Miracle-dz/UniFAR-FTUs huggingface dataset:
Stratification of Returns to Higher Education in Peru: The Role of Education Quality and Major Choices

In the last two decades, access to higher education has increased substantially in Latin America. The quantity of new programs available has created concerns about education quality, which has implications for the labor market. We use rich longitudinal data from a Peruvian cohort tracked from ages 8 to 26 (the Young Lives study) to analyze the profile of students enrolled in different 'types' of higher education, and to explore the returns to higher education before and during the COVID-19 crisis. We find evidence of stratification at higher education level: (a) students from the wealthiest households tend to enroll in universities (as opposed to technical institutes), and choose majors and institutions with the highest income rewards;(b) students with higher levels of cognitive skills and socio-emotional competencies tend to attend better quality universities;(c) there are hidden gender gaps: females are more likely to enroll in majors that are the least rewarded in the labor market. In the 2020 labor market, by age 26 we find that: (d) pre-COVID, positive returns to higher education are only observable for those that attended better quality universities;(e) during the pandemic, higher education became a protective factor, with the income premium being higher for everyone that attended this education level;(f) the male income premium doubled during the pandemic.
Adsorption of binary CO2/CH4 mixtures using carbon nanotubes: Effects of confinement and surface functionalization

ABSTRACT Effect of confinement and surface functionalization in carbon nanotubes (CNTs) on the competitive adsorption of a binary CO2/CH4 mixture has been investigated by grand canonical Monte Carlo simulations. Adsorption using CNTs with different functionalization arrangements, different diameters, different functionalization degrees, and different functional groups, such as –COOH, –CO, –OH, –CH3, is investigated. Effects of (a) the pore textural properties, such as pore size and accessible surface area, and (b) the gas–adsorbent interaction, especially the electrostatic interaction, are discussed. From these results, we discuss the impact that variables such as confinement and surface functionalization have on the performance for CO2 separation.
Double Glueing over Free Exponential: with Measure Theoretic Applications

This paper provides a compact method to lift the free exponential construction of Mellies-Tabareau-Tasson over the Hyland-Schalk double glueing for orthogonality categories. A condition"reciprocity of orthogonality"is presented simply enough to lift the free exponential over the double glueing in terms of the orthogonality. Our general method applies to the monoidal category TsK of the s-finite transition kernels with countable biproducts. We show (i) TsK^{op} has the free exponential, which is shown to be describable in terms of measure theory. (ii) The s-finite transition kernels have an orthogonality between measures and measurable functions in terms of Lebesgue integrals. The orthogonality is reciprocal, hence the free exponential of (i) lifts to the orthogonality category O_I(TsK^{op}), which subsumes Ehrhard et al's probabilistic coherent spaces as the full subcategory of countable measurable spaces. To lift the free exponential, the measure-theoretic uniform convergence theorem commuting Lebesgue integral and limit plays a crucial role. Our measure-theoretic orthogonality is considered as a continuous version of the orthogonality of the probabilistic coherent spaces for linear logic, and in particular provides a two layered decomposition of Crubille et al's direct free exponential for these spaces.
Transparency Communication for Reinforcement Learning in Human-Robot Interaction

Autonomous systems need to learn from their experience to improve their decisions. Machine-learning methods, particularly reinforcement learning, have successfully applied quantitative probabilities and utilities to a variety of real-world domains, including allowing robots to improve from their mistakes. The complexity of the domains that robots operate in and the inherent complexity of their decisions make understanding the inner workings of robotics systems increasingly challenging for their human teammates. While transparency communications have shown to alleviate such problems, the additional capability of self-improving, enabled by reinforcement learning, is likely to complicate the robot’s effort to reason transparently with human teammates. In this paper, we discuss the design of both model-based and model-free re-inforcement learning for the robots in a human-robot simulation testbed and the design of trans-parency communications that unpacks the components of the robot’s decision-making and learning process.
Fluorescence correlation spectroscopy diffusion laws in the presence of moving nanodomains

It has been shown by means of simulations that spot variation fluorescence correlation spectroscopy (sv-FCS) can be used for the identification and, to some extent, also characterization of immobile lipid nanodomains in model as well as cellular plasma membranes. However, in these simulations, the nanodomains were assumed to be stationary, whereas they actually tend to move like the surrounding lipids. In the present study, we investigated how such domain movement influences the diffusion time/spot-size dependence observed in FCS experiments, usually referred to as ‘diffusion law’ analysis. We show that domain movement might mask the effects of the ‘anomalous’ diffusion characteristics of membrane lipids or proteins predicted for stationary domains, making it difficult to identify such moving nanodomains by sv-FCS. More specifically, our simulations indicate that (i) for domains moving up to a factor of 2.25 slower than the surrounding lipids, such impeded diffusion cannot be observed and the diffusion behaviour of the proteins or lipids is indistinguishable from that of freely diffusing molecules, i.e. nanodomains are not detected; (ii) impeded protein/lipid diffusion behaviour can be observed in experiments where the radii of the detection volume are similar in size to the domain radii, the domain diffusion is about 10 times slower than that of the lipids, and the probes show a high affinity to the domains; and (iii) presence of nanodomains can only be reliably detected by diffraction limited sv-FCS when the domains move very slowly (about 200 times slower than the lipid diffusion). As nanodomains are expected to be in the range of tens of nanometres and most probes show low affinities to such domains, sv-FCS is limited to stationary domains and/or STED-FCS. However, even for that latter technique, diffusing domains smaller than 50 nm in radius are hardly detectable by FCS diffusion time/spot-size dependencies.
International Shipping Consequences of a Navigable Arctic

Global warming will open up trans-Arctic shipping lanes as ice cover recedes. Given the established causal effect of shipping distance on trade volume, these lanes will affect trade between regions for which trans-Arctic routing is preferable to current options. This paper calculates optimal naval routes between important container ports on four maps of the Earth: the current world map, projected world maps immediately after either the Northwest or Northeast Passages become navigable, and a projected world map with a totally navigable Arctic. Decreases in optimal route lengths due to trans-Arctic routing are integrated with existing literature on the relationship between shipping distance and trade volume in order to project the direct influence of Arctic navigability on trade volumes. Conclusions suggest a strongly regional effect—routing between certain northern economies is shortened by up to 45%, implying trade volume increases of up to 20%. Alongside clear benefactors in the global north, even ports as far south as Singapore and Santos, Brazil can take advantage of Arctic routing in certain circumstances. However, many other regions are unaffected.
An extension of the Budyko model with iceline

Budyko’s energy model is a historic system of differential equations, capturing the effect of the water albedo on climate trends. This has been analyzed by many researchers and is the basis of our work. The position of the Earth’s iceline, and its coupling with temperature, play a very important role in determining long term climate patterns (which range between extremes from a water covered to an ice covered Earth). We built upon established results of Budyko and others, and additionally studied how two important system parameters related to water purity (the glacier forming temperature, Tc, and the water albedo, αw) may impact the long-term climatic outcome. We found that perturbing either of these parameters had significant global effects. Moreover, these effects were enhanced when occurring in combination, and even more so when in the presence of an elevated greenhouse effect.
THE EFFECTS OF ETHYLENE (EDTA) ON UPTAKE OF ZINC (Zn

Dagari, M. S. , Department of Chemistry, Faculty of Science, Federal University Gashua, Yobe State, Nigeria. Department of Pure and Industrial Chemistry, Faculty of Physical Sciences, College of Natural and Pharmaceutical Sciences, Bayero University Kano, P.M. B. 3011, Nigeria. *Correspondence author: m_smusa@yahoo.com ABSTRACT This study was carried out to assess the effects of EDTA on zinc and copper uptake in the hydroponically grown mint leaf (Mentha in both chelated and unchelated treatments and Cu uptake by mint leaf (Mentha solutions in a greenhouse were investigated various doses of Zn and Cu concentration of EDTA (0.0025M). concentrations of Zn and Cu to control. Zn contents in the shoots showed more accumulation than in the roots (p<0.05) and also Cu content in the shoots was higher than in roots chelated treatments i.e. additio inhibits their uptake in both shoot and root of the plant at (0.0025, 0.005, 0.0075 mg/L) when compared to the unchelated treatment of same concentration. chelation does not have much e of the plant (p>0.05) as the uptake in both chelated and unchelated treatments was almost constant. Hence, chelation inhibits translocation of Zn the plant at highest concentration of zinc and copper in the solution.
Major bacterial pathogens of bovine respiratory disease and lung lesions in calves from selected areas of Ethiopia

Bovine respiratory disease (BRD) is a potential threat and an economically detrimental disease of calf-rearing in Ethiopia. This study was designed to isolate the major bacterial pathogens associated with respiratory disease of calves and the lung lesions involved. A cross-sectional study with a purposive sampling method was employed in 170 calves during the study period from April 2018 to October 2018. The Bacteriological and molecular assay revealed overall isolation of 156 (91.76%) bacterial pathogens. Gross inspection of pneumonic lungs revealed one or more lesions of hydatidosis (46.67%), emphysema (44.44%), congestion (35.56%), hemorrhage (20.0%) and atelectasis (15.56%). Bacterial pathogen identification showed 86 (50.59%) Mannheimia haemolytica, 31 (18.24%) Pasteurella multocida, 22 (12.94%) Bibersteina trehalosi and 17 (10.0%) Histophilous somni. The distribution of bacterial pathogens in the study areas indicates a higher incidence of M. haemolytica from the nasopharyngeal swab and pneumonic lung tissue at a rate of 68 (61.26%) and 18 (40.0%), respectively. Multiplex PCR of M. haemolytica revealed amplification of the PHSSA gene (~325 bp) and Rpt2 gene (~1022 bp). Conventional PCR assay of P. multocida showed amplification of the species-specific KMT1 gene (~460 bp) and B. trehalosi targeting sodA gene (~144 bp). The findings in the present study indicate that M. haemolytica is the major bacterial pathogen of BRD in calves in the study areas. Thus, it calls for the development of a vaccine from M. haemolytica strains and assessment of pathogen-specific risk factors associated with BRD in calves to design costeffective control strategies.
Social Skills Development in Bilingual Preschoolers : A Case Study

This article presents a case study of bilingual preschoolers’ social skills development in dimensions of communication, cooperation, emotional management, and problematic behaviours. This study utilized non-participant observation and semi-structured interviews for collecting data. Five bilingual preschoolers and two early childhood educators (ECEs) participated in this study. The results indicate that the main reason for the exclusion of bilingual children in group activities in childcare is their language limitations rather than their appearance or other factors that would contribute to them being considered as an outsider. The main barrier in the social skills development of bilingual children surrounds negative feelings, such as frustration and hesitation in their relationships with others. These feelings and experiences inhibit them from interacting with others. The role of ECEs, peers, and parents was significant to the development of children’s social skills.
Prediction of Ki67 scores from H&E stained breast cancer sections using convolutional neural networks

Ki67 is an established marker of proliferation in breast cancer, but currently has limited clinical value due to limitations of the analytical validity of immunohistochemistry (IHC) - based Ki67 scoring. While the inter-assessor variability of scoring can be improved through image analysis software, Ki67 IHC also suﬀers from a lack of standardized staining protocols and is not part of routine pathology workﬂow in most countries. This could potentially be alleviated through directly predicting Ki67 scores from routine hematoxylin and eosin (H&E) stained whole-slide-images (WSIs). We compared four diﬀerent deep learning based approaches to predict Ki67 scores from routine H&E stained WSIs in a dataset that consists of matched H&E and Ki67 WSIs from 126 breast cancer patients, resulting in a Spearman correlation between WSI cancer ROI averages of 0.546 for the best performing model in a 5-fold cross-validation (CV). These ﬁndings suggest that it is possible to predict the Ki67 score from H&E stained WSIs, but validation in a larger cohort is required to meaningfully distinguish the performance of the methods that were investigated.
A Survey on Recent Advances in Machine Learning Based Sleep Apnea Detection Systems

Sleep apnea is a sleep disorder that affects a large population. This disorder can cause or augment the exposure to cardiovascular dysfunction, stroke, diabetes, and poor productivity. The polysomnography (PSG) test, which is the gold standard for sleep apnea detection, is expensive, inconvenient, and unavailable to the population at large. This calls for more friendly and accessible solutions for diagnosing sleep apnea. In this paper, we examine how sleep apnea is detected clinically, and how a combination of advances in embedded systems and machine learning can help make its diagnosis easier, more affordable, and accessible. We present the relevance of machine learning in sleep apnea detection, and a study of the recent advances in the aforementioned area. The review covers research based on machine learning, deep learning, and sensor fusion, and focuses on the following facets of sleep apnea detection: (i) type of sensors used for data collection, (ii) feature engineering approaches applied on the data (iii) classifiers used for sleep apnea detection/classification. We also analyze the challenges in the design of sleep apnea detection systems, based on the literature survey.
Slicer4J: a dynamic slicer for Java

Dynamic program slicing is used in a variety of tasks, including program debugging and security analysis. Despite being extensively studied in the literature, the only dynamic slicing solution for Java programs that is publicly available today is a tool named JavaSlicer. Unfortunately, JavaSlicer only supports programs written in Java 6 or below and does not support multithreading. To address these limitations, this paper contributes a new dynamic slicing tool for Java, named Slicer4J. Slicer4J uses low-overhead instrumentation to collect a runtime execution trace; it then constructs a thread-aware, inter-procedural dynamic control-flow graph and uses the graph to compute the slice. To support slicing through Java framework methods and native code, Slicer4J relies on a set of pre-constructed data-flow summaries of the main framework methods. It also allows the users to further customize this set, adding user-defined methods when needed. We demonstrate the applicability of Slicer4J on ten benchmark and open-source Java programs, comparing it with JavaSlicer, and discuss how to use and extend the tool.
Dynamic Proximal Unrolling Network for Compressive Sensing Imaging

—Recovering an underlying image from under-sampled measurements, Compressive Sensing Imaging (CSI) is a challenging problem and has many practical applications. Recently, deep neural networks have been applied to this problem with promising results, owing to its implicitly learned prior to alleviate the ill-poseness of CSI. However, existing neural network approaches require separate models for each imaging parameter like sampling ratios, leading to training difﬁculties and overﬁtting to speciﬁc settings. In this paper, we present a dynamic proximal unrolling network (dubbed DPUNet), which can handle a variety of measurement matrices via one single model without retraining. Speciﬁcally, DPUNet can exploit both embedded physical model via gradient descent and imposing image prior with learned dynamic proximal mapping leading to joint reconstruction. A key component of DPUNet is a dynamic proximal mapping module, whose parameters can be dynamically adjusted at inference stage and make it adapt to any given imaging setting. Experimental results demonstrate that the proposed DPUNet can effectively handle multiple CSI modalities under varying sampling ratios and noise levels with only one model, and outperform the state-of-the-art approaches.
Quantifying the Quark Gluon Plasma

The study of heavy-ion collisions presents a challenge to both theoretical and experimental nuclear physics. Due to the extremely short lifetime and small size of the collision system, disentangling information provided by experimental observables and progress in physical insight requires the careful application of plausible reasoning. I apply a program of statistical methodologies, primarily Bayesian, to quantify properties of the medium in specific models, as well as compare and criticize differing models of the system. Of particular interest are estimations of the specific shear and bulk viscosities, where we find that information carried by the experimental data is still limited. In particular we find a large sensitivity to prior assumptions at high temperatures. Moreover, sensitivities to model assumptions are present at low temperatures, and this source of model uncertainty is propagated with model averaging and model mixing.
The Additional Representative Images for Legacy (ARI-L) Project for the ALMA Science Archive

The Additional Representative Images for Legacy (ARI-L) project is a European Development project for ALMA Upgrade approved by the Joint ALMA Observatory (JAO) and the European Southern Observatory (ESO), started in 2019 June. It aims to increase the legacy value of the ALMA Science Archive (ASA) by bringing the reduction level of ALMA data from Cycles 2–4 close to that of data from more recent Cycles processed for imaging with the ALMA Pipeline. As of mid-2021, more than 150,000 images have been returned to the ASA for public use. At its completion in 2022, the project will have provided enhanced products for at least 70% of the observational data from Cycles 2–4 processable with the ALMA Pipeline. In this paper, we present the project rationale, its implementation, and the new opportunities offered to ASA users by the ARI-L products. The ARI-L cubes and images complement the much-limited number of archival image products generated during the data quality assurance stages (QA2), which cover only a small fraction of the available data for those Cycles. ARI-L imaging products are highly relevant for many science cases and significantly enhance the possibilities for exploiting archival data. Indeed, ARI-L products facilitate archive access and data usage for science purposes even for non-expert data miners, provide a homogeneous view of all data for better data set comparisons and download selections, make the archive more accessible to visualization and analysis tools, and enable the generation of preview images and plots similar to those possible for subsequent Cycles.
A Decentralized Primal-Dual Framework for Non-Convex Smooth Consensus Optimization

In this work, we introduce ADAPD, A DecentrAlized Primal-Dual algorithmic framework for solving non-convex and smooth consensus optimization problems over a network of distributed agents. The proposed framework relies on a novel problem formulation that elicits ADMM-type updates, where each agent first inexactly solves a local strongly convex subproblem with any method of its choice and then performs a neighbor communication to update a set of dual variables. We present two variants that allow for a single gradient step for the primal updates or multiple communications for the dual updates, to exploit the tradeoff between the per-iteration cost and the number of iterations. When multiple communications are performed, ADAPD can achieve theoretically optimal communication complexity results for non-convex and smooth consensus problems. Numerical experiments on several applications, including a deep-learning one, demonstrate the superiority of ADAPD over several popularly used decentralized methods.
Automatically Generating Models of IT Systems

Information technology system (ITS), informally, consists of hardware and software infrastructure (e.g., workstations, servers, laptops, installed software packages, databases, LANs, firewalls, etc.), along with physical and logical connections and inter-dependencies between various items. Nowadays, every company owns and operates an ITS, but detailed information about the system is rarely publicly available. However, there are many situations where the availability of such data would be beneficial. For example, cyber ranges need descriptions of complex realistic IT systems in order to provide an effective training and education platform. Furthermore, various algorithms in cybersecurity, in particular attack tree generation, need to be validated on realistic models of IT systems. In this paper, we describe a system we call the Generator that, based on the high-level requirements such as the number of employees and the business area the target company belongs to, generates a model of an ITS that satisfies the given requirements. We put special emphasis on the following two criteria: the generated ITS models a large amount of details, and ideally resembles a real system. Our survey of related literature found no sufficiently similar prior works, so we believe that this is the first attempt of building something like this. We created a proof-of-concept implementation of the Generator, validated it by generating ITS models for a simplified fictional financial institution, and analyzed the Generators performance with respect to the problem size. The research was done in an iterative manner, with coauthors continuously providing feedback on intermediate results. The conducted experiments show that our approach is feasible. In the future, we intend to extend this prototype to allow probabilistic generation of IT systems when only a subset of parameters is explicitly defined, and further develop and validate our approach with the help of domain experts.