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Building on top of the success in AI-based atmospheric emulation, we propose an AI-based ocean emulation and downscaling framework focusing on the high-resolution regional ocean over Gulf of Mexico. Regional ocean emulation presents unique challenges owing to the complex bathymetry and lateral boundary conditions as well as from fundamental biases in deep learning-based frameworks, such as instability and hallucinations. In this paper, we develop a deep learning-based framework to autoregressively integrate ocean-surface variables over the Gulf of Mexico at $8$ Km spatial resolution without unphysical drifts over decadal time scales and simulataneously downscale and bias-correct it to $4$ Km resolution using a physics-constrained generative model. The framework shows both short-term skills as well as accurate long-term statistics in terms of mean and variability.

We introduce a single-view reconstruction technique of volumetric fields in which multiple light scattering effects are omnipresent, such as in clouds. We model the unknown distribution of volumetric fields using an unconditional diffusion model trained on a novel benchmark dataset comprising 1,000 synthetically simulated volumetric density fields. The neural diffusion model is trained on the latent codes of a novel, diffusion-friendly, monoplanar representation. The generative model is used to incorporate a tailored parametric diffusion posterior sampling technique into different reconstruction tasks. A physically-based differentiable volume renderer is employed to provide gradients with respect to light transport in the latent space. This stands in contrast to classic NeRF approaches and makes the reconstructions better aligned with observed data. Through various experiments, we demonstrate single-view reconstruction of volumetric clouds at a previously unattainable quality.

Neuromorphic computing, inspired by the brain's parallel and energy-efficient processing, offers a transformative approach to artificial intelligence. In this study, we fabricated optimized spin-transfer torque nano-oscillators (STNOs) and investigated their dynamic behaviors using a hybrid excitation scheme combining AC laser illumination and DC bias currents. Laser-induced thermal gradients generate pulsed thermoelectric voltages ($V_{\text{AC}}$) via the Tunnel Magneto-Seebeck (TMS) effect, while the addition of bias currents enhances this response, producing both $V_{\text{AC}}$ and a DC component ($V_{\text{DC}}$). Magnetic field sweeps reveal distinct switching between parallel (P) and antiparallel (AP) magnetization states in both voltage components, supporting multistate memory applications. Millivolt-range thermovoltage signals in open-circuit conditions demonstrate CMOS compatibility, enabling simplified, scalable neuromorphic systems. Under biased conditions, enhanced thermovoltage outputs exhibit intriguing phenomena, including spikes correlated with Barkhausen jumps and double-switching behavior, offering insights into magnetization dynamics and vortex transitions. These features resemble neural spiking behavior, suggesting applications in spiking neural networks, reservoir computing, multistate logic, analog computing, and high-resolution sensing. By bridging spintronic phenomena with practical applications, this work provides a versatile platform for next-generation AI technologies and adaptive computing architectures.

Neural network models implemented in embedded devices have been shown to be susceptible to side-channel attacks (SCAs), allowing recovery of proprietary model parameters, such as weights and biases. There are already available countermeasure methods currently used for protecting cryptographic implementations that can be tailored to protect embedded neural network models. Shuffling, a hiding-based countermeasure that randomly shuffles the order of computations, was shown to be vulnerable to SCA when the Fisher-Yates algorithm is used. In this paper, we propose a design of an SCA-secure version of the Fisher-Yates algorithm. By integrating the masking technique for modular reduction and Blakely's method for modular multiplication, we effectively remove the vulnerability in the division operation that led to side-channel leakage in the original version of the algorithm. We experimentally evaluate that the countermeasure is effective against SCA by implementing a correlation power analysis attack on an embedded neural network model implemented on ARM Cortex-M4. Compared to the original proposal, the memory overhead is $2\times$ the biggest layer of the network, while the time overhead varies from $4\%$ to $0.49\%$ for a layer with $100$ and $1000$ neurons, respectively.

Anisotropic materials with low symmetries hold significant promise for next-generation electronic and quantum devices. 2M-WS2, a candidate for topological superconductivity, has garnered considerable interest. However, a comprehensive understanding of how its anisotropic features contribute to unconventional superconductivity, along with a simple, reliable method to identify its crystal orientation, remains elusive. Here, we combine theoretical and experimental approaches to investigate angle- and polarization-dependent anisotropic Raman modes of 2M-WS2. Through first-principles calculations, we predict and analyze phonon dispersion and lattice vibrations of all Raman modes in 2M-WS2. We establish a direct correlation between their anisotropic Raman spectra and high-resolution transmission electron microscopy images. Finally, we demonstrate that anisotropic Raman spectroscopy can accurately determine the crystal orientation and twist angle between two stacked 2M-WS2 layers. Our findings provide insights into the electron-phonon coupling and anisotropic properties of 2M-WS2, paving the way for the use of anisotropic materials in advanced electronic and quantum devices., Comment: 19 pages, 5 figures

The effects of negative triangularity (NT) on boundary plasma turbulence in double-null (DN) configurations are investigated using global, nonlinear, three-dimensional, flux-driven two-fluid simulations. NT plasmas exhibit suppressed interchange-driven instabilities, resulting in enhanced confinement and lower fluctuation levels compared to positive triangularity (PT) plasmas. This reduction in interchange instability is associated with the weakening of curvature effects in the unfavorable region, caused by the stretching of magnetic field lines at the outer midplane. The magnetic disconnection between the turbulent low-field side (LFS) and the quiescent high-field side (HFS) results in most of the heat flux reaching the DN outer targets. In NT plasmas, the power load on the outer target is reduced, while it increases on the inner target, indicating a reduced in-out power asymmetry compared PT plasmas. Furthermore, the analysis of power load asymmetry between the upper and lower targets shows that the up-down power asymmetry is mitigated in NT plasmas, mainly due to the reduced total power crossing the separatrix. The reduction of interchange instabilities in NT plasmas also affects the blob dynamics. A three-dimensional blob analysis reveals that NT plasmas feature smaller blob sizes and slower propagation velocities. Finally, an analytical scaling law for blob size and velocity that includes plasma shaping effects is derived based on the two-region model and is found to qualitatively capture the trends observed in nonlinear simulations., Comment: 33 pages, 9 figures

In particle physics, it is needed to evaluate the possibility that excesses of events in mass spectra are due to statistical fluctuations as quantified by the standards of local and global significances. Without prior knowledge of a particle's mass, it is especially critical to estimate its global significance. The usual approach is to count the number of times a significance limit is exceeded in a collection of simulated Monte Carlo (MC) 'toy experiments.' To demonstrate this conventional method for global significance, we performed simulation studies according to a recent Compact Muon Solenoid (CMS) result to show its effectiveness. However, this counting method is not practical for computing large global significances. To address this problem, we developed a new 'extrapolation' method to evaluate the global significance. We compared the global significance estimated by our new method with that of the conventional approach, and verified its feasibility and effectiveness. This method is also applicable for cases where only small toy MC samples are available. In this approach, the significance is calculated based on p-values, assuming symmetrical Gaussian distributions., Comment: 17 pages, 15 figures

A Schr\"odinger operator that is bounded below and has a unique positive ground state can be transformed into a Dirichlet form operator by the ground state transformation. If the resulting Dirichlet form operator is hypercontractive, Davies and Simon call the Schr\"odinger operator ``intrinsically hypercontractive". I will show that if one adds a suitable potential onto an intrinsically hypercontractive Schr\"odinger operator it remains intrinsically hypercontractive. The proof uses a fortuitous relation between the WKB equation and logarithmic Sobolev inequalities. All bounds are dimension independent. The main theorem will be applied to several examples., Comment: 123 pages

Building on work by Fiore-Pronk-Paoli, we construct four model structures on the category of double categories, each modeling one of the following: simplicial spaces, Segal spaces, $(\infty,1)$-categories, and $\infty$-groupoids. Additionally, we provide an explicit formula for computing homotopy colimits in these models using the Grothendieck construction. We expect the model of double categories for $(\infty,1)$-categories to play a similar role than that of the model of categories for spaces or $\infty$-groupoids in Grothendieck's study of the homotopy theory of spaces., Comment: 37 pages; comments welcome

Recent pledges to triple global nuclear capacity by 2050 suggest a "nuclear renaissance," bolstered by reactor concepts such as sodium-cooled fast reactors, high-temperature reactors, and molten salt reactors. These technologies claim to address the challenges of today's high-capacity light-water reactors, i.e., cost overruns, delays, and social acceptance, while also offering additional non-electrical applications. However, this analysis reveals that none of these concepts currently meet the prerequisites of affordability, competitiveness, or commercial availability. We omit social acceptability. The cost analysis reveals optimistic FOAK cost assumptions of 5,623 to 9,511 USD per kW, and NOAK cost projections as low as 1,476 USD per kW. At FOAK cost, the applied energy system model includes no nuclear power capacity, and thus indicates that significant cost reductions would be required for these technologies to contribute to energy system decarbonization. In low-cost scenarios, reactors capable of producing high temperature heat become competitive with other low-carbon technologies. We conclude that, for reactor capacties to increase significantly, a focus on certain technology lines ist necessary. However, until a concept becomes viable and commercially available, policymakers should prioritize existing technologies to decarbonize energy systems.

We consider a massive vector Boson in a static patch of $D$-dimensional de Sitter space (dS$_D$). We argue that this field is controlled by an effective physical (squared) mass $\mu_{\mathrm{v}}^2 = m_{\mathrm{v}}^2 + 2(D-1)\ell_{\mathrm{dS}}^{-2}$ which differs from the naive "Lagrangian" (squared) mass $m_{\mathrm{v}}^2$ that appears in the usual form of the Proca Lagrangian/action. In particular, we conjecture that the theory remains well-defined in the naively tachyonic Lagrangian mass range $-2(D-1) < m_{\mathrm{v}}^2\ell_{\mathrm{dS}}^2 < 0$. We identify several interesting physical features of the "edge of stability" $m_{\mathrm{v}}^2\ell_{\mathrm{dS}}^2 = -2(D-1)$. Fixing a static patch breaks the $D$-dimensional de Sitter isometries down to a "static patch subgroup", which explains why our theory may continue to be well-defined in the above mass range despite not fitting into a unitary irreducible representation of SO$(D,1)$. We conjecture that for situations such as ours, the usual $\mathrm{SO}(D,1)$ "Higuchi bound" on unitarity is replaced by the concept of the edge of stability. In $D = 3$ spacetime dimensions, the $s$-wave sector of our theory remarkably simplifies, becoming equivalent to the $p$-wave sector of an ordinary massive scalar. In this case we can explicitly check that the $D = 3$ $s$-wave sector remains well-defined -- both classically and quantum mechanically -- in the above mass range. In the course of our analysis, we will derive the general classical solution and the quasinormal frequency spectrum for the massive vector Boson in the static patch of dS$_D$, generalizing previous work by Higuchi [1], which was done for the special case $D = 4$. While this work was being completed, we became aware of upcoming work by Grewal, Law, and Lochab [2] which will contain a similar derivation., Comment: 35 + 7 pages, 4 figures

The continuous scaling of metal-oxide-semiconductor field-effect transistors (MOSFETs) has led to device geometries where charged carriers are increasingly confined to ever smaller channel cross sections. This development is associated with reduced screening of long-range Coulomb interactions. To accurately predict the behavior of such ultra-scaled devices, electron-electron (e-e) interactions must be explicitly incorporated in their quantum transport simulation. In this paper, we present an \textit{ab initio} atomistic simulation framework based on density functional theory, the non-equilibrium Green's function formalism, and the self-consistent GW approximation to perform this task. The implemented method is first validated with a carbon nanotube test structure before being applied to calculate the transfer characteristics of a silicon nanowire MOSFET in a gate-all-around configuration. As a consequence of e-e scattering, the energy and spatial distribution of the carrier and current densities both significantly change, while the on-current of the transistor decreases owing to the Coulomb repulsion between the electrons. Furthermore, we demonstrate how the resulting bandgap modulation of the nanowire channel as a function of the gate-to-source voltage could potentially improve the device performance. To the best of our knowledge, this study is the first one reporting large-scale atomistic quantum transport simulations of nano-devices under non-equilibrium conditions and in the presence of e-e interactions within the GW approximation.

We describe the methods and results of a classification of the non-synchronizing primitive permutation groups of degree up to 624. We make use of theory and computation to determine the primitive groups of degree up to 624 that are non-separating, which is a necessary, and very often sufficient, condition for a primitive group to be non-synchronizing, and determine exactly which of these non-separating groups are non-synchronizing. A new infinite sequence of non-synchronizing graphs is discovered and new examples of non-separating, but synchronizing, groups are found., Comment: 18 pages

For safety, autonomous systems must be able to consider sudden changes and enact contingency plans appropriately. State-of-the-art methods currently find trajectories that balance between nominal and contingency behavior, or plan for a singular contingency plan; however, this does not guarantee that the resulting plan is safe for all time. To address this research gap, this paper presents Contingency-MPPI, a data-driven optimization-based strategy that embeds contingency planning inside a nominal planner. By learning to approximate the optimal contingency-constrained control sequence with adaptive importance sampling, the proposed method's sampling efficiency is further improved with initializations from a lightweight path planner and trajectory optimizer. Finally, we present simulated and hardware experiments demonstrating our algorithm generating nominal and contingency plans in real time on a mobile robot., Comment: 12 Pages, 6 Figures

Globally-consistent localization in urban environments is crucial for autonomous systems such as self-driving vehicles and drones, as well as assistive technologies for visually impaired people. Traditional Visual-Inertial Odometry (VIO) and Visual Simultaneous Localization and Mapping (VSLAM) methods, though adequate for local pose estimation, suffer from drift in the long term due to reliance on local sensor data. While GPS counteracts this drift, it is unavailable indoors and often unreliable in urban areas. An alternative is to localize the camera to an existing 3D map using visual-feature matching. This can provide centimeter-level accurate localization but is limited by the visual similarities between the current view and the map. This paper introduces a novel approach that achieves accurate and globally-consistent localization by aligning the sparse 3D point cloud generated by the VIO/VSLAM system to a digital twin using point-to-plane matching; no visual data association is needed. The proposed method provides a 6-DoF global measurement tightly integrated into the VIO/VSLAM system. Experiments run on a high-fidelity GPS simulator and real-world data collected from a drone demonstrate that our approach outperforms state-of-the-art VIO-GPS systems and offers superior robustness against viewpoint changes compared to the state-of-the-art Visual SLAM systems.

Leveraging reciprocal-space proximity effect between superconducting bulk and topological surface states (TSSs) offers a promising way to topological superconductivity. However, elucidating the mutual influence of bulk and TSSs on topological superconductivity remains a challenge. Here, we report pioneering transport evidence of a thickness-dependent transition from conventional to unconventional superconductivity in 2M-phase WS2 (2M-WS2). As the sample thickness reduces, we see clear changes in key superconducting metrics, including critical temperature, critical current, and carrier density. Notably, while thick 2M-WS2 samples show conventional superconductivity, with an in-plane (IP) upper critical field constrained by the Pauli limit, samples under 20 nm exhibit a pronounced IP critical field enhancement, inversely correlated with 2D carrier density. This marks a distinct crossover to unconventional superconductivity with strong spin-orbit-parity coupling. Our findings underscore the crucial role of sample thickness in accessing topological states in 2D topological superconductors, offering pivotal insights into future studies of topological superconductivity., Comment: 15 pages, 4 figures

Distributed model training needs to be adapted to challenges such as the straggler effect and Byzantine attacks. When coordinating the training process with multiple computing nodes, ensuring timely and reliable gradient aggregation amidst network and system malfunctions is essential. To tackle these issues, we propose \textit{dSTAR}, a lightweight and efficient approach for distributed stochastic gradient descent (SGD) that enhances robustness and convergence. \textit{dSTAR} selectively aggregates gradients by collecting updates from the first \(k\) workers to respond, filtering them based on deviations calculated using an ensemble median. This method not only mitigates the impact of stragglers but also fortifies the model against Byzantine adversaries. We theoretically establish that \textit{dSTAR} is (\(\alpha, f\))-Byzantine resilient and achieves a linear convergence rate. Empirical evaluations across various scenarios demonstrate that \textit{dSTAR} consistently maintains high accuracy, outperforming other Byzantine-resilient methods that often suffer up to a 40-50\% accuracy drop under attack. Our results highlight \textit{dSTAR} as a robust solution for training models in distributed environments prone to both straggler delays and Byzantine faults., Comment: 15 pages

We analyzed the 7.92$\times 10^{11}$ cosmic-ray-induced muon events collected by the IceCube Neutrino Observatory from May 13, 2011, when the fully constructed experiment started to take data, to May 12, 2023. This dataset provides an up-to-date cosmic-ray arrival direction distribution in the Southern Hemisphere with unprecedented statistical accuracy covering more than a full period length of a solar cycle. Improvements in Monte Carlo event simulation and better handling of year-to-year differences in data processing significantly reduce systematic uncertainties below the level of statistical fluctuations compared to the previously published results. We confirm the observation of a change in the angular structure of the cosmic-ray anisotropy between 10 TeV and 1 PeV, more specifically in the 100-300 TeV energy range.

We report on a search for millicharged particles (mCPs) produced in cosmic ray proton atmospheric interactions using data collected during the first science run of the LUX-ZEPLIN experiment. The mCPs produced by two processes -- meson decay and proton bremsstrahlung -- are considered in this study. This search utilized a novel signature unique to liquid xenon (LXe) time projection chambers (TPCs), allowing sensitivity to mCPs with masses ranging from 10 to 1000 MeV/c$^2$ and fractional charges between 0.001 and 0.02 of the electron charge e. With an exposure of 60 live days and a 5.5 tonne fiducial mass, we observed no significant excess over background. This represents the first experimental search for atmospheric mCPs and the first search for mCPs using an underground LXe experiment.

We present the development of a decay energy spectroscopy (DES) method for the analysis of radioactive impurities using magnetic microcalorimeters (MMCs). The DES system was designed to analyze radionuclides, such as Ra-226, Th-228, and their daughter nuclides, in materials like copper, commonly used in rare-event search experiments. We tested the DES system with a gold foil absorber measuring 20x20x0.05 mm^3, large enough to accommodate a significant drop of source solution. Using this large absorber and an MMC sensor, we conducted a long-term measurement over ten days of live time, requiring 11 ADR cooling cycles. The combined spectrum achieved an energy resolution of 45 keV FWHM, sufficient to identify most alpha and DES peaks of interest. Specific decay events from radionuclide contaminants in the absorber were identified. This experiment confirms the capability of the DES system to measure alpha decay chains of Ra-226 and Th-228, offering a promising method for radio-impurity evaluation in ultra-low background experiments., Comment: 5 pages, 5 figures

The International Muon Collider Collaboration (IMCC) is engaged in a design study for a future facility intended to collide muons. Subsequent to the initial linear acceleration, the counter-rotating muons and anti-muons are accelerated in a chain of rapid cycling synchrotrons (RCS) up to the multi-TeV collision energy. To maximise the number of muons available in the collider, it is essential to exploit the time dilation of the muon lifetime by employing a large accelerating gradient. The 1.3 GHz TESLA cavity serves as the baseline for the RCS chain. Considering the high bunch population and the small aperture of the cavity, the resulting beam-induced voltage per bunch passage is considerable, resulting in a substantial perturbation of the cavity voltage for subsequent bunch passages. In this contribution, the effects of beam loading during the acceleration cycle on the muons are calculated with the objective of determining the optimum parameters for minimising the cavity voltage transients. The interaction of the induced voltages, considering the counter-rotating beams, is studied., Comment: Appears in the proceedings of the 14th International Computational Accelerator Physics Conference (ICAP'24), 2-5 October 2024, Germany

A $(k,g,\underline{g+1})$-graph is a $k$-regular graph of girth $g$ which does not contain cycles of length $g+1$. Such graphs are known to exist for all parameter pairs $k \geq 3, g \geq 3 $, and we focus on determining the orders $n(k,g,\underline{g+1})$ of the smallest $(k,g,\underline{g+1})$-graphs. This problem can be viewed as a special case of the previously studied Girth-Pair Problem, the problem of finding the order of a smallest $k$-regular graph in which the length of a smallest even length cycle and the length of a smallest odd length cycle are prescribed. When considering the case of an odd girth $g$, this problem also yields results towards the Cage Problem, the problem of finding the order of a smallest $k$-regular graph of girth $g$. We establish the monotonicity of the function $n(k,g,\underline{g+1})$ with respect to increasing $g$, and present universal lower bounds for the values $n(k,g,\underline{g+1})$. We propose an algorithm for generating all $(k,g,\underline{g+1})$-graphs on $n$ vertices, use this algorithm to determine several of the smaller values $n(k,g,\underline{g+1})$, and discuss various approaches to finding smallest $(k,g,\underline{g+1})$-graphs within several classes of highly symmetrical graphs., Comment: 19 pages

Using a Milnor-Moore argument we show that, $K(2)$-locally at the prime $2$, the spectra $MU\langle 6\rangle$ and $MString$ split as direct sums of Morava $E$-theories after tensoring with a finite Galois extension of the sphere called $E^{hF_{3/2}}$. In the case of $MString$ we are able to refine this splitting in several ways: we show that the projection maps are determined by spin characteristic classes, that the Ando-Hopkins-Rezk orientation admits a unital section after tensoring with $E^{hF_{3/2}}$, and that the splitting can be improved to one of $E^{hH}\otimes MString$ into a direct sum of shifts of $TMF_0(3)$ where $H$ is an open subgroup of the Morava stabilizer group of index $4$., Comment: 32 pages, comments welcome!

Wall-based active and passive flow control for drag reduction in low Reynolds number (Re) turbulent flows can lead to three typical phenomena: i) attenuation or ii) amplification of the near-wall cycle, and iii) generation of spanwise rollers. The present study conducts direct numerical simulations (DNS) of a low Re turbulent channel flow and demonstrates that each flow response can be generated with a wall transpiration at two sets of spatial scales, termed "streak" and "roller" scales. The effect of the transpiration is controlled by its relative phase to the background flow, which can be parametrized by the wall pressure. Streak scales i) attenuate the near-wall cycle if transpiration and wall-pressure are approximately in-phase or ii) amplify it otherwise, and iii) roller scales energize spanwise rollers when transpiration and wall pressure are out-of-phase. The dynamics of the wall pressure and transpiration are coupled and robust relative phase relations, which are required to trigger the flow responses, can result if the source term of the linear fast or nonlinear slow pressure correlates with the wall transpiration over a scale-dependent height or if the temporal frequency content of the wall transpiration is approximately sparse. The importance of each condition depends on the relative magnitude of the pressure components, which is significantly altered by the transpiration. The analogy in flow response suggests that transpiration with the two scale families and their phase relations to the wall pressure represent fundamental building blocks for flows over tailored surfaces including riblets, porous, and permeable walls., Comment: 40 pages, 15 figures

Ergodicity has been one of the fundamental concepts underpinning our understanding of thermalisation in isolated systems since the first developments in classical statistical mechanics. Recently, a similar notion has been introduced for quantum systems, termed Complete Hilbert Space Ergodicity (CHSE), in which the evolving quantum state explores all of the available Hilbert space. This contrasts with the Eigenstate Thermalisation Hypothesis (ETH), in which thermalisation is formulated via the properties of matrix elements of local operators in the energy eigenbasis. In this work we explore how ETH-violation mechanisms, including quantum many-body scars and Hilbert space fragmentation can affect Complete Hilbert Space Ergodicity. We find that the presence of these mechanisms leads to CHSE in decoupled subspaces, a phenomenon we call Hilbert Subspace Ergodicity, and which represents a protocol for constructing t-designs in subspaces., Comment: 13+6 pages,12+7 figures

We present a novel approach for minimally invasive flexible needle manipulations by pairing a real-time finite element simulator with the cross-entropy method. Additionally, we demonstrate how a kinematic-driven bang-bang controller can complement the control framework for better tracking performance. We show how electromagnetic (EM) tracking can be readily incorporated into the framework to provide controller feedback. Tissue phantom experiment with EM tracking shows the average targeting error is $0.16 \pm 0.29mm$., Comment: Submitted to IEEE International Conference on Robotics and Automation 2025

Semantic Simultaneous Localization and Mapping (SLAM) systems struggle to map semantically similar objects in close proximity, especially in cluttered indoor environments. We introduce Semantic Enhancement for Object SLAM (SEO-SLAM), a novel SLAM system that leverages Vision-Language Models (VLMs) and Multimodal Large Language Models (MLLMs) to enhance object-level semantic mapping in such environments. SEO-SLAM tackles existing challenges by (1) generating more specific and descriptive open-vocabulary object labels using MLLMs, (2) simultaneously correcting factors causing erroneous landmarks, and (3) dynamically updating a multiclass confusion matrix to mitigate object detector biases. Our approach enables more precise distinctions between similar objects and maintains map coherence by reflecting scene changes through MLLM feedback. We evaluate SEO-SLAM on our challenging dataset, demonstrating enhanced accuracy and robustness in environments with multiple similar objects. Our system outperforms existing approaches in terms of landmark matching accuracy and semantic consistency. Results show the feedback from MLLM improves object-centric semantic mapping. Our dataset is publicly available at: jungseokhong.com/SEO-SLAM.

We show how the flow approach of Duch, with elementary differentials as coordinates, can be used to prove well-posedness for rough stochastic differential equations driven by fractional Brownian motion with Hurst index $H > \frac{1}{4}$. A novelty appearing here is that we use coordinates for the flow that are indexed by trees rather than multi-indices., Comment: Added statement and proof of global well-posedness

This paper details a significant milestone towards maturing a buoyant aerial robotic platform, or aerobot, for flight in the Venus clouds. We describe two flights of our subscale altitude-controlled aerobot, fabricated from the materials necessary to survive Venus conditions. During these flights over the Nevada Black Rock desert, the prototype flew at the identical atmospheric densities as 54 to 55 km cloud layer altitudes on Venus. We further describe a first-principle aerobot dynamics model which we validate against the Nevada flight data and subsequently employ to predict the performance of future aerobots on Venus. The aerobot discussed in this paper is under JPL development for an in-situ mission flying multiple circumnavigations of Venus, sampling the chemical and physical properties of the planet's atmosphere and also remotely sensing surface properties., Comment: Preprint submitted to AIAA Journal of Aircraft

Recent discoveries of gravitational wave sources have advanced our knowledge about the formation of compact object binaries. At present, many questions about the stellar origins of binary neutron stars remain open. We explore the evolution of binary neutron star progenitors with the population synthesis code COSMIC. We identify three dominant evolutionary tracks to form neutron star binaries that merge within the age of the Universe: a scenario that includes a common envelope phase between the first neutron star and its companion, a scenario with almost equal-mass progenitors that evolve quasi-simultaneously and which features a double-core common envelope, and a scenario involving the accretion-induced collapse of an oxygen-neon white dwarf into a neutron star. We show that the distribution of time delays between stellar formation and binary neutron star merger at a given progenitor metallicity does not follow a power-law, but instead features a complex structure that reflects the progenitor properties and the relative contribution of each evolutionary track. We also explore the evolution of the merger rate density with redshift and show that the scenario involving the accretion-induced collapse could be dominant at high redshifts. These results can have important implications for the study of the chemical enrichment of galaxies in r-process elements produced in kilonovae; and of short gamma-ray bursts offsets in their host galaxies., Comment: 14 pages, 10 figures. Accepted for publication in A&A

We present 3DGS-CD, the first 3D Gaussian Splatting (3DGS)-based method for detecting physical object rearrangements in 3D scenes. Our approach estimates 3D object-level changes by comparing two sets of unaligned images taken at different times. Leveraging 3DGS's novel view rendering and EfficientSAM's zero-shot segmentation capabilities, we detect 2D object-level changes, which are then associated and fused across views to estimate 3D changes. Our method can detect changes in cluttered environments using sparse post-change images within as little as 18s, using as few as a single new image. It does not rely on depth input, user instructions, object classes, or object models -- An object is recognized simply if it has been re-arranged. Our approach is evaluated on both public and self-collected real-world datasets, achieving up to 14% higher accuracy and three orders of magnitude faster performance compared to the state-of-the-art radiance-field-based change detection method. This significant performance boost enables a broad range of downstream applications, where we highlight three key use cases: object reconstruction, robot workspace reset, and 3DGS model update. Our code and data will be made available at https://github.com/520xyxyzq/3DGS-CD.

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

We extend prior work comparing linear multilevel models (MLM) and fixed effect (FE) models to the generalized linear model (GLM) setting, where the coefficient on a treatment variable is of primary interest. This leads to three key insights. (i) First, as in the linear setting, MLM can be thought of as a regularized form of FE. This explains why MLM can show large biases in its treatment coefficient estimates when group-level confounding is present. However, unlike the linear setting, there is not an exact equivalence between MLM and regularized FE coefficient estimates in GLMs. (ii) Second, we study a generalization of "bias-corrected MLM" (bcMLM) to the GLM setting. Neither FE nor bcMLM entirely solves MLM's bias problem in GLMs, but bcMLM tends to show less bias than does FE. (iii) Third, and finally, just like in the linear setting, MLM's default standard errors can misspecify the true intragroup dependence structure in the GLM setting, which can lead to downwardly biased standard errors. A cluster bootstrap is a more agnostic alternative. Ultimately, for non-linear GLMs, we recommend bcMLM for estimating the treatment coefficient, and a cluster bootstrap for standard errors and confidence intervals. If a bootstrap is not computationally feasible, then we recommend FE with cluster-robust standard errors.

The upheavals of the last few years within higher education and the labor force have catalyzed leaders in arts and design postsecondary education to question assumptions within their institutions about "what it means to be embarking on a career and life" in arts and design today (Novak-Leonard, Dempster, Scotto Adams, & Walters, 2022, p. 9). Ultimately, the questions being raised seek to better understand what constitutes "success" for alumni and who is determining those terms of success in the current, evolving contexts of education and work. To inform these critical considerations about student outcomes and notions of "success," this report uses data from the 2022 Strategic National Arts Alumni Project (SNAAP) survey to provide insights on the most recently measured employment outcomes for alumni of arts and design postsecondary programs and alumni reflections on their work and careers, including satisfaction with different aspects of their work and careers. The 2022 SNAAP survey asked alumni of postsecondary arts, design, and adjacent programs about their employment as of September 2022, and about their perspectives on connections between their postsecondary arts and design training and their employment and the development of their careers during Fall 2022. [This report was supported by funds provided by the University of Illinois College of Fine and Applied Arts, and the University of Illinois Investment for Growth Fund, in partnership with the Strategic National Arts Alumni Project (SNAAP).]

During the COVID-19 pandemic there were many changes in arts and design alumni's paid work, creative work, income and in the skills that they needed to be successful in their careers. Given the documented impact of the pandemic on the arts and culture sector, on arts and creative workers, and on institutions of higher education, the authors turn to data from the 2022 Strategic National Arts Alumni Project (SNAAP) survey to understand how the impact of the pandemic was felt by people doing arts, design, or creative work as part of their job or occupational roles and the varied experiences across socio-demographic characteristics. This report focuses on providing insight into arts and design graduates whose work is--or before the pandemic was--in the arts, design, or creative realm by examining their working lives, economic lives, and the skills that they needed to attain or update during the first two years of the pandemic in the United States. In addition to presenting analyses and findings about arts and design alumni's experiences of the first year of the pandemic along these dimensions of their creative and working lives, the authors also dedicate a section of the conclusion of this report to the impressions and challenges identified by higher education arts leaders in a series of formal conversation at the beginning of 2021 to frame commentary about university preparation for resilient arts and design alumni. [Additional funding for this report was provided by the University of Illinois College of Fine and Applied Arts, the University of Illinois Investment for Growth Fund, and the Department of Arts Administration, Education, and Policy at The Ohio State University.]

Fast and accurate physics simulation is an essential component of robot learning, where robots can explore failure scenarios that are difficult to produce in the real world and learn from unlimited on-policy data. Yet, it remains challenging to incorporate RGB-color perception into the sim-to-real pipeline that matches the real world in its richness and realism. In this work, we train a robot dog in simulation for visual parkour. We propose a way to use generative models to synthesize diverse and physically accurate image sequences of the scene from the robot's ego-centric perspective. We present demonstrations of zero-shot transfer to the RGB-only observations of the real world on a robot equipped with a low-cost, off-the-shelf color camera. website visit https://lucidsim.github.io, Comment: 17 pages, 19 figures

Band-to-band tunnelling field-effect transistors (TFETs) have long been considered as promising candidates for future low-power logic applications. However, fabricated TFETs rarely reach sub-60 mV/dec sub-threshold swings (SS) at room temperature. Previous theoretical studies identified Auger processes as possible mechanisms for the observed degradation of SS. Through first-principles quantum transport simulations incorporating carrier-carrier interactions within the Non-equilibrium Green's Function formalism and self-consistent GW approximation, we confirm here that Auger processes are indeed at least partly responsible for the poor performance of TFETs. Using a carbon nanotube TFET as testbed, we show that carrier-carrier scattering alone significantly increases the OFF-state current of these devices, thus worsening their sub-threshold behavior., Comment: 6 pages, 5 figures

We present jf1uids, a one-dimensional fluid solver that can, by virtue of a geometric formulation of the Euler equations, model radially symmetric fluid problems in a conservative manner, i.e., without losing mass or energy. For spherical problems, such as ideal supernova explosions or stellar wind-blown bubble expansions, simulating only along a radial dimension drastically reduces compute and memory demands compared to a full three-dimensional method. This simplification also alleviates constraints on backpropagation through the solver. Written in JAX, jf1uids is a GPU-compatible and fully differentiable simulator. We demonstrate the advantages of this differentiable physics simulator by retrieving the wind's parameters for an adiabatic stellar wind expansion from the final fluid state using gradient descent. As part of a larger "stellar winds, cosmic rays and machine learning" research track, jf1uids serves as a solid foundation to be extended with additional physics modules, foremost cosmic rays and a neural-net powered gas-cooling surrogate and improved by higher order and more accurate numerical schemes. All code is available under https://github.com/leo1200/jf1uids/., Comment: 8 pages, 4 figures, accepted for the Machine Learning and the Physical Sciences workshop at NeurIPS 2024

This research introduces a novel hydrofoil-based propulsion framework for unmanned aquatic robots, inspired by the undulating locomotion observed in select aquatic species. The proposed system incorporates a camber-modulating mechanism to enhance hydrofoil propulsive force generation and eventually efficiency. Through dynamic simulations, we validate the effectiveness of the camber-adjusting hydrofoil compared to a symmetric counterpart. The results demonstrate a significant improvement in horizontal thrust, emphasizing the potential of the cambering approach to enhance propulsive performance. Additionally, a prototype flipper design is presented, featuring individual control of heave and pitch motions, as well as a camber-adjustment mechanism. The integrated system not only provides efficient water-based propulsion but also offers the capacity for generating vertical forces during take-off maneuvers for seaplanes. The design is tailored to harness wave energy, contributing to the exploration of alternative energy resources. This work advances the understanding of bionic oscillatory principles for aquatic robots and provides a foundation for future developments in environmentally safe and agile underwater exploration., Comment: Peer-reviewed and accepted in Ubiquitous Robots 2024, New York City