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Utilizing data from the $Solar$ $Magnetism$ and $Activity$ $Telescope$ (SMAT), analytical solutions of polarized radiative transfer equations, and in-orbit test data from the Full-disk Magnetograph (FMG) onboard the Advanced Space based Solar Observatory (ASO-S), this study reveals the magnetic-sensitivity spectral positions for the Fe {\sc i} $\lambda$5234.19 A, working line used by FMG. From the experimental data of SMAT, it is found that the most sensitivity position is located at the line center for linear polarization (Stokes-Q/U), while it is about -0.07 A away from the line center for circular polarization (Stokes-V). Moreover, both the theoretical analysis and the in-orbit test data analysis of FMG prove again the above results. Additionally, the theoretical analysis suggests the presence of distinct spectral pockets (centered at 0.08-0.15 A) from the line, harboring intense magnetic sensitivity across all three Stokes parameters. Striking a balance between high sensitivity for both linear and circular polarization while capturing additional valuable information, a spectral position of -0.08 A emerges as the champion for routine FMG magnetic-field observations., Comment: 12pages,8figures

This work considers the decentralized successive convex approximation (SCA) method for minimizing stochastic non-convex objectives subject to convex constraints, along with possibly non-smooth convex regularizers. Although SCA has been widely applied in decentralized settings, its stochastic first order (SFO) complexity is unknown, and it is thought to be slower than the centralized momentum-enhanced SCA variants. In this work, we advance the state-of-the-art for SCA methods by proposing an accelerated variant, namely the \textbf{D}ecentralized \textbf{M}omentum-based \textbf{S}tochastic \textbf{SCA} (\textbf{D-MSSCA}) and analyze its SFO complexity. The proposed algorithm entails creating a stochastic surrogate of the objective at every iteration, which is minimized at each node separately. Remarkably, the D-MSSCA achieves an SFO complexity of $\mathcal{O}(\epsilon^{-3/2})$ to reach an $\epsilon$-stationary point, which is at par with the SFO complexity lower bound for unconstrained stochastic non-convex optimization in centralized setting.

We consider stochastic optimization problems with functional constraints. If the objective and constraint functions are not convex, the classical stochastic approximation algorithms such as the proximal stochastic gradient descent do not lead to efficient algorithms. In this work, we put forth an accelerated SCA algorithm that utilizes the recursive momentum-based acceleration which is widely used in the unconstrained setting. Remarkably, the proposed algorithm also achieves the optimal SFO complexity, at par with that achieved by state-of-the-art (unconstrained) stochastic optimization algorithms and match the SFO-complexity lower bound for minimization of general smooth functions. At each iteration, the proposed algorithm entails constructing convex surrogates of the objective and the constraint functions, and solving the resulting convex optimization problem. A recursive update rule is employed to track the gradient of the objective function, and contributes to achieving faster convergence and improved SFO complexity. A key ingredient of the proof is a new parameterized version of the standard Mangasarian-Fromowitz Constraints Qualification, that allows us to bound the dual variables and hence establish that the iterates approach an $\epsilon$-stationary point. We also detail a obstacle-avoiding trajectory optimization problem that can be solved using the proposed algorithm, and show that its performance is superior to that of the existing algorithms. The performance of the proposed algorithm is also compared against that of a specialized sparse classification algorithm on a binary classification problem., Comment: 32 pages, 4 figures, journal submission

Spectrum-Based Fault Localization (SBFL) is a technique to be used during debugging, the premise of which is that, based on the test case outcomes and code coverage, faulty code elements can be automatically detected. SBFL is popular among researchers because it is lightweight and easy to implement, and there is a lot of potential in it when it comes to research that aims to improve its effectiveness. Despite this, the technique cannot be found in contemporary development and debugging tools, only a handful of research prototypes are available. Reasons for this can be multiple, including the algortihms' sub-optimal effectiveness and other technical weaknesses. But, also the lack of clear functional and non-functional requirements for such a tool, either standalone or integrated into IDEs. In this paper, we attempt to provide such a list in form of recommendations, based on surveying the most popular SBFL tools and on our own researchers' and tool builders' experience., Comment: 6 pages

In this paper we investigate bounds for the zeros of a bicomplex polynomial using matrix method. In particular, we find analogue of Gershgorin disk theorem, Cauchy Theorem, theorem of Fujiwara, Walsh and other theorems concerning to zeros of a polynomial to bicomplex polynomials.

Over the past few years, The idea of edge computing has seen substantial expansion in both academic and industrial circles. This computing approach has garnered attention due to its integrating role in advancing various state-of-the-art technologies such as Internet of Things (IoT) , 5G, artificial intelligence, and augmented reality. In this chapter, we introduce computing paradigms for IoT, offering an overview of the current cutting-edge computing approaches that can be used with IoT. Furthermore, we go deeper into edge computing paradigms, specifically focusing on cloudlet and mobile edge computing. After that, we investigate the architecture of edge computing-based IoT, its advantages, and the technologies that make Edge computing-based IoT possible, including artificial intelligence and lightweight virtualization. Additionally, we review real-life case studies of how edge computing is applied in IoT-based Intelligent Systems, including areas like healthcare, manufacturing, agriculture, and transportation. Finally, we discuss current research obstacles and outline potential future directions for further investigation in this domain., Comment: 19 pages, 5 figures, Book Chapter In: Donta, P.K., Hazra, A., Lov\'en, L. (eds) Learning Techniques for the Internet of Things. Springer, Cham

With the help of a new architecture called Edge/Fog (E/F) computing, cloud computing services can now be extended nearer to data generator devices. E/F computing in combination with Deep Learning (DL) is a promisedtechnique that is vastly applied in numerous fields. To train their models, data producers in conventional DL architectures with E/F computing enable them to repeatedly transmit and communicate data with third-party servers, like Edge/Fog or cloud servers. Due to the extensive bandwidth needs, legal issues, and privacy risks, this architecture is frequently impractical. Through a centralized server, the models can be co-trained by FL through distributed clients, including cars, hospitals, and mobile phones, while preserving data localization. As it facilitates group learning and model optimization, FL can therefore be seen as a motivating element in the E/F computing paradigm. Although FL applications in E/F computing environments have been considered in previous studies, FL execution and hurdles in the E/F computing framework have not been thoroughly covered. In order to identify advanced solutions, this chapter will provide a review of the application of FL in E/F computing systems. We think that by doing this chapter, researchers will learn more about how E/F computing and FL enable related concepts and technologies. Some case studies about the implementation of federated learning in E/F computing are being investigated. The open issues and future research directions are introduced., Comment: 21 pages, 4 figures, Book chapter

To achieve seamless collaboration between robots and humans in a shared environment, accurately predicting future human movements is essential. Human motion prediction has traditionally been approached as a sequence prediction problem, leveraging historical human motion data to estimate future poses. Beginning with vanilla recurrent networks, the research community has investigated a variety of methods for learning human motion dynamics, encompassing graph-based and generative approaches. Despite these efforts, achieving accurate long-term predictions continues to be a significant challenge. In this regard, we present the Adversarial Motion Transformer (AdvMT), a novel model that integrates a transformer-based motion encoder and a temporal continuity discriminator. This combination effectively captures spatial and temporal dependencies simultaneously within frames. With adversarial training, our method effectively reduces the unwanted artifacts in predictions, thereby ensuring the learning of more realistic and fluid human motions. The evaluation results indicate that AdvMT greatly enhances the accuracy of long-term predictions while also delivering robust short-term predictions, Comment: The paper is under consideration at Pattern Recognition Letters

Conversational assistive robots can aid people, especially those with cognitive impairments, to accomplish various tasks such as cooking meals, performing exercises, or operating machines. However, to interact with people effectively, robots must recognize human plans and goals from noisy observations of human actions, even when the user acts sub-optimally. Previous works on Plan and Goal Recognition (PGR) as planning have used hierarchical task networks (HTN) to model the actor/human. However, these techniques are insufficient as they do not have user engagement via natural modes of interaction such as language. Moreover, they have no mechanisms to let users, especially those with cognitive impairments, know of a deviation from their original plan or about any sub-optimal actions taken towards their goal. We propose a novel framework for plan and goal recognition in partially observable domains -- Dialogue for Goal Recognition (D4GR) enabling a robot to rectify its belief in human progress by asking clarification questions about noisy sensor data and sub-optimal human actions. We evaluate the performance of D4GR over two simulated domains -- kitchen and blocks domain. With language feedback and the world state information in a hierarchical task model, we show that D4GR framework for the highest sensor noise performs 1% better than HTN in goal accuracy in both domains. For plan accuracy, D4GR outperforms by 4% in the kitchen domain and 2% in the blocks domain in comparison to HTN. The ALWAYS-ASK oracle outperforms our policy by 3% in goal recognition and 7%in plan recognition. D4GR does so by asking 68% fewer questions than an oracle baseline. We also demonstrate a real-world robot scenario in the kitchen domain, validating the improved plan and goal recognition of D4GR in a realistic setting., Comment: Published in IROS 2023

In this paper, we price European Call three different option pricing models, where the volatility is dynamically changing i.e. non constant. In stochastic volatility (SV) models for option pricing a closed form approximation technique is used, indicating that these models are computationally efficient and have the same level of performance as existing ones. We show that the calibration of SV models, such as Heston model and the High Order Moment based Stochastic Volatility (MSV) is often faster and easier. On 15 different datasets of index options, we show that models which incorporates stochastic volatility achieves accuracy comparable with the existing models. Further, we compare the In Sample and Out Sample pricing errors of each model on each date. Lastly, the pricing of models is compared among three different market to check model performance in different markets. Keywords: Option Pricing Model, Simulations, Index Options, Stochastic Volatility Models, Loss Function http://www.sci-int.com/pdf/638279543859822650.pdf