Search

We investigated two consecutive solar eruption events in the solar active region (AR) 12994 at the solar eastern limb on 2022 April 15. We found that the flare loops formed by the first eruption were involved in the second eruption. During the initial stage of the second flare, the middle part of these flare loops (E-loops) erupted outward along with the flux ropes below, while the parts of the flare loops (I-loops1 and I-loops2) on either side of the E-loops first rose and then contracted. Approximately 1 hour after the eruption, the heights of I-loops1 and I-loops2 decreased by 9 Mm and 45 Mm, respectively, compared to before the eruption. Their maximum descent velocities were 30 km/s and 130 km/s, respectively. The differential emission measure (DEM) results indicate that the plasma above I-loops1 and I-loops2 began to be heated about 23 minutes and 44 minutes after the start of the second flare, respectively. Within 20 minutes, the plasma temperature in these regions increased from ~3 MK to 6 MK. We proposed an adiabatic heating mechanism that magnetic energy would be converted into thermal and kinetic energy when the pre-stretched loops contract. Our calculations show that the magnetic energy required to heat the two high-temperature regions are 10^29-10^30 erg, which correspond to a loss of field strength of 2-3 G., Comment: The paper has been accepted for publication in the ApJ

Over the past decade, ride-sharing services have become increasingly important, with U.S. market leaders such as Uber and Lyft expanding to over 900 cities worldwide and facilitating billions of rides annually. This rise reflects their ability to meet users' convenience, efficiency, and affordability needs. However, in busy areas and surge zones, the benefits of these platforms can diminish, prompting riders to relocate to cheaper, more convenient locations or seek alternative transportation. While much research has focused on the strategic behavior of drivers, the strategic actions of riders, especially when it comes to riders walking outside of surge zones, remain under-explored. This paper examines the impact of rider-side strategic behavior on surge dynamics. We investigate how riders' actions influence market dynamics, including supply, demand, and pricing. We show significant impacts, such as spillover effects where demand increases in areas adjacent to surge zones and prices surge in nearby areas. Our theoretical insights and experimental results highlight that rider strategic behavior helps redistribute demand, reduce surge prices, and clear demand in a more balanced way across zones., Comment: 30 Pages

A well-designed document communicates not only through its words but also through its visual eloquence. Authors utilize aesthetic elements such as colors, fonts, graphics, and layouts to shape the perception of information. Thoughtful document design, informed by psychological insights, enhances both the visual appeal and the comprehension of the content. While state-of-the-art document AI models demonstrate the benefits of incorporating layout and image data, it remains unclear whether the nuances of document aesthetics are effectively captured. To bridge the gap between human cognition and AI interpretation of aesthetic elements, we formulated hypotheses concerning AI behavior in document understanding tasks, specifically anchored in document design principles. With a focus on legibility and layout quality, we tested four aspects of aesthetic effects: noise, font-size contrast, alignment, and complexity, on model confidence using correlational analysis. The results and observations highlight the value of model analysis rooted in document design theories. Our work serves as a trailhead for further studies and we advocate for continued research in this topic to deepen our understanding of how AI interprets document aesthetics.

Sparse basis recovery is a classical and important statistical learning problem when the number of model dimensions $p$ is much larger than the number of samples $n$. However, there has been little work that studies sparse basis recovery in the Federated Learning (FL) setting, where the client data's differential privacy (DP) must also be simultaneously protected. In particular, the performance guarantees of existing DP-FL algorithms (such as DP-SGD) will degrade significantly when $p \gg n$, and thus, they will fail to learn the true underlying sparse model accurately. In this work, we develop a new differentially private sparse basis recovery algorithm for the FL setting, called SPriFed-OMP. SPriFed-OMP converts OMP (Orthogonal Matching Pursuit) to the FL setting. Further, it combines SMPC (secure multi-party computation) and DP to ensure that only a small amount of noise needs to be added in order to achieve differential privacy. As a result, SPriFed-OMP can efficiently recover the true sparse basis for a linear model with only $n = O(\sqrt{p})$ samples. We further present an enhanced version of our approach, SPriFed-OMP-GRAD based on gradient privatization, that improves the performance of SPriFed-OMP. Our theoretical analysis and empirical results demonstrate that both SPriFed-OMP and SPriFed-OMP-GRAD terminate in a small number of steps, and they significantly outperform the previous state-of-the-art DP-FL solutions in terms of the accuracy-privacy trade-off., Comment: Paper under review

We have systematically investigated ultraviolet (UV) emission from molecular hydrogen (H$_{2}$) using the Interface Region Imaging Spectrometer (IRIS), during three X-ray flares of C5.1, C9.7 and X1.0 classes on Oct. 25, 2014. Significant emission from five H$_{2}$ spectral lines appeared in the flare ribbons, interpreted as photo-excitation (fluorescence) due to the absorption of UV radiation from two Si IV spectral lines. The H$_{2}$ profiles were broad and consisted of two non-stationary components in red and in the blue wings of the line in addition to the stationary component. The red (blue) wing components showed small redshifts (blue shifts) of ~5-15 km s$^{-1}$ (~5-10 km s$^{-1}$). The nonthermal velocities were found to be ~5-15 km s$^{-1}$. The interrelation between intensities of H$_{2}$ lines and their branching ratios confirmed that H$_{2}$ emission formed under optically thin plasma conditions. There is a strong spatial and temporal correlation between Si IV and H$_{2}$ emission, but the H$_{2}$ emission is more extended and diffuse, further suggesting H$_{2}$ fluorescence, and - by analogy with flare ''back-warming'' providing a means to estimate the depth from which the H$_{2}$ emission originates. We find that this is 1871$\pm$157 km and 1207$\pm$112 km below the source of the Si IV emission, in two different ribbon locations., Comment: 14 pages, 12 figures, accepted for publication in MNRAS

With changes in privacy laws, there is often a hard requirement for client data to remain on the device rather than being sent to the server. Therefore, most processing happens on the device, and only an altered element is sent to the server. Such mechanisms are developed by leveraging differential privacy and federated learning. Differential privacy adds noise to the client outputs and thus deteriorates the quality of each iteration. This distributed setting adds a layer of complexity and additional communication and performance overhead. These costs are additive per round, so we need to reduce the number of iterations. In this work, we provide an analytical framework for studying the convergence guarantees of gradient-based distributed algorithms. We show that our private algorithm minimizes the expected gradient variance by approximately $d^2$ rounds, where d is the dimensionality of the model. We discuss and suggest novel ways to improve the convergence rate to minimize the overhead using Importance Sampling (IS) and gradient diversity. Finally, we provide alternative frameworks that might be better suited to exploit client sampling techniques like IS and gradient diversity.

We have carried out a comprehensive study of the temperature structure of plasmoids, which successively occurred in recurrent active region jets. The multithermal plasmoids were seen to be travelling along the multi-threaded spire as well as at the footpoint region in the EUV/UV images recorded by the Atmospheric Imaging Assembly (AIA). The Differential Emission Measure (DEM) analysis was performed using EUV AIA images, and the high-temperature part of the DEM was constrained by combining X-ray images from the X-ray telescope (XRT/Hinode). We observed a systematic rise and fall in brightness, electron number densities and the peak temperatures of the spire plasmoid during its propagation along the jet. The plasmoids at the footpoint (FPs) (1.0-2.5 MK) and plasmoids at the spire (SPs) (1.0-2.24 MK) were found to have similar peak temperatures, whereas the FPs have higher DEM weighted temperatures (2.2-5.7 MK) than the SPs (1.3-3.0 MK). A lower limit to the electron number densities of plasmoids - SPs (FPs) were obtained that ranged between 3.4-6.1$\times$10$^{8}$ (3.3-5.9$\times$10$^{8}$) cm$^{-3}$ whereas for the spire, it ranged from 2.6-3.2$\times$10$^{8}$ cm$^{-3}$. Our analysis shows that the emission of these plasmoids starts close to the base of the jet(s), where we believe that a strong current interface is formed. This suggests that the blobs are plasmoids induced by a tearing-mode instability., Comment: 13 pages, 10 figures, Accepted for publication in Monthly Notices of the Royal Astronomical Society

We report the discovery and characterization of high-speed (>100 km/s) horizontal flows in solar active regions, making use of the Sun-as-a-star spectroscopy in the range 5-105 nm provided by the EVE (Extreme Ultraviolet Variability Experiment) spectrometers on the Solar Dynamics Observatory. These apparent flows are persistent on time scales of days, and are well observed in lines of Mg X, Si XII and Fe XVI for example. They are prograde, as evidenced directly by blueshifts/redshifts peaking at the east/west limb passages of isolated active regions. The high-speed flow behavior does not depend upon active-region latitude or solar cycle, with similar behavior in Cycles 24 and 25.

While running any experiment, we often have to consider the statistical power to ensure an effective study. Statistical power or power ensures that we can observe an effect with high probability if such a true effect exists. However, several studies lack the appropriate planning for determining the optimal sample size to ensure adequate power. Thus, careful planning ensures that the power remains high even under high measurement errors while keeping the type 1 error constrained. We study the impact of differential privacy on experiments and theoretically analyze the change in sample size required due to the Gaussian mechanisms. Further, we provide an empirical method to improve the accuracy of private statistics with simple bootstrapping., Comment: There are no significant alterations from version 1. We mistakenly included metadata from a journal Latex template while using its document class and it's now removed to avoid any confusion. This article has been accepted at the Workshop on Theory and Practice of Differential Privacy at ICML 2022

Statistical power estimation for studies with multiple model parameters is inherently a multivariate problem. Power for individual parameters of interest cannot be reliably estimated univariately since correlation and variance explained relative to one parameter will impact the power for another parameter, all usual univariate considerations being equal. Explicit solutions in such cases, especially for models with many parameters, are either impractical or impossible to solve, leaving researchers to the prevailing method of simulating power. However, the point estimates for a vector of model parameters are uncertain, and the impact of inaccuracy is unknown. In such cases, sensitivity analysis is recommended such that multiple combinations of possible observable parameter vectors are simulated to understand power trade-offs. A limitation to this approach is that it is computationally expensive to generate sufficient sensitivity combinations to accurately map the power trade-off function in increasingly high-dimensional spaces for the models that social scientists estimate. This paper explores the efficient estimation and graphing of statistical power for a study over varying model parameter combinations. We propose a simple and generalizable machine learning inspired solution to cut the computational cost to less than 10% of the brute force method while providing F1 scores above 90%. We further motivate the impact of transfer learning in learning power manifolds across varying distributions., Comment: Submitted to AI4Science (ICML workshop). Not published yet

The inception of modeling contextual information using models such as BERT, ELMo, and Flair has significantly improved representation learning for words. It has also given SOTA results in almost every NLP task - Machine Translation, Text Summarization and Named Entity Recognition, to name a few. In this work, in addition to using these dominant context-aware representations, we propose a Knowledge Aware Representation Learning (KARL) Network for Named Entity Recognition (NER). We discuss the challenges of using existing methods in incorporating world knowledge for NER and show how our proposed methods could be leveraged to overcome those challenges. KARL is based on a Transformer Encoder that utilizes large knowledge bases represented as fact triplets, converts them to a graph context, and extracts essential entity information residing inside to generate contextualized triplet representation for feature augmentation. Experimental results show that the augmentation done using KARL can considerably boost the performance of our NER system and achieve significantly better results than existing approaches in the literature on three publicly available NER datasets, namely CoNLL 2003, CoNLL++, and OntoNotes v5. We also observe better generalization and application to a real-world setting from KARL on unseen entities., Comment: 9 pages, 2 figures, 4 Tables

Recent years of research in Natural Language Processing (NLP) have witnessed dramatic growth in training large models for generating context-aware language representations. In this regard, numerous NLP systems have leveraged the power of neural network-based architectures to incorporate sense information in embeddings, resulting in Contextualized Word Embeddings (CWEs). Despite this progress, the NLP community has not witnessed any significant work performing a comparative study on the contextualization power of such architectures. This paper presents a comparative study and an extensive analysis of nine widely adopted Transformer models. These models are BERT, CTRL, DistilBERT, OpenAI-GPT, OpenAI-GPT2, Transformer-XL, XLNet, ELECTRA, and ALBERT. We evaluate their contextualization power using two lexical sample Word Sense Disambiguation (WSD) tasks, SensEval-2 and SensEval-3. We adopt a simple yet effective approach to WSD that uses a k-Nearest Neighbor (kNN) classification on CWEs. Experimental results show that the proposed techniques also achieve superior results over the current state-of-the-art on both the WSD tasks, Comment: 8 pages, 1 figure, 3 tables

Retinopathy of Prematurity (ROP) is a fibrovascular proliferative disorder, which affects the developing peripheral retinal vasculature of premature infants. Early detection of ROP is possible in stage 1 and stage 2 characterized by demarcation line and ridge with width, which separates vascularised retina and the peripheral retina. To detect demarcation line/ ridge from neonatal retinal images is a complex task because of low contrast images. In this paper we focus on detection of ridge, the important landmark in ROP diagnosis, using Convolutional Neural Network(CNN). Our contribution is to use a CNN-based model Mask R-CNN for demarcation line/ridge detection allowing clinicians to detect ROP stage 2 better. The proposed system applies a pre-processing step of image enhancement to overcome poor image quality. In this study we use labelled neonatal images and we explore the use of CNN to localize ridge in these images. We used a dataset of 220 images of 45 babies from the KIDROP project. The system was trained on 175 retinal images with ground truth segmentation of ridge region. The system was tested on 45 images and reached detection accuracy of 0.88, showing that deep learning detection with pre-processing by image normalization allows robust detection of ROP in early stages., Comment: Accepted in SPIE Medical Imaging 2019