Miracle-dz/UniFAR-FTUs huggingface dataset:
American Women in Transition.

This study undertaken for the National Committee for Research on the 1980 Census is the first in a planned series of volumes presenting analyses of the data from the 1980 U.S. census. The authors combine 1980 census data with data from previous censuses and national surveys to describe the changes affecting American women over the past 30 years particularly their move from the home to the labor force and from traditional family life to the realms of work and higher education. The authors "document womens increasing educational attainment and labor force participation their continued commitment to marriage and family and the balancing act necessitated by this overlap of roles. Pointing to the lack of affordable child care and womens lower earning power the authors describe the current realities of womens lives in a society that has not yet adapted to their changing status. " (EXCERPT)
Catch my Eyebrow, Catch my Mind: Examining the Effect of Upper Facial Expressions on Emotional Recognition for VR Avatars

Capturing the movement of the upper face in social VR was challenging due to the head-mounted display's occlusion, which hinders accurate emotional expression. In this paper, we conducted two user studies to examine the effect of upper facial expressions on users' emotional recognition ability with VR avatars. Study 1 found that compared with eyelids, restricting the movement of eyebrows would yield greater difficulty for the users to recognize the expressed emotions of $3\mathrm{D}$ avatars. Study 2 further tested different movement amplitudes of the upper facial expressions and results showed that the most realistic movement amplitude was optimal when balancing the tradeoff between emotion recognition accuracy and the Uncanny Valley effect.
SEGREGATION: THEORETICAL APPROACHES

Segregation and the study of its effects on socioeconomic variables has increasingly become an object of interest for economists. The multifaceted nature of the concept of segregation has led to the development of numerous indices which have tried to capture its different aspects. This chapter surveys the segregation literature focusing on axiomatic models. The axiomatic approach, by emphasizing the essential properties that the various measures share and those on which they differ, can help researchers to select the segregation indices that best fit their purposes. After presenting the basic notation, the chapter introduces the Lorenz segregation ordering on two-group cities and the four basic properties it satisfies. Examples of segregation indices are then illustrated. Additional axioms that segregation indices may satisfy, and some characterization results, are also presented. Finally, the case of variable number of groups is analysed and a further characterization theorem is formulated.
The Design of Wideband Nonreciprocal Transmission by Active Metasurface

The design of wideband nonreciprocal transmission is proposed by applying active metasurface, in which the meta-atom is composed of receiving antenna, guide-wave processing part, and transmitting antenna. Favorable spatial electromagnetic performance throughout a wideband is necessary for both the receiving and transmitting antennas. The guide-wave processing part enables one-way wave guiding by including the power amplifier with its bias circuits. By appropriately combining the three parts into one single meta-atom, good non-reciprocal transmission characteristics across a wideband were demonstrated in our field-circuit cosimulation. Furthermore, a prototype of metasurface consisting of 392 elements were fabricated and measured, yielding favorable wideband performance from 7.5 to 12.5 GHz, and wide-angle performance from −70° to +70° with more than 30 dB isolation between dual-direction transmission in the near-field measurement.
Tracking Modern Transportation with Feature-Based Algorithm in a Mobile Application in Butuan City (Bus Tracking System)

This work aims to provide commuters with a more efficient and effective bus transportation service by reducing queuing times and providing ease of use via mobile phone applications. The system has three types of mobile applications customized for each end-user: commuters, drivers, and company owner/admin. The system also includes an emergency feature that enables commuters directly contacting concerned agencies. The system also includes a passenger scanner for commuter identity monitoring, which may aid in contact tracing and criminal tracking. The commuter’s data will be recorded in the firebase database. This system was created using the JAVA Programming Language and IDE Android Studio. This application is relevant to our community, for this mobile application has features in connection to the Pandemic crisis. Also, we included high security for the commuters and give drivers’ an immediate response from the company’s technical team.
Enhanced photoelectric detection of NV magnetic resonances in diamond under dual-beam excitation

Support from EU (FP7 Project DIADEMS, Grant No. 611143) is acknowledged. A.G. acknowledges the Lendulet program of the Hungarian Academy of Sciences. The authors thank A. Jarmola and D. Budker from the Department of Physics of the University of California (Berkeley, California) for the preparation of the electron-irradiated type-Ib diamond.
Modification of organic underlayers by plasma during dry etching and its effect on the film properties

The manufacturing process of advanced logic devices has become ever more challenging than before due to continued shrinkage in dimensions from scaling down and increased complexity from the integration of new transistor structures such as gate-all-around (GAA). Underlayers are utilized as a mask to protect targeted device structures while selected areas of deposited metal is removed by wet etchant during replacement metal gate (RMG) process to construct the transistor. Reported studies describing the developmental strategies for such underlayers have been mostly focused on how to strengthen the adhesion towards the substrate with the designed film properties. In this paper, we identify the effect of plasma during dry etching of the RMG process as the factor to be considered in designing of the wet etch resistant underlayer. Physical and chemical properties of organic films after dry etching with plasmas of different gases have been investigated using various analysis techniques, and the subsequent effect of plasma-modification on the film properties such as resistance towards wet chemicals for various films was evaluated.
A Line Complex-Based Evolutionary Algorithm for Many-Objective Optimization

In solving many-objective optimization problems (MaOPs), existing nondominated sorting-based multi-objective evolutionary algorithms suffer from the fast loss of selection pressure. Most candidate solutions become nondominated during the evolutionary process, thus leading to the failure of producing off-spring toward Pareto-optimal front with diversity. Can we find a more effective way to select nondominated solutions and resolve this issue? To answer this critical question, this work proposes to evolve solutions through line complex rather than solution points in Euclidean space. First, Plücker coordinates are used to project solution points to line complex composed of position vectors and momentum ones. Besides position vectors of the solution points, momentum vectors are used to extend the comparability of non-dominated solutions and enhance selection pressure. Then, a new distance function designed for high-dimensional space is proposed to replace Euclidean distance as a more effective distance-based estimator. Based on them, a novel many-objective evolutionary algorithm (MaOEA) is proposed by integrating a line complex-based environmental selection strategy into the NSGA-III framework. The proposed algorithm is compared with the state of the art on widely used benchmark problems with up to 15 objectives. Experimental results demonstrate its superior competitiveness in solving MaOPs.
A Unified Performance Framework for Integrated Sensing-Communications Based on KL-Divergence

The need for integrated sensing and communication (ISAC) services has significantly increased in the last few years. This integration imposes serious challenges such as joint system design, resource allocation, optimization, and analysis. Since sensing and telecommunication systems have different approaches for performance evaluation, introducing a unified performance measure which provides a perception about the quality of sensing and telecommunication is very beneficial. To this end, this paper provides performance analysis for ISAC systems based on the information theoretical framework of the Kullback-Leibler divergence (KLD). The considered system model consists of a multiple-input-multiple-output (MIMO) base-station (BS) providing ISAC services to multiple communication user equipments (CUEs) and targets (or sensing-served users). The KLD framework allows for a unified evaluation of the error rate performance of CUEs, and the detection performance of the targets. The relation between the detection capability for the targets and error rate of CUEs on one hand, and the proposed KLD on the other hand is illustrated analytically. Theoretical results corroborated by simulations show that the derived KLD is very accurate and can perfectly characterize both subsystems, namely the communication and radar subsystems.
Virtual Reality applied to medical education and training on Diabetic Foot

Diabetic Foot is one of the most common complications of Diabetes Mellitus and the leading non-traumatic cause of lower-limb amputations in the World. These complications are preventable with early diagnosis and timely care. However, even in the context of expanding primary health care, this problem continues to increase, which suggests a gap in the training of primary health care professionals regarding the diagnosis and treatment of Diabetic Foot. This project proposes the development of a simulator with Virtual Reality for training students and professionals in primary health care, aiming to collaborate in filling this gap. The application will be developed using the Unity game engine, optimized for the Oculus Quest platform, and features gamification elements to increase user engagement. The context of medical care in primary care will be simulated with vari-ous clinical cases, including several virtual patients with different stages related to the Diabetic Foot, seeking to provide a range of credible and distinct experiences to potential users. We pretend to verify usability, effectiveness, and any side effects (cybersickness). The intention is to conduct field studies with qualified students and professionals to identify the main benefits and obstacles to applying the technology.
Evidential Markers of Virtual Reality: Linguistic Markup of the Navigation Route

The article explores the specifics of linguistic marking of movement in virtual reality, identified with the conceptual spatialization of the new semantic environment being mastered. Obscene lexemes are recognized as spatial tags. They are considered as evidential markers of the virtual reality narrative describing the location in virtual reality and the mental transformations experienced by the experimenter. In our work, we pay attention to the research of virtual reality, which allows movement within itself. The narrative commentary of virtual reality produced by the creator and owner of the channels "Kuplinov ► Play" Dmitry Kuplinov is chosen as the material of the analysis. By means of the narrative accompanying the movement of a person into virtual reality, a special transfer zone of semantics is constructed, represented by the specific morphology of the mental and own linguistic planes. Significant in the process of constructing the methodology of analysis was the recognition of the existence of an intermediate or consensus reality between external and internal reality, including three-dimensional positions and movements of macroscopic objects, other properties of the world for which there are general descriptions in terms of classical physics.
On the Covariance Singularity of Quaternion Estimators

The asymptotic behavior of the estimation error covariance of quaternion estimators is mathematically examined. It is proved that the condition number of the asymptotic covariance matrix is of the order of the inverse of its largest eigenvalue, so that this matrix becomes asymptotically ill-conditioned as its trace tends to zero. Nevertheless, it is proved that the aforementioned asymptotic behavior cannot be captured by low-order Taylor approximations of the covariance, such as the one computed by the extended Kalman fllter. The analytical results are demonstrated via a simulation study using the recently introduced quaternion particle fllter and the additive quaternion extended Kalman fllter.
Targeted Therapy for SLE—What Works, What Doesn’t, What’s Next

For many years, the failure of randomized controlled trials (RCTs) has prevented patients with systemic lupus erythematosus (SLE) from benefiting from biological drugs that have proved to be effective in other rheumatological diseases. Only two biologics are approved for SLE, however they can only be administered to a restricted proportion of patients. Recently, several phase II RCTs have evaluated the efficacy and safety of new biologics in extra-renal SLE and lupus nephritis. Six drug trials have reported encouraging results, with an improvement in multiple clinical and serological outcome measures. The possibility of combining B-cell depletion and anti-BLyS treatment has also been successfully explored.
Quadratically fast IRLS for sparse signal recovery

We present a new class of iterative algorithms for sparse recovery problems that combine iterative support detection and estimation. More precisely, these methods use a two state Gaussian scale mixture as a proxy for the signal model and can be interpreted both as iteratively reweighted least squares (IRLS) and Expectation/Maximization (EM) algorithms for the constrained maximization of the log-likelihood function. Under certain conditions, these methods are proved to converge to a sparse solution and to be quadratically fast in a neighborhood of that sparse solution, outperforming classical IRLS for lp-minimization. Numerical experiments validate the theoretical derivations and show that these new reconstruction schemes outperform classical IRLS for lp-minimization with p\in(0,1] in terms of rate of convergence and sparsity-undersampling tradeoff.
Bootstrap methods and their application: The Basic Bootstraps

Introduction In this chapter we discuss techniques which are applicable to a single, homogeneous sample of data, denoted by y 1 , …, y n . The sample values are thought of as the outcomes of independent and identically distributed random variables Y 1 , …, Y n whose probability density function (PDF) and cumulative distribution function (CDF) we shall denote by f and F , respectively. The sample is to be used to make inferences about a population characteristic, generically denoted by θ, using a statistic T whose value in the sample is t . We assume for the moment that the choice of T has been made and that it is an estimate for θ, which we take to be a scalar. Our attention is focused on questions concerning the probability distribution of T . For example, what are its bias, its standard error, or its quantiles? What are likely values under a certain null hypothesis of interest? How do we calculate confidence limits for θ using T ? There are two situations to distinguish, the parametric and the nonparametric. When there is a particular mathematical model, with adjustable constants or parameters ψ that fully determine f , such a model is called parametric and statistical methods based on this model are parametric methods. In this case the parameter of interest θ is a component of or function of ψ. When no such mathematical model is used, the statistical analysis is nonparametric , and uses only the fact that the random variables Y j are independent and identically distributed.
Effects of HAT-CN Layer Thickness on Molecular Orientation and Energy-Level Alignment with ZnPc

Efficient energy-level alignment is crucial for achieving high performance in organic electronic devices. Because the electronic structure of an organic semiconductor is significantly influenced by its molecular orientation, comprehensively understanding the molecular orientation and electronic structure of the organic layer is essential. In this study, we investigated the interface between a 1,4,5,8,9,11-hexaazatriphenylene hexacarbonitrile (HAT-CN) hole injection layer and a zinc-phthalocyanine (ZnPc) p-type organic semiconductor. To determine the energy-level alignment and molecular orientation, we conducted in situ ultraviolet and X-ray photoelectron spectroscopies, as well as angle-resolved X-ray absorption spectroscopy. We found that the HAT-CN molecules were oriented relatively face-on (40°) in the thin (5 nm) layer, whereas they were oriented relatively edge-on (62°) in the thick (100 nm) layer. By contrast, ZnPc orientation was not significantly altered by the underlying HAT-CN orientation. The highest occupied molecular orbital (HOMO) level of ZnPc was closer to the Fermi level on the 100 nm thick HAT-CN layer than on the 5 nm thick HAT-CN layer because of the higher work function. Consequently, a considerably low energy gap between the lowest unoccupied molecular orbital level of HAT-CN and the HOMO level of ZnPc was formed in the 100 nm thick HAT-CN case. This may improve the hole injection ability of the anode system, which can be utilized in various electronic devices.
Large‐scaled detection of COVID‐19 from X‐ray using transfer learning

The pandemic cause as a result of the outbreak of COVID‐19 disease continues to burden the healthcare system despite several interventions using vaccines and other preventive measures. Healthcare settings adopted the use of reverse transcription‐polymerase chain reaction (RT‐PCR) which is hampered by so many challenges such as miss‐diagnosis, false positive results, high cost, especially for those in remote and rural areas, the need for trained medical pathologists, the use of chemicals, and a lack of point‐of‐care detection. The use of radiographic images as an alternative or confirmatory approach has offered medical experts another option, but has some limitations, such as misinterpretation, and can be tedious for analyzing thousands of cases. In order to bridge this gap, we applied two AlexNet models for the classification of different types of pneumonia, including COVID‐19 using X‐ray. Considering the fact that the majority of articles in the literature reported binary classifications of radiographic images. This article utilizes X‐ray images for classification of COVID‐19, non‐COVID‐19 viral pneumonia, bacterial pneumonia, and normal cases using the AlexNet‐SoftMax classifier and the AlexNet‐SVM classifier. The research also evaluates performance based on 5k‐fold and 10k fold cross validation (CV). The results achieved in terms of accuracy, sensitivity, and specificity based on 70:30 partition, 5k, and 10k CV have shown that the models outperformed the majority of the state‐of‐the‐art deep learning architectures.
Semi-Supervised Modified-UNet for Lung Infection Image Segmentation

Automatic lung infection segmentation in computed tomography (CT) scans can offer great assistance in radiological diagnosis by improving accuracy and reducing time required for diagnosis. The biggest challenges for deep learning (DL) models in segmenting infection region are the high variances in infection characteristics, fuzzy boundaries between infected and normal tissues, and the troubles in getting a large number of annotated data for training. To resolve such issues, we propose a modified U-Net (Mod-UNet) model with minor architectural changes and significant modifications in the training process of vanilla 2-D U-Net. As part of these modifications, we updated the loss function, optimization function, and regularization methods, added a learning rate scheduler and applied advanced data augmentation techniques. Segmentation results on two Covid-19 Lung CT segmentation datasets show that the performance of Mod-UNet is considerably better than the baseline U-Net. Furthermore, to mitigate the issue of lack of annotated data, the Mod-UNet is used in a semi-supervised framework (Semi-Mod-UNet) which works on a random sampling approach to progressively enlarge the training dataset from a large pool of unannotated CT slices. Exhaustive experiments on the two Covid-19 CT segmentation datasets and on a real lung CT volume show that the Mod-UNet and Semi-Mod-UNet significantly outperform other state-of-the-art approaches in automated lung infection segmentation.
Comparative Analysis of Different Systematic Odometry Error Correction Methods on a Differential Drive Indoor Service Robot

The COVID-19 pandemic has led to a surge of interest in research work involving the development of robotic systems that reduce human-to-human interaction, as such a technology can greatly benefit healthcare industries in preventing the spread of highly infectious diseases. An indoor service robot is built and equipped with wheel odometry and a 2D LiDAR. However, the presence of the systematic odometry errors is evident during field testing. Hence, the possibility of minimizing systematic odometry errors is inspected using various methods of calculation, namely: UMBmark, Lee’s and Jung’s. The methods all use the Bidirectional Square Path test, performed together with ROS. It is found that Jung’s method is the most appropriate method showing a 20.4% improvement compared to the uncalibrated dead reckoning accuracy. Moreover, it is found that the presence of slippage, a nonsystematic error, greatly affects the return position errors of the robot. Consequently, it is recommended to improve the design of the wheelbase to minimize the effects of nonsystematic errors.
Bridging the Gap Between Experience-Based Knowledge and the Scientific Knowledge

The context for this study was to document fishers’ experience-based knowledge is reliable and can be included in citizen science as supplementary information to scientific research. Fishers’ experience-based knowledge contributes to important understanding of the environment and the marine ecosystem. Hence, fishers’ knowledge over time is not always regarded as supplementary knowledge. There is a lack of information on how fishers have acquired reliable knowledge when they have not had formal scientific education and in what way fishers’ knowledge can be used as empirical data. In this study, a survey was conducted by scientific educator within the fishing community of Mausund in Norway. The survey was limited to specifically targeting fishers' knowledge of the migration pattern of the edible crab (Cancer pagurus) in Sulfjorden, Mausund and Frohavet. This study performs in-depth interviews with five fishers at Mausund. The experience-based knowledge acquired by these fishers is first and foremost learnt through intergenerational learning from fathers, grandfathers, and uncles to sons. The study supports existing research on fishers’ experience-based knowledge, which is professional and very precise. Experience-based and local knowledge can be understood as a knowledge contribution of value to the research field of environment monitoring.
Moments of scale mixtures of skew-normal distributions and their quadratic forms

ABSTRACT We obtain the first four moments of scale mixtures of skew-normal distributions allowing for scale parameters. The first two moments of their quadratic forms are obtained using those moments. Previous studies derived the moments, but all relevant results do not allow for scale parameters. In particular, it is shown that the mean squared error becomes an unbiased estimator of σ2 with skewed and heavy-tailed errors. Two measures of multivariate skewness are calculated.