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Underwater Acoustic Sensor Networks (UW-ASNs) are predominantly used for underwater environments and find applications in many areas. However, a lack of security considerations, the unstable and challenging nature of the underwater environment, and the resource-constrained nature of the sensor nodes used for UW-ASNs (which makes them incapable of adopting security primitives) make the UW-ASN prone to vulnerabilities. This paper proposes an Adaptive decentralised Intrusion Detection and Prevention System called AIDPS for UW-ASNs. The proposed AIDPS can improve the security of the UW-ASNs so that they can efficiently detect underwater-related attacks (e.g., blackhole, grayhole and flooding attacks). To determine the most effective configuration of the proposed construction, we conduct a number of experiments using several state-of-the-art machine learning algorithms (e.g., Adaptive Random Forest (ARF), light gradient-boosting machine, and K-nearest neighbours) and concept drift detection algorithms (e.g., ADWIN, kdqTree, and Page-Hinkley). Our experimental results show that incremental ARF using ADWIN provides optimal performance when implemented with One-class support vector machine (SVM) anomaly-based detectors. Furthermore, our extensive evaluation results also show that the proposed scheme outperforms state-of-the-art bench-marking methods while providing a wider range of desirable features such as scalability and complexity.

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.

The detection of energy thefts is vital for the safety of the whole smart grid system. However, the detection alone is not enough since energy thefts can crucially affect the electricity supply leading to some blackouts. Moreover, privacy is one of the major challenges that must be preserved when dealing with clients' energy data. This is often overlooked in energy theft detection research as most current detection techniques rely on raw, unencrypted data, which may potentially expose sensitive and personal data. To solve this issue, we present a privacy-preserving energy theft detection technique with effective demand management that employs two layers of privacy protection. We explore a split learning mechanism that trains a detection model in a decentralised fashion without the need to exchange raw data. We also employ a second layer of privacy by the use of a masking scheme to mask clients' outputs in order to prevent inference attacks. A privacy-enhanced version of this mechanism also employs an additional layer of privacy protection by training a randomisation layer at the end of the client-side model. This is done to make the output as random as possible without compromising the detection performance. For the energy theft detection part, we design a multi-output machine learning model to identify energy thefts, estimate their volume, and effectively predict future demand. Finally, we use a comprehensive set of experiments to test our proposed scheme. The experimental results show that our scheme achieves high detection accuracy and greatly improves the privacy preservation degree.

IPv6 over Low-powered Wireless Personal Area Networks (6LoWPAN) have grown in importance in recent years, with the Routing Protocol for Low Power and Lossy Networks (RPL) emerging as a major enabler. However, RPL can be subject to attack, with severe consequences. Most proposed IDSs have been limited to specific RPL attacks and typically assume a stationary environment. In this article, we propose the first adaptive hybrid IDS to efficiently detect and identify a wide range of RPL attacks (including DIO Suppression, Increase Rank, and Worst Parent attacks, which have been overlooked in the literature) in evolving data environments. We apply our framework to networks under various levels of node mobility and maliciousness. We experiment with several incremental machine learning (ML) approaches and various 'concept-drift detection' mechanisms (e.g. ADWIN, DDM, and EDDM) to determine the best underlying settings for the proposed scheme.

The COVID-19 pandemic has proved to be one of the most disruptive public health emergencies in recent memory. Among non-pharmaceutical interventions, social distancing and lockdown measures are some of the most common tools employed by governments around the world to combat the disease. While mathematical models of COVID-19 are ubiquitous, few have leveraged network theory in a general way to explain the mechanics of social distancing. In this paper, we build on existing network models for heterogeneous, clustered networks with random link activation/deletion dynamics to put forth realistic mechanisms of social distancing using piecewise constant activation/deletion rates. We find our models are capable of rich qualitative behavior, and offer meaningful insight with relatively few intervention parameters. In particular, we find that the severity of social distancing interventions and when they begin have more impact than how long it takes for the interventions to take full effect.

The coordinated assurance of interrelated critical properties, such as system safety and cyber-security, is one of the toughest challenges in critical systems engineering. In this chapter, we summarise approaches to the coordinated assurance of safety and security. Then, we highlight the state of the art and recent challenges in human-robot collaboration in manufacturing both from a safety and security perspective. We conclude with a list of procedural and technological issues to be tackled in the coordinated assurance of collaborative industrial robots., Comment: 23 pages, 4 tables, 1 figure

A two-layer neural network model that systematically includes correlations among input variables to arbitrary order and is designed to implement Bayes inference has been adapted to classify breast cancer tumors as malignant or benign, assigning a probability for either outcome. The inputs to the network represent measured characteristics of cell nuclei imaged in Fine Needle Aspiration biopsies. The present machine-learning approach to diagnosis (known as HOPP, for higher-order probabilistic perceptron) is tested on the much-studied, open-access Breast Cancer Wisconsin (Diagnosis) Data Set of Wolberg et al. This set lists, for each tumor, measured physical parameters of the cell nuclei of each sample. The HOPP model can identify the key factors -- input features and their combinations -- most relevant for reliable diagnosis. HOPP networks were trained on 90\% of the examples in the Wisconsin database, and tested on the remaining 10\%. Referred to ensembles of 300 networks, selected randomly for cross-validation, accuracy of classification for the test sets of up to 97\% was readily achieved, with standard deviation around 2\%, together with average Matthews correlation coefficients reaching 0.94 indicating excellent predictive performance. Demonstrably, the HOPP is capable of matching the predictive power attained by other advanced machine-learning algorithms applied to this much-studied database, over several decades. Analysis shows that in this special problem, which is almost linearly separable, the effects of irreducible correlations among the measured features of the Wisconsin database are of relatively minor importance, as the Naive Bayes approximation can itself yield predictive accuracy approaching 95\%. The advantages of the HOPP algorithm will be more clearly revealed in application to more challenging machine-learning problems.

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

The California higher education system has not kept pace with changing workforce demands or the needs of California's racially diverse population. Fewer than half of adults in California have a college credential but by 2030, 60% will need a college education to meet workforce demand and racial equity gaps will need to be entirely eliminated. In the next 12 years, California will need to produce 1.65 million more college credentials and we are not on track to get there. The 2018 California Higher Education Report Card is a snapshot of California's performance in college preparation, access, affordability and completion today compared to where we need to be in 2030. The grades reflect progress toward a 60% college attainment goal and progress toward eliminating the racial equity gaps by 2030.

One of the most promising applications of near-term quantum computing is the simulation of quantum systems, a classically intractable task. Quantum simulation requires computationally expensive matrix exponentiation; Trotter-Suzuki decomposition of this exponentiation enables efficient simulation to a desired accuracy on a quantum computer. We apply the Covariance Matrix Adaptation Evolutionary Strategy (CMA-ES) algorithm to optimise the Trotter-Suzuki decompositions of a canonical quantum system, the Heisenberg Chain; we reduce simulation error by around 60%. We introduce this problem to the computational search community, show that an evolutionary optimisation approach is robust across runs and problem instances, and find that optimisation results generalise to the simulation of larger systems., Comment: A version of this paper is to appear in GECCO'19

Future remote sensing of exoplanets will be enhanced by a thorough investigation of our solar system Ice Giants (Neptune-size planets). What can the configuration of the magnetic field tell us (remotely) about the interior, and what implications does that field have for the structure of the magnetosphere; energy input into the atmosphere, and surface geophysics (for example surface weathering of satellites that might harbour sub-surface oceans). How can monitoring of auroral emission help inform future remote observations of emission from exoplanets? Our Solar System provides the only laboratory in which we can perform in-situ experiments to understand exoplanet formation, dynamos, systems and magnetospheres., Comment: Exoplanet Science Strategy White Paper, submitted to the National Academies of Sciences, Engineering and Medicine, Space Studies Board, 9 March 2018

Nuclear matter and finite nuclei exhibit the property of superfluidity by forming Cooper pairs. We review the microscopic theories and methods that are being employed to understand the basic properties of superfluid nuclear systems, with emphasis on the spatially extended matter encountered in neutron stars, supernova envelopes, and nuclear collisions. Our survey of quantum many-body methods includes techniques that employ Green functions, correlated basis functions, and Monte Carlo sampling of quantum states. With respect to empirical realizations of nucleonic and hadronic superfluids, this review is focused on progress that has been made toward quantitative understanding of their properties at the level of microscopic theories of pairing, with emphasis on the condensates that exist under conditions prevailing in neutron-star interiors. These include singlet $S$-wave pairing of neutrons in the inner crust, and, in the quantum fluid interior, singlet-$S$ proton pairing and triplet coupled $P$-$F$-wave neutron pairing. Additionally, calculations of weak-interaction rates in neutron-star superfluids within the Green function formalism are examined in detail. We close with a discussion of quantum vortex states in nuclear systems and their dynamics in neutron-star superfluid interiors., Comment: Review article, 63 pages, 28 figures, v4 matches journal version. [v2 and v3: 58 pages, 28 figures, v1: 50 pages, 27 figures]

The phase diagram of isospin-asymmetrical nuclear matter may feature a number of unconventional phases, which include the translationally and rotationally symmetric, but isospin-asymmetrical BCS condensate, the current-carrying Larkin-Ovchinnikov-Fulde-Ferrell (LOFF) phase, and the heterogeneous phase-separated phase. Because the Cooper pairs of the condensate carry a single unit of charge, these phases are charged superconductors and respond to electromagnetic gauge fields by either forming domains (type-I superconductivity) or quantum vortices (type-II superconductivity). We evaluate the Ginzburg-Landau (GL) parameter across the phase diagram and find that the unconventional phases of isospin-asymmetrical nuclear matter are good type-II superconductors and should form Abrikosov vortices with twice the quantum of magnetic flux. We also find that the LOFF phase at the boundary of the transition to the type-I state, with the GL parameter being close to the critical value $1/\sqrt{2}$., Comment: Invited contribution presented by A. S. at the 19th International Conference on Recent Progress in Many-Body Theories, June 25-30, 2017, APCTP, Pohang, Korea, v1: 8 pages, 2 figs

MeV-scale magnetic fields in the interiors of magnetars suppress the pairing of neutrons and protons in the $S$-wave state. In the case of a neutron condensate the suppression is the consequence of the Pauli-paramagnetism of the neutron gas, i.e., the alignment of the neutron spins along the magnetic field. The proton $S$-wave pairing is suppressed because of the Landau diamagnetic currents of protons induced by the field. The Ginzburg-Landau and BCS theories of the critical magnetic fields for unpairing are reviewed. The macrophysical implications of the suppression (unpairing) of the condensates are discussed for the rotational crust-core coupling in magnetars and the neutrino-dominated cooling era of their thermal evolution., Comment: 10 pages, 4 figures, Proceedings of "Compact Stars in the QCD phase diagram V", 23-27 May 2016 GSSI and LNGS, L'Aquila, Italy

This contribution will survey recent progress toward an understanding of diverse pairing phenomena in dilute nuclear matter at small and moderate isospin asymmetry, with results of potential relevance to supernova envelopes and proto-neutron stars. Application of {\it ab initio} many-body techniques has revealed a rich array of temperature-density phase diagrams, indexed by isospin asymmetry, which feature both conventional and unconventional superfluid phases. At low density there exist a homogeneous translationally invariant BCS phase, a homogeneous LOFF phase violating translational invariance, and an inhomogeneous translationally invariant phase-separated BCS phase. The transition from the BCS to the BEC phases is characterized in terms of the evolution, from weak to strong coupling, of the pairing gap, condensate wave function, and quasiparticle occupation numbers and spectra. Additionally, a schematic formal analysis of pairing in neutron matter at low to moderate densities is presented that establishes conditions for the emergence of both conventional and unconventional pairing solutions and encompasses the possibility of dineutron formation., Comment: v2: minor changes, 19 pages, 10 figures. Contribution presented by J. W. Clark at XVIII International Conference on Recent Progress in Many-Body Theories, August 16-21, 2015, Niagara Falls, NY USA

We obtain the critical magnetic field required for complete destruction of $S$-wave pairing in neutron matter, thereby setting limits on the pairing and superfluidity of neutrons in the crust and outer core of magnetars. We find that for fields $B \ge 10^{17}$ G the neutron fluid is non-superfluid -- if weaker spin-1 superfluidity does not intervene -- a result with profound consequences for the thermal, rotational, and oscillatory behavior of magnetars. Because the dineutron is not bound in vacuum, cold dilute neutron matter cannot exhibit a proper BCS-BEC crossover. Nevertheless, owing to the strongly resonant behavior of the $nn$ interaction at low densities, neutron matter shows a precursor of the BEC state, as manifested in Cooper-pair correlation lengths {being} comparable to the interparticle distance. We make a systematic quantitative study of this type of BCS-BEC crossover in the presence of neutron fluid spin-polarization induced by an ultra-strong magnetic field. We evaluate the Cooper pair wave-function, quasiparticle occupation numbers, and quasiparticle spectra for densities and temperatures spanning the BCS-BEC crossover region. The phase diagram of spin-polarized neutron matter is constructed and explored at different polarizations., Comment: 13 RevTex pages, 18 figures, v2: minor changes, references added, matches published version

We study the persistent homology of the data set of syntactic parameters of the world languages. We show that, while homology generators behave erratically over the whole data set, non-trivial persistent homology appears when one restricts to specific language families. Different families exhibit different persistent homology. We focus on the cases of the Indo-European and the Niger-Congo families, for which we compare persistent homology over different cluster filtering values. We investigate the possible significance, in historical linguistic terms, of the presence of persistent generators of the first homology. In particular, we show that the persistent first homology generator we find in the Indo-European family is not due (as one might guess) to the Anglo-Norman bridge in the Indo-European phylogenetic network, but is related to the position of Ancient Greek and the Hellenic branch within the network., Comment: 15 pages, 25 jpg figures

In this contribution we first review a method for obtaining the collective modes of pair-correlated neutron matter as found in a neutron star inner crust. We discuss two classes of modes corresponding to density and spin perturbations with energy spectra $\omega = \omega_0 + \alpha q^2$, where $\omega_0 = 2\Delta$ is the threshold frequency and $\Delta$ is the gap in the neutron fluid spectrum. For characteristic values of Landau parameters in neutron star crusts the exitonic density modes have $\alpha < 0$, therefore they live below the pair-breaking continuum $\omega_0$ and are undamped. For spin diffusive modes $\alpha > 0$ and they exist above $\omega_0$ which implies that these modes are damped. As an application of these findings we compute the thermal conductivity due to spin diffusive modes and show that it scales as $T^{1/2} \exp(-2\omega_0/T)$ in the case where their two-by-two scattering cross-section is weakly dependent on temperature., Comment: article style 8 pages, including 3 figures. Proceedings of "Compact Stars in the QCD Phase Diagram IV" (CSQCD IV), September 26-30, 2014, Prerow, Germany; v2: minor adjustments

We study the phase diagram of isospin-asymmetrical nuclear matter in the density-temperature plane, allowing for four competing phases of nuclear matter: (i) the unpaired phase, (ii) the translationally and rotationally symmetric, but isospin-asymmetrical BCS condensate, (iii) the current-carrying Larkin-Ovchinnikov-Fulde-Ferrell phase, and (iv) the heterogeneous phase-separated phase. The phase diagram of nuclear matter composed of these phases features two tri-critical points in general, as well as crossovers from the asymmetrical BCS phase to a BEC of deuterons plus a neutron gas, both for the homogeneous superfluid phase (at high temperatures) and for the heterogeneous phase (at low temperatures). The BCS-BEC type crossover in the condensate occurs as the density is reduced. We analyze in detail some intrinsic properties of these phases, including the Cooper-pair wave function, the coherence length, the occupation numbers of majority and minority nucleonic components, and the dispersion relations of quasiparticle excitations about the ground state. We show by explicit examples that the physics of the individual phases and the transition from weak to strong coupling can be well understood by tracing the behavior of these quantities., Comment: v1: 14 pages, 16 figures, uses RevTex 4, v2: matches published version

We report on recent progress in understanding pairing phenomena in low-density nuclear matter at small and moderate isospin asymmetry. A rich phase diagram has been found comprising various superfluid phases that include a homogeneous and phase-separated BEC phase of deuterons at low density and a homogeneous BCS phase, an inhomogeneous LOFF phase, and a phase-separated BCS phase at higher densities. The transition from the BEC phases to the BCS phases is characterized in terms of the evolution, from strong to weak coupling, of the condensate wavefunction and the second moment of its density distribution in $r$-space. We briefly discuss approaches to higher-order clustering in low-density nuclear matter., Comment: 8 pages, 3 figures, proceedings of the conference "The Modern Physics of Compact Stars and Relativistic Gravity", 18-21 September 2013 Yerevan, Armenia