Differential Entropy Research Articles

Relative State Counting for Semiclassical Black Holes

It has been shown that entropy differences between certain states of perturbative quantum gravity can be computed without specifying an ultraviolet completion. This is analogous to the situation in classical statistical mechanics, where entropy differences are defined but absolute entropy is not. Unlike in classical statistical mechanics, however, the entropy differences computed in perturbative quantum gravity do not have a clear physical interpretation. Here we construct a family of perturbative black hole states for which the entropy difference can be interpreted as a relative counting of states. Conceptually, this Letter begins with the algebra of mass fluctuations around a fixed black hole background, and points out that while this is a type I algebra, it is not a factor and therefore has no canonical definition of entropy. As in previous work, coupling the mass fluctuations to quantum matter embeds the mass algebra within a type II factor, in which entropy differences (but not absolute entropies) are well defined. It is then shown that for microcanonical wave functions of mass fluctuation, the type II entropy difference equals the logarithm of the dimension of the extra Hilbert space that is needed to map one microcanonical window to another using gauge-invariant unitaries. The Letter closes with comments on type II entropy difference in a more general class of states, where the von Neumann entropy difference does not have a physical interpretation, but “one-shot” entropy differences do. Published by the American Physical Society 2024

QCD evolution of entanglement entropy.

Entanglement entropy has emerged as a novel tool for probing nonperturbative quantum chromodynamics (QCD) phenomena, such as color confinement in protons. While recent studies have demonstrated its significant capability in describing hadron production in deep inelastic scatterings, the QCD evolution of entanglement entropy remains unexplored. In this work, we investigate the differential rapidity-dependent entanglement entropy within the proton and its connection to final-state hadrons, aiming to elucidate its QCD evolution. Our analysis reveals a strong agreement between the rapidity dependence of von Neumann entropy, obtained from QCD evolution equations, and the corresponding experimental data on hadron entropy. These findings provide compelling evidence for the emergence of a maximally entangled state, offering new insights into the nonperturbative structure of protons.

Moisture desorption isotherms and thermodynamic properties of ecalyptus globulus wood

This paper aimed to analyze the moisture desorption isotherms and specific thermodynamic properties of Algerian Eucalyptus globulus wood using the static gravimetric method. The study was conducted at height temperatures and relative humidity levels ranging from 5% to 90%. The desorption isotherms exhibited a sigmoidal shape, categorized as "type II." A suitable "thermodynamic" equation was chosen to describe the desorption isotherms under controlled conditions. The Clausius-Clapeyron relationship was employed to determine the isosteric heat of desorption. The results demonstrated that, for equilibrium moisture content below 4%, the differential enthalpy and entropy decreased exponentially with increasing moisture content. Specifically, the differential enthalpy decreased from 36 kJ/mol to 5 kJ/mol, while the entropy decreased from 76 J/ (mol K) to 13 J/ (mol K). The enthalpy-entropy compensation theory in the desorption reaction was validated by the observed difference between the isokinetic temperature and the harmonic temperature. Moreover, the wood-water desorption process was spontaneous, as confirmed by the negative value of Gibbs free energy (-1690 J/mol). With increasing temperature, the spreading pressure dropped; at 40°C, it was 2.47978 J/m², and at 80°C, it was 0.82328 J/m². Conversely, the rate of increase was 0.11% J/m² per relative humidity as the water activity increased.

Regularized entanglement entropy of electron-positron scattering with a witness photon

Regularized quantum information metrics are calculated for the scattering process e−e+→γ,Z→μ−μ+ that has a witness photon entangled with the initial electron-positron state. Unitarity implies the correct regularization of divergences that appear in both the final density matrix and von Neumann entanglement entropies. The entropies are found to quantify uncertainty or randomness. The variation of information, entanglement entropy, and correlation between the muon’s and witness photon’s helicities are found to convey equivalent information. The magnitude of the muon’s expected helicity rises (falls) as the helicity entropy falls (rises). Area, or the scattering cross section, is a source of entropy for the muon’s helicity entropy and momentum entropy. The muon’s differential angular entropy distribution is similar to the differential angular cross section distribution, capturing the forward-backward asymmetry at high center-of-mass energies. Published by the American Physical Society 2024

Quantifying the informativity of emission lines to infer physical conditions in giant molecular clouds

Context. Observations of ionic, atomic, or molecular lines are performed to improve our understanding of the interstellar medium (ISM). However, the potential of a line to constrain the physical conditions of the ISM is difficult to assess quantitatively, because of the complexity of the ISM physics. The situation is even more complex when trying to assess which combinations of lines are the most useful. Therefore, observation campaigns usually try to observe as many lines as possible for as much time as possible. Aims. We have searched for a quantitative statistical criterion to evaluate the full constraining power of a (combination of) tracer(s) with respect to physical conditions. Our goal with such a criterion is twofold. First, we want to improve our understanding of the statistical relationships between ISM tracers and physical conditions. Secondly, by exploiting this criterion, we aim to propose a method that helps observers to make their observation proposals; for example, by choosing to observe the lines with the highest constraining power given limited resources and time. Methods. We propose an approach based on information theory, in particular the concepts of conditional differential entropy and mutual information. The best (combination of) tracer(s) is obtained by comparing the mutual information between a physical parameter and different sets of lines. The presented analysis is independent of the choice of the estimation algorithm (e.g., neural network or χ2 minimization). We applied this method to simulations of radio molecular lines emitted by a photodissociation region similar to the Horsehead Nebula. In this simulated data, we considered the noise properties of a state-of-the-art single dish telescope such as the IRAM 30m telescope. We searched for the best lines to constrain the visual extinction, AVtot, or the ultraviolet illumination field, G0. We ran this search for different gas regimes, namely translucent gas, filamentary gas, and dense cores. Results. The most informative lines change with the physical regime (e.g., cloud extinction). However, the determination of the optimal (combination of) line(s) to constrain a physical parameter such as the visual extinction depends not only on the radiative transfer of the lines and chemistry of the associated species, but also on the achieved mean signal-to-noise ratio. The short integration time of the CO isotopologue J = 1 − 0 lines already yields much information on the total column density for a large range of (AVtot, G0) space. The best set of lines to constrain the visual extinction does not necessarily combine the most informative individual lines. Precise constraints on the radiation field are more difficult to achieve with molecular lines. They require spectral lines emitted at the cloud surface (e.g., [CII] and [CI] lines). Conclusions. This approach allows one to better explore the knowledge provided by ISM codes, and to guide future observation campaigns.

Machine-Learning-Based Depression Detection Model from Electroencephalograph (EEG) Data Obtained by Consumer-Grade EEG Device

Background/Objectives: There have been attempts to detect depression using medical-grade electroencephalograph (EEG) data based on a machine learning approach. EEG has garnered interest as a method for assessing brainwaves by attaching electrodes to the scalp to obtain electrical activity in the brain. Recently, machine learning has been applied to the EEG data to detect depression, with encouraging results. Specifically, studies using medical-grade EEG data have shown that depression can be accurately detected. However, there is a need to expand the range of applications by achieving a score with machine learning using simpler consumer-grade brain wave sensors. At present, a sufficient score has not been achieved.; Methods: To improve the score of depression detection, we quantified various EEG indices to train models such as power spectrum, asymmetry, complexity, and functional connectivity. In addition, feature selection was performed to ensure that the model learns only promising EEG indices for depression detection. The feature selection methods were Light Gradient Boosting Machine (LightGBM) feature importance, mutual information, ReliefF and ElasticNet coefficients. The selected EEG indices were learned by the LightGBM model, which is reported to be as accurate as the latest deep learning models. In cross-validation, the independence of test and training data was ensured to avoid excessively calculated score; Results: The results showed that the Macro F1 score was 91.59%, suggesting that a consumer-grade EEG can detect depression. In addition, analysis of the EEG indices selected by feature selection indicated that the Macro F1 score was about 80% for single EEG indices such as differential entropy in the frequency band β and functional connectivity in the left frontal region in the frequency band 1–128 Hz; Conclusions: Although the data were obtained from a consumer-grade EEG, the results suggest that these EEG indices are promising for detection depression.

Entropy-Based Volatility Analysis of Financial Log-Returns Using Gaussian Mixture Models

Volatility in financial markets refers to the variation in asset prices over time. High volatility indicates increased risk, making its evaluation essential for effective risk management. Various methods are used to assess volatility, with the standard deviation of log-returns being a common approach. However, this implicitly assumes that log-returns follow a Gaussian distribution, which is not always valid. In this paper, we explore the use of (differential) entropy to evaluate the volatility of financial log-returns. Estimation of entropy is obtained using a Gaussian mixture model to approximate the underlying density of log-returns. Following this modeling approach, popular risk measures such as Value at Risk and Expected Shortfall can also be computed. By integrating Gaussian mixture modeling and entropy into the analysis of log-returns, we aim to provide a more accurate and robust framework for assessing financial volatility and risk measures.

Context-Dependent Criteria for Dirichlet Process in Sequential Decision-Making Problems

In models with insufficient initial information, parameter estimation can be subject to statistical uncertainty, potentially resulting in suboptimal decision-making; however, delaying implementation to gather more information can also incur costs. This paper examines an extension of information-theoretic approaches designed to address this classical dilemma, focusing on balancing the expected profits and the information needed to be obtained about all of the possible outcomes. Initially utilized in binary outcome scenarios, these methods leverage information measures to harmonize competing objectives efficiently. Building upon the foundations laid by existing research, this methodology is expanded to encompass experiments with multiple outcome categories using Dirichlet processes. The core of our approach is centered around weighted entropy measures, particularly in scenarios dictated by Dirichlet distributions, which have not been extensively explored previously. We innovatively adapt the technique initially applied to binary case to Dirichlet distributions/processes. The primary contribution of our work is the formulation of a sequential minimization strategy for the main term of an asymptotic expansion of differential entropy, which scales with sample size, for non-binary outcomes. This paper provides a theoretical grounding, extended empirical applications, and comprehensive proofs, setting a robust framework for further interdisciplinary applications of information-theoretic paradigms in sequential decision-making.

A clustering coefficient structural entropy of complex networks

The structural entropy is a quantification of the topological structural complexity of the static complex networks, which is defined based on the structural characteristics and the Shannon entropy. For instance, the ’degree structural entropy’ is based on the network’s ’first-order’ topological properties: the degree of each node. The ’betweenness structural entropy’ is based on the betweenness centrality of nodes, which is a global topological structural property of static complex networks. The two different structural entropy give two completely different views of the network’s topological structural complexity. However, a ’mesoscopic’ structural entropy is still missing in the network theory. In this work, a clustering coefficient structural entropy of complex networks is proposed to quantify the structural complexity of static networks on the mesoscopic scale. The effectivity of the proposed ’mesoscopic’ structural entropy is verified in a series of networks that grow from two different seed networks under the Barabási–Albert and Erdős–Rényi rules. We also find that the quantification of structural entropy effectively reflects the impact of structural heterogeneity on the growth rule in the early stages of seed network growth. Finally, we observe that the structural ratio of the clustering coefficient structural entropy and degree structural entropy remains stable and unchanged when network growth reaches maturity. We also note that the convergence rate of the network’s structural entropy ratio varies under different guiding rules. These findings suggest that the differences in structural entropy can serve as a novel tool for measuring the stability of complex networks and provide fresh insights into achieving a ’balanced’ state in the dynamic evolution of complex networks.

Bivariate Pareto–Feller Distribution Based on Appell Hypergeometric Function

The Pareto–Feller distribution has been widely used across various disciplines to model “heavy-tailed” phenomena, where extreme events such as high incomes or large losses are of interest. In this paper, we present a new bivariate distribution based on the Appell hypergeometric function with marginal Pareto–Feller distributions obtained from two independent gamma random variables. The proposed distribution has the beta prime marginal distributions as special case, which were obtained using a Kibble-type bivariate gamma distribution, and the stochastic representation was obtained by the quotient of a scale mixture of two gamma random variables. This result can be viewed as a generalization of the standard bivariate beta I (or inverted bivariate beta distribution). Moreover, the obtained bivariate density is based on two confluent hypergeometric functions. Then, we derive the probability distribution function, the cumulative distribution function, the moment-generating function, the characteristic function, the approximated differential entropy, and the approximated mutual information index. Based on numerical examples, the exact and approximated expressions are shown.

Investigating deep learning strategies for fast denoising of 5D cardiac photon-counting micro-CT images


Photon-counting detectors (PCDs) for CT imaging use energy thresholds to simultaneously acquire projections at multiple energies, making them suitable for spectral imaging and material decomposition. Unfortunately, setting multiple energy thresholds results in noisy analytical reconstructions due to low photon counts in high-energy bins. Iterative reconstruction provides high quality photon-counting CT (PCCT) images but requires enormous computation time for 5D (3D + energy + time) in vivo cardiac imaging. 

Approach. 
We recently introduced UnetU, a deep learning (DL) approach that accurately denoises axial slices from 4D (3D + energy) PCCT reconstructions at various acquisition settings. In this study, we explore UnetU configurations for 5D cardiac PCCT denoising, focusing on singular value decomposition (SVD) modifications along the energy and time dimensions and alternate network architectures such as 3D U-net, FastDVDNet, and Swin Transformer UNet. We compare our networks to multi-energy non-local means (ME NLM), an established PCCT denoising algorithm. 

Main results. 
Our evaluation, using real mouse data and the digital MOBY phantom, revealed that all DL methods were more than 16 times faster than iterative reconstruction. DL denoising with SVD along the energy dimension was most effective, consistently providing low root mean square error and spatio-temporal reduced reference entropic difference, alongside strong qualitative agreement with iterative reconstruction. This superiority was attributed to lower effective rank along the energy dimension than the time dimension in 5D cardiac PCCT reconstructions. ME NLM sometimes outperformed DL with time SVD or time and energy SVD, but lagged behind iterative reconstruction and DL with energy SVD. Among alternate DL architectures with energy SVD, none consistently outperformed UnetU Energy (2D). 

Significance. 
Our study establishes UnetU Energy as an accurate and efficient method for 5D cardiac PCCT denoising, offering a 32-fold speed increase from iterative reconstruction. This advancement sets a new benchmark for DL applications in cardiovascular imaging.

7286 Higher Order Associations Linking Tryptophan, Kynurenine, And Tyrosine As Serum Markers Of Glucocorticoid Downstream Action In Adipose Tissue

Abstract Disclosure: K. Bennett: None. T. Garner: None. D. Chantzichristos: Speaker; Self; Sanofi, Takeda. P. Jansson: None. A. Stevens: None. G. Johannsson: Consulting Fee; Self; Novo Nordisk, Shire, AstraZeneca. Speaker; Self; Novo Nordisk, Pfizer, Inc.. Introduction: Treatment of Adrenal Insufficiency (AI) by glucocorticoid (GC) replacement therapy is mainly guided by clinical judgement, as there is no biomarker of GC action to individualise its dosage. Hypergraphs are networks modelling higher order associations (HOAs), which are interactions between more than two ‘omic elements. The aim of this study was to use hypergraphs to identify serum markers that reflect the action of GCs in adipose tissue (AT). Methods: In a randomised, cross-over trial, 10 patients with primary AI received saline i.v. (GC withdrawal), or circadian infusion of hydrocortisone (GC exposure), with > 2 weeks apart. After 26 hours (8AM), AT biopsy and PBMCs were collected for transcriptomic analysis, and abdominal subcutaneous microdialysis and PBMCs were collected for metabolomic profiling using LC/MS. Hypergraph models were generated to integrate serum and AT transcriptomics and metabolomics, identifying clusters of putatively causally linked metabolites which could represent serum markers of downstream GC action in AT. Hypergraph structure was assessed in AT and serum using entropy to infer associations within pre-defined metabolic pathways. Differences in entropy between the interventions was measured using Bayes Factor. Univariate ROC curve analysis was used to calculate AUCs for the serum markers. Results: In AT dialysate, 22 metabolites were identified as significantly different between GC exposure and withdrawal states. These included metabolites found in lipolysis and proteolysis pathways, such as acyl carnitines, lysophosphatidylcholines, fatty acids, and peptide derivatives. Nine metabolites were significantly different between the interventions in the serum. DEGs in AT (n=2048) were found to be enriched in metabolic pathways. Hypergraphs were used to refine clusters in the serum based on their HOAs with AT differentially expressed genes and metabolites. Tryptophan, Kynurenine, and Tyrosine clustered together, demonstrating a relationship between their expression in serum, and the metabolomic and transcriptomic profile of AT. These HOAs were shared with cortisol in the exposure intervention, but not in GC withdrawal, suggesting GC dependent regulation. All 3 metabolites had AUC-ROCs > 0.8, with Kynurenine performing the best at 0.86. Tissue specific differences were seen in the entropy of metabolic pathways between GC exposure and withdrawal, with similar changes seen in lipid and proteolysis pathways, but no difference seen in serum in carbohydrate and amino acid biosynthesis. Conclusions: During near physiological exposure, we identified changes to hypergraph structure and metabolite concentrations in lipid, protein, and carbohydrate pathways in AT and serum, compared to GC withdrawal. Hypergraph integration of multi-omic data identified tryptophan, kynurenine, and tyrosine as potential serum markers reflecting GC action in AT. Presentation: 6/3/2024

7847 Radiomic Ultrasound Checkup of Thyroid Nodules Before Radiodiodine Therapy

Abstract Disclosure: A.A. Trukhin: Grant Recipient; Self; Russian Science Foundation (project N 22-15-00135). A.V. Manaev: Grant Recipient; Self; Russian Science Foundation (project N 22-15-00135). S.M. Zakharova: Grant Recipient; Self; Russian Science Foundation (project N 22-15-00135). M.S. Sheremeta: None. E.A. Troshina: Grant Recipient; Self; Russian Science Foundation (project N 22-15-00135). Background: Over 83 years radioiodine therapy (RIT) of autonomous thyroid nodules still have uncovered pages, according to its complexity and thyroid nodules variety. There are two main criteria to allow RIT – no malignancy and presence of iodine uptake, the last one could be checked by Tc-99m-pertachnetate or I-123, I-131 iodine scintigraphy. Case with thyroid nodules first undergo ultrasound (US) TI-RADS classification, then fine needle aspiration stratification according to the Bethesda system. Publications applied such algorithm shows 75-85% accuracy in malignancy determination, that in 15-25% can lead to improper RIT application. Aim: Method development for thyroid nodules ultrasound checkup before I-131 administration and I-131 24h uptake prognosis. Methods: Study includes 150 nodules (65 benign, 60 malignant, 25 autonomous benign thyroid nodules). Both US longitudinal and transversal thyroid nodule projections were obtained via GE Voluson E8 (36% benign cases / 27% malignant cases) and GE Logiq E (64% benign cases / 73% malignant cases) in cinematic loop regime. Autonomous thyroid nodules underwent I-131 pharmacokinetics examination with 24h uptake determination and RIT. Statistical textural features were obtained through spatial adjacency matrix. Results: Analysis of 16 statistical textural features shows weak correlation between 11 statistical textural features E (Energy), S (Entropy), LU (Local homogeneity), MP (Maximum probability), TR (Matrix trace), CORR (Correlation), IMC (information measure of correlation), DV (Difference variance), DE (Difference entropy), SE (Sum entropy), SA (Average value of the sum). ROC-analysis using 11 statistical textural features shows the best model for benign/malignance determination with AUC = 0.83, Se = 74%, Sp = 73%, PPV = 75%, NPV = 72%, Acc = 74%. I-131 pharmacokinetics examination shows strong correlation SA to I-131 24h uptake. Conclusions: The performed study demonstrates the possibility of using textural statistical features of ultrasound images as instrument for thyroid nodules ultrasound checkup before I-131 administration, for extra FNA prescription and prognosis the I-131 24h important in exact therapeutic I-131 activity calculation. Presentation: 6/2/2024

Integrating hydrological knowledge into deep learning for DEM super-resolution

Deep learning-based super-resolution methods have been successfully applied to digital elevation model (DEM) downscaling studies by designing structures and loss functions of the model. However, little attention has been paid to the design of super-resolution models that can maintain the hydrological characteristics of the DEM, which is important for hydrological studies. This study introduces a super-resolution model that integrates hydrologic knowledge (HKSRCGAN), with the aim to effectively maintain topographic features as well as the hydrologic connectivity of the DEM. The hydrological knowledge derived from surface flow direction and hydrological features are integrated into a deep learning algorithm to guide model training. The 30 m spatial resolution FABDEM is used to demonstrate the utility of the proposed method. Results show that the HKSRCGAN outperforms the bicubic interpolation, SRCNN, SRGAN, SRResNet and TfaSR methods in reducing topographic errors and maintaining hydrologic characteristics. In the test area, the entropy difference analysis shows that the DEM generated by HKSRCGAN is similar to the information contained in the reference DEM. Furthermore, super-resolution models integrating hydrological knowledge are valuable for modeling terrain primarily shaped by gravity and surface water flows. In the future, deep learning-based models integrating hydrologic knowledge are expected to be applied in DEM upscaling to maintain consistent hydrological characteristics.

MAS-DGAT-Net: A dynamic graph attention network with multibranch feature extraction and staged fusion for EEG emotion recognition

In recent years, with the rise of deep learning technologies, EEG-based emotion recognition has garnered significant attention. However, most existing methods tend to focus on the spatiotemporal information of EEG signals while overlooking the potential topological information of brain regions. To address this issue, this paper proposes a dynamic graph attention network with multi-branch feature extraction and staged fusion (MAS-DGAT-Net), which integrates graph convolutional neural networks (GCN) for EEG emotion recognition. Specifically, the differential entropy (DE) features of EEG signals are first reconstructed into a correlation matrix using the Spearman correlation coefficient. Then, the brain-region connectivity-feature extraction (BCFE) module is employed to capture the brain connectivity features associated with emotional activation states. Meanwhile, this paper introduces a dual-branch cross-fusion feature extraction (CFFE) module, which consists of an attention-based cross-fusion feature extraction branch (A-CFFEB) and a cross-fusion feature extraction branch (CFFEB). A-CFFEB efficiently extracts key channel-frequency information from EEG features by using an attention mechanism and then fuses it with the output features from the BCFE. The fused features are subsequently input into the proposed dynamic graph attention module with a broad learning system (DGAT-BLS) to mine the brain connectivity feature information further. Finally, the deep features output by DGAT-BLS and CFFEB are combined for emotion classification. The proposed algorithm has been experimentally validated on SEED, SEED-IV, and DEAP datasets in subject-dependent and subject-independent settings, with the results confirming the model's effectiveness. The source code is publicly available at: https://github.com/cvmdsp/MAS-DGAT-Net

On an Empirical Likelihood Based Solution to the Approximate Bayesian Computation Problem

ABSTRACTApproximate Bayesian computation (ABC) methods are applicable to statistical models specified by generative processes with analytically intractable likelihoods. These methods try to approximate the posterior density of a model parameter by comparing the observed data with additional process‐generated simulated data sets. For computational benefit, only the values of certain well‐chosen summary statistics are usually compared, instead of the whole data set. Most ABC procedures are computationally expensive, justified only heuristically, and have poor asymptotic properties. In this article, we introduce a new empirical likelihood‐based approach to the ABC paradigm called ABCel. The proposed procedure is computationally tractable and approximates the target log posterior of the parameter as a sum of two functions of the data—namely, the mean of the optimal log‐empirical likelihood weights and the estimated differential entropy of the summary functions. We rigorously justify the procedure via direct and reverse information projections onto appropriate classes of probability densities. Past applications of empirical likelihood in ABC demanded constraints based on analytically tractable estimating functions that involve both the data and the parameter; although by the nature of the ABC problem such functions may not be available in general. In contrast, we use constraints that are functions of the summary statistics only. Equally importantly, we show that our construction directly connects to the reverse information projection and estimate the relevant differential entropy by a k‐NN estimator. We show that ABCel is posterior consistent and has highly favorable asymptotic properties. Its construction justifies the use of simple summary statistics like moments, quantiles, and so forth, which in practice produce accurate approximation of the posterior density. We illustrate the performance of the proposed procedure in a range of applications.

EEG-based responses of patients with disorders of consciousness and healthy controls to familiar and non-familiar emotional videos

ObjectiveTo investigate the differences in the brain responses of healthy controls (HC) and patients with disorders of consciousness (DOC) to familiar and non-familiar audiovisual stimuli and their consistency with the clinical progress. MethodsEEG responses of 19 HC and 19 patients with DOC were recorded while watching emotionally-valenced familiar and non-familiar videos. Differential entropy of the EEG recordings was used to train machine learning models aimed to distinguish brain responses to stimuli type. The consistency of brain responses with the clinical progress of the patients was also evaluated. ResultsModels trained using data from HC outperformed those for patients. However, the performance of the models for patients was not influenced by their clinical condition. The models were successfully trained for over 75% of participants, regardless of their clinical condition. More than 75% of patients whose CRS-R scores increased post-study displayed distinguishable brain responses to both stimuli. ConclusionsResponses to emotionally-valenced stimuli enabled modelling classifiers that were sensitive to the familiarity of the stimuli, regardless of the clinical condition of the participants and were consistent with their clinical progress in most cases. SignificanceEEG responses are sensitive to familiarity of emotionally-valenced stimuli in HC and patients with DOC.

Spectrum aided vision enhancer enhances mucosal visualization by hyperspectral imaging in capsule endoscopy.

Narrow-band imaging (NBI) is more efficient in detecting early gastrointestinal cancer than white light imaging (WLI). NBI technology is available only in conventional endoscopy, but unavailable in magnetic-assisted capsule endoscopy (MACE) systems due to MACE's small size and obstacles in image processing issues. MACE is an easy, safe, and convenient tool for both patients and physicians to avoid the disadvantages of conventional endoscopy. Enabling NBI technology in MACE is mandatory. We developed a novel method to improve mucosal visualization using hyperspectral imaging (HSI) known as Spectrum Aided Visual Enhancer (SAVE, Transfer N,Hitspectra Intelligent Technology Co., Kaohsiung, Taiwan). The technique was developed by converting the WLI image captured by MACE to enhance SAVE images. The structural similarity index metric (SSIM) between the WLI MACE images and the enhanced SAVE images was 91%, while the entropy difference between the WLI MACE images and the enhanced SAVE images was only 0.47%. SAVE algorithm can identify the mucosal break on the esophagogastric junction in patients with gastroesophageal reflux disorder. We successfully developed a novel image-enhancing technique, SAVE, in the MACE system, showing close similarity to the NBI from the conventional endoscopy system. The future application of this novel technology in the MACE system can be promising.

Characteristics of Differential Entropy Generation in a Transonic Rotor and Its Applications to Casing Treatment Designs

Characteristics of Differential Entropy Generation in a Transonic Rotor and Its Applications to Casing Treatment Designs

Shannon differential entropy properties of consecutive k-out-of-n:G systems

Shannon differential entropy properties of consecutive k-out-of-n:G systems