Portfolio Optimization, CAPM & Factor Modeling Project Report

In this Portfolio Optimization Project, we used Markowitz¡¯s modern portfolio theory for portfolio optimization. We selected fifteen stocks traded on the New York Stock Exchange and gathered these stocks¡¯ historical data from Yahoo Finance [1]. Then we used Markowitz¡¯s theory to analyze this data in order to obtain the optimal weights of our initial portfolio. To maintain our investment in a current tangency portfolio, we recalculated the optimal weights and rebalanced the positions every week. In the CAPM project, we used the security characteristic line to calculate the stocks¡¯ daily returns. We also computed the risk of each asset, portfolio beta, and portfolio epsilons. In the Factor Modeling project, we computed estimates of each asset¡¯s expected returns and return variances of fifteen stocks for each of our factor models. Also we computed estimates of the covariances among our asset returns. In order to find which model performs best, we compared each portfolio¡¯s actual return with its corresponding estimated portfolio return

Efficient Bounds and Estimates for Canonical Angles in Randomized Subspace Approximations

Randomized subspace approximation with "matrix sketching" is an effective approach for constructing approximate partial singular value decompositions (SVDs) of large matrices. The performance of such techniques has been extensively analyzed, and very precise estimates on the distribution of the residual errors have been derived. However, our understanding of the accuracy of the computed singular vectors (measured in terms of the canonical angles between the spaces spanned by the exact and the computed singular vectors, respectively) remains relatively limited. In this work, we present bounds and estimates for canonical angles of randomized subspace approximation that can be computed efficiently either a priori or a posterior. Under moderate oversampling in the randomized SVD, our prior probabilistic bounds are asymptotically tight and can be computed efficiently, while bringing a clear insight into the balance between oversampling and power iterations given a fixed budget on the number of matrix-vector multiplications. The numerical experiments demonstrate the empirical effectiveness of these canonical angle bounds and estimates on different matrices under various algorithmic choices for the randomized SVD

Synthesis of Epoxidatied Castor Oil and Its Effect on the Properties of Waterborne Polyurethane

AbstractIn this study, a new method for synthesis poxidatied castor oil (ECO) is engaged. A series of waterborne polyurethane dispersions (WPUs) were synthesized using polytetramethylene ether glycol (PTMEG), toluene diisocyanate (TDI-80), and ECO. These WPUs can be crosslinked spontaneously upon drying, without extra additives or processing steps. Moreover, the particle size, and morphology of WPUs were examined with light scattering ultrafine particle analyzer, and transmission electron microscopy. The anti-water, thermal and mechanical properties were also studied. Results reveal that the particle size of WPUs mainly depends on the concentrations of ECO. The particle size decreases when the ECO is used. Furthermore, increased amount of ECO results in an improvement of the anti-water, thermal and mechanical properties of WPU films

Self-tuning vibration absorber and the effect of its installation position on damping characteristics

A kind of self-tuning vibration absorber is presented. The relationship between the installation position and the vibration damping effect of the self-tuning vibration absorber is established, the influence on the damping effect is discussed. Then, on the vibration test bed, the theoretical analysis results are tested and verified. The results show that, installation position of the self-tuning vibration absorber has a significant influence on its vibration damping effect. When installed near the source location, the self-tuning vibration absorber has a better vibration damping effect. It is should be avoided in the area of vibration deterioration

Cluster-aware Semi-supervised Learning: Relational Knowledge Distillation Provably Learns Clustering

Despite the empirical success and practical significance of (relational) knowledge distillation that matches (the relations of) features between teacher and student models, the corresponding theoretical interpretations remain limited for various knowledge distillation paradigms. In this work, we take an initial step toward a theoretical understanding of relational knowledge distillation (RKD), with a focus on semi-supervised classification problems. We start by casting RKD as spectral clustering on a population-induced graph unveiled by a teacher model. Via a notion of clustering error that quantifies the discrepancy between the predicted and ground truth clusterings, we illustrate that RKD over the population provably leads to low clustering error. Moreover, we provide a sample complexity bound for RKD with limited unlabeled samples. For semi-supervised learning, we further demonstrate the label efficiency of RKD through a general framework of cluster-aware semi-supervised learning that assumes low clustering errors. Finally, by unifying data augmentation consistency regularization into this cluster-aware framework, we show that despite the common effect of learning accurate clusterings, RKD facilitates a "global" perspective through spectral clustering, whereas consistency regularization focuses on a "local" perspective via expansion

Robust Blockwise Random Pivoting: Fast and Accurate Adaptive Interpolative Decomposition

The interpolative decomposition (ID) aims to construct a low-rank approximation formed by a basis consisting of row/column skeletons in the original matrix and a corresponding interpolation matrix. This work explores fast and accurate ID algorithms from five essential perspectives for empirical performance: (a) skeleton complexity that measures the minimum possible ID rank for a given low-rank approximation error, (b) asymptotic complexity in FLOPs, (c) parallelizability of the computational bottleneck as matrix-matrix multiplications, (d) error-revealing property that enables automatic rank detection for given error tolerances without prior knowledge of target ranks, (e) ID-revealing property that ensures efficient construction of the optimal interpolation matrix after selecting the skeletons. While a broad spectrum of algorithms have been developed to optimize parts of the aforementioned perspectives, practical ID algorithms proficient in all perspectives remain absent. To fill in the gap, we introduce robust blockwise random pivoting (RBRP) that is parallelizable, error-revealing, and exact-ID-revealing, with comparable skeleton and asymptotic complexities to the best existing ID algorithms in practice. Through extensive numerical experiments on various synthetic and natural datasets, we empirically demonstrate the appealing performance of RBRP from the five perspectives above, as well as the robustness of RBRP to adversarial inputs

SRSF2 Is Essential For Hematopoiesis and Its Mutations Dysregulate Alternative RNA Splicing In MDS

Abstract Myelodysplastic syndromes (MDS) are a group of neoplasms that are ineffective in generating multiple lineages of myeloid cells and have various risks to progress to acute myeloid leukemia. Recent genome-wide sequencing studies reveal that mutations in genes of splicing factors are commonly associated with MDS. However, the importance of these splicing factors in hematopoiesis has been unclear and the causal effect of their mutations on MDS development remains to be determined. One of these newly identified genes is SRSF2, and its mutations have been linked to poor survival among MDS patients. Interestingly, most of SRSF2 mutations occur at proline 95 and the majority of these mutations change this proline to histidine (P95H). Given that SRSF2 is a well-characterized splicing factor involved in both constitutive and regulated splicing, we hypothesize that SRSF2 plays an important role in normal hematopoiesis and the SRSF2 mutations induce specific changes in alternative splicing that favor disease progression. We first examined the role of SRSF2 in hematopoiesis by generating Srsf2 null mutation in mouse blood cells via crossing conditional Srsf2 knockout mice (Srsf2f/f) with blood cell-specific Cre transgenic mice (Vav-Cre). The mutant mice produced significantly fewer definitive blood cells (10% of wild type controls), exhibited increased apoptosis in the remaining blood cells, and died during embryonic development. Importantly, we detected no hematopoietic stem/progenitor cells (lineage-/cKit+) in E14 fetal livers of Vav-Cre/Srsf2f/f mice. These results indicate that SRSF2 is essential for hematopoiesis during embryonic development. We next examined the role of SRSF2 in adult hematopoiesis by injecting polyIC into mice that carry a polyIC inducible Cre expression unit. Unexpectedly, after multiple polyIC treatments, the Srsf2f/f mice stayed alive during several months of observation. Time course genotyping analyses of polyIC treated mice revealed an increased rate of incomplete Srsf2 deletion in peripheral blood cells. These observations suggest that Srsf2 ablation did not cause immediate cell lethality in differentiated blood cells, but the gene is indispensable for the function of blood stem/progenitor cells. Since mutations of splicing factors are generally heterozygous in MDS patients, we also examined mice with Srsf2+/- blood cells. No obvious defect of hematopoiesis was observed under normal conditions or in response to stress with 5-FU treatment and sublethal irradiation. To gain molecular insight into the splicing activity of MDS-associated mutant forms of SRSF2, we performed large-scale alternative splicing surveys by using RNA-mediated oligonucleotide annealing, selection, and ligation coupled with next-generation sequencing (RASL-seq) previously developed in our lab, which offers a robust and cost-effective platform for splicing profiling. Compared to vector transduction controls, we found that overexpression of both wild type and P95H SRSF2 induced many, but distinct changes in alternative splicing in lineage-negative bone marrow cells, and importantly, we noted several changes in genes with known roles in hematopoietic malignancies that were uniquely induced by the mutant SRSF2. To further link the mutations to altered splicing in MDS patients, we also applied RASL-seq to a large number of MDS patient samples with or without mutations in SRSF2 or other splicing regulators. The data revealed a specific set of alternative splicing events that are commonly linked to MDS with splicing factor mutations. These findings strongly suggest that many of these mutations in splicing regulators are gain-of-function mutations that are causal to MDS. In conclusion, we report that SRSF2 plays an essential role in hematopoietic stem/progenitor cells and that the MDS-associated mutations in SRSF2 have a dominant effect on RNA alternative splicing. These findings provide functional information and molecular basis of SRSF2 and its MDS-related mutations in hematopoiesis and related clinical disorders. Disclosures: No relevant conflicts of interest to declare

Design of the Reverse Logistics System for Medical Waste Recycling Part I: System Architecture, Classification & Monitoring Scheme, and Site Selection Algorithm

With social progress and the development of modern medical technology, the amount of medical waste generated is increasing dramatically. The problem of medical waste recycling and treatment has gradually drawn concerns from the whole society. The sudden outbreak of the COVID-19 epidemic further brought new challenges. To tackle the challenges, this study proposes a reverse logistics system architecture with three modules, i.e., medical waste classification & monitoring module, temporary storage & disposal site selection module, as well as route optimization module. This overall solution design won the Grand Prize of the "YUNFENG CUP" China National Contest on Green Supply and Reverse Logistics Design ranking 1st. This paper focuses on the description of architectural design and the first two modules, especially the module on site selection. Specifically, regarding the medical waste classification & monitoring module, three main entities, i.e., relevant government departments, hospitals, and logistics companies, are identified, which are involved in the five management functions of this module. Detailed data flow diagrams are provided to illustrate the information flow and the responsibilities of each entity. Regarding the site selection module, a multi-objective optimization model is developed, and considering different types of waste collection sites (i.e., prioritized large collection sites and common collection sites), a hierarchical solution method is developed employing linear programming and K-means clustering algorithms sequentially. The proposed site selection method is verified with a case study and compared with the baseline, it can immensely reduce the daily operational costs and working time. Limited by length, detailed descriptions of the whole system and the remaining route optimization module can be found at https://shorturl.at/cdY59.Comment: 8 pages, 6 figures, submitted to and under review by the IEEE Intelligent Vehicles Symposium (IV 2023

Design of the Reverse Logistics System for Medical Waste Recycling Part II: Route Optimization with Case Study under COVID-19 Pandemic

Medical waste recycling and treatment has gradually drawn concerns from the whole society, as the amount of medical waste generated is increasing dramatically, especially during the pandemic of COVID-19. To tackle the emerging challenges, this study designs a reverse logistics system architecture with three modules, i.e., medical waste classification & monitoring module, temporary storage & disposal site (disposal site for short) selection module, as well as route optimization module. This overall solution design won the Grand Prize of the "YUNFENG CUP" China National Contest on Green Supply and Reverse Logistics Design ranking 1st. This paper focuses on the design of the route optimization module. In this module, a route optimization problem is designed considering transportation costs and multiple risk costs (e.g., environment risk, population risk, property risk, and other accident-related risks). The Analytic Hierarchy Process is employed to determine the weights for each risk element, and a customized genetic algorithm is developed to solve the route optimization problem. A case study under the COVID-19 pandemic is further provided to verify the proposed model. Limited by length, detailed descriptions of the whole system and the other modules can be found at https://shorturl.at/cdY59.Comment: 6 pages, 4 figures, under review by the 26th IEEE International Conference on Intelligent Transportation Systems (ITSC 2023

Antidepressants : a content analysis of healthcare providers' tweets

This study aims to analyse the Twitter posts of healthcare providers related to antidepressants after the impact of the COVID-19 pandemic and to explore the healthcare providers’ engagement and their areas of interest