Miracle-dz/UniFAR-FTUs huggingface dataset:
Flare models and radio emission

Flare-associated radio emissions, specifically microwave bursts, type III bursts and type III-like bursts, provide information on the spectrum of suprathermal electrons accelerated in a flare, and on the location of the acceleration region. They can also be used to map out both closed and open magnetic structures. The radio data also suggest that the energy release process in flares is highly structured in space and time, and provides indirect evidence that the corona is highly inhomogeneous on a finer scale than can be resolved. Of the many flare models proposed two are now favored by observational data. There is indirect evidence for the open field model from Yohkoh data, and there is strong evidence in favor of an emerging flux model from vector magnetogram data. The evidence that the emerging flux is already twisted implies that the energy released in a flare is already stored in the associated current system in the emerging flux, so that this energy is transported into the corona as the emerging flux loop rises. A specific emerging flux model, in which reconnection between two current-carrying flux loops releases magnetic energy, is reviewed briefly.
A versatile cubic-curved triangle mesh generator using subparametric transformation for FEM and image processing applications

Image processing applications are often affected by noise during the segmentation of curved edges. In this article, a versatile curved triangle mesh development method is proposed for finite element-based image processing applications using MATLAB. This approach first creates meshes using an open-source finite element mesh generator. The proposed technique then generates and visualizes cubic-order curved triangular elements using subparametric transformation. The resulting meshes can be used to solve a variety of problems using finite element analysis for image and video processing applications. The results of the presented work include a cubic-order curved triangular mesh using subparametric transformation, node coordinates, connectivity matrix, and border nodes for finite element implementations. The results obtained by the proposed mesh generator are verified to obtain the most accurate numerical results for an eigenvalue problem by FEM. It can be utilized to improve numerical results very well for various finite element analysis applications. This technology can be utilized in areas such as medical simulation, image processing, material engineering, video processing, computational materials science, optical flow estimation, computational electromagnetics, and so on.
Table‐top cosmology with Bose‐Einstein condensates

Cosmic inﬂation is the theory thatthe universe underwent a violent,rapid expansion at the beginning oftime, giving rise to much of the ob-served structure in the universe [1]. This theory is supported by indirectobservational evidence from manydifferent sources (e.g. see [2]). How-ever, direct experimental investiga-tion of inﬂation is of course impos-sible. For this reason there has beenconsiderableinterestinanalogmod-els for astrophysics (e.g. see [3]), inwhichanalogousbehaviorisstudiedin systems that are experimentallyconvenient.Due to their cleanness and greatﬂexibility, dilute ultra-cold atomicgases (particularly Bose-Einsteincondensates) form an attractivesystem for realizing such analogmodels. Indeed, a number of suchtable-top-astrophysics experimentshave been conducted with Bose-Einstein condensates. For example,supernova-like collapse and explo-sion dynamics [4], controlled bymodifying inter-atomic interactionsusing Feshbach resonances, andlater interpreted as cosmologicalparticle creation [5]; Faster-than-sound ﬂow of a condensate hasbeen used to produce a sonic blackholeanalog[6];The
Validation and comparison of methods to estimate (co)variance components for MACE

Methods to estimate (co)variance components across countries include the methods described by Sigurdsson et al. (1996), Klei & Weigel (1998) and Madsen et al. (2000). The first two EM-REML based procedures have earlier been validated on simulated data for traits with a relative high heritability (Sigurdsson et al., 1996; Klei & Weigel, 1998). They have also been compared for low heritability traits on field data (Mark et al., 2000). Based on these studies it can be concluded that both methods yield unbiased results, when there is sufficient information about the genetic components in the across country inference (well-connected subset of bulls and an intermediate heritability). However, it was also shown that the method of Sigurdsson et al. (1996) can underestimate the true value and yield substantially lower estimates compared with the method of Klei & Weigel (1998), when the data are not well connected across countries and heritability is low.
Machine Learning/Deep Learning Algorithms & Variability in Grading Improves Early Detection of DR

Health condition which mainly influence human retina i.e. cognate by Diabetic Mellitus (DM) is a main thread of Diabetic Retinopathy (DR). As a result of the damage of the retina, it causes vision loss. In accordance with census diabetic individuals who had suffered from diabetics in a long time also have DR issues. As a result, DR has become a critical issue that needs a primary stage screening and assessment in order to prevent vision loss and blindness. Physical diagnosis of the condition is time-consuming and prone to inaccuracy. Furthermore, it is not possible to find an ophthalmologist regardless of location or time. As a result, the need for a highly advanced and computerized intelligent system arises, which can be used to diagnose DR in its early stages. Researchers have proposed a number of Machine Learning (ML) algorithms for the diagnosis of DR for decapods. For determining retinal lesion significantly and for initial stage DR diagnosis various feature extraction and analyzing approaches are recommended. Traditional Machine Learning models, on the other hand, suffer from poor generalization during feature extraction due to limited datasets. Using Deep Learning models, more datasets and high computer processing unit weak generalization problem can be reduced. This study intends to provide a DR overview as well as a brief explanation of previous efforts and current automated methods and improvements, in order to the staring exposure of DR. This paper also discusses the most up-to-date DR lesions as well as the causes and symptoms of DR and focus on how AI/ML approaches helpful in early diagnosis of DR and we have to study more on variability in grading to evaluate the best possible result for screening and improving eye disease mainly caused by diabetics.
Large monitors reduce tracking error in robot-assisted visual-motor tasks

Robot-assisted rehabilitation often makes use of virtual environments to present the therapy tasks. Virtual reality has the ability of providing valuable visual feedback and enjoyable interaction to the patients; therefore, the way they are displayed to users becomes crucial. Is the monitor size an important feature that influences how the task is perceived and thus affects patients’ performance? This study on healthy participants investigates the influence of displays in perceiving haptic effects. The participants performed an experiment using an end-effector robot, where they followed a moving target around a trajectory while disturbed by a simulated perturbation and assisted by an adaptive algorithm. The experiment was presented on two different monitors to assess whether a different size affects their performance. Statistically significant results show that the performance achieved with the large monitor features lower error compared to the small monitor, implying that large monitors might be a better solution for rehabilitation with virtual tasks and assistive robots.
ScSer: Supervised Contrastive Learning for Speech Emotion Recognition using Transformers

Emotion recognition from the speech is a key challenging task and an active area of research in effective Human-Computer Interaction (HCI). Though many deep learning and machine learning approaches have been proposed to tackle the problem, they lack in both accuracy and learning robust representations agnostic to changes in voice. Additionally, there is a lack of sufficient labelled speech data for bigger models. To overcome these issues, we propose supervised contrastive learning with transformers for the task of speech emotion recognition (ScSer) and evaluate it on different standard datasets. Further, we experiment the supervised contrastive setting with different augmentations from WavAugment library and some custom augmentations. Finally, we propose a custom augmentation random cyclic shift with which ScSer outperforms other competitive methods and produce a state of the art accuracy of 96% on RAVDESS dataset with 7600 samples (Big-Ravdess) and a 2-4% boost over other wav2vec methods.
Introducing OHFV Analyzer, a Tool for Automated Analysis of Omsk Hemorrhagic Fever Virus Dynamics

Viral population surveillance is an essential task in public health, especially in preventing pandemics. The Omsk hemorrhagic fever virus (OHFV) is an etiological agent which is close to tick-borne encephalitis virus. Mainly, it is geographically limited to western Siberian regions, including Omsk, Novosibirsk, Kurgan, and Tyumen. Earlier, we presented an online platform, TBEV-Analyzer, for analysis and monitoring of TBEV. That platform relies on a model called clusteron structure, constructed based on phylogenetic networks. Using that experience, we introduce here OHFV Analyzer, an online analytical platform designed for specifically monitoring the Omsk hemorrhagic fever virus. The platform can automatically determine the phylogenetic history of a query strain based on analysis of a specific E protein fragment. In addition, the platform provides the specific amino acid signature of the clusteron to which the query belongs and presents a visualization of it superimposed on the envelope glycoprotein surface. We believe the proposed platform can serve as an exploratory tool for OHFV monitoring while helping to better understand the pathogen's genetic diversity and evolutionary history.
Classification of cardiac cohorts based on morphological and hemodynamic features derived from 4D PC-MRI data

An accurate assessment of the cardiovascular system and prediction of cardiovascular diseases (CVDs) are crucial. Cardiac blood flow data provide insights about patient-specific hemodynamics. However, there is a lack of machine learning approaches for a feature-based classification of heart-healthy people and patients with CVDs. In this paper, we investigate the potential of morphological and hemodynamic features extracted from measured blood flow data in the aorta to classify heart-healthy volunteers (HHV) and patients with bicuspid aortic valve (BAV). Furthermore, we determine features that distinguish male vs. female patients and elderly HHV vs. BAV patients. We propose a data analysis pipeline for cardiac status classification, encompassing feature selection, model training, and hyperparameter tuning. Our results suggest substantial differences in flow features of the aorta between HHV and BAV patients. The excellent performance of the classifiers separating between elderly HHV and BAV patients indicates that aging is not associated with pathological morphology and hemodynamics. Our models represent a first step towards automated diagnosis of CVS using interpretable machine learning models.
On-chip Subwavelength Tilt Fork Grating for Vortex Beam Generation and Manipulation

We propose an integrated optical vortex beam generator with tunable emission angle based on a tilt subwavelength fork grating. By adding a linear phase gradient in the grating design, the emission angle could be manipulated. OCIS codes: (130.0130) Integrated optics; (230.3120) Integrated optics devices; (030.4070) Modes.
A Dual-Band Transmitarray Antenna with Independent Beam Control at Ka Band

This paper presents a three-layer, dual-band, linearly polarized, transmitarray antenna (TA) operating in the Ka band (19 GHz/29 GHz) for satellite communication systems. The proposed 5.2×5.2 mm2 unit-cell is based on two folded rectangular strips on the receiving and transmitting layers, which are connected using metalized vias. Each folded rectangular strip is designed to achieve a 180° phase resolution (i.e., 1-bit phase quantization) at its corresponding operating frequency. The proposed radiating element can form independent linearly polarized beams at each frequency band using the same radiating aperture. This TA design provides a maximum gain of 21.2 and 24.5 dBi at 19 GHz and 29 GHz, respectively.
Reinforced Resource Allocation based on n-Dimensional Matrix Diagram for Multi-Modal Optical Networks

This paper proposes a reinforcement-learning-based resource allocation algorithm in multi-model optical networks, which is designed based on n-dimensional matrix diagram. Simulation results show that the proposed algorithm can efficiently optimize resource allocation. ©2021 The Author(s) OCIS codes: (060.1155) All-optical networks; (060.4251) Networks, assignment and routing algorithms.
Design and Analysis of high speed low power CMOS comparator with charge distribution technique

Comparators are fundamental blocks in the architectures of analog to digital converters. Due to the requirement of low power and high speed converters, the dynamic comparators are the natural choice. Existing dynamic comparators have issues of higher power consumption and delay. To overcome these drawbacks, a low power dynamic comparator with charge distribution technique is proposed in this paper. The proposed comparator reduces the regeneration time delay with the reduction in the power consumption considerably. The proposed design and simulation is carried out in 180 nm CMOS technology. Results show reduced power consumption of 260 µW and delay of 220 ps with supply voltage of 1.8 V at 0.5 GHz of frequency.
Object Classification using Micro-Doppler Signature and Clutter Suppression in MIMO Radar

Radar(Radio-Detection and Ranging) has become very popular for several applications-in meteorology, defence, aircraft system and navigation system. The main purpose of the radar is to detect any object-moving or stationary. However, to get the micro-motion(internal motions of an object), we need to get micro-Doppler feature. In this report, the micro-Doppler feature of birds have been discussed using MIMO radar system as combination of several bistatic radar system. Also classification of multiple birds has been discussed in presence of noise and without noise. Also, adaptive signal processing technique (LMS,RLS,NLMS) has been discussed for clutter suppression . Finally application of LMS-Newton method has been discussed for clutter suppression.It can be seen that it has faster convergence compared to other algorithms.
Asymmetrical Continuous Mode Doherty Power Amplifier Using Complex Combining Load Impedance

This work presents a broadband asymmetrical continuous-mode class J Doherty power amplifier (PA) using complex combining load. The DPA functions as a class J PA when operating in the low power region. The proposed Doherty PA (DPA) achieves continuous mode class J characteristics by varying the reactive part of the complex load. This variable complex combing load has been realized by a novel post-matching network (PMN). A detailed theoretical analysis is presented that shows the impedance transformation procedure from the asymmetrical DPA impedance trajectory to a class J. To validate the proposed technique, a prototype PA has been designed with Cree GaN devices for 9 dB output power back-off (OBO) operating from 1.8 GHz to 2.6 GHz. In the aforementioned frequency band, the PA achieves 61%–76% efficiency at saturation and 43%–60% efficiency at OBO. The PA maintains 10 dB gain and an average of 43 dBm power over the entire frequency range.
Investigating the Driving Forces of the Formation of International Premium Hot Products

: Through cross-case study of 4 high-end brand hot products, this paper explores the characteristics of high-end brand hot products to reveal their driving forces and the mechanism of their creation. The study indicates that the driving factors for the creation of high-end brand hot products are ultimate product power, unique brand culture and spirit, and successful marketing communication and promotion.
dysfunction, and CC16 in children.

Traffic-related air pollution, biomarkers of metabolic oxidative stress, BACKGROUND: Previous research has revealed links between air pollution exposure and metabolic syndrome in adults; however, these associations are less explored in children. OBJECTIVE: This study aims to investigate the association between traf ﬁ c-related air pollutants (TRAP) and biomarkers of metabolic dysregulation, oxidative stress, and lung epithelial damage in children. METHODS: We conducted cross-sectional analyses in a sample of predominantly Latinx, low-income children ( n = 218) to examine associations between air pollutants (nitrogen dioxide (NO 2 ), nitrogen oxides (NO x ), elemental carbon, polycyclic aromatic hydrocarbons, carbon monoxide (CO), ﬁ ne particulates (PM 2.5 )) and biomarkers of metabolic function (high-density lipoprotein (HDL), hemoglobin A1c (HbA1c), oxidative stress (8-isoprostane), and lung epithelial damage (club cell protein 16 (CC16)). RESULTS: HDL cholesterol showed an inverse association with NO 2 and NO x , with the strongest relationship between HDL and 3-month exposure to NO 2 ( – 15.4 mg/dL per IQR increase in 3-month NO 2 , 95% CI = – 27.4, – 3.4). 8-isoprostane showed a consistent pattern of increasing values with 1-day and 1-week exposure across all pollutants. Non-signi ﬁ cant increases in % HbA1c were found during 1-month time frames and decreasing CC16 in 3-month exposure time frames. CONCLUSION: Our results suggest that TRAP is signi ﬁ cantly associated with decreased HDL cholesterol in longer-term time frames and elevated 8-isoprostane in shorter-term time frames. TRAP could have the potential to in ﬂ uence lifelong metabolic patterns, through metabolic effects in childhood. quantify a relationship between biomarker levels pollutant exposures, tidyverse, tinytex for data manipulation/presentation, corrarray associations between additive a p-spline for seasonality. of a transformation of results are absolute changes in % hemoglobin A1c per interquartile range (IQR) of the pollutant. These estimates come from a GAM model that adjusted for whether or not the child lives with a smoker, whether or not the child is Latinx, physical activity, household income, and a smoothed term for day of study. results are multiplicative changes in CC16 (ng/mL) per interquartile range (IQR) of the pollutant. These estimates come from a GAM model that adjusted for whether or not the child lives with a smoker, whether or not the child is Latinx, physical activity, household income,
The physical origin of dispersion in accumulation in InGaAs based metal oxide semiconductor gate stacks

Dispersion in accumulation is a widely observed phenomenon in technologically important InGaAs gate stacks. Two principal different interface defects were proposed as the physical origin of this phenomenon—disorder induced gap states and border traps. While the gap states are located at the semiconductor side of the interface, the border traps are related to the dielectric side. The study of Al2O3, HfO2, and an intermediate composition of HfxAlyO deposited on InGaAs enabled us to find a correlation between the dispersion and the dielectric/InGaAs band offset. At the same time, no change in the dispersion was observed after applying an effective pre-deposition treatment which results in significant reduction of the interface states. Both observations prove that border traps are the physical origin of the dispersion in accumulation in InGaAs based metal-oxide-semiconductor gate stacks.
Secure Batch Deduplication Without Dual Servers in Backup System

—Cloud storage provides highly available and low cost resources to users. However, as massive amounts of outsourced data grow rapidly, an effective data deduplication scheme is necessary. This is a hot and challenging ﬁeld, in which there are quite a few researches. However, most of previous works require dual-server fashion to be against brute-force attacks and do not support batch checking. It is not practicable for the massive data stored in the cloud. In this paper, we present a secure batch deduplication scheme for backup system. Besides, our scheme resists the brute-force attacks without the aid of other servers. The core idea of the batch deduplication is to separate users into different groups by using short hashes. Within each group, we leverage group key agreement and symmetric encryption to achieve secure batch checking and semantically secure storage. We also extensively evaluate its performance and overhead based on different datasets. We show that our scheme saves the data storage by up to 89.84 % . These results show that our scheme is efﬁcient and scalable for cloud backup system and can also ensure data conﬁdentiality.
REINFORCEMENT LEARNING FOR STOCK PORTFOLIO MANAGEMENT

: Stock trading techniques play the essential aspect of functioning as a key factor when it comes to investment. In this case Paper, It proposes a way of learning a stock trading strategy that maximizes return on investment using Deep Reintorcement.Researchers developed ways to buy and sell using deep reinforcement learning agents using ensemble trading strategies for actor-critical-based models on these 5 Algorithms: Proximal Policy Optimization (PPO), Actor-Critic Gain (A2C), Deep Deterministic Policy Gradient (DDPG), Twin-Delayed Deep Deterministic Policy Gradient (TD3), Soft Actor-Critic(SAC). Ensemble strategy inherits and integrates the excellent features of these five Algorithms, thereby robustly adapting to the exceptional market situation.To avoid immense memory usage in training networks with an area of continuous movement,For processing massive amounts of data, researchers employ the load-on-demand approach, 30 Dow Jones Stocks with enough liquidity are used to test the algorithms.The overall performance of the trading agency was evaluated with significant enhancement of the Reinforcement algorithm and compared to both the Dow Jones trading stock index and the traditional minimum deviation portfolio allocation procedure. The proposed deep ensemble strategy outperforms baselines and five algorithms in trade-weighted returns by the Sharpe ratio.