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Smart grids are inherently susceptible to various types of malicious cyberattacks that have all been documented in the recent literature. Traditional cybersecurity research on power systems often utilizes simplified models that fail to capture the interactions between dynamic and steady-state behaviors, potentially underestimating the impact of cyber threats. This paper presents the first attempt to design and assess stealthy false data injection attacks (FDIAs) against nonlinear differential algebraic equation (NDAE) models of power networks. NDAE models, favored in industry for their ability to accurately capture both dynamic and steady-state behaviors, provide a more accurate representation of power system behavior by coupling dynamic and algebraic states. We propose novel FDIA strategies that simultaneously evade both dynamic and static intrusion detection systems while respecting the algebraic power flow and operational constraints inherent in NDAE models. We demonstrate how the coupling between dynamic and algebraic states in NDAE models significantly restricts the attacker's ability to manipulate state estimates while maintaining stealthiness. This highlights the importance of using more comprehensive power system models in cybersecurity analysis and reveals potential vulnerabilities that may be overlooked in simplified representations. The proposed attack strategies are validated through simulations on the IEEE 39-bus system.

We report a new CO observation survey of LHAASO J0341$+$5258 using the Nobeyama Radio Observatory (NRO) 45-m telescope. LHAASO J0341$+$5258 is one of the unidentified ultra-high-energy (UHE; E $>$100 TeV) gamma-ray sources detected by LHAASO. Our CO observations were conducted in February and March 2024, with a total observation time of 36 hours, covering the LHAASO source ($\sim$0.3-0.5 degrees in radius) and its surrounding area (1$\times$1.5 degrees). Within the LHAASO source extent, we identified five compact ($<$ 2 pc) molecular clouds at nearby distances ($<$ 1-4 kpc). These clouds can serve as proton-proton collision targets, producing hadronic gamma rays via neutral pion decays. Based on the hydrogen densities (700-5000 cm$^{-3}$) estimated from our CO observations and archived HI data from the DRAO survey, we derived the total proton energy of $W_p$ (E $>$ 1 TeV) $\sim$ 10$^{45}$ erg to account for the gamma-ray flux. One of the molecular clouds appears to be likely associated with an asymptotic giant branch (AGB) star with an extended CO tail, which may indicate some particle acceleration activities. However, the estimated maximum particle energy below 100 TeV makes the AGB-like star unlikely to be a PeVatron site. We conclude that the UHE emission observed in LHAASO J0341$+$5258 could be due to hadronic interactions between the newly discovered molecular clouds and TeV-PeV protons originating from a distant SNR or due to leptonic emission from a pulsar wind nebula candidate, which is reported in our companion X-ray observation paper (DiKerby et al. 2025)., Comment: Accepted for publication in ApJ; 18 pages, 11 figures, and 4 tables

The astronomical origin of the most energetic galactic cosmic rays and gamma rays is still uncertain. X-ray followup of candidate "PeVatrons", systems producing cosmic rays with energies exceeding 1 PeV, can constrain their spatial origin, identify likely counterparts, and test particle emission models. Using 120 ks of XMM-Newton observations, we report the discovery of a candidate pulsar wind nebula, a possible counterpart for the LHAASO PeVatron J0343+5254u. This extended source has a power law X-ray spectrum with spectral index of 1.9 - softer at greater distance from the center - and asymmetric spatial extension out to 2'. We conduct leptonic modeling of the X-ray and gamma ray radiation from this complex system, showing that a fully leptonic model with elevated IR photon fields can explain the multiwavelength emission from this source, similar to other VHE pulsar wind nebulae; excess gamma ray emissivity not explained by a leptonic model may be due to hadronic interactions in nearby molecular cloud regions, which might also produce detectable astroparticle flux., Comment: 14 pages, 7 figures, 3 tables, accepted for publication in ApJ

Efficiently solving sparse linear systems $Ax=b$, where $A$ is a large, sparse, symmetric positive semi-definite matrix, is a core challenge in scientific computing, machine learning, and optimization. A major bottleneck in Gaussian elimination for these systems is fill-in, the creation of non-zero entries that increase memory and computational cost. Minimizing fill-in is NP-hard, and existing heuristics like Minimum Degree and Nested Dissection offer limited adaptability across diverse problem instances. We introduce \textit{ReFill}, a reinforcement learning framework enhanced by Graph Neural Networks (GNNs) to learn adaptive ordering strategies for fill-in minimization. ReFill trains a GNN-based heuristic to predict efficient elimination orders, outperforming traditional heuristics by dynamically adapting to the structure of input matrices. Experiments demonstrate that ReFill outperforms strong heuristics in reducing fill-in, highlighting the untapped potential of learning-based methods for this well-studied classical problem., Comment: appendix added with remaining experiments

Understanding the nature of the accretion disk, its interplay with the X-ray corona, and assessing black hole spin demographics are some open challenges in astrophysics. In this work, we examine the predictions of the standard $\alpha$-disk model, origin of the soft X-ray excess, and measure the black hole spin parameter by applying the updated high-density disk reflection model to the XMM-Newton/NuSTAR broadband (0.3$-$78 keV) X-ray spectra of a sample of Type-1 AGN. Our Bayesian analysis confirms that the high-density relativistic reflection model with a broken power-law emissivity profile can simultaneously fit the soft X-ray excess, broad iron K line, and Compton hump for $\sim$70% of the sample, while an additional warm Comptonization model is still required to describe the observed soft X-ray excess for the remaining sources. Our first-ever calculation of the disk-to-corona power transfer fraction reveals that the fraction of power released from the accretion disk into the hot corona can have diverse values, the sample median of which is $0.7_{-0.4}^{+0.2}$. We find that the transferred power from the accretion disk can potentially soften the X-ray spectrum of the hot corona. The median values of the hot coronal temperature and optical depth for the sample are estimated to be $63_{-11}^{+23}$ keV and $0.85_{-0.27}^{+0.12}$, respectively. Finally, through joint XMM-Newton+NuSTAR relativistic reflection spectroscopy, we systematically constrain the black hole spin parameter across the broad range of black hole masses, $\log(M_{\rm BH}/M_{\odot}) \sim 5.5-9.0$, and increase the available spin measurements in the AGN population by $\sim$20%., Comment: 42 pages, 14 figures, 3 tables with 2 long tables, and 5 additional figures in the APPENDIX. Comments welcome

We present MOFA, an open-source generative AI (GenAI) plus simulation workflow for high-throughput generation of metal-organic frameworks (MOFs) on large-scale high-performance computing (HPC) systems. MOFA addresses key challenges in integrating GPU-accelerated computing for GPU-intensive GenAI tasks, including distributed training and inference, alongside CPU- and GPU-optimized tasks for screening and filtering AI-generated MOFs using molecular dynamics, density functional theory, and Monte Carlo simulations. These heterogeneous tasks are unified within an online learning framework that optimizes the utilization of available CPU and GPU resources across HPC systems. Performance metrics from a 450-node (14,400 AMD Zen 3 CPUs + 1800 NVIDIA A100 GPUs) supercomputer run demonstrate that MOFA achieves high-throughput generation of novel MOF structures, with CO$_2$ adsorption capacities ranking among the top 10 in the hypothetical MOF (hMOF) dataset. Furthermore, the production of high-quality MOFs exhibits a linear relationship with the number of nodes utilized. The modular architecture of MOFA will facilitate its integration into other scientific applications that dynamically combine GenAI with large-scale simulations., Comment: 13 pages, 10 figures

The o1 model series is trained with large-scale reinforcement learning to reason using chain of thought. These advanced reasoning capabilities provide new avenues for improving the safety and robustness of our models. In particular, our models can reason about our safety policies in context when responding to potentially unsafe prompts, through deliberative alignment. This leads to state-of-the-art performance on certain benchmarks for risks such as generating illicit advice, choosing stereotyped responses, and succumbing to known jailbreaks. Training models to incorporate a chain of thought before answering has the potential to unlock substantial benefits, while also increasing potential risks that stem from heightened intelligence. Our results underscore the need for building robust alignment methods, extensively stress-testing their efficacy, and maintaining meticulous risk management protocols. This report outlines the safety work carried out for the OpenAI o1 and OpenAI o1-mini models, including safety evaluations, external red teaming, and Preparedness Framework evaluations.

A recent report on the detection of very-high-energy gamma rays from V4641 Sagittarii (V4641 Sgr) up to ~0.8 peta-electronvolt has made it the second confirmed "PeVatron" microquasar. Here we report on the observation of V4641 Sgr with X-Ray Imaging and Spectroscopy Mission (XRISM) in September 2024. Thanks to the large field of view and low background, the CCD imager Xtend successfully detected for the first time X-ray extended emission around V4641 Sgr with a significance of > 4.5 sigma and > 10 sigma based on our imaging and spectral analysis, respectively. The spatial extent is estimated to have a radius of $7 \pm 3$ arcmin ($13 \pm 5$ pc at a distance of 6.2 kpc) assuming a Gaussian-like radial distribution, which suggests that the particle acceleration site is within ~10 pc of the microquasar. If the X-ray morphology traces the diffusion of accelerated electrons, this spatial extent can be explained by either an enhanced magnetic field (~80 uG) or a suppressed diffusion coefficient (~$10^{27}$ cm$^2$ s$^{-1}$ at 100 TeV). The integrated X-ray flux, (4-6)$\times 10^{-12}$ erg s$^{-1}$ cm$^{-2}$ (2-10 keV), would require a magnetic field strength higher than the galactic mean (> 8 uG) if the diffuse X-ray emission originates from synchrotron radiation and the gamma-ray emission is predominantly hadronic. If the X-rays are of thermal origin, the measured extension, temperature, and plasma density can be explained by a jet with a luminosity of ~$2\times 10^{39}$ erg s$^{-1}$, which is comparable to the Eddington luminosity of this system., Comment: 9 pages, 5 figures, accepted for publication in ApJL

Here, we present the outcomes from the second Large Language Model (LLM) Hackathon for Applications in Materials Science and Chemistry, which engaged participants across global hybrid locations, resulting in 34 team submissions. The submissions spanned seven key application areas and demonstrated the diverse utility of LLMs for applications in (1) molecular and material property prediction; (2) molecular and material design; (3) automation and novel interfaces; (4) scientific communication and education; (5) research data management and automation; (6) hypothesis generation and evaluation; and (7) knowledge extraction and reasoning from scientific literature. Each team submission is presented in a summary table with links to the code and as brief papers in the appendix. Beyond team results, we discuss the hackathon event and its hybrid format, which included physical hubs in Toronto, Montreal, San Francisco, Berlin, Lausanne, and Tokyo, alongside a global online hub to enable local and virtual collaboration. Overall, the event highlighted significant improvements in LLM capabilities since the previous year's hackathon, suggesting continued expansion of LLMs for applications in materials science and chemistry research. These outcomes demonstrate the dual utility of LLMs as both multipurpose models for diverse machine learning tasks and platforms for rapid prototyping custom applications in scientific research., Comment: Updating author information, the submission remains largely unchanged. 98 pages total

We present a detailed X-ray investigation of a region (S1) exhibiting non-thermal X-ray emission within the supernova remnant (SNR) CTB 37B hosting the magnetar CXOU J171405.7$-$381031. Previous analyses modeled this emission with a power law (PL), inferring various values for the photon index ($\Gamma$) and absorbing column density ($N_{\rm H}$). Based on these, S1 was suggested to be the SNR shell, a background pulsar wind nebula (PWN), or an interaction region between the SNR and a molecular cloud. Our analysis of a larger dataset favors a steepening (broken or curved PL) spectrum over a straight PL, with the best-fit broken power-law (BPL) parameters of $\Gamma=1.23\pm0.23$ and $2.24\pm0.16$ below and above a break at $5.57\pm0.52$ keV, respectively. However, a simple PL or srcut model cannot be definitively ruled out. For the BPL model, the inferred $N_{\rm H}=(4.08\pm0.72)\times 10^{22}\rm \ cm^{-2}$ towards S1 is consistent with that of the SNR, suggesting a physical association. The BPL-inferred spectral break $\Delta \Gamma \approx 1$ and hard $\Gamma$ can be naturally explained by a non-thermal bremsstrahlung (NTB) model. We present an evolutionary NTB model that reproduces the observed spectrum, which indicates the presence of sub-relativistic electrons within S1. However, alternate explanations for S1, an unrelated PWN or the SNR shock with unusually efficient acceleration, cannot be ruled out. We discuss these explanations and their implications for gamma-ray emission from CTB 37B, and describe future observations that could settle the origin of S1., Comment: 11 pages. 4 figures, Accepted for publication in Apj

Cracks are among the earliest indicators of deterioration in concrete structures. Early automatic detection of these cracks can significantly extend the lifespan of critical infrastructures, such as bridges, buildings, and tunnels, while simultaneously reducing maintenance costs and facilitating efficient structural health monitoring. This study investigates whether leveraging multi-temporal data for crack segmentation can enhance segmentation quality. Therefore, we compare a Swin UNETR trained on multi-temporal data with a U-Net trained on mono-temporal data to assess the effect of temporal information compared with conventional single-epoch approaches. To this end, a multi-temporal dataset comprising 1356 images, each with 32 sequential crack propagation images, was created. After training the models, experiments were conducted to analyze their generalization ability, temporal consistency, and segmentation quality. The multi-temporal approach consistently outperformed its mono-temporal counterpart, achieving an IoU of $82.72\%$ and a F1-score of $90.54\%$, representing a significant improvement over the mono-temporal model's IoU of $76.69\%$ and F1-score of $86.18\%$, despite requiring only half of the trainable parameters. The multi-temporal model also displayed a more consistent segmentation quality, with reduced noise and fewer errors. These results suggest that temporal information significantly enhances the performance of segmentation models, offering a promising solution for improved crack detection and the long-term monitoring of concrete structures, even with limited sequential data.

Security experts reverse engineer (decompile) binary code to identify critical security vulnerabilities. The limited access to source code in vital systems - such as firmware, drivers, and proprietary software used in Critical Infrastructures (CI) - makes this analysis even more crucial on the binary level. Even with available source code, a semantic gap persists after compilation between the source and the binary code executed by the processor. This gap may hinder the detection of vulnerabilities in source code. That being said, current research on Large Language Models (LLMs) overlooks the significance of decompiled binaries in this area by focusing solely on source code. In this work, we are the first to empirically uncover the substantial semantic limitations of state-of-the-art LLMs when it comes to analyzing vulnerabilities in decompiled binaries, largely due to the absence of relevant datasets. To bridge the gap, we introduce DeBinVul, a novel decompiled binary code vulnerability dataset. Our dataset is multi-architecture and multi-optimization, focusing on C/C++ due to their wide usage in CI and association with numerous vulnerabilities. Specifically, we curate 150,872 samples of vulnerable and non-vulnerable decompiled binary code for the task of (i) identifying; (ii) classifying; (iii) describing vulnerabilities; and (iv) recovering function names in the domain of decompiled binaries. Subsequently, we fine-tune state-of-the-art LLMs using DeBinVul and report on a performance increase of 19%, 24%, and 21% in the capabilities of CodeLlama, Llama3, and CodeGen2 respectively, in detecting binary code vulnerabilities. Additionally, using DeBinVul, we report a high performance of 80-90% on the vulnerability classification task. Furthermore, we report improved performance in function name recovery and vulnerability description tasks.

Young supernova remnants (SNRs) are believed to be the origin of energetic cosmic rays (CRs) below the "knee" of their spectrum at $\sim3$ petaelectronvolt (PeV, $10^{15}$ eV). Nevertheless, the precise location, duration, and operation of CR acceleration in young SNRs are open questions. Here, we report on multi-epoch X-ray observations of Cassiopeia A (Cas A), a 350-year-old SNR, in the 15-50 keV band that probes the most energetic CR electrons. The observed X-ray flux decrease $(15\pm1\%)$, contrary to the expected $>$90\% decrease based on previous radio, X-ray, and gamma-ray observations, provides unambiguous evidence for CR electron acceleration operating in Cas A. A temporal model for the radio and X-ray data accounting for electron cooling and continuous injection finds that the freshly injected electron spectrum is significantly harder (exponential cutoff power law index $q=2.15$), and its cutoff energy is much higher ($E_{cut}=36$ TeV) than the relic electron spectrum ($q=2.44\pm0.03$, $E_{cut}=4\pm1$ TeV). Both electron spectra are naturally explained by the recently developed modified nonlinear diffusive shock acceleration (mNLDSA) mechanism. The CR protons producing the observed gamma rays are likely accelerated at the same location by the same mechanism as those for the injected electron. The Cas A observations and spectral modeling represent the first time radio, X-ray, gamma ray and CR spectra have been self-consistently tied to a specific acceleration mechanism -- mNLDSA -- in a young SNR., Comment: Accepted for publication in ApJ. 14 pages, 4 figures, 2 tables

How black holes are formed remains an open and fundamental question in Astrophysics. Despite theoretical predictions, it lacks observations to understand whether the black hole formation experiences a supernova explosion. Here we report the discovery of an X-ray shell north of the Galactic micro-quasar SS 433 harboring a stellar-mass black hole spatially associated with radio continuum and polarization emissions, and an HI cloud. Its spectrum can be reproduced by a 1-keV under-ionized plasma, from which the shell is inferred to have been created by a supernova explosion 20-30 kyr ago and its properties constitute evidence for canonical SN explosions to create some black holes. Our analysis precludes other possible origins including heated by jets or blown by disk winds. According to the lower mass limit of the compact object in SS 433, we roughly deduced that the progenitor should be more massive than 25 M$_{\odot}$. The existence of such a young remnant in SS 433 can also lead to new insights into the supercritical accretion in young microquasars and the ${\gamma}$-ray emission of this system. The fallback ejecta may provide accretion materials within tens of thousands of years while the shock of the supernova remnant may play a crucial role in the cosmic ray (re)acceleration., Comment: 20 pages, 8 figures, accepted for publication in ApJL

With the recent unprecedented advancements in Artificial Intelligence (AI) computing, progress in Large Language Models (LLMs) is accelerating rapidly, presenting challenges in establishing clear guidelines, particularly in the field of security. That being said, we thoroughly identify and describe three main technical challenges in the security and software engineering literature that spans the entire LLM workflow, namely; \textbf{\textit{(i)}} Data Collection and Labeling; \textbf{\textit{(ii)}} System Design and Learning; and \textbf{\textit{(iii)}} Performance Evaluation. Building upon these challenges, this paper introduces \texttt{SecRepair}, an instruction-based LLM system designed to reliably \textit{identify}, \textit{describe}, and automatically \textit{repair} vulnerable source code. Our system is accompanied by a list of actionable guides on \textbf{\textit{(i)}} Data Preparation and Augmentation Techniques; \textbf{\textit{(ii)}} Selecting and Adapting state-of-the-art LLM Models; \textbf{\textit{(iii)}} Evaluation Procedures. \texttt{SecRepair} uses a reinforcement learning-based fine-tuning with a semantic reward that caters to the functionality and security aspects of the generated code. Our empirical analysis shows that \texttt{SecRepair} achieves a \textit{12}\% improvement in security code repair compared to other LLMs when trained using reinforcement learning. Furthermore, we demonstrate the capabilities of \texttt{SecRepair} in generating reliable, functional, and compilable security code repairs against real-world test cases using automated evaluation metrics.

We introduce a new balanced separator theorem for unit-disk graphs involving two shortest paths combined with the 1-hop neighbours of those paths and two other vertices. This answers an open problem of Yan, Xiang and Dragan [CGTA '12] and improves their result that requires removing the 3-hop neighborhood of two shortest paths. Our proof uses very different ideas, including Delaunay triangulations and a generalization of the celebrated balanced separator theorem of Lipton and Tarjan [J. Appl. Math. '79] to systems of non-intersecting paths., Comment: To appear in ESA 2024. 15 pages, 7 figures

The energy landscape of multiply connected superconducting structures is ruled by fluxoid quantization due to the implied single-valuedness of the complex wave function. The transitions and interaction between these energy states, each defined by a specific phase winding number, are governed by classical and/or quantum phase slips. Understanding these events requires the ability to probe, non-invasively, the state of the ring. Here, we employ a niobium resonator to examine the superconducting properties of an aluminum loop. By applying a magnetic field, adjusting temperature, and altering the loop's dimensions via focused ion beam milling, we correlate resonance frequency shifts with changes in the loop's kinetic inductance. This parameter is a unique indicator of the superconducting condensate's state, facilitating the detection of phase slips in nanodevices and providing insights into their dynamics. Our method presents a proof-of-principle spectroscopic technique with promising potential for investigating the Cooper pair density in inductively coupled superconducting nanostructures.

In the classical prophet inequality settings, a gambler is given a sequence of $n$ random variables $X_1, \dots, X_n$, taken from known distributions, observes their values in this (potentially adversarial) order, and select one of them, immediately after it is being observed, so that its value is as high as possible. The classical \emph{prophet inequality} shows a strategy that guarantees a value at least half of that an omniscience prophet that picks the maximum, and this ratio is optimal. Here, we generalize the prophet inequality, allowing the gambler some additional information about the future that is otherwise privy only to the prophet. Specifically, at any point in the process, the gambler is allowed to query an oracle $\mathcal{O}$. The oracle responds with a single bit answer: YES if the current realization is greater than the remaining realizations, and NO otherwise. We show that the oracle model with $m$ oracle calls is equivalent to the \textsc{Top-$1$-of-$(m+1)$} model when the objective is maximizing the probability of selecting the maximum. This equivalence fails to hold when the objective is maximizing the competitive ratio, but we still show that any algorithm for the oracle model implies an equivalent competitive ratio for the \textsc{Top-$1$-of-$(m+1)$} model. We resolve the oracle model for any $m$, giving tight lower and upper bound on the best possible competitive ratio compared to an almighty adversary. As a consequence, we provide new results as well as improvements on known results for the \textsc{Top-$1$-of-$m$} model.

HESS J1641-463 is an unidentified gamma-ray source with a hard TeV gamma-ray spectrum, and thus it has been proposed to be a possible candidate for cosmic ray (CR) accelerators up to PeV energies (a PeVatron candidate). The source spatially coincides with the radio supernova remnant (SNR) G338.5+0.1, but has not yet been fully explored in the X-ray band. We analyzed newly taken NuSTAR data, pointing at HESS J1641-463, with 82 ks effective exposure time. There is no apparent X-ray counterpart of HESS J1641-463, while nearby stellar cluster, Mercer 81, and stray-light X-rays are detected. Combined with the archival Chandra data, partially covering the source, we derived an upper limit of $\sim 6\times 10^{-13}$ erg cm$^{-2}$ s$^{-1}$ in 2-10 keV ($\sim 3\times 10^{-13}$ erg cm$^{-2}$ s$^{-1}$ in 10-20 keV). If the gamma-ray emission is originated from decay of $\pi^0$ mesons produced in interactions between CR protons and ambient materials, secondary electrons in the proton-proton interactions can potentially emit synchrotron photons in the X-ray band, which can be tested by our X-ray observations. Although the obtained X-ray upper limits cannot place a constraint on the primary proton spectrum, it will be possible with a future hard X-ray mission., Comment: 15 pages, 8 figures, 4 tables, accepted for publication in ApJ

Supernova remnants (SNRs) exert strong influence on the physics and chemistry of the nearby molecular clouds (MCs) through shock waves and the cosmic rays (CRs) they accelerate. To investigate the SNR-cloud interaction in the prototype interacting SNR W28 (G6.4$-$0.1), we present new observations of $\rm HCO^+$, HCN and HNC $J=1\text{--}0$ lines, supplemented by archival data of CO isotopes, $\rm N_2H^+$ and $\rm H^{13}CO^+$. We compare the spatial distribution and spectral line profiles of different molecular species. Using local thermodynatic equilibrium (LTE) assumption, we obtain an abundance ratio $N({\rm HCO^+})/N({\rm CO})\sim10^{-4}$ in the northeastern shocked cloud, which is higher by an order of magnitude than the values in unshocked clouds. This can be accounted for by the chemistry jointly induced by shock and CRs, with the physical parameters previously obtained from observations: preshock density $n_{\rm H}\sim 2\times 10^{5}\rm \ cm^{-3}$, CR ionization rate $\zeta=2.5\times 10^{-15} \rm \ s^{-1}$ and shock velocity $V_{\rm s}=15\text{--}20\rm \ km\ s^{-1}$. Towards a point outside the northeastern boundary of W28 with known high CR ionization rate, we estimate the abundance ratio $ N({\rm HCO^+})/N({\rm N_2H^+}) \approx 0.6\text{--}3.3$, which can be reproduced by a chemical simulation if a high density $n_{\rm H}\sim 2\times 10^5 \ \rm cm^{-3}$ is adopted., Comment: 19 pages, 15 figures, accepted by ApJ

In software development, the predominant emphasis on functionality often supersedes security concerns, a trend gaining momentum with AI-driven automation tools like GitHub Copilot. These tools significantly improve developers' efficiency in functional code development. Nevertheless, it remains a notable concern that such tools are also responsible for creating insecure code, predominantly because of pre-training on publicly available repositories with vulnerable code. Moreover, developers are called the "weakest link in the chain" since they have very minimal knowledge of code security. Although existing solutions provide a reasonable solution to vulnerable code, they must adequately describe and educate the developers on code security to ensure that the security issues are not repeated. Therefore we introduce a multipurpose code vulnerability analysis system \texttt{SecRepair}, powered by a large language model, CodeGen2 assisting the developer in identifying and generating fixed code along with a complete description of the vulnerability with a code comment. Our innovative methodology uses a reinforcement learning paradigm to generate code comments augmented by a semantic reward mechanism. Inspired by how humans fix code issues, we propose an instruction-based dataset suitable for vulnerability analysis with LLMs. We further identify zero-day and N-day vulnerabilities in 6 Open Source IoT Operating Systems on GitHub. Our findings underscore that incorporating reinforcement learning coupled with semantic reward augments our model's performance, thereby fortifying its capacity to address code vulnerabilities with improved efficacy.

We report on the X-ray emission properties of the pulsar PSR J1849$-$0001 and its wind nebula (PWN), as measured by Chandra, XMM-Newton, NICER, Swift, and NuSTAR. In the X-ray data, we detected the 38-ms pulsations of the pulsar up to $\sim$60 keV with high significance. Additionally, we found that the pulsar's on-pulse spectral energy distribution displays significant curvature, peaking at $\approx$60 keV. Comparing the phase-averaged and on-pulse spectra of the pulsar, we found that the pulsar's off-pulse emission exhibits a spectral shape that is very similar to its on-pulse emission. This characterization of the off-pulse emission enabled us to measure the $>$10 keV spectrum of the faint and extended PWN using NuSTAR's off-pulse data. We measured both the X-ray spectrum and the radial profiles of the PWN's brightness and photon index, and we combined these X-ray measurements with published TeV results. We then employed a multizone emission scenario to model the broadband data. The results of the modeling suggest that the magnetic field within the PWN is relatively low ($\approx 7\mu \rm G$) and that electrons are accelerated to energies $\stackrel{>}{_{\sim}}$400 TeV within this PWN. The electrons responsible for the TeV emission outside the X-ray PWN may propagate to $\sim$30 pc from the pulsar in $\sim$10 kyr., Comment: 18 pages, 6 figures, Accepted for publication in Apj

We present a novel diffusion-based approach to generate synthetic histopathological Whole Slide Images (WSIs) at an unprecedented gigapixel scale. Synthetic WSIs have many potential applications: They can augment training datasets to enhance the performance of many computational pathology applications. They allow the creation of synthesized copies of datasets that can be shared without violating privacy regulations. Or they can facilitate learning representations of WSIs without requiring data annotations. Despite this variety of applications, no existing deep-learning-based method generates WSIs at their typically high resolutions. Mainly due to the high computational complexity. Therefore, we propose a novel coarse-to-fine sampling scheme to tackle image generation of high-resolution WSIs. In this scheme, we increase the resolution of an initial low-resolution image to a high-resolution WSI. Particularly, a diffusion model sequentially adds fine details to images and increases their resolution. In our experiments, we train our method with WSIs from the TCGA-BRCA dataset. Additionally to quantitative evaluations, we also performed a user study with pathologists. The study results suggest that our generated WSIs resemble the structure of real WSIs.

Ransomware is a type of malware which encrypts user data and extorts payments in return for the decryption keys. This cyberthreat is one of the most serious challenges facing organizations today and has already caused immense financial damage. As a result, many researchers have been developing techniques to counter ransomware. Recently, the federated learning (FL) approach has also been applied for ransomware analysis, allowing corporations to achieve scalable, effective detection and attribution without having to share their private data. However, in reality there is much variation in the quantity and composition of ransomware data collected across multiple FL client sites/regions. This imbalance will inevitably degrade the effectiveness of any defense mechanisms. To address this concern, a modified FL scheme is proposed using a weighted cross-entropy loss function approach to mitigate dataset imbalance. A detailed performance evaluation study is then presented for the case of static analysis using the latest Windows-based ransomware families. The findings confirm improved ML classifier performance for a highly imbalanced dataset., Comment: 6 pages, 4 figures, 3 tables

Compact objects and supernova remnants provide nearby laboratories to probe the fate of stars after they die, and the way they impact, and are impacted by, their surrounding medium. The past five decades have significantly advanced our understanding of these objects, and showed that they are most relevant to our understanding of some of the most mysterious energetic events in the distant Universe, including Fast Radio Bursts and Gravitational Wave sources. However, many questions remain to be answered. These include: What powers the diversity of explosive phenomena across the electromagnetic spectrum? What are the mass and spin distributions of neutron stars and stellar mass black holes? How do interacting compact binaries with white dwarfs - the electromagnetic counterparts to gravitational wave LISA sources - form and behave? Which objects inhabit the faint end of the X-ray luminosity function? How do relativistic winds impact their surroundings? What do neutron star kicks reveal about fundamental physics and supernova explosions? How do supernova remnant shocks impact cosmic magnetism? This plethora of questions will be addressed with AXIS - the Advanced X-ray Imaging Satellite - a NASA Probe Mission Concept designed to be the premier high-angular resolution X-ray mission for the next decade. AXIS, thanks to its combined (a) unprecedented imaging resolution over its full field of view, (b) unprecedented sensitivity to faint objects due to its large effective area and low background, and (c) rapid response capability, will provide a giant leap in discovering and identifying populations of compact objects (isolated and binaries), particularly in crowded regions such as globular clusters and the Galactic Center, while addressing science questions and priorities of the US Decadal Survey for Astronomy and Astrophysics (Astro2020)., Comment: 61 pages, 33 figures. This White Paper is part of a series commissioned for the AXIS Probe Concept Mission

The emergence of pandemics has significantly emphasized the need for effective solutions in healthcare data analysis. One particular challenge in this domain is the manual examination of medical images, such as X-rays and CT scans. This process is time-consuming and involves the logistical complexities of transferring these images to centralized cloud computing servers. Additionally, the speed and accuracy of image analysis are vital for efficient healthcare image management. This research paper introduces an innovative healthcare architecture that tackles the challenges of analysis efficiency and accuracy by harnessing the capabilities of Artificial Intelligence (AI). Specifically, the proposed architecture utilizes fog computing and presents a modified Convolutional Neural Network (CNN) designed specifically for image analysis. Different architectures of CNN layers are thoroughly explored and evaluated to optimize overall performance. To demonstrate the effectiveness of the proposed approach, a dataset of X-ray images is utilized for analysis and evaluation. Comparative assessments are conducted against recent models such as VGG16, VGG19, MobileNet, and related research papers. Notably, the proposed approach achieves an exceptional accuracy rate of 99.88% in classifying normal cases, accompanied by a validation rate of 96.5%, precision and recall rates of 100%, and an F1 score of 100%. These results highlight the immense potential of fog computing and modified CNNs in revolutionizing healthcare image analysis and diagnosis, not only during pandemics but also in the future. By leveraging these technologies, healthcare professionals can enhance the efficiency and accuracy of medical image analysis, leading to improved patient care and outcomes.

Accurate latency computation is essential for the Internet of Things (IoT) since the connected devices generate a vast amount of data that is processed on cloud infrastructure. However, the cloud is not an optimal solution. To overcome this issue, fog computing is used to enable processing at the edge while still allowing communication with the cloud. Many applications rely on fog computing, including traffic management. In this paper, an Intelligent Traffic Congestion Mitigation System (ITCMS) is proposed to address traffic congestion in heavily populated smart cities. The proposed system is implemented using fog computing and tested in a crowded city. Its performance is evaluated based on multiple metrics, such as traffic efficiency, energy savings, reduced latency, average traffic flow rate, and waiting time. The obtained results are compared with similar techniques that tackle the same issue. The results obtained indicate that the execution time of the simulation is 4,538 seconds, and the delay in the application loop is 49.67 seconds. The paper addresses various issues, including CPU usage, heap memory usage, throughput, and the total average delay, which are essential for evaluating the performance of the ITCMS. Our system model is also compared with other models to assess its performance. A comparison is made using two parameters, namely throughput and the total average delay, between the ITCMS, IOV (Internet of Vehicle), and STL (Seasonal-Trend Decomposition Procedure based on LOESS). Consequently, the results confirm that the proposed system outperforms the others in terms of higher accuracy, lower latency, and improved traffic efficiency.

The Advanced X-ray Imaging Satellite (AXIS) is a Probe-class concept that will build on the legacy of the Chandra X-ray Observatory by providing low-background, arcsecond-resolution imaging in the 0.3-10 keV band across a 450 arcminute$^2$ field of view, with an order of magnitude improvement in sensitivity. AXIS utilizes breakthroughs in the construction of lightweight segmented X-ray optics using single-crystal silicon, and developments in the fabrication of large-format, small-pixel, high readout rate CCD detectors with good spectral resolution, allowing a robust and cost-effective design. Further, AXIS will be responsive to target-of-opportunity alerts and, with onboard transient detection, will be a powerful facility for studying the time-varying X-ray universe, following on from the legacy of the Neil Gehrels (Swift) X-ray observatory that revolutionized studies of the transient X-ray Universe. In this paper, we present an overview of AXIS, highlighting the prime science objectives driving the AXIS concept and how the observatory design will achieve these objectives., Comment: Published in Proceedings of SPIE Optics & Photonics 2023, San Diego

5G New Radio (NR) has stringent demands on both performance and complexity for the design of low-density parity-check (LDPC) decoding algorithms and corresponding VLSI implementations. Furthermore, decoders must fully support the wide range of all 5G NR blocklengths and code rates, which is a significant challenge. In this paper, we present a high-performance and low-complexity LDPC decoder, tailor-made to fulfill the 5G requirements. First, to close the gap between belief propagation (BP) decoding and its approximations in hardware, we propose an extension of adjusted min-sum decoding, called generalized adjusted min-sum (GA-MS) decoding. This decoding algorithm flexibly truncates the incoming messages at the check node level and carefully approximates the non-linear functions of BP decoding to balance the error-rate and hardware complexity. Numerical results demonstrate that the proposed fixed-point GAMS has only a minor gap of 0.1 dB compared to floating-point BP under various scenarios of 5G standard specifications. Secondly, we present a fully reconfigurable 5G NR LDPC decoder implementation based on GA-MS decoding. Given that memory occupies a substantial portion of the decoder area, we adopt multiple data compression and approximation techniques to reduce 42.2% of the memory overhead. The corresponding 28nm FD-SOI ASIC decoder has a core area of 1.823 mm2 and operates at 895 MHz. It is compatible with all 5G NR LDPC codes and achieves a peak throughput of 24.42 Gbps and a maximum area efficiency of 13.40 Gbps/mm2 at 4 decoding iterations., Comment: 14 pages, 15 figures, accepted by IEEE Transactions on Circuits and Systems I: Regular Paper

Simulation is an essential tool to develop and benchmark autonomous vehicle planning software in a safe and cost-effective manner. However, realistic simulation requires accurate modeling of nuanced and complex multi-agent interactive behaviors. To address these challenges, we introduce Waymax, a new data-driven simulator for autonomous driving in multi-agent scenes, designed for large-scale simulation and testing. Waymax uses publicly-released, real-world driving data (e.g., the Waymo Open Motion Dataset) to initialize or play back a diverse set of multi-agent simulated scenarios. It runs entirely on hardware accelerators such as TPUs/GPUs and supports in-graph simulation for training, making it suitable for modern large-scale, distributed machine learning workflows. To support online training and evaluation, Waymax includes several learned and hard-coded behavior models that allow for realistic interaction within simulation. To supplement Waymax, we benchmark a suite of popular imitation and reinforcement learning algorithms with ablation studies on different design decisions, where we highlight the effectiveness of routes as guidance for planning agents and the ability of RL to overfit against simulated agents.

G106.3$+$2.7, commonly considered a composite supernova remnant (SNR), is characterized by a boomerang-shaped pulsar wind nebula (PWN) and two distinct ("head" & "tail") regions in the radio band. A discovery of very-high-energy (VHE) gamma-ray emission ($E_\gamma > 100$ GeV) followed by the recent detection of ultra-high-energy (UHE) gamma-ray emission ($E_\gamma > 100$ TeV) from the tail region suggests that G106.3$+$2.7 is a PeVatron candidate. We present a comprehensive multi-wavelength study of the Boomerang PWN (100" around PSR J2229+6114) using archival radio and Chandra data obtained from two decades ago, a new NuSTAR X-ray observation from 2020, and upper limits on gamma-ray fluxes obtained by Fermi and VERITAS observatories. The NuSTAR observation allowed us to detect a 51.67 ms spin period from the pulsar PSR J2229+6114 and the PWN emission characterized by a power-law model with $\Gamma = 1.52\pm0.06$ up to 20 keV. Contrary to the previous radio study by Kothes et al. 2006, we prefer a much lower PWN B-field ($B\sim3$ $\mu$G) and larger distance ($d \sim 8$ kpc) based on (1) the non-varying X-ray flux over the last two decades, (2) the energy-dependent X-ray PWN size resulting from synchrotron burn-off and (3) the multi-wavelength spectral energy distribution (SED) data. Our SED model suggests that the PWN is currently re-expanding after being compressed by the SNR reverse shock $\sim 1000$ years ago. In this case, the head region should be formed by GeV--TeV electrons injected earlier by the pulsar propagating into the low density environment.