A Four-week Stock Simulation Research by Yuxin Wu and Chengyu Jiang

This project is a four-week stock simulation research. The goal of the project is for the two team members to learn stock trading strategies through a real-time simulation so that they can make wise and proper decisions on stock investment in the future. This project reviewed the current stock market, analysed two popular trading techniques and ran stock simulations on ten selected companies with an investment of $500,000 for each trading method. The results indicated that the Day Trading method is more profitable than the Swing & News Trading method in short-term investment (one month). This project provided the team members with valuable trading experiences that will be beneficial to future investments

Global and Local Hierarchy-aware Contrastive Framework for Implicit Discourse Relation Recognition

Due to the absence of explicit connectives, implicit discourse relation recognition (IDRR) remains a challenging task in discourse analysis. The critical step for IDRR is to learn high-quality discourse relation representations between two arguments. Recent methods tend to integrate the whole hierarchical information of senses into discourse relation representations for multi-level sense recognition. Nevertheless, they insufficiently incorporate the static hierarchical structure containing all senses (defined as global hierarchy), and ignore the hierarchical sense label sequence corresponding to each instance (defined as local hierarchy). For the purpose of sufficiently exploiting global and local hierarchies of senses to learn better discourse relation representations, we propose a novel GLobal and LOcal Hierarchy-aware Contrastive Framework (GLOF), to model two kinds of hierarchies with the aid of contrastive learning. Experimental results on the PDTB dataset demonstrate that our method remarkably outperforms the current state-of-the-art model at all hierarchical levels.Comment: 13 pages, 10 figure

Scenimefy: Learning to Craft Anime Scene via Semi-Supervised Image-to-Image Translation

Automatic high-quality rendering of anime scenes from complex real-world images is of significant practical value. The challenges of this task lie in the complexity of the scenes, the unique features of anime style, and the lack of high-quality datasets to bridge the domain gap. Despite promising attempts, previous efforts are still incompetent in achieving satisfactory results with consistent semantic preservation, evident stylization, and fine details. In this study, we propose Scenimefy, a novel semi-supervised image-to-image translation framework that addresses these challenges. Our approach guides the learning with structure-consistent pseudo paired data, simplifying the pure unsupervised setting. The pseudo data are derived uniquely from a semantic-constrained StyleGAN leveraging rich model priors like CLIP. We further apply segmentation-guided data selection to obtain high-quality pseudo supervision. A patch-wise contrastive style loss is introduced to improve stylization and fine details. Besides, we contribute a high-resolution anime scene dataset to facilitate future research. Our extensive experiments demonstrate the superiority of our method over state-of-the-art baselines in terms of both perceptual quality and quantitative performance.Comment: ICCV 2023. The first two authors contributed equally. Code: https://github.com/Yuxinn-J/Scenimefy Project page: https://yuxinn-j.github.io/projects/Scenimefy.htm

Therapeutic role of MiR-140-5p for the treatment of non-small cell lung cancer

Background/Aim: Lung cancer is the second most common cancer in both men and women, after prostate and breast cancer, respectively. There are two main types of primary lung cancer, non-small cell lung cancer (NSCLC), which accounts for approximately 85-90% of all lung cancer cases, and small cell lung cancer (SCLC), which accounts for the other 10-15% of lung cancers. MiRNAs are small molecules that post-transcriptionally regulate many genes and contribute to many disease aetiologies, including tumours. In lung cancer, the down-regulation of miR-140-5p leads to disease progression. Materials and Methods: In this study a miR-140-5p-only treatment and miR-140-5p combined with other chemotherapeutics have been studied in vitro. Results: When transfected into NSCLC, the overexpression of miR-140-5p reduced the migration and invasion properties of malignant cells and, also improved their adhesion onto the artificial extracellular matrix. When miRNA-140-5p replacement treatment was combined with other drugs commonly used in clinical practice, such as gefinitib, DMH1 and cisplatin, it enhanced their efficacy by reducing the migration and invasion ability of cancer cells, thus suggesting that it acts synergistically with known compounds for the treatment of NSCLC. Additionally, some endothelial mesenchymal transition (EMT) markers appeared to be regulated by miR-140-5p. Conclusion: Novel direct targets of miR-140-5p have not been investigated in this study, but our results indicate the involvement of miR-140-5p in lung cancer invasion. The preliminary data from this study imply that when miR-140-5p levels are restored; maybe synergistically support current therapies for NSCLC though further validation, especially in vivo is required

Lion: Adversarial Distillation of Proprietary Large Language Models

The practice of transferring knowledge from a sophisticated, proprietary large language model (LLM) to a compact, open-source LLM has garnered considerable attention. Previous works have focused on a unidirectional knowledge distillation way by aligning the responses of the student model with those of the teacher model to a set of instructions. Nevertheless, they overlooked the possibility of incorporating any reciprocal "feedback"--identifying challenging instructions where the student model's performance falls short--to boost the student model's proficiency iteratively. To this end, we propose a novel adversarial distillation framework for a more efficient knowledge transfer. Leveraging the versatile role adaptability of LLMs, we prompt the teacher model to identify "hard" instructions and generate new "hard" instructions for the student model, creating a three-stage adversarial loop of imitation, discrimination, and generation. By applying this adversarial framework, we successfully transfer knowledge from ChatGPT to a student model (named Lion), using a mere 70k training data. Our results show that Lion-13B not only achieves comparable open-ended generation capabilities to ChatGPT but surpasses conventional state-of-the-art (SOTA) instruction-tuned models like Vicuna-13B by 55.4% in challenging zero-shot reasoning benchmarks such as BIG-Bench Hard (BBH) and 16.7% on AGIEval. Code and model can be found at https://github.com/YJiangcm/Lion.Comment: 21 pages, 5 figures, EMNLP 2023 main conferenc

Significance and therapeutic implications of endothelial progenitorcells in angiogenic-mediated tumour metastasis

Cancer conveys profound social and economic consequences throughout the world. Metastasis is respon-sible for approximately 90% of cancer-associated mortality and, when it occurs, cancer becomes almostincurable. During metastatic dissemination, cancer cells pass through a series of complex steps includingthe establishment of tumour-associated angiogenesis. The human endothelial progenitor cells (hEPCs)are a cell population derived from the bone marrow which are required for endothelial tubulogenesisand neovascularization. They also express abundant inflammatory cytokines and paracrine angiogenicfactors. Clinically hEPCs are highly correlated with relapse, disease progression, metastasis and treatmentresponse in malignancies such as breast cancer, ovarian cancer and non-small-cell lung carcinoma. It hasbecome evident that the hEPCs are involved in the angiogenesis-required progression and metastasis oftumours. However, it is not clear in what way the signalling pathways, controlling the normal cellularfunction of human BM-derived EPCs, are hijacked by aggressive tumour cells to facilitate tumour metas-tasis. In addition, the actual roles of hEPCs in tumour angiogenesis-mediated metastasis are not wellcharacterised. In this paper we reviewed the clinical relevance of the hEPCs with cancer diagnosis, pro-gression and prognosis. We further summarised the effects of tumour microenvironment on the hEPCsand underlying mechanisms. We also hypothesized the roles of altered hEPCs in tumour angiogenesisand metastasis. We hope this review may enhance our understanding of the interaction between hEPCsand tumour cells thus aiding the development of cellular-targeted anti-tumour therapies

The axis of CXCR4/SDF-1 plays a role in colon cancer cell adhesion through regulation of the AKT and IGF1R signalling pathways

Background/aim: Colorectal cancer (CRC) is the third most common cancer in the world. The high mortality of this tumor is mainly due to its invasive properties, as it forms metastases in multiple organs, preferentially in the liver. There has evidence showing that C-X-C chemokine receptor type 4 (CXCR-4) and its ligand, stromal cell-derived factor-1 (SDF-1), plays an important role in cancer progression and metastasis. However, the molecular mechanism underling the CRCR4-mediated CRC metastasis has not been well characterized. In this study, we aimed to investigate the roles of CXCR4 in colorectal cancer using the clustered regularly interspaced short palindromic repeats (CRISPR)/CRISPR-associated protein 9 (Cas9)-based genomic editing technique. Materials and methods: We knocked-down CXCR4 using specific guide-RNA linked CRISPR/Cas9 in HT115 and COLO201 colon cancer cell lines which exhibited high levels of endogenous CXCR4 gene expression. Stable HT115 cells with CXCR4 knock-down were established by CRISPR plasmid transfection and validation was confirmed using T7 endonuclease 1 (T7EN1), flow cytometry (FACS) and western blotting assays. Results: Knock-down of CXCR4 did not decrease proliferation of HT115 cells, but decreased the adhesion potential of cells to the human umbilical vein endothelial cells (HUVEC) and extracellular matrix. We further demonstrated that the AKT and type 1 insulin-like growth factor receptor (IGF1R) signalling pathways may be involved in the alteration of adhesion in CRC cells when CXCR4 is knocked down. Conclusion: Our data suggest that CXCR4 plays a key role in colorectal cancer progression via the mediation of tumor cell adhesion. The Axis of CXCR4/SDF-1 Plays a Role in Colon Cancer Cell Adhesion Through... | Request PDF. Available from: https://www.researchgate.net/publication/319179295_The_Axis_of_CXCR4SDF-1_Plays_a_Role_in_Colon_Cancer_Cell_Adhesion_Through_Regulation_of_the_AKT_and_IGF1R_Signalling_Pathways [accessed Jan 08 2018]