Miracle-dz/UniFAR-FTUs huggingface dataset:
A method to estimate a temporally stable spectral envelope for periodic signals

Vocoder-based speech synthesis system requires fundamental frequency (F0) and spectral envelope. Since the sound quality of synthesized speech depends on the estimation performance, methods that can accurately estimate two parameters are crucial to synthesize natural speech. In particular, spectral envelope estimation is more difficult than F0 estimation. One of the problems in spectral envelope estimation is that the result depends on the windowing time, type of window function, and its length. Conventional methods such as LPC or Cepstrum cannot remove the temporal variation. TANDEM-STRAIGHT can estimate a temporally stable spectral envelope, whereas the processing that consists of averaging two power spectra, smoothing and post-processing is complex. To simplify the processing, we propose a method based on pitch synchronous analysis and spectral smoothing. The proposed method can estimate a temporally stable spectral envelope from only one power spectrum processed by a specialized window function. The window function and its length are determined to remove the temporal variation. The objective evaluation was conducted to verify the temporal variance and the estimation performance. The result suggested that the proposed method can estimate the temporally stable power spectrum as well as TANDEM-STRAIGHT.
THE VARIANCE RATIO TEST: AN ANALYSIS OF SIZE AND POWER BASED ON A CONTINUOUS-TIME ASYMPTOTIC FRAMEWORK

We consider the statistical properties of the variance ratio statistic in the context of testing for market efficiency defined by uncorrelated returns. The statistic is then the ratio of the variance of K-period returns to the variance of one-period returns scaled by K. We use a continuous-time asymptotic framework whereby we let the sample size increase to infinity keeping the span of the data fixed. We also let the aggregation parameter K increase such that K/T → κ as T, the sample size, increases to infinity. We consider the limit of the statistic under the null hypothesis and under three alternative hypotheses that have been popular in the finance literature. Our analysis permits us to address size and power issues with respect to κ and the sampling interval used. Our theoretical and simulation results show that power is initially increasing as κ increases but then decreases with further increases in κ. This shows that for any given alternative there exists a value of K relative to T that will maximize power. We thus investigate the properties of a test that is the maximal value of the variance ratio over a range of possible values for K. The importance of the trimming to define this range is highlighted.This paper is drawn from chapter 2 of Vodounou's Ph.D. dissertation at the Université de Montréal (Vodounou, 1997). We thank two referees for useful comments and Tomoyoshi Yabu and Xiaokang Zhu for research assistance. Perron acknowledges financial support from the Social Sciences and Humanities Research Council of Canada (SSHRC), the Natural Sciences and Engineering Council of Canada (NSERC), the Fonds pour la Formation de Chercheurs et l'Aide à la Recherche du Québec (FCAR), and the National Science Foundation (grant SES-0078492).
Comparative analysis of clutter removal techniques over experimental IR images

Infrared surveillance systems have the task of detecting small moving targets having low signal-to-clutter ratio. Detection is usually accomplished by (1) removing the background structures from each frame and (2) integrating the target signal over consecutive frames of the residual sequence. We focus on the analysis of background removal techniques based on linear and nonlinear two-dimensional filters such as the window average, median, max-median, and max-mean. We introduce two modified versions of the window average and max-mean filters, where an appropriate guard window is used to reduce the bias due to the target. We define an ad hoc methodology to compare the different background estimation techniques on the basis of their ability to suppress background structures and to preserve the target of interest. Finally, we present and discuss the results obtained over two experimental IR sequences containing a highly structured background.
Design of new kernel density estimator for entropy maximization in independent component analysis

This paper proposes a new algorithm for estimating the score function using maximum entropy theory and kernel density estimation. The main idea is to control smoothing parameters for maximizing entropy in kernel density estimation. To generate score function, directly partial derivative equation from kernel density estimator is derived. To find suitable smoothing parameter, we adopted gradient descent method. Finally, the new kernel density estimator is experimented in blind separation and discuss on properties of the proposed learning algorithm.
Galactic evolution of 7Li

Abstract Lithium represents a key element in cosmology, as it is one of the few nuclei synthesized during the Big Bang. The primordial abundance of 7Li allows us to impose constraints on the primordial nucleosynthesis and on the baryon density of the universe. However, 7Li is not only produced during the Big Bang but also during galactic evolution: measures of stellar Li in our Galaxy suggest an almost constant Li abundance (the so-called Spite plateau) at low metallicities and a subsequent increase in the disk stars, leading to a Li abundance in Population I stars higher by a factor of ten than in Population II stars. This means that there must exist several possible stellar sources of 7Li: asymptotic giant branch stars, supernovae, novae, red giant stars. 7Li is also partly produced in spallation processes while 6Li is entirely produced by such processes. All of these sources have been included in galactic chemical evolution models and constraints have been derived on the primordial 7Li and its evolution, as well on stellar models. I will review these models and their results and what we have learned about 7Li evolution. Some still open problems, such as the disagreement between the primordial 7Li abundance as derived by WMAP and as measured in Population II stars, and the uncertainties about the main sources of stellar 7Li will be discussed.
Inversion of real transient radar data using the distorted-Born iterative algorithm

Real transient (time-domain) radar data are inverted using the distorted-Born iterative algorithm with a finite difference time domain (FDTD) forward solver. Application is to the nondestructive evaluation of concrete structures. Despite the fact that the source pulse shape was unknown, that its amplitude had to be calibrated by hand, that a 2-D wave propagation model was used, and that grid dispersion errors were present, reasonable reconstruction images were obtained. Certain artifacts that appeared in the reconstructed images may be attributed to the FDTD forward solver and are not inherent in the distorted-Born iterative method. Therefore, improvement in the inversion algorithm can be achieved by improving on the forward solver. <<ETX>>
Procedure for correct refocusing of the Rotman lens according to Snell's law

A procedure is derived for proper refocusing of a dielectric-filled Rotman lens with beam port locations determined according to Snell's law. The theoretical performance of such a lens is compared with that of a conventional design at wide scan angle and the design equations for correct refocusing of the refracting lens derived. The approach provides an alternative lens configuration which may give a wider field of scan, at a given focal length, for lenses fabricated in microstrip or stripline. The presence of grating lobes in the lens is discussed. >
Multisymplectic Relative Equilibria, Multiphase Wavetrains, and Coupled NLS Equations

The article begins with a geometric formulation of two‐phase wavetrain solutions of coupled nonlinear Schrödinger equations. It is shown that these solutions come in natural four‐parameter families, associated with symmetry, and a geometric instability condition can be deduced from the parameter structure that generalizes Roskes' instability criterion. It is then shown that this geometric structure is universal in the sense that it does not depend on the particular equation, only on the structure of the equations. The theory also extends to the case without symmetry, where small divisors may be present, but gives a new formal geometric framework for multiphase wavetrains.
Death Penalty Charging in Los Angeles County

The authors analyze death penalty charging data for Los Angeles County involving homicides from 1990 to 1994. The data were collected by the Los Angeles Times. This data set is one of the largest tabulations of homicide defendant data yet collected. A Bayesian logistic regression analysis is applied with a proper prior formulated to provide conservative inferences. The authors illustrate procedures for inferences for polytomous predictors and report three analyses with three partially overlapping sets of covariates.
Crystallinity of Ce Substituted YIG Films Prepared by RF Sputtering

The crystallinity of Ce-substituted yttrium iron garnet ( Ce1\colonYIG) film was studied by measuring X-ray diffraction (XRD) and deformation and by transmission electron microscope (TEM) observation. The Ce1\colonYIG films were prepared on anisotropic etched or unetched GGG and cation doped GGG (GCGMZG) substrates by RF sputtering. They have a narrow crystallization region as regards substrate temperature and this region becomes narrower as the O2 gas flow rate is increased. The XRD measurements and TEM observation revealed that crystallized Ce1\colonYIG films have a multilayered structure except for those on GCGMZG substrates. Their layered structure was dependent on their thickness, the substrate treatment, the lattice constant difference between the film and the substrate and the O2 flow rate. They also exhibited compressive deformation which originated mainly from the lattice constant difference between the film and the substrate. The crystal growth mechanism of Ce1\colonYIG films was considered on the basis of recent work on the growth mechanism of compressively deformed strained semiconductor film.
Numerical solution of saddle point problems

Large linear systems of saddle point type arise in a wide variety of applications throughout computational science and engineering. Due to their indefiniteness and often poor spectral properties, such linear systems represent a significant challenge for solver developers. In recent years there has been a surge of interest in saddle point problems, and numerous solution techniques have been proposed for this type of system. The aim of this paper is to present and discuss a large selection of solution methods for linear systems in saddle point form, with an emphasis on iterative methods for large and sparse problems.
Mycosynthesis of silver nanoparticles by endophytic Fungi: Mechanism, characterization techniques and their applications

Fungi are attractive as reducing and stabilizing agents in the biogenic synthesis of silver nanoparticles due to the production of considerable amounts of proteins, high yields, simplicity of handling, and low toxicity of the residues. Nanotechnology is one of the prospective technologies that could be utilized to address the recent issues as agrichemical production lines, nanotechnology production lines can also have drawbacks. As a result, a brand-new branch of nanotechnology called as "green nanotechnology" was developed, integrating biological ideas with physical and chemical procedures to produce nanosized particles with specific uses that are ecologically friendly. Biochemical synthesis methods utilizing microorganisms and plants are being investigated and developed in an effort to create nanoparticles (NPs) in a sustainable and eco-friendly manner. Biological synthesis has grown in favor as a possible alternative to the drawbacks related to physical and chemical methods of synthesis. The current analysis discusses the potential for enhancing plant health and disease resistance using AgNPs produced by endophytic fungi, which will contribute to a greater level of agricultural sustainability. Future research should focus on understanding this important method, it is advised.
The Retrieval of Drop Size Distribution Parameters Using a Dual-Polarimetric Radar

The raindrop size distribution (DSD) is vital for applications such as quantitative precipitation estimation, understanding microphysical processes, and validation/improvement of two-moment bulk microphysical schemes. We trace the history of the DSD representation and its linkage to polarimetric radar observables from functional forms (exponential, gamma, and generalized gamma models) and its normalization (un-normalized, single/double-moment scaling normalized). The four-parameter generalized gamma model is a good candidate for the optimal representation of the DSD variability. A radar-based disdrometer was found to describe the five archetypical shapes (from Montreal, Canada) consisting of drizzle, the larger precipitation drops and the ‘S’-shaped curvature that occurs frequently in between the drizzle and the larger-sized precipitation. Similar ‘S’-shaped DSDs were reproduced by combining the disdrometric measurements of small-sized drops from an optical array probe and large-sized drops from 2DVD. A unified theory based on the double-moment scaling normalization is described. The theory assumes the multiple power law among moments and DSDs are scaling normalized by the two characteristic parameters which are expressed as a combination of any two moments. The normalized DSDs are remarkably stable. Thus, the mean underlying shape is fitted to the generalized gamma model from which the ‘optimized’ two shape parameters are obtained. The other moments of the distribution are obtained as the product of power laws of the reference moments M3 and M6 along with the two shape parameters. These reference moments can be from dual-polarimetric measurements: M6 from the attenuation-corrected reflectivity and M3 from attenuation-corrected differential reflectivity and the specific differential propagation phase. Thus, all the moments of the distribution can be calculated, and the microphysical evolution of the DSD can be inferred. This is one of the major findings of this article.
Stability of image restoration by minimizing regularized objective functions

We address the general problem of the recovery of an unknown image, x/spl isin/R/sup p/, from noisy data, y/spl isin/R/sup q/, by minimizing a regularized objective function /spl epsi/(x,y). We focus on typical situations when the objective function is C/sup m/-smooth and is composed of a quadratic data-fidelity term and a general regularization term: /spl epsi/(x,y)=/spl par/Ax-y/spl par//sup 2/+/spl Phi/(x), where A is a linear operator. Many authors have shown that especially nonconvex regularizers /spl Phi/ allow the restoration of images involving both sharp edges and smoothly varying regions. The main limitation in using such regularizers is that, being highly nonconvex, the resultant objective functions are intricate to minimize. On the other hand since very few facts are known about the minimizers of such functions, the properties and in particular the stability of the resultant solutions are difficult to control. This state of the art limits the practical use of such functions. This work is devoted to the stability of the local and global minimizers x of objective functions /spl epsi/ as specified above, under the assumption that A is injective. We thus have shown that the global minimizers of /spl epsi/ are stable under small perturbations of the data.
An Optimization-Linked Intelligent Security Algorithm for Smart Healthcare Organizations

IoT-enabled healthcare apps are providing significant value to society by offering cost-effective patient monitoring solutions in IoT-enabled buildings. However, with a large number of users and sensitive personal information readily available in today’s fast-paced, internet, and cloud-based environment, the security of these healthcare systems must be a top priority. The idea of safely storing a patient’s health data in an electronic format raises issues regarding patient data privacy and security. Furthermore, with traditional classifiers, processing large amounts of data is a difficult challenge. Several computational intelligence approaches are useful for effectively categorizing massive quantities of data for this goal. For many of these reasons, a novel healthcare monitoring system that tracks disease processes and forecasts diseases based on the available data obtained from patients in distant communities is proposed in this study. The proposed framework consists of three major stages, namely data collection, secured storage, and disease detection. The data are collected using IoT sensor devices. After that, the homomorphic encryption (HE) model is used for secured data storage. Finally, the disease detection framework is designed with the help of Centered Convolutional Restricted Boltzmann Machines-based whale optimization (CCRBM-WO) algorithm. The experiment is conducted on a Python-based cloud tool. The proposed system outperforms current e-healthcare solutions, according to the findings of the experiments. The accuracy, precision, F1-measure, and recall of our suggested technique are 96.87%, 97.45%, 97.78%, and 98.57%, respectively, according to the proposed method.
The Spectrum of Meson States in the Isovector Non-strange T+1u Channel by the Stochastic LapH Method in Lattice QCD

A new method of estimating quark propagation, known as Stochastic LapH, has provided a way of computing many observables in lattice QCD which have previously been inaccessible due to computational expense. In this work, the first large-scale applications of the Stochastic LapH method to excited meson spectroscopy are presented. We discuss the construction of single-meson and two-meson operators with LapH smearing, and procedures for selecting and correlating large sets of these operators are described. We then present the analysis of a 56 × 56 correlator matrix of operators in the isovector nonstrange T 1u channel. We extract the energy of the ρ meson, and discuss the identification and energy extractions of excited ρ mesons. Operators are included for all flavor combinations that can result in the I = 1, S = 0 total quantum numbers of the desired symmetry channel. The correlation matrix is calculated on 551 configurations of an anisotropic 24×128 gauge field ensemble with clover-improved Wilson fermions. Good signal quality is obtained even for correlators involving same-time quark lines and isoscalar mesons as part of two-meson operators. Results are compared with experimental values compiled by the Particle Data Group.
Arsenic, cadmium, and lead exposition: a risk factor for breast and prostate cancers? – Protocol for a systematic review

Objective: This study aims to develop a protocol for a systematic review of the literature published until April/2021 related to exposure to heavy metals and the development of breast and prostate cancer, to understand the relationship between metals such as arsenic, cadmium, and lead and these types of cancer, considering the characteristics of bioaccumulation of these elements and the intensification of their use in contemporary society. Methodology: The mnemonic PECOS (Population, Exposure, Control, Outcome, and Study Design) will guide the search strategy that will be carried out in eight databases (Pubmed, Pubmed PMC, BVS, Embase, Scopus, Web of Science, Cochrane, and ProQuest). The selection process and data extraction will be conducted by two authors independently. Articles that meet the pre-established inclusion criteria (age over 18 years, exposed metals, type of cancer, case-control, and cohort studies) will be included in the review and, after reading the complete text, will have the data and characteristics extracted and organized in a table containing the bibliometric data and characteristics of the studies (population, data obtained, conclusions and results). Results: They will be presented in a descriptive synthesis followed by tables and diagrams.
Hybrid-AI-Based iBeacon Indoor Positioning Cybersecurity: Attacks and Defenses

iBeacon systems have been increasingly established in public areas to assist users in terms of indoor location navigation and positioning. People receive the services through the Bluetooth Low Energy (BLE) installed on their mobile phones. However, the positioning and navigation functions of an iBeacon system may be compromised when faced with cyberattacks issued by hackers. In other words, its security needs to be further considered and enhanced. This study took the iBeacon system of Taipei Main Station, the major transportation hub with daily traffic of at least three hundred thousand passengers, as an example for exploring its potential attacks and further studying the defense technologies, with the assistance of AI techniques and human participation. Our experiments demonstrate that in the early stage of iBeacon system information security planning, information security technology and a rolling coding encryption should be included, representing the best defense methods at present. In addition, we believe that the adoption of rolling coding is the most cost-effective defense. However, if the security of critical infrastructure is involved, the most secure defense method should be adopted, namely a predictable and encrypted rolling coding method.
Optimization of Steam Distillation of Essential Oil of Eucalyptus tereticornis by Response Surface Methodology

This paper deals with optimization of yield of Eucalyptus tereticornis oil in steam distillation using response surface methodology (RSM). The factors considered were mass of solute/solvent ratio (A), extraction time (B) and steam rate (C). These parameters were varied at two levels. Conditions of optimum oil yield predicted were 105 min of extraction time and 0.032 kg/h steam rate. These factors gave an optimum oil yield of 2.05 %. Significant model terms were time, steam rate, and the interactions between them. Analysis of variance (ANOVA) indicates that the model was significant as evidenced from R2 of 0.9844 and the model F-value of 49.13. The oil yield predicted by the model was closed to the experimentally determined values (1.85 % and 1.87 % respectively); hence the model can be used for prediction of oil yield in essential oil extraction from E. tereticornis leaf via steam distillation method.
Spitzer Observations of NGC 2547: The Disk Population at 25 Million Years

We present Spitzer observations of the young cluster NGC 2547, obtaining photometry at 3.6, 4.5, 5.8, 8.0, and 24 μm, reaching significantly fainter infrared sensitivities than previous studies. With these observations, we investigate the disk frequency in this cluster. A total of 3770, 2408, 1988, 1238, and 1123 sources were detected in the common region of the five respective bands. The detection limits were 14.9, 14.0, 13.5, 13.3, and 12.0 mag, respectively. The large majority of sources are associated with 2MASS objects. From this large ensemble, we utilize the criteria of Naylor et al. to identify 184 likely members of the cluster. The analyses in this paper are focused on these likely members. Of the 184 candidates, 162 were detected in at least one Infrared Array Camera (IRAC) band. These objects form a well-defined family in the J-K,K-[3.6] color-color diagram. There is no evidence for excess emission at 3.6 μm for cluster candidates with J-K < 0.8, corresponding to spectral types earlier than late K. For later type stars, only 12 have evidence for a 3.6 μm excess. Hence, we derive a 3.6 μm emitting disk fraction of less than 7%. The lack of excess for the more massive stars may indicate a difference in the disk dissipation timescales for different mass stars. At 24 μm, 32 of the sources are detected. Most of them have photospheric K-[3.6] and K-[24] colors, but approximately a quarter show an excess at 24 μm. This observation may be evidence for cool disks with central holes.