Reducing Communication for Split Learning by Randomized Top-k Sparsification

Split learning is a simple solution for Vertical Federated Learning (VFL), which has drawn substantial attention in both research and application due to its simplicity and efficiency. However, communication efficiency is still a crucial issue for split learning. In this paper, we investigate multiple communication reduction methods for split learning, including cut layer size reduction, top-k sparsification, quantization, and L1 regularization. Through analysis of the cut layer size reduction and top-k sparsification, we further propose randomized top-k sparsification, to make the model generalize and converge better. This is done by selecting top-k elements with a large probability while also having a small probability to select non-top-k elements. Empirical results show that compared with other communication-reduction methods, our proposed randomized top-k sparsification achieves a better model performance under the same compression level.Comment: Accepted by IJCAI 202

PPGenCDR: A Stable and Robust Framework for Privacy-Preserving Cross-Domain Recommendation

Privacy-preserving cross-domain recommendation (PPCDR) refers to preserving the privacy of users when transferring the knowledge from source domain to target domain for better performance, which is vital for the long-term development of recommender systems. Existing work on cross-domain recommendation (CDR) reaches advanced and satisfying recommendation performance, but mostly neglects preserving privacy. To fill this gap, we propose a privacy-preserving generative cross-domain recommendation (PPGenCDR) framework for PPCDR. PPGenCDR includes two main modules, i.e., stable privacy-preserving generator module, and robust cross-domain recommendation module. Specifically, the former isolates data from different domains with a generative adversarial network (GAN) based model, which stably estimates the distribution of private data in the source domain with ́Renyi differential privacy (RDP) technique. Then the latter aims to robustly leverage the perturbed but effective knowledge from the source domain with the raw data in target domain to improve recommendation performance. Three key modules, i.e., (1) selective privacy preserver, (2) GAN stabilizer, and (3) robustness conductor, guarantee the cost-effective trade-off between utility and privacy, the stability of GAN when using RDP, and the robustness of leveraging transferable knowledge accordingly. The extensive empirical studies on Douban and Amazon datasets demonstrate that PPGenCDR significantly outperforms the state-of-the-art recommendation models while preserving privacy

Intra- and Inter-group Optimal Transport for User-Oriented Fairness in Recommender Systems

Recommender systems are typically biased toward a small group of users, leading to severe unfairness in recommendation performance, i.e., User-Oriented Fairness (UOF) issue. Existing research on UOF exhibits notable limitations in two phases of recommendation models. In the training phase, current methods fail to tackle the root cause of the UOF issue, which lies in the unfair training process between advantaged and disadvantaged users. In the evaluation phase, the current UOF metric lacks the ability to comprehensively evaluate varying cases of unfairness. In this paper, we aim to address the aforementioned limitations and ensure recommendation models treat user groups of varying activity levels equally. In the training phase, we propose a novel Intra- and Inter-GrOup Optimal Transport framework (II-GOOT) to alleviate the data sparsity problem for disadvantaged users and narrow the training gap between advantaged and disadvantaged users. In the evaluation phase, we introduce a novel metric called ?-UOF, which enables the identification and assessment of various cases of UOF. This helps prevent recommendation models from leading to unfavorable fairness outcomes, where both advantaged and disadvantaged users experience subpar recommendation performance. We conduct extensive experiments on three real-world datasets based on four backbone recommendation models to prove the effectiveness of ?-UOF and the efficiency of our proposed II-GOOT

Key theoretical and technical issues and countermeasures for effective development of Gulong shale oil, Daqing Oilfield, NE China

Aiming at the four issues of underground storage state, exploitation mechanism, crude oil flow and efficient recovery, the key theoretical and technical issues and countermeasures for effective development of Gulong shale oil are put forward. Through key exploration and research on fluid occurrence, fluid phase change, exploitation mechanism, oil start-up mechanism, flow regime/pattern, exploitation mode and enhanced oil recovery (EOR) of shale reservoirs with different storage spaces, multi-scale occurrence states of shale oil and phase behavior of fluid in nano confined space were provided, the multi-phase, multi-scale flow mode and production mechanism with hydraulic fracture-shale bedding fracture-matrix infiltration as the core was clarified, and a multi-scale flow mathematical model and recoverable reserves evaluation method were preliminarily established. The feasibility of development mode with early energy replenishment and recovery factor of 30% was discussed. Based on these, the researches of key theories and technologies for effective development of Gulong shale oil are proposed to focus on: (1) in-situ sampling and non-destructive testing of core and fluid; (2) high-temperature, high-pressure, nano-scale laboratory simulation experiment; (3) fusion of multi-scale multi-flow regime numerical simulation technology and large-scale application software; (4) waterless (CO2) fracturing technique and the fracturing technique for increasing the vertical fracture height; (5) early energy replenishment to enhance oil recovery; (6) lifecycle technical and economic evaluation. Moreover, a series of exploitation tests should be performed on site as soon as possible to verify the theoretical understanding, optimize the exploitation mode, form supporting technologies, and provide a generalizable development model, thereby supporting and guiding the effective development and production of Gulong shale oil

lncRNA GAS6-AS1 inhibits progression and glucose metabolism reprogramming in LUAD via repressing E2F1-mediated transcription of GLUT1

Glucose metabolism reprogramming is one of the hallmarks of cancer cells, although functional and regulatory mechanisms of long noncoding RNA (lncRNA) in the contribution of glucose metabolism in lung adenocarcinoma (LUAD) remain incompletely understood. The aim of this study was to uncover the role of GAS6-AS1 in the regulation of progression and glucose metabolism in LUAD. We discovered that overexpression of GAS6-AS1 suppressed tumor progression of LUAD both in vitro and in vivo. Metabolism-related assays revealed that GAS6-AS1 inhibited glucose metabolism reprogramming. Mechanically, GAS6-AS1 was found to repress the expression of glucose transporter GLUT1, a key regulator of glucose metabolism. Ectopic expression of GLUT1 restored the inhibition effect of GAS6-AS1 on cancer progression and glucose metabolism reprogramming. Further investigation identified that GAS6-AS1 directly interacted with transcription factor E2F1 and suppressed E2F1-mediated transcription of GLUT1, and GAS6-AS1 was downregulated in LUAD tissues and correlated with clinicopathological characteristics and survival of patients. Taken together, our results identified GAS6-AS1 as a novel tumor suppressor in LUAD and unraveled its underlying molecular mechanism in reprogramming glucose metabolism. GAS6-AS1 potentially may serve as a prognostic marker and therapeutic target in LUAD

Laser Cutting Technologies and Corresponding Pollution Control Strategy

In conjunction with the increasing demand for material cutting, such as the decommissioning and dismantling of nuclear facilities, advanced cutting technologies need be developed to increase precision and cost-effectiveness. As compared with other cutting technologies, laser cutting offers advantages of greater cutting precision, accuracy, and customization. In this work, we investigated the constitution, classification, and current status of this technology. Pollutant emission during laser cutting, corresponding pollution control methods and apparatus were proposed as well. Laser cutting equipment mainly comprises an automated system integrating a fiber laser, industrial computer, servo motor control, electrical control, and detection technology. It mainly consists of mechanical and electrical control parts. Laser cutting equipment is distinguished by light source, power, and cutting dimensions. Known variants of laser cutting technology involve vaporization, fusion, reactive fusion, and controlled fracture cutting. During the cutting process, dust, smoke, and aerosols can be released, which is an environmental concern and poses a threat to public health. The selection of the dedusting method and design of apparatus should take into account the dust removal rate, initial capital cost, maintenance cost, etc. Multi-stage filtration such as bag filtration combined with activated carbon filtration or electrostatic filtration is accepted