Miracle-dz/UniFAR-FTUs huggingface dataset:
EXPLORING TRANSITIONS OF SPACE PLASMAS OUT OF EQUILIBRIUM

Space plasmas from the solar wind to planetary magnetospheres and the outer heliosphere are systems in stationary states out of equilibrium. Empirical kappa distributions, which naturally emerge from Tsallis Statistics, successfully describe these space plasmas. The Tsallis formalism offers a solid statistical foundation and provides a set of proven tools for understanding these distributions, including a consistent definition of temperature—the physical temperature, which characterizes the non-equilibrium stationary states. Here, we develop a measure of the “thermodynamic distance” of stationary states away from equilibrium. The stationary states are labeled by the value of the entropic q-index, lying in a spectrum from q = 1 (equilibrium) to the maximum value of q, which specifies the furthest possible stationary state from equilibrium. We call this the “q-frozen state,” because as a system approaches this state, it behaves analogously to when its temperature approaches absolute zero. We also introduce a novel isothermal procedure that describes a system's transition into different stationary states by varying the q-index, and show how the variation of temperature can be realized using an “iso-metastability” procedure, in which the system remains in a fixed stationary state. These innovations allow a generalization of the zeroth law of thermodynamics to cover stationary states out of equilibrium. By expressing the entropy in terms of the q-index, we show the detailed paths by which the transition of stationary states evolves toward equilibrium following the dynamics of a characteristic difference equation along the q-indices. This naturally exhibits certain stationary states out of equilibrium that are frequently observed in space plasmas.
Three-Zone Segmentation-Based Motion Compensation for Video Compression

Motion compensation has been widely employed for removing temporal redundancies in typical hybrid video coding framework. The popular video compression standards, such as H.264/AVC and HEVC, adopt the block-based partitioning model to describe the motion field due to its high-compression efficiency and relatively low-computational complexity. However, block-based motion compensation may not align with the actual object motion boundaries, potentially limiting the compression efficiency. In view of this, we propose a three-zone segmentation-based motion compensation scheme to improve the description accuracy of motion field as well as the coding efficiency. In particular, the segmentation information is implied in the reference frame instead of being explicitly signalled. Based on the segmentation information, three motion compensation zones can be identified, including one edge, one foreground, and one background zone. The foreground zone is motion compensated by the signalled motion vector of the block, and the background zone is motion compensated by the motion information implicitly derived from the local motion field. Regarding the edge zone, it is viewed as an overlapped area and the weighted compensation strategy is adopted. The proposed algorithm is implemented into the reference software VTM-1.0 of versatile video coding (VVC), and the simulation results show that the algorithm can achieve 1.14% and 1.06% bitrate savings for random access and low-delay configurations, respectively.
Using Fuzzy Rough Concept Lattices Merging and Presenting Domain Ontology

As the foundation of the semantic web, ontology is a formal, explicit specification of a shared conceptual model. It becomes an important problem on ontology application to merg and present ontology. However, there are some defects between several kinds of existing ontology application methods in many aspects. The paper offers a methodology of ontology merging for knowledge sharing and reusing based on fuzzy concept concept lattice union in order to makes up these defects.This paper proposes the means of ontology presenting based on the form of concept lattices visualizing in three-dimensional space. The experimental results indicate that this method has great validity.
The probability that a point of a tree is fixed

Abstract Using arguments involving combinatorial enumeration and asymptotics we compute the probability that a point of a random tree is fixed. The method is also applied to homeomorphically irreducible trees to illustrate how it works for other species of trees. To the nearest per cent, the limiting probability of a fixed point in a randomtree is 70%, and for homeomorphically irreducible trees it is 20%.
The magnetic structure of sunspots and starspots

The single most important quantity determining the properties of sunspots and presumably starspots is their magnetic field. First an overview of the magnetic structure of sunspots is given, some of the progress made in recent years is described and some of the unsolved questions are pointed out. Both observational and theoretical aspects are dealt with. Finally, the magnetic structure of starspots is discussed. After presenting the evidence for (and against) their magnetic nature the signature of starspots in Zeeman Doppler images is described. It is pointed out that if the properties of sunspots are extrapolated to giant spots on rapidly rotating stars then the observed signature can be at least qualitatively explained.
Size dependence of electrostatic lens effect in vertical MOSFETs

The size dependence of the electrostatic lens effect in the channel of a nanoscale vertical pillar-type metal–oxide–semiconductor field-effect transistor (V-MOSFET) is studied by quantum dynamics simulation. Our findings indicate that the applicable diameter of the pillar for the efficient current-path control by the electrostatic lens effect on the V-MOSFET is in the range of about 10–30 nm. In the large-diameter pillar (30 nm diameter), the lens effect at the interfaces between the source and the body, and between the body and the source works well owing to the ballistic transport of electrons. On the other hand, in a slim pillar (10 nm diameter or less), the lens effect does not work well, because it is difficult to handle the electron dynamics by the analogy of classical geometrical optics, even though the electrons show ballistic transport. Our results indicate that the proposed technique is applicable for many nanoscale pillar-type devices.
Linear Estimation of a Regression Relationship from Censored Data—Part II Best Linear Unbiased Estimation and Theory

In many regression problems, data on the dependent variable are censored; that is, the values of some observations are known only to be above or else below some value. Such data often arise in accelerated life testing where life is the dependent variable and temperature or stress is the independent variable, and some test units have not failed at the time of the analysis. In such situations, the standard techniques of least squares estimation for the parameters of a linear regression model cannot be used, since the values of the censored observations are not known. This is Part II of a two-part series on the theory and application of linear estimation methods for regression analysis using the ordered observations of censored data. Part II deals with best (minimum variance) linear unbiased estimators for the parameters of a linear regression model. The use of these methods is illustrated with analyses of censored data from tandem specimens in a creep-rupture test on an alloy. The theory underlying the vari.
Filtrations on the Homology of Algebraic Varieties

Introduction Questions and speculations Abelian monoid varieties Chow varieties and Lawson homology Correspondences and Lawson homology "Multiplication" of algebraic cycles Operations in Lawson homology Filtrations Appendix A. Mixed Hodge structures, homology, and cycle classes Appendix B. Trace maps and the Dold-Thom theorem Appendix Q. On the group completion of a simplicial monoid Bibliography.
FNMF-ITWC Algorithm Applied to the Cancer Gene Expression Data

The clustering analysis of the cancer gene expression data can provide bases for the early diagnosis of cancer and accurate classification of the cancer subtypes. For the characteristics of cancer gene expression data, an algorithm named FNMF-ITWC (Fast Nonnegative Matrix Factorization Interrelated Two_Way Clustering) is proposed. FNMF-ITWC algorithm firstly selects genes from the original gene expression data, implements non-negative matrix factorization on the row (gene dimension), and then performs clustering on the column (sample dimension). Matlab experimental results show that FNMF-ITWC algorithm improves the computing speed of the algorithm and reduces the data storage space. At the same time, it is able to reveal correlation among genes under certain experimental conditions and the correlation among experiments for some genes.
The Thermal‐Noise Limit in the Detection of Underwater Acoustic Signals

The criteria for an “optimum” hydrophone receiving system for underwater acoustic signals are considered, and the equilibrium thermal‐noise pressure spectrum expression is shown.Experimental ambient sea‐noise spectrum levels are in excess of the thermal noise at all frequencies below 25 kc. Extrapolation of experimental curves (−5 db/octave) shows intersection with thermal‐noise spectrum (+6 db/octave) between 50 and 200 kc, depending on sea state. (For a definition of “sea state,” see Instructions to Marine Meteorological Observers (U. S. Department of Commerce Weather Bureau, U. S. Government Printing Office, Washington, D. C., June, 1941), seventh edition, circular M. ) The ambient‐noise level is expressed as a function of the equivalent temperature.Formulas are then derived for the required plane‐wave signal level for which electrical signal‐to‐noise ratio is unity. The operating noise factor is defined in terms of the electroacoustical efficiency of the linear reversible hydrophone, the amplifier nois.
Can late-type active stars be explained by a dipole magnetic trap?

In this paper we review four different types of X-ray and/or radio observations of active late-type stars. We then consider if a single magnetic source configuration – a toroidal dipole magnetic trap – can possibly explain these various different observations. We conclude that, indeed, dipole magnetic confinement (similar to the magnetic configurations of the Earth's radiation belts and the case of Jupiter and the Io torus) can explain all the diverse observational data. We take this to be very strong observational support for this type of magnetic confinement scheme. We also consider that this magnetic configuration is only likely to be established and maintained in the most active stars.
Insights on Upper Mantle Melting, Rheology, and Anelastic Behavior From Seismic Shear Wave Tomography

In seismic tomography we map the wave speed structure inside the Earth, but we ultimately seek to interpret those images in terms of physical parameters. This is challenging because many parameters can trade‐off with each other to produce a given wave speed. The problem is compounded by the convention of mapping seismic structures as perturbations relative to a 1‐D reference model, rather than absolute wave speeds. Using a full waveform tomography model of Europe as a case study, we quantify the extent to which thermochemical and dynamic properties can be constrained using only S wave speed, expressed in absolute values. The wave speed distributions of this tomography model are compared with 4 million thermochemical models, whose seismic properties are computed via thermodynamic modeling. These models sample the full range of realistic mantle compositions, including variable water and melt contents, and mineral intrinsic anelasticity is taken into account. Intrinsic anelasticity causes waves to travel more slowly at higher temperatures, leading to seismic attenuation, but the sensitivity of the wave speed reduction to temperature is, in turn, controlled by the wave frequency. Global studies of surface waves indicate an anticorrelation between S wave speed and attenuation. We therefore only retain thermochemical models satisfying this anticorrelation. Our study indicates that the frequency dependence of anelasticity, α, depends on temperature or rheology, with α ≈ 0.1 being most appropriate in cold or lithospheric mantle and α ≈ 0.3 in warmer regions (i.e., the asthenosphere). Additionally, the slowest regions require specific compositions and/or a velocity‐weakening mechanism, such as partial melting, elastically accommodated grain boundary sliding, or water.
A Generalization of a Formula of Eisenstein

where E[p\ denotes the non-zero p-torsion of E. (Equation (0.1) was probably known to Eisenstein: he published a similar formula. See [1] for a proof of (0.1) and related history. ) The automorphism x—*(ox, y^>y acts on E[p]', and (0.1) gives a non-canonical way to extract a cube root of p. This played a crucial role in Matthews's proof of Cassels's conjecture on the value of the cubic Gauss sum [1,8]. The purpose of this paper is to produce an analogue of (0.1), relating integers in Z[e] to points on a curve of genus 2. Specifically, let C be the curve of genus 2 given by y = x + l (0.2)
Deep Group-Wise Fully Convolutional Network for Co-Saliency Detection With Graph Propagation

A key problem in co-saliency detection is how to effectively model the interactive relationship of a whole image group and the individual perspective of each image in a united data-driven manner. In this paper, we propose a group-wise deep co-saliency detection approach to address the co-saliency object discovery problem based on the fully convolutional network (FCN). The proposed approach captures the group-wise interaction information for group images by learning a semantics-aware image representation based on a convolutional neural network, which adaptively learns the group-wise features for co-saliency detection. Furthermore, the proposed approach discovers the collaborative and interactive relationships between group-wise feature representation and single-image individual feature representation and models this in a collaborative learning framework. Then, we set up a unified deep learning scheme to jointly optimize the process of group-wise feature representation learning and collaborative learning, leading to more reliable and robust co-saliency detection results. Finally, we present a graph Laplacian regularized nonlinear regression model for saliency refinement. The experimental results demonstrate the effectiveness of our approach in comparison with the state-of-the-art approaches.
Multimodal biometric authentication based on score level fusion using support vector machine

Fusion of multiple biometrics for human authentication performance improvement has received considerable attention. This paper presents a novel multimodal biometric authentication method integrating face and iris based on score level fusion. For score level fusion, support vector machine (SVM) based fusion rule is applied to combine two matching scores, respectively from Laplacianface based face verifier and phase information based iris verifier, to generate a single scalar score which is used to make the final decision. Experimental results show that the performance of the proposed method can bring obvious improvement comparing to the unimodal biometric identification methods and the previous fused face-iris methods.
ON SYNCHRONIZABILITY OF DYNAMICAL LOCAL-WORLD NETWORKS

Synchronization phenomena in dynamical systems with local-world topologies are investigated. The effect of local-world size M on the synchronizability is studied. A larger M makes networks more synchronizable. Then the ability of dynamical networks to resist random errors and attacks is analyzed and compared with those of scale-free and exponential networks. Two attacking strategies are adopted, and a better quantity is used to measure the changes in synchronizability after node removal.
Imaging Formation Algorithm of the Ground and Space-Borne Hybrid BiSAR Based on Parameters Estimation from Direct Signal

This paper proposes a novel image formation algorithm for the bistatic synthetic aperture radar (BiSAR) with the configuration of a noncooperative transmitter and a stationary receiver in which the traditional imaging algorithm failed because the necessary imaging parameters cannot be estimated from the limited information from the noncooperative data provider. In the new algorithm, the essential parameters for imaging, such as squint angle, Doppler centroid, and Doppler chirp-rate, will be estimated by full exploration of the recorded direct signal (direct signal is the echo from satellite to stationary receiver directly) from the transmitter. The Doppler chirp-rate is retrieved by modeling the peak phase of direct signal as a quadratic polynomial. The Doppler centroid frequency and the squint angle can be derived from the image contrast optimization. Then the range focusing, the range cell migration correction (RCMC), and the azimuth focusing are implemented by secondary range compression (SRC) and the range cell migration, respectively. At last, the proposed algorithm is validated by imaging of the BiSAR experiment configured with china YAOGAN 10 SAR as the transmitter and the receiver platform located on a building at a height of 109 m in Jiangsu province. The experiment image with geometric correction shows good accordance with local Google images.
TrEKBQA:Traversing Knowledge Graph Embedding for Multi-hop Knowledge Base Question Answering

Recent research apply KG embedding to multi-hop Knowledge Base Question Answering(KBQA) to predict missing links, however, it is often affected by the skewed distribution of nodes in the knowledge graph, resulting in poor generalization of the model. Therefore, we propose a method TrEKBQA based on traversing the knowledge graph embedding space for multi-hop KBQA, which performs path traversal in the KG embedding space instead of KG itself for link prediction to complete the knowledge graph, thus improving the accuracy of multi-hop KBQA.TrEKBQA model complex relationships using correlations between individual links and longer paths connecting the same pair of entities to traverse the KG embedding space to mitigate the effects of biased distribution of nodes and improve the performance of link prediction. In the pre-processing process, TrEKBQA uses the PRN algorithm to extract subgraphs related to the problem entity to reduce the number of target entities. Through experiments on multiple benchmark datasets, we demonstrate the effectiveness of TrEKBQA on KBQA tasks.
Range expansion of Ixodes scapularis ticks and of Borrelia burgdorferi by migratory birds

Recent studies have suggested that the risk of exposure to Lyme disease is emerging in Canada because of the expanding range of I. scapularis ticks. The wide geographic breeding range of I. scapularis-carrying migratory birds is consistent with the widespread geographical occurrence of I. scapularis in Canada. However, how important migratory birds from the United States are for the establishment and the stable endemic transmission cycle of Lyme disease in Canada remains an issue of theoretical challenge and practical significance. In this paper, we design and analyze a periodic model of differential equations with a forcing term modeling the annual bird migration to address the aforementioned issue. Our results show that ticks can establish in any migratory bird stopovers and breeding sites. Moreover, bird-transported ticks may increase the probability of B. burgdorferi establishment in a tick-endemic habitat.
Streak artefact quantification for abdominal CT

Streaking artefacts in computed tomography (CT) can be caused by photon starvation caused by highly attenuating regions. Patient positioning can influence the attenuation e.g. by arms raised or down in an abdominal CT scan. Positioning the arms alongside the body increases attenuation, therefore higher dose can be expected. Additionally the artefacts can cause a decrease in image quality. Measuring this quality decrease is the purpose of this article. We implemented different methods to quantise streaking artefacts and correlated them to the judgement of two radiologists in a study of 80 patients. High significance was found for a correlation coefficient of 0.57. This correlation from measurements and clinical usability (represented by the radiologists' ratings) enables to predict the usability by means of image processing alone. This can be included in the patient image as a correlate to the diagnostic usability, resp. a new volume can be made depending on the number.
The Mapping Properties for a Class of Oscillatory Integrals

In this paper we show that for p = a + b b that the operators given by Tf ( x ) = (cid:1) ∞ 0 e ig ( x,y ) ϕ ( x,y ) f ( y ) dy, map L p into itself with g ( x,y ) = x b y a + γ ( x b/a ) γ 2 ( y ). The conditions on γ,γ 2 and ϕ as deﬁned within.
Particle reacceleration and apparent radio source structure

The radio galaxy model which uses magnetohydrodynamic turbulence generated by surface instabilities to reaccelerate the radiating electrons has striking consequences for apparent source structure. Strong wave damping in the plasma results in a narrow turbulent edge. Particles accelerated in this edge must diffuse across field lines into the radio source; this predicts strong limb brightening in some cases. The structure of this edge and diffusion into the source are described. The relevance of this model to jets, radio tails, and standard double sources is discussed.
Light-SRNet: a lightweight dual-attention feature fusion network for hand-drawn sketch recognition

Abstract. Free-hand sketches play a vital role in graphically portraying ideas and concepts in image recognition systems. Most recently proposed learning-based sketch recognition methods have achieved marked progress in recognition accuracy, but they rarely optimize the use of the sparsity features of sketch images. Although several attention-based sketch recognition models have been presented, they endure complex computations and large model sizes. To address these challenges, we present a lightweight convolutional neural network called Light-SRNet based on a dual-attention mechanism to improve the accuracy of sketch recognition while retaining its lightweight nature. In the proposed model, we introduced both the spatial and channel attention mechanisms into the feature extraction network to highlight more discriminative feature representations to enhance its powerful sketch recognition ability. We compared the proposed Light-SRNet with its competitors on the TU-Berlin dataset, Sketchy dataset, and QuickDrawExtended dataset. Extensive experimental results show that Light-SRNet achieves a recognition accuracy of 73.14%, which is comparable to other similar sketch recognition techniques, while requiring only about a quarter of the model parameters.
Chaos and control in Josephson system subjected to combined bounded noise and harmonic excitation

In this paper, nonlinear dynamic and chaotic behaviors of Josephson system subjected to combined bounded noise and harmonic excitation are investigated. Theoretically, the stochastic Melnikov method is employed to establish the necessary conditions of existence of chaotic motion in the system. Numerical simulations, including the safe basins, the maximal Lyapunov exponent and the Poincare map, are carried out according to the necessary conditions for the system’s parameters. They show that routes of the system turn faster into random chaos as the amplitude of the stochastic noise excitation increases. Finally by using sliding mode control method, the controller is designed to eliminate the adverse effect of the noise and improve the working performance of the system.