Search Results (2,958)

Recorded audio often contains speech from multiple people in conversation. It is useful to label such signals with speaker turns, noting when each speaker is talking and identifying each speaker. This paper discusses how to process speech signals to do such speaker diarization (SD). We examine the nature of speech signals, to identify the possible acoustical features that could assist this clustering task. Traditional speech analysis techniques are reviewed, as well as measures of spectral similarity and clustering. Speech activity detection requires separating speech from background noise in general audio signals. SD may use stochastic models (hidden Markov and Gaussian mixture) and embeddings such as x-vectors. Modern neural machine learning methods are examined in detail. Suggestions are made for future improvements. Full article

Soil is the most dynamic matrix in the environment and where biogeochemical cycles take place through the activities of microorganisms such as bacteria. A 16S rRNA sequence analysis of seven different soil samples from different geographical locations in the northeastern part of the United States of America was conducted in order to determine bacterial community composition and diversity and whether geographical distance affects community composition. Microbial DNA was extracted from each soil sample and next generation sequencing was performed. Overall, the predominant bacterial phyla with high relative abundance in each soil were found to be members of Pseudomonadota, Actinomycetota, Acidobacteriota, Chloroflexota, and Bacteroidota which comprised the core microbiome in all 7 soils analyzed. At the order level, the top four bacteria belonged to Rhizobiales, Actinomycetales, Gaiellales, and Solirubrobacterales. Bacterial identification at the genus level were predominantly unclassified with an average of 58%. However, when identification was possible, the most abundant genera detected were Bradyrhizobium and Rhodoplanes. Surface soil samples from the states of New York, Maryland, and Delaware showed the lowest bacterial diversity when compared to suburban soil samples from the state of New Jersey. Similarity between bacterial communities decreased with increasing distance, indicating the dispersal limitations of some bacteria to colonize different habitats where some types show high relative abundance and others did not. However, in some samples, deterministic factors such as land management and possible vehicle emissions probably affected the assemblage and diversity of bacterial communities. Stochastic and deterministic processes might have determined the biogeographical distribution of bacteria in soils influencing the community structure and diversity. Full article

The need to manage the risks related to the COVID-19 epidemic in health, economics, finance, and insurance became obvious after its outbreak. As a basis for the respective quantitative methods, this paper models, in a novel manner, the dynamics of an epidemic via a four-dimensional stochastic differential equation. Crucial time-dependent input parameters include the reproduction number, the average number of externally and newly infected cases, and the average number of new vaccinations. The proposed model is driven by a single Brownian motion process. When fitted to COVID-19 data, it generates the observed features. It captures widely observed fluctuations in the number of newly infected cases. The fundamental probabilistic properties of the dynamics of an epidemic can be deduced from the proposed model. These can form the basis for successfully managing an epidemic and the related economic and financial risks. As a general tool for quantitative studies, a simulation algorithm is provided. A case study illustrates the model and discusses strategies for reopening an economy during an epidemic. Full article

Any application of nanoparticles is influenced by the unavoidable tendency of these particles to agglomerate. As a result, one obtains a more or less broad distribution of agglomerate sizes. This may influence the properties significantly. Looking at agglomeration processes, one has to distinguish two different phenomena: the generally discussed problem, where each particle has the chance to combine with any other particle, or the case, where an agglomeration is possible only with direct neighbors. The latter case, which is the subject of this study, is observed when the particles are stored in a box. In contrast to conventional analyses, the calculations for this paper are based on Markov chain Monte Carlo calculations. This paper describes the formation and development of these agglomerates and the resulting distributions. For an improved depiction of the results, a new quantity derived from entropy, the ‘integral entropy’, was developed. This quantity allows efficient visualization of the development of the agglomerates as a function of the iteration steps resulting from these calculations; additionally, applying the integral reduces the statistical scattering of the results. Furthermore, different mechanisms and interaction parameters were assumed and compared. The results were analyzed to show progress that depends on the number of iteration steps. An important result of these calculations is the distribution of agglomerate sizes and the number of agglomerates as a function of the number of iterations. The calculations are based on different assumptions on the agglomeration and arrangements of the particles. Full article

Emergency scenario characterization and analysis is an essential approach to describing and understanding the future development of emergencies and assisting in response decision-making. This paper aims to develop a method for emergency evolution analysis in a scenario-based way to improve “scenario response” decision-making. A systematic conceptual framework for emergency scenario evolution (ESE) analysis has been developed based on the domain knowledge of emergency management and the disaster system, combined with the representational ability of the knowledge element model. In addition, a modelling approach for ESE based on the generalized stochastic Petri net (ESEGSPN) is proposed to depict the evolutionary uncertainty through basic control flow and to optimize the parameter uncertainty using fuzzy theory. Finally, the COVID-19 pandemic is used as a case study to show how ESEGSPN works. The results indicate that ESEGSPN can simulate the emergency evolution process, identify critical states and trigger actions, present the evolution trend of typical scenario elements, and assist decision-makers in deploying more targeted emergency responses in dynamically changing situations. Full article

Understanding the influence of topography on wettability is essential for improving the modeling of superhydrophobic surfaces. Moreover, wetting predictions can foresee corrosion, biological contamination, self-cleaning properties, and all phenomena related to wetting. In this context, this research work reports the experimental corroboration of a novel theoretical model for stochastic surfaces that relates the static contact angle for the heterogeneous wetting of surfaces to the root mean square (RMS) slope of the surface structures, allowing wetting prediction through topography. For this study, hydrophobic and superhydrophobic alumina thin films with gradual roughness were constructed. The films were deposited on glass using the dip-coating technique, textured with boiling water, and functionalized to achieve low surface energy using Dynasylan F-8815. Surface wettability was characterized using the sessile drop technique, and the RMS slope of the alumina surfaces was quantified using the atomic force microscopy (AFM) technique. The model, presented here for the first time, fits the experimental data, allowing wetting prediction for hydrophobic and superhydrophobic surfaces considering static contact angles. As expected, topography plays a fundamental role in achieving superhydrophobicity. Therefore, defining a topographic criterion, as performed here, for obtaining superhydrophobic surfaces is highly relevant to reduce the production costs of these surfaces and also enable new production processes and designs. Full article

With the swift evolution of artificial intelligence in the automotive sector, autonomous driving has ascended as a pivotal research frontier for automotive manufacturers. Environmental perception, as the cornerstone of autonomous driving, necessitates innovative solutions to address the intricate challenges posed by data sensitivity during vehicle operations. To this end, federated learning (FL) emerges as a promising paradigm, offering a balance between data privacy preservation and performance optimization for perception tasks. In this paper, we pioneer the integration of FL into 3D object detection, presenting personalized FedM²former, a novel multi-modal framework tailored for autonomous driving. This framework aims to elevate the accuracy and robustness of 3D object detection while mitigating concerns over data sensitivity. Recognizing the heterogeneity inherent in user data, we introduce a personalization strategy leveraging stochastic gradient descent optimization prior to local training, ensuring the global model’s adaptability and generalization across diverse user vehicles. Furthermore, to address the sparsity of point cloud data, we innovate the attention layer within our detection model. Our balanced window attention mechanism innovatively processes both point cloud and image data in parallel within each window, significantly enhancing model efficiency and performance. Extensive experiments on benchmark datasets, including nuScenes, ONCE, and Waymo, demonstrate the efficacy of our approach. Notably, we achieve state-of-the-art results with test mAP and NDS of 71.2% and 73.6% on nuScenes, 67.14% test mAP on ONCE, and 83.9% test mAP and 81.8% test mAPH on Waymo, respectively. These outcomes underscore the feasibility of our method in enhancing object detection performance and speed while safeguarding privacy and data security, positioning Personalized FedM²former as a significant advancement in the autonomous driving landscape. Full article

This paper addresses the portfolio optimisation problem within the jump-diffusion stochastic differential equations (SDEs) framework. We begin by recalling a fundamental theoretical result concerning the existence of solutions to the Black–Scholes–Merton partial differential equation (PDE), which serves as the cornerstone for subsequent analysis. Then, we explore a range of financial applications, spanning scenarios characterised by the absence of jumps, the presence of jumps following a log-normal distribution, and jumps following a distribution of greater generality. Additionally, we delve into optimising more complex portfolios composed of multiple risky assets alongside a risk-free asset, shedding new light on optimal allocation strategies in these settings. Our investigation yields novel insights and potentially groundbreaking results, offering fresh perspectives on portfolio management strategies under jump-diffusion dynamics. Full article

Probabilistic models in physics often require the evaluation of normalized Boltzmann factors, which in turn implies the computation of the partition function Z. Obtaining the exact value of Z, though, becomes a forbiddingly expensive task as the system size increases. A possible way to tackle this problem is to use the Annealed Importance Sampling (AIS) algorithm, which provides a tool to stochastically estimate the partition function of the system. The nature of AIS allows for an efficient and parallel implementation in Restricted Boltzmann Machines (RBMs). In this work, we evaluate the partition function of magnetic spin and spin-like systems mapped into RBMs using AIS. So far, the standard application of the AIS algorithm starts from the uniform probability distribution and uses a large number of Monte Carlo steps to obtain reliable estimations of Z following an annealing process. We show that both the quality of the estimation and the cost of the computation can be significantly improved by using a properly selected mean-field starting probability distribution. We perform a systematic analysis of AIS in both small- and large-sized problems, and compare the results to exact values in problems where these are known. As a result, we propose two successful strategies that work well in all the problems analyzed. We conclude that these are good starting points to estimate the partition function with AIS with a relatively low computational cost. The procedures presented are not linked to any learning process, and therefore do not require a priori knowledge of a training dataset. Full article

Dissipation and irreversibility are two central concepts of classical thermodynamics that are often treated as synonymous. Dissipation D is lost or dissipated work $W_{\mathrm{diss}}\ge 0$ but is commonly quantified by entropy generation ${\Delta }_{\mathrm{i}}S$ in an isothermal irreversible macroscopic process that is often expressed as Kullback–Leibler distance $D_{\mathrm{KL}}$ in modern literature. We argue that $D_{\mathrm{KL}}$ is nonthermodynamic, and is erroneously justified for quantification by mistakenly equating exchange microwork ${\Delta }_{\mathrm{e}}{W}_k$ with the system-intrinsic microwork $\Delta W_k={\Delta }_{\mathrm{e}}{W}_k+{\Delta }_{\mathrm{i}}{W}_k$, which is a very common error permeating stochastic thermodynamics as was first pointed out several years ago, see text. Recently, it is discovered that dissipation D is properly identified by ${\Delta }_{\mathrm{i}}W\ge 0$ for all spontaneously irreversible processes and all temperatures T, positive and negative in an isolated system. As T plays an important role in the quantification, dissipation allows for ${\Delta }_{\mathrm{i}}S\ge 0$ for $T>0$, and ${\Delta }_{\mathrm{i}}S<0$ for $T<0$, a surprising result. The connection of D with $W_{\mathrm{diss}}$ and its extension to interacting systems have not been explored and is attempted here. It is found that D is not always proportional to ${\Delta }_{\mathrm{i}}S$. The determination of D requires $d_{\mathrm{i}}{p}_k$, but we show that Fokker-Planck and master equations are not general enough to determine it, which is contrary to the common belief. We modify the Fokker-Planck equation to fix the issue. We find that detailed balance also allows for all microstates to remain disconnected without any transition among them in an equilibrium macrostate, another surprising result. We argue that Liouville’s theorem should not apply to irreversible processes, contrary to the claim otherwise. We suggest to use nonequilibrium statistical mechanics in extended space, where $p_k$’s are uniquely determined to evaluate D. Full article

Our aim in this paper is to analytically compute the at-the-money second derivative of the Bachelier implied volatility curve as a function of the strike price for correlated stochastic volatility models. We also obtain an expression for the short-term limit of this second derivative in terms of the first and second Malliavin derivatives of the volatility process and the correlation parameter. Our analysis does not need the volatility to be Markovian and can be applied to the case of fractional volatility models, both with $H<1/2$ and $H>1/2.$ More precisely, we start our analysis with an adequate decomposition formula of the curvature as the curvature in the uncorrelated case (where the Brownian motions describing asset price and volatility dynamics are uncorrelated) plus a term due to the correlation. Then, we compute the curvature in the uncorrelated case via Malliavin calculus. Finally, we add the corresponding correlation correction and we take limits as the time to maturity tends to zero. The presented results can be an interesting tool in financial modeling and in the computation of the corresponding Greeks. Moreover, they allow us to obtain general formulas that can be applied to a wide class of models. Finally, they provide us with a precise interpretation of the impact of the Hurst parameter H on this curvature. Full article

Caching the contents of unmanned aerial vehicles (UAVs) could significantly improve the content fetching performance of request users (RUs). In this paper, we study UAV trajectory design, content fetching, power allocation, and content placement problems in multi-UAV-aided networks, where multiple UAVs can transmit contents to the assigned RUs. To minimize the energy consumption of the system, we develop a constrained optimization problem that simultaneously designs UAV trajectory, power allocation, content fetching, and content placement. Since the original minimization problem is a mixed-integer nonlinear programming (MINLP) problem that is difficult to solve, the optimization problem was first transformed into a semi-Markov decision process (SMDP). Next, we developed a new technique to solve the joint optimization problem: option-based hierarchical deep reinforcement learning (OHDRL). We define UAV trajectory planning and power allocation as the low-level action space and content placement and content fetching as the high-level option space. Stochastic optimization can be handled using this strategy, where the agent makes high-level option selections, and the action is carried out at a low level based on the chosen option’s policy. When comparing the proposed approach to the current technique, the numerical results show that it can produce more consistent learning performance and reduced energy consumption. Full article

This study investigates the control of seismic and wind-induced responses in reinforced concrete chimneys, incorporating material nonlinearity and soil–structure interaction. Using the Force Analogy Method (FAM), an innovative approach for nonlinear dynamic analysis, the performance of tuned mass dampers (TMDs) is evaluated under diverse loading scenarios, including seismic sequences with mainshock–aftershock events and wind-induced loads, due to synoptic and hurricane winds, modeled as stochastic processes. The FAM approach enables computationally efficient analysis, effectively capturing the complex interactions between seismic and wind actions across varying soil foundation conditions. The results demonstrate the effectiveness of TMDs in reducing structural responses across various loading and foundation scenarios, emphasizing their efficiency in enhancing the safety and resilience of slender structures. This research provides valuable insights into the application of vibration control strategies for safeguarding critical infrastructure against extreme environmental loads. Full article

Microorganisms are instrumental in river ecosystems and participate in biogeochemical cycles. It is thought that dynamic hydrological processes in rivers influence microbial community assembly, but the seasonal succession and community assembly of river sediments on the Loess Plateau remain unclear. This study used high-throughput sequencing technology (16S and ITS) and the neutral community model to analyze seasonal succession and the assembly processes associated with microbial communities in the Dali River, a tributary of the Yellow River on the Loess Plateau. The results showed that sediment bacterial and fungal community diversity indexes in non-flood season were 1.03–3.15 times greater than those in flood season. There were obvious variations between non-flood and flood seasons in sediment microorganisms. The similarities among all, abundant, and rare microbial communities decreased as geographical distance increased. Proteobacteria (52.5–99.6%) and Ascomycota (22.0–34.2%) were the primary microbial phyla in all, abundant, and rare microbial communities. Sediment ammonia nitrogen, water temperature, and sediment organic carbon significantly affected (p < 0.05) the structure of all, abundant, and rare sediment microorganism communities. The ecological networks for the bacterial community of non-flood season and fungal community of flood season had complex topological parameters. The bacterial community in river sediments was driven by deterministic processes, while the fungal community was dominated by stochastic processes. These results expanded understanding about sediment microbial community characteristics in rivers on the Loess Plateau and provided insights into the assembly processes and the factors driving microbial communities in river networks. Full article

Path planning is of great research significance as it is key to affecting the efficiency and safety of mobile robot autonomous navigation task execution. The traditional gray wolf optimization algorithm is widely used in the field of path planning due to its simple structure, few parameters, and easy implementation, but the algorithm still suffers from the disadvantages of slow convergence, ease of falling into the local optimum, and difficulty in effectively balancing exploration and exploitation in practical applications. For this reason, this paper proposes a multi-strategy improved gray wolf optimization algorithm (MSIAR-GWO) based on reinforcement learning. First, a nonlinear convergence factor is introduced, and intelligent parameter configuration is performed based on reinforcement learning to solve the problem of high randomness and over-reliance on empirical values in the parameter selection process to more effectively coordinate the balance between local and global search capabilities. Secondly, an adaptive position-update strategy based on detour foraging and dynamic weights is introduced to adjust the weights according to changes in the adaptability of the leadership roles, increasing the guiding role of the dominant individual and accelerating the overall convergence speed of the algorithm. Furthermore, an artificial rabbit optimization algorithm bypass foraging strategy, by adding Brownian motion and Levy flight perturbation, improves the convergence accuracy and global optimization-seeking ability of the algorithm when dealing with complex problems. Finally, the elimination and relocation strategy based on stochastic center-of-gravity dynamic reverse learning is introduced for the inferior individuals in the population, which effectively maintains the diversity of the population and improves the convergence speed of the algorithm while avoiding falling into the local optimal solution effectively. In order to verify the effectiveness of the MSIAR-GWO algorithm, it is compared with a variety of commonly used swarm intelligence optimization algorithms in benchmark test functions and raster maps of different complexities in comparison experiments, and the results show that the MSIAR-GWO shows excellent stability, higher solution accuracy, and faster convergence speed in the majority of the benchmark-test-function solving. In the path planning experiments, the MSIAR-GWO algorithm is able to plan shorter and smoother paths, which further proves that the algorithm has excellent optimization-seeking ability and robustness. Full article