Miracle-dz/UniFAR-FTUs huggingface dataset:
Filipino and Indonesian Migrant Domestic Workers in Hong Kong: Their Life Courses in Migration

This article presents a portrayal of Filipino and Indonesian female domestic workers’ life courses in migration, using the life history calendar data from the 2017 survey of migrant domestic workers in Hong Kong. Applying sequence analysis, we first analyzed migration trajectory features such as individual migration trajectories, duration spent in each state, and longitudinal diversity of state distributions. We found that Indonesian domestic workers, compared with their Filipino counterparts, are more diverse in their migration histories, indicating involvements in serial migration. We also conducted a cluster analysis of the domestic workers’ migratory trajectories. The analysis yielded three meaningful clusters/types of migrant workers—those moved late in life, those who participated in serial migration, and those migrated directly from their home country to Hong Kong. Finally, we investigated the effect of a complex migration history on job satisfaction and the characteristics of membership in the three ideal-typical migration types among the domestic workers older than 39 years.
A Model of Double Descent for High-Dimensional Logistic Regression

We consider a model for logistic regression where only a subset of features of size p is used for training a linear classifier over n training samples. The classifier is obtained by running gradient-descent (GD) on the logistic-loss. For this model, we investigate the dependence of the classification error on the overparameterization ratio κ = p/n. First, building on known deterministic results on convergence properties of the GD, we uncover a phase-transition phenomenon for the case of Gaussian features: the classification error of GD is the same as that of the maximum-likelihood (ML) solution when κ < κ⋆, and that of the max-margin (SVM) solution when κ > κ⋆. Next, using the convex Gaussian min-max theorem (CGMT), we sharply characterize the performance of both the ML and SVM solutions. Combining these results, we obtain curves that explicitly characterize the test error of GD for varying values of κ. The numerical results validate the theoretical predictions and unveil "double-descent" phenomena that complement similar recent observations in linear regression settings.
Deep Residual Network for MSFA Raw Image Denoising

Multispectral filter arrays (MSFA) is increasingly used in multispectral imaging. While many previous works studied the denoising algorithms for CFA based cameras, denoising MSFA raw images is little discussed. The major challenges for denoising MSFA data include 1) more channels than CFA and no predominant channel; 2) compatibility between denoising and the subsequent demosaicking process. To overcome these challenges, we propose a new deep residual network designed to account for the uniqueness of MSFA mosaic patterns. First, a split and stride convolution layer is innovated to match the mosaic pattern of the MSFA raw image. Then, data augmentation using MSFA shifting and dynamic noise is proposed to make the model robust to different noise levels. In addition, a new network optimization criteria is also suggested by using the noise standard deviation to normalize the L1 loss function. Comprehensive experiments demonstrate that the proposed deep residual network outperforms the state-of-the-art denoising algorithms in MSFA field.
Taxonomy of the family Campylobacteraceae

This chapter presents an overview of the biological diversity of Campylobacter species and Arcobacter species and also addresses the taxonomic information that has become available through whole-genome sequence analysis. Classical biochemical tests routinely used for the identification of clinical bacteria often yielded negative or variable results within Campylobacter species. This poor biochemical reactivity and lack of clear-cut differential characters led to the wide application of vernacular names for many groups of Campylobacter-like organisms (CLOs). Some of the CLO groups were later classified as novel species, but several were identified as biochemical variants of well-known species. The first isolated Campylobacter was almost certainly Campylobacter fetus. In 1914, a researcher observed a vibrio, later classified as Vibrio sputorum, in sputum of a patient with bronchitis. Similar bacteria isolated from the bovine vagina and semen were classified as V. bubulus. During a study of the bacterial flora of the cloacae of whooping cranes, 10 atypical Campylobacter isolates were recovered on two separate occasions and were classified as C. canadensis. Strain NP4, isolated from groundwater with high arsenic concentrations, is classified as a Sulfurospirillum species on the basis of 16S rRNA gene sequence analysis, and it differs from the other Sulfurospirillum isolates in its carbon source and electron acceptor usage profiles. Representative strains of Bacteroides ureolyticus species were included in a polyphasic taxonomic study to elucidate their taxonomic status. Strains have been isolated from superficial ulcers and soft tissue infections, urethritis, vaginosis, and periodontal disease.
Possible Oncogenic Potential of ΔNp73: A Newly Identified Isoform of Human p73

p73, a recently identified gene highly homologous to p53, can transactivate p53 target genes and induce apoptosis. Here we report the identification of an NH(2)-terminal truncated isoform of human p73, DeltaNp73, which is capable of suppressing p53- and p73-dependent transactivation. We speculate that this suppression is achieved by competing for the DNA binding site in the case of p53 and by direct association in the case of TAp73. Expression of DeltaNp73 in cancer cell lines also inhibited suppressive activity of p53 and TAp73 in colony formation, implying possible involvement of DeltaNp73 in oncogenesis by inhibiting the tumor-suppressive function of p53 and TAp73. Also reported is the identification of TAp73eta, a new member of the COOH-terminal truncated isoform of p73 and tissue-specific expression of these isoforms, along with other previously identified p73 isoforms.
Effects of an Advancing Tenure on CEO Cognitive Complexity

We study how the cognitive complexity of chief executive officers (CEOs) changes during their tenures. Drawing from prior theory and research, we argue that CEOs attain gradually greater role-specific knowledge, or expertise, as their tenures advance, which yields more complex thinking. Beyond examining the main effect of CEO tenure on cognitive complexity, we consider three moderators of this relationship, each of which is expected to influence the accumulation of expertise over a CEO’s time in office: industry dynamism, industry jolts, and CEO positional power. We conduct our tests on a sample of 684 CEOs of public corporations. The analytic centerpiece of our study is a novel index of CEO cognitive complexity based on CEOs’ language patterns in the question-and-answer portions of quarterly conference calls. As part of our extensive theory of measurement, we provide evidence of the reliability and validity of our index. Our results indicate that CEOs, in general, experience substantial increases in cognitive complexity over their time in office. Examined moderators somewhat, but modestly, alter this general trajectory, and nonlinearities are not observed. We discuss the implications of our findings.
What is organizational inequality? Why is it increasing as macroeconomic inequality increases?

Inequality has been increasing for decades in both rich and developing countries and the academic literature addressing it struggles to provide explanations, let alone solutions. This article is concerned with a relatively underexplored area, the relationship between macro-level inequality and organizational inequality. The core focus of the article is the recognition that the two phenomena are closely bound up one with the other. This is made possible by adopting Rousseau’s notion of inequality as hierarchy and willingness to accept subordination to authority and disparity of treatment. In doing so, we highlight similarities and dissimilarities between Rousseau and Marx. Inequality remains an issue of hierarchy at both the macro and organizational levels. As it was for Rousseau, so it is today but it is much more layered than in Rousseau’s day: inequality in society is the accepted degree of hierarchy among its members, inequality in the economy and at work is the extent to which, accepted or not, there is an imbalance of power, financial resources, remuneration of work and access to opportunities and services. The increase in inequality is due to a radical change in the socio-economic model of advanced economies. This change involves a shift towards financialization, a pressure on labour through flexibility, the decline of trade unions’ power and the retrenchment of public social spending.
Correlation of Expression Transforming Growth Factor-Î²1, E-cadherin, and Ki-67 in Meningiomas

BACKGROUND: Meningioma is the most common primary intracranial tumors in adults, accounts for 36% of total intracranial tumors. Obtaining the clinicopathological characteristics of patients with meningioma and investigating, the association between signaling pathways with disease progression could provide a basis for therapeutic development.AIM: This study aims to investigate the expression of transforming growth factor-I²1 (TGF-I²1), E-cadherin, and Ki-67 in meningiomas.MATERIALS AND METHODS: This study examined the expression levels of E-cadherin, Ki-67, and TGF-I²1 with respect to the WHO grade in patients with meningioma. A total of 62 meningioma samples were analyzed. By the WHO criteria, 54 specimens were diagnosed as the WHO Grade 1, 6 as Grade 2, and 2 as Grade 3. Grade 1 was classified as low-grade meningioma, while Grade 2 and Grade 3 were classified as high-grade meningioma (HGM). RESULTS: In this study, the mean age diagnosis was 41.97 Â± 9.79 years old, with female: male ratio of 8:1. There was no association between age, sex, and tumor location with the progression of meningioma. Immune-characterization revealed that HGM was associated with the higher number of Ki-67+ cells (p < 0.0001) and lower expression of TGF-I²1 and E-cadherin (p < 0.001). The number of Ki-67+ cells was inversely correlated with TGF-I²1 and E-cadherin (p < 0.05). TGF-I²1 expression was positively correlated with E-cadherin expression (p < 0.0001). CONCLUSION: This study concluded that HGM was highly proliferative (high Ki-67+) and invasive (low E-cadherin), with dysregulated TGF-I²1 signaling. In addition, younger age at diagnosis and high female: male ratio in our series suggests that Indonesian females might possess specific risk factors for having meningioma.
Direct methanol fuel cell modeling based on the norm optimal iterative learning control

Direct methanol fuel cells are one of the most promisingly critical fuel cell technologies for portable applications. Due to the strong dependency between actual operating conditions and electrical power, acquiring an explicit model becomes difficult. In this article, the behavioral model of direct methanol fuel cell is proposed with satisfactory accuracy, using only input/output measurement data. First, using the generated data which are tested on the direct methanol fuel cell, the frequency response of the direct methanol fuel cell is estimated as a primary model in lower accuracy. Then, the norm optimal iterative learning control is used to improve the estimated model of the direct methanol fuel cell with a predictive trial information algorithm. Iterative learning control can be used for controlling systems with imprecise models as it is capable of correcting the input control signal in each trial. The proposed algorithm uses not only the past trial information but also the future trials which are predicted. It is found that better performance, as well as much more convergence speed, can be achieved with the predicted future trials. In addition, applying the norm optimal iterative learning control on the proposed procedure, resulted from the solution of a quadratic optimization problem, leads to the optimal selection of the control inputs. Simulation results demonstrate the effectiveness of the proposed approach by practical data.
Nursing Students’ Attitudes and Stigma toward Mental Health Nursing: A Systematic Review

Purpose: This systematic review seeks to assess undergraduate nursing students’ attitudes and stigma beliefs towards mental illness. Design and Methods: A systematic review of the literature was performed. Findings: Twenty studies met the inclusion criteria. Most of the results show students had poor attitudes and stigmatising beliefs towards mental illness. However, this improved when students exposed to psychiatric course and a greater improvement toward these stigmatizing attitudes was observed in clinical placements than in theory. Practical Implications: Mental-health-specific training seems to improve perceptions toward mental health. Clinical placement underpins theory, leading to a decrease in negative attitudes and stigma regarding mental health.
Gender Wage Gap in Hospitality

This article analyzes the gender wage gap in the hospitality sector. First, it explores whether the gender wage gap is partly explained by the economic sector. Second, it measures how this gap changes across the wage distribution using quantile regression. Third, it decomposes the gender wage gap in the hospitality sector to distinguish which part can be explained by observed attributes and which part is explained by other factors (unobserved characteristics or gender discrimination). Methodologically, this article introduces the use of quantile regression to the analysis of the gender wage gap and its decomposition in the hospitality sector. The main findings are as follows. First, on average in the hospitality sector, wages (without taking into account worker skills) are below the overall average wages. However, if a deeper look is taken, this research reveals that unskilled workers are better paid in hospitality than in most of the other sectors. The opposite is true for skilled workers, since mid- and high-wage workers in the hospitality sector receive wages below their counterparts in other sectors. Second, the gender wage gap is particularly low in the hospitality sector and the gap changes across the wage distribution. Third, a large part of the gender wage gap in hospitality is not explained by worker or company characteristics. The segregation of women into worse-paid jobs and gender discrimination (or unobserved characteristics) seem to be the main sources of the gender wage gap.
Sparse and Parametric Modeling with Applications to Acoustics and Audio

Recent advances in signal processing, machine learning and deep learning with sparse intrinsic structure of data have paved the path for solving inverse problems in acoustics and audio. The main task of this thesis was to bridge the gap between the powerful mathematical tools and challenging problems in acoustics and audio. This thesis consists out of two main parts. The first part of the thesis focuses on the questions related to acoustic simulations that comply with the “real world” constraints and the acoustic data acquisition inside of closed spaces. The simulated and measured data is used to solve various types of inverse problems with underlying sparsity. By using the technique of compressed sensing, we estimate the room modes, localize sound sources in a room and also estimate room’s geometry. The Finite Rate of Innovation technique is coupled with non-convex optimization for the task of blind deconvolution in the context of echo retrieval. We also invent a new statistical measure for the echo density for the purpose of detecting the type of acoustic environment from its acoustic impulse response, even beyond fully closed spaces. These types solutions can have an application in the blooming domain of virtual, augmented and mixed reality for sound compression and rendering. The second part of the thesis focuses on the recent trends in machine learning that are centered around deep learning. Large scale data acquisition of acoustic impulse responses is still a challenging and very expensive task. Also, the existing databases tend to be too heterogeneous to be merged, due to the lack of the standardization of the acquisition procedure, and also the available metadata tends to be incomplete. In order to keep up with the recent trends and avoid the difficulties that come from the lack of large scale acoustical data, the last part of research in this thesis has diverged from the rest and is devoted to deep learning applied to classification problems in audio with the focus on speech and environmental sounds. The learning procedure is parametrized, which results in an off-grid learning procedure for audio classification. Learned trends align with perceptual trends, which helps the interpretation of the achieved results.
Brain Metastases: quest on primary site

Background: Secondaries of brain (Brain metastases, BM) are most common central nervous system (CNS) tumors and it may be initial manifestation of systemic malignancies. Histomorphology of brain metastatic tumors along with immunohistochemistry (IHC) will helpful in the detection of unknown primary site (UPS) of brain metastases. Aim: To evaluate unknown primary site of brain metastases with help of immunohistochemistry. Material and Method: This study was conducted at the department of onco-pathology of our institute. Five years data from 2014 to 2018 were obtained from online Laboratory Information System (LIS), department of onco-pathology of our institute. IHC was performed on basis of histomorphology. Results: Total 128 brain metastatic tumors were retrieved. Of which the present study have applied IHC on 107 cases and in remaining 21 cases, systemic malignancies were already diagnosed. Out of 107 cases, primary site was detected in 92.53% cases (n=99) and in 7.47% cases (n = 8), not able to detect primary site even after IHC. Lung was the most common primary site detected (n = 73) followed Gastrointestinal tract (GIT, n = 9). Conclusion: Lung followed by breast are the most common primary malignancies for secondaries of brain (BM). Systemic approach using immunohistochemistry is really helpful in detection of unknown primary site of secondaries of brain (BM).
Studies on seasonal changes in malacocenoses of the nature reserve Buki nad jeziorem Lutomskim

A 15-month study in a beech forest and alder forest in a nature reserve Buki nad Jeziorem Lutomskim revealed the presence of 36 land gastropod species of 14 families. Euryoecious species and forest species of European distribution dominated. The mean density of gastropods in the beech forest was 36 indiv. /m 2 , in the alder forest 27 indiv. /m 2 . The density in the beech and alder forest was the highest in winter, the lowest in summer. A highly significant correlation was found between the density and the number of spe- cies. In the beech forest malacocenosis, the superdominant was Discus rotundatus (O. F. Muller), the eudominant being Cochlodina laminata (Montagu). In the alder forest, no superdominant could be distin- guished, the eudominants being Discus rotundatus (O. F. Muller) and Cochlodina laminata (Montagu). Domi- nant species, as well as the number of species, species diversity, equitability and frequency varied between months. In the beech forest Discus rotundatus (O. F. Muller) - an accessory species - showed the highest fre- quency; in the alder forest all species were accidental. Juvenile individuals were more numerous than adults in spring samples from the beech forest and in autumn samples from the alder forest.
Three delay model: to find out the reason for maternal deaths

Background: Each year in India, roughly 28 million women experience pregnancy and 26 million have a live birth. Of these, an estimated 67,000 maternal deaths and one million new-born deaths occur each year. A woman dies as a result of complication arising during pregnancy and childbirth every 90 seconds in the world, and every 7 minutes in India. The three delay model can be used to find the causes of delays in relation to maternal deaths. Methods: A retrospective study was carried out in a tertiary care centre. All cases of maternal mortality between July 2010 to June 2016 were included in the study. Then data analysis was done. Results: Out of the total 382 maternal deaths, Majority of maternal deaths 43% were due to type 1 delay. 150 patients had delay in seeking help, 9 patients refused treatment and 5 patients refused admission to health care centre. 13% maternal deaths were due to type 3 delay which include delay in receiving adequate treatment, Delay in diagnosis and intervention, Lack of facilities etc. Conclusions: Type-1 delay was major contributors of maternal deaths in the study region. Therefore, to prevent the preventable maternal deaths effective action should be taken.
Improving patients loyalty: A guide for healthcare service providers and policy makers

ABSTRACT Objectives: This study aims to identify the factors affecting hospital choice decisions of patients, to categorize the decision-makers involved in the selection of hospitals, and to predict patients’ repurchase intention model based on identified factors for tertiary-level healthcare services. Methodology: Data were collected from in-patients of multispecialty hospitals located in northern India. Factor analysis and linear discriminant analysis techniques have been used to analyze the data. Findings: Study has revealed that factors that affect hospital choice decisions are basic amenities, fame and quality, building and infrastructure, ease and affordability, personal substances, responsiveness of services, recommendations and suggestions, clinical support, privacy and information sharing, and range of services. It has been found that family member and doctors individually or jointly, friends/relatives, and patients themselves take decisions to choose the hospitals. Study has also resulted in a model consisting of linear discriminant equation to predict patients’ repurchase intentions for the services of same hospitals.
Tanaidacea (Crustacea: Peracardia) of the Gulf of Mexico. VIII.

Pseudosphyrapus siegi, n. sp. is described from benthic samples collected in deep water (595-2400 m) from the northem Gulf of Mexico. Of the five currently recognized species of Pseudosphyrupus C u p , 1980, P. siegi is most closely related to P. dispar (Lang, 1968), a southwestem Pacific cognate and only other member of the genus having a squama on antenna 2. Pseudosphyrapus siegi is distinguished from P . dispar by: (1) a more triangular, but less acute, rostrum (2) the presence of two 4-pronged spines on the second maxilla (homologous spines on P . dispar are 3-pronged), and (3) the absence of a spinifom process on the chelipedal carpus. Other less noticeable differences between the two species occur in the setation or spination of the maxilliped and pereopods. Except for a small genital cone on males and developing oostegites on females, no noticeable sexual dimorphism (e.g., setation of antenna 1, enlargement or modification of chela and pereopod 1 in males) was exhibited by the specimens of P. siegi examined. It is probable, however, that more fully developed and sexually dimorphic males exist for P . siegi. The limited collection data suggests that body size in P . siegi may increase with depth. A key to the species of the genus Pseudosphyrupus is included.
The effects of funding policy change on the scientific performance of government research institutes

ABSTRACT Despite growing government financial support for scientific development, our understanding of the effect of funding practices on scientific performance is limited. This study investigates the effects of funding policy change on the scientific performance measured by research output of government research institutes (GRIs) in South Korea. This paper analyzes the balanced panel data to examine performance at Korean GRIs during 90's. Our findings indicate that the introduction of a new funding policy improved the journal impact factors of papers.