A-SFS: Semi-supervised Feature Selection based on Multi-task Self-supervision

Feature selection is an important process in machine learning. It builds an interpretable and robust model by selecting the features that contribute the most to the prediction target. However, most mature feature selection algorithms, including supervised and semi-supervised, fail to fully exploit the complex potential structure between features. We believe that these structures are very important for the feature selection process, especially when labels are lacking and data is noisy. To this end, we innovatively introduce a deep learning-based self-supervised mechanism into feature selection problems, namely batch-Attention-based Self-supervision Feature Selection(A-SFS). Firstly, a multi-task self-supervised autoencoder is designed to uncover the hidden structure among features with the support of two pretext tasks. Guided by the integrated information from the multi-self-supervised learning model, a batch-attention mechanism is designed to generate feature weights according to batch-based feature selection patterns to alleviate the impacts introduced by a handful of noisy data. This method is compared to 14 major strong benchmarks, including LightGBM and XGBoost. Experimental results show that A-SFS achieves the highest accuracy in most datasets. Furthermore, this design significantly reduces the reliance on labels, with only 1/10 labeled data needed to achieve the same performance as those state of art baselines. Results show that A-SFS is also most robust to the noisy and missing data.Comment: 18 pages, 7 figures, accepted by knowledge-based system

A Survey on Causal Reinforcement Learning

While Reinforcement Learning (RL) achieves tremendous success in sequential decision-making problems of many domains, it still faces key challenges of data inefficiency and the lack of interpretability. Interestingly, many researchers have leveraged insights from the causality literature recently, bringing forth flourishing works to unify the merits of causality and address well the challenges from RL. As such, it is of great necessity and significance to collate these Causal Reinforcement Learning (CRL) works, offer a review of CRL methods, and investigate the potential functionality from causality toward RL. In particular, we divide existing CRL approaches into two categories according to whether their causality-based information is given in advance or not. We further analyze each category in terms of the formalization of different models, ranging from the Markov Decision Process (MDP), Partially Observed Markov Decision Process (POMDP), Multi-Arm Bandits (MAB), and Dynamic Treatment Regime (DTR). Moreover, we summarize the evaluation matrices and open sources while we discuss emerging applications, along with promising prospects for the future development of CRL.Comment: 29 pages, 20 figure

Effect of dexmedetomidine on miR-144-3p expression and epithelial mesenchymal transition in gastric cancer cells

Purpose: To investigate the effect of dexmedetomidine (DEX) on epithelial mesenchymal transition (EMT) in gastric cancer cells, and the role of microRNA-144-3p (miR-144-3p) in the process.Methods: The effect of DEX on miRNA expression profile was analyzed using GEO database(https://www.ncbi.nlm.nih.gov/gds/). Human gastric cancer cells were cultured in vitro, and one group of cells was treated with saline for 48 h (control group). Cells treated with DEX at doses of 0.01, 0.1 and 1.0 μmol/L for 48 h were marked as low-, medium- and high-DEX concentration groups. The mRNA expression levels of miR-144-3p, ZEB1, E-cadherin and vimentin were determined using real-time quantitative polymerase chain reaction (RT-PCR), while the protein expressions of ZEB1, E-cadherin and vimentin were assayed with Western blotting. Cell proliferation was determined with CCK-8 assay, while metastasis was measured using Transwell assay.Results: The GEO database demonstrated that the expression of miR-144-3p in rat cardiomyocytes was significantly decreased after DEX treatment (p < 0.05). The expression of miR-144-3p was decreased in all groups, when compared to the control group, but the expressions of ZEB1 and vimentin were increased, while that of E-cadherin was down-regulated (p < 0.05). Cell proliferation in the high-DEX concentration group was decreased (p < 0.05). The degrees of cell invasion and migration were increased in the medium- and high-DEX concentration groups (p < 0.05).Conclusion: DEX promotes the metastasis of gastric cancer cells by regulation of epithelialmesenchymal transition (EMT) and the expression of miR-144-3p. This finding provides a new insight into the treatment of gastric cancer

Learning to Skip for Language Modeling

Overparameterized large-scale language models have impressive generalization performance of in-context few-shot learning. However, most language models allocate the same amount of parameters or computation to each token, disregarding the complexity or importance of the input data. We argue that in language model pretraining, a variable amount of computation should be assigned to different tokens, and this can be efficiently achieved via a simple routing mechanism. Different from conventional early stopping techniques where tokens can early exit at only early layers, we propose a more general method that dynamically skips the execution of a layer (or module) for any input token with a binary router. In our extensive evaluation across 24 NLP tasks, we demonstrate that the proposed method can significantly improve the 1-shot performance compared to other competitive baselines only at mild extra cost for inference

Experimental Study of Granular Clogging in Two-Dimensional Hopper

We experimentally investigate the clogging process of granular materials in a two-dimensional hopper, and present a self-consistent physical mechanism of clogging based on preformed dynamic chain structures in the flow. We found that these chain structures follow a specific modified restricted random walk, and clogging occurs when they are mechanically stable enough to withstand the flow fluctuations, resulting in the formation of an arch at the outlet. We introduce a simple model which can explain the clogging probability by incorporating an analytical expression for chain formation and its transition into an arch. Our results provide insight into the microscopic mechanism of clogging in hopper flow.Comment: 22 pages, 8 figure

Effective inhibition of HCMV UL49 gene expression and viral replication by oligonucleotide external guide sequences and RNase P

Abstract Background Human cytomegalovirus (HCMV) is a ubiquitous herpesvirus that typically causes asymptomatic infections in healthy individuals but may lead to serious complications in newborns and immunodeficient individuals. The emergence of drug-resistant strains of HCMV has posed a need for the development of new drugs and treatment strategies. Antisense molecules are promising gene-targeting agents for specific regulation of gene expression. External guide sequences (EGSs) are oligonucleotides that consist of a sequence complementary to a target mRNA and recruit intracellular RNase P for specific degradation of the target RNA. The UL49-deletion BAC of HCMV was significantly defective in growth in human foreskin fibroblasts. Therefore, UL49 gene may serve as a potential target for novel drug development to combat HCMV infection. In this study, DNA-based EGS molecules were synthesized to target the UL49 mRNA of human cytomegalovirus (HCMV). Results By cleavage activity assessing in vitro, the EGS aimed to the cleavage site 324 nt downstream from the translational initiation codon of UL49 mRNA (i.e. EGS324) was confirmed be efficient to direct human RNase P to cleave the target mRNA sequence. When EGS324 was exogenously administered into HCMV-infected human foreskin fibroblasts (HFFs), a significant reduction of ~76% in the mRNA and ~80% in the protein expression of UL49 gene, comparing with the cells transfected with control EGSs. Furthermore, a reduction of about 330-fold in HCMV growth were observed in HCMV-infected HFFs treated with the EGS. Conclusions These results indicated that UL49 gene was essential for replication of HCMV. Moreover, our study provides evidence that exogenous administration of a DNA-based EGS can be used as a potential therapeutic approach for inhibiting gene expression and replication of a human virus.</p

Learning by Doing: An Online Causal Reinforcement Learning Framework with Causal-Aware Policy

As a key component to intuitive cognition and reasoning solutions in human intelligence, causal knowledge provides great potential for reinforcement learning (RL) agents' interpretability towards decision-making by helping reduce the searching space. However, there is still a considerable gap in discovering and incorporating causality into RL, which hinders the rapid development of causal RL. In this paper, we consider explicitly modeling the generation process of states with the causal graphical model, based on which we augment the policy. We formulate the causal structure updating into the RL interaction process with active intervention learning of the environment. To optimize the derived objective, we propose a framework with theoretical performance guarantees that alternates between two steps: using interventions for causal structure learning during exploration and using the learned causal structure for policy guidance during exploitation. Due to the lack of public benchmarks that allow direct intervention in the state space, we design the root cause localization task in our simulated fault alarm environment and then empirically show the effectiveness and robustness of the proposed method against state-of-the-art baselines. Theoretical analysis shows that our performance improvement attributes to the virtuous cycle of causal-guided policy learning and causal structure learning, which aligns with our experimental results

Trends, Drivers, and Mitigation of CO₂ Emissions in the Guangdong–Hong Kong–Macao Greater Bay Area

The Guangdong–Hong Kong–Macao Greater Bay Area (GBA) is a national initiative aimed at building a world-class city cluster in China and whose trends, socioeconomic drivers of CO2 emissions, and mitigation pathways are of great significance to the high-quality regional economic development. This study compiled the CO2 emission inventories of the GBA from 2000 to 2019 and explored the key drivers of CO2 emissions using the logarithmic mean Divisia index method. The results showed that CO2 emissions in GBA slowed significantly after 2017 and have already been decoupled from gross domestic product (GDP) growth. Economic growth and energy intensity are the major factors driving and inhibiting the increase in GBA's CO2 emissions, respectively. The energy production and heavy manufacturing sectors have reduced their roles in driving the growth of GBA's CO2 emissions. GBA achieved remarkable results in low-carbon development through industrial restructuring and upgrading. Industrial upgrades in Shenzhen and Hong Kong and technological advances in Shenzhen, Guangzhou, and Foshan have significantly curbed the growth in the GBA's CO2 emissions. The heterogeneity of cities in the GBA greatly increases the complexity of formalizing the allocation of emission reduction tasks and developing a roadmap for regional carbon neutrality. Graded emission reduction strategies and carbon peaking and neutrality policy recommendations for GBA cities are proposed. This study provides a scientific basis for the development of an action program for carbon peaking and neutrality in GBA cities and low-carbon development plans for other cities and regions.</p