Anisotropic inverse problems with internal measurements

This thesis concerns the hybrid inverse problem of reconstructing a tensor-valued conductivity from knowledge of internal measurements. This problem finds applications in the medical imaging modalities Current Density Imaging and Magnetic Resonance Electrical Impedance Tomography. In the first part of the thesis, we investigate the reconstruction of the anisotropic conductivity in a second-order elliptic partial differential equation, from knowledge of internal current densities. We show that the unknown coefficient can be uniquely and stably reconstructed via explicit inversion formulas with a loss of one derivative compared to errors in the measurement. This improves the resolution of quantitative reconstructions in Calderón's problem (i.e. reconstruction problems from knowledge of boundary measurements). We then extend the problem to the full anisotropic Maxwell system and show that the complex-valued anisotropic admittivity can be uniquely reconstructed from knowledge of several internal magnetic fields. We also proved a unique continuation property and Runge approximation property for an anisotropic Maxwell system. In the second part, we performed some numerical experiments to demonstrate the computational feasibility of the reconstruction algorithms and assess their robustness to noisy measurements

Predict the Future from the Past? On the Temporal Data Distribution Shift in Financial Sentiment Classifications

Temporal data distribution shift is prevalent in the financial text. How can a financial sentiment analysis system be trained in a volatile market environment that can accurately infer sentiment and be robust to temporal data distribution shifts? In this paper, we conduct an empirical study on the financial sentiment analysis system under temporal data distribution shifts using a real-world financial social media dataset that spans three years. We find that the fine-tuned models suffer from general performance degradation in the presence of temporal distribution shifts. Furthermore, motivated by the unique temporal nature of the financial text, we propose a novel method that combines out-of-distribution detection with time series modeling for temporal financial sentiment analysis. Experimental results show that the proposed method enhances the model's capability to adapt to evolving temporal shifts in a volatile financial market.Comment: EMNLP 2023 main conferenc

The Expression Levels of XLF and Mutant P53 Are Inversely Correlated in Head and Neck Cancer Cells.

XRCC4-like factor (XLF), also known as Cernunnos, is a protein encoded by the human NHEJ1 gene and an important repair factor for DNA double-strand breaks. In this study, we have found that XLF is over-expressed in HPV(+) versus HPV(-) head and neck squamous cell carcinoma (HNSCC) and significantly down-regulated in the HNSCC cell lines expressing high level of mutant p53 protein versus those cell lines harboring wild-type TP53 gene with low p53 protein expression. We have also demonstrated that Werner syndrome protein (WRN), a member of the NHEJ repair pathway, binds to both mutant p53 protein and NHEJ1 gene promoter, and siRNA knockdown of WRN leads to the inhibition of XLF expression in the HNSCC cells. Collectively, these findings suggest that WRN and p53 are involved in the regulation of XLF expression and the activity of WRN might be affected by mutant p53 protein in the HNSCC cells with aberrant TP53 gene mutations, due to the interaction of mutant p53 with WRN. As a result, the expression of XLF in these cancer cells is significantly suppressed. Our study also suggests that XLF is over-expressed in HPV(+) HNSCC with low expression of wild type p53, and might serve as a potential biomarker for HPV(+) HNSCC. Further studies are warranted to investigate the mechanisms underlying the interactive role of WRN and XLF in NHEJ repair pathway

SWOT Analysis of Green Logistics Development and Strategic Options for China

グリーン物流は1990年代以降に現れた新しい概念である。グリーン物流は持続可能な社会経済の発展に寄与し、物流管理の新しい潮流である。しかし、中国におけるグリーン物流の発展現状はあまり明るくない。グリーン物流の概念がまだ普及していない、政策が不足し、技術が遅れて、専門人材が不足しているなどの問題がある。このため、筆者がマーケティング分野におけるSWOT 分析を応用して、中国のグリーン物流発展の現状を研究し、中国に適したグリーン物流発展戦略を提案する。Green logistics is a relatively recent concept proposed in the 20th century. Its purpose is to achieve sustainable socio-economic development and is now a new trend in logistics management. In China, however, the development of green logistics is not optimistic, because of problems such as, the concept of green logistics not having gained widespread acceptance, a lack of government policies, outdated technology, and shortage of professional human resources. Therefore, the author uses SWOT analysis to study the current situation of green logistics development in China and suggest suitable green logistics development strategies.departmental bulletin pape

SWOT Analysis of Green Logistics Development and Strategic Options for China

グリーン物流は1990年代以降に現れた新しい概念である。グリーン物流は持続可能な社会経済の発展に寄与し、物流管理の新しい潮流である。しかし、中国におけるグリーン物流の発展現状はあまり明るくない。グリーン物流の概念がまだ普及していない、政策が不足し、技術が遅れて、専門人材が不足しているなどの問題がある。このため、筆者がマーケティング分野におけるSWOT 分析を応用して、中国のグリーン物流発展の現状を研究し、中国に適したグリーン物流発展戦略を提案する。Green logistics is a relatively recent concept proposed in the 20th century. Its purpose is to achieve sustainable socio-economic development and is now a new trend in logistics management. In China, however, the development of green logistics is not optimistic, because of problems such as, the concept of green logistics not having gained widespread acceptance, a lack of government policies, outdated technology, and shortage of professional human resources. Therefore, the author uses SWOT analysis to study the current situation of green logistics development in China and suggest suitable green logistics development strategies.departmental bulletin pape

CoRide: Joint Order Dispatching and Fleet Management for Multi-Scale Ride-Hailing Platforms

How to optimally dispatch orders to vehicles and how to tradeoff between immediate and future returns are fundamental questions for a typical ride-hailing platform. We model ride-hailing as a large-scale parallel ranking problem and study the joint decision-making task of order dispatching and fleet management in online ride-hailing platforms. This task brings unique challenges in the following four aspects. First, to facilitate a huge number of vehicles to act and learn efficiently and robustly, we treat each region cell as an agent and build a multi-agent reinforcement learning framework. Second, to coordinate the agents from different regions to achieve long-term benefits, we leverage the geographical hierarchy of the region grids to perform hierarchical reinforcement learning. Third, to deal with the heterogeneous and variant action space for joint order dispatching and fleet management, we design the action as the ranking weight vector to rank and select the specific order or the fleet management destination in a unified formulation. Fourth, to achieve the multi-scale ride-hailing platform, we conduct the decision-making process in a hierarchical way where a multi-head attention mechanism is utilized to incorporate the impacts of neighbor agents and capture the key agent in each scale. The whole novel framework is named as CoRide. Extensive experiments based on multiple cities real-world data as well as analytic synthetic data demonstrate that CoRide provides superior performance in terms of platform revenue and user experience in the task of city-wide hybrid order dispatching and fleet management over strong baselines.Comment: CIKM 201

Combining Sparse Group Lasso and Linear Mixed Model Improves Power to Detect Genetic Variants Underlying Quantitative Traits

Genome-Wide association studies (GWAS), based on testing one single nucleotide polymorphism (SNP) at a time, have revolutionized our understanding of the genetics of complex traits. In GWAS, there is a need to consider confounding effects such as due to population structure, and take groups of SNPs into account simultaneously due to the “polygenic” attribute of complex quantitative traits. In this paper, we propose a new approach SGL-LMM that puts together sparse group lasso (SGL) and linear mixed model (LMM) for multivariate associations of quantitative traits. LMM, as has been often used in GWAS, controls for confounders, while SGL maintains sparsity of the underlying multivariate regression model. SGL-LMM first sets a fixed zero effect to learn the parameters of random effects using LMM, and then estimates fixed effects using SGL regularization. We present efficient algorithms for hyperparameter tuning and feature selection using stability selection. While controlling for confounders and constraining for sparse solutions, SGL-LMM also provides a natural framework for incorporating prior biological information into the group structure underlying the model. Results based on both simulated and real data show SGL-LMM outperforms previous approaches in terms of power to detect associations and accuracy of quantitative trait prediction