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The observation of a Hall effect, a finite transverse voltage induced by a longitudinal current, usually requires the breaking of time-reversal symmetry, for example through the application of an external magnetic field or the presence of long range magnetic order in a sample. Recently it was suggested that under certain symmetry conditions, the presence of finite Berry curvatures in the band structure of a system with time-reversal symmetry but without inversion symmetry can give rise to a nonlinear Hall effect in the presence of a probe current. In order to observe the nonlinear Hall effect, one requires a finite component of a so-called Berry dipole along the direction of the probe current. We report here measurements of the nonlinear Hall effect in two-dimensional electron gases fabricated on the surface of KTaO$_3$ with different surface crystal orientations as a function of the probe current, a transverse electric field and back gate voltage. For all three crystal orientations, the transverse electric field modifies the nonlinear Hall effect. We discuss our results in the context of the current understanding of the nonlinear Hall effect as well as potential experimental artifacts that may give rise to the same effects.

Over the past few years, a growing number of data platforms have emerged, including data commons, data repositories, and databases containing biomedical, environmental, social determinants of health and other data relevant to improving health outcomes. With the growing number of data platforms, interoperating multiple data platforms to form data meshes, data fabrics and other types of data ecosystems reduces data silos, expands data use, and increases the potential for new discoveries. In this paper, we introduce ten principles, which we call pillars, for data meshes. The goals of the principles are 1) to make it easier, faster, and more uniform to set up a data mesh from multiple data platforms; and, 2) to make it easier, faster, and more uniform, for a data platform to join one or more data meshes. The hope is that the greater availability of data through data meshes will accelerate research and that the greater uniformity of meshes will lower the cost of developing meshes and connecting a data platform to them., Comment: 10 pages, 1 figure

Reproducibility in research remains hindered by complex systems involving data, models, tools, and algorithms. Studies highlight a reproducibility crisis due to a lack of standardized reporting, code and data sharing, and rigorous evaluation. This paper introduces the concept of Continuous Analysis to address the reproducibility challenges in scientific research, extending the DevOps lifecycle. Continuous Analysis proposes solutions through version control, analysis orchestration, and feedback mechanisms, enhancing the reliability of scientific results. By adopting CA, the scientific community can ensure the validity and generalizability of research outcomes, fostering transparency and collaboration and ultimately advancing the field., Comment: 11 pages, 3 figures

We describe 'RingSim', a phenomenological agent-based model that allows numerical simulation of magnetic nanowire networks with areas of hundreds of micrometers squared for durations of hundreds of seconds; a practical impossibility for general-purpose micromagnetic simulation tools. In RingSim, domain walls (DWs) are instanced as mobile agents which respond to external magnetic fields, and their stochastic interactions with pinning sites and other DWs are described via simple phenomenological rules. We first present a detailed description of the model and its algorithmic implementation for simulating the behaviours of arrays of interconnected ring-shaped nanowires, which have previously been proposed as hardware platforms for unconventional computing applications. The model is then validated against a series of experimental measurements of an array's static and dynamic responses to rotating magnetic fields. The robust agreement between the modelled and experimental data demonstrates that agent-based modelling is a powerful tool for exploring mesoscale magnetic devices, enabling time scales and device sizes that are inaccessible to more conventional magnetic simulation techniques.

Traumatic brain injury (TBI) presents a significant public health challenge, often resulting in mortality or lasting disability. Predicting outcomes such as mortality and Functional Status Scale (FSS) scores can enhance treatment strategies and inform clinical decision-making. This study applies supervised machine learning (ML) methods to predict mortality and FSS scores using a real-world dataset of 300 pediatric TBI patients from the University of Colorado School of Medicine. The dataset captures clinical features, including demographics, injury mechanisms, and hospitalization outcomes. Eighteen ML models were evaluated for mortality prediction, and thirteen models were assessed for FSS score prediction. Performance was measured using accuracy, ROC AUC, F1-score, and mean squared error. Logistic regression and Extra Trees models achieved high precision in mortality prediction, while linear regression demonstrated the best FSS score prediction. Feature selection reduced 103 clinical variables to the most relevant, enhancing model efficiency and interpretability. This research highlights the role of ML models in identifying high-risk patients and supporting personalized interventions, demonstrating the potential of data-driven analytics to improve TBI care and integrate into clinical workflows.

We examined theoretically, experimentally and numerically the origin of the acoustothermal effect using a standing surface acoustic wave actuated sessile water droplet system. Despite a wealth of experimental studies and a few recent theoretical explorations, a profound understanding of the acoustothermal mechanism remains elusive. This study bridges the existing knowledge gap by pinpointing the fundamental causes of acoustothermal heating. Theory broadly applicable to any acoustofluidic system at arbitrary Reynolds numbers going beyond the regular perturbation analysis is presented. Relevant parameters responsible for the phenomenon are identified and an exact closed form expression delineating the underlining mechanism is presented. Furthermore, an analogy between the acoustothermal effect and electromagnetic heating is drawn, thereby deepening understanding of the acoustothermal process., Comment: 7 pages, 4 figures

We calculate the 2024 impact factors of 36 most widely used journals in Astrophysics, using the citations collated by NASA/ADS (Astrophysics Data System) and compare them to the official impact factors. This includes journals which publish papers outside of astrophysics such as PRD, EPJC, Nature etc. We also propose a new metric to gauge the impact factor based on the median number of citations in a journal and calculate the same for all the journals. We find that the ADS-based impact factors are mostly in agreement, albeit higher than the official impact factors for most journals. The journals with the maximum fractional difference in median-based and old impact factors are JHEAP and PTEP. We find the maximum difference between the ADS and official impact factor for Nature., Comment: 5 pages. Comments welcome

Recent work has suggested that nanoconfined water may exhibit superionic proton transport at lower temperatures and pressures than bulk water. Using first-principles-level simulations, we study the role of nuclear quantum effects in inducing this superionicity in nanoconfined water. We show that nuclear quantum effects increase the ionic conductivity of nanoconfined hexatic water, leading to superionic behaviour at lower temperatures and pressures than previously thought possible. Our work suggests that superionic water may be accessible in graphene nanocapillary experiments., Comment: 8 pages, 3 figures

Machine learning potentials have revolutionised the field of atomistic simulations in recent years and are becoming a mainstay in the toolbox of computational scientists. This paper aims to provide an overview and introduction into machine learning potentials and their practical application to scientific problems. We provide a systematic guide for developing machine learning potentials, reviewing chemical descriptors, regression models, data generation and validation approaches. We begin with an emphasis on the earlier generation of models, such as high-dimensional neural network potentials (HD-NNPs) and Gaussian approximation potential (GAP), to provide historical perspective and guide the reader towards the understanding of recent developments, which are discussed in detail thereafter. Furthermore, we refer to relevant expert reviews, open-source software, and practical examples - further lowering the barrier to exploring these methods. The paper ends with selected showcase examples, highlighting the capabilities of machine learning potentials and how they can be applied to push the boundaries in atomistic simulations.

Skid-steered wheel mobile robots (SSWMRs) operate in a variety of outdoor environments exhibiting motion behaviors dominated by the effects of complex wheel-ground interactions. Characterizing these interactions is crucial both from the immediate robot autonomy perspective (for motion prediction and control) as well as a long-term predictive maintenance and diagnostics perspective. An ideal solution entails capturing precise state measurements for decisions and controls, which is considerably difficult, especially in increasingly unstructured outdoor regimes of operations for these robots. In this milieu, a framework to identify pre-determined discrete modes of operation can considerably simplify the motion model identification process. To this end, we propose an interactive multiple model (IMM) based filtering framework to probabilistically identify predefined robot operation modes that could arise due to traversal in different terrains or loss of wheel traction.

Stabilizing vertical dynamics for on-road and off-road vehicles is an important research area that has been looked at mostly from the point of view of ride comfort. The advent of autonomous vehicles now shifts the focus more towards developing stabilizing techniques from the point of view of onboard proprioceptive and exteroceptive sensors whose real-time measurements influence the performance of an autonomous vehicle. The current solutions to this problem of managing the vertical oscillations usually limit themselves to the realm of active suspension systems without much consideration to modulating the vehicle velocity, which plays an important role by the virtue of the fact that vertical and longitudinal dynamics of a ground vehicle are coupled. The task of stabilizing vertical oscillations for military ground vehicles becomes even more challenging due lack of structured environments, like city roads or highways, in off-road scenarios. Moreover, changes in structural parameters of the vehicle, such as mass (due to changes in vehicle loading), suspension stiffness and damping values can have significant effect on the controller's performance. This demands the need for developing deep learning based control policies, that can take into account an extremely large number of input features and approximate a near optimal control action. In this work, these problems are addressed by training a deep reinforcement learning agent to minimize the vertical acceleration of a scaled vehicle travelling over bumps by controlling its velocity.

This article presents an innovative study in exploring, evaluating, and implementing deep learning architectures for the calibration of multi-modal sensor systems. The focus behind this is to leverage the use of sensor fusion to achieve dynamic, real-time alignment between 3D LiDAR and 2D Camera sensors. static calibration methods are tedious and time-consuming, which is why we propose utilizing Conventional Neural Networks (CNN) coupled with geometrically informed learning to solve this issue. We leverage the foundational principles of Extrinsic LiDAR-Camera Calibration tools such as RegNet, CalibNet, and LCCNet by exploring open-source models that are available online and comparing our results with their corresponding research papers. Requirements for extracting these visual and measurable outputs involved tweaking source code, fine-tuning, training, validation, and testing for each of these frameworks for equal comparisons. This approach aims to investigate which of these advanced networks produces the most accurate and consistent predictions. Through a series of experiments, we reveal some of their shortcomings and areas for potential improvements along the way. We find that LCCNet yields the best results out of all the models that we validated., Comment: 8 pages, 10 figures

Contrary to on-road autonomous navigation, off-road autonomy is complicated by various factors ranging from sensing challenges to terrain variability. In such a milieu, data-driven approaches have been commonly employed to capture intricate vehicle-environment interactions effectively. However, the success of data-driven methods depends crucially on the quality and quantity of data, which can be compromised by large variability in off-road environments. To address these concerns, we present a novel workflow to recreate the exact vehicle and its target operating conditions digitally for domain-specific data generation. This enables us to effectively model off-road vehicle dynamics from simulation data using the Koopman operator theory, and employ the obtained models for local motion planning and optimal vehicle control. The capabilities of the proposed methodology are demonstrated through an autonomous navigation problem of a 1:5 scale vehicle, where a terrain-informed planner is employed for global mission planning. Results indicate a substantial improvement in off-road navigation performance with the proposed algorithm (5.84x) and underscore the efficacy of digital twinning in terms of improving the sample efficiency (3.2x) and reducing the sim2real gap (5.2%).

This study presents a novel algorithm based on machine learning (ML) for the precise segmentation and measurement of detonation cells from soot foil images, addressing the limitations of manual and primitive edge detection methods prevalent in the field. Using advances in cellular biology segmentation models, the proposed algorithm is designed to accurately extract cellular patterns without a training procedure or dataset, which is a significant challenge in detonation research. The algorithm's performance was validated using a series of test cases that mimic experimental and numerical detonation studies. The results demonstrated consistent accuracy, with errors remaining within 10%, even in complex cases. The algorithm effectively captured key cell metrics such as cell area and span, revealing trends across different soot foil samples with uniform to highly irregular cellular structures. Although the model proved robust, challenges remain in segmenting and analyzing highly complex or irregular cellular patterns. This work highlights the broad applicability and potential of the algorithm to advance the understanding of detonation wave dynamics., Comment: 23 pages, 12 figures, submitted to Comb. and Flame; v2 - added section

Divergent solutions are ubiquitous with perturbation methods. We use continued function such as continued exponential to converge divergent series in perturbation approaches for energy eigenvalues of Helium, Stark effect and Zeeman effect on Hydrogen. We observe that convergence properties are obtained similar to that of the Pad\'e approximation which is extensively used in literature. Free parameters are not used which influence the convergence and only first few terms in the perturbation series are implemented.

Quasiparticle relaxation in pure superconductors is thought to be determined by the intrinsic inelastic scattering rate in the material. In certain applications, i.e. superconducting qubits and circuits, excess quasiparticles exist at densities far beyond the thermal equilibrium level, potentially leading to dephasing and energy loss. In order to engineer superconductors with shorter overall quasiparticle lifetimes, we consider the impact of a proximity layer on the transport of quasiparticles in a superconductor. We find that a normal metal layer can be used to significantly increase the relaxation rate of quasiparticles in a superconductor, as seen by a large reduction in the quasiparticle charge imbalance in a fully proximitized Cu/Al bilayer wire. The mechanism for this effect may be useful for preventing quasiparticle poisoning of qubits using carefully chosen proximity bilayers consisting of clean superconductors and disordered normal metals., Comment: 8 pages, 6 figures

Quantum key distribution (QKD) can secure cryptographic communication between two distant users, as guaranteed by the laws of quantum mechanics rather than computational assumptions. The twin-field scheme, which employs counter-propagated weak coherent light pulses, doubles the secure distance of standard QKD without using quantum repeaters. Here, we study a method to extend the twin-field key distribution protocol to a scheme for multi-party quantum key agreement. We study our protocol's security using a minimum error discrimination analysis and derive the asymptotic key rate based on the entanglement-based source-replacement scheme. We also simulate it on the ANSYS Interconnect platform to study the protocol's performance in certain practical situations.

We study a variety of finite quasiperiodic configurations with magnetodielectric $\delta$-function plates created from simple substitution rules. While previous studies for $N$ bodies involved interactions mediated by a scalar field, we extended our analysis of Green's function and corresponding Casimir energy to the electromagnetic field using plates with magnetic and dielectric properties for handling finite-size quasiperiodic lattices. The Casimir energy is computed for a class of quasiperiodic structures built from $N$ purely conducting or permeable $\delta$-plates. The Casimir energy of this quasiperiodic sequence of plates turns out to be either positive or negative, indicating that the pressure from the quantum vacuum tends to cause the stack of plates to expand or contract depending on their arrangement. We also handle the transverse electric and transverse magnetic mode Green's functions for $\delta$-plates and derive the Faddeev-like equation with the transition matrix for $N$ purely conducting or permeable plates.

The application for random numbers is ubiquitous. We experimentally build a well-studied quantum random number generator from homodyne measurements on the quadrature of the vacuum fluctuations. Semi-device-independence in this random number generator is usually obtained using phase modulators to shift the phase of the laser and obtain random sampling from both X and P quadrature measurements of the vacuum state in previous implementations. We characterize the experimental parameters for optimal performance of this source-device independent quantum random number generator by measuring the two quadratures concurrently using two homodyne detectors. We also study the influence of these parameters on randomness, which can be extracted based on Shannon entropy and von Neumann entropy, which correspond to an eavesdropper listening to classical and quantum side information, respectively.

Multiterminal Josephson junctions (MTJJs), devices in which a normal metal is in contact with three or more superconducting leads, have been proposed as artificial analogs of topological crystals. The topological nature of MTJJs manifests as a modulation of the quasiparticle density of states (DOS) in the normal metal that may be probed by tunneling measurements. We show that one can reveal this modulation by measuring the resistance of diffusive MTJJs with normal contacts, which shows rich structure as a function of the phase differences $\{\phi_i \}$. Our approach demonstrates a simple yet powerful technique for exploring topological effects in MTJJs., Comment: 6 pages, 4 figures

Vibrational spectroscopy is a cornerstone technique for molecular characterization and offers an ideal target for the computational investigation of molecular materials. Building on previous comprehensive assessments of efficient methods for infrared (IR) spectroscopy, this study investigates the predictive accuracy and computational efficiency of gas-phase IR spectra calculations, accessible through a combination of modern semiempirical quantum mechanical and transferable machine learning potentials. A composite approach for IR spectra prediction based on the doubly-harmonic approximation, utilizing harmonic vibrational frequencies in combination squared derivatives of the molecular dipole moment, is employed. This approach allows for methodical flexibility in the calculation of IR intensities from molecular dipoles and the corresponding vibrational modes. Various methods are systematically tested to suggest a suitable protocol with an emphasis on computational efficiency. Among these methods, semiempirical extended tight-binding (xTB) models, classical charge equilibrium models, and machine learning potentials trained for dipole moment prediction are assessed across a diverse dataset of organic molecules. We particularly focus on the recently reported machine learning potential MACE-OFF23 to address the accuracy limitations of conventional low-cost quantum mechanical and force-field methods. This study aims to establish a standard for the efficient computational prediction of IR spectra, facilitating the rapid and reliable identification of unknown compounds and advancing automated analytical workflows in chemistry.

The efficient readout of the relevant information is pivotal for quantum simulation experiments. Often only single observables are accessed by performing standard projective measurements. In this work, we implement a generalized measurement scheme based on controlled outcoupling of atoms. This gives us simultaneous access to number imbalance and relative phase in a system of two tunnel-coupled 1D Bose gases, which realize a quantum simulator of the sine-Gordon field theory. We demonstrate that our measurement is quantum limited by accessing number squeezing and show that we can track Josephson oscillation dynamics with the generalized measurements. Finally, we show that the scheme allows the extraction of atoms while maintaining the system's coherent dynamics, which opens up the door to accessing multi-time correlation functions. Our scheme constitutes a step towards accessing quantum properties of the sine-Gordon field theory and, in the future, studying spatially extended systems under continuous monitoring., Comment: 7 pages, 5 figures

Modern day studies show a high degree of correlation between high yielding crop varieties and plants with upright leaf angles. It is observed that plants with upright leaf angles intercept more light than those without upright leaf angles, leading to a higher rate of photosynthesis. Plant scientists and breeders benefit from tools that can directly measure plant parameters in the field i.e. on-site phenotyping. The estimation of leaf angles by manual means in a field setting is tedious and cumbersome. We mitigate the tedium using a combination of the Mask R-CNN instance segmentation neural network, and Line Segment Transformer (LETR), a vision transformer. The proposed Computer Vision (CV) pipeline is applied on two image datasets, Summer 2015-Ames ULA and Summer 2015- Ames MLA, with a combined total of 1,827 plant images collected in the field using FieldBook, an Android application aimed at on-site phenotyping. The leaf angles estimated by the proposed pipeline on the image datasets are compared to two independent manual measurements using ImageJ, a Java-based image processing program developed at the National Institutes of Health and the Laboratory for Optical and Computational Instrumentation. The results, when compared for similarity using the Cosine Similarity measure, exhibit 0.98 similarity scores on both independent measurements of Summer 2015-Ames ULA and Summer 2015-Ames MLA image datasets, demonstrating the feasibility of the proposed pipeline for on-site measurement of leaf angles.

Charged particle reconstruction is one the most computationally heavy components of the full event reconstruction of Large Hadron Collider (LHC) experiments. Looking to the future, projections for the High Luminosity LHC (HL-LHC) indicate a superlinear growth for required computing resources for single-threaded CPU algorithms that surpass the computing resources that are expected to be available. The combination of these facts creates the need for efficient and computationally performant pattern recognition algorithms that will be able to run in parallel and possibly on other hardware, such as GPUs, given that these become more and more available in LHC experiments and high-performance computing centres. Line Segment Tracking (LST) is a novel such algorithm which has been developed to be fully parallelizable and hardware agnostic. The latter is achieved through the usage of the Alpaka library. The LST algorithm has been tested with the CMS central software as an external package and has been used in the context of the CMS HL-LHC High Level Trigger (HLT). When employing LST for pattern recognition in the HLT tracking, the physics and timing performances are shown to improve with respect to the ones utilizing the current pattern recognition algorithms. The latest results on the usage of the LST algorithm within the CMS HL-LHC HLT are presented, along with prospects for further improvements of the algorithm and its CMS central software integration.

Ferroelectricity is characterized by the presence of spontaneous and switchable macroscopic polarization. Scaling limits of ferroelectricity have been of both fundamental and technological importance, but the probes of ferroelectricity have often been indirect due to confounding factors such as leakage in the direct electrical measurements. Recent interest in low-voltage switching electronic devices squarely puts the focus on ultrathin limits of ferroelectricity in an electronic device form, specifically on the robustness of ferroelectric characteristics such as retention and endurance for practical applications. Here, we illustrate how manipulating the kinetic energy of the plasma plume during pulsed laser deposition can yield ultrathin ferroelectric capacitor heterostructures with high bulk and interface quality, significantly low leakage currents and a broad "growth window". These heterostructures venture into previously unexplored aspects of ferroelectric properties, showcasing ultralow switching voltages ($<$0.3 V), long retention times ($>$10$^{4}$s), and high endurance ($>$10$^{11}$cycles) in 20 nm films of the prototypical perovskite ferroelectric, BaTiO$_{3}$. Our work demonstrates that materials engineering can push the envelope of performance for ferroelectric materials and devices at the ultrathin limit and opens a direct, reliable and scalable pathway to practical applications of ferroelectrics in ultralow voltage switches for logic and memory technologies.

We present an efficient and scalable algorithm for performing matrix-vector multiplications ("matvecs") for block Toeplitz matrices. Such matrices, which are shift-invariant with respect to their blocks, arise in the context of solving inverse problems governed by autonomous systems, and time-invariant systems in particular. In this article, we consider inverse problems that are solved for inferring unknown parameters from observational data of a linear time-invariant dynamical system given in the form of partial differential equations (PDEs). Matrix-free Newton-conjugate-gradient methods are often the gold standard for solving these inverse problems, but they require numerous actions of the Hessian on a vector. Matrix-free adjoint-based Hessian matvecs require solution of a pair of linearized forward/adjoint PDE solves per Hessian action, which may be prohibitive for large-scale inverse problems, especially when efficient low-rank approximations of the Hessian are not readily available, such as for hyperbolic PDE operators. Time invariance of the forward PDE problem leads to a block Toeplitz structure of the discretized parameter-to-observable (p2o) map defining the mapping from inputs (parameters) to outputs (observables) of the PDEs. This block Toeplitz structure enables us to exploit two key properties: (1) compact storage of the p2o map and its adjoint; and (2) efficient fast Fourier transform (FFT)-based Hessian matvecs. The proposed algorithm is mapped onto large multi-GPU clusters and achieves more than 80 percent of peak bandwidth on an NVIDIA A100 GPU. Excellent weak scaling is shown for up to 48 A100 GPUs. For the targeted problems, the implementation executes Hessian matvecs within fractions of a second, orders of magnitude faster than can be achieved by the conventional matrix-free Hessian matvecs via forward/adjoint PDE solves.

The contribution of nuclear quantum effects (NQEs) to the properties of various hydrogen-bound systems, including biomolecules, is increasingly recognized. Despite the development of many acceleration techniques, the computational overhead of incorporating NQEs in complex systems is sizable, particularly at low temperatures. In this work, we leverage deep learning and multiscale coarse-graining techniques to mitigate the computational burden of path integral molecular dynamics (PIMD). Specifically, we employ a machine-learned potential to accurately represent corrections to classical potentials, thereby significantly reducing the computational cost of simulating NQEs. We validate our approach using four distinct systems: Morse potential, Zundel cation, single water molecule, and bulk water. Our framework allows us to accurately compute position-dependent static properties, as demonstrated by the excellent agreement obtained between the machine-learned potential and computationally intensive PIMD calculations, even in the presence of strong NQEs. This approach opens the way to the development of transferable machine-learned potentials capable of accurately reproducing NQEs in a wide range of molecular systems.

Designing and manipulating virtual human heads is essential across various applications, including AR, VR, gaming, human-computer interaction and VFX. Traditional graphic-based approaches require manual effort and resources to achieve accurate representation of human heads. While modern deep learning techniques can generate and edit highly photorealistic images of faces, their focus remains predominantly on 2D facial images. This limitation makes them less suitable for 3D applications. Recognizing the vital role of editing within the UV texture space as a key component in the 3D graphics pipeline, our work focuses on this aspect to benefit graphic designers by providing enhanced control and precision in appearance manipulation. Research on existing methods within the UV texture space is limited, complex, and poses challenges. In this paper, we introduce SemUV: a simple and effective approach using the FFHQ-UV dataset for semantic manipulation directly within the UV texture space. We train a StyleGAN model on the publicly available FFHQ-UV dataset, and subsequently train a boundary for interpolation and semantic feature manipulation. Through experiments comparing our method with 2D manipulation technique, we demonstrate its superior ability to preserve identity while effectively modifying semantic features such as age, gender, and facial hair. Our approach is simple, agnostic to other 3D components such as structure, lighting, and rendering, and also enables seamless integration into standard 3D graphics pipelines without demanding extensive domain expertise, time, or resources., Comment: CVIP 2024 Preprint

Water confined in nanoscale cavities plays a crucial role in everyday phenomena in geology and biology, as well as technological applications at the water-energy nexus. However, even understanding the basic properties of nano-confined water is extremely challenging for theory, simulations, and experiments. In particular, determining the melting temperature of quasi-one-dimensional ice polymorphs confined in carbon nanotubes has proven to be an exceptionally difficult task, with previous experimental and classical simulations approaches report values ranging from $\sim 180 \text{ K}$ up to $\sim 450 \text{ K}$ at ambient pressure. In this work, we use a machine learning potential that delivers first principles accuracy to study the phase diagram of water for confinement diameters $ 9.5 < d < 12.5 \text{ \AA}$. We find that several distinct ice polymorphs melt in a surprisingly narrow range between $\sim 280 \text{ K}$ and $\sim 310 \text{ K}$, with a melting mechanism that depends on the nanotube diameter. These results shed new light on the melting of ice in one-dimension and have implications for the operating conditions of carbon-based filtration and desalination devices.

Biphasic interfaces are complex but fascinating regimes that display a number of properties distinct from those of the bulk. The CO$_2$-H$_2$O interface, in particular, has been the subject of a number of studies on account of its importance for the carbon life cycle as well as carbon capture and sequestration schemes. Despite this attention, there remain a number of open questions on the nature of the CO$_2$-H$_2$O interface, particularly concerning the interfacial tension and phase behavior of CO$_2$ at the interface. In this paper, we seek to address these ambiguities using ab initio-quality simulations. Harnessing the benefits of machine-learned potentials and enhanced statistical sampling methods, we present an ab initio-level description of the CO$_2$-H$_2$O interface. Interfacial tensions are predicted from 1-500 bar and found to be in close agreement with experiment at the pressures for which experimental data is available. Structural analyses indicate the build-up of an adsorbed, saturated CO$_2$ film forming at low pressure (20 bar) with properties similar to those of the bulk liquid, but preferential perpendicular alignment with respect to the interface. CO$_2$ monolayer build-up coincides with a reduced structuring of water molecules close to the interface. This study highlights the predictive nature of machine-learned potentials for complex macroscopic properties of biphasic interfaces, and the mechanistic insight obtained into carbon dioxide aggregation at the water interface is of high relevance for geoscience, climate research, and materials science., Comment: 14 pages, 10 figures

Preferential equality is an equivalence relation on fuzzy subsets of finite sets and is a generalization of classical equality of subsets. In this paper we introduce a tightened version of the preferential equality on fuzzy subsets and derive some important combinatorial formulae for the number of such tight fuzzy subsets of an n-element set where n is a natural number. We also offer some asymptotic results

Offering a promising solution to the scalability challenges associated with human evaluation, the LLM-as-a-judge paradigm is rapidly gaining traction as an approach to evaluating large language models (LLMs). However, there are still many open questions about the strengths and weaknesses of this paradigm, and what potential biases it may hold. In this paper, we present a comprehensive study of the performance of various LLMs acting as judges, focusing on a clean scenario in which inter-human agreement is high. Investigating thirteen judge models of different model sizes and families, judging answers of nine different 'examtaker models' - both base and instruction-tuned - we find that only the best (and largest) models achieve reasonable alignment with humans. However, they are still quite far behind inter-human agreement and their assigned scores may still differ with up to 5 points from human-assigned scores. In terms of their ranking of the nine exam-taker models, instead, also smaller models and even the lexical metric contains may provide a reasonable signal. Through error analysis and other studies, we identify vulnerabilities in judge models, such as their sensitivity to prompt complexity and length, and a tendency toward leniency. The fact that even the best judges differ from humans in this comparatively simple setup suggest that caution may be wise when using judges in more complex setups. Lastly, our research rediscovers the importance of using alignment metrics beyond simple percent alignment, showing that judges with high percent agreement can still assign vastly different scores.

The Casimir energy for $N$ $\delta$-function plates depends on multiple scattering parameter $\Delta$. This $N$ body interaction was distributed into two body interactions with nearest neighbour scattering and next-to-nearest neighbour scattering based on partitions of $N-1$ and its permutations. Implementing this methodology, we investigate Casimir energy for multiple plates with constant conductivity relatable to Graphene. We also study Casimir interaction between a perfect magnetic conductor and multiple constant conductivity $\delta$ plates, which results in Boyer repulsion. In the asymptotic limit for ideal boundary conditions, the results become simple where multiple scattering parameter $\Delta$ consists only of nearest neighbour scattering term.

Quantum technology can enable secure communication for cryptography purposes using quantum key distribution. Quantum key distribution protocols provide a secret key between two users with security guaranteed by the laws of quantum mechanics. To define the proper implementation of a quantum key distribution system using a particular cryptography protocol, it is crucial to critically and meticulously assess the device's performance due to technological limitations in the components used. We perform simulations on the ANSYS Interconnect platform to characterise the practical implementation of these devices using distributed-phase-reference protocols differential-phase-shift and coherent-one-way quantum key distribution. Further, we briefly describe and simulate some possible eavesdropping attempts, backflash attack, trojan-horse attack and detector-blinding attack exploiting the device imperfections.