Miracle-dz/UniFAR-FTUs huggingface dataset:
Thermally Activated Processes for Ferromagnet Intercalation in Graphene-Heavy Metal Interfaces.

The development of graphene (Gr) spintronics requires the ability to engineer epitaxial Gr heterostructures with interfaces of high quality, in which the intrinsic properties of Gr are modified through proximity with a ferromagnet to allow for efficient room temperature spin manipulation or the stabilization of new magnetic textures. These heterostructures can be prepared in a controlled way by intercalation through graphene of different metals. Using photoelectron spectroscopy (XPS) and Scanning Tunneling Microscopy (STM), we achieve a nanoscale control of thermal activated intercalation of homogeneous ferromagnetic (FM) layer underneath epitaxial Gr grown onto (111)-oriented heavy metal (HM) buffers deposited in turn onto insulating oxide surfaces. XPS and STM demonstrate that Co atoms evaporated on top of Gr arrange in 3D clusters, and, upon thermal annealing, penetrate through and diffuse below Gr in a 2D fashion. The complete intercalation of the metal occurs at specific temperatures depending on the type of metallic buffer. The activation energy and the optimum temperature for the intercalation processes are determined. We describe a reliable method to fabricate and characterize in-situ high quality Gr-FM/HM heterostructures enabling the realization of novel spin-orbitronic devices that exploits the extraordinary properties of Gr.
Semiparametric Estimation of Correlated Random Coefficient Models without Instrumental Variables

We study a linear random coefficient model where slope parameters may be correlated with some continuous covariates. Such a model specification may occur in empirical research, for instance, when quantifying the effect of a continuous treatment observed at two time periods. We show one can carry identification and estimation without instruments. We propose a semiparametric estimator of average partial effects and of average treatment effects on the treated. We showcase the small sample properties of our estimator in an extensive simulation study. Among other things, we reveal that it compares favorably with a control function estimator. We conclude with an application to the effect of malaria eradication on economic development in Colombia.
CNN-based Dual-Chain Models for Knowledge Graph Learning

Knowledge graph learning plays a critical role in integrating domain specific knowledge bases when deploying machine learning and data mining models in practice. Existing methods on knowledge graph learning primarily focus on modeling the relations among entities as translations among the relations and entities, and many of these methods are not able to handle zero-shot problems, when new entities emerge. In this paper, we present a new convolutional neural network (CNN)-based dual-chain model. Different from translation based methods, in our model, interactions among relations and entities are directly captured via CNN over their embeddings. Moreover, a secondary chain of learning is conducted simultaneously to incorporate additional information and to enable better performance. We also present an extension of this model, which incorporates descriptions of entities and learns a second set of entity embeddings from the descriptions. As a result, the extended model is able to effectively handle zero-shot problems. We conducted comprehensive experiments, comparing our methods with 15 methods on 8 benchmark datasets. Extensive experimental results demonstrate that our proposed methods achieve or outperform the state-of-the-art results on knowledge graph learning, and outperform other methods on zero-shot problems. In addition, our methods applied to real-world biomedical data are able to produce results that conform to expert domain knowledge.
Surface charges toolkit for gravity

These notes provide a detailed catalog of surface charge formulas for different classes of gravity theories. The present catalog reviews and extends the existing literature on the topic. Part of the focus is on reviewing the method to compute quasi-local surface charges for gauge theories in order to clarify conceptual issues and their range of applicability. Many surface charge formulas for gravity theories are expressed in metric, tetrads-connection, Chern-Simons connection, and even BF variables. For most of them, the language of differential forms is exploited and contrasted with the more popular metric components language. The gravity theory is coupled with matter fields as scalar, Maxwell, Skyrme, Yang-Mills, and spinors. Furthermore, three examples with ready-to-download notebook codes, show the method in full action. Several new results are highlighted through the notes.
Use of Anti-inflammatory Medications by Urban Adolescents with Asthma MEDICAL RESEARCH REPORT

The National Heart, Lung, and Blood Institute has issued guidelines recommending the use of anti-inflammatory medications in all patients with persistent asthma. Little is known about adherence to these guidelines in asthmatic adolescents. To examine the patterns of asthma severity and the use of anti-inflammatory medications among adolescents with asthma, we employed a multistaged stratified sample approach at a high school located in the Bronx, New York. First, an asthma screening survey was administered to 3,800 registered students, ages 13 to 18 years. Then, we identified a subset of 200 children who answered "yes" to all of the following questions: 1) does your doctor think that you have asthma, 2) do your parents think that you have asthma, and 3) do you think that you have asthma? Next, a self-completion questionnaire was administered to a sample drawn from this cohort. Subjects were surveyed regarding severity of asthma symptoms, use of anti-inflammatory medications, and demographic information. Differences in proportion were tested by Chisquare analyses or Fisher’s exact test, as appropriate. One hundred sixty subjects participated. Sixty-three percent of respondents were female, 68% were Hispanic, and 26% were African American. Thirty-three percent had weekly symptoms, and 14% had daily symptoms. Only 33% of adolescents with daily symptoms and 24.5% of those with weekly symptoms were on inhaled steroids. However, those with daily symptoms were significantly more likely to be on anti-inflammatory medications compared to subjects with monthly symptoms (33% vs. 6.5%, p = 0.001). These data show that the majority of adolescents with persistent asthma were not on daily steroids in accordance with the National Heart, Lung, and Blood Institute guidelines
Cenozoic sediment budget of West Africa and the 1 Niger delta 2 3

(1) Géosciences Environnement Toulouse, Université de Toulouse, CNRS, IRD, UPS, CNES, F-31400 6 Toulouse, France 7 8 (2) now at MINES ParisTech, PSL Research University, Centre de Géosciences, 35 rue St Honoré, 77305 9 Fontainebleau Cedex, France 10 11 (3) IRD and Département des Sciences de la Terre, Université Ouaga I Professeur Joseph Ki-Zerbo, 01 PB 12 182 Ougadougou 01, Burkina Faso 13 14 (4) Aix-Marseille Univ, CNRS, IRD, Coll France, CEREGE, BP 80, 13545 Aix-en-Provence, Cedex 4, 15 France 16 17
On Value Discrepancy of Imitation Learning

Imitation learning trains a policy from expert demonstrations. Imitation learning approaches have been designed from various principles, such as behavioral cloning via supervised learning, apprenticeship learning via inverse reinforcement learning, and GAIL via generative adversarial learning. In this paper, we propose a framework to analyze the theoretical property of imitation learning approaches based on discrepancy propagation analysis. Under the infinite-horizon setting, the framework leads to the value discrepancy of behavioral cloning in an order of O((1-\gamma)^{-2}). We also show that the framework leads to the value discrepancy of GAIL in an order of O((1-\gamma)^{-1}). It implies that GAIL has less compounding errors than behavioral cloning, which is also verified empirically in this paper. To the best of our knowledge, we are the first one to analyze GAIL's performance theoretically. The above results indicate that the proposed framework is a general tool to analyze imitation learning approaches. We hope our theoretical results can provide insights for future improvements in imitation learning algorithms.
Describing Video with Multiple Descriptions

Automatically describing video content with text description is challenging but important task, which has been at-tracting a lot of attention in CV community. Previous works mainly strive for the accuracy of the generated sentences, while ignoring the sentences diversity, which is inconsistent with human behavior. In this paper, we aim to caption each video with multiple descriptions and propose a novel framework. Concretely, for a given video, the intermediate latent variables of conventional encode-decode process are utilized as input to the conditional generative adversarial network (CGAN) with the purpose of generating diverse sentences. We adopt the combination of LSTM and CNN as our generator that produces descriptions conditioned on latent variables and the CNNs as discriminator that assesses the quality of generated sentences. Simultaneously, a novel DCE metric is designed to assess the diverse captions. We evaluate our method on the benchmark datasets, where it demonstrates its ability to generate diverse descriptions and achieves competitive or even superior results against other state-of-the-art methods.
GPT Conjecture: Understanding the Trade-offs between Granularity, Performance and Timeliness in Control-Flow Integrity

Performance/security trade-off is widely noticed in CFI research, however, we observe that not every CFI scheme is subject to the trade-off. Motivated by the key observation, we ask three questions. Although the three questions probably cannot be directly answered, they are inspiring. We find that a deeper understanding of the nature of the trade-off will help answer the three questions. Accordingly, we proposed the GPT conjecture to pinpoint the trade-off in designing CFI schemes, which says that at most two out of three properties (fine granularity, acceptable performance, and preventive protection) could be achieved.
Topological based classification using graph convolutional networks

In colored graphs, node classes are often associated with either their neighbors class or with information not incorporated in the graph associated with each node. We here propose that node classes are also associated with topological features of the nodes. We use this association to improve Graph machine learning in general and specifically, Graph Convolutional Networks (GCN). First, we show that even in the absence of any external information on nodes, a good accuracy can be obtained on the prediction of the node class using either topological features, or using the neighbors class as an input to a GCN. This accuracy is slightly less than the one that can be obtained using content based GCN. Secondly, we show that explicitly adding the topology as an input to the GCN does not improve the accuracy when combined with external information on nodes. However, adding an additional adjacency matrix with edges between distant nodes with similar topology to the GCN does significantly improve its accuracy, leading to results better than all state of the art methods in multiple datasets.
Vortex regeneration mechanism in the self-sustaining process of wall-bounded flows

The streamwise vortex regeneration mechanism in the self-sustaining process (SSP) of wall-bounded turbulence is investigated. Resolvent analysis is used to identify the principal forcing mode which produces the maximum amplification of the velocities in direct numerical simulations (DNS) of the minimal channel for the buffer layer. The identified mode is then projected out from the nonlinear term of the Navier-Stokes equations at each time step from the DNS of the corresponding minimal channel. The results show that the removal of the principal forcing mode is able to inhibit turbulence while removing the subsequent modes instead of the principal one only marginally affects the flow. Analysis of the dyadic interactions in the nonlinear term shows that the contributions toward the principal forcing mode come from a limited number of wavenumber interactions. Using conditional averaging, the flow structures that are responsible for generating the principal forcing mode, and thus the nonlinear interaction to self-sustain turbulence, are identified to be spanwise rolls interacting with oblique streaks. This corroborates previous studies on the vortex regeneration mechanism and characterises the underlying quadratic interactions in the SSP of the minimal channel using resolvent analysis.
Postpartum Ovarian Resumption in Native Dairy Cows in Upper Egypt and their Relation to Oxidant Antioxidant Status

The present study aimed to characterize the follicular pattern and ovarian resumption during the postpartum period in lactating dairy cows in Upper Egypt and study the impact of different components of the antioxidant system on follicular dynamics. Sixteen native dairy cows in their second lactation period were assigned for the current study. Cows were kept indoor and subjected to ultrasonographic examination and bled daily. Data regarding the change in the follicular dynamics and incidence of short cycles were collected and tabulated then statistically analyzed. Blood samples were used to determine nitric oxide (NO), Vitamin A, B-Carotene, Vitamin C and Vitamin E. results of the present study revealed that The first postpartum behavioral estrus observed 35-47 days (41.5 ± 1.86 days postpartum). The average postpartum days required for the animal to come in estrus in cows with transient CL was 44 ± 1.76 days and in cows without transient CL was 37 ± 1.18 days. In cows with transient corpus luteum, first ovulation was not accompanied by behavioral estrus (Quiet ovulation) at 23 ±1.02 days. Vit A, B-carotene, Vit E, NO, but not Vit C, increased significantly during the first observable postpartum heat in the studied cows and their level in the plasma correlated differently with the different classes of ovarian follicles. It is concluded that most antioxidants in the present study, except ascorbic acid, increases at the time of estrus and they are not affected by the day of postpartum period or the number and size of the dominant follicles. Short cycles are frequent in the postpartum cows (50% of cows had a short cycles in the present work). Follicular growth starts immediately – may be before the studied time- in postpartum dairy cows.
Exploiting Token and Path-based Representations of Code for Identifying Security-Relevant Commits

Public vulnerability databases such as CVE and NVD account for only 60% of security vulnerabilities present in open-source projects, and are known to suffer from inconsistent quality. Over the last two years, there has been considerable growth in the number of known vulnerabilities across projects available in various repositories such as NPM and Maven Central. Such an increasing risk calls for a mechanism to infer the presence of security threats in a timely manner. We propose novel hierarchical deep learning models for the identification of security-relevant commits from either the commit diff or the source code for the Java classes. By comparing the performance of our model against code2vec, a state-of-the-art model that learns from path-based representations of code, and a logistic regression baseline, we show that deep learning models show promising results in identifying security-related commits. We also conduct a comparative analysis of how various deep learning models learn across different input representations and the effect of regularization on the generalization of our models.
Lawrence Berkeley National Laboratory Recent Work Title Effect of Technetium-99 sources on its retention in low activity waste glass Permalink

8 Small-scale crucible melting tests on simulated waste glass were performed with 9 technetium-99 (Tc-99) introduced as different species in a representative low activity waste 10 simulant. The glass saw an increase in Tc-99 retention when TcO2·2H2O and various Tc-minerals 11 containing reduced tetravalent Tc were used compared to tests in which pertechnetate with 12 heptavalent Tc was used. We postulate that the increase of Tc retention is likely caused by 13 different reaction paths for Tc incorporation into glass during early stages of melting, rather 14 than the low volatility of reduced tetravalent Tc compounds, which has been a generally 15 accepted idea. Additional studies are needed to clarify the exact mechanisms relevant to the 16 effect of reduced Tc compounds on Tc incorporation into or volatilization from the glass melt. 17
An Integral Equation Formulation of the N-body Dielectric Spheres Problem. Part II: Complexity Analysis

This article is the second in a series of two papers concerning the mathematical study of a boundary integral equation of the second kind that describes the interaction of N dielectric spherical particles undergoing mutual polarisation. The first article presented the numerical analysis of the Galerkin method used to solve this boundary integral equation and derived N-independent convergence rates for the induced surface charges and total electrostatic energy. The current article will focus on computational aspects of the algorithm. We provide a convergence analysis of the iterative method used to solve the underlying linear system and show that the number of liner solver iterations required to obtain a solution is independent of N. Additionally, we present two linear scaling solution strategies for the computation of the approximate induced surface charges. Finally, we consider a series of numerical experiments designed to validate our theoretical results and explore the dependence of the numerical errors and computational cost of solving the underlying linear system on different system parameters.
Demand-Private Coded Caching and the Exact Trade-off for N=K=2

The distributed coded caching problem has been studied extensively in the recent past. While the known coded caching schemes achieve an improved transmission rate, they violate the privacy of the users since in these schemes the demand of one user is revealed to others in the delivery phase. In this paper, we consider the coded caching problem under the constraint that the demands of the other users remain information theoretically secret from each user. We first show that the memory-rate pair (M, min{ N, K}(1- M / N)) is achievable under information theoretic demand privacy, while using broadcast transmissions. Using this, we show that perfectly demand-private coded caching rate is order optimal for all parameter regimes. We then show that a demand-private scheme for $N$ files and $K$ users can be obtained from a non-private scheme that satisfies only a restricted subset of demands of N K users for $N$ files. We then focus on the demand-private coded caching problem for $K$ = 2 users, $N$ = 2 files. We characterize the exact memory-rate trade-off for this case. To show the achievability, we use our first result to construct a demand-private scheme from a non-private scheme satisfying a restricted demand subset that is known from an earlier work by Tian. Further, by giving a converse based on the extra requirement of privacy, we show that the obtained achievable region is the exact memory-rate trade-off.
Knowledge of dental students toward HIV/AIDS and its transmission

Although worldwide prevention and community information about AIDS has been developed in recent decades, it is still considered as a problem for public health. Dental students are at increased risk for HIV infection because of overutilization and unsafe injections during surgery or other procedures. Knowledge of dental students about HIV infection and its transmission ways is an important subject in health care system. The purpose of current study was to evaluate knowledge of dental students of Hormozgan University of Medical Sciences. This was a cross sectional study carried out among all of the dental students of Hormozgan University of Medical Sciences (HUMS) in 2012-2013. A questionnaire containing demographic question (age, gender) and 20 questions (7 general questions and 13 specialized questions ) about knowledge toward HIV/AIDS was distributed among the participants. Data was analyzed by frequency, one way t-test, chi-square and spearman correlation test using the SPSS (V.19). The significant level was set as < 0.05. Totally 78 student participated in this study. The mean age of participants was 21.44 ± 2.54. Forty five (57.7%) were preclinical students. The mean score of questionnaires was 52.38 ± 4. This score among preclinical students was 52.28 ± 4.2 and in clinical students was 52.92 ± 3.4. There was no significant difference between total scores of respondents among clinical and preclinical students (P > 0.05). Only two students (2.5%) got below 41 points (poor knowledge). Fifteen students had achieved between 41 to 50 points (moderate knowledge) and the remains (N = 61, p = 78.2%) achieved higher than 51 points (good knowledge). Although, the knowledge of dental students was high, they didn’t have enough knowledge about post exposure prophylaxis which is an important subject and any oral health worker may experience it in their professional life. Educating health care workers especially dental students regarding the prophylaxis strategies in preclinical course is recommended.
Mathematical Modeling of Systemic Risk in Financial Networks: Managing Default Contagion and Fire Sales

As impressively shown by the financial crisis in 2007/08, contagion effects in financial networks harbor a great threat for the stability of the entire system. Without sufficient capital requirements for banks and other financial institutions, shocks that are locally confined at first can spread through the entire system and be significantly amplified by various contagion channels. The aim of this thesis is thus to investigate in detail two selected contagion channels of this so-called systemic risk, provide mathematical models and derive consequences for the systemic risk management of financial institutions. The first contagion channel we consider is default contagion. The underlying effect is here that insolvent institutions cannot service their debt or other financial obligations anymore - at least partially. Debtors and other directly impacted parties in the system are thus forced to write off their losses and can possibly be driven into insolvency themselves due to their incurred financial losses. This on the other hand starts a new round in the default contagion process. In our model we simplistically describe each institution by all the financial positions it is exposed to as well as its initial capital. In doing so, our starting point is the work of Detering et al. (2017) - a model for contagion in unweighted networks - which particularly considers the exact network configuration to be random and derives asymptotic results for large networks. We extend this model such that weighted networks can be considered and an application to financial networks becomes possible. More precisely, for any given initial shock we deduce an explicit asymptotic expression for the total damage caused in the system by contagion and provide a necessary and sufficient criterion for an unshocked financial system to be stable against small shocks. Moreover, .