Miracle-dz/UniFAR-FTUs huggingface dataset:
Unhealthy Immigrants: Sources for Health Gaps Between Immigrants and Natives in Israel

The study focuses on sources for health gaps between Jewish immigrants and native-born Israelis. Unlike traditional immigrant societies where immigration is viewed as economically motivated, immigrants returning to Israel are viewed as the “returning diaspora”. Because immigrants in Israel are entitled to the same health benefits and medical services as native-born, we expect Israel to attract unhealthy immigrants in disproportionate numbers. The data for the analysis are obtained from the Israeli National Health Interview Survey (2013–2015). The data set provides detailed information on health status and illness, sociodemographic attributes and origin of immigrants. Three major origin groups of immigrants are distinguished: the former Soviet Union, Western Europeans or the Americans (mostly Ashkenazim), and Asians or North Africans (mostly Sephardim). Our findings lend support to the expectations that the health status of all immigrant groups is poorer than that of native-born Israelis. The nativity–illness gap is most pronounced in the case of male immigrants (from Europe or the Americas or South Africa or Australia) and for female immigrants (from countries in the Middle East or North Africa) and least pronounced in the case of immigrants arriving from the former Soviet Union for both gender groups. Decomposition of the gaps into components reveals that some portion of the illness gap can be attributed to nativity status, but the largest portion of the gap is attributed to demographic characteristics. Neither socioeconomic status nor health-related behavior accounts for a substantial portion of the nativity–illness gap for all subgroups of immigrants.
Cognitive Processes to Understand Alcohol Use Disorder

Cognitive psychology represents useful approach for understanding clinical psychiatric conditions. It is also an important tool to understand the neuronal foundation of alcohol use disorder (AUD), promises the discovery of a possible endophenotype and could help to create proper pharmacological and psychological treatments (Bernardin et al. 2014; Fein et al. 1990). Then again, identifying brain processes and related cognitive dysfunctions resulting in an increased vulnerability for AUD may provide a possibility to recognize AUD at an earlier stage and as a consequence it could be important in creating better prevention programs too. It is well known for a long time that alcohol is a neurotoxic agent that influences numerous cognitive processes, and it is also widely accepted that the abstinence period after alcohol cessation shows a gradual improvement of these functions (Fein et al. 1990; Crews and Nixon 2009). Therefore, in our research we focused in particular on memory functions and mentalization processes in an attempt to investigate patients after longer periods of abstinence (more than 6 months) to exclude the direct toxic effect of alcohol and allow a longer regeneration period. The main goals of this work were to investigate the following: I. To examine the associative learning functions in intermediate-term abstinent alcohol dependence patients. II. To assess social cognitive functions by employing tests to investigate complex emotional recognition in intermediate-term alcohol dependence subjects.
Plasmodium falciparum Multidrug Resistance Proteins (pfMRPs)

The capacity of the lethal Plasmodium falciparum parasite to develop resistance against anti-malarial drugs represents a central challenge in the global control and elimination of malaria. Historically, the action of drug transporters is known to play a pivotal role in the capacity of the parasite to evade drug action. MRPs (Multidrug Resistance Protein) are known in many phylogenetically diverse groups to be related to drug resistance by being able to handle a large range of substrates, including important endogenous substances as glutathione and its conjugates. P. falciparum MRPs are associated with in vivo and in vitro altered drug response, and might be important factors for the development of multi-drug resistance phenotypes, a latent possibility in the present, and future, combination therapy environment. Information on P. falciparum MRPs is scattered in the literature, with no specialized review available. We herein address this issue by reviewing the present state of knowledge.
EDGE: Explaining Deep Reinforcement Learning Policies

With the rapid development of deep reinforcement learning (DRL) techniques, there is an increasing need to understand and interpret DRL policies. While recent research has developed explanation methods to interpret how an agent determines its moves, they cannot capture the importance of actions/states to a game’s ﬁnal result. In this work, we propose a novel self-explainable model that augments a Gaussian process with a customized kernel function and an interpretable predictor. Together with the proposed model, we also develop a parameter learning procedure that leverages inducing points and variational inference to improve learning efﬁciency. Using our proposed model, we can predict an agent’s ﬁnal rewards from its game episodes and extract time step importance within episodes as strategy-level explanations for that agent. Through experiments on Atari and MuJoCo games, we verify the explanation ﬁdelity of our method and demonstrate how to employ interpretation to understand agent behavior, discover policy vulnerabilities, remediate policy errors, and even defend against adversarial attacks.
Layout-Based Evaluation of Read/Write Performance of SOT-MRAM and SOTFET-RAM

This paper presents a comparison of array-level performance of non-volatile SOT-MRAM and SOTFET-RAM to conventional 6T CMOS SRAM using a specially developed simulation suite that merges physics-based compact models and layout-based parasitic extraction. Unlike prior work, our characterization framework generates a full layout of the memory array including all peripheral logic and routing. The framework uses an industry-standard parasitic extraction tool to generate the full netlist including parasitics which is then simulated using compact models for the appropriate emerging non-volatile device. Using this framework, we show about 1.8x energy savings for total read operations and write operations, and 2x area savings for the SOT-based memories relative to a comparable CMOS SRAM for a $256\times 128$ array size. Our unique full-layout approach also enables important insights that challenge conventional wisdom based on higher-level modeling.
The effectiveness of an electric “wash toothbrush” on oral plaque control – A pilot study - Online DZZ

Introduction: Mechanical plaque control by means of self-responsible, homebased oral hygiene is essential for the prevention of caries and periodontal diseases. In this respect, many elderly patients are at increased risk. It has already been shown that an electric toothbrush with oscillating-rotating movement and a continuous water supply has a positive effect on dental plaque control when compared to a manual toothbrush. The aim of the present pilot study was to evaluate if sonic toothbrushes likewise benefit from a continuous water supply during the brushing process and if they have a positive effect on dental plaque control in younger seniors.
Warming with Borders: Climate Refugees and Carbon Pricing

As climate changes and natural disasters intensify, the threat of large and sudden human displacement increases. This paper explores how carbon should be taxed in the presence of international displacement caused by climate change. It first provides empirical evidence on the migration response to natural disasters from developing to developed countries. Second, it introduces climate refugees into a climate-economy growth model and theoretically characterizes global and unilateral optimal carbon prices taking into account the economic and social impact of climate refugees. Third, it quantifies global and unilateral carbon prices in a North-South calibration. The main finding is that forced migration enhances the incentives of host regions to fight climate change—with a 26% increase in the unilateral carbon price, more so if political conflict is taken into account. This stands in contrast to the global and the unilateral policy in origin regions, which barely change in magnitude after accounting for the presence of climate refugees.
Do Developers Really Know How to Use Git Commands? A Large-scale Study Using Stack Overflow

Git, a cross-platform and open source distributed version control tool, provides strong support for non-linear development and is capable of handling everything from small to large projects with speed and efficiency. It has become an indispensable tool for millions of software developers and is the de facto standard of version control in software development nowadays. However, despite its widespread use, developers still frequently face difficulties when using various Git commands to manage projects and collaborate. To better help developers use Git, it is necessary to understand the issues and difficulties that they may encounter when using Git. Unfortunately, this problem has not yet been comprehensively studied. To fill this knowledge gap, in this article, we conduct a large-scale study on Stack Overflow, a popular Q&A forum for developers. We extracted and analyzed 80,370 relevant questions from Stack Overflow, and reported the increasing popularity of the Git command questions. By analyzing the questions, we identified the Git commands that are frequently asked and those that are associated with difficult questions on Stack Overflow to help understand the difficulties developers may encounter when using Git commands. In addition, we conducted a survey to understand how developers learn Git commands in practice, showing that self-learning is the primary learning approach. These findings provide a range of actionable implications for researchers, educators, and developers.
Deep Clustering Based on Implicit Likelihood Maximization

Georgios Vardakas, M.Sc. in Data and Computer Systems Engineering, Department of Computer Science and Engineering, School of Engineering, University of Ioannina, Greece, October 2021. Deep Clustering Based on Implicit Likelihood Maximization. Advisor: Aristidis Likas, Professor. Deep Learning is a type of machine learning and artificial intelligence that imitates how the human brain can learn. It is one of the essential elements of data science, which includes statistics and predictive modeling. Although DL started mainly for supervised tasks, lately, it has found success in several unsupervised learning fields, like clustering, dimensionality reduction, etc. Clustering belongs to unsupervised machine learning and is defined as a process of assigning objects to groups so that the data share common characteristics. Therefore, the main goal of clustering is for objects belonging to the same group to be similar (or related) to each other and differ (or not be related) to objects in different groups. This way, clustering explores the data and aims to find (hidden) structures in them. At the same time, clustering is one of the most challenging problems in the field of machine learning. In this master’s thesis, we study Deep Clustering methods. Deep clustering is a new promising area of clustering algorithms that emerged in recent years. The main goal of Deep Clustering is to create clustering algorithms merged with Deep Learning methods to exploit their representational power. Τherefore, in this thesis, we will clearly describe the new machine learning area of Deep Clustering and why it is considered promising. Afterward, we will present two Deep Clustering algorithms that were studied. The first Deep Clustering algorithm that we will discuss is the ClusterGan, which makes use of a modified Generative Adversarial Networks’ architecture in order to cluster the data in latent space Z. The second Deep Clustering method that we will present is our contribution, and it is based on a generative Deep Neural Network model that is trained by Implicit Likelihood Maximization (IMLE). IMLE provides an effective way of maximizing the likelihood of the model indirectly. The Deep Clustering methodology that is based on IMLE also clusters the data in the latent space. Finally, we will analyze the experiments that took place and present the experimental results.
Credit Risk and Underlying Asset Risk

This paper develops the credit risk of simple risky bond (Merton 1974) as expected option return to the maturity and analytically presents that the credit risk is influenced by the underlying asset risks. The paper moreover shows that the direction and magnitude of the influence depends on what the underlying asset risks are. Simulation results indicate that the relations between the credit risk and the asset risks are different among asset risks.
SEMANTIC BASED APPROACH: THE UPSHOT OF WORD CATEGORIZATION ON IRANIAN ON EFL MEDICAL LEARNER’S SHORT- TERM MEMORY

This survey study was conducted to explore the efficacy of word categorization on Iranian EFL learner’s short-term memory. To this end, the developed two kinds of instruments were employed. PET vocabulary tests for assessing the retention of vocabulary in short-term memory, a researcher-made questionnaire, consisting of 7-point Likert-type items, which measured the learner’s attitude were used. Therefore, 56 Iranian EFL students who were purposively selected (28 students in experimental group and 28 ones in control group) from among high school students to participate the study. The collected data through a pretest, posttest, and a questionnaire were analyzed via independent sample t-test and descriptive analysis. The obtained data which were analyzed using SPSS software indicated how efficient word categorization technique would be to count on this line. The findings revealed that there were statistically significant differences at (p=.000, p≤0.05) between the two experimental and control groups in the post test in favor of the experimental group. The outcomes in the questionnaires and students’ perceptions also confirmed that they had a positive attitude toward utilizing the aforementioned strategy.
Continuous permanent unobserved heterogeneity in dynamic discrete choice models

In dynamic discrete choice (DDC) analysis, it is common to use mixture models to control for unobserved heterogeneity. However, consistent estimation typically requires both restrictions on the support of unobserved heterogeneity and a high-level injectivity condition that is difficult to verify. This paper provides primitive conditions for point identification of a broad class of DDC models with multivariate continuous permanent unobserved heterogeneity. The results apply to both finite- and infinite-horizon DDC models, do not require a full support assumption, nor a long panel, and place no parametric restriction on the distribution of unobserved heterogeneity. In addition, I propose a seminonparametric estimator that is computationally attractive and can be implemented using familiar parametric methods.
AL-AʿJĀZ, (Jan-June 2020) Gauging the Influence of Social Media Addiction on Employees

Social media addiction (SMA) has been increased in the office environment. It raised serious concern about its effects on employees and organization performances. The addiction blowout without age, gender, culture, and social class differentiation. To study its consequences on various facets of life has grew more importance. In this study, role of SMA among the employees and its consequences on the organization performance has been investigated through a cross-sectional survey method. The data were collected through adopted scales from middle-line managers of Islamic banks. The date then was analyzed by applying linear regression techniques in SPSS 24 software package to analyze the causality of relationships between the variables. It was found that there is a significant positive relationship exist between SMA and organization performance. While, SMA has been found less significant in influencing employees’ performance. The relationship of SMA and employee’s performance had been moderated by masculinity. Their performances have been interactive differently between males and females.
LTER Synthesis Working Group: State Changes and Threshold Dynamics

A three-day workshop on state changes and threshold dynamics was held at the Harvard Forest LTER May 2-4, 2011 (May 1 and May 5 were travel days). Participants included (*graduate student; **post-doc): Brandon Bestelmeyer (JRN), Aaron Ellison (HFR), Bill Fraser (PAL), Kristen Gorman (PAL)*, Sally Holbrook (SBC), Christine Laney (JRN)*, Mark Ohman (CCE), Finn Pillsbury (JRN)**, Andrew Rassweiler (SBC)**, and Sapna Sharma (NTL)**. This workshop built on a 2009 Ecotrends workshop at JRN (May 1-2, 2009: http://intranet2.lternet.edu/content/ workshop-promote-synthesis-products-ecotrends-project) and a follow-up workshop at the 2009 ASM (http://asm.lternet.edu/2009/workgroups/understanding-state-change-long-term-datasets).
THE DEVELOPMENT OF A RELIABLE PRIVACY-PRESERVING ENVIRONMENT FOR MACHINE LEARNING IN INDUSTRIAL INTERNET OF THINGS SYSTEMS

: To establish an acceptable balance among quality of service (QoS) and users' confidentiality, as well as to measure security breach in the social Internet of Things, is very difficult (IoT). In the context of social IoT, we present a privacy-preserving customised service mechanism (Persian) based on a static Bayesian game to provide privacy protection tailored to the unique security needs of users. We utilise fuzzy uncertainty reasoning to categorise individuals based on their unique privacy preferences, which is quantified in our method. The categorization findings make it easier for trustworthy cloud service providers (CSPs) to offer customers with services at the appropriate degrees of sophistication. Aside from that, the CSP takes a strategic decision with the aim of increasing reputation by participating in a decision-making game with prospective opponents. Using Shannon information entropy, we can determine the degree of privacy disclosure based on the likelihood of game mixed strategy equilibrium, which is the goal of our method. Despite the presence of attackers, experimental results demonstrate that Persian provides high quality of service and successfully protects user privacy.
Multi-prover interactive proofs and the quantum PCP conjectures

In a previous lecture we introduced the quantum “Hamiltonian” PCP conjecture, which is a conjecture on the hardness of approximating the smallest eigenvalue (“ground state energy”) of a local Hamiltonian. The conjecture is inspired by the classical PCP theorem, which makes an analogous statement about the hardness of approximating the largest possible fraction of clauses that can be satisfied in an instance of a classical constraint satisfaction problem.
MANAGEMENT OF SEVERELY ATROPHIC MANDIBULAR RIDGE USING HOLLOW DENTURE AND NEUTRAL ZONE TECHNIQUE: A CASE REPORT

Complete edentulism is a condition with which an individual may have to live for decades. The typical life span of a conventional denture does not last more than 5 years, the main reason being perhaps the changing tissues on which it rests. Resorption of alveolar bone after tooth loss and denture wearing is an irreversible process and complex Prosthodontic rehabilitations are more likely to have them as one of the components of complexity. Rehabilitation of the rigorously resorbed mandibular ridge poses every prosthodontist a challenge in terms of retention, stability and support. We present a case of an elderly female patient who had previously worn two complete dentures, but was not satisfied with their outcome. Her chief concern was retention and stability, although the mandibular ridges were severely resorbed. A conventional complete denture prosthesis was planned with feature being the incorporation of teeth within the neutral zone and enhancing retention by hollowing out the mandibular denture. A neutrocentric occlusion with zero anterior guidance and use of non anatomic cuspid teeth were incorporated in the occlusal scheme. The patient received the complete denture prosthesis with much enthusiasm, especially with the weight of the lower denture.
PR ] 2 1 Ja n 20 19 STEADY STATE SENSITIVITY ANALYSIS OF CONTINUOUS TIME MARKOV CHAINS

In this paper we study Monte Carlo estimators based on the likelihood ratio approach for steady-state sensitivity. We first extend the result of Glynn and Olvera-Cravioto [doi: 10.1287/stsy.2018. 002] to the setting of continuous time Markov chains with a countable state space which include models such as stochastic reaction kinetics and kinetic Monte Carlo lattice systems. We show that the variance of the centered likelihood ratio estimators does not grow in time. This result suggests that the centered likelihood ratio estimators should be favored for sensitivity analysis when the mixing time of the underlying continuous time Markov chain is large, which is typically the case when systems exhibit multiscale behavior. We demonstrate a practical implication of this analysis with two numerical benchmarks of biochemical reaction networks.
Human-in-the-loop Techniques in Machine Learning

Human-in-the-loop techniques are playing more and more signiﬁcant roles in the machine learning pipeline, which consists of data preprocessing, data labeling, model training and inference. Humans can not only provide training data for machine learning applications, but also directly accomplish some tasks that are hard for the computer in the pipeline, with the help of machine-based approaches. In this paper, we ﬁrst summarize the human-in-the-loop techniques in machine learning, including: (1) Data Extraction: Non-structured data always needs to be transformed to structured data for feature engineering, where humans can provide training data or generate rules for extraction. (2) Data Integration: In order to enrich data or features, data integration is proposed to join other tables. Humans can help to address some machine-hard join operations. (3) Data Cleaning: In real world, data is always dirty. We can leverage humans’ intelligence to clean the data and further induce rules to clean more. (4) Data Annotation and Iterative labeling. Machine learning always requires a large volume of high-quality training data, and humans can provide high quality data for training. When the budget is limited, iterative labeling is proposed to label the informative examples. (5) Model training and inference. For different applications(e.g. classiﬁcation, clustering), given human labels, we have different ML techniques to train and infer the model. Then we summarize several commonly used techniques in human-in-the-loop machine learning applied in the above modules, including quality improvement, cost reduction, latency reduction, active learning and weak supervision. Finally, we provide some open challenges and opportunities.