A simplified stock-flow consistent dynamic model of the systemic financial fragility in the 'New Capitalism'

In the last few years, many financial analysts and heterodox economists (but even some ‘dissenters’ among orthodox economists) have referred to the contribution of Hyman P. Minsky as fundamental to understanding the current crisis. However, it is well-known that the traditional formulation of Minsky’s ‘financial instability hypothesis’ shows serious internal logical problems. Furthermore, Minsky’s analysis of capitalism must be updated on the basis of the deep changes which, during the last three decades, have concerned the world economy. In order to overcome these theoretical and empirical troubles, this paper, first, introduces the reader to the ‘mechanics’ of the financial instability theory, according to the formulation of the traditional Minskian literature (section 2). Second, it shows ‘why’ Minsky’s theory cannot be regarded as a general theory of the business cycle (section 3). Third, the paper attempts to supply a simplified, but consistent, re-formulation of Minsky’s theory by inter-breeding it with inputs coming from the ‘New Cambridge’ theories and the current ‘formal Minskian literature’. The aim of this is to analyze the impact of both capital-asset inflation and consumer credit on the financial ‘soundness’ of the non-financial business sector (sections 4-7). Some concluding remarks are provided in the last part of the paper (section 8).Financial Instability; Stock-Flow Consistency; Capital-asset Inflation

Saint-Venant's principle in dynamical porous thermoelastic media with memory for heat flux

In the present paper, we study a linear thermoelastic porous material with a constitutive equation for heat flux with memory. An approximated theory of thermodynamics is presented for this model and a maximal pseudo free energy is determined. We use this energy to study the spatial behaviour of the thermodynamic processes in porous materials. We obtain the domain of influence theorem and establish the spatial decay estimates inside of the domain of influence. Further, we prove a uniqueness theorem valid for finite or infinite body. The body is free of any kind of a priori assumptions concerning the behaviour of solutions at infinity.Comment: 18 pages, accepted on Journal of Thermal Stresse

Spatial behaviour in dynamical thermoelasticity backward in time for porous media

The aim of this paper is to study the spatial behaviour of the solutions to the boundary-final value problems associated with the linear theory of elastic materials with voids. More precisely the present study is devoted to porous materials with a memory effect for the intrinsic equilibrated body forces. An appropriate time-weighted volume measure is associated with the backward in time thermoelastic processes. Then, a first-order partial differential inequality in terms of such measure is established and further is shown how it implies the spatial exponential decay of the thermoelastic process in question.Comment: 12 pages, accepted by Journal of Thermal Stresse

Some Theorems in Thermoelasticity for Micropolar Porous Media

Within the context of a linear theory of heat-flux dependent thermoelasticity for micropolar porous media some variational principles and a reciprocal relation are derived.Comment: 15 pages, accepted by Rev.Roum.Sci.Tech.-Mec.App

D2D Data Offloading in Vehicular Environments with Optimal Delivery Time Selection

Within the framework of a Device-to-Device (D2D) data offloading system for cellular networks, we propose a Content Delivery Management System (CDMS) in which the instant for transmitting a content to a requesting node, through a D2D communication, is selected to minimize the energy consumption required for transmission. The proposed system is particularly fit to highly dynamic scenarios, such as vehicular networks, where the network topology changes at a rate which is comparable with the order of magnitude of the delay tolerance. We present an analytical framework able to predict the system performance, in terms of energy consumption, using tools from the theory of point processes, validating it through simulations, and provide a thorough performance evaluation of the proposed CDMS, in terms of energy consumption and spectrum use. Our performance analysis compares the energy consumption and spectrum use obtained with the proposed scheme with the performance of two benchmark systems. The first one is a plain classic cellular scheme, the second is a D2D data offloading scheme (that we proposed in previous works) in which the D2D transmissions are performed as soon as there is a device with the required content within the maximum D2D transmission range..

SPoT: Representing the Social, Spatial, and Temporal Dimensions of Human Mobility with a Unifying Framework

Modeling human mobility is crucial in the analysis and simulation of opportunistic networks, where contacts are exploited as opportunities for peer-topeer message forwarding. The current approach with human mobility modeling has been based on continuously modifying models, trying to embed in them the mobility properties (e.g., visiting patterns to locations or specific distributions of inter-contact times) as they came up from trace analysis. As a consequence, with these models it is difficult, if not impossible, to modify the features of mobility or to control the exact shape of mobility metrics (e.g., modifying the distribution of inter-contact times). For these reasons, in this paper we propose a mobility framework rather than a mobility model, with the explicit goal of providing a exible and controllable tool for modeling mathematically and generating simulatively different possible features of human mobility. Our framework, named SPoT, is able to incorporate the three dimensions - spatial, social, and temporal - of human mobility. The way SPoT does it is by mapping the different social communities of the network into different locations, whose members visit with a configurable temporal pattern. In order to characterize the temporal patterns of user visits to locations and the relative positioning of locations based on their shared users, we analyze the traces of real user movements extracted from three location-based online social networks (Gowalla, Foursquare, and Altergeo). We observe that a Bernoulli process effectively approximates user visits to locations in the majority of cases and that locations that share many common users visiting them frequently tend to be located close to each other. In addition, we use these traces to test the exibility of the framework, and we show that SPoT is able to accurately reproduce the mobility behavior observed in traces. Finally, relying on the Bernoulli assumption for arrival processes, we provide a throughout mathematical analysis of the controllability of the framework, deriving the conditions under which heavy-tailed and exponentially-tailed aggregate inter-contact times (often observed in real traces) emerge

Power-management policies for mobile computing

Abbiamo studiato architetture di rete per il power-saving in ambito di wireless LAN infrastrutturate. Abbiamo proposto protocolli power-saving di livello middleware, indipendenti dalla tecnologia wireless impiegata. Tali protocolli sono stati valutati approfonditamente, risultando molto efficienti. Abbiamo poi valutato in maniera estensiva il meccanismo di power-saving dello standard 802.11. Ne abbiamo evidenziato i limiti, ed abbiamo definito un framework cross-layer di power-management. Tale framewok integra i protocolli middleware studiati inizialmente e lo standard 802.11. L'incremento delle prestazioni ottenute rispetto allo standard 802.11 arriva al 90% in termini di power saving

Machine Learning for Earth Systems Modeling, Analysis and Predictability

Artificial intelligence (AI) and machine learning (ML) methods and applications have been continuously explored in many areas of scientific research. While these methods have lead to many advances in climate science, there remains room for growth especially in Earth System Modeling, analysis and predictability. Due to their high computational expense and large volumes of complex data they produce, earth system models (ESMs) provide an abundance of potential for enhancing both our understanding of the climate system as well as improving performance of ESMs themselves using ML techniques. Here I demonstrate 3 specific areas of development using ML: statistical downscaling, predictability using non-linear latent spaces and emulation of complex parametrization. These three areas of research illustrate the ability of innovative ML methods to advance our understanding of climate systems through ESMs. In Aim 1, I present a first application of a fast super resolution convolutional neural network (FSRCNN) based approach for downscaling earth system model (ESM) simulations. We adapt the FSRCNN to improve reconstruction on ESM data, we term the FSRCNN-ESM. We find that FSRCNN-ESM outperforms FSRCNN and other super-resolution methods in reconstructing high resolution images producing finer spatial scale features with better accuracy for surface temperature, surface radiative fluxes and precipitation. In Aim 2, I construct a novel Multi-Input Multi-Output Autoencoder-decoder (MIMO-AE) in an application of multi-task learning to capture the non-linear relationship of Southern California precipitation (SC-PRECIP) and tropical Pacific Ocean sea surface temperature (TP-SST) on monthly time-scales. I find that the MIMO-AE index provides enhanced predictability of SC-PRECIP for a lead-time of up-to four months as compared to Ni{\~n}o 3.4 index and the El Ni{\~n}o Southern Oscillation Longitudinal Index. I also use a MTL method to expand on a convolutional long short term memory (conv-LSTM) to predict Nino 3.4 index by including multiple input variables known to be associated with ENSO, namely sea level pressure (SLP), outgoing longwave radiation (ORL) and surface level zonal winds (U). In Aim 3, I demonstrate the capability of DNNs for learning computationally expensive parameterizations in ESMs. This study develops a DNN to replace the full radiation model in the E3SM