Intensity oscillations in coronal XBPs from Hinode/XRT observations

Our aim is to investigate the intensity oscillations in coronal X-ray Bright Points (XBPs). We analysed a 7-hours long time sequence of the soft X-ray images obtained on April 14, 2007 with 2-min cadence using X-Ray Telescope (XRT) on-board the Hinode mission. We use SSW in IDL to derive the time series of 14 XBPs and 2 background regions. For the first time, we have tried to use power spectrum analysis on XBPs data to determine the periods of intensity oscillations. coronal X-ray Bright Points (XBPs). The power spectra of XBPs show several significant peaks at different frequencies corresponding to a wide variety of time scales which range from a few minutes to hours. The light curves of all the XBPs give the impression that the XBPs can be grouped into three classes depending on emission levels: (i) weak XBPs; (ii) bright XBPs; and (iii) very strong XBPs. The periods of intensity oscillation are consistent in all the XBPs and are independent of their brightness level, suggesting that the heating mechanisms in all the three groups of XBPs are similar. The different classes of XBPs may be related to the different strengths of the magnetic field with which they have been associated.Comment: 7 pages, 3 figure

Study of Inhomogeneities in the Solar Atmosphere

We have analysed a large number of Ca II line profiles at the site of the bright points in the interior of the network using a 35-minute long time sequence spectra obtained at hte Vacuum Tower Telesope (VTT) of hte Sacramento Peak Observatory on a quiet region of the solar disc and studied the dynamical processes associated with these structures. Our analysis shows that the profiles can be grouped into three classes in terms of their evolutionary behavior. It is surmised that the differences in their behavior is directly linked with the inner network photospheric magnetic points to which they have been observed to bear a spatial correspondence. The light curves of these bright points give the impression that the"main pulse" which is the upward propagating disturbance carrying energy throws the medium within the bright point into a resonant mode of oscillation that are seen as the follower pulses. The main pulse as well as the follower pulses have identical periods of intensity oscillations, with a mean value around 190 ± 20 secs. We show that the energy transported by these main pulses at the sits of the bright points over the entire visible solar surface can account for a substantial freedom of the radiative loss from the quiet chromosphere according to current models

MAZE: Data-Free Model Stealing Attack Using Zeroth-Order Gradient Estimation

Model Stealing (MS) attacks allow an adversary with black-box access to a Machine Learning model to replicate its functionality, compromising the confidentiality of the model. Such attacks train a clone model by using the predictions of the target model for different inputs. The effectiveness of such attacks relies heavily on the availability of data necessary to query the target model. Existing attacks either assume partial access to the dataset of the target model or availability of an alternate dataset with semantic similarities. This paper proposes MAZE -- a data-free model stealing attack using zeroth-order gradient estimation. In contrast to prior works, MAZE does not require any data and instead creates synthetic data using a generative model. Inspired by recent works in data-free Knowledge Distillation (KD), we train the generative model using a disagreement objective to produce inputs that maximize disagreement between the clone and the target model. However, unlike the white-box setting of KD, where the gradient information is available, training a generator for model stealing requires performing black-box optimization, as it involves accessing the target model under attack. MAZE relies on zeroth-order gradient estimation to perform this optimization and enables a highly accurate MS attack. Our evaluation with four datasets shows that MAZE provides a normalized clone accuracy in the range of 0.91x to 0.99x, and outperforms even the recent attacks that rely on partial data (JBDA, clone accuracy 0.13x to 0.69x) and surrogate data (KnockoffNets, clone accuracy 0.52x to 0.97x). We also study an extension of MAZE in the partial-data setting and develop MAZE-PD, which generates synthetic data closer to the target distribution. MAZE-PD further improves the clone accuracy (0.97x to 1.0x) and reduces the query required for the attack by 2x-24x

Nonlinear Force-Free Field Modeling of the Solar Magnetic Carpet and Comparison with SDO/HMI and Sunrise/IMaX Observations

In the quiet solar photosphere, the mixed polarity fields form a magnetic carpet, which continuously evolves due to dynamical interaction between the convective motions and magnetic field. This interplay is a viable source to heat the solar atmosphere. In this work, we used the line-of-sight (LOS) magnetograms obtained from the Helioseismic and Magnetic Imager (HMI) on the \textit{Solar Dynamics Observatory} (\textit{SDO}), and the Imaging Magnetograph eXperiment (IMaX) instrument on the \textit{Sunrise} balloon-borne observatory, as time dependent lower boundary conditions, to study the evolution of the coronal magnetic field. We use a magneto-frictional relaxation method, including hyperdiffusion, to produce time series of three-dimensional (3D) nonlinear force-free fields from a sequence of photospheric LOS magnetograms. Vertical flows are added up to a height of 0.7 Mm in the modeling to simulate the non-force-freeness at the photosphere-chromosphere layers. Among the derived quantities, we study the spatial and temporal variations of the energy dissipation rate, and energy flux. Our results show that the energy deposited in the solar atmosphere is concentrated within 2 Mm of the photosphere and there is not sufficient energy flux at the base of the corona to cover radiative and conductive losses. Possible reasons and implications are discussed. Better observational constraints of the magnetic field in the chromosphere are crucial to understand the role of the magnetic carpet in coronal heating.Comment: Accepted for publication in The Astrophysical Journal (13 pages, 10 figures

Understanding and Mitigating Privacy Vulnerabilities in Deep Learning

Advancements in Deep Learning (DL) have enabled leveraging large-scale datasets to train models that perform challenging tasks at a level that mimics human intelligence. In several real-world scenarios, the data used for training, the trained model, and the data used for inference can be private and distributed across multiple distrusting parties, posing a challenge for training and inference. Several privacy-preserving training and inference frameworks have been developed to address this challenge. For instance, frameworks like federated learning and split learning have been proposed to train a model collaboratively on distributed data without explicitly sharing the private data to protect training data privacy. To protect model privacy during inference, the model owners have adopted a client-server architecture to provide inference services, wherein the end-users are only allowed black-box access to the model’s predictions for their input queries. The goal of this thesis is to provide a better understanding of the privacy properties of the DL frameworks used for privacy-preserving training and inference. While these frameworks have the appearance of keeping the data and model private, the information exchanged during training/inference has the potential to compromise the privacy of the parties involved by leaking sensitive data. We aim to understand if these frameworks are truly capable of preventing the leakage of model and training data in realistic settings. In this pursuit, we discover new vulnerabilities that can be exploited to design powerful attacks that can overcome the limitations of prior works and break the illusion of privacy. Our findings highlight the limitations of these frameworks and underscore the importance of principled techniques to protect privacy. Furthermore, we leverage our improved understanding to design better defenses that can significantly deter the efficacy of an attack.Ph.D

SHAP@k:Efficient and Probably Approximately Correct (PAC) Identification of Top-k Features

The SHAP framework provides a principled method to explain the predictions of a model by computing feature importance. Motivated by applications in finance, we introduce the Top-k Identification Problem (TkIP), where the objective is to identify the k features with the highest SHAP values. While any method to compute SHAP values with uncertainty estimates (such as KernelSHAP and SamplingSHAP) can be trivially adapted to solve TkIP, doing so is highly sample inefficient. The goal of our work is to improve the sample efficiency of existing methods in the context of solving TkIP. Our key insight is that TkIP can be framed as an Explore-m problem--a well-studied problem related to multi-armed bandits (MAB). This connection enables us to improve sample efficiency by leveraging two techniques from the MAB literature: (1) a better stopping-condition (to stop sampling) that identifies when PAC (Probably Approximately Correct) guarantees have been met and (2) a greedy sampling scheme that judiciously allocates samples between different features. By adopting these methods we develop KernelSHAP@k and SamplingSHAP@k to efficiently solve TkIP, offering an average improvement of $5\times$ in sample-efficiency and runtime across most common credit related datasets