Miracle-dz/UniFAR-FTUs huggingface dataset:
Markov Chain Monte Carlo: 10 Years and Still Running!

Although Markov chain Monte Carlo (MCMC) techniques can be traced back to the early 1950s and are almost contemporary with independent Monte Carlo methods, the use of these techniques has only really taken off in the past 10 years, at least in "mainstream" statistics, with the work of Gelfand and Smith (1990) and the rediscovery of Metropolis's and Hastings's papers. The impact on the discipline is deep and durable, because these methods have opened new horizons in the scale of the problems that one can deal with, thus enhancing the position of statistics in most applied fields. The MCMC "revolution" has in particular boosted Bayesian statistics to new heights by providing a virtually universal tool for dealing with integration (and optimization) problems. This can be seen in, for instance, the explosion of papers dealing with complex models, hierarchical modelings, nonparametric Bayesian estimation, and spatial statistics. This trend has also created new synergies with mathematicians and probabilists, as well as econometricians, engineers, ecologists, astronomers, and others, for theoretical requests and practical implications of MCMC techniques. It is impossible to provide here a complete perspective on MCMC methods and applications. For this, as well as for other references, we refer the reader to the books by Gilks, Richardson, and Spiegelhalter (1995) and Robert and Casella (1999). Here we focus on four particular aspects of MCMC algorithms: theoretical foundations, practical implementation, Bayesian inference, and prospects. The Web provides many relevant sites, including the MCMC preprint server (http: //www.statslab.cam.ac.uk/ r'mcmc/), which offers a most useful window to the current research on MCMC; Wilson's site on perfect simulation (http://dimacs.rutgers.edu/-'dbwilson/exact.h), which keeps track of papers and applets on this topic; and a site on convergence control techniques (http://www.ensae.fr/ crest/statistique/robert/McDiag). Even more recent developments allow for on-line computations through applets, like Breyer's (http://www.maths.lancs.ac.uk/-'breyer/java. html), which impressively illustrates the behavior of different samplers for simple problems. Note also that the BUGS and CODA software are freely available at http://www.mrc-bsu.cam.ac.uk/bu.
Depth-dependent x-ray diffraction using extremely asymmetric reflections

Investigations of semiconductor heterostructures by x-ray diffraction are characterized by the absence of any direct depth information in the scattering profile. This paper demonstrates a new method of obtaining depth-dependent information about heterostructures by x-ray diffraction. The distances involved here are limited by the absorption of the x-rays in the material. The absorption depth can be varied continuously for asymmetric reflections by rotating the sample around the scattering vector. Two different methods for this rotation as well as experimental set-ups are discussed. We present experimental results that conclusively demonstrate the usefulness of depth-dependent measurements. The data can be used directly for the improvement of semiconductor devices.
Performance Limitation of Networked Systems with Network‐Induced Delay and Packet‐Dropout Constraints

In this paper, we deeply investigate the tracking performance limitation of single‐input single‐output (SISO) networked systems with packet dropouts and network‐induced delay constrains, where the power of the tracking error is adopted to measure of the performance and is minimized by searching through all stabilizing one‐parameter controllers, with/without considering the channel input power. Moreover, the explicit expressions of the tracking performance limitation are obtained by applying the spectral factorization technique. Theoretical analysis shows that the tracking performance limitation is closely dependent on non‐minimum phase zeros, unstable poles of a given plant, the characteristics of the reference signals, network‐induced delay and packet dropouts probability of communication channel. Finally, some typical examples and simulations are conducted to illustrate the theoretical results.
Convective Instability Underneath Midlevel Clouds: Comparisons between Numerical Simulations and VHF Radar Observations

Deep turbulent layers can sometimes be observed on the underside of clouds that extend above upper-level frontal zones. In a recent study based on 3-D numerical simulations with idealized initial conditions, it was found that mid-level cloud-base turbulence (MCT) can result from Rayleigh-Benard-like convection due to cooling by sublimation of precipitating snow into dry and weakly stratified subcloud layers. In the present study, numerically simulated MCT were compared with a turbulent layer detected by the very high frequency (VHF) middle and upper atmosphere (MU) radar during the passage of an upper-level front topped by clouds. The simulations were initialized with thermodynamic parameters derived from simultaneous radiosonde data. It was found that some important features of the simulated MCT (such as the scale of convection and vertical wind velocity perturbations) agreed quantitatively well with those reported in radar observations. Even if the possibility of other generation mechanisms cannot be ruled out, the good agreement strongly suggests that the MU radar actually detected MCT.
Comparison of atmospheric parameters derived from meteor observations with CIRA

In this paper we compare monthly averages of the atmospheric parameter T/ p (where T is temperature and p is pressure) derived from the decay of underdense meteor echoes with the CIRA (1986) atmospheric model. The meteor data were collected with the Buckland Park VHF radar situated ∼40km north of Adelaide, Australia. We examine the overall agreement between the meteor observations and CIRA as well as seasonal differences between the two. Comparison is made with the results of Hocking et al. [1997]. Our results are complimentary to those of Hocking et al.; our data were obtained in the Southern Hemisphere as opposed to the Northern Hemisphere. A discussion on the effect of the geomagnetic field on the diffusion of meteor trails and its effect on the measurement of atmospheric parameters is also included. We note that the geomagnetic field is a very important consideration when using meteors for the derivation of atmospheric temperatures and pressures above heights of around 92–93 km. This effect is required to be taken into account above these heights as failure to do so leads to errors in the interpretation of the data. Recent researchers have avoided this problem by restricting their data to below 90 km.
Numerical issues in integrating holonomic kinematic inversion algorithms for redundant manipulators

Abstract Redundant robotic manipulators under kinematic control may exhibit unpredictable behaviours at joint level, since closed loop trajectories in Cartesian space do not in general map in closed loop trajectories in joint space. Holonomic kinematic inversion algorithms avoid this problem. In this paper, we point out some numerical problems that may arise when discretizing a holonomic kinematic inversion algorithm. It is shown that the knowledge of the additional set of holonomic constraints is needed in order to overcome such drawbacks. A simple case study that shows the potential effects of discretization is discussed.
Integrity Management of the Reachable Space With Lane Grid Maps

For an autonomous vehicle, reliable situation understanding is a key component of safe navigation. An incorrect prediction of an upcoming situation means that erroneous information may be supplied to the decision-making process, leading to hazardous outcomes. It is therefore of great importance to estimate the driving areas that are reachable by other interacting road users, without introducing misleading information. This paper presents a means of handling the integrity of prediction information, given the imperfection of object prediction, via a Lane Grid Map, that is to say a spatial representation of the situation at a tactical level, based on the topological layer of a high-definition map. We demonstrate experimentally, using real data, how the spatial sampling step of the grid representation can be used to manage the integrity of prediction information. Moreover, addressing interactions during the prediction makes it possible to handle some particular situations safely. We show how some interactions can be utilized via the concept of neutralization. To quantify the integrity of the prediction, we propose the use of two metrics, namely False Negative Rate and Neutralized Time Interval. Experiments were carried out with three vehicles to evaluate the integrity of the prediction using these metrics.
Fear of COVID-19, Intolerance of Uncertainty, dan Health Anxiety Pada Kelompok Usia Produktif Di Era Pandemi COVID-19

Based on a survey from the Task Force for Handling COVID-19 in 2021, the productive age group (19-45 years) is the age group with the most confirmed positive case of COVID-19. Fear, anxiety, and uncertainty are common psychological responses found during pandemic. However, if this response develops in a maladaptive direction, it can have negative impacts on the individual in the longer term. The purpose of this study was to describe the fear of COVID-19, intolerance of uncertainty (IU), and health anxiety in individuals of productive age, especially in Greater Jakarta. This study uses a quantitative approach, with the type of correlation research. The measurement instrument that will be used is the fear of COVID-19, IUS-12, and WI-8 questionnaires. The participants in this study were individuals aged 19-45 years domiciled in Greater Jakarta. The analysis technique that will be used in this research is correlation test and regression test. The results of this study indicate that there is a significant positive correlation between the variables fear of COVID-19, health anxiety, and intolerance of uncertainty.
Erratum: “Chandra Observations of High-Mass Young Stellar Objects in the Monoceros R2 Molecular Cloud” (ApJ, 567, 423 [2002])

Errors were made in the calculations of emission measures (EMs) using the best-fit normalizations in XSPEC. All the EMs in Table 1 should be divided by 10; the log(EM) in the first row, for example, is 52.5(52.1–52.9), not 53.5(53.1–53.9). All the spectral parameters except EM are unchanged. These errors of EM do not affect any of the conclusions of the paper. The Astrophysical Journal, 580:626, 2002November 20 # 2002. The American Astronomical Society. All rights reserved. Printed in U.S.A.
Procedure for loading diamond cells with high‐pressure gas

We devised an apparatus and technique for loading diamond‐anvil cells with gases at high pressure. Cells were filled quickly and conveniently at room temperature with helium and hydrogen isotopes to initial densities exceeding those of the normal liquids. We report preliminary values of 4He and D2 melting points based on the ruby pressure scale and compare them with extrapolations from our earlier melting curves measured to 20 kbar in a piston‐cylinder device. Our filling procedure now makes it possible to use 4He as a pressure medium in diamond cells. The technique is also useful for loading cells with gaseous mixtures.
Ugo Fano. 28 July 1912 — 13 February 2001

Ugo Fano was a lively, challenging and creative physicist, known for his wide-ranging studies in atomic physics, radiological physics and radiation biology, and statistical physics and relaxation. He stimulated and guided a cadre of students, postdoctoral associates, and colleagues whose admiration and affection for him continued, as did their interactions with him, throughout his life.
CH stars in the Large Magellanic Cloud and in our Galaxy

Infrared JHK photometry is given for all the proposed LMC CH stars in the list (43 stars) of Hartwick & Cowley (1985, A. J., 334). Their luminosities range up to M(bol) ∼ −6 or brighter, making them the most massive carbon stars in the LMC. However, the kinematic discussion of Hartwick & Cowley suggests an age of a few Gyr for them. They may thus be the products of binary mergers, i.e. evolutionary products of blue stragglers of an intermediate-age population. Some analogous stars in the halo of our Galaxy may have evolved from globular cluster type blue stragglers
Effective Crude Oil Trading Techniques Using Long Short-Term Memory and Convolution Neural Networks

—Crude oil plays a vital role in the global economy and forecasting crude oil prices is crucial for both government and private sectors. However, the crude oil price is high volatility, influenced by various factors and challenging to predict. Thus, various machine learning techniques have been proposed to predict crude oil prices for decades. In this study, we propose an Artificial Neural Network (ANN) with different combinations of Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) to improve the trend forecasting of crude oil prices for better trading signals compared to traditional strategies. As the crude oil price is a time series data, it is appropriate to apply CNN and LSTM for forecasting. The concept of our model is that CNN could detect features or patterns in different locations of time series data, while LSTM could maintain both short-term and long-term memory along with time series data. The collaboration of their abilities could help the neural network model understand complex relationships of historical data and trends of crude oil prices. Our study found that the combination of CNN and LSTM could significantly enhance trading performance in the long run.
Star Power Factor Correction Architecture

Conventional two-stage power factor correction (PFC) architecture based on a boost PFC front end and an isolated dc–dc back end is increasingly challenged by the recent trend toward higher power density and efficiency. One key reason for the challenge is the difficulty of increasing the operating frequency of the boost PFC stage. This article presents a new star PFC architecture that allows the boost PFC stage to operate efficiently at a higher frequency while maintaining the performance of the second dc–dc stage, leading to a high-power-density and high-efficiency ac–dc converter design across a universal input. The high performance of star architecture is enabled by realizing: 1) continuous-conduction-mode (CCM) operation of the boost PFC stage; and 2) full-range zero-voltage-switching (ZVS) of all active switches. In addition, the star architecture can: 1) operate at a constant frequency via a proper selection of the circuit topology and modulation method; and 2) be controlled based on simple and low-cost analog circuits. A 300-kHz, 240-W, 48-V-output, and universal-input prototype is built to verify the performance of the star architecture, showcasing high power factor, constant output voltage, 97.1 % full-load efficiency, 55.6 W/in$^{3}$ power density by box volume.
Unified gauge theories and the baryon number of the universe

I suggest that the dominance of matter over antimatter in the present universe is a consequence of baryon-number--nonconserving reactions in the very early fireball. Unified gauge theories of weak, electromagnetic, and strong interactions provide a basis for such a conjecture and a computation in specific SU(5) models gives a small ratio of baryon- to photon-number density in rough agreement with observation.
A Review of Machine Learning Approaches for Human Detection through Feature Based Classification

: Human detection has always been a task of sincere importance for many automation activities under computer vision. The problem concentrates on identifying regions of human presence in an image or frames of running video. The application areas may have the varied role of human detection objectives ranging from normal to serious and very critical / sensitive. Vision based attendance, tra ffi c flow analysis, driver assistance, etc., are examples of applications with a normal role. Simultaneously, it plays a serious role in vision based theft identification system and a critical role in intruder detection in the border or sensitive places. This paper presents and explores various machine learning based human detection techniques. These techniques include feature learning based and deep learning based human detection paradigms. Through experiments, it has been found that Deep Neural Network based human detection techniques provide more e ffi cient results than feature learning based techniques in terms of detection accuracy
A note on the convergence of an inertial version of a diagonal hybrid projection-point algorithm

In this note, an inertial and relaxed version of a diagonal hybrid projection-proximal point algorithm is considered, in order to find the minimum of a function f approximated by a sequence of functions (in general, smoother than f or taking into account some constraints of the problem). Two convergence theorems are proved under different kind of assumptions, which allows to apply the method in various cases.
Multiscale tip asymptotics in hydraulic fracture with leak-off

This paper is concerned with an analysis of the near-tip region of a fluid-driven fracture propagating in a permeable saturated rock. The analysis is carried out by considering the stationary problem of a semi-infinite fracture moving at constant speed V. Two basic dissipative processes are taken into account: fracturing of the rock and viscous flow in the fracture, and two fluid balance mechanisms are considered – leak-off and storage of the fracturing fluid in the fracture. It is shown that the solution is characterized by a multiscale singular behaviour at the tip, and that the nature of the dominant singularity depends both on the relative importance of the dissipative processes and on the scale of reference. This solution provides a framework to understand the interaction of representative physical processes near the fracture tip, as well as to track the changing nature of the dominant tip process(es) with the tip velocity and its impact on the global fracture response. Furthermore, it gives a universal scaling of the near-tip processes on the scale of the entire fracture and sets the foundation for developing efficient numerical algorithms relying on accurate modelling of the tip region.
EFFECT OF BLACKCURRANT AND KAMCHATKA HONEYSUCKLE EXTRACTS ON QUALITY PROPERTIES OF RAW-COOKED MEAT PRODUCT

In recent years we notice customers demand for substitution of synthetic food additives, such as antioxidants, for natural and possibly healthier options. Lipid oxidation is the most important meat deterioration process. To retard the oxidation meat industry use variety of synthetic antioxidants. However, due to recent customer demands, industry is trying to replace those synthetic antioxidants for their natural counterparts. This is a challenging process because not only new additives must adequate antioxidant ability, but it also cannot alter sensory quality of products. Fruit extract seems to provide such an option. In our study, blackcurrant (Ribes nigrum L.) and Kamchatka honeysuckle (Lonicera caerulea var. kamtschatica) was incorporated into raw-cooked meat products (Frankfurters). Frankfurters were vacuum packed and stored at 4 °C for 21 days. During the storage period no significant differences in pH was recorded, with average value of 5.54. Also texture analysis did not reveal differences between the control and experimental groups. Both extracts decrease the amount of malondialdehyde (MDA) created in samples. Control groups increase of MDA levels at the end of storage was 108%, blackcurrant and honeysuckle extract groups were just 44 and 39% respectively. Sensory evaluation however revealed that honeysuckle extract altered taste of final product in significant way. This could pose a problem with customers’ acceptance of such a product.
Image super resolution with adaptive edge enhancement algorithm

In this paper, an adaptive edge enhancement algorithm is proposed to reconstruct a super resolution image from a single low resolution one. In order to improve the results of the high resolution reconstruction, edge statistics is learned from the scenes using a statistical analysis of the maximum likelihood estimation to approximate edge boosting weight that helps to significantly enhance edge information in the high frequency area. The edge sketch image will be adaptively combined with the results of wiener filter according to the values of the local variance. The experimental results on several test images show the success in reconstructing the super resolution both quantitatively and perceptually.
Marginal Maximum A Posteriori Item Parameter Estimation for the Generalized Graded Unfolding Model

A marginal maximum a posteriori (MMAP) procedure was implemented to estimate item parameters in the generalized graded unfolding model (GGUM). Estimates from the MMAP method were compared with those derived from marginal maximum likelihood (MML) and Markov chain Monte Carlo (MCMC) procedures in a recovery simulation that varied sample size, questionnaire length, and number of item response categories. MMAP item parameter estimates were generally the most accurate and had the smallest standard errors on average. In contrast, the accuracy and variability of MML estimates suffered substantially when the number of item response categories was small and the true item locations were extreme. MMAP estimates were also more computationally efficient than corresponding MCMC estimates. Consequently, the MMAP procedure is recommended for estimation of GGUM item parameters.
Phenotypic Spectrum of NFIA Haploinsufficiency: Two Additional Cases and Review of the Literature

The NFIA (nuclear factor I/A) gene encodes for a transcription factor belonging to the nuclear factor I family and has key roles in various embryonic differentiation pathways. In humans, NFIA is the major contributor to the phenotypic traits of “Chromosome 1p32p31 deletion syndrome”. We report on two new cases with deletions involving NFIA without any other pathogenic protein-coding gene alterations. A cohort of 24 patients with NFIA haploinsufficiency as the sole anomaly was selected by reviewing the literature and public databases in order to analyze all clinical features reported and their relative frequencies. This process was useful because it provided an overall picture of the phenotypic outcome of NFIA haploinsufficiency and helped to define a cluster of phenotypic traits that can facilitate clinicians in identifying affected patients. NFIA haploinsufficiency can be suspected by a careful observation of the dysmorphisms (macrocephaly, craniofacial, and first-finger anomalies), and this potential diagnosis is strengthened by the presence of intellectual and developmental disabilities or other neurodevelopmental disorders. Further clues of NFIA haploinsufficiency can be provided by instrumental tests such as MRI and kidney urinary tract ultrasound and confirmed by genetic testing.