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Due to the wide employment of automated reasoning in the analysis and construction of correct systems, the results reported by automated reasoning engines must be trustworthy. For Boolean satisfiability (SAT) solvers - and more recently SAT-based maximum satisfiability (MaxSAT) solvers - trustworthiness is obtained by integrating proof logging into solvers, making solvers capable of emitting machine-verifiable proofs to certify correctness of the reasoning steps performed. In this work, we enable for the first time proof logging based on the VeriPB proof format for multi-objective MaxSAT (MO-MaxSAT) optimization techniques. Although VeriPB does not offer direct support for multi-objective problems, we detail how preorders in VeriPB can be used to provide certificates for MO-MaxSAT algorithms computing a representative solution for each element in the non-dominated set of the search space under Pareto-optimality, without extending the VeriPB format or the proof checker. By implementing VeriPB proof logging into a state-of-the-art multi-objective MaxSAT solver, we show empirically that proof logging can be made scalable for MO-MaxSAT with reasonable overhead.

Stellar feedback is an essential step in the baryon cycle of galaxies, but it remains unconstrained beyond Cosmic Noon. We study the ionized gas kinematics, dynamical mass and gas-flow properties of a sample of 16 sub-$L^{\star}$ star-forming galaxies at $4\leq z\leq7.6$, using high-resolution JWST/NIRSpec observations. The emission lines are resolved, with velocity dispersions ($\sigma_{\rm gas}{\rm~(km~s^{-1})}\simeq38-96$) comparable to more massive galaxies at Cosmic Noon. From $\sigma_{\rm gas}$ and the galaxy size ($r_e=400-960~$pc), we estimate the dynamical mass to be $\log M_{\rm dyn}/M_{\odot}=9.25-10.25$. Stellar-to-dynamical mass ratios are low ($\log M_{\star}/M_{\rm dyn}\in[-0.5,-2]$) and decrease with increasing SFR surface density ($\Sigma_{\rm SFR}$). We estimate the gas surface densities assuming a star-formation law, but the gas masses do not balance the baryon-to-dynamical mass ratios, which would require a decrease in the star-formation efficiency. We find evidence of ionized outflows in five out of the sixteen galaxies, based on the need of broad components to reproduce the emission-line wings. We only observe outflows from galaxies undergoing recent bursts of star formation ${\rm SFR_{10}/SFR_{100}\geq1}$, with elevated $\Sigma_{\rm SFR}$ and low $M_{\star}/M_{\rm dyn}$. This links high gas surface densities to increased outflow incidence and lower $M_{\star}/M_{\rm dyn}$. With moderate outflow velocities ($v_{\rm flow}{\rm~(km~s^{-1})}=150-250$) and mass outflow rates ($\dot{M}_{\rm flow}/{\rm M_{\odot} yr^{-1}}=0.2-5$), these high-redshift galaxies appear more efficient at removing baryons than low-redshift galaxies with similar $M_{\star}$, showing mass loading-factors of $\dot{M}_{\rm flow}/{\rm SFR}=0.04-0.4$. For their given dynamical mass, the outflow velocities exceed the escape velocities, meaning that they may eventually enrich the Circumgalactic Medium., Comment: 15 pages, 11 figures, 3 tables; submitted to MNRAS

Hybrid clinical trials, that borrow real-world data (RWD), are gaining interest, especially for rare diseases. They assume RWD and randomized control arm be exchangeable, but violations can bias results, inflate type I error, or reduce power. A two-step hybrid design first tests exchangeability, reducing inappropriate borrowing but potentially inflating type I error (Yuan et al., 2019). We propose four methods to better control type I error. Approach 1 estimates the variance of test statistics, rejecting the null hypothesis based on large sample normal approximation. Approach 2 uses a numerical approach for exact critical value determination. Approach 3 splits type I error rates by equivalence test outcome. Approach 4 adjusts the critical value only when equivalence is established. Simulation studies using a hypothetical ALS scenario, evaluate type I error and power under various conditions, compared to the Bayesian power prior approach (Ibrahim et al., 2015). Our methods and the Bayesian power prior control type I error, whereas Yuan et al. (2019) increases it under exchangeability. If exchangeability doesn't hold, all methods fail to control type I error. Our methods show type I error inflation of 6%-8%, compared to 10% for Yuan et al. (2019) and 16% for the Bayesian power prior.

Detecting the first generation of stars, Population III (PopIII), has been a long-standing goal in astrophysics, yet they remain elusive even in the JWST era. Here we present a novel NIRCam-based selection method for PopIII galaxies, and carefully validate it through completeness and contamination simulations. We systematically search ~500 arcmin$^{2}$ across JWST legacy fields for PopIII candidates, including GLIMPSE which, assisted by gravitational lensing, has produced JWST's deepest NIRCam imaging thus far. We discover one promising PopIII galaxy candidate (GLIMPSE-16043) at $z=6.50^{+0.03}_{-0.24}$, a moderately lensed galaxy (mu=2.9) with an intrinsic UV magnitude of $M_{UV}$=-15.89. It exhibits key PopIII features: strong H$\alpha$ emission (rest-frame EW $2810\pm550$\AA); a Balmer jump; no dust (UV slope $\beta=-2.34\pm0.36$); and undetectable metal lines (e.g., [OIII]; [OIII]/H$\beta$<0.44) implying a gas-phase metallicity of Zgas/Zsun<0.5%. These properties indicate the presence of a nascent, metal-deficient young stellar population (<5Myr) with a stellar mass of $\simeq10^{5}M_{\odot}$. Intriguingly, this source deviates significantly from the extrapolated UV-metallicity relation derived from recent JWST observations at $z=4-10$, consistent with UV enhancement by a top-heavy PopIII initial mass function or the presence of an extremely metal-poor AGN. We also derive the first observational constraints on the PopIII UV luminosity function at z~6-7. The volume density of GLIMPSE-16043 ($\approx10^{-4}$ cMpc$^{-3}$) is in excellent agreement with theoretical predictions, independently reinforcing its plausibility. This study demonstrates the power of our novel NIRCam method to finally reveal distant galaxies even more pristine than the Milky Way's most metal-poor satellites, thereby promising to bring us closer to the first generation of stars than we have ever been before., Comment: 41 pages, 20 figures, and 10 Tables. Submitted to ApJ. Comments are welcome!

Longitudinal MRI analysis is crucial for predicting disease outcomes, particularly in chronic conditions like hepatocellular carcinoma (HCC), where early detection can significantly influence treatment strategies and patient prognosis. Yet, due to challenges like limited data availability, subtle parenchymal changes, and the irregular timing of medical screenings, current approaches have so far focused on cross-sectional imaging data. To address this, we propose HCCNet, a novel model architecture that integrates a 3D adaptation of the ConvNeXt CNN architecture with a Transformer encoder, capturing both the intricate spatial features of 3D MRIs and the complex temporal dependencies across different time points. HCCNet utilizes a two-stage pre-training process tailored for longitudinal MRI data. The CNN backbone is pre-trained using a self-supervised learning framework adapted for 3D MRIs, while the Transformer encoder is pre-trained with a sequence-order-prediction task to enhance its understanding of disease progression over time. We demonstrate the effectiveness of HCCNet by applying it to a cohort of liver cirrhosis patients undergoing regular MRI screenings for HCC surveillance. Our results show that HCCNet significantly improves predictive accuracy and reliability over baseline models, providing a robust tool for personalized HCC surveillance. The methodological approach presented in this paper is versatile and can be adapted to various longitudinal MRI screening applications. Its ability to handle varying patient record lengths and irregular screening intervals establishes it as an invaluable framework for monitoring chronic diseases, where timely and accurate disease prognosis is critical for effective treatment planning., Comment: Submitted for publication to Biomedical Signal Processing and Control; Incorrect notation corrected

Biosensing based on optically trapped fluorescent nanodiamonds is an intriguing research direction potentially allowing to resolve biochemical processes inside living cells. Towards this goal, we investigate infrared near (NIR) laser irradiation at 1064 nm on fluorescent nanodiamonds (FNDs) containing nitrogen-vacancy (NV) centers. By conducting comprehensive experiments, we aim to understand how NIR exposure influences the fluorescence and sensing properties of FNDs and to determine the potential implications for the use of FNDs in various sensing applications. The experimental setup involved exposing FNDs to varying intensities of NIR laser light and analyzing the resultant changes in their optical and physical properties. Key measurements included T1 relaxation times, optical spectroscopy, and optically detected magnetic resonance (ODMR) spectra. The findings reveal how increased NIR laser power correlates with alterations in ODMR central frequency but also that charge state dynamics under NIR irradiation of NV centers plays a role. We suggest protocols with NIR and green light that mitigate the effect of NIR, and demonstrate that FND biosensing works well with such a protocol.

Deep learning has proven to be a suitable alternative to least-squares (LSQ) fitting for parameter estimation in various quantitative MRI (QMRI) models. However, current deep learning implementations are not robust to changes in MR acquisition protocols. In practice, QMRI acquisition protocols differ substantially between different studies and clinical settings. The lack of generalizability and adoptability of current deep learning approaches for QMRI parameter estimation impedes the implementation of these algorithms in clinical trials and clinical practice. Neural Controlled Differential Equations (NCDEs) allow for the sampling of incomplete and irregularly sampled data with variable length, making them ideal for use in QMRI parameter estimation. In this study, we show that NCDEs can function as a generic tool for the accurate prediction of QMRI parameters, regardless of QMRI sequence length, configuration of independent variables and QMRI forward model (variable flip angle T1-mapping, intravoxel incoherent motion MRI, dynamic contrast-enhanced MRI). NCDEs achieved lower mean squared error than LSQ fitting in low-SNR simulations and in vivo in challenging anatomical regions like the abdomen and leg, but this improvement was no longer evident at high SNR. NCDEs reduce estimation error interquartile range without increasing bias, particularly under conditions of high uncertainty. These findings suggest that NCDEs offer a robust approach for reliable QMRI parameter estimation, especially in scenarios with high uncertainty or low image quality. We believe that with NCDEs, we have solved one of the main challenges for using deep learning for QMRI parameter estimation in a broader clinical and research setting., Comment: 29 pages, 10 figures, 7 supplementary figures, pre-print

The incompressibility of infinite nuclear matter (K_\infty) is a parameter in the description of the nuclear equation of state that governs the energy cost associated with density oscillations near the saturation density. The most direct experimental method for studying this property of infinite nuclear matter is to probe the isoscalar giant monopole resonance (ISGMR) in finite nuclei. This work explores the use of 6Li as a probe to study the ISGMR in several stable nuclei: 58Ni, 90Zr, 116Sn, and 208Pb, as complementary to using inelastic scattering of {\alpha}-particles, which has been used to great effect over the last several decades. Elastic and inelastic scattering data for these targets were collected with 343-MeV 6Li beams. In all nuclei studied in this work, the ISGMR strength distributions extracted from multipole decomposition analyses of the inelastic scattering spectra agree very well with the previously measured ISGMR responses from {\alpha}-particle scattering, establishing the feasibility of employing 6Li inelastic scattering in investigations of the ISGMR., Comment: Accepted for publication in Phys. Rev. C

Scalable quantum networks rely on optical connections between long-lived qubits to distribute entanglement. Tin vacancies in diamond have emerged as promising long-lived qubits, offering extended spin coherence times at liquid helium temperatures and spin-dependent, highly coherent optical transitions for effective photon-based communication. Connecting remote nodes requires quantum interference of indistinguishable photons, which is challenging in an inhomogeneous solid-state environment. Here, we demonstrate a two-node experiment with tin vacancies in diamond, which exhibit a resonant frequency distribution spanning approximately 8 GHz. To overcome the frequency mismatch, we tune the resonant frequencies of one node using the Stark effect. We achieve tunability up to 4 GHz while maintaining optical coherence. As a demonstration, we achieve detuning-dependent remote two-photon interference between separate nodes, obtaining 80(6)% interference visibility without postprocessing when the defects' optical transitions are tuned into resonance, and 63(8)% with detuning up to 20 times their natural linewidths. These results highlight the potential of tin-vacancy centres in diamond for establishing robust optical links between remote quantum registers.

As AI systems increasingly make critical decisions, deceptive AI poses a significant challenge to trust and safety. We present Self-Other Overlap (SOO) fine-tuning, a promising approach in AI Safety that could substantially improve our ability to build honest artificial intelligence. Inspired by cognitive neuroscience research on empathy, SOO aims to align how AI models represent themselves and others. Our experiments on LLMs with 7B, 27B, and 78B parameters demonstrate SOO's efficacy: deceptive responses of Mistral-7B-Instruct-v0.2 dropped from 73.6% to 17.2% with no observed reduction in general task performance, while in Gemma-2-27b-it and CalmeRys-78B-Orpo-v0.1 deceptive responses were reduced from 100% to 9.3% and 2.7%, respectively, with a small impact on capabilities. In reinforcement learning scenarios, SOO-trained agents showed significantly reduced deceptive behavior. SOO's focus on contrastive self and other-referencing observations offers strong potential for generalization across AI architectures. While current applications focus on language models and simple RL environments, SOO could pave the way for more trustworthy AI in broader domains. Ethical implications and long-term effects warrant further investigation, but SOO represents a significant step forward in AI safety research., Comment: NeurIPS 2024 Safe Generative AI Workshop

We propose a mechanism which can generate supercurrents in spin-orbit coupled superconductors with charged magnetic inclusions. The basic idea is that through spin-orbit interaction, the in-plane electric field near the edge of each inclusion appears to the electrons as an effective spin-dependent gauge field; if Cooper pairs can be partially spin polarized, then each pair experiences a nonzero \textit{net} transverse pseudo-gauge field. We explore the phenomenology of our mechanism within a Ginzburg-Landau theory, with parameters determined from a microscopic model. Depending on parameters, our mechanism can either enhance or reduce the total magnetization upon superconducting condensation. Given an appropriate distribution of inclusions, we show how our mechanism can generate superconducting vortices without any applied orbital magnetic field. Surprisingly, the vortices form \textit{nonlocally}; they are situated in between the inclusions. Our mechanism can produce similar qualitative behavior to the "magnetic memory effect" observed in 4Hb-TaS$_2$. However, the magnitude of the effect in that material seems larger than our model can naturally explain., Comment: 11 pages, 2 figures

Robot learning is witnessing a significant increase in the size, diversity, and complexity of pre-collected datasets, mirroring trends in domains such as natural language processing and computer vision. Many robot learning methods treat such datasets as multi-task expert data and learn a multi-task, generalist policy by training broadly across them. Notably, while these generalist policies can improve the average performance across many tasks, the performance of generalist policies on any one task is often suboptimal due to negative transfer between partitions of the data, compared to task-specific specialist policies. In this work, we argue for the paradigm of training policies during deployment given the scenarios they encounter: rather than deploying pre-trained policies to unseen problems in a zero-shot manner, we non-parametrically retrieve and train models directly on relevant data at test time. Furthermore, we show that many robotics tasks share considerable amounts of low-level behaviors and that retrieval at the "sub"-trajectory granularity enables significantly improved data utilization, generalization, and robustness in adapting policies to novel problems. In contrast, existing full-trajectory retrieval methods tend to underutilize the data and miss out on shared cross-task content. This work proposes STRAP, a technique for leveraging pre-trained vision foundation models and dynamic time warping to retrieve sub-sequences of trajectories from large training corpora in a robust fashion. STRAP outperforms both prior retrieval algorithms and multi-task learning methods in simulated and real experiments, showing the ability to scale to much larger offline datasets in the real world as well as the ability to learn robust control policies with just a handful of real-world demonstrations., Comment: Project website at https://weirdlabuw.github.io/strap/

Optimization plays an important role in chemical engineering, impacting cost-effectiveness, resource utilization, product quality, and process sustainability metrics. This chapter broadly focuses on data-driven optimization, particularly, on model-based derivative-free techniques, also known as surrogate-based optimization. The chapter introduces readers to the theory and practical considerations of various algorithms, complemented by a performance assessment across multiple dimensions, test functions, and two chemical engineering case studies: a stochastic high-dimensional reactor control study and a low-dimensional constrained stochastic reactor optimization study. This assessment sheds light on each algorithm's performance and suitability for diverse applications. Additionally, each algorithm is accompanied by background information, mathematical foundations, and algorithm descriptions. Among the discussed algorithms are Bayesian Optimization (BO), including state-of-the-art TuRBO, Constrained Optimization by Linear Approximation (COBYLA), the Ensemble Tree Model Optimization Tool (ENTMOOT) which uses decision trees as surrogates, Stable Noisy Optimization by Branch and Fit (SNOBFIT), methods that use radial basis functions such as DYCORS and SRBFStrategy, Constrained Optimization by Quadratic Approximations (COBYQA), as well as a few others recognized for their effectiveness in surrogate-based optimization. By combining theory with practice, this chapter equips readers with the knowledge to integrate surrogate-based optimization techniques into chemical engineering. The overarching aim is to highlight the advantages of surrogate-based optimization, introduce state-of-the-art algorithms, and provide guidance for successful implementation within process systems engineering.

We study the Dominating set problem and Independent Set Problem for dynamic graphs in the vertex-arrival model. We say that a dynamic algorithm for one of these problems is $k$-stable when it makes at most $k$ changes to its output independent set or dominating set upon the arrival of each vertex. We study trade-offs between the stability parameter $k$ of the algorithm and the approximation ratio it achieves. We obtain the following results. 1. We show that there is a constant $\varepsilon^*>0$ such that any dynamic $(1+\varepsilon^*)$-approximation algorithm the for Dominating set problem has stability parameter $\Omega(n)$, even for bipartite graphs of maximum degree 4. 2. We present algorithms with very small stability parameters for the Dominating set problem in the setting where the arrival degree of each vertex is upper bounded by $d$. In particular, we give a $1$-stable $(d+1)^2$-approximation algorithm, a $3$-stable $(9d/2)$-approximation algorithm, and an $O(d)$-stable $O(1)$-approximation algorithm. 3. We show that there is a constant $\varepsilon^*>0$ such that any dynamic $(1+\varepsilon^*)$-approximation algorithm for the Independent Set Problem has stability parameter $\Omega(n)$, even for bipartite graphs of maximum degree $3$. 4. Finally, we present a $2$-stable $O(d)$-approximation algorithm for the Independent Set Problem, in the setting where the average degree of the graph is upper bounded by some constant $d$ at all times. We extend this latter algorithm to the fully dynamic model where vertices can also be deleted, achieving a $6$-stable $O(d)$-approximation algorithm., Comment: 23 pages, an initial version has been published as part of the proceedings of APPROX 2023

Let $P$ be a set of points in the plane and let $m$ be an integer. The goal of Max Cover by Unit Disks problem is to place $m$ unit disks whose union covers the maximum number of points from~$P$. We are interested in the dynamic version of Max Cover by Unit Disks problem, where the points in $P$ appear and disappear over time, and the algorithm must maintain a set \cDalg of $m$ disks whose union covers many points. A dynamic algorithm for this problem is a $k$-stable $\alpha$-approximation algorithm when it makes at most $k$ changes to \cDalg upon each update to the set $P$ and the number of covered points at time $t$ is always at least $\alpha \cdot \opt(t)$, where $\opt(t)$ is the maximum number of points that can be covered by m disks at time $t$. We show that for any constant $\varepsilon>0$, there is a $k_{\varepsilon}$-stable $(1-\varepsilon)$-approximation algorithm for the dynamic Max Cover by Unit Disks problem, where $k_{\varepsilon}=O(1/\varepsilon^3)$. This improves the stability of $\Theta(1/\eps^4)$ that can be obtained by combining results of Chaplick, De, Ravsky, and Spoerhase (ESA 2018) and De~Berg, Sadhukhan, and Spieksma (APPROX 2023). Our result extends to other fat similarly-sized objects used in the covering, such as arbitrarily-oriented unit squares, or arbitrarily-oriented fat ellipses of fixed diameter. We complement the above result by showing that the restriction to fat objects is necessary to obtain a SAS. To this end, we study the Max Cover by Unit Segments problem, where the goal is to place $m$ unit-length segments whose union covers the maximum number of points from $P$. We show that there is a constant $\varepsilon^* > 0$ such that any $k$-stable $(1 + \varepsilon^*)$-approximation algorithm must have $k=\Omega(m)$, even when the point set never has more than four collinear points., Comment: 18 pages

This paper investigates C IV absorption in the circumgalactic medium (CGM) of L* galaxies and its relationship with galaxy star formation rates. We present new observations from the C IV in L* survey (CIViL*; PID$\#$17076) using the Hubble Space Telescope/Cosmic Origins Spectrograph. By combining these measurements with archival C IV data (46 observations total), we estimate detection fractions for star-forming (sSFR $>$ 10$^{-11}$ yr$^{-1}$) and passive galaxies (sSFR $\leq$ 10$^{-11}$ yr$^{-1}$) to be 72$_{-18}^{+14}$\% [21/29] and 23$_{-15}^{+27}$\% [3/13], respectively. This indicates a significant dichotomy in C IV presence between L* star-forming and passive galaxies, with over 99% confidence. This finding aligns with Tumlinson et al. (2011), which noted a similar dichotomy in O VI absorption. Our results imply a substantial carbon reservoir in the CGM of L* galaxies, suggesting a minimum carbon mass of $\gtrsim$ 3.03 $\times$ 10$^{6}$ M$_{\odot}$ out to 120 kpc. Together, these findings highlight a strong connection between star formation in galaxies and the state of their CGM, providing insight into the mechanisms governing galaxy evolution., Comment: 14 pages, 4 figures, ApJ Letters, accepted

A set of edges $F$ in a graph $G$ is an edge dominating set if every edge in $G$ is either in $F$ or shares a vertex with an edge in $F$. $G$ is said to be well-edge-dominated if all of its minimal edge dominating sets have the same cardinality. Recently it was shown that any triangle-free well-edge-dominated graph is either bipartite or in the set $\{C_5, C_7, C_7^*\}$ where $C_7^*$ is obtained from $C_7$ by adding a chord between any pair of vertices distance three apart. In this paper, we completely characterize all well-edge-dominated graphs containing exactly one triangle, of which there are two infinite families. We also prove that there are only eight well-edge-dominated outerplanar graphs, most of which contain at most one triangle.

We present a spectroscopic analysis of two star-forming galaxies at z~5 observed with JWST/NIRSpec as part of the Early eXtragalactic Continuum and Emission Line Science (EXCELS) survey. The detection of the C III]$\lambda\lambda$1906,09, [O II]$\lambda\lambda$3726,29, [O III]$\lambda\lambda$4363,5007, and [N II]$\lambda$6584 nebular emission lines enables investigation of the C/O, N/O, and C/N abundance ratios using the temperature-sensitive method. The two galaxies have stellar masses of log($M_{\star}$/M$_{\odot}$ ) = 8.13$\pm$0.09 and log($M_{\star}$/M$_{\odot}$ )=8.52$\pm$0.13 and corresponding metallicities of Z~0.2Z$_{\odot}$ and Z~0.3Z$_{\odot}$. These metallicities are somewhat higher than is typical for other z>5 galaxies with similar stellar mass and are in fact comparable to high-redshift analogue galaxies at z~0. Both galaxies display evidence for N/O enhancement with respect to the z~0 sample, with log(N/O)=-1.07$\pm$0.17 and log(N/O)=-0.86$\pm$0.15 respectively. In contrast, we find low C abundances, with log(C/O)=-0.82$\pm$0.22 and log(C/O)=-1.02$\pm$0.22, consistent with the predicted yields of core-collapse supernovae. Following the trend observed in other high-redshift sources, we find that the C/N ratios are lower at fixed O/H compared to the majority of local galaxies. In contrast to the top-heavy IMF invoked in some studies to explain low C/N ratios in metal-poor galaxies, we find, via comparison to chemical evolution models, that a standard or bottom-heavy IMF better explains the observed abundance ratios in more enriched systems due to an increase in N-enrichment from intermediate mass (4-7M$_{\odot}$) stars. Our results demonstrate that robust measurements of CNO abundances with JWST can reveal unique enrichment pathways in galaxies as a function of both metallicity and redshift., Comment: 17 pages, 8 Figures, and 3 Tables. Submitted to MNRAS. Comments Welcome

Lubrication flows between two solid surfaces can be found in a variety of biological and engineering settings. In many of these systems, the lubricant exhibits viscoelastic properties, which modify the associated lubrication forces. Here, we experimentally study viscoelastic lubrication by considering the motion of a submerged cylinder sliding down an incline. We demonstrate that cylinders move faster when released in a viscoelastic Boger liquid compared to a Newtonian liquid with similar viscosity. Cylinders exhibit pure sliding motion in viscoelastic liquids, in contrast to the stick-slip motion observed in Newtonian liquids. We rationalize our results by using the second-order fluid model, which predicts a lift force on the cylinder arising from the normal-stress differences provided by the dissolved polymers. The interplay between viscoelastic lift, viscous friction, and gravity leads to a prediction for the sliding speed, which is consistent with our experimental results for weakly viscoelastic flows. Finally, we identify a remarkable difference between the lubrication of cylindrical and spherical contacts, as the latter does not exhibit any lift for weak viscoelasticity.

Modern cloud computing workloads are composed of multiresource jobs that require a variety of computational resources in order to run, such as CPU cores, memory, disk space, or hardware accelerators. A single cloud server can typically run many multiresource jobs in parallel, but only if the server has sufficient resources to satisfy the demands of every job. A scheduling policy must therefore select sets of multiresource jobs to run in parallel in order to minimize the mean response time across jobs -- the average time from when a job arrives to the system until it is completed. Unfortunately, achieving low response times by selecting sets of jobs that fully utilize the available server resources has proven to be a difficult problem. In this paper, we develop and analyze a new class of policies for scheduling multiresource jobs, called Markovian Service Rate (MSR) policies. While prior scheduling policies for multiresource jobs are either highly complex to analyze or hard to implement, our MSR policies are simple to implement and are amenable to response time analysis. We show that the class of MSR policies is throughput-optimal in that we can use an MSR policy to stabilize the system whenever it is possible to do so. We also derive bounds on the mean response time under an MSR algorithm that are tight up to an additive constant. These bounds can be applied to systems with different preemption behaviors, such as fully preemptive systems, non-preemptive systems, and systems that allow preemption with setup times. We show how our theoretical results can be used to select a good MSR policy as a function of the system arrival rates, job service requirements, the server's resource capacities, and the resource demands of the jobs.

HI-rich absorbers seen within quasar spectra contain the bulk of neutral gas in the Universe. However, the spatial extent of these reservoirs are not extensively studied due to the pencil beam nature of quasar sightlines. Using two giant gravitational arc fields (at redshifts 1.17 and 2.06) as 2D background sources with known strong MgII absorption observed with the MUSE integral field spectrograph (IFS), we investigated whether spatially mapped MgII absorption can predict the presence of strong HI systems, and determine both the physical extent and HI mass of the two absorbing systems. We created a simple model of an ensemble of gas clouds in order to simultaneously predict the HI column density and gas covering fraction of HI-rich absorbers based on observations of the MgII rest-frame equivalent width in IFS spaxels. We first test the model on the field with HI observations already available from the literature, finding that we can recover HI column densities consistent with the previous estimates (although with large uncertainties). We then use our framework to simultaneously predict the gas covering fraction, HI column density and total HI mass ($M_{\rm{HI}}$) for both fields. We find that both of the observed strong systems have a covering fraction of $\approx70$% and are likely damped Lyman $\alpha$ systems (DLAs) with $M_{\rm{HI}}>10^9\ M_{\odot}$. Our model shows that the typical MgII metrics used in the literature to identify the presence of DLAs are sensitive to the gas covering fraction. However, these MgII metrics are still sensitive to strong HI, and can be still applied to absorbers towards gravitational arcs or other spatially extended background sources. Based on our results, we speculate that the two strong absorbers are likely representative of a neutral inner circumgalactic medium and are a significant reservoir of fuel for star formation within the host galaxies., Comment: 15 pages, 6 Figures, 3 appendices. Accepted for publication on Astronomy & Astrophysics

Upper bounds are obtained for the $p$-capacity of compact sets in $\R^d$, with $d \ge 2$ and $1

We investigate an interface in the transverse field quantum Ising chain connecting an ordered ferromagnetic phase and a disordered paramagnetic phase that are Kramers-Wannier duals of each other. Unlike prior studies focused on non-invertible defects, this interface exhibits a symmetry that combines Kramers-Wannier transformation with spatial reflection. We demonstrate that, under open boundary conditions, this setup gives rise to a discrete $\mathbb{Z}_4$ symmetry, encompassing the conventional $\mathbb{Z}_2$ Ising parity as a subgroup, while in a closed geometry a non-invertible symmetry emerges. Using the Jordan-Wigner transformation, we map the spin chain onto a solvable quadratic Majorana fermion system. In this formulation, the $\mathbb{Z}_4$ symmetry is realized manifestly as a parity-dependent reflection operation with respect to a Majorana site, in contrast to the conventional reflection which mirrors with respect to the central link of the Majorana chain. Additionally, we construct Majorana strong zero modes that retain the $\mathbb{Z}_4$ symmetry, ensure degeneracies of all energy eigenstates, and are robust under generic local symmetry-preserving perturbations of the fermion model, including interactions. In an open geometry of the studied model, we identify two regimes that exhibit different degeneracy patterns. We discuss whether this is a manifestation of distinct fermion symmetry-protected topological orders protected by the $\mathbb{Z}_4$ symmetry., Comment: 33 pages, 6 figures, 1 table

Rest-frame far-ultraviolet (FUV) observations from JWST are revolutionizing our understanding of the high-z galaxies that drove reionization and the mechanisms by which they accomplished it. To fully interpret these observations, we must be able to diagnose how properties of the interstellar medium (ISM; e.g., column density, covering fraction, outflow velocity) directly relate to the absorption features produced. Using the high-S/N and high-resolution FUV spectra of 45 nearby star-forming galaxies from CLASSY, we present the largest uniform, simultaneous characterization of neutral and low-ionization state (LIS) interstellar UV absorption lines (OI, SiII, SII, CII, AlII) across a wide range of galaxy properties. We also present 21-cm HI observations for 35 galaxies, multiple of which are gas-poor or non-detected, possibly indicating the onset of a post-starburst phase. We find that our simultaneous 1-component Voigt profile fits are capable of accurately modeling the LIS absorption for ~75% of galaxies, mitigating challenges associated with saturation, infilling, and degeneracies. While the most massive galaxies require additional components, our 1-component fits return average properties of the absorbing gas and follow the scaling relations described by a single gas cloud. We explore connections between LIS absorption and direct tracers of the neutral ISM (OI, Ly-alpha, HI 21-cm), finding that CII most closely traces the neutral gas trends while other ions exhibit weaker correlations. Given the challenges with directly observing HI at higher-z, we demonstrate that LIS absorption can be a powerful means to study the neutral ISM and present empirical relationships for predicting neutral gas properties., Comment: 25 pages with 16 figures and 2 tables. Long appendix with figure sets and tables. Accepted to ApJ

Object detection and semantic segmentation are both scene understanding tasks yet they differ in data structure and information level. Object detection requires box coordinates for object instances while semantic segmentation requires pixel-wise class labels. Making use of one task's information to train the other would be beneficial for multi-task partially supervised learning where each training example is annotated only for a single task, having the potential to expand training sets with different-task datasets. This paper studies various weak losses for partially annotated data in combination with existing supervised losses. We propose Box-for-Mask and Mask-for-Box strategies, and their combination BoMBo, to distil necessary information from one task annotations to train the other. Ablation studies and experimental results on VOC and COCO datasets show favorable results for the proposed idea. Source code and data splits can be found at https://github.com/lhoangan/multas., Comment: Accepted for publishing in BMVC 2024

The photo-electric effect was a historic milestone in the development of quantum theory, revealing the first evidence of discrete energy of the electromagnetic field through hallmark signatures such as the threshold frequency, intensity-independent energy transfer, and the near instantaneous ejection of photo-electrons. In this work, we revisit the photo-electric effect through the lens of semi-classical, quantum, and neo-classical models to explore their implications on energy conservation and discrete photon exchange. We extend this analysis to include the gravito-phononic effect through modelling energy transfer between coupled harmonic oscillators, rather than traditional atom-field interactions. Our approach demonstrates that two coupled harmonic oscillators, under a beam-splitter interaction, can exhibit hallmark signatures analogous to the photo-electric effect, including resonance conditions and quantised energy absorption. We discuss the implications of this model for the recently proposed gravito-phononic detections of single gravitons. This further clarifies that a gravitational version of the photo-electric effect, modelled as discrete energy transfer between harmonic oscillators can provide the first evidence of the graviton., Comment: 5 + 1 pages, 1 figure. A short note answering common questions

We report the discovery of five galaxy candidates at redshifts between $15.9

ABC-stacked rhombohedral graphene multilayers exhibit a wide variety of electronic ground states characterized by broken isospin symmetry and superconductivity. Recently, indirect evidence of inter-valley coherent (IVC) order has been reported in rhombohedral trilayer graphene (RTG), with possible implications for the origin of superconductivity. Here, we report the direct visualization of IVC order in RTG using scanning tunneling microscopy and spectroscopy. Tuning the chemical potential through the Van Hove singularity near the edge of the valence band, we observe a cascade of phase transitions associated with the formation of half- and quarter-metal states. IVC phases, distinguished by an enlarged real space unit cell, are directly imaged near both the high- and low-density boundaries of the half-metal phase. At high hole density, we precisely reconstruct the IVC band structure through quasiparticle interference. Intriguingly, the charge density modulations reveal a C3-symmetric incommensurate IVC order that agrees with the recent prediction of an IVC-crystal phase. Our findings demonstrate that IVC phases are a widespread symmetry-broken ground state within graphene systems., Comment: 22 pages, 4 figures

Time-resolved high-resolution X-ray Computed Tomography (4D $\mu$CT) is an imaging technique that offers insight into the evolution of dynamic processes inside materials that are opaque to visible light. Conventional tomographic reconstruction techniques are based on recording a sequence of 3D images that represent the sample state at different moments in time. This frame-based approach limits the temporal resolution compared to dynamic radiography experiments due to the time needed to make CT scans. Moreover, it leads to an inflation of the amount of data and thus to costly post-processing computations to quantify the dynamic behaviour from the sequence of time frames, hereby often ignoring the temporal correlations of the sample structure. Our proposed 4D $\mu$CT reconstruction technique, named DYRECT, estimates individual attenuation evolution profiles for each position in the sample. This leads to a novel memory-efficient event-based representation of the sample, using as little as three image volumes: its initial attenuation, its final attenuation and the transition times. This third volume represents local events on a continuous timescale instead of the discrete global time frames. We propose a method to iteratively reconstruct the transition times and the attenuation volumes. The dynamic reconstruction technique was validated on synthetic ground truth data and experimental data, and was found to effectively pinpoint the transition times in the synthetic dataset with a time resolution corresponding to less than a tenth of the amount of projections required to reconstruct traditional $\mu$CT time frames., Comment: 13 pages, 10 figures, article. Submitted to IEEE Transactions on Computational Imaging 23/10/2024

We determine the spectroscopic properties of ~1000 ostensibly star-forming galaxies at redshifts (z=4-10) using prism spectroscopy from JWST/NIRSpec. With rest-wavelength coverage between Lya and [S II] in the optical, we stack spectra as a function of nebular conditions, and compare UV spectral properties with stellar age. This reveals UV lines of N III], N IV], C III], C IV, He II, and O III] in the average high-z galaxy. All UV lines are more intense in younger starbursts. We measure electron temperatures from the collisionally excited [O III] line ratios, finding Te=18000-22000 K for the O++ regions. We also detect a significant nebular Balmer Jump from which we estimate only Te=8000-13000 K. Accounting for typical temperature offsets between zones bearing doubly and singly ionized oxygen, these two temperatures remain discrepant by around 40%. We use the [O III] temperatures to estimate abundances of carbon, nitrogen, and oxygen. We find that log(C/O) is consistently ~-1, with no evolution of C/O with metallicity or stellar age. The average spectra are mildly enhanced in Nitrogen, with higher N/O than low-z starbursts, but are less enhanced than samples of high-z galaxies with visible UV N III] and N IV]. Whatever processes produce the N-enhancement in the individual galaxies must also be ongoing, at lower levels, in the median galaxy in the early Universe. The strongest starbursts are a source of significant ionizing emission: ionizing photon production efficiencies reach 10^25.7 Hz/erg, and show multiple signatures of high Lyman continuum escape, including Mg II escape fractions nearing 100%, significant deficits in [S II] emission, high degrees of ionization, and blue UV colors., Comment: Submitted to the ApJ. 23 pages, 10 figures, 8 tables, 8 authors, 7 sections, ~3MB, one world. Comments welcome

Finding interpretable biomechanical models can provide insight into the functionality of organs with regard to physiology and disease. However, identifying broadly applicable dynamical models for in vivo tissue remains challenging. In this proof of concept study we propose a reconstruction framework for data-driven discovery of dynamical models from experimentally obtained undersampled MRI spectral data. The method makes use of the previously developed spectro-dynamic framework which allows for reconstruction of displacement fields at high spatial and temporal resolution required for model identification. The proposed framework combines this method with data-driven discovery of interpretable models using Sparse Identification of Non-linear Dynamics (SINDy). The design of the reconstruction algorithm is such that a symbiotic relation between the reconstruction of the displacement fields and the model identification is created. Our method does not rely on periodicity of the motion. It is successfully validated using spectral data of a dynamic phantom gathered on a clinical MRI scanner. The dynamic phantom is programmed to perform motion adhering to 5 different (non-linear) ordinary differential equations. The proposed framework performed better than a 2-step approach where the displacement fields were first reconstructed from the undersampled data without any information on the model, followed by data-driven discovery of the model using the reconstructed displacement fields. This study serves as a first step in the direction of data-driven discovery of in vivo models., Comment: 11 pages regular paper with 8 figures, 9 pages supplementary material with 6 figures, 1 supplementary video

We prove that in the algebraic metacomplexity framework, the decomposition of metapolynomials into their isotypic components can be implemented efficiently, namely with only a quasipolynomial blowup in the circuit size. This means that many existing algebraic complexity lower bound proofs can be efficiently converted into isotypic lower bound proofs via highest weight metapolynomials, a notion studied in geometric complexity theory. In the context of algebraic natural proofs, our result means that without loss of generality algebraic natural proofs can be assumed to be isotypic. Our proof is built on the Poincar\'e--Birkhoff--Witt theorem for Lie algebras and on Gelfand--Tsetlin theory, for which we give the necessary comprehensive background., Comment: 29 pages. Comments are welcome!

In this work, the seasonal predictive capabilities of Neural Radiance Fields (NeRF) applied to satellite images are investigated. Focusing on the utilization of satellite data, the study explores how Sat-NeRF, a novel approach in computer vision, performs in predicting seasonal variations across different months. Through comprehensive analysis and visualization, the study examines the model's ability to capture and predict seasonal changes, highlighting specific challenges and strengths. Results showcase the impact of the sun direction on predictions, revealing nuanced details in seasonal transitions, such as snow cover, color accuracy, and texture representation in different landscapes. Given these results, we propose Planet-NeRF, an extension to Sat-NeRF capable of incorporating seasonal variability through a set of month embedding vectors. Comparative evaluations reveal that Planet-NeRF outperforms prior models in the case where seasonal changes are present. The extensive evaluation combined with the proposed method offers promising avenues for future research in this domain.

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

Large Language Models (LLMs) demonstrate promising capabilities in solving simple scientific problems but often produce hallucinations for complex ones. While integrating LLMs with tools can increase reliability, this approach typically results in over-reliance on tools, diminishing the model's ability to solve simple problems through basic reasoning. In contrast, human experts first assess problem complexity using domain knowledge before choosing an appropriate solution approach. Inspired by this human problem-solving process, we propose a novel two-component fine-tuning method. In the first component World Knowledge Distillation (WKD), LLMs learn directly from solutions generated using tool's information to internalize domain knowledge. In the second component Tool Usage Adaptation (TUA), we partition problems into easy and hard categories based on the model's direct answering accuracy. While maintaining the same alignment target for easy problems as in WKD, we train the model to intelligently switch to tool usage for more challenging problems. We validate our method on six scientific benchmark datasets, spanning mathematics, climate science and epidemiology. On average, our models demonstrate a 28.18% improvement in answer accuracy and a 13.89% increase in tool usage precision across all datasets, surpassing state-of-the-art models including GPT-4o and Claude-3.5., Comment: 26 pages, 15 figures

The synthesis of heavy elements in supernovae is affected by low-energy (n,p) and (p,n) reactions on unstable nuclei, yet experimental data on such reaction rates are scarce. The SECAR (SEparator for CApture Reactions) recoil separator at FRIB (Facility for Rare Isotope Beams) was originally designed to measure astrophysical reactions that change the mass of a nucleus significantly. We used a novel approach that integrates machine learning with ion-optical simulations to find an ion-optical solution for the separator that enables the measurement of (p,n) reactions, despite the reaction leaving the mass of the nucleus nearly unchanged. A new measurement of the $^{58}$Fe(p,n)$^{58}$Co reaction in inverse kinematics with a 3.66$\pm$0.12 MeV/nucleon $^{58}$Fe beam (corresponding to 3.69$\pm$0.12 MeV proton energy in normal kinematics) yielded a cross-section of 20.3$\pm$6.3 mb and served as a benchmark for the new technique demonstrating its effectiveness in achieving the required performance criteria. This novel approach marks a significant advancement in experimental nuclear astrophysics, as it paves the way for studying astrophysically important (p,n) reactions on unstable nuclei produced at FRIB.

We use JWST/NIRISS slitless spectroscopy from the Next Generation Deep Extragalactic Exploratory Public (NGDEEP) Survey to investigate the physical condition of star-forming galaxies at $1.7 < z < 3.4$. At these redshifts, the deep NGDEEP NIRISS slitless spectroscopy covers the [O II]$\lambda\lambda$3726,3729, [O III]$\lambda\lambda$4959,5007, H$\beta$ and H$\alpha$ emission features for galaxies with stellar masses $\log(\mathrm{M_\ast/M_\odot}) \gtrsim 7$, nearly a factor of a hundred lower than previous studies. We focus on the [O III]/[O II] (O$_{32}$) ratio which is primarily sensitive to the ionization state and with a secondary dependence on the gas-phase metallicity of the interstellar medium. We find significant ($\gtrsim5\sigma$) correlations between the O$_{32}$ ratio and galaxy properties as O$_{32}$ increases with decreasing stellar mass, decreasing star formation rate (SFR), increasing specific SFR (sSFR$\equiv \mathrm{SFR}/M_*$), and increasing equivalent width (EW) of H$\beta$ and H$\alpha$. These trends suggest a tight connection between the ionization parameter and these galaxy properties. Galaxies at $z\sim2-3$ exhibit a higher O$_{32}$ than local normal galaxies with the same stellar masses and SFRs, indicating that they have a higher ionization parameter and lower metallicity than local normal galaxies. In addition, we observe an evolutionary trend in the O$_{32}$ -- EW(H$\beta$) relation from $z\sim0$ and $z\gtrsim5$, such that higher redshift galaxies have higher EW(H$\beta$) and higher O$_{32}$ at fixed EW. We argue that both the enhanced recent star formation activity and the higher star formation surface density may contribute to the increase in O$_{32}$ and the ionization parameter., Comment: 27 pages, 14 figures

After over three decades of unsuccessful attempts, we report the first detection of molecular gas emission in Malin 1, the largest spiral galaxy observed to date, and one of the most iconic giant low surface brightness galaxies. Using ALMA, we detect significant $^{12}$CO(J=1-0) emission in the galaxy's central region and tentatively identify CO emission across three regions on the disc. These observations allow for a better estimate of the H$_2$ mass and molecular gas mass surface density, both of which are remarkably low given the galaxy's scale. By integrating data on its HI mass, we derive a very low molecular-to-atomic gas mass ratio. Overall, our results highlight the minimal presence of molecular gas in Malin 1, contrasting sharply with its extensive, homogeneous atomic gas reservoir. For the first time, we position Malin 1 on the Kennicutt-Schmidt (K-S) diagram, where it falls below the main sequence for normal spirals, consistent with previous upper limits but now with more accurate figures. These findings are crucial for constraining our understanding of star formation processes in environments characterized by extremely low molecular gas densities and for refining models of galaxy formation, thereby improving predictions concerning the formation, evolution, and distribution of these giant, elusive galaxies., Comment: Accepted for publication in The Astrophysical Journal Letters. Published

We present a novel approach for the reconstruction of time-resolved free-running first-pass myocardial perfusion MRI, named CMR-MOTUS. This method builds upon the MR-MOTUS framework and addresses the challenges of a contrast varying reference image. By integrating a low-rank plus sparse (L+S) decomposition, CMR-MOTUS efficiently captures both motion fields and contrast variations. This innovative technique eliminates the need for acquiring a motion-static reference image prior to the examination, thereby reducing examination time and complexity for cardiac MRI examinations. Our results demonstrate that CMR-MOTUS can successfully disentangle different dynamic components, offering high-quality motion fields and motion correct a myocardial first-pass perfusion image series.

The pitch-angle diffusion coefficient quantifies the effect of pitch-angle scattering on charged particles propagating through turbulent magnetic fields and is a key ingredient in understanding the diffusion of these particles along the background magnetic field. Despite its significance, only a limited number of studies have calculated the pitch-angle diffusion coefficient from test particle simulations in synthetic magnetic turbulence, employing various, often quite different, techniques for this purpose. In this study, we undertake a comparative analysis of nine different methods for calculating the pitch-angle diffusion coefficient from full-orbit simulations. Our objective is to find the strengths and limitations of each method and to determine the most reliable approach. Although all nine methods should theoretically yield comparable results, certain methods may be ill-suited for numerical investigations, while others may not be applicable under conditions of strong turbulence. Through this investigation, we aim to provide recommendations for best practices when employing these methods in future numerical studies of pitch-angle scattering.

The use of foundation models in climate science has recently gained significant attention. However, a critical issue remains: the lack of a comprehensive evaluation framework capable of assessing the quality and scientific validity of model outputs. To address this issue, we develop ClimaGen (Climate QA Generator), an automated algorithmic framework that generates question-answer pairs from graduate textbooks with climate scientists in the loop. As a result, we present ClimaQA-Gold, an expert-annotated benchmark dataset alongside ClimaQA-Silver, a large-scale, comprehensive synthetic QA dataset for climate science. Finally, we develop evaluation strategies and compare different Large Language Models (LLMs) on our benchmarks. Our results offer novel insights into various approaches used to enhance climate foundation models.

We consider two-dimensional periodically driven systems of fermions with particle-hole symmetry. Such systems support non-trivial topological phases, including ones that cannot be realized in equilibrium. We show that a space-time defect in the driving Hamiltonian, dubbed a ``time vortex,'' can bind $\pi$ Majorana modes. A time vortex is a point in space around which the phase lag of the Hamiltonian changes by a multiple of $2\pi$. We demonstrate this behavior on a periodically driven version of Kitaev's honeycomb spin model, where $\mathbb{Z}_2$ fluxes and time vortices can realize any combination of $0$ and $\pi$ Majorana modes. We show that a time vortex can be created using Clifford gates, simplifying its realization in near-term quantum simulators., Comment: 11 pages, 6 figures

Network verification (NWV), broadly defined as the verification of properties of distributed protocols used in network systems, cannot be efficiently solved on classical hardware via brute force. Prior work has developed a variety of methods that scale by observing a structure in the search space and then evaluating classes within the search space instead of individual instances. However, even these classification mechanisms have their limitations. In this paper, we consider a radically different approach: applying quantum computing to more efficiently solve NWV problems. We provide an overview of how to map variants of NWV problems into unstructured search problems that can be solved via quantum computing with quadratic speedup, making the approach feasible in theory to problems that are double in size (of the input). Emerging quantum systems cannot yet tackle problems of practical interest, but rapid advances in hardware and algorithm development make now a great time to start thinking about their application. With this in mind, we explore the limits of scale of the problem for which quantum computing can solve NWV problems as unstructured search.

Large Language Model (LLM) Agents are an emerging computing paradigm that blends generative machine learning with tools such as code interpreters, web browsing, email, and more generally, external resources. These agent-based systems represent an emerging shift in personal computing. We contribute to the security foundations of agent-based systems and surface a new class of automatically computed obfuscated adversarial prompt attacks that violate the confidentiality and integrity of user resources connected to an LLM agent. We show how prompt optimization techniques can find such prompts automatically given the weights of a model. We demonstrate that such attacks transfer to production-level agents. For example, we show an information exfiltration attack on Mistral's LeChat agent that analyzes a user's conversation, picks out personally identifiable information, and formats it into a valid markdown command that results in leaking that data to the attacker's server. This attack shows a nearly 80% success rate in an end-to-end evaluation. We conduct a range of experiments to characterize the efficacy of these attacks and find that they reliably work on emerging agent-based systems like Mistral's LeChat, ChatGLM, and Meta's Llama. These attacks are multimodal, and we show variants in the text-only and image domains., Comment: website: https://imprompter.ai code: https://github.com/Reapor-Yurnero/imprompter v2 changelog: add new results to Table 3, correct several typos

We present protocols for preparing two-dimensional abelian and non-abelian topologically ordered states by employing finite depth unitary circuits composed of long-ranged, simultaneous, and mutually commuting two-qubit gates. Our protocols are motivated by recent proposals for circuits in trapped ion systems, which allow each qubit to participate in multiple gates simultaneously. Our circuits are shown to be optimal, in the sense that the number of two-qubit gates and ancilla qubits scales as $O(L^2)$, where $L$ is the linear size of the system. Examples include the ground states of the toric code, certain Kitaev quantum double models, and string net models. Going beyond two dimensions, we extend our scheme to more general Calderbank-Shor-Steane (CSS) codes. As an application, we present protocols for realizing the three-dimensional Haah's code and X-Cube fracton models., Comment: 15 pages, 9 figures

We conducted systematic radiative transfer (RT) modeling of the Mg II doublet line profiles for 33 low-redshift Lyman continuum (LyC) leakers, and Ly$\alpha$ modeling for a subset of six objects, using a multiphase, clumpy circumgalactic medium (CGM) model. Our RT models successfully reproduced the Mg II line profiles for all 33 galaxies, revealing a necessary condition for strong LyC leakage: high maximum clump outflow velocity ($v_{\rm MgII,\,max} \gtrsim 390\,\rm km\,s^{-1}$) and low total Mg II column density ($N_{\rm MgII,\,tot} \lesssim 10^{14.3}\,\rm cm^{-2}$). We found that the clump outflow velocity and total Mg II column density have the most significant impact on Mg II spectra and emphasized the need for full RT modeling to accurately extract the CGM gas properties. In addition, using archival HST COS/G160M data, we modeled Ly$\alpha$ profiles for six objects and found that their spectral properties do not fully align with the conventional LyC leakage criteria, yet no clear correlation was identified between the modeled parameters and observed LyC escape fractions. We inferred LyC escape fractions based on HI properties from Ly$\alpha$ RT modeling and found that LyC leakage is primarily governed by the number of optically thick HI clumps per sightline ($f_{\rm cl}$). Intriguingly, two galaxies with relatively low observed LyC leakage exhibited the highest RT-inferred LyC escape fractions due to their lowest $f_{\rm cl}$ values, driven by the strong blue peaks of their Ly$\alpha$ emission. Future high-resolution, spatially resolved observations are crucial for resolving this puzzle. Overall, our results support a "picket fence" geometry over a "density-bounded" scenario for the CGM, where a combination of high Mg II outflow velocities and low Mg II column densities may be correlated with the presence of more low-density HI channels that facilitate LyC escape., Comment: 22 pages, 16 figures, comments are welcome

A switching method is a graph operation that results in cospectral graphs (graphs with the same spectrum). Work by Wang and Xu [Discrete Math. 310 (2010)] suggests that most cospectral graphs with cospectral complements can be constructed using regular orthogonal matrices of level 2, which has relevance for Haemers' conjecture. We present two new switching methods and several combinatorial and geometrical reformulations of existing switching operations of level 2. We also introduce the concept of reducibility and use it to classify all irreducible switching methods that correspond to a conjugation with a regular orthogonal matrix of level 2 with one nontrivial indecomposable block, up to switching sets of size 12, extending previous results.

Teasers are an effective tool for promoting content in entertainment, commercial and educational fields. However, creating an effective teaser for long videos is challenging for it requires long-range multimodal modeling on the input videos, while necessitating maintaining audiovisual alignments, managing scene changes and preserving factual accuracy for the output teasers. Due to the lack of a publicly-available dataset, progress along this research direction has been hindered. In this work, we present DocumentaryNet, a collection of 1,269 documentaries paired with their teasers, featuring multimodal data streams of video, speech, music, sound effects and narrations. With DocumentaryNet, we propose a new two-stage system for generating teasers from long documentaries. The proposed TeaserGen system first generates the teaser narration from the transcribed narration of the documentary using a pretrained large language model, and then selects the most relevant visual content to accompany the generated narration through language-vision models. For narration-video matching, we explore two approaches: a pretraining-based model using pretrained contrastive language-vision models and a deep sequential model that learns the mapping between the narrations and visuals. Our experimental results show that the pretraining-based approach is more effective at identifying relevant visual content than directly trained deep autoregressive models.

Despite advances in diffusion-based text-to-music (TTM) methods, efficient, high-quality generation remains a challenge. We introduce Presto!, an approach to inference acceleration for score-based diffusion transformers via reducing both sampling steps and cost per step. To reduce steps, we develop a new score-based distribution matching distillation (DMD) method for the EDM-family of diffusion models, the first GAN-based distillation method for TTM. To reduce the cost per step, we develop a simple, but powerful improvement to a recent layer distillation method that improves learning via better preserving hidden state variance. Finally, we combine our step and layer distillation methods together for a dual-faceted approach. We evaluate our step and layer distillation methods independently and show each yield best-in-class performance. Our combined distillation method can generate high-quality outputs with improved diversity, accelerating our base model by 10-18x (230/435ms latency for 32 second mono/stereo 44.1kHz, 15x faster than comparable SOTA) -- the fastest high-quality TTM to our knowledge. Sound examples can be found at https://presto-music.github.io/web/.

We present indirect constraints on the absolute escape fraction of ionizing photons ($f_{\rm esc}^{\rm LyC}$) of the system GN 42912 which comprises two luminous galaxies ($M_{\rm UV}$ magnitudes of -20.89 and -20.37) at $z\sim7.5$, GN 42912-NE and GN 42912-SW, to determine their contribution to the ionizing photon budget of the Epoch of Reionization (EoR). The high-resolution James Webb Space Telescope NIRSpec and NIRCam observations reveal the two galaxies are separated by only ~0.1$"$ (0.5 kpc) on the sky and have a 358 km s$^{-1}$ velocity separation. GN 42912-NE and GN 42912-SW are relatively massive for this redshift (log($M_\ast/M_\odot$) $\sim$ 8.4 and 8.9, respectively), with gas-phase metallicities of 18 per cent and 23 per cent solar, O$_{32}$ ratios of 5.3 and $>5.8$, and $\beta$ slopes of $-1.92$ and $-1.51$, respectively. We use the Mg II$\lambda\lambda$2796,2803 doublet to constrain $f_{\rm esc}^{\rm LyC}$. Mg II has an ionization potential close to that of neutral hydrogen and, in the optically thin regime, can be used as an indirect tracer of the LyC leakage. We establish realistic conservative upper limits on $f_{\rm esc}^{\rm LyC}$ of 8.5 per cent for GN 42912-NE and 14 per cent for GN 42912-SW. These estimates align with $f_{\rm esc}^{\rm LyC}$ trends observed with $\beta$, O$_{32}$, and the H$\beta$ equivalent width at $z<4$. The small inferred ionized region sizes ($<0.3$ pMpc) around both galaxies indicate they have not ionized a significant fraction of the surrounding neutral gas. While these $z>7$ $f_{\rm esc}^{\rm LyC}$ constraints do not decisively determine a specific reionization model, they support a minor contribution from these two relatively luminous galaxies to the EoR., Comment: 18 pages, submitted to MNRAS