Miracle-dz/UniFAR-FTUs huggingface dataset:
A Comparison of the Variable Sampling Interval (VSI) and Variable Sample Size (VSS) Coefficient of Variation Charts

An efficient way to perform quality monitoring is important for a lot of industries, particularly in the manufacturing industry. Control charts are one of the important tools to monitor quality. Recently, control charts monitoring the coefficient of variation (CV) are proposed to monitor processes which do not have a constant mean and a standard deviation which changes with the mean. These processes cannot be monitored by standard control charts which monitor the mean and/or standard deviation. The Variable Sampling Interval (VSI) and Variable Sample Size (VSS) CV charts are two recent charts proposed to improve the detection ability of CV charts. This paper compares the performance between these two charts in terms of the Average Time to Signal (ATS). The comparison shows that the VSS CV chart shows better performance when either a small sample size is taken or when a small shift in the CV needs to be detected. Alternatively, the VSI CV chart outperforms the VSS CV chart. This paper will enable practitioners to know the conditions where the VSS CV chart outperforms the VSI CV chart, and vice versa. Hence, practitioners will be able to decide whether it is better to monitor their processes with a VSI or VSS scheme.
Osmotic Effects on Dynamics of a Colloidal Rigid Sphere in a Near-Critical Binary Fluid Mixture

When a rigid spherical particle is put in a binary fluid mixture lying in the homogeneous phase near the demixing critical point, one of the fluid components is expected to be significantly concentrated around the particle because it is attracted preferentially by the particle surface. The resultant gradient of the mixture composition causes an additional pressure including osmotic pressure. We study how this pressure changes the particle motion by using the Gaussian free-energy functional for the mixture and assuming a correlation length much smaller than the particle size. The drag coefficient in this situation was calculated in the regime of sufficiently weak preferential attraction in a previous study [R. Okamoto, Y. Fujitani, and S. Komura, J. Phys. Soc. Jpn. 82, 084003 (2013)]. We here devise a new procedure beyond the regime to calculate the drag coefficient and the long-time tail of the velocity correlation of the particle. Our results cannot be explained totally by effective enlargement of the pa.
Effect of alkyl side-chain length on the photophysical, morphology and photoresponse properties of poly(3-alkylthiophene)

The effect of alkyl side-chain length on the photophysical, morphology and photoresponse properties of poly (3-alkylthiophene) (P3AT) has been investigated. Butyl, hexyl, octyl, decyl, and dodecyl side chains have been studied. In solution state, the average lifetime of poly (3-octylthiophene) (P3OT) is the shortest among P3AT solution owing to the strong intrachain interaction. The fluorescence lifetime of P3AT solution increases with the side-chain length increasing. A similar trend occurs in the P3AT films. This phenomenon illustrates conformation ‘memory’ property evolving from solution state to solid film. The four lifetimes represent different photophysical processes and have been systematically analyzed. The photodetector based on P3DDT is a suitable candidate for practical application owing to the fast, reversible photoresponse and photostability. The high performance of P3DDT-based photodetector is correlated to the optimized morphology and strong interchain interaction which promotes the exciton delocalization.
Global Bifurcations of Critical Orbits via Equivariant Conley Index

Abstract Let V be a finite-dimensional representation of a compact connected Lie group G and let ϕ : V × [λ1, λ2] → ℝ be a family of G-invariant potentials of the class C2 such that ∇vϕ(0, λ) = 0 for every λ ∈ [λ1, λ2], where ∇vϕ is the gradient of ϕ with respect to the first coordinate. Denote by V′i a direct sum of eigenspaces of the isomorphism ∇v2 ϕ(0, λi) corresponding to positive eigenvalues, i = 1, 2. We have proved that if dimV′1 ≧ dimV′2 and the representation V′1 is not equivalent (as a linear representation of G) to the representation V′2 ⊕ ℝ[2k], k ∈ {0} ∪ ℕ (where ℝ[2k] is a 2k-dimensional trivial representation of G), then there is a global bifurcation of G-orbits of non-zero solutions of problem ∇vϕ(v, λ) = 0 from the interval {0} × [λ1, λ2]. We have proved that a global bifurcation and symmetrybreaking of solutions phenomenon occurs simultaneously for elliptic differential equations considered by Smoller and Wasserman in [25], which improves results of [25].
Dynamics of the Transition from Spiral Rainbands to a Secondary Eyewall in Hurricane Earl (2010)

Airborne Doppler radar captured the inner core of Hurricane Earl during the early stages of secondary eyewall formation (SEF), providing needed insight into the SEF dynamics. An organized rainband complex outside of the primary eyewall transitioned into an axisymmetric secondary eyewall containing a low-level tangential wind maximum. During this transition, the downshear-left quadrant of the storm exhibited several notable features. A mesoscale descending inflow (MDI) jet persistently occurred across broad stretches of stratiform precipitation in a pattern similar to previous studies. This negatively buoyant jet traveled radially inward and descended into the boundary layer. Farther inward, enhanced low-level inflow and intense updrafts appeared. The updraft adjacent to the MDI was likely triggered by a region of convergence and upward acceleration (induced by the negatively buoyant MDI) entering the high-θe boundary layer. This updraft and the MDI in the downshear-left quadrant accelerated the tangential winds in a radial range where the axisymmetric wind maximum of the secondary eyewall soon developed. This same quadrant eventually exhibited the strongest overturning circulation and wind maximum of the forming secondary eyewall. Given these features occurring in succession in the downshear-left quadrant, we hypothesize that the MDI plays a significant dynamical role in SEF. The MDI within a mature rainband complex persistently perturbs the boundary layer, which locally forces enhanced convection and tangential winds. These perturbations provide steady low-level forcing that projects strongly onto the axisymmetric field, and forges the way for secondary eyewall development via one of several SEF theories that invoke axisymmetric dynamical processes.
Watch Your Step: Learning Graph Embeddings Through Attention

Graph embedding methods represent nodes in a continuous vector space, preserving information from the graph (e.g. by sampling random walks). There are many hyper-parameters to these methods (such as random walk length) which have to be manually tuned for every graph. In this paper, we replace random walk hyper-parameters with trainable parameters that we automatically learn via backpropagation. In particular, we learn a novel attention model on the power series of the transition matrix, which guides the random walk to optimize an upstream objective. Unlike previous approaches to attention models, the method that we propose utilizes attention parameters exclusively on the data (e.g. on the random walk), and not used by the model for inference. We experiment on link prediction tasks, as we aim to produce embeddings that best-preserve the graph structure, generalizing to unseen information. We improve state-of-the-art on a comprehensive suite of real world datasets including social, collaboration, and biological networks. Adding attention to random walks can reduce the error by 20% to 45% on datasets we attempted. Further, our learned attention parameters are different for every graph, and our automatically-found values agree with the optimal choice of hyper-parameter if we manually tune existing methods.
A simple measurement method of molecular relaxation in a gas by reconstructing acoustic velocity dispersion

Recently, a decomposition method of acoustic relaxation absorption spectra was used to capture the entire molecular multimode relaxation process of gas. In this method, the acoustic attenuation and phase velocity were measured jointly based on the relaxation absorption spectra. However, fast and accurate measurements of the acoustic attenuation remain challenging. In this paper, we present a method of capturing the molecular relaxation process by only measuring acoustic velocity, without the necessity of obtaining acoustic absorption. The method is based on the fact that the frequency-dependent velocity dispersion of a multi-relaxation process in a gas is the serial connection of the dispersions of interior single-relaxation processes. Thus, one can capture the relaxation times and relaxation strengths of N decomposed single-relaxation dispersions to reconstruct the entire multi-relaxation dispersion using the measurements of acoustic velocity at 2N + 1 frequencies. The reconstructed dispersion spectra are in good agreement with experimental data for various gases and mixtures. The simulations also demonstrate the robustness of our reconstructive method.
Influence of a square pulse voltage on argon-ethyl lactate discharges and their plasma-deposited coatings using time-resolved spectroscopy and surface characterization

By comparing time-resolved optical emission spectroscopy measurements and the predictions of a collisional-radiative model, the evolutions of electron temperature (Te) and number density of argon metastable atoms [n(Arm)] were determined in argon-ethyl lactate dielectric barrier discharges. The influence of a square pulse power supply on Te, n(Arm), and discharge current is evaluated and correlated with the chemistry and the topography of plasma-deposited coatings. Pulsed discharges were found to have shorter (100 ns) but stronger (1 A) current peaks and higher electron temperatures (0.7 eV) than when using a 35 kHz sinusoidal power supply (2 μs, 30 mA, 0.3 eV). The n(Arm) values seemed to be rather stable around 1011 cm−3 with a sinus power supply. In contrast, with a pulse power supply with long time off (i.e., time without discharge) between each pulse, a progressive increase in n(Arm) from 1011 cm−3 up to 1012–1013 cm−3 was observed. When the time off was reduced, this increase was measured in sync with the current peak. The chemical composition of the coatings was not significantly affected by using a pulse signal, whereas the topography was strongly influenced and led to powder formations when reducing the time off.By comparing time-resolved optical emission spectroscopy measurements and the predictions of a collisional-radiative model, the evolutions of electron temperature (Te) and number density of argon metastable atoms [n(Arm)] were determined in argon-ethyl lactate dielectric barrier discharges. The influence of a square pulse power supply on Te, n(Arm), and discharge current is evaluated and correlated with the chemistry and the topography of plasma-deposited coatings. Pulsed discharges were found to have shorter (100 ns) but stronger (1 A) current peaks and higher electron temperatures (0.7 eV) than when using a 35 kHz sinusoidal power supply (2 μs, 30 mA, 0.3 eV). The n(Arm) values seemed to be rather stable around 1011 cm−3 with a sinus power supply. In contrast, with a pulse power supply with long time off (i.e., time without discharge) between each pulse, a progressive increase in n(Arm) from 1011 cm−3 up to 1012–1013 cm−3 was observed. When the time off was reduced, this increase was measured in sync wi.
Laminar and Transitional Flow in open channels for non- Newtonian fluids

The laminar-turbulent transition for pipelines has been studied intensively and several models are available to predict transition with various degrees of success. Generally the Reynolds number of 2100 is used to describe transition in pipelines. Three smooth rectangular tilting flumes with flow widths of 75, 150 and 300 mm were used to test various concentrations of Kaolin and Bentonite suspensions as well as Carboxymethylcellulose (CMC) solutions. By using the appropriate Reynolds number, all the laminar data could be collapsed onto a standard Moody diagram for water. In laminar flow the friction factor can be predicted for all the materials tested. Transitional behaviour was observed not at one Reynolds number, but over a range. For the higher concentration fluids, transition occurred at lower Reynolds numbers and vice versa. This makes it very difficult to accurately predict the transition of non-Newtonian fluids in open channels.
Linear Mixed-Effect State-Space Forecasting: Completing the algorithm

Statistical downscaling techniques are used to translate global climate scenarios into local impact forecasts. In this study, I propose a new algorithm for downscaling by using a linear mixed-effect state-space model (LMESS). The rationale to use this model in a climate data context is that it allows for both time-varying and fixed relations between dependent and explanatory variables. My findings show the importance of identifying the correct random and fixed effects. I develop a new method for selection based on the state-space formulation with fixed parameters by Chow (1984). I apply the proposed methods to climate data at five different weather stations in the Netherlands. My findings show that the LMESS model is not able to consistently outperform a multivariate linear regression forecast method.
Seasonality, size effect, and delisting bias

We study the seasonality pattern of stock returns within the year. We found that while overall the size premium is small after adjusting for delisting bias, the January returns are still larger than returns from remaining months and it is more significant for small sized companies. This obvious conflict is caused by the consistent negative returns around September, October and November. And the absolute value for small sized companies during these months are larger than large size companies. Extending to different time period and using different delisting returns do not change this pattern. We argue that demand pressure cause the results.
Soficity and Other Dynamical Aspects of Groupoids and Inverse Semigroups

This thesis is divided into four chapters. In the rst one, all the pre-requisite theory of semigroups and groupoids is introduced, as well as a few new results such as a short study of ∨-ideals and quotients in distributive semigroups and a non-commutative Loomis-Sikorski Theorem. In the second chapter, we motivate and describe the so c property for probability measure-preserving groupoids and prove several permanence properties for the class of so c groupoids. This provides a common ground for similar results in the particular cases of groups ([44]) and equivalence relations ([41, 142]). In particular, we prove that so city is preserved under nite index extensions of groupoids. We also prove that so city can be determined in terms of the full group alone, answering a question by Conley, Kechris and Tucker-Drob. In the third chapter we turn to the classical problem of reconstructing a topological space from a suitable structure on the space of continuous functions. We prove that a locally compact Hausdor space can be recovered from classes of functions with values on a Hausdor space together with an appropriate notion of disjointness, as long as some natural regularity hypotheses are satis ed. This allows us to recover (and even generalize) classical theorem by Kaplansky, Milgram, Banach-Stone, among others, as well as recent results of the similar nature, and obtain new consequences as well. Furthermore, we extend the techniques used here to obtain structural theorems related to topological groupoids. In the fourth and nal chapter, we study dynamical aspects of partial actions of inverse semigroups, and in particular how to construct groupoids of germs and (partial) crossed products and how do they relate to each other. This chapter is based on joint work with Viviane Beuter.
Euclidean Equivalent of Minkowski’s Space-Time Theory and the Corresponding Model of Elementary Particles (“Cuantex” Model)

This document communicates some of the main results obtained from a theoretical work which performs a type of Wick’s rotation, where Lorentz’s group is connected in the resulting Euclidean metric, and as a consequence models the particles with rest mass as photons in a compacted additional dimension (for a photon of the ordinary 3-dimensional space, they do not go through the 4-dimension due to null angle in this dimension). Among its reported results are new explanations, much more elegant than the current ones, of the material waves of De Broglie, the uncertainty principle, the dilation of the proper time, the Higgs field, the existence of the antiparticles and specifically of the electron-positron annihilation, among others. It also leaves open the possibility of unifying at least three of the fundamental forces and the different types of particles under a single model of photon and compact dimension. Additionally, two experimental results are proposed that can only currently be explained by this theory.
Periodic dynamic factor models: estimation approaches and applications

: A periodic dynamic factor model (PDFM) is introduced as a dynamic factor modeling approach to multivariate time series data exhibiting cyclical behavior and, in particular, periodic dependence structure. In the PDFM, the loading matrices are allowed to depend on the “season” and the factors are assumed to follow a periodic vector autoregressive (PVAR) model. Estimation of the loading matrices and the underlying PVAR model is studied. A simulation study is presented to assess the performance of the introduced estimation procedures, and applications to several real data sets are provided.
Detection of Power Line Insulator Defects Using Aerial Images Analyzed With Convolutional Neural Networks

As the failure of power line insulators leads to the failure of power transmission systems, an insulator inspection system based on an aerial platform is widely used. Insulator defect detection is performed against complex backgrounds in aerial images, presenting an interesting but challenging problem. Traditional methods, based on handcrafted features or shallow-learning techniques, can only localize insulators and detect faults under specific detection conditions, such as when sufficient prior knowledge is available, with low background interference, at certain object scales, or under specific illumination conditions. This paper discusses the automatic detection of insulator defects using aerial images, accurately localizing insulator defects appearing in input images captured from real inspection environments. We propose a novel deep convolutional neural network (CNN) cascading architecture for performing localization and detecting defects in insulators. The cascading network uses a CNN based on a region proposal network to transform defect inspection into a two-level object detection problem. To address the scarcity of defect images in a real inspection environment, a data augmentation method is also proposed that includes four operations: 1) affine transformation; 2) insulator segmentation and background fusion; 3) Gaussian blur; and 4) brightness transformation. Defect detection precision and recall of the proposed method are 0.91 and 0.96 using a standard insulator dataset, and insulator defects under various conditions can be successfully detected. Experimental results demonstrate that this method meets the robustness and accuracy requirements for insulator defect detection.
Simulation of Surface Wetting by Droplets Using a Phase Field Model

In the proposed phase field model a continuous order parameter indicates the phase distribution (liquid/gas). An energy density functional which is dependent on the surface tensions and defined by three contributions yields the total energy of the system. An equilibrium state is then computed by minimizing this energy of the system using an evolution equation. Details of the algorithmic implementation are discussed by illustrative examples. (© 2016 Wiley‐VCH Verlag GmbH & Co. KGaA, Weinheim)
ISPRS Journal of Photogrammetry and Remote Sensing

Due to its many applications, multi-class geospatial object detection has attracted increasing research interest in recent years. In the literature, existing methods highly depend on costly bounding box annotations. Based on the observation that scene-level tags provide important cues for the presence of objects, this paper proposes a weakly supervised deep learning (WSDL) method for multi-class geospatial object detection using scene-level tags only. Compared to existing WSDL methods which take scenes as isolated ones and ignore the mutual cues between scene pairs when optimizing deep networks, this paper exploits both the separate scene category information and mutual cues between scene pairs to su ﬃ ciently train deep networks for pursuing the superior object detection performance. In the ﬁ rst stage of our training method, we leverage pair-wise scene-level similarity to learn discriminative convolutional weights by exploiting the mutual information between scene pairs. The second stage utilizes point-wise scene-level tags to learn class-speci ﬁ c activation weights. While considering that the testing remote sensing image generally covers a large region and may contain a large number of objects from multiple categories with large size variations, a multi-scale scene-sliding-voting strategy is developed to calculate the class-speci ﬁ c activation maps (CAM) based on the aforementioned weights. Finally, objects can be detected by segmenting the CAM. The deep networks are trained on a seemingly unrelated remote sensing image scene classi ﬁ cation dataset. Additionally, the testing phase is conducted on a publicly open multi-class geospatial object detection dataset. The experimental results demonstrate that the proposed deep networks dramatically outperform the state-of-the-art methods.
Numerical simulations of wire array Z-pinches under variations of global magnetic fields

During the last five years a lot of work has been done with the computational code GORGON [1,2] to simulate the dynamics of transient plasmas. The main work has been focused on studying plasmas in different configurations of wire arrays. These configurations include conical arrays [3,4,5], asymmetrical cylindrical arrays [6]. This thesis focuses on two primary areas. First, how does spatial resolution affect the reliability of observed simulation results? This was studied by simulating conical wire array Z-pinches with varying resolutions. Second, the dynamics of Zpinch plasmas generated by asymmetrical wire arrays are examined. This causes the magnetic field topology to become asymmetrical, thus causing the plasma to move away from the geometrical center [6]. Regarding the conical arrays studied in this work, a low electronic density region was initially observed, which was located between two high density regions, similar to what has been observed in stellar phenomena like the Herbig-Haro objects [7]. However, subsequent simulations, with resolution increased by a factor of 2,5, have disproved this supposition. The second main aspect of this work shows how undermassed aluminium wire configurations can be used for accurate control of plasma dynamics, by modifying