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Any kind of network can be naturally represented by a Directed Acyclic Graph (DAG); additionally, activation functions can determine the reaction of each node of the network with respect to the signal(s) incoming. We study the characterization of the signal distribution in a network under the lens of tensor algebra. More specifically, we describe every activation function as tensor distributions with respect to the nodes, called \textit{activation tensors}. The distribution of the signal is encoded in the \textit{total tensor} of the network. We formally prove that the total tensor can be obtained by computing the \textit{Batthacharya-Mesner Product} (BMP), an $n$-ary operation for tensors of order $n$, on the set of the activation tensors properly ordered and processed via two basic operations, that we call \textit{blow} and \textit{forget}. Our theoretical framework can be validated through the related code developed in Python., Comment: 12 pages, 4 figures. Submitted to MEGA24

Externally driven glassy systems may undergo non-equilibrium phase transitions (NEPTs). In particular, ac-driven systems display special features, like those observed in the vortex matter of $\mathrm{NbSe_2}$, where oscillatory drives reorganize the system into partially ordered vortex lattices. We provide experimental evidence for this dynamic reorganization and we show an unambiguous signature of its connection with an NEPT driven by ac forces. We perform a scaling analysis and we estimate critical exponents for this transition. Our results share similarities with some glass-to-viscous-liquid NEPTs and invite to search for similar physics in other elastic disordered media., Comment: 11 pages, 7 figures

Gas demand is made of three components: Residential, Industrial, and Thermoelectric Gas Demand. Herein, the one-day-ahead prediction of each component is studied, using Italian data as a case study. Statistical properties and relationships with temperature are discussed, as a preliminary step for an effective feature selection. Nine "base forecasters" are implemented and compared: Ridge Regression, Gaussian Processes, Nearest Neighbours, Artificial Neural Networks, Torus Model, LASSO, Elastic Net, Random Forest, and Support Vector Regression (SVR). Based on them, four ensemble predictors are crafted: simple average, weighted average, subset average, and SVR aggregation. We found that ensemble predictors perform consistently better than base ones. Moreover, our models outperformed Transmission System Operator (TSO) predictions in a two-year out-of-sample validation. Such results suggest that combining predictors may lead to significant performance improvements in gas demand forecasting.

Evidence of nematic effects in the mixed superconducting phase of slightly underdoped $\text{Ba}(\text{Fe}_{1-x}\text{Co}_x)_2\text{As}_2$ is reported. We have found strong in-plane resistivity anisotropy for crystals in different strain conditions. For these compositions, there is no magnetic long range order, so the description may be ascribed to the interplay between the superconducting and nematic order parameters. A piezoelectric-based apparatus is used to apply tensile or compressive strain to tune nematic domain orientation in order to examine intrinsic nematicity. Measurements are done under a rotating magnetic field and the analysis of the angular dependence of physical quantities identifies the cases in which the sample is {\em detwinned}. Furthermore, the angular dependence of the data allows us to evaluate the effects of nematicity on the in-plane superconductor stiffness. Our results show that although nematicity contributes in a decisive way in the conduction properties, its contributions to the anisotropy properties of the stiffness of the superconducting order parameter is not as significant in these samples., Comment: 9 pages, 9 figures. Published in Phys. Rev. B on 28 February 2019

Gas demand forecasting is a critical task for energy providers as it impacts on pipe reservation and stock planning. In this paper, the one-day-ahead forecasting of residential gas demand at country level is investigated by implementing and comparing five models: Ridge Regression, Gaussian Process (GP), k-Nearest Neighbour, Artificial Neural Network (ANN), and Torus Model. Italian demand data from 2007 to 2017 are used for training and testing the proposed algorithms. The choice of the relevant covariates and the most significant aspects of the pre-processing and feature extraction steps are discussed in-depth, lending particular attention to the role of one-day-ahead temperature forecasts. Our best model, in terms of Root Mean Squared Error (RMSE), is the ANN, closely followed by the GP. If the Mean Absolute Error (MAE) is taken as an error measure, the GP becomes the best model, although by a narrow margin. A main novel contribution is the development of a model describing the propagation of temperature errors to gas forecasting errors that is successfully validated on experimental data. Being able to predict the quantitative impact of temperature forecasts on gas forecasts could be useful in order to assess potential improvement margins associated with more sophisticated weather forecasts. On the Italian data, it is shown that temperature forecast errors account for some 18% of the mean squared error of gas demand forecasts provided by ANN.

Energy companies need efficient procedures to perform market calibration of stochastic models for commodities. If the Black framework is chosen for option pricing, the bottleneck of the market calibration is the computation of the variance of the asset. Energy commodities are commonly represented by multi-factor linear models, whose variance obeys a matrix Lyapunov differential equation. In this paper, analytical and numerical methods to derive the variance are discussed: the Lyapunov approach is shown to be more straightforward than ad-hoc derivations found in the literature and can be readily extended to higher-dimensional models. A case study is presented, where the variance of a two-factor mean-reverting model is embedded into the Black formulae and the model parameters are calibrated against listed options. The analytical and numerical method are compared, showing that the former makes the calibration 14 times faster. A Python implementation of the proposed methods is available as open-source software on GitHub., Comment: Accepted for publication in Applied Stochastic Models in Business and Industry

In type-II superconductors, the macroscopic response of vortex matter to an external perturbation depends on the local interaction of flux lines with the pinning landscape (pinscape). The (Campbell) penetration depth $\lambda_{\mathrm{\scriptscriptstyle C}}$ of an $ac$ field perturbation is often associated with a phenomenological pinning curvature. However, this basic approach is unable to capture thermal hysteresis effects observed in a variety of superconductors. The recently developed framework of strong-pinning theory has established a quantitative relationship between the microscopic pinscape and macroscopic observables. Specifically, it identifies history-dependent vortex arrangements as the primary source for thermal hysteresis in the Campbell response. In this work, we show that this interpretation is well suited to capture the experimental results of the clean superconductor $\mathrm{Nb}\mathrm{Se}_{2}$; as observed through Campbell response (linear $ac$ susceptibility) and small-angle neutron scattering measurements. Furthermore, we exploit the hysteretic Campbell response upon thermal cycling to extract the temperature dependence of microscopic pinning parameters from bulk measurements; specifically the pinning force and pinning length. This spectroscopic tool may stimulate further pinscape characterization in other superconducting systems., Comment: 7 pages, 5 figures

We studied thermal and dynamic history effects in the vortex lattice (VL) near the order-disorder transition in clean NbSe$_2$ single crystals. Comparing the evolution of the effective vortex pinning and the bulk VL structure, we observed metastable superheated and supercooled VL configurations that coexist with a hysteretic effective pinning response due to thermal cycling of the system. A novel scenario, governed by the interplay between (lower) elastic and (higher) plastic energy barriers, is proposed as an explanation for our observations: Plastic barriers, which prevent the annihilation or creation of topological defects, require dynamic assistance to be overcome and to achieve a stable VL at each temperature. Conversely, thermal hysteresis in the pining response is ascribed to low energy barriers, which inhibit rearrangement within a single VL correlation volume and are easily overcome as the relative strength of competing interactions changes with temperature., Comment: 9 pages, 5 figures. To be published in Physical Review B

We report structural evidence of dynamic reorganization in vortex matter in clean NbSe$_2$ by joint small angle neutron scattering and ac-susceptibility measurements. The application of oscillatory forces in a transitional region near the order-disorder transition results in robust bulk vortex lattice configurations with an intermediate degree of disorder. These dynamically-originated configurations correlate with intermediate pinning responses previously observed, resolving a long standing debate regarding the origin of such responses., Comment: 9 pages, 7 figures. To be published in Physical Review Letters