Miracle-dz/UniFAR-FTUs huggingface dataset:
Optimal linear labelling for the minimization of both source and channel distortion

We present an approach for joint source-channel coding based on the minimisation of channel distortion and then source distortion. It has previously been proved that distortion due to a binary symmetric channel is minimized by a linear labelling. In this paper, we show how to obtain an asymptotically optimal linear labelling which also minimizes the source distortion for Gaussian sources. This linear labelling is based on the notion of component diversity which can be obtained by algebraic constructions derived from number theory.
A Probabilistic Perspective on Re-Identifiability

A quasi-identifier is a set of attributes that can be used to re-identify entries in anonymized data sets. A group of individuals is considered about whom quasi-identifying numerical information is disclosed such as date of birth, age, weight, and height. The fraction of individuals is determined whose information is unique in that group and hence is identifiable unambiguously. Nonuniformity can be captured well by a single number, the Kullback-Leibler distance. For example sets of real microdata, given approximations based on Kullback-Leibler distances are accurate. Second, the effect of disclosing more specific or less specific information is analyzed experimentally. Third, the effect of correlation between numerical attributes is measured. A formula gives the re-identifiability level. The approximations are validated using publicly available demographic data sets.
L2 and L2 - L model reduction via linear matrix inequalities

Necessary and sufficient conditions are derived for the existance of a solution to the continuous-time and discrete-time suboptimal L2 and L2 - L model reduction problems. These conditions are expressed in terms of linear matrix inequalities (LMIs) and a coupling non-convex rank constraint. In addition, explicit parametrizations of all reduced-order models that correspond to a feasible solution are presented in terms of contractive matrices.
Flow-dependent networks : Existence and behavior at Steiner points

Given boundary points, prescribed flows between each pair of boundary points, and a cost per unit length as a function of flow, we seek a cost-minimizing network. Such flow-dependent networks model, e.g., traffic networks with a given flow of traffic between pairs of cities or communication networks with a given number of channels between pairs of locations. We prove the existence of cost-minimizing networks, study their junctions, and prove that edges can meet in fours as well as in threes.
A Midlatitude Cirrus Cloud Climatology from the Facility for Atmospheric Remote Sensing. Part I: Macrophysical and Synoptic Properties

Abstract A uniquely extensive high cloud dataset has been collected from the University of Utah Facility for Atmospheric Remote Sensing in support of the First (ISCCP) International Satellite Cloud Climatology Project Regional Experiment extended time observations satellite validation effort. Here in Part I of a series of papers examining the climatological properties of the cirrus clouds studied over Salt Lake City, Utah, ∼2200 h of data collected from 1986–96 is used to create a subset of 1389 hourly polarization ruby (0.694 μm) lidar measurements of cloud layer heights. These data were obtained within ±3 h of the local 0000 UTC National Weather Service radiosonde launches to provide reliable cloud temperature, pressure, and wind data. Future parts of this series will consider the inferred cirrus cloud microphysical and radiative properties. In addition to describing the cirrus macrophysical properties in terms of their yearly, seasonal, and monthly means and variabilities, the synoptic weather patterns.
AgO investigated by photoelectron spectroscopy: Evidence for mixed valence

We present photoelectron spectroscopy investigations of in-situ prepared AgO. The sample was prepared by room temperature oxidation of Ag in an electron cyclotron resonance O 2 plasma. In contrast to other measurements based on ex situ prepared AgO powder samples, our investigations show a distinct double peak structure of the O 1s signal with a remarkable chemical shift of 2.9 eV between the two O 1s components. These two components can not be motivated from a crystallographic point of view as the oxygen sites are all equivalent in the unit cell. We interpret this double peak structure as a characteristic feature of AgO and discuss it in terms of mixed valences.
The new ECMWF VAREPS (Variable Resolution Ensemble Prediction System)

The European Centre for Medium‐Range Weather Forecasts (ECMWF) Variable Resolution Ensemble Prediction System (VAREPS) is a system designed to provide skilful predictions of small‐scale, severe‐weather events in the early forecast range, and accurate large‐scale forecast guidance in the extended forecast range (say beyond forecast day 7). In this work, first the rationale behind VAREPS is presented, and then the performance of VAREPS with a truncation at forecast day 7, i.e. TL399L40(d0–7) and TL255L40(d7–15), is discussed and compared to the performance of two constant resolution systems, a TL255L40 and a TL319L40 (this latter one requires similar computing resources to VAREPS).
Longitudinal asymmetry of the Io plasma torus

Remote observations of the Io plasma torus have revealed a persistent longitudinal asymmetry in the brightness of optical S+ emission lines but not of ultraviolet S++ emission lines. The S+ asymmetry (with respect to the corotating System III coordinates) has been attributed to an asymmetric plasma source; the question then arises, why do the higher ionization states of sulfur not reflect a similar asymmetry? The explanation proposed here is based on the fact that torus ions do not quite corotate at the System III rate because of mass loading. If the resulting System III drift period of the ions is intermediate between the ionization lifetimes of S+ and S++, then the asymmetry of the source will be obscured by longitudinal drift for the relatively long-lived multiply-ionized sulfur ions, but not for the short-lived S+.
A measurement of the mass fluctuation spectrum from the cluster X-ray temperature function

A statistically complete sample of galaxy clusters is presented which also has complete X-ray temperature information. Using this sample, the cluster bolometric luminosity and temperature functions are derived. The temperature function constrains the shape and amplitude of the mass fluctuation power spectrum. For a power law, i.e., with the fluctuation power spectrum proportional to k exp n, n = {minus}(1.7 + 0.65, {minus} 0.35), and an amplitude is obtained which implies that the rms value of {delta}{minus}M/M is 0.59 + or {minus} 0.02 on a scale of 8/h Mpc at the present epoch. 31 refs.
The treatment of moving problem for constant-frequency double-rotor generator in finite element analysis

The constant-frequency double-rotor generator has the potential to be used in the marine energy conversions, such as tides and marine current power generation system due to the advantages. However, there are two different rotational speed movement boundaries in the constant-frequency double-rotor generator, especially the inner moving part boundary between the inner wound rotor and outer permanent magnet rotor. This complicates the finite element analysis and greatly increases the analysis time. In this article, a new finite element analysis method based on double boundaries interpolation multiple reference frame is proposed. The principle of the interpolation multiple reference frame method is introduced, and the analysis and comparison with the traditional method are carried out. The analysis results show that the interpolation multiple reference frame method simplifies the preprocessing procedure, reduces computation time, ensures high calculation accuracy, and has the potential to be applied in all kinds of multiple mechanical port machines.
A Supersymmetric Form of the Nonlinear Sigma Model in Two-Dimensions

A supersymmetric form of the two-dimensional nonlinear $\ensuremath{\sigma}$ model is described; the primary purpose is to explore further the analogy between the $\ensuremath{\sigma}$ model and four-dimensional Yang-Mills theory. In the case of three components, the theory contains an enlarged supersymmetry algebra and is equivalent to the ordinary $\ensuremath{\sigma}$ model with an additional logarithmic interaction between pseudoparticles.
Broad-band photometry and long-slit spectroscopy of the peculiar ring galaxy FM 287-14

Aims. We report detailed morphological and kinematical insights into the disturbed galaxy FM 287-14, which is reported in the literature as a member of strongly interacting galaxies with a clear case of mergers. The main purpose of this paper is to provide a first observational study of this double-nuclei object based on photometry and spectroscopy. Methods. The study is based on BVR photometry with the 1.6-m telescope at OPD, and long-slit spectroscopy with Gemini South’s GMOS spectrograph, with two-gratings centered at 5 011 A and 6 765 A. Image enhancement techniques were applied to the direct images, and the main lines in the spectra were used to determine the radial velocity. Line ratios and color-color diagnostic diagrams helped determine the nature of this object. Results. The external NE section is populated with well-defined H ii regions and is bluer than the SW region with less evidence of H ii regions. A double nucleus has been identified. The (B − R) × (B − V) diagram shows that the main nucleus is redder than the second one; the internal “ring-like” structure is redder than the external one. The main object redshift is z = 0.0311, corresponding to a line-of-sight velocity of 9334 km s −1 (a distance of 133.3 Mpc), and the second nucleus has a redshift of z = 0.0310, corresponding to a line-of-sight velocity of 9300 km s −1 (a distance of 132.9 Mpc). Both nuclei show characteristics of early-type galaxies, but the main one also shows modest emission evidence of the [N ii]λ6583 line. Conclusions. The peculiarities of FM 287-14 can be adequately interpreted as an ongoing merging-stage of interaction of the main object with a companion galaxy.
Voltage tunable surface acoustic wave phase shifter on AlGaN/GaN

A voltage tunable surface acoustic wave (SAW) phase shifter on a two-dimensional electron gas (2DEG) AlGaN/GaN heterostructure has been developed. The acoustoelectric interaction is field-effect modulated by means of an insulated gate placed in the acoustic path of a SAW delay line. The phase velocity under the gate is tuned by modifying the conductance of the 2DEG with a dc bias, while the insulating layer prevents the screening of the SAW piezoelectric fields by the gate. The device is compatible with the nitride transistor technology for signal processing and frequency control applications.
Data fusion for three-dimensional tracking using particle techniques

Robustness and tracking speed are two important indices for evaluating the performance of real-time 3-D tracking. We propose a new approach to fuse sensing data of the most current observation into a 3-D visual tracker with particle techniques. With the proposed data fusion method, the importance density function in the particle filter can be designed to represent posterior states by particle crowds in a better way. This makes the tracking system more robust to noise and outliers. On the other hand, because particle interpretation is performed in a much more efficient fashion, the number of particles used in tracking is greatly reduced, which improves the real-time performance of the system. Simulation and experimental results verified the effectiveness of the proposed method.
Plasmonic Corrugated Horn Structure for Optical Transmission Enhancement

We propose and theoretically analyze a plasmonic corrugated horn structure for enhanced optical transmission. It makes use of the enhancement of unidirectional propagating surface plasmon polaritons at oblique incidence. Geometric parameters such as the groove depth and width are optimized. Analysis shows that it presents a better performance than the bull's eye structure for a small number of grooves.
Numerical characterization of piezoelectric resonant transducer modes for acoustic wave excitation in optical fibers

The control of light in optical fibers by acoustic waves often requires a piezoelectric transducer as acoustic driver. As the fiber acoustic excitation depends on the deformation and frequency of the transducer, the analysis of the resonances and piezo mechanical behavior provides more efficient operation for acousto-optic devices. In this paper, an acousto-optic modulator is modeled using a three-dimensional finite element method in order to investigate the effect of the vibration modes of a piezoelectric disc in the excitation of acoustic waves in a fiber segment. The results indicate that in specific resonances, transducer radial and thickness modes are useful for the excitation of flexural and longitudinal acoustic waves in optical fiber.
Total radiation losses and emissivity profiles in RFX

In the framework of adding new information to the reversed field pinch (RFP) confinement database, the radiation emitted by RFX plasmas has been investigated using an eight chord bolometric camera, whose detectors have been calibrated absolutely. From the experimental data the emissivity profiles have been reconstructed by means of a generalized tomography reconstruction algorithm. This analysis confirms that the radiation emitted in RFX is systematically concentrated at the edge. The dependence of the emitted power on the plasma density shows that the radiation increases approaching the high density regime, but it rarely goes beyond 30% of the input power for stationary discharges. This behaviour is strongly dependent on the concentration of impurities but, in any case, in RFX there is no evidence of disruptions. A simple local energy balance allows a preliminary evaluation of the radial heat flux profile to be obtained. These measurements indicate that an active impurity screening mechanism is acting in the edge and that transport is the major energy loss mechanism in RFX.
Sensitivity of quantitative precipitation forecast to soil moisture initialization and microphysics parametrization

In contrast to many other aspects of numerical weather prediction, quantitative precipitation forecasts did not gain much from a continuously improved physics over the past decades. A gap of knowledge remains as to how different processes contributing to the uncertainty in precipitation forecasts compare to each other. This research aims at gaining further insight into how two aspects influencing moist processes interrelate in atmospheric models. For this, high‐resolution (3000 m) integrations with the non‐hydrostatic Advanced Regional Prediction System were performed for two cases of extreme convection in Belgium, one driven by strong buoyancy and no vertical wind shear and the other driven by strong vertical wind shear and moderate buoyancy. Sensitivity experiments consisted of three integrations with different soil moisture initialization and three experiments with different size distribution assumptions in the microphysical parametrization scheme. The gain of spatially distributed soil moisture information was small in our simulations, but it was found important to have at least the mean soil moisture content right for the realistic simulation of cold‐pool intensity. A mechanism, based on cold‐pool and buoyancy interaction was proposed to explain the inverse relation between surface precipitation and soil moisture content. Changing the size distribution assumptions of the precipitating hydrometeors in the microphysics parametrization had a larger impact on the hydrometeor distribution than on surface precipitation. Weighting the size distribution assumptions of large hail to small graupel led to a poor representation of the general storm structure but did not affect the surface precipitation as much as was found in previous idealized studies. Copyright © 2010 Royal Meteorological Society
Ytterbium fiber laser emitting at 1160 nm

We have realized ytterbium fiber laser emitting at 1160 nm. To avoid a competition with a spontaneous lasing at shorter wavelengths we pumped laser into the core at wavelength of 1070 nm. Cladding pumped Yb-laser was used as a pump source. Also, the active fiber was heated up to 70°C to increase the absorption of the pump power and suppress the spontaneous emission. The lasing efficiency slope of 45% was measured. A maximum output power of 3.2 W was achieved.
Improved Bayesian MRI reconstruction involving neural priors based on a regularization approach

The goal of this paper is to present the development of a new reconstruction methodology for restoring magnetic resonance images (MRI) from reduced scans in k-space. The proposed approach considers the combined use of regularized neural network models and Bayesian restoration, in the problem of MRI image extraction from sparsely sampled k-space, following several different sampling schemes, including spiral and radial. Effective solutions to this problem are indispensable especially when dealing with MRI of dynamic phenomena since then, rapid sampling in k-space is required. The goal is such a case is to make measurement time smaller by reducing scanning trajectories as much as possible. In this way, however, underdetermined equations are introduced and poor image reconstruction follows. It is suggested here that significant improvements could be achieved concerning quality of the extracted image, by judiciously applying regularization based neural networks and Bayesian estimation methods to the k-space data. More specifically, it is demonstrated that regularization based neural network techniques could construct efficient priors and introduce them in the procedure of Bayesian reconstruction. These regularized ANN priors are independent of specific image properties and probability distributions. They are based on training supervised multilayer perceptron (MLP) regularized neural filters to estimate the missing samples of complex k-space and thus, to improve k-space information capacity. Such a neural filter based prior is integrated to the maximum likelihood procedure involved in the Bayesian reconstruction. It is found that the proposed methodology leads to enhanced image extraction results favorably compared to the ones obtained by the traditional Bayesian MRI reconstruction approach as well as by the pure MLP based reconstruction approach.
A fast computing method to distinguish the hyperbolic trajectory of an non-autonomous system

Attempting to find a fast computing method to DHT (distinguished hyperbolic trajectory), this study first proves that the errors of the stable DHT can be ignored in normal direction when they are computed as the trajectories extend. This conclusion means that the stable flow with perturbation will approach to the real trajectory as it extends over time. Based on this theory and combined with the improved DHT computing method, this paper reports a new fast computing method to DHT, which magnifies the DHT computing speed without decreasing its accuracy.
Orbifold principal bundles on an elliptic fibration and parabolic principal bundles on a Riemann surface

Let X be a compact Riemann surface and associated to each point p-i of a finite subset S of X is a positive integer m-i. Fix an elliptic curve C. To this data we associate a smooth elliptic surface Z fibered over X. The group C acts on Z with X as the quotient. It is shown that the space of all vector bundles over Z equipped with a lift of the action of C is in bijective correspondence with the space of all parabolic bundles over X with parabolic structure over S and the parabolic weights at any p-i being integral multiples of 1 / m-i. A vector bundle V over Z equipped with an action of C is semistable (respectively, polystable) if and only if the parabolic bundle on X corresponding to V is semistable (respectively, polystable). This bijective correspondence is extended to the context of principal bundles.
Boundary Value Problems on Semi-Infinite Intervals and Their Numerical Solution

To solve boundary value problems posed on semi-infinite intervals the problem is frequently replaced by one on a finite interval. We devise a theory for doing this correctly for both linear and nonlinear problems. In brief the method consists in determining appropriate asymptotic boundary conditions (ABC) to use at a finite point. In the linear case these are “projections” into the subspace of bounded solutions. In the nonlinear case, nonlinear boundary conditions result. They involve possibly unknown projections for the problem linearized about the solution at infinity. A linear eigenvalue problem for the Schrodinger equation and a nonlinear elasticity problem are solved to show the power of the new methods.
Development of Deep Belief Network for Tool Faults Recognition

The controlled interaction of work material and cutting tool is responsible for the precise outcome of machining activity. Any deviation in cutting parameters such as speed, feed, and depth of cut causes a disturbance to the machining. This leads to the deterioration of a cutting edge and unfinished work material. Recognition and description of tool failure are essential and must be addressed using intelligent techniques. Deep learning is an efficient method that assists in dealing with a large amount of dynamic data. The manufacturing industry generates momentous information every day and has enormous scope for data analysis. Most intelligent systems have been applied toward the prediction of tool conditions; however, they must be explored for descriptive analytics for on-board pattern recognition. In an attempt to recognize the variation in milling operation leading to tool faults, the development of a Deep Belief Network (DBN) is presented. The network intends to classify in total six tool conditions (one healthy and five faulty) through image-based vibration signals acquired in real time. The model was designed, trained, tested, and validated through datasets collected considering diverse input parameters.
ANALISA MACHINE LEARNING DENGAN ALGORITMA MULTI-LAYER PERCEPTRON UNTUK PENANGANAN KEJAHATAN PHISHING

Setelah pandemic merajarela, semua kebijakan mengharuskan bekerja dan bersekolah dari rumah. Oleh karena itu banyak pekerjaan dikomunikasikan melalui email. Dengan meningkatnya penggunaan email dikalangan pekerja, diimbangi juga dengan kriminalitas cyber. Dan salah satunya adalah tindak pidana Phishing melalui email. Motif kejahatan Phishing ini adalah untuk mendapatkan data pribadi dari korban, yakni user dan password agar mendapatkan keuntungan baik secara material dan non-material. Oleh karena itu penulis ingin melakukan penangan tindak kejahatan cyber dengan menggunakan machine learning yaitu Multi-layer Perceptron. Diharapkan dengan machine learning tersebut dapat mencegah terjadinya kejahatan Phishing secara maksimal dan akurat. Dalam penelitin ini, peneliti menggunakan dataset email yang bersumber dari Kaggle yang terdiri dari 1368 email Phishing & 4538 yang bukan email Phishing.dan pengguna akan menguji tingkat akurasi dengan menggunakan algoritma Multi-layer Perceptron. Setelah dilakukan pengujian, tingkat akurasi didapatkan 99.65%. ini membuktikan bahwasanya model Multi-layer Perceptron cukup efektif untuk menangani tindak pindana kejahatan Phishing.