The Effects of Organizational Forms of Mutual Fund Management Company on Mutual Fund Performance

The organizational form of a company indicates whether it is publicly-traded or privately-held. The effects of the organizational forms on a company’s operations and performances have been well documented. However, because the organizational form of companies in the finance industry is so different from those in other industries, the effect on performance is quite different. There has been little research done to determine how the organizational form of mutual fund management companies affect the performance of their mutual funds. This thesis examines the impact of mutual fund management companies on the performance of their managed funds using data that cover the period 2007 to 2016 on 782 different firms. The results showed that the performance of mutual funds managed by publicly-traded mutual fund management companies was significantly compared to those managed by privately-held companies. Based on the sample data, the hypothesis of this thesis is that publicly-traded and privately-held fund management companies have different incentives and interests that impact mutual fund performance. The thesis also addresses the issues of discontinuous returns and endogenous organizational form variables. The test results examined in this thesis support the notion that mutual funds managed by publicly-traded companies underperform compared to industry benchmarks. In addition, funds managed by publicly-traded management companies perform poorer in general compared to funds managed by privately-held companies

Discovering Organizational Correlations from Twitter

Organizational relationships are usually very complex in real life. It is difficult or impossible to directly measure such correlations among different organizations, because important information is usually not publicly available (e.g., the correlations of terrorist organizations). Nowadays, an increasing amount of organizational information can be posted online by individuals and spread instantly through Twitter. Such information can be crucial for detecting organizational correlations. In this paper, we study the problem of discovering correlations among organizations from Twitter. Mining organizational correlations is a very challenging task due to the following reasons: a) Data in Twitter occurs as large volumes of mixed information. The most relevant information about organizations is often buried. Thus, the organizational correlations can be scattered in multiple places, represented by different forms; b) Making use of information from Twitter collectively and judiciously is difficult because of the multiple representations of organizational correlations that are extracted. In order to address these issues, we propose multi-CG (multiple Correlation Graphs based model), an unsupervised framework that can learn a consensus of correlations among organizations based on multiple representations extracted from Twitter, which is more accurate and robust than correlations based on a single representation. Empirical study shows that the consensus graph extracted from Twitter can capture the organizational correlations effectively.Comment: 11 pages, 4 figure

Engineering design of artificial vascular junctions for 3D printing

Vascular vessels, including arteries, veins and capillaries, are being printed using additive manufacturing technologies, also known as 3D printing. This paper demonstrates that it is important to follow the vascular design by nature as close as possible when 3D printing artificial vascular branches. In previous work, the authors developed an algorithm of computational geometry for constructing smooth junctions for 3D printing. In this work, computational fluid dynamics (CFDs) is used to compare the wall shear stress and blood velocity field for the junctions of different designs. The CFD model can reproduce the expected wall shear stress at locations remote from the junction. For large vessels such as veins, it is shown that ensuring the smoothness of the junction and using smaller joining angles as observed in nature is very important to avoid high wall shear stress and recirculation. The issue is however less significant for capillaries. Large joining angles make no difference to the hemodynamic behavior, which is also consistent with the fact that most capillary junctions have large joining angles. The combination of the CFD analysis and the junction construction method form a complete design method for artificial vascular vessels that can be 3D printed using additive manufacturing technologies

The effect of geometry on mechanical properties of biodegradable polylactic-acid tensile-test specimens by material extrusion

Additive manufactured biomedical devices have been widely used in the biomedical fields due to the development of biomaterials and manufacturing techniques. Biodegradable Polylactic Acid-based polymers are the most common material that can be manufactured using material extrusion, one of the most widely known additive manufacturing methods. However, medical grade polymers are too expensive for degradation studies with common tensile specimens. Therefore, this paper aims to reduce the volume of the material used for manufacturing tensile specimen by introducing a new tensile specimen, Micro-X tensile specimen, developed for steel. Young’s Modulus and Ultimate Tensile Strength of micro-X tensile specimens were compared with the ASTM D1708 standard specimens. The experimental results showed that there is no significant difference in terms of mechanical properties. Furthermore, the micro-X tensile specimen was reduced the volume and as well as the cost by approximately 91% in comparison to ASTM D1708 standard tensile specimen

A Kernel-space POF virtual switch

Protocol Oblivious Forwarding (POF) aims at providing a standard southbound interface for sustainable Software Defined Networking (SDN) evolvement. It overcomes the limitations of popular Open Flow protocols (an existing widely-adopted southbound interface), through the enhancement of SDN forwarding plane. This paper pioneers the design and implementation of a Kernel-space POF Virtual Switch (K_POFVS) on Linux platform. K_POFVS can improve the packet processing speed, through fast packet forwarding and the capability of adding/deleting/modifying protocol fields in kernel space. In addition, it is able to enhance flow table matching speed, by separating the mask table (consisting of flow entry masks used to figure out the matching field) and the flow table under a caching mechanism. Furthermore, K_POFVS can achieve efficient communication between the kernel space and the user space, via extending the Netlink communication between them. Experimental results show that K_POFVS can provide much better performance than existing user-space POF virtual switches, in terms of packet forwarding delay, packet processing delay and packet transmission rateThis work is partially supported by the National Program on Key Basic Research Project of China (973 Program) under Grant No. 2012CB315803, the Strategic Priority Research Program of the Chinese Academy of Sciences under grant No. XDA06010306, the National Natural Science Foundation of China under Grant No. 61303241, and the University of Exeter’s Innovation Platform – Link Fund under Award No. LF207

LESS: Label-efficient Multi-scale Learning for Cytological Whole Slide Image Screening

In computational pathology, multiple instance learning (MIL) is widely used to circumvent the computational impasse in giga-pixel whole slide image (WSI) analysis. It usually consists of two stages: patch-level feature extraction and slide-level aggregation. Recently, pretrained models or self-supervised learning have been used to extract patch features, but they suffer from low effectiveness or inefficiency due to overlooking the task-specific supervision provided by slide labels. Here we propose a weakly-supervised Label-Efficient WSI Screening method, dubbed LESS, for cytological WSI analysis with only slide-level labels, which can be effectively applied to small datasets. First, we suggest using variational positive-unlabeled (VPU) learning to uncover hidden labels of both benign and malignant patches. We provide appropriate supervision by using slide-level labels to improve the learning of patch-level features. Next, we take into account the sparse and random arrangement of cells in cytological WSIs. To address this, we propose a strategy to crop patches at multiple scales and utilize a cross-attention vision transformer (CrossViT) to combine information from different scales for WSI classification. The combination of our two steps achieves task-alignment, improving effectiveness and efficiency. We validate the proposed label-efficient method on a urine cytology WSI dataset encompassing 130 samples (13,000 patches) and FNAC 2019 dataset with 212 samples (21,200 patches). The experiment shows that the proposed LESS reaches 84.79%, 85.43%, 91.79% and 78.30% on a urine cytology WSI dataset, and 96.88%, 96.86%, 98.95%, 97.06% on FNAC 2019 dataset in terms of accuracy, AUC, sensitivity and specificity. It outperforms state-of-the-art MIL methods on pathology WSIs and realizes automatic cytological WSI cancer screening.Comment: This paper was submitted to Medical Image Analysis. It is under revie

DMCVR: Morphology-Guided Diffusion Model for 3D Cardiac Volume Reconstruction

Accurate 3D cardiac reconstruction from cine magnetic resonance imaging (cMRI) is crucial for improved cardiovascular disease diagnosis and understanding of the heart's motion. However, current cardiac MRI-based reconstruction technology used in clinical settings is 2D with limited through-plane resolution, resulting in low-quality reconstructed cardiac volumes. To better reconstruct 3D cardiac volumes from sparse 2D image stacks, we propose a morphology-guided diffusion model for 3D cardiac volume reconstruction, DMCVR, that synthesizes high-resolution 2D images and corresponding 3D reconstructed volumes. Our method outperforms previous approaches by conditioning the cardiac morphology on the generative model, eliminating the time-consuming iterative optimization process of the latent code, and improving generation quality. The learned latent spaces provide global semantics, local cardiac morphology and details of each 2D cMRI slice with highly interpretable value to reconstruct 3D cardiac shape. Our experiments show that DMCVR is highly effective in several aspects, such as 2D generation and 3D reconstruction performance. With DMCVR, we can produce high-resolution 3D cardiac MRI reconstructions, surpassing current techniques. Our proposed framework has great potential for improving the accuracy of cardiac disease diagnosis and treatment planning. Code can be accessed at https://github.com/hexiaoxiao-cs/DMCVR.Comment: Accepted in MICCAI 202