Miracle-dz/UniFAR-FTUs huggingface dataset:
Passive scalar mixing at finite correlation times and the Batchelor spectrum

An elegant model for passive scalar mixing was given by Kraichnan (1968) assuming the velocity to be delta-correlated in time. For realistic random flows this assumption becomes invalid. We generalize the Kraichnan model to include the effects of a finite correlation time, $\tau$, using renewing flows. The generalized evolution equation for the 3-D passive scalar spectrum $\hat{M}(k,t)$ or its correlation function $M(r,t)$, gives the Kraichnan equation when $\tau \to 0$, and extends it to the next order in $\tau$. It involves third and fourth order derivatives of $M$ or $\hat{M}$ (in the high $k$ limit). For small-$\tau$ (or small Strouhl number), it can be recast using the Landau-Lifshitz approach, to one with at most second derivatives of $\hat{M}$. We present both a scaling solution to this equation neglecting diffusion and a more exact solution including diffusive effects. Interestingly, to leading order in $\tau$, we show that the steady state 1-D passive scalar spectrum, preserves the Batchelor (1959) form, $E_\theta(k) \propto k^{-1}$, in the viscous-convective limit, independent of $\tau$. When passive scalar fluctuations decay, we show that the decay rate is reduced for finite $\tau$, but the spectrum $E_\theta(k) \propto k^{1/2}$ independent of $\tau$ . We also present results from high resolution ($1024^3$) direct numerical simulations of passive scalar mixing. We find reasonable agreement with predictions of the Batchelor spectrum, during steady state. The scalar spectrum during decay is however shallower than what theory predicts, a feature which remains intriguing.
Interpolation effects in tabulated interatomic potentials

Empirical interatomic potentials are widely used in atomistic simulations due to their ability to compute the total energy and interatomic forces quickly relative to more accurate quantum calculations. The functional forms in these potentials are sometimes stored in a tabulated format, as a collection of data points (argument–value pairs), and a suitable interpolation (often spline-based) is used to obtain the function value at an arbitrary point. We explore the effect of these interpolations on the potential predictions by calculating the quasi-harmonic thermal expansion and finite-temperature elastic constant of a one-dimensional chain compared with molecular dynamics simulations. Our results show that some predictions are affected by the choice of interpolation regardless of the number of tabulated data points. Our results clearly indicate that the interpolation must be considered part of the potential definition, especially for lattice dynamics properties that depend on higher-order derivatives of the potential. This is facilitated by the Knowledgebase of Interatomic Models (KIM) project, in which both the tabulated data (‘parameterized model’) and the code that interpolates them to compute energy and forces (‘model driver’) are stored and given unique citeable identifiers. We have developed cubic and quintic spline model drivers for pair functional type models (EAM, FS, EMT) and uploaded them to the OpenKIM repository (https://openkim.org).
CS-LeCT: Chained Secure and Low-Energy Consumption Data Transmission Based on Compressive Sensing

Energy consumption and security are two major challenges faced by telemedicine systems. Due to the simplicity and the randomness in information acquisition process, compressive sensing (CS) has a vast prospect in dealing with these two problems. Recently, a chained CS (CCS) scheme has been proposed, which can implement a secure and low-energy consumption data transmission. However, due to its chain computation and the lossy compression nature of CS, there exists an accumulative error in the existing CCS scheme. Moreover, block-based CS decoding also leads to significant performance deterioration. In this article, a new chained secure and low-energy consumption data transmission (CS-LeCT) scheme has been proposed, whose reconstruction performance is far superior to the CCS. First, to overcome the accumulative error of the CCS scheme, square matrices instead of underdetermined matrices were used to encode the signal so that in the proposed CS-LeCT, the decoding process can be carried out by adopting a lossless inverse operation. Second, inspired by the structurally random matrix (SRM), an active packet drop strategy was designed to achieve low-energy consumption data transmission, whose decoding can be implemented by means of the SRM-based CS reconstruction. The performance gain of the CS-LeCT has not only been validated through simulation experiments but has also been analyzed theoretically. Safety analysis also shows that the proposed CS-LeCT can resist several potential attacks, such as ciphertext-only attacks (COAs), known-plaintext attacks (KPAs), and man-in-the-middle attacks (MiTMs).
Branch and bound with simplicial partitions for global optimization

Abstract Branch and bound methods for global optimization are considered in this paper. Advantages and disadvantages of simplicial partitions for branch and bound are shown. A new general combinatorial approach for vertex triangulation of hyper‐rectangular feasible regions is presented. Simplicial partitions may be used to vertex triangulate feasible regions of non rectangular shape defined by linear inequality constraints. Linear inequality constraints may be used to avoid symmetries in optimization problems.
An essay on Interbull evaluations from an AI-industry perspective

About ten years ago Schaeffer (1994) introduced MACE as a method for Multiplecountry comparison of dairy sires. The two key issues of MACE are that breeding values from different countries are modelled as different traits, and that DYD’s or deregressed proofs are used as data. Important issues that were raised by Schaeffer are: the importance of genetic groups, connectedness, the role of preferential treatment, unique identification, estimation of variances when using DYD’s as data. Also, Schaeffer observer that “the multiple-country model may be most applicable to conformation traits that are not measured or scored in the same manner in each country, and, therefore, the genetic correlation between countries may be low or negative”. This remark by Schaeffer is especially interesting in light of the discussions going on the last year in the international breeding literature.
On the Optimality of the Hamming Metric for Decoding Block Codes over Binary Additive Noise Channels

Most of the basic concepts of algebraic coding theory are derived for the memoryless binary symmetric channel. These concepts do not necessarily hold for time-varying channels or for channels with memory. However, errors in real-life channels seem to occur in bursts rather than independently, suggesting that these channels exhibit some statistical dependence or memory. Nonetheless, the same algebraic codes are still commonly used in current communication systems that employ interleaving to spread channel error bursts over the set of received codewords to make the channel appear memoryless to the block decoder. This method suffers from immediate shortcomings as it fails to exploit the channel’s memory while adding delay to the system. We study optimal maximum likelihood block decoding of binary codes sent over several binary additive channels with infinite and finite memory. We derive conditions on general binary codes and channels parameters under which maximum likelihood and minimum distance decoding are equivalent. The channels considered in this work are the infinite and finite memory Polya contagion channels [1], the queue-based channel [29], and the Gilbert-Elliott channel [9, 12]. We also present results on the optimality of classical perfect and quasi-perfect codes when used over the aforementioned channels under maximum likelihood decoding.
Experimental study of a microstrip patch antenna with an L-shaped probe

The L-shaped probe is shown to be an attractive feed for the thick microstrip antenna (thickness around 10% of the operating wavelength). A parametric study on the rectangular patch antenna is presented. It is found that the antenna attains 36% impedance bandwidth (SWR/spl les/2) as well as gain bandwidth and about 7-dBi average gain. A two-element array fed by L-probes is also proposed. Experiments show that the array design can substantially suppress the cross polarization of the proposed antenna. Both the antennas have stable radiation patterns across the passband. Moreover, the measured resonant frequencies of the proposed antenna agree well with an existing formula and the L-probe does not have much effect on the resonant frequency.
Fuzzy Fourier transforms and their application to fingerprint identification

Abstract We present and study Fourier transforms of fuzzy-number-valued functions. As an application, we present a new approach to fingerprint coding. From each fingerprint we obtain a fuzzy-number-valued function. Using the Fourier transforms of this function we perform fingerprint coding. Using the fingercodes obtained as above, matching in large fingerprint databases will be more efficient.
From User Stories to End-to-end Web Testing

In agile development of web applications, user stories are a common aid to develop the software. In this paper, we exploit this for deriving test cases from these user stories, to check and ensure the functioning of the application. Our step-by-step method is based on a literature review and interviews with professional testers. Besides steps for transforming user stories into test cases, the method also includes guidelines to select user stories beforehand, and guidelines on how to implement automatically executable test cases, afterwards. We demonstrate the method’s applicability with case studies on three web applications from different application domains.
High-Mass Drell-Yan Cross-Section and Search for New Phenomena in Multi-Electron/Positron Final States with the ATLAS Detector

The Standard Model of particle physics is a very successful theory which describes nearly all known processes of particle physics very precisely. Nevertheless, there are several observations which cannot be explained within the existing theory. In this thesis, two analyses with high energy electrons and positrons using data of the ATLAS detector are presented. One, probing the Standard Model of particle physics and another searching for phenomena beyond the Standard Model. The production of an electron-positron pair via the Drell-Yan process leads to a very clean signature in the detector with low background contributions. This allows for a very precise measurement of the cross-section and can be used as a precision test of perturbative quantum chromodynamics (pQCD) where this process has been calculated at next-to-next-to-leading order (NNLO). The invariant mass spectrum mee is sensitive to parton distribution functions (PFDs), in particular to the poorly known distribution of antiquarks at large momentum fraction (Bjoerken x). The measurement of the high-mass Drell-Yan cross-section in proton-proton collisions at a center-of-mass energy of √ s = 7TeV is performed on a dataset collected with the ATLAS detector, corresponding to an integrated luminosity of 4.7 fb−1. The differential cross-section of pp → Z0/γ∗ + X → e+e− + X is measured as a function of the invariant mass in the range 116 GeV < mee < 1500 GeV. The background is estimated using a datadriven method and Monte Carlo simulations. The final cross-section is corrected for detector effects and different levels of final state radiation corrections. A comparison is made to various event generators and to predictions of pQCD calculations at NNLO. A good agreement within the uncertainties between measured cross-sections and Standard Model predictions is observed. Examples of observed phenomena which can not be explained by the Standard Model are the amount of dark matter in the universe and neutrino oscillations. To explain these phenomena several extensions of the Standard Model are proposed, some of them leading to new processes with a high multiplicity of electrons and/or positrons in the final state. A model independent search in multi-object final states, with objects defined as electrons and positrons, is performed to search for these phenomenas. The dataset collected at a center-of-mass energy of √ s = 8TeV, corresponding to an integrated luminosity of 20.3 fb−1 is used. The events are separated in different categories using the object multiplicity. The data-driven background method, already used for the cross-section measurement was developed further for up to five objects to get an estimation of the number of events including fake contributions. Within the uncertainties the comparison between data and Standard Model predictions shows no significant deviations. iii Cl ick to B UY N OW ! PD F-X Change Eitor trcker-software . co m C lic k t o B UY N OW ! PD F-X Change Eitor trcker-software . co m
Improved Slime Mould Algorithm Using Logical Chaos Perturbation and Reference Point Non-Dominated Sorting for Multi-Objective Optimization

The Slime Mould Algorithm (SMA) has gained significant attention from researchers due to its powerful multi-point search capability and its simple and practical structure. Several advanced versions of SMA have been proposed. However, most existing methods primarily focus on the single-objective research domain, leaving the research on multi-objective SMA relatively limited. Additionally, the basic SMA lacks robust global search capability, and extending it to the multi-objective domain often results in a loss of solution diversity. To address these limitations, this paper introduces a general multi-objective SMA framework. It incorporates a logical chaotic single-dimensional perturbation mechanism to enhance individuals’ search traversal in the decision space. Furthermore, a non-dominated sorting mechanism based on the reference point is employed to select a more diverse set of solutions for the subsequent evolution of the next generation. Through experiments conducted on 28 basis functions using seven advanced multi-objective algorithms (CMOPSO, NSGA-II, NSGA-III, MOEAD, PSEA-II, SPEA-II, and NSLS), the results demonstrate that the multi-objective SMA outperforms other algorithms in terms of convergence, accuracy, and diversity.
Theoretical Normalization and Generalization of PCA Joint Subspace Model

For the classification of high-dimensional data,PCA joint subspace model can accurately describe the probability distribution of the sample data of each class and thus improve the classification accuracy of the corresponding Bayesian classifier. In this paper,we firstly make certain theoretical normalization of the PCA joint subspace. Particularly,its two basic assumptions are proposed. Moreover,it is proved that the used heuristic setting of the parameter referred to as"representative eigenvalue"for the residual subspace is just its maximum likelihood estimate. We further generalize the expression of the probability distribution of the residual subspace and establish the generalized class-wise joint subspace algorithm for Bayesian classification. Finally,the experimental results on several real-world datasets demonstrate the superiority of the generalized class-wise joint subspace algorithm.
Graph Spatiotemporal Pattern Learning Network for Real-Time Road Network Traffic Abnormal Incident Detection

To improve the efficiency of detecting abnormal traffic incidents on the road network and reduce the false alarm rate, a real-time traffic anomaly detection framework based on a graph spatiotemporal pattern learning (GSTPL) network is proposed. In this framework, a traffic pattern search algorithm based on a fluctuation similarity measure is designed to screen traffic flow data with the same traffic pattern, and a traffic pattern graph tuple is constructed as the input of the network model to avoid the sample imbalance problem and the effect of single-sample randomness for traffic pattern learning. Then the GSTPL network is designed to extract, unsupervised, the traffic spatiotemporal pattern features and make a reasonable prediction of future traffic parameters as the basis for anomaly evaluation. To further restrain the effect of random fluctuations in traffic flow parameters, an abnormal state evaluation method is designed to calculate the anomaly state likelihood by prediction error distribution learning. The overall detection framework realizes stable prediction of network key node traffic parameters by using spatiotemporal pattern features to construct the traffic pattern graph tuple, and gives incident evaluation results in real time by combination with the detection data. The experiment uses I90 and I405 highway traffic data in Seattle, WA, from 2015. Through comparative analysis, the proposed incident detection method based on GSTPL has a higher detection rate and lower false alarm rate, can adaptively learn dynamic changes of the traffic pattern, and has strong adaptability and stability to different traffic environments.
A Bayesian Neural Network for Severe-Hail Size Prediction

The National Severe Storms Laboratory has developed algorithms that compute a number of Doppler radar and environmental attributes known to be relevant for the detection/prediction of severe hail. Based on these attributes, two neural networks have been developed for the estimation of severe-hail size: one for predicting the severe-hail size in a physical dimension, and another for assigning a probability of belonging to one of three hail size classes. Performance is assessed in terms of multidimensional (i.e., nonscalar) measures. It is shown that the network designed to predict severe-hail size outperforms the existing method for predicting severe-hail size. Although the network designed for classifying severe-hail size produces highly reliable and discriminatory probabilities for two of the three hail-size classes (the smallest and the largest), forecasts of midsize hail, though highly reliable, are mostly nondiscriminatory.
Haplotype Analysis of GmSGF14 Gene Family Reveals Its Roles in Photoperiodic Flowering and Regional Adaptation of Soybean

Flowering time and photoperiod sensitivity are fundamental traits that determine soybean adaptation to a given region or a wide range of geographic environments. The General Regulatory Factors (GRFs), also known as 14-3-3 family, are involved in protein–protein interactions in a phosphorylation-dependent manner, thus regulating ubiquitous biological processes, such as photoperiodic flowering, plant immunity and stress response. In this study, 20 soybean GmSGF14 genes were identified and divided into two categories according to phylogenetic relationships and structural characteristics. Real-time quantitative PCR analysis revealed that GmSGF14g, GmSGF14i, GmSGF14j, GmSGF14k, GmSGF14m and GmSGF14s were highly expressed in all tissues compared to other GmSGF14 genes. In addition, we found that the transcript levels of GmSGF14 family genes in leaves varied significantly under different photoperiodic conditions, indicating that their expression responds to photoperiod. To explore the role of GmSGF14 in the regulation of soybean flowering, the geographical distribution of major haplotypes and their association with flowering time in six environments among 207 soybean germplasms were studied. Haplotype analysis confirmed that the GmSGF14mH4 harboring a frameshift mutation in the 14-3-3 domain was associated with later flowering. Geographical distribution analysis demonstrated that the haplotypes related to early flowering were frequently found in high-latitude regions, while the haplotypes associated with late flowering were mostly distributed in low-latitude regions of China. Taken together, our results reveal that the GmSGF14 family genes play essential roles in photoperiodic flowering and geographical adaptation of soybean, providing theoretical support for further exploring the function of specific genes in this family and varietal improvement for wide adaptability.
Synthesis of Four-Bar Linkages by Four Infinitely Close Relative Positions and Pressure Angle

A computer-applicable linear mathematical model has been developed to determine Burmester’s curves for infinitely close relative positions (cubic of stationary curvature), which indirectly uses Carter-Hall’s circle. By varying a free parameter and using elements of kinematic and analytical geometry, an incomparably simpler solution is achieved than that obtained by the third-degree equations of the Burmester's curves for stationary curvature. The mathematical model for the synthesis of four-bar linkages includes and a condition for the pressure angle, whereupon is uniquely defined the kinematic diagram of the mechanism. Of the pressure angle, the reactions of the forces in the kinematic pairs and the force sizing of the mechanism depend. The model would facilitate the engineers in the synthesis of four-bar linkages by generating a function approximating a given function in the vicinity of a given position, where the two functions have four infinitely close common points (3rd-order approximation). An example of the synthesis of a four-bar linkage illustrates the application of the model, which is linear - it includes only equations of straight lines written in Cartesian coordinates, which is why it is convenient for computer calculations.
Hand Gesture Recognition Pad Using an Array of Inductive Sensors

Gesture recognition is a field of study within human–computer interaction technology and is used in an increasing number of applications. So far, the systems designed for gesture recognition have been able to sense multiple static and dynamic gestures. However, they suffer from some limitations. This study introduces a novel sensing pad system consisting of an array of inductive sensors which can recognize and differentiate specific static hand gestures through machine learning algorithms (MLAs). It is designed to be a non-contact apparatus where the gestures made by a user can be perceived by the system. It uses five coils, one for each finger, and can sense the fingers that are unfolded while making a particular gesture. Ten volunteer users participated in this study. Ten gestures, numbers 1–10 of the American Sign Language (ASL) are chosen to be tested upon, ten times each for every user. The responses from the sensing coils were measured via a data acquisition board and sent to the PC for processing. A total of 1000 responses were recorded and processed using MLAs which provided an accuracy of 98.7% using fivefold cross-validation (5F-CV) and 97.3% using leave-one-subject-out CV (LOSO-CV) proving that the system can successfully distinguish hand gestures instantly.