Miracle-dz/UniFAR-FTUs huggingface dataset:
Understanding strong molecular hydrogen emission in astronomical environments

Here I describe recent studies of objects with molecular hydrogen emission that is strong relative to other spectral lines. Large telescopes and fast spectrometers have made the 2 {\mu}m window accessible even for relatively faint objects. I summarize several environments where strong molecular hydrogen 2.121 micron emission is observed. The line is hard to excite due to its large excitation potential, and is most emissive in regions that have temperatures that are nearly high enough to dissociate molecular hydrogen. I outline several case studies. In the Helix planetary nebula strong emission is produced by rapidly flowing molecular gas that is exposed to an intense ionizing radiation field. This advective production of molecular hydrogen is a fundamentally non-equilibrium process. In the filaments surrounding brightest cluster galaxies in cool core clusters ionizing particles penetrate into magnetically confined molecular cores and excite the gas. Finally, I outline ongoing work on the Crab Nebula, where the first complete maps of molecular emission have only recently been completed. Both ionizing particles and high-energy photons may be important. Finally I speculate on the origin of the correlation between H2 / H I intensity ratios and other properties found in Active Galaxies. This is suggestive of a hardening of the radiation field along the Eigenvector 1 sequence. In all of this work I take the approach of understanding H2 emission along with emission from low and moderate ionization species, a necessary step if we are to really understand the context in which molecular hydrogen emission forms.
Detection and Classification of Sexism on Social Media Using Multiple Languages, Transformers, and Ensemble Models

Identifying and classifying sexist content in social media posts is a highly complex and relevant problem. Some characteristics such as sarcasm and multiple forms of sexism increase the difficulty of detecting and identifying this type of content. Nevertheless, it is essential to improve prediction quality to improve decision-making such as post removal, and user ban, among others. The main objective of this work is to propose a methodology and explore the use of different transformers architectures for two tasks in English and Spanish: sexism detection and sexism classification. Single-language and multilingual versions of the BERT, RoBERTa, and single-language versions of the Electra, and GPT2 architectures were evaluated on the EXIST 2022 shared task challenge at IberLEF 2022 dataset. It was observed that: (i) the use of the translation of the posts to English and then using single-language English and multilingual models present the best results; (ii) the best architectures were BERT and RoBERTa; (iii) using single-language Spanish models provided the worst results; (iv) sexism classification was more difficult than sexism detection; and (v) the use of ensembles were better than the GPT2 and Electra models, but worse than English single-language generally and multilingual models. An in-depth hyperparameters analysis was also conducted.
Deciding Emptiness for Constraint Automata on Strings with the Prefix and Suffix Order

We study constraint automata that accept data languages on finite string values. Each transition of the automaton is labelled with a constraint restricting the string value at the current and the next position of the data word in terms of the prefix and the suffix order. We prove that the emptiness problem for such constraint automata with Büchi acceptance condition is NL-complete. We remark that since the constraints are formed by two partial orders, prefix and suffix, we cannot exploit existing techniques for similar formalisms. Our decision procedure relies on a decidable characterization for those infinite paths in the graph underlying the automaton that can be completed with string values to yield a Büchi-accepting run. Our result is - to the best of our knowledge - the first work in this context that considers both prefix and suffix, and it is a first step into answering an open question posed by Demri and Deters.
Editorial: Virtual, mixed, and augmented reality in cognitive neuroscience and neuropsychology

COPYRIGHT © 2022 Burin, Salatino and Ziat. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. Editorial: Virtual, mixed, and augmented reality in cognitive neuroscience and neuropsychology
Diversity of endophytic bacterial and fungal microbiota associated with the medicinal lichen Usnea longissima at high altitudes

Endophytic microbial communities of lichen are emerging as novel microbial resources and for exploration of potential biotechnological applications. Here, we focused on a medicinal lichen Usnea longissima, and investigated its bacterial and fungal endophytes. Using PacBio 16S rRNA and ITS amplicon sequencing, we explored the diversity and composition of endophytic bacteria and fungi in U. longissima collected from Tibet at five altitudes ranging from 2,989 to 4,048 m. A total of 6 phyla, 12 classes, 44 genera, and 13 species of the bacterial community have been identified in U. longissima. Most members belong to Alphaproteobacteria (42.59%), Betaproteobacteria (33.84%), Clostridia (13.59%), Acidobacteria (7%), and Bacilli (1.69%). As for the fungal community, excluding the obligate fungus sequences, we identified 2 phyla, 15 classes, 65 genera, and 19 species. Lichen-related fungi of U. longissima mainly came from Ascomycota (95%), Basidiomycota (2.69%), and unidentified phyla (2.5%). The presence of the sequences that have not been characterized before suggests the novelty of the microbiota. Of particular interest is the detection of sequences related to lactic acid bacteria and budding yeast. In addition, the possible existence of harmful bacteria was also discussed. To our best knowledge, this is the first relatively detailed study on the endophytic microbiota associated with U. longissima. The results here provide the basis for further exploration of the microbial diversity in lichen and promote biotechnological applications of lichen-associated microbial strains.
Advances in fluorescence diagnosis to track footprints of cancer progression in vivo

Fluorescence spectroscopy and imaging have been widely used for in vivo cancer diagnosis and therapy monitoring in preclinical models, as well as clinical translation. Great attempts have been made to develop novel fluorescence techniques and improve on existing ones, which can now be used in conjunction with newly developed fluorescent probes for specific cancer imaging. In this review, a broad overview of fluorescence techniques is provided, including photodynamic diagnosis, laser confocal endomicroscopy and fluorescence lifetime imaging, coupled with endogenous and exogenous fluorophores. In particular, endogenous fluorophores, such as nicotinamide adenine dinucleotide (NADH) and flavin adenine dinucleotide (FAD), are highlighted as they are linked to cellular metabolism in precancer growth. The use of near‐infrared dyes, such as indocynanine green (ICG), for imaging deep‐tissue regions is also reviewed. In addition, diagnostic algorithms used for tissue classification and cancer detection will be discussed. Lastly, emerging technologies in fluorescence diagnosis will also be included.
Biomedical Named Entity Recognition in Eight Languages with Zero Code Changes

Named entity recognition (NER) is one of the most important building blocks of NLP tasks in the medical domain by extracting meaningful chunks from clinical notes and reports, which are then fed to downstream tasks like assertion status detection, entity resolution, relation extraction, and de-identification. Due to the growing volume of healthcare data in unstructured format, an increasingly important challenge is providing high accuracy implementations of state-of-the-art deep learning (DL) algorithms at scale. On the other hand, when it comes to low-resource languages, collecting high quality annotated data sets in the biomedical domain is still a big challenge. In this study, we train production-grade biomedical NER models on eight different biomedical datasets published within the LivingNER competition [1]. Transformer based Bert for token classification and BiLSTM-CNN-Char based NER algorithms from Spark NLP library are utilized during this study and we trained 28 different NER models in total with decent accuracies (0.9243 F1 test score in Spanish) without changing the underlying DL architecture. The trained models are available within a production-grade code base as part of the Spark NLP library; can scale up for training and inference in any Spark cluster; has GPU support and libraries for popular programming languages such as Python, R, Scala and Java.
Correlations between smartphone addiction and alexithymia, attachment style, and subjective well-being: A meta-analysis

Background Smartphone addiction (SA) has become a social problem that affects peoples’ quality of life and is frequently reported to be correlated with alexithymia, avoidant or anxious attachment styles, and subjective well-being. This study aimed to investigate the relationship between SA and alexithymia, attachment style, and subjective well-being. Methods A meta-analysis was conducted following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. The following electronic databases were searched: PubMed, Web of Science, Embase, PsycINFO, PsycArticles, China National Knowledge Infrastructure (CNKI), WANFANG DATA, and Chongqing VIP Information Co., Ltd. (VIP). Stata 16.0 was used to analyze the overall effect and test the moderating effect. Results One hundred and ten studies were included, involving a total of 96,680 participants. SA had a significantly high positive correlation with alexithymia (r = 0.40), attachment anxiety (r = 0.37), and negative emotions (r = 0.31), and a low positive correlation with attachment avoidance (r = 0.17). In addition, there was a high negative correlation between SA and subjective well-being (r = –0.33) and a low negative correlation between SA, life satisfaction (r = –0.17), and positive emotions (r = –0.18). A moderation analysis revealed that age significantly moderated the relationship between SA and positive emotions. The tools for measuring SA significantly moderated the relationship between SA, alexithymia, attachment anxiety, and subjective well-being. Meanwhile, subjective well-being measurement tools significantly moderated the relationships between SA, subjective well-being, and negative emotions. Conclusion SA was closely related to alexithymia, attachment style, and subjective well-being. In the future, longitudinal research can be conducted to better investigate the dynamic changes in the relationship between them. Systematic review registration [www.crd. york.ac. uk/PROSPERO/], identifier [CRD42022334798].
Uncovering patterns in student work: machine learning to understand human learning

When we observe thousands and sometimes millions of students learning simultaneously what patterns unfold? Being able to autonomously understand students as they learn, both in terms of assessing knowledge and being able to provide feedback, is a grand challenge in education and one that has been studied for hundreds of years. Massive online classes, which were recently popularized, have provided a serendipitous opportunity to break ground on this important problem. In my dissertation I will explore data driven solutions in the domain of students learning to program – a discipline with rich, structured assignments. I will discuss emergent patterns in; the space of student partial solutions, in how students navigate open ended assignments and in how students work through a series of problems. Highlights include, (1) a method which yields a noteworthy improvement in the state of the art for the task of Knowledge Tracing (2) Discovery of the Poison Path pattern in how students navigate solution spaces that both: predicts how teachers would suggest a learner make forward progress and has an almost perfect logarithmic relationship with the probability of a student succeeding in the future and (3) A leading method of using deep neural networks to autonomously embed student programs into Euclidian space. This work has been featured in: Khan Academy, Coursera and Code.org. My thesis is meant to depict a nascent space of education research, and where it could go. The results we present more than anything show the extent to which many open, fascinating problems remain.
Local Instrumental Variables

This paper unites the treatment effect literature and the latent variable literature. The economic questions answered by the commonly used treatment effect parameters are considered. We demonstrate how the marginal treatment effect parameter can be used in a latent variable framework to generate the average treatment effect, the effect of treatment on the treated and the local average treatment effect, thereby establishing a new relationship among these parameters. The method of local instrumental variables directly estimates the marginal treatment effect parameters, and thus can be used to estimate all of the conventional treatment effect parameters when the index condition holds and the parameters are identified. When they are not, the method of local instrumental variables can be used to produce bounds on the parameters with the width of the bounds depending on the width of the support for the index generating the choice of the observed potential outcome.
Learning structured probabilistic models for semantic role labeling

Teaching a computer to read is one of the most interesting and important artificial intelligence tasks. Due to the complexity of this task, many sub-problems have been defined, mostly in the area of natural language processing (NLP). In this thesis, we focus on semantic role labeling (SRL), one important processing step on the road from raw text to a full semantic representation. Given an input sentence and a target verb in that sentence, the SRL task is to label the semantic arguments, or roles, of that verb. For example, in the sentence “Tom eats an apple,” the verb “eat” has two roles, Eater = “Tom” and Thing Eaten = “apple”. Most SRL systems, including the ones presented in this thesis, take as input a syntactic analysis built by an automatic syntactic parser. SRL systems rely heavily on path features constructed from the syntactic parse, which capture the syntactic relationship between the target verb and the phrase being classified. However, there are several issues with these path features. First, the path feature does not always contain all relevant information for the SRL task. Second, the space of possible path features is very large, resulting in very sparse features that are hard to learn. In this thesis, we consider two ways of addressing these issues. First, we experiment with a number of variants of the standard syntactic features for SRL. We include a large number of syntactic features suggested by previous work, many of which are designed to reduce sparsity of the path feature. We also suggest several new features, most of which are designed to capture additional information about the sentence not included in the standard path feature. We add each feature individually to a baseline SRL model, finding that the sparsity-reducing features are not very helpful, while
Towards Better Understanding of the Performance and Design of Datalog Systems

Recent years have seen a resurgence of interest in the Datalog language and its syntactic extensions from both the industry and academia. Such interest has motivated a line of work to build efficient Datalog systems that support expressing data analytics of different types such as program and graph analyses in a concise and simple way. However, besides the performance improvement of different systems presented in these works over existing competitors, little understanding has been gained about the property of varying Datalog workloads (i.e., program and data), and the computation resource usage of different systems (i.e., memory and CPU utilization), which are crucial for understanding the essence behind the performance difference observed in different systems. When such knowledge is gained, clear guidance could be provided for users to choose between different Datalog systems depending on their use cases and system builders to improve the existing system or build more efficient new systems. In this paper, we propose the general profiling of the Datalog program evaluation and present the corresponding visualizations. We further discuss the insights gained from the produced visualizations and how these insights shed light on the pros and cons of existing Datalog systems, which further provides guidance on making improvements over the existing system and designing/building more efficient new systems.
Becoming a Digital Nomad Entrepreneur: A Process Theory

ABSTRACT:There is an opportunity to expand the travel and technology literature by integrating additional provision for the entrepreneurial side of digital nomadism. Digital nomad entrepreneurship (DNE) is a distinctive approach to life, travel, and entrepreneurship, and is a growing phenomenon. Previous research has tended to focus on either the philosophical/lifestyle or the entrepreneurial-level aspects of DNE. This study uses grounded theory method to investigate the merger of philosophical/ lifestyle and business factors that merge to form the process for how an individual becomes a digital nomad entrepreneur. The findings reveal a model termed the "Digital Nomad Entrepreneurship & Lifestyle Design Process," which is comprised of four conceptual sub-categories: Pre-Day Zero-Lamentation & Discovery; Plugging In-Transition & Acculturation; Just Get Started-1,000 Messy Days; and The Other Side-Mentality Shifts and Scale Lessons. With the increasing focus on remote work, intensified by the COVID-19 pandemic, this process theory uses insights from digital nomad entrepreneurs to inductively generate a practical guide for aligning income generation with a lifestyle characterized by geographic flexibility.
Non-fragile guaranteed cost control of discrete-time fuzzy bilinear system

This paper focuses on the problem of non-fragile guaranteed cost control for a class of T-S discrete-time fuzzy bilinear systems (DFBS). Based on the parallel distributed compensation (PDC) approach, the sufficient conditions are derived such that the closed-loop system is asymptotically stable and the cost function value is no more than a certain upper bound in the presence of the additive controller gain perturbations. The non-fragile guaranteed cost controller can be obtained by solving a set of bilinear matrix inequalities (BMIs). The Van de Vusse model is utilized to demonstrate the validity and effectiveness of the proposed approach.
BindingSite-AugmentedDTA: enabling a next-generation pipeline for interpretable prediction models in drug repurposing

While research into Drug-Target Interaction (DTI) prediction is fairly mature, generalizability and interpretability are not always addressed in the existing works in this field. In this paper, we propose a deep learning-based framework, called BindingSite-AugmentedDTA, which improves Drug-Target Affinity (DTA) predictions by reducing the search space of potential binding sites of the protein, thus making the binding affinity prediction more efficient and accurate. Our BindingSite-AugmentedDTA is highly generalizable as it can be integrated with any DL-based regression model, while it significantly improves their prediction performance. Also, unlike many existing models, our model is highly interpretable due to its architecture and self-attention mechanism, which can provide a deeper understanding of its underlying prediction mechanism by mapping attention weights back to protein binding sites. The computational results confirm that our framework can enhance the prediction performance of seven state-of-the-art DTA prediction algorithms in terms of 4 widely used evaluation metrics, including Concordance Index (CI), Mean Squared Error (MSE), modified squared correlation coefficient , and the Area Under the Precision Curve (AUPC). We also contribute to the two most commonly used DTA benchmark datasets, namely Kiba and Davis, by including additional information on 3D structure of all proteins contained in these two datasets. We manually extracted this information from Protein Data Bank (PDB) files of proteins available at https://www.uniprot. org/. Furthermore, we experimentally validate the practical potential of our proposed framework through in-lab experiments. We measure the binding interaction between several drug candidate compounds for the inhibition of binding between (SARS-CoV-2 S-protein RBD) Spike and ACE-2 (host cell binding target) proteins. We then compare the computationally-predicted results against the ones experimentally-observed in the laboratory. The relatively high agreement between computationally-predicted and experimentally-observed binding interactions supports the potential of our framework as the next-generation pipeline for prediction models in drug repurposing.
The Two-Component System YesMN Promotes Pneumococcal Host-to-Host Transmission and Regulates Genes Involved in Zinc Homeostasis

The ability to sense and respond rapidly to the dynamic environment of the upper respiratory tract (URT) makes Streptococcus pneumoniae (Spn) a highly successful human pathogen. Two-component systems (TCSs) of Spn sense and respond to multiple signals it encounters allowing Spn to adapt and thrive in various host sites. Spn TCS have been implicated in their ability to promote pneumococcal colonization of the URT and virulence. ABSTRACT The ability to sense and respond rapidly to the dynamic environment of the upper respiratory tract (URT) makes Streptococcus pneumoniae (Spn) a highly successful human pathogen. Two-component systems (TCSs) of Spn sense and respond to multiple signals it encounters allowing Spn to adapt and thrive in various host sites. Spn TCS have been implicated in their ability to promote pneumococcal colonization of the URT and virulence. As the disease state can be a dead-end for a pathogen, we considered whether TCS would contribute to pneumococcal transmission. Herein, we determined the role of YesMN, an understudied TCS of Spn, and observe that YesMN contributes toward pneumococcal shedding and transmission but is not essential for colonization. The YesMN regulon includes genes involved in zinc homeostasis and glycan metabolism, which are upregulated during reduced zinc availability in a YesMN-dependent fashion. Thus, we identified the YesMN regulon and a potential molecular signal it senses that lead to the activation of genes involved in zinc homeostasis and glycan metabolism. Furthermore, in contrast to Spn monoinfection, we demonstrate that YesMN is critical for high pneumococcal density in the URT during influenza A virus (IAV) coinfection. We attribute reduced colonization of the yesMN mutant possibly due to increased association with and clearance by the mucus covering the URT epithelial surface. Thus, our results highlight the dynamic interactions that occur between Spn and IAV in the URT, and the role that TCSs play in modulation of these interactions.