Effective dynamicsof a coupled microscopic-macroscopic stochastic system

A conceptual model for microscopic-macroscopic slow-fast stochastic systems is considered. A dynamical reduction procedure is presented in order to extract effective dynamics for this kind of systems. Under appropriate assumptions, the effective system is shown to approximate the original system, in the sense of a probabilistic convergence.Comment: 14 page

A Monte Carlo Study of Erraticity Behavior in Nucleus-Nucleus Collisions at High Energies

It is demonstrated using Monte Carlo simulation that in different nucleus$-$nucleus collision samples, the increase of the fluctuation of event factorial moments with decreasing phase space scale, called erraticity, is still dominated by the statistical fluctuations. This result does not depend on the Monte Carlo models. Nor does it depend on the concrete conditions, e.g. the collision energy, the mass of colliding nuclei, the cut of phase space, etc.. This means that the erraticity method is sensitive to the appearance of novel physics in the central collisions of heavy nuclei.Comment: 9 pages, 4 figures (in eps form

MIMOCrypt: Multi-User Privacy-Preserving Wi-Fi Sensing via MIMO Encryption

Wi-Fi signals may help realize low-cost and non-invasive human sensing, yet it can also be exploited by eavesdroppers to capture private information. Very few studies rise to handle this privacy concern so far; they either jam all sensing attempts or rely on sophisticated technologies to support only a single sensing user, rendering them impractical for multi-user scenarios. Moreover, these proposals all fail to exploit Wi-Fi's multiple-in multiple-out (MIMO) capability. To this end, we propose MIMOCrypt, a privacy-preserving Wi-Fi sensing framework to support realistic multi-user scenarios. To thwart unauthorized eavesdropping while retaining the sensing and communication capabilities for legitimate users, MIMOCrypt innovates in exploiting MIMO to physically encrypt Wi-Fi channels, treating the sensed human activities as physical plaintexts. The encryption scheme is further enhanced via an optimization framework, aiming to strike a balance among i) risk of eavesdropping, ii) sensing accuracy, and iii) communication quality, upon securely conveying decryption keys to legitimate users. We implement a prototype of MIMOCrypt on an SDR platform and perform extensive experiments to evaluate its effectiveness in common application scenarios, especially privacy-sensitive human gesture recognition.Comment: IEEE S&P 2024, 19 pages, 22 figures, including meta reviews and response

Differences in the link between social trait judgment and socio-emotional experience in neurotypical and autistic individuals

Neurotypical (NT) individuals and individuals with autism spectrum disorder (ASD) make different judgments of social traits from others\u27 faces; they also exhibit different social emotional responses in social interactions. A common hypothesis is that the differences in face perception in ASD compared with NT is related to distinct social behaviors. To test this hypothesis, we combined a face trait judgment task with a novel interpersonal transgression task that induces measures social emotions and behaviors. ASD and neurotypical participants viewed a large set of naturalistic facial stimuli while judging them on a comprehensive set of social traits (e.g., warm, charismatic, critical). They also completed an interpersonal transgression task where their responsibility in causing an unpleasant outcome to a social partner was manipulated. The purpose of the latter task was to measure participants\u27 emotional (e.g., guilt) and behavioral (e.g., compensation) responses to interpersonal transgression. We found that, compared with neurotypical participants, ASD participants\u27 self-reported guilt and compensation tendency was less sensitive to our responsibility manipulation. Importantly, ASD participants and neurotypical participants showed distinct associations between self-reported guilt and judgments of criticalness from others\u27 faces. These findings reveal a novel link between perception of social traits and social emotional responses in ASD

Food Consumption in China: the revolution continues

Recent decades have seen China's domestic consumption in sectors such as food, housing, health care, education and travel greatly increase. This important book assesses China's current food consumption trends and the outlook for its future needs of such a crucial commodity. Key features of this book include: -A systematic examination of the key elements shaping food consumption, with particular attention to factors peculiar to China; -An evaluation of changes in food consumption between rural and urban residents, the rich and poor, and consumers of different regions and identification of the key drivers behind such changes; -A comprehensive coverage of all major food items including food grains, meats and other animal products, fruits and vegetables, alcoholic drinks, and aquacultural products; and -A projection for China's food import needs by 2020. This book will be of great relevance to anyone who is interested in the dynamics of Chinese food consumption, such as commodity traders, leaders of agri-food industries, food trade officials, and food market researchers. It will also prove a valuable reference for undergraduate and postgraduate students majoring in food marketing and trade, general food and agricultural economics, and scholars studying food consumption issues

Hybrid-learning-based classification and quantitative inference of driver braking intensity of an electrified vehicle

The recognition of driver's braking intensity is of great importance for advanced control and energy management for electric vehicles. In this paper, the braking intensity is classified into three levels based on novel hybrid unsupervised and supervised learning methods. First, instead of selecting threshold for each braking intensity level manually, an unsupervised Gaussian Mixture Model is used to cluster the braking events automatically with brake pressure. Then, a supervised Random Forest model is trained to classify the correct braking intensity levels with the state signals of vehicle and powertrain. To obtain a more efficient classifier, critical features are analyzed and selected. Moreover, beyond the acquisition of discrete braking intensity level, a novel continuous observation method is proposed based on Artificial Neural Networks to quantitative analyze and recognize the brake intensity using the prior determined features of vehicle states. Experimental data are collected in an electric vehicle under real-world driving scenarios. Finally, the classification and regression results of the proposed methods are evaluated and discussed. The results demonstrate the feasibility and accuracy of the proposed hybrid learning methods for braking intensity classification and quantitative recognition with various deceleration scenarios