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Thermionic emission has been exploited to give rise to the theory of thermionic cooling also known as electron transpiration cooling, which can potentially serve as a powerful and engineerable cooling mode for hypersonic leading edges that can reach temperatures exceeding 2000 {\deg}C. However, the contribution to this cooling mode by photoexcited electrons remains relatively unexplored. Here, we present a numerical model of thermionic emission and photoemission driven cooling and heat spreading, examining the trajectories of electrons emitted based on a random energy model within a prescribed potential space. By simulating surfaces with two different temperature gradients, and imposing potential spaces derived for Cartesian, cylindrical, and spherical coordinate systems, we demonstrate that heat spreading can be significant for temperature gradients on a length scale comparable to the electron spreading distance. Additionally, by testing two different leading edge radii, we find that heat spreading affects a larger percentage of surface area for a smaller leading edge radius.

Leading edges of hypersonic vehicles can reach temperatures greater than 2000 {\deg}C, and radii of curvature smaller than 1 cm, at which thermionic emission (also known as electron transpiration) can play a significant role in cooling the leading edge alongside other heat transfer modes such as convection and radiation. Existing theoretical analyses of thermionic cooling with space-charge effects at a leading edge are limited to one-dimensional (1D), analytical and numerical models that do not capture the influences of geometric curvature of the leading edge or temperature gradients along the leading edge. The key to understanding space-charge effects is development of the plasma sheath potential, and to that end we demonstrate a generalized methodology to calculate the sheath potential space in one-dimensional Cartesian, cylindrical, and spherical coordinate systems. We accomplish this by extending Takamura's approach beyond the Cartesian system, and motivate sheath formation conditions for potential sheathes with and without a virtual cathode similar in nature to how Bohm originally presented his criterion of minimum Mach number for a valid 1D Cartesian sheath. By observing for what parameter inputs we satisfy the sheath formation conditions, we illustrate parameter spaces of minimum Mach number, potential derivative at the wall, and net current, for each coordinate system and for two different input work functions; we also show example potential spaces for each coordinate system. With our numerical approach, generalized to multiple coordinate systems, we enable computationally efficient and higher fidelity analysis of thermionic emission with space-charge effects for more realistic system geometries.

This paper proposes ShapeShifter, a new 3D generative model that learns to synthesize shape variations based on a single reference model. While generative methods for 3D objects have recently attracted much attention, current techniques often lack geometric details and/or require long training times and large resources. Our approach remedies these issues by combining sparse voxel grids and point, normal, and color sampling within a multiscale neural architecture that can be trained efficiently and in parallel. We show that our resulting variations better capture the fine details of their original input and can handle more general types of surfaces than previous SDF-based methods. Moreover, we offer interactive generation of 3D shape variants, allowing more human control in the design loop if needed.

Existing research that addressed cable manipulation relied on two-fingered grippers, which make it difficult to perform similar cable manipulation tasks that humans perform. However, unlike dexterous manipulation of rigid objects, the development of dexterous cable manipulation skills in robotics remains underexplored due to the unique challenges posed by a cable's deformability and inherent uncertainty. In addition, using a dexterous hand introduces specific difficulties in tasks, such as cable grasping, pulling, and in-hand bending, for which no dedicated task definitions, benchmarks, or evaluation metrics exist. Furthermore, we observed that most existing dexterous hands are designed with structures identical to humans', typically featuring only one thumb, which often limits their effectiveness during dexterous cable manipulation. Lastly, existing non-task-specific methods did not have enough generalization ability to solve these cable manipulation tasks or are unsuitable due to the designed hardware. We have three contributions in real-world dexterous cable manipulation in the following steps: (1) We first defined and organized a set of dexterous cable manipulation tasks into a comprehensive taxonomy, covering most short-horizon action primitives and long-horizon tasks for one-handed cable manipulation. This taxonomy revealed that coordination between the thumb and the index finger is critical for cable manipulation, which decomposes long-horizon tasks into simpler primitives. (2) We designed a novel five-fingered hand with 25 degrees of freedom (DoF), featuring two symmetric thumb-index configurations and a rotatable joint on each fingertip, which enables dexterous cable manipulation. (3) We developed a demonstration collection pipeline for this non-anthropomorphic hand, which is difficult to operate by previous motion capture methods., Comment: 17 pages, 14 figures, Project Link:https://sites.google.com/view/dexterous-cable-manipulation/home

Sub-Neptunes are the most common type of planet in our galaxy. Interior structure models suggest that the coldest sub-Neptunes could host liquid water oceans underneath their hydrogen envelopes - sometimes called 'hycean' planets. JWST transmission spectra of the $\sim$ 250 K sub-Neptune K2-18 b were recently used to report detections of CH$_4$ and CO$_2$, alongside weaker evidence of (CH$_3$)$_2$S (dimethyl sulfide, or DMS). Atmospheric CO$_2$ was interpreted as evidence for a liquid water ocean, while DMS was highlighted as a potential biomarker. However, these notable claims were derived using a single data reduction and retrieval modeling framework, which did not allow for standard robustness tests. Here we present a comprehensive reanalysis of K2-18 b's JWST NIRISS SOSS and NIRSpec G395H transmission spectra, including the first analysis of the second-order NIRISS SOSS data. We incorporate multiple well-tested data reduction pipelines and retrieval codes, spanning 60 different data treatments and over 250 atmospheric retrievals. We confirm the detection of CH$_4$ ($\approx$ 4$\sigma$), with a volume mixing ratio of log CH$_4$ = $-1.15^{+0.40}_{-0.52}$, but we find no statistically significant or reliable evidence for CO$_2$ or DMS. Finally, we quantify the observed atmospheric composition using photochemical-climate and interior models, demonstrating that our revised composition of K2-18 b can be explained by an oxygen-poor mini-Neptune without requiring a liquid water surface or life., Comment: 42 pages, 20 figures. Submitted to AAS Journals

This report provides an overview of the workshop titled Autonomy and Safety Assurance in the Early Development of Robotics and Autonomous Systems, hosted by the Centre for Robotic Autonomy in Demanding and Long-Lasting Environments (CRADLE) on September 2, 2024, at The University of Manchester, UK. The event brought together representatives from six regulatory and assurance bodies across diverse sectors to discuss challenges and evidence for ensuring the safety of autonomous and robotic systems, particularly autonomous inspection robots (AIR). The workshop featured six invited talks by the regulatory and assurance bodies. CRADLE aims to make assurance an integral part of engineering reliable, transparent, and trustworthy autonomous systems. Key discussions revolved around three research questions: (i) challenges in assuring safety for AIR; (ii) evidence for safety assurance; and (iii) how assurance cases need to differ for autonomous systems. Following the invited talks, the breakout groups further discussed the research questions using case studies from ground (rail), nuclear, underwater, and drone-based AIR. This workshop offered a valuable opportunity for representatives from industry, academia, and regulatory bodies to discuss challenges related to assured autonomy. Feedback from participants indicated a strong willingness to adopt a design-for-assurance process to ensure that robots are developed and verified to meet regulatory expectations., Comment: 7 pages, 2 figures

We present a study of new 7.7-11.3 $\mu$m data obtained with the James Webb Space Telescope Mid-InfraRed Instrument in the starburst galaxy M 82. In particular, we focus on the dependency of the integrated CO(1-0) line intensity on the MIRI-F770W and MIRI-F1130W filter intensities to investigate the correlation between CO content and the 7.7 and 11.3 $\mu$m features from polycyclic aromatic hydrocarbons (PAH) in M 82's outflows. To perform our analysis, we identify CO clouds using archival $^{12}$CO($J$=1-0) NOEMA moment 0 map within 2 kpc from the center of M 82, with sizes ranging between $\sim$21 and 270 pc; then, we compute the CO-to-PAH relations for the 306 validated CO clouds. On average, the power-law slopes for the two relations in M 82 are lower than what is seen in local main-sequence spirals. In addition, there is a moderate correlation between $I_{\rm CO(1-0)}$-$I_{\rm 7.7\mu m} /I_{\rm 11.3\mu m}$ for most of the CO cloud groups analyzed in this work. Our results suggest that the extreme conditions in M 82 translate into CO not tracing the full budget of molecular gas in smaller clouds, perhaps as a consequence of photoionization and/or emission suppression of CO molecules due to hard radiation fields from the central starburst.

Bayesian inference promises a framework for principled uncertainty quantification of neural network predictions. Barriers to adoption include the difficulty of fully characterizing posterior distributions on network parameters and the interpretability of posterior predictive distributions. We demonstrate that under a discretized prior for the inner layer weights, we can exactly characterize the posterior predictive distribution as a Gaussian mixture. This setting allows us to define equivalence classes of network parameter values which produce the same likelihood (training error) and to relate the elements of these classes to the network's scaling regime -- defined via ratios of the training sample size, the size of each layer, and the number of final layer parameters. Of particular interest are distinct parameter realizations that map to low training error and yet correspond to distinct modes in the posterior predictive distribution. We identify settings that exhibit such predictive multimodality, and thus provide insight into the accuracy of unimodal posterior approximations. We also characterize the capacity of a model to "learn from data" by evaluating contraction of the posterior predictive in different scaling regimes., Comment: Mathematics of Modern Machine Learning Workshop at NeurIPS 2024

In recent years, observations with the JWST have started to map out the rapid metal enrichment of the early Universe, while (sub)millimeter observations have simultaneously begun to reveal the ubiquity of dust beyond $z\gtrsim6$. However, the pathways that led to the assembly of early dust reservoirs remain poorly quantified, and require pushing our understanding of key scaling relations between dust, gas and metals into the early Universe. We investigate the dust build-up in twelve $6.5 \lesssim z \lesssim 7.7$ galaxies drawn from the REBELS survey that benefit from (i) JWST/NIRSpec strong-line metallicity measurements, (ii) ALMA [CII]-based redshifts and gas masses, and (iii) dust masses from single- or multi-band ALMA continuum observations. Combining these measurements, we investigate the dust-to-gas (DtG), dust-to-metal (DtM), and dust-to-stellar mass (DtS) ratios of our sample as a function of metallicity. While our analysis is limited by systematic uncertainties related to the [CII]-to-H$_2$ conversion factor and dust temperature, we explore a wide range of possible values, and carefully assess their impact on our results. Under a fiducial set of assumptions, we find an average $\log(\mathrm{DtG}) = -3.02 \pm 0.23$, only slightly below that of local metal-rich galaxies. On the other hand, at fixed metallicity our average $\log(\mathrm{DtS}) = -2.15 \pm 0.42$ is significantly larger than that of low-redshift galaxies. Finally, through a comparison to various theoretical models of high-redshift dust production, we find that assembling the dust reservoirs in massive galaxies at $z\approx7$ likely requires the combination of rapid supernova enrichment and efficient ISM dust growth., Comment: 18 pages + appendices, 5 figures in main text, submitted to MNRAS

Metallicity is a crucial tracer of galaxy evolution, providing insights into gas accretion, star formation, and feedback. At high redshift, these processes reveal how early galaxies assembled and enriched their interstellar medium. In this work, we present rest-frame optical spectroscopy of 12 massive ($\log(M_*/\mathrm{M_{\odot}})>9$) galaxies at $z\sim 6$-$8$ from the REBELS ALMA large program, observed with JWST NIRSpec/IFU in the prism mode. These observations span emission lines from [OII]$\lambda$3727,9 to [SII]$\lambda$6716,31, providing key information on nebular dust attenuation, ionisation states, and chemical abundances. We find lower O32 ratios (average $\sim3.7$) and [OIII]$\lambda$5007 equivalent widths (average ${EW_{[OIII]}}\sim390$\AA) than are generally found in existing large spectroscopic surveys at $z>6$, indicating less extreme ionising conditions. Strong-line diagnostics suggest that these systems are some of the most metal-rich galaxies observed at $z>6$ (average $Z_{\mathrm{gas}}\sim 0.4 Z_{\odot}$), including sources with near-solar oxygen abundances, in line with their high stellar masses (average $\log{M_*/\mathrm{M_{\odot}}}\sim9.5$). Supplementing with literature sources at lower masses, we investigate the mass-metallicity and fundamental metallicity relations (MZR and FMR, respectively) over a 4 dex stellar mass range at $63$ studies., Comment: Submitted to MNRAS, 22 pages

We present measurements of the dust attenuation curves of 12 massive ($9~<~\log$($M_{\star}/{M}_{\odot})$ $<~10$) Lyman-break galaxies at $z=6.5-7.7$ derived from James Webb Space Telescope (JWST) NIRSpec integral field unit (IFU) spectroscopy. The galaxies are drawn from the Atacama Large Millimeter/submillimeter Array (ALMA) Reionization Era Bright Emission Line Survey (REBELS) large program. The dust attenuation curves were obtained by fitting spectral energy distribution (SED) models with a flexible dust law to the full galaxy spectra over observed wavelengths $0.6-5.3$ $\mu$m. These attenuation curves show a range of recovered slopes ($-0.39\leq\delta\leq0.08$) that are on average slightly flatter than seen in local sources of the same stellar masses, with none exhibiting very steep slopes. Three galaxies exhibit evidence for a 2175 \r{A} dust bump ($>4\sigma$) and we find SED fitting excluding the bump can overestimate derived stellar masses by up to $0.4$ dex. Correcting for the dust attenuation with our best-fit attenuation curves we recover a range of intrinsic UV-slopes ($-2.5\leq\beta_0\leq-2.2$). The galaxies show moderate reddening ($A_V~=~0.1-0.6$ mag) and the $A_V$ to stellar mass relation is consistent with local sources. The attenuation law slope is found to correlate with $A_V$, while we see no strong correlation with stellar mass, ${M_{\rm UV}}$, or gas-phase metallicity. Overall, our results show little evolution in dust properties in the REBELS sources compared to the local Universe. Comparing our recovered trends to empirical models suggests that the most important factor driving the variation in the attenuation curves in our sample is the dust-star geometry, not the properties of the dust grains themselves., Comment: 17 Pages, 6 Figures, Submitted to MNRAS

Thermodynamical explosions of White Dwarfs (WD)are one of the keys to high precision cosmology. Nebular spectra, namely mid-infrared (MIR) with JWST are an effective tool to probe for the multi-dimensional imprints of the explosion physics of WDs and their progenitor systems but also pose a challenge for simulations. What we observe as SNe Ia are low-energy photons, namely light curves, and spectra detected some days to years after the explosion. The light is emitted from a rapidly expanding envelope consisting of a low-density and low-temperature plasma with atomic population numbers far from thermodynamical equilibrium. SNe Ia are powered radioactive decays which produce hard X- and gamma-rays and MeV leptons which are converted within the ejecta to low-energy photons. We find that the optical and IR nebular spectra depend sensitively on the proper treatment of the physical conversion of high to low energies. The low-energy photons produced by forbidden line transitions originate from a mostly optically thin envelope. However, the UV is optically thick because of a quasi-continuum formed by allowed lines and bound-free transitions even several years after the explosion. We find that stimulated recombination limits the over-ionization of high ions with populations governed by the far UV. The requirements to simulate nebular spectra are well beyond both classical stellar atmospheres and nebulae. Using our full non-LTE HYDrodynamical RAdiation code (HYDRA) as a test-bed, the sensitivity on the physics on synthetic spectra are demonstrated using observations as a benchmark. At some examples, we establish the power of high-precision nebular spectroscopy as quantitative tool. Centrally ignited, off-center delayed-detonation near Chandrasekhar-mass models can reproduce line-ratios and line profiles of Branch-normal and underluminous SNe Ia observed with JWST., Comment: 10 Pages, 5 figures submitted Dec. 2024, revised Jan. 3, accepted Jan. 6th (Journal of Physics, Conference Series, in press)

Power systems naturally experience disturbances, some of which can damage equipment and disrupt consumers. It is important to quickly assess the likely consequences of credible disturbances and take preventive action, if necessary. However, assessing the impact of potential disturbances is challenging because many of the influential factors, such as loading patterns, controller settings and load dynamics, are not precisely known. To address this issue, the paper introduces the concept of parameter-space recovery regions. For each disturbance, the corresponding recovery region is the region of parameter space for which the system will recover to the desired operating point. The boundary of the recovery region establishes the separation between parameter values that result in trouble-free recovery and those that incur undesirable non-recovery. The safety margin for a given set of parameter values is defined as the smallest distance (in parameter space) between the given values and the recovery boundary. Novel numerical algorithms with theoretical guarantees are presented for efficiently computing recovery boundaries and safety margins. Unlike prior methods, which tend to be overly conservative and restricted to low dimensional parameter space, these methods compute safety margins to arbitrary user-specified accuracy and do so efficiently in high dimensional parameter space. The efficacy of the methods is demonstrated using the IEEE 39-bus benchmark power system, where safety margins are computed for cases that consider up to 86 parameters, and reveal unexpected safety implications that would not have been observed otherwise., Comment: 9 pages

Physical systems experience nonlinear disturbances which have the potential to disrupt desired behavior. For a particular disturbance, whether or not the system recovers from the disturbance to a desired stable equilibrium point depends on system parameter values, which are typically uncertain and time-varying. Therefore, to quantify proximity to vulnerability we define the safety margin to be the smallest change in parameter values from a nominal value such that the system will no longer be able to recover from the disturbance. Safety margins are valuable but challenging to compute as related methods, such as those for robust region of attraction estimation, are often either overly conservative or computationally intractable for high dimensional systems. Recently, we developed algorithms to compute safety margins efficiently and non-conservatively by exploiting the large sensitivity of the system trajectory near the region of attraction boundary to small perturbations. Although these algorithms have enjoyed empirical success, they lack theoretical guarantees that would ensure their generalizability. This work develops a novel characterization of safety margins in terms of trajectory sensitivities, and uses this to derive well-posedness and convergence guarantees for these algorithms, enabling their generalizability and successful application to a large class of nonlinear systems., Comment: 16 pages

Bayesian inference is conceptually elegant, but calculating posterior expectations can entail a heavy computational cost. Monte Carlo methods are reliable and supported by strong asymptotic guarantees, but do not leverage smoothness of the integrand. Solving Stein equations has emerged as a possible alternative, providing a framework for numerical approximation of posterior expectations in which smoothness can be exploited. However, existing numerical methods for Stein equations are associated with high computational cost due to the need to solve large linear systems. This paper considers the combination of iterative linear solvers and preconditioning strategies to obtain fast approximate solutions of Stein equations. Our main finding is that these methods can be effective, but that the performance of different preconditioning strategies is context-dependent, so that no single strategy can be universally preferred.

Spin states in semiconductors provide exceptionally stable and noise-resistant environments for qubits, positioning them as optimal candidates for reliable quantum computing technologies. The proposal to use nuclear and electronic spins of donor atoms in silicon, introduced by Kane in 1998, sparked a new research field focused on the precise positioning of individual impurity atoms for quantum devices, utilising scanning tunnelling microscopy and ion implantation. This roadmap article reviews the advancements in the 25 years since Kane's proposal, the current challenges, and the future directions in atomic-scale semiconductor device fabrication and measurement. It covers the quest to create a silicon-based quantum computer and expands to include diverse material systems and fabrication techniques, highlighting the potential for a broad range of semiconductor quantum technological applications. Key developments include phosphorus in silicon devices such as single-atom transistors, arrayed few-donor devices, one- and two-qubit gates, three-dimensional architectures, and the development of a toolbox for future quantum integrated circuits. The roadmap also explores new impurity species like arsenic and antimony for enhanced scalability and higher-dimensional spin systems, new chemistry for dopant precursors and lithographic resists, and the potential for germanium-based devices. Emerging methods, such as photon-based lithography and electron beam manipulation, are discussed for their disruptive potential. This roadmap charts the path toward scalable quantum computing and advanced semiconductor quantum technologies, emphasising the critical intersections of experiment, technological development, and theory., Comment: 94 pages

The proposed Habitable Worlds Observatory is intended to observe the atmospheres of nearby terrestrial exoplanets with a resolution greater than that of any previous instrument. While these observations present a substantial opportunity for astrobiology, they also incur the risk of false positives and false negatives. Here, we explore the use of systems science (in the form of network theory and thermochemical kinetics) to mitigate these risks, and briefly describe the technical specifications HWO would require in order to use these methodologies.

Continuous gravitational waves (CWs) emission from neutron stars carries information about their internal structure and equation of state, and it can provide tests of General Relativity. We present a search for CWs from a set of 45 known pulsars in the first part of the fourth LIGO--Virgo--KAGRA observing run, known as O4a. We conducted a targeted search for each pulsar using three independent analysis methods considering the single-harmonic and the dual-harmonic emission models. We find no evidence of a CW signal in O4a data for both models and set upper limits on the signal amplitude and on the ellipticity, which quantifies the asymmetry in the neutron star mass distribution. For the single-harmonic emission model, 29 targets have the upper limit on the amplitude below the theoretical spin-down limit. The lowest upper limit on the amplitude is $6.4\!\times\!10^{-27}$ for the young energetic pulsar J0537-6910, while the lowest constraint on the ellipticity is $8.8\!\times\!10^{-9}$ for the bright nearby millisecond pulsar J0437-4715. Additionally, for a subset of 16 targets we performed a narrowband search that is more robust regarding the emission model, with no evidence of a signal. We also found no evidence of non-standard polarizations as predicted by the Brans-Dicke theory., Comment: main paper: 12 pages, 6 figures, 4 tables

We explore, through a combination of x-ray diffraction and elastoresistivity measurements, the effect of disorder on the strain-tuned charge density wave and associated multicriticality in Pd$_x$ErTe$_3$ (x = 0, 0.01, 0.02 and 0.026). We focus particularly on the behavior near the strain-tuned bicritical point that occurs in pristine ErTe$_3$ (x=0). Our study reveals that while Pd intercalation somewhat broadens the signatures of the CDW phase transitions, the line of first-order transitions at which the CDW reorients as a function of applied strain persists in the presence of disorder and still seemingly terminates at a critical point. The critical point occurs at a lower temperature and a lower strain compared to pristine ErTe$_3$. Similarly, the nematic elastoresistance of Pd$_x$ErTe$_3$, though suppressed in magnitude and broadened relative to that of ErTe$_3$, has a markedly more symmetric response around the critical point. These observations point to disorder driving a reduction in the system's electronic orthorhombicity even while the material remains irrevocably orthorhombic due to the presence of a glide plane in the crystal structure. Disorder, it would appear, reinforces the emergence of a "pseudo-tetragonal" electronic response in this fundamentally orthorhombic material.

Recent probabilistic methods for 3D triangular meshes capture diverse shapes by differentiable mesh connectivity, but face high computational costs with increased shape details. We introduce a new differentiable mesh processing method in 2D and 3D that addresses this challenge and efficiently handles meshes with intricate structures. Additionally, we present an algorithm that adapts the mesh resolution to local geometry in 2D for efficient representation. We demonstrate the effectiveness of our approach on 2D point cloud and 3D multi-view reconstruction tasks. Visit our project page (https://sonsang.github.io/dmesh2-project) for source code and supplementary material., Comment: 26 pages, 27 figures, 4 tables

The study of the homology of diagram algebras has emerged as an interesting and important field. In many cases, the homology of a diagram algebra can be identified with the homology of a group. In this paper we have two main aims. Firstly, we study the (co)homology of new families of diagram algebras such as the blob algebras and the walled Brauer algebras, both of which exhibit new phenomena in the field. Secondly, we show that in the cases where the homology of a diagram algebra can be identified with group homology one can also identify the cohomology of the algebra with the cohomology of a group. We use this to establish an integer-graded cohomology theory for these diagram algebras and identify this with the Tate cohomology of a group., Comment: 36 pages

Formal verification using proof assistants, such as Coq, enables the creation of high-quality software. However, the verification process requires significant expertise and manual effort to write proofs. Recent work has explored automating proof synthesis using machine learning and large language models (LLMs). This work has shown that identifying relevant premises, such as lemmas and definitions, can aid synthesis. We present Rango, a fully automated proof synthesis tool for Coq that automatically identifies relevant premises and also similar proofs from the current project and uses them during synthesis. Rango uses retrieval augmentation at every step of the proof to automatically determine which proofs and premises to include in the context of its fine-tuned LLM. In this way, Rango adapts to the project and to the evolving state of the proof. We create a new dataset, CoqStoq, of 2,226 open-source Coq projects and 196,929 theorems from GitHub, which includes both training data and a curated evaluation benchmark of well-maintained projects. On this benchmark, Rango synthesizes proofs for 32.0% of the theorems, which is 29% more theorems than the prior state-of-the-art tool Tactician. Our evaluation also shows that Rango adding relevant proofs to its context leads to a 47% increase in the number of theorems proven., Comment: In Proceedings of the 47th International Conference on Software Engineering (ICSE), Ottawa, ON, Canada, April 2025

Non-equilibrium states generated by ultrafast laser pulses are characterized by specific phenomena that are not accessible in static measurements. Previous time- and angle-resolved photoemission spectroscopy (TARPES) studies on rare-earth tritelluride materials have revealed the laser-driven melting of the charge density wave order as well as its collective amplitude mode excitation. Variation of the excess energy deposited by optical pumping in the material promises pathways to control the dynamic material response. To this end, we use an optical parametric amplifier to generate a tunable pump photon energy. Studying LaTe3 we compare the dynamics driven by pumping resonantly across the charge density wave energy gap with the effect of pumping at a twice higher photon energy in a TARPES pump-probe experiment. We clearly identify a pump photon energy dependent behavior. At the larger pump photon energy, the excess electronic energy generates lattice heating mediated by e-ph coupling and softening of the amplitude mode frequency from 3 to 2 THz. Remarkably, the resonant pumping across the CDW gap results in a time-independent amplitude mode frequency. We conclude that the resonant excitation across the energy gap excites the amplitude mode selectively while additional electronic excess energy deposited at higher pump photon energy modifies the crystal properties transiently by incoherent dissipative processes., Comment: 10 pages, 3 figures

Pattern images are everywhere in the digital and physical worlds, and tools to edit them are valuable. But editing pattern images is tricky: desired edits are often programmatic: structure-aware edits that alter the underlying program which generates the pattern. One could attempt to infer this underlying program, but current methods for doing so struggle with complex images and produce unorganized programs that make editing tedious. In this work, we introduce a novel approach to perform programmatic edits on pattern images. By using a pattern analogy -- a pair of simple patterns to demonstrate the intended edit -- and a learning-based generative model to execute these edits, our method allows users to intuitively edit patterns. To enable this paradigm, we introduce SplitWeave, a domain-specific language that, combined with a framework for sampling synthetic pattern analogies, enables the creation of a large, high-quality synthetic training dataset. We also present TriFuser, a Latent Diffusion Model (LDM) designed to overcome critical issues that arise when naively deploying LDMs to this task. Extensive experiments on real-world, artist-sourced patterns reveals that our method faithfully performs the demonstrated edit while also generalizing to related pattern styles beyond its training distribution., Comment: Website: https://bardofcodes.github.io/patterns/

We consider the optimal experimental design problem of allocating subjects to treatment or control when subjects participate in multiple, separate controlled experiments within a short time-frame and subject covariate information is available. Here, in addition to subject covariates, we consider the dependence among the responses coming from the subject's random effect across experiments. In this setting, the goal of the allocation is to provide precise estimates of treatment effects for each experiment. Deriving the precision matrix of the treatment effects and using D-optimality as our allocation criterion, we demonstrate the advantage of collaboratively designing and analyzing multiple experiments over traditional independent design and analysis, and propose two randomized algorithms to provide solutions to the D-optimality problem for collaborative design. The first algorithm decomposes the D-optimality problem into a sequence of subproblems, where each subproblem is a quadratic binary program that can be solved through a semi-definite relaxation based randomized algorithm with performance guarantees. The second algorithm involves solving a single semi-definite program, and randomly generating allocations for each experiment from the solution of this program. We showcase the performance of these algorithms through a simulation study, finding that our algorithms outperform covariate-agnostic methods when there are a large number of covariates.

We investigate a quantum dynamical phase transition induced by the competition between local unitary evolution and dissipation in a qubit chain with a strong, on-site $\mathbb{Z}_2$ symmetry. While the steady-state of this evolution is always maximally-mixed, we show that the dynamical behavior of certain non-local observables on the approach to this steady-state is dictated by a quantum Ising model with a $\textit{complex}$ transverse-field (cTFIM). We investigate these observables analytically, uncovering a dynamical phase transition as the relative rate of unitary evolution and dissipation is tuned. We show that the weak-dissipation limit corresponds to a cTFIM with a large magnitude of the imaginary transverse-field, for which the many-body "ground-state" (with smallest real eigenvalue) is gapless, exhibiting quasi-long-range correlations of the local magnetization with a continuously-varying exponent. Correspondingly, the dynamics of the non-local observables show oscillatory behavior with an amplitude decaying exponentially in time. The strong-dissipation limit corresponds to a gapped ferromagnetic phase of the cTFIM, and non-local observables show exponential decay on the approach to equilibrium. This transition in (1+1)-dimensions has a peculiar, "two-sided" nature appearing as either first- or second-order depending on the phase from which the transition is approached, an analytic result which is corroborated by numerical studies. In higher dimensions, we present a field-theoretic understanding of the first-order nature of this transition, when approaching from the ferromagnetic phase of the cTFIM, though the nature of the phase with large imaginary transverse-field remains to be understood., Comment: 15 + 14 pages, 8 figures

The paper aims at proposing an efficient and stable quasi-interpolation based method for numerically computing the Helmholtz-Hodge decomposition of a vector field. To this end, we first explicitly construct a matrix kernel in a general form from polyharmonic splines such that it includes divergence-free/curl-free/harmonic matrix kernels as special cases. Then we apply the matrix kernel to vector decomposition via the convolution technique together with the Helmholtz-Hodge decomposition. More precisely, we show that if we convolve a vector field with a scaled divergence-free (curl-free) matrix kernel, then the resulting divergence-free (curl-free) convolution sequence converges to the corresponding divergence-free (curl-free) part of the Helmholtz-Hodge decomposition of the field. Finally, by discretizing the convolution sequence via certain quadrature rule, we construct a family of (divergence-free/curl-free) quasi-interpolants for the Helmholtz-Hodge decomposition (defined both in the whole space and over a bounded domain). Corresponding error estimates derived in the paper show that our quasi-interpolation based method yields convergent approximants to both the vector field and its Helmholtz-Hodge decomposition

We present Argos, a simple approach for adding verifiability to fully homomorphic encryption (FHE) schemes using trusted hardware. Traditional approaches to verifiable FHE require expensive cryptographic proofs, which incur an overhead of up to seven orders of magnitude on top of FHE, making them impractical. With Argos, we show that trusted hardware can be securely used to provide verifiability for FHE computations, with minimal overhead relative to the baseline FHE computation. An important contribution of Argos is showing that the major security pitfall associated with trusted hardware, microarchitectural side channels, can be completely mitigated by excluding any secrets from the CPU and the memory hierarchy. This is made possible by focusing on building a platform that only enforces program and data integrity and not confidentiality (which is sufficient for verifiable FHE, since all data remain encrypted at all times). All secrets related to the attestation mechanism are kept in a separate coprocessor (e.g., a TPM) inaccessible to any software-based attacker. Relying on a discrete TPM typically incurs significant performance overhead, which is why (insecure) software-based TPMs are used in practice. As a second contribution, we show that for FHE applications, the attestation protocol can be adapted to only incur a fixed cost. Argos requires no dedicated hardware extensions and is supported on commodity processors from 2008 onward. Our prototype implementation introduces 6% overhead to the FHE evaluation, and 8% for more complex protocols. In particular, we show that Argos can be adapted for real-world applications of FHE, such as PIR and PSI. By demonstrating how to combine cryptography with trusted hardware, Argos paves the way for widespread deployment of FHE-based protocols beyond the semi-honest setting, without the overhead of cryptographic proofs.

In this work, we perform on-chip quantum random number generation (QRNG) that uses a novel differential amplifier configuration for conjugate homodyne detection. Leveraging separate integrated photonics and integrated analog circuit platforms, we present an alternative method for QRNG. This approach exploits the observable $\hat{\text{Z}}$, derived from the sum of squared conjugate quadrature distributions which we compare to the traditional single quadrature approach. Utilizing this method, we report a shot noise clearance (SNC) of 25.6 dB and a common mode rejection ratio (CMRR) of 69 dB for our homodyne detection system. We used a variety of design tools to model and predict performance and compare results with our measurements. The realization of our QRNG system consists of a 90{\deg} optical hybrid, a dual differential transimpedance amplifier (TIA), and a field-programmable gate array (FPGA) used for the real-time post-processing to produce a uniform random bitstream. The randomness extraction is implemented using a Toeplitz hashing algorithm and is validated by the National Institute of Standards and Technology (NIST) randomness test suites., Comment: 8 pages + appendix. 10 figures + 2 tables

As modern AI models become integral to everyday tasks, concerns about their inherent biases and their potential impact on human decision-making have emerged. While bias in models are well-documented, less is known about how these biases influence human decisions. This paper presents two interactive experiments investigating the effects of partisan bias in AI language models on political decision-making. Participants interacted freely with either a biased liberal, biased conservative, or unbiased control model while completing political decision-making tasks. We found that participants exposed to politically biased models were significantly more likely to adopt opinions and make decisions aligning with the AI's bias, regardless of their personal political partisanship. However, we also discovered that prior knowledge about AI could lessen the impact of the bias, highlighting the possible importance of AI education for robust bias mitigation. Our findings not only highlight the critical effects of interacting with biased AI and its ability to impact public discourse and political conduct, but also highlights potential techniques for mitigating these risks in the future.

Feedbacks between evolution and ecology are ubiquitous, with ecological interactions determining which mutants are successful, and these mutants in turn modifying community structure. We study the evolutionary dynamics of several ecological models with overlapping niches, including consumer resource and Lotka-Volterra models. Evolution is assumed slow and extinctions are permanent, with ecological dynamics reaching a stable fixed point between introductions of invaders or mutants. When new strains are slowly added to the community, the ecosystem converges, after an initial evolutionary transient, to a diverse eco-evolutionary steady state. In this "Red Queen" phase of continual evolution, the biodiversity continues to turn over without the invasion probability of new variants getting any smaller. For resource-mediated interactions, the Red Queen phase obtains for any amount of asymmetry in the interactions between strains, and is robust to "general fitness" differences in the intrinsic growth rates of strains. Via a dynamical mean field theory framework valid for high-dimensional phenotype space, we analytically characterize the Red Queen eco-evolutionary steady state in a particular limit of model parameters. Scaling arguments enable a more general understanding of the steady state and evolutionary transients toward it. This work therefore establishes simple models of continual evolution in an ecological context without host-pathogen arms races, and points to the generality of Red Queen evolution. However, we also find other eco-evolutionary phases in simple models: For generalized Lotka-Volterra models with weakly asymmetric interactions an "oligarch" phase emerges in which the evolutionary dynamics continually slow down and a substantial fraction of the community's abundance condenses into a handful of slowly turning-over strains., Comment: 36 pages, 22 figures

We present measurements of the specific angular momentum $j_\star$ of 41 star-forming galaxies at $1.51$ in some cases) in low-mass haloes that decrease with increasing mass, suggesting a significant role of efficient angular momentum transport in these gas-rich systems, aided by the removal of low-$j_\star$ gas via feedback-driven outflows in low-mass galaxies., Comment: 29 pages, 18 figures, 24 appendix figures. Accepted for publication in MNRAS

Incorporating artificial intelligence (AI) into digital pathology offers promising prospects for automating and enhancing tasks such as image analysis and diagnostic processes. However, the diversity of tissue samples and the necessity for meticulous image labeling often result in biased datasets, constraining the applicability of algorithms trained on them. To harness synthetic histopathological images to cope with this challenge, it is essential not only to produce photorealistic images but also to be able to exert control over the cellular characteristics they depict. Previous studies used methods to generate, from random noise, semantic masks that captured the spatial distribution of the tissue. These masks were then used as a prior for conditional generative approaches to produce photorealistic histopathological images. However, as with many other generative models, this solution exhibits mode collapse as the model fails to capture the full diversity of the underlying data distribution. In this work, we present a pipeline, coined PriorPath, that generates detailed, realistic, semantic masks derived from coarse-grained images delineating tissue regions. This approach enables control over the spatial arrangement of the generated masks and, consequently, the resulting synthetic images. We demonstrated the efficacy of our method across three cancer types, skin, prostate, and lung, showcasing PriorPath's capability to cover the semantic mask space and to provide better similarity to real masks compared to previous methods. Our approach allows for specifying desired tissue distributions and obtaining both photorealistic masks and images within a single platform, thus providing a state-of-the-art, controllable solution for generating histopathological images to facilitate AI for computational pathology.

A charge density wave (CDW) is a phase of matter characterized by a periodic modulation of the valence electron density accompanied by a distortion of the lattice structure. The microscopic details of CDW formation are closely tied to the dynamic charge susceptibility, $\chi(q,\omega)$, which describes the behavior of electronic collective modes. Despite decades of extensive study, the behavior of $\chi(q,\omega)$ in the vicinity of a CDW transition has never been measured with high energy resolution ($\sim$meV). Here, we investigate the canonical CDW transition in ErTe$_3$ using momentum-resolved electron energy loss spectroscopy (M-EELS), a technique uniquely sensitive to valence band charge excitations. Unlike phonons in these materials, which undergo conventional softening due to the Kohn anomaly at the CDW wavevector, the electronic excitations display purely relaxational dynamics that are well described by a diffusive model. The diffusivity peaks around 250 K, just below the critical temperature. Additionally, we report, for the first time, a divergence in the real part of $\chi(q,\omega)$ in the static limit ($\omega \rightarrow 0$), a phenomenon predicted to characterize CDWs since the 1970s. These results highlight the importance of energy- and momentum-resolved measurements of electronic susceptibility and demonstrate the power of M-EELS as a versatile probe of charge dynamics in materials.

We study in detail the postselection problem in a specific model: bosons hopping on a lattice subjected to continuous local measurements of quadrature observables. We solve the model analytically and show that the postselection overhead can be reduced by postprocessing the entire measurement record into one or two numbers for each trajectory and then postselecting based only on these numbers. We then provide a step-by-step protocol designed to recover connected two-point functions of the quantum trajectories, which display an exponentially decaying profile that is not observable in the unconditional, trajectory averaged, state. With the analytical solution in hand, we analyse the features of this postprocessing stage with the intention of abstracting away the properties that make postselection feasible in this model and may help in mitigating postselection in more general settings. We also test the protocol numerically in a way that utilizes only experimentally accessible information, showing that various quantum trajectory observables can be recovered with a few repetitions of the numerical experiment, even after including inevitable coarse-graining procedures expected under realistic experimental conditions. Furthermore, all the information required to design the postprocessing stage is independently present both in the unconditional dynamics and also in the measurement record, thus bypassing the need to solve for the conditional evolution of the model. We finalize by providing experimental implementations of these models in cavity-QED and circuit-QED., Comment: 16 + 8 pages, 11 figures

Formal verification of robotic applications presents challenges due to their hybrid nature and distributed architecture. This paper introduces ROSMonitoring 2.0, an extension of ROSMonitoring designed to facilitate the monitoring of both topics and services while considering the order in which messages are published and received. The framework has been enhanced to support these novel features for ROS1 -- and partially ROS2 environments -- offering improved real-time support, security, scalability, and interoperability. We discuss the modifications made to accommodate these advancements and present results obtained from a case study involving the runtime monitoring of specific components of a fire-fighting Uncrewed Aerial Vehicle (UAV)., Comment: In Proceedings FMAS2024, arXiv:2411.13215

The multivariate Gaussian distribution underpins myriad operations-research, decision-analytic, and machine-learning models (e.g., Bayesian optimization, Gaussian influence diagrams, and variational autoencoders). However, despite recent advances in adversarial machine learning (AML), inference for Gaussian models in the presence of an adversary is notably understudied. Therefore, we consider a self-interested attacker who wishes to disrupt a decisionmaker's conditional inference and subsequent actions by corrupting a set of evidentiary variables. To avoid detection, the attacker also desires the attack to appear plausible wherein plausibility is determined by the density of the corrupted evidence. We consider white- and grey-box settings such that the attacker has complete and incomplete knowledge about the decisionmaker's underlying multivariate Gaussian distribution, respectively. Select instances are shown to reduce to quadratic and stochastic quadratic programs, and structural properties are derived to inform solution methods. We assess the impact and efficacy of these attacks in three examples, including, real estate evaluation, interest rate estimation and signals processing. Each example leverages an alternative underlying model, thereby highlighting the attacks' broad applicability. Through these applications, we also juxtapose the behavior of the white- and grey-box attacks to understand how uncertainty and structure affect attacker behavior., Comment: 30 pages, 6 figures; 4 tables

Autonomous systems use independent decision-making with only limited human intervention to accomplish goals in complex and unpredictable environments. As the autonomy technologies that underpin them continue to advance, these systems will find their way into an increasing number of applications in an ever wider range of settings. If we are to deploy them to perform safety-critical or mission-critical roles, it is imperative that we have justified confidence in their safe and correct operation. Verification is the process by which such confidence is established. However, autonomous systems pose challenges to existing verification practices. This paper highlights viewpoints of the Roadmap Working Group of the IEEE Robotics and Automation Society Technical Committee for Verification of Autonomous Systems, identifying these grand challenges, and providing a vision for future research efforts that will be needed to address them.

Video understanding has made huge strides in recent years, relying largely on the power of the transformer architecture. As this architecture is notoriously expensive and video is highly redundant, research into improving efficiency has become particularly relevant. This has led to many creative solutions, including token merging and token selection. While most methods succeed in reducing the cost of the model and maintaining accuracy, an interesting pattern arises: most methods do not outperform the random sampling baseline. In this paper we take a closer look at this phenomenon and make several observations. First, we develop an oracle for the value of tokens which exposes a clear Pareto distribution where most tokens have remarkably low value, and just a few carry most of the perceptual information. Second, we analyze why this oracle is extremely hard to learn, as it does not consistently coincide with visual cues. Third, we observe that easy videos need fewer tokens to maintain accuracy. We build on these and further insights to propose a lightweight video model we call LITE that can select a small number of tokens effectively, outperforming state-of-the-art and existing baselines across datasets (Kinetics400 and Something-Something-V2) in the challenging trade-off of computation (GFLOPs) vs accuracy.