Extrinsic local regression on manifold-valued data

We propose an extrinsic regression framework for modeling data with manifold valued responses and Euclidean predictors. Regression with manifold responses has wide applications in shape analysis, neuroscience, medical imaging and many other areas. Our approach embeds the manifold where the responses lie onto a higher dimensional Euclidean space, obtains a local regression estimate in that space, and then projects this estimate back onto the image of the manifold. Outside the regression setting both intrinsic and extrinsic approaches have been proposed for modeling i.i.d manifold-valued data. However, to our knowledge our work is the first to take an extrinsic approach to the regression problem. The proposed extrinsic regression framework is general, computationally efficient and theoretically appealing. Asymptotic distributions and convergence rates of the extrinsic regression estimates are derived and a large class of examples are considered indicating the wide applicability of our approach

Essays on financial markets

This thesis comprises three empirical studies, which investigate the influential factors of financial markets and their participants’ behaviour. These studies can be read independently. Using a sample of European banks, the first study, “Corruption culture and bank short-termism”, provides evidence that country-level corruption is strongly associated with short-termism (a behaviour that focuses on short-term benefits at the expense of long-term shareholders’ wealth growth). To measure short-termism, I construct a multi-dimensional index which combines earnings management, tail risk, and short-term debt ratio. I show that banks headquartered in countries that are more corrupt behave more short-sightedly than banks headquartered in countries that are less corrupt. I further demonstrate that foreign shareholders act as a channel through which corruption is imported. For banks located in countries with a lower than average corruption level, having more shareholdings by investors domiciled in countries that are more corrupt leads to more short-termism. This study highlights the link between bank short-termism and corruption of both headquartered countries and foreign shareholders. The second study, “Macroeconomic and political uncertainty and cross sectional return dispersion around the world”, examines whether return dispersion (the cross sectional variation in stock returns) could be used to measure the macroeconomic and political uncertainty of international financial markets. I show that return dispersion is able to capture uncertainties including local and global business cycles, international political instability, market general uncertainties, international country risk, and economic policy uncertainty. Stocks that are more sensitive to return dispersion have higher returns. Moreover, I compare return dispersion with another commonly used uncertainty measure: implied volatility. I find that they capture different aspects of uncertainty. This study aims to provide a simple and easy-to-use measure of uncertainty for both academic purposes and professional practice. The third study, “The performance of asset allocation strategies across datasets and over time”, evaluates the ex-ante performance of various commonly used asset allocation strategies including equal weighting, mean variance weighting, risk parity weighting, minimum variance weighting, equal risk contribution weighting, and maximum diversification weighting. The results show that there are no statistically significant differences between asset allocation strategies if the portfolios are based on country or industry indices. If the portfolios are formed of stocks or multi-asset classes, then the differences between strategies are large; however, none of the strategies can consistently outperform the others over time. I also identify that the implementation of the mean variance rule leads to wide fluctuation in risk shifting, which is undesirable for investors. Last but not least, I illustrate how all of the strategies are far from ex-ante optimal

Incorporating simulated spatial context information improves the effectiveness of contrastive learning models

Visual learning often occurs in a specific context, where an agent acquires skills through exploration and tracking of its location in a consistent environment. The historical spatial context of the agent provides a similarity signal for self-supervised contrastive learning. We present a unique approach, termed Environmental Spatial Similarity (ESS), that complements existing contrastive learning methods. Using images from simulated, photorealistic environments as an experimental setting, we demonstrate that ESS outperforms traditional instance discrimination approaches. Moreover, sampling additional data from the same environment substantially improves accuracy and provides new augmentations. ESS allows remarkable proficiency in room classification and spatial prediction tasks, especially in unfamiliar environments. This learning paradigm has the potential to enable rapid visual learning in agents operating in new environments with unique visual characteristics. Potentially transformative applications span from robotics to space exploration. Our proof of concept demonstrates improved efficiency over methods that rely on extensive, disconnected datasets

Inequalities for Permanents and Permanental Minors of Row Substochastic Matrices

In this paper, some inequalities for permanents and permanental minors of row substochastic matrices are proved. The convexity of the permanent function on the interval between the identity matrix and an arbitrary row substochastic matrix is also proved. In addition, a conjecture about the permanent and permanental minors of square row substochastic matrices with fixed row and column sums is formulated

Extrinsic Local Regression on Manifold-Valued Data

We propose an extrinsic regression framework for modeling data with manifold valued responses and Euclidean predictors. Regression with manifold responses has wide applications in shape analysis, neuroscience, medical imaging and many other areas. Our approach embeds the manifold where the responses lie onto a higher dimensional Euclidean space, obtains a local regression estimate in that space, and then projects this estimate back onto the image of the manifold. Outside the regression setting both intrinsic and extrinsic approaches have been proposed for modeling i.i.d manifold-valued data. However, to our knowledge our work is the first to take an extrinsic approach to the regression problem. The proposed extrinsic regression framework is general, computationally efficient and theoretically appealing. Asymptotic distributions and convergence rates of the extrinsic regression estimates are derived and a large class of examples are considered indicating the wide applicability of our approach

Transcriptome Analysis Reveals a Comprehensive Insect Resistance Response Mechanism in Cotton to Infestation by the Phloem Feeding Insect Bemisia Tabaci (Whitefly)

The whitefly (Bemisia tabaci) causes tremendous damage to cotton production worldwide. However, very limited information is available about how plants perceive and defend themselves from this destructive pest. In this study, the transcriptomic differences between two cotton cultivars that exhibit either strong resistance (HR) or sensitivity (ZS) to whitefly were compared at different time points (0, 12, 24 and 48 h after infection) using RNA‐Seq. Approximately one billion paired‐end reads were obtained by Illumina sequencing technology. Gene ontology and KEGG pathway analysis indicated that the cotton transcriptional response to whitefly infestation involves genes encoding protein kinases, transcription factors, metabolite synthesis, and phytohormone signalling. Furthermore, a weighted gene co‐expression network constructed from RNA‐Seq datasets showed that WRKY40 and copper transport protein are hub genes that may regulate cotton defenses to whitefly infestation. Silencing GhMPK3 by virus‐induced gene silencing (VIGS) resulted in suppression of the MPK‐WRKY‐JA and ET pathways and lead to enhanced whitefly susceptibility, suggesting that the candidate insect resistant genes identified in this RNA‐Seq analysis are credible and offer significant utility. Taken together, this study provides comprehensive insights into the cotton defense system to whitefly infestation and has identified several candidate genes for control of phloem‐feeding pests

A Service Composition Approach Based on Pre-joined Service Network in Graph Database

We solve the service composition problem with plugin semantic matching in a graph database. We present a Prejoined Service Network (PJSN) approach which firstly constructs and stores a service composition network with all services and compositions in a graph database. Then, this approach fetches a satisfying solution by converting the composition request into Cypher and querying the graph database. We evaluate the performance of the proposed PJSN approach by conducting experiments and comparing with that of the Pre-joined Semantic Indexing Graph (PJSIG) method. The experiment results show that compared with the PJSIG method, the proposed approach can always find a solution and lead to higher user’s satisfaction

A Survey of Personalized Medicine Recommendation

Mining potential and valuable medical knowledge from massive medical data to support clinical decision-making has become an important research field. Personalized medicine recommendation is an important research direction in this field, aiming to recommend the most suitable medicines for each patient according to the health status of the patient. Personalized medicine recommendation can assist clinicians to make clinical decisions and avoid the occurrence of medical abnormalities, so it has been widely concerned by many researchers. Based on this, this paper makes a comprehensive review of personalized medicine recommendation. Specifically, we first make clear the definition of personalized medicine recommendation problem; then, starting from the key theories and technologies, the personalized medicine recommendation algorithms proposed in recent years are systematically classified (medicine recommendation based on multi-disease, medicine recommendation with combination pattern, medicine recommendation with additional knowledge, and medicine recommendation based on feedback) and in-depth analyzed; and this paper also introduces how to evaluate personalized medicine recommendation algorithms and some common evaluation indicators; finally, the challenges of personalized medicine recommendation problem are put forward, and the future research direction and development trends are prospected

Research progress in cardiotoxicity of organophosphate esters

Organophosphate esters (OPEs) have been extensively utilized worldwide as a substitution for brominated flame retardants. With an increased awareness of the need for environmental protection, the potential health risks and ecological hazards of OPEs have attracted widespread attention. As the dynamic organ of the circulatory system, the heart plays a significant role in maintaining normal life activities. Currently, there is a lack of systematic appraisal of the cardiotoxicity of OPEs. This article summarized the effects of OPEs on the morphological structure and physiological functions of the heart. It is found that these chemicals can lead to pericardial edema, abnormal looping, and thinning of atrioventricular walls in the heart, accompanied by alterations in heart rate, with toxic effects varying by the OPE type. These effects are primarily associated with the activation of endoplasmic reticulum stress response, the perturbation of cytoplasmic and intranuclear signal transduction pathways in cardiomyocytes. This paper provides a theoretical basis for further understanding of the toxic effects of OPEs and contributes to environmental protection and OPEs’ ecological risk assessment