Miracle-dz/UniFAR-FTUs huggingface dataset:
Expression Changes of Genes Involved in Autophagy of the Endoplasmic Reticulum Network in Animal Models of Alzheimer’s Disease

Background and Aim This study aimed to investigate the association between Endoplasmic Reticulum au� tophagy (ER-phagy) and Alzheimer’s Disease (AD) by analyzing the expression patterns of related genes in animal models. Methods & Materials Microarray data of AD patients’ brain tissues were extracted from the Gene Expres� sion Omnibus (GEO) database. These data were first analyzed in GEO2R online tool. Then, the expression of ER-phagy related genes were isolated and the protein interaction networks were plotted by STRING database for the genes with increased expression. Finally, the relationship between the genes that had significant increased expression were designed, and the expression of new identified genes in each study was examined. Ethical Considerations All ethical principles were considered in this article. Results Genes involved in ER-phagy showed a sporadic expression in different AD models. An increase in the expression of ER-phagy regulatory 1 (FAM134B) gene was observed in studies with the mutation in both Microtubule-associated Protein Tau (MAPT) and Amyloid Precursor Protein (APP) genes. Increase in the expression of NPC intracellular cholesterol transporter 1 (NPC1) gene was observed in two studies that had mutations in APP, Presenilin 1 (PSEN1) and MAPT genes. Moreover, SEC62 homolog and Cell Cycle Progression 1 (CCPG1) genes both showed decreased expression in one study. Finally, the expres� sion of Reticulon 3 (RTN3) was not significant in any of the studies. Conclusion The genes involved in ER-phagy have a sporadic expression in AD models, where only two genes FAM134B and NPC1 are involved in AD. The FAM134B gene seems to interact with the Wnk1 gene, which plays a role in cell survival and proliferation, in the hippocampus and forebrain. It also interacts with the Map1lc3b gene, which has a role in phagosome deletion and protein ubiquitination, in the forebrain. It also interacts with the Map1lc3b gene, which has a role in phagosome deletion and protein ubiquitination, in the forebrain. NPC1 had interaction with the Abcg1 gene, which activates lipid homeo� stasis, in the subventricular zone.
Update on a search for light NMSSM Higgs bosons in H ( 125 ) → φ 1 φ 1 → 4 τ decays at √ s = 13 TeV Student :

Introduction The Next-to-Minimal Supersymmetric Standard Model (NMSSM) predicts the existence of two chargedh±, three CP-even -h1,h2 h3 and two CP-odd a1,a2 Higgs bosons [1]. In contrast to MSSM, the lightest bosons h1 and a1 can be much lighter than the discovered Higgs boson of mass 125 GeV, denoted H (125) [2]. The H (125) state can be either h1 or h2 and may decay into a pair of light bosons, a1a1 or h1h1. An update on a search for light NMSSM Higgs bosons produced in decays of the H (125) state and decaying into τ leptons is presented. The analysis considered only gluon-gluon fusion (ggH) production of H (125) so far. In this report we estimate the impact of the Vector Boson Fusion (VBF) process on the analysis sensitivity.
Nesting ecology and reproductive biology of the hawksbill turtle, Eretmochelys imbricata, at Kish Island, Persian Gulf

The ecology and reproductive biology of the hawksbill turtle, Eretmochelys imbricata were studied in Kish Island, Persian Gulf. The studied parameters include: environmental factors such as air temperature and humidity, soil types in different habitats during the breeding season; parameters related to the females' body and hatchlings biometrics i.e. weight, curved carapace length (CCL), straight carapace length (SCL), curved carapace width (CCW) and straight carapace width (SCW); and parameters related to reproductive biology, i.e. breeding time during diurnal, total eggs laid, the numbers of normal and abnormal eggs, weight and diameter of the eggs, incubation period and hatching success (HS). The temperature, humidity and soil size in different nest sites were 18.5–31°C, 70 to 88% and 0.063 to 4 mm, respectively. Means of weight, CCL, SCL, CCW and SCW of the females were 39.8 kg, 71.6, 65.1, 65.2 and 51.8 cm, respectively. The average of total egg numbers, normal and abnormal eggs by each individual female were 92.9, 75.2 and 17.7 respectively. Diameter and weight of every egg measured 38.4 mm and 33.6 g. Average of incubation period and HS were 60.9 days and 75.8%. According to the positive and significant correlation between CCL and CCW with weight took exponential regression models.
Understanding the Effect of Voice Quality and Accent on Talker Similarity

Abstract This paper presents a methodology to study the role of nonnative accents on talker recognition by humans. The methodology combines a state-of-the-art accent-conversion system to resynthesize the voice of a speaker with a different accent of her/his own, and a protocol for perceptual listening tests to measure the relative contribution of accent and voice quality on speaker similarity. Using a corpus of non-native and native speakers, we generated accent conversions in two different directions: non-native speakers with native accents, and native speakers with non-native accents. Then, we asked listeners to rate the similarity between 50 pairs of real or synthesized speakers. Using a linear mixed effects model, we find that (for our corpus) the effect of voice quality is five times as large as that of non-native accent, and that the effect goes away when speakers share the same (native) accent. We discuss the potential significance of this work in earwitness identification and sociophonetics.
A Rigorous Machine Learning Analysis Pipeline for Biomedical Binary Classification: Application in Pancreatic Cancer Nested Case-control Studies with Implications for Bias Assessments

Machine learning (ML) offers a collection of powerful approaches for detecting and modeling associations, often applied to data having a large number of features and/or complex associations. Currently, there are many tools to facilitate implementing custom ML analyses (e.g. scikit-learn). Interest is also increasing in automated ML packages, which can make it easier for non-experts to apply ML and have the potential to improve model performance. ML permeates most subfields of biomedical research with varying levels of rigor and correct usage. Tremendous opportunities offered by ML are frequently offset by the challenge of assembling comprehensive analysis pipelines, and the ease of ML misuse. In this work we have laid out and assembled a complete, rigorous ML analysis pipeline focused on binary classification (i.e. case/control prediction), and applied this pipeline to both simulated and real world data. At a high level, this 'automated' but customizable pipeline includes a) exploratory analysis, b) data cleaning and transformation, c) feature selection, d) model training with 9 established ML algorithms, each with hyperparameter optimization, and e) thorough evaluation, including appropriate metrics, statistical analyses, and novel visualizations. This pipeline organizes the many subtle complexities of ML pipeline assembly to illustrate best practices to avoid bias and ensure reproducibility. Additionally, this pipeline is the first to compare established ML algorithms to 'ExSTraCS', a rule-based ML algorithm with the unique capability of interpretably modeling heterogeneous patterns of association. While designed to be widely applicable we apply this pipeline to an epidemiological investigation of established and newly identified risk factors for pancreatic cancer to evaluate how different sources of bias might be handled by ML algorithms.
Some peculiarities of water freezing at small sub-zero temperatures

I consider the kinetics of water freezing and show that, at small sub-zero temperatures, (i) the time of ice nucleation within the bulk water environment is enormous and therefore cannot take place either in lakes of in living cells; (ii) that the ice nucleation needs some ice-binding surfaces to occur, but (iii) even this kind of ice nucleation can take place, as a rule, only at the temperatures that are a few degrees below 0oC. Further, I discuss factors that can drastically reduce the ice nucleation time at nearly-zero temperatures both in open reservoirs, where water contacts with air, and in cells, where there is no such contact.
Temporal Mental Health Dynamics on Social Media

We describe a set of experiments for building a temporal mental health dynamics system. We utilise a pre-existing methodology for distant-supervision of mental health data mining from social media platforms and deploy the system during the global COVID-19 pandemic as a case study. Despite the challenging nature of the task, we produce encouraging results, both explicit to the global pandemic and implicit to a global phenomenon, Christmas Depression, supported by the literature. We propose a methodology for providing insight into temporal mental health dynamics to be utilised for strategic decision-making.
Endohyphal bacteria from fungal endophytes of the Mediterranean cypress (Cupressus sempervirens) exhibit in vitro bioactivity

Summary Endohyphal organisms of endophytic fungi can induce, or promote, beneficial effects of their respective host fungi to the host plant that harbours them. The Cupressaceae plant family (Coniferales) hosts highly bioactive endophytic fungi. Here, we show that a fraction of such endophytic fungi harbours bioactive endohyphal bacteria with a non-obligatory symbiotic lifestyle. Indeed, 5 of 16 (31.25%) endophytic fungi of Cupressus sempervirens harboured endofungal bacterial strains of Bacillaceae (G+, Bacilli) and Sphingomonadaceae (G−, α-Proteobacteria), that is Bacillus pumilus (from the fungi Leptosphaeria CSE211 and Pyrenochaeta CSE134), Bacillus subtilis (from the fungi Leptosphaeria CSE212 and Ascorhizoctonia CSE148) and Sphingomonas paucimobilis (from the fungus Ascorhizoctonia CSE195). Notably, each endophytic fungal species contained only one endofungal bacterial species that was stably maintained in symbiosis over several rounds of subculturing. Moreover, we investigated whether cypress endofungal bacteria (CEB) could benefit their host fungus competing with other fungi and bacteria or the host plant against the invading microorganisms. In vitro assays indicated that CEB possessed antagonistic activity against cypress endophytic and pathogenic microbiome. Also, CEB metabolites and volatile compounds (VOCs) exhibited antifungal and antibacterial activity against the target microbiome. Bioactivity of CEB was less than that of the endophytic microbiome of Cupressaceae, on which we reported earlier. In conclusion, our work is the first to document endohyphal bacteria of fungal endophytes of C. sempervirens and the bioactivity of such endohyphal symbionts. These findings implicate a complicated interrelationship among host plant, endophytic microbiome and endofungal bacteria, which might be of high importance for evolutionary, as well as environmental and agricultural studies. Eventually, endohyphal bacteria may be introduced as a novel source for lead molecule discovery.
A Dataset of Human Motion Status Using IR-UWB Through-wall Radar

Ultra-wideband (UWB) through-wall radar has a wide range of applications in non-contact human information detection and monitoring. With the integration of machine learning technology, its potential prospects include the physiological monitoring of patients in the hospital environment and the daily monitoring at home. Although many target detection methods of UWB through-wall radar based on machine learning have been proposed, there is a lack of an opensource dataset to evaluate the performance of the algorithm. This published dataset was measured by impulse radio UWB (IR-UWB) through-wall radar system. Three test subjects were measured in different environments and several defined motion statuses. Using the presented dataset, we propose a human-motion-status recognition method using a convolutional neural network (CNN), the detailed dataset partition method and recognition process flow is given. On the well-trained network, the recognition accuracy of testing data for three kinds of motion statuses is higher than 99.7%. The dataset presented in this paper considers a simple environment. Therefore, we call on all organizations in the UWB radar field to cooperate to build opensource datasets to further promote the development of UWB through-wall radar.
BiLO-CPDP: Bi-Level Programming for Automated Model Discovery in Cross-Project Defect Prediction

Cross-Project Defect Prediction (CPDP), which borrows data from similar projects by combining a transfer learner with a classifier, have emerged as a promising way to predict software defects when the available data about the target project is insufficient. However, developing such a model is challenge because it is difficult to determine the right combination of transfer learner and classifier along with their optimal hyper-parameter settings. In this paper, we propose a tool, dubbed BiLO-CPDP, which is the first of its kind to formulate the automated CPDP model discovery from the perspective of bi-level programming. In particular, the bi-level programming proceeds the optimization with two nested levels in a hierarchical manner. Specifically, the upper-level optimization routine is designed to search for the right combination of transfer learner and classifier while the nested lower-level optimization routine aims to optimize the corresponding hyper-parameter settings. To evaluate BiLO-CPDP, we conduct experiments on 20 projects to compare it with a total of 21 existing CPDP techniques, along with its single-level optimization variant and Auto-Sklearn, a state-of-the-art automated machine learning tool. Empirical results show that BiLO-CPDP champions better prediction performance than all other 21 existing CPDP techniques on 70% of the projects, while being overwhelmingly superior to Auto-Sklearn and its single-level optimization variant on all cases. Furthermore, the unique bi-level formalization in BiLO-CPDP also permits to allocate more budget to the upper-level, which significantly boosts the performance.
Human-in-the-Loop Methods for Data-Driven and Reinforcement Learning Systems

Recent successes combine reinforcement learning algorithms and deep neural networks, despite reinforcement learning not being widely applied to robotics and real world scenarios. This can be attributed to the fact that current state-of-the-art, end-to-end reinforcement learning approaches still require thousands or millions of data samples to converge to a satisfactory policy and are subject to catastrophic failures during training. Conversely, in real world scenarios and after just a few data samples, humans are able to either provide demonstrations of the task, intervene to prevent catastrophic actions, or simply evaluate if the policy is performing correctly. This research investigates how to integrate these human interaction modalities to the reinforcement learning loop, increasing sample efficiency and enabling real-time reinforcement learning in robotics and real world scenarios. This novel theoretical foundation is called Cycle-of-Learning, a reference to how different human interaction modalities, namely, task demonstration, intervention, and evaluation, are cycled and combined to reinforcement learning algorithms. Results presented in this work show that the reward signal that is learned based upon human interaction accelerates the rate of learning of reinforcement learning algorithms and that learning from a combination of human demonstrations and interventions is faster and more sample efficient when compared to traditional supervised learning algorithms. Finally, Cycle-of-Learning develops an effective transition between policies learned using human demonstrations and interventions to reinforcement learning. The theoretical foundation developed by this research opens new research paths to human-agent teaming scenarios where autonomous agents are able to learn from human teammates and adapt to mission performance metrics in real-time and in real world scenarios.
Empirical Likelihood Weighted Estimation of Average Treatment Effects

There has been growing attention on how to effectively and objectively use covariate information when the primary goal is to estimate the average treatment effect (ATE) in randomized clinical trials (RCTs). In this paper, we propose an effective weighting approach to extract covariate information based on the empirical likelihood (EL) method. The resulting two-sample empirical likelihood weighted (ELW) estimator includes two classes of weights, which are obtained from a constrained empirical likelihood estimation procedure, where the covariate information is effectively incorporated into the form of general estimating equations. Furthermore, this ELW approach separates the estimation of ATE from the analysis of the covariate-outcome relationship, which implies that our approach maintains objectivity. In theory, we show that the proposed ELW estimator is semiparametric efficient. We extend our estimator to tackle the scenarios where the outcomes are missing at random (MAR), and prove the double robustness and multiple robustness properties of our estimator. Furthermore, we derive the semiparametric efficiency bound of all regular and asymptotically linear semiparametric ATE estimators under MAR mechanism and prove that our proposed estimator attains this bound. We conduct simulations to make comparisons with other existing estimators, which confirm the efficiency and multiple robustness property of our proposed ELW estimator. An application to the AIDS Clinical Trials Group Protocol 175 (ACTG 175) data is conducted.
Evaluating Knowledge Transfer In Neural Network for Medical Images

Deep learning and knowledge transfer techniques have permeated the field of medical imaging and are considered as key approaches for revolutionizing diagnostic imaging practices. However, there are still challenges for the successful integration of deep learning into medical imaging tasks due to a lack of large annotated imaging data. To address this issue, we propose a teacher-student learning framework to transfer knowledge from a carefully pre-trained convolutional neural network (CNN) teacher to a student CNN. In this study, we explore the performance of knowledge transfer in the medical imaging setting. We investigate the proposed network's performance when the student network is trained on a small dataset (target dataset) as well as when teacher's and student's domains are distinct. The performances of the CNN models are evaluated on three medical imaging datasets including Diabetic Retinopathy, CheXpert, and ChestX-ray8. Our results indicate that the teacher-student learning framework outperforms transfer learning for small imaging datasets. Particularly, the teacher-student learning framework improves the area under the ROC Curve (AUC) of the CNN model on a small sample of CheXpert (n=5k) by 4% and on ChestX-ray8 (n=5.6k) by 9%. In addition to small training data size, we also demonstrate a clear advantage of the teacher-student learning framework in the medical imaging setting compared to transfer learning. We observe that the teacher-student network holds a great promise not only to improve the performance of diagnosis but also to reduce overfitting when the dataset is small.
Multi-Modal End-User Programming of Web-Based Virtual Assistant Skills

While Alexa can perform over 100,000 skills on paper, its capability covers only a fraction of what is possible on the web. To reach the full potential of an assistant, it is desirable that individuals can create skills to automate their personal web browsing routines. Many seemingly simple routines, however, such as monitoring COVID-19 stats for their hometown, detecting changes in their child's grades online, or sending personally-addressed messages to a group, cannot be automated without conventional programming concepts such as conditional and iterative evaluation. This paper presents VASH (Voice Assistant Scripting Helper), a new system that empowers users to create useful web-based virtual assistant skills without learning a formal programming language. With VASH, the user demonstrates their task of interest in the browser and issues a few voice commands, such as naming the skills and adding conditions on the action. VASH turns these multi-modal specifications into skills that can be invoked invoice on a virtual assistant. These skills are represented in a formal programming language we designed called WebTalk, which supports parameterization, function invocation, conditionals, and iterative execution. VASH is a fully working prototype that works on the Chrome browser on real-world websites. Our user study shows that users have many web routines they wish to automate, 81% of which can be expressed using VASH. We found that VASH Is easy to learn, and that a majority of the users in our study want to use our system.
A Square Equal-area Map Projection

A novel square equal-area map projection is proposed. The projection combines closed-form forward and inverse solutions with relatively low angular distortion and minimal cusps, a combination of properties not manifested by any previously published square equal-area projection. Thus, the new projection has lower angular distortion than any previously published square equal-area projection with a closed-form solution. Utilizing a quincuncial arrangement, the new projection places the north pole at the center of the square and divides the south pole between its four corners; the projection can be seamlessly tiled. The existence of closed-form solutions makes the projection suitable for real-time visualization applications, both in cartography and in other areas, such as for the display of panoramic images.
Development of a method for solving structural optimization problems.

In practice, optimization tasks have some structure that allows developing new algorithms for every problem with faster convergence rates. Using the structure of optimization tasks, we can propose algorithms with more optimistic convergence rates for the following optimization problems: functions with Holder continuous gradients, superposition of functions (min-max problems), transportation problems, clustering by electorial model. In this work, we propose the unification of gradient-type methods into one method using a special concept of inexact model and develop a series of methods that can solve generalized optimization problem statements and use its structure with the aid of the proposed concept of inexact model. We constructed the gradient method for problems with relative smoothness, the primal--dual adaptive gradient and fast gradient methods, and the stochastic nonadaptive gradient methods that support an inexact model of a function. Moreover, the concept of inexact model is supported by different examples of optimization problems.
Puzzle-AE: Novelty Detection in Images through Solving Puzzles

Autoencoder, as an essential part of many anomaly detection methods, is lacking flexibility on normal data in complex datasets. U-Net is proved to be effective for this purpose but overfits on the training data if trained by just using reconstruction error similar to other AE-based frameworks. Puzzle-solving, as a pretext task of self-supervised learning (SSL) methods, has earlier proved its ability in learning semantically meaningful features. We show that training U-Nets based on this task is an effective remedy that prevents overfitting and facilitates learning beyond pixel-level features. Shortcut solutions, however, are a big challenge in SSL tasks, including jigsaw puzzles. We propose adversarial robust training as an effective automatic shortcut removal. We achieve competitive or superior results compared to the State of the Art (SOTA) anomaly detection methods on various toy and real-world datasets. Unlike many competitors, the proposed framework is stable, fast, data-efficient, and does not require unprincipled early stopping.