Meta-optimized Joint Generative and Contrastive Learning for Sequential Recommendation

Sequential Recommendation (SR) has received increasing attention due to its ability to capture user dynamic preferences. Recently, Contrastive Learning (CL) provides an effective approach for sequential recommendation by learning invariance from different views of an input. However, most existing data or model augmentation methods may destroy semantic sequential interaction characteristics and often rely on the hand-crafted property of their contrastive view-generation strategies. In this paper, we propose a Meta-optimized Seq2Seq Generator and Contrastive Learning (Meta-SGCL) for sequential recommendation, which applies the meta-optimized two-step training strategy to adaptive generate contrastive views. Specifically, Meta-SGCL first introduces a simple yet effective augmentation method called Sequence-to-Sequence (Seq2Seq) generator, which treats the Variational AutoEncoders (VAE) as the view generator and can constitute contrastive views while preserving the original sequence's semantics. Next, the model employs a meta-optimized two-step training strategy, which aims to adaptively generate contrastive views without relying on manually designed view-generation techniques. Finally, we evaluate our proposed method Meta-SGCL using three public real-world datasets. Compared with the state-of-the-art methods, our experimental results demonstrate the effectiveness of our model and the code is available

Investigation of TiO2 thin film deposited by microwave plasma assisted sputtering and its application in 3D glasses

TiO2 deposition using separate regions for sputtering and oxidation is not well investigated. We optimized process parameter for such as oxygen flow and microwave power to produce high quality TiO2 filters for Stereo/3D imaging applications. This deposition technique was chosen for its unique advantages: high deposition rates while increasing the probability of obtaining stoichiometric oxides, reduces possibility of target poisoning and provides better stability of process. Various characterization methods, such as scanning electron microscopy (SEM), atomic force microscopy (AFM), Raman, X-ray diffraction (XRD), transmission spectroscopy, were used in compliment to simulations for detailed analysis of deposited TiO2 thin films. Process parameters were optimized to achieve TiO2 films with low surface scattering and absorption for fabricating multi-passbands interference filter for 3D glasses. From observations and quantitative analysis of surfaces, it was seen that surface roughness increases while oxygen flow or microwave power increases. As the content of anatase phase also increases with higher microwave power and higher oxygen flow, the formation of anatase grains can cause higher surface roughness. Optical analysis of samples validates these trends and provided additional information for absorption trends. Optimized parameters for deposition process are then obtained and the final fabricated 3D glasses filters showed high match to design, within 0.5% range for thickness error

Association between H-RAS T81C genetic polymorphism and gastrointestinal cancer risk: A population based case-control study in China

<p>Abstract</p> <p>Background</p> <p>Gastrointestinal cancer, such as gastric, colon and rectal cancer, is a major medical and economic burden worldwide. However, the exact mechanism of gastrointestinal cancer development still remains unclear. <it>RAS </it>genes have been elucidated as major participants in the development and progression of a series of human tumours and the single nucleotide polymorphism at <it>H-RAS </it>cDNA position 81 was demonstrated to contribute to the risks of bladder, oral and thyroid carcinoma. Therefore, we hypothesized that this polymorphisms in <it>H-RAS </it>could influence susceptibility to gastrointestinal cancer as well, and we conducted this study to test the hypothesis in Chinese population.</p> <p>Methods</p> <p>A population based case-control study, including 296 cases with gastrointestinal cancer and 448 healthy controls selected from a Chinese population was conducted. <it>H-RAS </it>T81C polymorphism was genotyped by Polymerase Chain Reaction-Restriction Fragment Length Polymorphism (PCR-RFLP) assay.</p> <p>Results</p> <p>In the healthy controls, the TT, TC and CC genotypes frequencies of <it>H-RAS </it>T81C polymorphism, were 79.24%, 19.87% and 0.89%, respectively, and the C allele frequency was 10.83%. Compared with TT genotype, the TC genotype was significantly associated with an increased risk of gastric cancer (adjusted OR = 3.67, 95%CI = 2.21–6.08), while the CC genotype showed an increased risk as well (adjusted OR = 3.29, 95%CI = 0.54–19.86), but it was not statistically significant. In contrast, the frequency of TC genotype was not significantly increased in colon cancer and rectal cancer patients. Further analysis was performed by combining TC and CC genotypes compared against TT genotype. As a result, a statistically significant risk with adjusted OR of 3.65 (95%CI, 2.22–6.00) was found in gastric cancer, while no significant association of <it>H-RAS </it>T81C polymorphism with colon cancer and rectal cancer was observed.</p> <p>Conclusion</p> <p>These findings indicate, for the first time, that there is an <it>H-RAS </it>T81C polymorphism existing in Chinese population, and this SNP might be a low penetrance gene predisposition factor for gastric cancer.</p

The Online Path Planning Method of UAV Autonomous Inspection in Distribution Network

In this paper, the problem of online path planning for autonomous inspection of distribution network lines by UAV is studied. Because the distribution lines are mostly distributed around cities, counties and mountainous areas, the lines and their surrounding environment are uncertain and dynamic. These factors will affect the safety of UAV inspection, making the off-line pre-planned path for UAV unavailable. This paper designs an improved iteration random tree algorithm (IRRT) algorithm, which can quickly plan the path of UAV in dynamic environment

Optimal Layout of Electric Vehicle Charging Station Locations Considering Dynamic Charging Demand

This paper proposes an optimization method for electric vehicle charging station locations considering dynamic charging demand. Firstly, the driving characteristics and charging characteristics of the electric vehicle are obtained based on the driving trajectory of the electric vehicle, and the charging demand is predicted using a Monte Carlo simulation. Then a mathematical model with the goal of minimizing the overall cost is constructed, and the impact on carbon emissions is considered in the model. In order to better solve the location model, an improved whale optimization algorithm based on a hybrid strategy is proposed. Finally, the location problem of Shenzhen electric taxi charging stations is analyzed as an example. The results show that when the number of charging stations is set to 19, the comprehensive cost is the smallest and the energy saving and emission reduction effect is good. The improved whale optimization algorithm also has higher solution accuracy and convergence speed than other classical algorithms

Machine Unlearning by Reversing the Continual Learning

Recent legislations, such as the European General Data Protection Regulation (GDPR), require user data holders to guarantee the individual’s right to be forgotten. This means that user data holders must completely delete user data upon request. However, in the field of machine learning, it is not possible to simply remove these data from the back-end database wherein the training dataset is stored, because the machine learning model still retains this data information. Retraining the model using a dataset with these data removed can overcome this problem; however, this can lead to expensive computational overheads. In order to remedy this shortcoming, we propose two effective methods to help model owners or data holders remove private data from a trained model. The first method uses an elastic weight consolidation (EWC) constraint term and a modified loss function to neutralize the data to be removed. The second method approximates the posterior distribution of the model as a Gaussian distribution, and the model after unlearning is computed by decreasingly matching the moment (DMM) of the posterior distribution of the neural network trained on all data and the data to be removed. Finally, we conducted experiments on three standard datasets using backdoor attacks as the evaluation metric. The results show that both methods are effective in removing backdoor triggers in deep learning models. Specifically, EWC can reduce the success rate of backdoor attacks to 0. IMM can ensure that the model prediction accuracy is higher than 80% and keep the success rate of backdoor attacks below 10%

The Mechanism of Transcription Factor Swi6 in Regulating Growth and Pathogenicity of <i>Ceratocystis fimbriata</i>: Insights from Non-Targeted Metabolomics

Ceratocystis fimbriata (C. fimbriata) is a notorious pathogenic fungus that causes sweet potato black rot disease. The APSES transcription factor Swi6 in fungi is located downstream of the cell wall integrity (CWI)-mitogen-activated protein kinase (MAPK) signaling pathway and has been identified to be involved in cell wall integrity and virulence in several filamentous pathogenic fungi. However, the specific mechanisms by which Swi6 regulates the growth and pathogenicity of plant pathogenic fungi remain elusive. In this study, the SWI6 deletion mutants and complemented strains of C. fimbriata were generated. Deletion of Swi6 in C. fimbriata resulted in aberrant growth patterns. Pathogenicity assays on sweet potato storage roots revealed a significant decrease in virulence in the mutant. Non-targeted metabolomic analysis using LC-MS identified a total of 692 potential differentially accumulated metabolites (PDAMs) in the ∆Cfswi6 mutant compared to the wild type, and the results of KEGG enrichment analysis demonstrated significant enrichment of PDAMs within various metabolic pathways, including amino acid metabolism, lipid metabolism, nucleotide metabolism, GPI-anchored protein synthesis, and ABC transporter metabolism. These metabolic pathways were believed to play a crucial role in mediating the growth and pathogenicity of C. fimbriata through the regulation of CWI. Firstly, the deletion of the SWI6 gene led to abnormal amino acid and lipid metabolism, potentially exacerbating energy storage imbalance. Secondly, significant enrichment of metabolites related to GPI-anchored protein biosynthesis implied compromised cell wall integrity. Lastly, disruption of ABC transport protein metabolism may hinder intracellular transmembrane transport. Importantly, this study represents the first investigation into the potential regulatory mechanisms of SWI6 in plant filamentous pathogenic fungi from a metabolic perspective. The findings provide novel insights into the role of SWI6 in the growth and virulence of C. fimbriata, highlighting its potential as a target for controlling this pathogen

Top-down estimate of China's black carbon emissions using surface observations: Sensitivity to observation representativeness and transport model error

This study examines the sensitivity of “top-down” quantification of Chinese black carbon (BC) emissions to the temporal resolution of surface observations and to the transport model error associated with the grid resolution and wet deposition. At two rural sites (Miyun in North China Plain and Chongming in Yangtze River Delta), the model-inferred emission bias based on hourly BC observations can differ by up to 41% from that based on monthly mean observations. This difference relates to the intrinsic inability of the grid-based model in simulating high pollution plumes, which often exert a larger influence on the arithmetic mean of observations at monthly time steps. Adopting the variation of BC to carbon monoxide correlation slope with precipitation as a suitable measure to evaluate the model's wet deposition, we found that wet removal of BC in the model was too weak, due in part to the model's underestimation of large precipitation events. After filtering out the observations during high pollution plumes and large precipitation events for which the transport model error should not be translated into the emission error, the inferred emission bias changed from −11% (without filtering) to −2% (with filtering) at the Miyun site, and from −22% to +1% at the Chongming site. Using surface BC observations from three more rural sites (located in Northeast, Central, and Central South China, respectively) as constraints, our top-down estimate of total BC emissions over China was 1.80 ± 0.65 Tg/yr in 2006, 0.5% lower than the bottom-up inventory of Zhang et al. (2009) but with smaller uncertainty.Engineering and Applied Science

Clinicopathological Characteristics and Prognostic Profiles of Breast Carcinoma with Neuroendocrine Features

Background: Breast carcinoma with neuroendocrine features includes neuroendocrine neoplasm of the breast and invasive breast cancer with neuroendocrine differentiation. This study aimed to investigate the clinicopathological features and prognosis of this disease according to the fifth edition of the World Health Organization classification of breast tumors. Materials and Methods: A total of 87 patients with breast carcinoma with neuroendocrine features treated in the First Medical Center, Chinese PLA General Hospital from January 2001 to January 2022 were retrospectively enrolled in this study. Results: More than half of the patients were postmenopausal patients, especially those with neuroendocrine neoplasm (62.96%). There were more patients with human epidermal growth factor receptor 2 negative and hormone receptor positive tumors, and most of them were Luminal B type (71.26%). The multivariate analysis showed that diabetes and stage IV disease were related to the progression-free survival of breast carcinoma with neuroendocrine features patients (p = 0.004 and p < 0.001, respectively). Conclusion: Breast carcinoma with neuroendocrine features tended to be human epidermal growth factor receptor 2 negative and hormone receptor positive tumors, most of them were Luminal B type, and the related factors of progression-free survival were diabetes and stage IV disease