Miracle-dz/UniFAR-FTUs huggingface dataset:
An Efficient Porcine Acoustic Signal Denoising Technique Based on EEMD-ICA-WTD

Automatic monitoring of group-housed pigs in real time through porcine acoustic signals has played a crucial role in automated farming. In the process of data collection and transmission, acoustic signals are generally interfered with noise. In this paper, an effective porcine acoustic signal denoising technique based on ensemble empirical mode decomposition (EEMD), independent component analysis (ICA), and wavelet threshold denoising (WTD) is proposed. Firstly, the porcine acoustic signal is decomposed into intrinsic mode functions (IMFs) by EEMD. In addition, permutation entropy (PE) is adopted to distinguish noise-dominant IMFs from the IMFs. Secondly, ICA is employed to extract the independent components (ICs) of the noise-dominant IMFs. The correlation coefficients of ICs and the first IMF are calculated to recognize noise ICs. The noise ICs will be removed. Then, WTD is applied to the other ICs. Finally, the porcine acoustic signal is reconstructed by the processed components. Experimental results show that the proposed method can effectively improve the denoising performance of porcine acoustic signal.
Vanishing Point Detection Method Using Multiple Initial Vanishing Points

In this paper, we propose a vanishing point detection method using multiple initial vanishing points. Vanishing points are important geometric information that is used for reconstructing 3D structures. Three vanishing points are detected for indoor scenes. In the previous work, it could be inaccurate to detect only one initial vanishing point, because initial vanishing point getting most highest sum of voting could be deferent from the best initial vanishing point. Therefore the method which sets multiple initial vanishing point and detects a best vanishing point from them gives us preparation for the prior case. Also in this paper, we propose a adjusting vanishing point method by postprocessing of detected vanishing points. We could detect more accurate vanishing point by using postprocessing. Experimental results show that the accuracy of the vanishing point detection is about 1～2% higher than that of the existing method through the proposed method and the performance is improved accordingly
Phased Hemispherical Lens Antenna for 1-D Wide-Angle Beam Scanning

A $Ka$ -band hemispherical lens antenna fed by a phased array antenna (PAA) for 1-D wide-angle beam scanning is presented. The hemispherical lens antenna with two dielectric layers is designed based on the combination of geometrical optics (GOs) and finite-element method (FEM), thus the computational complexity can be reduced without loss of accuracy. A modification procedure is then proposed to efficiently modify the lens antenna for wide-angle scanning property. Finally, the lens is fabricated through 3-D printing technology in spite of its complex shapes. A linear patch array is employed to feed the hemispherical lens antenna. By utilizing the conjugate field matching (CFM) method, the proposed lens antenna is able to achieve 1-D beam scanning with about 2.6 dB/5 dB gain loss as scanning to ±45°/60°. The measured results show that the proposed lens antenna has a low profile with a focus-to-diameter ratio (F/D) of only 0.14, and aperture efficiency for the on-axis beam is 52% at 27.3 GHz. The proposed antenna is highly suitable for 5G mm-wave wireless communication with the advantages of wide-angle beam scanning, low cost, and ease of fabrication.
Impacts of Ge Preamorphization Implantation and Si Capping on the Specific Contact Resistivity of Ni(Pt)SiGe/p+-SiGe Contacts

This article explores the impacts of both Ge preamorphization implantation (PAI) and Si capping on the specific contact resistivity (<inline-formula> <tex-math notation="LaTeX">$\rho _{c}$ </tex-math></inline-formula>) of Ni(Pt)SiGe/p<sup>+</sup>-SiGe contacts elaborately. Both Ge PAI and Si capping are found to be beneficial in reducing the <inline-formula> <tex-math notation="LaTeX">$\rho _{c}$ </tex-math></inline-formula> of Ni(Pt)SiGe/p<sup>+</sup>-SiGe contacts remarkably, with <inline-formula> <tex-math notation="LaTeX">$\rho _{c}$ </tex-math></inline-formula> values reduced from the original <inline-formula> <tex-math notation="LaTeX">${20.03} \times {10}^{-{8}} \Omega $ </tex-math></inline-formula>-cm<sup>2</sup> to <inline-formula> <tex-math notation="LaTeX">${9.92} \times {10}^{-{8}}\,\,\Omega $ </tex-math></inline-formula>-cm<sup>2</sup> with Ge PAI and <inline-formula> <tex-math notation="LaTeX">${1.44} \times {10}^{-{8}} \Omega $ </tex-math></inline-formula>-cm<sup>2</sup> with Si capping, respectively. Improved interfacial uniformity and morphology are thought to be primarily responsible for such significant reductions in <inline-formula> <tex-math notation="LaTeX">$\rho _{c}$ </tex-math></inline-formula> since neither Ge PAI nor Si capping induces an obvious difference in B concentrations at the interface of Ni(Pt)SiGe/p<sup>+</sup>-SiGe. Moreover, by combining Ge PAI or Si capping with extra B implantation before germanosilicidation to intentionally introduce B dopant segregation (DS) at the interface of Ni(Pt)SiGe/p<sup>+</sup>-SiGe contacts, further reduction of <inline-formula> <tex-math notation="LaTeX">$\rho _{c}$ </tex-math></inline-formula> values is accomplished as anticipated.
A Deep Convolutional Neural Wavelet Network for Classification of Medical Images

: This work present a new solution for medical image classification using the Neural Network (NN) and Wavelet Network (WN) based on the Fast Wavelet Transform (FWT) and the Adaboost algorithm. This method is divided in two stages: The learning stage and the classification stage. The first consists to extract the features using the FWT based on the MultiResolution Analysis (MRA). These features are used to calculate the inputs of the hidden layer. Then, those inputs are filtered by using the Adaboost algorithm to select the best ones corresponding to each image. The second consist to create an AutoEncoder (AE) using the best-selected wavelets of all images. Then, after a series of Stacked AE, a pooling is applied for each hidden layer to get our Convolutional Deep Neural Wavelet Network (CDNWN) architecture for the classification phase. Our experiments were performed on two different datasets and the obtained classifications rates given by our approach show a clear improvement compared to those cited in this article.
ON THE SOLUTION OF INVERSE EQUIVALENT SURFACE-SOURCE PROBLEMS

Various formulations of the inverse equivalent surface-source problem and corresponding solution approaches are discussed and investigated. Starting from the radiation integrals of electric and magnetic surface current densities, the probe-corrected inverse equivalent source formulation is set up together with different forms of side constraints such as the zero-field or Love condition. The linear systems of equations resulting from the discretized forms of these equations are solved by the normal residual (NR) and normal error (NE) systems of equations. As expected and as demonstrated by the solution of a variety of inverse equivalent surface-source problems, related to synthetic as well as realistic antenna near-field measurement data, it is found that the iterative solution of the NE equations allows for a better control of the solution error and leads in general to a slightly faster convergence. Moreover, the results show that the incorporation of the zero-field condition into the solution process is in general not beneficial, which is also supported by the structure of the NE systems of equations. If desired, Love surface current densities, or just fields in general, can more easily be computed in a post-processing step. The accuracy of the obtained near-fields and far-fields depends more on the stopping criterion of the inverse source solver than on the particular choice of the equivalent surface-source representation, where the zero-field condition may influence the stopping criterion in a rather unpredictable way.
Molecular detection of mycoplasma infection in milk of clinically mastitic buffaloes.

investigation, 1250 primiparous and multiparous dairy buffaloes located in different villages of Assiut and Sohag Governorates, South Egypt were clinically inspected. Eighty-nine cases showed signs of clinical mastitis and their mammary secretions were tested for the presence of Mycoplasma infection using Polymerase Chain Reaction (PCR). The clinically mastitic buffaloes were categorized into peracute (nine cases), acute (n = 36) and subacute/chronic (n = 44) forms. Mycoplasma infection was detected in 20.22 % of the tested buffaloes. All molecularly Mycoplasma positive—buffaloes were multiparous and characterized by recurrent attacks of mastitis. be Clinical of as a considerable as a in comparison in
Nonlinear Distribution Regression for Remote Sensing Applications

In many remote sensing applications, one wants to estimate variables or parameters of interest from observations. When the target variable is available at a resolution that matches the remote sensing observations, standard algorithms, such as neural networks, random forests, or the Gaussian processes, are readily available to relate the two. However, we often encounter situations where the target variable is only available at the group level, i.e., collectively associated with a number of remotely sensed observations. This problem setting is known in statistics and machine learning as multiple instance learning (MIL) or distribution regression (DR). This article introduces a nonlinear (kernel-based) method for DR that solves the previous problems without making any assumption on the statistics of the grouped data. The presented formulation considers distribution embeddings in reproducing kernel Hilbert spaces and performs standard least squares regression with the empirical means therein. A flexible version to deal with multisource data of different dimensionality and sample sizes is also presented and evaluated. It allows working with the native spatial resolution of each sensor, avoiding the need for matchup procedures. Noting the large computational cost of the approach, we introduce an efficient version via random Fourier features to cope with millions of points and groups. Real experiments involve the Soil Moisture Active Passive (SMAP) vegetation optical depth (VOD) data for the estimation of crop production in the U.S. Corn Belt and the Moderate Resolution Imaging Spectroradiometer (MODIS) and Multi-angle Imaging SpectroRadiometer (MISR) reflectances for the estimation of aerosol optical depth (AOD). An exhaustive empirical evaluation of the method is done against naive (linear and nonlinear) approaches based on input-space means as well as previously presented methods for MIL. We provide source code of our methods in http://isp.uv. es/code/dr.html.
Adaptive Fuzzy Prescribed Performance Control of Nontriangular Structure Nonlinear Systems

In this article, a new n-step fuzzy adaptive output tracking prescribed performance control problem is investigated for a class of nontriangular structure nonlinear systems. In the control design process, the mean value theorem is used to separate the virtual state variables needed for the control design, and the implicit function theorem is exploited to assert the existence of the desired continuous control. The fuzzy logic systems are used to identify the unknown nonlinear functions and ideal controller, respectively. By constructing a novel iterative Lyapunov function, a new n-step adaptive backstepping control design algorithm is established. The prominent characteristics of the proposed adaptive fuzzy backstepping control design algorithm are as follows: one is that it can ensure the closed-loop control system is the semiglobally uniformly ultimately bounded and the tracking error can converge within the prescribed performance bounds. The other is that it solves the controller design problem for the nontriangular nonlinear systems that the previous adaptive backstepping design techniques cannot deal with. Two examples are provided to show the effectiveness of the presented control method.
Simultaneous high-probability bounds on the false discovery proportion in structured, regression and online settings

While traditional multiple testing procedures prohibit adaptive analysis choices made by users, Goeman and Solari (2011) proposed a simultaneous inference framework that allows users such flexibility while preserving high-probability bounds on the false discovery proportion (FDP) of the chosen set. In this paper, we propose a new class of such simultaneous FDP bounds, tailored for nested sequences of rejection sets. While most existing simultaneous FDP bounds are based on closed testing using global null tests based on sorted p-values, we additionally consider the setting where side information can be leveraged to boost power, the variable selection setting where knockoff statistics can be used to order variables, and the online setting where decisions about rejections must be made as data arrives. Our finite-sample, closed form bounds are based on repurposing the FDP estimates from false discovery rate (FDR) controlling procedures designed for each of the above settings. These results establish a novel connection between the parallel literatures of simultaneous FDP bounds and FDR control methods, and use proof techniques employing martingales and filtrations that are new to both these literatures. We demonstrate the utility of our results by augmenting a recent knockoffs analysis of the UK Biobank dataset.
Pengaruh Faktor-Faktor Psikologis terhadapKeputusan Individual Investasi Sahamdi Pulau Jawa

Penelitian ini bertujuan untuk menjelaskan pengaruh dari perilaku herding, optimisme berlebihan, kecenderungan risiko dan konservatisme terhadap keputusan individual investasi saham di Pulau Jawa. Populasi dari penelitian ini adalah investor saham individual yang ada di Pulau Jawa. Sebanyak 114 data sampel berhasil dikumpulkan dengan metode survey dan menggunakan teknik purposive sampling. Analisis data dilakukan menggunakan metode Structural Equation Modeling (SEM) dengan aplikasi SmartPLS 3. Hasil pengujian menunjukkan bahwa perilaku herding dan kecenderungan risikotidak berpengaruh terhadap keputusan individual investasi saham. Sedangkan, optimisme berlebihan dan konservatisme berpengaruh positif terhadap keputusan individual investasi saham. Kata kunci: perilaku herding, optimisme berlebihan, kecenderungan risiko, konservatisme, keputusan investasi
Impedance Transformer for Recessed Ground Patch Antenna at 60 GHz

This letter presents the concept of tapered recessed ground (TRG) below the microstrip feedline to transform the source impedance (50 Ω) to the edge impedance (<italic>Z</italic><sub>in</sub>) of the recessed ground microstrip patch antenna. The dimensions of tapering parameters, including its length, width, and depth, are studied in detail to match <italic>Z</italic><sub>in. </sub> The use of this method overcomes the fabrication challenges of narrow slots of inset-feed technique, especially on high-indexed substrates. Prototypes of 60 GHz recessed ground patch antennas, using inset-feed (A<sub>1</sub>) and TRG techniques [A<sub>2</sub> and A<sub>3</sub> (with different tapering dimensions for each)], are fabricated on a 0.127 mm thick alumina substrate <inline-formula><tex-math notation="LaTeX">$(\varepsilon _{r}= 9.8)$</tex-math></inline-formula> as a proof of concept. The measured results indicate a −10 dB impedance bandwidth of 7.5% (A<sub>1</sub>) and 7% (A<sub>2</sub> and A<sub>3</sub>) with return loss better than 30 dB at 60 GHz. The measured gain and radiation efficiency of antenna A<sub>2</sub> are more by 2.35 dB and 8.3%, respectively, than antenna A<sub>1</sub>. The measured and simulated results agree reasonably well and validate the concept of TRG.
A pathophysiologic evaluation of the receptor for advanced glycation end products (RAGE) in the lung

The receptor for advanced glycation end products (RAGE) is a member of the immunoglobulin super-family of cell surface receptors whose activation has been suggested to contribute to various pathologies. RAGE has been primarily studied in diabetes where its upregulation has been linked to disease in the kidney, vasculature, and nervous system. This protein is highly expressed in the lung under normal conditions, but its function is unknown. We therefore investigated the normal function of RAGE in the lung and its pulmonary expression in two disease states.Idiopathic pulmonary fibrosis (IPF) is a debilitating disease with both high morbidity and mortality. Unfortunately, there are currently no effective therapies for IPF necessitating mechanistic insight into the disease pathogenesis. We found that pulmonary fibrosis led to a depletion of RAGE in both animal models and tissue from patients with idiopathic pulmonary fibrosis. In contrast to other diseases in which RAGE signaling promotes pathology, we found that aged RAGE null mice spontaneously develop pulmonary fibrosis-like alterations and more severe fibrosis in response to asbestos injury. In addition, we found that RAGE null mice were fully protected from the fibrotic effects of bleomycin. In addition, we investigated the expression of RAGE in the lungs of diabetic rodents. Diabetes has been shown to alter RAGE expression in a number of tissues that do not normally express RAGE. We hypothesized that diabetes would alter pulmonary RAGE expression and contribute to the susceptibility to pulmonary injury. We found that pulmonary RAGE expression was unaltered in five rodent models suggesting that diabetes does not effect RAGE expression in the lung.Lastly, we identified that RAGE has a very high affinity for components in the basement membrane of the lung. A few RAGE studies suggested that it might serve a role as an adhesion molecule. We found that RAGE extensively colocalized with the alveolar basement membrane and had very high affinity for collagen I, collagen IV, and laminin, but not fibronectin. These findings along with the fact that RAGE null mice spontaneously develop fibrosis suggest a potential homeostatic function of RAGE in the lung. This is in stark contrast to the vast majority of studies, which suggest that its expression is solely pathologic.
Influence of Room Temperature Sputtered Al-Doped Zinc Oxide on Passivation Quality in Silicon Heterojunction Solar Cells

Al-doped zinc oxide (AZO) is a potential candidate to substitute tin-doped indium oxide in silicon heterojunction (SHJ) solar cells due to its low cost and low ecological impact. The AZO, sputtered at room temperature (RT), is of particular interest because of low thermal budget and potential for high throughput production with the well-established industrial methods. In SHJ solar cells, high effective carrier lifetime prerequisite for the high open-circuit voltage is achieved with surface passivation by intrinsic amorphous silicon layers followed by doped silicon layers. The passivation quality may be affected by the subsequent sputtering of an AZO layer especially at RT. In this article, we investigated the influence of the AZO sputtering and postdeposition annealing on the effective carrier lifetime in symmetrical silicon layer stacks with n- or p-type doped silicon layers and solar cell precursors. It has been found that the effective carrier lifetime significantly decreased after AZO sputtering at RT. The detrimental effect of AZO sputtering is substrate temperature dependent and is smaller or even absent at elevated temperatures. However, postdeposition annealing, equivalent to the Ag paste curing, mostly recovered the effective carrier lifetime in the symmetrical stacks as well as in the cell precursors. Finally, an aperture area efficiency of 21.2% has been achieved for the 19 mm × 19 mm SHJ solar cell prepared with room temperature sputtered AZO.
Amplitude-Fluctuation Separation Based on BPM Waveform Reconstruction in MIMO Systems

In Multi-input-multi-output (MIMO) systems, the orthogonality among sub-waves could be realized by binary phase modulation (BPM) encoding. Especially in the case of insufficient matched filters, Hadamard-matrix-based BPM encoding could be employed to separate the transmitted signal from echo effectively, which is verified firstly in this paper by utilizing target location, when introducing fluctuations in amplitude as well as in phase. Hence the amplitude-fluctuation separation method based on waveform reconstruction is put forward. The waveform is reconstructed on basis of the known array structure, and then the results are optimized obeying the minimum mean square error (MSE) criterion to obtain more accurate estimation. Compared with the traditional single-channel blind source separation approach, this method shows significant computation reduction by taking full use of the phase and structure information. Monte-Carlo experiments validate the method application in angle estimation improvement.
Location-Aware Deep Collaborative Filtering for Service Recommendation

With the widespread application of service-oriented architecture (SOA), a flood of similarly functioning services have been deployed online. How to recommend services to users to meet their individual needs becomes the key issue in service recommendation. In recent years, methods based on collaborative filtering (CF) have been widely proposed for service recommendation. However, traditional CF typically exploits only low-dimensional and linear interactions between users and services and is challenged by the problem of data sparsity in the real world. To address these issues, inspired by deep learning, this article proposes a new deep CF model for service recommendation, named location-aware deep CF (LDCF). This model offers the following innovations: 1) the location features are mapped into high-dimensional dense embedding vectors; 2) the multilayer-perceptron (MLP) captures the high-dimensional and nonlinear characteristics; and 3) the similarity adaptive corrector (AC) is first embedded in the output layer to correct the predictive quality of service. Equipped with these, LDCF can not only learn the high-dimensional and nonlinear interactions between users and services but also significantly alleviate the data sparsity problem. Through substantial experiments conducted on a real-world Web service dataset, results indicate that LDCF’s recommendation performance obviously outperforms nine state-of-the-art service recommendation methods.
A 1+1 Lifshitz Weyl Anomaly From a Schrodinger-invariant Non-relativistic Chern-Simons Action

The main result of this note is that the Weyl anomaly of a $z=2$ (1+1)-dimensional Lifshitz effective action found in \cite{Oz2014} can be derived from a (2+1)-dimensional non-relativistic Schrodinger-invariant Chern-Simons (NRSCS) action constructed in \cite{ObersNRSCS} which was shown to be equivalent to a specific Weyl-invariant non-projectable Horava-Lifshitz action of gravity. On a manifold with a boundary, we will show that the (1+1)-dimensional Lifshitz Weyl anomaly can be derived from a specific term, the torsional CS (tCS) term, in the NRSCS action built from the gauge fields of the Weyl and special conformal symmetry generators of the centrally-extended Schrodinger algebra. We also focus on the $z=1$ Lifshitz Weyl anomaly and attempt to elicit its geometric and physical nature, in particular its relationship with the Lorentz anomaly of a (1+1)-dimensional CFT effective actions. We show that it is directly related to the curvature scalar of the dual Lorentz connection $d\star\omega$, the integral of which is known to be a topological invariant. We also point out that making the anomalous Lifshitz quantum effective action Weyl-invariant amounts to obtaining an for a stationary chiral boson, the spatial-component of the acceleration vector. By putting boundary conditions on the spatial slices, the time dependence of the lapse function in the Arnowitt, Deser and Misner (ADM) decomposition is eliminated and the result is a Rindler metric. We finally discuss several issues related to the (1+1)-dimensional Lifshitz Weyl anomaly regarding edge physics of fractional quantum Hall states and anomaly cancellation by anomaly inflow.