Miracle-dz/UniFAR-FTUs huggingface dataset:
Structural Change in Labor Supply

This paper studies how structural change in labor supply along the development spectrum shapes cross-country differences in hours worked. We emphasize two main forces: sectoral reallocation from self-employment to wage work, and declining fixed costs of wage work. We show that these forces are crucial for understanding how the extensive margin (the employment rate) and intensive margin (hours per worker) of aggregate hours worked vary with income per capita. To do so we build and estimate a quantitative model of labor supply featuring a traditional self-employment sector and a modern wage-employment sector. When estimated to match cross-country data, the model predicts that sectoral reallocation explains more than half of the total hours decrease at lower levels of development. Declining fixed costs drive the rise in employment rates at higher levels of income per capita, and imply higher hours in the future, in contrast to the lower hours resulting from income effects and expansions in tax-and-transfer systems. Email: alexander.bick@asu.edu, fuchs@wiwi.uni-frankfurt.de, lagakos@bu.edu, and hitoshi.tsujiyama@hof.uni-frankfurt.de. We thank Isaac Otoo and Paul Reimers for outstanding research assistance. For helpful comments and assistance we thank Timo Boppart, Peter Egger, Per Krusell, Kjetil Storesletten, Nora Strecker, Stephen Terry, Johanna Wallenius, and audiences at Athens, BI Norwegian Business School, Bocconi, Bonn, the CEPR Annual Macro Conference, Columbia, CREI, Florida, Goethe University Frankfurt, Hitotsubashi University, the IFS, the IIES Stockholm, Johns Hopkins SAIS, Melbourne, Monash, MOVE Barcelona, Oslo, Queensland, the Reserve Bank of Australia, the SED Annual Meetings, Sciences Po, Stockholm School of Economics, the T2M conference, University College Dublin, Vienna, Western Australia, and Zurich. Fuchs-Schündeln gratefully acknowledges financial support from the European Research Council under the ERC Consolidator Grant No. 815378 and from the DFG under the Leibniz-Preis. All potential errors are our own.
Serum Ferritin and Iron/TIBC of Pregnant Women Attending Nnamdi Azikiwe University Teaching Hospital, Nnewi, Anambra State, Nigeria: A Longitudinal Study

Background: During pregnancy, serum ferritin level is decreased because of the increased need of iron which triggers ferritin mobilization from its stores. Iron is needed during pregnancy to expand the red blood cell mass and for fetal and placental growth. Aim: The study was aimed at determining the levels of serum ferritin and serum iron/TIBC of pregnant women at different trimesters and comparing them with that of non-pregnant women. Methods: One hundred and sixty apparently healthy pregnant women attending antenatal clinic at Nnamdi Azikiwe University Teaching Hospital, Nnewi at booking in their first trimesters constituted the study population. Similarly, 160 age-matched nonpregnant women were used as the control groups. Five milliliters of venous blood were collected from each subject by means of a hypodermic needle and syringe. One milliliter of blood was aliquoted into potassium CDTA container and used for malaria parasite screening. The remaining 4 ml was placed into gel tubes and used for screening for HIV I and II, Hepatitis B surface Antigen (HBSAg), Hepatitis C virus (HCV), Veneral Disease Research Laboratory (VDRL), and for serum ferritin and Iron. The same procedures were conducted on same pregnant women at the second and third trimesters. At the second trimester, only 156 pregnant women were followed up, while 140 women completed the study at the last trimester. Results: Serum ferritin (ng/ml) and serum iron (μg/dl) were significantly decreased from the first trimester (28.83 ± 19.39) and (74.18 ± 23.92), second (23.76 ± 18.74) and (67.77 ± 17.22), and to the third (20.45 ± 18.42) and (61.23 ± 17.35) (F=10.2, P < 0.001) and (F = 21.9, P < 0.001), respectively. On the other hand, TIBC increased significantly from the first trimester (349.50 ± 52.69), second (364.67 ± 54.53), and to the third (374.40 ± 55.40) (F = 10.9, P < 0.001), respectively. When compared to non-pregnant controls, the serum ferritin and iron in the first (28.83 ± 19.39) and (74.18 ± 23.92), second (23.76 ± 18.74) and (67.77 ± 17.22), and third (20.45 ± 18.42) and (61.23 ± 17.35) trimester were significantly decreased compared to the non-pregnant controls (39.67 ± 54.70) and (80.78 ± 38.19) (F = 14.2, P < 0.001) and (F = 33.7, P < 0.001), respectively. The TIBC in the first (349.50 ± 52.69), second (364.67 ± 54.53), and third (374.40 ± 55.40) trimesters showed a significant increase compared to the controls (338.37 ± 57.68) (F = 17.1, P < 0.001), respectively. Conclusion: This study showed that levels of serum ferritin and serum iron/TIBC in pregnant women are altered in pregnancy. Therefore, there is need to monitor pregnancies at risk to prevent adverse outcomes.
The Effect of "Drug Compliance Therapy" on Compliance, Attitude toward Medication, and Insight among Hospitalized Patients with Psychotic Disorders

Patient’s attitude toward medication and insight into illness are the main predictors of compliance. An innovative multi-component approach thought to be advantageous in addressing and possibly improving the above mentioned factors is "drug compliance therapy". Objective: To examine the effect of "drug compliance therapy" on compliance, attitude toward medication, and insight among hospitalized patients with psychotic disorders. Setting: The study was conducted at El-Maamoura Hospital for Psychiatric Medicine in Alexandria, Egypt. Subjects: Comprised 80 noncompliant patients. They were divided into two equal groups; a study group and a control one (40 patients each). Tools: Four tools were used; Psychotic Patients' Socio-demographic and Clinical Data Structured Interview Schedule, Clinician Rating of Compliance Scale (CRCS), Drug Attitude Inventory (DAI-30) and Schedule for Assessment of Insight-Expanded version (SAI-E). Results: Patients in the study group showed significant improvements in their mean scores of the CRCS, DAI-30, and SAI-E following the implementation of drug compliance therapy. Conclusion: Drug compliance therapy can bring significant improvements in the levels of compliance, attitudes, and insight among hospitalized patients with psychotic disorders. Recommendations: Drug compliance therapy should be incorporated into the routine psychiatric clinical practice, psychiatric nurses need to be trained to deliver it in the inpatient and outpatient settings, and further researches are needed to evaluate the effectiveness of this therapy over long-term follow-up periods.
Pregabalin for Postoperative Analgesia after Idiopathic Scoliosis Surgery

Introduction: Postoperative pain management in children after spinal surgery is a crucial issue that deserves much attention. Multimodal analgesia appears to be the most appropriate approach to optimize pain control. Pregabalin has proven its efficacy in improving postoperative pain scores and considerably reducing opioid consumption in adults. The aim of the study was to demonstrate the analgesic effect of pregabalin for postoperative pain relief after scoliosis surgery in adolescents. Methods: Forty adolescents with idiopathic scoliosis scheduled to undergo a posterior corrective spinal surgery, were assigned to receive either pregabalin 150 mg orally (pregabalin group, n=20) or a placebo (control group, n=20) one hour before surgery and 12 hours postoperatively. Patient received also, paracetamol 15 mg/Kg four times per day, 20 mg of piroxicam orally twice-a-day and morphine administered via PCA pump, for 48 hours. Total postoperative morphine consumption and severity of pain at rest and on movement using a 10 cm-Visual analogic scale pain score were measured 48 hours following surgery. Results: Total 48-hour PCA morphine consumption was significantly lower in the pregabalin group than in the control group [mean (SD) 29.8 mg (13.2) vs. 43.1 mg (20.7), P=0.014]. However, there were no differences between groups in the median VAS pain scores at rest and on movement. No significant differences were found with regard to side effects in the initial 48 hours after surgery. Conclusions: A per operative total dose of 300 mg oral pregabalin in adolescents undergoing surgical correction of idiopathic scoliosis, was associated with a significant reduction in 48-h morphine consumption.
Efficient transfer learning for NLP with ELECTRA

Clark et al. [2020] claims that the ELECTRA approach is highly efficient in NLP performances relative to computation budget. As such, this reproducibility study focus on this claim, summarized by the following question: Can we use ELECTRA to achieve close to SOTA performances for NLP in low-resource settings, in term of compute cost?
UNIVERSIDAD NACIONAL DE SAN AGUSTÍN DE AREQUIPA FACULTAD DE INGENIERIA DE PROCESOS ESCUELA PROFESIONAL DE INGENIERÍA DE INDUSTRIAS ALIMENTARIAS

The objective of this work was to evaluate the stabilisation of betalains and phenolic compounds extracted from red cactus pear by spray and freeze-drying. After hydroethanolic extraction and partial solvent removal under reduced pressure, the highly coloured extracts were enriched with oca starch and maltodextrin as drying aids in different ratios (100:0, 70:30, and 50:50, oca starch: maltodextrin) prior to spray and freezedrying. The obtained microencapsulated extract powders were characterised by moisture content, hygroscopicity, solubility and morphology. In addition, the stability of the encapsulated betalains and phenolics was evaluated during storage at room temperature for 105 days. All microcapsules showed high retentions of betacyanins (69.9–86.5% after 105 days), betaxanthins (72.2–81.9%), phenolic compounds (46.5– 63.5%) and antioxidant capacity (60.1–64.9%, FRAP method; 49.7–57.5%, ABTS method). The system with 70:30 starch:maltodextrin ratio as drying aids showed the highest values of retention regarding the polyphenol content (63.5%), antioxidant capacity (64.9% to FRAP method) and betacyanin content (86.5%), as well as a low degradation rate constant of betacyanins (1,23 x 10 days) and a long half-life (563 days). Oca starch used alone or in combination with maltodextrin has been shown to work adequately as a microencapsulating agent and stabilizer of pigments and antioxidants derived from red cactus pear.
Reasons for no-show to referrals at a university eye clinic after eye examination via a mobile ophthalmic unit in a Brazilian region.

PURPOSE This study aimed to identify patient's reason for no-show at a university eye clinic after ophthalmic examination via a mobile ophthalmic unit, which provides comprehensive ophthalmic care to underserved communities in a region of Brazil. METHODS In 2017/2018, this prospective observational study searched for no-shows at referrals to a university eye clinic after an outreach program screening via a mobile ophthalmic unit in 10 municipalities in the central-western region of São Paulo, Brazil. A total of 1,928 patients underwent a comprehensive eye examination at no cost, and 37.1% of them needed referral to a university eye clinic for specialized examinations or surgeries. We used the following two main tools: (1) comparative analysis between patients who attended the referral and those who did not; (2) active search using a questionnaire to assess reasons for no-show. RESULTS Attendance to referrals was not influenced by age, gender, distance from the university hospital, number of ophthalmologists in the municipality, average family income, and visual acuity. The main cause for referrals was cataract (350 cases). No-show was most common among glaucoma/suspected glaucoma (54.1%) cases, followed by strabismus (45%) and anterior segment disease (33.6%) cases. Many patients who did not attend the referral sought another service. CONCLUSION Patient's issues and lack of knowledge regarding their ophthalmic condition are the main reasons for no-show at referrals for free ophthalmic care. Thus, educational campaigns are needed to achieve consistently high attendance to prevent avoidable blindness.
Effects of Extrusion on Physicochemical Properties of Oat Polysaccharides and Its Improvement in Flour Dough Extensibility and Gumminess

The first two authors contributed to the work equally and should be regarded as co-first authors. This work evaluated the influence of extrusion on the physicochemical properties of oat polysaccharides and its improvement in flour dough quality. Solubility, solvent retention capacity, and water binding capacity of extruded oat bran polysaccharides (EOPs) were effectively improved when compared to normal oat bran polysaccharides (NOP S ), whereas the gelation temperature markedly decreased. The EOP solution revealed strong viscoelasticity. Moreover, the extensibility and gumminess of EOP-added dough were significantly improved when compared to blank dough. Therefore, EOPs are highly potential for use as new functional ingredients in food.
Can Corruption Enhance Innovation? New Evidence from China’s Anti-Corruption Campaign

The bulk of extant literature suggests that corruption impedes innovation; however, we find evidence of the opposite of this in China, such that corruption in the form of grease money enhances corporate innovation. Our identification strategy exploits China’s anti-corruption campaign as a shock to corruption. Using a difference-in-differences model, we find that corrupt firms experience an 18.94% decrease in patent applications and a 17.30% decrease in patent approvals after the campaign relative to their peers. Additional tests of generalizability confirm a positive corruption–innovation relationship. The positive effects of corruption on innovation are more significant when firms have limited political power and connections. Government subsidies are underlying channels through which the campaign affects corporate innovation. Our evidence sheds light on the “greasing-the-wheels” perspective on corruption.
Measuring the H i Content of Individual Galaxies Out to the Epoch of Reionization with [C ii]

The H i gas content is a key ingredient in galaxy evolution, the study of which has been limited to moderate cosmological distances for individual galaxies due to the weakness of the hyperfine H i 21 cm transition. Here we present a new approach that allows us to infer the H i gas mass M HI of individual galaxies up to z ≈ 6, based on a direct measurement of the [C ii]-to-H i conversion factor in star-forming galaxies at z ≳ 2 using γ-ray burst afterglows. By compiling recent [C ii]-158 μm emission line measurements we quantify the evolution of the H i content in galaxies through cosmic time. We find that M HI starts to exceed the stellar mass M ⋆ at z ≳ 1, and increases as a function of redshift. The H i fraction of the total baryonic mass increases from around 20% at z = 0 to about 60% at z ∼ 6. We further uncover a universal relation between the H i gas fraction M HI/M ⋆ and the gas-phase metallicity, which seems to hold from z ≈ 6 to z = 0. The majority of galaxies at z > 2 are observed to have H i depletion times, t dep,HI = M HI/SFR, less than ≈2 Gyr, substantially shorter than for z ∼ 0 galaxies. Finally, we use the [C ii]-to-H i conversion factor to determine the cosmic mass density of H i in galaxies, ρ HI, at three distinct epochs: z ≈ 0, z ≈ 2, and z ∼ 4–6. These measurements are consistent with previous estimates based on 21 cm H i observations in the local universe and with damped Lyα absorbers (DLAs) at z ≳ 2, suggesting an overall decrease by a factor of ≈5 in ρ HI(z) from the end of the reionization epoch to the present.
On Proximal Causal Inference With Synthetic Controls

We aim to evaluate the causal impact of an intervention when time series data on a single treated unit and multiple untreated units are available, in preand posttreatment periods. In their seminal work, Abadie & Gardeazabal (2003) and Abadie et al. (2010) proposed a synthetic control (SC) method as an approach to relax the parallel trend assumption on which difference-in-differences methods typically rely upon. The term “synthetic control” refers to a weighted average of control units built to match the treated unit’s pre-treatment outcome trajectory, such that the SC’s post-treatment outcome predicts the treated unit’s unobserved potential outcome under no treatment. The treatment effect is then estimated as the difference in post-treatment outcomes between the treated unit and the SC. A common practice to estimate the weights is to regress the pre-treatment outcome process of the treated unit on that of control units using ordinary or weighted (constrained) least squares. However, it has been established that these estimators can fail to be consistent under standard time series asymptotic regimes. Furthermore, inferences with synthetic controls are typically performed by placebo test which lacks formal justification. In this paper, we introduce a proximal causal inference framework for the synthetic control approach and formalize identification and inference for both the synthetic control weights and the treatment effect on the treated unit. We further extend the traditional linear interactive fixed effects model to accommodate more general nonlinear models allowing for binary and count outcomes which are currently under-studied in the synthetic control literature. We illustrate our proposed methods with simulation studies and an application to the evaluation of the 1990 German Reunification. K eywords: consistency; inference; panel data; proximal causal inference; synthetic control. 1 ar X iv :2 10 8. 13 93 5v 1 [ st at . M E ] 3 1 A ug 2 02 1
End-to-End Monocular Vanishing Point Detection Exploiting Lane Annotations

Vanishing points (VPs) play a vital role in various computer vision tasks, especially for recognizing the 3D scenes from an image. In the real-world scenario of automobile applications, it is costly to manually obtain the external camera parameters when the camera is attached to the vehicle or the attachment is accidentally perturbed. In this paper we introduce a simple but effective end-to-end vanishing point detection. By automatically calculating intersection of the extrapolated lane marker annotations, we obtain geometrically consistent VP labels and mitigate human annotation errors caused by manual VP labeling. With the calculated VP labels we train end-to-end VP Detector via heatmap estimation. The VP Detector realizes higher accuracy than the methods utilizing manual annotation or lane detection, paving the way for accurate online camera calibration.
Beyond Model Extraction: Imitation Attack for Black-Box NLP APIs

Machine-learning-as-a-service (MLaaS) has attracted millions of users to their outperforming sophisticated models. Although published as black-box APIs, the valuable models behind these services are still vulnerable to imitation attacks. Recently, a series of works have demonstrated that attackers manage to steal or extract the victim models. Nonethe-less, none of the previous stolen models can outperform the original black-box APIs. In this work, we take the ﬁrst step of showing that attackers could potentially surpass victims via unsupervised domain adaptation and multi-victim ensemble. Extensive experiments on benchmark datasets and real-world APIs validate that the imitators can succeed in outper-forming the original black-box models. We consider this as a milestone in the research of imitation attack, especially on NLP APIs, as the superior performance could inﬂuence the defense or even publishing strategy of API providers.
EG-Booster: Explanation-Guided Booster of ML Evasion Attacks

The widespread usage of machine learning (ML) in a myriad of domains has raised questions about its trustworthiness in high-stakes environments. Part of the quest for trustworthy ML is assessing robustness to test-time adversarial examples. Inline with the trustworthy ML goal, a useful input to potentially aid robustness evaluation is feature-based explanations of model predictions. In this paper, we present a novel approach, called EG-Booster, that leverages techniques from explainable ML to guide adversarial example crafting for improved robustness evaluation of ML models. The key insight in EG-Booster is the use of feature-based explanations of model predictions to guide adversarial example crafting by adding consequential perturbations (likely to result in model evasion) and avoiding non-consequential perturbations (unlikely to contribute to evasion). EG-Booster is agnostic to model architecture, threat model, and supports diverse distance metrics used in the literature. We evaluate EG-Booster using image classification benchmark datasets: MNIST and CIFAR10. Our findings suggest that EG-Booster significantly improves the evasion rate of state-of-the-art attacks while performing a smaller number of perturbations. Through extensive experiments that cover four white-box and three black-box attacks, we demonstrate the effectiveness of EG-Booster against two undefended neural networks trained on MNIST and CIFAR10, and an adversarially-trained ResNet model trained on CIFAR10. Furthermore, we introduce a stability assessment metric and evaluate the reliability of our explanation-based attack boosting approach by tracking the similarity between the model's predictions across multiple runs of EG-Booster. Our results over 10 separate runs suggest that EG-Booster's output is stable across distinct runs. Combined with state-of-the-art attacks, we hope EG-Booster will be used towards improved robustness assessment of ML models against evasion attacks.
Decision Tree-Based Predictive Models for Academic Achievement Using College Students' Support Networks

In this study, we examine a set of primary data collected from 484 students enrolled in a large public university in the Mid-Atlantic United States region during the early stages of the COVID-19 pandemic. The data, called Ties data, included students' demographic and support network information. The support network data comprised of information that highlighted the type of support, (i.e. emotional or educational;routine or intense). Using this data set, models for predicting students' academic achievement, quantified by their self-reported GPA, were created using Chi-Square Automatic Interaction Detection (CHAID), a decision tree algorithm, and cforest, a random forest algorithm that uses conditional inference trees. We compare the methods' accuracy and variation in the set of important variables suggested by each algorithm. Each algorithm found different variables important for different student demographics with some overlap. For White students, different types of educational support were important in predicting academic achievement, while for non-White students, different types of emotional support were important in predicting academic achievement. The presence of differing types of routine support were important in predicting academic achievement for cisgender women, while differing types of intense support were important in predicting academic achievement for cisgender men.
Weak continuity of curvature for connections in $L^p$

We study the weak continuity of two interrelated non-linear partial differential equations, the Yang–Mills equations and the Gauß–Codazzi–Ricci equations, involving Lintegrable connections. Our key finding is that underlying cancellations in the curvature form, especially the div-curl structure inherent in both equations, are sufficient to pass to the limit in the non-linear terms. We first establish the weak continuity of Yang–Mills equations and prove that any weakly converging sequence of weak Yang–Mills connections in L converges to a weak Yang–Mills connection. We then prove that, for a sequence of isometric immersions with uniformly bounded second fundamental forms in L, the curvatures are weakly continuous, which leads to the weak continuity of the Gauß–Codazzi–Ricci equations with respect to sequences of isometric immersions with uniformly bounded second fundamental forms in L. Our methods are independent of dimensions and do not rely on gauge changes.