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Accurate estimation of nuclear masses and their prediction beyond the experimentally explored domains of the nuclear landscape are crucial to an understanding of the fundamental origin of nuclear properties and to many applications of nuclear science, most notably in quantifying the $r$-process of stellar nucleosynthesis. Neural networks have been applied with some success to the prediction of nuclear masses, but they are known to have shortcomings in application to extrapolation tasks. In this work, we propose and explore a novel type of neural network for mass prediction in which the usual neuron-like processing units are replaced by complex-valued product units that permit multiplicative couplings of inputs to be learned from the input data. This generalized network model is tested on both interpolation and extrapolation data sets drawn from the Atomic Mass Evaluation. Its performance is compared with that of several neural-network architectures, substantiating its suitability for nuclear mass prediction. Additionally, a prediction-uncertainty measure for such complex-valued networks is proposed that serves to identify regions of expected low prediction error.

Risk budgeting is a portfolio strategy where each asset contributes a prespecified amount to the aggregate risk of the portfolio. In this work, we propose an efficient numerical framework that uses only simulations of returns for estimating risk budgeting portfolios. Besides a general cutting planes algorithm for determining the weights of risk budgeting portfolios for arbitrary coherent distortion risk measures, we provide a specialised version for the Expected Shortfall, and a tailored Stochastic Gradient Descent (SGD) algorithm, also for the Expected Shortfall. We compare our algorithm to standard convex optimisation solvers and illustrate different risk budgeting portfolios, constructed using an especially designed Julia package, on real financial data and compare it to classical portfolio strategies.

Information encoding in barcodes using magnetic-based technology is a unique strategy to read data buried underneath non-transparent surfaces since a direct line-of-sight between the code and the reader is not required. This technology is of particular interest in secure labelling and recyclable packaging applications. However, current magnetic reading heads, such as those employed for magnetic ink character recognition, need to be placed in contact with the magnetic structures, limiting the depths at which the information can be read. This paper describes a strategy to overcome that limitation by replacing the traditional inductive heads with tunnel magnetoresistive (TMR) sensors. Soft-magnetic codes can be printed using conventional LaserJet toners and, by having their magnetisation set with a permanent magnet included in the device, the resulting magnetic field can be read using a TMR sensor. We demonstrate that such a device can read barcodes at depths of at least 1 mm. It can also resolve individual structures as thin as 200 {\mu}m when used in contact., Comment: 4 pages, 5 figures, Manuscript accepted for publication in IEEE Transactions on Magnetics

Risk-averse multistage stochastic programs appear in multiple areas and are challenging to solve. Stochastic Dual Dynamic Programming (SDDP) is a well-known tool to address such problems under time-independence assumptions. We show how to derive a dual formulation for these problems and apply an SDDP algorithm, leading to converging and deterministic upper bounds for risk-averse problems., Comment: 20 pages, 3 figures

We establish the existence of solutions to a class of non-linear stochastic differential equation of reaction-diffusion type in an infinite-dimensional space, with diffusion corresponding to a given transition kernel. The solution obtained is the scaling limit of a sequence of interacting particle systems, and satisfies the martingale problem corresponding to the target differential equation., Comment: 24 pages

Artificial neural networks are trained by a standard backpropagation learning algorithm with regularization to model and predict the systematics of -decay of heavy and superheavy nuclei. This approach to regression is implemented in two alternative modes: (i) construction of a statistical global model based solely on available experimental data for alpha-decay half-lives, and (ii) modeling of the {\it residuals} between the predictions of state-of-the-art phenomenological model (specifically, the effective liquid-drop model (ELDM)) and experiment. Analysis of the results provide insights on the strengths and limitations of this application of machine learning (ML) to exploration of the nuclear landscape in regions beyond the valley of stability., Comment: 12 pages, 5 figures

Spin-based electronics has evolved into a major field of research that broadly encompasses different classes of materials, magnetic systems, and devices. This review describes recent advances in spintronics that have the potential to impact key areas of information technology and microelectronics. We identify four main axes of research: nonvolatile memories, magnetic sensors, microwave devices, and beyond-CMOS logic. We discuss state-of-the-art developments in these areas as well as opportunities and challenges that will have to be met, both at the device and system level, in order to integrate novel spintronic functionalities and materials in mainstream microelectronic platforms., Comment: Review written by the SpinFactory European Consortium

We propose a new algorithm for solving multistage stochastic mixed integer linear programming (MILP) problems with complete continuous recourse. In a similar way to cutting plane methods, we construct nonlinear Lipschitz cuts to build lower approximations for the non-convex cost to go functions. An example of such a class of cuts are those derived using Augmented Lagrangian Duality for MILPs. The family of Lipschitz cuts we use is MILP representable, so that the introduction of these cuts does not change the class of the original stochastic optimization problem. We illustrate the application of this algorithm on two simple case studies, comparing our approach with the convex relaxation of the problems, for which we can apply SDDP, and for a discretized approximation, applying SDDiP., Comment: 35 pages, 12 figures

As the Internet of Things (IoT) becomes more integral to how we live our lives and society becomes ever increasingly connected, the need for efficient nanoscale radio frequency detection is becoming more of a pressing issue. The commonly used technique of a superheterodyne conversion of the incoming signal to an intermediate frequency (IF), requires a relatively large electronic circuit, including a local oscillator, mixer, various filters and a rectifier (i.e. diode). In order to radically simplify this process, we propose a spintronics-based nanometric single device whose resistance varies linearly as a function of the incoming frequency. This analogue frequency sensor allows a direct measurement of the incoming frequency and can work over many frequency bands and could replace a significantly larger conventional electronics circuit, thus reducing the footprint and potentially the energy cost. Three types of operation of the frequency sensor are demonstrated: a HF/VHF sensor based on a subharmonic modulation working across 9 different bands with 1-10 MHz bandwidth and 200 kHz resolution, a VHF/UHF sensor based on direct gyrotropic excitation operating between 150-225MHz with 250 kHz resolution and a UHF/SHF sensor based on indirect spin wave excitation operating between 2-4GHz with a 2.5 MHz resolution.