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We present an experimental study of immiscible, two-phase fluid flow through a three-dimensional porous medium consisting of randomly-packed, monodisperse glass spheres. Our experiments combine refractive-index matching and laser-induced fluorescence imaging to resolve the morphology and stability of the moving interface resulting from the injection of one fluid into another. The imposed injection rate sets a balance between gravitational and viscous forces, producing interface morphologies which range from unstable collections of tangled fingers at low rates to stable sheets at high rates. The image data are complemented by time-resolved pressure measurements. We develop a stability criterion for the fluid interface based on the analysis of the 3D images and the pressure data. This criterion involves the Darcy permeability in each of the two phases and the time derivative of the pressure drop across the medium. We observe that the relative permeability encountered by the invading fluid is modified by the imposed flow rate in our experiment, which impacts the two-phase flow dynamics. We show that, in addition to the balance between the relevant forces driving the dynamics, local regions of crystalline order in the beadpack (crystallites) affect the stability of the invading front. This work provides insights into how disorder on multiple length scales in porous media can interact with viscous, capillary, and gravitational forces to determine the stability and dynamics of immiscible fluid interfaces., Comment: 40 pages, 11 figures

The use of copyrighted materials in training generative language models raises critical legal and ethical questions. This paper presents a framework for and the results of empirically assessing the impact of copyrighted materials on the performance of large language models (LLMs) for Norwegian. We found that both books and newspapers contribute positively when the models are evaluated on a diverse set of Norwegian benchmarks, while fiction works possibly lead to decreased performance. Our experiments could inform the creation of a compensation scheme for authors whose works contribute to AI development., Comment: pre-print, under review

The formation of iron oxide nanoparticles (NPs) presents challenges such as efficiency losses and fine dust emissions in practical iron combustion systems, highlighting the need for deeper understanding of the formation mechanisms and thermochemical conditions. This study combines experiments and multi-scale simulations to analyze NP clouds generated by single iron particles burning in high-temperature oxidizing environments. The ambient gas conditions were provided by a laminar flat flame burner, with post-flame oxygen mole fractions varied between 20, 30, and 40 vol% at a constant temperature of ~1800K. High-speed in-situ diagnostics were used to measure particle size, NP initiation, NP cloud evolution, and microparticle surface temperature history. The experimental setup utilized three 10kHz imaging systems: one for two-color pyrometry and two for diffusive-backlight illumination (DBI), targeting particle size and NP measurements. The findings showcase the powerful capabilities of multi-physics diagnostics in quantifying NP initiation time and temperature, which depend on particle size and ambient oxygen concentration. CFD simulations revealed enhanced convection velocity driven by increased Stefan flow, which transported NPs toward parent iron particles under high-oxygen conditions. This delayed the detection of NP clouds, leading to higher microparticle temperatures at NP initiation. Molecular dynamics (MD) simulations uncovered FeO2(g) as a key NP precursor, forming when Fe atoms dissociate from the liquid phase. The initial temperature significantly influenced the resulting nanocluster composition, with Fe(II) dominating at higher temperatures and Fe(III) at lower temperatures. This integrated approach enhances understanding of NP formation in iron combustion, offering insights into the conditions affecting nanoparticle characteristics.

We study consistent query answering via different graph representations. First, we introduce solution-conflict hypergraphs in which nodes represent facts and edges represent either conflicts or query solutions. Considering a monotonic query and a set of antimonotonic constraints, we present an explicit algorithm for counting the number of repairs satisfying the query based on a tree decomposition of the solution-conflict hypergraph. The algorithm not only provides fixed-parameter tractability results for data complexity over expressive query and constraint classes, but also introduces a novel and potentially implementable approach to repair counting. Second, we consider the Gaifman graphs arising from MSO descriptions of consistent query answering. Using a generalization of Courcelle's theorem, we then present fixed-parameter tractability results for combined complexity over expressive query and constraint classes.

This study evaluates the performance of OpenAI's o1-preview model in higher-order cognitive domains, including critical thinking, systematic thinking, computational thinking, data literacy, creative thinking, logical reasoning, and scientific reasoning. Using established benchmarks, we compared the o1-preview models's performance to human participants from diverse educational levels. o1-preview achieved a mean score of 24.33 on the Ennis-Weir Critical Thinking Essay Test (EWCTET), surpassing undergraduate (13.8) and postgraduate (18.39) participants (z = 1.60 and 0.90, respectively). In systematic thinking, it scored 46.1, SD = 4.12 on the Lake Urmia Vignette, significantly outperforming the human mean (20.08, SD = 8.13, z = 3.20). For data literacy, o1-preview scored 8.60, SD = 0.70 on Merk et al.'s "Use Data" dimension, compared to the human post-test mean of 4.17, SD = 2.02 (z = 2.19). On creative thinking tasks, the model achieved originality scores of 2.98, SD = 0.73, higher than the human mean of 1.74 (z = 0.71). In logical reasoning (LogiQA), it outperformed humans with average 90%, SD = 10% accuracy versus 86%, SD = 6.5% (z = 0.62). For scientific reasoning, it achieved near-perfect performance (mean = 0.99, SD = 0.12) on the TOSLS,, exceeding the highest human scores of 0.85, SD = 0.13 (z = 1.78). While o1-preview excelled in structured tasks, it showed limitations in problem-solving and adaptive reasoning. These results demonstrate the potential of AI to complement education in structured assessments but highlight the need for ethical oversight and refinement for broader applications.

Recently, the ParaOpt algorithm was proposed as an extension of the time-parallel Parareal method to optimal control. ParaOpt uses quasi-Newton steps that each require solving a system of matching conditions iteratively. The state-of-the-art parallel preconditioner for linear problems leads to a set of independent smaller systems that are currently hard to solve. We generalize the preconditioner to the nonlinear case and propose a new, fast inversion method for these smaller systems, avoiding disadvantages of the current options with adjusted boundary conditions in the subproblems.

The framework of templicial vector spaces was put forth in arXiv:2302.02484v2 as a suitable generalization of simplicial sets in order to develop a theory of enriched quasi-categories, called quasi-categories in vector spaces. We construct a lift of Faonte's $A_{\infty}$-nerve arXiv:1312.2127v2 which lands in templicial vector spaces. Further, we show that when restricted to dg-categories, this nerve recovers the templicial dg-nerve of arXiv:2005.04778v4, and that the nerve of any $A_{\infty}$-category is a quasi-category in vector spaces., Comment: 26 pages; comments welcome

We investigate the second-order photon correlation function in cavity-magnon systems, focusing on ferromagnetic and antiferromagnetic cavities within the ultra-strong coupling regime, and extending beyond the rotating-wave approximation. By deriving exact integral solutions for the second-order correlation function, we demonstrate that counter-rotating magnon-photon interactions induce quadrature squeezing in the cavity mode. Furthermore, we show that tuning the anisotropic magnon-cavity couplings enhance the squeezing effect by changing the level repulsion of the magnon-cavity photon hybrid mode without increasing cavity photon occupation number. Our study reveals distinct quantum correlation behaviors in ferromagnetic and antiferromagnetic cavities: For ferromagnetic cavities, we show that squeezing increases with coupling strength asymmetry, whereas in antiferromagnetic case, magnon modes with opposite chirality suppress quantum effects and imposes a lower bound on correlation functions. These findings provide a pathway to optimize photon blockade and quantum information processing in magnon-cavity systems in the ultrastrong coupling limit., Comment: 14 pages, 6 figures

Compact binary mergers detectable in gravitational waves can be accompanied by a kilonova, an electromagnetic transient powered by radioactive decay of newly synthesised r-process elements. A few kilonova candidates have been observed during short gamma-ray burst follow-up, and one found associated with a gravitational wave detection, GW170817. However, robust kilonova candidates are yet to be found in un-triggered, wide-field optical surveys, that is, a search not requiring an initial gravitational wave or gamma-ray burst trigger. Here we present the first observing run for the Kilonova and Transients Program (KNTraP) using the Dark Energy Camera. The first KNTraP run ran for 11 nights, covering 31 fields at a nightly cadence in two filters. The program can detect transients beyond the LIGO/Virgo/KAGRA horizon, be agnostic to the merger orientation, avoid the Sun and/or Galactic plane, and produces high cadence multi-wavelength light curves. The data were processed nightly in real-time for rapid identification of transient candidates, allowing for follow-up of interesting candidates before they faded away. Three fast-rising candidates were identified in real-time, however none had the characteristics of the kilonova AT2017gfo associated with GW170817 or with the expected evolution for kilonovae from our fade-rate models. After the run, the data were reprocessed, then subjected to stringent filtering and model fitting to search for kilonovae offline. Multiple KNTraP runs (3+) are expected to detect kilonovae via this optical-only search method. No kilonovae were detected in this first KNTraP run using our selection criteria, constraining the KN rate to $R < 1.8\times10^{5}$ Gpc$^{-3}$ yr$^{-1}$., Comment: 17 pages, 6 figures, 3 tables, submitted to MNRAS

The widely-used, weight-only quantized large language models (LLMs), which leverage low-bit integer (INT) weights and retain floating-point (FP) activations, reduce storage requirements while maintaining accuracy. However, this shifts the energy and latency bottlenecks towards the FP activations that are associated with costly memory accesses and computations. Existing LLM accelerators focus primarily on computation optimizations, overlooking the potential of jointly optimizing FP computations and data movement, particularly for the dominant FP-INT GeMM operations in LLM inference. To address these challenges, we investigate the sensitivity of activation precision across various LLM modules and its impact on overall model accuracy. Based on our findings, we first propose the Anda data type: an adaptive data format with group-shared exponent bits and dynamic mantissa bit allocation. Secondly, we develop an iterative post-training adaptive precision search algorithm that optimizes the bit-width for different LLM modules to balance model accuracy, energy efficiency, and inference speed. Lastly, a suite of hardware optimization techniques is proposed to maximally exploit the benefits of the Anda format. These include a bit-plane-based data organization scheme, Anda-enhanced processing units with bit-serial computation, and a runtime bit-plane Anda compressor to simultaneously optimize storage, computation, and memory footprints. Our evaluations on FPINT GeMM operations show that Anda achieves a 2.4x speedup, 4.0x area efficiency, and 3.1x energy efficiency improvement on average for popular LLMs including OPT, LLaMA, and LLaMA-2 series over the GPU-like FP-FP baseline. Anda demonstrates strong adaptability across various application scenarios, accuracy requirements, and system performance, enabling efficient LLM inference across a wide range of deployment scenarios., Comment: To appear in 2025 IEEE International Symposium on High-Performance Computer Architecture (HPCA 2025)

Recent advances in semiconductor spin qubits have achieved linear arrays exceeding ten qubits. Moving to two-dimensional (2D) qubit arrays is a critical next step to advance towards fault-tolerant implementations, but it poses substantial fabrication challenges, particularly because enabling control of nearest-neighbor entanglement requires the incorporation of interstitial exchange gates between quantum dots in the qubit architecture. In this work, we present a 2D array of silicon metal-oxide-semiconductor (MOS) quantum dots with tunable interdot coupling between all adjacent dots. The device is characterized at 4.2 K, where we demonstrate the formation and isolation of double-dot and triple-dot configurations. We show control of all nearest-neighbor tunnel couplings spanning up to 30 decades per volt through the interstitial exchange gates and use advanced modeling tools to estimate the exchange interactions that could be realized among qubits in this architecture. These results represent a significant step towards the development of 2D MOS quantum processors compatible with foundry manufacturing techniques., Comment: 9 pages, 5 figures

We, the QAIMS lab lab at the Aerospace Faculty of TU Delft, participated as finalists in the Airbus/BMW Quantum Computing Challenge 2024. Stacking sequence retrieval, a complex combinatorial task within a bi-level optimization framework, is crucial for designing laminated composites that meet aerospace requirements for weight, strength, and stiffness. This document presents the scientifically relevant sections of our submission, which builds on our prior research on applying quantum computation to this challenging design problem. For the competition, we expanded our previous work in several significant ways. First, we incorporated a full set of manufacturing constraints into our algorithmic framework, including those previously established theoretically but not yet demonstrated, thereby aligning our approach more closely with real-world manufacturing demands. We implemented the F-VQE algorithm, which enhances the probability shaping of optimal solutions, improving on simpler variational quantum algorithms. Our approach also demonstrates flexibility by accommodating diverse objectives as well as finer ply-angle increments alongside the previously demonstrated conventional ply angles. Scalability was tested using the DMRG algorithm, which, despite limitations in entanglement representation, enabled simulations with up to 200 plies. Results were directly compared to conventional stacking sequence retrieval algorithms with DMRG showing high competitiveness. Given DMRG's limited entanglement capabilities, it serves as a conservative baseline, suggesting potential for even greater performance on fully realized quantum systems. This document serves to make our competition results publicly available as we prepare a formal publication on these findings and their implications for aerospace materials design optimization., Comment: Scientifically relevant sections of a submission to 2024 Airbus/BMW Quantum Computing Challenge. 26 pages, 10 figures, 3 tables

Attributing outputs from Large Language Models (LLMs) in adversarial settings-such as cyberattacks and disinformation-presents significant challenges that are likely to grow in importance. We investigate this attribution problem using formal language theory, specifically language identification in the limit as introduced by Gold and extended by Angluin. By modeling LLM outputs as formal languages, we analyze whether finite text samples can uniquely pinpoint the originating model. Our results show that due to the non-identifiability of certain language classes, under some mild assumptions about overlapping outputs from fine-tuned models it is theoretically impossible to attribute outputs to specific LLMs with certainty. This holds also when accounting for expressivity limitations of Transformer architectures. Even with direct model access or comprehensive monitoring, significant computational hurdles impede attribution efforts. These findings highlight an urgent need for proactive measures to mitigate risks posed by adversarial LLM use as their influence continues to expand., Comment: 7 pages, 1 figure

In combustion theory, flames are usually described in terms of the dynamics of iso-surfaces of a specific scalar. The flame displacement speed is then introduced as a local variable quantifying the progression of these iso-surfaces relative to the flow field. While formally defined as a scalar, the physical meaning of this quantity allows relating it with a vector pointing along the normal direction of the scalar iso-surface. In this work, this one-dimensional concept is extended by the introduction of a generalized flame displacement velocity vector, which is associated with the dynamics of iso-surfaces of two generic scalars, $\alpha$ and $\beta$. It is then shown how a new flamelet paradigm can be built around this velocity vector, which leads to a very compact and generic set of two-dimensional flamelet equations for thermochemical quantities and the conditioning scalar gradients, $g_{\alpha} = \lvert \nabla \alpha \rvert$ and $g_{\beta} = \lvert \nabla \beta \rvert$. The most important features of the developed framework are discussed in the context of partially-premixed flames, which provides significant insights into several aspects of the theory, including the nature of the different contributions to the flamelet equations for the conditioning scalar gradients and the fact that different flamelet coordinate systems (orthogonal and non-orthogonal) can be characterized by the same flame displacement velocity vector. This approach opens an entire spectrum of possibilities for the definition of new two-dimensional composition spaces, which represents a very promising basis for the development of new variants of flamelet theory.

We improve bounds on the degree and sparsity of Boolean functions representing the Legendre symbol as well as on the $N$th linear complexity of the Legendre sequence. We also prove similar results for both the Liouville function for integers and its analog for polynomials over $\mathbb{F}_2$, or more general for any (binary) arithmetic function which satisfies $f(2n)=-f(n)$ for $n=1,2,\ldots$

In this paper, we propose an extension of the cabling methods to bijective non-degenerate solutions of the Yang--Baxter equation, with applications to indecomposable and simple solutions. We address two main challenges in extending this technique to the non-involutive case. First, we establish that the indecomposability of a solution can be assessed through its injectivization or the associated biquandle. Second, we show that the canonical embedding into the structure monoid resolves issues related to the pullback of subsolutions. Our results not only extend the theorems of Lebed, Ramirez and Vendramin to non-involutive solutions but also provide numerical criteria for indecomposability., Comment: 12 pages

The rapid expansion of Internet of Things (IoT), edge, and embedded devices in the past decade has introduced numerous challenges in terms of security and configuration management. Simultaneously, advances in cloud-native development practices have greatly enhanced the development experience and facilitated quicker updates, thereby enhancing application security. However, applying these advances to IoT, edge, and embedded devices remains a complex task, primarily due to the heterogeneous environments and the need to support devices with extended lifespans. WebAssembly and the WebAssembly System Interface (WASI) has emerged as a promising technology to bridge this gap. As WebAssembly becomes more popular on IoT, edge, and embedded devices, there is a growing demand for hardware interface support in WebAssembly programs. This work presents WASI proposals and proof-of-concept implementations to enable hardware interaction with I2C and USB, which are two commonly used protocols in IoT, directly from WebAssembly applications. This is achieved by running the device drivers within WebAssembly as well. A thorough evaluation of the proof of concepts shows that WASI-USB introduces a minimal overhead of at most 8% compared to native operating system USB APIs. However, the results show that runtime initialization overhead can be significant in low-latency applications., Comment: Preprint of article submitted to IEEE/IFIP Network Operations and Management Symposium 2025

Social dilemmas where the good of a group is at odds with individual interests are usually considered as static -- the dilemma does not change over time. In the COVID-19 pandemic, social dilemmas occurred in the mitigation of epidemic spread: Should I reduce my contacts or wear a mask to protect others? In the context of respiratory diseases, which are predominantly spreading during the winter months, some of these situations re-occur seasonally. We couple a game theoretical model, where individuals can adjust their behavior, to an epidemiological model with seasonal forcing. We find that social dilemmas can occur annually and that behavioral reactions to them can either decrease or increase the peaks of infections in a population. Our work has not only implications for seasonal infectious diseases, but also more generally for oscillatory social dilemmas: A complex interdependence between behavior and external dynamics emerges. To be effective and to exploit behavioral dynamics, intervention measures to mitigate re-occuring social dilemmas have to be timed carefully.

Spin Seebeck effect (SSE) refers to the creation of spin currents due to a temperature gradient in the magnetic materials or across magnet-normal metal interfaces, which can be electrically detected through the inverse spin Hall effect (ISHE) when in contact with heavy metals. It offers fundamental insights into the magnetic properties of materials, including the magnetic phase transition, static magnetic order, and magnon excitations. However, the SSE in van der Waals antiferromagnet is still elusive, especially across the spin-flip transition. Here, we demonstrate the SSE in the weak exchange coupled van der Waals antiferromagnet CrPS$_4$. The SSE increases as the magnetic field increases before the spin-flip transition due to the enhancement of the thermal spin current as a function of the applied field. A peak of SSE is observed at the spin-flip field, which is related to the magnon mode edges across the spin-flip field. Our results extend SSE research to van der Waals antiferromagnets and demonstrate an enhancement of SSE at the spin-flip transition., Comment: 18 pages, 4 figures

The negatively-charged nitrogen vacancy (NV$^{-}$) center in diamond is widely used for quantum sensing since the sensitivity of the spin triplet in the electronic ground state to external perturbations such as strain and electromagnetic fields make it an excellent probe for changes in these perturbations. The spin state can be measured through optically detected magnetic resonance (ODMR), which is most commonly achieved by detecting the photoluminescence (PL) after exciting the spin-triplet transition. Recently, methods have been proposed and demonstrated that use the absorption of the infrared singlet transition at 1042 nm instead. These methods however require cryogenic temperatures or external cavities to enhance the absorption signal. Here, we report on our optimization efforts of the magnetometer sensitivity at room temperature and without cavities. We reach sensitivities of 18 pT$/\sqrt{\mathrm{Hz}}$, surpassing previously reported values. We also report on a defect that is native to CVD-grown diamond and thus absent in HPHT diamond, the excitation of which impacts the measured singlet absorption signal., Comment: 6 pages with 4 figures

The integration of artificial intelligence into business processes has significantly enhanced decision-making capabilities across various industries such as finance, healthcare, and retail. However, explaining the decisions made by these AI systems poses a significant challenge due to the opaque nature of recent deep learning models, which typically function as black boxes. To address this opacity, a multitude of explainability techniques have emerged. However, in practical business applications, the challenge lies in selecting an appropriate explainability method that balances comprehensibility with accuracy. This paper addresses the practical need of understanding differences in the output of explainability techniques by proposing a novel method for the assessment of the agreement of different explainability techniques. Based on our proposed methods, we provide a comprehensive comparative analysis of six leading explainability techniques to help guiding the selection of such techniques in practice. Our proposed general-purpose method is evaluated on top of one of the most popular deep learning architectures, the Vision Transformer model, which is frequently employed in business applications. Notably, we propose a novel metric to measure the agreement of explainability techniques that can be interpreted visually. By providing a practical framework for understanding the agreement of diverse explainability techniques, our research aims to facilitate the broader integration of interpretable AI systems in business applications., Comment: 10 pages, 5 figures

We report the discovery of a peculiar X-ray transient, EP240408a, by Einstein Probe (EP) and follow-up studies made with EP, Swift, NICER, GROND, ATCA and other ground-based multi-wavelength telescopes. The new transient was first detected with Wide-field X-ray Telescope (WXT) on board EP on April 8th, 2024, manifested in an intense yet brief X-ray flare lasting for 12 seconds. The flare reached a peak flux of 3.9x10^(-9) erg/cm2/s in 0.5-4 keV, about 300 times brighter than the underlying X-ray emission detected throughout the observation. Rapid and more precise follow-up observations by EP/FXT, Swift and NICER confirmed the finding of this new transient. Its X-ray spectrum is non-thermal in 0.5-10 keV, with a power-law photon index varying within 1.8-2.5. The X-ray light curve shows a plateau lasting for about 4 days, followed by a steep decay till becoming undetectable about 10 days after the initial detection. Based on its temporal property and constraints from previous EP observations, an unusual timescale in the range of 7-23 days is found for EP240408a, which is intermediate between the commonly found fast and long-term transients. No counterparts have been found in optical and near-infrared, with the earliest observation at 17 hours after the initial X-ray detection, suggestive of intrinsically weak emission in these bands. We demonstrate that the remarkable properties of EP240408a are inconsistent with any of the transient types known so far, by comparison with, in particular, jetted tidal disruption events, gamma-ray bursts, X-ray binaries and fast blue optical transients. The nature of EP240408a thus remains an enigma. We suggest that EP240408a may represent a new type of transients with intermediate timescales of the order of about 10 days. The detection and follow-ups of more of such objects are essential for revealing their origin., Comment: 25 pages, 11 figures

The spatial distribution of spurious dots in SiMOS single-electron transistors (SETs), fabricated on an industrial 300 mm process line, has been statistically analyzed. To have a deeper understanding of the origin of these spurious dots, we analyzed SETs with three different oxide thicknesses: 8 nm, 12 nm and 20 nm. By combining spurious dot triangulation cryo-measurement with simulations of strain, gate bias, and location of the electron wave function, we demonstrate that most spurious dots are formed through the combined effects of strain and gate bias, leading to variations in the conduction band energy. Despite the similar thermal expansion coefficients of polycrystalline silicon gates and single-crystalline silicon substrates, strain remains a crucial factor in spurious dots formation. This learning can be use to optimize the device design and the oxide thickness, to reduce the density of spurious dot while keeping quantum dot tunability., Comment: 10 pages, 9 figures

Customer churn, particularly in the telecommunications sector, influences both costs and profits. As the explainability of models becomes increasingly important, this study emphasizes not only the explainability of customer churn through machine learning models, but also the importance of identifying multivariate patterns and setting soft bounds for intuitive interpretation. The main objective is to use a machine learning model and fuzzy-set theory with top-\textit{k} HUIM to identify highly associated patterns of customer churn with intuitive identification, referred to as Highly Associated Fuzzy Churn Patterns (HAFCP). Moreover, this method aids in uncovering association rules among multiple features across low, medium, and high distributions. Such discoveries are instrumental in enhancing the explainability of findings. Experiments show that when the top-5 HAFCPs are included in five datasets, a mixture of performance results is observed, with some showing notable improvements. It becomes clear that high importance features enhance explanatory power through their distribution and patterns associated with other features. As a result, the study introduces an innovative approach that improves the explainability and effectiveness of customer churn prediction models., Comment: 18 pages single columns, 4 figures, This paper is an extended version of a work originally presented at the 6th International Workshop on Utility-Driven Mining and Learning (held in conjunction with the 28th Pacific-Asia Conference on Knowledge Discovery and Data Mining - PAKDD 2024) on May 7, 2024

Standard approaches to quantum computing require significant overhead to correct for errors. The hardware size for conventional quantum processors in solids often increases linearly with the number of physical qubits, such as for transmon qubits in superconducting circuits or electron spin qubits in quantum dot arrays. While photonic circuits based on flying qubits do not suffer from decoherence or lack of potential scalability, they have encountered significant challenges to overcome photon loss in long delay circuits. Here, we propose an alternative approach that utilizes flying electronic wave packets propagating in solid-state quantum semiconductor circuits. Using a novel time-bin architecture for the electronic wave packets, hardware requirements are drastically reduced because qubits can be created on-demand and manipulated with a common hardware element, unlike the localized approach of wiring each qubit individually. The electronic Coulomb interaction enables reliable coupling and readout of qubits. Improving upon previous devices, we realize electronic interference at the level of a single quantized mode that can be used for manipulation of electronic wavepackets. This important landmark lays the foundation for fault-tolerant quantum computing with a compact and scalable architecture based on electron interferometry in semiconductors.

We present parametric breadth-first search (PBFS), a new algorithm for solving the parametric minimum cut problem in a network with source-sink-monotone capacities. The objective is to find the set of breakpoints, i.e., the points at which the minimum cut changes. It is well known that this problem can be solved in the same asymptotic runtime as the static minimum cut problem. However, existing algorithms that achieve this runtime bound involve fairly complicated steps that are inefficient in practice. PBFS uses a simpler approach that discovers the breakpoints in ascending order, which allows it to achieve the desired runtime bound while still performing well in practice. We evaluate our algorithm on benchmark instances from polygon aggregation and computer vision. Polygon aggregation was recently proposed as an application for parametric minimum cut, but the monotonicity property has not been exploited fully. PBFS outperforms the state of the art on most benchmark instances, usually by a factor of 2-3. It is particularly strong on instances with many breakpoints, which is the case for polygon aggregation. Compared to the existing min-cut-based approach for polygon aggregation, PBFS scales much better with the instance size. On large instances with millions of vertices, it is able to compute all breakpoints in a matter of seconds.

Fabrication of quantum processors in advanced 300 mm wafer-scale complementary metal-oxide-semiconductor (CMOS) foundries provides a unique scaling pathway towards commercially viable quantum computing with potentially millions of qubits on a single chip. Here, we show precise qubit operation of a silicon two-qubit device made in a 300 mm semiconductor processing line. The key metrics including single- and two-qubit control fidelities exceed 99% and state preparation and measurement fidelity exceeds 99.9%, as evidenced by gate set tomography (GST). We report coherence and lifetimes up to $T_\mathrm{2}^{\mathrm{*}} = 30.4$ $\mu$s, $T_\mathrm{2}^{\mathrm{Hahn}} = 803$ $\mu$s, and $T_1 = 6.3$ s. Crucially, the dominant operational errors originate from residual nuclear spin carrying isotopes, solvable with further isotopic purification, rather than charge noise arising from the dielectric environment. Our results answer the longstanding question whether the favourable properties including high-fidelity operation and long coherence times can be preserved when transitioning from a tailored academic to an industrial semiconductor fabrication technology., Comment: 10 pages, 4 figures, 4 extended data figures

Modeling complex systems that evolve toward equilibrium distributions is important in various physical applications, including molecular dynamics and robotic control. These systems often follow the stochastic gradient descent of an underlying energy function, converging to stationary distributions around energy minima. The local covariance of these distributions is shaped by the energy landscape's curvature, often resulting in anisotropic characteristics. While flow-based generative models have gained traction in generating samples from equilibrium distributions in such applications, they predominately employ isotropic conditional probability paths, limiting their ability to capture such covariance structures. In this paper, we introduce Hessian-Informed Flow Matching (HI-FM), a novel approach that integrates the Hessian of an energy function into conditional flows within the flow matching framework. This integration allows HI-FM to account for local curvature and anisotropic covariance structures. Our approach leverages the linearization theorem from dynamical systems and incorporates additional considerations such as time transformations and equivariance. Empirical evaluations on the MNIST and Lennard-Jones particles datasets demonstrate that HI-FM improves the likelihood of test samples., Comment: In submission

Variational Autoencoders (VAEs) are a powerful framework for learning compact latent representations, while NeuralODEs excel in learning transient system dynamics. This work combines the strengths of both to create fast surrogate models with adjustable complexity. By leveraging the VAE's dimensionality reduction using a non-hierarchical prior, our method adaptively assigns stochastic noise, naturally complementing known NeuralODE training enhancements and enabling probabilistic time series modeling. We show that standard Latent ODEs struggle with dimensionality reduction in systems with time-varying inputs. Our approach mitigates this by continuously propagating variational parameters through time, establishing fixed information channels in latent space. This results in a flexible and robust method that can learn different system complexities, e.g. deep neural networks or linear matrices. Hereby, it enables efficient approximation of the Koopman operator without the need for predefining its dimensionality. As our method balances dimensionality reduction and reconstruction accuracy, we call it Balanced Neural ODE (B-NODE). We demonstrate the effectiveness of this method on academic test cases and apply it to a real-world example of a thermal power plant., Comment: Conference paper under review

As artificial intelligence (AI) continues to advance, it demonstrates capabilities comparable to human intelligence, with significant potential to transform education and workforce development. This study evaluates OpenAI o1-preview's ability to perform higher-order cognitive tasks across 14 dimensions, including critical thinking, systems thinking, computational thinking, design thinking, metacognition, data literacy, creative thinking, abstract reasoning, quantitative reasoning, logical reasoning, analogical reasoning, and scientific reasoning. We used validated instruments like the Ennis-Weir Critical Thinking Essay Test and the Biological Systems Thinking Test to compare the o1-preview's performance with human performance systematically. Our findings reveal that o1-preview outperforms humans in most categories, achieving 150% better results in systems thinking, computational thinking, data literacy, creative thinking, scientific reasoning, and abstract reasoning. However, compared to humans, it underperforms by around 25% in logical reasoning, critical thinking, and quantitative reasoning. In analogical reasoning, both o1-preview and humans achieved perfect scores. Despite these strengths, the o1-preview shows limitations in abstract reasoning, where human psychology students outperform it, highlighting the continued importance of human oversight in tasks requiring high-level abstraction. These results have significant educational implications, suggesting a shift toward developing human skills that complement AI, such as creativity, abstract reasoning, and critical thinking. This study emphasizes the transformative potential of AI in education and calls for a recalibration of educational goals, teaching methods, and curricula to align with an AI-driven world., Comment: An assessment of OpenAI o1-Preview for Higher Order Thinking in Education

Streamlining the deployment of Deep Neural Networks (DNNs) on heterogeneous edge platforms, coupling within the same micro-controller unit (MCU) instruction processors and hardware accelerators for tensor computations, is becoming one of the crucial challenges of the TinyML field. The best-performing DNN compilation toolchains are usually deeply customized for a single MCU family, and porting to a different heterogeneous MCU family implies labor-intensive re-development of almost the entire compiler. On the opposite side, retargetable toolchains, such as TVM, fail to exploit the capabilities of custom accelerators, resulting in the generation of general but unoptimized code. To overcome this duality, we introduce MATCH, a novel TVM-based DNN deployment framework designed for easy agile retargeting across different MCU processors and accelerators, thanks to a customizable model-based hardware abstraction. We show that a general and retargetable mapping framework enhanced with hardware cost models can compete with and even outperform custom toolchains on diverse targets while only needing the definition of an abstract hardware model and a SoC-specific API. We tested MATCH on two state-of-the-art heterogeneous MCUs, GAP9 and DIANA. On the four DNN models of the MLPerf Tiny suite MATCH reduces inference latency by up to 60.88 times on DIANA, compared to using the plain TVM, thanks to the exploitation of the on-board HW accelerator. Compared to HTVM, a fully customized toolchain for DIANA, we still reduce the latency by 16.94%. On GAP9, using the same benchmarks, we improve the latency by 2.15 times compared to the dedicated DORY compiler, thanks to our heterogeneous DNN mapping approach that synergically exploits the DNN accelerator and the eight-cores cluster available on board., Comment: 13 pages, 11 figures, 4 tables

In rapidly evolving e-commerce industry, the capability of selecting high-quality data for model training is essential. This study introduces the High-Utility Sequential Pattern Mining using SHAP values (HUSPM-SHAP) model, a utility mining-based active learning strategy to tackle this challenge. We found that the parameter settings for positive and negative SHAP values impact the model's mining outcomes, introducing a key consideration into the active learning framework. Through extensive experiments aimed at predicting behaviors that do lead to purchases or not, the designed HUSPM-SHAP model demonstrates its superiority across diverse scenarios. The model's ability to mitigate labeling needs while maintaining high predictive performance is highlighted. Our findings demonstrate the model's capability to refine e-commerce data processing, steering towards more streamlined, cost-effective prediction modeling., Comment: 7 pages, 2 figures, preprint version

Radiologists play a crucial role by translating medical images into medical reports. However, the field faces staffing shortages and increasing workloads. While automated approaches using vision-language models (VLMs) show promise as assistants, they require exceptionally high accuracy. Most current VLMs in radiology rely solely on supervised fine-tuning (SFT). Meanwhile, in the general domain, additional preference fine-tuning has become standard practice. The challenge in radiology lies in the prohibitive cost of obtaining radiologist feedback. We propose a scalable automated preference alignment technique for VLMs in radiology, focusing on chest X-ray (CXR) report generation. Our method leverages publicly available datasets with an LLM-as-a-Judge mechanism, eliminating the need for additional expert radiologist feedback. We evaluate and benchmark five direct alignment algorithms (DAAs). Our results show up to a 57.4% improvement in average GREEN scores, a LLM-based metric for evaluating CXR reports, and a 9.2% increase in an average across six metrics (domain specific and general), compared to the SFT baseline. We study reward overoptimization via length exploitation, with reports lengthening by up to 3.2x. To assess a potential alignment tax, we benchmark on six additional diverse tasks, finding no significant degradations. A reader study involving four board-certified radiologists indicates win rates of up to 0.62 over the SFT baseline, while significantly penalizing verbosity. Our analysis provides actionable insights for the development of VLMs in high-stakes fields like radiology.

Biomedical knowledge is uniquely complex and structured, requiring distinct reasoning strategies compared to other scientific disciplines like physics or chemistry. Biomedical scientists do not rely on a single approach to reasoning; instead, they use various strategies, including rule-based, prototype-based, and case-based reasoning. This diversity calls for flexible approaches that accommodate multiple reasoning strategies while leveraging in-domain knowledge. We introduce KGARevion, a knowledge graph (KG) based agent designed to address the complexity of knowledge-intensive medical queries. Upon receiving a query, KGARevion generates relevant triplets by using the knowledge base of the LLM. These triplets are then verified against a grounded KG to filter out erroneous information and ensure that only accurate, relevant data contribute to the final answer. Unlike RAG-based models, this multi-step process ensures robustness in reasoning while adapting to different models of medical reasoning. Evaluations on four gold-standard medical QA datasets show that KGARevion improves accuracy by over 5.2%, outperforming 15 models in handling complex medical questions. To test its capabilities, we curated three new medical QA datasets with varying levels of semantic complexity, where KGARevion achieved a 10.4% improvement in accuracy.

Projective measurement of single electron spins, or spin readout, is among the most fundamental technologies for spin-based quantum information processing. Implementing spin readout with both high-fidelity and scalability is indispensable for developing fault-tolerant quantum computers in large-scale spin-qubit arrays. To achieve high fidelity, a latching mechanism is useful. However, the fidelity can be decreased by spin relaxation and charge state leakage, and the scalability is currently challenging. Here, we propose and demonstrate a double-latching high-fidelity spin readout scheme, which suppresses errors via an additional latching process. We experimentally show that the double-latching mechanism provides significantly higher fidelity than the conventional latching mechanism and estimate a potential spin readout fidelity of 99.94% using highly spin-dependent tunnel rates. Due to isolation from error-inducing processes, the double-latching mechanism combined with scalable charge readout is expected to be useful for large-scale spin-qubit arrays while maintaining high fidelity., Comment: 17 pages, 5 figures

With the introduction of autonomous technology into our society, e.g. autonomous shipping, it is important to assess and assure the safety of autonomous systems in a real-world context. Simulation-based testing is a common approach to attempt to verify performance of autonomous systems, but assurance also requires formal evidence. This paper introduces the Assurance of Digital Assets (ADA) framework, a structured method for the assurance of digital assets, i.e. novel, complex, or intelligent systems enabled by digital technologies, using contract-based design. Results are shown for an autonomous ferry assurance case, focusing on collision avoidance during the ferry's transit. Further, we discuss the role of simulation-based testing in verifying compliance to contract specifications, to build the necessary evidence for an assurance case., Comment: 12 pages, 3 figures, final draft submitted to ICMASS/MTEC 2024 conference

We present team semantics for two of the most important linear and branching time specification languages, Linear Temporal Logic (LTL) and Computation Tree Logic (CTL). With team semantics, LTL is able to express hyperproperties, which have in the last decade been identified as a key concept in the verification of information flow properties. We study basic properties of the logic and classify the computational complexity of its satisfiability, path, and model checking problem. Further, we examine how extensions of the basic logic react to adding additional atomic operators. Finally, we compare its expressivity to the one of HyperLTL, another recently introduced logic for hyperproperties. Our results show that LTL with team semantics is a viable alternative to HyperLTL, which complements the expressivity of HyperLTL and has partially better algorithmic properties. For CTL with team semantics, we investigate the computational complexity of the satisfiability and model checking problem. The satisfiability problem is shown to be EXPTIME-complete while we show that model checking is PSPACE-complete.

Background: Centralized collection and processing of healthcare data across national borders pose significant challenges, including privacy concerns, data heterogeneity and legal barriers. To address some of these challenges, we formed an interdisciplinary consortium to develop a feder-ated health data network, comprised of six institutions across five countries, to facilitate Nordic-Baltic cooperation on secondary use of health data. The objective of this report is to offer early insights into our experiences developing this network. Methods: We used a mixed-method ap-proach, combining both experimental design and implementation science to evaluate the factors affecting the implementation of our network. Results: Technically, our experiments indicate that the network functions without significant performance degradation compared to centralized simu-lation. Conclusion: While use of interdisciplinary approaches holds a potential to solve challeng-es associated with establishing such collaborative networks, our findings turn the spotlight on the uncertain regulatory landscape playing catch up and the significant operational costs., Comment: 24 pages (including appendices), 1 figure

In order to solve problems of practical importance, quantum computers will likely need to incorporate quantum error correction, where a logical qubit is redundantly encoded in many noisy physical qubits. The large physical-qubit overhead typically associated with error correction motivates the search for more hardware-efficient approaches. Here, using a microfabricated superconducting quantum circuit, we realize a logical qubit memory formed from the concatenation of encoded bosonic cat qubits with an outer repetition code of distance $d=5$. The bosonic cat qubits are passively protected against bit flips using a stabilizing circuit. Cat-qubit phase-flip errors are corrected by the repetition code which uses ancilla transmons for syndrome measurement. We realize a noise-biased CX gate which ensures bit-flip error suppression is maintained during error correction. We study the performance and scaling of the logical qubit memory, finding that the phase-flip correcting repetition code operates below threshold, with logical phase-flip error decreasing with code distance from $d=3$ to $d=5$. Concurrently, the logical bit-flip error is suppressed with increasing cat-qubit mean photon number. The minimum measured logical error per cycle is on average $1.75(2)\%$ for the distance-3 code sections, and $1.65(3)\%$ for the longer distance-5 code, demonstrating the effectiveness of bit-flip error suppression throughout the error correction cycle. These results, where the intrinsic error suppression of the bosonic encodings allows us to use a hardware-efficient outer error correcting code, indicate that concatenated bosonic codes are a compelling paradigm for reaching fault-tolerant quantum computation., Comment: Comments on the manuscript welcome!

The Mittelman-di Cicco-Walker (MDW) H$\alpha$ Sky Survey is an autonomously-operated and ongoing all-sky imaging survey in the narrowband H$\alpha$ wavelength. The survey was founded by amateur astronomers, and is presented here in its first stage of refinement for rigorous scientific use. Each field is exposed through an H$\alpha$ filter with a 3nm bandwidth for a total of four hours, with a pixel scale of 3.2 arcsec. Here, we introduce the first Data Release of the MDW H$\alpha$ Survey (Data Release 0, or DR0), spanning 238 fields in the region of Orion (~3100 deg$^2$). DR0 includes: calibrated mean fields, star-removed mean fields, a point source catalog matched to Data Release 1 of the Panoramic Survey Telescope and Rapid Response System (Pan-STARRS1) and the INT Galactic Plane Survey (IGAPS), and mosaics.

Online adaptation of exoskeleton control based on muscle activity sensing is a promising way to personalize exoskeletons based on the user's biosignals. While several electromyography (EMG) based methods have been shown to improve joint torque estimation, EMG sensors require direct skin contact and complex post-processing. In contrast, force myography (FMG) measures normal forces from changes in muscle volume due to muscle activity. We propose an FMG-based method to estimate knee and ankle joint torques by combining joint angles and velocities with muscle activity information. We learn a model for joint torque estimation using Gaussian process regression (GPR). The effectiveness of the proposed FMG-based method is validated on isokinetic motions performed by two subjects. The model is compared to a baseline model using only joint angle and velocity, as well as a model augmented by EMG data. The results show that integrating FMG into exoskeleton control improves the joint torque estimation for the ankle and knee and is therefore a promising way to improve adaptability to different exoskeleton users., Comment: This work has been submitted to the IEEE for possible publication

The A-hierarchy is a parametric analogue of the polynomial hierarchy in the context of paramterised complexity theory. We give a new characterisation of the A-hierarchy in terms of a generalisation of the SUBSET-SUM problem.

Drainage in porous media can be broken down into two main mechanisms: a primary piston-like displacement of the interfaces through the bulk of pore bodies and throats, and a secondary slow flow through corners and films in the wake of the invasion front. In granular porous media, this secondary drainage mechanism unfolds in connected pathways of pendular structures, such as capillary bridges and liquid rings, formed between liquid clusters. To represent both mechanisms, we proposed a dynamic dual-network model for drainage, considering that a gas displaces a wetting liquid from quasi-2D granular porous media. For this model, dedicated analyses of the capillary bridge shapes and hydraulic conductivity were conducted so that the secondary drainage mechanism could be properly quantified at finite speeds. With the model, an investigation of the wetting-phase connectivity and flow during drainage was carried out, covering a broad range of flow conditions. Results indicate that the span of liquid-connected structures in the unsaturated region, as well as their ability to contribute to flow, varies significantly with Capillary and Bond numbers.

Quantum computing is rapidly advancing, harnessing the power of qubits' superposition and entanglement for computational advantages over classical systems. However, scalability poses a primary challenge for these machines. By implementing a hybrid workflow between classical and quantum computing instances, D-Wave has succeeded in pushing this boundary to the realm of industrial use. Furthermore, they have recently opened up to mixed integer linear programming (MILP) problems, expanding their applicability to many relevant problems in the field of optimisation. However, the extent of their suitability for diverse problem categories and their computational advantages remains unclear. This study conducts a comprehensive examination by applying a selection of diverse case studies to benchmark the performance of D-Wave's hybrid solver against that of industry-leading solvers such as CPLEX, Gurobi, and IPOPT. The findings indicate that D-Wave's hybrid solver is currently most advantageous for integer quadratic objective functions and shows potential for quadratic constraints. To illustrate this, we applied it to a real-world energy problem, specifically the MILP unit commitment problem. While D-Wave can solve such problems, its performance has not yet matched that of its classical counterparts.

When estimating models of a multivariable dynamic system, a typical condition for consistency is to require the input signals to be persistently exciting, which is guaranteed if the input spectrum is positive definite for a sufficient number of frequencies. In this paper it is investigated how such a condition can be relaxed by exploiting prior structural information on the multivariable system, such as structural zero elements in the transfer matrix or entries that are a priori known and therefore not parametrized. It is shown that in particular situations the data-informativity condition can be decomposed into different MISO (multiple input single output) situations, leading to relaxed conditions for the MIMO (multiple input multiple output) model. When estimating a single module in a linear dynamic network, the data-informativity conditions can generically be formulated as path-based conditions on the graph of the network. The new relaxed conditions for data-informativity will then also lead to relaxed path-based conditions on the network graph. Additionally the new expressions are shown to be closely related to earlier derived conditions for (generic) single module identifiability., Comment: 16 pages, 4 figures

Recently significant attention has been paid to magnetic-field-dependent photoluminescence (PL) features of the negatively charged nitrogen-vacancy (NV) centers in diamond. These features are used for microwave-free sensing and are indicative of the spin-bath properties in the diamond sample. Examinating the temperature dependence of the PL features allows to identify both temperature dependent and independent features, and to utilize them in diamond-based quantum sensing and dynamic nuclear polarization applications. Here, we study the thermal variability of many different features visible in a wide range of magnetic fields. To this end, we first discuss the origin of the features and tentatively assign the previously unidentified features to cross relaxation of NV center containing multi-spin systems. The experimental results are compared with theoretically predicted temperature shifts deduced from a combination of thermal expansion and electron-phonon interactions. A deeper insight into the thermal behavior of a wide array of the features may come with important consequences for various applications in high-precision NV thermometry, gyroscopes, solid-state clocks, and biomagnetic measurements., Comment: 12 pages, 16 figures