Linear Context Transform Block

Squeeze-and-Excitation (SE) block presents a channel attention mechanism for modeling global context via explicitly capturing dependencies across channels. However, we are still far from understanding how the SE block works. In this work, we first revisit the SE block, and then present a detailed empirical study of the relationship between global context and attention distribution, based on which we propose a simple yet effective module, called Linear Context Transform (LCT) block. We divide all channels into different groups and normalize the globally aggregated context features within each channel group, reducing the disturbance from irrelevant channels. Through linear transform of the normalized context features, we model global context for each channel independently. The LCT block is extremely lightweight and easy to be plugged into different backbone models while with negligible parameters and computational burden increase. Extensive experiments show that the LCT block outperforms the SE block in image classification task on the ImageNet and object detection/segmentation on the COCO dataset with different backbone models. Moreover, LCT yields consistent performance gains over existing state-of-the-art detection architectures, e.g., 1.5$\sim$1.7% AP$^{bbox}$ and 1.0$\sim$1.2% AP$^{mask}$ improvements on the COCO benchmark, irrespective of different baseline models of varied capacities. We hope our simple yet effective approach will shed some light on future research of attention-based models.Comment: AAAI-2020 accepte

On file-based content distribution over wireless networks via multiple paths: Coding and delay trade-off

With the emergence of the adaptive bit rate (ABR) streaming technology, the video/content streaming technology is shifting toward a file-based content distribution. That is, video content is encoded into a set of smaller media files containing video of 2-10 seconds before transmission. This file-based content distribution, coupled with increasingly rapid adoption of smartphones, requires an efficient file-based distribution algorithm to satisfy the QoS demand in wireless networks. In this paper, we study the transmission of a finite-sized file over wireless networks using multipath routing, with the objective to minimize file transmission delay instead of average packet delay. The file transmission delay is defined as the time interval from the instant that a file is first transmitted to the time at which the file can be reconstructed in the destination node. We observe that file transmission delay depends not only on the mean of the packet delay but also on its distribution, especially the tail. This observation leads to a better understanding of the file transfer delay in wireless networks and a minimum delay file transmission strategy. In a wireless multipath communication scenario, we propose to use packet level erasure code (e.g., digital fountain code) to transmit data file with redundancy. Given that a file with k packets is encoded into n packets for transmission, the use of digital fountain code allows the file to be received when only k out of n packets are received. By adding redundant packets, the destination node does not have to wait for the packet to arrive late, hence reducing the delay of the file transmission. We characterize the tradeoff between the code rate (i.e., the ratio of the number of transmitted packets to the number of the original packets) and the file delay reduction. As a rule of thumb, we provide practical guidelines in determining an appropriate code rate for a fixed file to achieve a reasonable transmission delay. We show that only- - a few redundant packets are needed to achieve a significant reduction in file transmission delay

A Regularized Opponent Model with Maximum Entropy Objective

In a single-agent setting, reinforcement learning (RL) tasks can be cast into an inference problem by introducing a binary random variable o, which stands for the "optimality". In this paper, we redefine the binary random variable o in multi-agent setting and formalize multi-agent reinforcement learning (MARL) as probabilistic inference. We derive a variational lower bound of the likelihood of achieving the optimality and name it as Regularized Opponent Model with Maximum Entropy Objective (ROMMEO). From ROMMEO, we present a novel perspective on opponent modeling and show how it can improve the performance of training agents theoretically and empirically in cooperative games. To optimize ROMMEO, we first introduce a tabular Q-iteration method ROMMEO-Q with proof of convergence. We extend the exact algorithm to complex environments by proposing an approximate version, ROMMEO-AC. We evaluate these two algorithms on the challenging iterated matrix game and differential game respectively and show that they can outperform strong MARL baselines.Comment: Accepted to International Joint Conference on Artificial Intelligence (IJCA2019

Understanding and treating suboptimal health status through tourism engagement: An exploratory study of Chinese domestic tourists

Objectives: Suboptimal health status (SHS) is a global public health concern of worldwide academic interest. The topic is popular in the medical sciences, including public health; however, other disciplines have paid little attention to this condition despite aging societies. This study introduced SHS into the tourism literature, a logical connection given the established positive correlation between well-being and tourism engagement. Lifestyle factors are crucial to SHS. Accordingly, this study examines tourists’ sociodemographic characteristics, tourism-related attributes, and lifestyle behaviors to compare individuals with SHS to those with optimal health status. Methods: Chinese tourists in Shandong Province, China who had visited Mount Tai within 6 months of study recruitment (October to December 2021) completed a pen-and-pencil survey to answer questions for this cross-sectional research study. In total, 360 surveys were eligible for analysis following initial screening. Results: The self-report SHS status survey, SHSQ-25, was used to determine the portion of study participants exhibiting symptoms of SHS. The descriptive analysis indicated that 36.4 % (n = 131) of the sample (N = 360) presented with SHS. Three lifestyle behavior factors (i.e., work stress, sleep length, and drinking length), five SHS domains (i.e., fatigue, cardiovascular system, immune system, digestive system, and mental status), and two tourism-related attributes (i.e., international travel frequency and travel expenditure) were integrated using canonical correlation analysis to determine relationships among these three domains. Conclusions: Results from this study demonstrated the meaningful relationships among lifestyle behaviors, tourism-related attributes, and SHS. Previous work has implied that tourism participation may enhance individuals’ health status and well-being; however, conclusions from this study are contradictory to those findings. For travelers with SHS to fully benefit from tourism, more information is needed to develop suitable marketing strategies and tourism products. This study provides a starting point to direct future research to further explore motivations of and strategies to benefit tourists with SHS

Simulation of bubbles

International audienceWe present a novel framework based on a continuous fluid simulator for general simulation of realistic bubbles, with which we can handle as many significant dynamic bubble effects as possible. To capture a very thin liquid film of bubbles, we have developed a regional level set method allowing multi-manifold interface tracking. Based on the definitions of regional distance and its five operators, the implementation of the regional level set method is very easy. An implicit surface of liquid film with arbitrary thickness can be reconstructed from the regional level set function. To overcome the numerical instability problem, we exploit a new semi-implicit surface tension model which is unconditionally stable and makes the simulation of surface tension dominated phenomena much more efficient. An approximated film thickness evolution model is proposed to control the bubble's lifecycle. All these new techniques combine into a general framework that can produce various realistic dynamic effects of bubbles

Visual Simulation of Multiple Unmixable Fluids

International audienceWe present a novel grid-based method for simulating multiple unmixable fluids moving and interacting. Unlike previous methods that can only represent the interface between two fluids (usually between liquid and gas), this method can handle an arbitrary number of fluids through multiple independent level sets coupled with a constrain condition. To capture the fluid surface more accurately, we extend the particle level set method to a multi-fluid version. It shares the advantages of the particle level set method, and has the ability to track the interfaces of multiple fluids. To handle the dynamic behavior of different fluids existing together, we use a multiphase fluid formulation based on a smooth weight function