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In the search for accurate approximate solutions of the many-body Schr\"odinger equation, reduced density matrices play an important role, as they allow to formulate approximate methods with polynomial scaling in the number of particles. However, these methods frequently encounter the issue of $N$-representability, whereby in self-consistent applications of the methods, the reduced density matrices become unphysical. A number of algorithms have been proposed in the past to restore a given set of $N$-representability conditions once the reduced density matrices become defective. However, these purification algorithms have either ignored symmetries of the Hamiltonian related to conserved quantities, or have not incorporated them in an efficient way, thereby modifying the reduced density matrix to a greater extent than is necessary. In this paper, we present an algorithm capable of efficiently performing all of the following tasks in the least invasive manner: restoring a given set of $N$-representability conditions, maintaining contraction consistency between successive orders of reduced density matrices, and preserving all conserved quantities. We demonstrate the superiority of the present purification algorithm over previous ones in the context of the time-dependent two-particle reduced density matrix method applied to the quench dynamics of the Fermi-Hubbard model., Comment: 13 pages, 4 figures

This paper presents a rigorous derivation of equations to evaluate the macroscopic stress tensor, the couple stress tensor, and the flux vector equivalent to underlying microscopic fields in continuous and discrete heterogeneous systems with independent displacements and rotations. Contrary to the classical asymptotic expansion homogenization, finite size representative volume is considered. First, the macroscopic quantities are derived for a heterogeneous Cosserat continuum. The resulting continuum equations are discretized to provide macroscopic quantities in discrete heterogeneous systems. Finally, the expressions for discrete system are derived once again, this time considering the discrete nature directly. The formulations are presented in two variants, considering either internal or external forces, couples, and fluxes. The derivation is based on the virtual work equivalence and elucidates the fundamental significance of the couple stress tensor in the context of balance equations and admissible virtual deformation modes. Notably, an additional term in the couple stress tensor formula emerges, explaining its dependence on the reference system and position of the macroscopic point. The resulting equations are verified by comparing their predictions with known analytical solutions and results of other numerical models under both steady state and transient conditions., Comment: 26 pages, 15 figures

The growing importance of real-time simulation in the medical field has exposed the limitations and bottlenecks inherent in the digital representation of complex biological systems. This paper presents a novel methodology aimed at advancing current lines of research in soft tissue simulation. The proposed approach introduces a hybrid model that integrates the geometric bias of graph neural networks with the physical bias derived from the imposition of a metriplectic structure as soft and hard constrains in the architecture, being able to simulate hepatic tissue with dissipative properties. This approach provides an efficient solution capable of generating predictions at high feedback rate while maintaining a remarkable generalization ability for previously unseen anatomies. This makes these features particularly relevant in the context of precision medicine and haptic rendering. Based on the adopted methodologies, we propose a model that predicts human liver responses to traction and compression loads in as little as 7.3 milliseconds for optimized configurations and as fast as 1.65 milliseconds in the most efficient cases, all in the forward pass. The model achieves relative position errors below 0.15\%, with stress tensor and velocity estimations maintaining relative errors under 7\%. This demonstrates the robustness of the approach developed, which is capable of handling diverse load states and anatomies effectively. This work highlights the feasibility of integrating real-time simulation with patient-specific geometries through deep learning, paving the way for more robust digital human twins in medical applications.

Constructing precise global maps is a key task in robotics and is required for localization, surveying, monitoring, or constructing digital twins. To build accurate maps, data from mobile 3D LiDAR sensors is often used. Mapping requires correctly aligning the individual point clouds to each other to obtain a globally consistent map. In this paper, we investigate the problem of multi-scan alignment to obtain globally consistent point cloud maps. We propose a 3D LiDAR bundle adjustment approach to solve the global alignment problem and jointly optimize the available data. Utilizing a continuous-time trajectory allows us to consider the ego-motion of the LiDAR scanner while recording a single scan directly in the least squares adjustment. Furthermore, pruning the search space of correspondences and utilizing out-of-core circular buffer enables our approach to align thousands of point clouds efficiently. We successfully align point clouds recorded with a handheld LiDAR, as well as ones mounted on a vehicle, and are able to perform multi-session alignment., Comment: Submitted to ICRA 2025

Artificial intelligence-based chatbots are increasingly influencing physics education due to their ability to interpret and respond to textual and visual inputs. This study evaluates the performance of two large multimodal model-based chatbots, ChatGPT-4 and ChatGPT-4o on the Brief Electricity and Magnetism Assessment (BEMA), a conceptual physics inventory rich in visual representations such as vector fields, circuit diagrams, and graphs. Quantitative analysis shows that ChatGPT-4o outperforms both ChatGPT-4 and a large sample of university students, and demonstrates improvements in ChatGPT-4o's vision interpretation ability over its predecessor ChatGPT-4. However, qualitative analysis of ChatGPT-4o's responses reveals persistent challenges. We identified three types of difficulties in the chatbot's responses to tasks on BEMA: (1) difficulties with visual interpretation, (2) difficulties in providing correct physics laws or rules, and (3) difficulties with spatial coordination and application of physics representations. Spatial reasoning tasks, particularly those requiring the use of the right-hand rule, proved especially problematic. These findings highlight that the most broadly used large multimodal model-based chatbot, ChatGPT-4o, still exhibits significant difficulties in engaging with physics tasks involving visual representations. While the chatbot shows potential for educational applications, including personalized tutoring and accessibility support for students who are blind or have low vision, its limitations necessitate caution. On the other hand, our findings can also be leveraged to design assessments that are difficult for chatbots to solve.

To efficiently manage plant diseases, Agriculture Cyber-Physical Systems (A-CPS) have been developed to detect and localize disease infestations by integrating the Internet of Agro-Things (IoAT). By the nature of plant and pathogen interactions, the spread of a disease appears as a focus with density of infected plants and intensity of infection diminishing outwards. This gradient of infection needs variable rate and precision pesticide spraying to efficiently utilize resources and effectively handle the diseases. This article, SprayCraft presents a graph based method for disease management A-CPS to identify disease hotspots and compute near optimal path for a spraying drone to perform variable rate precision spraying. It uses graph to represent the diseased locations and their spatial relation, Message Passing is performed over the graph to compute the probability of a location to be a disease hotspot. These probabilities also serve as disease intensity measures and are used for variable rate spraying at each location. Whereas, the graph is utilized to compute tour path by considering it as Traveling Salesman Problem (TSP) for precision spraying by the drone. Proposed method has been validated on synthetic data of locations of diseased locations in a farmland., Comment: 28 pages, 40 figures, 2 tables

Hamiltonian Truncation (HT) methods provide a powerful numerical approach for investigating strongly coupled QFTs. In this work, we develop HT techniques to analyse a specific Renormalization Group (RG) flow recently proposed in Refs. [1, 3]. These studies put forward Ginzburg-Landau descriptions for the conformal minimal models $M(3,10)$ and $M(3,8)$, as well as the RG flow connecting them. Specifically, the RG-flow is defined by deforming the $M(3,10)$ with the relevant primary operator $\phi_{1,7}$ (whose indices denote its position in the Kac table), yielding $M(3,10)+ \phi_{1,7}$. From the perspective of HT, realising such an RG-flow presents significant challenges, as the $\phi_{1,7}$ deformation requires renormalizing the UV theory up to third order in the coupling constant of the deformation. In this study, we carry out the necessary calculations to formulate HT for this theory and numerically investigate the spectrum of $M(3,10)+ \phi_{1,7}$ in the large coupling regime, finding strong evidence in favour of the proposed flow., Comment: 46 pages, 6 figures and 6 tables

Although pandemics are often studied as if populations are well-mixed, disease transmission networks exhibit a multi-scale structure stretching from the individual all the way up to the entire globe. The COVID-19 pandemic has led to an intense debate about whether interventions should prioritize public health or the economy, leading to a surge of studies analyzing the health and economic costs of various response strategies. Here we show that describing disease transmission in a self-similar (fractal) manner across multiple geographic scales allows for the design of multi-scale containment measures that substantially reduce both these costs. We characterize response strategies using multi-scale reproduction numbers -- a generalization of the basic reproduction number $R_0$ -- that describe pandemic spread at multiple levels of scale and provide robust upper bounds on disease transmission. Stable elimination is guaranteed if there exists a scale such that the reproduction number among regions of that scale is less than $1$, even if the basic reproduction number $R_0$ is greater than $1$. We support our theoretical results using simulations of a heterogeneous SIS model for disease spread in the United States constructed using county-level commuting, air travel, and population data.

The recent discovery of superconductivity in $\rm La_3Ni_2O_7$ has attracted significant attention due to its high critical temperature and analogy to cuprate oxides. The oxidation and spin states of Ni ions are among the most important local properties in this compound, extensively discussed in the context of its superconductivity. Despite their direct link to the electron filling configurations of the relevant $\rm 3d_{x^2-y^2}$ and $\rm 3d_{z^2}$ orbitals, these local electronic properties of $\rm La_3Ni_2O_7$ yet to be systematically investigated. In this work, we address this issue using x-ray absorption spectroscopy (XAS) and x-ray emission spectroscopy (XES) measurements under pressure. Comparison of Ni \textit{K}-edge XAS and $\rm K\beta$ XES with the reference spectra of $\rm NiO$ and $\rm LaNiO_3$ shows that Ni ions, with an average valence of $\sim 2.53+$, are in a low-spin ($\rm S = 1/2$) ground state under ambient conditions. High pressure XAS and XES data clearly show that the oxidation ($\sim 2.5+$) and spin ($\rm S = 1/2$) states of Ni ions remain stable across the investigated pressure (up to 30 GPa) and temperature (down to 10 K) ranges, ruling out previously proposed spin transition scenarios.

Magnetic fields remain one of the least understood aspects of exoplanetary systems. A deeper understanding of planetary dynamos and the evolution of surface magnetic properties throughout a planet's lifetime is a key scientific purpose, with implications for planetary evolution, habitability, and atmospheric dynamics. This study models the evolution of magnetic fields generated by dynamo action in cold giant gaseous planets. We solve the resistive magnetohydrodynamic (MHD) equations under anelastic approximation with a 3D pseudo-spectral spherical shell MHD code. We employ 1D thermodynamical hydrostatic profiles taken from gas giant evolutionary models as the background states of our MHD models. Numerical integration leads to saturated dynamo solutions. Such calculations are performed with radial profiles corresponding to different planetary ages so that we can interpret them as different snapshots of the magnetoconvection evolution during the long-term planetary evolution. We characterize magnetic fields across different evolutionary stages of a cold gaseous planet in terms of topology and strength. We find the occurrence of a transition from multipolar to dipolar-dominated dynamo regime throughout the life of a Jovian planet. During the planetary evolution and the cooling down phase, we observe a decrease in the average magnetic field strength near the dynamo surface as $\sim t^{-0.2}-t^{-0.3}$, a trend compatible with previously proposed scaling laws. We also find that some dimensionless parameters evolve differently for the multipolar to dipolar branch, possibly reflecting a force balance change. This approach can be extended to study hot gaseous planets, offering a versatile tool for interpreting the magnetic properties of giant planets., Comment: 19 pages, submitted for publication, comments are welcome

We present a novel approach to generating mathematical conjectures using Large Language Models (LLMs). Focusing on the solubilizer, a relatively recent construct in group theory, we demonstrate how LLMs such as ChatGPT, Gemini, and Claude can be leveraged to generate conjectures. These conjectures are pruned by allowing the LLMs to generate counterexamples. Our results indicate that LLMs are capable of producing original conjectures that, while not groundbreaking, are either plausible or falsifiable via counterexamples, though they exhibit limitations in code execution., Comment: 23 pages, 10 figures, NeurIPS MathAI Workshop 2024

To comply with high productivity demands, software developers reuse free open-source software (FOSS) code to avoid reinventing the wheel when incorporating software features. The reliance on FOSS reuse has been shown to improve productivity and the quality of delivered software; however, reusing FOSS comes at the risk of exposing software projects to public vulnerabilities. Massacci and Pashchenko have explored this trade-off in the Java ecosystem through the lens of technical leverage: the ratio of code borrowed from FOSS over the code developed by project maintainers. In this paper, we replicate the work of Massacci and Pashchenko and we expand the analysis to include level-1 transitive dependencies to study technical leverage in the fastest-growing NPM ecosystem. We investigated 14,042 NPM library releases and found that both opportunities and risks of technical leverage are magnified in the NPM ecosystem. Small-medium libraries leverage 2.5x more code from FOSS than their code, while large libraries leverage only 3\% of FOSS code in their projects. Our models indicate that technical leverage shortens the release cycle for small-medium libraries. However, the risk of vulnerability exposure is 4-7x higher for libraries with high technical leverage. We also expanded our replication study to include the first level of transitive dependencies, and show that the results still hold, albeit with significant changes in the magnitude of both opportunities and risks of technical leverage. Our results indicate the extremes of opportunities and risks in NPM, where high technical leverage enables fast releases but comes at the cost of security risks., Comment: Manuscript accepted for publication in Empirical Software Engineering (EMSE)

The merger of a black hole (BH) and a neutron star (NS) in most cases is expected to leave no material around the remnant BH; therefore, such events are often considered as sources of gravitational waves without electromagnetic counterparts. However, a bright counterpart can emerge if the NS is strongly magnetized, as its external magnetosphere can experience radiative shocks and magnetic reconnection during/after the merger. We use magnetohydrodynamic simulations in the dynamical spacetime of a merging BH-NS binary to investigate its magnetospheric dynamics. We find that the magnetosphere develops compressive waves that steepen into shocks. After swallowing the NS, the BH acquires a magnetosphere that quickly evolves into a split monopole configuration and then undergoes an exponential decay (balding), enabled by magnetic reconnection and also assisted by the ring-down of the remnant BH. This spinning BH drags the split monopole into rotation, forming a transient pulsar-like state. It emits a striped wind if the swallowed magnetic dipole moment is inclined to the spin axis. We predict two types of transients from this scenario: (1) a fast radio burst emitted by the shocks as they expand to large radii and (2) an X/gamma-ray burst emitted by the $e^\pm$ outflow heated by magnetic dissipation., Comment: 18 pages, 12 figures, submitted to ApJL

Background. In modern software development, the use of external libraries and packages is increasingly prevalent, streamlining the software development process and enabling developers to deploy feature-rich systems with little coding. While this reliance on reusing code offers substantial benefits, it also introduces serious risks for deployed software in the form of malicious packages - harmful and vulnerable code disguised as useful libraries. Aims. Popular ecosystems, such PyPI, receive thousands of new package contributions every week, and distinguishing safe contributions from harmful ones presents a significant challenge. There is a dire need for reliable methods to detect and address the presence of malicious packages in these environments. Method. To address these challenges, we propose a data-driven approach that uses machine learning and static analysis to examine the package's metadata, code, files, and textual characteristics to identify malicious packages. Results. In evaluations conducted within the PyPI ecosystem, we achieved an F1-measure of 0.94 for identifying malicious packages using a stacking ensemble classifier. Conclusions. This tool can be seamlessly integrated into package vetting pipelines and has the capability to flag entire packages, not just malicious function calls. This enhancement strengthens security measures and reduces the manual workload for developers and registry maintainers, thereby contributing to the overall integrity of the ecosystem.

Sulfide solid state electrolytes are promising candidates to realize all solid state batteries due to their superior ionic conductivity and excellent ductility. However, their hypersensitivity to moisture requires processing environments that are not compatible with todays lithium ion battery manufacturing infrastructure. Herein, we present a reversible surface modification strategy that enables the processability of sulfide SSEs under humid ambient air. We demonstrate that a long chain alkyl thiol, undecanethiol, is chemically compatible with the electrolyte with negligible impact on its ion conductivity. Importantly, the thiol modification extends the amount of time that the sulfide SSE can be exposed to air with 33 percent relative humidity with limited degradation of its structure while retaining a conductivity of above 1 mS per cm for up to 2 days, a more than 100 fold improvement in protection time over competing approaches. Experimental and computational results reveal that the thiol group anchors to the SSE surface, while the hydrophobic hydrocarbon tail provides protection by repelling water. The modified Li6PS5Cl SSE maintains its function after exposure to ambient humidity when implemented in a Li0.5In LiNi0.8Co0.1Mn0.1O2 ASSB. The proposed protection strategy based on surface molecular interactions represents a major step forward towards cost competitive and energy efficient sulfide SSE manufacturing for ASSB applications., Comment: 38 pages, 6 figures

Spiking neural networks (SNNs) emulated on dedicated neuromorphic accelerators promise to offer energy-efficient signal processing. However, the neuromorphic advantage over traditional algorithms still remains to be demonstrated in real-world applications. Here, we describe an intensity-modulation, direct-detection (IM/DD) task that is relevant to high-speed optical communication systems used in data centers. Compared to other machine learning-inspired benchmarks, the task offers several advantages. First, the dataset is inherently time-dependent, i.e., there is a time dimension that can be natively mapped to the dynamic evolution of SNNs. Second, small-scale SNNs can achieve the target accuracy required by technical communication standards. Third, due to the small scale and the defined target accuracy, the task facilitates the optimization for real-world aspects, such as energy efficiency, resource requirements, and system complexity.

Code reviews are an integral part of software development and have been recognized as a crucial practice for minimizing bugs and favouring higher code quality. They serve as an important checkpoint before committing code and play an essential role in knowledge transfer between developers. However, code reviews can be time-consuming and can stale the development of large software projects. In a recent study, Guo et al. assessed how ChatGPT3.5 can help the code review process. They evaluated the effectiveness of ChatGPT in automating the code refinement tasks, where developers recommend small changes in the submitted code. While Guo et al. 's study showed promising results, proprietary models like ChatGPT pose risks to data privacy and incur extra costs for software projects. In this study, we explore alternatives to ChatGPT in code refinement tasks by including two open-source, smaller-scale large language models: CodeLlama and Llama 2 (7B parameters). Our results show that, if properly tuned, the Llama models, particularly CodeLlama, can achieve reasonable performance, often comparable to ChatGPT in automated code refinement. However, not all code refinement tasks are equally successful: tasks that require changing existing code (e.g., refactoring) are more manageable for models to automate than tasks that demand new code. Our study highlights the potential of open-source models for code refinement, offering cost-effective, privacy-conscious solutions for real-world software development.

We investigate the nonlinear dynamics of vertically emitting Kerr microcavities under detuned optical injection, considering the impact of slow thermal effects. Our model integrates thermal detuning caused by refractive index shifts due to heating. Through numerical and analytical approaches, we uncover a rich spectrum of dynamical behaviors, including excitable thermo-optical pulses, mixed-mode oscillations, and chaotic spiking, governed by a higher-dimensional canard scenario. Introducing a long external feedback loop with time delays comparable to the microcavity photon lifetime but shorter than thermal relaxation timescales, reveals how delay affects excitability and stabilizes temporal localized states. Our findings extend the understanding of excitable systems, demonstrating how thermal and feedback mechanisms interplay to shape nonlinear optical dynamics. Further, our approach paves the way for the study of cavity stabilization and cavity cooling using an additional control beam.

Medical patient data is always multimodal. Images, text, age, gender, histopathological data are only few examples for different modalities in this context. Processing and integrating this multimodal data with deep learning based methods is of utmost interest due to its huge potential for medical procedure such as diagnosis and patient treatment planning. In this work we retrain a multimodal transformer-based model for disease classification. To this end we use the text-image pair dataset from OpenI consisting of 2D chest X-rays associated with clinical reports. Our focus is on fusion methods for merging text and vision information extracted from medical datasets. Different architecture structures with a LLaMA II backbone model are tested. Early fusion of modality specific features creates better results with the best model reaching 97.10% mean AUC than late fusion from a deeper level of the architecture (best model: 96.67% mean AUC). Both outperform former classification models tested on the same multimodal dataset. The newly introduced multimodal architecture can be applied to other multimodal datasets with little effort and can be easily adapted for further research, especially, but not limited to, the field of medical AI., Comment: 9 pages, 6 figures, conference: AIRoV -- The First Austrian Symposium on AI, Robotics, and Vision 25.-27.3.2024, Innsbruck

Recent advances in Diffusion Models have enabled the generation of images from text, with powerful closed-source models like DALL-E and Midjourney leading the way. However, open-source alternatives, such as StabilityAI's Stable Diffusion, offer comparable capabilities. These open-source models, hosted on Hugging Face, come equipped with ethical filter protections designed to prevent the generation of explicit images. This paper reveals first their limitations and then presents a novel text-based safety filter that outperforms existing solutions. Our research is driven by the critical need to address the misuse of AI-generated content, especially in the context of information warfare. DiffGuard enhances filtering efficacy, achieving a performance that surpasses the best existing filters by over 14%.

Accurate electrical load forecasting is crucial for optimizing power system operations, planning, and management. As power systems become increasingly complex, traditional forecasting methods may fail to capture the intricate patterns and dependencies within load data. Machine learning (ML) techniques have emerged as powerful alternatives, offering superior prediction accuracy and the ability to model non-linear and complex temporal relationships. This study presents a comprehensive comparison of prominent ML models: feedforward neural networks, recurrent neural networks, long short-term memory networks, gated recurrent units, and the attention temporal graph convolutional network; for short-term load forecasting of the Energy Corridor distribution system in Houston, Texas. Using a 24-hour look-back window, we train the models on datasets spanning one and five years, to predict the load demand for the next hour and assess performance. Our findings aim to identify the most effective ML approach for accurate load forecasting, contributing to improved grid reliability and system optimization.

We initiate a systematic study of utilizing predictions to improve over approximation guarantees of classic algorithms, without increasing the running time. We propose a systematic method for a wide class of optimization problems that ask to select a feasible subset of input items of minimal (or maximal) total weight. This gives simple (near-)linear time algorithms for, e.g., Vertex Cover, Steiner Tree, Min-Weight Perfect Matching, Knapsack, and Clique. Our algorithms produce optimal solutions when provided with perfect predictions and their approximation ratios smoothly degrade with increasing prediction error. With small enough prediction error we achieve approximation guarantees that are beyond reach without predictions in the given time bounds, as exemplified by the NP-hardness and APX-hardness of many of the above problems. Although we show our approach to be optimal for this class of problems as a whole, there is a potential for exploiting specific structural properties of individual problems to obtain improved bounds; we demonstrate this on the Steiner Tree problem. We conclude with an empirical evaluation of our approach.

To achieve net-zero carbon emissions, electrification in the transportation sector plays an important role. Significant increase of electric vehicles (EV) has been observed nationally and globally. While the transition to EVs presents substantial environmental benefits, it would lead to several challenges to the power grid due to EV charging activities. Growing EVs greatly increase peak loads on residential grids, particularly during evening charging periods. This surge can result in operational challenges, including greater voltage drops, increased power losses, and potential overloading violations, compromising grid reliability and efficiency. This study focuses on determining ampacity violations, and analyzing line loading levels in a 240-bus distribution system with 1120 customers, located in the Midwest U.S. By simulating a range of charging scenarios and evaluating EV chargers with varying power capacities under different distribution system voltage levels, this research aims to identify lines at risk of ampacity violations for various EV charging penetration rates up to 100%. The findings will provide valuable insights for utilities and grid operators, informing strategies for voltage level adjustments and necessary infrastructure reinforcements to effectively accommodate the growing energy demands associated with widespread EV adoption.

Symmetries of quantum field theories are characterized by topological defects/operators organized into a higher category. In this paper we consider the Axion-Maxwell field theory in four dimensions and, building on the construction of its topological defects by Choi, Lam, Shao, Hidaka, Nitta and Yokokura, we discuss field theoretical methods to compute some aspects of the higher structure of such category. In particular, we determine explicitly the generalized $F$-symbols for the non-invertible electric 1-form symmetry of the theory. Along the way we clarify various aspects of the bottom-up worldvolume approach towards the calculus of defects., Comment: 68 pages, 17 figures

Global Categorical Symmetries are a powerful new tool for analyzing quantum field theories. This volume compiles lecture notes from the 2022 and 2023 summer schools on Global Categorical Symmetries, held at the Perimeter Institute for Theoretical Physics and at the Swiss Map Research Station in Les Diableret. Specifically, this volume collects the lectures: * An introduction to symmetries in quantum field theory, Kantaro Ohmori * Introduction to anomalies in quantum field theory, Clay C\'ordova * Symmetry Categories 101, Michele Del Zotto * Applied Cobordism Hypothesis, David Jordan * Finite symmetry in QFT, Daniel S. Freed These volumes are devoted to interested newcomers: we only assume (basic) knowledge of quantum field theory (QFT) and some relevant maths. We try to give appropriate references for non-standard materials that are not covered. Our aim in this first volume is to illustrate some of the main questions and ideas together with some of the methods and the techniques necessary to begin exploring global categorical symmetries of QFTs., Comment: Lecture notes from the Summer Schools of the Simons Collaboration on Global Categorical Symmetries

Segmentation of cardiac magnetic resonance images (MRI) is crucial for the analysis and assessment of cardiac function, helping to diagnose and treat various cardiovascular diseases. Most recent techniques rely on deep learning and usually require an extensive amount of labeled data. To overcome this problem, few-shot learning has the capability of reducing data dependency on labeled data. In this work, we introduce a new method that merges few-shot learning with a U-Net architecture and Gaussian Process Emulators (GPEs), enhancing data integration from a support set for improved performance. GPEs are trained to learn the relation between the support images and the corresponding masks in latent space, facilitating the segmentation of unseen query images given only a small labeled support set at inference. We test our model with the M&Ms-2 public dataset to assess its ability to segment the heart in cardiac magnetic resonance imaging from different orientations, and compare it with state-of-the-art unsupervised and few-shot methods. Our architecture shows higher DICE coefficients compared to these methods, especially in the more challenging setups where the size of the support set is considerably small., Comment: Accepted at Statistical Atlases and Computational Modeling of the Heart (STACOM) Workshop 2024

Photonic integrated circuits find ubiquitous use in various technologies, from communication, to computing and sensing, and therefore play a crucial role in the quantum technology counterparts. Several systems are currently under investigation, each showing distinct advantages and drawbacks. For this reason, efforts are made to effectively combine different platforms in order to benefit from their respective strengths. In this work, 3D laser written photonic wire bonds are employed to interface triggered sources of quantum light, based on semiconductor quantum dots embedded into etched microlenses, with low-loss silicon-nitride photonics. Single photons at telecom wavelengths are generated by the In(Ga)As quantum dots which are then funneled into a silicon-nitride chip containing single-mode waveguides and beamsplitters. The second-order correlation function of g(2)(0) = 0.11+/-0.02, measured via the on-chip beamsplitter, clearly demonstrates the transfer of single photons into the silicon-nitride platform. The photonic wire bonds funnel on average 28.6+/-8.8% of the bare microlens emission (NA = 0.6) into the silicon-nitride-based photonic integrated circuit even at cryogenic temperatures. This opens the route for the effective future up-scaling of circuitry complexity based on the use of multiple different platforms.

In a previous publication [J. Chem. Phys., 161, 044105 (2024)], it has been shown that Rothe's method can be used to solve the time-dependent Schr\"odinger equation (TDSE) for the hydrogen atom in a strong laser field using time-dependent Gaussian wave packets. Here, we generalize these results, showing that Rothe's method can propagate arbitrary numbers of thawed, complex-valued, explicitly correlated Gaussian functions (ECGs) with dense correlation matrices for systems with varying dimensionality. We consider the multidimensional Henon-Heiles potential, and show that the dynamics can be quantitatively reproduced using only 30 Gaussians in 2D, and that accurate spectra can be obtained using 20 Gaussians in 2D and 30 to 40 Gaussians in 3D and 4D. Thus, the relevant multidimensional dynamics can be described at high quality using only a small number of ECGs that give a very compact representation of the wave function. This efficient representation, along with the demonstrated ability of Rothe's method to propagate Gaussian wave packets in strong fields and ECGs in complex potentials, paves the way for accurate molecular dynamics calculations beyond the Born-Oppenheimer approximation in strong fields., Comment: 17 pages, 13 figures

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.

The purpose of this paper is to characterize one-dimensional local domains, or more in general reduced, in terms of its Macaulay's inverse system. This leads to study almost finitely generated modules in the divided power ring. We specialize the results to a numerical semigroup ring by computing explicitly its inverse system. In the graded case we characterize reduced arithmetically Gorenstein $0$-dimensional schemes.

This document is comprised of a collection of updated preliminary parameters for the key parts of the muon collider. The updated preliminary parameters follow on from the October 2023 Tentative Parameters Report. Particular attention has been given to regions of the facility that are believed to hold greater technical uncertainty in their design and that have a strong impact on the cost and power consumption of the facility. The data is collected from a collaborative spreadsheet and transferred to overleaf.

Large vision-language models (VLLMs) exhibit promising capabilities for processing multi-modal tasks across various application scenarios. However, their emergence also raises significant data security concerns, given the potential inclusion of sensitive information, such as private photos and medical records, in their training datasets. Detecting inappropriately used data in VLLMs remains a critical and unresolved issue, mainly due to the lack of standardized datasets and suitable methodologies. In this study, we introduce the first membership inference attack (MIA) benchmark tailored for various VLLMs to facilitate training data detection. Then, we propose a novel MIA pipeline specifically designed for token-level image detection. Lastly, we present a new metric called MaxR\'enyi-K%, which is based on the confidence of the model output and applies to both text and image data. We believe that our work can deepen the understanding and methodology of MIAs in the context of VLLMs. Our code and datasets are available at https://github.com/LIONS-EPFL/VL-MIA., Comment: NeurIPS 2024

We analyze the dynamics of a gravitational bounded cloud of generic bosons or a boson star in the non-relativistic regime, assuming from the very beginning that the boson star is in a Bose-Einstein condensed state. Also, we have taken into account multi-particle interactions within the boson star, which are described through the so-called logarithmic potential. Assuming energy conservation conditions at any time, we are able to describe the boson star as a some kind of gravitational quantum liquid, or more specifically, as a macroscopic gravitational quantum droplet. The assumption of logarithmic interactions, lead us to establish several scenarios in which the system is able to oscillates around the equilibrium radius. The approach analyzed in the present report shows that the interpretation of a boson star as a macroscopic gravitational quantum droplet can be applied to the dynamics analysis of a large number of systems on different size scales., Comment: 15 pages, 10 figures

The magnetised near detector (ND280) of the T2K long-baseline neutrino oscillation experiment has been recently upgraded aiming to satisfy the requirement of reducing the systematic uncertainty from measuring the neutrinonucleus interaction cross section, which is the largest systematic uncertainty in the search for leptonic charge-parity symmetry violation. A key component of the upgrade is SuperFGD, a 3D segmented plastic scintillator detector made of approximately 2,000,000 optically-isolated 1 cm3 cubes. It will provide a 3D image of GeV neutrino interactions by combining tracking and stopping power measurements of final state particles with sub-nanosecond time resolution. The performance of SuperFGD is characterized by the precision of its response to charged particles as well as the systematic effects that might affect the physics measurements. Hence, a detailed Geant4 based optical simulation of the SuperFGD building block, i.e. a plastic scintillating cube read out by three wavelength shifting fibers, has been developed and validated with the different datasets collected in various beam tests. In this manuscript the description of the optical model as well as the comparison with data are reported., Comment: 31 pages, 15 figures

Merging supermassive black hole (SMBH) binaries will likely be surrounded by a circumbinary accretion disk. Close to merger, gravitational radiation-driven inspiral will happen on timescales faster than the effective viscous time at the disk cavity wall, leading to a decoupling of the inner binary dynamics from the surrounding gaseous environment. Here we perform the first simulation of this decoupling process from a magnetically arrested circumbinary accretion disk. In this regime, the central cavity is filled with very strong vertical magnetic flux, regulating accretion onto the binary. Our simulations identify three main stages of this process: (1) Large-scale magnetic flux loss prior to decoupling. (2) Rayleigh-Taylor-driven accretion streams onto the binary during and after decoupling, which can power magnetic tower-like outflows, resembling dual jets. (3) Post merger, the cavity wall becomes unstable and the magnetic flux trapped inside the cavity will get ejected in large coherent outbreak episodes with implications for potential multi-messenger transients to merging SMBH binaries., Comment: 9 pages, 4 figures

The fluid dynamics community has found success in explaining both the onset and coherent structure formation in wall-bounded turbulence through examining transient growth and pseudoresonance. Whether similar effects are important in plasmas well-described by magnetohydrodynamics is an open question. In nuclear fusion experiments, the onset of turbulence often enhances undesirable heat transport between the core and the edge, and a vast number of studies have attempted to understand these phenomena via linear and nonlinear numerical codes. If there are plasma experiments where instabilities and turbulence onset are dominated by nonmodal effects, the tools of non-modal stability and resolvent analysis could prove to be essential to practitioners. Towards that goal, in this work we provide a detailed derivation of non-modal stability tools and the resolvent operator for incompressible, resistive magnetohydrodynamics (MHD). Concretely, optimal initial conditions that maximize transient growth are computed, along with bounds on linear transient growth, and are numerically verified with a nonlinear solver to show that these linear effects can be reproduced in nonlinear simulations. The potential of such initial conditions to initiate the tearing mode in a spectrally stable system is explored. We found the approach common in nonmodal hydrodynamic stability of using pseudospectral methods to be infeasible for many parameter combinations due to numerical ill-conditioning. Further, transient growth in the Harris current sheet proved to be highly norm-dependent, with the observed differences being of an order of magnitude. Nonlinear simulations showed that transient growth occurs to some extent in all norms, both in the infinitesimal as well as finite limit. No case was observed of transient growth being strong enough to trigger the full plasmoid instability., Comment: I would like to aknowledge my advisors and collaborators: Alan A. Kaptanoglu, Christopher J. Hansen, Luca Comisso, Steven L. Brunton, Patrick Jenny

Modern day audio signal classification techniques lack the ability to classify low feature audio signals in the form of spectrographic temporal frequency data representations. Additionally, currently utilized techniques rely on full diverse data sets that are often not representative of real-world distributions. This paper derives several first-of-its-kind machine learning methodologies to analyze these low feature audio spectrograms given data distributions that may have normalized, skewed, or even limited training sets. In particular, this paper proposes several novel customized convolutional architectures to extract identifying features using binary, one-class, and siamese approaches to identify the spectrographic signature of a given audio signal. Utilizing these novel convolutional architectures as well as the proposed classification methods, these experiments demonstrate state-of-the-art classification accuracy and improved efficiency than traditional audio classification methods.

Sonar based audio classification techniques are a growing area of research in the field of underwater acoustics. Usually, underwater noise picked up by passive sonar transducers contains all types of signals that travel through the ocean and is transformed into spectrographic images. As a result, the corresponding spectrograms intended to display the temporal-frequency data of a certain object often include the tonal regions of abundant extraneous noise that can effectively interfere with a 'contact'. So, a majority of spectrographic samples extracted from underwater audio signals are rendered unusable due to their clutter and lack the required indistinguishability between different objects. With limited clean true data for supervised training, creating classification models for these audio signals is severely bottlenecked. This paper derives several new techniques to combat this problem by developing a novel Score-CAM based denoiser to extract an object's signature from noisy spectrographic data without being given any ground truth data. In particular, this paper proposes a novel generative adversarial network architecture for learning and producing spectrographic training data in similar distributions to low-feature spectrogram inputs. In addition, this paper also a generalizable class activation mapping based denoiser for different distributions of acoustic data, even real-world data distributions. Utilizing these novel architectures and proposed denoising techniques, these experiments demonstrate state-of-the-art noise reduction accuracy and improved classification accuracy than current audio classification standards. As such, this approach has applications not only to audio data but for countless data distributions used all around the world for machine learning.

Large language models have drastically changed the prospects of AI by introducing technologies for more complex natural language processing. However, current methodologies to train such LLMs require extensive resources including but not limited to large amounts of data, expensive machinery, and lengthy training. To solve this problem, this paper proposes a new tokenization method inspired by universal Lempel-Ziv-Welch data compression that compresses repetitive phrases into multi-word tokens. With MultiTok as a new tokenizing tool, we show that language models are able to be trained notably more efficiently while offering a similar accuracy on more succinct and compressed training data. In fact, our results demonstrate that MultiTok achieves a comparable performance to the BERT and GPT-2 standards as both a stand-alone tokenizer and an add-on to existing tokenizers while also providing close to 2.5x faster training with more than 30% less training data.