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We propose a novel layer-wise parameterization for convolutional neural networks (CNNs) that includes built-in robustness guarantees by enforcing a prescribed Lipschitz bound. Each layer in our parameterization is designed to satisfy a linear matrix inequality (LMI), which in turn implies dissipativity with respect to a specific supply rate. Collectively, these layer-wise LMIs ensure Lipschitz boundedness for the input-output mapping of the neural network, yielding a more expressive parameterization than through spectral bounds or orthogonal layers. Our new method LipKernel directly parameterizes dissipative convolution kernels using a 2-D Roesser-type state space model. This means that the convolutional layers are given in standard form after training and can be evaluated without computational overhead. In numerical experiments, we show that the run-time using our method is orders of magnitude faster than state-of-the-art Lipschitz-bounded networks that parameterize convolutions in the Fourier domain, making our approach particularly attractive for improving robustness of learning-based real-time perception or control in robotics, autonomous vehicles, or automation systems. We focus on CNNs, and in contrast to previous works, our approach accommodates a wide variety of layers typically used in CNNs, including 1-D and 2-D convolutional layers, maximum and average pooling layers, as well as strided and dilated convolutions and zero padding. However, our approach naturally extends beyond CNNs as we can incorporate any layer that is incrementally dissipative.

This article investigates equivariant parametrized cellular cohomology, a cohomology theory introduced by Costenoble-Waner for spaces with an action by a compact Lie group $G$. The theory extends the $RO(G)$-graded cohomology of a $G$-space $B$ to a cohomology graded by $RO(\Pi B)$, the representations of the equivariant fundamental groupoid of $B$. This paper is meant to serve as a guide to this theory and contains some new computations. We explain the key ingredients for defining parametrized cellular cohomology when $G$ is a finite group, with particular attention to the case of the cyclic group $G=C_2$. We compute some examples and observe that $RO(\Pi B)$ is not always free. When $G$ is the trivial group, we explain how to identify equivariant parametrized cellular cohomology with cellular cohomology in local coefficients. Finally, we illustrate the theory with some new computations of parametrized cellular cohomology for several spaces with $G = C_2$ and $G=C_4$.

Non-invasive imaging of microwave (MW) magnetic fields with microscale lateral resolution is pivotal for various applications, such as MW technologies and integrated circuit failure analysis. Diamond nitrogen-vacancy (NV) center magnetometry has emerged as an ideal tool, offering $\mu$m-scale resolution, millimeter-scale field of view, high sensitivity, and non-invasive imaging compatible with diverse samples. However, up until now, it has been predominantly used for imaging of static or low-frequency magnetic fields or, concerning MW field imaging, to directly characterize the same microwave device used to drive the NV spin transitions. In this work we leverage an NV center ensemble in diamond for wide-field imaging of MW magnetic fields generated by a test device employing a differential measurement protocol. The microscope is equipped with a MW loop to induce Rabi oscillations between NV spin states, and the MW field from the device-under-test is measured through local deviations in the Rabi frequency. This differential protocol yields magnetic field maps of a 2.57 GHz MW field with a sensitivity of $\sim$ 9 $\mu$T Hz$^{-1/2}$ for a total measurement duration of $T = 357$ s, covering a $340\times340$ $\mu$m$^2$ field of view with a $\mu$m-scale spatial resolution and a DUT input power dynamic range of 30 dB. This work demonstrates a novel NV magnetometry protocol, based on differential Rabi frequency measurement, that extends NV wide-field imaging capabilities to imaging of weak MW magnetic fields that would be difficult to measure directly through standard NV Rabi magnetometry.

This paper introduces a novel model-free control strategy for a complex multi-stage gearbox electromechanical linear actuator (EMLA) system, driven by a permanent magnet synchronous motor (PMSM) with non-ideal ball screw characteristics. The proposed control approach aims to (1) manage user-specified safety constraints, (2) identify optimal control parameters for minimizing tracking errors, (3) ensure robustness, and (4) guarantee uniformly exponential stability. First, this paper employs a trajectory-setting interpolation-based algorithm to specify the piecewise definition of a smooth and jerk-bounded reference trajectory. Then, a dual robust subsystem-based barrier Lyapunov function (DRS-BLF) control is proposed for the PMSM-powered EMLA system to track the reference motions, guaranteeing user-specified safety related to constraints on system characteristics and alleviating control signal efforts. This methodology guarantees robustness and uniform exponential convergence. Lastly, optimal control parameter values are determined by customizing a swarm intelligence technique known as the Jaya (a term derived from the Sanskrit word for `victory') algorithm to minimize tracking errors. Experimental results validate the performance of the DRS-BLF control., Comment: This work has been accepted in the IEEE Power Electronics and Motion Control (IEEE-PEMC 2024) Conference

Ultra-luminous X-ray sources (ULXs) are high-mass X-ray binaries with an X-ray luminosity above $10^{39}$ erg s$^{-1}$. These ULXs can be powered by black holes that are more massive than $20M_\odot$, accreting in a standard regime, or lighter compact objects accreting supercritically. There are only a few ULXs with known optical or UV counterparts, and their nature is debated. Determining whether optical/UV radiation is produced by the donor star or by the accretion disc is crucial for understanding ULX physics and testing massive binary evolution. We conduct, for the first time, a fully consistent multi-wavelength spectral analysis of a ULX and its circumstellar nebula. We aim to establish the donor star type and test the presence of strong disc winds in the prototypical ULX Holmberg II X-1 (Ho II X-1). We intent to obtain a realistic spectral energy distribution of the ionising source, which is needed for robust nebula analysis. We acquired new UV spectra of Ho II X-1 with the HST and complemented them with archival optical and X-ray data. We explored the spectral energy distribution of the source and analysed the spectra using the stellar atmosphere code PoWR and the photoionisation code Cloudy. Our analysis of the X-ray, UV, and optical spectra of Ho II X-1 and its nebula consistently explains the observations. We do not find traces of disc wind signatures in the UV and the optical, rejecting previous claims of the ULX being a supercritical accretor. The optical/UV counterpart of HoII X-1 is explained by a B-type supergiant donor star. Thus, the observations are fully compatible with Ho II X-1 being a close binary consisting of an $\gtrsim 66\,M_\odot$ black hole accreting matter from an $\simeq 22 M_\odot$ B-supergiant companion. Also, we propose a possible evolution scenario for the system, suggesting that Ho II X-1 is a potential gravitational wave source progenitor., Comment: Accepted in A&A. 14 pages (12 main body + 2 appendix), 6 figures, 6 tables

In-wheel drive (IWD) systems enhance the responsiveness, traction, and maintenance efficiency of vehicles by enabling each wheel to operate independently. This paper proposes a novel robust torque-observed valve-based control (RTOVC) framework to address velocity tracking in hydraulic IWDs that actuate heavy-duty wheeled mobile robots (HWMRs), considering such challenges as wheel slippages, sensor limitations, rough terrains, and modeling uncertainties. To overcome the sensor-dependent control systems associated with the closed-loop torque/pressure in hydraulic IWD-actuated HWMRs, a robust observer network based on an adaptive barrier Lyapunov function (BLF) is proposed to estimate the required in-wheel motor torque to track the velocity references. Then, another adaptive BLF for valve control signals is employed to modulate the hydraulic fluid to generate the estimated torque for each IWD. The RTOVC strategy ensures user-defined safety within the logarithmic BLF framework by constraining the valve control signal, actual velocity, velocity tracking error, and torque of each hydraulic IWD in an HWMR to avoid exceeding specified limits. Despite its safety constraints, external disturbances, and modeling uncertainties, robustness and uniformly exponential stability of the RTOVC-applied hydraulic IWD mechanism are ensured in HWMRs. Experimental investigations using a 6,500-kg HWMR, actuated by four independent IWDs under intense disturbances and safety-defined constraints, validate the performance of the RTOVC., Comment: This work has been submitted for possible publication in the IEEE

Data in the healthcare domain arise from a variety of sources and modalities, such as x-ray images, continuous measurements, and clinical notes. Medical practitioners integrate these diverse data types daily to make informed and accurate decisions. With recent advancements in language models capable of handling multimodal data, it is a logical progression to apply these models to the healthcare sector. In this work, we introduce a framework that connects small language models to multiple data sources, aiming to predict the risk of various diseases simultaneously. Our experiments encompass 12 different tasks within a multitask learning setup. Although our approach does not surpass state-of-the-art methods specialized for single tasks, it demonstrates competitive performance and underscores the potential of small language models for multimodal reasoning in healthcare., Comment: 6 pages, submitted to ICMLA

This paper addresses navigation in crowded environments by integrating goal-conditioned generative models with Sampling-based Model Predictive Control (SMPC). We introduce goal-conditioned autoregressive models to generate crowd behaviors, capturing intricate interactions among individuals. The model processes potential robot trajectory samples and predicts the reactions of surrounding individuals, enabling proactive robotic navigation in complex scenarios. Extensive experiments show that this algorithm enables real-time navigation, significantly reducing collision rates and path lengths, and outperforming selected baseline methods. The practical effectiveness of this algorithm is validated on an actual robotic platform, demonstrating its capability in dynamic settings.

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

Context. B supergiants (BSGs) represent an important connection between the main sequence and more extreme evolutionary stages of massive stars. Additionally, lying toward the cool end of the hot star regime, determining their wind properties is crucial to constrain the evolution and feedback of massive stars as, for instance, they might manifest the bi-stability jump phenomenon. Aims. We undertake a detailed analysis of a representative sample of 18 Small Magellanic Cloud (SMC) BSGs within the ULLYSES and XShootU datasets. Our UV and optical analysis spans BSGs from B0 to B8 - covering the bi-stability jump region. We aim to evaluate their evolutionary status and verify what their wind properties say about the bi-stability jump in a low-metallicity environment. Methods. We used the CMFGEN to model the spectra and photometry (from UV to infrared) of our sample. We compare our results with different evolutionary models, with previous determinations in the literature of OB stars, and with diverging mass-loss recipes at the bi-stability jump. Additionally, we provide the first BSG models in the SMC including X-rays. Results. (i) Within a single-stellar evolution framework, the evolutionary status of early BSGs seem less clear than that of late BSGs, which agree with H-shell burning models. (ii) UV analysis shows evidence that BSGs contain X-rays in their atmospheres, for which we provide constraints. In general, we find higher X-ray luminosity (close to the standard log(L_X/L) ~ -7) for early BSGs. For cooler BSGs, lower values are preferred, log(L_X/L) ~ -8.5. (iii) The obtained mass-loss rates suggest neither a jump nor a monotonic decrease with temperature. Instead, a rather constant trend is observed, which is at odds with the increase found for Galactic BSGs. (iv) The wind velocity behavior with temperature shows a sharp drop at ~19 kK, similar to what is observed for Galactic BSGs., Comment: 33 pages (23+10)+(22 at Zenodo), 34 figures (21+13)+(21 at Zenodo), 7 tables (3+4)+(1 at Zenodo), accepted for publication

Wide-field magnetic microscopy using nitrogen-vacancy (NV) centers in diamond can yield high-quality magnetic images of DC and AC magnetic fields. The unique combination of micron-scale spatial resolution of scalar or vector fields at room temperature and parallel camera readout make this an appealing technique for applications in biology, geology, condensed-matter physics, and electronics. However, while NV magnetic microscopy has achieved great success in these areas, historically the accessible frequency range has been limited. In this paper, we overcome this limitation by implementing the recently developed technique of quantum frequency mixing. With this approach, we generate wide-field magnetic images of test structures driven by alternating currents up to 70 MHz, well outside the reach of DC and Rabi magnetometry methods. With further improvements, this approach could find utility in hyperspectral imaging for electronics power spectrum analysis, electronics diagnostics and troubleshooting, and quantum computing hardware validation., Comment: 8 pages main text, 3 pages supplement

Precision optical measurements of the electron-spin precession of nitrogen-vacancy (NV) centers in diamond form the basis of numerous applications. The most sensitivity-demanding applications, such as femtotesla magnetometry, require the ability to measure changes in GHz spin transition frequencies at the sub-millihertz level, corresponding to a fractional resolution of better than 10^{-12}. Here we study the impact of microwave (MW) phase noise on the response of an NV sensor. Fluctuations of the phase of the MW waveform cause undesired rotations of the NV spin state. These fluctuations are imprinted in the optical readout signal and, left unmitigated, are indistinguishable from magnetic field noise. We show that the phase noise of several common commercial MW generators results in an effective pT s^{1/2}-range noise floor that varies with the MW carrier frequency and the detection frequency of the pulse sequence. The data are described by a frequency domain model incorporating the MW phase noise spectrum and the filter-function response of the sensing protocol. For controlled injection of white and random-walk phase noise, the observed NV magnetic noise floor is described by simple analytic expressions that accurately capture the scaling with pulse sequence length and the number of pi pulses. We outline several strategies to suppress the impact of MW phase noise and implement a version, based on gradiometry, that realizes a >10-fold suppression. Our study highlights an important challenge in the pursuit of sensitive diamond quantum sensors and is applicable to other qubit systems with a large transition frequency., Comment: Main text: 8 pages, 5 figures. Entire manuscript including Appendices: 23 pages, 14 figures, 6 tables

CONTEXT: The spectral analysis of hot, massive stars is a fundamental astrophysical method to obtain their intrinsic properties and their feedback. Quantitative spectroscopy for hot, massive stars requires detailed numerical modeling of the atmosphere and an iterative treatment to obtain the best solution within a given framework. AIMS: We present an overview of different techniques for the quantitative spectroscopy of hot stars employed within the X-Shooting ULLYSES collaboration, from grid-based approaches to tailored fits. By performing a blind test, we gain an overview about the similarities and differences of the resulting parameters. Our study aims to provide an overview of the parameter spread caused by different approaches. METHODS: For three different stars from the sample (SMC O5 star AzV 377, LMC O7 star Sk -69 50, and LMC O9 star Sk -66 171), we employ different atmosphere codes (CMFGEN, Fastwind, PoWR) and strategies to determine their best-fitting model. For our analyses, UV and optical spectra are used to derive the properties with some methods relying purely on optical data for comparison. To determine the overall spectral energy distribution, we further employ additional photometry from the literature. RESULTS: Effective temperatures for each of three sample stars agree within 3 kK while the differences in log g can be up to 0.2 dex. Luminosity differences of up to 0.1 dex result from different reddening assumptions, which seem to be larger for the methods employing a genetic algorithm. All sample stars are nitrogen-enriched. CONCLUSIONS: We find a reasonable agreement between the different methods. Tailored fitting tends to be able to minimize discrepancies obtained with more course or automatized treatments. UV spectral data is essential for the determination of realistic wind parameters. For one target (Sk -69 50), we find clear indications of an evolved status., Comment: 19+15 pages, 21+4 figures, accepted version (A&A 689, A30) including language editing, condensed abstract

We are exposed to much information trying to influence us, such as teaser messages, debates, politically framed news, and propaganda - all of which use persuasive language. With the recent interest in Large Language Models (LLMs), we study the ability of LLMs to produce persuasive text. As opposed to prior work which focuses on particular domains or types of persuasion, we conduct a general study across various domains to measure and benchmark to what degree LLMs produce persuasive language - both when explicitly instructed to rewrite text to be more or less persuasive and when only instructed to paraphrase. We construct the new dataset Persuasive-Pairs of pairs of a short text and its rewrite by an LLM to amplify or diminish persuasive language. We multi-annotate the pairs on a relative scale for persuasive language: a valuable resource in itself, and for training a regression model to score and benchmark persuasive language, including for new LLMs across domains. In our analysis, we find that different 'personas' in LLaMA3's system prompt change persuasive language substantially, even when only instructed to paraphrase.

On the route towards merging neutron stars and stripped-envelope supernovae, binary population synthesis predicts a large number of post-interaction systems with massive stars that have stripped off their outer layers. Yet, observations of such stars in the intermediate-mass regime below the Wolf-Rayet masses are rare. Using X-Shooting ULLYSES (XShootU) data, we discovered three partially stripped star + Be/Oe binaries in the Magellanic Clouds. We analyzed the UV and optical spectra using the PoWR model atmosphere code by superimposing model spectra corresponding to each component. The estimated current masses of the partially stripped stars fall within the intermediate mass range of 4-8 $M_{\odot}$. These objects are overluminous for their stellar masses, matching core He-burning luminosities. Their Be/Oe secondaries have much higher masses than their stripped primaries (mass ratio > 2). All three partially stripped stars show significant nitrogen enrichment and carbon and oxygen depletion on their surfaces. Additionally, one of our sample stars exhibits significant helium enrichment. Our study provides the first comprehensive determination of the wind parameters of partially stripped stars in the intermediate mass range. The wind mass-loss rates of these stars are found to be on the order of $10^{-7} M_\odot$ yr$^{-1}$, which is over ten times higher than that of OB stars of the same luminosity. Current evolutionary models characterizing this phase typically employ OB or WR mass-loss rates, which underestimate or overestimate stripped stars' mass-loss rates by an order of magnitude. Binary evolution models indicate that the observed primaries had initial masses of 12-17 $M_{\odot}$, making them potential candidates for stripped-envelope supernovae that form neutron stars. If they survive the explosion, these systems may become Be X-ray binaries and later double neutron stars., Comment: Accepted for publication in Astronomy & Astrophysics

We characterize the superconducting diode effect (SDE) in two-dimensional diffusive structures with Rashba spin-orbit coupling using the quasiclassical formalism. We consider both homogeneous superconductors and Josephson junctions. In the first case, the diode effect monotonically increases as the magnetic field is increased and the temperature is reduced, in contrast to the non-monotonic behavior found in clean structures. In Josephson junctions, SDE dramatically increases and changes its sign close to the $0-\pi$ transition of the junction, which occurs at specific junction lengths and strengths of the magnetic field. We show that the SDE is strongly suppressed in narrow junctions. Our results are relevant for understanding recent experiments that measure SDE in mesoscopic nanostructures, where significant disorder is unavoidable., Comment: 9 pages, 6 figures

Context. Recent astrometric and spectroscopic surveys of OB stars have revealed a few stellar-mass black holes (BHs) with orbital periods as low as 10 days. No X-ray counterpart has been detected, due to the absence of a radiatively efficient accretion disk around the BH. Yet, dissipative processes in the hot, dilute and strongly magnetized plasma around the BH (so-called BH corona) can still lead to non-thermal X-ray emission (e.g. synchrotron). Aims. We determine the X-ray luminosity distribution from BH+OB star binaries up to orbital periods of a few thousand days. Methods. We use detailed binary evolution models computed with MESA for initial primary masses of 10-90 $M_{\odot}$ and orbital periods from 1-3000 d. The X-ray luminosity is computed for a broad range of radiative efficiencies. Results. We show that particle acceleration through magnetic reconnection can heat the BH corona. A substantial fraction of the gravitational potential energy from the accreted plasma is converted into non-thermal X-ray emission. Our population synthesis analysis predicts at least 28 (up to 72) BH+OB star binaries in the Large Magellanic Cloud (LMC) to produce X-ray luminosity above 10$^{31}$ erg$\,$s$^{-1}$, observable through focused Chandra observations. We identify a population of SB1 systems in the LMC and HD96670 in the Milky Way comprising O stars with unseen companions of masses above 2.3 $M_{\odot}$ that aligns well with our predictions. The predicted luminosities of the OB companions to these X-ray-emitting BHs are 10$^{4.5-5.5}$ $L_{\odot}$. Conclusions. These results make the case for long-time exposure in X-rays of the stellar-mass BH candidates identified around OB stars. It will constrain the underlying population of X-ray-faint BHs, the evolution from single to double degenerate binaries, and the progenitors of gravitational wave mergers. (Abridged), Comment: A&A, 690, A256. DOI: https://doi.org/10.1051/0004-6361/202450940

With the rapid increase in mobile subscribers, there is a drive towards achieving higher data rates, prompting the use of higher frequencies in future wireless communication technologies. Wave propagation channel modeling for these frequencies must be considered in conjunction with measurement results. This paper presents a ray-launching (RL)-based simulation in a complex urban scenario characterized by an undulating terrain with a high density of trees. The simulation results tend to closely match the reported measurements when more details are considered. This underscores the benefits of using the RL method, which provides detailed space-time and angle-delay results., Comment: 6 pages, 7 figures, published on 2024 International Conference on Electromagnetics in Advanced Applications (ICEAA 2024)

Operational contextuality forms a rapidly developing subfield of quantum information theory. However, the characterization of the quantum mechanical entities that fuel the phenomenon has remained unknown with many partial results existing. Here, we present a resolution to this problem by connecting operational contextuality one-to-one with the no-broadcasting theorem. The connection works both on the level of full quantum theory and subtheories thereof. We demonstrate the connection in various relevant cases, showing especially that for quantum states the possibility of demonstrating contextuality is exactly characterized by non-commutativity, and for measurements this is done by a norm-1 property closely related to repeatability. Moreover, we show how techniques from broadcasting can be used to simplify known foundational results in contextuality.

Redescription mining is a data analysis technique that has found applications in diverse fields. The most used redescription mining approaches involve two phases: finding matching pairs among data attributes and extending the pairs. This process is relatively efficient when the number of attributes remains limited and when the attributes are Boolean, but becomes almost intractable when the data consist of many numerical attributes. In this paper, we present new algorithms that perform the matching and extension orders of magnitude faster than the existing approaches. Our algorithms are based on locality-sensitive hashing with a tailored approach to handle the discretisation of numerical attributes as used in redescription mining., Comment: 20 pages, 4 figures, to appear at ECML-PKDD 2024

The real estate market shows an inherent connection to space. Real estate agencies unevenly operate and specialize across space, price and type of properties, thereby segmenting the market into submarkets. We introduce here a methodology based on multipartite networks to detect the spatial segmentation emerging from data on housing online listings. Considering the spatial information of the listings, we build a bipartite network that connects agencies and spatial units. This bipartite network is projected into a network of spatial units, whose connections account for similarities in the agency ecosystem. We then apply clustering methods to this network to segment markets into spatially-coherent regions, which are found to be robust across different clustering detection algorithms, discretization of space and spatial scales, and across countries with case studies in France and Spain. This methodology addresses the long-standing issue of housing market segmentation, relevant in disciplines such as urban studies and spatial economics, and with implications for policymaking., Comment: 9 pages, 10 figures

This paper is devoted to the estimation of the Lipschitz constant of neural networks using semidefinite programming. For this purpose, we interpret neural networks as time-varying dynamical systems, where the $k$-th layer corresponds to the dynamics at time $k$. A key novelty with respect to prior work is that we use this interpretation to exploit the series interconnection structure of neural networks with a dynamic programming recursion. Nonlinearities, such as activation functions and nonlinear pooling layers, are handled with integral quadratic constraints. If the neural network contains signal processing layers (convolutional or state space model layers), we realize them as 1-D/2-D/N-D systems and exploit this structure as well. We distinguish ourselves from related work on Lipschitz constant estimation by more extensive structure exploitation (scalability) and a generalization to a large class of common neural network architectures. To show the versatility and computational advantages of our method, we apply it to different neural network architectures trained on MNIST and CIFAR-10.

Massive stars rotate faster, on average, than lower mass stars. Stellar rotation triggers hydrodynamical instabilities which transport angular momentum and chemical species from the core to the surface. Models of high-mass stars that include these processes predict that chemical mixing is stronger at lower metallicity. We aim to test this prediction by comparing the surface abundances of massive stars at different metallicities. We performed a spectroscopic analysis of single O stars in the Magellanic Clouds (MCs) based on the ULLYSES and XshootU surveys. We determined the fundamental parameters and helium, carbon, nitrogen, and oxygen surface abundances of 17 LMC and 17 SMC non-supergiant O6-9.5 stars. We complemented these determinations by literature results for additional MCs and also Galactic stars to increase the sample size and metallicity coverage. We investigated the differences in the surface chemical enrichment at different metallicities and compared them with predictions of three sets of evolutionary models. Surface abundances are consistent with CNO-cycle nucleosynthesis. The maximum surface nitrogen enrichment is stronger in MC stars than in Galactic stars. Nitrogen enrichment is also observed in stars with higher surface gravities in the SMC than in the Galaxy. This trend is predicted by models that incorporate chemical transport caused by stellar rotation. The distributions of projected rotational velocities in our samples are likely biased towards slow rotators. A metallicity dependence of surface abundances is demonstrated. The analysis of larger samples with an unbiased distribution of projected rotational velocities is required to better constrain the treatment of chemical mixing and angular momentum transport in massive single and binary stars., Comment: 15 pages + appendix. Accepted in Astronomy & Astrophysics

Textual domain is a crucial property within the Natural Language Processing (NLP) community due to its effects on downstream model performance. The concept itself is, however, loosely defined and, in practice, refers to any non-typological property, such as genre, topic, medium or style of a document. We investigate the core notion of domains via human proficiency in identifying related intrinsic textual properties, specifically the concepts of genre (communicative purpose) and topic (subject matter). We publish our annotations in *TGeGUM*: A collection of 9.1k sentences from the GUM dataset (Zeldes, 2017) with single sentence and larger context (i.e., prose) annotations for one of 11 genres (source type), and its topic/subtopic as per the Dewey Decimal library classification system (Dewey, 1979), consisting of 10/100 hierarchical topics of increased granularity. Each instance is annotated by three annotators, for a total of 32.7k annotations, allowing us to examine the level of human disagreement and the relative difficulty of each annotation task. With a Fleiss' kappa of at most 0.53 on the sentence level and 0.66 at the prose level, it is evident that despite the ubiquity of domains in NLP, there is little human consensus on how to define them. By training classifiers to perform the same task, we find that this uncertainty also extends to NLP models., Comment: Accepted at LREC-COLING 2024

From the perspective of control theory, convolutional layers (of neural networks) are 2-D (or N-D) linear time-invariant dynamical systems. The usual representation of convolutional layers by the convolution kernel corresponds to the representation of a dynamical system by its impulse response. However, many analysis tools from control theory, e.g., involving linear matrix inequalities, require a state space representation. For this reason, we explicitly provide a state space representation of the Roesser type for 2-D convolutional layers with $c_\mathrm{in}r_1 + c_\mathrm{out}r_2$ states, where $c_\mathrm{in}$/$c_\mathrm{out}$ is the number of input/output channels of the layer and $r_1$/$r_2$ characterizes the width/length of the convolution kernel. This representation is shown to be minimal for $c_\mathrm{in} = c_\mathrm{out}$. We further construct state space representations for dilated, strided, and N-D convolutions.

As quantum computing hardware becomes more complex with ongoing design innovations and growing capabilities, the quantum computing community needs increasingly powerful techniques for fabrication failure root-cause analysis. This is especially true for trapped-ion quantum computing. As trapped-ion quantum computing aims to scale to thousands of ions, the electrode numbers are growing to several hundred with likely integrated-photonic components also adding to the electrical and fabrication complexity, making faults even harder to locate. In this work, we used a high-resolution quantum magnetic imaging technique, based on nitrogen-vacancy (NV) centers in diamond, to investigate short-circuit faults in an ion trap chip. We imaged currents from these short-circuit faults to ground and compared to intentionally-created faults, finding that the root-cause of the faults was failures in the on-chip trench capacitors. This work, where we exploited the performance advantages of a quantum magnetic sensing technique to troubleshoot a piece of quantum computing hardware, is a unique example of the evolving synergy between emerging quantum technologies to achieve capabilities that were previously inaccessible., Comment: 8 pages main text; 2 pages supplement

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

It is known that strongly involutive polyhedra are closely related to self-dual maps where the antipodal function acts as duality isomorphism. Such a family of polyhedra appears in different combinatorial, topological and geometric contexts, and is thus attractive to be studied. In this note, we determine the 10 antipodal pairings among the classification of the 24 self-dual pairings $Dual(G)\rhd Aut(G)$ of self-dual maps G. We also present the orbifold associated to each antipodal pairing and describe explicitly the corresponding fundamental regions. We finally explain how to construct two infinite families of strongly involutive polyhedra (one of them new) by using their doodles and the action of the corresponding orbifolds., Comment: 16 pages, 21 figures, 1 table

Recently, semidefinite programming (SDP) techniques have shown great promise in providing accurate Lipschitz bounds for neural networks. Specifically, the LipSDP approach (Fazlyab et al., 2019) has received much attention and provides the least conservative Lipschitz upper bounds that can be computed with polynomial time guarantees. However, one main restriction of LipSDP is that its formulation requires the activation functions to be slope-restricted on $[0,1]$, preventing its further use for more general activation functions such as GroupSort, MaxMin, and Householder. One can rewrite MaxMin activations for example as residual ReLU networks. However, a direct application of LipSDP to the resultant residual ReLU networks is conservative and even fails in recovering the well-known fact that the MaxMin activation is 1-Lipschitz. Our paper bridges this gap and extends LipSDP beyond slope-restricted activation functions. To this end, we provide novel quadratic constraints for GroupSort, MaxMin, and Householder activations via leveraging their underlying properties such as sum preservation. Our proposed analysis is general and provides a unified approach for estimating $\ell_2$ and $\ell_\infty$ Lipschitz bounds for a rich class of neural network architectures, including non-residual and residual neural networks and implicit models, with GroupSort, MaxMin, and Householder activations. Finally, we illustrate the utility of our approach with a variety of experiments and show that our proposed SDPs generate less conservative Lipschitz bounds in comparison to existing approaches., Comment: accepted as a conference paper at ICLR 2024

Model Predictive Control (MPC) can be applied to safety-critical control problems, providing closed-loop safety and performance guarantees. Implementation of MPC controllers requires solving an optimization problem at every sampling instant, which is challenging to execute on embedded hardware. To address this challenge, we propose a framework that combines a tightened soft constrained MPC formulation with supervised learning to approximate the MPC value function. This combination enables us to obtain a corresponding optimal control law, which can be implemented efficiently on embedded platforms. The framework ensures stability and constraint satisfaction for various nonlinear systems. While the design effort is similar to that of nominal MPC, the proposed formulation provides input-to-state stability (ISS) with respect to the approximation error of the value function. Furthermore, we prove that the value function corresponding to the soft constrained MPC problem is Lipschitz continuous for Lipschitz continuous systems, even if the optimal control law may be discontinuous. This serves two purposes: First, it allows to relate approximation errors to a sufficiently large constraint tightening to obtain constraint satisfaction guarantees. Second, it paves the way for an efficient supervised learning procedure to obtain a continuous value function approximation. We demonstrate the effectiveness of the method using a nonlinear numerical example.

Accurate determination of mass-loss rates from massive stars is important to understanding stellar and galactic evolution and enrichment of the interstellar medium. Large-scale structure and variability in stellar winds have significant effects on mass-loss rates. Time-series observations provide direct quantification of such variability. Observations of this nature are available for some Galactic early supergiant stars but not yet for stars in lower metallicity environments such as the Magellanic Clouds. We utilise ultraviolet spectra from the Hubble Space Telescope ULLYSES program to demonstrate that the presence of structure in stellar winds of supergiant stars at low metallicities may be discerned from single-epoch spectra. We find evidence that, for given stellar luminosities and mean stellar wind optical depths, structure is more prevalent at higher metallicities. We confirm, at Large Magellanic Cloud (0.5 Z_solar), Small Magellanic Cloud (0.2 Z_solar) and lower (0.14 -- 0.1 Z_solar) metallicities, earlier Galactic results that there does not appear to be correlation between the degree of structure in stellar winds of massive stars and stellar effective temperature. Similar lack of correlation is found with regard to terminal velocity of stellar winds. Additional and revised values for radial velocities of stars and terminal velocities of stellar winds are presented. Direct evidence of temporal variability, on timescales of several days, in stellar wind at low metallicity is found. We illustrate that narrow absorption components in wind-formed profiles of Galactic OB stellar spectra remain common in early B supergiant spectra at low metallicities, providing means for better constraining hot, massive star mass-loss rates., Comment: 18 pages plus Appendices, 9 figures plus 37 figures in Appendices. Accepted by MNRAS

We generalize the notion of joint measurability to continuous variable systems by extending a recently introduced compression algorithm of quantum measurements to this realm. The extension results in a property that asks for the minimal dimensional quantum system required for representing a given set of quantum measurements. To illustrate the concept, we show that the canonical pair of position and momentum is completely incompressible. We translate the concept of measurement compression to the realm of quantum correlations, where it results in a generalisation of continuous variable quantum steering. In contrast to the steering scenario, which detects entanglement, the generalisation detects the dimensionality of entanglement. We illustrate the bridge between the concepts by showing that an analogue of the original EPR argument is genuinely infinite-dimensional with respect to our figure of merit, and that a fundamental discrete variable result on preparability of unsteerable state assemblages with separable states does not directly carry over to the continuous variable setting. We further prove a representation result for partially entanglement breaking channels that can be of independent interest.