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Multi-spacecraft observations of solar energetic particle (SEP) events not only enable a deeper understanding and development of particle acceleration and transport theories, but also provide important constraints for model validation efforts. However, because of computational limitations, a given physics-based SEP model is usually best-suited to capture a particular phase of an SEP event, rather than its whole development from onset through decay. For example, magnetohydrodynamic (MHD) models of the heliosphere often incorporate solar transients only at the outer boundary of their so-called coronal domain -- usually set at a heliocentric distance of 20-30 $R_{\odot}$. This means that particle acceleration at CME-driven shocks is also computed from this boundary onwards, leading to simulated SEP event onsets that can be many hours later than observed, since shock waves can form much lower in the solar corona. In this work, we aim to improve the modelled onset of SEP events by inserting a "fixed source" of particle injection at the outer boundary of the coronal domain of the coupled WSA-Enlil 3D MHD model of the heliosphere. The SEP model that we employ for this effort is SEPMOD, a physics-based test-particle code based on a field line tracer and adiabatic invariant conservation. We apply our initial tests and results of SEPMOD's fixed-source option to the 2021 October 9 SEP event, which was detected at five well-separated locations in the inner heliosphere -- Parker Solar Probe, STEREO-A, Solar Orbiter, BepiColombo, and near-Earth spacecraft., Comment: 31 pages, 8 figures, 4 tables, accepted for publication in Journal of Space Weather and Space Climate

Motivated by an experiment on a superconducting quantum processor [Mi et al., Science 378, 785 (2022)], we study level pairings in the many-body spectrum of the random-field Floquet quantum Ising model. The pairings derive from Majorana zero and $\pi$ modes when writing the spin model in Jordan-Wigner fermions. Both splittings have lognormal distributions with random transverse fields. In contrast, random longitudinal fields affect the zero and $\pi$ splittings in drastically different ways. While zero pairings are rapidly lifted, the $\pi$ pairings are remarkably robust, or even strengthened, up to vastly larger disorder strengths. We explain our results within a self-consistent Floquet perturbation theory and study implications for boundary spin correlations. The robustness of $\pi$ pairings against longitudinal disorder may be useful for quantum information processing., Comment: 5+7 pages, 3 figures

Emerging reconfigurable optical communication technologies allow to enhance datacenter topologies with demand-aware links optimized towards traffic patterns. This paper studies the algorithmic problem of jointly optimizing topology and routing in such demand-aware networks to minimize congestion, along two dimensions: (1) splittable or unsplittable flows, and (2) whether routing is segregated, i.e., whether routes can or cannot combine both demand-aware and demand-oblivious (static) links. For splittable and segregated routing, we show that the problem is generally $2$-approximable, but APX-hard even for uniform demands induced by a bipartite demand graph. For unsplittable and segregated routing, we establish upper and lower bounds of $O\left(\log m/ \log\log m \right)$ and $\Omega\left(\log m/ \log\log m \right)$, respectively, for polynomial-time approximation algorithms, where $m$ is the number of static links. We further reveal that under un-/splittable and non-segregated routing, even for demands of a single source (resp., destination), the problem cannot be approximated better than $\Omega\left(\frac{c_{\max}}{c_{\min}} \right)$ unless P=NP, where $c_{\max}$ (resp., $c_{\min}$) denotes the maximum (resp., minimum) capacity. It remains NP-hard for uniform capacities, but is tractable for a single commodity and uniform capacities. Our trace-driven simulations show a significant reduction in network congestion compared to existing solutions., Comment: 10 pages

Packet buffers in datacenter switches are shared across all the switch ports in order to improve the overall throughput. The trend of shrinking buffer sizes in datacenter switches makes buffer sharing extremely challenging and a critical performance issue. Literature suggests that push-out buffer sharing algorithms have significantly better performance guarantees compared to drop-tail algorithms. Unfortunately, switches are unable to benefit from these algorithms due to lack of support for push-out operations in hardware. Our key observation is that drop-tail buffers can emulate push-out buffers if the future packet arrivals are known ahead of time. This suggests that augmenting drop-tail algorithms with predictions about the future arrivals has the potential to significantly improve performance. This paper is the first research attempt in this direction. We propose Credence, a drop-tail buffer sharing algorithm augmented with machine-learned predictions. Credence can unlock the performance only attainable by push-out algorithms so far. Its performance hinges on the accuracy of predictions. Specifically, Credence achieves near-optimal performance of the best known push-out algorithm LQD (Longest Queue Drop) with perfect predictions, but gracefully degrades to the performance of the simplest drop-tail algorithm Complete Sharing when the prediction error gets arbitrarily worse. Our evaluations show that Credence improves throughput by $1.5$x compared to traditional approaches. In terms of flow completion times, we show that Credence improves upon the state-of-the-art approaches by up to $95\%$ using off-the-shelf machine learning techniques that are also practical in today's hardware. We believe this work opens several interesting future work opportunities both in systems and theory that we discuss at the end of this paper.

On 2022 February 15, an impressive filament eruption was observed off the solar eastern limb from three remote-sensing viewpoints, namely Earth, STEREO-A, and Solar Orbiter. In addition to representing the most-distant observed filament at extreme ultraviolet wavelengths -- captured by Solar Orbiter's field of view extending to above 6 $R_{\odot}$ -- this event was also associated with the release of a fast ($\sim$2200 km$\cdot$s$^{-1}$) coronal mass ejection (CME) that was directed towards BepiColombo and Parker Solar Probe. These two probes were separated by 2$^{\circ}$ in latitude, 4$^{\circ}$ in longitude, and 0.03 au in radial distance around the time of the CME-driven shock arrival in situ. The relative proximity of the two probes to each other and to the Sun ($\sim$0.35 au) allows us to study the mesoscale structure of CMEs at Mercury's orbit for the first time. We analyse similarities and differences in the main CME-related structures measured at the two locations, namely the interplanetary shock, the sheath region, and the magnetic ejecta. We find that, despite the separation between the two spacecraft being well within the typical uncertainties associated with determination of CME geometric parameters from remote-sensing observations, the two sets of in-situ measurements display some profound differences that make understanding of the overall 3D CME structure particularly challenging. Finally, we discuss our findings within the context of space weather at Mercury's distances and in terms of the need to investigate solar transients via spacecraft constellations with small separations, which has been gaining significant attention during recent years., Comment: 31 pages, 13 figures, 5 tables, accepted for publication in The Astrophysical Journal

Ground-based high-contrast instruments have yielded reflected light images of protoplanetary disks. Quantitative measurements of the reflected radiation provide strong constraints on the scattering dust which can clarify the dust particle evolution in these disks and the composition of the forming planets. This study aimed to derive the wavelength dependence of polarized reflectivity $(\hat{Q}_{\varphi}/I_\star)_\lambda$ for 11 disks, constraining dust properties and identifying systematic differences. Using ESO archive data from SPHERE/ZIMPOL and SPHERE/IRDIS instruments, we obtained accurate intrinsic polarized reflectivity $\hat{Q}_\varphi/I_\star$ values at wavelengths from 0.62$\mu$m to 2.2$\mu$m. Polarized reflectivities ranged from $Q_\varphi/I_\star\approx 0.1\%$ to 1.0$\%$, with PSF-corrected values averaging 1.6 times higher than observed. Accurate PSF calibrations reduced systematic errors to $\Delta\hat{Q}_\varphi/\hat{Q}_\varphi\approx 10\%$ or less. For each disk, we derived a polarized reflectivity color $\eta_{V/IR}$ between a visible band $\lambda<1~\mu$m and a near-IR band $\lambda>1~\mu$m and other wavelength combinations. Wavelength gradients $\eta$ varied significantly among objects. Disks around Herbig stars (HD 169142, HD 135334B, HD 100453, MWC 758, and HD 142527) showed a red color $\eta_{\rm V/IR}>0.5$, suggesting rather compact dust grains. T-Tauri star disks (PDS 70, TW Hya, RX J1615, and PDS 66) were predominantly gray $-0.5<\eta_{\rm V/IR}<0.5$, with an absence of blue colors incompatible with porous aggregates. Exceptional red colors for LkCa15 and MWC758 were attributed to potential extra reddening from hot dust near the star. Future studies incorporating parameters like fractional polarization $\langle p_\varphi \rangle$ hold promise for advancing our understanding of dust properties within protoplanetary disks., Comment: 15 pages, 9 figures, 5 tables, 2 appendices. Accepted by A&A

We study the effect of acceleration and deceleration on the stability of channel flows. To do so, we derive an exact solution for laminar profiles of channel flows with arbitrary, time-varying wall motion and pressure gradient. This solution then allows us to investigate the stability of any unsteady channel flow. In particular, we restrict our investigation to the nonnormal growth of perturbations about time-varying base flows with exponentially decaying acceleration and deceleration, with comparisons to growth about a constant base flow (i.e., the time-invariant simple shear or parabolic profile). We apply this acceleration and deceleration through the velocity of the walls and through the flow rate. For accelerating base flows, perturbations never grow larger than perturbations about a constant base flow, while decelerating flows show massive amplification of perturbations - at a Reynolds number of 500, properly timed perturbations about the decelerating base flow grow $\mathscr{O}(10^5)$ times larger than perturbations grow about a constant base flow. This amplification increases as we raise the rate of deceleration and the Reynolds number. We find that this amplification arises due to a transition from spanwise perturbations leading to the largest amplification, to streamwise perturbations leading to the largest amplification that only occurs in the decelerating base flow. By evolving the optimal perturbations through the linearized equations of motion, we reveal that the decelerating base flow achieves this massive amplification through the Orr mechanism, or the down-gradient Reynolds stress mechanism, which accelerating and constant base flows cannot maintain.

We consider the problem of optimally controlling stochastic, Markovian systems subject to joint chance constraints over a finite-time horizon. For such problems, standard Dynamic Programming is inapplicable due to the time correlation of the joint chance constraints, which calls for non-Markovian, and possibly stochastic, policies. Hence, despite the popularity of this problem, solution approaches capable of providing provably-optimal and easy-to-compute policies are still missing. We fill this gap by introducing an augmented binary state to the system dynamics, allowing us to characterize the optimal policies and propose a Dynamic Programming based solution method. Our analysis provides a deep insight into the impact of joint chance constraints on the optimal control policies.

The Accelerator Neutrino Neutron Interaction Experiment (ANNIE) is a 26-ton water Cherenkov neutrino detector installed on the Booster Neutrino Beam (BNB) at Fermilab. Its main physics goals are to perform a measurement of the neutron yield from neutrino-nucleus interactions, as well as a measurement of the charged-current cross section of muon neutrinos. An equally important focus is placed on the research and development of new detector technologies and target media. Specifically water-based liquid scintillator (WbLS) is of interest as a novel detector medium, as it allows for the simultaneous detection of scintillation and Cherenkov light. This paper presents the deployment of a 366L WbLS vessel in ANNIE in March 2023 and the subsequent detection of both Cherenkov light and scintillation from the WbLS. This proof-of-concept allows for the future development of reconstruction and particle identification algorithms in ANNIE, as well as dedicated analyses, such as the search for neutral current events and the hadronic scintillation component within the WbLS volume., Comment: 19 pages, 16 figures

Large language models have achieved great success in recent years, so as their variants in vision. Existing vision-language models can describe images in natural languages, answer visual-related questions, or perform complex reasoning about the image. However, it is yet unclear how localization tasks, such as word grounding or referring localization, can be performed using large language models. In this work, we aim to develop a vision-language model that can take locations, for example, a set of points or boxes, as either inputs or outputs. When taking locations as inputs, the model performs location-conditioned captioning, which generates captions for the indicated object or region. When generating locations as outputs, our model regresses pixel coordinates for each output word generated by the language model, and thus performs dense word grounding. Our model is pre-trained on the Localized Narrative dataset, which contains pixel-word-aligned captioning from human attention. We show our model can be applied to various location-aware vision-language tasks, including referring localization, location-conditioned captioning, and dense object captioning, archiving state-of-the-art performance on RefCOCO and Visual Genome. Project page: https://jerryxu.net/PixelLLM ., Comment: Project page: https://jerryxu.net/PixelLLM

Polarisation is a powerful remote-sensing tool to study the nature of particles scattering the starlight. It is widely used to characterise interplanetary dust particles in the Solar System and increasingly employed to investigate extrasolar dust in debris discs' systems. We aim to measure the scattering properties of the dust from the debris ring around HD 181327 at near-infrared wavelengths. We obtained high-contrast polarimetric images of HD 181327 in the H band with the SPHERE / IRDIS instrument on the Very Large Telescope (ESO). We complemented them with archival data from HST / NICMOS in the F110W filter reprocessed in the context of the Archival Legacy Investigations of Circumstellar Environments (ALICE) project. We developed a combined forward-modelling framework to simultaneously retrieve the scattering phase function in polarisation and intensity. We detected the debris disc around HD 181327 in polarised light and total intensity. We measured the scattering phase function and the degree of linear polarisation of the dust at 1.6 micron in the birth ring. The maximum polarisation is 23.6% +/- 2.6% and occurs between a scattering angle of 70 deg and 82 deg. We show that compact spherical particles made of a highly refractive and relatively absorbing material in a differential power-law size distribution of exponent $-3.5$ can simultaneously reproduce the polarimetric and total intensity scattering properties of the dust. This type of material cannot be obtained with a mixture of silicates, amorphous carbon, water ice, and porosity, and requires a more refracting component such as iron-bearing minerals. We reveal a striking analogy between the near-infrared polarisation of comets and that of HD 181327. The methodology developed here combining VLT/SPHERE and HST/NICMOS may be applicable in the future to combine the polarimetric capabilities of SPHERE with the sensitivity of JWST., Comment: 21 pages, 22 figures, accepted in Astronomy & Astrophysics

Recent approaches to point tracking are able to recover the trajectory of any scene point through a large portion of a video despite the presence of occlusions. They are, however, too slow in practice to track every point observed in a single frame in a reasonable amount of time. This paper introduces DOT, a novel, simple and efficient method for solving this problem. It first extracts a small set of tracks from key regions at motion boundaries using an off-the-shelf point tracking algorithm. Given source and target frames, DOT then computes rough initial estimates of a dense flow field and visibility mask through nearest-neighbor interpolation, before refining them using a learnable optical flow estimator that explicitly handles occlusions and can be trained on synthetic data with ground-truth correspondences. We show that DOT is significantly more accurate than current optical flow techniques, outperforms sophisticated "universal" trackers like OmniMotion, and is on par with, or better than, the best point tracking algorithms like CoTracker while being at least two orders of magnitude faster. Quantitative and qualitative experiments with synthetic and real videos validate the promise of the proposed approach. Code, data, and videos showcasing the capabilities of our approach are available in the project webpage: https://16lemoing.github.io/dot ., Comment: Accepted to CVPR 2024

Modeling the ratio of two dependent components as a function of covariates is a frequently pursued objective in observational research. Despite the high relevance of this topic in medical studies, where biomarker ratios are often used as surrogate endpoints for specific diseases, existing models are based on oversimplified assumptions, assuming e.g.\@ independence or strictly positive associations between the components. In this paper, we close this gap in the literature and propose a regression model where the marginal distributions of the two components are linked by Frank copula. A key feature of our model is that it allows for both positive and negative correlations between the components, with one of the model parameters being directly interpretable in terms of Kendall's rank correlation coefficient. We study our method theoretically, evaluate finite sample properties in a simulation study and demonstrate its efficacy in an application to diagnosis of Alzheimer's disease via ratios of amyloid-beta and total tau protein biomarkers., Comment: 32 pages, 6 figures, 5 tables