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The leaf area index determines crop health and growth. Traditional methods for calculating it are time-consuming, destructive, costly, and limited to a scale. In this study, we automate the index estimation method using drone image data of grapevine plants and a machine learning model. Traditional feature extraction and deep learning methods are used to obtain helpful information from the data and enhance the performance of the different machine learning models employed for the leaf area index prediction. The results showed that deep learning based feature extraction is more effective than traditional methods. The new approach is a significant improvement over old methods, offering a faster, non-destructive, and cost-effective leaf area index calculation, which enhances precision agriculture practices., Comment: Accepted in 2024 IEEE INTERNATIONAL WORKSHOP ON Metrology for Agriculture and Forestry

In many research fields, researchers aim to identify significant associations between a set of explanatory variables and a response while controlling the false discovery rate (FDR). To this aim, we develop a fully Bayesian generalization of the classical model-X knockoff filter. Knockoff filter introduces controlled noise in the model in the form of cleverly constructed copies of the predictors as auxiliary variables. In our approach we consider the joint model of the covariates and the response and incorporate the conditional independence structure of the covariates into the prior distribution of the auxiliary knockoff variables. We further incorporate the estimation of a graphical model among the covariates, which in turn aids knockoffs generation and improves the estimation of the covariate effects on the response. We use a modified spike-and-slab prior on the regression coefficients, which avoids the increase of the model dimension as typical in the classical knockoff filter. Our model performs variable selection using an upper bound on the posterior probability of non-inclusion. We show how our model construction leads to valid model-X knockoffs and demonstrate that the proposed characterization is sufficient for controlling the BFDR at an arbitrary level, in finite samples. We also show that the model selection is robust to the estimation of the precision matrix. We use simulated data to demonstrate that our proposal increases the stability of the selection with respect to classical knockoff methods, as it relies on the entire posterior distribution of the knockoff variables instead of a single sample. With respect to Bayesian variable selection methods, we show that our selection procedure achieves comparable or better performances, while maintaining control over the FDR. Finally, we show the usefulness of the proposed model with an application to real data.

Mouse-tracking data, which record computer mouse trajectories while participants perform an experimental task, provide valuable insights into subjects' underlying cognitive processes. Neuroscientists are interested in clustering the subjects' responses during computer mouse-tracking tasks to reveal patterns of individual decision-making behaviors and identify population subgroups with similar neurobehavioral responses. These data can be combined with neuro-imaging data to provide additional information for personalized interventions. In this article, we develop a novel hierarchical shrinkage partition (HSP) prior for clustering summary statistics derived from the trajectories of mouse-tracking data. The HSP model defines a subjects' cluster as a set of subjects that gives rise to more similar (rather than identical) nested partitions of the conditions. The proposed model can incorporate prior information about the partitioning of either subjects or conditions to facilitate clustering, and it allows for deviations of the nested partitions within each subject group. These features distinguish the HSP model from other bi-clustering methods that typically create identical nested partitions of conditions within a subject group. Furthermore, it differs from existing nested clustering methods, which define clusters based on common parameters in the sampling model and identify subject groups by different distributions. We illustrate the unique features of the HSP model on a mouse tracking dataset from a pilot study and in simulation studies. Our results show the ability and effectiveness of the proposed exploratory framework in clustering and revealing possible different behavioral patterns across subject groups., Comment: Biometrics

Covariate-dependent graph learning has gained increasing interest in the graphical modeling literature for the analysis of heterogeneous data. This task, however, poses challenges to modeling, computational efficiency, and interpretability. The parameter of interest can be naturally represented as a three-dimensional array with elements that can be grouped according to two directions, corresponding to node level and covariate level, respectively. In this article, we propose a novel dual group spike-and-slab prior that enables multi-level selection at covariate-level and node-level, as well as individual (local) level sparsity. We introduce a nested strategy with specific choices to address distinct challenges posed by the various grouping directions. For posterior inference, we develop a tuning-free Gibbs sampler for all parameters, which mitigates the difficulties of parameter tuning often encountered in high-dimensional graphical models and facilitates routine implementation. Through simulation studies, we demonstrate that the proposed model outperforms existing methods in its accuracy of graph recovery. We show the practical utility of our model via an application to microbiome data where we seek to better understand the interactions among microbes as well as how these are affected by relevant covariates.

Count data play a crucial role in sports analytics, providing valuable insights into various aspects of the game. Models that accurately capture the characteristics of count data are essential for making reliable inferences. In this paper, we propose the use of the Conway-Maxwell-Poisson (CMP) model for analyzing count data in sports. The CMP model offers flexibility in modeling data with different levels of dispersion. Here we consider a bivariate CMP model that models the potential correlation between home and away scores by incorporating a random effect specification. We illustrate the advantages of the CMP model through simulations. We then analyze data from baseball and soccer games before, during, and after the COVID-19 pandemic. The performance of our proposed CMP model matches or outperforms standard Poisson and Negative Binomial models, providing a good fit and an accurate estimation of the observed effects in count data with any level of dispersion. The results highlight the robustness and flexibility of the CMP model in analyzing count data in sports, making it a suitable default choice for modeling a diverse range of count data types in sports, where the data dispersion may vary., Comment: Journal of Quantitative Analysis in Sports (in press)

Motivated by an increasing demand for models that can effectively describe features of complex multivariate time series, e.g. from sensor data in biomechanics, motion analysis, and sports science, we introduce a novel state-space modeling framework where the state equation encodes the evolution of latent partitions of the data over time. Building on the principles of dynamic linear models, our approach develops a random partition model capable of linking data partitions to previous ones over time, using a straightforward Markov structure that accounts for temporal persistence and facilitates changepoint detection. The selection of changepoints involves multiple dependent decisions, and we address this time-dependence by adopting a non-marginal false discovery rate control. This leads to a simple decision rule that ensures more stringent control of the false discovery rate compared to approaches that do not consider dependence. The method is efficiently implemented using a Gibbs sampling algorithm, leading to a straightforward approach compared to existing methods for dependent random partition models. Additionally, we show how the proposed method can be adapted to handle multi-view clustering scenarios. Simulation studies and the analysis of a human gesture phase dataset collected through various sensing technologies show the effectiveness of the method in clustering multivariate time series and detecting changepoints.

We study the impact of mechanical vibrations on the performance of the photonic "hourglass" structure, which is predicted to emit single photons on-demand with near-unity efficiency and indistinguishability. Previous investigations neglected the impact of vibrational modes inherent to this quasi-1D geometry, relying instead on a three-dimensional bulk assumption for the phonon modes. However, it has been shown that phonon decoherence has a much stronger impact in 1D structures as compared with bulk media. Here, we surprisingly demonstrate the robustness of the photonic hourglass design, achieving close-to-unity indistinguishability even by incorporating a detailed description of the vibrational modes. We explain this unexpected result in terms of the large Purcell enhancement of the hourglass single-photon source, which eliminates the negative effect of phonons. Our findings highlight the key role of high-Q optical cavities in mitigating the detrimental effect of phonon decoherence, even for structures of reduced dimensionality., Comment: 7 pages, 5 figures

While the semiconductor quantum dot placed in a solid-state material allows for deterministic emission of single photons, the photon indistinguishability is strongly influenced by the intrinsic coupling to lattice vibrations, phonons, of the solid-state environment. This work investigates the phonon-induced decoherence for a quantum dot placed in the one-dimensional system of a homogeneous cylindrical nanowire. Such a structure supports multiple longitudinal phonon branches, and we consider both a linear and a quadratic coupling of the emitter to these modes. Under a polaron approach, we initially derive an analytical expression for the 1D pure dephasing rate, which leads to a reduced pure dephasing rate compared with bulk. By implementing these results into a full cavity quantum electrodynamic model, we demonstrate that multimode coupling is necessary to correctly predict the indistinguishability in a 1D system, which may otherwise be significantly underestimated., Comment: 21 pages, 14 figures

The present work provides an application of Global Sensitivity Analysis to supervised machine learning methods such as Random Forests. These methods act as black boxes, selecting features in high--dimensional data sets as to provide accurate classifiers in terms of prediction when new data are fed into the system. In supervised machine learning, predictors are generally ranked by importance based on their contribution to the final prediction. Global Sensitivity Analysis is primarily used in mathematical modelling to investigate the effect of the uncertainties of the input variables on the output. We apply it here as a novel way to rank the input features by their importance to the explainability of the data generating process, shedding light on how the response is determined by the dependence structure of its predictors. A simulation study shows that our proposal can be used to explore what advances can be achieved either in terms of efficiency, explanatory ability, or simply by way of confirming existing results.

In this paper, we propose Varying Effects Regression with Graph Estimation (VERGE), a novel Bayesian method for feature selection in regression. Our model has key aspects that allow it to leverage the complex structure of data sets arising from genomics or imaging studies. We distinguish between the predictors, which are the features utilized in the outcome prediction model, and the subject-level covariates, which modulate the effects of the predictors on the outcome. We construct a varying coefficients modeling framework where we infer a network among the predictor variables and utilize this network information to encourage the selection of related predictors. We employ variable selection spike-and-slab priors that enable the selection of both network-linked predictor variables and covariates that modify the predictor effects. We demonstrate through simulation studies that our method outperforms existing alternative methods in terms of both feature selection and predictive accuracy. We illustrate VERGE with an application to characterizing the influence of gut microbiome features on obesity, where we identify a set of microbial taxa and their ecological dependence relations. We allow subject-level covariates including sex and dietary intake variables to modify the coefficients of the microbiome predictors, providing additional insight into the interplay between these factors.

The swing-up of quantum emitter population (SUPER) scheme allows to populate the excited state of a quantum emitter with near-unity fidelity using two red-detuned laser pulses. Its off-resonant, yet fully coherent nature has attracted significant interest in quantum photonics as a valuable tool for preparing single-photon sources in their excited state on demand, while simultaneously ensuring straightforward spectral filtering of the laser. However, the physical understanding of this mechanism in terms of energy exchange between the electromagnetic field and the emitter is still lacking. Here, we present a fully quantized model of the swing-up excitation and demonstrate that it is in fact a multi-photon process, where one of the modes loses two or more photons while the other gains at least one. Our findings provide an unexpected physical interpretation of the SUPER scheme and unveil a new non-linear interaction between single emitters and multiple field modes., Comment: 15 pages, 6 figures + Supplemental document (4 pages, 2 figures)

We consider the problem of analyzing multivariate time series collected on multiple subjects, with the goal of identifying groups of subjects exhibiting similar trends in their recorded measurements over time as well as time-varying groups of associated measurements. To this end, we propose a Bayesian model for temporal biclustering featuring nested partitions, where a time-invariant partition of subjects induces a time-varying partition of measurements. Our approach allows for data-driven determination of the number of subject and measurement clusters as well as estimation of the number and location of changepoints in measurement partitions. To efficiently perform model fitting and posterior estimation with Markov Chain Monte Carlo, we derive a blocked update of measurements' cluster-assignment sequences. We illustrate the performance of our model in two applications to functional magnetic resonance imaging data and to an electroencephalogram dataset. The results indicate that the proposed model can combine information from potentially many subjects to discover a set of interpretable, dynamic patterns. Experiments on simulated data compare the estimation performance of the proposed model against ground-truth values and other statistical methods, showing that it performs well at identifying ground-truth subject and measurement clusters even when no subject or time dependence is present.

Anisotropic laminates with a negative Poisson's ratio for at least some directions are called auxetic. In this paper, we consider the conditions for optimizing the auxeticity of an orthotropic laminate, namely: for a laminate composed by a given material, (i) how to obtain the lowest, i.e. the highest negative, Poisson's ratio and (ii) how to maximize the auxetic zone, i.e. the set of directions where the Poisson's ratio is negative. It is shown that in both the cases the optimal solution is found on the boundary of the feasible domain and in particular that it can be obtained using angle-ply sequences of identical layers. The polar method with dimensionless moduli is employed for representing the anisotropic behavior of the laminate, which allows, on the one hand, to reduce the dimensionality of the problem and, on the other hand, to have an effective mathematical representation of anisotropy by dimensionless invariants., Comment: 16 pages, 9 figure, 3 tables. arXiv admin note: text overlap with arXiv:2403.16057

The excitation scheme is essential for single-photon sources as it prepares the exciton state, defines the decay dynamics, and influences the spectral diffusion of the emitted single photons. Here, we investigate the impact of different optical excitation strategies on the single-photon emission characteristics of bilayer WSe$_2$ quantum emitters. Under phonon-assisted excitation, we achieve narrow and stable single-photon emission with an excellent purity reaching $ 0.94\pm 0.02\,$. Furthermore, the decay time is reduced by more than an order of magnitude from $(16.65 \pm 2.39)\,$ns for above-band excitation to $(1.33 \pm 0.04)\,$ns for phonon-assisted excitation. Finally, we observe a suppressed spectral wandering along with a two-fold reduction of the spectral linewidth. Our comprehensive investigation highlights the critical role of the excitation method in optimizing the performance of WSe$_2$-based quantum emitters.

We propose a flexible Bayesian approach for sparse Gaussian graphical modeling of multivariate time series. We account for temporal correlation in the data by assuming that observations are characterized by an underlying and unobserved hidden discrete autoregressive process. We assume multivariate Gaussian emission distributions and capture spatial dependencies by modeling the state-specific precision matrices via graphical horseshoe priors. We characterize the mixing probabilities of the hidden process via a cumulative shrinkage prior that accommodates zero-inflated parameters for non-active components, and further incorporate a sparsity-inducing Dirichlet prior to estimate the effective number of states from the data. For posterior inference, we develop a sampling procedure that allows estimation of the number of discrete autoregressive lags and the number of states, and that cleverly avoids having to deal with the changing dimensions of the parameter space. We thoroughly investigate performance of our proposed methodology through several simulation studies. We further illustrate the use of our approach for the estimation of dynamic brain connectivity based on fMRI data collected on a subject performing a task-based experiment on latent learning

Functional concurrent, or varying-coefficient, regression models are commonly used in biomedical and clinical settings to investigate how the relation between an outcome and observed covariate varies as a function of another covariate. In this work, we propose a Bayesian nonparametric approach to investigate how clusters of these functional relations evolve over time. Our model clusters individual functional trajectories within and across time periods while flexibly accommodating the evolution of the partitions across time periods with covariates. Motivated by mobile health data collected in a novel, smartphone-based smoking cessation intervention study, we demonstrate how our proposed method can simultaneously cluster functional trajectories, accommodate temporal dependence, and provide insights into the transitions between functional clusters over time.

Functional data analysis, which models data as realizations of random functions over a continuum, has emerged as a useful tool for time series data. Often, the goal is to infer the dynamic connections (or time-varying conditional dependencies) among multiple functions or time series. For this task, a dynamic and Bayesian functional graphical model is introduced. The proposed modeling approach prioritizes the careful definition of an appropriate graph to identify both time-invariant and time-varying connectivity patterns. A novel block-structured sparsity prior is paired with a finite basis expansion, which together yield effective shrinkage and graph selection with efficient computations via a Gibbs sampling algorithm. Crucially, the model includes (one or more) graph changepoints, which are learned jointly with all model parameters and incorporate graph dynamics. Simulation studies demonstrate excellent graph selection capabilities, with significant improvements over competing methods. The proposed approach is applied to study of dynamic connectivity patterns of sea surface temperatures in the Pacific Ocean and reveals meaningful edges., Comment: Revised for Computational Statistics and Data Analysis

In this paper, we develop an analytical approach for estimating brain connectivity networks that accounts for subject heterogeneity. More specifically, we consider a novel extension of a multi-subject Bayesian vector autoregressive model that estimates group-specific directed brain connectivity networks and accounts for the effects of covariates on the network edges. We adopt a flexible approach, allowing for (possibly) non-linear effects of the covariates on edge strength via a novel Bayesian nonparametric prior that employs a weighted mixture of Gaussian processes. For posterior inference, we achieve computational scalability by implementing a variational Bayes scheme. Our approach enables simultaneous estimation of group-specific networks and selection of relevant covariate effects. We show improved performance over competing two-stage approaches on simulated data. We apply our method on resting-state fMRI data from children with a history of traumatic brain injury and healthy controls to estimate the effects of age and sex on the group-level connectivities. Our results highlight differences in the distribution of parent nodes. They also suggest alteration in the relation of age, with peak edge strength in children with traumatic brain injury (TBI), and differences in effective connectivity strength between males and females.

In the context of signal detection in the presence of an unknown time-varying channel parameter, receivers based on the Expectation Propagation (EP) framework appear to be very promising. EP is a message-passing algorithm based on factor graphs with an inherent ability to combine prior knowledge of system variables with channel observations. This suggests that an effective estimation of random channel parameters can be achieved even with a very limited number of pilot symbols, thus increasing the payload efficiency. However, achieving satisfactory performance often requires ad-hoc adjustments in the way the probability distributions of latent variables - both data and channel parameters - are combined and projected. Here, we apply EP to a classical problem of coded transmission on a strong Wiener phase noise channel, employing soft-input soft-output decoding. We identify its limitations and propose new strategies which reach the performance benchmark while maintaining low complexity, with a primary focus on challenging scenarios where the state-of-the-art algorithms fail.

The problem of obtaining anisotropic auxetic composite laminates, i.e. having a negative Poisson's ratio for at least some directions, is examined in this paper. In particular, the possibility of obtaining auxeticity stacking uni-directional identical plies is considered. It is shown that if the ply is composed by isotropic matrix and fibers, then it is impossible to obtain totally auxetic orthotropic laminates, i.e. auxeticity for each direction, unless at least one among matrix and fibers is auxetic itself. Moreover, it is shown what are the conditions, in terms of the mechanical properties of the constituents and of the volume fraction of the fibers, to fabricate uni-directional plies with which to realize laminates having a negative Poisson's ratio for some directions. Several existing materials are also examined. All the analysis is done using the polar formalism, very effective for the study of plane anisotropic problems., Comment: 21 figures

Non-Hermitian models describe the physics of ubiquitous open systems with gain and loss. One intriguing aspect of non-Hermitian models is their inherent topology that can produce intriguing boundary phenomena like resilient higher-order topological insulators (HOTIs) and non-Hermitian skin effects (NHSE). Recently, time-multiplexed lattices in synthetic dimensions have emerged as a versatile platform for the investigation of these effects free of geometric restrictions. Despite holding broad applications, studies of these effects have been limited to static cases so far, and full dynamical control over the non-Hermitian effects has remained elusive. Here, we demonstrate the emergence of topological non-Hermitian corner states with remarkable temporal controllability and robustness in a two-dimensional photonic synthetic time lattice. Specifically, we showcase various dynamic control mechanisms for light confinement and flow, including spatial mode tapering, sequential non-Hermiticity on-off switching, dynamical corner state relocation, and light steering. Moreover, we establish the corner state's robustness in the presence of intensity modulation randomness and quantitatively determine its breakdown regime. Our findings extend non-Hermitian and topological photonic effects into higher synthetic dimensions, offering remarkable flexibility and real-time control possibilities. This opens avenues for topological classification, quantum walk simulations of many-body dynamics, and robust Floquet engineering, free from the limitations of physical geometries.

Quantum emitters in transition metal dichalcogenides (TMDs) have recently emerged as a promising platform for generating single photons for optical quantum information processing. In this work, we present an approach for deterministically controlling the polarization of fabricated quantum emitters in a tungsten diselenide (WSe$_2$) monolayer. We employ novel nanopillar geometries with long and sharp tips to induce a controlled directional strain in the monolayer, and we report on fabricated WSe$_2$ emitters producing single photons with a high degree of polarization $(99\pm 4 \%)$ and high purity ($g^{(2)}(0) = 0.030 \pm 0.025$). Our work paves the way for the deterministic integration of TMD-based quantum emitters for future photonic quantum technologies.

Within optical quantum information processing, single-photon sources based on a two-level system in a semiconductor material allow for on-demand generation of single photons. To initiate the spontaneous emission process, it is necessary to efficiently populate the excited state. However, reconciling the requirement for on-demand excitation with both high efficiency and high photon indistinguishability remains a challenge due to the presence of charge noise and phonon-induced decoherence in the solid-state environment. Here, we propose a method for reconstructing the phonon spectral density experienced by WSe$_{2}$ quantum emitters in the emission process. Using the reconstructed phonon spectral density, we analyze the performance of the resonant, phonon-assisted, and Swing-UP of the quantum EmitteR population (SUPER) swing-up excitation schemes. Under resonant excitation, we obtain an exciton preparation fidelity limited to $\sim$0.80 by the strong phonon coupling, which improves to 0.96 for the SUPER scheme (or 0.89, depending on the type of emitter considered). Under near-resonant phonon-assisted excitation, we observe near-unity excitation fidelity up to 0.976 (0.997). Additionally, we demonstrate that, assuming the suppression of the phonon sidebands, residual dephasing mechanisms such as charge/spin fluctuations are the dominating decoherence mechanisms undermining the photon indistinguishability., Comment: 13 pages, 5 figures

Tensor-based representations are being increasingly used to represent complex data types such as imaging data, due to their appealing properties such as dimension reduction and the preservation of spatial information. Recently, there is a growing literature on using Bayesian scalar-on-tensor regression techniques that use tensor-based representations for high-dimensional and spatially distributed covariates to predict continuous outcomes. However surprisingly, there is limited development on corresponding Bayesian classification methods relying on tensor-valued covariates. Standard approaches that vectorize the image are not desirable due to the loss of spatial structure, and alternate methods that use extracted features from the image in the predictive model may suffer from information loss. We propose a novel data augmentation-based Bayesian classification approach relying on tensor-valued covariates, with a focus on imaging predictors. We propose two data augmentation schemes, one resulting in a support vector machine (SVM) classifier, and another yielding a logistic regression classifier. While both types of classifiers have been proposed independently in literature, our contribution is to extend such existing methodology to accommodate high-dimensional tensor valued predictors that involve low rank decompositions of the coefficient matrix while preserving the spatial information in the image. An efficient Markov chain Monte Carlo (MCMC) algorithm is developed for implementing these methods. Simulation studies show significant improvements in classification accuracy and parameter estimation compared to routinely used classification methods. We further illustrate our method in a neuroimaging application using cortical thickness MRI data from Alzheimer's Disease Neuroimaging Initiative, with results displaying better classification accuracy throughout several classification tasks., Comment: 27 pages, 3 figures, and 7 tables

The paper addresses the problem of finding the necessary and sufficient conditions to be satisfied by the engineering moduli of an anisotropic material for the elastic energy to be positive for each state of strain or stress. The problem is solved first in the most general case of a triclinic material and then each possible case of elastic syngony is treated as a special case. The method of analysis is based upon a rather forgotten theorem of linear algebra and, in the most general case, the calculations, too much involved, are carried out using a formal computation code. New, specific bounds, concerning some of the technical constants, are also found., Comment: Preprint

Event-related potentials (ERPs) extracted from electroencephalography (EEG) data in response to stimuli are widely used in psychological and neuroscience experiments. A major goal is to link ERP characteristic components to subject-level covariates. Existing methods typically follow two-step approaches, first identifying ERP components using peak detection methods and then relating them to the covariates. This approach, however, can lead to loss of efficiency due to inaccurate estimates in the initial step, especially considering the low signal-to-noise ratio of EEG data. To address this challenge, we propose a semiparametric latent ANOVA model (SLAM) that unifies inference on ERP components and their association to covariates. SLAM models ERP waveforms via a structured Gaussian process prior that encodes ERP latency in its derivative and links the subject-level latencies to covariates using a latent ANOVA. This unified Bayesian framework provides estimation at both population- and subject- levels, improving the efficiency of the inference by leveraging information across subjects. We automate posterior inference and hyperparameter tuning using a Monte Carlo expectation-maximization algorithm. We demonstrate the advantages of SLAM over competing methods via simulations. Our method allows us to examine how factors or covariates affect the magnitude and/or latency of ERP components, which in turn reflect cognitive, psychological or neural processes. We exemplify this via an application to data from an ERP experiment on speech recognition, where we assess the effect of age on two components of interest. Our results verify the scientific findings that older people take a longer reaction time to respond to external stimuli because of the delay in perception and brain processes.

In the equivalent single layer theories of anisotropic laminates, the coupling tensor B describes the relation between the in- and out-of plane behavior of the plate. This tensor has some peculiar characteristics and in particular it is not defined, so it is normally considered as an unbounded tensor. We show in this paper a way to determine some relations between the polar invariants of B and those of the tensors describing the in- and out-of- plane behaviors of the laminate, A and D respectively. These relations constitute a set of bounds for the invariants of all the tensors of the laminate, A,B and D. It is hence shown that also the components of B must satisfy some conditions. Some peculiar cases, interesting for applications, are also considered., Comment: 20 pages, 3 figures

The complete set of bounds for the technical constants of an elastic layer, plate or laminate is given. The bounds are valid in general, also for completely anisotropic bodies. They are obtained transforming the polar bounds previously found. These bounds complete the knowledge of classical elasticity at least in the two-dimensional case and are useful in several situations, e.g., for determining the correct feasibility domain in design problems or as necessary conditions for accepting the results of laboratory tests on anisotropic layers., Comment: 11 pages, no figures, no tables

We consider in this paper the general properties of laminates designed to be isotropic in extension and in bending and with a coupling between the in- and out-of plane responses. In particular, we analyze the mathematical properties of the tensors describing the elastic and thermal behavior and the mechanical consequences of these properties. The differences, from the mathematical and mechanical point of view, between the hybrid laminates, i.e. composed by layers of different materials, and those made of identical plies, are pointed out and analyzed. The polar formalism for planar tensors is used in this study., Comment: 31 pages, 9 figures, 6 tables

The analysis of the mathematical and mechanical properties of thermoelastic coupling tensors in anisotropic laminates is the topic of this paper. Some theoretical results concerning the compliance tensors are shown and their mechanical consequences analyzed. Moreover, the case of thermally stable laminates, important for practical applications, is also considered. The study is carried out in the framework of the polar method, a mathematical formalism particularly well suited for the analysis of planar anisotropic problems, introduced by Prof. G. Verchery in 1979., Comment: 35 pages, 18 figures

In this paper, we demonstrate that a few-period circular Bragg reflector around an infinite nanowire can increase the $\beta$ factor of the fundamental mode up to 0.999 due to further suppression of the emission into radiation modes caused by a photonic band gap effect. We then apply this strategy in the practically relevant case of the finite-sized SPS based on tapered nanowires and demonstrate that the collection efficiency can be further increased. Additionally, we also show the beneficial effects of adding optimized high-index rings around the micropillar SPS.

This paper contains a set of theoretical results concerning the coupling tensor B of an anisotropic laminate and of its compliance corresponding B. The theoretical analysis and the mechanical results are obtained through an extensive use of the so-called polar formalism, introduced as early as 1979 by Prof. G. Verchery.

This paper aims at tackling the problem of signal detection in flat-fading channels. In this context, receivers based on the expectation propagation framework appear to be very promising although presenting some critical issues. We develop a new algorithm based on this framework where, unlike previous works, convergence is achieved after a single forward-backward pass, without additional inner detector iterations. The proposed message scheduling, together with novel adjustments of the approximating distributions' parameters, allows to obtain significant performance advantages with respect to the state-of-the-art solution. Simulation results show the applicability of this algorithm when sparser pilot configurations have to be adopted and a considerable gain compared to the current available strategies.

Understanding the Hubbard model is crucial for investigating various quantum many-body states and its fermionic and bosonic versions have been largely realized separately. Recently, transition metal dichalcogenides heterobilayers have emerged as a promising platform for simulating the rich physics of the Hubbard model. In this work, we explore the interplay between fermionic and bosonic populations, using a $\rm{WS}_2$/$\rm{WSe}_2$ heterobilayer device that hosts this hybrid particle density. We independently tune the fermionic and bosonic populations by electronic doping and optical injection of electron-hole pairs, respectively. This enables us to form strongly interacting excitons that are manifested in a large energy gap in the photoluminescence spectrum. The incompressibility of excitons is further corroborated by measuring exciton diffusion, which remains constant upon increasing pumping intensity, as opposed to the expected behavior of a weakly interacting gas of bosons, suggesting the formation of a bosonic Mott insulator. We explain our observations using a two-band model including phase space filling. Our system provides a controllable approach to the exploration of quantum many-body effects in the generalized Bose-Fermi-Hubbard model., Comment: Main text: 7 pages, 5 figures. Supplementary Material: 10 pages, 7 figures

We propose a sparse vector autoregressive (VAR) hidden semi-Markov model (HSMM) for modeling temporal and contemporaneous (e.g. spatial) dependencies in multivariate nonstationary time series. The HSMM's generic state distribution is embedded in a special transition matrix structure, facilitating efficient likelihood evaluations and arbitrary approximation accuracy. To promote sparsity of the VAR coefficients, we deploy an $l_1$-ball projection prior, which combines differentiability with a positive probability of obtaining exact zeros, achieving variable selection within each switching state. This also facilitates posterior estimation via Hamiltonian Monte Carlo (HMC). We further place non-local priors on the parameters of the HSMM dwell distribution improving the ability of Bayesian model selection to distinguish whether the data is better supported by the simpler hidden Markov model (HMM), or the more flexible HSMM. Our proposed methodology is illustrated via an application to human gesture phase segmentation based on sensor data, where we successfully identify and characterize the periods of rest and active gesturing, as well as the dynamical patterns involved in the gesture movements associated with each of these states., Comment: 27 pages, 9 figures

We present a detailed analysis of the physics governing the collection efficiency and the Purcell enhancement of the nanopost single-photon source. We show that a standard single-mode Fabry-P\'erot model is insufficient to describe the device performance, which benefits significantly from scattering from the fundamental mode to radiation modes. We show how the scattering mechanism decouples the collection efficiency from the Purcell enhancement, such that maximum collection efficiency is obtained off-resonance. Finally, we discuss how this scattering mechanism can be beneficial for future single-photon source designs.