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We report observations of direct evidence of energetic protons being accelerated above ~400 keV within the reconnection exhaust of a heliospheric current sheet (HCS) crossing by NASA's Parker Solar Probe (PSP) at a distance of ~16.25 solar radii (Rs) from the Sun. Inside the extended exhaust, both the reconnection-generated plasma jets and the accelerated protons propagated toward the Sun, unambiguously establishing their origin from HCS reconnection sites located beyond PSP. Within the core of the exhaust, PSP detected stably trapped energetic protons up to ~400 keV, which is approximately 1000 times greater than the available magnetic energy per particle. The differential energy spectrum of the accelerated protons behaved as a pure power-law with spectral index of about -5. Supporting simulations using the kglobal model suggest that the trapping and acceleration of protons up to ~400 keV in the reconnection exhaust is likely facilitated by merging magnetic islands with a guide field between ~0.2-0.3 of the reconnecting magnetic field, consistent with the observations. These new results, enabled by PSP's proximity to the Sun, demonstrate that magnetic reconnection in the HCS is a significant new source of energetic particles in the near-Sun solar wind. The discovery of in-situ particle acceleration via magnetic reconnection at the HCS provides valuable insights into this fundamental process which frequently converts the large magnetic field energy density in the near-Sun plasma environment and may be responsible for heating the sun's atmosphere, accelerating the solar wind, and energizing charged particles to extremely high energies in solar flares., Comment: 24 pages, 4 figures

The vulnerability of machine learning models in adversarial scenarios has garnered significant interest in the academic community over the past decade, resulting in a myriad of attacks and defenses. However, while the community appears to be overtly successful in devising new attacks across new contexts, the development of defenses has stalled. After a decade of research, we appear no closer to securing AI applications beyond additional training. Despite a lack of effective mitigations, AI development and its incorporation into existing systems charge full speed ahead with the rise of generative AI and large language models. Will our ineffectiveness in developing solutions to adversarial threats further extend to these new technologies? In this paper, we argue that overly permissive attack and overly restrictive defensive threat models have hampered defense development in the ML domain. Through the lens of adversarial evasion attacks against neural networks, we critically examine common attack assumptions, such as the ability to bypass any defense not explicitly built into the model. We argue that these flawed assumptions, seen as reasonable by the community based on paper acceptance, have encouraged the development of adversarial attacks that map poorly to real-world scenarios. In turn, new defenses evaluated against these very attacks are inadvertently required to be almost perfect and incorporated as part of the model. But do they need to? In practice, machine learning models are deployed as a small component of a larger system. We analyze adversarial machine learning from a system security perspective rather than an AI perspective and its implications for emerging AI paradigms.

In this paper, we investigate some properties of symbolic powers and symbolic Rees algebras of binomial edge ideals associated with some classes of block graphs. First, it is shown that symbolic powers of binomial edge ideals of pendant cliques graphs coincide with the ordinary powers. Furthermore, we see that binomial edge ideals of a generalization of these graphs are symbolic $F$-split. Consequently, net-free generalized caterpillar graphs are also a class of block graphs with symbolic $F$-split binomial edge ideals. Finally, it turns out that symbolic Rees algebras of binomial edge ideals associated with these two classes, namely pendant cliques graphs and net-free generalized caterpillar graphs, are strongly $F$-regular.

Instead of randomly acquiring training data points, Uncertainty-based Active Learning (UAL) operates by querying the label(s) of pivotal samples from an unlabeled pool selected based on the prediction uncertainty, thereby aiming at minimizing the labeling cost for model training. The efficacy of UAL critically depends on the model capacity as well as the adopted uncertainty-based acquisition function. Within the context of this study, our analytical focus is directed toward comprehending how the capacity of the machine learning model may affect UAL efficacy. Through theoretical analysis, comprehensive simulations, and empirical studies, we conclusively demonstrate that UAL can lead to worse performance in comparison with random sampling when the machine learning model class has low capacity and is unable to cover the underlying ground truth. In such situations, adopting acquisition functions that directly target estimating the prediction performance may be beneficial for improving the performance of UAL.

Evolving networks are complex data structures that emerge in a wide range of systems in science and engineering. Learning expressive representations for such networks that encode their structural connectivity and temporal evolution is essential for downstream data analytics and machine learning applications. In this study, we introduce a self-supervised method for learning representations of temporal networks and employ these representations in the dynamic link prediction task. While temporal networks are typically characterized as a sequence of interactions over the continuous time domain, our study focuses on their discrete-time versions. This enables us to balance the trade-off between computational complexity and precise modeling of the interactions. We propose a recurrent message-passing neural network architecture for modeling the information flow over time-respecting paths of temporal networks. The key feature of our method is the contrastive training objective of the model, which is a combination of three loss functions: link prediction, graph reconstruction, and contrastive predictive coding losses. The contrastive predictive coding objective is implemented using infoNCE losses at both local and global scales of the input graphs. We empirically show that the additional self-supervised losses enhance the training and improve the model's performance in the dynamic link prediction task. The proposed method is tested on Enron, COLAB, and Facebook datasets and exhibits superior results compared to existing models., Comment: 16 pages, 6 figures

In computation-intensive domains such as digital signal processing, encryption, and neural networks, the performance of arithmetic units, including adders and multipliers, is pivotal. Conventional numerical systems often fall short of meeting the efficiency requirements of these applications concerning area, time, and power consumption. Innovative approaches like residue number systems (RNS) and redundant number systems have been introduced to surmount this challenge, markedly elevating computational efficiency. This paper examines from multiple perspectives how the fusion of redundant number systems with RNS (termed R-RNS) can diminish latency and enhance circuit implementation, yielding substantial benefits in practical scenarios. We conduct a comparative analysis of four systems - RNS, redundant number system, Binary Number System (BNS), and Signed-Digit Redundant Residue Number System (SD-RNS)-and appraise SD-RNS through an advanced Deep Neural Network (DNN) utilizing the CIFAR-10 dataset. Our findings are encouraging, demonstrating that SD-RNS attains computational speedups of 1.27 times and 2.25 times over RNS and BNS, respectively, and reduces energy consumption by 60% compared to BNS during sequential addition and multiplication tasks., Comment: This paper has been accepted by the 21st International SoC Conference (ISOCC), 2024, 2 pages

In this paper we extend some set theoretic concepts of numerical semigroups for arbitrary sub-semigroups of natural numbers. Then we characterized gapsets which leads to a more efficient computational approach towards numerical semigroups and finally we introduce the extension of gapsets and prove that the sequence of the number of gapsets of size $g$ is non-decreasing as a weak version of Bras-Amor\'os's conjecture., Comment: 14 pages, 2 algorithms

Blockchain is a decentralized network to increase trust, integrity, and transparency of transactions. With the exponential growth of transactions in the realm of Blockchain, especially in Bitcoin, Blockchain size increases as all transactions must be stored and verified. In Bitcoin, validating M of N transactions involves the necessity of M authentic signatures out of the total N transactions. This procedure is so time-consuming and needs a significant storage capacity. To address these issues, several multi signature schemes have been proposed, enabling users to interactively generate a common signature on a single message. Recently, some lattice based multi signature schemes have been presented to deal with the threats of quantum computers. However, none of them have met all desirable features of multi signature schemes like aggregate public key, low numbers of communication rounds, or resistant to quantum computers. Within this paper, we present a new multi signature scheme based on lattices, known as Razhims, that has aggregate public key, necessitates solely a single round of communication, and is resistant to quantum computers. In Razhims, the aggregate public key size and the final signature size are equal to the public key size and the final signature size of a standard signature respectively, and are independent of the number of signers., Comment: This article has 18 pages, 6 figures, and 4 tables

The present paper investigates the limit $G$-space $\mathcal{J}_{G}$ generated by the self-similar action of automatic groups on a regular rooted tree. The limit space $\mathcal{J}_{G}$ is the Gromov-Hausdorff limit of the family of Schreier graphs $\Gamma_{n}$; therefore, $\mathcal{J}_{G}$ can be approximated by Schreier graphs on level $n$-th when $n$ tends to infinity. We propose a computer program whose code is written in Wolfram language computes the adjacency matrix of Schreier graph $\Gamma_{n}$ at each specified level of the regular rooted tree. In this paper, the Schreier graphs corresponding to each automatic group is computed by applying the program to some collection of automata groups, including classic automatic groups.

The emerging paradigm of Non-Conventional Internet of Things (NC IoT), which is focused on the usefulness of information as opposed to the notion of high volume data collection and transmission, will be an important and dominant part of human life in the near future. This paper proposes a novel semantic-based approach for addressing the unique challenges posed by underwater NC IoT. We present an intelligent sensing strategy for exploring the semantics of the underwater environment by judiciously selecting the data to transmit, thereby minimizing redundancy for utmost relevant data transmission. We introduce an evolutionary function for the selection of the semantic-empowered messages relevant to the specific task within a minimum Age of Information (AoI), a freshness metric of the collected information, and for monitoring the underwater environment for performance optimization. A DNN-empowered Bayesian integrated with an adaptive surrogate model optimization will determine the optimal placement strategy of the sensors and the uncertainty level of the underwater landscape. An Adaptive Expected Improvement (AEI) mechanism is introduced to predict the optimal arrival rate for enabling a synchronized data sensing and transmission ecosystem, ensuring efficiency and timeliness. Simulation results show that the proposed solution outperforms conventional approaches.

In the evolving era of Unmanned Aerial Vehicles (UAVs), the emphasis has moved from mere data collection to strategically obtaining timely and relevant data within the Internet of Drones (IoDs) ecosystem. However, the unpredictable conditions in dynamic IoDs pose safety challenges for drones. Addressing this, our approach introduces a multi-UAV framework using spatial-temporal clustering and the Frechet distance for enhancing reliability. Seamlessly coupled with Integrated Sensing and Communication (ISAC), it enhances the precision and agility of UAV networks. Our Multi-Agent Reinforcement Learning (MARL) mechanism ensures UAVs adapt strategies through ongoing environmental interactions and enhancing intelligent sensing. This focus ensures operational safety and efficiency, considering data capture and transmission viability. By evaluating the relevance of the sensed information, we can communicate only the most crucial data variations beyond a set threshold and optimize bandwidth usage. Our methodology transforms the UAV domain, transitioning drones from data gatherers to adept information orchestrators, establishing a benchmark for efficiency and adaptability in modern aerial systems.

With the growth of textual data across online platforms, sentiment analysis has become crucial for extracting insights from user-generated content. While traditional approaches and deep learning models have shown promise, they cannot often capture complex relationships between entities. In this paper, we propose leveraging Relational Graph Convolutional Networks (RGCNs) for sentiment analysis, which offer interpretability and flexibility by capturing dependencies between data points represented as nodes in a graph. We demonstrate the effectiveness of our approach by using pre-trained language models such as BERT and RoBERTa with RGCN architecture on product reviews from Amazon and Digikala datasets and evaluating the results. Our experiments highlight the effectiveness of RGCNs in capturing relational information for sentiment analysis tasks.

Edge storage presents a viable data storage alternative for application vendors (AV), offering benefits such as reduced bandwidth overhead and latency compared to cloud storage. However, data cached in edge computing systems is susceptible to intentional or accidental disturbances. This paper proposes a decentralized integrity auditing scheme to safeguard data integrity and counter the traditional reliance on centralized third-party auditors (TPA), which are unfit for distributed systems. Our novel approach employs edge servers (ES) as mutual auditors, eliminating the need for a centralized entity. This decentralization minimizes potential collusion with malicious auditors and biases in audit outcomes. Using a strategic game model, we demonstrate that ESs are more motivated to audit each other than TPAs. The auditing process is addressed as a Nash Equilibrium problem, assuring accurate integrity proof through incentives for ESs. Our scheme's security and performance are rigorously assessed, showing it is secure within the random oracle model, offers improved speed, and is cost-effective compared to existing methods.

In this paper, we investigate whether the symbolic and ordinary powers of a binomial edge ideal $J_{G}$ are equal. We show that the equality $J_{G}^{t}=J_{G}^{(t)}$ holds for every $t \geq 1$ when $|Ass(J_{G})|=2$. Moreover, if $G$ is a caterpillar tree, then one has the same equality. Finally, we characterize the generalized caterpillar graphs which the equality of symbolic and ordinary powers of $J_{G}$ occurs.