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Since SSH’s standardization nearly 20 years ago, real-world requirements for a remote access protocol and our understanding of how to build secure cryptographic network protocols have both evolved significantly. In this work, we introduce Hop, a transport and remote access protocol designed to support today’s needs. Building on modern cryptographic advances, Hop reduces SSH protocol complexity and overhead while simultaneously addressing many of SSH’s shortcomings through a cryptographically-mediated delegation scheme, native host identification based on lessons from TLS and ACME, client authentication for modern enterprise environments, and support for client roaming and intermittent connectivity. We present concrete design requirements for a modern remote access protocol, describe our proposed protocol, and evaluate its performance. We hope that our work encourages discussion of what a modern remote access protocol should look like in the future. 

Abstract Generating high-quality synthetic networks with realistic community structure is vital to effectively evaluate community detection algorithms. In this study, we propose a new synthetic network generator called the Edge-Connected Stochastic Block Model (EC-SBM). The goal of EC-SBM is to take a given clustered real-world network and produce a synthetic network that resembles the clustered real-world network with respect to both network and community-specific criteria. In particular, we focus on simulating the internal edge connectivity of the clusters in the reference clustered network. Our performance study on large real-world networks shows that EC-SBM is generally more accurate with respect to network and community criteria than currently used approaches for this problem. Furthermore, we demonstrate that EC-SBM can complete analyses on several real-world networks with millions of nodes. 

Adeno-associated viruses (AAVs) are a leading vector for gene therapy, yet their clinical utility is limited by the lack of robust quality control methods to distinguish between empty (AAVempty), partially loaded (AAVpartial), and fully DNA loaded (AAVfull) capsids. Current analytical techniques provide partial insights but remain limited in sensitivity, throughput, or resolution. Here we present a multimodal plasmonic nanopore sensor that integrates optical trapping with electrical resistive-pulse sensing to characterize AAV9 capsids at the single-particle level in tens of μL sample volumes and fM range concentrations. As a model system, we employed AAV9 capsids not loaded with DNA, capsids loaded with a self-complementary 4.7 kbp DNA (AAVscDNA), and ones loaded with single-stranded 4.7 kbp DNA (AAVssDNA). Ground-truth validation was performed with analytical ultracentrifugation (AUC). Nanosensor data were acquired concurrently for optical step changes (occurring at AAV trapping and un-trapping) both in transmittance and reflectance geometries, and electrical nanopore resistive pulse signatures, making for a total of five data dimensions. The acquired data was then filtered and clustered by Gaussian mixture models (GMMs), accompanied by spectral clustering stability analysis, to successfully separate between AAV species based on their DNA load status (AAVempty, AAVpartial, AAVfull) and DNA load type (AAVscDNA versus AAVssDNA). The motivation for quantifying the AAVempty and AAVpartial population fractions is that they reduce treatment efficacy and increase immunogenicity. Likewise, the motivation to identify AAVscDNA population fractions is that these have much higher transfection rates. Importantly, the results showed that the nanosensor could differentiate between AAVscDNA and AAVssDNA despite their identical masses. In contrast, AUC could not differentiate between AAVscDNA and AAVssDNA. An equimolar mixture of AAVscDNA, AAVssDNA and AAVempty was also measured with the sensor, and the results showed the expected population fractions, supporting the capacity of the method to differentiate AAV load status in heterogeneous solutions. In addition, less common optical and electrical signal signatures were identified in the acquired data, which were attributed to debris, rapid entry re-entry to the optical trap, or weak optical trap exits, representing critical artifacts to recognize for correct interpretation of the data. Together, these findings establish plasmonic nanopore sensing as a promising platform for quantifying AAV DNA loading status and genome type with the potential to extend ultra-sensitive single-particle characterization beyond the capabilities of existing methods. 

With the increasing integration of cyber-physical systems (CPS) into critical applications, ensuring their resilience against cyberattacks is paramount. A particularly concerning threat is the vulnerability of CPS to deceptive attacks that degrade system performance while remaining undetected. This article investigates perfectly undetectable false data injection attacks (FDIAs) targeting the trajectory tracking control of a nonholonomic mobile robot. The proposed attack method utilizes affine transformations of intercepted signals, exploiting weaknesses inherent in the partially linear dynamic properties and symmetry of the nonlinear plant. The feasibility and potential impact of these attacks are validated through experiments using a Turtlebot 3 platform, highlighting the urgent need for sophisticated detection mechanisms and resilient control strategies to safeguard CPS against such threats. Furthermore, a novel approach for detection of these attacks called the state monitoring signature function (SMSF) is introduced. An example SMSF, a carefully designed function resilient to FDIA, is shown to be able to detect the presence of an FDIA through signatures based on system states. 

This article presents the Smart Quad, a tangible user interface (TUI) designed to enhance quadrilateral learning by integrating physical and digital educational tools. Developed through an iterative, user-centered design process, the Smart Quad is a tangible device that pairs with the PhET SimulationQuadrilateralcreating a multimodal, inclusive learning environment for geometry. We conducted a pilot study with four users with blindness or low vision (BLV) and a formal user study in two settings: a classroom study with 15 students from grades 6 to 8 and individual sessions with five students with BLV from grades 7 to 9. Our findings demonstrate the efficacy of the Smart Quad in facilitating hands-on, interactive learning experiences, particularly for students with BLV, and highlight the potential for TUIs to bridge gaps in mathematics education by supporting diverse learning needs and preferences. The results suggest that TUIs like the Smart Quad can significantly improve engagement and understanding of geometric concepts, offering a promising direction for future educational tools.