Novel ensemble algorithms for random two-domain parabolic problems

In this paper, three efficient ensemble algorithms are proposed for fast-solving the random fluid-fluid interaction model. Such a model can be simplified as coupling two heat equations with random diffusion coefficients and a friction parameter due to its complexity and uncertainty. We utilize the Monte Carlo method for the coupled model with random inputs to derive some deterministic fluid-fluid numerical models and use the ensemble idea to realize the fast computation of multiple problems. Our remarkable feature of these algorithms is employing the same coefficient matrix for multiple linear systems, significantly reducing the computational cost. By data-passing partitioned techniques, we can decouple the numerical models into two smaller sub-domain problems and achieve parallel computation. Theoretically, we derive that both algorithms are unconditionally stable and convergent. Finally, numerical experiments are conducted not only to support the theoretical results but also to validate the exclusive feature of the proposed algorithms

Accelerating Stochastic Recursive and Semi-stochastic Gradient Methods with Adaptive Barzilai-Borwein Step Sizes

The mini-batch versions of StochAstic Recursive grAdient algoritHm and Semi-Stochastic Gradient Descent method, employed the random Barzilai-Borwein step sizes (shorted as MB-SARAH-RBB and mS2GD-RBB), have surged into prominence through timely step size sequence. Inspired by modern adaptors and variance reduction techniques, we propose two new variant rules in the paper, referred to as RHBB and RHBB+, thereby leading to four algorithms MB-SARAH-RHBB, MB-SARAH-RHBB+, mS2GD-RHBB and mS2GD-RHBB+ respectively. RHBB+ is an enhanced version that additionally incorporates the importance sampling technique. They are aggressive in updates, robust in performance and self-adaptive along iterative periods. We analyze the flexible convergence structures and the corresponding complexity bounds in strongly convex cases. Comprehensive tuning guidance is theoretically provided for reference in practical implementations. Experiments show that the proposed methods consistently outperform the original and various state-of-the-art methods on frequently tested data sets. In particular, tests on the RHBB+ verify the efficacy of applying the importance sampling technique to the step size level. Numerous explorations display the promising scalability of our iterative adaptors.Comment: 44 pages, 33 figure

Using Data Mining Techniques to Assess the Impact of COVID-19 on the Auto Insurance Industry in China

Since coronavirus disease 2019 (COVID-19) was discovered at the end of 2019, the whole world has been severely affected. The insurance industry, regarded as an important factor in recovery, has also been affected by COVID-19. However, effective data mining techniques have rarely been utilized in the insurance industry in China, especially under the circumstances of COVID-19. Although some traditional statistical analysis methods have been applied to this area, the limitation of the lack of data distribution still cannot be efficiently overcome. With the machine learning technique proposed in this thesis, this limitation can be solved by using a stacking model with great generalization ability. In this research, the ElasticNet, LightGBM, and Random Forest approaches were employed as base learners; ridge and LASSO regression were used as meta-models to increase the prediction accuracy; and the SHAP value was utilized to explain the impact of COVID-19 on the insurance industry in China. The stacking meta-model in this thesis has a mean absolute percentage error (MAPE) of 12.57134, whereas the average value in the past week is 21.50972, and the MAPE of ElasticNet is 22.57935. In conclusion, COVID-19 affects the auto insurance industry in China

Sex-Interacting mRNA- and miRNA-eQTLs and Their Implications in Gene Expression Regulation and Disease

Despite sex being an important epidemiological and physiological factor, not much is known about how sex works to interact with genotypes to result in different phenotypes. Both messenger RNA (mRNA) and microRNA (miRNA) may be differentially expressed between the sexes in different physiological conditions, and both may be differentially regulated between males and females. Using whole transcriptome data on lymphoblastoid cell lines from 338 samples of European origin, we tried to uncover genes differentially expressed between the two sexes and sex-interacting expression quantitative trait loci (ss-eQTLs). Two miRNAs were found to be differentially expressed between the two sexes, both of which were found to be functionally implicated in breast cancer. Using two stage linear regression analysis, 21 mRNA ss-eQTL and 3 miRNA ss-eQTLs were discovered. We replicated two of the mRNA ss-eQTLs (p < 0.1) using a separate dataset of gene expression data derived from monocytes. Three mRNA ss-eQTLs are in high linkage disequilibrium with variants also found to be associated with sexually dimorphic traits. Taken together, we believe the ss-eQTLs presented will assist researchers in uncovering the basis of sex-biased gene expression regulation, and ultimately help us understand the genetic basis of differences in phenotypes between sexes

Exploring determinants of attraction and helpfulness of online product review:a consumer behaviour perspective

To assist filtering and sorting massive review messages, this paper attempts to examine the determinants of review attraction and helpfulness. Our analysis divides consumers’ reading process into “notice stage” and “comprehend stage” and considers the impact of “explicit information” and “implicit information” of review attraction and review helpfulness. 633 online product reviews were collected from Amazon China. A mixed-method approach is employed to test the conceptual model proposed for examining the influencing factors of review attraction and helpfulness. The empirical results show that reviews with negative extremity, more words, and higher reviewer rank easily gain more attraction and reviews with negative extremity, higher reviewer rank, mixed subjective property, and mixed sentiment seem to be more helpful. The research findings provide some important insights, which will help online businesses to encourage consumers to write good quality reviews and take more active actions to maximise the value of online reviews

SoK: Diving into DAG-based Blockchain Systems

Blockchain plays an important role in cryptocurrency markets and technology services. However, limitations on high latency and low scalability retard their adoptions and applications in classic designs. Reconstructed blockchain systems have been proposed to avoid the consumption of competitive transactions caused by linear sequenced blocks. These systems, instead, structure transactions/blocks in the form of Directed Acyclic Graph (DAG) and consequently re-build upper layer components including consensus, incentives, \textit{etc.} The promise of DAG-based blockchain systems is to enable fast confirmation (complete transactions within million seconds) and high scalability (attach transactions in parallel) without significantly compromising security. However, this field still lacks systematic work that summarises the DAG technique. To bridge the gap, this Systematization of Knowledge (SoK) provides a comprehensive analysis of DAG-based blockchain systems. Through deconstructing open-sourced systems and reviewing academic researches, we conclude the main components and featured properties of systems, and provide the approach to establish a DAG. With this in hand, we analyze the security and performance of several leading systems, followed by discussions and comparisons with concurrent (scaling blockchain) techniques. We further identify open challenges to highlight the potentiality of DAG-based solutions and indicate their promising directions for future research.Comment: Full versio

Rational Ponzi Games in Algorithmic Stablecoin

Algorithmic stablecoins (AS) are one special type of stablecoins that are not backed by any asset (equiv. without collateral). They stand to revolutionize the way a sovereign fiat operates. As implemented, these coins are poorly stabilized in most cases, easily deviating from the price target or even falling into a catastrophic collapse (a.k.a. Death spiral), and are as a result dismissed as a Ponzi scheme. However, is this the whole picture? In this paper, we try to reveal the truth and clarify such a deceptive concept. We find that Ponzi is basically a financial protocol that pays existing investors with funds collected from new ones. Running a Ponzi, however, does not necessarily imply that any participant is in any sense losing out, as long as the game can be perpetually rolled over. Economists call such realization as a \textit{rational Ponzi game}. We thereby propose a rational model in the context of AS and draw its holding conditions. We apply the model to examine: \textit{whether or not the algorithmic stablecoin is a rational Ponzi game.} Accordingly, we discuss two types of algorithmic stablecoins (\text{Rebase} \& \text{Seigniorage shares}) and dig into the historical market performance of two impactful projects (\text{Ampleforth} \& \text{TerraUSD}, respectively) to demonstrate the effectiveness of our model.Comment: Accepted by CryptoEx@ICBC 202

Virtual Reality Based Robot Teleoperation via Human-Scene Interaction

Robot teleoperation gains great success in various situations, including chemical pollution rescue, disaster relief, and long-distance manipulation. In this article, we propose a virtual reality (VR) based robot teleoperation system to achieve more efficient and natural interaction with humans in different scenes. A user-friendly VR interface is designed to help users interact with a desktop scene using their hands efficiently and intuitively. To improve user experience and reduce workload, we simulate the process in the physics engine to help build a preview of the scene after manipulation in the virtual scene before execution. We conduct experiments with different users and compare our system with a direct control method across several teleoperation tasks. The user study demonstrates that the proposed system enables users to perform operations more instinctively with a lighter mental workload. Users can perform pick-and-place and object-stacking tasks in a considerably short time, even for beginners. Our code is available at https://github.com/lingxiaomeng/VR_Teleoperation_Gen3

Provably Secure Single Sign-on Scheme in Distributed Systems and Networks

Distributed systems and networks have been adopted by telecommunications, remote educations, businesses, armies and governments. A widely applied technique for distributed systems and networks is the single sign-on (SSO) which enables a user to use a unitary secure credential (or token) to access multiple computers and systems where he/she has access permissions. However, most existing SSO schemes have not been formally proved to satisfy credential privacy and soundness of credential based authentication. To overcome this drawback, we formalise the security model of single sign-on scheme with authenticated key exchange. Specially, we point out the difference between soundness and credential privacy, and define them together in one definition. Also, we propose a provably secure single sign-on authentication scheme, which satisfies soundness, preserves credential privacy, meets user anonymity, and supports session key exchange. The proposed scheme is very efficient so that it suits for mobile devices in distributed systems and networks. 2012 IEEE

An Efficient Generic Framework for Three-Factor Authentication With Provably Secure Instantiation

Remote authentication has been widely studied and adapted in distributed systems. The security of remote authentication mechanisms mostly relies on one of or the combination of three factors: 1) something users know - password; 2) something users have - smart card; and 3) something users are - biometric characteristics. This paper introduces an efficient generic framework for three-factor authentication. The proposed generic framework enhances the security of existing two-factor authentication schemes by upgrading them to three-factor authentication schemes, without exposing user privacy. In addition, we present a case study by upgrading a secure two-factor authentication scheme to a secure three-factor authentication scheme. Furthermore, implementation analysis, formal proof, and privacy discussion are provided to show that the derived scheme is practical, secure, and privacy preserving