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High harmonic generation is a resource of extremely broad frequency combs of ultrashort light pulses. The non-classical nature of this new quantum source has been recently evidenced in semiconductors by showing that high harmonic generation generates multimode squeezed states of light. Applications in quantum information science require the knowledge of the mode structure of the created states, defining how the quantum properties distribute over the spectral modes. To achieve that, an effective Schmidt decomposition of the reduced photonic state is performed on a tripartite harmonic set by simultaneously measuring the second- and third-order intensity correlation function. A relatively high Schmidt number is predicated which indicates a rich mode structure, a useful resource in quantum technology. By modelling our data with a multimode displaced squeezed state, we retrieve the dependencies of the measured correlation as a function of the high harmonic driving laser intensity. The effective high-harmonic mode distribution is retrieved, and the strength of the contributing squeezing modes is estimated. Additionally, we demonstrate a significant violation of a Cauchy-Schwarz-type inequality for three biseparable partitions by multiple standard deviations. Our results confirm and extend the analysis of non-classical properties in semiconductor high-harmonic generation. The source operates at room temperature with compact lasers, and it could become a useful resource for future applications in quantum technologies., Comment: 25 pages, 9 figures

We study the quantum Hall effect (QHE) in the three-dimensional topological insulator HgTe, which features topological Dirac-type surface states in a bulk gap opened by strain. Despite the co-existence of multiple carrier subsystems, the system exhibits perfectly quantized Hall plateaus at high magnetic fields. Here we study the system using three different experimental techniques: Transport experiments, capacitance measurements including the quantum capacitance, and current distribution measurements using electrostatically sensitive scanning probe microscopy. Our key finding is that at sufficiently high magnetic fields, the different electronic subsystems merge into one, and the current in a quantum Hall plateau is distributed across the entire width of the Hall bar device., Comment: 9 pages, 9 figures. Submitted to PRR

Driven by advances in recording technology, large-scale high-dimensional datasets have emerged across many scientific disciplines. Especially in biology, clustering is often used to gain insights into the structure of such datasets, for instance to understand the organization of different cell types. However, clustering is known to scale poorly to high dimensions, even though the exact impact of dimensionality is unclear as current benchmark datasets are mostly two-dimensional. Here we propose MNIST-Nd, a set of synthetic datasets that share a key property of real-world datasets, namely that individual samples are noisy and clusters do not perfectly separate. MNIST-Nd is obtained by training mixture variational autoencoders with 2 to 64 latent dimensions on MNIST, resulting in six datasets with comparable structure but varying dimensionality. It thus offers the chance to disentangle the impact of dimensionality on clustering. Preliminary common clustering algorithm benchmarks on MNIST-Nd suggest that Leiden is the most robust for growing dimensions.

Light clients are gaining increasing attention in the literature since they obviate the need for users to set up dedicated blockchain full nodes. While the literature features a number of light client instantiations, most light client protocols optimize for long offline phases and implicitly assume that the block headers to be verified are signed by highly dynamic validators. In this paper, we show that (i) most light clients are rarely offline for more than a week, and (ii) validators are unlikely to drastically change in most permissioned blockchains and in a number of permissionless blockchains, such as Cosmos and Polkadot. Motivated by these findings, we propose a novel practical system that optimizes for such realistic assumptions and achieves minimal communication and computational costs for light clients when compared to existing protocols. By means of a prototype implementation of our solution, we show that our protocol achieves a reduction by up to $90$ and $40000\times$ (respectively) in end-to-end latency and up to $1000$ and $10000\times$ (respectively) smaller proof size when compared to two state-of-the-art light client instantiations from the literature.

We study the phase between the oscillating (rf) excitation field and the detected (light) power modulation in scalar rf-driven optically pumped magnetometers (OPMs), in particular in the $M_x$ configuration. While the static dependence of the demodulation phase on the direction of the external static magnetic field vector can be largely overcome by aligning the oscillating rf field along the light propagation direction, we show that a dynamic (transient) phase response can appear under tilts of the magnetic field. We analytically solve the corresponding modified Bloch equation and obtain agreement with experimental observations in an $M_x$ magnetometer setup using a paraffin-coated Cs vapor cell. The results reveal fundamental limitations of $M_x$ magnetometers in terms of response time and accuracy, in particular when operated with active electronic feedback, e.g., using a phase-locked loop. Thus, the work is highly relevant in important magnetometry applications, where the direction of the quasi-static magnetic field of interest is not known \textit{a priori} and/or not constant over time, or in measurements, in which a large detection bandwidth is paramount. Such conditions are encountered, e.g., in geomagnetic surveying, in particular with mobile platforms., Comment: 11 pages, 9 figures

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, version 3: minor changes, 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