Search Results (565)

Precise odor source localization in turbulent and sparse environments plays a vital role in enabling robotic systems for hazardous chemical monitoring and effective disaster response. To address this, we propose Cooperative Gravitational-Rényi Infotaxis (CGRInfotaxis), a distributed decision-optimization framework that combines multi-agent collaboration with hybrid cognitive strategy to improve search efficiency and robustness. The method integrates a gravitational potential field for rapid source convergence and Rényi divergence-based probabilistic exploration to handle sparse detections, dynamically balanced via a regulation factor. Particle filtering optimizes posterior probability estimation to autonomously refine search areas while preserving computational efficiency, alongside a distributed interactive-optimization mechanism for real-time decision updates through agent cooperation. The algorithm’s performance is evaluated in scenarios with fixed and randomized odor source locations, as well as with varying numbers of agents. Results demonstrate that CGRInfotaxis achieves a near-100% success rate with high consistency across diverse conditions, outperforming existing methods in stability and adaptability. Increasing the number of agents further enhances search efficiency without compromising reliability. These findings suggest that CGRInfotaxis significantly advances multi-agent odor source localization in turbulent, sparse environments, offering practical utility for real-world applications. Full article

We propose a novel interpretability framework that integrates instance-wise feature selection with causal reasoning to explain decisions made by black-box image classifiers. Instead of relying on feature importance or mutual information, our method identifies input regions that exert the greatest causal influence on model predictions. Causal influence is formalized using a structural causal model and quantified via a conditional mutual information term. To optimize this objective efficiently, we employ continuous subset sampling and the matrix-based Rényi’s $\alpha$-order entropy functional. The resulting explanations are compact, semantically meaningful, and causally grounded. Experiments across multiple vision datasets demonstrate that our method outperforms existing baselines in terms of predictive fidelity. Full article

Since the outbreak of the Russia–Ukraine conflict in 2022, Ukraine’s agricultural production has faced significant disruption, leading to widespread cropland abandonment. These croplands were abandoned at different stages, primarily due to war-related destruction and displacement of people. Existing methods for detecting abandoned cropland fail to account for crop type differences and distinguish abandonment stages, leading to inaccuracies. Therefore, this study proposes a novel framework combining crop-type classification with the Bias-weighted Time-Weighted Dynamic Time Warping (BTWDTW) method, distinguishing between sowing and harvest abandonment. Additionally, the proposed framework improves accuracy by integrating a more nuanced analysis of crop-specific patterns, thus offering more precise insights into abandonment dynamics. The overall accuracy of the proposed method reached 88.9%. The results reveal a V-shaped trajectory of cropland abandonment, with abandoned areas increasing from 28,184 km² in 2022 to 33,278 km² in 2024, with 2023 showing an abandoned area of 24,007.65 km². Spatially, about 70% of sowing abandonment occurred in high-conflict areas, with hotspots of unplanted abandonment shifting from southern Ukraine to the northeast, while unharvested abandonment was observed across the entire country. Significant variations were found across crop types, with maize experiencing the highest rate of unharvested abandonment, while wheat exhibited a more balanced pattern of sowing and harvest losses. The proposed method and results provide valuable insights for post-conflict agricultural recovery and decision-making in recovery planning. Full article

Traditional 4-dimensional variational data assimilation methods have limitations due to the Gaussian distribution assumption of observation errors, and the gradient of the objective functional is vulnerable to observation noise and outliers. To address these issues, this paper proposes a non-Gaussian nonlinear data assimilation method called α-4DVar, based on Rényi entropy and the α-generalized Gaussian distribution. By incorporating the heavy-tailed property of Rényi entropy, the objective function and its gradient suitable for non-Gaussian errors are derived, and numerical experiments are conducted using the Lorenz-63 model. Experiments are conducted with Gaussian and non-Gaussian errors as well as different initial guesses to compare the assimilation effects of traditional 4DVar and α-4DVar. The results show that α-4DVar performs as well as traditional method without observational errors. Its analysis field is closer to the truth, with RMSE rapidly dropping to a low level and remaining stable, particularly under non-Gaussian errors. Under different initial guesses, the RMSE of both the background and analysis fields decreases quickly and stabilizes. In conclusion, the α-4DVar method demonstrates significant advantages in handling non-Gaussian observational errors, robustness against noise, and adaptability to various observational conditions, thus offering a more reliable and effective solution for data assimilation. Full article

Schmidt entropy is used as a common denotation for all Hilbert space entropies that can be defined via the Schmidt decomposition theorem; they include quantum entanglement entropies and classical separability entropies. Exact results about the asymptotic growth in time of such entropies (in the form of Renyi entropies of any order $\ge 1$) are directly derived from the Schmidt decompositions. Such results include a proof that pure point spectra entail boundedness in time of all entropies of order larger than 1; and that slower than exponential transport forbids faster than logarithmic asymptotic growth. Applications to coupled Quantum Kicked Rotors and to Floquet systems are presented. Full article

Under complex operating conditions, vibration signals from rotating machinery often exhibit non-stationary characteristics with non-proportional and closely spaced instantaneous frequency (IF) components. Traditional time–frequency analysis (TFA) methods struggle to accurately extract such features due to energy leakage and component mixing. In response to these issues, an enhanced time–frequency analysis approach, termed Local Entropy Selection Scaling–Reassigning Chirplet Transform (LESSRCT), has been developed to improve the representation accuracy for complex non-stationary signals. This approach constructs multi-channel time–frequency representations (TFRs) by introducing multiple scales of chirp rates (CRs) and utilizes a Rényi entropy-based criterion to adaptively select multiple optimal CRs at the same time center, enabling accurate characterization of multiple fundamental components. In addition, a frequency reassignment mechanism is incorporated to enhance energy concentration and suppress spectral diffusion. Extensive validation was conducted on a representative synthetic signal and three categories of real-world data—bat echolocation, inner race bearing faults, and wind turbine gearbox vibrations. In each case, the proposed LESSRCT method was compared against SBCT, GLCT, CWT, SET, EMCT, and STFT. On the synthetic signal, LESSRCT achieved the lowest Rényi entropy of 13.53, which was 19.5% lower than that of SET (16.87) and 35% lower than GLCT (18.36). In the bat signal analysis, LESSRCT reached an entropy of 11.53, substantially outperforming CWT (19.91) and SBCT (15.64). For bearing fault diagnosis signals, LESSRCT consistently achieved lower entropy across varying SNR levels compared to all baseline methods, demonstrating strong noise resilience and robustness. The final case on wind turbine signals demonstrated its robustness and computational efficiency, with a runtime of 1.31 s and excellent resolution. These results confirm that LESSRCT delivers robust, high-resolution TFRs with strong noise resilience and broad applicability. It holds strong potential for precise fault detection and condition monitoring in domains such as aerospace and renewable energy systems. Full article

Given finite-dimensional random vectors Y, X, and Z that form a Markov chain in that order ($Y\to X\to Z$), we derive the upper bounds on the excess minimum risk using generalized information divergence measures. Here, Y is a target vector to be estimated from an observed feature vector X or its stochastically degraded version Z. The excess minimum risk is defined as the difference between the minimum expected loss in estimating Y from X and from Z. We present a family of bounds that generalize a prior bound based on mutual information, using the Rényi and $\alpha$-Jensen–Shannon divergences, as well as Sibson’s mutual information. Our bounds are similar to recently developed bounds for the generalization error of learning algorithms. However, unlike these works, our bounds do not require the sub-Gaussian parameter to be constant, and therefore, apply to a broader class of joint distributions over Y, X, and Z. We also provide numerical examples under both constant and non-constant sub-Gaussianity assumptions, illustrating that our generalized divergence-based bounds can be tighter than the ones based on mutual information for certain regimes of the parameter $\alpha$. Full article

This study presents a one-dimensional solver of the shallow water equations designed for the wet-bed step Riemann problem. Nonlinear mass and momentum equations incorporating shock and rarefaction waves in a straight one-dimensional channel are expressed as a pair of equations that depend solely on local depth values either side of the step. These unified equations are uniquely designed for the four conditions involving shock and rarefaction waves that can occur in the Step Riemann Problem. The Levenberg–Marquardt method is used to solve these simplified nonlinear equations. Four verification tests are considered for shallow free surface flow in a wet-bed channel with a step. These cases involve two rarefactions, opposing shock-like hydraulic bores, and a rarefaction and shock-like bore. The numerical predictions are in close agreement with existing theory, demonstrating that the method is very effective at solving the wet-bed step Riemann problem. Full article

This paper introduces the record-based transmuted Rayleigh distribution of order 3 (rbt-R), a three-parameter extension of the classical Rayleigh model designed to address data characterized by high skewness and heavy tails. While traditional generalizations of the Rayleigh distribution enhance model flexibility, they often lack sufficient adaptability to capture the complexity of empirical distributions encountered in applied statistics. The rbt-R model incorporates two additional shape parameters, a and b, enabling it to represent a wider range of distributional shapes. Parameter estimation for the rbt-R model is performed using the maximum likelihood method. Simulation studies are conducted to evaluate the asymptotic properties of the estimators, including bias and mean squared error. The performance of the rbt-R model is assessed through empirical applications to four datasets: nicotine yields and carbon monoxide emissions from cigarette data, as well as breaking stress measurements from carbon-fiber materials. Model fit is evaluated using standard goodness-of-fit criteria, including AIC, AIC_c, BIC, and the Kolmogorov–Smirnov statistic. In all cases, the rbt-R model demonstrates a superior fit compared to existing Rayleigh-based models, indicating its effectiveness in modeling highly skewed and heavy-tailed data. Full article

Late Paleozoic to Early Mesozoic granitoids in the East Kunlun Orogenic Belt (EKOB) provide critical insights into the complex and debated relationship between Paleo–Tethyan magmatism and tectonics. This study presents integrated bulk-rock geochemical and zircon isotopic data for the Xingshugou monzogranite (MG) to address these controversies. LA-ICP-MS zircon U-Pb dating constrains the emplacement age of the MG to 247.1 ± 1.5 Ma. The MG exhibits a peraluminous and low Na₂O A₂-type granite affinity, characterized by high K₂O (4.69–6.80 wt.%) and Zr + Nb + Ce + Y (>350 ppm) concentrations, coupled with high Y/Nb (>1.2) and A/CNK ratios (1.54–2.46). It also displays low FeO^T, MnO, TiO₂, P₂O₅, and Mg^# values (26–49), alongside pronounced negative Eu anomalies (Eu/Eu* = 0.37–0.49) and moderately fractionated rare earth element (REE) patterns ((La/Yb)_N = 3.30–5.11). The MG exhibits enrichment in light rare earth elements (LREEs) and large ion lithophile elements (LILEs; such as Sr and Ba), and depletion in high field strength elements (HFSEs; such as Nb, Ta, and Ti), collectively indicating an arc magmatic affinity. Zircon saturation temperatures (T_Zr = 868–934 °C) and geochemical discriminators suggest that the MG was generated under high-temperature, low-pressure, relatively dry conditions. Combined with positive zircon ε_Hf(t) (1.8 to 4.7) values, it is suggested that the MG was derived from partial melting of juvenile crust. Synthesizing regional data, this study suggests that the Xingshugou MG was formed in an extensional tectonic setting triggered by slab rollback of the Paleo-Tethys Oceanic slab. Full article

Automatic classification of oil-painting styles holds significant promise for art history, digital archiving, and forensic investigation by offering objective, scalable analysis of visual artistic attributes. In this paper, we introduce a deep conditional information bottleneck (CIB) framework, built atop ResNet-50, for fine-grained style classification of oil paintings. Unlike traditional information bottleneck (IB) approaches that minimize the mutual information $I(X;Z)$ between input X and latent representation Z, our CIB minimizes the conditional mutual information $I(X;Z\mid Y)$, where Y denotes the painting’s style label. We implement this conditional term using a matrix-based Rényi’s entropy estimator, thereby avoiding costly variational approximations and ensuring computational efficiency. We evaluate our method on two public benchmarks: the Pandora dataset (7740 images across 12 artistic movements) and the OilPainting dataset (19,787 images across 17 styles). Our method outperforms the prevalent ResNet with a relative performance gain of $13.1\%$ on Pandora and $11.9\%$ on OilPainting. Beyond quantitative gains, our approach yields more disentangled latent representations that cluster semantically similar styles, facilitating interpretability. Full article

Compressive sensing in the ambiguity domain facilitates high-performance reconstruction of time-frequency distributions (TFDs) for non-stationary signals. However, identifying the optimal regularization parameter in the absence of prior knowledge remains a significant challenge. The Rényi entropy-based two-step iterative shrinkage/thresholding (RTwIST) algorithm addresses this issue by incorporating local component estimates to guide adaptive thresholding, thereby improving interpretability and robustness. Nevertheless, RTwIST may struggle to accurately isolate components in cases of significant amplitude variations or component intersections. In this work, an enhanced RTwIST framework is proposed, integrating the random sample consensus (RANSAC)-based refinement stage that iteratively extracts individual components and fits smooth trajectories to their peaks. The best-fitting curves are selected by minimizing a dedicated objective function that balances amplitude consistency and trajectory smoothness. Experimental validation on both synthetic and real-world electroencephalogram (EEG) signals demonstrates that the proposed method achieves superior reconstruction accuracy, enhanced auto-term continuity, and improved robustness compared to the original RTwIST and several state-of-the-art algorithms. Full article

This research proposes a new method for time–frequency analysis, termed the Local Entropy Optimization–Adaptive Demodulation Reassignment Transform (LEOADRT), which is specifically designed to efficiently analyze complex, non-stationary mechanical vibration signals that exhibit multiple instantaneous frequencies or where the instantaneous frequency ridges are in close proximity to each other. The method introduces a demodulation term to account for the signal’s dynamic behavior over time, converting each component into a stationary signal. Based on the local optimal theory of Rényi entropy, the demodulation parameters are precisely determined to optimize the time–frequency analysis. Then, the energy redistribution of the ridges already generated in the time–frequency map is performed using the maximum local energy criterion, significantly improving time–frequency resolution. Experimental results demonstrate that the performance of the LEOADRT algorithm is superior to existing methods such as SBCT, EMCT, VSLCT, and GLCT, especially in processing complex non-stationary signals with non-proportionality and closely spaced frequency intervals. This method provides strong support for mechanical fault diagnosis, condition monitoring, and predictive maintenance, making it particularly suitable for real-time analysis of multi-component and cross-frequency signals. Full article

Deploying text-to-image (T2I) models is challenging due to high computational demands, extensive data needs, and the persistent goal of enhancing generation quality and diversity, particularly on resource-constrained devices. We introduce a lightweight T2I framework that uses a dual-conditioned Conditional Variational Autoencoder (CVAE), leveraging CLIP embeddings for semantic guidance and enabling explicit attribute control, thereby reducing computational load and data dependency. Key to our approach is a specialized mapping network that bridges CLIP text–image modalities for improved fidelity and Rényi divergence for latent space regularization to foster diversity, as evidenced by richer latent representations. Experiments on CelebA demonstrate competitive generation (FID: 40.53, 42 M params, 21 FPS) with enhanced diversity. Crucially, our model also shows effective generalization to the more complex MS COCO dataset and maintains a favorable balance between visual quality and efficiency (8 FPS at 256 × 256 resolution with 54 M params). Ablation studies and component validations (detailed in appendices) confirm the efficacy of our contributions. This work offers a practical, efficient T2I solution that balances generative performance with resource constraints across different datasets and is suitable for specialized, data-limited domains. Full article

Fusarium acuminatum is a major pathogenic fungus causing root rot in alfalfa (Medicago sativa). DNA polymerase theta is known to play a crucial role in repairing DNA double-strand breaks. However, its biological function in F. acuminatum remains unknown. In this study, the POLQ gene was deleted by homologous recombination using Agrobacterium tumefaciens-mediated transformation. Compared to the wild type (with the POLQ gene), the mutants (without the POLQ gene) showed significant phenotypic changes: they produced brown-yellow pigments instead of pink, slowed mycelial growth, and exhibited changes in macroconidia size and shape. The virulence of the mutants was greatly reduced, inducing only mild symptoms in alfalfa. In addition, FITC-WGA staining showed impaired spore germination and hyphal growth. These results suggest that POLQ is a key gene regulating growth and development of F. acuminatum, indicating that DNA repair may play an essential role in the pathogenicity of the pathogen in alfalfa. The POLQ gene could thus be a promising target for limiting F. acuminatum infections in alfalfa. Full article