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In recent years, law enforcement authorities have increasingly used mathematical tools to support criminal investigations, such as those related to terrorism. In this work, two relevant questions are discussed "How can the different roles of members of a terrorist organization be recognized?" and "are there early signs of impending terrorist acts?" These questions are addressed using the tools of entropy and network theory, more specifically centralities (degree, betweenness, clustering) and their entropies. These tools were applied to data (physical contacts) of four real terrorist networks from different countries. The different roles of the members are clearly recognized from the values of the selected centralities. An early sign of impending terrorist acts is the evolutionary pattern of the values of the entropies of the selected centralities. These results have been confirmed in all four terrorist networks. The conclusion is expected to be useful to law enforcement authorities to identify the roles of the members of terrorist organizations as the members with high centrality and to anticipate when a terrorist attack is imminent, by observing the evolution of the entropies of the centralities.I propose a quantum metrology protocol for measuring frequencies and weak forces based on a periodic modulating quantum Jahn-Teller system composed of a single spin and two bosonic modes. I show that, in the first order of the frequency drive, the time-independent effective Hamiltonian describes spin-dependent interaction between the two bosonic modes. In the limit of high-frequency drive and low bosonic frequency, the quantum Jahn-Teller system exhibits critical behavior which can be used for high-precision quantum estimation. A major advantage of the scheme is the robustness of the system against spin decoherence, which allows it to perform parameter estimation with measurement time not limited by spin dephasing.To monitor the Earth's surface, the satellite of the NASA Landsat program provides us image sequences of any region on the Earth constantly over time. These image sequences give us a unique resource to study the Earth's surface, changes of the Earth resource over time, and their implications in agriculture, geology, forestry, and more. Besides natural sciences, image sequences are also commonly used in functional magnetic resonance imaging (fMRI) of medical studies for understanding the functioning of brains and other organs. In practice, observed images almost always contain noise and other contaminations. For a reliable subsequent image analysis, it is important to remove such contaminations in advance. Selleck APD334 This paper focuses on image sequence denoising, which has not been well-discussed in the literature yet. To this end, an edge-preserving image denoising procedure is suggested. The suggested method is based on a jump-preserving local smoothing procedure, in which the bandwidths are chosen such that the possible spatio-temporal correlations in the observed image intensities are accommodated properly. Both theoretical arguments and numerical studies show that this method works well in the various cases considered.This paper investigates the nested Monte Carlo tree search (NMCTS) for feature selection on regression tasks. NMCTS starts out with an empty subset and uses search results of lower nesting level simulation. Level 0 is based on random moves until the path reaches the leaf node. link2 In order to accomplish feature selection on the regression task, the Gamma test is introduced to play the role of the reward function at the end of the simulation. link3 The concept Vratio of the Gamma test is also combined with the original UCT-tuned1 and the design of stopping conditions in the selection and simulation phases. The proposed GNMCTS method was tested on seven numeric datasets and compared with six other feature selection methods. It shows better performance than the vanilla MCTS framework and maintains the relevant information in the original feature space. The experimental results demonstrate that GNMCTS is a robust and effective tool for feature selection. It can accomplish the task well in a reasonable computation budget.We present new PAC-Bayesian generalisation bounds for learning problems with unbounded loss functions. This extends the relevance and applicability of the PAC-Bayes learning framework, where most of the existing literature focuses on supervised learning problems with a bounded loss function (typically assumed to take values in the interval [0;1]). In order to relax this classical assumption, we propose to allow the range of the loss to depend on each predictor. This relaxation is captured by our new notion of HYPothesis-dependent rangE (HYPE). Based on this, we derive a novel PAC-Bayesian generalisation bound for unbounded loss functions, and we instantiate it on a linear regression problem. To make our theory usable by the largest audience possible, we include discussions on actual computation, practicality and limitations of our assumptions.An extensive survey of open literature reveals the need for a unifying approach for characterizing the degradation of tribo-pairs. This paper focuses on recent efforts made towards developing unified relationships for adhesive-type wear under unlubricated conditions through a thermodynamic framework. It is shown that this framework can properly characterize many complex scenarios, such as degradation problems involving unidirectional, bidirectional (oscillatory and reciprocating motions), transient operating conditions (e.g., during the running-in period), and variable loading/speed sequencing.Based on elastic mechanics, the fluid-structure coupling theory and the finite element method, a high-speed railway wheel-rail rolling-aerodynamic noise model is established to realize the combined simulation and prediction of the vibrations, rolling noise and aerodynamic noise in wheel-rail systems. The field test data of the Beijing-Shenyang line are considered to verify the model reliability. In addition, the directivity of each sound source at different frequencies is analyzed. Based on this analysis, noise reduction measures are proposed. At a low frequency of 300 Hz, the wheel-rail area mainly contributes to the aerodynamic noise, and as the frequency increases, the wheel-rail rolling noise becomes dominant. When the frequency is less than 1000 Hz, the radiated noise fluctuates around the cylindrical surface, and the directivity of the sound is ambiguous. When the frequency is in the middle- and high-frequency bands, exceeding 1000 Hz, both the rolling and total noise exhibit a notable directivity in the directions of 20-30° and 70-90°, and thus, noise reduction measures can be implemented in these directions.In this paper, we propose a new system for a sequential secret key agreement based on 6 performance metrics derived from asynchronously recorded EEG signals using an EMOTIV EPOC+ wireless EEG headset. Based on an extensive experiment in which 76 participants were engaged in one chosen mental task, the system was optimized and rigorously evaluated. The system was shown to reach a key agreement rate of 100%, a key extraction rate of 9%, with a leakage rate of 0.0003, and a mean block entropy per key bit of 0.9994. All generated keys passed the NIST randomness test. The system performance was almost independent of the EEG signals available to the eavesdropper who had full access to the public channel.A long-standing tradition, largely present in both the physical and the philosophical literature, regards the advent of (special) relativity-with its block-universe picture-as the failure of any indeterministic program in physics. On the contrary, in this paper, we note that upholding reasonable principles of finiteness of information hints at a picture of the physical world that should be both relativistic and indeterministic. We thus rebut the block-universe picture by assuming that fundamental indeterminacy itself should also be regarded as a relative property when considered in a relativistic scenario. We discuss the consequence that this view may have when correlated randomness is introduced, both in the classical case and in the quantum one.Calculation of the thermal conductivity of nanofluids by molecular dynamics (MD) is very common. Regrettably, general MD can only be employed to simulate small systems due to the huge computation workload. Instead, the computation workload can be considerably reduced due to the coarse-grained fluid when multiparticle collision dynamics (MPCD) is employed. Hence, such a method can be utilized to simulate a larger system. However, the selection of relevant parameters of MPCD noticeably influences the calculation results. To this end, parameterization investigations for various bin sizes, number densities, time-steps, rotation angles and temperatures are carried out, and the influence of these parameters on the calculation of thermal conductivity are analyzed. Finally, the calculations of thermal conductivity for liquid argon, water and Cu-water nanofluid are performed, and the errors compared to the theoretical values are 3.4%, 1.5% and 1.2%, respectively. This proves that the method proposed in the present work for calculating the thermal conductivity of nanofluids is applicable.One of the important steps in the annotation of genomes is the identification of regions in the genome which code for proteins. One of the tools used by most annotation approaches is the use of signals extracted from genomic regions that can be used to identify whether the region is a protein coding region. Motivated by the fact that these regions are information bearing structures we propose signals based on measures motivated by the average mutual information for use in this task. We show that these signals can be used to identify coding and noncoding sequences with high accuracy. We also show that these signals are robust across species, phyla, and kingdom and can, therefore, be used in species agnostic genome annotation algorithms for identifying protein coding regions. These in turn could be used for gene identification.A class of models for non-Gaussian spatial random fields is explored for spatial field reconstruction in environmental and sensor network monitoring. The family of models explored utilises a class of transformation functions known as Tukey g-and-h transformations to create a family of warped spatial Gaussian process models which can support various desirable features such as flexible marginal distributions, which can be skewed, leptokurtic and/or heavy-tailed. The resulting model is widely applicable in a range of spatial field reconstruction applications. To utilise the model in applications in practice, it is important to carefully characterise the statistical properties of the Tukey g-and-h random fields. In this work, we study both the properties of the resulting warped Gaussian processes as well as using the characterising statistical properties of the warped processes to obtain flexible spatial field reconstructions. In this regard we derive five different estimators for various important quantities often considered in spatial field reconstruction problems. These include the multi-point Minimum Mean Squared Error (MMSE) estimators, the multi-point Maximum A-Posteriori (MAP) estimators, an efficient class of multi-point linear estimators based on the Spatial-Best Linear Unbiased (S-BLUE) estimators, and two multi-point threshold exceedance based estimators, namely the Spatial Regional and Level Exceedance estimators. Simulation results and real data examples show the benefits of using the Tukey g-and-h transformation as opposed to standard Gaussian spatial random fields in a real data application for environmental monitoring.
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