Miracle-dz/UniFAR-FTUs huggingface dataset:
A Comprehensive Memory Management Framework for CPU-FPGA Heterogenous SoCs

Efficient utilization of restrained memory resources is of paramount importance in CPU-FPGA heterogeneous multiprocessor system-on-chip (HMPSoC)-based system design for memory-intensive applications. State-of-the-art high level synthesis (HLS) tools rely on the system programmers to manually determine the data placement within the complex memory hierarchy. Different data placement policies may lead to different system performance, and finding an optimal data placement policy is a nontrivial problem. For instance, we show counter-intuitive results that traditional frequency and locality-based data placement strategy designed for CPU architecture leads to nonoptimal system performance in CPU-FPGA HMPSoCs. In this work, we first propose an automatic data placement framework for field programmable gate array (FPGA) kernels to determine whether each array object should be accessed via the on-chip BRAM, shared CPU L2-cache, or DDR memory to achieve the optimal performance. Moreover, we find that when the CPU kernel and the FPGA kernel are executed in parallel, memory contentions may degrade the performance and the optimal data placement policy designed for the FPGA kernel alone will not achieve the optimal overall system performance. In this article, we proposed to use cache partitioning to alleviate the impact brought by memory contentions. We extend the framework designed for FPGA by adding the cross-layer memory contentions analysis to automatically generate an optimal data placement policy and cache partitioning mechanism for the parallel executing kernels. The proposed data placement framework can be seamlessly integrated with the commercial Vivado HLS. The experimental results on the Zedboard platform show an average $1.5\times $ performance speedup for FPGA kernels compared with a greedy-based allocation strategy. When FPGA kernels and CPU kernels are executed in parallel, the FPGA kernel and the CPU kernel have a performance speedup of $1.62\times $ and $1.10\times $ on average, respectively.
Automated Agronomic Bot for Green Ailment Scanner

Agriculture's productivity has a big impact on the Indian economy. Plant disease identification is key to agricultural output. Early detection of sick plants reduces productivity and volume losses. Plant diseases are studied by examining the plant's apparent characteristics. Long-term farming requires monitoring crop health. Handling plant disease outbreaks is tough. Huge effort, plant disease knowledge, and processing time are needed. Early identification is crucial since it can affect output quantity and quality. When crops on large farms become apparent on the plant's leaves, an automated method will be useful. Image processing is used to identify plant diseases. Disease detection involves picture capture, pre-processing, segmentation, feature extraction, and classification. This study looked for plant illnesses using leaf pictures. In this work, leaf pictures were analysed to diagnose plant illnesses. Some strategies for recognising plant diseases were also addressed. Neural Networks were used to classify leaf diseases in this article. AGRI ROBOT helped with this.
Magnetomechanical coupling, ΔE effect, and permeability in FeSiB and FeNiMoB alloys

Measurements of magnetomechanical coupling, ΔE effect, and relative permeability have been made as a function of bias field and annealing temperature and time in transversely annealed samples of Metglas R a) alloy 2605S‐2 (Fe78Si9B13), Metglas alloy 2605S‐3 (Fe79Si5B16), and Metglas alloy 2826MB (Fe40Ni38Mo4B18). Annealing temperatures and times were varied from 300 to 450 °C and 15 to 60 min, respectively. The bias field which generates the largest coupling factor in these alloys is less than 1 Oe. From an application point of view, this low optimum field has a distinct advantage in power requirements over the 11 Oe field required for Metglas alloy 2605CO1 but the low field requirement is a marked disadvantage when considering bias field stability. That is, a 0.5 Oe change in field can result in greatly reduced coupling in the present alloys versus a very small change in 2605CO.
Exact Skewness–Kurtosis Tests for Multivariate Normality and Goodness‐of‐Fit in Multivariate Regressions with Application to Asset Pricing Models*

Dans cet article, nous proposons des tests sur la forme de la distribution des erreurs dans un modele de regression lineaire multivarie (RLM). Les tests que nous developpons sont fonction des residus obtenus par moindres carres multivaries, lesquels sont standardises de facon a ce que leur distribution soit invariante a la matrice de covariance, inconnue, des erreurs. Notre approche utilise des mesures empiriques d'asymetrie et d'aplatissement de la distribution des erreurs, que nous comparons a des estimations engendrees par simulation de ces caracteristiques sous cette meme hypothese distributionnelle. Les cas specifiques que nous etudions comprennent des tests sur les erreurs du modele dans le cadre des lois normale, t de Student, melange de normales et stable. Dans le cas gaussien, nous obtenons des versions exactes de tests d'ajustement standards sur l'asymetrie et l'aplatissement des erreurs dans le cas multivarie. A cette fin, nous utilisons des tests de Monte Carlo simples, doubles et multiples. Dans les cas non-gaussiens, comme les familles de lois dependent de parametres de nuisance, nous proposons des regions de confiance pour ces derniers et la distribution des erreurs. Les procedures introduites dans cet article sont alors evalulees par une simulation de petite taille. Finalement, les tests proposes sont appliques a un modele d'evaluation d'actifs impliquant un taux d'interet sans risque observable et utilisant les rendements de portefeuilles mensuels de titres inscrits a la bourse de New York, sur des sous-periodes de cinq ans allant de janvier 1926 a decembre 1995.
Adaptive speckle filters and scene heterogeneity

The presence of speckle in radar images makes the radiometric and textural aspects less efficient for class discrimination. Many adaptive filters have been developed for speckle reduction, the most well known of which are analyzed. It is shown that they are based on a test related to the local coefficient of variation of the observed image, which describes the scene heterogeneity. Some practical criteria are introduced to modify the filters in order to make them more efficient. The filters are tested on a simulated synthetic aperture radar (SAR) image and an SAR-580 image. As was expected, the new filters perform better, i.e. they average the homogeneous areas better and preserve texture information, edges, linear features, and point target responses better at the same time. Moreover, they can be adapted to features other than the coefficient of variation to reduce the speckle while preserving the corresponding information. >
The Research on Computer Recognition Technology for Blurred Alphabet Images

This paper mainly studies the recognition issue of blurred alphabet images. If alphabets in an image is blurred, it is difficult t be recognized. To avoid the defect, the paper proposes a computer recognition method for blurred alphabet images based on grayscale class variance algorithm. The algorithm executes non-linear transformation on feature parameters of alphabets images extracted to obtain eigenvector coefficient weights, and then calculates characteristic correlation coefficient through wavelet transform to realize the blurred alphabet recognition. Experiments show that, the proposed method improves the accuracy of the blurred alphabets recognition and achieves satisfactory results.
Stress-induced growth of bismuth nanowires

We report a method of making nanowires of bismuth (Bi) with diameters ranging from 30 to 200 nm and lengths up to several millimeters. The nanowires are extruded spontaneously at the rate of a few micrometers per second at room temperature from the surfaces of freshly grown composite thin films consisting of Bi and chrome–nitride. The high compressive stress in these composite thin films is the driving force responsible for the nanowire formation. This mechanism can also be used to create nanowires of other materials.
An investigation of semiconductor nanoparticles for application to all-optical switching

A practical all-optical switch is necessary to alleviate electronic bottlenecks in fibre optic networks. Thus, a new alloptical switch is introduced here—exhibiting femtojoule switching energies and femtosecond switching times. The alloptical switches use 40 μm dielectric spheres to direct high-intensity photonic nanojets into peripheral coatings of semiconductor nanoparticles. Semiconductor nanoparticle coatings of Si, CdTe, InP, and CuO are studied and found to yield switching energies of approximately 1 pJ, 500 fJ, 400 fJ, and 300 fJ with switching times of 2 ps, 2.3 ps, 900 fs, and 350 fs, respectively.
Efficient Fourier shape descriptor for industrial defect images using wavelets

The use of image retrieval and classification has several applications in industrial imaging systems, which typically use large image archives. In these applications, the matter of computational efficiency is essential and therefore compact visual descriptors are necessary to describe image content. A novel approach to contour-based shape description using wavelet transform combined with Fourier transform is presented. The proposed method outperforms ordinary Fourier descriptors in the retrieval of complicated industrial shapes without increasing descriptor dimensionality.
3 - Calcul de translation et rotation par la transformation de Fourier

Dans l'idee de completer les capteurs mecaniques de mouvement par des techniques a base de vision, nous analysons le deplacement d'une image pour en deduire les parametres de rotation de la camera. L'approche choisie est celle de la transformation de Fourier dont on utilise les proprietes d'invariance par rotation et de dephasage par translation. L'application, reduite pour cette etude aux rotations de camera, peut s'etendre a tous les domaines lies au recalage d'images.
Mathematisch strenge Theorie elastischer Schalen

Es wird eine allgemeine lineare Theorie elastischer Schalen entwickelt, die von den ublichen Annahmen keinen Gebrauch macht. Die Schubverzerrungen, die Spannungen normal zur Schalenmittelflache werden berucksichtigt, und die Randbedingungen an den Schalenlaibungen streng erfullt. Auch hinsichtlich der Schalendicke sind keinerlei Einschrankungen erforderlich. In den endgultigen Schalengleichungen treten als Unbekannte nur die auch in der bisher ublichen Theorie vorhandenen Schnitt- und Verformungsgrosen auf. A general linear theory of elastic shells is developed which is free from usual assumptions. The shearing strain and the stress normal to the middle surface of the shell are taken into account, and the boundary conditions on the faces of the shell are strictly satisfied. No restrictions as to shell thickness are introduced. The only unknown quantities occuring in the final equations are those occurring in the current theory, too, viz. stress resultants and displacements.
Modelling Merging and Fragmentation in Multiphase Flows with SURFER

We introduce a new numerical method, called "SURFER," for the simulation of two- and three-dimensional flows with several fluid phases and free interfaces between them. We consider incompressible fluids obeying the Navier-Stokes equation with Newtonian viscosity in the bulk of each phase. Capillary forces are taken into account even when interfaces merge or break up. Fluid interfaces are advanced in time using an exactly volume conserving variant of the volume of fluid algorithm, thus allowing for full symmetry between fluid phases. The Navier-Stokes equation is solved using staggered finite differences on a MAC grid and a split-explicit time differencing scheme, while incompressibility is enforced using an iterative multigrid Poisson solver. Capillary effects are represented as a stress tensor computed from gradients of the volume fraction function. This formulation is completely independent of the topology of interfaces and relatively easy to implement in 3D. It also allows exact momentum conservation in the discretized algorithm. Numerical spurious effects or "parasite currents" are noticed and compared to similar effects in Boltzmann lattice gas methods for immiscible fluids. Simulations of droplets pairs colliding in 2D and in 3D are shown. Interface reconnection is performed easily, despite the large value of capillary forces during reconnection.
Exceptional Dynamically Invariant Sections

Abstract In [33] we developed a notion of a Green’s section that generalized the of Green’s currents that arise in higher dimensional holomorphic dynamical systems. In such systems there may be totally invariant currents other than the Green’s currents. These are called exceptional currents. We thus define a corresponding notion of an exceptional section for a continuous self map f : X → X of a topological space X. Our notion requires the use of sheaves on X and cohomorphisms of these sheaves. For holomorphic endomorphisms for which the methods of [33] give Green’s currents we show that corresponding exceptional hypersurfaces are attracting and give rise to a function, plurisubharmonic off the attractor, which measures the rate of contraction. We also show that if such an exceptional hypersurface can be perturbed to a smooth current then the Green’s current is supported in the closure of the basin of attraction. Lastly, we give conditions under which exceptional sections in more general settings are necessarily attracting.
Simulation of the flow between rotating disks with Discontinuous Galerkin method

In this work Discontinuous Galerkin (DG) method is applied to the simulation of the incompressible, viscous flow between rotating disks. The code is based on the projection method and accepts unstructured meshes with hexahedral elements. It is explicit in time and allows for arbitrary partition of the computational domain between processors in parallel computing (communication patterns are automatically assigned). Two cases (configurations) are considered: flat disks (structured mesh) and disk with a step placed at the center of the cavity (unstructured mesh). In both configurations the upper disk is rotating, the other one and the remaining walls are stationary. The results for the flat disks are compared to DNS data and very good agreement is obtained. The second case shows capability of the new approach with handling complex geometries.
New agri-environmental measures have a direct effect on wildlife and economy on conventional agricultural land

The objective of this article is to evaluate economic profits along with return on investment and also the impact of newly designed agri-environmental measures (AEM) on the presence of bioindicator species—European hare and roe deer—in comparison to conventionally cultivated agricultural land. The abundance of European hare was, on average, 4.5–6.7 times higher on AEM compared to the standard agricultural regime and 3.5–6.4 times higher in the case of roe deer in 2020 and 2021. From an economic point of view, the highest incomes were found for extensive orchard alleys and standard conventional crops—wheat and rapeseed rotation. The cash flow from extensive orchard was 4.3 times larger and wheat and rapeseed were 3.5 times larger than from the clover grass mixture. Moreover, the lowest value of operational expenses was found in the case of extensive orchard alleys. The payback period ranged from 16.02 years (wheat and rapeseed rotation) to 53.6 years (clover grass mixture). It is crucial not to assess the economic parameters separately but optimize them with sustainable wildlife management and other benefits that provide ecological and efficient directions of AEM for future generations. However, the performed economic analysis highlights the significantly lower incomes of most components of AEM. We see a principal issue of AEMs usage in the lack of strong incentives for farmers to maximize conservation outcomes. Therefore, the AEMs are often placed in locations with presumed low agriculture profit, which is often related to insignificant conservation effects. Thus, the incomparable AEMs profitability compared to conventional agriculture has to be reflected by the agricultural policy at the European Union level and subsidy policy of particular member states.
Asymptotically correct defect control software for boundary value ordinary differential equations

xiv Acknowledgements . xv Chapter
Asymptotic state of the finite-Larmor-radius guiding-centre plasma

The equilibrium properties of a two-dimensional plasma are examined theoretically, using a model where the finite-Larmor-radius corrections to the simple guiding-centre description are included. The analysis is carried out in a truncated Fourier representation of the resulting equations. This system has three ‘rugged’ quadratic invariants. A canonical-ensemble probability distribution characterized by three temperatures is derived. The resulting partition function is obtained and the equilibrium spectral energy density is calculated. The possibility of negative-temperature states leading to an inverse energy cascade is pointed out.
Genetic characterization of brown type donkeys of Andhra Pradesh using microsatellite markers

The brown type donkeys of Andhra Pradesh, which are mainly concentrated in Kurnool and Anathapur districts, were evaluated for within breed genetic diversity and bottlenecks using heterologous microsatellite markers. The genomic DNA, isolated from 28 blood samples collected from Kurnool district, were amplified by PCR using FAM and HEX labeled primers and resolved for alleles on automatic DNA sequencer. In all, twenty loci of the horse origin were tested and only 12 loci gave scorable results. Rest of the loci either did not amplify (HMS3 and HMS7) or did not resolve properly (VHL20) or showed less than 4 alleles (HMS5, HMS6, HTG4, ASB17 and COR22) in the studied population. At the 12 loci included in the final analysis, the PCR product size range varied from 76-92 bp at locus HTG6 to 257-273 bp at locus COR18. The observed number of alleles varied from 4 (VHL209) to 10 (AHT5 and HTG7) with a mean of 6.92±1.83. The effective number of alleles ranged from 1.62 (VHL209) to 7.91 (AHT5) with a mean of 4.21±2.06. The observed heterozygosity ranged from 0.32 (HMS2) to 0.92 (AHT5) with a mean of 0.57±0.2. The expected heterozygosity ranged between 0.39 (VHL209) to 0.89 (AHT5 and HTG7) with a mean of 0. 72Â±0.14. The mean genetic diversity estimate (FIS) was 0.21 indicating moderately high levels of inbreeding. The cumulative exclusion probability (PE) of these loci was 0.999892 indicating their suitability for parentage testing in these donkeys. The sign test, standardized differences test, the Wilcoxon test using the allelic frequency data at the studied loci as well as normal 'L' shaped distribution of the allelic frequency indicated the absence of any recent genetic bottleneck in Brown type donkeys of Andhra Pradesh. When these donkeys were compared to Spiti donkeys of Himachal Pradesh on the basis of allelic frequency data at these loci, they showed Nei's standard and unbiased genetic distances of 0.32 and 0.29, respectively.
Comparing statistical techniques to classify the structure of mountain forest stands using CHM-derived metrics in Trento province (Italy)

Abstract In some cases a canopy height model (CHM) is the only available source of forest height information. For these cases it is important to understand the predictive power of CHM data for forest attributes. In this study we examined the use of lidar-derived CHM metrics to predict forest structure classes according to the amount of basal area present in understory, midstory, and overstory trees. We evaluated two approaches to predict size- based forest classifications: in the first, we attempted supervised classification with both linear discriminant analysis (LDA) and random forest (RF); in the second, we predicted basal areas of lower, mid, and upper canopy trees from CHM-derived variables by k-nearest neighbour imputation (k-NN) and parametric regression, and then classified observations based on their predicted basal areas. We used leave-one-out cross-validation to evaluate our ability to predict forest structure classes from CHM data and in the case of prediction-based classification approach we look at the performances in predicting basal area. The strategies proved moderately successful with a best overall classification accuracy of 41% in the case of LDA. In general, we were most successful in predicting the basal areas of small and large trees (R2 respectively of 71% and 69% in the case of k-NN imputation).
Research progress of multifunctional metamaterials based on vanadium dioxide

Metasurface has the ability to manipulate electromagnetic wave in the subwavelength range, and has great application potential in stealth, detection, nuclear magnetism, high magnetic field, solar energy and microwave energy utilization, so it has been paid great attention. The physical properties of vanadium dioxide can change from semiconductor phase to metal state under certain temperature conditions, and the change is reversible. By using vanadium dioxide to design and fabricate metamaterials, temperature controlled multifunctional metamaterials can be made. This metamaterial has a much wider range of applications. Metamaterials based on vanadium dioxide have developed rapidly in recent years. Researchers have used vanadium dioxide to switch terahertz waves from spectral to spatial characteristics, tuning and dynamic control. One promising application of metamaterials is the realization of high-quality holograms. Metamaterials designed with vanadium dioxide can be used to make temperature-dependent dynamic holography. Vanadium dioxide can also be used in the optical band to regulate the absorption and reflection of photoelectric devices. In this paper, the research status in these related fields is reviewed.
Neural simulation pipeline: Enabling container-based simulations on-premise and in public clouds

In this study, we explore the simulation setup in computational neuroscience. We use GENESIS, a general purpose simulation engine for sub-cellular components and biochemical reactions, realistic neuron models, large neural networks, and system-level models. GENESIS supports developing and running computer simulations but leaves a gap for setting up today's larger and more complex models. The field of realistic models of brain networks has overgrown the simplicity of earliest models. The challenges include managing the complexity of software dependencies and various models, setting up model parameter values, storing the input parameters alongside the results, and providing execution statistics. Moreover, in the high performance computing (HPC) context, public cloud resources are becoming an alternative to the expensive on-premises clusters. We present Neural Simulation Pipeline (NSP), which facilitates the large-scale computer simulations and their deployment to multiple computing infrastructures using the infrastructure as the code (IaC) containerization approach. The authors demonstrate the effectiveness of NSP in a pattern recognition task programmed with GENESIS, through a custom-built visual system, called RetNet(8 × 5,1) that uses biologically plausible Hodgkin–Huxley spiking neurons. We evaluate the pipeline by performing 54 simulations executed on-premise, at the Hasso Plattner Institute's (HPI) Future Service-Oriented Computing (SOC) Lab, and through the Amazon Web Services (AWS), the biggest public cloud service provider in the world. We report on the non-containerized and containerized execution with Docker, as well as present the cost per simulation in AWS. The results show that our neural simulation pipeline can reduce entry barriers to neural simulations, making them more practical and cost-effective.