Dynamically Adjusting the Mining Capacity in Cryptocurrency with Binary Blockchain

Many cryptocurrencies rely on Blockchain for its operation. Blockchain serves as a public ledger where all the completed transactions can be looked up. To place transactions in the Blockchain, a mining operation must be performed. However, due to a limited mining capacity, the transaction confirmation time is increasing. To mitigate this problem many ideas have been proposed, but they all come with own challenges. We propose a novel parallel mining method that can adjust the mining capacity dynamically depending on the congestion level. It does not require an increase in the block size or a reduction of the block confirmation time. The proposed scheme can increase the number of parallel blockchains when the mining congestion is experienced, which is especially effective under DDoS attack situation. We describe how and when the Blockchain is split or merged, how to solve the imbalanced mining problem, and how to adjust the difficulty levels and rewards. We then show the simulation results comparing the performance of binary blockchain and the traditional single blockchain

Hadoop Performance Analysis Model with Deep Data Locality

Background: Hadoop has become the base framework on the big data system via the simple concept that moving computation is cheaper than moving data. Hadoop increases a data locality in the Hadoop Distributed File System (HDFS) to improve the performance of the system. The network traffic among nodes in the big data system is reduced by increasing a data-local on the machine. Traditional research increased the data-local on one of the MapReduce stages to increase the Hadoop performance. However, there is currently no mathematical performance model for the data locality on the Hadoop. Methods: This study made the Hadoop performance analysis model with data locality for analyzing the entire process of MapReduce. In this paper, the data locality concept on the map stage and shuffle stage was explained. Also, this research showed how to apply the Hadoop performance analysis model to increase the performance of the Hadoop system by making the deep data locality. Results: This research proved the deep data locality for increasing performance of Hadoop via three tests, such as, a simulation base test, a cloud test and a physical test. According to the test, the authors improved the Hadoop system by over 34% by using the deep data locality. Conclusions: The deep data locality improved the Hadoop performance by reducing the data movement in HDFS

To what extent is digit patterning a Turing System?

Building precise, robust patterns and structures from an initially homogeneous state is fundamental to developmental biology. Digit patterning is a representative example of a periodic pattern in development. Previous studies have shown that a reaction–diffusion (Turing) system, in which diffusible activators and inhibitors interact, is the most likely explanation of how the spatial pattern of the digits is formed. Although self-organisation mechanisms such as the Turing system successfully recapitulate many aspects of digit patterning, critical questions remain regarding its timing and behaviour. First I addressed the question of timing, or how long reaction-diffusion plays a role in the developing digits. I perturbed the digit patterning process of embryonic limbs by inserting beads that contain morphogens involved in the reaction-diffusion mechanism. Then I quantified the degree of pattern change, or plasticity of the patterning, from limbs harvested at different developmental timing throughout the digit patterning stage. For quantification, I developed a custom image analytic pipeline that extracts relevant topology and represents the difference between perturbed and unperturbed patterns. Modelling the plasticity profile over the digit patterning process, through extensive interplay of experiments and modelling, revealed that plasticity during digit patterning decreases in a sigmoidal manner. Transcriptomics analysis that matches with the sigmoidal decrease observed in expression patterns further identified gene candidates that could be critical to the digit patterning. Further, the timing of reaction-diffusion is discussed in the context of the tissue movements, revealing that Sox9 digit patterning happens significantly earlier than cell density changes. The second part aims at improving our understanding about which pathways and components of the pathways are involved in the digit forming Turing network. Previously identified digit patterning Turing network, such as BSW model, abstracts the entire Wnt and Bmp signalling pathways’ activities into each node. Thus there is insufficient knowledge on the mechanistic role of Wnt signalling mediated Sox9 repression. To further clarify detailed mechanisms of the Turing network, I used an unbiased screening approach to systematically perturb digit patterning using small molecule inhibitors, ligands, and peptides at different doses in systems such as limb culture and micromass. Out of multiple steps critical to Wnt signalling, including Wnt production, Wnt receptor interaction, Wnt canonical pathway cytosolic interactions, and Wnt canonical pathway transcriptional interactions, I identified that inhibition of Wnt production and Wnt transcriptional component inhibition category most effectively disrupt digit patterning. I also identified candidate ligands such as sFRP1 and Dkk1 as potential extracellular Wnt inhibitors that effectively change digit patterning upon application. These results provide the first quantitative insight into the duration of the reaction-diffusion based mechanism in a biological system, and how a screening approach complemented with data driven modelling can complement and clarify workings of a reaction diffusion based system. Further work in improving our knowledge on the Turing system with tissue growth, cell movements, and ectodermal-mesenchymal interaction will eventually allow generation of a complete organogenesis simulation model

Punishing without rewards? A comprehensive examination of the asymmetry in economic voting

It has been controversial whether incumbents are punished more for a bad economy than they are rewarded for a good economy due to mixed results from previous studies on one or handful number of countries. This paper makes an empirical contribution to this lingering question by conducting extensive tests on whether this asymmetry hypothesis is a cross-nationally generalizable phenomenon using all currently available modules of the Comparative Study of Electoral Systems survey from 122 elections in 42 representative democracies between 1996 and 2016, as well as macro-economic indicators and individual-level economic perception. In general, this paper finds little support for the asymmetry hypothesis; although the evidence of such asymmetric economic voting is found in some subpopulations using certain economic indicators, these conditional effects are largely inconsistent, suggesting that it is still safe to assume a linear relationship between economic conditions and support for the incumbent

分子動力学シミュレーションを用いた凝縮系のシングルビーム２次元ラマン分光法

京都大学新制・課程博士博士(理学)甲第23030号理博第4707号新制||理||1675(附属図書館)京都大学大学院理学研究科化学専攻(主査)教授 谷村 吉隆, 教授 渡邊 一也, 教授 林 重彦学位規則第4条第1項該当Doctor of ScienceKyoto UniversityDGA

A Lab Experiment on Committee Hearings: Preferences, Power, and a Quest for Information

In principle, committees hold hearings to gather and provide information to their principals, but some hearings are characterized as political showcases. This article investigates conditions that moderate committee members' incentives to hold an informative hearing by presenting a game‐theoretic model and a lab experiment. Specifically, it studies when committees hold hearings and which types of hearing they hold by varying policy preferences of committee members and the principal and political gains from posturing. Findings provide new insights to how preferences and power distribution affect individuals' incentives to be informed when they make decisions as members of a committee in many contexts

Maximum quantum battery charging power is not an entanglement monotone

We establish a general implementation-independent approach to assess the potential advantage of using highly entangled quantum states for enhancing the maximum charging power of quantum batteries. It is shown that the impact of entanglement on power can be separated from both the global quantum speed limit associated to an optimal choice of driving Hamiltonian and the energy gap of the batteries. We then demonstrate that the quantum state advantage of battery charging, defined as the power obtainable for given quantum speed limit and energy gap, is not an entanglement monotone. A striking example we provide is that, counterintuitively, independent thermalization of the local batteries, completely destroying any entanglement, can lead to larger charging power than that of the initial maximally entangled state. Highly entangled states can thus also be potentially $disadvantageous$ when compared to simple product states representing the well known classical limit of battery charging. We also demonstrate that taking the considerable effort of producing highly entangled states, such as W or $k$-locally entangled states is not sufficient to obtain quantum-enhanced scaling behavior with the number of battery cells.Comment: 11 pages, 2 figure