Self-adaptive node-based PCA encodings

In this paper we propose an algorithm, Simple Hebbian PCA, and prove that it is able to calculate the principal component analysis (PCA) in a distributed fashion across nodes. It simplifies existing network structures by removing intralayer weights, essentially cutting the number of weights that need to be trained in half

Historical seismograms for unravelling a mysterious earthquake: The 1907 Sumatra Earthquake

History of instrumental seismology is short. Seismograms are available only for a little more than 100 years; high-quality seismograms are available only for the last 50 years and the seismological database is very limited in time. To extend the database, seismograms of old events are of vital importance. Many unusual earthquakes are known to have occurred, but their seismological characteristics are poorly known. The 1907 Sumatra earthquake is one of them (1907 January 4, M= 7.6). Gutenberg and Richter located this event in the outer-rise area of the Sunda arc. This earthquake is known to be anomalous because of its extensive tsunami, which is disproportionate of its magnitude. The tsunami affected the coastal areas over 950 km along the Sumatran coast. We investigated this earthquake using the historical seismograms we could collect from several seismological observatories. We examined the P-wave arrival times listed in the Strassburg Bulletin (1912) and other station bulletins. The scatter of the Observed−Computed traveltime residuals ranges from –30 to 30 s, too large to locate the event accurately. The uncertainty of the epicentre estimated from an S-P grid-search relocation study is at least 1° (~110 km). We interpreted the Omori seismograms from Osaka, Mizusawa and Tokyo, and the Wiechert seismograms from Göttingen and Uppsala by comparing them with the seismograms simulated from modern broad-band seismograms of the 2002, 2008 and two 2010 Sumatra earthquakes which occurred near the 1907 earthquake. From the amplitude of Rayleigh waves recorded on the Omori seismograms we conclude that the magnitude of the 1907 earthquake at about 30 to 40 s is about 7.8 (i.e. 7.5 to 8.0). The SH waveforms recorded on the Göttingen and Uppsala seismograms suggest that the 1907 earthquake is a thrust earthquake at a shallow depth around 30 km. The most likely scenario is that the 1907 earthquake initiated on the subduction interface, and slowly ruptured up-dip into the shallow sediments and caused the extensive tsunami. Although their quantity and quality are limited, historical seismograms provide key quantitative information about old events that cannot be obtained otherwise. This underscores the importance of preserving historical seismograms

Jumps in Rank and Expected Returns. Introducing Varying Cross-sectional Risk

Decision theorists claim that an ordinal measure of risk may be sufficient for an agent to make a rational choice under uncertainty. We propose a measure of financial risk, namely the Varying Cross-sectional Risk (VCR), that is based on a ranking of returns. VCR is defined as the probability of a sharp jump over time in the position of an asset return within the cross-sectional return distribution of the assets that constitute the market, which is represented by the Standard and Poor's 500 Index (SP500). We model the joint dynamics of the cross-sectional position and the asset return by analyzing (1) the marginal probability distribution of a sharp jump in the cross-sectional position within the context of a duration model, and (2) the probability distribution of the asset return conditional on a jump, for which we specify different return dynamics depending upon whether or not a jump has taken place. As a result, the marginal probability distribution of returns is a mixture of distributions. The performance of our model is assessed in an out-of-sample exercise. We design a set of trading rules that are evaluated according to their profitability and riskiness. A trading rule based on our VCR model is dominant providing superior mean trading returns and accurate estimation of the Value-at-Risk.Duration, Mixture of distributions, Nonlinearity, Reality check, Trading rule, VaR

On M2M Micropayments : A Case Study of Electric Autonomous Vehicles

The proliferation of electric vehicles has spurred the research interest in technologies associated with it, for instance, batteries, and charging mechanisms. Moreover, the recent advancements in autonomous cars also encourage the enabling technologies to integrate and provide holistic applications. To this end, one key requirement for electric vehicles is to have an efficient, secure, and scalable infrastructure and framework for charging, billing, and auditing. However, the current manual charging systems for EVs may not be applicable to the autonomous cars that demand new, automatic, secure, efficient, and scalable billing and auditing mechanism. Owing to the distributed systems such as blockchain technology, in this paper, we propose a new charging and billing mechanism for electric vehicles that charge their batteries in a charging-on-the-move fashion. To meet the requirements of billing in electric vehicles, we leverage distributed ledger technology (DLT), a distributed peer-to-peer technology for micro-transactions. Our proof-of-concept implementation of the billing framework demonstrates the feasibility of such system in electric vehicles. It is also worth noting that the solution can easily be extended to the electric autonomous cars (EACs)

An architecture for distributed ledger-based M2M auditing for Electric Autonomous Vehicles

Electric Autonomous Vehicles (EAVs) promise to be an effective way to solve transportation issues such as accidents, emissions and congestion, and aim at establishing the foundation of Machine-to-Machine (M2M) economy. For this to be possible, the market should be able to offer appropriate charging services without involving humans. The state-of-the-art mechanisms of charging and billing do not meet this requirement, and often impose service fees for value transactions that may also endanger users and their location privacy. This paper aims at filling this gap and envisions a new charging architecture and a billing framework for EAV which would enable M2M transactions via the use of Distributed Ledger Technology (DLT)