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Radial jet drilling (RJD) technology has been applied to enhance the recovery of difficult-to-produce reserves by multiple horizontal micro-holes. The micro-hole length drilled by high-pressure water jets is of vital importance for the oil and gas recovery effect and is usually tens of meters long for applications in maturing oil fields in China. The water jets are generated by multiple orifices nozzle generally. Many studies focused on improving the self-propelled force generated by water jets to increase the micro-hole length. However, there are few researches on improving the micro-hole extension capacity in terms of optimizing the flexible hose that acts as the drill pipe in conventional drilling technology. This paper firstly studied the relationship between the flexible hose length and the micro-hole extension limit according to the analytical model to calculate the micro-hole extension limit. Then, the method to optimize the flexible hose length and the flow rate was developed aiming to obtain maximum micro-hole extension limit. The results show that the micro-hole extension limit decreases logarithmically with the increase in the flexible hose length under the condition that the takes the maximum value. The optimization model is applied by a field case and is proved to be effective to increase the micro-hole extension limit. This study is significant to improve the micro-hole extension capacity. Moreover, it provides a reference for the design of the hydraulics and selection of flexible hose for the RJD.

In the collector’s problem with group drawings, s out of n different types of coupon are sampled with replacement. In the uniform case, each s-subset of the types has the same probability of being sampled. For this case, we derive a Poisson limit theorem for the number of types that are sampled at most $c-1$ times, where $c \ge 1$ is fixed. In a specified approximate nonuniform setting, we prove a Poisson limit theorem for the special case $c=1$ . As corollaries, we obtain limit distributions for the waiting time for c complete series of types in the uniform case and a single complete series in the approximate nonuniform case.

We study shot noise processes with cluster arrivals, in which entities in each cluster may experience random delays (possibly correlated), and noises within each cluster may be correlated. We prove functional limit theorems for the process in the large-intensity asymptotic regime, where the arrival rate gets large while the shot shape function, cluster sizes, delays, and noises are unscaled. In the functional central limit theorem, the limit process is a continuous Gaussian process (assuming the arrival process satisfies a functional central limit theorem with a Brownian motion limit). We discuss the impact of the dependence among the random delays and among the noises within each cluster using several examples of dependent structures. We also study infinite-server queues with cluster/batch arrivals where customers in each batch may experience random delays before receiving service, with similar dependence structures.

Statement of the problem. The article presents a computational model, an algorithm for computational analysis and the results of calculating the nonlinear static-dynamic deformation of reinforced concrete frames in out-of-limit states caused by the sudden removal of one of the supporting structures. Results. To design a numerical model of the static-dynamic loading mode of a structural system, the LS-DYNA software package was used that makes use of a detailed 3D model implementing an explicit finite element method. During the computational analysis, the physical and mechanical characteristics of the deformation of materials were taken in three variants: those obtained based on the experimental data by G. A. Geniev, the experimental data by N. V. Fedorova and M. D. Medyankin under the static-dynamic uniaxial testing mode of a limited number of standard samples of prisms and according to the Russian standards SP (СП) 385.1325800.2018. Conclusions. Numerical analysis of the static-dynamic deformation of the reinforced concrete framerod system of a multi-storey building has established that the differentiated accounting of the quantitative value of the concrete viscosity modulus and, accordingly, the time and level of static-dynamic loading of the structure allows one to identify the criteria for the special limit state of the elements of reinforced concrete structural systems of buildings and structures in a more rigid manner.

Consideration is given to the distribution of sensitivity along the area of indium antimonide FPA pixel obtained with the aid of the nondestructive method of the scanning mask on the basis of the fast testing open probe installation.

The issue of heavy metals contamination of environmental segments is becoming serious day by day with industrial and electronic developments. Economic growth and environmental issue are parallel in...

We consider a strongly supercritical branching process in random environment with immigration stopped at a distant time 𝑛. The offspring reproduction law in each generation is assumed to be geometric. The process is considered under the condition of its extinction after time 𝑛. Two limit theorems for this process are proved: the first one is for the time interval from 0 till 𝑛, and the second one is for the time interval from 𝑛 till + ∞ +\infty .

У роботі розглядаються алгоритми машинного навчання. Увага зосереджена на напівконтрольному навчанні, яке здається балансом між точністю навчання з учителем та витратами методів навчання без учителя. Розглядаються приклади ретельного опрацювання мічених наборів даних, для яких навчання під наглядом може бути дуже ефективним. Порівнюються підходи semi-supervised та supervised та проаналізована ефективність кожного. В роботі розглядаються підходи S3VM та TSVM. Метою роботи було дослідити чи можуть напівконтрольовані підходи конкурувати з контрольованими або навіть їх перевершити. Описується застосування даних підходів до запропонованого датасету для визначення більш точної класифікації даних, а саме на опорній межі.

In this paper, we consider the concentration property of solutions to the dispersive Ginzburg-Landau (or Gross-Pitaevskii) equation in three dimensions. On a spatial domain, it has long been conjectured that such a solution concentrates near some curve evolving according to the binormal curvature flow, and conversely, that a curve moving this way can be realized in a suitable sense by some solution to the dispersive Ginzburg-Landau equation. Some partial results are known with rather strong symmetry assumptions. Our main theorems here provide affirmative answer to both conjectures under certain small curvature assumption. The results are valid for small but fixed material parameter in the equation, in contrast to the general practice to take this parameter to its zero limit. The advantage is that we can retain precise description of the vortex filament structure. The results hold on a long but finite time interval, depending on the curvature assumption., Comment: 26 pp

У роботі доведено граничну теорему для послідовності точкових процесів, які опи- сують моменти (r + 1)-х надходжень різних типів з загальної кількості в n типів в узагальненій задачі про дні народження. Класична задача про дні народження, відо- ма з популярної літератури, відповідає параметрам r = 1 (достатньо одного збігу) та n = 365 (кількість днів у невисокосному році). Доведення базується на застосуванні техніки пуассонізації/депуассонізації. Цей результат далі використовується для про- стого доведення деяких класичних граничних теорем у задачі про дні народження, які фактично описують асимптотичну поведінку різних змістовних функціоналів від побудованих процесів.

Naive Bayes (NB) was once awarded as one of the top 10 data mining algorithms, but the unreliable probability estimation and the unrealistic attribute conditional independence assumption limit its performance. To alleviate these two primary weaknesses simultaneously, instance and attribute weighting has been recently proposed. However, the existing approach learns instance and attribute weights separately, without considering their interactions at all, which restricts the performance of the learned model. Therefore, in this study, we propose a novel approach to learning instance and attribute weights collaboratively and call the resulting model collaboratively weighted naive Bayes (CWNB). In CWNB, we first learn the weight of each training instance iteratively based on its estimated posterior probability loss to make the prior and conditional probabilities more accurate, then we incorporate these two probabilities into the conditional log-likelihood (CLL) formula, and at last we search the optimal weight of each attribute by maximizing the CLL. Extensive experimental results show that CWNB significantly outperforms the standard NB and all the other existing state-of-the-art competitors.

Unsupervised subsequence outlier detection on multivariate time series (MTS) is a valuable problem in practice that can observably save the cost of labeling and provide interpretability in real applications. For the task, most of the classic methods are under two strong assumptions: (i) stationary MTS. it may have difficulty coping with the phenomenon of time drift. (ii) Attribute-level IIDness (independent and identically distributed), it may ignore the relationship between attribute when measuring the similarity between multivariate subsequences. The above assumptions limit the availability of existing methods in real scenarios. To address this issue, this paper introduces a novel coupled generalized local outlier detection model for MTS, which extends the traditional generalized local outlier detection model to cope with subsequence outlier detection tasks by incorporating a novel Non-IID similarity metric. Specifically, the proposed method mainly includes three aspects: (i) represents the MTS relationship in symbolic space which provides a lower complexity and satisfactory sensitivity. (ii) Proposes a Non-IID coupled similarity metric (TSDis) which considers the intrinsic intra-attribute and inter-attribute coupling between segments. (iii) Extends the traditional generalized local outlier detection model to handle subsequence outlier detection tasks by embedding Non-IID coupled similarity metric. Experimental results show the proposed method can utilize the potential characteristics of MTS effectively and stably. Meanwhile, it detects outliers more accurately than baseline approaches on 12 time-series datasets.

We study the process of two-phase flow in thin porous media domains of Brinkman-type. This is generally described by a model of coupled, mixed-type differential equations of fluids' saturation and pressure. To reduce the model complexity, different approaches that utilize the thin geometry of the domain have been suggested. We focus on a reduced model that is formulated as a single nonlocal evolution equation of saturation. It is derived by applying standard asymptotic analysis to the dimensionless coupled model, however, a rigid mathematical derivation is still lacking. In this paper, we prove that the reduced model is the analytical limit of the coupled two-phase flow model as the geometrical parameter of domain's width--length ratio tends to zero. Precisely, we prove the convergence of weak solutions for the coupled model to a weak solution for the reduced model as the geometrical parameter approaches zero.

For a $\psi $ -mixing process $\xi _0,\xi _1,\xi _2,\ldots $ we consider the number ${\mathcal N}_N$ of multiple returns $\{\xi _{q_{i,N}(n)}\in {\Gamma }_N,\, i=1,\ldots ,\ell \}$ to a set ${\Gamma }_N$ for n until either a fixed number N or until the moment $\tau _N$ when another multiple return $\{\xi _{q_{i,N}(n)}\in {\Delta }_N,\, i=1,\ldots ,\ell \}$ , takes place for the first time where ${\Gamma }_N\cap {\Delta }_N=\emptyset $ and $q_{i,N}$ , $i=1,\ldots ,\ell $ are certain functions of n taking on non-negative integer values when n runs from 0 to N. The dependence of $q_{i,N}(n)$ on both n and N is the main novelty of the paper. Under some restrictions on the functions $q_{i,N}$ we obtain Poisson distributions limits of ${\mathcal N}_N$ when counting is until N as $N\to \infty $ and geometric distributions limits when counting is until $\tau _N$ as $N\to \infty $ . We obtain also similar results in the dynamical systems setup considering a $\psi $ -mixing shift T on a sequence space ${\Omega }$ and studying the number of multiple returns $\{ T^{q_{i,N}(n)}{\omega }\in A^a_n,\, i=1,\ldots ,\ell \}$ until the first occurrence of another multiple return $\{ T^{q_{i,N}(n)}{\omega }\in A^b_m,\, i=1,\ldots ,\ell \}$ where $A^a_n,\, A_m^b$ are cylinder sets of length n and m constructed by sequences $a,b\in {\Omega }$ , respectively, and chosen so that their probabilities have the same order.

Methods for solving the Schrodinger equation without approximation are in high demand but are notoriously computationally expensive. In practical terms, there are just three primary factors that currently limit what can be achieved: 1) system size/dimensionality; 2) energy level excitation; and 3) numerical convergence accuracy. Broadly speaking, current methods can deliver on any two of these three goals, but achieving all three at once remains an enormous challenge. In this paper, we shall demonstrate how to "hit the trifecta" in the context of molecular vibrational spectroscopy calculations. In particular, we compute the lowest 1000 vibrational states for the six-atom acetonitrile molecule (CH3CN), to a numerical convergence of accuracy 10-2 cm-1 or better. These calculations encompass all vibrational states throughout most of the dynamically relevant range (i.e., up to ∼4250 cm-1 above the ground state), computed in full quantum dimensionality (12 dimensions), to near spectroscopic accuracy. To our knowledge, no such vibrational spectroscopy calculation has ever previously been performed.

We study higher order expansions both in the Berry-Ess\'een estimate (Edgeworth expansions) and in the local limit theorems for Birkhoff sums of chaotic probability preserving dynamical systems. We establish general results under technical assumptions, discuss the verification of these assumptions and illustrate our results by different examples (subshifts of finite type, Young towers, Sinai billiards, random matrix products), including situations of unbounded observables with integrability order arbitrarily close to the optimal moment condition required in the i.i.d. setting., Comment: 66 pages, 2 figures

We study the higher integrability of weights satisfying a reverse Hölder inequality ( ⨏ I u β ) 1 β ≤ B ⁢ ( ⨏ I u α ) 1 α {\biggl{(}\fint_{I}u^{\beta}\biggr{)}^{\frac{1}{\beta}}}\leq B{\biggl{(}\fint_{I}u^{\alpha}\biggr{)}^{\frac{1}{\alpha}}} for some B > 1 B>1 and given α < β \alpha , in the limit cases when α ∈ { - ∞ , 0 } \alpha\in\{-\infty,0\} and/or β ∈ { 0 , + ∞ } \beta\in\{0,+\infty\} . The results apply to the Gehring and Muckenhoupt weights and their corresponding limit classes.

An effective fraction of data with missing values from various physiochemical sensors in the Internet of Things is still emerging owing to unreliable links and accidental damage. This phenomenon will limit the predicative ability and performance for supporting data analyses by IoT-based platforms. Therefore, it is necessary to exploit a way to reconstruct these lost data with high accuracy. A new data reconstruction method based on spectral k-support norm minimization (DR-SKSNM) is proposed for NB-IoT data, and a relative density-based clustering algorithm is embedded into model processing for improving the accuracy of reconstruction. First, sensors are grouped by similar patterns of measurement. A relative density-based clustering, which can effectively identify clusters in data sets with different densities, is applied to separate sensors into different groups. Second, based on the correlations of sensor data and its joint low rank, an algorithm based on the matrix spectral k-support norm minimization with automatic weight is developed. Moreover, the alternating direction method of multipliers (ADMM) is used to obtain its optimal solution. Finally, the proposed method is evaluated by using two simulated and real sensor data sources from Panzhihua environmental monitoring station with random missing patterns and consecutive missing patterns. From the simulation results, it is proved that our algorithm performs well, and it can propagate through low-rank characteristics to estimate a large missing region’s value.

In this paper, we investigate the solvability, regularity and the vanishing dissipation limit of solutions to the three-dimensional viscous magnetohydrodynamic (MHD) equations in bounded domains. On the boundary, the velocity field fulfills a Navier-slip condition, while the magnetic field satisfies the insulating condition. It is shown that the initial boundary value problem has a global weak solution for a general smooth domain. More importantly, for a flat domain, we establish the uniform local well-posedness of the strong solution with higher-order uniform regularity and the asymptotic convergence with a rate to the solution of the ideal MHD equation as the dissipations tend to zero.

The Qian–Sheng model is a system describing the hydrodynamics of nematic liquid crystals in the Q-tensor framework. When the inertial effect is included, it is a hyperbolic-type system involving a second-order material derivative coupling with forced incompressible Navier–Stokes equations. If formally letting the inertial constant [Formula: see text] go to zero, the resulting system is the corresponding parabolic model. We provide the result on the rigorous justification of this limit in [Formula: see text] with small initial data, which validates mathematically the parabolic Qian–Sheng model. To achieve this, an initial layer is introduced to not only overcome the disparity of the initial conditions between the hyperbolic and parabolic models, but also make the convergence rate optimal. Moreover, a novel [Formula: see text]-dependent energy norm is carefully designed, which is non-negative only when [Formula: see text] is small enough, and handles the difficulty brought by the second-order material derivative.

I analyze the role of infinite idealizations used in the renormalization group (RG hereafter) method in explaining universality across microscopically different physical systems in critical phenomena. I argue that despite the reference to infinite limit systems such as systems with infinite correlation lengths during the RG process, the key to explaining universality in critical phenomena need not involve infinite limit systems. I develop my argument by introducing what I regard as the explanatorily relevant property in RG explanations: linearization* property; I then motivate and prove a proposition about the linearization* property in support of my view. As a result, infinite limit systems in RG explanations are dispensable.

The design of engineered earthworks is predominantly conducted through limit equilibrium analysis, requiring strain-independent strength criteria. Previous studies on deep-seated first-time failures within over-consolidated clay cut slopes have proposed the use of fully softened strength parameters for design. A study investigating shallow first-time failures in clay cut slopes due to seasonal stress cycles was undertaken using a validated numerical model capable of capturing seasonal ratcheting and progressive failure. It was found that fully softened strength criteria are inappropriate for the assessment of shallow first-time failures due to seasonal ratcheting and slopes at angles between the material's fully softened and residual friction angle may be at risk of failure in the future due to this behaviour. However, adopting residual strength parameters will likely result in overly conservative solutions considering the required design life of geotechnical assets. It was found that the strain-softening behaviour of clay defines the rate of strength deterioration and the operational life of engineered slopes. While general guidelines for analysis considering shallow first-time failures in clay cut slopes are provided, detailed understanding of a material's strain-softening behaviour, the magnitude and rate of strength reduction with strains, is needed to establish strength criteria for limit equilibrium analysis.

The article advances two related arguments with regard to term limit decisions in Sub-Saharan Africa. We should take a long-term perspective on term limit decisions and not describe and explain the...

In this article we use MP2 and CCSD(T) calculations for the A24 and S66 data sets to explore how midbond functions can be used to generate cost effective counterpoise corrected supramolecular interaction energies of noncovalent complexes. We use the A24 data set to show that the primary role of midbond functions is not to approach the complete basis set limit, but rather to ensure a balanced description of the molecules and the interaction region (unrelated to the basis set superposition error). The need for balance is a consequence of using atom centered basis sets. In the complete basis set limit, the error will disappear, but reaching the complete basis set limit is not feasible beyond small systems. For S66 we investigate the need for increasing the number of midbond centers. Results show that adding a second midbond center increases the accuracy, but the effect is secondary to changing the atom centered basis set. Further, by comparing calculations using the 3s3p2d1f1g midbond set with using aug-cc-pVDZ and aug-cc-pVTZ as midbond sets, we see that the requirements for the midbond set to be effective, is not just that it contains diffuse functions, but also that high angular momentum functions are included. By comparing two approaches for placing midbond centers we show that results are not particularly sensitive to placement as long as the placement is reasonable.

In this paper, we develop a general approach to investigate limit dynamics of infinite-dimensional dissipative impulsive systems whose initial conditions do not uniquely determine their long time behavior. Based on the notion of an uniform attractor, we show how to describe limit behavior of such complex systems with the help of properties of their components. More precisely, we prove the existence of the uniform attractor for an impulsive multivalued system in terms of properties of nonimpulsive semiflow and impulsive parameters. We also give an application of these abstract results to the impulsive reaction-diffusion system without uniqueness.

When studying the Influence Maximization (IM) problem in social networks, it is found that the initial seed nodes selected by some heuristic algorithms have the characteristics of aggregation. This situation is called the rich club phenomenon of seed sets. Once the seed node is over-aggregated, it will limit the spread of influence. Therefore, analyzing the rich-club phenomenon is necessary for solving the IM problem. The paper proposes the rich club coefficient and reactivation rate to quantitatively analyze this phenomenon. The analysis mainly focuses on the relationship between the hop distance and the propagation probability. In addition, the relationship between the rich club phenomenon and the IM problem is also an aspect of concern. When dealing with the main aspects, a key problem needs to be solved, which is that the optimal range of hop is different under different propagation probabilities. To solve this problem, the Multi Hop Remove (MHR) algorithm is proposed, which is based on the independent cascade model. By the MHR algorithm, the hop range is determined under different propagation probabilities. According to our experimental results, the more serious the rich club phenomenon accumulates, the smaller the influence spread. To reduce the obstruction of this phenomenon, the multi-hop selection of seed nodes is a superior solution.

Over the last two decades, the local approximation has been successfully used to extend the range of applicability of the "gold standard" singles and doubles coupled-cluster method with perturbative triples CCSD(T) to systems with hundreds of atoms. The local approximation error grows in absolute value with the increasing system size, i.e., by increasing the number of electron pairs in the system. In this study, we demonstrate that the recently introduced two-point extrapolation scheme for approaching the complete pair natural orbital (PNOs) space limit in domain-based pair natural orbital CCSD(T) calculations drastically reduces the dependence of the error on the system size, thus opening up unprecedented opportunities for the calculation of benchmark quality relative energies for large systems.

Teaching a multicultural classroom has always been challenging for educators. With the rush toward global standardization, there is a concern that uniformity in education will sacrifice students’ freedom in expressing their skills and knowledge. This problem can be seen in the way students are being assessed. The rapid growth of standardized tests and the demand to meet yearly progress thresholds are tangible examples of how assessments may limit the way students demonstrate their learning attainment. This article aims at presenting several alternatives teachers could take to implement Universal Design for Learning (UDL) and inclusive assessment principles in assessing multicultural classrooms. This analytic autoethnography study yielded three themes: providing options of assessment forms, providing accommodation in conducting assessments, and using the strength-based language in assessment feedback.

Machine learning provides a probabilistic framework for metabolic pathway inference from genomic sequence information at different levels of complexity and completion. However, several challenges including pathway features engineering, multiple mapping of enzymatic reactions and emergent or distributed metabolism within populations or communities of cells can limit prediction performance. In this paper, we present triUMPF,triple non-negative matrix factorization (NMF) with community detection formetabolicpathway inference, that combines three stages of NMF to capture myriad relationships between enzymes and pathways within a graph network. This is followed by community detection to extract higher order structure based on the clustering of vertices which share similar statistical properties. We evaluated triUMPF performance using experimental datasets manifesting diverse multi-label properties, including Tier 1 genomes from the BioCyc collection of organismal Pathway/Genome Databases and low complexity microbial communities. Resulting performance metrics equaled or exceeded other prediction methods on organismal genomes with improved precision on multi-organismal datasets.Availability and implementationThe software package, and installation instructions are published ongithub.com/triUMPF

This paper studies the number of limit cycles in an ( m + 1 ) th order Lienard differential system, which are bifurcated from the families of periodic orbits near two centres and a double homoclinic loop of a Hamiltonian system with an elliptic Hamiltonian function H = 1 2 y 2 − 1 2 x 2 + 1 4 x 4 . It is proved that this kind of systems can have at least 7 [ m − 2 5 ] + 2 r − [ r 3 ] ⋅ mod ( r + 1 , 3 ) number of limit cycles, where r = mod ( m − 2 , 5 ) and 3 ≤ m ≤ 18 except m = 8 . When the Lienard differential system is of centrally symmetric, the lower bound on the number of limit cycles could be 7 [ m 6 ] + 2 r + [ r 2 ] with r = mod ( m 2 , 3 ) and m ≤ 124 even. In any of the cases the bounds can be reached with the help of Maple programs. Here mod ( a , b ) represents the remainder of a divided by b.

Mixing is an essential manufacturing process in various industries. The processing procedure and final product quality depend on the homogeneity of mixing. Because it is difficult to evaluate mixing systems experimentally, the discrete element method is commonly employed. However, as the number of particles increases, this approach incurs huge computational costs. The coarse grain model offers a potential solution, but its applicability has not been widely demonstrated; this study aimed to elucidate the upper limit for applying the coarse grain model. To determine the appropriate simulation parameters, calibrations were performed by comparing the powder bed in experiments versus simulations. Various mixing processes were numerically evaluated, and the mixing characteristics were qualitatively consistent among all coarse-grained ratios. These mixing systems were also evaluated quantitatively based on Lacey’s mixing index, which indicated that the upper limit of the coarse-grained ratio was five times. It is therefore important to secure a sufficient number of particles in each cell and to use an appropriate number of cells. This study clarified the upper application limit and criteria for the coarse grain model and verified the maximum coarse-grained ratio (five times). This approach can be used to determine the coarse-grained ratio and reduce computational costs.

This paper presents a stabilization technique for the finite element modelling of contact-impact problems of elastic bars via a bi-penalty method for enforcing contact constraints while employing an explicit predictor–corrector time integration algorithms. The present proposed method combines three salient features in carrying out explicit transient analysis of contact-impact problems: the addition of a penalty term associated with a kinetic energy expression of gap constraints, in addition to the conventional internal energy penalty term of the gap constraints; an explicit integration method that alleviates spurious oscillations; and, a judicious selection of two penalty parameters such that the stable time steps of the resulting explicit method is least compromised. Numerical experiments have been carried out with three explicit methods: the standard central difference method, the stabilized predictor–corrector method (Wu, 2003 [50] ) and a method for mitigating spurious oscillations (Park et al., 2012 [44] ) as applied to simulate one-dimensional contact-impact problems of the Signorini problem and the impact of two elastic bars. Results indicate that the proposed method can maintain the contact-free stability limit of the central difference and yield improved accuracy compared with existing bi-penalty methods.

We consider the stochastic heat equation driven by a Rosenblatt sheet and we study the limit behavior in distribution of the spatial average of the solution. By analyzing the cumulants of the solut...

We study the critical Ising model with free boundary conditions on finite domains in $\mathbb{Z}^d$ with $d\geq4$. Under the assumption, so far only proved completely for high $d$, that the critical infinite volume two-point function is of order $|x-y|^{-(d-2)}$ for large $|x-y|$, we prove the same is valid on large finite cubes with free boundary conditions, as long as $x, y$ are not too close to the boundary. This confirms a numerical prediction in the physics literature by showing that the critical susceptibility in a finite domain of linear size $L$ with free boundary conditions is of order $L^2$ as $L\rightarrow\infty$. We also prove that the scaling limit of the near-critical (small external field) Ising magnetization field with free boundary conditions is Gaussian with the same covariance as the critical scaling limit, and thus the correlations do not decay exponentially. This is very different from the situation in low $d$ or the expected behavior in high $d$ with bulk boundary conditions., small changes to the abstract and introduction, three new references

Recently, deep learning models have achieved great success in computer vision applications, relying on large-scale class-balanced datasets. However, imbalanced class distributions still limit the wide applicability of these models due to degradation in performance. To solve this problem, in this paper, we concentrate on the study of cross entropy which mostly ignores output scores on incorrect classes. This work discovers that neutralizing predicted probabilities on incorrect classes improves the prediction accuracy for imbalanced image classification. This paper proposes a simple but effective loss named complement cross entropy based on this finding. The proposed loss makes the ground truth class overwhelm the other classes in terms of softmax probability, by neutralizing probabilities of incorrect classes, without additional training procedures. Along with it, this loss facilitates the models to learn key information especially from samples on minority classes. It ensures more accurate and robust classification results on imbalanced distributions. Extensive experiments on imbalanced datasets demonstrate the effectiveness of the proposed method., 8 pages, Accepted to Pattern Recognition Letters (PRL), August 2021

Reasonable numerical conditions are critical for the precision and accuracy of the numerical consequences of hydrocyclones. In this paper, the effects of resistance coefficient, wall roughness, and friction packing limit on particle velocity and distribution as well as the separation behaviors were studied in a cylindrical hydrocyclone using the two-fluid model. The results show that the friction packing limit and wall roughness can significantly influence the numerical results, whereas the effect of the resistance coefficient is negligible. As the friction packing limit increases from 0.4 to 0.55, the cut size gradually decreases with improving separation sharpness, and it reaches the minimum when the friction parking limit is 0.5. The wall roughness monotonously increases the cut size and reduces the separation sharpness. Simulation results were also compared with experimental data, and a reasonable prediction can be obtained with resistance coefficient, friction packing limit, and wall roughness height of 0.95, 0.5, and 50 μm, respectively.

In planar analytic vector fields, a monodromic singularity can be distinguished between a focus or a center by means of the Lyapunov coefficients, which are given in terms of the power series coefficients of the first-return map defined around the singularity. In this paper, we are interested in an analogous problem for monodromic tangential singularities of piecewise analytic vector fields Z = ( Z + , Z − ) . First, we prove that the first-return map, defined in a neighborhood of a monodromic tangential singularity, is analytic, which allows the definition of the Lyapunov coefficients. Then, as a consequence of a general property for pair of involutions, we obtain that the index of the first non-vanishing Lyapunov coefficient is always even. In addition, a general recursive formula together with a Mathematica algorithm for computing the Lyapunov coefficients is obtained. We also provide results regarding limit cycles bifurcating from monodromic tangential singularities. Several examples are analyzed.

At high elevation or latitude, trees reach low-temperature range limits. In attempting an explanation, the range limits of individual tree species (set by freezing tolerance) and the general limit of the life-form tree (set by thermal growth constraints) need to be distinguished. The general cold edge of the fundamental niche of trees is termed the treeline, by definition, the lower edge of the alpine belt, a most important bioclimatological reference line. Trees can be absent from the treeline due to disturbances or biotic interactions. The actual local edge of tree distribution, the delineation of the realized niche, is driven by stochastic effects. Therefore, treeline theory and hypothesis testing is inevitably tied to the fundamental niche concept.

International audience; Both compressible and incompressible porous medium models are used in the literature to describe the mechanical properties of living tissues. These two classes of models can be related using a stiff pressure law. In the incompressible limit, the compressible model generates a free boundary problem of Hele-Shaw type where incompressibility holds in the saturated phase. Here we consider the case with a nutrient. Then, a badly coupled system of equations describes the cell density number and the nutrient concentration. For that reason, the derivation of the free boundary (incompressible) limit was an open problem, in particular a difficulty is to establish the so-called complementarity relation which allows to recover the pressure using an elliptic equation. To establish the limit, we use two new ideas. The first idea, also used recently for related problems, is to extend the usual Aronson-Bénilan estimates in $L^\infty$ to an $L^2$ setting. The second idea is to derive a sharp uniform $L^4$ estimate on the pressure gradient, independently of space dimension.

With impressive density and coding capacity, DNA offers a promising solution for building long-lasting data archival storage systems. In recent implementations, data retrieval such as random access typically relies on a large library of non-interacting PCR primers. While several algorithms automate the primer design process, the capacity and scalability of DNA-based storage systems are still fundamentally limited by the availability of experimentally validated orthogonal primers. In this work, we combine the nested and semi-nested PCR techniques to virtually enforce multidimensional data organization in large DNA storage systems. The strategy effectively pushes the limit of DNA storage capacity and reduces the number of primers needed for efficient random access from very large address space. Specifically, our design requires k * n unique primers to index nk data entries, where k specifies the number of dimensions and n indicates the number of data entries stored in each dimension. We strategically leverage forward/reverse primer pairs from the same or different address layers to virtually specify and maintain data retrievals in the form of rows, columns, tables, and blocks with respect to the original storage pool. This architecture enables various random-access patterns that could be tailored to preserve the underlying data structures and relations (e.g., files and folders) within the storage content. With just one or two rounds of PCR, specific data subsets or individual datum from the large multidimensional storage can be selectively enriched for simple extraction by gel electrophoresis or readout via sequencing.Abstract Figure

Identification of influential spreaders is still a challenging issue in network science. Therefore, it attracts increasing attention from both computer science and physical societies, and many algorithms to identify influential spreaders have been proposed so far. Degree centrality, as the most widely used neighborhood-based centrality, was introduced into the network world to evaluate the spreading ability of nodes. However, degree centrality always assigns too many nodes with the same value, so it leads to the problem of resolution limitation in distinguishing the real influences of these nodes, which further affects the ranking efficiency of the algorithm. The k-shell decomposition method also faces the same problem. In order to solve the resolution limit problem, we propose a high-resolution index combining both degree centrality and the k-shell decomposition method. Furthermore, based on the proposed index and the well-known gravity law, we propose an improved gravity model to measure the importance of nodes in propagation dynamics. Experiments on ten real networks show that our model outperforms most of the state-of-the-art methods. It has a better performance in terms of ranking performance as measured by the Kendall’s rank correlation, and in terms of ranking efficiency as measured by the monotonicity value.

Negative handprints or hand-stencils, which occur in many prehistoric sites around the world, occupy a particular place in accounts of rock art. Although they frequently occur alongside paintings, their indexical status as imprints leads them to be treated separately from other types of representations that are more easily accepted as such. This article argues that the negative handprint operates as a kind of limit-case for definitions of art. I examine how it has given rise to imagined scenarios of making — what we might call primal scenes of art — by writers including Georges Bataille, Maurice Blanchot and Marguerite Duras. While its logic of presence invites us to think about it as a point of origin, a trace that connects us to our earliest human ancestors, I show how it can be read against that logic of presence through the lens of one particular ‘primal scene’, that imagined by Jean-Luc Nancy. In this reading, it is precisely the question of absence or distance that gives the handprint its status as a point of origin that undoes the very idea of origins.

We develop an empirical behavioural order-driven (EBOD) model, which consists of an order placement process and an order cancellation process. Price limit rules are introduced in the definition of relative price. The order placement process is determined by several empirical regularities: the long memory in order directions, the long memory in relative prices, the asymmetric distribution of relative prices, and the nonlinear dependence of the average order size and its standard deviation on the relative price. Order cancellation follows a Poisson process with the arrival rate determined from real data and the cancelled order is determined according to the empirical distributions of relative price level and relative position at the same price level. All these ingredients of the model are derived based on the empirical microscopic regularities in the order flows of stocks on the Shenzhen Stock Exchange. The model is able to produce the main stylized facts in real markets. Computational experiments uncover that asymmetric setting of price limits will cause the stock price diverging exponentially when the up price limit is higher than the down price limit and vanishing vice versus. We also find that asymmetric price limits have influences on stylized facts. Our EBOD model provides a suitable computational experiment platform for academics, market participants and policy makers., 19 pages, 8 figures and 7 tables