Learning to Incorporate Texture Saliency Adaptive Attention to Image Cartoonization

Image cartoonization is recently dominated by generative adversarial networks (GANs) from the perspective of unsupervised image-to-image translation, in which an inherent challenge is to precisely capture and sufficiently transfer characteristic cartoon styles (e.g., clear edges, smooth color shading, abstract fine structures, etc.). Existing advanced models try to enhance cartoonization effect by learning to promote edges adversarially, introducing style transfer loss, or learning to align style from multiple representation space. This paper demonstrates that more distinct and vivid cartoonization effect could be easily achieved with only basic adversarial loss. Observing that cartoon style is more evident in cartoon-texture-salient local image regions, we build a region-level adversarial learning branch in parallel with the normal image-level one, which constrains adversarial learning on cartoon-texture-salient local patches for better perceiving and transferring cartoon texture features. To this end, a novel cartoon-texture-saliency-sampler (CTSS) module is proposed to dynamically sample cartoon-texture-salient patches from training data. With extensive experiments, we demonstrate that texture saliency adaptive attention in adversarial learning, as a missing ingredient of related methods in image cartoonization, is of significant importance in facilitating and enhancing image cartoon stylization, especially for high-resolution input pictures.Comment: Proceedings of the 39th International Conference on Machine Learning, PMLR 162:7183-7207, 202

Collaborative Video Analytics on Distributed Edges with Multiagent Deep Reinforcement Learning

Deep Neural Network (DNN) based video analytics empowers many computer vision-based applications to achieve high recognition accuracy. To reduce inference delay and bandwidth cost for video analytics, the DNN models can be deployed on the edge nodes, which are proximal to end users. However, the processing capacity of an edge node is limited, potentially incurring substantial delay if the inference requests on an edge node is overloaded. While efforts have been made to enhance video analytics by optimizing the configurations on a single edge node, we observe that multiple edge nodes can work collaboratively by utilizing the idle resources on each other to improve the overall processing capacity and resource utilization. To this end, we propose a Multiagent Reinforcement Learning (MARL) based approach, named as EdgeVision, for collaborative video analytics on distributed edges. The edge nodes can jointly learn the optimal policies for video preprocessing, model selection, and request dispatching by collaborating with each other to minimize the overall cost. We design an actor-critic-based MARL algorithm with an attention mechanism to learn the optimal policies. We build a multi-edge-node testbed and conduct experiments with real-world datasets to evaluate the performance of our method. The experimental results show our method can improve the overall rewards by 33.6%-86.4% compared with the most competitive baseline methods

EFFECTS OF RUNNING FATIGUE ON KNEE JOINT SYMMETRY AMONG AMATEUR RUNNERS

The purpose of this study was to reveal the effects of running fatigue on the symmetry of lower limb dynamics and kinematics parameters. 18 male amateur runners participated in this study. The marker trajectories and ground reaction forces were collected via an 8-camera VICON and Kistler 3D force platform before and after the running-induced fatigue protocol. Symmetry angles (SA) of joint moments, range of motions (ROM), and joint stiffness in three planes were calculated pre- and post-fatigue. SA of knee Extension Angle, Internal rotation, Abduction moment, coronal ROM and joint stiffness significantly increased after fatigue(

PrefixMol: Target- and Chemistry-aware Molecule Design via Prefix Embedding

Is there a unified model for generating molecules considering different conditions, such as binding pockets and chemical properties? Although target-aware generative models have made significant advances in drug design, they do not consider chemistry conditions and cannot guarantee the desired chemical properties. Unfortunately, merging the target-aware and chemical-aware models into a unified model to meet customized requirements may lead to the problem of negative transfer. Inspired by the success of multi-task learning in the NLP area, we use prefix embeddings to provide a novel generative model that considers both the targeted pocket's circumstances and a variety of chemical properties. All conditional information is represented as learnable features, which the generative model subsequently employs as a contextual prompt. Experiments show that our model exhibits good controllability in both single and multi-conditional molecular generation. The controllability enables us to outperform previous structure-based drug design methods. More interestingly, we open up the attention mechanism and reveal coupling relationships between conditions, providing guidance for multi-conditional molecule generation

PLANTAR FORCE COMPARISONS BETWEEN THE CHASSE STEP AND ONE STEP FOOTWORK DURING TOPSPIN FOREHAND USING STATISTICAL PARAMETRIC MAPPING

The purpose of this study was to investigate the plantar force characteristics of the chasse step and one step footwork during table tennis topspin stroke using one-dimensional statistical parameter mapping (SPM 1d). Twelve national players volunteered to participate in the study. The plantar force of the right foot during the chasse step and one step backward phase (BP) and forward phase (FP) was recorded by instrumented insole systems. Paired sample T tests in SPSS 24.0 (SPSSs Inc, Chicago, IL, USA) were used to analyze peak pressure of each plantar region. For SPM analysis, the plantar force time series curves were marked as a 100% process. A paired-samples T-test in MATLAB was used to analyze differences in plantar force. One step produced a greater plantar force than the chasse step during 6.92-11.22% BP (P=0.039). The chasse step produced a greater plantar force than one step during 53.47-99.01% BP (

Modified p-y curves for monopile foundation with different length-to-diameter ratio

The soil reaction of the monopile foundation subjected to lateral loading in offshore wind turbines is typically assessed relying on p-y curves advocated by API. However, this method is inadequate for gradually increasing monopile diameters and significantly underestimates the lateral soil confinement. In the present works, a 3D pile-soil interaction finite element model was first established, considering the soil suction and strain hardening characteristics for the normally consolidated clay in China’s sea. Modifications to the p-y curves in API were accomplished in the comparative process between the lateral soil resistance-displacement curves retrieved from the finite element model and the representative expression. Furthermore, the prediction accuracy for the corrected p-y curves has been proved by forecasting the monopile lateral bearing capacity with varying length-to-diameter ratios, which also demonstrates that the modified p-y curves could successfully reflect the lateral soil confinement of the normally consolidated clay and flexible piles. It also provides an approach to assess the deformation response and horizontal ultimate bearing capacity of monopiles with different length-to-diameter ratios

2nd Place Winning Solution for the CVPR2023 Visual Anomaly and Novelty Detection Challenge: Multimodal Prompting for Data-centric Anomaly Detection

This technical report introduces the winning solution of the team Segment Any Anomaly for the CVPR2023 Visual Anomaly and Novelty Detection (VAND) challenge. Going beyond uni-modal prompt, e.g., language prompt, we present a novel framework, i.e., Segment Any Anomaly + (SAA$+$), for zero-shot anomaly segmentation with multi-modal prompts for the regularization of cascaded modern foundation models. Inspired by the great zero-shot generalization ability of foundation models like Segment Anything, we first explore their assembly (SAA) to leverage diverse multi-modal prior knowledge for anomaly localization. Subsequently, we further introduce multimodal prompts (SAA$+$) derived from domain expert knowledge and target image context to enable the non-parameter adaptation of foundation models to anomaly segmentation. The proposed SAA$+$ model achieves state-of-the-art performance on several anomaly segmentation benchmarks, including VisA and MVTec-AD, in the zero-shot setting. We will release the code of our winning solution for the CVPR2023 VAN.Comment: The first two author contribute equally. CVPR workshop challenge report. arXiv admin note: substantial text overlap with arXiv:2305.1072

Neighbourhood satisfaction in rural resettlement residential communities: the case of Suqian, China

Against the background of large-scale urbanisation and rural land expropriation, rural resettlement residential housing has been built to accommodate local rural residents in the peripheral areas of China. To explore the context-specific policy implications for improving neighbourhood satisfaction (NS) of residents in rural resettlement residential communities (RRRCs), this paper examines the determinants of NS, and their spatial effects, in rural resettlement residential neighbourhoods using Suqian, in Jiangsu Province, as a case study. This study contributes to the current literature in two ways: it constitutes the first attempt to examine NS among RRRCs; second, our spatial model helps to gain further understanding of horizontal and vertical spatial dependence effects. Our results indicate that income, gender, age, family structure, number of years living in a community, transport and architectural age all have significant effects on NS in RRRCs

Burden of disease resulting from chronic mountain sickness among young Chinese male immigrants in Tibet

BACKGROUND: In young Chinese men of the highland immigrant population, chronic mountain sickness (CMS) is a major public health problem. The aim of this study was to measure the disease burden of CMS in this population. METHODS: We used disability-adjusted life years (DALYs) to estimate the disease burden of CMS. Disability weights were derived using the person trade-off methodology. CMS diagnoses, symptom severity, and individual characteristics were obtained from surveys collected in Tibet in 2009 and 2010. The DALYs of individual patients and the DALYs/1,000 were calculated. RESULTS: Disability weights were obtained for 21 CMS health stages. The results of the analyses of the two surveys were consistent with each other. At different altitudes, the CMS rates ranged from 2.1-37.4%; the individual DALYs of patients ranged from 0.13-0.33, and the DALYs/1,000 ranged from 3.60-52.78. The age, highland service years, blood pressure, heart rate, smoking rate, and proportion of the sample working in engineering or construction were significantly higher in the CMS group than in the non-CMS group (p < 0.05). These variables were also positively associated with the individual DALYs (p < 0.05). Among the symptoms, headaches caused the largest proportion of DALYs. CONCLUSION: The results show that CMS imposes a considerable burden on Chinese immigrants to Tibet. Immigrants with characteristics such as a higher residential altitude, more advanced age, longer highland service years, being a smoker, and working in engineering or construction were more likely to develop CMS and to increase the disease burden. Higher blood pressure and heart rate as a result of CMS were also positively associated with the disease burden. The authorities should pay attention to the highland disease burden and support the development and application of DALYs studies of CMS and other highland diseases