Search

The recent success of diffusion-based generative models in image and natural language processing has ignited interest in diffusion-based trajectory optimization for nonlinear control systems. Existing methods cannot, however, handle the nonlinear equality constraints necessary for direct trajectory optimization. As a result, diffusion-based trajectory optimizers are currently limited to shooting methods, where the nonlinear dynamics are enforced by forward rollouts. This precludes many of the benefits enjoyed by direct methods, including flexible state constraints, reduced numerical sensitivity, and easy initial guess specification. In this paper, we present a method for diffusion-based optimization with equality constraints. This allows us to perform direct trajectory optimization, enforcing dynamic feasibility with constraints rather than rollouts. To the best of our knowledge, this is the first diffusion-based optimization algorithm that supports the general nonlinear equality constraints required for direct trajectory optimization.

The development of large-scale quantum networks requires reliable quantum channels, the quality of which can be quantified by the framework of quantum process tomography. In this work, we leverage ancilla-assisted process tomography and Bayesian inference to probe a 1.6 km deployed fiber-optic link. We send one of two polarization-entangled photons from Alice in one building to Bob in another, exploiting the local qubit as an ancilla system to characterize the corresponding quantum channel. Monitoring over a 24 h period returns a steady process fidelity of 95.1(1)%, while controllable spectral filtering with passbands from 0.025-4.38 THz finds fidelities that first increase, then level off with bandwidth, suggesting both stable operation with time and minimal polarization mode dispersion. To our knowledge, these results represent the first AAPT of a deployed quantum link, revealing a valuable tool for in situ analysis of entanglement-based quantum networks.

KIC 10685175 is a roAp star whose polar magnetic field is predicted to be 6 kG through a non-adiabatic axisymmetric pulsation theoretical model. In this work, we aim to measure the magnetic field strength of KIC 10685175 using high-resolution spectropolarimetric observations, and compare it with the one predicted by the theoretical model. From the study of two high-resolution unpolarized spectra, we obtained [$T_{\rm eff}$, $\log g$, [Fe/H], [$\alpha$/Fe], $V_{mic}$]=[8250 $\pm$ 200\,K, 4.4 $\pm$ 0.1, -0.4 $\pm$ 0.2, 0.16 $\pm$ 0.1, 1.73 $\pm$ 0.2\,km~s$^{-1}$]. Although the Fe absorption lines appear relatively weak in comparison to typical Ap stars with similar $T_{\rm eff}$, the lines belonging to rare earth elements (Eu and Nd) are stronger than that in chemically normal stars, indicating the peculiar nature of KIC~10685175. The mean longitudinal magnetic field $\langle B_\ell \rangle=-226\pm39$\,G has been measured in the polarized spectrum, but magnetically split lines were not detected. No significant line profile variability was evident in our spectra. Also the longitudinal magnetic field strengths measured using line masks constructed for different elements have been rather similar. Due to a poor rotation phase coverage of our data, additional spectroscopic and polarimetric observations are needed to allow us to conclude on the inhomogeneous element distribution over the stellar surface. The estimated polar magnetic field is $4.8 \pm 0.8$\,kG, which is consistent with the predicted polar magnetic field strength of about 6\,kG within 3$\sigma$. This work therefore provides support for the pulsation theoretical model.

Achieving human-like dexterity is a longstanding challenge in robotics, in part due to the complexity of planning and control for contact-rich systems. In reinforcement learning (RL), one popular approach has been to use massively-parallelized, domain-randomized simulations to learn a policy offline over a vast array of contact conditions, allowing robust sim-to-real transfer. Inspired by recent advances in real-time parallel simulation, this work considers instead the viability of online planning methods for contact-rich manipulation by studying the well-known in-hand cube reorientation task. We propose a simple architecture that employs a sampling-based predictive controller and vision-based pose estimator to search for contact-rich control actions online. We conduct thorough experiments to assess the real-world performance of our method, architectural design choices, and key factors for robustness, demonstrating that our simple sampling-based approach achieves performance comparable to prior RL-based works. Supplemental material: https://caltech-amber.github.io/drop., Comment: Extended version, updated appendix. Submitted to ICRA 2025

In the realm of robust optimization the $k$-adaptability approach is one promising method to derive approximate solutions for two-stage robust optimization problems. Instead of allowing all possible second-stage decisions, the $k$-adaptability approach aims at calculating a limited set of $k$ such decisions already in the first-stage before the uncertainty reveals. The parameter $k$ can be adjusted to control the quality of the approximation. However, not much is known on how many solutions $k$ are needed to achieve an optimal solution for the two-stage robust problem. In this work we derive bounds on $k$ which guarantee optimality for general non-linear problems with integer decisions where the uncertainty appears in the objective function or in the constraints. We show that for objective uncertainty the bound is the same as for the linear case and depends linearly on the dimension of the uncertainty, while for constraint uncertainty the dependence can be exponential, still providing the first generic bound for a wide class of problems. The results give new insights on how many solutions are needed for problems as the decision dependent information discovery problem or the capital budgeting problem with constraint uncertainty.

To gain better understanding of the Ap stars with the longest rotation periods, we obtained high resolution spectra of a sample of super-slowly rotating Ap (ssrAp) star candidates identified by a TESS photometric survey, to confirm that they are indeed Ap stars, to check that their v sin i values are compatible with super-slow rotation, and to obtain a first estimate of their magnetic field strengths. We determined whenever possible their mean magnetic field modulus, their mean quadratic magnetic field, and an upper limit of their projected equatorial velocities. Eighteen of the 27 stars studied are typical Ap stars; most of the other nine appear to be misclassified. One of the Ap stars is not a slow rotator; it must be seen nearly pole-on. The properties of the remaining 17 are compatible with moderately to extremely long rotation periods. Eight new stars with resolved magnetically split lines in the visible range were discovered; their mean magnetic field modulus and their mean quadratic magnetic field were measured. The mean quadratic field could also be determined in five more stars. Five new spectroscopic binaries containing an Ap star were identified. Among the misclassified stars, one SB2 system with two similar, sharp-lined Am components was also discovered. The technique that we used to carry out a search for ssrAp star candidates using TESS data is validated, but appears limited by uncertainties in the spectral classification of Ap stars. The new magnetic field measurements obtained as part of this study lend further support to the tentative conclusions of our previous studies: the absence of periods longer than ~150 d in stars with magnetic fields stronger than ~7.5 kG, the lower rate of occurrence of super-slow rotation for field strengths less than ~2 kG than in the range ~3-7.5 kG, and the deficiency of slowly rotating Ap stars with field strengths between ~2 and ~3 kG., Comment: 34 pages, 30 figures, accepted for publication in A&A

Supervised machine learning is powerful. In recent years, it has enabled massive breakthroughs in computer vision and natural language processing. But leveraging these advances for optimal control has proved difficult. Data is a key limiting factor. Without access to the optimal policy, value function, or demonstrations, how can we fit a policy? In this paper, we show how to automatically generate supervised learning data for a class of continuous-time nonlinear stochastic optimal control problems. In particular, applying the Feynman-Kac theorem to a linear reparameterization of the Hamilton-Jacobi-Bellman PDE allows us to sample the value function by simulating a stochastic process. Hardware accelerators like GPUs could rapidly generate a large amount of this training data. With this data in hand, stochastic optimal control becomes supervised learning., Comment: CDC 2024

The Transiting Exoplanet Survey Satellite (TESS) has enabled the discovery of numerous tidally tilted pulsators (TTPs), which are pulsating stars in close binaries where the presence of a tidal bulge has the effect of tilting the primary star's pulsation axes into the orbital plane. Recently, the modeling framework developed to analyze TTPs has been applied to the emerging class of tri-axial pulsators, which exhibit nonradial pulsations about three perpendicular axes. In this work, we report on the identification of the second-ever discovered tri-axial pulsator, with sixteen robustly-detected pulsation multiplets, of which fourteen are dipole doublets separated by 2$\nu_{\rm orb}$. We jointly fit the spectral energy distribution (SED) and TESS light curve of the star, and find that the primary is slightly evolved off the zero-age main sequence, while the less massive secondary still lies on the zero-age main sequence. Of the fourteen doublets, we associate eight with $Y_{10x}$ modes and six with novel $Y_{10y}$ modes. We exclude the existence of $Y_{11x}$ modes in this star and show that the observed pulsation modes must be $Y_{10y}$. We also present a toy model for the tri-axial pulsation framework in the context of this star. The techniques presented here can be utilized to rapidly analyze and confirm future tri-axial pulsator candidates., Comment: 19 pages, 11 figures, 4 tables. Accepted for publication in ApJ

We introduce a cluster-based generative image segmentation framework to encode higher-level representations of visual concepts based on one-shot learning inspired by the Omniglot Challenge. The inferred parameters of each component of a Gaussian Mixture Model (GMM) represent a distinct topological subpart of a visual concept. Sampling new data from these parameters generates augmented subparts to build a more robust prototype for each concept, i.e., the Abstracted Gaussian Prototype (AGP). This framework addresses one-shot classification tasks using a cognitively-inspired similarity metric and addresses one-shot generative tasks through a novel AGP-VAE pipeline employing variational autoencoders (VAEs) to generate new class variants. Results from human judges reveal that the generative pipeline produces novel examples and classes of visual concepts that are broadly indistinguishable from those made by humans. The proposed framework leads to impressive but not state-of-the-art classification accuracy; thus, the contribution is two-fold: 1) the system is uniquely low in theoretical and computational complexity and operates in a completely standalone manner compared while existing approaches draw heavily on pre-training or knowledge engineering; and 2) in contrast with competing neural network models, the AGP approach addresses the importance of breadth of task capability emphasized in the Omniglot challenge (i.e., successful performance on generative tasks). These two points are critical as we advance toward an understanding of how learning/reasoning systems can produce viable, robust, and flexible concepts based on literally nothing more than a single example.

This paper addresses the key challenge of estimating the asymptotic covariance associated with the Markov chain central limit theorem, which is essential for visualizing and terminating Markov Chain Monte Carlo (MCMC) simulations. We focus on summarizing batching, spectral, and initial sequence covariance estimation techniques. We emphasize practical recommendations for modern MCMC simulations, where positive correlation is common and leads to negatively biased covariance estimates. Our discussion is centered on computationally efficient methods that remain viable even when the number of iterations is large, offering insights into improving the reliability and accuracy of MCMC output in such scenarios., Comment: 31 pages, 10 figures

The EdWeek Research Center surveyed K-12 educators and high school students to learn more about how they view the conditions in their schools. This report highlights findings from surveys conducted in early 2024. The findings shed light on the ways that students' perspectives can differ along socioeconomic lines and provide data on practices that have the potential to improve school environments. [Additional funding was provided by Panorama Education.]

Stellar theory enables us to understand the properties of stars at different stages of their evolution, and contributes to other fields of astrophysics such as galactic and exoplanet studies. Assessing the accuracy of stellar theories necessitates high precision, model-independent measurements of the properties of real stars, such as those obtainable for the components of double lined eclipsing binaries (DLEBs), while asteroseismology offers probing power of the stellar interior if one or both components pulsate. KIC 4851217 is a DLEB containing two late A-type stars and exhibits pulsations of the $\delta$ Scuti type. By analysing high resolution HERMES and moderate resolution ISIS spectra, jointly with Kepler and TESS light curves, we measured the masses, radii and effective temperatures of the components to precisions of ~0.5, ~1.1 and ~1 per cent, respectively. We additionally report the discovery and characterisation of a tertiary M-dwarf companion. Models of the system's spectral energy distribution agree with an age of 0.82 Gyr, with the more massive and larger secondary component near the end of the main sequence lifetime. An examination of the pulsating component's pulsation frequencies reveals 39 pulsation multiplets that are split by the orbital frequency. For most of these, it is evident that the pulsation axes have been tilted into the orbital plane. This makes KIC 4851217 a tidally tilted pulsator (TTP). This precisely characterized $\delta$ Scuti DLEB is an ideal candidate for advancing intermediate-mass stellar theory, contributing to our understanding of hierarchichal systems as well as to the topic of TTPs., Comment: 23 pages

Lymphoid infiltration at tumor margins is a key prognostic marker in solid tumors, playing a crucial role in guiding immunotherapy decisions. Current assessment methods, heavily reliant on immunohistochemistry (IHC), face challenges in tumor margin delineation and are affected by tissue preservation conditions. In contrast, we propose a Hematoxylin and Eosin (H&E) staining-based approach, underpinned by an advanced lymphocyte segmentation model trained on a public dataset for the precise detection of CD3+ and CD20+ lymphocytes. In our colorectal cancer study, we demonstrate that our H&E-based method offers a compelling alternative to traditional IHC, achieving comparable results in many cases. Our method's validity is further explored through a Turing test, involving blinded assessments by a pathologist of anonymized curves from H&E and IHC slides. This approach invites the medical community to consider Turing tests as a standard for evaluating medical applications involving expert human evaluation, thereby opening new avenues for enhancing cancer management and immunotherapy planning., Comment: Published in Medical Optical Imaging and Virtual Microscopy Image Analysis (MOVI) at MICCAI 2024

Open-source LLMs have shown great potential as fine-tuned chatbots, and demonstrate robust abilities in reasoning and surpass many existing benchmarks. Retrieval-Augmented Generation (RAG) is a technique for improving the performance of LLMs on tasks that the models weren't explicitly trained on, by leveraging external knowledge databases. Numerous studies have demonstrated the effectiveness of RAG to more successfully accomplish downstream tasks when using vector datasets that consist of relevant background information. It has been implicitly assumed by those in the field that if adversarial background information is utilized in this context, that the success of using a RAG-based approach would be nonexistent or even negatively impact the results. To address this assumption, we tested several open-source LLMs on the ability of RAG to improve their success in answering multiple-choice questions (MCQ) in the medical subspecialty field of Nephrology. Unlike previous studies, we examined the effect of RAG in utilizing both relevant and adversarial background databases. We set up several open-source LLMs, including Llama 3, Phi-3, Mixtral 8x7b, Zephyr$\beta$, and Gemma 7B Instruct, in a zero-shot RAG pipeline. As adversarial sources of information, text from the Bible and a Random Words generated database were used for comparison. Our data show that most of the open-source LLMs improve their multiple-choice test-taking success as expected when incorporating relevant information vector databases. Surprisingly however, adversarial Bible text significantly improved the success of many LLMs and even random word text improved test taking ability of some of the models. In summary, our results demonstrate for the first time the countertintuitive ability of adversarial information datasets to improve the RAG-based LLM success., Comment: 24 pages, 3 figures, 11 tables

Visual Question Answering for Remote Sensing (RSVQA) is a task that aims at answering natural language questions about the content of a remote sensing image. The visual features extraction is therefore an essential step in a VQA pipeline. By incorporating attention mechanisms into this process, models gain the ability to focus selectively on salient regions of the image, prioritizing the most relevant visual information for a given question. In this work, we propose to embed an attention mechanism guided by segmentation into a RSVQA pipeline. We argue that segmentation plays a crucial role in guiding attention by providing a contextual understanding of the visual information, underlying specific objects or areas of interest. To evaluate this methodology, we provide a new VQA dataset that exploits very high-resolution RGB orthophotos annotated with 16 segmentation classes and question/answer pairs. Our study shows promising results of our new methodology, gaining almost 10% of overall accuracy compared to a classical method on the proposed dataset., Comment: Accepted to IGARSS 2024

Synthesized beam (PSF) synthetic observations with and without the antennas in Mexico are analyzed. For a simple continuum observing setup, we generated visibility files and their associated PSF images for a grid of parameters (robust weighting, tapering, and declination). The tests were done for both the MID and MID+Spiral+Core configurations and their cropped versions without antennas in Mexico. We show that the performance of the Array, in terms of the beam properties, is in general significantly better when both the MID array antennas are present in Northern Mexico and observations target southern sources. At a declination of -40 deg, there are increments in the ellipticity of at least ~1.3X and 1.2X for a tapering of 3.0 and 4.0 mas, if the antennas in Mexico are not included. For the parameter space tested, the changes in ellipticity of the MID and MID+Spiral+Core configurations differ by ~10%. Larger tapering values help to reduce the ellipticity for cropped configurations at all declinations, but it will impose more constraints in terms of angular resolution., Comment: 10 pages, 8 figures

We find that the observed pressure-mode rotational splittings of slowly/moderately rotating Delta Scuti stars and Beta Cephei stars mostly have a positive asymmetry. That is, the left frequency spacing is larger than the right spacing in the dipole mode splitting triplets and the $l=2$ mode splitting multiplets (considering $m=1, 0, -1$ modes only). This is in agreement with the second-order perturbative effect of the rotational non-spherical distortion: both the prograde and retrograde modes have their frequencies shifted towards lower values relative to the $m=0$ modes. We thus study the rotational perturbation both in the first and second order, as well as the near-degeneracy mode coupling effect in MESA models representing Delta Scuti stars. For faster rotators, the near-degeneracy mode coupling between the nearest radial and quadrupole modes can significantly shift the $m=0$ modes, reduce the splitting asymmetry, and even change its sign. We find the theoretical splitting asymmetry from the second-order non-spherical distortion is larger than observed asymmetry. To facilitate future detections, we predict correlations between splitting asymmetry, splitting amplitude, and pulsation frequency. We also discuss additional factors that can influence splitting asymmetry, including embedded magnetic fields, resonant mode coupling, and binarity., Comment: MNRAS, submitted

The quantum conductance and its classical wave analogue, the transmittance, are given by the sum of the eigenvalues of the transmission matrix. The lowest transmission eigenvalue in diffusive media might be expected to play a negligible role in the conductance, and, in any case, to be too small to be observed. Here, we observe the lowest transmission eigenchannel in microwave waveguides, though it is orders of magnitude below the nominal noise level, and show that the transmittance is pulled down by global correlation among transmission eigenvalues and among zeros and poles of the transmission matrix. Transmission vanishes either when the energy density on the sample output vanishes at topological transmission zeros or when the longitudinal velocity vanishes precisely at the crossover to a new channel. This lowers the conductance by an amount proportional to the modulation of the density of states. In accord with the correspondence principle, the conductance approaches Ohms law as the number of channels increases with sample width. The exploration of the transmission matrix opens the door to a new understanding of mesoscopic transport and ultrasensitive detection techniques.

Optical control of correlated electronic states promises unprecedented tunability of novel functional materials. Tailored optical excitations can steer a system along non-equilibrium pathways to metastable states with specific structural or electronic properties. A much-desired feature is the reproducible and ultrafast switching to functional states. The light-induced hidden state of 1T-TaS$_{2}$, with its strongly enhanced conductivity and exceptionally long lifetime, represents a unique model system for studying the switching of correlated electronic states using femtosecond optical stimuli. However, despite intense investigation, the switching mechanism and the structural origins of the distinctive electronic properties of the hidden state have not been fully uncovered. Here, we use surface-sensitive electron diffraction in combination with a femtosecond optical quench to reveal coexistent charge-density wave chiralities as a new structural feature of the hidden state. We find that a single-pulse optical quench produces a state with long-range structural order and different weights of the two chiral enantiomorphs of the charge-density wave. Harnessing a double-pulse optical quench, we trace the origin of the mixed chirality to the transient electronic excitation of the host crystal. The coexistent long-range-order of both chiralities suggests the presence of extended heterochiral charge-density wave interfaces, which results in a higher-level, fractal-type moir\'{e} superstructure. Density functional theory simulations for such a charge-density wave moir\'{e} superstructure reveal multiple flat bands, Dirac cones, and a kagome electronic subsystem around the Fermi energy. Our findings shed light on novel electronic properties gained by chiral interface engineering, and create avenues for light-induced moir\'{e} superstructures in quasi-two-dimensional materials.

The concept of counterfactual explanations (CE) has emerged as one of the important concepts to understand the inner workings of complex AI systems. In this paper, we translate the idea of CEs to linear optimization and propose, motivate, and analyze three different types of CEs: strong, weak, and relative. While deriving strong and weak CEs appears to be computationally intractable, we show that calculating relative CEs can be done efficiently. By detecting and exploiting the hidden convex structure of the optimization problem that arises in the latter case, we show that obtaining relative CEs can be done in the same magnitude of time as solving the original linear optimization problem. This is confirmed by an extensive numerical experiment study on the NETLIB library.

Efficient and precise quantification of lymphocytes in histopathology slides is imperative for the characterization of the tumor microenvironment and immunotherapy response insights. We developed a data-centric optimization pipeline that attain great lymphocyte detection performance using an off-the-shelf YOLOv5 model, without any architectural modifications. Our contribution that rely on strategic dataset augmentation strategies, includes novel biological upsampling and custom visual cohesion transformations tailored to the unique properties of tissue imagery, and enables to dramatically improve model performances. Our optimization reveals a pivotal realization: given intensive customization, standard computational pathology models can achieve high-capability biomarker development, without increasing the architectural complexity. We showcase the interest of this approach in the context of breast cancer where our strategies lead to good lymphocyte detection performances, echoing a broadly impactful paradigm shift. Furthermore, our data curation techniques enable crucial histological analysis benchmarks, highlighting improved generalizable potential.

Large language models (LLMs) have revolutionized Natural Language Processing (NLP), but their size creates computational bottlenecks. We introduce a novel approach to create accurate, sparse foundational versions of performant LLMs that achieve full accuracy recovery for fine-tuning tasks at up to 70% sparsity. We achieve this for the LLaMA-2 7B model by combining the SparseGPT one-shot pruning method and sparse pretraining of those models on a subset of the SlimPajama dataset mixed with a Python subset of The Stack dataset. We exhibit training acceleration due to sparsity on Cerebras CS-3 chips that closely matches theoretical scaling. In addition, we establish inference acceleration of up to 3x on CPUs by utilizing Neural Magic's DeepSparse engine and 1.7x on GPUs through Neural Magic's nm-vllm engine. The above gains are realized via sparsity alone, thus enabling further gains through additional use of quantization. Specifically, we show a total speedup on CPUs for sparse-quantized LLaMA models of up to 8.6x. We demonstrate these results across diverse, challenging tasks, including chat, instruction following, code generation, arithmetic reasoning, and summarization to prove their generality. This work paves the way for rapidly creating smaller and faster LLMs without sacrificing accuracy.

We would like to enable a collaborative multiagent team to navigate at long length scales and under uncertainty in real-world environments. In practice, planning complexity scales with the number of agents in the team, with the length scale of the environment, and with environmental uncertainty. Enabling tractable planning requires developing abstract models that can represent complex, high-quality plans. However, such models often abstract away information needed to generate directly-executable plans for real-world agents in real-world environments, as planning in such detail, especially in the presence of real-world uncertainty, would be computationally intractable. In this paper, we describe the deployment of a planning system that used a hierarchy of planners to execute collaborative multiagent navigation tasks in real-world, unknown environments. By developing a planning system that was robust to failures at every level of the planning hierarchy, we enabled the team to complete collaborative navigation tasks, even in the presence of imperfect planning abstractions and real-world uncertainty. We deployed our approach on a Clearpath Husky-Jackal team navigating in a structured outdoor environment, and demonstrated that the system enabled the agents to successfully execute collaborative plans., Comment: Accepted to the IEEE ICRA Workshop on Field Robotics 2024

Spatial heterogeneity and phase competition are hallmarks of strongly-correlated materials, promising tunable functionality on the nanoscale. Light-induced switching of a correlated insulator to a metallic state is well established. However, optical excitation generally lacks the specificity to select sub-wavelength domains and control final textures. Here, we employ valley-selective photodoping to drive the domain-specific quench of a textured Peierls insulator. Polarized excitation leverages the anisotropy of quasi-one-dimensional states at the correlated gap to initiate an insulator-to-metal transition with minimal electronic heating. We find that averting dissipation facilitates domain-specific carrier confinement, control over nanotextured phases, and a prolonged lifetime of the metastable metallic state. Complementing existing manipulation schemes, valley-selective photoexcitation will enable the activation of electronic phase separation beyond thermodynamic limitations, facilitating optically-controlled hidden states, engineered heterostructures, and polarization-sensitive percolation networks.

Significantly simplifying the creation of optimization models for real-world business problems has long been a major goal in applying mathematical optimization more widely to important business and societal decisions. The recent capabilities of Large Language Models (LLMs) present a timely opportunity to achieve this goal. Therefore, we propose research at the intersection of LLMs and optimization to create a Decision Optimization CoPilot (DOCP) - an AI tool designed to assist any decision maker, interacting in natural language to grasp the business problem, subsequently formulating and solving the corresponding optimization model. This paper outlines our DOCP vision and identifies several fundamental requirements for its implementation. We describe the state of the art through a literature survey and experiments using ChatGPT. We show that a) LLMs already provide substantial novel capabilities relevant to a DOCP, and b) major research challenges remain to be addressed. We also propose possible research directions to overcome these gaps. We also see this work as a call to action to bring together the LLM and optimization communities to pursue our vision, thereby enabling much more widespread improved decision-making.

HD 12098 is an roAp star pulsating in the most distorted dipole mode yet observed in this class of star. Using TESS Sector 58 observations we show that there are photometric spots at both the magnetic poles of this star. It pulsates obliquely primarily in a strongly distorted dipole mode with a period of $P_{\rm puls} = 7.85$ min ($\nu_{\rm puls} = 183.34905$ d$^{-1}$; 2.12210 mHz) that gives rise to an unusual quadruplet in the amplitude spectrum. Our magnetic pulsation model cannot account for the strong distortion of the pulsation in one hemisphere, although it is successful in the other hemisphere. There are high-overtone p~modes with frequencies separated by more than the large separation, a challenging problem in mode selection. The mode frequencies observed in the TESS data are in the same frequency range as those previously observed in ground-based Johnson $B$ data, but are not for the same modes. Hence the star has either changed modes, or observations at different atmospheric depth detect different modes. There is also a low-overtone p mode and possibly g modes that are not expected theoretically with the $> 1$ kG magnetic field observed in this star., Comment: 7 pages; 5 figures

Bilevel optimization deals with nested problems in which a leader takes the first decision to minimize their objective function while accounting for a follower's best-response reaction. Constrained bilevel problems with integer variables are particularly notorious for their hardness. While exact solvers have been proposed for mixed-integer linear bilevel optimization, they tend to scale poorly with problem size and are hard to generalize to the non-linear case. On the other hand, problem-specific algorithms (exact and heuristic) are limited in scope. Under a data-driven setting in which similar instances of a bilevel problem are solved routinely, our proposed framework, Neur2BiLO, embeds a neural network approximation of the leader's or follower's value function, trained via supervised regression, into an easy-to-solve mixed-integer program. Neur2BiLO serves as a heuristic that produces high-quality solutions extremely fast for the bilevel knapsack interdiction problem, the "critical node game" from network security, a donor-recipient healthcare problem, and discrete network design from transportation planning. These problems are diverse in that they have linear or non-linear objectives/constraints and integer or mixed-integer variables, making Neur2BiLO unique in its versatility.

Pulsations of rapidly oscillating Ap stars and their interaction with the stellar magnetic field have not been studied in the near-IR region despite the benefits these observations offer compared to visual wavelengths. The main advantage of the near-IR is the quadratic dependence of the Zeeman effect on the wavelength, as opposed to the linear dependence of the Doppler effect. To test pulsation diagnostics of roAp stars in the near-IR, we aim to investigate the pulsation behaviour of one of the brightest magnetic roAp stars, $\gamma$Equ, which possesses a strong surface magnetic field of the order of several kilogauss and exhibits magnetically split spectral lines in its spectra. Two magnetically split spectral lines belonging to different elements, the triplet Fe I at 1563.63nm and the pseudo-doublet Ce III at 1629.2nm, were recorded with CRIRES+ over about one hour in the H band with the aim of understanding the character of the line profile variability and the pulsation behaviour of the magnetic field modulus. The profile shapes of both studied magnetically split spectral lines vary in a rather complex manner probably due to a significant decrease in the strength of the longitudinal field component and an increase in the strength of the transverse field components over the last decade. A mean magnetic field modulus of 3.9kG was determined for the Fe I, whereas for the Ce III we observe only about 2.9kG. For comparison, a mean field modulus of 3.4kG was determined using the Zeeman doublet Fe II at 6249.25 in optical PEPSI spectra recorded just about two weeks before the CRIRES+ observations. Different effects that may lead to the differences in the field modulus values are discussed. Our measurements of the mean magnetic field modulus using the line profiles recorded in different pulsational phase bins suggest a field modulus variability of 32G for the Fe I and 102G for the Ce III., Comment: Accepted to be published in A&A