Search

Spin engineering of advanced pulse sequences has had a transformative impact on the development of nuclear magnetic resonance (NMR) spectroscopy, to an extending degree also electron paramagnetic resonance (EPR), and the hybrid between the two, dynamic nuclear polarization (DNP). Based on a simple formalism, we demonstrate that (i) single-crystal static-sample optimisations may tremendously ease design of experiments for rotating powders and (ii) pulse sequences may readily be exchanged between these distinct spectroscopies. Specifically, we design broadband heteronuclear solid-state NMR magic-angle-spinning (MAS) dipolar recoupling experiments based on the recently developed PLATO (PoLarizAtion Transfer via non-linear Optimization) microwave (MW) pulse sequence optimized on a single crystal for powder static-sample DNP. Using this concept, we demonstrate design of ultra-broadband 13C-15N and 2H-13C cross-polarization experiments, using PLATO on the 13C radio-frequency (RF) channel and square/ramped or RESPIRATION (Rotor Echo Short Pulse IRrAdiaTION) RF irradiation on the 15N and 2H RF channels, respectively.

Dielectric structures can support low-absorption optical modes, which are attractive for engineering light-matter interactions with excitonic resonances in two-dimensional (2D) materials. However, the coupling strength is often limited by the electromagnetic field being confined inside the dielectric, reducing spatial overlap with the active excitonic material. Here, we demonstrate a scheme for enhanced light-matter coupling by embedding excitonic tungsten disulfide (WS$_2$) within dielectric hexagonal boron nitride (hBN), forming a van der Waals (vdW) heterostructure that optimizes the field overlap and alignment between excitons and optical waveguide modes. To tailor diffractive coupling between free-space light and the waveguide modes in the vdW heterostructure, we fabricate Fourier surfaces in the top hBN layer using thermal scanning-probe lithography and etching, producing sinusoidal topographic landscapes with nanometer precision. We observe the formation of exciton-polaritons with a Rabi splitting indicating that the system is at the onset of strong coupling. These results demonstrate the potential of Fourier-tailored vdW heterostructures for exploring advanced optoelectronic and quantum devices.

The ability of tumors to evolve and adapt by developing subclones in different genetic and epigenetic states is a major challenge in oncology. Traditional tools like multi-regional sequencing used to study tumor evolution and the resultant intra-tumor heterogeneity (ITH) are often impractical because of their resource-intensiveness and limited scalability. Here, we present MorphoITH, a novel framework that leverages histopathology slides to deconvolve molecular ITH through tissue morphology. MorphoITH integrates a self-supervised deep learning similarity measure to capture phenotypic variation across multiple dimensions (cytology, architecture, and microenvironment) with rigorous methods to eliminate spurious sources of variation. Using a prototype of ITH, clear cell renal cell carcinoma (ccRCC), we show that MorphoITH captures clinically-significant biological features, such as vascular architecture and nuclear grades. Furthermore, we find that MorphoITH recognizes differential biological states corresponding to subclonal changes in key driver genes (BAP1/PBRM1/SETD2). Finally, by applying MorphoITH to a multi-regional sequencing experiment, we postulate evolutionary trajectories that largely recapitulate genetic evolution. In summary, MorphoITH provides a scalable phenotypic lens that bridges the gap between histopathology and genomics, advancing precision oncology.

This paper considers a class of multi-objective optimization problems known as Minkowski sum problems. Minkowski sum problems have a decomposable structure, where the global nondominated (Pareto) set corresponds to the Minkowski sum of several local nondominated sets. In some cases, the vectors of local sets does not contribute to the generation of the global nondominated set, and may therefore lead to wasted computational efforts. Therefore, we investigate theoretical properties of both necessary and redundant vectors, and propose an algorithm based on bounding sets for identifying unnecessary local vectors. We conduct extensive numerical experiments to test the the impact of varying characteristics of the instances on the resulting global nondominated set and the number of redundant vectors.

The occurrences of cyber attacks on the power grids have been increasing every year, with novel attack techniques emerging every year. In this paper, we address the critical challenge of detecting novel/zero-day attacks in digital substations that employ the IEC-61850 communication protocol. While many heuristic and machine learning (ML)-based methods have been proposed for attack detection in IEC-61850 digital substations, generalization to novel or zero-day attacks remains challenging. We propose an approach that leverages the in-context learning (ICL) capability of the transformer architecture, the fundamental building block of large language models. The ICL approach enables the model to detect zero-day attacks and learn from a few examples of that attack without explicit retraining. Our experiments on the IEC-61850 dataset demonstrate that the proposed method achieves more than $85\%$ detection accuracy on zero-day attacks while the existing state-of-the-art baselines fail. This work paves the way for building more secure and resilient digital substations of the future.

The "imaging-by-smashing" technique has been developed recently in relativistic nuclear collisions. By smashing heavy nuclei at RHIC and the LHC and analyzing the anisotropic expansion (flow) of the final state produced particles, unique information on the structure of the collided nuclei has been obtained. Existing efforts primarily focus on the colliding mode of heavy nuclei collisions. In contrast, nuclear structure studies with collisions of light nuclei and the fixed target mode, despite their significant impact and broad interest, have not been thoroughly explored. In this Letter, we investigate the $\alpha$-clustering signature of $^{20}$Ne and $^{16}$O in the fixed-target $^{208}$Pb--$^{20}$Ne and $^{208}$Pb--$^{16}$O collisions at $\sqrt{s_{_\mathrm{NN}}}$ = 68.5 GeV, using the parton transport model AMPT. The results of two- and four-particle cumulants of anisotropic flow demonstrate a robust $\alpha$-clustering signature that persists regardless of the complex dynamic evolution of the created systems. This study highlights the significant impact of the LHCb SMOG (SMOG2) project in discovering the $\alpha$-clustering signature of light nuclei at relativistic energies., Comment: 7 pages, 3 figures

Multi-modal models require aligned, shared embedding spaces. However, common CLIP-based approaches need large amounts of samples and do not natively support 3D or tabular data, both of which are crucial in the medical domain. To address these issues, we revisit CLIP-style alignment by training a domain-specific 3D foundation model as an image encoder and demonstrate that modality alignment is feasible with only 62 MRI scans. Our approach is enabled by a simple embedding accumulation strategy required for training in 3D, which scales the amount of negative pairs across batches in order to stabilize training. We perform a thorough evaluation of various design choices, including the choice of backbone and loss functions, and evaluate the proposed methodology on zero-shot classification and image-retrieval tasks. While zero-shot image-retrieval remains challenging, zero-shot classification results demonstrate that the proposed approach can meaningfully align the representations of 3D MRI with tabular data., Comment: 10 pages, 2 figures. To be published in ISBI 2025

Ultra-thin two-dimensional (2D) materials have gained significant attention for making next-generation optoelectronic devices. Here, we report a large-area heterojunction photodetector fabricated using a liquid metal-printed 2D $\text{SnO}_2$ layer transferred onto CdTe thin films. The resulting device demonstrates efficient broadband light sensing from visible to near-infrared wavelengths, with enhanced detectivity and faster photo response than bare CdTe photodetectors. Significantly, the device shows a nearly $10^5$-fold increase in current than the dark current level when illuminated with a 780 nm laser and achieves a specific detectivity of around $10^{12} \, \text{Jones}$, nearly two orders of magnitude higher than a device with pure CdTe thin film. Additionally, temperature-dependent optoelectronic testing shows that the device maintains a stable response up to $140^\circ \text{C}$ and generates distinctive photocurrent at temperatures up to $80^\circ \text{C}$, demonstrating its thermal stability. Using band structure analysis, density functional theory (DFT) calculations, and photocurrent mapping, the formation of a $p$-$n$ junction is indicated, contributing to the enhanced photo response attributed to the efficient carrier separation by the built-in potential in the hetero-junction and the superior electron mobility of 2D $\text{SnO}_2$. Our results highlight the effectiveness of integrating liquid metal-exfoliated 2D materials for enhanced photodetector performance.

This paper describes the synthesis of the room acoustics challenge as a part of the generative data augmentation workshop at ICASSP 2025. The challenge defines a unique generative task that is designed to improve the quantity and diversity of the room impulse responses dataset so that it can be used for spatially sensitive downstream tasks: speaker distance estimation. The challenge identifies the technical difficulty in measuring or simulating many rooms' acoustic characteristics precisely. As a solution, it proposes generative data augmentation as an alternative that can potentially be used to improve various downstream tasks. The challenge website, dataset, and evaluation code are available at https://sites.google.com/view/genda2025., Comment: Accepted to the Workshop on Generative Data Augmentation at ICASSP 2025. Challenge website: https://sites.google.com/view/genda2025

X-ray observations of the optically selected $z=6.025$ QSO CFHQS J164121+375520 (hereafter J1641) revealed that its flux dropped by a factor $\gtrsim7$ from 2018, when it was a bright and soft X-ray source, to 2021. Such a strong variability amplitude has not been observed before among $z>6$ QSOs, and the underlying physical mechanism was unclear. We carried out a new X-ray and rest-frame UV monitoring campaign of J1641 over 2022-2024. We detected J1641 with Chandra in the 2-7 keV band, while no significant emission is detected at softer X-ray energies, making J1641 an X-ray changing look QSO at $z>6$. Comparing with the 2018 epoch, the 0.5-2 keV flux dropped dramatically by a factor $>20$. We ascribe this behaviour to intervening, and still ongoing, obscuration by Compton-thick gas intercepting our line of sight between 2018 and 2021. The screening material could be an inner disk or a failed nuclear wind that increased their thickness. Another possibility is that we have witnessed an occultation event due to dust-free clouds located at sub-pc/pc scales, similar to those recently invoked to explain the remarkable X-ray weakness of AGN discovered by JWST. These interpretations are also consistent with the lack of strong variations of the QSO rest-frame UV lightcurve over the same period. Future monitoring of J1641 and the possible discovery of other X-ray changing look QSOs at $z>6$ will provide us with precious information about the physics of rapid supermassive black-hole growth at high redshift., Comment: Accepted for publication on A&A Letters

Secure and reliable certification of quantum resources is a fundamental challenge in the advancement of next-generation quantum technologies, particularly as devices become more complex and integrated into practical applications, where parts of the system may be untrusted or inaccessible by users. Addressing this challenge requires certification methods that rely on minimal assumptions and limited trust while still faithfully and reliably verifying the quantum resources in question. For infinite-dimensional bosonic systems, existing certification methods rely on fully trusted and well calibrated measurement systems, leaving significant security vulnerabilities open. In this work, we present the first experimental demonstration of measurement-device-independent (MDI) certification schemes for infinite-dimensional bosonic systems, where the certification process is conducted using entirely untrusted measurement devices and assuming only the trusted preparation of coherent states. Specifically, we implement schemes for the MDI certification of continuous-variable (CV) entanglement and the operation of an elementary optical CV quantum memory. We leverage techniques of Bayesian metrology and exploit the practicality and accessibility of Gaussian quantum optics to achieve secure and efficient certification. These results demonstrate the potential of the MDI framework to enhance trust in quantum technologies with applications in quantum communication and quantum computing., Comment: 22 pages, 12 figures

We present results from fitting $p$-mode spectra derived from 7-d segments of Sun-as-a-star helioseismic observations from the Birmingham Solar Oscillations Network covering 32 yr. The results show a clear dependence of the mode frequencies on solar activity, and the frequency dependence of the sensitivity to activity can also be seen. Because we use data segments that cover less than half of a solar rotation, we are able to test for the effect of activity on the solar far side. By fitting with a model that takes into account activity on the far side of the Sun, we show that the frequency shifts are sensitive to activity from the whole Sun, not just the side facing the observer. Our results suggest that there is potential to investigate activity-related asteroseismic frequency shifts in solar-like oscillators using short time series of observations., Comment: 6 pages, 7 figures. Accepted by MNRAS 14-Jan-25

For any arc-weighted oriented graph $D=(V(D), A(D),w)$, we write ${\rm fas}_w(D)$ to denote the minimum weight of a feedback arc set in $D$. In this paper, we consider upper bounds on ${\rm fas}_w(D)$ for arc-weight oriented graphs $D$ with bounded maximum degrees and directed girth. We obtain such bounds by introducing a new parameter ${\rm fasd}(D)$, which is the maximum integer such that $A(D)$ can be partitioned into ${\rm fasd}(D)$ feedback arc sets. This new parameter seems to be interesting in its own right. We obtain several bounds for both ${\rm fas}_w(D)$ and ${\rm fasd}(D)$ when $D$ has maximum degree $\Delta(D)\le \Delta$ and directed girth $g(D)\geq g$. In particular, we show that if $\Delta(D)\leq~4$ and $g(D)\geq 3$, then ${\rm fasd}(D) \geq 3$ and therefore ${\rm fas}_w(D)\leq \frac{w(D)}{3}$ which generalizes a tight bound for an unweighted oriented graph with maximum degree at most 4. We also show that ${\rm fasd}(D)\geq g$ and ${\rm fas}_w(D) \leq \frac{w(D)}{g}$ if $\Delta(D)\leq 3$ and $g(D)\geq g$ for $g\in \{3,4,5\}$ and these bounds are tight. However, for $g=10$ the bound ${\rm fasd}(D)\geq g$ does not always hold when $\Delta(D)\leq 3$. Finally we give some bounds for the cases when $\Delta$ or $g$ are large.

We construct a Euclidean domain with no multiplicative Euclidean norm to a compatibly well-ordered monoid, and hence with no multiplicative Euclidean norm to $\mathbb{R}$ (under its usual order). A key step in the proof is showing that the UFD property is preserved when adjoining a free factorization.

We report the discovery and characterization of two sub-Saturns from the Transiting Exoplanet Survey Satellite (\textit{TESS}) using high-resolution spectroscopic observations from the MaHPS spectrograph at the Wendelstein Observatory and the SOPHIE spectrograph at the Haute-Provence Observatory. Combining photometry from TESS, KeplerCam, LCOGT, and MuSCAT2 with the radial velocity measurements from MaHPS and SOPHIE we measure precise radii and masses for both planets. TOI-5108 b is a sub-Saturn with a radius of $6.6 \pm 0.1$ $R_\oplus$ and a mass of $32 \pm 5$ $M_\oplus$. TOI-5786 b is similar to Saturn with a radius of $8.54 \pm 0.13$ $R_\oplus$ and a mass of $73 \pm 9$ $M_\oplus$. The host star for TOI-5108 b is a moderately bright (Vmag 9.75) G-type star. TOI-5786 is a slightly dimmer (Vmag 10.2) F-type star. Both planets are close to their host stars with periods of 6.75 days and 12.78 days respectively. This puts TOI-5108 b just inside the bounds of the Neptune desert while TOI-5786 b is right above the upper edge. We estimate hydrogen-helium envelope mass fractions of $38 \%$ for TOI-5108 b and $74 \% $ for TOI-5786 b. However, using a model for the interior structure that includes tidal effects the envelope fraction of TOI-5108 b could be much lower ($\sim 20\,\%$) depending on the obliquity. We estimate mass-loss rates between 1.0 * $10^9$ g/s and 9.8 * $10^9$ g/s for TOI-5108 b and between 3.6 * $10^8$ g/s and 3.5 * $10^9$ g/s for TOI-5786 b. Given their masses, this means that both planets are stable against photoevaporation. We also detect a transit signal for a second planet candidate in the TESS data of TOI-5786 with a period of 6.998 days and a radius of $3.83 \pm 0.16$ $R_\oplus$. Using our RV data and photodynamical modeling, we are able to provide a 3-$\sigma$ upper limit of 26.5 $M_\oplus$ for the mass of the potential inner companion to TOI-5786 b., Comment: 25 pages, 13 figures. Accepted for publication in A&A

Continuous gravitational waves (CWs) emission from neutron stars carries information about their internal structure and equation of state, and it can provide tests of General Relativity. We present a search for CWs from a set of 45 known pulsars in the first part of the fourth LIGO--Virgo--KAGRA observing run, known as O4a. We conducted a targeted search for each pulsar using three independent analysis methods considering the single-harmonic and the dual-harmonic emission models. We find no evidence of a CW signal in O4a data for both models and set upper limits on the signal amplitude and on the ellipticity, which quantifies the asymmetry in the neutron star mass distribution. For the single-harmonic emission model, 29 targets have the upper limit on the amplitude below the theoretical spin-down limit. The lowest upper limit on the amplitude is $6.4\!\times\!10^{-27}$ for the young energetic pulsar J0537-6910, while the lowest constraint on the ellipticity is $8.8\!\times\!10^{-9}$ for the bright nearby millisecond pulsar J0437-4715. Additionally, for a subset of 16 targets we performed a narrowband search that is more robust regarding the emission model, with no evidence of a signal. We also found no evidence of non-standard polarizations as predicted by the Brans-Dicke theory., Comment: main paper: 12 pages, 6 figures, 4 tables

Seagrass meadows play a crucial role in marine ecosystems, providing important services such as carbon sequestration, water quality improvement, and habitat provision. Monitoring the distribution and abundance of seagrass is essential for environmental impact assessments and conservation efforts. However, the current manual methods of analyzing underwater video transects to assess seagrass coverage are time-consuming and subjective. This work explores the use of deep learning models to automate the process of seagrass detection and coverage estimation from underwater video data. A dataset of over 8,300 annotated underwater images was created, and several deep learning architectures, including ResNet, InceptionNetV3, DenseNet, and Vision Transformer, were evaluated for the task of binary classification of ``Eelgrass Present'' and ``Eelgrass Absent'' images. The results demonstrate that deep learning models, particularly the Vision Transformer, can achieve high performance in predicting eelgrass presence, with AUROC scores exceeding 0.95 on the final test dataset. The use of transfer learning and the application of the Deep WaveNet underwater image enhancement model further improved the models' capabilities. The proposed methodology allows for the efficient processing of large volumes of video data, enabling the acquisition of much more detailed information on seagrass distributions compared to current manual methods. This information is crucial for environmental impact assessments and monitoring programs, as seagrasses are important indicators of coastal ecosystem health. Overall, this project demonstrates the value that deep learning can bring to the field of marine ecology and environmental monitoring.

Nanolasers operating at low power levels are strongly affected by intrinsic quantum noise, affecting both intensity fluctuations and laser coherence. Starting from semi-classical rate equations and making a simple hypothesis for the phase of the laser field, a simple stochastic model for the laser quantum noise is suggested. The model is shown to agree quantitatively with quantum master equations for microscopic lasers with a small number of emitters and with classical Langevin equations for macroscopic systems. In contrast, neither quantum master equations nor classical Langevin equations adequately address the mesoscopic regime. The stochastic approach is used to calculate the linewidth throughout the transition to lasing, where the linewidth changes from being dominated by the particle-like nature of photons below threshold to the wave-like nature above threshold, where it is strongly influenced by index fluctuations enhancing the linewidth., Comment: 6 pages, 4 figures. Submitted for PRL

In this paper, we introduce a numerical framework designed to solve problems within the emerging field of Waveguide Quantum Electrodynamics (WQED). The framework is based on collision quantum optics, where a localized quantum system interacts sequentially with individual time-bin modes. This approach provides a physically intuitive model that allows researchers familiar with tools such as QuTiP in Python, Quantum Optics Toolbox for Matlab, or QuantumOptics.jl in Julia to efficiently set up and execute WQED simulations. Despite its conceptual simplicity, we demonstrate the framework's robust ability to handle complex WQED scenarios. These applications include the scattering of single- or two-photon pulses by quantum emitters or cavities, as well as the exploration of non-Markovian dynamics, where emitted photons are reflected back, thereby introducing feedback mechanisms., Comment: 14 pages, 7 figures

Video streaming services depend on the underlying communication infrastructure and available network resources to offer ultra-low latency, high-quality content delivery. Open Radio Access Network (ORAN) provides a dynamic, programmable, and flexible RAN architecture that can be configured to support the requirements of time-critical applications. This work considers a setup in which the constrained network resources are supplemented by \gls{GAI} and \gls{MEC} {techniques} in order to reach a satisfactory video quality. Specifically, we implement a novel semantic control channel that enables \gls{MEC} to support low-latency applications by tight coupling among the ORAN xApp, \gls{MEC}, and the control channel. The proposed concepts are experimentally verified with an actual ORAN setup that supports video streaming. The performance evaluation includes the \gls{PSNR} metric and end-to-end latency. Our findings reveal that latency adjustments can yield gains in image \gls{PSNR}, underscoring the trade-off potential for optimized video quality in resource-limited environments.

The Global Carbon Budget, maintained by the Global Carbon Project, summarizes Earth's global carbon cycle through four annual time series beginning in 1959: atmospheric CO$_2$ concentrations, anthropogenic CO$_2$ emissions, and CO$_2$ uptake by land and ocean. We analyze these four time series as a multivariate (cointegrated) system. Statistical tests show that the four time series are cointegrated with rank three and identify anthropogenic CO$_2$ emissions as the single stochastic trend driving the nonstationary dynamics of the system. The three cointegrated relations correspond to the physical relations that the sinks are linearly related to atmospheric concentrations and that the change in concentrations equals emissions minus the combined uptake by land and ocean. Furthermore, likelihood ratio tests show that a parametrically restricted error-correction model that embodies these physical relations and accounts for the El Ni\~no/Southern Oscillation cannot be rejected on the data. The model can be used for both in-sample and out-of-sample analysis. In an application of the latter, we demonstrate that projections based on this model, using Shared Socioeconomic Pathways scenarios, yield results consistent with established climate science.

Let $O(p,q)$ be the orthogonal groups of signature $(p,q)$ over the reals. It is shown that an element of the commutator subgroup $O(p,q)'$ of $O(p,q)$ is bireflectional (product of 2 involutions in $O(p,q)'$) if and only if it is reversible (conjugate to its inverse). Moreover, the bireflectional elements of $O(p, q)'$ are classified., Comment: 7 pages. arXiv admin note: text overlap with arXiv:2411.01323

Ultra-light axion-like particles (ALPs) can be a viable solution to the dark matter problem. The scalar field associated with ALPs, coupled to the electromagnetic field, acts as an active birefringent medium, altering the polarisation properties of light through which it propagates. In particular, oscillations of the axionic field induce monochromatic variations of the plane of linearly polarised radiation of astrophysical signals. The radio emission of millisecond pulsars provides an excellent tool to search for such manifestations, given their high fractional linear polarisation and negligible fluctuations of their polarisation properties. We have searched for the evidence of ALPs in the polarimetry measurements of pulsars collected and preprocessed for the European Pulsar Timing Array (EPTA) campaign. Focusing on the twelve brightest sources in linear polarisation, we searched for an astrophysical signal from axions using both frequentist and Bayesian statistical frameworks. For the frequentist analysis, which uses Lomb-Scargle periodograms at its core, no statistically significant signal has been found. The model used for the Bayesian analysis has been adjusted to accommodate multiple deterministic systematics that may be present in the data. A statistically significant signal has been found in the dataset of multiple pulsars with common frequency between $10^{-8}$ Hz and $2\times10^{-8}$ Hz, which can most likely be explained by the residual Faraday rotation in the terrestrial ionosphere. Strong bounds on the coupling constant $g_{a\gamma}$, in the same ballpark as other searches, have been obtained in the mass range between $6\times10^{-24}$ eV and $5\times10^{-21}$ eV. We conclude by discussing problems that can limit the sensitivity of our search for ultra-light axions in the polarimetry data of pulsars, and possible ways to resolve them., Comment: 24 pages, 15 figures

Student dropout is a significant concern for educational institutions due to its social and economic impact, driving the need for risk prediction systems to identify at-risk students before enrollment. We explore the accuracy of such systems in the context of higher education by predicting degree completion before admission, with potential applications for prioritizing admissions decisions. Using a large-scale dataset from Danish higher education admissions, we demonstrate that advanced sequential AI models offer more precise and fair predictions compared to current practices that rely on either high school grade point averages or human judgment. These models not only improve accuracy but also outperform simpler models, even when the simpler models use protected sociodemographic attributes. Importantly, our predictions reveal how certain student profiles are better matched with specific programs and fields, suggesting potential efficiency and welfare gains in public policy. We estimate that even the use of simple AI models to guide admissions decisions, particularly in response to a newly implemented nationwide policy reducing admissions by 10 percent, could yield significant economic benefits. However, this improvement would come at the cost of reduced human oversight and lower transparency. Our findings underscore both the potential and challenges of incorporating advanced AI into educational policymaking.

Alzheimer's disease (AD) is the leading cause of dementia, and its early detection is crucial for effective intervention, yet current diagnostic methods often fall short in sensitivity and specificity. This study aims to detect significant indicators of early AD by extracting and integrating various imaging biomarkers, including radiomics, hippocampal texture descriptors, cortical thickness measurements, and deep learning features. We analyze structural magnetic resonance imaging (MRI) scans from the Alzheimer's Disease Neuroimaging Initiative (ADNI) cohorts, utilizing comprehensive image analysis and machine learning techniques. Our results show that combining multiple biomarkers significantly improves detection accuracy. Radiomics and texture features emerged as the most effective predictors for early AD, achieving AUCs of 0.88 and 0.72 for AD and MCI detection, respectively. Although deep learning features proved to be less effective than traditional approaches, incorporating age with other biomarkers notably enhanced MCI detection performance. Additionally, our findings emphasize the continued importance of classical imaging biomarkers in the face of modern deep-learning approaches, providing a robust framework for early AD diagnosis., Comment: SPIE Medical Imaging (MI25)

This research brief describes recent methodological initiatives with the Public Libraries Survey. It describes how starting with the 2022 data, Institute of Museum and Library Services (IMLS) updated geographic identifiers to better align Census Bureau geography types with the library's legal service area, with the goal of enabling data users to link PLS data with other geographical data more easily (e.g., the American Community Survey) and is assessing the feasibility of outlet-level data collection over the next year.

The recently launched James Webb Space Telescope (JWST) is opening new observing windows on the distant universe. Among JWST's instruments, the Mid Infrared Instrument (MIRI) offers the unique capability of imaging observations at wavelengths $\lambda > 5\mu$m. This enables unique access to the rest frame near infra-red (NIR, $\lambda \ge 1$\mum) emission from galaxies at redshifts $z>4$ and the visual ($\lambda \gtrsim 5000$\AA) rest frame for $z>9$. We here report on the guaranteed time observations (GTO) from the MIRI European Consortium, of the Hubble Ultra Deep Field (HUDF), forming the MIRI Deep Imaging Survey (MIDIS), consisting of an on source integration time of $\sim41$ hours in the MIRI/F560W (5.6 $\mu$m) filter. To our knowledge, this constitutes the longest single filter exposure obtained with JWST of an extragalactic field as yet., Comment: submitted to A&A on July 30, 2024

The AU Microscopii planetary system is only 24 Myr old, and its geometry may provide clues about the early dynamical history of planetary systems. Here, we present the first measurement of the Rossiter-McLaughlin effect for the warm sub-Neptune AU Mic c, using two transits observed simultaneously with the European Southern Observatory's (ESO's) Very Large Telescope (VLT)/Echelle SPectrograph for Rocky Exoplanets and Stable Spectroscopic Observations (ESPRESSO), CHaracterising ExOPlanet Satellite (CHEOPS), and Next-Generation Transit Survey (NGTS). After correcting for flares and for the magnetic activity of the host star, and accounting for transit-timing variations, we find the sky-projected spin-orbit angle of planet c to be in the range $\lambda_c=67.8_{-49.0}^{+31.7}$\,degrees (1-$\sigma$). We examine the possibility that planet c is misaligned with respect to the orbit of the inner planet b ($\lambda_b=-2.96_{-10.30}^{+10.44}$\,degrees), and the equatorial plane of the host star, and discuss scenarios that could explain both this and the planet's high density, including secular interactions with other bodies in the system or a giant impact. We note that a significantly misaligned orbit for planet c is in some degree of tension with the dynamical stability of the system, and with the fact that we see both planets in transit, though these arguments alone do not preclude such an orbit. Further observations would be highly desirable to constrain the spin-orbit angle of planet c more precisely., Comment: Accepted for publication in MNRAS

As global life expectancy increases, so does the burden of chronic diseases, yet individuals exhibit considerable variability in the rate at which they age. Identifying biomarkers that distinguish fast from slow ageing is crucial for understanding the biology of ageing, enabling early disease detection, and improving prevention strategies. Using contrastive deep learning, we show that skin biopsy images alone are sufficient to determine an individual's age. We then use visual features in histopathology slides of the skin biopsies to construct a novel biomarker of ageing. By linking with comprehensive health registers in Denmark, we demonstrate that visual features in histopathology slides of skin biopsies predict mortality and the prevalence of chronic age-related diseases. Our work highlights how routinely collected health data can provide additional value when used together with deep learning, by creating a new biomarker for ageing which can be actively used to determine mortality over time., Comment: 20 pages, 5 tables, 5 figures Under review: npj Digital Medicine

Detection and differentiation of circulating tumor cells (CTCs) and non-CTCs in blood draws of cancer patients pose multiple challenges. While the gold standard relies on tedious manual evaluation of an automatically generated selection of images, machine learning (ML) techniques offer the potential to automate these processes. However, human assessment remains indispensable when the ML system arrives at uncertain or wrong decisions due to an insufficient set of labeled training data. This study introduces a human-in-the-loop (HiL) strategy for improving ML-based CTC detection. We combine self-supervised deep learning and a conventional ML-based classifier and propose iterative targeted sampling and labeling of new unlabeled training samples by human experts. The sampling strategy is based on the classification performance of local latent space clusters. The advantages of the proposed approach compared to naive random sampling are demonstrated for liquid biopsy data from patients with metastatic breast cancer.

TOI-396 is an F6V star ($V\approx6.4$) orbited by three transiting planets. The orbital periods of the two innermost planets are close to the 5:3 commensurability ($P_b \sim3.6$ d and $P_c \sim6.0$ d). To measure the masses of the three planets, refine their radii, and investigate whether planets b and c are in MMR, we carried out HARPS RV observations and retrieved photometric data from TESS. We extracted the RVs via a skew-normal fit onto the HARPS CCFs and performed an MCMC joint analysis of the Doppler measurements and transit photometry, while employing the breakpoint method to remove stellar activity from the RV time series. We also performed a thorough TTV dynamical analysis of the system. Our analysis confirms that the three planets have similar sizes: $R_b=2.004_{-0.047}^{+0.045}R_{\oplus}$; $R_c=1.979_{-0.051}^{+0.054}R_{\oplus}$; $R_d=2.001_{-0.064}^{+0.063}R_{\oplus}$. For the first time, we have determined the RV masses for TOI-396b and d: $M_b=3.55_{-0.96}^{+0.94}M_{\oplus}$ ($\rho_b=2.44_{-0.68}^{+0.69}$ g cm$^{-3}$) and $M_d=7.1\pm1.6M_{\oplus}$ ($\rho_d=4.9_{-1.1}^{+1.2}$ g cm$^{-3}$). Our results suggest a quite unusual system architecture, with the outermost planet being the densest. The Doppler reflex motion induced by TOI-396c remains undetected in our RV time series, likely due to the proximity of $P_c$ to the star's rotation period ($P_{\mathrm{rot}}=6.7\pm1.3$ d). We also discovered that TOI-396b and c display significant TTVs. While the TTV dynamical analysis returns a formally precise mass for TOI-396c ($M_{c,\mathrm{dyn}}=2.24^{+0.13}_{-0.67}M_{\oplus}$), the result might not be accurate owing to the poor sampling of the TTV phase. We also conclude that TOI-396b and c are close to but out of the 5:3 MMR. Our numerical simulation suggests TTV semi-amplitudes of up to 5 hours over a temporal baseline of $\sim$5.2 years., Comment: 25 pages (7 in the Appendix; also available electronically), 15 Figures (2 in the Appendix), 10 Tables (5 in the Appendix). Accepted for publication in A&A

Contrastive learning has proven instrumental in learning unbiased representations of data, especially in complex environments characterized by high-cardinality and high-dimensional sensitive information. However, existing approaches within this setting require predefined modelling assumptions of bias-causing interactions that limit the model's ability to learn debiased representations. In this work, we propose a new method for fair contrastive learning that employs an attention mechanism to model bias-causing interactions, enabling the learning of a fairer and semantically richer embedding space. In particular, our attention mechanism avoids bias-causing samples that confound the model and focuses on bias-reducing samples that help learn semantically meaningful representations. We verify the advantages of our method against existing baselines in fair contrastive learning and show that our approach can significantly boost bias removal from learned representations without compromising downstream accuracy.

Empirical exoplanet mass-radius relations have been used to study the demographics and compositions of small exoplanets for many years. However, the heterogeneous nature of these measurements hinders robust statistical analysis of this population, particularly with regard to the masses of planets. For this reason, we perform a homogeneous and consistent re-analysis of the radial velocity (RV) observations of 85 small exoplanets using publicly available HARPS RV data and the fitting toolkit Pyaneti. For the entire sample, we run 12 different models to investigate the impact of modelling choices, including the use of multi-dimensional Gaussian Processes (GPs) to mitigate stellar activity. We find that the way orbital eccentricity is modelled can significantly impact the RV amplitude found in some cases. We also find that the addition of a GP to mitigate stellar activity does impact the RV amplitude found - though if the GP is modelled on activity indicators as well as the RVs the results are more robust. The RV amplitude found for every planet in our sample using all the models is made available for other groups to perform demographics studies. Finally, we provide a list of recommendations for the RV community moving forward., Comment: Accepted for publication in A&A

There is a growing need to understand how digital systems can support clinical decision-making, particularly as artificial intelligence (AI) models become increasingly complex and less human-interpretable. This complexity raises concerns about trustworthiness, impacting safe and effective adoption of such technologies. Improved understanding of decision-making processes and requirements for explanations coming from decision support tools is a vital component in providing effective explainable solutions. This is particularly relevant in the data-intensive, fast-paced environments of intensive care units (ICUs). To explore these issues, group interviews were conducted with seven ICU clinicians, representing various roles and experience levels. Thematic analysis revealed three core themes: (T1) ICU decision-making relies on a wide range of factors, (T2) the complexity of patient state is challenging for shared decision-making, and (T3) requirements and capabilities of AI decision support systems. We include design recommendations from clinical input, providing insights to inform future AI systems for intensive care.

Bristled and membranous insect wings have co-evolved despite apparently serving the same functionality. We emulate flight physics using an automated free-fall experiment to better understand how and why several distinct wing forms may have developed. Biomimetic two-dimensional lattice animals were laser cut from a continuous sheet of paper, and their descent in a settling tank was tracked using a camera. Data from 31 generic symmetric polyominos (1,692 experiments) reveal that morphology impacts the drag coefficient $C_D$ and hence flight efficiency. Some polyominos rapidly sediment while others remain suspended for longer. Positioning the search for an optimal shape within an evolutionary context, we relinquished control of the automated setup to a genetic algorithm. Hereditary information passes between generations in proportion to fitness and is augmented by rare mutations. High-performing morphologies are observed, but {experimental repeats do} not re-evolve the same shapes. Adaptation rates also differ if the selective pressure is reversed from suspension to sedimentation. Our data (50,086 experiments) reveal several physical sources of indeterminism in the simulated natural selection process, including fluid flow variability and morphological entropy. This provides experimental support for the idea that the lack of convergence in insect wing shape may partly reflect the competition between fitness and entropy, making it nearly impossible to achieve unique optimal forms., Comment: Corrected author list

Several initiatives have been undertaken to conceptually model the domain of scholarly data using ontologies and to create respective Knowledge Graphs. Yet, the full potential seems unleashed, as automated means for automatic population of said ontologies are lacking, and respective initiatives from the Semantic Web community are not necessarily connected: we propose to make scholarly data more sustainably accessible by leveraging Wikidata's infrastructure and automating its population in a sustainable manner through LLMs by tapping into unstructured sources like conference Web sites and proceedings texts as well as already existing structured conference datasets. While an initial analysis shows that Semantic Web conferences are only minimally represented in Wikidata, we argue that our methodology can help to populate, evolve and maintain scholarly data as a community within Wikidata. Our main contributions include (a) an analysis of ontologies for representing scholarly data to identify gaps and relevant entities/properties in Wikidata, (b) semi-automated extraction -- requiring (minimal) manual validation -- of conference metadata (e.g., acceptance rates, organizer roles, programme committee members, best paper awards, keynotes, and sponsors) from websites and proceedings texts using LLMs. Finally, we discuss (c) extensions to visualization tools in the Wikidata context for data exploration of the generated scholarly data. Our study focuses on data from 105 Semantic Web-related conferences and extends/adds more than 6000 entities in Wikidata. It is important to note that the method can be more generally applicable beyond Semantic Web-related conferences for enhancing Wikidata's utility as a comprehensive scholarly resource. Source Repository: https://github.com/scholarly-wikidata/ DOI: https://doi.org/10.5281/zenodo.10989709 License: Creative Commons CC0 (Data), MIT (Code), Comment: 17 pages, accepted at EKAW-24

In the last few decades planet search surveys have been focusing on solar type stars, and only recently the high-mass regimes. This is mostly due to challenges arising from the lack of instrumental precision, and more importantly, the inherent active nature of fast rotating massive stars. Here we report NGTS-33b (TOI-6442b), a super-Jupiter planet with mass, radius and orbital period of 3.6 $\pm$ 0.3 M$_{\rm jup}$, 1.64 $\pm$ 0.07 R$_{\rm jup}$ and $2.827972 \pm 0.000001$ days, respectively. The host is a fast rotating ($0.6654 \pm 0.0006$ day) and hot (T$_{\rm eff}$ = 7437 $\pm$ 72 K) A9V type star, with a mass and radius of 1.60 $\pm$ 0.11 M$_{\odot}$ and 1.47 $\pm$ 0.06 R$_{\odot}$, respectively. Planet structure and Gyrochronology models shows that NGTS-33 is also very young with age limits of 10-50 Myr. In addition, membership analysis points towards the star being part of the Vela OB2 association, which has an age of $\sim$ 20-35 Myr, thus providing further evidences about the young nature of NGTS-33. Its low bulk density of 0.19$\pm$0.03 g cm$^{-3}$ is 13$\%$ smaller than expected when compared to transiting hot Jupiters with similar masses. Such cannot be solely explained by its age, where an up to 15$\%$ inflated atmosphere is expected from planet structure models. Finally, we found that its emission spectroscopy metric is similar to JWST community targets, making the planet an interesting target for atmospheric follow-up. Therefore, NGTS-33b's discovery will not only add to the scarce population of young, massive and hot Jupiters, but will also help place further strong constraints on current formation and evolution models for such planetary systems., Comment: 19 pages, 17 figures, 7 tables, accepted for publication in MNRAS

Contemporary machine learning models, such as language models, are powerful, but come with immense resource requirements both at training and inference time. It has been shown that decoder-only language models can be trained to a competitive state with ternary weights (1.58 bits per weight), facilitating efficient inference. Here, we start our exploration with non-transformer model architectures, investigating 1.58-bit training for multi-layer perceptrons and graph neural networks. Then, we explore 1.58-bit training in other transformer-based language models, namely encoder-only and encoder-decoder models. Our results show that in all of these settings, 1.58-bit training is on par with or sometimes even better than the standard 32/16-bit models., Comment: 10 pages, 2 tables, 6 figures