Miracle-dz/UniFAR-FTUs huggingface dataset:
Statistical Computing Software Reviews

Abstract This department includes the two sections New Developments in Statistical Computing and Statistical Computing Software Reviews; suitable contents for each of these sections is described under the respective section heading. Articles submitted for the department, outside the two sections, should not be highly technical and should be relevant to the teaching or practice of statistical computing. This section is similar in organization to a Book Review section in other journals; however, software of interest to statisticians is the subject of review here. Emphasis is on software for microcomputers. Programs that operate only in larger mainframe computers will seldom receive review. Normally, producers of programs make a copy of their product available to the Section Editor, who then selects one or more persons to test the product and prepare a review. Producers of computer software who wish to have their product reviewed are invited to contact the Section Editor, Professor Kenneth Berk, Department o.
Multifractal analysis of gray-scale images

The basis of fractal (and multifractal) theory and its application to image analysis is presented. In the multifractal approach the image is described by a measure /spl mu/, or by capacities. Through the multifractal spectrum f(/spl alpha/) both local and global information of the image regularity can be derived. By using a different fractal dimension different parts of analyzed images can be detected, without loosing or introducing any information. This feature is very useful for a global image analysis, edge detection, segmentation, texture classification, etc. The applicability of the multifractal approach to image analysis is verified through different medical images.
The Dynamic Lot-Size Model with Stochastic Lead Times

Optimal solutions for the dynamic lot-sizing problem with deterministic demands but stochastic lead times are "lumpy. " If lead time distributions are arbitrary except that they are independent of order size and do not allow orders to cross in time, then each order in an optimal solution will exactly satisfy a consecutive sequence of demands, a natural extension of the classic results by Wagner and Whitin. If, on the other hand, orders can cross in time, then optimal solutions are still "lumpy" in the sense that each order will satisfy a set, not necessarily consecutive, of the demands. An example shows how this characterization can be used to find a solution to a problem where interdependence of lead times is critical. This characterization of optimal solutions facilitates dynamic programming approaches to this problem.
Electric field measurements above a radar scattering volume producing 'diffuse' auroral echoes

A coordinated experiment was conducted on March 7, 1972, in which electric fields, the mean E region neutral wind velocity, and the electron density profile were obtained in the vicinity of a radar scattering volume producing diffuse auroral echoes. The measurements were made by the Chatanika incoherent scatter radar, from the motions of barium ion clouds released by a NASA rocket. These plasma parameters were compared with the Doppler velocity maps obtained by a scanning auroral backscatter radar located at Homer, Alaska. From a threshold analysis of the data it is shown that the diffuse auroral echoes observed were not produced by primary plasma waves generated by the Buneman- Farley two-stream instability. (AIP)
Low-cost broadband circularly polarized patch antenna

A novel single-feed broadband circularly polarized patch antenna is proposed. The proposed antenna has a simple structure, consisting of a corner-truncated square radiating patch, an L-shaped ground plane, and a probe feed. Through a via hole in the vertical ground of the L-shaped ground plane, the radiating patch is easily excited by a probe feed oriented in the same plane as the patch, and circular polarization (CP) radiation over a wide frequency range (>10%) is achieved. Experimental results of a constructed prototype with the center operating frequency at about 2500 MHz showed that the antenna has an impedance bandwidth (1.5:1 VSWR) of about 30%, a 3-dB axial-ratio CP bandwidth of about 10.4%, and a gain level of 8.5 dBi or larger within the CP bandwidth. In addition to the low cost of the proposed antenna due to its simple structure, the obtained CP performance is among the best that have been reported for single-feed single-element patch antennas.
Carbon isotopic abundance ratios in S-type stars

We have used the IRAM 30 m telescope on Pico de Veleta to observe 11 S-type stars in the J = 1–0 and J = 2–1 transitions of both 12 C 16 Oa nd 13 C 16 O in order to derive the 12 CO/ 13 CO ratio in their circumstellar envelopes. Detections of high signal to noise were achieved for 7 stars. A mean isotope ratio of 35.3 ± 6.2 was found. This ratio fits between the ratio for late M stars of about 12 ± 3a nd that of carbon stars of 58 ± 17. Hence, we confirm the often proposed evolutionary sequence from late M through type S to carbon
Stereo Calibration with a Free-Moving Stick

We propose a new method to stereo rig calibration. The method consists of upgrading a 3D projective representation to Euclidean using a free-moving 1D calibration apparatus (points aligned on a line). For higher accuracy, a nonlinear technique based on the maximum likelihood criterion is used to estimate the stereo rig parameters (the intrinsic and extrinsic parameters). Experiments with real images validate the new method and show that the results of the technique are comparable to the performance of the most complex methods.
Shielding and radiation characteristics of planar layered inhomogeneous composites

We propose an analytical study in the spectral domain of inhomogeneous layers that are characterized in terms of transmission matrices leading to an equivalent circuit representation of the whole multilayered structure. Within the framework of this two-port formalism, we present a new contribution to the computation of the Green's function arising in the analysis of multilayered integrated antennas loaded with inhomogeneous materials. Such materials offer new degrees of freedom and, besides, may allow pattern control, frequency shifting, and enlargement of the bandwidth. We also propose an analytical study of the shielding effectiveness of isotropic inhomogeneous materials located in a multilayered configuration. The expression for the shielded fields sustained both by plane wave and arbitrary sources is deduced in a closed analytical form. Numerical results are also presented showing the effects of the electromagnetic parameters on radiation pattern, matching properties and radar cross section of the integrated structure.
Extended spectral analysis of internal quantum efficiency

A powerful new method for identifying the performance-limiting mechanisms in silicon solar cells has been developed and tested at Sandia. This method uses the internal quantum efficiency (IQE) of the device at both near-infrared and near-bandgap wavelengths. The conventional interpretation of IQE is expanded to accommodate textured surfaces and long diffusion lengths, and extended to near-bandgap wavelengths where internal optical effects play an important role. This paper describes how the information available from this extended analysis can be used to obtain a value for the internal optical reflectance of the back surface, and to separate the effects of diffusion length from back-surface recombination. Results from experimental tests verify the method. The information obtained can be used to compute recombination components for the cell, and to quantify the light-trapping effectiveness of the device. <<ETX>>
COROTATING AND TRANSIENT SOLAR WIND FLOWS IN THREE DIMENSIONS

▪ Abstract Two types of flows dominate the large-scale structure of the solar wind: corotating flows and transient disturbances. Corotating flows are associated with spatial variability in the coronal expansion and solar rotation, whereas transient disturbances are associated with episodic ejections of material into interplanetary space from coronal regions not previously participating in the solar wind expansion. Ulysses' recent epic journey over the poles of the Sun has provided new insights on the three-dimensional nature of both corotating flows and transient disturbances in the solar wind and their evolution with heliocentric distance and latitude. This paper provides a simple physical description of the origins and dynamics of both of these types of solar wind flows, highlighting new understanding gained from the unique Ulysses high-latitude observations.
Choosing a robustness tuning parameter

A novel method is proposed for choosing the tuning parameter associated with a family of robust estimators. It consists of minimising estimated mean squared error, an approach that requires pilot estimation of model parameters. The method is explored for the family of minimum distance estimators proposed by [Basu, A. , Harris, I.R. , Hjort, N.L. and Jones, M.C. , 1998, Robust and efficient estimation by minimising a density power divergence. Biometrika, 85, 549–559. ] Our preference in that context is for a version of the method using the L 2 distance estimator [Scott, D.W. , 2001, Parametric statistical modeling by minimum integrated squared error. Technometrics, 43, 274–285. ] as pilot estimator.
Multiplier techniques for linear multistep methods

A theory is developed for the fixed-h stability of integration schemes based on A(α)-stable formulas when applied to nonlinear parabolic-like stiff equations. The theory is based on a general multiplier technique whose properties we fully develop. Assuming that a few simply checkable criteria which are related to monotonicity properties of the nonlinearity around the computed solution are satisfied, we obtain various error bounds and boundedness results for that solution. Some practical implications of the theory are also given.
Structural Characteristics of Knowledge Graphs Determine the Quality of Knowledge Graph Embeddings Across Model and Hyperparameter Choices

The realm of biomedicine is producing information at a rate far beyond the capacity of clinicians, researchers, and machine learning experts to analyse in full. Recently, developments in Knowledge Graphs (KGs) have facilitated the representation of all this information in an easily-integrable and easily-queryable format. With increasing academic and clinical interest in Knowledge Graph Em-beddings (KGEs), various KGE models have been developed to allow machine learning to efficiently run on these large Knowledge Graphs and predict new, previously unseen information about the domain. However, the need to validate hyperparameters for every new dataset, especially considering the time and expertise needed for validation and model training, have limited the use of KGEs in biology to those who have expertise in machine learning and knowledge engineering. This research presents a framework by which the effect of hyperparam-eters on model performance for a given KG can be modelled as a function of KG structure. The presented evaluation of the framework finds a clear effect of graph structure on hyperparameter fitness. This leads to the conclusion that more research into cross-dataset hyperparameter prediction and re-use holds promise for increasing the accessibility and usability of KGEs for biomedical applications.
An Analysis of Video Games Genre as Moral Educational Game

The trend of using video games as learning media and education game in Indonesia is on the rising with the growth of the video game industry. As one of the critical aspects of modern education, moral education also attempts to integrate video games as educational game. In this study, we examined how video games can be used to enhance moral education in Indonesia. This study attempts to determine which genre of video games is suitable as a moral educational game by quantitative analysis by length, complexity, and fitness to subject parameter. The analysis results in select your adventure archetype is the most suitable genre as moral education game
Soothing Music Recommendation System for Mothers with Postpartum Depression Using CRNN Method

Postpartum depression is a significant disease but rarely recognized and treated. There are several treatments can be done to reduce the occurrence of depression, one of them are music therapy. Everyone can apply music therapy by choosing the music that suits them. For postpartum depression sufferers, the selection of soothing music is very important, so a music recommendation system is needed to choose soothing music. The purpose of this paper is how to develop a soothing music recommendation system for mothers with postpartum depression or who are at risk of suffering from postpartum depression and whether the method used can provide accurate recommendations. The method used is Convolutional Recurrent Neural Network or CRNN. The results obtained from experiments using CNN and CRNN methods in developing a music recommendation system are quite good results in detecting music tempo for both methods.
Optimal Control of a Gold Cyanide Leaching Process Based on Reinforcement Learning

Leaching by cyanide solutions in aerated alkaline slurry is the main process for gold extraction from ores. In this study, the model-based reinforcement learning (MBRL) is used to design an optimal controller for a gold cyanidation leaching process (GCLP) by automatically learning a control policy. The simulation experiments are performed on a first principle model to verify the effectiveness of the proposed method. The results show that the MBRL method can learn an optimal control policy for the GCLP.
Breaking an FPGA-Integrated NIST SP 800-193 Compliant TRNG Hard-IP Core with On-Chip Voltage-Based Fault Attacks

Practical cryptographic systems rely on a true random number generator (TRNG), which is a necessary component in any hardware Root-of-Trust (RoT). Hardware trust anchors are also integrated into larger chips, for instance as hard-IP cores in FPGAs, where the remaining FPGA fabric is freely programmable. To provide security guarantees, proper operation of the TRNG is critical. By that, adversaries are interested to tamper with the ability of TRNGs to produce unpredictable random numbers. In this paper, we show that an FPGA on-chip attack can reduce the true randomness of a TRNG integrated as a hard-IP module in the FPGA. This module is considered to be an immutable security module, compliant with NIST SP 800– 193 Platform Firmware Resilience Guidelines (PFR), which is a well known guideline for system resilience, and it is also certified by the Cryptographic Algorithm Validation Program (CAVP). By performing an on-chip voltage drop-based fault attack with user-programmable FPGA logic, the random numbers produced by the IP core fail NIST SP 800–22 and BSI AIS31 tests, meaning they are not truly random anymore. By that, this paper shows that new attack vectors can break even verified IP cores, since on-chip attacks are usually not considered in the threat model, which can still affect highly integrated systems.
Adaptive fuzzy fixed-time congestion control for TCP/AWM networks

This paper investigates the fixed-time congestion control problem for a class of TCP/AWM networks. Inspired by existing network models, a modified TCP/AWM network model with the variable number of TCP sessions and the external disturbance is first established. Moreover, fuzzy logic systems and bound estimation method are respectively introduced to deal with the nonlinear functions and the external disturbance in the system. At the same time, a new performance function, called fixed-time performance function, is first defined in this paper, where combining it with prescribed performance control guarantees that the desired queue length is tracked by the queue length in fixed-time. In addition, the proposed congestion control algorithm with the aid of backstepping technique can ensure that all the signals of the closed-loop system remain semi-globally uniformly ultimately bounded. Finally, a set of simulation studies are provided to clarify the effectiveness and superiority of the designed congestion controller.
Empirical likelihood in long‐memory time series models

This article studies the empirical likelihood method for long‐memory time series models. By virtue of the Whittle likelihood, one obtains a score function that can be viewed as an estimating equation of the parameters of a fractional integrated autoregressive moving average (ARFIMA) model. This score function is used to obtain an empirical likelihood ratio which is shown to be asymptotically chi‐square distributed. Confidence regions for the parameters are constructed based on the asymptotic distribution of the empirical likelihood ratio. Bartlett correction and finite sample properties of the empirical likelihood confidence regions are examined.
SOC Estimation Method of Lithium-ion Batteries Based on Relaxation Effect and GRU-RNN

State of charge (SOC) of lithium-ion battery is one of the key parameters for battery management system (BMS). This paper proposes a SOC estimation method of lithium-ion batteries based on relaxation effect and gated recurrent unit recurrent neural network (GRU-RNN). Firstly, the SOC estimating model based on relaxation effect is proposed using GRU-RNN. Then, a periodic discharge experiment is designed according to the characteristics of relaxation effect, which is used to collect voltage relaxation dataset. Finally, the proposed model is trained using the voltage relaxation dataset, and the SOC estimation results of proposed model under different discharge currents are verified.
On the Performance of Non-Orthogonal Transmission Enabled Two-Tier CoMP Networks

Non-orthogonal multiple access (NOMA) enables more flexible transmission designs for next generation wireless communication systems. Integrating NOMA with coordinated multi-point transmission (CoMP) can further boost the benefit brought in by joint transmission. In this paper, we investi-gate the performance of a two-tier CoMP hotspot network with NOMA under Nakagami-m fading environment. In the considered system, a macro base station (MBS) applies zero-forcing beamforming (ZF - BF) to provide multiple interference-free signal streams. The signals of two user equipments (UEs) associated with the MBS and a small-cell base station (SBS), are superimposed in each stream. Cross-tier joint transmission is applied for SBS UEs (SUEs) to efficiently improve received signal quality. Under stochastic geometry framework, we derive the analytical expressions of outage probability and study the influence of key parameter settings. Numerical results show that although a slight degradation can be expected for MBS UEs, NOMA still improves the overall outage performance for the system, especially at high target spectral efficiency region.
An Interactive Multi Model prediction-based Radar-Camera Fusion Algorithm for Autonomous Vehicles

Continuous and reliable object detection is vital for environment perception, planning and control of autonomous vehicles. As most popular sensors, the millimeter-wave radar and camera both suffer from false detection in the presence of disturbances, such as electromagnetic interference for radar and fuzzy image for camera. Therefore, in this paper, an interactive multi model prediction-based radar-camera fusion(IMM-RCF) algorithm for autonomous vehicle is proposed to tackle the aforementioned issues. While detecting the position of the same target using camera and radar, confidence level of measurements from camera and radar are evaluated by the target’s predicted location from a compound model with a weighted sum of multiple kinematic sub models. A relatively more reliable signal, either from the camera or from the radar, can be easily selected by comparing the deviation between compound model estimates and the camera or radar measurements. This allows a reliable and fast distinguishing of detection result. To improve the accuracy of the prediction result, weighting factors for the output of each sub-model are updated by observing the estimation error of each sub-model. Additionally, for a smoother output, a radar and camera signals fusion algorithm is proposed following the philosophy of Kalman filter. Experimental results on an autonomous bus show that the IMM-RCF algorithm reduced the false detection rate to 1.86% also decreases measurement error to 0.60 m for objects 39.21 m ahead of the bus, demonstrating 53.4% and 54.4% improvements relative to radar and camera measurement results in distance accuracy.
A Machine Learning Based Analysis Between Climate Change and Human Health: A Correlational Study

Climate change has huge impact in human health. Social and environmental determinants of health are affected by climate change. According to World Health Organization (WHO) states that the strokes, most heart diseases, cancers, diabetes, chronic kidney diseases are the top causes of death. Although there are direct factors behind these diseases, climate change could have an invisible role in the rise of these diseases. Researchers are using various technologies to find correlations between climate change and human health, particularly trying to find out which elements of the weather are more responsible. Although there are explicit reasons for the formation of these diseases. But few studies have been conducted on passive factors that have a hidden but serious effect on the formation of these diseases. In this regard, machine learning approach can help us to correlate between the features of climate and various human diseases. Following that, the study uses Pearson, Spearman and Phi-K algorithms to determine the possibilities of correlation between human health and climate change. The research states that Carbon Monoxide (CO) have 98% of correlation and carbon dioxide (CO2) has 95% of correlation with cardiovascular disease (Ca).
Multi-Modal Deep Reinforcement Learning in ViZDoom with Audio Component

In this work, the domain of audiovisual reinforcement learning is discussed in the context of ViZDoom environment with the addition of audio component. Two models with audiovisual features were compared: Asynchronous Proximal Policy Optimization (APPO) and Importance Weighted Actor-Learner Architecture (IMPALA). We trained the agents in two different scenarios for ViZDoom environment: Music Recognition and Duel. Agents learned to play in Duel scenario, while they achieve stable performance in Music recognition scenario. IMPALA managed to outperform APPO in Duel scenario, while APPO showed twice better results than IMPALA in Music Recognition scenario. Both agents are not able to achieve decent results in Music Recognition task and future research with provided directions of improvement could be made.
Offline Data-Driven Particle Swarm Optimization Assisted by Selective Surrogate Ensembles and Hybrid Search Strategies

Real-world optimization problems usually involve the case of offline data-driven optimization, where any real fitness evaluations are not allowed during the optimization. Although surrogate models can be used for fitness approximation, it is still a grand challenge for applying evolutionary algorithms to solve offline data-driven optimization problems. To address this issue, a new algorithm, called DDmPSO-SE, is presented in this paper. In the proposed approach, both the surrogate ensemble and local surrogate are employed to fully exploit the useful information of the offline data and the entire population. Besides, hybrid search strategies are designed to improve the search efficiency of the swarm, and a simple yet effective replacement method is put forward to enhance the optimization performance further. The experimental results indicate that DDmPSO-SE is a promising method for handling offline data-driven optimization problems and achieves competitive results compared to other offline algorithms.