Zero-Shot 3D Drug Design by Sketching and Generating

Drug design is a crucial step in the drug discovery cycle. Recently, various deep learning-based methods design drugs by generating novel molecules from scratch, avoiding traversing large-scale drug libraries. However, they depend on scarce experimental data or time-consuming docking simulation, leading to overfitting issues with limited training data and slow generation speed. In this study, we propose the zero-shot drug design method DESERT (Drug dEsign by SkEtching and geneRaTing). Specifically, DESERT splits the design process into two stages: sketching and generating, and bridges them with the molecular shape. The two-stage fashion enables our method to utilize the large-scale molecular database to reduce the need for experimental data and docking simulation. Experiments show that DESERT achieves a new state-of-the-art at a fast speed.Comment: NeurIPS 2022 camera-read

Application of Radiant Floor Heating in Large Space Buildings with Significant Cold Air Infiltration through Door Openings

Radiant Floor Heating System (RFHS) has been commonly used in railway stations in cold regions of China for its advantages in thermal comfort and energy efficiency. However, the uneven distribution and extremely cold area of the heating floor, caused by cold air infiltration through door openings, are commonly found in our filed measurements. This impact is not considered in the standardized design methods, resulting in an underestimation of the design heat flux. In this paper, CFD simulations are used to quantify the impacts of natural infiltration on surface heat transfer process. Model validation was made against field measurements. 13 simulations were performed for different speeds. As a result, the mean radiant heat flux at floor surface decreased by 36.8% as the infiltration air speed increased from 0.05 m/s to 1.2 m/s, and the noneffective area increased more than 16 times. This result highlights a significant influence of natural infiltration. Regression models were finally developed as a simple method for rough estimation of this impact on radiation, which can make up the limitations of current methods and inform designers to improve their initial design of RFHS when natural infiltration is present

CAG: A Real-time Low-cost Enhanced-robustness High-transferability Content-aware Adversarial Attack Generator

Deep neural networks (DNNs) are vulnerable to adversarial attack despite their tremendous success in many AI fields. Adversarial attack is a method that causes the intended misclassfication by adding imperceptible perturbations to legitimate inputs. Researchers have developed numerous types of adversarial attack methods. However, from the perspective of practical deployment, these methods suffer from several drawbacks such as long attack generating time, high memory cost, insufficient robustness and low transferability. We propose a Content-aware Adversarial Attack Generator (CAG) to achieve real-time, low-cost, enhanced-robustness and high-transferability adversarial attack. First, as a type of generative model-based attack, CAG shows significant speedup (at least 500 times) in generating adversarial examples compared to the state-of-the-art attacks such as PGD and C\&W. CAG only needs a single generative model to perform targeted attack to any targeted class. Because CAG encodes the label information into a trainable embedding layer, it differs from prior generative model-based adversarial attacks that use $n$ different copies of generative models for $n$ different targeted classes. As a result, CAG significantly reduces the required memory cost for generating adversarial examples. CAG can generate adversarial perturbations that focus on the critical areas of input by integrating the class activation maps information in the training process, and hence improve the robustness of CAG attack against the state-of-art adversarial defenses. In addition, CAG exhibits high transferability across different DNN classifier models in black-box attack scenario by introducing random dropout in the process of generating perturbations. Extensive experiments on different datasets and DNN models have verified the real-time, low-cost, enhanced-robustness, and high-transferability benefits of CAG

Towards Long-Tailed Recognition for Graph Classification via Collaborative Experts

Graph classification, aiming at learning the graph-level representations for effective class assignments, has received outstanding achievements, which heavily relies on high-quality datasets that have balanced class distribution. In fact, most real-world graph data naturally presents a long-tailed form, where the head classes occupy much more samples than the tail classes, it thus is essential to study the graph-level classification over long-tailed data while still remaining largely unexplored. However, most existing long-tailed learning methods in visions fail to jointly optimize the representation learning and classifier training, as well as neglect the mining of the hard-to-classify classes. Directly applying existing methods to graphs may lead to sub-optimal performance, since the model trained on graphs would be more sensitive to the long-tailed distribution due to the complex topological characteristics. Hence, in this paper, we propose a novel long-tailed graph-level classification framework via Collaborative Multi-expert Learning (CoMe) to tackle the problem. To equilibrate the contributions of head and tail classes, we first develop balanced contrastive learning from the view of representation learning, and then design an individual-expert classifier training based on hard class mining. In addition, we execute gated fusion and disentangled knowledge distillation among the multiple experts to promote the collaboration in a multi-expert framework. Comprehensive experiments are performed on seven widely-used benchmark datasets to demonstrate the superiority of our method CoMe over state-of-the-art baselines.Comment: Accepted by IEEE Transactions on Big Data (TBD 2024

Wearable Knee Assistive Devices for Kneeling Tasks in Construction

Construction workers regularly perform tasks that require kneeling, crawling, and squatting. Working in awkward kneeling postures for prolonged time periods can lead to knee pain, injuries, and osteoarthritis. In this paper, we present lightweight, wearable sensing and knee assistive devices for construction workers during kneeling and squatting tasks. Analysis of kneeling on level and slopped surfaces (0, 10, 20 degs) is performed for single- and double-leg kneeling tasks. Measurements from the integrated inertial measurement units are used for real-time gait detection and lower-limb pose estimation. Detected gait events and pose estimation are used to control the assistive knee-joint torque provided by lightweight exoskeletons with powerful quasi-direct drive actuation. Human subject experiments are conducted to validate the effectiveness of the proposed analysis and control design. The results show reduction in knee extension/flexion muscle activation (up to 39%) during stand-to-kneel and kneel-to-stand tasks. Knee-ground contact forces/pressures are also reduced (up to 15%) under robotic assistance during single-leg kneeling. Increasing assistive knee torque shows redistribution of the subject’s weight from the knee in contact with the ground to both supporting feet. The proposed system provides an enabling tool to potentially reduce musculoskeletal injury risks of construction workers

Association of Lifestyle Factors with Multimorbidity Risk in China: A National Representative Study

Multimorbidity significantly impacts health, well-being, and the economy; therefore, exploring notable factors associated with multimorbidity across all age groups is critical. For this investigation, we focused on the relationship between four lifestyle factors and multimorbidity risk. We recruited 11,031 Chinese citizens aged ≥ 12 years from 31 provinces between July 2021 and September 2021 using a quota sampling strategy to ensure that the socioeconomic characteristics (sex, age, rural–urban distribution) of those participating in this research were representative of national demographics. In the first stage, multivariable logistic regression models were utilized as a means of investigating the relationship between lifestyle factors and multimorbidity. Then, a multinomial logistic regression model was used with the aim of examining the Healthy Lifestyle Profile (HLP) related to the number of chronic diseases. Multivariable logistic regression models assessed the interaction effects and joint association among the four lifestyle factors. Overall, 18% of the participants had at least one disease, and 5.9% had multimorbidity. Approximately two-thirds of the participants were physically inactive, 40% had consumed alcohol, 39% were underweight or overweight, and 20% were or had been smokers. Participants who maintained one HLP showed a 34% lower multimorbidity risk (adjusted OR, 0.66; 95% CI, 0.48 to 0.92), while participants who maintained 4 HLP showed a 73% lower multimorbidity risk (adjusted OR, 0.27; 95% CI, 0.17 to 0.43), as compared to those who had 0 HLP. The joint association analysis revealed that participants with all four healthy lifestyle factors had 0.92 times lower odds of multimorbidity (95% CI: 0.90, 0.94) in comparison with the all-unhealthy reference cluster. Notably, individuals with a combination of healthy smoking status and healthy body weight had the highest minimized odds of multimorbidity (OR: [0.92], 95% CI: 0.91, 0.94). Common lifestyle habits, alone or in combination, are associated with multimorbidity risk. This study provides insights for public health programs to promote a healthy lifestyle at a younger age and to alleviate multimorbidity risk in older people