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The Perception of Security (PoS) refers to people's opinions about security or insecurity in a place or situation. While surveys have traditionally been the primary means to capture such perceptions, they need to be improved in their ability to offer real-time monitoring or predictive insights into future security perceptions. Recent evidence suggests that social network content can provide complementary insights into quantifying these perceptions. However, the challenge of accurately predicting these perceptions, with the capacity to anticipate them, still needs to be explored. This article introduces an innovative approach to PoS within short time frames using social network data. Our model incorporates external factors that influence the publication and reposting of content related to security perceptions. Our results demonstrate that this proposed model achieves competitive predictive performance and maintains a high degree of interpretability regarding the factors influencing security perceptions. This research contributes to understanding how temporal patterns and external factors impact the anticipation of security perceptions, providing valuable insights for proactive security planning., Comment: 31 pages, 5 figures, 1 Appendix

Clinical practice in psychiatry is burdened with the increased demand for healthcare services and the scarce resources available. New paradigms of health data powered with machine learning techniques could open the possibility to improve clinical workflow in critical stages of clinical assessment and treatment in psychiatry. In this work, we propose a machine learning system capable of predicting, detecting, and explaining individual changes in symptoms of patients with Schizophrenia by using behavioral digital phenotyping data. We forecast symptoms of patients with an error rate below 10%. The system detects decreases in symptoms using changepoint algorithms and uses counterfactual explanations as a recourse in a simulated continuous monitoring scenario in healthcare. Overall, this study offers valuable insights into the performance and potential of counterfactual explanations, predictive models, and change-point detection within a simulated clinical workflow. These findings lay the foundation for further research to explore additional facets of the workflow, aiming to enhance its effectiveness and applicability in real-world healthcare settings. By leveraging these components, the goal is to develop an actionable, interpretable, and trustworthy integrative decision support system that combines real-time clinical assessments with sensor-based inputs.

We present the mathematical context of the predictive accuracy index and then introduce the definition of integral average transform. We establish the relation of our definition with two variables kernels $K({\bf y},{\bf x})$. As an example of an application we show that integrating against the fundamental solution of the Laplace operator, that is, solving the Poisson equation, can be re-interpreted as an integral of averages of the forcing term over balls. As a result, we obtained a novel integral representation of the solution of the Poisson equation. Our motivation comes from the need for a better mathematical understanding of the prediction accuracy index. This index is used to identify hot spots in predictive security and other applications.

Regular perturbation is applied to space-division multiplexing (SDM) on optical fibers and motivates a correlated rotation-and-additive noise (CRAN) model. For S spatial modes, or 2S complex-alphabet channels, the model has 4S(S+1) hidden independent real Gauss-Markov processes, of which 2S model phase noise, 2S(2S-1) model spatial mode rotation, and 4S model additive noise. Achievable information rates of multi-carrier communication are computed by using particle filters. For S=2 spatial modes with strong coupling and a 1000 km link, joint processing of the spatial modes gains 0.5 bits/s/Hz/channel in rate and 1.4 dB in power with respect to separate processing of 2S complex-alphabet channels without considering CRAN., Comment: This paper has been accepted for presentation at the IEEE 17th International Symposium on Wireless Communication Systems (ISWCS) 2021. Copyright (c) 2021 IEEE. Personal use is permitted. For any other purposes, permission must be obtained from the IEEE by emailing pubs-permissions@ieee.org

Regular perturbation is applied to the Manakov equation and motivates a generalized correlated phase-and-additive noise model for wavelength-division multiplexing over dual-polarization optical fiber channels. The model includes three hidden Gauss-Markov processes: phase noise, polarization rotation, and additive noise. Particle filtering is used to compute lower bounds on the capacity of multi-carrier communication with frequency-dependent powers and delays. A gain of 0.17 bits/s/Hz/pol in spectral efficiency or 0.8 dB in power efficiency is achieved with respect to existing models at their peak data rate. Frequency-dependent delays also increase the spectral efficiency of single-polarization channels., Comment: Published in JLT (http://dx.doi.org/10.1109/JLT.2021.3069686)

We extend the analysis, initiated in arXiv:1901.01269, of the thermodynamic stability of four-dimensional asymptotically flat hairy black holes by considering a general class of exact solutions in Einstein-Maxwell-dilaton theory with a non-trivial dilaton potential. We find that, regardless of the values of the parameters of the theory, there always exists a sub-class of hairy black holes that are thermodynamically stable and have the extremal limit well defined. This generic feature that makes the equilibrium configurations locally stable should be related to the properties of the dilaton potential that is decaying towards the spatial infinity, but behaves as a box close to the horizon. We prove that these thermodynamically stable solutions are also dynamically stable under spherically symmetric perturbations., Comment: 35 pages, 15 figures

Analytic expressions are derived for the Wiener filter (WF), also known as the linear minimum mean square error (LMMSE) estimator, for an intensity-modulation/direct-detection (IM/DD) short-haul fiber-optic communication system. The link is purely dispersive and the nonlinear square-law detector (SLD) operates at the thermal noise limit. The achievable rates of geometrically shaped PAM constellations are substantially increased by taking the SLD into account as compared to a WF that ignores the SLD., Comment: Accepted to IEEE Communications Letters

A correlated phase-and-additive-noise (CPAN) mismatched model is developed for wavelength division multiplexing over optical fiber channels governed by the nonlinear Schr\"odinger equation. Both the phase and additive noise processes of the CPAN model are Gauss-Markov whereas previous work uses Wiener phase noise and white additive noise. Second order statistics are derived and lower bounds on the capacity are computed by simulations. The CPAN model characterizes nonlinearities better than existing models in the sense that it achieves better information rates. For example, the model gains 0.35 dB in power at the peak data rate when using a single carrier per wavelength. For multiple carriers per wavelength, the model combined with frequency-dependent power allocation gains 0.14 bits/s/Hz in rate and 0.8 dB in power at the peak data rate., Comment: This work has been accepted for publication in the IEEE/OSA Journal of Lightwave Technology