Inside out: rethinking contemporary Chinese art and global creative economy

This thesis is to destabilize the Western dominant understanding of contemporary Chinese art when it circulates on the global art market, such as the all-to-common narratives only celebrating Chinese artists who are politically criticizing or resist the Chinese authority. Meanwhile, I also question the authority control inside of China, especially the mainstream criteria of aesthetics and art. Cultural anthropologist Aihwa Ong observes that some Western scholars believe contemporary Chinese art to be “crass opportunism with reduced aesthetic value.”Chinese American artist and art critic Chen Danqing criticizes contemporary Chinese art from the last ten years as too utilitarian: “During the Cultural Revolution, all [Chinese] artists worried about their artworks not being ‘revolutionary’; today, I see they only worry about their ‘tricks’ are not ‘contemporary’ enough.” He critically argued that Chinese art today is a consequence of learning Western art due to a lack of cultural confidence. I argue that contemporary Chinese art is not market-driven or simply copy Western arts. It has been shaped by the context of its particular socio-political and economic condition since the middle of the twentieth century. I also emphasize the specialness of “contemporaneity” in contemporary Chinese art

A Correct Smoothed Particle Method to Model Structure-Ice Interaction

This paper studies the effect of ice resistance on the icebreaking capacity and speed of an icebreaking vessel. We combine an improved Correct Smoothed Particle Method (CSPM) with a material low-speed collision fracture model to numerically simulate the continuous icebreaking and rolling process of crushed. Using this model, we investigate the icebreaking resistance and immersion resistance during the icebreaking process, taking into account the fluid (water) as the elastic boundary support and the fluid-solid coupling interaction. We compare the icebreaking resistance and broken ice fracture shapes obtained by the numerical calculation with the theoretical analytical results, and thus validate the improved CSPM method. Further, we compare the immersion resistance results from our simulation against that from Puntigliano [Puntigliano, Hamburgische Schiffbau-Versuchsanstalt GmbH (1995)], and demonstrate that the proposed method can accurately predict ice resistance

A Screening Strategy for Structured Optimization Involving Nonconvex ℓq,p\ell_{q,p} Regularization

In this paper, we develop a simple yet effective screening rule strategy to improve the computational efficiency in solving structured optimization involving nonconvex $\ell_{q,p}$ regularization. Based on an iteratively reweighted $\ell_1$ (IRL1) framework, the proposed screening rule works like a preprocessing module that potentially removes the inactive groups before starting the subproblem solver, thereby reducing the computational time in total. This is mainly achieved by heuristically exploiting the dual subproblem information during each iteration.Moreover, we prove that our screening rule can remove all inactive variables in a finite number of iterations of the IRL1 method. Numerical experiments illustrate the efficiency of our screening rule strategy compared with several state-of-the-art algorithms

Physics Sensor Based Deep Learning Fall Detection System

Fall detection based on embedded sensor is a practical and popular research direction in recent years. In terms of a specific application: fall detection methods based upon physics sensors such as [gyroscope and accelerator] have been exploited using traditional hand crafted features and feed them in machine learning models like Markov chain or just threshold based classification methods. In this paper, we build a complete system named TSFallDetect including data receiving device based on embedded sensor, mobile deep-learning model deploying platform, and a simple server, which will be used to gather models and data for future expansion. On the other hand, we exploit the sequential deep-learning methods to address this falling motion prediction problem based on data collected by inertial and film pressure sensors. We make a empirical study based on existing datasets and our datasets collected from our system separately, which shows that the deep-learning model has more potential advantage than other traditional methods, and we proposed a new deep-learning model based on the time series data to predict the fall, and it may be superior to other sequential models in this particular field

Improving Learning Engagement Among Ethnic Minority High School Students in China Through School-Based Social Support: An Intervention Study

The study aimed to examine the effect of school-based social support, specifically the integrated peer and teacher support on the learning engagement of ethnic minority high school students in China. Researchers designed a customized reading program to incorporate peer and teacher support through collaborative learning and dual-teacher classroom mechanisms. The intervention groups involve 192 first year high school students in an underdeveloped area in Yunnan province. Learning engagement was measured using a self-report interview and observation field note during the intervention. Results showed that the school-based social support has effectively promoted the student’s self-confidence, learning motivation, self-identity, and develop a positive learning environment. These findings suggest that school-based social support can be a productive way to improve learning engagement among ethnic minority high school students in China. Implications for education practice and future research are discussed

Multi-Agent Robust Control Synthesis from Global Temporal Logic Tasks

This paper focuses on the heterogeneous multi-agent control problem under global temporal logic tasks. We define a specification language, called extended capacity temporal logic (ECaTL), to describe the required global tasks, including the number of times that a local or coupled signal temporal logic (STL) task needs to be satisfied and the synchronous requirements on task satisfaction. The robustness measure for ECaTL is formally designed. In particular, the robustness for synchronous tasks is evaluated from both the temporal and spatial perspectives. Mixed-integer linear constraints are designed to encode ECaTL specifications, and a two-step optimization framework is further proposed to realize task-satisfied motion planning with high spatial robustness and synchronicity. Simulations are conducted to demonstrate the expressivity of ECaTL and the efficiency of the proposed control synthesis approach.Comment: 7 pages, 3 figure

Ductility and Ultimate Capacity of Concrete-Filled Lattice Rectangular Steel Tube Columns

A kind of concrete-filled lattice rectangular steel tube (CFLRST) column was put forward. The numerical simulation was modeled to analyze the mechanical characteristic of CFLRST column. By comparing the load-deformation curves from the test results, the rationality and reliability of the finite element model has been confirmed, moreover, the change of the section stiffness and stress in the forcing process and the ultimate bearing capacity of the column were analyzed. Based on the model, the comparison of ultimate bearing capacity and ductility between CFLRST column and reinforced concrete (RC) column were also analyzed. The results of the finite element analysis show that the loading process of CFLRST column consists of elastic stage, yield stage and failure stage. The failure modes are mainly strength failure and failure of elasto-plastic instability. CFLRST column has higher bearing capacities in comparison with reinforced concrete columns with the same steel ratio. In addition, the stiffness degeneration of CFLRST column is slower than RC column and CFLRST column has good ductility

Wild2Avatar: Rendering Humans Behind Occlusions

Rendering the visual appearance of moving humans from occluded monocular videos is a challenging task. Most existing research renders 3D humans under ideal conditions, requiring a clear and unobstructed scene. Those methods cannot be used to render humans in real-world scenes where obstacles may block the camera's view and lead to partial occlusions. In this work, we present Wild2Avatar, a neural rendering approach catered for occluded in-the-wild monocular videos. We propose occlusion-aware scene parameterization for decoupling the scene into three parts - occlusion, human, and background. Additionally, extensive objective functions are designed to help enforce the decoupling of the human from both the occlusion and the background and to ensure the completeness of the human model. We verify the effectiveness of our approach with experiments on in-the-wild videos

A survey on fairness of large language models in e-commerce: progress, application, and challenge

This survey explores the fairness of large language models (LLMs) in e-commerce, examining their progress, applications, and the challenges they face. LLMs have become pivotal in the e-commerce domain, offering innovative solutions and enhancing customer experiences. This work presents a comprehensive survey on the applications and challenges of LLMs in e-commerce. The paper begins by introducing the key principles underlying the use of LLMs in e-commerce, detailing the processes of pretraining, fine-tuning, and prompting that tailor these models to specific needs. It then explores the varied applications of LLMs in e-commerce, including product reviews, where they synthesize and analyze customer feedback; product recommendations, where they leverage consumer data to suggest relevant items; product information translation, enhancing global accessibility; and product question and answer sections, where they automate customer support. The paper critically addresses the fairness challenges in e-commerce, highlighting how biases in training data and algorithms can lead to unfair outcomes, such as reinforcing stereotypes or discriminating against certain groups. These issues not only undermine consumer trust, but also raise ethical and legal concerns. Finally, the work outlines future research directions, emphasizing the need for more equitable and transparent LLMs in e-commerce. It advocates for ongoing efforts to mitigate biases and improve the fairness of these systems, ensuring they serve diverse global markets effectively and ethically. Through this comprehensive analysis, the survey provides a holistic view of the current landscape of LLMs in e-commerce, offering insights into their potential and limitations, and guiding future endeavors in creating fairer and more inclusive e-commerce environments.Comment: 21 pages, 9 figure

On the Nonlinear Vibrational Responses of a Large Vessel with a Broad Bow Flare under Wave Excitation: Theory and Experiment

A fully coupled nonlinear three-dimensional (3D) hydroelastic method is developed to investigate vibrational responses of a large ship with a pronounced bow flare subjected to high seas. This numerical model consists of a 3D boundary element method, 1D Euler-Bernoulli beam model, and a 2D generalized Wagner model. Green water loads were considered. Experimental study was carried out in a towing tank on a self-propelled segmented model with nonuniform steel backbones. The ship model was tested in regular incident waves of large amplitude. Impact pressure and nonlinear vertical bending moments were measured and compared with numerical predictions. The proposed nonlinear model produced similar results to the experimental model. Furthermore, the effects of elastic modes and nonlinearities on the numerical results were analyzed