Gromov-Witten theory and cycle-valued modular forms

In this paper, we proved generating functions of Gromov-Witten cycles of the elliptic orbifold lines with weights (3,3,3), (4,4,2), and (6,3,2) are cycle-valued quasi-modular forms. This is a generalization of Milanov and Ruan's work on cycle-valued level. First we construct a global cohomology field theory (CohFT) for simple elliptic singularities (modulo an extension problem) and prove its modularity. Then, we apply Teleman's reconstruction theorem to prove mirror theorems on cycled-valued level and match it with a CohFT from Gromov-Witten theory of a corresponding orbifold.This solves the extension property as well as inducing the modularity for a Gromov-Witten CohFT

A Trust Management Framework for Decision Support Systems

In the era of information explosion, it is critical to develop a framework which can extract useful information and help people to make “educated” decisions. In our lives, whether we are aware of it, trust has turned out to be very helpful for us to make decisions. At the same time, cognitive trust, especially in large systems, such as Facebook, Twitter, and so on, needs support from computer systems. Therefore, we need a framework that can effectively, but also intuitively, let people express their trust, and enable the system to automatically and securely summarize the massive amounts of trust information, so that a user of the system can make “educated” decisions, or at least not blind decisions. Inspired by the similarities between human trust and physical measurements, this dissertation proposes a measurement theory based trust management framework. It consists of three phases: trust modeling, trust inference, and decision making. Instead of proposing specific trust inference formulas, this dissertation proposes a fundamental framework which is flexible and can be adapted by many different inference formulas. Validation experiments are done on two data sets: the Epinions.com data set and the Twitter data set. This dissertation also adapts the measurement theory based trust management framework for two decision support applications. In the first application, the real stock market data is used as ground truth for the measurement theory based trust management framework. Basically, the correlation between the sentiment expressed on Twitter and stock market data is measured. Compared with existing works which do not differentiate tweets’ authors, this dissertation analyzes trust among stock investors on Twitter and uses the trust network to differentiate tweets’ authors. The results show that by using the measurement theory based trust framework, Twitter sentiment valence is able to reflect abnormal stock returns better than treating all the authors as equally important or weighting them by their number of followers. In the second application, the measurement theory based trust management framework is used to help to detect and prevent from being attacked in cloud computing scenarios. In this application, each single flow is treated as a measurement. The simulation results show that the measurement theory based trust management framework is able to provide guidance for cloud administrators and customers to make decisions, e.g. migrating tasks from suspect nodes to trustworthy nodes, dynamically allocating resources according to trust information, and managing the trade-off between the degree of redundancy and the cost of resources

Receipt-Freeness and Coercion Resistance in Remote E-Voting Systems

Abstract: Remote electronic voting (E-voting) is a more convenient and efficient methodology when compared with traditional voting systems. It allows voters to vote for candidates remotely, however, remote E-voting systems have not yet been widely deployed in practical elections due to several potential security issues, such as vote-privacy, robustness and verifiability. Attackers' targets can be either voting machines or voters. In this paper, we mainly focus on three important security properties related to voters: receipt-freeness, vote-selling resistance, and voter-coercion resistance. In such scenarios, voters are willing or forced to cooperate with attackers. We provide a survey of existing remote E-voting systems, to see whether or not they are able to satisfy these three properties to avoid corresponding attacks. Furthermore, we identify and summarise what mechanisms they use in order to satisfy these three security properties

Using Twitter trust network for stock market analysis

Online social networks are now attracting a lot of attention not only from their users but also from researchers in various fields. Many researchers believe that the public mood or sentiment expressed in social media is related to financial markets. We propose to use trust among users as a filtering and amplifying mechanism for the social media to increase its correlation with financial data in the stock market. Therefore, we used the real stock market data as ground truth for our trust management system. We collected stock-related data (tweets) from Twitter, which is a very popular Micro-blogging forum, to see the correlation between the Twitter sentiment valence and abnormal stock returns for eight firms in the S&P 500. We developed a trust management framework to build a user-to-user trust network for Twitter users. Compared with existing works, in addition to analyzing and accumulating tweets’ sentiment, we take into account the source of tweets – their authors. Authors are differentiated by their power or reputation in the whole community, where power is determined by the user-to-user trust network. To validate our trust management system, we did the Pearson correlation test for an eight months period (the trading days from 01/01/2015 through 08/31/2015). Compared with treating all the authors equally important, or weighting them by their number of followers, our trust network based reputation mechanism can amplify the correlation between a specific firm’s Twitter sentiment valence and the firm’s stock abnormal returns. To further consider the possible auto-correlation property of abnormal stock returns, we constructed a linear regression model, which includes historical stock abnormal returns, to test the relation between the Twitter sentiment valence and abnormal stock returns. Again, our results showed that by using our trust network power based method to weight tweets, Twitter sentiment valence reflect abnormal stock returns better than treating all the authors equally important or weighting them by their number of followers

Survey of Return-Oriented Programming Defense Mechanisms

A prominent software security violation-buffer overflow attack has taken various forms and poses serious threats until today. One such vulnerability is return-oriented programming attack. An return-oriented programming attack circumvents the dynamic execution prevention, which is employed in modern operating systems to prevent execution of data segments, and attempts to execute unintended instructions by overwriting the stack exploiting the buffer overflow vulnerability. Numerous defense mechanisms have been proposed in the past few years to mitigate/prevent the attack – compile time methods that add checking logic to the program code before compilation, dynamic methods that monitor the control-flow integrity during execution and randomization methods that aim at randomizing instruction locations. This paper discusses (i) these different static, dynamic, and randomization techniques proposed recently and (ii) compares the techniques based on their effectiveness and performances

Morphological diversity of single neurons in molecularly defined cell types.

Dendritic and axonal morphology reflects the input and output of neurons and is a defining feature of neuronal types1,2, yet our knowledge of its diversity remains limited. Here, to systematically examine complete single-neuron morphologies on a brain-wide scale, we established a pipeline encompassing sparse labelling, whole-brain imaging, reconstruction, registration and analysis. We fully reconstructed 1,741 neurons from cortex, claustrum, thalamus, striatum and other brain regions in mice. We identified 11 major projection neuron types with distinct morphological features and corresponding transcriptomic identities. Extensive projectional diversity was found within each of these major types, on the basis of which some types were clustered into more refined subtypes. This diversity follows a set of generalizable principles that govern long-range axonal projections at different levels, including molecular correspondence, divergent or convergent projection, axon termination pattern, regional specificity, topography, and individual cell variability. Although clear concordance with transcriptomic profiles is evident at the level of major projection type, fine-grained morphological diversity often does not readily correlate with transcriptomic subtypes derived from unsupervised clustering, highlighting the need for single-cell cross-modality studies. Overall, our study demonstrates the crucial need for quantitative description of complete single-cell anatomy in cell-type classification, as single-cell morphological diversity reveals a plethora of ways in which different cell types and their individual members may contribute to the configuration and function of their respective circuits

Methylenetetrahydrofolate reductase <it>C677T</it> polymorphism is associated with estimated glomerular filtration rate in hypertensive Chinese males

<p>Abstract</p> <p>Background</p> <p>Plasma level of total homocysteine (tHcy) is negatively correlated with kidney function in general population. However, the causal mechanism of this correlation is poorly understood. The purpose of this study is to investigate the association of methylenetetrahydrofolate reductase (<it>MTHFR</it>) <it>C677T</it> gene polymorphism, which is a major genetic determinant of the plasma tHcy level, with estimated glomerular filtration rate (eGFR) in Chinese.</p> <p>Methods</p> <p>A total of 18 814 hypertensive patients (6 914 males, 11 900 females) were included in the study.</p> <p>Results</p> <p>Association between the eGFR and <it>MTHFR C677T</it> genotype was examined by sex-specific regression analyses. In males, TT genotype was associated with 1.37 ml/min/1.73 m² decrease in eGFR (p = 0.004) and with an increased risk (OR = 1.32, p = 0.008) for the lowest quintile of eGFR after adjusting for age, BMI, and blood pressures. However, such association was not observed in females (p > 0.05). This association suggests <it>MTHFR C677T</it> polymorphism may play a role in the regulation of eGFR in males.</p> <p>Conclusions</p> <p><it>MTHFR 677 T</it> is a risk allele for decreased kidney function in Chinese males, implicating this gene in the pathogenesis of chronic kidney disease (CKD).</p