Miracle-dz/UniFAR-FTUs huggingface dataset:
TREND TOWARDS THE USE OF COMPLEX NETWORKS IN CLOUD COMPUTING ENVIRONMENT

As a consequences of convulsion of the spreading strategy and topological nature of the cloud computing, it's partner troublesome assignment to portray the hubs that region unit significantly more Powerful than others. The total target of this paper is to take a gander at cutting edge organizing based distributed computing. Propelled systems region unit those systems whose structure is unbalanced, confounded and developing progressively. Its fundamental concentration could likewise be the investigation of moment, systems to it express of framework with thousands or inconceivable hub. It's been found from the survey that none of the method is considerably higher for distributed computing. Hence, this paper closes with the long run degree to determine these issues.
Trace: Tennessee Research and Creative Exchange

Neuromorphic electronics studies the physical realization of neural networks in discrete circuit components. Hardware implementations of neural networks take advantage of highly parallelized computing power with low energy systems. The hardware designed for these systems functions as a low power, low area alternative to computer simulations. With on-line learning in the system, hardware implementations of neural networks can further improve their solution to a given task. In this work, the analog computational system presented is the computational core for running a spiking neural network model. This component of a neural network, the neuron, is one of the building blocks used to create neural networks. The neuron takes inputs from the connected synapses, which each store a weight value. The inputs are stored in the neuron and checked against a threshold. The neuron activates, causing a ﬁring event, when the neuron’s internal storage crosses its threshold. The neuron designed is an Axon-Hillock neuron utilizing memristive synapses for low area and energy operation.
Revision of the Age of Construction Phases of a Mound Dated to the Late Copper–Early Bronze Age in Eastern Hungary Relying on 14C-Based Chronologies

ABSTRACT Ecse Mound is a burial mound in the Hortobágy region of eastern Hungary. Built by prehistoric nomadic peoples from the east, it now stands on the border between two modern settlements. The construction of the mound was assumed to be related to representatives of the Pit Grave Culture populating the area between the Late Copper and Bronze Ages. This theory considered similarities in shape, orientation, and stratigraphy of this mound with other absolute-dated ones in the Hortobágy region alone. The mound comprises two construction layers as indicated by magnetic susceptibility and on-site stratigraphic observations. According to detailed sedimentological, geochemical analyses of samples taken from the bedrock, artificial stratigraphic horizons, and the overlying topsoil, there is a marked similarity between the soil forming the body of the mound in both artificial horizons and the underlying bedrock soil. In contrast the pedological, geological character of the modern topsoil is utterly different. According to our dating results, the uppermost stratigraphic horizon is coeval with the absolute-dated mounds in the region, assigning it to the period of the Pit Grave Culture. However, the lower anthropological horizon is older and dates to between the Early and Late Copper Ages.
Evaluating the robustness of machine learning algorithm on Human Activity Recognition

Physical inactivity can cause several diseases, like diabetes, obesity, hypertension, cardiovascular diseases, depression, osteoporosis, and many other physical problems. It is needed to monitor the movements performed by someone and classify them as one of the Activities of Daily Living, like walking, laying down, standing, and other activities to diagnose physical inactivity and avoid this kind of problems. In this context, signals from smartphone sensors can be used as input to machine learning models to perform such classification. However, some of these activities, as sitting and laying, produce almost the same, resulting in noise among data labels thus making the training process harder. This work aims to analyze the impact of data with label noise defining the most robust machine learning technique for that kind of data.
Deep learning models for COVID-19 chest x-ray classification: Preventing shortcut learning using feature disentanglement

In response to the COVID-19 global pandemic, recent research has proposed creating deep learning based models that use chest radiographs (CXRs) in a variety of clinical tasks to help manage the crisis. However, the size of existing datasets of CXRs from COVID-19+ patients are relatively small, and researchers often pool CXR data from multiple sources, for example, using different x-ray machines in various patient populations under different clinical scenarios. Deep learning models trained on such datasets have been shown to overfit to erroneous features instead of learning pulmonary characteristics - a phenomenon known as shortcut learning. We propose adding feature disentanglement to the training process, forcing the models to identify pulmonary features from the images while penalizing them for learning features that can discriminate between the original datasets that the images come from. We find that models trained in this way indeed have better generalization performance on unseen data; in the best case we found that it improved AUC by 0.13 on held out data. We further find that this outperforms masking out non-lung parts of the CXRs and performing histogram equalization, both of which are recently proposed methods for removing biases in CXR datasets.
Atlantic Water Pathways Along the North‐Western Svalbard Shelf Mapped Using Vessel‐Mounted Current Profilers

A large amount of warm Atlantic water (AW) enters the Arctic as a boundary current through Fram Strait (West Spitsbergen Current [WSC]) and is the major oceanic heat source to the Arctic Ocean. Along the north‐western Svalbard shelf, the WSC splits into the shallow Svalbard Branch, the Yermak Branch that follows the slope of the Yermak Plateau, and the Yermak Pass Branch flowing across the plateau. The WSC has previously been studied using moorings, dedicated oceanographic transects, and models. In this study, we mapped the circulation patterns and AW flow around Svalbard using Vessel‐Mounted Acoustic Doppler Current Profiler data from multiple surveys during four consecutive summers (2014–2017). Despite the scattered nature of this compiled data set, persistent circulation patterns could be discerned. Spatial interpolation showed a meandering boundary current west of Svalbard and a more homogeneous AW flow, centered around the 1,000‐m isobath north of Svalbard. In all summers, we observed a northward jet between 79 and 80°N and the 1,000‐ and 500‐m isobaths, before the WSC divided into the three branches. North of Svalbard, the shallow Svalbard Branch reunited with the Yermak Pass Branch between 10 and 15°E and a part of the AW circulated within Hinlopen Trench. The calculated volume transport of 2 Sv in the upper 500 m compares well with model results and previous observations. Our results further show that the Yermak Pass Branch can be as important as the Svalbard Branch in transporting AW across the Yermak Plateau during summer. Plain Language Summary We mapped how seawater flows from the Atlantic into the Arctic Ocean around the Svalbard archipelago. To know where and how much water flows from the Atlantic into the Arctic Ocean is important because Atlantic water is the major source of heat, nutrients, and plankton for the Arctic Ocean. Heat from Atlantic water plays a role in the increased melting of sea ice, and the nutrients and plankton drifting with the currents are a major food supply for the Arctic marine ecosystem.Wemapped the ocean currents with acoustic current meters that are mounted to research vessels that surveyed the area west and north of the Svalbard archipelago. We found that the Atlantic water flowing around Svalbard can take three different pathways: the shallow Svalbard Branch close to the north‐western edge of Svalbard, the Yermak Branch that follows the slope of an underwater plateau north‐west of Svalbard, and the Yermak Pass Branch that flows across the plateau and which has not been observed in summer before. We also showed that Atlantic water circulates within Hinlopen trench, a large passage splitting the archipelago, which could create a local ecological hot spot due to the favorable nutrient and plankton supply.
Minimized Capillary End Effect During CO2 Displacement in 2‐D Micromodel by Manipulating Capillary Pressure at the Outlet Boundary in Lattice Boltzmann Method

We propose a new outflow boundary condition to minimize the capillary end effect for a pore‐scale CO2 displacement simulation. The Rothman‐Keller lattice Boltzmann method with multi‐relaxation time is implemented to manipulate a nonflat wall and inflow‐outflow boundaries with physically acceptable fluid properties in 2‐D microfluidic chip domain. Introducing a mean capillary pressure acting at CO2 ‐water interface to the nonwetting fluid at the outlet effectively prevents CO2 injection pressure from suddenly dropping upon CO2 breakthrough such that the continuous CO2 invasion and the increase of CO2 saturation are allowed. This phenomenon becomes most pronounced at capillary number of logCa = −5.5, while capillary fingering and massive displacement of CO2 prevail at low and high capillary numbers, respectively. Simulations with different domain length in homogeneous and heterogeneous domains reveal that capillary pressure and CO2 saturation near the inlet are reproducible compared with those with a proposed boundary condition. The residual CO2 saturation uniquely follows the increasing tendency with increasing capillary number, corroborated by experimental evidences. The determination of the mean capillary pressure and its sensitivity are also discussed. The proposed boundary condition is commonly applicable to other pore‐scale simulations to accurately capture the spatial distribution of nonwetting fluid and corresponding displacement ratio.
Facial Micro-Expression Analysis

The Micro-expressions (MEs) are very important nonverbal communication clues. However, due to their local and short nature, spotting them is challenging. In this thesis, we address this problem by using a dedicated local and temporal pattern (LTP) of facial movement. This pattern has a specific shape (S-pattern) when ME are displayed. Thus, by using a classical classification algorithm (SVM), MEs are distinguished from other facial movements. We also propose a global final fusion analysis on the whole face to improve the distinction between ME (local) and head (global) movements. However, the learning of S-patterns is limited by the small number of ME databases and the low volume of ME samples. Hammerstein models (HMs) are known to be a good approximation of muscle movements. By approximating each S-pattern with a HM, we can both filter outliers and generate new similar S-patterns. By this way, we perform a data augmentation for S-pattern training dataset and improve the ability to differentiate MEs from other facial movements. In the first ME spotting challenge of MEGC2019, we took part in the building of the new result evaluation method. In addition, we applied our method to spotting ME in long videos and provided the baseline result for the challenge. The spotting results, performed on CASME I and CASME II, SAMM and CAS(ME)2, show that our proposed LTP outperforms the most popular spotting method in terms of F1-score. Adding the fusion process and data augmentation improve even more the spotting performance.
Robust Vehicular Localization and Map Matching in Urban Environments Through IMU, GNSS, and Cellular Signals

A framework for ground vehicle localization that uses cellular signals of opportunity (SOPs), a digital map, an inertial measurement unit (IMU), and a Global Navigation Satellite System (GNSS) receiver is developed. This framework aims to enable localization in an urban environment where GNSS signals could be unusable or unreliable. The proposed framework employs an extended Kalman filter (EKF) to fuse pseudorange observables extracted from cellular SOPs, IMU measurements, and GNSS-derived position estimates (when available). The EKF is coupled with a map-matching approach. The framework assumes the positions of the cellular towers to be known, and it estimates the vehicle's states (position, velocity, orientation, and IMU biases) along with the difference between the vehicle-mounted receiver clock error states (bias and drift) and each cellular SOP clock error state. The proposed framework is evaluated experimentally on a ground vehicle navigating in a deep urban area with a limited sky view. Results show a position root-mean-square error (RMSE) of 2.8 m across a 1,380-m trajectory when GNSS signals are available and an RMSE of 3.12 m across the same trajectory when GNSS signals are unavailable for 330 m. Moreover, compared to localization with a loosely coupled GNSS? IMU integrated system, a 22% reduction in the localization error is obtained whenever GNSS signals are available, and an 81% reduction in the localization error is obtained whenever GNSS signals are unavailable.
Toward Automatic Fake News Classification

The interaction of technology with humans have many adverse effects. The rapid growth and outreach of the social media and the Web have led to the dissemination of questionable and untrusted content among a wider audience, which has negatively influenced their lives and judgment. Different election campaigns around the world highlighted how ”fake news” misinformation that looks genuine can be targeted towards specific communities to manipulate and confuse them. Ever since, automatic fake news detection has gained widespread attention from the scientific community. As a result, many re-search studies have been conducted to tackle the detection and spreading of fake news. While the first step of such tasks would be to classify claims associated based on their credibility, the next steps would involve identifying hidden patterns in style, syntax, and content of such news claims. We provide a comprehensive overview of what has already been done in this domain and other similar fields, and then propose a generalized method based on Deep Neural Networks to identify if a given claim is fake or genuine. By using different features like the authenticity of the source, perceived cognitive authority, style, and content-based factors, and natural language features, it is possible to predict fake news accurately. We have used a modular approach by combining techniques from information retrieval, natural language processing, and deep learning. Our classifier comprises two main sub-modules. The first sub-module uses the claim to retrieve relevant articles from the know-ledge base which can then be used to verify the truth of the claim. It also uses word-level features for prediction. The second sub-module uses a deep neural network to learn the underlying style of fake content. Our experiments conducted on bench-mark datasets show that for the given classification task we can obtain up to 82.4% accuracy by using a combination of two models; the first model was up to 72% accurate while the second model was around 81% accurate. Our detection model has the potential to automatically detect and prevent the spread of fake news, thus, limiting the caustic influence of technology in the human lives.
A New Image Psychovisual Coding Quality Measurement based Region of Interest

region of interest, we propose a new objective metric evaluation adapted to wavelet foveation-based image compression quality measurement, which exploits a foveation setup filter implementation technique in the DWT domain, based especially on the point and region of fixation of the human eye. This model is then used to predict the visible divergences between an original and compressed image with respect to this region field and yields an adapted and local measure error by removing all peripheral errors. The technique, which we call foveation wavelet visible difference prediction (FWVDP), is demonstrated on a number of noisy images all of which have the same local peak signal to noise ratio (PSNR), but visibly different errors. We show that the FWVDP reliably predicts the fixation areas of interest where error is masked, due to high image contrast, and the areas where the error is visible, due to low image contrast. The paper also suggests ways in which the FWVDP can be used to determine a visually optimal quantization strategy for foveation-based wavelet coefficients and to produce a quantitative local measure of image quality.
Node-Oriented Workflow (NOW): A Command Template Workflow Management Tool for High Throughput Data Analysis Pipelines

Next generation sequencing (NGS) systems produce vast quantities of data that require substantial computational resources for typical analysis tasks. In addition, data that are generated by different NGS systems are not homogeneous. Moreover, there are an overwhelming number of tools available for performing typical tasks. Managing NGS workflows involves writing custom scripts that quickly grow in complexity, often resulting in unwieldy workflows that underutilize typical high performance compute resources, and increase the demands of the staff managing these workflows. We present Node-Oriented Workflow (NOW), a dynamic command template workflow engine for high performance distributed computing (HPC) systems. Our system provides a simple-to-use browserbased front end for designing and managing complex workflows. Workflows are configured using a simple browser interface, and are managed by the integrated job engine, which initializes nodes, monitors node status, and processes results of individual jobs across nodes in an HPC configuration. We reduce excessive messaging across nodes by placing the burden on nodes to start tasks in a workflow when dependencies are met, i.e., node oriented workflow. Our system was designed for NGS processing in the clinical research setting, emphasizing user simplicity, tool scalability, minimization of redundancy in workflows, while maximizing throughput in an HPC environment. Furthermore, NOW is not restricted to NGS pipeline management, but can used to manage any computational pipeline.
Scaling laws: legal and social complexity in US localities

Law sets out the rules for society and the economy, particularly important for interactions between strangers. Legal code is a form of non-rival infrastructure, a public good important for investment and innovation. This paper investigates whether legal code complexity scales with population size in US localities. We analyse a corpus of municipal codes from 3259 cities and measure legal complexity using various metrics, including number of words, bytes, and compressed bytes. We find that legal complexity scales geometrically with jurisdiction population, with a scaling parameter of approximately 0.2 and an R2 of approximately 0.2. The estimated scaling parameter is similar to gross domestic product per capita, consistent with an interpretation of legal codes as regulating social interactions per capita in cities. This article is part of the theme issue ‘A complexity science approach to law and governance’.
Advanced lung organoids for respiratory system and pulmonary disease modeling

Amidst the recent coronavirus disease 2019 (COVID-19) pandemic, respiratory system research has made remarkable progress, particularly focusing on infectious diseases. Lung organoid, a miniaturized structure recapitulating lung tissue, has gained global attention because of its advantages over other conventional models such as two-dimensional (2D) cell models and animal models. Nevertheless, lung organoids still face limitations concerning heterogeneity, complexity, and maturity compared to the native lung tissue. To address these limitations, researchers have employed co-culture methods with various cell types including endothelial cells, mesenchymal cells, and immune cells, and incorporated bioengineering platforms such as air-liquid interfaces, microfluidic chips, and functional hydrogels. These advancements have facilitated applications of lung organoids to studies of pulmonary diseases, providing insights into disease mechanisms and potential treatments. This review introduces recent progress in the production methods of lung organoids, strategies for improving maturity, functionality, and complexity of organoids, and their application in disease modeling, including respiratory infection and pulmonary fibrosis.
Generalized Integrate-and-Fire Models of Neuronal Activity Approximate Spike Trains of a Detailed Model to a High Degree of Accuracy

[PDF] [Full Text] [Abstract] , August 1, 2006; 96 (2): 872-890. J Neurophysiol Q. J. M. Huys, M. B. Ahrens and L. Paninski Efficient Estimation of Detailed Single-Neuron Models [PDF] [Full Text] [Abstract] , September 1, 2006; 18 (9): 2146-2210. Neural Comput. M. Rudolph and A. Destexhe Event-Driven Simulation Strategies. Analytical Integrate-and-Fire Neuron Models with Conductance-Based Dynamics for [PDF] [Full Text] [Abstract] , December 1, 2006; 96 (6): 3448-3464. J Neurophysiol G. La Camera, A. Rauch, D. Thurbon, H. -R. Luscher, W. Senn and S. Fusi Neurons Multiple Time Scales of Temporal Response in Pyramidal and Fast Spiking Cortical [PDF] [Full Text] [Abstract] , February 21, 2007; 27 (8): 2058-2073. J. Neurosci. J. G. Mancilla, T. J. Lewis, D. J. Pinto, J. Rinzel and B. W. Connors Synchronization of Electrically Coupled Pairs of Inhibitory Interneurons in Neocortex [PDF] [Full Text] [Abstract] , April 1, 2007; 97 (4): 2744-2757. J Neurophysiol B. Doiron, A. -M. M. Oswald and L. Maler Interval Coding. II. Dendrite-Dependent Mechanisms
An Online Pediatric Palliative Care Education and Mentoring (Project ECHO) in Nepal: A Program Implementation Case Study and Assessment of Changes in Healthcare Providers’ Knowledge, Confidence, and Attitudes

OBJECTIVES The goal of this implementation study was to describe the implementation and evaluation of the impact of an online pediatric palliative care training program in Nepal, using the Project ECHO model. METHODS The study used mixed methods, including a program case study describing the online learning program and before-and-after surveys of program participants, assessing learning through changes in knowledge, comfort, and attitudes. An end-of-program survey was used to evaluate participants’ overall experiences with the learning program and use of the learning resources. RESULTS A literature review, stakeholder surveys, and expert input informed the design of the intervention. The course used the Project ECHO model of online education, with modifications based on the leadership team's previous ECHO experiences and local stakeholder input. The intervention occurred over 9 months, with 22 online teaching sessions. Each session consisted of a didactic lecture, case presentation, and interactive discussion with expert clinical teachers. Fifty-five clinicians in Nepal participated, including physicians (47%), nurses (44%), and psychotherapists (5%). Clinicians reported improvements in knowledge, skills, and attitudes after program participation. Program acceptability scores were high, with 93% of participants reporting that the course provided effective learning. CONCLUSIONS Project ECHO can be successfully implemented to deliver continuing professional development in Nepal. Delivering palliative care education online using the Project ECHO model, leads to improved knowledge, skills, and attitudes for clinicians. Project ECHO suggests an innovative solution which can provide training and support to clinicians in settings where educational opportunities in palliative care are limited.