New Problem, Benchmark and Algorithm for Robust Anti-UAV System

Unmanned Aerial Vehicles (UAVs) have been widely used in many areas, including transportation, surveillance, and military. However, their potential for safety and privacy violations is an increasing issue and highly limits their broader applications, underscoring the critical importance of UAV perception and defense (anti-UAV). Still, previous works have simplified such an anti-UAV task as a tracking problem, where the prior information of UAVs is always provided; such a scheme fails in real-world anti-UAV tasks (i.e. complex scenes, indeterminate-appear and -reappear UAVs, and real-time UAV surveillance). In this paper, we first formulate a new and practical anti-UAV problem featuring the UAVs perception in complex scenes without prior UAVs information. To benchmark such a challenging task, we propose the largest UAV dataset dubbed AntiUAV600 and a new evaluation metric. The AntiUAV600 comprises 600 video sequences of challenging scenes with random, fast, and small-scale UAVs, with over 723K thermal infrared frames densely annotated with bounding boxes. Finally, we develop a novel anti-UAV approach via an evidential collaboration of global UAVs detection and local UAVs tracking, which effectively tackles the proposed problem and can serve as a strong baseline for future research. Extensive experiments show our method outperforms SOTA approaches and validate the ability of AntiUAV600 to enhance UAV perception performance due to its large scale and complexity. Our dataset, pretrained models, and source codes will be released publically

The 3rd Anti-UAV Workshop & Challenge: Methods and Results

The 3rd Anti-UAV Workshop & Challenge aims to encourage research in developing novel and accurate methods for multi-scale object tracking. The Anti-UAV dataset used for the Anti-UAV Challenge has been publicly released. There are two main differences between this year's competition and the previous two. First, we have expanded the existing dataset, and for the first time, released a training set so that participants can focus on improving their models. Second, we set up two tracks for the first time, i.e., Anti-UAV Tracking and Anti-UAV Detection & Tracking. Around 76 participating teams from the globe competed in the 3rd Anti-UAV Challenge. In this paper, we provide a brief summary of the 3rd Anti-UAV Workshop & Challenge including brief introductions to the top three methods in each track. The submission leaderboard will be reopened for researchers that are interested in the Anti-UAV challenge. The benchmark dataset and other information can be found at: https://anti-uav.github.io/.Comment: Technical report for 3rd Anti-UAV Workshop and Challenge. arXiv admin note: text overlap with arXiv:2108.0990

Target SSR-Seq: A Novel SSR Genotyping Technology Associate With Perfect SSRs in Genetic Analysis of Cucumber Varieties

Simple sequence repeats (SSR) – also known as microsatellites – have been used extensively in genetic analysis, fine mapping, quantitative trait locus (QTL) mapping, as well as marker-assisted selection (MAS) breeding and other techniques. Despite a plethora of studies reporting that perfect SSRs with stable motifs and flanking sequences are more efficient for genetic research, the lack of a high throughput technology for SSR genotyping has limited their use as genetic targets in many crops. In this study, we developed a technology called Target SSR-seq that combined the multiplexed amplification of perfect SSRs with high throughput sequencing. This method can genotype plenty of SSR loci in hundreds of samples with highly accurate results, due to the substantial coverage afforded by high throughput sequencing. We also detected 844 perfect SSRs based on 182 resequencing datasets in cucumber, of which 91 SSRs were selected for Target SSR-seq. Finally, 122 SSRs, including 31 SSRs for varieties identification, were used to genotype 382 key cucumber varieties readily available in Chinese markets using our Target SSR-seq method. Libraries of PCR products were constructed and then sequenced on the Illumina HiSeq X Ten platform. Bioinformatics analysis revealed that 111 filtered SSRs were accurately genotyped with an average coverage of 1289× at an extremely low cost; furthermore, 398 alleles were observed in 382 cucumber cultivars. Genetic analysis identified four populations: northern China type, southern China type, European type, and Xishuangbanna type. Moreover, we acquired a set of 16 core SSRs for the identification of 382 cucumber varieties, of which 42 were isolated as backbone cucumber varieties. This study demonstrated that Target SSR-seq is a novel and efficient method for genetic research