Miracle-dz/UniFAR-FTUs huggingface dataset:
Nonequilibrium phase transitions and dynamical scaling regimes

A general problem in physics is the prediction of a system's macroscopic behaviour given a microscopic model. The established procedure—known as statistical physics—is to try and determine the probability distribution of microscopic configurations. Once this is known, one can evaluate macroscopic quantities such as the average energy, volume or pressure of the system. In recent years, the application of statistical mechanics to nonequilibrium systems, and quite specifically the probabilistic modelling of nonequilibrium microscopic dynamics, has become a major research topic. However, in contrast to the equilibrium case, there is currently no general framework within which nonequilibrium systems are understood. Hence the aim of this thesis is to improve our understanding of nonequilibrium systems through the study of a range of systems with probabilistic microscopic dynamics and the collective phenomena—notably phase transitions and the onset of scaling regimes—that arise. In this thesis I briefly review general aspects of mathematical models of probabilistic dynamics (stochastic processes), with a particular emphasis on steady-state properties and the origin of phase transitions. Then I consider separately four specific types of nonequilibrium dynamics. Firstly, I introduce and solve exactly a model of a particle reaction system. The solution, which employs commutation properties of the q-deformed harmonic oscillator algebra, reveals that phase transitions in the analytic form of the particle density as a function of time arise as a direct consequence of randomness in the reaction dynamics. I also use similar mathematical techniques to solve the partially asymmetric exclusion process, an important prototype of a physical system that is driven by its environment. This model is also found to exhibit phase transitions, although in this case their origin lies in the nonequilibrium interactions between the system and its surroundings. Then I examine the scaling behaviour associated with the nonequilibrium directed percolation continuous phase transition. This transition is related to the presence of an absorbing state and I provide evidence for such a transition in a wetting model that does not possess an absorbing state. Finally, I generalise the wetting model to two dimensions and study its interfacial scaling behaviour. This is found to belong to the Kardar-Parisi-Zhang universality class, although there are strong crossover effects—which I quantify—that obscure the scaling regime.
(I,q)‐graded Lie algebraic extensions of the Poincaré algebra, constraints on I and q

The constraints on the index set I and on the function q of the (I,q)‐graded Lie algebras over K containing the Poincare Lie algebra are studied. By using the single‐grading model, particular choices for I and q consistent with the found constraints are determined for K=C. Gradings are then found for which I⊆I=Z2×(Z4N×Z4N)×Gre, with N∈N and Gre an Abelian group. These gradings provide a way for algebraic extensions of the Poincare Lie algebra beyond the Z2‐gradings of supersymmetry and supergravity. In these algebraic extensions, each other commuting space–time parameter can either commute or anticommute with the further parameters of the (I,q)‐graded (super) manifold. Different field representations can have—with each other—generalized commutative behavior beyond commutativity and anticommutativity.
How to estimate the Green’s function of a heterogeneous medium between two passive sensors? Application to acoustic waves

The exact Green’s function of a heterogeneous medium can be retrieved from the crosscorrelation of the fields received by two passive sensors. We propose a physical interpretation based on time-reversal symmetry. We address the issue of causality and show the role of multiple scattering for the reconstruction of the Green’s function. Ultrasonic experimental results are presented to illustrate the argument. Applications to geophysics and ocean acoustics are discussed.
Introduction: Faults and fractures in rocks: mechanics, occurrence, dating, stress history and fluid flow

1Sorbonne Université, CNRS-INSU, Institut des Sciences de la Terre de Paris – ISTeP, UMR 7193, Paris, France; 2Università degli Studi di Napoli Federico II, Naples, Italy; 3Consiglio Nazionale delle Ricerche, IGAG, Rome, Italy; 4Aix Marseille Univ., CNRS, IRD, INRAE, CEREGE, Aix-en-Provence, France; 5Università di Parma, Parma, Italy and 6Università della Basilicata, Dipartimento di Scienze, Potenza, Italy
Psychometric properties of the Center for Epidemiologic Studies Depression (CES-D) scale in stroke survivors

ABSTRACT Purpose This study aimed to evaluate the psychometric properties of the Center for Epidemiologic Studies Depression (CES-D) scale in adults with stroke. Methods A secondary analysis of the Stroke Recovery in Underserved Populations Cohort Study. The CES-D was administrated to 828 stroke patients at discharge from inpatient rehabilitation facilities and at 3- and 12-month follow-ups. Data were analyzed using classical test theory (CTT) and Rasch measurement model. Results Confirmatory factor analyses of the CES-D items showed excellent fit of a four-factor model (CFI = 0.98; TLI = 0.98; RMSEA = 0.05). CTT analyses revealed satisfactory reliability and validity. Rasch analyses also supported the unidimensionality of each factor (subscale). Wright maps indicated a floor effect and item gaps. A few items displayed differential item functioning: 3 items (1 depressed affect and 2 somatic symptoms) across gender, 1 item (depressed affect) across time of assessment and all # somatic symptom items across time of assessment. Conclusion The four-factor structure of the CES-D was confirmed and its psychometric properties were validated, supporting the use of four subscales to characterize depressive symptomatology in adults with stroke. Supplementary assessments are needed for evaluating and comparing somatic symptoms over time. A refinement of the CES-D was recommended to better differentiate stroke survivors with subtle depressive symptoms.
Electron emission from glow-discharge cathode materials due to neon and argon ion bombardment

We have measured the electron yields of 7 glow‐discharge cathode materials under bombardment by neon and argon ions with energies between 1 and 20 keV. The surfaces of the samples were conditioned by operating the materials as cold cathodes in a high‐voltage glow discharge before the electron yield measurement. The materials studied are oxidized magnesium, oxidized aluminum, a molybdenum‐aluminum oxide sintered composite, molybdenum, stainless steel, copper, and graphite. The dependence of electron yield on ion velocity was found to be approximately linear for all materials and gases, with the slopes being strongly material dependent. The corresponding glow‐discharge current intensities were observed to have a supralinear dependence on the electron yield. The results are relevant to the design and modeling of cold cathode high‐voltage glow discharges.
Multilayers and crystal for a multi-bandpass monochromator

VESPERS beamline is a hard X-ray microprobe beamline dedicated to micro-diffraction and micro-fluorescence analysis at the Canadian Light Source; it requires multi-bandpass X-ray beams for different types of samples and experiments. A specially designed double crystal/multilayer monochromator was built for this purpose with three different bandpasses: 0.01%, 1.6% and 10%. The diffraction elements used for the monochromator have a triple-stripe design using Si(111) crystal as a single substrate with two differing stripes of Mo/B4C multilayers deposited thereon. The uncovered Si(111) section provides a 0.01% bandpass, while the periodic and depth-graded Mo/B4C multilayers provide 1.6% and 10% bandpasses, respectively. This paper outlines the requirements and specifications of the diffracting elements as well as the design, deposition and optimization of the multilayers. The performance of the deposited multilayer structures has been tested using Cu-Kα radiation line with a Huber diffractometer.
Downregulation of RBM17 enhances cisplatin sensitivity and inhibits cell invasion in human hypopharyngeal cancer cells

Abstract Most of advanced hypopharyngeal squamous cell carcinoma (HSCC) are resistant to chemotherapy, and there is still lack of effective treatment for HSCC now. The present study aimed to investigate whether downregulation of RNA-binding motif protein 17 (RBM17) could enhance cisplatin sensitivity and inhibit cell invasion in HSCC and the underlying mechanism. We observed that RBM17 was upregulated in tumor tissues and associated with poor progression. Treatment of FaDu cells with cisplatin increased RBM17 expression in mRNA levels. Downregulation of RBM17 enhanced cisplatin-mediated inhibition of FaDu cells. In addition, downregulation of RBM17 effectively suppressed tumor cell migration and invasion through the reversion of epithelial–mesenchymal transition. Moreover, downregulation of RBM17 could significantly slow tumor growth in FaDu xenograft tumor model. Liquid chromatography–mass spectrometry/mass spectrometry detection and independent PRM analysis showed that 21 differentially expressed proteins were associated with the downregulation of RBM17. Taken together, our study implied that downregulation of RBM17 could serve as a novel approach to enhance cisplatin sensitivity in HSCC.
Classification by Opinion-Changing Behavior: A Mixture Model Approach

We illustrate the use of a class of statistical models, finite mixture models, that can be used to allow for differences in model parameterizations across groups, even in the absence of group labels. We also introduce a methodology for fitting these models, data augmentation. Neither finite mixture models nor data augmentation is routine in the world of political science methodology, but both are quite standard in the statistical literature. The techniques are applied to an investigation of the empirical support for a theory (developed fully by Hill and Kriesi 2001) that extends Converse's (1964) “black-and-white” model of response stability. Our model formulation enables us (1) to provide reliable estimates of the size of the two groups of individuals originally distinguished in this model, opinion holders and unstable opinion changers; (2) to examine the evidence for Converse's basic claim that these unstable changers truly exhibit nonattitudes; and (3) to estimate the size of a newly defined group, durable changers, whose members exhibit more stable opinion change. Our application uses survey data collected at four time points over nearly 2 years which track Swiss citizens' readiness to support pollution-reduction policies. The results, combined with flexible model checks, provide support for portions of Converse and Zaller's (1992) theories on response instability and appear to weaken the measurement-error arguments of Achen (1975) and others. This paper concentrates on modeling issues and serves as a companion paper to Hill and Kriesi (2001), which uses the same data set and model but focuses more on the details of the opinion-changing behavior debate.
Optimal combination of power load based on particle swarm optimization and its application in distribution network planning

The power grid planning method based on load optimal combination is to reduce the load peak-valley difference of the corresponding regions and improve the utilization efficiency and operation economy of distribution equipment through optimal combination of loads in different power supply regions of the distribution network, in response to the power characteristics of increasingly refined social division of labor and increasingly prominent load diversity. In this paper, the analysis of typical loads is taken as the starting point, and the maximum benefit load (the difference between load and loss) is taken as the objective to build a distribution network planning model based on load optimal combination, and the integer planning problem of load optimal combination is solved through binary particle swarm optimization (PSO). Finally, through the actual power grid case analysis, the correctness of the method proposed in this paper is verified, and a specific example is used to illustrate the positive significance of load optimal combination for distribution network planning.
Research on Automatic Sleep Staging of Sleep Based on Different Reference Montages of Single Channel EEG

Sleep staging is very important for the effective diagnosis and pre-intervention of patients with sleep disorders. The traditional sleep staging method based on manual scoring is highly subjective, time-consuming and the collection methods are complicated and cumbersome. From the perspective of optimizing the selection of EEG channels in automatic sleep staging, we collected PSG signals from 26 subjects in the sleep laboratory, extracted the frequency domain features based on power spectral density and the time-frequency features of wavelet transform of 12 channel sleep EEG signals located in the forehead-central area. The separability and validity of the extracted features were explored by the One Way Analysis of Variance (ANOVA). The light Gradient Boosting Machine (LGB) and leave-one-out cross-validation method is used to classify the four stages of sleep: wakefulness (W), light sleep (LS), deep sleep (DS), and rapid eye movement (REM). Through channel screening in different EEG reference montages, an EEG channel selection that can be applied to wearable sleep monitoring devices is proposed. Moreover, in order to verify the generalization ability of the staging model. Sleep-EDFx, a public data set provided by PhysioNet, is used to evaluate and compare the performance of the proposed model. The experimental results show that the T4-AVG channel with ear electrodes as a reference achieves the highest classification accuracy of 92.25% and 0.857 kappa coefficient, which provides better wearability and portability than electrodes placed in the parietal-occipital region. All datasets show relatively stable classification performance, which reflects the portability and robustness of the model.
Research and implementation of a trend prediction model based on trend similarity for the changing trends of fashion elements in clothing

Trend forecasting of clothing fashion elements is an important guide for product development and sales of garment companies. Existing work can only capture simple changing trend laws and patterns of mutual influence between trends but cannot give effective and practical guidance on the trend changes of clothing fashion elements. This paper uses user information to group rich fashion elements in a more accurate and meaningful way to predict the trend of future trends in fashion elements. By comparing the similarity between the recent trend changes and the historical trend information, we continuously evaluate the next change trend information from the similar historical trend information, learn the laws and patterns of clothing fashion element change trends and predict the future trend change direction. Our experiments show that the model proposed in this paper can effectively capture the changing laws of clothing fashion elements and the patterns that affect each other to predict the changing trends. Compared with the baseline method, the model has the best performance in MAE and MAPE indicators.
Ensemble Learning for Diabetic Foot Ulcer Segmentation based on DFUC2022 Dataset

In order to increase the segmentation impact of the Diabetic Foot Ulcer Challenge 2022 dataset, we train a selection of popular deep learning segmentation algorithms and improve training methods, such as adding Dice term to loss function, employing transfer learning and poly learning rate update strategy, etc., in this paper. Experiments show that our method is effective, we get a Dice score of 0.7045, which is better than the official baseline result of 0.6277. Moreover, we integrate the above segmentation models using four ensemble methods to evaluate segmentation performance, such as Averaging, Weighting, Voting, and Stacking. We observed that our proposed one-layer CNN stacking network exhibits superior segmentation performance (Dice score: 0.7142) compared to single CNN model and other three ensemble methods. Our performance surpasses the baseline result, placing us in the top 10 in the Diabetic Foot Ulcer Challenge 2022.
Joint extraction method of entity relationship in Chinese Medicine based on Data Augmentation and Deep Learning

TCM texts are rich in evidence-based information, and a crucial step in knowledge mining is using high-tech tools to organize and store TCM texts in a systematic manner. Knowledge graphs are better suited to organizing and preserving the knowledge of TCM texts with complex relationships than typical databases are. Building high-quality knowledge maps requires accurate and efficient entity relationship extraction, and completely automated entity relationship extraction necessitates the creation of a sizable amount of high-quality corpus data, which increases expenses and lowers productivity. Because the same entity can generate many relations in the joint extraction of TCM entity relations and there are insufficient corpus data, these issues must be addressed. The joint extraction model of TCM entity interactions proposed in this paper is based on deep learning and data augmentation. Using a multi-head selective bidirectional long and short-term memory network (multi-head-BILSTM), the relationship overlap problem is first solved, and the data is then enhanced using five mechanisms: entity replacement, random addition, random deletion, random replacement, and integrated enhancement. We quantitatively assess the benefits and drawbacks of several relationship extraction algorithms as well as the performance improvements of TCM entity relationship joint extraction brought about by five alternative data augmentation mechanisms. In conclusion, our research has the potential to significantly enhance the efficiency of joint TCM entity relationship extraction.
A new decoding method of the grammatical evolution

GE (grammatical Evolution) is an evolutionary algorithm that evolves programs in an arbitrary language using a variable-length binary string. The binary genome determines which production rules from the grammar definition of the Backs-Naur form(BNF) are used for the program in the genotype-phenotype mapping process. However, the matter relating to the completeness of individual phenotype throughout population formation isn't well self-addressed, so limiting both the convergence speed and the accuracy of the evaluations to some extent. In this paper, we propose an improved GE algorithm that aims to guarantee the completeness of individual phenotypes during initialization as well as subsequent evolution. A comparison of the improved algorithm (IGE) with the classical GE algorithm (CGE) and NGE(integer-coded grammatical evolution) conducted on the symbolic regression problems shows that the improved algorithm (IGE) not only reduces the search space and improves the accuracy of the algorithm, but also speeds up the convergence of the algorithm in constructing both. Definition 1. “Incomplete”: An individual is called an incomplete individual if its corresponding sentential form contains a non-terminal symbol, and its corresponding mapping process is called incomplete mapping. Definition 2. "Recursive" production rule: Refers to the BNF in which the same non-terminal symbol appears on the left and right sides of the production rule. Definition 3. Combined production rule: For a production rule with a non-terminal symbol in the right part, if it can be followed by only one type of production rule, it is said that the production rule and its subsequent followable production rule are combined production rules. Definition 4. Non-combined production rule: Production rule except for combined production rule
Interpreting Deep Networks: by Ensemble Learning and Visualizing the Perturbation

Most deep neural network models are black boxes and lack interpretability. As the interpretability is critical for deep models applied to high-risk tasks, many visual approaches are proposed to reveal what the model learns in recent years. However, the quality and completeness of important regions, which are exported by the most existing visualization methods, are still unsatisfactory. Thus, a method, interpreting deep networks by ensemble learning and visualizing the perturbation, is presented in this paper. The method firstly perturbs the original image using the mask to obtain the perturbed image. Secondly, it imports the perturbed image into the model to obtain the loss of the update mask. Finally, an Adaboost-like algorithm, which combines the extremal perturba- tions and the meaningful perturbation, is introduced to improve the interpretation. Experimental results confirm that the method can improve the quality and completeness of the important regions, and provide a more reasonable explanation for the prediction results.
Heart Disease Type Prediction Model Based on SVM-ANN

Due to factors e.g., incorrect diet and exercise habits of modern people, the number of heart disease patients is rising yearly. It is extremely important to find a good method to try to predict the different types of heart diseases. Because it is so hard to distinguish the different types of heart disease. Considering this, the use of different models is important to predict the type of heart disease. Then this study is trying to find the suitable models to solve these problems. In this work, the different models were used, which are the combination of ANN and SVM to get the accuracy of the models. First, ANN was used to obtain the accuracy of the model. Next, the SVM and ANN were used together to predict the result. Also the use of ResNet, which solve the gradient disappearance problem. Besides, comparing the use of Naïve Bayes, the use of Random Forest is trying to make the model stable and reduce the risk of overfitting. Also, the PCA model did the feature extractor and emerge the new features. The methods achieve the accuracy is about 90% of the Random Forest. Also, the confusion matrix of the model was obtained. After using 10 epochs, the accuracy of the combination of SVM and ANN is about 90.56%.
Direct Position Determination using Distributed Unfold Coprime Arrays with Unknown Mutual Coupling: based on Reduced-Dimension Search

The direct position determination (DPD) approach has higher localization accuracy and better robustness than the classical two-step approach when localizing multiple sources with distributed antenna arrays. This paper focuses on the DPD algorithm using multiple Unfolded Coprime Arrays (UCAs) with unknown mutual coupling. To reduce the adverse effects of the mutual coupling, we first expand the unfolded coprime arrays into the DPD scenario. Subsequently, we introduce the HD-DPD-Capon algorithm, which fuses all inverse covariance matrices of distributed arrays, simultaneously searching for multiple unknown mutual coupling coefficients and source positions. Finally, in advance of the reduced-dimension search, we propose the RMCD-ICF algorithm, which only needs to search the two-dimension position, to reduce the high computational complexity of the HD-DPD-Capon algorithm caused by the high-dimensional search. Simulation results verify the superiority of the proposed algorithm on computation complexity and localization accuracy.