Miracle-dz/UniFAR-FTUs huggingface dataset:
The Parton Model and the Bethe-Salpeter Wave Function

The quark-parton model is discussed from the point of view that the quark partons are the quanta created by the Fourier transforms of the quark fields at constant x/sup +/. We argue using this picture that, for a given behavior of a hadron's deep-inelastic structure function W/sub 2/(x) as x . -->. 1, the Drell-Yan-West relation provides a lower bound on the behavior of its form factor F/sub 1/(Q/sup 2/) as Q/sup 2/ . -->. infinity. The connection between the parton and Bethe-Salpeter descriptions of hadron structure is described and used to translate known information about the pion's Bethe-Salpeter wave function into information about the amplitude for a pion to consist of precisely two quark partons. We find that the two-parton contributions to . nu.W/sub 2/(x) and F (Q/sup 2/) behave roughly like . nu.W/sub 2/ proportional (1 - x)/sup 2/ for small (1 - x) and F (Q/sup 2/) proportional (Q/sup 2/)/sup -1/ for large Q/sup 2/, respectively.
Joint Precoding Scheme with Enhanced Spectral Efficiency for Cell-Free Massive MIMO Downlink

The cell-free massive multiple-input multiple-output (MIMO) system is a promising technology for B5G and 6G, it owns large spectral and power efficiency. However, the precoding scheme can affect the system downlink spectral efficiency obviously. Maximum-ratio transmission (MRT) is the most common precoding scheme used in cell-free massive MIMO systems, which maximizes the power of the desired signal, but is unable to suppress interference. Centralized zero-forcing (CZF) is a precoding scheme with strong interference cancellation ability, but can not support too many users because of the high computational complexity and limited front-haul capacity. In order to suppress interference and satisfy the requirement of system scalability, we propose a joint precoding scheme with enhanced spectral efficiency, named Scalable Joint Zero-Forcing Precoding (SJZF), which deploys different zero-forcing precoding schemes with the degree of inter-APs interference. We derive a closed-form expression of the achievable spectral efficiency (SE) for SJZF, under the assumption of independent rayleigh fading channel and pilot contamination. Based on the characteristics of the SE expression, we propose a novel random pilot assignment method, and a max-min fairness power optimization algorithm, to improve the system's SE obviously. Numerical results show that the SE of our SJZF outperforms other conventional precoding schemes, approaching CZF, but have affordable computation complexity.
Land snail diversity in central China: revision of Laeocathaica Möllendorff, 1899 (Gastropoda, Camaenidae), with descriptions of seven new species

﻿Abstract Central China harbors the native dart-sac-bearing camaenids Laeocathaica. The genus is revised and seven new species are proposed based on museum material and newly collected specimens. This work confirmed that most Laeocathaica species have restricted habitats. The comparison of the dart sac apparatus among the dart-sac-bearing camaenid genera indicated the importance of the presence of the proximal accessory sac that might be analogous to the membranous/muscular sac surrounding the proximal dart sac and/or the distal region of the vagina near the atrium, which also plays a significant role in the diagnosis of Laeocathaica species based on its number, symmetry and position on the dart sac. Species with similar shell morphology were studied using geometric morphometric methods to detect variations in shell shape. A molecular phylogenetic analysis based on 16S and ITS2 sequence data of partial Laeocathaica species and many other dart-sac-bearing taxa suggested that Laeocathaica might be monophyletic. Furthermore, the present phylogeny indicated that Stilpnodiscus, Cathaica, Bradybaena, and Pseudobuliminus might be polyphyletic and therefore the taxonomy of dart-sac-bearing camaenids in this region requires a thorough revision. This work reconfirms that the Southern Gansu Plateau is important as a hotspot for malacodiversity conservation on the Chinese mainland.
Primary and secondary instabilities in soft bilayered systems

Wrinkling phenomena emerging from mechanical instabilities in inhomogeneously compressed soft bilayered systems can evoke a wide variety of surface morphologies. Applications range from undesired instabilities in engineering structures such as sandwich panels, via fabricating surfaces with controlled buckling patterns of unique properties, to wrinkling phenomena in living matter such as lungs, mucosas, and brain convolutions. While moderate compression evokes periodic sinusoidal wrinkles, higher compression induces secondary instabilities ‐ the surface bifurcates into increasingly complex morphologies. Periodic wrinkling has already been extensively studied, but the rich pattern formation in the highly nonlinear post‐buckling regime remains poorly understood. Here, we establish a computational model of differential growth to explore the evolving buckling pattern of a growing layer bonded to a non‐growing substrate. Our model provides a mechanistic understanding of growth‐induced primary and secondary instabilities. We show that amongst all possible secondary bifurcations, the mode of period‐doubling is energetically favorable. We experimentally validate our numerical results by examining buckling of a compressed polymer film on a soft foundation. Our computational studies have broad applications in the microfabrication of distinct surface patterns as well as in the morphogenesis of living systems, where growth is progressive and the formation of structural instabilities is critical to biological function. (© 2015 Wiley‐VCH Verlag GmbH & Co. KGaA, Weinheim)
Towards an Ontological Framework for Integrating Domain Expert Knowledge with Random Forest Classification

This paper proposes an ontological framework that combines semantic-based methodologies and data-driven random forests (RF) to enable the integration of domain expert knowledge with machine-learning models. To achieve this, the RF classification process is firstly deconstructed and converted into semantic-based rules, which are combined with external rules constructed from the knowledge of domain experts. The combined rule set is applied to an ontological reasoner for inference, producing two classifications: (1) from simulating the selected RF voting strategy, (2) from the knowledge-driven rules, where the latter is prioritised. A case study in the steel manufacturing domain is presented that uses the proposed framework for real-world predictive maintenance purposes. Results are validated and compared to typical machine-learning approaches.
External Coil Coupled Loss Induced Quench (E-CLIQ) System for the Protection of LTS Magnets

The next generation of Nb$_{3}$Sn accelerator magnets are pushed to higher magnetic fields, higher stored energy and higher energy density. This brings additional challenges when it comes to quench protection. At CERN, a fast quench protection system is under development that introduces normal zones directly in the coil's conductor by inductive heating. This system is called External coil Coupled Loss Induced Quench (E-CLIQ). The E-CLIQ device comprises many small copper coils that are strategically placed near the main coils. Once a quench is detected, a capacitor is discharged in to the E-CLIQ coils, resulting in the characteristic damped oscillating current of an RLC circuit. This results in a large, but local applied dB/dt on the conductor of the main magnet system, which locally heats it by induced interfilament and interstrand coupling loss. Such a system has the advantage of being fast by introducing normal zones in potentially less than 10 ms, it doesn't rely on heat flow through the insulation layers of the coil, as is the case with traditional quench heaters, and it is not electrically connected to the main circuit. In this paper, the E-CLIQ system is introduced, several first demonstrator coils are presented and the results of a first proof-of-concept test are shown.
Synergies between synaptic and HCN channel plasticity dictates firing rate homeostasis and mutual information transfer in hippocampal model neuron

Homeostasis is a precondition for any physiological system of any living organism. Nonetheless, models of learning and memory that are based on processes of synaptic plasticity are unstable by nature according to Hebbian rules, and it is not fully clear how homeostasis is maintained during these processes. This is where theoretical and computational frameworks can help in gaining a deeper understanding of the various cellular processes that enable homeostasis in the face of plasticity. A previous simplistic single compartmental model with a single synapse showed that maintaining input/output response homeostasis and stable synaptic learning could be enabled by introducing a linear relationship between synaptic plasticity and HCN conductance plasticity. In this study, we aimed to examine whether this approach could be extended to a more morphologically realistic model that entails multiple synapses and gradients of various VGICs. In doing so, we found that a linear relationship between synaptic plasticity and HCN conductance plasticity was able to maintain input/output response homeostasis in our morphologically realistic model, where the slope of the linear relationship was dependent on baseline HCN conductance and synaptic permeability values. An increase in either baseline HCN conductance or synaptic permeability value led to a decrease in the slope of the linear relationship. We further show that in striking contrast to the single compartment model, here linear relationship was insufficient in maintaining stable synaptic learning despite maintaining input/output response homeostasis. Additionally, we showed that homeostasis of input/output response profiles was at the expense of decreasing the mutual information transfer due to the increase in noise entropy, which could not be fully rescued by optimizing the linear relationship between synaptic and HCN conductance plasticity. Finally, we generated a place cell model based on theta oscillations and show that synaptic plasticity disrupts place cell activity. Whereas synaptic plasticity accompanied by HCN conductance plasticity through linear relationship maintains the stability of place cell activity. Our study establishes potential differences between a single compartmental model and a morphologically realistic model.
ARC-learning: A Self-tuning Cache Policy under Dynamic Popularity

With the development of content applications, CDN servers have begun to carry more and more network traffic. Cache policy determines which object to store plays an essential role in the edge server's performance, and, in recent years, people have begun to focus on cache design through learning-based algorithms to adapt to the more varied scene and achieve better performance than heuristic algorithms. However, these algorithms have assumed that the objects they predicted have sufficient past information to use, which leads to poor performance when the load popularity rapidly changes. Motivated by this scene, we propose a cache strategy combining a learning-based method with a self-tuning strategy to improve the poor performance of the learning-based method under the above situation. We verify our strategy using real traces (from YouTube and Wikipedia) and synthetic traces with different dynamic levels, and the simulation results show that ARC-learning achieves the best performance compared with other algorithms in both static and non-static environments.
Results of an implementation of the dual surface method to treat the non-uniqueness in solving acoustic exterior problems using the boundary element method

The problem of non-uniqueness (NU) of the solution of exterior acoustic problems when using the boundary element method (BEM) is well known. Methods like the Burton-Miller technique or the CHIEF method are used to solve this challenge at the expense of more complex procedures for handling hypersingular integrals and/or higher computing times due to higher complexity of the algorithm or additional equations. The dual surface method, commonly used for electromagnetic problems, was adapted for acoustic radiation and scattering problems. The basic principles of methods to solve the NU problem are outlined and results for different models and solution procedures are presented, taking into account quality, solution time, and the numerical advantages when using iterative solvers.
Fat rays revisited: a synthesis of physical and geometrical optics with Gauβlets

Decomposed into Gauβlets (Gaussian beamlets or “fat rays”), a diffracting wavefield can be propagated through an optical system by simple ray tracing. As an update to a 1990 ILDC presentation, the method’s progress since then is covered along with new comparisons against more precise solutions.
An Audio Information Hiding Algorithm with High-Capacity Which Based on Chaotic and Wavelet Transform

Audio information hiding hides the secret message into the audio data flows by some characteristics of human auditory system.An audio information hiding algorithm with high-capacity which based on chaotic and wavelet transform is proposed,which,scrambles the secret message by chaotic sequences,and then one-to-one hides those into the high and low frequency of the wavelet field of the cover message by changing the cover message's wavelet domain coefficient's polarity based on the features that audio sample dots inversion has little influence on the human hearing.Experimental results show that this algorithm not only has good imperceptibility and is robust against different kinds of attacks,such as noise adding,re-quantizing,re-sampling,MP3 compression,and so on,but also has high-capacity,and can realize the blind detection.
One-dimensional quantum magnetism in the anhydrous alum KTi(SO4)2

The anhydrous alum KTi(SO4)2, where the Ti3+ (d1, S = 1/2) ions form an anisotropic triangular lattice, has been prepared by a new hydrothermal route and characterized by magnetic susceptibility and neutron scattering measurements. Contrary to expectations, fits to the magnetic susceptibility indicate that the spins are isotropic (i.e. Heisenberg) and that the frustrating couplings are weak; indeed, the system is well modelled by nearly isolated chains. The inelastic neutron scattering data furthermore shows excellent agreement with an exact theoretical calculation for the one-dimensional spinon continuum. The unexpected magnetic properties of KTi(SO4)2 are explained in the light of density functional calculations, which reveal an unusual orbital ground state for the Ti3 + ion.
Fast Parathyroid Glands Segmentation on Blurred Near-Infrared Autofluorescence Medical Images

Thyroid cancer is a common malignancy with a high incidence in the female population and it ranks as the 11th top cancer worldwide. Radical surgery is the most important method of treating thyroid cancer. One of the most challenging task during the surgical operation of thyroid cancer is to accurately avoid false excision or damage of the parathyroid glands(PG). In practice, the size and anatomic location-variations of parathyroid could highly likely results in miscutting of them during the surgery, causing unrecoverable medical malpractice and harm to the patient (transient or permanent hypocalcemia). Nir-autofluorescence(NIRAF) imaging has shown significant advantages in intraoperative identification of parathyroid glands with good predictability. In this work, we combined near-infrared autofluorescence imaging technology with fast saliency detection algorithm to perform pixel-wise inference on PG on the spontaneous fluorescence image, our method achieved 97.71% precision through experiments. This work increased the real-time performance of intraoperative detection of PG and made the self-fluorescence PG diagnostic more intuitive and accurate.
Hazardous Entity Recommendation for Safety Production Inspection Based on Multi-task Learning

The large number and wide variety of hazardous entities is contradicted with the limited law enforcement strength of safety production, resulting in duplicate or missed inspections. In order to realize the key entity recommendation for safety production inspection, we introduce recommendation algorithms into this field. Data sparsity and cold start problems are inevitable in traditional recommendations, while knowledge graphs can be added as side information to solve the problems. Due to the strong sparsity of safety inspection data and the severe overfitting of existing models, we adaptively improve the multi-task learning algorithm by dividing the model into high layers and low layers and designing the structures respectively. A recommendation model based on multi-task learning and convolutional structures (CMKR) is proposed in this paper to provide better hazardous entity recommendations for safety production inspection. To solve the serious problem of over-fitting of the original multi-task learning algorithm, the convolutional neural network with the characteristics of sparse connection and weight sharing displaces a fully-connected multi-layer perceptron (MLP). ConvKB, an embedding model using CNN for the knowledge graph completion task is used at the high layers to improve the generalization ability of the model. In click-through rate prediction, ACC reaches 0.7061 and AUC reaches 0.7112 on hazardous entity recommendations of key sites. Compared with previous algorithms, the proposed method effectively controls the overfitting problem and improves the overall performance.
The Combination of Color and Depth Information for Importance Map

In order to extract important map, a lot of image resizing algorithms only apply color map (such as gradient or salience information, etc.) to settle this question. On the analysis of depth map, this article thinks about depth information and combines with color map in order to put forward a combined important map. This new important map can identify the main body of source image accurately. Guided by this important map, image resizing results can not only protect the important area, but also obtain better processing effect. The image resizing algorithm can be segmented into two phases: the perception phase is the extraction for the importance map on the source image. The accuracy of important map affects the resizing effect directly. For importance map, most resizing algorithm only apply color information (such as gradient or salience information, etc.) to cope with the question. The importance map which is based on color map reflects the focus area of human eye, it is not quite accurately and its accuracy still needs to be improved further. In view of this situation, this paper thinks about depth information which is based on physiology (1) and psychology theory (2-4). Comparing with other automatic algorithm to calculate the importance map, quantitative results and qualitative analysis show that new algorithm may extract the importance map much more accurate. II. COMBINATION EXTRACTION ALGORITHM The joint of depth and color information map, the process of the combination extraction algorithm is shown on figure 1. The source information derives from the RGB-D camera. Combination extraction algorithm was divided into four parts: edge test, salience map detection, depth information generation and importance map's extraction. The edge test and salience detection belong to color information processing's category, depth information generation reside in depth information processing's scope, the extraction of importance map not only thinks about the color information in the source image (gradient or salience map), but also combines with the depth map. A. The Edge Test and Salience Detection With regard to edge and saliency test, we use sobel operator (5) to calculate the gradient information and adopt Itti model (6) to detect important map. New algorithm converts color image to grayscale image firstly, and then deals with grayscale image for edge test and saliency detection. Edge test is finished by applying the Sobel model which makes the calculated features keep the continuity on the source image's edge. Sobel model is a discrete difference operator and was applied to label brightness function's approximation on image. In the image's any pixels, we will calculate a corresponding gradient vector by using this operator. This operator contains two sets matrices respectively, they are the horizontal and vertical operator, which was used to deal with the plane convolution on image, we can get the brightness value of the difference approximation.
CUBN: A clustering algorithm based on density and distance

In data mining, clustering is used to discover groups and identify interesting distribution on the underlying data. Traditional clustering algorithm favors clusters with spherical shapes and similar sizes. We propose a new clustering algorithm called CUBN that integrates density-based and distance-based clustering. Firstly CUBN finds border points by using erosion operation that is one of the basic operations in mathematical morphology, then, it clusters the border points and inner points according to the nearest distance. Our experimental results show that CUBN can identify clusters having non-spherical shapes and wide variances in size, and its computational complexity is O(n). Therefore, this algorithm facilitates the clustering of a very large data set.
Contribution of the Second Order Terms to the Nonlinear Shallow Water Waves

Contribution of the second order terms in the reductive perturbation theory has been investigated for the nonlinear shallow water waves. The fundamental equations are reduced to a coupled set of the Korteweg-de Vries equation for the first order horizontal velocity and a linear inhomogeneous equation for the second order arbitrary function. Structure of the coupled set of equations turns out to be the same as in the case of nonlinear ion acoustic wave. A steady state solution of the coupled set of equations has been examined in comparison with Laiton's analysis of the second order contribution of the Friedrich's expansion for the nonlinear shallow water waves.
Experimental Investigation on the Performance of Angle-of-Arrival-based Asset Localization in a Warehouse

Accurate asset tracking and indoor localization (IL) is of interest in many applications including IoT sensing, shipping warehouse organization, and indoor navigation. Angle-based IL methods, such as angle-of-arrival (AOA), are known for their excellent accuracy. Moreover, their built-in support in the Bluetooth low energy (BLE) stack enables scalable and straight forward IL implementations. In this investigation, important considerations regarding the localization system parameters, including the number and location of anchor nodes, and tracked assets are explored.
Folded C-Type SIW Slot Array Antenna with Sidelobe Suppression for mm-wave Application

In this article, a millimeter wave (mm-wave) multibeam array antenna based on folded C-type substrate integrated waveguide (FCSIW) is proposed, which can suppress the sidelobe level of the E-plane and H-plane radiation patterns. By adjusting the size of the slot element, the excitation amplitude of the slot element can be regulated. So that the excitation amplitude of the slot element presents the Chebyshev distribution, and the sidelobe level with −25 dB of the H-plane radiation pattern can be achieved. In addition, the $4 \times 8$ butler matrix (BM) based on FCSIW is used to excite the $8\times 4$ slot array antenna, which can make the excitation amplitude of the slot array antenna present an approximate Chebyshev distribution along the E-plane, and the sidelobe level with −17dB of the E-plane radiation pattern can be obtained. Finally, the mm-wave multibeam array antenna with sidelobe suppression is designed, which can be used as one of the candidates for 5G mm-wave communication in the future.