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Spam and phishing remain critical threats in cybersecurity, responsible for nearly 90% of security incidents. As these attacks grow in sophistication, the need for robust defensive mechanisms intensifies. Bayesian spam filters, like the widely adopted open-source SpamAssassin, are essential tools in this fight. However, the emergence of large language models (LLMs) such as ChatGPT presents new challenges. These models are not only powerful and accessible, but also inexpensive to use, raising concerns about their misuse in crafting sophisticated spam emails that evade traditional spam filters. This work aims to evaluate the robustness and effectiveness of SpamAssassin against LLM-modified email content. We developed a pipeline to test this vulnerability. Our pipeline modifies spam emails using GPT-3.5 Turbo and assesses SpamAssassin's ability to classify these modified emails correctly. The results show that SpamAssassin misclassified up to 73.7% of LLM-modified spam emails as legitimate. In contrast, a simpler dictionary-replacement attack showed a maximum success rate of only 0.4%. These findings highlight the significant threat posed by LLM-modified spam, especially given the cost-efficiency of such attacks (0.17 cents per email). This paper provides crucial insights into the vulnerabilities of current spam filters and the need for continuous improvement in cybersecurity measures., Comment: EAI International Conference on Digital Forensics & Cyber Crime 2024

We present an analysis method to search for exotic low-mass field (ELF) bursts generated during large energy astrophysical events such as supernovae, binary black hole or binary neutron star mergers, and fast radio bursts using the Global Network of Optical Magnetometers for Exotic physics searches (GNOME). In our model, the associated gravitational waves or electromagnetic signals herald the arrival of the ELF burst that interacts via coupling to the spin of fermions in the magnetometers. This enables GNOME to serve as a tool for multi-messenger astronomy. The algorithm employs a model-agnostic excess-power method to identify network-wide candidate events to be subjected to a model-dependent generalized likelihood-ratio test to determine their statistical significance. We perform the first search with this technique on GNOME data coincident with the binary black hole merger S200311bg detected by LIGO/Virgo on the 11th of March 2020 and find no significant events. We place the first lab-based limits on combinations of ELF production and coupling parameters.

The faces of a convex set owe their relevance to an interplay between convexity and topology that is systematically studied in the work of Rockafellar. Infinite-dimensional convex sets are excluded from this theory as their relative interiors may be empty. Shirokov and the present author answered this issue by proving that every point in a convex set lies in the relative algebraic interior of the face it generates. This theorem is proved here in a simpler way, connecting ideas scattered throughout the literature. This article summarizes and develops methods for faces and their relative algebraic interiors and applies them to spaces of probability measures., Comment: 18 pages, 1 figure, version 2: improved connection to the literature, comments welcomed

Weak coin flipping is an important cryptographic primitive -- it is the strongest known secure two-party computation primitive that classically becomes secure only under certain assumptions (e.g. computational hardness), while quantumly there exist protocols that achieve arbitrarily close to perfect security. This breakthrough result was established by Mochon in 2007 [arXiv:0711.4114]. However, his proof relied on the existence of certain unitary operators which was established by a non-constructive argument. Consequently, explicit protocols have remained elusive. In this work, we give exact constructions of related unitary operators. These, together with a new formalism, yield a family of protocols approaching perfect security thereby also simplifying Mochon's proof of existence. We illustrate the construction of explicit weak coin flipping protocols by considering concrete examples (from the aforementioned family of protocols) that are more secure than all previously known protocols., Comment: 51 pages (+ 9 appendix), 12 figures. This is a self-contained, concise version of our main results in arXiv:1811.02984 (STOC '19) and arXiv:1911.13283v2 (SODA '21). The Cryptology ePrint 2022/1101 is the comprehensive version, subsuming the above

In recent years, the diagnosis of gliomas has become increasingly complex. Analysis of glioma histopathology images using artificial intelligence (AI) offers new opportunities to support diagnosis and outcome prediction. To give an overview of the current state of research, this review examines 83 publicly available research studies that have proposed AI-based methods for whole-slide histopathology images of human gliomas, covering the diagnostic tasks of subtyping (23/83), grading (27/83), molecular marker prediction (20/83), and survival prediction (29/83). All studies were reviewed with regard to methodological aspects as well as clinical applicability. It was found that the focus of current research is the assessment of hematoxylin and eosin-stained tissue sections of adult-type diffuse gliomas. The majority of studies (52/83) are based on the publicly available glioblastoma and low-grade glioma datasets from The Cancer Genome Atlas (TCGA) and only a few studies employed other datasets in isolation (16/83) or in addition to the TCGA datasets (15/83). Current approaches mostly rely on convolutional neural networks (63/83) for analyzing tissue at 20x magnification (35/83). A new field of research is the integration of clinical data, omics data, or magnetic resonance imaging (29/83). So far, AI-based methods have achieved promising results, but are not yet used in real clinical settings. Future work should focus on the independent validation of methods on larger, multi-site datasets with high-quality and up-to-date clinical and molecular pathology annotations to demonstrate routine applicability.

Onion routing and mix networks are fundamental concepts to provide users with anonymous access to the Internet. Various corresponding solutions rely on the efficient Sphinx packet format. However, flaws in Sphinx's underlying proof strategy were found recently. It is thus currently unclear which guarantees Sphinx actually provides, and, even worse, there is no suitable proof strategy available. In this paper, we restore the security foundation for all these works by building a theoretical framework for Sphinx. We discover that the previously-used DDH assumption is insufficient for a security proof and show that the Gap Diffie-Hellman (GDH) assumption is required instead. We apply it to prove that a slightly adapted version of the Sphinx packet format is secure under the GDH assumption. Ours is the first work to provide a detailed, in-depth security proof for Sphinx in this manner. Our adaptations to Sphinx are necessary, as we demonstrate with an attack on sender privacy that would be possible otherwise.

The Next Generation Very Large Array (ngVLA) is a planned radio interferometer providing unprecedented sensitivity at wavelengths between 21 cm and 3 mm. Its 263 antenna element array will be spatially distributed across North America to enable both superb low surface brightness recovery and sub-milliarcsecond angular resolution imaging. The project was developed by the international astronomy community under the lead of the National Radio Astronomy Observatory (NRAO), and is anticipated to be built between 2027 and 2037. Two workshops have been held in 2022 and 2023 with the goal to discuss and consolidate the scientific interests in the ngVLA within the German astronomical community. This community paper constitutes a collection of 48 science ideas which the German community aims to pursue with the ngVLA in the 2030s. This is not a complete list and the ideas are not developed at the level of a "Science Book", such that the present document is mainly meant provide a basis for further discussion within the community. As such, additional contributions are welcome, and will be considered for inclusion in future revisions., Comment: Version 2.0 (status June 18, 2024): 169 pages, comments and future contributions welcome [v2.0: 7 new science cases added, some minor revisions to other chapters]

Machine learning and deep learning have been celebrating many successes in the application to biological problems, especially in the domain of protein folding. Another equally complex and important question has received relatively little attention by the machine learning community, namely the one of prediction of complex traits from genetics. Tackling this problem requires in-depth knowledge of the related genetics literature and awareness of various subtleties associated with genetic data. In this guide, we provide an overview for the machine learning community on current state of the art models and associated subtleties which need to be taken into consideration when developing new models for phenotype prediction. We use height as an example of a continuous-valued phenotype and provide an introduction to benchmark datasets, confounders, feature selection, and common metrics.

To what extent magnetic fields affect how molecular clouds (MCs) fragment and create dense structures is an open question. We present a numerical study of cloud fragmentation using the SILCC-Zoom simulations. These simulations follow the self-consistent formation of MCs in a few hundred parsec sized region of a stratified galactic disc; and include magnetic fields, self-gravity, supernova-driven turbulence, as well as a non-equilibrium chemical network. To discern the role of magnetic fields in the evolution of MCs, we study seven simulated clouds, five with magnetic fields, and two without, with a maximum resolution of 0.1 parsec. Using a dendrogram we identify hierarchical structures which form within the clouds. Overall, the magnetised clouds have more mass in a diffuse envelope with a number density between 1-100 cm$^{-3}$. We find that six out of seven clouds are sheet-like on the largest scales, as also found in recent observations, and with filamentary structures embedded within, consistent with the bubble-driven MC formation mechanism. Hydrodynamic simulations tend to produce more sheet-like structures also on smaller scales, while the presence of magnetic fields promotes filament formation. Analysing cloud energetics, we find that magnetic fields are dynamically important for less dense, mostly but not exclusively atomic structures (typically up to $\sim 100 - 1000$~cm$^{-3}$), while the denser, potentially star-forming structures are energetically dominated by self-gravity and turbulence. In addition, we compute the magnetic surface term and demonstrate that it is generally confining, and some atomic structures are even magnetically held together. In general, magnetic fields delay the cloud evolution and fragmentation by $\sim$ 1 Myr., Comment: 21 pages, 13 figures, accepted for publication in MNRAS

This article reports a comparative study of bulk and surface properties in the transition metal dichalcogenide 1T-TaS$_2$. When heating the sample, the surface displays an intermediate insulating phase that persists for $\sim 10$ K on top of a metallic bulk. The weaker screening of Coulomb repulsion and stiffer Charge Density Wave (CDW) explain such resilience of a correlated insulator in the topmost layers. Both time resolved ARPES and transient reflectivity are employed to investigate the dynamics of electrons and CDW collective motion. It follows that the amplitude mode is always stiffer at the surface and displays variable coupling to the Mott-Peierls band, stronger in the low temperature phase and weaker in the intermediate one.

Nanoelectromechanical systems offer unique functionalities in photonics: The ability to elastically and reversibly deform dielectric beams with subwavelength dimensions enable electrical control of the propagation of light with a power consumption orders of magnitude below that of competing technologies, such as thermo-optic tuning. We present a study of the design, fabrication, and characterization of compact electrostatic comb-drive actuators tailored for integrated nanoelectromechanical silicon photonic circuits. Our design has a footprint of $1.2 \times 10^{3} \mu$m$^{2}$ and is found to reach displacements beyond 50 nm at 5 V with a mechanical resonance above 200 kHz, or, using different spring constants and skeletonization, a mechanical resonance above 2.5 MHz with displacements beyond 50 nm at 28 V. This is sufficient to induce very large phase shifts and other optical effects in nanoelectromechanical reconfigurable photonic circuits., Comment: 7 pages, 4 figure, 1 supplementary animation

The evolutionary relationships and mechanisms governing the behavior of the wide variety of luminous stars populating the upper H-R diagram are not well established. Luminous blue variables (LBVs) are particularly rare, with only a few dozen identified in the Milky Way and nearby galaxies. Since 2012, the Barber Observatory Luminous Stars Survey has monitored more than 100 luminous targets in M33, including M33C-4119 which has recently undergone photometric and spectroscopic changes consistent with an S Doradus eruption of an LBV., Comment: 6 pages, 4 figures, 1 Table

Numerous observations suggest that there exist undiscovered beyond-the-Standard-Model particles and fields. Because of their unknown nature, these exotic particles and fields could interact with Standard Model particles in many different ways and assume a variety of possible configurations. Here we present an overview of the Global Network of Optical Magnetometers for Exotic physics searches (GNOME), our ongoing experimental program designed to test a wide range of exotic physics scenarios. The GNOME experiment utilizes a worldwide network of shielded atomic magnetometers (and, more recently, comagnetometers) to search for spatially and temporally correlated signals due to torques on atomic spins from exotic fields of astrophysical origin. We survey the temporal characteristics of a variety of possible signals currently under investigation such as those from topological defect dark matter (axion-like particle domain walls), axion-like particle stars, solitons of complex-valued scalar fields (Q-balls), stochastic fluctuations of bosonic dark matter fields, a solar axion-like particle halo, and bursts of ultralight bosonic fields produced by cataclysmic astrophysical events such as binary black hole mergers., Comment: 22 pages, 12 figures, submitted to Annalen der Physik

We report on a search for a new, short-range, spin-dependent interaction using a modified version of the experimental apparatus used to measure the permanent neutron electric dipole moment at the Paul Scherrer Institute. This interaction, which could be mediated by axion-like particles, concerned the unpolarized nucleons (protons and neutrons) near the material surfaces of the apparatus and polarized ultracold neutrons stored in vacuum. The dominant systematic uncertainty resulting from magnetic-field gradients was controlled to an unprecedented level of approximately 4 pT/cm using an array of optically-pumped cesium vapor magnetometers and magnetic-field maps independently recorded using a dedicated measurement device. No signature of a theoretically predicted new interaction was found, and we set a new limit on the product of the scalar and the pseudoscalar couplings $g_sg_p\lambda^2 < 8.3 \times 10^{-28}\,\text{m}^2$ (95% C.L.) in a range of $5\,\mu\text{m} < \lambda < 25\,\text{mm}$ for the monopole-dipole interaction. This new result confirms and improves our previous limit by a factor of 2.7 and provides the current tightest limit obtained with free neutrons.

Despite tremendous progress in the research on self-assembled nanotechnological building blocks such as macromolecules, nanowires, and two-dimensional materials, synthetic self-assembly methods bridging nanoscopic to macroscopic dimensions remain unscalable and inferior to biological self-assembly. In contrast, planar semiconductor technology has had an immense technological impact owing to its inherent scalability, yet it appears unable to reach the atomic dimensions enabled by self-assembly. Here we use surface forces including Casimir-van der Waals interactions to deterministically self-assemble and self-align suspended silicon nanostructures with void features well below the length scales possible with conventional lithography and etching, despite using nothing more than conventional lithography and etching. The method is remarkably robust and the threshold for self-assembly depends monotonically on all governing parameters across thousands of measured devices. We illustrate the potential of these concepts by fabricating nanostructures, which are impossible to make with any other known method: Waveguide-coupled high-Q silicon photonic cavities that confine telecom photons to 2 nm air gaps with an aspect ratio of 100, corresponding to mode volumes more than 100 times below the diffraction limit. Scanning transmission electron microscopy measurements confirm the ability to build devices even with subnanometer dimensions. Our work constitutes the first steps towards a new generation of fabrication technology that combines the atomic dimensions enabled by self-assembly with the scalability of planar semiconductors.

The strong anharmonicity and high coherence times inherent to fluxonium superconducting circuits are beneficial for quantum information processing. In addition to requiring high-quality physical qubits, a quantum processor needs to be assembled in a manner that minimizes crosstalk and decoherence. In this paper, we report work on fluxonium qubits packaged in a flip-chip architecture, where a classical control and readout chip is bump-bonded to the quantum chip, forming a multi-chip module (MCM). The modular approach allows for improved connectivity between the qubits and control/readout elements, and separate fabrication processes. We characterize the coherence properties of the individual fluxonium qubits, demonstrate high fidelity single-qubit gates with 6 ns microwave pulses (without DRAG), and identify the main decoherence mechanisms to improve on the reported results.

Neuromorphic computing implementing spiking neural networks (SNN) is a promising technology for reducing the footprint of optical transceivers, as required by the fast-paced growth of data center traffic. In this work, an SNN nonlinear demapper is designed and evaluated on a simulated intensity-modulation direct-detection link with chromatic dispersion. The SNN demapper is implemented in software and on the analog neuromorphic hardware system BrainScaleS-2 (BSS-2). For comparison, linear equalization (LE), Volterra nonlinear equalization (VNLE), and nonlinear demapping by an artificial neural network (ANN) implemented in software are considered. At a pre-forward error correction bit error rate of 2e-3, the software SNN outperforms LE by 1.5 dB, VNLE by 0.3 dB and the ANN by 0.5 dB. The hardware penalty of the SNN on BSS-2 is only 0.2 dB, i.e., also on hardware, the SNN performs better than all software implementations of the reference approaches. Hence, this work demonstrates that SNN demappers implemented on electrical analog hardware can realize powerful and accurate signal processing fulfilling the strict requirements of optical communications., Comment: 9 pages, 5 figures, accepted for publication by the IEEE/Optica Publishing Group Journal of Lightwave Technology

Kippenhahn discovered that the numerical range of a complex square matrix is the convex hull of a plane real algebraic curve. Here, we present an example of a convex set, which has a similar algebraic description as the numerical range, whereas the analogue of Kippenhahn's construction fails regarding isolated, singular points of the curve. This example prompted us to carefully review Kippenhahn's assertion and to highlight aspects of a complete proof that was achieved with methods of convex geometry and real algebraic geometry., Comment: 10 pages, accepted for publication in the proceedings of IWOTA 2022

We introduce Manifold tensor categories, which make precise the notion of a tensor category with a manifold of simple objects. A basic example is the category of vector spaces graded by a Lie group. Unlike classic tensor category theory, our setup keeps track of the smooth (and topological) structure of the manifold of simple objects. We set down the necessary definitions for Manifold tensor categories and a generalisation we term Orbifold tensor categories. We also construct a number of examples, most notably two families of examples we call Interpolated Tambara-Yamagami categories and Interpolated quantum group categories. Finally, we show conditions under which pointwise duality data in an Orbifold tensor category automatically assembles into smooth duality data. Our proof uses the classic Implicit function theorem from differential geometry., Comment: This document is an edited excerpt of the author's thesis