Generating Synergistic Formulaic Alpha Collections via Reinforcement Learning

In the field of quantitative trading, it is common practice to transform raw historical stock data into indicative signals for the market trend. Such signals are called alpha factors. Alphas in formula forms are more interpretable and thus favored by practitioners concerned with risk. In practice, a set of formulaic alphas is often used together for better modeling precision, so we need to find synergistic formulaic alpha sets that work well together. However, most traditional alpha generators mine alphas one by one separately, overlooking the fact that the alphas would be combined later. In this paper, we propose a new alpha-mining framework that prioritizes mining a synergistic set of alphas, i.e., it directly uses the performance of the downstream combination model to optimize the alpha generator. Our framework also leverages the strong exploratory capabilities of reinforcement learning~(RL) to better explore the vast search space of formulaic alphas. The contribution to the combination models' performance is assigned to be the return used in the RL process, driving the alpha generator to find better alphas that improve upon the current set. Experimental evaluations on real-world stock market data demonstrate both the effectiveness and the efficiency of our framework for stock trend forecasting. The investment simulation results show that our framework is able to achieve higher returns compared to previous approaches.Comment: Accepted by KDD '23, ADS trac

DocStormer: Revitalizing Multi-Degraded Colored Document Images to Pristine PDF

For capturing colored document images, e.g. posters and magazines, it is common that multiple degradations such as shadows, wrinkles, etc., are simultaneously introduced due to external factors. Restoring multi-degraded colored document images is a great challenge, yet overlooked, as most existing algorithms focus on enhancing color-ignored document images via binarization. Thus, we propose DocStormer, a novel algorithm designed to restore multi-degraded colored documents to their potential pristine PDF. The contributions are: firstly, we propose a "Perceive-then-Restore" paradigm with a reinforced transformer block, which more effectively encodes and utilizes the distribution of degradations. Secondly, we are the first to utilize GAN and pristine PDF magazine images to narrow the distribution gap between the enhanced results and PDF images, in pursuit of less degradation and better visual quality. Thirdly, we propose a non-parametric strategy, PFILI, which enables a smaller training scale and larger testing resolutions with acceptable detail trade-off, while saving memory and inference time. Fourthly, we are the first to propose a novel Multi-Degraded Colored Document image Enhancing dataset, named MD-CDE, for both training and evaluation. Experimental results show that the DocStormer exhibits superior performance, capable of revitalizing multi-degraded colored documents into their potential pristine digital versions, which fills the current academic gap from the perspective of method, data, and task

Experimental study on effects of gas flow rate on soot characteristics in diffusion flames coupled with plasma

This study examined the evolution of morphology and nanostructure of soot particles from the plasma-flame interaction for various gas flow rates. The current study used both optical diagnostic and sampling methods to explore the soot production and combustion characteristics. Soot particles were characterized at the same positions downstream from the flame zone by thermophoretic sampling and transmission electron microscopy. Moreover, X-ray diffraction analysis, and thermogravimetric analysis were performed to study the nanostructure and oxidation reactivity of soot. A reduction in soot concentration was found with the plasma addition, which illustrated an inhibition effect of plasma on soot emission. The increased gas flow rate promoted soot concentration since a growing number of carbons participated in the combustion process. Depending on the gas flow rate (carbon content) variation and plasma activation, either liquid-like soot material with irregularly shaped protrusions or chain-like structure, or a mixture of both, were generated from the diffusion flames. The soot produced by plasma-flame interaction also demonstrated a high correlation between nanostructure and reactivity. The soot from lower carbon content with plasma activation had a shorter fringe length and larger fringe tortuosity related to higher oxidation reactivity. On the contrary, soot from the highest carbon content without plasma-flame interaction exhibited prevalent fullerene-like nanostructures with evident large or small shells and also had a higher carbonization degree resulting in lower oxidation reactivity

MicroRNA-206: Effective Inhibition of Gastric Cancer Progression through the c-Met Pathway

MicroRNAs are endogenous short chain nucleotide RNAs that regulate gene function by direct binding of target mRNAs. In this study, we investigated the effects of microRNA-206 (miR-206) on the development of gastric cancer. miR-206 was first confirmed to be downregulated in gastric cancer specimens. Conversely, upregulation of c-Met was confirmed in tissue samples of human gastric cancer, with its level inversely correlated with miR-206 expression. Introduction of miR-206 inhibited cellular proliferation by inducing G1 cell cycle arrest, as well as migration and invasion. Moreover, important proliferation and/or migration related molecules such as c-Met, CDK4, p-Rb, p-Akt and p-ERK were confirmed to be downregulated by Western blot analysis. Targeting of c-Met also directly affected AGS cell proliferation, migration and invasion. In vivo, miR-206 expressing tumor cells also displayed growth delay in comparison to unaffected tumor cells. Our results demonstrated that miR-206 suppressed c-Met expression in gastric cancer and could function as a potent tumor suppressor in c-Met overexpressing tumors. Inhibition of miR-206 function could contribute to aberrant cell proliferation and migration, leading to gastric cancer development

novoPathFinder: a webserver of designing novel-pathway with integrating GEM-model

To increase the number of value-added chemicals that can be produced by metabolic engineering and synthetic biology, constructing metabolic space with novel reactions/pathways is crucial. However, with the large number of reactions that existed in the metabolic space and complicated metabolisms within hosts, identifying novel pathways linking two molecules or heterologous pathways when engineering a host to produce a target molecule is an arduous task. Hence, we built a user-friendly web server, novoPathFinder, which has several features: (i) enumerate novel pathways between two specified molecules without considering hosts; (ii) construct heterologous pathways with known or putative reactions for producing target molecule within Escherichia coli or yeast without giving precursor; (iii) estimate novel pathways with considering several categories, including enzyme promiscuity, Synthetic Complex Score (SCScore) and LD50 of intermediates, overall stoichiometric conversions, pathway length, theoretical yields and thermodynamic feasibility. According to the results, novoPathFinder is more capable to recover experimentally validated pathways when comparing other rule-based web server tools. Besides, more efficient pathways with novel reactions could also be retrieved for further experimental exploration. novoPathFinder is available at http://design.rxnfinder.org/novopathfinder/

Targeted Deletion of the Murine Lgr4 Gene Decreases Lens Epithelial Cell Resistance to Oxidative Stress and Induces Age-Related Cataract Formation

Oxidative stress contributes to the formation of cataracts. The leucine rich repeat containing G protein-coupled receptor 4 (LGR4, also known as GPR48), is important in many developmental processes. Since deletion of Lgr4 has previously been shown to lead to cataract formation in mice, we sought to determine the specific role that Lgr4 plays in the formation of cataracts. Initially, the lens opacities of Lgr4−/− mice at different ages without ocular anterior segment dysgenesis (ASD) were evaluated with slit-lamp biomicroscopy. Lenses from both Lgr4−/− and wild-type mice were subjected to oxidation induced protein denaturation to assess the ability of the lens to withstand oxidation. The expression of antioxidant enzymes was evaluated with real-time quantitative PCR. Phenotypically, Lgr4−/− mice showed earlier onset of lens opacification and higher incidence of cataract formation compared with wild-type mice of similar age. In addition, Lgr4−/− mice demonstrated increased sensitivity to environmental oxidative damage, as evidenced by altered protein expression. Real-time quantitative PCR showed that two prominent antioxidant defense enzymes, catalase (CAT) and superoxidase dismutase-1 (SOD1), were significantly decreased in the lens epithelial cells of Lgr4−/− mice. Our results suggest that the deletion of Lgr4 can lead to premature cataract formation, as well as progressive deterioration with aging. Oxidative stress and altered expression of several antioxidant defense enzymes contribute to the formation of cataracts