High Order Maximum Principle Preserving Semi-Lagrangian Finite Difference WENO schemes for the Vlasov Equation

In this paper, we propose the parametrized maximum principle preserving (MPP) flux limiter, originally developed in [Z. Xu, Math. Comp., (2013), in press], to the semi- Lagrangian finite difference weighted essentially non-oscillatory scheme for solving the Vlasov equation. The MPP flux limiter is proved to maintain up to fourth order accuracy for the semi-Lagrangian finite difference scheme without any time step restriction. Numerical studies on the Vlasov-Poisson system demonstrate the performance of the proposed method and its ability in preserving the positivity of the probability distribution function while maintaining the high order accuracy

Micro-encapsulated phase change material (MPCM) slurries: characterization and building applications

© 2017 Micro-encapsulated Phase Change Material (MPCM) slurries, acting as the heat transfer fluids or thermal storage mediums, have gained applications in various building thermal energy systems, significantly enhancing their energy efficiency and operational performance. This paper presents a review of research on MPCM slurries and their building applications. The research collects information on the currently available MPCM particles and shells, studies of the physical, structural and thermal stability, and rheological properties of MPCM slurries, and identification/determination of the critical parameters and dimensionless numbers relating to the MPCM slurries’ heat transfer. The research suggests possible approaches for enhancing the heat transfer between a MPCM slurry and its surroundings, while several controversial phenomena and potential causes were also investigated. Furthermore, the research presents mathematical correlations established between different thermal and physical parameters relating to the MPCM slurries, and introduces a number of practical applications of the MPCM slurries in building thermal energy systems. Based on such extensive review and analyses, the research will help in identifying the current status, potential problems in existence, and future directions in research, development and practical application of MPCM slurries. It will also promote the development and application of cost-effective and energy-efficient PCM materials and thus contribute to achieving the UK and international targets in energy saving and carbon emission reductions in the building sector and beyond

Cell aging preserves cellular immortality in the presence of lethal levels of damage.

Cellular aging, a progressive functional decline driven by damage accumulation, often culminates in the mortality of a cell lineage. Certain lineages, however, are able to sustain long-lasting immortality, as prominently exemplified by stem cells. Here, we show that Escherichia coli cell lineages exhibit comparable patterns of mortality and immortality. Through single-cell microscopy and microfluidic techniques, we find that these patterns are explained by the dynamics of damage accumulation and asymmetric partitioning between daughter cells. At low damage accumulation rates, both aging and rejuvenating lineages retain immortality by reaching their respective states of physiological equilibrium. We show that both asymmetry and equilibrium are present in repair mutants lacking certain repair chaperones, suggesting that intact repair capacity is not essential for immortal proliferation. We show that this growth equilibrium, however, is displaced by extrinsic damage in a dosage-dependent response. Moreover, we demonstrate that aging lineages become mortal when damage accumulation rates surpass a threshold, whereas rejuvenating lineages within the same population remain immortal. Thus, the processes of damage accumulation and partitioning through asymmetric cell division are essential in the determination of proliferative mortality and immortality in bacterial populations. This study provides further evidence for the characterization of cellular aging as a general process, affecting prokaryotes and eukaryotes alike and according to similar evolutionary constraints

The valley version of the Extended Delta Conjecture

The Shuffle Theorem of Carlsson and Mellit gives a combinatorial expression for the bigraded Frobenius characteristic of the ring of diagonal harmonics, and the Delta Conjecture of Haglund, Remmel and the second author provides two generalizations of the Shuffle Theorem to the delta operator expression $\Delta'_{e_k} e_n$. Haglund et al. also propose the Extended Delta Conjecture for the delta operator expression $\Delta'_{e_k} \Delta_{h_r}e_n$, which is analogous to the rise version of the Delta Conjecture. Recently, D'Adderio, Iraci and Wyngaerd proved the rise version of the Extended Delta Conjecture at the case when $t=0$. In this paper, we propose a new valley version of the Extended Delta Conjecture. Then, we work on the combinatorics of extended ordered multiset partitions to prove that the two conjectures for $\Delta'_{e_k} \Delta_{h_r}e_n$ are equivalent when $t$ or $q$ equals 0, thus proving the valley version of the Extended Delta Conjecture when $t$ or $q$ equals 0.Comment: 28 pages, 9 figure

A Twitter-Based Prediction Market: Social Network Approach

Information aggregation mechanisms are designed explicitly for collecting and aggregating dispersed information. Prediction markets represent one of the best examples of how this kind of wisdom of the crowds can be used. We use a Twitter-based prediction market to suggest that carefully designed market mechanisms can bring to light trends in dispersed information that improves the accuracy of our predictions. The information system we are developing combines the power of prediction markets with the popularity of Twitter. Simulation results show that our network-embedded prediction market can produce better predictions using information exchange in social networks and can outperform other prediction markets that do not use social networks. We also demonstrate that as cost decreases and more and more agents acquire information, the prediction market prices fully incorporate all available information, and the forecasting performance of the network-embedded prediction market is better

Information Exchange in Prediction Markets: How Social Networks Promote Forecast Efficiency

This paper studies the effects of social networks on the performance of prediction markets with endogenous information acquisition. We provide a game-theoretic framework to resolve the question: Can social networks and information exchange promote the forecast efficiency in prediction markets? Our study shows that the use of social networks could be detrimental to forecast performance when the cost of information acquisition is high. Although social networks can provide internal communications among participants, they reduce the incentive to acquire information because of free riding. We also study the effects of social networks on information acquisition in prediction markets. In the symmetric Bayes-Nash Equilibrium, all participants use a threshold strategy, and the equilibrium action of information acquisition is decreasing in the number of participant\u27s friends and increasing in the network density

Diffeomorphic Metric Mapping and Probabilistic Atlas Generation of Hybrid Diffusion Imaging based on BFOR Signal Basis

We propose a large deformation diffeomorphic metric mapping algorithm to align multiple b-value diffusion weighted imaging (mDWI) data, specifically acquired via hybrid diffusion imaging (HYDI), denoted as LDDMM-HYDI. We then propose a Bayesian model for estimating the white matter atlas from HYDIs. We adopt the work given in Hosseinbor et al. (2012) and represent the q-space diffusion signal with the Bessel Fourier orientation reconstruction (BFOR) signal basis. The BFOR framework provides the representation of mDWI in the q-space and thus reduces memory requirement. In addition, since the BFOR signal basis is orthonormal, the L2 norm that quantifies the differences in the q-space signals of any two mDWI datasets can be easily computed as the sum of the squared differences in the BFOR expansion coefficients. In this work, we show that the reorientation of the $q$-space signal due to spatial transformation can be easily defined on the BFOR signal basis. We incorporate the BFOR signal basis into the LDDMM framework and derive the gradient descent algorithm for LDDMM-HYDI with explicit orientation optimization. Additionally, we extend the previous Bayesian atlas estimation framework for scalar-valued images to HYDIs and derive the expectation-maximization algorithm for solving the HYDI atlas estimation problem. Using real HYDI datasets, we show the Bayesian model generates the white matter atlas with anatomical details. Moreover, we show that it is important to consider the variation of mDWI reorientation due to a small change in diffeomorphic transformation in the LDDMM-HYDI optimization and to incorporate the full information of HYDI for aligning mDWI

Manipulation: Online Platforms’ Inescapable Fate

Online platforms are prone to abuse and manipulation from strategic parties. For example, social media and review websites suffer from the presence of opinion spam and fake reviews. Applying the economic concept of rational expectation equilibrium (REE), we explore the impact of manipulation on consumer welfare in a Twitter-like environment. We argue that the REE outcome can be decomposed into a firm-centric effect and a rational expectation effect, and the relative strength of these effects determines the final level of manipulation. We also examine the effect of competition on firms’ manipulation levels. We find that the combination of a competition effect and a rational expectation effect determines the overall effect of competition on strategic manipulation. This research sheds light on the reliability of opinion mining, and contributes to our understanding of strategic manipulation in the context of sentiment analysis