Graph Masked Autoencoder for Sequential Recommendation

While some powerful neural network architectures (e.g., Transformer, Graph Neural Networks) have achieved improved performance in sequential recommendation with high-order item dependency modeling, they may suffer from poor representation capability in label scarcity scenarios. To address the issue of insufficient labels, Contrastive Learning (CL) has attracted much attention in recent methods to perform data augmentation through embedding contrasting for self-supervision. However, due to the hand-crafted property of their contrastive view generation strategies, existing CL-enhanced models i) can hardly yield consistent performance on diverse sequential recommendation tasks; ii) may not be immune to user behavior data noise. In light of this, we propose a simple yet effective Graph Masked AutoEncoder-enhanced sequential Recommender system (MAERec) that adaptively and dynamically distills global item transitional information for self-supervised augmentation. It naturally avoids the above issue of heavy reliance on constructing high-quality embedding contrastive views. Instead, an adaptive data reconstruction paradigm is designed to be integrated with the long-range item dependency modeling, for informative augmentation in sequential recommendation. Extensive experiments demonstrate that our method significantly outperforms state-of-the-art baseline models and can learn more accurate representations against data noise and sparsity. Our implemented model code is available at https://github.com/HKUDS/MAERec.Comment: This paper has been published as a full paper at SIGIR 202

Graph Transformer for Recommendation

This paper presents a novel approach to representation learning in recommender systems by integrating generative self-supervised learning with graph transformer architecture. We highlight the importance of high-quality data augmentation with relevant self-supervised pretext tasks for improving performance. Towards this end, we propose a new approach that automates the self-supervision augmentation process through a rationale-aware generative SSL that distills informative user-item interaction patterns. The proposed recommender with Graph TransFormer (GFormer) that offers parameterized collaborative rationale discovery for selective augmentation while preserving global-aware user-item relationships. In GFormer, we allow the rationale-aware SSL to inspire graph collaborative filtering with task-adaptive invariant rationalization in graph transformer. The experimental results reveal that our GFormer has the capability to consistently improve the performance over baselines on different datasets. Several in-depth experiments further investigate the invariant rationale-aware augmentation from various aspects. The source code for this work is publicly available at: https://github.com/HKUDS/GFormer.Comment: Accepted by SIGIR'202

Experimental and numerical studies on indoor thermal comfort in fluid flow: a case study on primary school classrooms

Indoor thermal comfort in primary classrooms is important to students' learning and health. The studies focusing on it, especially under the subtropical plateau monsoon climate, are scarce. In this study, the indoor thermal comfort surveys and parameter measurements were made over the period from October 2018 to December 2018 in Kunming, China. A series of indoor thermal comfort and outdoor parameters were measured each 1 h and subjective questionnaire surveys were performed on the selected 20 students every week except on holidays. A series of three-dimensional numerical simulations were carried out using ANSYS Fluent

INTERMITTENT HYPOXIA-INDUCED RENAL ANTIOXIDANTS AND OXIDA- TIVE DAMAGE IN MALE MICE: HORMETIC DOSE RESPONSE

Obstructive sleep apnea causes cardiovascular disease via chronic intermittent hypoxia (IH), which may be related to oxidative stress. Nuclear factor-erythroid 2-related factor 2 (Nrf2) is an important cellular defense mechanism against oxidative stress by regulating its down-stream multiple antioxidants. The present study was to define whether IH can induce renal pathogenic damage and if so, whether Nrf2 and its down-stream antioxidants are involved in IH-induced pathogenic changes. Mice were culled for exposure to inter- mittent air as control or IH that consisted of 20.9% O2/ 8% O2 FIO2 alternation cycles (30 episodes per h) with 20 seconds at the nadir FIO2 for 12 h a day during daylight. Short term IH exposure (3 – 7 days) induced significant increases in renal inflammatory response and antioxidant levels along with a reduction of the spontaneous content of mal- ondialdehyde while long-term IH exposure (8 weeks) induced a significant decrease of antioxidant levels and significant increases of renal inflammation, oxidative damage, cell death, and fibrosis. This study suggests that IH induces a hormetic response, i.e.: short term IH exposure is able to induce a protective response to protect the kidney from oxidative damage while long-term IH exposure is able to induce a damage effect on the kidney

Molecular simulation study on hydration of low-rank coal particles and formation of hydration film

Water molecules in low-rank coal (LRC) significantly influence its upgrading and utilization. To investigate the hydration of LRC particles and the formation of a hydration film, molecular simulation techniques were innovatively used, including molecular dynamics (MD) simulations and density functional theory (DFT) calculations. The adsorption of water molecules on LRC and various oxygen-containing groups was analyzed. The results show that water molecules adsorb close to the LRC surface and form a large overlapping layer at the LRC/water interface. The radial distribution functions (RDFs) show that the adsorption affinity of water molecules on oxygen-containing sites is stronger than that on carbon-containing sites, and the RDF peaks indicate the existence of a hydration film. Moreover, the differences in adsorption between various oxygen-containing groups depend on both the number of hydrogen bonds and the adsorption distances. The calculated binding energies indicate that the adsorption capacity follows the order carboxyl > phenolic hydroxyl > alcoholic hydroxyl > ether linkage > carbonyl. Experimental results show that a high sorption rate exists between water vapor and LRC samples at the beginning of sorption, which verified the simulation results

Machine Learning-Based Stealing Attack of the Temperature Monitoring System for the Energy Internet of Things

With the development of the Energy Internet of Things (EIoT), it is of great practical significance to study the security strategy and intelligent control system for solar thermal utilization system to optimize the operation efficiency and carry out intelligent dynamic adjustment. For buildings integrated with solar water heating systems, computational fluid dynamics simulation was used in analyzing the process of solar energy output. A method based on machine learning is proposed to predict energy conversion. Besides, the simulation and analysis are carried out in combination with the possible safety problems such as the vibration of the control system. This paper proposed a novel platform of EIoT for machine learning-based cybersecurity study and implemented the platform for the temperature monitoring system. After the evaluation of the machine learning-based cybersecurity study, the EIoT system demonstrated a high performance with the Extreme Gradient Boosting (XGBoost) training algorithm

A Metalearning-based Sparse Aperture ISAR Imaging Method

Sparse Aperture-Inverse Synthetic Aperture Radar (SA-ISAR) imaging methods aim to reconstruct high-quality ISAR images from the corresponding incomplete ISAR echoes. The existing SA-ISAR imaging methods can be roughly divided into two categories: model-based and deep learning-based methods. Model-based SA-ISAR methods comprise physical ISAR imaging models based on explicit mathematical formulations. However, due to the high nonconvexity and ill-posedness of the SA-ISAR problem, model-based methods are often ineffective compared with deep learning-based methods. Meanwhile, the performance of the existing deep learning-based methods depends on the quality and quantity of the training data, which are neither sufficient nor precisely labeled in space target SA-ISAR imaging tasks. To address these issues, we propose a metalearning-based SA-ISAR imaging method for space target ISAR imaging tasks. The proposed method comprises two primary modules: the learning-aided alternating minimization module and the metalearning-based optimization module. The learning-aided alternating minimization module retains the explicit ISAR imaging formulations, guaranteeing physical interpretability without data dependency. The metalearning-based optimization module incorporates a non-greedy strategy to enhance convergence performance, ensuring the ability to escape from poor local modes during optimization. Extensive experiments validate that the proposed algorithm demonstrates superior performance, excellent generalization capability, and high efficiency, despite the lack of prior training or access to labeled training samples, compared to existing methods