Maximum Entropy Research Articles

Habitat suitability models reveal extensive distribution of deep warm-water coral frameworks in the Red Sea

AbstractDeep-sea coral frameworks are understudied in the Red Sea, where conditions in the deep are conspicuously warm and saline compared to other basins. Habitat suitability models can be used to predict the distribution pattern of species or assemblages where direct observation is difficult. Here we show how coral frameworks, built by species within the families Caryophylliidae and Dendrophylliidae, are distributed between water depths of 150 m and 700 m in the northern Red Sea and Gulf of Aqaba. To extrapolate the known (ground-truthed) positions of these deep frameworks, we use environmental and geomorphometric variables to inform well-performing maximum entropy models. Over 250 km2 of seafloor in our study area are identified as suitable for such frameworks, equivalent to at least 35% of the area of photic-zone coral reefs in the same region. We hence contend that deep-water coral frameworks are an important and underappreciated repository of Red Sea biodiversity.

Assessing the impact of hard data patterns on Bayesian Maximum Entropy: a simulation study.

This study empirically tested the robustness of Bayesian Maximum Entropy (BME) in predicting spatiotemporal data, with an emphasis on skewness, sample size, and spatial dependency level. Simulated data, both Gaussian and non-Gaussian, were generated using the unconditional sequential simulation method, with sample sizes ranging from 100 to 500 at the interval length of 50 and varying skewness (0, 1, 3, 6 and 9) and spatial dependency levels (weak, moderate, and strong). Findings revealed sample size variations and spatial dependence levels did not significantly influence BME prediction's Mean Square Error (MSE) and bias. While skewness significantly impacted MSE (p-value < 0.001), bias remained unaffected. Moreover, skewness and spatial dependence interactions affected both MSE and bias. Despite this, BME proved robust to sample size and skewness, demonstrating a negligible MSE on the graphical plot (heatmap).

Comparative study of fibers extracted from the stems and roots of the Cameroonian pennissetum purpureum for their applications in compressed earth brick reinforcement and textile engineering

This work focuses on the extraction and experimental characterization of pennisetum purpureum fibers extracted from stems and roots, harvested in the Batié Kingdom, in the West Region of Cameroon. After extracting fibers using the boiling water technique, they are chemically treated to improve their properties and performance and to facilitate their incorporation into various composite materials. For the physical characterizations, it is measured: the absolute and apparent densities, the linear mass, the water absorption rate, and the diameter via the microscope. The mean values of the diameters and the measure of their frequency distributions are calculated, followed by the statistical analysis using the maximum entropy principle, in order to find the most probable diameter necessary for technological applications. For the mechanical properties, only the tensile tests are performed, with the determination of the young modulus of both the stems and roots. The results thus obtained showed that the fibers of the stems have an absolute density of (1.35 g/cm3), a linear mass of (54.6 tex), an apparent density of (0.45 g/cm3), a water content of (12.73%), an absorption rate of (142.46%), a porosity of (65.91%), a mean diameter of (7 mm), an elastic modulus of (3.98 GPa), a tensile strength of value of (1186.59 MPa) and an elongation of 16.17%, while the root fibers have an absolute density of (1.34 g/cm3), a linear mass (16.76 tex), an apparent density of (0.37845 g/cm3), a water content of (12.25%), an absorption rate of (193.16%), a porosity of (71.92%), a diameter of (4 mm), an elastic modulus of (1.55 GPa), a tensile strength of a value of (1960.35 MPa) and an elongation of 60.6%. Thus, the fibers of the stems have good mechanical properties, which make them an appropriate material in several applications, such as the reinforcement of composite materials.

Nanodomains and Their Temperature Dependence in a Phosphonium-Based Ionic Liquid: A Single-Molecule Tracking Study.

Ionic liquids (ILs) exhibit a unique nanoscale structure (i.e., nanodomains) characterized by their organization into distinct domains. We present evidence of nanodomains in trihexyl(tetradecyl)phosphonium chloride, [P66614][Cl], using single-molecule tracking (SMT) and the maximum entropy method (MEM) to analyze single-molecule trajectories. The diffusion properties of ATTO 647N were assessed as the temperature of [P66614][Cl] increased from 20 °C (4020 cP), 35 °C (1239 cP), 45 °C (599 cP) to 50 °C (439 cP). The MEM analysis revealed a distinct two-population distribution of diffusion coefficients representing nanodomains in [P66614][Cl] at 20 °C (4020 cP). The slow population accounts for 16%, with a diffusion coefficient of 0.104 μm2/s, while the fast population constitutes 84% with a diffusion coefficient of 0.634 μm2/s. Two diffusing populations were also measured for the chemically different probes ATTO 647N, DiD, and Nile Blue chloride in [P66614][Cl] at 20 °C. In contrast, only a single fast population was measured in [P66614][Cl] at 50 °C. At a similar viscosity (640 cP) but a lower temperature of 20 °C, trihexyl(tetradecyl)phosphonium bis[(trifluoromethyl)-sulfonyl]imide, [P66614][NTf2], also showed only a single diffusing population. The elimination of the slow population and the presence of a single diffusing population in [P66614][Cl] as the temperature increases and the viscosity decreases is consistent with liquid-liquid phase separation (LLPS) as a mechanism of nanodomain formation. In addition, the measurement of two diffusing populations for three fluorophores with different chemical structures is also consistent with a physical mechanism, and not a chemical mechanism, for nanodomain formation.

The INS/GPS integrated navigation based on autonomous underwater vehicle using modified extended Kalman filter

Ensuring highhtab accuracy and robustness remains a paramount concern in the realm of underwater positioning and navigation research. This paper proposes a filtering algorithm grounded in Inertial Measurement Unit/Global Positioning System (INS/GPS) integrated navigation to effectively address the challenges posed by non-Gaussian noise, stemming from outliers and measurement inaccuracies. Currently, most filtering algorithms are developed based on the criteria of minimum mean square error (MMSE) or maximum entropy criteria (MCC). To address the challenge of handling complex non-Gaussian noises, this paper uses the Minimum Error Entropy Criterion (MEE) and Extended Kalman Filter (EKF) with Kernel Risk-Sensitive Loss (KRSL) instead of MMSE or MCC to develop an online filtering algorithm with a recursive process, and adjusts the kernel size of MEE within a reasonable range by constructing an adaptive factor to deal with outliers generated during measurement and excessive convergence caused by the inapplicability of the kernel size. Through extensive target tracking simulations and surface Autonomous Underwater Vehicle (AUV) localization experiments, results demonstrate the efficacy of the proposed algorithm. Notably, the algorithm optimizes kernel sizes for different types of noises, ensuring robust state estimation and rapid convergence without compromising filtering accuracy.

A Novel Calibration Scheme of Gas Sensor Array for a More Accurate Measurement Model of Mixed Gases.

Gas sensor arrays (GSAs) usually encounter challenges due to the cross-contamination of mixed gases, leading to reduced accuracy in measuring gas mixtures. However, with the advent of artificial intelligence, there is a promising avenue for addressing this issue effectively. In pursuit of more accurate mixed gas measurements, we proposed a measurement model leveraging neural networks. Our approach involved employing the encoder of an autoencoder network (AEN) to extract features from experimental data, while fully connected layers were utilized for predicting concentrations of mixed gases. To refine the neural network parameters, we employed a variational autoencoder to generate additional data resembling the distribution of experimental data. Subsequently, we designed a domain difference maximum entropy technique to identify optimal concentration points for the calibration data. These calibration points were instrumental in training the fully connected layers, enhancing the model's accuracy. During practical usage, with the AEN configuration fixed, the model can be fine-tuned by using a small subset of test points across large-scale GSA deployments. Simulation and practical measurement results demonstrated the efficacy of our proposed measurement model, boasting high accuracy, with confidence intervals for relative errors of the four gas measurements below 3% at the 95% confidence level. Besides, the calibration scheme reduced the number of test points compared with traditional methods, reducing the cost of labor and equipment.

Investigating the effect of non-resonant background variation on the CARS data analysis of bacteria samples and classification using machine learning

Non-resonant background (NRB) plays a significant role in coherent anti-Stokes Raman scattering (CARS) spectroscopic applications. All the recent works primarily focused on removing the NRB using different deep learning methods, and only one study explored the effect of NRB. Hence, in this work, we systematically investigated the impact of NRB variation on Raman signal retrieval. The NRB is simulated as a linear function with different strengths relative to the resonant Raman signal, and the variance also changes for each NRB strength. The resonant part of nonlinear susceptibility is extracted from real experimental Raman data; hence, the simulated CARS data better approximate the experimental CARS spectra. Then, the corresponding Raman signal is retrieved by four different methods: maximum entropy method (MEM), Kramers-Kronig (KK), convolutional neural network (CNN), and long short-term memory (LSTM) network. Pearson correlation measurements and principal component analysis combined with linear discriminant analysis modeling revealed that MEM and KK methods have an edge over LSTM and CNN for higher NRB strengths. It is also demonstrated that normalizing the input data favors LSTM and CNN predictions. In contrast, background removal from the predictions significantly influenced Pearson correlation but not the classification accuracies for MEM and KK. Further, the LSTM performance is found to be limited and can only be applied for low NRB strengths. This comprehensive study has the potential to impact the CARS spectroscopy and microscopy applications in different areas.

SmoQyDEAC.jl: A differential evolution package for the analytic continuation of imaginary time correlation functions

We introduce the SmoQyDEAC.jl package, a Julia implementation of the Differential Evolution Analytic Continuation (DEAC) algorithm [N. S. Nichols et al., Phys. Rev. E 106, 025312 (2022)] for analytically continuing noisy imaginary time correlation functions to the real frequency axis. Our implementation supports fermionic and bosonic correlation functions on either the imaginary time or Matsubara frequency axes, and treatment of the covariance error in the input data. This paper presents an overview of the DEAC algorithm and the features implemented in the SmoQyDEAC.jl package. It also provides detailed benchmarks of the package’s output against the popular maximum entropy and stochastic analytic continuation methods. The code for this package can be downloaded from our GitHub repository at https://github.com/SmoQySuite/SmoQyDEAC.jl or installed using the Julia package manager. The online documentation, including examples, can be accessed at https://smoqysuite.github.io/SmoQyDEAC.jl/stable/.

Codebase release r1.1 for SmoQyDEAC.jl

We introduce the SmoQyDEAC.jl package, a Julia implementation of the Differential Evolution Analytic Continuation (DEAC) algorithm [N. S. Nichols et al., Phys. Rev. E 106, 025312 (2022)] for analytically continuing noisy imaginary time correlation functions to the real frequency axis. Our implementation supports fermionic and bosonic correlation functions on either the imaginary time or Matsubara frequency axes, and treatment of the covariance error in the input data. This paper presents an overview of the DEAC algorithm and the features implemented in the SmoQyDEAC.jl package. It also provides detailed benchmarks of the package’s output against the popular maximum entropy and stochastic analytic continuation methods. The code for this package can be downloaded from our GitHub repository at https://github.com/SmoQySuite/SmoQyDEAC.jl or installed using the Julia package manager. The online documentation, including examples, can be accessed at https://smoqysuite.github.io/SmoQyDEAC.jl/stable/.

An Ecoinformatic Model Using Rényi's Entropy Predicts Soil Chemistry Effect on Tree Species Abundance Distributions

ABSTRACTAimAn ecological community consists of species of various abundances that reflect their responses to the environmental conditions. A classic macroecological pattern, the species abundance distribution (SAD), has been studied for diverse taxa and communities and integrated into numerous modelling tools. Despite its widespread use, a mathematical model that can capture variations in the empirical SAD and describe its response to environmental changes is still lacking. By integrating the Maximum Entropy Theory of Ecology (METE) with a generalised entropy called Rényi's entropy, we aim to develop a new ecoinformatic model that can predict the variation of empirical SAD along multiple environmental gradients.LocationPanama.TaxonAngiosperms.MethodsWe extend the METE using the Rényi's entropy as an uncertainty measure. We apply this extended METE, called Rényi model, to the tree abundance data from 49 plots in Panama and predict the SAD within each plot. We estimate Rényi's parameter q by fitting the predicted SAD to the empirical SAD in each plot. We further compile climate and soil data from the Panama plots and analyse their relationships with the estimated q using multiple regressions.ResultsRényi model provides adequate description of the empirical SADs and outperforms lognormal or log‐series models in 40 of the 49 tree plots, according to the Akaike information criterion. Variations in Renyi's q estimates (from 1/2 to 1) reflect shifts in the empirical SADs. Multiple regressions reveal that P, Al and NH4, three soil chemicals that are important for tree growth and species distribution, significantly affect Renyi's q across plots.Main ConclusionsThese findings suggest that the Rényi model and Rényi's q can characterise the SAD of communities under environmental changes. They also indicate the potential of using generalised entropies to predict macroecological patterns in stressed ecosystems.

Cyberbullying Detection and Prevention System for Enhancing Online Platform Safety Using Maximum Entropy Model

ABSTRACTThe widespread proliferation of social media platforms in the 21st century has undoubtedly brought about a significant shift in how individuals establish connections. However, it is essential to acknowledge that this technological innovation has also given rise to negative repercussions. The proliferation of online abuse, harassment, cyberbullying, and trolling has emerged as a significant concern, overshadowing the otherwise advantageous aspects of digital networking platforms. The detrimental effect of cyberbullying is of considerable concern, which often causes profound psychological and physiological anguish among its targets, sometimes resulting in contemplation of suicide. The proliferation of online harassment occurrences, including disclosing confidential dialogues and spreading detrimental rumors, has engendered extensive apprehension. The present research aims to address the pressing need to identify and mitigate cyberbullying within the context of social media platforms. A rigorous approach was used to gather data from Kaggle, which was further processed utilizing sophisticated methods like tokenization, stemming, and lemmatization. The research used the Maximum Entropy Model to develop a detection system to identify instances of bullying in texts or messages shared on social media sites. The algorithm exhibited a praiseworthy mean accuracy rate of 77% in detecting cases of cyberbullying. The results highlight the capacity of the algorithm to anticipate if remarks entered by individuals on social media might be classified as cyberbullying, representing a noteworthy advancement in addressing this widespread problem. This study supports the broader deployment of the created cyberbullying detection system across all social media platforms. The proactive use of this novel technology enables online platforms to effectively detect and mitigate cyberbullying, cultivating a digital environment characterized by enhanced safety and respect. This research illuminates the significant issue of cyberbullying. It presents a concrete remedy to protect the welfare of those who engage in online activities, particularly those more susceptible to harm, such as women and children.

The Future Migration Direction of Deer and Japanese Yew Is Consistent Under Climate Change

Climate change is becoming an important driver of biodiversity loss by altering the habitat, distribution and interspecific relationships of species. Japanese yew (Taxus cuspidata) is a first class protected plant in China, which has important ecological significance and occupies a certain position in the feeding habit of wapiti (Cervus elaphus) and Siberian roe deer (Capreolus pygargus). Due to human and animal damage, the number of Japanese yew has gradually decreased. Therefore, understanding the potential distribution of Japanese yew and the suitable areas for deer to browse on it under climate change will help to further protect these three species in Northeast China, especially migrate to more suitable areas in different scenarios in the future. From July 2021 to July 2024, we collected the information of species distribution and the variables associated with the species’ ecological limits in Muling National Nature Reserve to cross-reflect the current and future distribution and feeding area of the two species to assess each other’s impacts with Maximum entropy model (MaxEnt). The results showed that under the SSP2-4.5 and SSP5-8.5 scenarios, feeding pressure, driest quarter precipitation (BIO17) and seasonal temperature variation coefficient (BIO4) were the main variables affecting the distribution of Japanese yew, and the driest quarter precipitation (BIO17) and annual precipitation (BIO12) were the main variables affecting wapiti and Siberian roe deer foraging them. Under SSP2-4.5 and SSP5-8.5 scenarios, the suitable area of Japanese yew and the feeding area of the two species of deer gradually decreased from 2041 to 2100. Compared with wapiti, Siberian roe deer has a greater impact on the distribution range of Japanese yew, and the suitable feeding area is wider. It is expected that the potential centroid of Japanese yew, wapiti and Siberian roe deer will migrate to higher latitudes in the future. These findings provide a scientific basis for the reserve to develop relevant measures and plans and effectively protect the three species.

FLSAD: Defending Backdoor Attacks in Federated Learning via Self-Attention Distillation

Federated Learning (FL), as a distributed machine learning framework, can effectively learn symmetric and asymmetric patterns from large-scale participants. However, FL is susceptible to malicious backdoor attacks through attackers injecting triggers into the backdoored model, resulting in backdoor samples being misclassified as target classes. Due to the stealthy nature of backdoor attacks in FL, it is difficult for users to discover the symmetric and asymmetric backdoor properties. Currently, backdoor defense methods in FL cause model performance degradation while reducing backdoors. In addition, some methods will assume the existence of clean samples, which does not match the realistic scenarios. To address such issues, we propose FLSAD, an effective backdoor defense method in FL via self-attention distillation. FLSAD can recover the triggers using an entropy maximization estimator. Based on the recovered triggers, we leverage the self-attention distillation to eliminate the backdoor. Compared with the baseline backdoor defense methods, FLSAD can reduce the success rates of different state-of-the-art backdoor attacks to 2% on four real-world datasets through extensive evaluation.

Using iNaturalist presence data to produce suitability maps for Triatoma protracta, T. rubida and T. recurva in the American Southwest, Texas and northern Mexico, to identify potential transmission zones of Chagas disease.

There are 11 species of triatomines in the USA, with seven reported in the American Southwest. These species are capable of transmitting Trypanosoma cruzi, the etiological agent of Chagas disease, but are generally sylvatic and rarely infect humans in the USA. iNaturalist is one of the most popular citizen science data portals, where users record encounters with any individual organism. As of November 2023, iNaturalist reported 722 confirmed triatomine observations in the American Southwest. Maximum entropy ecological niche modelling and bioclimatic environmental variables were used to predict zones of highest human-triatomine interaction. Models fit well for Triatoma protracta (0.85), T. rubida (0.94), and T. recurva (0.96). Precipitation in the warmest quarter was most predictive of T. protracta presence, while precipitation in the driest quarter was most predictive of T. rubida and T. recurva. Locations identified in the American Southwest with highest potential for human-triatomine interaction, and potential T. cruzi transmission, were coastal California, the Sierra Nevada foothills, southern Arizona and border areas of northern Mexico. Study findings can be used for targeted surveillance efforts to address the paucity of contemporary information regarding triatomine species diversity and geographical and ecological associations in the American Southwest.

Express[p] in expressive phonology: analysis of a nicknaming pattern using ‘princess’ in Japanese

Abstract Recent studies have shown that sound-symbolic patterns can be modelled using phonological theory. The purpose of the current study is to describe a new Japanese nicknaming pattern, pime-yobi, wherein [h] alternates with [p] to express cuteness, and to model it using Maximum Entropy Harmonic Grammar. The current study, building on the analysis of Alderete &amp; Kochetov (2017), proposes a sound-symbolic constraint, Express[p], which requires output forms to contain [p]. The results of two experiments show that Japanese speakers found names containing [p]s to be cuter than those without them and that pime-yobi nicknaming exhibits intra- and inter-speaker variation in acceptability and cuteness. Based on these results, theoretical analysis shows that the weight of Express[p] varies both across different speakers and within the same speaker.

Habitat Associated with Ramps/Wild Leeks (Allium tricoccum Ait.) in Pennsylvania, USA: Guidance for Forest Farming Site Selection

Ramps or wild leeks (Allium tricoccum) are a popular foraged non-timber forest product in North America consumed for their edible bulbs and leaves. The agroforestry practice of forest farming is a possible solution to conservation challenges surrounding the wild exploitation of this species, but it requires proper site selection to be successful. In this study, maximum entropy (Maxent) modeling using 163 occurrence points and field data collected at 30 wild populations were combined to determine the characteristics of the ramp habitat in Pennsylvania. Both Maxent modeling and field measurements highlighted moist, lower slope positions with base-rich bedrock types as suitable for ramps. Sites shared 50% of their floristic associates on average, with 252 species documented in total. Forest communities associated with ramps included many species indicative of base-rich mesic soil conditions, but the relative abundance of some indicator species differed by region. The confirmation of model variables by field measurements and forest community types points to the usefulness of these characteristics in identifying suitable forest farming sites. When used in tandem, these results can help to guide site selection for forest farming and other conservation strategies.

Application of Maximum Entropy Fuzzy Clustering Algorithm with Soft computing in Migration Anomaly Detection

Application of Maximum Entropy Fuzzy Clustering Algorithm with Soft computing in Migration Anomaly Detection

Evidence for Nanostructures of at Least 20 nm in a Phosphonium Ionic Liquid at Room Temperature Using Fluorescence Correlation Spectroscopy.

Fluorescence correlation spectroscopy (FCS) measurements are performed on the ionic liquid (IL), tetradecyl(trihexyl) phosphonium chloride, [P66614+][Cl-], using fluorescent probes of varying sizes: ATTO 532, ∼2 nm; and 20- and 40 nm fluorescent beads. The fluorescence correlation function, G(t), is analyzed in terms of a distribution of diffusion coefficients using a maximum entropy method (MEM). For ATTO 532 and the 20 nm beads, the fit to G(t) yields two well-defined distributions; for the 40 nm beads, however, only one is obtained. These results are consistent with the existence of two nanodomains whose size is greater than or equal to 20 nm and less than 40 nm. The origin of such nanodomains is attributed to a liquid-liquid phase transition. Other groups have observed liquid-liquid phase transitions experimentally in a number of systems, including [P66614+][Cl-]. We suggest that because large regions (i.e., greater than 1-2 nm) resulting from the liquid-liquid phase transition would be expected to have different properties, such as viscosity, and because their presence would necessarily increase the number of interfaces in the IL, these regions may provide an explanation for the exceptional behavior of ILs in various separation systems.

Distinct Ecological Habits and Habitat Responses to Future Climate Change in Two Subspecies of Magnolia sieboldii K. Koch, a Tree Endemic to East Asia.

Magnolia sieboldii, an important ornamental tree native to East Asia, comprises two subspecies in distinct regions, with wild populations facing suboptimal survival. This study aimed to understand the potential habitat distribution of these subspecies under future climate-change conditions to support climate-adaptive conservation. The maximum entropy (MaxEnt) model was used with occurrence and environmental data to simulate the current and future suitable habitats under various climate scenarios. Precipitation in the warmest quarter played a crucial role in shaping the potential habitats of both subspecies; however, they exhibited different sensitivities to temperature-related variables and altitude. Magnolia sieboldii subsp. sieboldii is more sensitive to temperature seasonality and annual mean temperature, whereas Magnolia sieboldii subsp. japonica is more affected by altitude, mean temperature in the driest quarter, and isothermality. Currently, the subsp. sieboldii is predicted to have larger, more contiguous suitable habitats across northeastern China, the Korean Peninsula, and Japan, whereas the subsp. japonica occupies smaller, more disjunct habitats scattered in central and western Japan and the southern Chinese mountains. These two subspecies will respond differently to future climate change. Potentially suitable habitats for subsp. sieboldii are expected to expand significantly northward over time, especially under the SSP585 scenario compared with the SSP126 scenario. In contrast, moderately and highly suitable habitats for the subsp. japonica are projected to contract southward significantly. Therefore, we recommend prioritizing the conservation of the subsp. japonica over that of the subsp. sieboldii. Strategies include in situ and ex situ protection, introduction and cultivation, regional hybridization, and international cooperation. Our study offers valuable insights for the development of targeted conservation strategies for both subspecies of M. sieboldii to counteract the effects of climate change.

Exploring the Multiplication of Resonant Modes in Off-Center-Driven Chladni Plates from Maximum Entropy States

In this study, the resonant characteristics of the off-center-driven Chladni plates were systematically investigated for the square and equilateral triangle shapes. Experimental results reveal that the number of the resonant modes is considerably increased for the plates under the off-center-driving in comparison to the on-center-driving. The Green’s functions derived from the nonhomogeneous Helmholtz equation are exploited to numerically analyze the information entropy distribution and the resonant nodal-line patterns. The experimental resonant modes are clearly confirmed to be in good agreement with the maximum entropy states in the Green’s functions. Furthermore, the information entropy distribution of the Green’s functions can be used to reveal that more eigenmodes can be triggered in the plate under the off-center-driving than the on-center-driving. By using the multiplication of the resonant modes in the off-center-driving, the dispersion relation between the experimental frequency and the theoretical wave number can be deduced with more accuracy. It is found that the deduced dispersion relations agree quite well with the Kirchhoff–Love plate theory.