Miracle-dz/UniFAR-FTUs huggingface dataset:
Node-Based Resilience Measure Clustering with Applications to Noisy and Overlapping Communities in Complex Networks

This paper examines a schema for graph-theoretic clustering using node-based resilience measures. Node-based resilience measures optimize an objective based on a critical set of nodes whose removal causes some severity of disconnection in the network. Beyond presenting a general framework for the usage of node based resilience measures for variations of clustering problems, we experimentally validate the usefulness of such methods in accomplishing the following: (i) clustering a graph in one step without knowing the number of clusters a priori; (ii) removing noise from noisy data; and (iii) detecting overlapping communities. We demonstrate that this clustering schema can be applied successfully using a wide range of data, including both real and synthetic networks, both natively in graph form and also expressed as point sets.
Fault Location Based on Voltage Measurement at Secondary Side of Low-Voltage Transformer in Distribution Network

This article introduces a fault location method based on the voltage measurement at the secondary side of low-voltage transformer in distribution networks. It can be divided into fault section location and fault distance estimation. The first step for fault section location, the measured voltage with different positions is grouped to distinguish the upstream and downstream of the fault by the nearest neighbor clustering algorithm. And the second step is fault distance estimation. The voltage amplitude of the smart meter in the downstream side of the fault, the voltage, and current of the substation are used to estimate fault distance via an iteration search in the estimated fault section. Simulation results are presented in different scenarios in the IEEE 34 Bus and 134 Bus test network. Various issues such as fault resistance, fault type, measurement errors, DG, and the neutral grounding mode are investigated in simulations. The simulation results confirm the performance of the proposed method for fault location.
CONSTRUCTION OF QUANTIZED ENVELOPING BY COCYCLE DEFORMATION ALGEBRAS

By using cocycle deformation, we construct a certain class of Hopf algebras, containing the quantized enveloping algebras and their analogues, from what we call pre-Nichols algebras. Our construction generalizes in some sense the known construction by (generalized) quantum doubles, but unlike in the known situation, it saves us from difficulties in checking complicated defining relations. MSC (2000): 16W30, 17B37
Mixed-Effects Models in S and S-Plus

discussion of applied concepts. I expect to use this book again as the textbook for the course, but I plan to more fully integrate my notes with the material of the book. I also plan for an even more hands-on course than usual. The book’s website is rich in support material, containing updates to the ARC software, documents that extend the scope of the text, additional datasets, an errata page for the book, and many other links to material on regression graphics. The preface to the book indicates that a teacher’s manual is available; however, I was not able to locate a link to the manual on the Internet site. Overall, with its up-to-date discussion of regression graphics at a very accessible level, Applied Regression Including Computing and Graphics is a must for everyone working in the area of regression analysis. I strongly recommend it as a text for a  rst full course on applied regression for senior undergraduate statistics majors or  rst-year graduate students—if the students have easy access to computing facilities and if their previous studies included an introduction to multiple linear regression.
An LSTM-GRU based hybrid framework for secured stock price prediction

Abstract The prediction of the stock prices is a very challenging task as the data is associated with nonlinearity and volatility. The machine learning and artificial intelligence methods have been found to make this task more efficient and the advent of high throughput computes have proved to be beneficial in these tasks. In this work a hybrid LSTM-GRU network has been used for prediction of the adjusted closing price of the Standard & Poor 500 index. Also, the initial number of six features have been increased to 25 features by adding several technical indicators. The performance indicators like Return ratio, R2, MSE, Optimism and Pessimism ratios are used to compare the proposed model with stand-alone LSTM, GRU and MLP models. This comparison establishes that the proposed model is capable of more accurate prediction of the stock market prices.
Slowly Varying Solitary Wave Solutions of the Perturbed Korteweg‐de Vries Equation Revisited

Using a multi-scale perturbation expansion we reconsider the slowly varying solitary wave asymptotic solution of the perturbed Korteweg-de Vries equation. The well-known results for the variation of the solitary-wave amplitude and the accompanying trailing tail are recovered. Here the analysis is carried through to second order so as to determine a general expression for the first-order speed correction. The result obtained here generalizes and improves previous results.
Vitamin D Ameliorates Apoptosis and Inflammation by Targeting the Mitochondrial and MEK1/2-ERK1/2 Pathways in Hyperoxia-Induced Bronchopulmonary Dysplasia

Purpose Bronchopulmonary dysplasia (BPD) is a common and severe complication in preterm infants. Vitamin D (VitD) has been reported to protect against BPD; however, its role in the mitochondria-mediated and MEK1/2-ERK1/2 pathways has not yet been reported. Methods We first performed in vivo studies using neonatal C57BL/6 mice in which we induced BPD by exposing them to a hyperoxic environment (85% O2). The mice were divided into room air (RA; 21% O2), RA+VitD, BPD, and BPD+VitD groups. Hematoxylin and eosin and Masson’s trichrome staining were used to evaluate lung injury. Inflammation and apoptosis were measured using ELISA, RT-qPCR, and TUNEL assays. We then analyzed BEAS-2B cells divided into the same groups along with an additional BPD+VitD+inhibitor group. Mitochondrial apoptosis was evaluated by transmission electron microscopy, mitochondrial membrane potential, and Western blotting. We then used VDR-shRNA to silence the Vitamin D Receptor (VDR) in the BEAS-2B cells. The inflammation, apoptotic rate, and the phosphorylated forms of MEK1/2 and ERK1/2 in cells were detected by RT-qPCR, flow cytometry, and Western blotting. Results The mean linear intercept, septal thickness, and abnormal fibrosis increased, while radial alveolar count decreased in BPD lungs compared to RA lungs. VitD administration was able to ameliorate the phenotype in BPD lungs. IL-6, IFN-γ, and TNF-α expression and the apoptotic rate decreased in the BPD+VitD lung group. VitD pretreatment restored abnormal mitochondrial morphology, reduced mitochondrial membrane loss, and reduced the expression of cleaved caspase-3, Bax, and Bcl-2 in BEAS-2B cells. VitD administration also reduced IL-6, IFN-γ, and TNF-α mRNA, as well as pMEK1/2 and pERK1/2 expression and apoptosis rate in cells exposed to hyperoxia. Conclusion We concluded that VitD treatment ameliorated apoptosis and inflammation by targeting the mitochondrial pathway and via the MEK1/2-ERK1/2 signaling pathway in BPD, thus supporting its potential therapeutic use in this condition.
DVAEGMM: Dual Variational Autoencoder With Gaussian Mixture Model for Anomaly Detection on Attributed Networks

A significant aspect of today’s digital information is attributed networks, which combine multiple node attributes with the basic network topology to extract knowledge. Anomaly Detection on attributed networks has recently drawn significant attention from researchers and is widely used in several high-impact areas. Most current approaches focus on shallow learning methods such as community analysis, ego network or selection of subspace method. These approaches have network sparsity and data nonlinearity problems, and they do not even capture the intricate relationships between various information sources. Deep learning approaches like graph autoencoders are utilized to perform anomaly detection through obtaining node embeddings while dealing with the network nonlinearity and sparsity issues. However, they suffer from the problem of ignoring the latent codes’ embedding distribution, which results in poor representation in many instances. In this paper, we propose a new framework called DVAEGMM to detect anomalies on attributed networks. First, our framework utilizes a dual variational autoencoder for capturing the complex cross-modality relationships between node attributes and network structure, like vanilla autoencoders, but it also considers the potential data distribution and makes use of a generative adversarial network (GAN) for an adversarial regularization approach. An adversarial mechanism makes the encoder make more accurate estimates of how potential features might be distributed. As a result, decoders can make graphs that are more like the original graph. Each input data point is represented by a low-dimensional representation and a probability of reconstruction by the algorithm. Lastly, the Gaussian Mixture Model, a distinct estimation network, is used to approximate the latent vector density, resulting in the detection of anomalies from measuring sample energy. They are trained jointly as an end-to-end framework. DVAEGMM helps in the simultaneous optimization of the mixture model, generative adversarial network, and variational autoencoder parameters. The joint optimization balances the reconstruction probability, the latent representation density approximation, and regularization. Extensive experiments on attributed networks prove that DVAEGMM significantly beats the existing methods, proving the efficiency of the presented approach. The AUC scores of our proposed framework for the BlogCatalog, Flickr, Enron, and Amazon datasets are 0.89380, 0.87130, 0.72480, and 0.75102, respectively.
The liquid identity of adolescents with disabilities: Changes in the identity of adolescents with disabilities in social media

Studies on liquid identity have been done by many previous researchers. However, this article sees it differently where liquid identity is experienced by adolescents with disabilities who basically have many difficulties, obstacles in interacting with society in general. This study aimed to look at the process, intention, and purpose of the liquid identity experienced by adolescents with disabilities on social media. This study used a virtual ethnographic method with informants who are members of the Facebook community named “Indonesian Association of People with Disabilities. ” The number of informants is 23 adolescents with disabilities who are users of social media. The findings of this study showed that adolescents with disabilities take various ways to get recognition and acceptance by a wide audience according to their type and characteristics. This is done by using social media called liquid identity. To get recognition and acceptance on social media, the practice of liquid identity of adolescents with disabilities was shown by hiding their real identity, having multiple accounts, and sharing positive things on social media. This study concluded that support from various parties is needed, either from the community, parents, friends, schools, and the environment. The role of these parties is important to provide motivation, enthusiasm, and encouragement so that adolescents with disabilities can show value on social media with their identity that is recognized by the public as a positive identity.
Television News, Political Comedy, Party, and Political Knowledge in Global Warming Belief: Evidence From a Large-Scale Panel Survey

Secondary analysis of panel data found, after controlling for prior belief and many covariates, that 2011 Fox News viewing was negatively related to belief in global warming in 2016 and 2019, while MSNBC, broadcast news, and political comedy viewing in 2016 and CNN in 2019 had positive relationships. Partisan belief gaps in 2016 widened with CNN viewing and narrowed with viewing broadcast news, Fox News (especially with higher knowledge) and MSNBC (especially with lower knowledge), while political comedy viewing was associated both with wider (with lower knowledge) and narrower gaps (with higher knowledge). Implications for practice and theory are discussed.
GeoDLS: A Deep Learning-Based Corn Disease Tracking and Location System Using RTK Geolocated UAS Imagery

Deep learning-based solutions for precision agriculture have recently achieved promising results. Deep learning has been used to identify crop diseases at the initial stages of disease development in an effort to create effective disease management systems. However, the use of deep learning and unmanned aerial system (UAS) imagery to track the spread of diseases, identify diseased regions within cornfields, and notify users with actionable information remains a research gap. Therefore, in this study, high-resolution, UAS-acquired, real-time kinematic (RTK) geotagged, RGB imagery at an altitude of 12 m above ground level (AGL) was used to develop the Geo Disease Location System (GeoDLS), a deep learning-based system for tracking diseased regions in corn fields. UAS images (resolution 8192 × 5460 pixels) were acquired in cornfields located at Purdue University’s Agronomy Center for Research and Education (ACRE), using a DJI Matrice 300 RTK UAS mounted with a 45-megapixel DJI Zenmuse P1 camera during corn stages V14 to R4. A dataset of 5076 images was created by splitting the UAS-acquired images using tile and simple linear iterative clustering (SLIC) segmentation. For tile segmentation, the images were split into tiles of sizes 250 × 250 pixels, 500 × 500 pixels, and 1000 × 1000 pixels, resulting in 1804, 1112, and 570 image tiles, respectively. For SLIC segmentation, 865 and 725 superpixel images were obtained using compactness (m) values of 5 and 10, respectively. Five deep neural network architectures, VGG16, ResNet50, InceptionV3, DenseNet169, and Xception, were trained to identify diseased, healthy, and background regions in corn fields. DenseNet169 identified diseased, healthy, and background regions with the highest testing accuracy of 100.00% when trained on images of tile size 1000 × 1000 pixels. Using a sliding window approach, the trained DenseNet169 model was then used to calculate the percentage of diseased regions present within each UAS image. Finally, the RTK geolocation information for each image was used to update users with the location of diseased regions with an accuracy of within 2 cm through a web application, a smartphone application, and email notifications. The GeoDLS could be a potential tool for an automated disease management system to track the spread of crop diseases, identify diseased regions, and provide actionable information to the users.
The development, and day-to-day variation, of a Military-Specific Auditory N-Back Task and Shoot-/Don’t -Shoot Task

Abstract During military operations, soldiers are required to successfully complete numerous physical and cognitive tasks concurrently. Understanding the typical variance in research tools that may be used to provide insight into the interrelationship between physical and cognitive performance is therefore highly important. This study assessed the inter-day variability of two military-specific cognitive assessments: a Military-Specific Auditory N-Back Task (MSANT) and a Shoot-/Don’t-Shoot Task (SDST) in 28 participants. Limits of agreement ±95% confidence intervals, standard error of the mean, and smallest detectable change were calculated to quantify the typical variance in task performance. All parameters within the MSANT and SDST demonstrated no mean difference for trial visit in either the seated or walking condition, with equivalency demonstrated for the majority of comparisons. Collectively, these data provided an indication of the typical variance in MSANT and SDST performance, while demonstrating that both assessments can be used during seated and walking conditions.
Laser-Induced Graphene for Heartbeat Monitoring with HeartPy Analysis

The HeartPy Python toolkit for analysis of noisy signals from heart rate measurements is an excellent tool to use in conjunction with novel wearable sensors. Nevertheless, most of the work to date has focused on applying the toolkit to data measured with commercially available sensors. We demonstrate the application of the HeartPy functions to data obtained with a novel graphene-based heartbeat sensor. We produce the sensor by laser-inducing graphene on a flexible polyimide substrate. Both graphene on the polyimide substrate and graphene transferred onto a PDMS substrate show piezoresistive behavior that can be utilized to measure human heartbeat by registering median cubital vein motion during blood pumping. We process electrical resistance data from the graphene sensor using HeartPy and demonstrate extraction of several heartbeat parameters, in agreement with measurements taken with independent reference sensors. We compare the quality of the heartbeat signal from graphene on different substrates, demonstrating that in all cases the device yields results consistent with reference sensors. Our work is a first demonstration of successful application of HeartPy to analysis of data from a sensor in development.
RFBG Thermal Aging Evaluation Above Regeneration Temperature

Regenerated fiber Bragg grating (RFBG) sensors for thermal aging at high temperatures from 600 °C to 1000 °C are produced and analyzed in this work. Seed FBGs are regenerated at 850 °C–900 °C and thermal characterized at the increasing steps of 50 °C–100 °C. Signal decay is measured from the reflected spectra until imminent erasure. The objective is to analyze the RFBG sensors lifetime above the regeneration temperature for long periods after annealing cycles with increasing thermal levels. Lifetime results from RFBG were reported to be a few hours above their regeneration temperature. Our results are significantly better. The sensors resisted for the time intervals of one to eight days up to 100 °C above the regeneration temperature. In addition, some sensors remained stable in terms of the spectral amplitude for more than four days in that thermal condition.
Vertical modes of quasi-geostrophic flows in an ocean with bottom topography : evolution equation and energetics

Quasi-geostrophic current is expanded in terms of vertical modes such as barotropic and baroclinic ones. Then the evolution of quasi-geostrophic motion is understood from the behavior of each vertical mode. There are some subtle issues, however, as regards vertical modes: boundary conditions, difference between a level model and a layer model, and so on. A comprehensive formulation is given of the expanSiOn of the quasi-geostrophic flows in terms of vertical modes both for a level model and for a layer model. Vertical modes are defined in almost the same manner for a level model and a layer model. Evolution equation and energetics of each mode are derived and argued. In addition to the ordinary nonlinear advective three-mode interaction, a gentle relief of bottom topography allows inter-modal energy transfer via bottom topography; in particular the so-called JEBAR effect and topographic torque is classified into one of such two-mode interaction (coupling). Under the rigid lid surface condition, the barotropic mode does not make a triplet with two different baroclinic modes, in the three-mode nonlinear advective interaction. Detailed balance turns out to hold for inter-modal energy transfer with respect to three-mode nonlinear advective interaction and two-mode coupling via topography. Potential enstrophy transfer among vertical modes is formulated also. It is shown that detailed balance is assured for inter-modal enstrophy transfer as well, if potential enstrophy is chosen properly. On the other hand detailed balance is not expected for inter-modal enstrophy transfer by two-mode coupling via bottom topography.
A High Precision Artificial Neural Networks Model for Short-Term Energy Load Forecasting

One of the most important research topics in smart grid technology is load forecasting, because accuracy of load forecasting highly influences reliability of the smart grid systems. In the past, load forecasting was obtained by traditional analysis techniques such as time series analysis and linear regression. Since the load forecast focuses on aggregated electricity consumption patterns, researchers have recently integrated deep learning approaches with machine learning techniques. In this study, an accurate deep neural network algorithm for short-term load forecasting (STLF) is introduced. The forecasting performance of proposed algorithm is compared with performances of five artificial intelligence algorithms that are commonly used in load forecasting. The Mean Absolute Percentage Error (MAPE) and Cumulative Variation of Root Mean Square Error (CV-RMSE) are used as accuracy evaluation indexes. The experiment results show that MAPE and CV-RMSE of proposed algorithm are 9.77% and 11.66%, respectively, displaying very high forecasting accuracy.
Automated Detection of Retrogressive Thaw Slumps in the High Arctic Using High-Resolution Satellite Imagery

Retrogressive thaw slumps (RTS) are considered one of the most dynamic permafrost disturbance features in the Arctic. Sub-meter resolution multispectral imagery acquired by very high spatial resolution (VHSR) commercial satellite sensors offer unique capacities in capturing the morphological dynamics of RTSs. The central goal of this study is to develop a deep learning convolutional neural net (CNN) model (a UNet-based workflow) to automatically detect and characterize RTSs from VHSR imagery. We aimed to understand: (1) the optimal combination of input image tile size (array size) and the CNN network input size (resizing factor/spatial resolution) and (2) the interoperability of the trained UNet models across heterogeneous study sites based on a limited set of training samples. Hand annotation of RTS samples, CNN model training and testing, and interoperability analyses were based on two study areas from high-Arctic Canada: (1) Banks Island and (2) Axel Heiberg Island and Ellesmere Island. Our experimental results revealed the potential impact of image tile size and the resizing factor on the detection accuracies of the UNet model. The results from the model transferability analysis elucidate the effects on the UNet model due the variability (e.g., shape, color, and texture) associated with the RTS training samples. Overall, study findings highlight several key factors that we should consider when operationalizing CNN-based RTS mapping over large geographical extents.
Some Numerical Criteria for the Nash Problem on Arcs for Surfaces

Abstract Let (X, O) be a germ of a normal surface singularity, π: → X be the minimal resolution of singularities and let A = (ai,j ) be the n × n symmetrical intersection matrix of the exceptional set of In an old preprint Nash proves that the set of arcs on a surface singularity is a scheme , and defines a map from the set of irreducible components of to the set of exceptional components of the minimal resolution of singularities of (X,O). He proved that this map is injective and ask if it is surjective. In this paper we consider the canonical decomposition • For any couple (Ei,Ej ) of distinct exceptional components, we define Numerical Nash condition (NN (i,j)). We have that (NN (i,j)) implies In this paper we prove that (NN (i,j)) is always true for at least the half of couples (i,j). • The condition (NN (i,j)) is true for all couples (i,j) with i ≠ j, characterizes a certain class of negative definite matrices, that we call Nash matrices. If A is a Nash matrix then the Nash map N is bijective. In particular our results depend only on A and not on the topological type of the exceptional set. • We recover and improve considerably almost all results known on this topic and our proofs are new and elementary. • We give infinitely many other classes of singularities where Nash Conjecture is true. The proofs are based on my old work [8] and in Plenat [10].