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Koopman operator theory offers a rigorous treatment of dynamics and has been emerging as a powerful modeling and learning-based control method enabling significant advancements across various domains of robotics. Due to its ability to represent nonlinear dynamics as a linear operator, Koopman theory offers a fresh lens through which to understand and tackle the modeling and control of complex robotic systems. Moreover, it enables incremental updates and is computationally inexpensive making it particularly appealing for real-time applications and online active learning. This review comprehensively presents recent research results on advancing Koopman operator theory across diverse domains of robotics, encompassing aerial, legged, wheeled, underwater, soft, and manipulator robotics. Furthermore, it offers practical tutorials to help new users get started as well as a treatise of more advanced topics leading to an outlook on future directions and open research questions. Taken together, these provide insights into the potential evolution of Koopman theory as applied to the field of robotics.

Surveys in the Milky Way and Large Magellanic Cloud revealed that the majority of massive stars will interact with companions during their lives. However, knowledge of the binary properties of massive stars at low metallicity, which approaches the conditions of the Early Universe, remains sparse. We present the Binarity at LOw Metallicity (BLOeM) campaign - an ESO large programme designed to obtain 25 epochs of spectroscopy for 929 massive stars in the SMC - the lowest metallicity conditions in which multiplicity is probed to date (Z = 0.2 Zsun). BLOeM will provide (i) the binary fraction, (ii) the orbital configurations of systems with periods P < 3 yr, (iii) dormant OB+BH binaries, and (iv) a legacy database of physical parameters of massive stars at low metallicity. The stars are observed with the LR02 setup of the giraffe instrument of the Very Large Telescope (3960-4570A, resolving power R=6200; typical signal-to-noise ratio S/N=70-100). This paper utilises the first 9 epochs obtained over a three-month time. We describe the survey and data reduction, perform a spectral classification of the stacked spectra, and construct a Hertzsprung-Russell diagram of the sample via spectral-type and photometric calibrations. The sample covers spectral types from O4 to F5, spanning the effective temperature and luminosity ranges 6.5

We constrain wind parameters of a sample of 18 O-type stars in the LMC, through analysis with stellar atmosphere and wind models including the effects of optically thick clumping. This allows us to determine the most accurate spectroscopic mass-loss and wind structure properties of massive stars at sub-solar metallicity to date and gain insight into the impact of metallicity on massive stellar winds. Combining high signal to noise (S/N) ratio spectroscopy in the UV and optical gives us access to diagnostics of multiple different physical processes in the stellar wind. We produce synthetic spectra using the stellar atmosphere modelling code FASTWIND, and reproduce the observed spectra using a genetic algorithm based fitting technique. We empirically constrain 15 physical parameters associated with the stellar and wind properties, including temperature, surface gravity, surface abundances, rotation, macroturbulence and wind parameters. We find, on average, mass-loss rates a factor of 4-5 lower than those predicted by Vink et al. 2001, in good agreement with predictions from Bjorklund et al. 2021, and the best agreement with those from Krticka et al. 2018. In the 'weak-wind' regime we find mass-loss rates orders of magnitude below any theoretical predictions. We find a positive correlation of clumping factors (fcl) with effective temperature with an average fcl = 14 +- 8 for the full sample. Above 38 kK an average 46 +- 24% of the wind velocity span is covered by clumps and the interclump density is 10-30% of the mean wind. Below an effective temperature of roughly 38 kK there must be additional light leakage for supergiants. For dwarf stars at low temperatures there is a statistical preference for very low clump velocity spans, however it is unclear if this can be physically motivated as there are no clearly observable wind signatures in UV diagnostics., Comment: 12 pages with 6 figures, plus 16 pages and 18 figures in appendix. Accepted for publication in A&A

Computational notebooks have become the primary coding environment for data scientists. However, research on their code quality is still emerging, and the code shared is often of poor quality. Given the importance of maintenance and reusability, understanding the metrics that affect notebook code comprehensibility is crucial. Code understandability, a qualitative variable, is closely tied to user opinions. Traditional approaches to measuring it either use limited questionnaires to review a few code pieces or rely on metadata such as likes and votes in software repositories. Our approach enhances the measurement of Jupyter notebook understandability by leveraging user comments related to code understandability. As a case study, we used 542,051 Kaggle Jupyter notebooks from our previous research, named DistilKaggle. We employed a fine-tuned DistilBERT transformer to identify user comments associated with code understandability. We established a criterion called User Opinion Code Understandability (UOCU), which considers the number of relevant comments, upvotes on those comments, total notebook views, and total notebook upvotes. UOCU proved to be more effective than previous methods. Furthermore, we trained machine learning models to predict notebook code understandability based solely on their metrics. We collected 34 metrics for 132,723 final notebooks as features in our dataset, using UOCU as the label. Our predictive model, using the Random Forest classifier, achieved 89% accuracy in predicting the understandability levels of computational notebooks.

Quasicrystals represent a category of rarely structured solids that challenge traditional periodicity in crystal materials. Recent advancements in the synthesis of two-dimensional (2D) van der Waals materials have paved the way for exploring the unique physical properties of these systems. Here, we report on the synthesis of 2D quasicrystals featuring 30{\deg} alternating twist angles between multiple graphene layers, using chemical vapor deposition (CVD). Strikingly, we observed periodic Moir\'e patterns in the quasicrystal, a finding that has not been previously reported in traditional alloy-based quasicrystals. The Moir\'e periodicity, varying with the parity of the constituent layers, aligns with the theoretical predictions that suggest a stress cancellation mechanism in force. The emergence of Moir\'e fringes is attributed to the spontaneous mismatched lattice constant in the oriented graphene layers, proving the existence of atomic relaxation. This phenomenon, which has been largely understudied in graphene systems with large twist angles, has now been validated through our use of scanning transmission electron microscopy (STEM). Our CVD-grown Moir\'e quasicrystal provides an ideal platform for exploring the unusual physical properties that arise from Moir\'e periodicity within quasicrystals.

Quantum valley Hall (QVH) domain wall states are a new class of one-dimensional (1D) one-way conductors that are topologically protected in the absence of valley mixing. Development beyond a single QVH channel raises important new questions as to how QVH channels in close spatial proximity interact with each other, and how that interaction may be controlled. Scalable epitaxial bilayer graphene synthesis produces layer stacking wall (LSW) bundles, where QVH channels are bound, providing an excellent platform to study QVH channel interactions. Here we show that distinct strain sources lead to the formation of both well-separated LSWs and close packed LSW bundles. Comparative studies of electronic transport in these two regimes reveal that close-packed LSW bundles support electrically tunable magnetoconductance. The coexistence of different strain sources offers a potential pathway to realize scalable quantum transport platform based on LSWs where electrically tunability enables programmable functionality.

Studies of the magnetic characteristics of massive stars have recently received significant attention because they are progenitors of highly magnetised compact objects. Stars initially more massive than about 8M_sun leave behind neutron stars and black holes by the end of their evolution. The merging of binary compact remnant systems produces astrophysical transients detectable by gravitational wave observatories. Studies of magnetic fields in massive stars with low metallicities are of particular interest because they provide important information on the role of magnetic fields in the star formation of the early Universe. While several detections of massive Galactic magnetic stars have been reported in the last few decades, the impact of a low-metallicity environment on the occurrence and strength of stellar magnetic fields has not yet been explored. Because of the similarity between Of?p stars in the Magellanic Clouds (MCs) and Galactic magnetic Of?p stars, which possess globally organised magnetic fields, we searched for magnetic fields in Of?p stars in the MCs. Additionally, we observed the massive contact binary Cl* NGC 346 SSN7 in the Small Magellanic Cloud to test the theoretical scenario that the origin of magnetic fields involves a merger event or a common envelope evolution. We obtained and analysed measurements of the magnetic field in four massive Of?p stars in the MCs and the binary Cl* NGC 346 SSN7 using the ESO/VLT FORS2 spectrograph in spectropolarimetric mode. We detected kilogauss-scale magnetic fields in two Of?p-type stars and in the contact binary Cl* NGC 346 SSN7. These results suggest that the impact of low metallicity on the occurrence and strength of magnetic fields in massive stars is low. However, because the explored stellar sample is very small, additional observations of massive stars in the MCs are necessary., Comment: 7 pages, 2 tables, 8 figures, published as a letter in A&A

The performance of deep neural networks is enhanced by ensemble methods, which average the output of several models. However, this comes at an increased cost at inference. Weight averaging methods aim at balancing the generalization of ensembling and the inference speed of a single model by averaging the parameters of an ensemble of models. Yet, naive averaging results in poor performance as models converge to different loss basins, and aligning the models to improve the performance of the average is challenging. Alternatively, inspired by distributed training, methods like DART and PAPA have been proposed to train several models in parallel such that they will end up in the same basin, resulting in good averaging accuracy. However, these methods either compromise ensembling accuracy or demand significant communication between models during training. In this paper, we introduce WASH, a novel distributed method for training model ensembles for weight averaging that achieves state-of-the-art image classification accuracy. WASH maintains models within the same basin by randomly shuffling a small percentage of weights during training, resulting in diverse models and lower communication costs compared to standard parameter averaging methods.

Radio surveys of early-type stars have revealed a number of non-thermal emitters. Most of these have been shown to be binaries, where the collision between the two stellar winds is responsible for the non-thermal emission. HD 168112 is a non-thermal radio emitter, whose binary nature has only recently been confirmed spectroscopically. We obtained independent spectroscopic observations to determine its orbit, in addition to radio observations to see if the thermal or non-thermal nature of the emission changes during the periastron passage. We monitored HD 168112 spectroscopically for a 13 year time span. From these data, we determined the orbital parameters, which we compared to the previous results in the literature. From the spectral index of the radio observations, we found how the nature of the emission changes as the system goes through periastron. Combining our results with other literature data allowed us to further constrain the orbital and stellar parameters. We find HD 168112 to have an orbital period of P = 512.17+0.41-0.11 d, an eccentricity of e = 0.7533+0.0053-0.0124, and a mass ratio close to one. From our spectroscopic modelling, we derived the stellar parameters, but we had difficulty arriving at a spectroscopic mass ratio of one. The radio observations around periastron show only thermal emission, suggesting that most of the synchrotron photons are absorbed in the two stellar winds at that phase. Combining our data with the optical interferometry detection, we could constrain the inclination angle to i ~ 63 deg, and the mass of each component to ~ 26 Msun. We have provided an independent spectroscopic confirmation of the binary nature of HD 168112. Although detected as a non-thermal radio emitter, near periastron the radio emission of this highly eccentric system is thermal and is mainly formed in the colliding-wind region. [abridged], Comment: 13 pages, 7 figures, accepted for publication in Astronomy & Astrophysics. Corrected values of Omega on p. 9

Hybrid quantum-classical optimization and learning strategies are among the most promising approaches to harnessing quantum information or gaining a quantum advantage over classical methods. However, efficient estimation of the gradient of the objective function in such models remains a challenge due to several factors including the exponential dimensionality of the Hilbert spaces, and information loss of quantum measurements. In this work, we study generic parameterized circuits in the context of variational methods. We develop a framework for gradient estimation that exploits the algebraic symmetries of Hamiltonian characterized through Lie algebra or group theory. Particularly, we prove that when the dimension of the dynamical Lie algebra is polynomial in the number of qubits, one can estimate the gradient with polynomial classical and quantum resources. This is done by a series of Hadamard tests applied to the output of the ansatz with no change to its circuit. We show that this approach can be equipped with classical shadow tomography to further reduce the measurement shot complexity to scale logarithmically with the number of parameters.

Superconducting Nb thin films have recently attracted significant attention due to their utility for quantum information technologies. In the processing of Nb thin films, fluoride-based chemical etchants are commonly used to remove surface oxides that are known to affect superconducting quantum devices adversely. However, these same etchants can also introduce hydrogen to form Nb hydrides, potentially negatively impacting microwave loss performance. Here, we present comprehensive materials characterization of Nb hydrides formed in Nb thin films as a function of fluoride chemical treatments. In particular, secondary-ion mass spectrometry, X-ray scattering, and transmission electron microscopy reveal the spatial distribution and phase transformation of Nb hydrides. The rate of hydride formation is determined by the fluoride solution acidity and the etch rate of Nb2O5, which acts as a diffusion barrier for hydrogen into Nb. The resulting Nb hydrides are detrimental to Nb superconducting properties and lead to increased power-independent microwave loss in coplanar waveguide resonators. However, Nb hydrides do not correlate with two-level system loss or device aging mechanisms. Overall, this work provides insight into the formation of Nb hydrides and their role in microwave loss, thus guiding ongoing efforts to maximize coherence time in superconducting quantum devices.

A common practice in deep learning involves training large neural networks on massive datasets to achieve high accuracy across various domains and tasks. While this approach works well in many application areas, it often fails drastically when processing data from a new modality with a significant distribution shift from the data used to pre-train the model. This paper focuses on adapting a large object detection model trained on RGB images to new data extracted from IR images with a substantial modality shift. We propose Modality Translator (ModTr) as an alternative to the common approach of fine-tuning a large model to the new modality. ModTr adapts the IR input image with a small transformation network trained to directly minimize the detection loss. The original RGB model can then work on the translated inputs without any further changes or fine-tuning to its parameters. Experimental results on translating from IR to RGB images on two well-known datasets show that our simple approach provides detectors that perform comparably or better than standard fine-tuning, without forgetting the knowledge of the original model. This opens the door to a more flexible and efficient service-based detection pipeline, where a unique and unaltered server, such as an RGB detector, runs constantly while being queried by different modalities, such as IR with the corresponding translations model. Our code is available at: https://github.com/heitorrapela/ModTr., Comment: ECCV 2024: European Conference on Computer Vision, Milan Italy

The performance of deep models, including Vision Transformers, is known to be vulnerable to adversarial attacks. Many existing defenses against these attacks, such as adversarial training, rely on full-model fine-tuning to induce robustness in the models. These defenses require storing a copy of the entire model, that can have billions of parameters, for each task. At the same time, parameter-efficient prompt tuning is used to adapt large transformer-based models to downstream tasks without the need to save large copies. In this paper, we examine parameter-efficient prompt tuning of Vision Transformers for downstream tasks under the lens of robustness. We show that previous adversarial defense methods, when applied to the prompt tuning paradigm, suffer from gradient obfuscation and are vulnerable to adaptive attacks. We introduce ADAPT, a novel framework for performing adaptive adversarial training in the prompt tuning paradigm. Our method achieves competitive robust accuracy of ~40% w.r.t. SOTA robustness methods using full-model fine-tuning, by tuning only ~1% of the number of parameters.

Using the medium resolution spectrograph X-shooter, spectra of 235 OB and Wolf-Rayet (WR) stars in sub-solar metallicity environments have been secured. [...]This second paper focuses on the optical observations of 232 Magellanic Clouds targets. It describes the uniform reduction of the UVB (300 - 560 nm) and VIS (550 - 1020 nm) XShootU data as well as the preparation of advanced data products [...] . The data reduction of the raw data is based on the ESO CPL X-shooter pipeline. We paid particular attention to the determination of the response curves [...] We implemented slit-loss correction, absolute flux calibration, (semi-)automatic rectification to the continuum, and a correction for telluric lines. The spectra of individual epochs were corrected for the barycentric motion, re-sampled and co-added, and the spectra from the two arms were merged into a single flux calibrated spectrum covering the entire optical range with maximum signal-to-noise ratio. [...] We provide three types of data products: (i) two-dimensional spectra for each UVB and VIS exposure; (ii) one-dimensional UVB and VIS spectra before and after response-correction, as well as after applying various processing, including absolute flux calibration, telluric removal, normalisation and barycentric correction; and (iii) co-added flux-calibrated and rectified spectra over the full optical range, for which all available XShootU exposures were combined. For many of the targets, the final signal-to-noise ratio per resolution element is above 200 in both the UVB and the VIS co-added spectra. The reduced data and advanced scientific data products will be made available to the community upon publication of this paper. [...], Comment: 22 pages, 19 figures; accepted for publication in Astronomy & Astrophysics. Links to online tables and databases will be included upon publication

A popular way to approximate the Koopman operator's action on a finite-dimensional subspace of functions is via orthogonal projections. The quality of the projected model directly depends on the selected subspace, specifically on how close it is to being invariant under the Koopman operator. The notion of invariance proximity provides a tight upper bound on the worst-case relative prediction error of the finite-dimensional model. However, its direct calculation is computationally challenging. This paper leverages the geometric structure behind the definition of invariance proximity to provide a closed-form expression in terms of Jordan principal angles on general inner product spaces. Unveiling this connection allows us to exploit specific isomorphisms to circumvent the computational challenges associated with spaces of functions and enables the use of existing efficient numerical routines to compute invariance proximity., Comment: 11 pages

Over the past decades, libraries of stellar spectra have been used in a large variety of science cases, including as sources of reference spectra for a given object or a given spectral type. Despite the existence of large libraries and the increasing number of projects of large-scale spectral surveys, there is to date only one very high-resolution spectral library offering spectra from a few hundred objects from the southern hemisphere (UVES-POP) . We aim to extend the sample, offering a finer coverage of effective temperatures and surface gravity with a uniform collection of spectra obtained in the northern hemisphere. Between 2010 and 2020, we acquired several thousand echelle spectra of bright stars with the Mercator-HERMES spectrograph located in the Roque de Los Muchachos Observatory in La Palma, whose pipeline offers high-quality data reduction products. We have also developed methods to correct for the instrumental response in order to approach the true shape of the spectral continuum. Additionally, we have devised a normalisation process to provide a homogeneous normalisation of the full spectral range for most of the objects. We present a new spectral library consisting of 3256 spectra covering 2043 stars. It combines high signal-to-noise and high spectral resolution over the entire range of effective temperatures and luminosity classes. The spectra are presented in four versions: raw, corrected from the instrumental response, with and without correction from the atmospheric molecular absorption, and normalised (including the telluric correction)., Comment: 17 pages, 18 figures Preview and access to the library: https://www.royer.se/melchiors.html

Data augmentation is one of the most effective techniques for regularizing deep learning models and improving their recognition performance in a variety of tasks and domains. However, this holds for standard in-domain settings, in which the training and test data follow the same distribution. For the out-of-domain case, where the test data follow a different and unknown distribution, the best recipe for data augmentation is unclear. In this paper, we show that for out-of-domain and domain generalization settings, data augmentation can provide a conspicuous and robust improvement in performance. To do that, we propose a simple training procedure: (i) use uniform sampling on standard data augmentation transformations; (ii) increase the strength transformations to account for the higher data variance expected when working out-of-domain, and (iii) devise a new reward function to reject extreme transformations that can harm the training. With this procedure, our data augmentation scheme achieves a level of accuracy that is comparable to or better than state-of-the-art methods on benchmark domain generalization datasets. Code: \url{https://github.com/Masseeh/DCAug}

A powerful way to adapt a visual recognition model to a new domain is through image translation. However, common image translation approaches only focus on generating data from the same distribution as the target domain. Given a cross-modal application, such as pedestrian detection from aerial images, with a considerable shift in data distribution between infrared (IR) to visible (RGB) images, a translation focused on generation might lead to poor performance as the loss focuses on irrelevant details for the task. In this paper, we propose HalluciDet, an IR-RGB image translation model for object detection. Instead of focusing on reconstructing the original image on the IR modality, it seeks to reduce the detection loss of an RGB detector, and therefore avoids the need to access RGB data. This model produces a new image representation that enhances objects of interest in the scene and greatly improves detection performance. We empirically compare our approach against state-of-the-art methods for image translation and for fine-tuning on IR, and show that our HalluciDet improves detection accuracy in most cases by exploiting the privileged information encoded in a pre-trained RGB detector. Code: https://github.com/heitorrapela/HalluciDet, Comment: IEEE/CVF Winter Conference on Applications of Computer Vision (WACV) 2024

This paper introduces the Koopman Control Family (KCF), a mathematical framework for modeling general discrete-time nonlinear control systems with the aim of providing a solid theoretical foundation for the use of Koopman-based methods in systems with inputs. We demonstrate that the concept of KCF captures the behavior of nonlinear control systems on a (potentially infinite-dimensional) function space. By employing a generalized notion of subspace invariance under the KCF, we establish a universal form for finite-dimensional models, which encompasses the commonly used linear, bilinear, and linear switched models as specific instances. In cases where the subspace is not invariant under the KCF, we propose a method for approximating models in general form and characterize the model's accuracy using the concept of invariance proximity. We end by discussing how the proposed framework naturally lends itself to data-driven modeling of control systems., Comment: 17 pages

Observations of individual massive stars, super-luminous supernovae, gamma-ray bursts, and gravitational-wave events involving spectacular black-hole mergers, indicate that the low-metallicity Universe is fundamentally different from our own Galaxy. Many transient phenomena will remain enigmatic until we achieve a firm understanding of the physics and evolution of massive stars at low metallicity (Z). The Hubble Space Telescope has devoted 500 orbits to observe 250 massive stars at low Z in the ultraviolet (UV) with the COS and STIS spectrographs under the ULLYSES program. The complementary ``X-Shooting ULLYSES'' (XShootU) project provides enhanced legacy value with high-quality optical and near-infrared spectra obtained with the wide-wavelength coverage X-shooter spectrograph at ESO's Very Large Telescope. We present an overview of the XShootU project, showing that combining ULLYSES UV and XShootU optical spectra is critical for the uniform determination of stellar parameters such as effective temperature, surface gravity, luminosity, and abundances, as well as wind properties such as mass-loss rates in function of Z. As uncertainties in stellar and wind parameters percolate into many adjacent areas of Astrophysics, the data and modelling of the XShootU project is expected to be a game-changer for our physical understanding of massive stars at low Z. To be able to confidently interpret James Webb Space Telescope (JWST) spectra of the first stellar generations, the individual spectra of low Z stars need to be understood, which is exactly where XShootU can deliver., Comment: Accepted in A&A - 35 Pages, 12 Figures, 4 Tables, 2 Large Tables