Spectral partitioning in equitable graphs

Graph partitioning problems emerge in a wide variety of complex systems, ranging from biology to finance, but can be rigorously analyzed and solved only for a few graph ensembles. Here, an ensemble of equitable graphs, i.e., random graphs with a block-regular structure, is studied, for which analytical results can be obtained. In particular, the spectral density of this ensemble is computed exactly for a modular and bipartite structure. Kesten-McKay's law for random regular graphs is found analytically to apply also for modular and bipartite structures when blocks are homogeneous. An exact solution to graph partitioning for two equal-sized communities is proposed and verified numerically, and a conjecture on the absence of an efficient recovery detectability transition in equitable graphs is suggested. A final discussion summarizes results and outlines their relevance for the solution of graph partitioning problems in other graph ensembles, in particular for the study of detectability thresholds and resolution limits in stochastic block models

A Fair Governance: On Inequality, Power and Democracy

Can governments keep the pace of global markets? It is a defining characteristic of the present times, tested and measured within multiple studies, that we are living in an increasingly interconnected economy in which giant companies emerge and compete presenting new goods and products at a global scale. The competing environment of international markets produces quickly growing creatures that old nation-states struggle to understand, monitor and, consequently, regulate. In this regard, the selection process taking place in the market seems to be far more effective and greedy than the selection process we apply to our governments. In this paper I discuss the basic theoretical mechanisms for the persistence of wealth concentration, introducing a general game-theoretical framework to connect governance, market economy, and wealth distribution, and to rethink democracy and fairness in policy-making, especially with the aim of global sustainability governance

On the thermal dynamic behaviour of the helium-cooled DEMO fusion reactor

The EU-DEMO conceptual design is being conducted among research institutions and universities from 26 countries of European Union, Switzerland and Ukraine. Its mission is to realise electricity from nuclear fusion reaction by 2050. As DEMO has been conceived to deliver net electricity to the grid, the choice of the Breeding Blanket (BB) coolant plays a pivotal role in the reactor design having a strong influence on plant operation, safety and maintenance. In particular, due to the pulsed nature of the heat source, the Primary Heat Transfer System (PHTS) becomes a very important actor of the Balance of Plant (BoP) together with the Power Conversion System (PCS). Moreover, aiming to mitigate the potential negative impact of plasma pulsing on BoP equipment, for the DEMO plant is also being investigated a "heat transfer chain" option which envisages an Intermediate Heat Transfer System (IHTS) equipped with an Energy Storage System (ESS) between PHTS and PCS. Within this framework, a preliminary study has been carried out to analyse the thermal dynamic behaviour of the IHTS system for the Helium-Cooled Pebble Bed (HCPB) BB concept during pulse/dwell transition which should be still considered as the normal operating mode of a fusion power plant. Starting from preliminary thermal-hydraulic calculations made in order to size the main BoP components, the global performances of DEMO BoP have been quantitatively assessed focusing the attention on the attitude of the whole IHTS to smooth the sudden power variations which come from the plasma. The paper describes criteria and rationale followed to develop a numerical model which manages to simulate simple transient scenarios of DEMO BoP. Results of numerical simulations are presented and critically discussed in order to point out the main issues that DEMO BoP has to overcome to achieve a viable electricity power output

Reinforcement Learning for Systematic FX Trading

We explore online inductive transfer learning, with a feature representation transfer from a radial basis function network formed of Gaussian mixture model hidden processing units to a direct, recurrent reinforcement learning agent. This agent is put to work in an experiment, trading the major spot market currency pairs, where we accurately account for transaction and funding costs. These sources of profit and loss, including the price trends that occur in the currency markets, are made available to the agent via a quadratic utility, who learns to target a position directly. We improve upon earlier work by targeting a risk position in an online transfer learning context. Our agent achieves an annualised portfolio information ratio of 0.52 with a compound return of 9.3%, net of execution and funding cost, over a 7-year test set; this is despite forcing the model to trade at the close of the trading day at 5 pm EST when trading costs are statistically the most expensive

Network models of financial systemic risk: A review

The global financial system can be represented as a large complex network in which banks, hedge funds and other financial institutions are interconnected to each other through visible and invisible financial linkages. Recently, a lot of attention has been paid to the understanding of the mechanisms that can lead to a breakdown of this network. This can happen when the existing financial links turn from being a means of risk diversification to channels for the propagation of risk across financial institutions. In this review article, we summarize recent developments in the modeling of financial systemic risk. We focus in particular on network approaches, such as models of default cascades due to bilateral exposures or to overlapping portfolios, and we also report on recent findings on the empirical structure of interbank networks. The current review provides a landscape of the newly arising interdisciplinary field lying at the intersection of several disciplines, such as network science, physics, engineering, economics, and ecology

Forward-looking solvency contagion

Solvency contagion risk is a key channel through which systemic risk can come about. We introduce a model that accounts not only for losses transmitted after banks default, but also for losses due to the fact that creditors revalue their exposures when probabilities of default of their counterparties change. We apply the model to run a series of simplified stress tests of the UK banking system from 2008 to 2016, based on two datasets of real interbank exposures between the seven major UK banks. We show that risks due to solvency contagion decrease markedly from the peak of the crisis, to the point of becoming negligible. We also characterise the distributions of both vulnerabilities and systemic importances of individual banks, thereby tracking the evolution of risk concentration

Glassy magnetic behavior and correlation length in nanogranular Fe-oxide and Au/Fe-oxide samples

In nanoscale magnetic systems, the possible coexistence of structural disorder and competing magnetic interactionsmay determine the appearance of a glassy magnetic behavior, implying the onset of a low-temperature disordered collective state of frozen magnetic moments. This phenomenology is the object of an intense research activity, stimulated by a fundamental scientific interest and by the need to clarify how disordered magnetism effects may affect the performance of magnetic devices (e.g., sensors and data storage media). We report the results of a magnetic study that aims to broaden the basic knowledge of glassy magnetic systems and concerns the comparison between two samples, prepared by a polyol method. The first can be described as a nanogranular spinel Fe-oxide phase composed of ultrafine nanocrystallites (size of the order of 1 nm); in the second, the Fe-oxide phase incorporated non-magnetic Au nanoparticles (10-20 nm in size). In both samples, the Fe-oxide phase exhibits a glassy magnetic behavior and the nanocrystallite moments undergo a very similar freezing process. However, in the frozen regime, the Au/Fe-oxide composite sample is magnetically softer. This effect is explained by considering that the Au nanoparticles constitute physical constraints that limit the length of magnetic correlation between the frozen Fe-oxide moments

Identifying clusters of anomalous payments in the salvadorian payment system

We develop an unsupervised methodology to group payments and identify possible anomalies. With our methodology, we identify clusters based on a set of network features, using transactional (unlabeled) information from a systemically important payment system of El Salvador. We first preprocess network features, such as degree and strength, through a principal components analysis we reduce the dimensionality of the newly defined data, then we place the main variables into clustering algorithms (k-means and DBSCAN) to analyze anomalous payments. We then analyze, these clusters using random forest to obtain the main network feature. Our results suggest that the proposed methodology works very well to detect anomalous payments, and it is very important to study the case of El Salvador, because of the recent restructuring of the Massive Payment System in El Salvador (promoted by the Transfer365 project), because the authorities want to increase financial inclusion. This change will make the SPM available to the public, to diversify services and incorporate more participants because, historically, it has operated with only three active participants. We expected that Transfer365 will interconnect the LBTR participants' systems with their banking core, the systems of the Ministry of Finance, and other authorized participants to channel large payment flows. Then, identifying possible anomalies through methodology will enhance risk monitoring and management by payment systems overseers

Biomedical Co-Cr-Mo Components Produced by Direct Metal Laser Sintering

Direct Metal Laser Sintering (DMLS) is an additive manufacturing technique based on a laser power source that sinters powdered materials using a 3D CAD model. The mechanical components produced by this procedure typically show higher residual porosity and poorer mechanical properties than those obtained by traditional manufacturing techniques. In this study, samples were produced by DMLS starting from a Co-Cr-Mo powder (in the \u3b3 phase) with a composition suitable for biomedical applications. Samples were submitted to hardness measurements and structural characterization. The samples showed a hardness value remarkably higher that those commonly obtained for the same cast or wrought alloys. In fact, the HRC value measured for the samples is 47 HRC, while the usual range for CAST Co-Cr-Mo is from 25 to 35 HRC. The samples microstructure was investigated by X-ray diffraction (XRD), electron microscopy (SEM and TEM) and energy dispersive microanalysis (EDX) in order to clarify the origin of this unexpected result. The laser treatment induced a melting of the metallic Co-Cr-Mo powder, generating a phase transformation from the \u3b3 (fcc) to the e (hcp) phase. The rapid cooling of the melted powder produced the formation of e (hcp) nano-lamellae inside the \u3b3 (fcc) phase. The nano-lamellae formed an intricate network responsible for the measured hardness increase. The results suggest possible innovative applications of the DMLS technique to the production of mechanical parts in the medical and dental fields, where a high degree of personalization is required

Solid-state phase transformations in thermally treated Ti-6Al-4V alloy fabricated via laser powder bed fusion

Laser Powder Bed Fusion (LPBF) technology was used to produce samples based on the Ti-6Al-4V alloy for biomedical applications. Solid-state phase transformations induced by thermal treatments were studied by neutron diffraction (ND), X-ray diffraction (XRD), scanning transmission electron microscopy (STEM) and energy-dispersive spectroscopy (EDS). Although, ND analysis is rather uncommon in such studies, this technique allowed evidencing the presence of retained \u3b2 in \u3b1' martensite of the as-produced (#AP) sample. The retained \u3b2 was not detectable byXRDanalysis, nor by STEM observations. Martensite contains a high number of defects, mainly dislocations, that anneal during the thermal treatment. Element diffusion and partitioning are the main mechanisms in the \u3b1 \u2194 \u3b2 transformation that causes lattice expansion during heating and determines the final shape and size of phases. The retained \u3b2 phase plays a key role in the \u3b1' \u2192 \u3b2 transformation kinetics