Thermodynamic Depth of Causal States: When Paddling around in Occam's Pool Shallowness Is a Virtue

Thermodynamic depth is an appealing but flawed structural complexity measure. It depends on a set of macroscopic states for a system, but neither its original introduction by Lloyd and Pagels nor any follow-up work has considered how to select these states. Depth, therefore, is at root arbitrary. Computational mechanics, an alternative approach to structural complexity, provides a definition for a system's minimal, necessary causal states and a procedure for finding them. We show that the rate of increase in thermodynamic depth, or {\it dive}, is the system's reverse-time Shannon entropy rate, and so depth only measures degrees of macroscopic randomness, not structure. To fix this we redefine the depth in terms of the causal state representation---$\epsilon$-machines---and show that this representation gives the minimum dive consistent with accurate prediction. Thus, $\epsilon$-machines are optimally shallow.Comment: 11 pages, 9 figures, RevTe

An Algorithm for Pattern Discovery in Time Series

We present a new algorithm for discovering patterns in time series and other sequential data. We exhibit a reliable procedure for building the minimal set of hidden, Markovian states that is statistically capable of producing the behavior exhibited in the data -- the underlying process's causal states. Unlike conventional methods for fitting hidden Markov models (HMMs) to data, our algorithm makes no assumptions about the process's causal architecture (the number of hidden states and their transition structure), but rather infers it from the data. It starts with assumptions of minimal structure and introduces complexity only when the data demand it. Moreover, the causal states it infers have important predictive optimality properties that conventional HMM states lack. We introduce the algorithm, review the theory behind it, prove its asymptotic reliability, use large deviation theory to estimate its rate of convergence, and compare it to other algorithms which also construct HMMs from data. We also illustrate its behavior on an example process, and report selected numerical results from an implementation.Comment: 26 pages, 5 figures; 5 tables; http://www.santafe.edu/projects/CompMech Added discussion of algorithm parameters; improved treatment of convergence and time complexity; added comparison to older method

Cubesat mission with technological demonstrator payload for high data rate downlink and health monitoring

The HyperCube payload will be composed by two different technological experiment, an high data-rate C band antenna, and a demonstrator for a remote structural health monitoring system. The first one has been thought with the aim to give Cubesats the capability to download an high quantity of data; it could be useful either if the data requiring the high data-rate downlink is on-board generated or simply retransmitted. The applications for which this payload could be used are several; an example for the first category of application is to download the data generated by another payload; the high data-rate capability could be necessary due to the narrow visibility window with the ground station, affected also by the absence of an active AOCS subsystem, which makes difficult the alignment of the on board antenna with the ground one. But the C band antenna could also be used to act as a “space–repeater”, downloading up–linked information. The second payload is related to the need to take under strictly control the health of the structures (not only the ones strictly belonging to primary structures, but also that of any subsystem component). In order to do that, smart materials are integrated into the structural component that need to be monitored; in particular, piezoelectric patches are used as sensors. As the structure is stressed, and the integrated piezoelectric sensors are subjected to mechanical deformation, they produce an electric signal; acquiring and properly studying the produced signal it is possible to monitor the mechanical condition of the structures. The health monitoring system is completed by a MicroController Unit which acquires, samples and stores the signal produced, and a transmitting system, which could be the C band antenna, or the TT&C antenna which each satellite needs

Controlled morphing of architected liquid crystal elastomer elements: modeling and simulations

Liquid crystal elastomers (LCE) are elastomeric materials possessing a network microstructure made of chains with a preferential orientation, induced by mesogen units embedded in the material prior to polymerization. This peculiarity can be harnessed to induce deformation of an LCE element by making its network switch from the preferentially oriented nematic state to the isotropic one, as occurs for instance by rising the temperature above a transition value characteristic of the material. This mechanism can be combined with an architected arrangement of LCE elements, whose nematic orientation and transition temperature are properly differentiated among the different zones constituting the element. In this way, interesting morphing capabilities can be obtained out of an architected elastomer made of LCE portions (ALCE), leading to a morphing structure whose deformation can be activated and precisely tuned by heating up or cooling down the material. In this research, we propose some simple architected LCE elements showing the capability of producing a variety of deformed shapes. A micromechanical theoretical model for LCE is firstly illustrated and several examples of morphing of architected LCE elements, whose mechanical response is obtained through finite element (FE) numerical analyses based on the proposed micromechanical model, are illustrated and critically discussed

Customer Complaining and Probability of Default in Consumer Credit

In many countries, Banking Authorities have adopted an Alternative Dispute Resolution (ADR) procedure to manage complaints that customers and financial intermediaries cannot solve by themselves. As a consequence, banks have had to implement complaint management systems in order to deal with customers’ demands. The growth rate of customer complaints has been increasing during the last few years. This does not seem to be only related to the quality of financial services or to lack of compliance in banking products. Another reason lies in the characteristics of the procedures themselves, which are very simple and free of charge. The paper analyzes some determinants regarding the willingness to complain. In particular, it examines whether a high customers’ probability of default leads to an increase in non-valid complaints. The paper uses a sample of approximately 1,000 customers who received a loan and made a claim against the lender. The analysis shows that customers with higher Probability of Default are more likely to make claims against Financial Institutions. Moreover, it shows that opportunistic behaviors and non-valid complaints are more likely if the customer is supported by a lawyer or other professionals and if the reason for the claim may result in a refund or damage compensation

Bio-inspired ganglion cell models for detecting horizontal and vertical movements

The retina performs the earlier stages of image processing in living beings and is composed of six different groups of cells, namely, the rods, cones, horizontal, bipolar, amacrine and ganglion cells. Each of those group of cells can be sub-divided into other types of cells that vary in shape, size, connectivity and functionality. Each cell is responsible for performing specific tasks in these early stages of biological image processing. Some of those cells are sensitive to horizontal and vertical movements. This paper proposes a multi-hierarchical spiking neural network architecture for detecting horizontal and vertical movements using a custom dataset which was generated in laboratory settings. The proposed architecture was designed to reflect the connectivity, behaviour and the number of layers found in the majority of vertebrates retinas, including humans. The architecture was trained using 2303 images and tested using 816 images. Simulation results revealed that each cell model is sensitive to vertical and horizontal movements with a detection error of 6.75 percent

Sustainable development and european banks: A non-financial disclosure analysis

none4noThis paper aims at contributing to the debate on the relationships between the European financial sector and sustainable development. Using a non-financial disclosure analysis of 262 European banks, the research sought, first, to investigate the "scope" of the contribution of European banks to the Sustainable Development Goals (SDGs) and, second, to explore the factors that seem to differentiate the SDGs approach among banks. The results show that country of origin, legal system, and adoption of an integrated report seem to differentiate banks in terms of contribution to the SDGs. The business model and stock exchange listing, conversely, do not seem to represent discriminatory factor in the contribution of banks toward the SDGs. The study can be useful for managers and decision makers to develop policies to support organizations in contributing to the SDGs.openCosma S.; Venturelli A.; Schwizer P.; Boscia V.Cosma, S.; Venturelli, A.; Schwizer, P.; Boscia, V

Mechanical characterization of additively manufactured photopolymerized polymers

Photopolymerization, based on light-induced radical polymerization, is nowadays exploited in additive manufacturing (AM) technologies enabling to achieve high dimensional quality. The mechanical properties of the obtained material are heavily dependent on the chemistry of the photopolymer and on the way the AM process is performed. Here we study, through experiments and theoretical modeling, how the mechanical properties of liquid crystal shutter (LCD) printed photopolymers depend on the printing process setup, namely UV exposure time and layer thickness. To this end, a multi-physics simulation tool considering the light diffusion, chemical kinetics, and the micro-mechanics at the network level, has been developed

NatCSNN: a convolutional spiking neural network for recognition of objects extracted from natural images

Biological image processing is performed by complex neural networks composed of thousands of neurons interconnected via thousands of synapses, some of which are excitatory and others inhibitory. Spiking neural models are distinguished from classical neurons by being biological plausible and exhibiting the same dynamics as those observed in biological neurons. This paper proposes a Natural Convolutional Neural Network (NatCSNN) which is a 3-layer bio-inspired Convolutional Spiking Neural Network (CSNN), for classifying objects extracted from natural images. A two-stage training algorithm is proposed using unsupervised Spike Timing Dependent Plasticity (STDP) learning (phase 1) and ReSuMe supervised learning (phase 2). The NatCSNN was trained and tested on the CIFAR-10 dataset and achieved an average testing accuracy of 84.7% which is an improvement over the 2-layer neural networks previously applied to this datase