Miracle-dz/UniFAR-FTUs huggingface dataset:
Evaluation of the Lidar–Radar Cloud Ice Water Content Retrievals Using Collocated in Situ Measurements

AbstractRemote sensing and in situ measurements made during the Colorado Airborne Multiphase Cloud Study, 2010–2011 (CAMPS) with instruments aboard the University of Wyoming King Air aircraft are used to evaluate lidar–radar-retrieved cloud ice water content (IWC). The collocated remote sensing and in situ measurements provide a unique dataset for evaluation studies. Near-flight-level IWC retrieval is compared with an in situ probe: the Colorado closed-path tunable diode laser hygrometer (CLH). Statistical analysis showed that the mean radar–lidar IWC is within 26% of the mean in situ measurements for pure ice clouds and within 9% for liquid-topped mixed-phase clouds. Considering their different measurement techniques and different sample volumes, the comparison shows a statistically good agreement and is close to the measurement uncertainty of the CLH, which is around 20%. It is shown that ice cloud microphysics including ice crystal shape and orientation has a significant impact on IWC retrievals. These.
Effects of noise and speaker's language background on plosive perception

This study examined the effect of speakers' language background and noise on the perception of American English (AE) plosives. Six normal-hearing, young-adults volunteered for the study. Participants listened to speech tokens of six plosives, /p/, /b/, /t/, /d/, /k/, and /g/ in the initial, medial, and final positions in the context of three vowels, /a/, /i/, and /u/, spoken by native Bangladeshi Bengali (BB) and AE speakers. Tokens were presented at 45, 65, and 65 dB SPL, either in quiet or noise. Multitalker babble, speech noise, and 1000–2000 Hz, 2000–4000 Hz, and 500–5000 Hz noise bands were used as noise. Significant differences were found with all noise types across both language backgrounds; the most difficult noise type condition being the multitalker babble. Listeners performed the best at 65 dB SPL. Listeners perceived plosives spoken by AE than BB speakers with significantly greater accuracy, more so in noise, except in the final positions. For BB-spoken tokens, listeners had the most difficult.
On the Weierstrass-Stone Theorem

Let S be a compact Hausdorff space, and let E be a normed space over the reals. Let C(S; E) be the linear space of all E-valued continuous functions ? on S with the uniform norm ||? || = sup{||? (t)||; t ? S}. When E = R, the Weierstrass-Stone Theorem describes the uniform closure of a subalgebra of C(S;R). We extend this classical result in two ways: we admit vector-valued functions and describe the uniform closure of arbitrary subsets of C(S;E). The classical Weierstrass-Stone Theorem is obtained as a corollary, without Zorn? s Lemma.
Fundamentos pedagógicos de la enseñanza comprensiva del deporte : una revisión de la literatura

In recent years a special interest in developing a theory of knowledge related to the teaching of sports has emerged. The comprehensive model of sport teaching or Teaching Games for Understanding Model and its variants have stood out. This focus is related to cognitive and constructivist theories of learning and to situated learning, and it is considered to be innovative in the teaching of sport. The objective of this work was to describe and analyse the basis on which the comprehensive teaching of sport is built, and, to achieve this, a systematic revision of the bibliography was carried out. Data indicated that the comprehensive focus is based, fundamentally, on premises related to the theories of cognitive, constructivist and situated learning. In this sense, some elements stand out. They make a reference to sport as teaching content (games modified, contextualized teaching, transfer of learning and technical-tactical teaching); to the student/player (learner/player-centred, existing knowledge and motivation); and to the teacher/coach (role of the teacher/coach, progressive teaching, problem-solving and questioning).
Analysis of large-strain shear in rate-dependent face-centred cubic polycrystals: correlation of micro- and macromechanics

Micro- and macroscopic aspects of large-strain deformation are examined through analyses of shear by using physical and phenomenological models. Past experiments and analyses are first reviewed to reveal current issues and put the present work in perspective. These issues are addressed by a complete set of simulations of large-strain shear with a finite-strain, rate-dependent polycrystal model. The model is based on a rigorous constitutive theory for crystallographic slip that accounts for the development of crystallographic texture and the effects of texture on constitutive response. The influences of strain hardening, latent hardening, strain-rate sensitivity, boundary constraints, and initial textures on texture evolution and constitutive response are studied. Coupled stress and strain effects such as axial elongation during unconstrained shear and the development of normal stresses during constrained shear are related to material properties, boundary constraint and texture. The formation of ideal textures and their role in determining polycrystalline behaviour is discussed in quantitative terms. Large-strain shear is also studied by using several phenomenological constitutive theories including J2-flow theory, J2-corner theory, and two versions of finite-strain kinematic hardening theory. The behaviours predicted by these phenomenological theories and the physically based polycrystal model are directly compared. A noteworthy outcome is the close correspondence found between the predictions of J2-corner theory and those of the micromechanically based physical model.
Magnetic field rotation of a strong resonance radiation polarization plane in the media with Doppler-broadened transitions of 01 and 10 types

For atomic media with resonant Doppler-broadened transitions of J=0 to J'=1 or J=1 to J'=0 type and taking into account the relaxation due to depolarizing collisions a theory of nonlinear magneto-optic polarization phenomena is developed. The theory is valid in the case of both arbitrary resonance linearly-polarized radiation intensity and arbitrary external magnetic field spatial orientation. The Faraday and Voigt geometries are analysed in detail, and in the case of the Faraday geometry the effect is considered of a noncompensated magnetic field transverse component on the shape of a dependence relating the radiation polarization plane rotation angle to a magnetic field longitudinal component. In the case of the Voigt geometry an essential effect is found of the angle between the radiation initial polarization and transverse magnetic field vectors on the character of the dependence relating the radiation polarization plane rotation angle to the transverse magnetic field magnitude.
Theory of resonant tunneling in a variably spaced multiquantum well structure: An Airy function approach

A theoretical study of resonant tunneling in multilayered heterostructures is presented based on an exact solution of the Schrodinger equation under the application of a constant electric field. By use of the transfer matrix approach, the transmissivity of the structure is determined as a function of the incident electron energy. The approach presented herein is easily extended to many layer structures where it is more accurate than other existing transfer matrix or Wentzel–Kramers–Brillouin (WKB) models. The transmission resonances are compared to the bound‐state energies calculated for a finite square well under bias using either an asymmetric square‐well model or the exact solution of an infinite square well under the application of an electric field. The results show good agreement with other existing models as well as with the bound‐state energies. The calculations were then applied to a new superlattice structure, the variably spaced superlattice energy filter, which is designed such that under bias.
The Growth of Cross Helicity in the Protogalactic Dynamo

The protogalactic dynamo theory of Kulsrud and coworkers raises the possibility that a strong magnetic field, which is coherent over galaxy-size length scales, emerges from a nonlinear turbulent dynamo during the protogalactic collapse. After galaxy formation, it is not clear that this galactic-scale field could be sustained by turbulence generated by supernovae because the turbulent stirring of supernovae is at the comparatively small length scale of 100 pc. However, it is known that if there is sufficient cross helicity ⟨ v ∙ b ⟩ (the angle brackets denote an average over an ensemble of random motions) in the protogalactic turbulence, some portion of the protogalactic magnetic energy will be immune to the turbulent cascade of energy, and the field generated by the protogalactic dynamo will be long-lived. In this paper, the evolution of the cross helicity in the protogalactic dynamo is investigated under the assumptions that (1) the protogalactic turbulence is supported by external forcing due to shocks, (2) resistivity is negligible compared to viscosity, (3) the initial magnetic energy and initial cross helicity are minuscule, and (4) the magnetic helicity ⟨ a ∙ b ⟩ and kinetic helicity ⟨ v ∙ 𝛁 × v ⟩ are zero. A numerical calculation based upon the direct interaction approximation (DIA) shows that while the magnetic energy grows rapidly due to line stretching, the cross helicity either decays or at most grows very slowly. This suggests that cross helicity cannot be invoked as a mechanism for sustaining the protogalactic magnetic field. Other mechanisms, however, may exist.
Towards a colony counting system using hyperspectral imaging

Colony counting is a procedure used in microbiology laboratories for food quality monitoring, environmental management, etc. Its purpose is to detect the level of contamination due to the presence and growth of bacteria, yeasts and molds in a given product. Current automated counters require a tedious training and setup procedure per product and bacteria type and do not cope well with diversity. This contrasts with the setting at microbiology laboratories, where a wide variety of food and bacteria types have to be screened on a daily basis. To overcome the limitations of current systems, we propose the use of hyperspectral imaging technology and examine the spectral variations induced by factors such as illumination, bacteria type, food source and age and type of the agar. To this end, we perform experiments making use of two alternative hyperspectral processing pipelines and compare our classification results to those yielded by color imagery. Our results show that colony counting may be automated through the automatic recovery of the illuminant power spectrum and reflectance. This is consistent with the notion that the recovery of the illuminant should minimize the variations in the spectra due to reflections, shadows and other photometric artifacts. We also illustrate how, with the reflectance at hand, the colonies can be counted making use of classical segmentation and classification algorithms.
The Effects of Dietary Interventions on Brain Aging and Neurological Diseases

Dietary interventions can ameliorate age-related neurological decline. Decades of research of in vitro studies, animal models, and clinical trials support their ability and efficacy to improve behavioral outcomes by inducing biochemical and physiological changes that lead to a more resilient brain. Dietary interventions including calorie restriction, alternate day fasting, time restricted feeding, and fasting mimicking diets not only improve normal brain aging but also slow down, or even reverse, the progression of neurological diseases. In this review, we focus on the effects of intermittent and periodic fasting on improving phenotypic outcomes, such as cognitive and motor-coordination decline, in the normal aging brain through an increase in neurogenesis and synaptic plasticity, and decrease in neuroinflammation, mitochondrial dysfunction, and oxidative stress. We summarize the results of various dietary interventions in animal models of age-related neurological diseases such as Alzheimer’s disease, Parkinson’s disease, epilepsy, and Multiple Sclerosis and discuss the results of clinical trials that explore the feasibility of dietary interventions in the prevention and treatment of these diseases.
Low-power radix 2 division algorithm with minimum add/sub operations

Reducing power consumption has become an important issue in many design problems. However, in some cases this should be done without affecting the speed of operation. In this paper we present a division algorithm which minimizes the number of add/sub operations. By splitting a single iteration of the algorithm into two steps we are able to reduce the clock period and make the execution time independent of quotient digits. The redundancy of the signed-digit quotient representation is exploited to reduce the comparison precision and use a CSA. This reduces the hardware complexity (hence lower power consumption) and reduces the propagation delay (hence faster operation). Finally, a comparison is given between the proposed and existing algorithms. The proposed algorithm reduces the power consumption by 15% over radix 4 division algorithm and by 45% over the radix 2 division algorithm.
Gaze Guidance in the Real-world by Changing Color Saturation of Objects

In this study, we propose a method for real-world gaze guidance by projecting an image onto a real-world object and changing its appearance based on visual saliency. In the proposed method, an image of the object is first acquired. Next, the image is changed such that the visual prominence of the object is increased and the image is changed so that the visual prominence of other parts of the object is decreased. Finally, the modified image is re-projected onto the object itself. Consequently, the object’s appearance and visual prominence are altered, and the user’s gaze is focused on the desired object. In this study, we propose an image processing method that changes the saturation of an object. We call this the "saturation filter. " A coaxial projector-camera system was used to apply the proposed gaze guidance method proposed in this study to a 3D object. The coaxial projector-camera system does not need to be recalibrated when an object moves. In this study, two experiments were conducted to verify the effectiveness of the proposed method in guiding a viewer’s gaze. As a result, it was confirmed that the proposed method can achieve the effect of gaze guidance.
Improved pivotal methods for constructing confidence regions with directional data

Abstract The importance of pivoting is well established in the context of nonparametric confidence regions. It ensures enhanced coverage accuracy. However, pivoting for directional data cannot be achieved simply by rescaling. A somewhat cumbersome pivotal method, which involves passing first into a space of higher dimension, has been developed by Fisher and Hall for samples of unit vectors. Although that method has some advantages over nonpivotal techniques, it does suffer from certain drawbacks—in particular, the operation of passing to a higher dimension. Here we suggest alternative pivotal approaches, the implementation of which does not require us to increase the intrinsic dimension of the data and which in practice seem to achieve greater coverage accuracy. These methods are of two types: new pivotal bootstrap techniques and techniques that exploit the “implicit pivotalness” of the empirical likelihood algorithm. Unlike the method proposed by Fisher and Hall, these methods are also applicable to axia.
A Study of Modelling IoT Security Systems with Unified Modelling Language (UML)

—The Internet of Things (IoT) has emerged as a technology with the application in different areas. Hence, security is one of the major challenges that has the potential to stifle the growth of the IoT. In fact, IoT is vulnerable to several cyber attacks and needs challenging techniques to achieve its security. In this paper, the use of a UML (Unified Modelling Language) aims at modeling IoT systems in various views. The purpose of this study is to discuss the need for more modeling in terms of security. For this reason, this paper focuses on modeling security of IoT systems. The objective is to make a comparison in terms of layers by describing the IoT architecture and presenting its components. In other words, the research question is to look for the modeling of security in the IoT layers. There is no standard that takes into account the security of the IoT architecture, there are different proposals of IoT levels, which means that each author has his own vision and own proposition. Moreover, there is a lack of modelling languages for IoT security systems. The main interest of this study is to choose the layer on which we should be interested. The question then is as follows: “which is the layer whose modeling is relevant? ” The obtained results were conclusive and provided the best insight into all the specifications of each layer of the IoT architecture studied.
DFGA: Digital Human Faces Generation and Animation from the RGB Video using Modern Deep Learning Technology

High-quality and personalized digital human faces have been widely used in media and entertainment, from film and game production to virtual reality. However, the existing technology of generating digital faces requires extremely intensive labor, which prevents the large-scale popularization of digital face technology. In order to tackle this problem, the proposed research will investigate deep learning-based facial modeling and animation technologies to 1) create personalized face geometry from a single image, including the recognizable neutral face shape and believable personalized blendshapes; (2) generate personalized production-level facial skin textures from a video or image sequence; (3) automatically drive and animate a 3D target avatar by an actor’s 2D facial video or audio. Our innovation is to achieve these tasks both efficiently and precisely by using the end-to-end framework with modern deep learning technology (StyleGAN, Transformer, NeRF).
Benchmarking Offline Reinforcement Learning Algorithms for E-Commerce Order Fraud Evaluation

Amazon and other e-commerce sites must employ mechanisms to protect their millions of customers from fraud, such as unauthorized use of credit cards. One such mechanism is order fraud evaluation, where systems evaluate orders for fraud risk, and either"pass"the order, or take an action to mitigate high risk. Order fraud evaluation systems typically use binary classification models that distinguish fraudulent and legitimate orders, to assess risk and take action. We seek to devise a system that considers both financial losses of fraud and long-term customer satisfaction, which may be impaired when incorrect actions are applied to legitimate customers. We propose that taking actions to optimize long-term impact can be formulated as a Reinforcement Learning (RL) problem. Standard RL methods require online interaction with an environment to learn, but this is not desirable in high-stakes applications like order fraud evaluation. Offline RL algorithms learn from logged data collected from the environment, without the need for online interaction, making them suitable for our use case. We show that offline RL methods outperform traditional binary classification solutions in SimStore, a simplified e-commerce simulation that incorporates order fraud risk. We also propose a novel approach to training offline RL policies that adds a new loss term during training, to better align policy exploration with taking correct actions.
Development and Implementation of an Intergenerational Bonding Program in a Co-Located Model: A Case Study in Singapore

There is a well-established body of evidence that intergenerational bonding programs (IGPs) can improve the overall well-being of older adults and strengthen relationships and understanding between generations. There is limited literature on the experience of IGPs in an Asian context, despite many of these countries facing faster rates of population ageing than other Western countries. In Singapore, intergenerational bonding is a priority in national efforts to encourage successful ageing. This paper presents a case study of the development and implementation of a co-located (shared site) model IGP in Singapore. Drawing on interviews with key stakeholders, the aim of this case study is to present the realities of the evolution of an IGP from conceptualisation through to implementation, and used the nursing home’s COVID-19 experience to illustrate issues of sustainability affecting IGPs with vulnerable populations. The findings will inform the development and implementation of similar future programs.
Antibacterial Properties of Gold Nanoparticles in the Modification of Medical Implants: A Systematic Review

The widespread occurrence of bacterial infections and their increased resistance to antibiotics has led to the development of antimicrobial coatings for multiple medical implants. Owing to their desirable properties, gold nanoparticles (AuNPs) have been developed as antibacterial agents. This systematic investigation sought to analyze the antibacterial effects of implant material surfaces modified with AuNPs. The data from 27 relevant studies were summed up. The included articles were collected from September 2011 to September 2021. According to the retrieved literature, we found that medical implants modified by AuNPs have good antibacterial effects against gram-positive and gram-negative bacteria, and the antibacterial effects would be improved by near-infrared (NIR) radiation.
TGF-β Inhibitors for Therapeutic Management of Kidney Fibrosis

Kidney fibrosis is a common pathophysiological mechanism of chronic kidney disease (CKD) progression caused by several underlying kidney diseases. Among various contributors to kidney fibrosis, transforming growth factor-β1 (TGF-β1) is the major factor driving fibrosis. TGF-β1 exerts its profibrotic attributes via the activation of canonical and non-canonical signaling pathways, which induce proliferation and activation of myofibroblasts and subsequent accumulation of extracellular matrix. Over the past few decades, studies have determined the TGF-β1 signaling pathway inhibitors and evaluated whether they could ameliorate the progression of CKD by hindering kidney fibrosis. However, therapeutic strategies that block TGF-β1 signaling have usually demonstrated unsatisfactory results. Herein, we discuss the therapeutic concepts of the TGF-β1 signaling pathway and its inhibitors and review the current state of the art regarding regarding TGF-β1 inhibitors in CKD management.
Graph Reinforcement Learning for Network Control via Bi-Level Optimization

Optimization problems over dynamic networks have been extensively studied and widely used in the past decades to formulate numerous real-world problems. However, (1) traditional optimization-based approaches do not scale to large networks, and (2) the design of good heuristics or approximation algorithms often requires significant manual trial-and-error. In this work, we argue that data-driven strategies can automate this process and learn efficient algorithms without compromising optimality. To do so, we present network control problems through the lens of reinforcement learning and propose a graph network-based framework to handle a broad class of problems. Instead of naively computing actions over high-dimensional graph elements, e.g., edges, we propose a bi-level formulation where we (1) specify a desired next state via RL, and (2) solve a convex program to best achieve it, leading to drastically improved scalability and performance. We further highlight a collection of desirable features to system designers, investigate design decisions, and present experiments on real-world control problems showing the utility, scalability, and flexibility of our framework.