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To date, most investigations on surprisal and entropy effects in reading have been conducted on the group level, disregarding individual differences. In this work, we revisit the predictive power of surprisal and entropy measures estimated from a range of language models (LMs) on data of human reading times as a measure of processing effort by incorporating information of language users' cognitive capacities. To do so, we assess the predictive power of surprisal and entropy estimated from generative LMs on reading data obtained from individuals who also completed a wide range of psychometric tests. Specifically, we investigate if modulating surprisal and entropy relative to cognitive scores increases prediction accuracy of reading times, and we examine whether LMs exhibit systematic biases in the prediction of reading times for cognitively high- or low-performing groups, revealing what type of psycholinguistic subject a given LM emulates. Our study finds that in most cases, incorporating cognitive capacities increases predictive power of surprisal and entropy on reading times, and that generally, high performance in the psychometric tests is associated with lower sensitivity to predictability effects. Finally, our results suggest that the analyzed LMs emulate readers with lower verbal intelligence, suggesting that for a given target group (i.e., individuals with high verbal intelligence), these LMs provide less accurate predictability estimates., Comment: ACL 2024 Findings

Semantic segmentation is an essential component of medical image analysis research, with recent deep learning algorithms offering out-of-the-box applicability across diverse datasets. Despite these advancements, segmentation failures remain a significant concern for real-world clinical applications, necessitating reliable detection mechanisms. This paper introduces a comprehensive benchmarking framework aimed at evaluating failure detection methodologies within medical image segmentation. Through our analysis, we identify the strengths and limitations of current failure detection metrics, advocating for the risk-coverage analysis as a holistic evaluation approach. Utilizing a collective dataset comprising five public 3D medical image collections, we assess the efficacy of various failure detection strategies under realistic test-time distribution shifts. Our findings highlight the importance of pixel confidence aggregation and we observe superior performance of the pairwise Dice score (Roy et al., 2019) between ensemble predictions, positioning it as a simple and robust baseline for failure detection in medical image segmentation. To promote ongoing research, we make the benchmarking framework available to the community., Comment: This work has been submitted for possible publication

Type Ia supernova (SN Ia) cosmology relies on the estimation of lightcurve parameters to derive precision distances that leads to the estimation of cosmological parameters. The empirical SALT2 lightcurve modeling that relies on only two parameters, a stretch x1, and a color c, has been used by the community for almost two decades. In this paper we study the ability of the SALT2 model to fit the nearly 3000 cosmology-grade SN Ia lightcurves from the second release of the Zwicky Transient Facility (ZTF) cosmology science working group. While the ZTF data was not used to train SALT2, the algorithm is modeling the ZTF SN Ia optical lightcurves remarkably well, except for lightcurve points prior to -10 d from maximum, where the training critically lacks statistics. We find that the lightcurve fitting is robust against the considered choice of phase-range, but we show the [-10; +40] d range to be optimal in terms of statistics and accuracy. We do not detect any significant features in the lightcurve fit residuals that could be connected to the host environment. Potential systematic population differences related to the SN Ia host properties might thus not be accountable for by the addition of extra lightcurve parameters. However, a small but significant inconsistency between residuals of blue- and red-SN Ia strongly suggests the existence of a phase-dependent color term, with potential implications for the use of SNe Ia in precision cosmology. We thus encourage modellers to explore this avenue and we emphasize the importance that SN Ia cosmology must include a SALT2 retraining to accurately model the lightcurves and avoid biasing the derivation of cosmological parameters., Comment: 10 pages, 9 figures. Submitted to Astronomy and Astrophysics

As Type Ia supernova cosmology transitions from a statistics dominated to a systematics dominated era, it is crucial to understand leftover unexplained uncertainties affecting their luminosity, such as the ones stemming from astrophysical biases. Indeed, SNe Ia are standardisable candles, whose absolute magnitude reach a 0.15~mag scatter once empirical correlations with their lightcurve stretch and colour and with their environment are accounted for. In this paper, we investigate how the standardisation process of SNe Ia depends on environment, to ultimately reduce their scatter in magnitude, focusing on colour standardisation. We use the volume-limited ZTF SN Ia DR2 sample, which offers unprecedented statistics for the low redshift ($z<0.06$) range. We first study the colour distribution, focusing on the effects of dust, to then select a dustless subsample of objects from low stellar mass environments and from the outskirts of their host galaxies. We then look at the colour-residuals relation and its associated parameter $\beta$. Finally, we investigate the colour dependency of the environment-dependent magnitude offsets (steps), to try to disentangle intrinsic and extrinsic colour origin. Our sample probes well the red tail of the colour distribution, up to $c=0.8$. The dustless sample exhibits a significantly lower red tail ($4.6\sigma$) in comparison to the whole sample. This suggests that reddening above $c\geq0.2$ is dominated by host interstellar dust absorption. Looking at the colour-residuals relation, we find it to be linear with lightcurve colour. We show hints of a potential evolution of $\beta$ with host stellar mass at a $2.5\sigma$ level. Finally, unlike recent claims from the literature, we see no evolution of steps as a function of lightcurve colour, suggesting that dust may not be the dominating mechanism responsible for the environmental dependency of SNe Ia magnitude., Comment: 10 pages, 7 figures, submitted to Astronomy and Astrophysics

Identifying predictive biomarkers, which forecast individual treatment effectiveness, is crucial for personalized medicine and informs decision-making across diverse disciplines. These biomarkers are extracted from pre-treatment data, often within randomized controlled trials, and have to be distinguished from prognostic biomarkers, which are independent of treatment assignment. Our study focuses on the discovery of predictive imaging biomarkers, aiming to leverage pre-treatment images to unveil new causal relationships. Previous approaches relied on labor-intensive handcrafted or manually derived features, which may introduce biases. In response, we present a new task of discovering predictive imaging biomarkers directly from the pre-treatment images to learn relevant image features. We propose an evaluation protocol for this task to assess a model's ability to identify predictive imaging biomarkers and differentiate them from prognostic ones. It employs statistical testing and a comprehensive analysis of image feature attribution. We explore the suitability of deep learning models originally designed for estimating the conditional average treatment effect (CATE) for this task, which previously have been primarily assessed for the precision of CATE estimation, overlooking the evaluation of imaging biomarker discovery. Our proof-of-concept analysis demonstrates promising results in discovering and validating predictive imaging biomarkers from synthetic outcomes and real-world image datasets., Comment: 19 pages, 12 figures

Understanding Type Ia supernovae (SNe~Ia) and the empirical standardisation relations that make them excellent distance indicators is vital to improving cosmological constraints. SN~Ia ``siblings", i.e. two or more SNe~Ia in the same host or parent galaxy offer a unique way to infer the standardisation relations and their diversity across the population. We analyse a sample of 25 SN~Ia pairs, observed homogeneously by the Zwicky Transient Factory (ZTF) to infer the SNe~Ia light curve width-luminosity and colour-luminosity parameters $\alpha$ and $\beta$. Using the pairwise constraints from siblings, allowing for a diversity in the standardisation relations, we find $\alpha = 0.218 \pm 0.055 $ and $\beta = 3.084 \pm 0.312$, respectively, with a dispersion in $\alpha$ and $\beta$ of $\leq 0.195$ and $\leq 0.923$, respectively, at 95$\%$ C.L. While the median dispersion is large, the values within $\sim 1 \sigma$ are consistent with no dispersion. Hence, fitting for a single global standardisation relation, we find $\alpha = 0.228 \pm 0.029 $ and $\beta = 3.160 \pm 0.191$. We find a very small intrinsic scatter of the siblings sample $\sigma_{\rm int} \leq 0.10$ at 95\% C.L. compared to $\sigma_{\rm int} = 0.22 \pm 0.04$ when computing the scatter using the Hubble residuals without comparing them as siblings. Splitting the sample based on host galaxy stellar mass, we find that SNe~Ia in both subsamples have consistent $\alpha$ and $\beta$. The $\beta$ value is consistent with the value for the cosmological sample. However, we find a higher $\alpha$ by $\sim 2.5 - 3.5 \sigma$. The high $\alpha$ is driven by low $x_1$ pairs, potentially suggesting that the slow and fast declining SN~Ia have different slopes of the width-luminosity relation. We can confirm or refute this with increased statistics from near future time-domain surveys. (abridged), Comment: 12 pages, 7 figures, submitted to A&A

To get distances, Type Ia Supernovae magnitudes are corrected for their correlation with lightcurve width and colour. Here we investigate how this standardisation is affected by the SN environment, with the aim to reduce scatter and improve standardisation. We first study the SN Ia stretch distribution, as well as its dependence on environment, as characterised by local and global (g-z) colour and stellar mass. We then look at the standardisation parameter $\alpha$, which accounts for the correlation between residuals and stretch, along with its environment dependence and linearity. We finally compute magnitude offsets between SNe in different astrophysical environments after colour and stretch standardisation, aka steps. This analysis is made possible due to the unprecedented statistics of the ZTF SN Ia DR2 volume-limited sample. The stretch distribution exhibits a bimodal behaviour, as previously found in literature. However, we find the distribution means to decrease with host stellar mass at a 9.0$\sigma$ significance. We demonstrate, at the 14.3$\sigma$ level, that the stretch-magnitude relation is non-linear, challenging the usual linear stretch-residuals relation. Fitting for a broken-$\alpha$ model, we indeed find two different slopes between stretch regimes ($x_1<-0.49\pm0.06$): $\alpha_{low}=0.28\pm0.01$ and $\alpha_{high}=0.09\pm0.01$, a $\Delta_{\alpha}=-0.19\pm0.01$ difference. As the relative proportion of SNe Ia in the high-/low-stretch modes evolves with redshift and environment, this implies that a linear $\alpha$ also evolves with redshift and environment. Concerning the environmental magnitude offset $\gamma$, we find it to be greater than 0.14 mag regardless of the considered environmental tracer used (local or global colour and stellar mass), all measured at the $\geq 6\sigma$ level, increased to $\sim0.18\pm0.01$ mag when accounting for the stretch-non linearity., Comment: 15 pages, 9 figures, submitted to Astronomy and Astrophysics

SNe Ia are used to determine the distance-redshift relation and build the Hubble diagram. Neglecting their host-galaxy peculiar velocities (PVs) may bias the measurement of cosmological parameters. The smaller the redshift, the larger the effect is. We use realistic simulations of SNe Ia observed by the Zwicky Transient Facility (ZTF) to investigate the effect of different methods to take into account PVs. We study the impact of neglecting galaxy PVs and their correlations in an analysis of the SNe Ia Hubble diagram. We find that it is necessary to use the PV full covariance matrix computed from the velocity power spectrum to take into account the sample variance. Considering the results we have obtained using simulations, we determine the PV systematic effects in the context of the ZTF DR2 SNe Ia sample. We determine the PV impact on the intercept of the Hubble diagram, $a_B$, which is directly linked to the measurement of $H_0$. We show that not taking into account PVs and their correlations results in a shift of the $H_0$ value of about $1.0$km.s$^{-1}$.Mpc$^{-1}$ and a slight underestimation of the $H_0$ error bar., Comment: 12 pages, 4 figures

Purpose: Semantic segmentation and landmark detection are fundamental tasks of medical image processing, facilitating further analysis of anatomical objects. Although deep learning-based pixel-wise classification has set a new-state-of-the-art for segmentation, it falls short in landmark detection, a strength of shape-based approaches. Methods: In this work, we propose a dense image-to-shape representation that enables the joint learning of landmarks and semantic segmentation by employing a fully convolutional architecture. Our method intuitively allows the extraction of arbitrary landmarks due to its representation of anatomical correspondences. We benchmark our method against the state-of-the-art for semantic segmentation (nnUNet), a shape-based approach employing geometric deep learning and a CNN-based method for landmark detection. Results: We evaluate our method on two medical dataset: one common benchmark featuring the lungs, heart, and clavicle from thorax X-rays, and another with 17 different bones in the paediatric wrist. While our method is on pair with the landmark detection baseline in the thorax setting (error in mm of $2.6\pm0.9$ vs $2.7\pm0.9$), it substantially surpassed it in the more complex wrist setting ($1.1\pm0.6$ vs $1.9\pm0.5$). Conclusion: We demonstrate that dense geometric shape representation is beneficial for challenging landmark detection tasks and outperforms previous state-of-the-art using heatmap regression. While it does not require explicit training on the landmarks themselves, allowing for the addition of new landmarks without necessitating retraining.}

Using a sample of $448.1 \times 10^6$ $\psi(2S)$ events collected with the BESIII detector, we perform a study of the decay $J/\psi\rightarrow K^+K^-$ via $\psi(2S)\rightarrow \pi^+\pi^-J/\psi$. The branching fraction of $J/\psi\rightarrow K^+K^-$ is determined to be $\mathcal{B}_{K^+K^-}=(3.072\pm 0.023({\rm stat.})\pm 0.050({\rm syst.}))\times 10^{-4}$, which is consistent with previous measurements but with significantly improved precision., Comment: to be submitted to PRD

This study identifies a gap in data-driven approaches to robot-centric pedestrian interactions and proposes a corresponding pipeline. The pipeline utilizes unsupervised learning techniques to identify patterns in interaction data of urban environments, specifically focusing on conflict scenarios. Analyzed features include the robot's and pedestrian's speed and contextual parameters such as proximity to intersections. They are extracted and reduced in dimensionality using Principal Component Analysis (PCA). Finally, K-means clustering is employed to uncover underlying patterns in the interaction data. A use case application of the pipeline is presented, utilizing real-world robot mission data from a mid-sized German city. The results indicate the need for enriching interaction representations with contextual information to enable fine-grained analysis and reasoning. Nevertheless, they also highlight the need for expanding the data set and incorporating additional contextual factors to enhance the robots situational awareness and interaction quality.

We study the phenomenon of quantum synchronization from the viewpoint of quantum metrology. By interpreting quantum self-sustained oscillators as dissipative quantum sensors, we develop a framework to characterize several aspects of quantum synchronization. We show that the quantum Fisher information (QFI) serves as a system-agnostic measure of quantum synchronization that also carries a clear operational meaning, viz., it quantifies the precision with which the amplitude of a weak synchronizing drive can be measured. We extend our analysis to study many-body oscillators subjected to multiple drives. We show how the QFI matrix can be used to determine the optimal drive that maximizes quantum synchronization, and also to quantitatively differentiate the synchronization responses induced by different drives. Our work highlights multiple connections between quantum synchronization and quantum metrology, paving a route towards finding quantum technological applications of quantum synchronization., Comment: Main: 6 pages, 2 figures; Supplemental: 9 pages, 1 figure

QUIC is a new network protocol standardized in 2021. It was designed to replace the TCP/TLS stack and is based on UDP. The most current web standard HTTP/3 is specifically designed to use QUIC as transport protocol. QUIC claims to provide secure and fast transport with low-latency connection establishment, flow and congestion control, reliable delivery, and stream multiplexing. To achieve the security goals, QUIC enforces the usage of TLS 1.3. It uses authenticated encryption with additional data (AEAD) algorithms to not only protect the payload but also parts of the header. The handshake relies on asymmetric cryptography, which will be broken with the introduction of powerful quantum computers, making the use of post-quantum cryptography inevitable. This paper presents a detailed evaluation of the impact of cryptography on QUIC performance. The high-performance QUIC implementations LSQUIC, quiche, and MsQuic are evaluated under different aspects. We break symmetric cryptography down to the different security features. To be able to isolate the impact of cryptography, we implemented a NOOP AEAD algorithm which leaves plaintext unaltered. We show that QUIC performance increases by 10 to 20% when removing packet protection. The header protection has negligible impact on performance, especially for AES ciphers. We integrate post-quantum cryptographic algorithms into QUIC, demonstrating its feasibility without major changes to the QUIC libraries by using a TLS library that implements post-quantum algorithms. Kyber, Dilithium, and FALCON are promising candidates for post-quantum secure QUIC, as they have a low impact on the handshake duration. Algorithms like SPHINCS+ with larger key sizes or more complex calculations significantly impact the handshake duration and cause additional issues in our measurements., Comment: Presented at the 2024 IFIP Networking Conference (IFIP Networking)

We present the first search for the leptonic decays $D^{*+}\to e^+\nu_e$ and $D^{*+}\to \mu^+\nu_\mu$ by analyzing a data sample of electron-positron collisions recorded with the BESIII detector at center-of-mass energies between 4.178 and 4.226 GeV, corresponding to an integrated luminosity of 6.32~fb$^{-1}$. No significant signal is observed. The upper limits on the branching fractions for $D^{*+}\to e^+\nu_e$ and $D^{*+}\to \mu^+\nu_\mu$ are set to be $1.1 \times 10^{-5}$ and $4.3 \times 10^{-6}$ at 90\% confidence level, respectively., Comment: 14 pages, 7 figures

Modern transient surveys now routinely discover flares resulting from tidal disruption events (TDEs) which occur when stars, typically $\sim0.5-2$ M$_{\odot}$, are ripped apart after passing too close to a supermassive black hole. We present three examples of a new class of extreme nuclear transients (ENTs) that we interpret as the tidal disruption of intermediate mass ($\sim3-10$ M$_{\odot}$) stars. Each is coincident with their host-galaxy nucleus and exhibits a smooth ($<10$% excess variability), luminous ($2-7\times10^{45}$ erg s$^{-1}$), and long-lived ($>150$ days) flare. ENTs are extremely rare ($\geq1\times10^{-3}$ Gpc$^{-1}$ yr$^{-1}$) compared to any other known class of transients. They are at least twice as energetic ($0.5-2.5\times 10^{53}$ erg) as any other known transient and these extreme energetics rule out stellar origins., Comment: Submitted to Science

The solid-state C$_2$H$_2$ chemistry in interstellar H$_2$O-rich ice has been proposed to explain astronomically observed complex organic molecules (COMs), including ketene (CH$_2$CO), acetaldehyde (CH$_3$CHO), and ethanol (CH$_3$CH$_2$OH), toward early star-forming regions. This formation mechanism is supported by recent laboratory studies and theoretical calculations for the reactions of C$_2$H$_2$+OH/H. However, the analog reaction of C$_2$H$_2$+NH$_2$ forming N-bearing species has been suggested to have a relatively low rate constant that is orders of magnitude lower than the value of C$_2$H$_2$+OH. This work extends our previous laboratory studies on O-bearing COM formation to investigate the interactions between C$_2$H$_2$ and NH$_3$ ice triggered by cosmic ray-induced secondary UV photons under molecular cloud conditions. Experiments were performed in an ultra-high vacuum chamber to investigate the UV photolysis of the C$_2$H$_2$:NH$_3$ ice mixture at 10 K. The studied ice chemistry of C$_2$H$_2$ with NH$_2$ radicals and H atoms resulting from the UV photodissociation of NH$_3$ leads to the formation of several N-bearing COMs, including vinylamine (CH$_2$CHNH$_2$), acetaldimine (CH$_3$CHNH), acetonitrile (CH$_3$CN), ketenimine (CH$_2$CNH), and tentatively ethylamine (CH$_3$CH$_2$NH$_2$). The experimental results show an immediate and abundant CH$_2$CHNH$_2$ yield as the first-generation product, which is further converted into other chemical derivatives. The effective destruction and formation cross-section values of parent species and COMs were derived, and we discuss the chemical links among these molecules and their astronomical relevance., Comment: 22 pages, 9 figures

Organic electro-optic materials have demonstrated promising performance in developing electro-optic phase shifters. Their integration with other silicon photonic processes, nanofabrication complexities, and durability remain to be developed. While the required poling step in electro-optic polymers limits their potential and utilization on a large scale, devices made of paraelectric nematic liquid crystals suffer from slow bandwidth. In ferroelectric nematic liquid crystals, we report an additional GHz-fast phase shift that ultimately allows for significant second-order nonlinear optical coefficients and the related Pockels effect. It avoids poling issues and can pave the way for hybrid silicon-organic systems with CMOS-foundry compatibility. We report DC and AC modulation efficiencies of $\approx$0.25 V$\cdot$mm and $\approx$25.7 V$\cdot$mm, respectively, an on-chip insertion loss of $\approx$2.1 dB, and an electro-optic bandwidth of $f_{-6dB}$>4.18 GHz, employing improved light-matter interaction in a waveguide architecture that calls for only one lithography step., Comment: 25 pages, 5 figures

We propose a decentralized authentication system for networks of unmanned aerial vehicles. A blockchain-based public key infrastructure allows the usage of public key cryptography and public key based authentication protocols. The blockchain provides a common storage of the public keys and their relations and can provide the required information for the authentication process. Furthermore, the unmanned aerial vehicles store selected parts of the blockchain in order to operate independently in areas where they might not have access to the Internet. This allows unmanned aerial vehicles to authenticate entities of the network, like other unmanned aerial vehicles, cloud services, cars, and any computer., Comment: 5 pages

As organisations move away from locally hosted computer services toward Cloud platforms, there is a corresponding need to ensure the forensic integrity of such instances. The primary reasons for concern are (i) the locus of responsibility, and (ii) the associated risk of legal sanction and financial penalty. Building upon previously proposed techniques for intrusion monitoring, we highlight the multi-level interpretation problem, propose enhanced monitoring of Cloud-based systems at diverse operational and data storage level as a basis for review of historical change across the hosted system and afford scope to identify any data impact from hostile action or 'friendly fire'., Comment: 6 pages

Successive interference cancellation is used to detect discrete modulation symbols transmitted over a 1000 km fiber-optic link. A transmitter and receiver are presented that have linear complexity in the number of transmitted symbols and achieve the information rates of previous studies that use continuous modulations., Comment: Submitted to European Conference on Optical Communications (ECOC) 2024

In the fields of photogrammetry, computer vision and computer graphics, the task of neural 3D scene reconstruction has led to the exploration of various techniques. Among these, 3D Gaussian Splatting stands out for its explicit representation of scenes using 3D Gaussians, making it appealing for tasks like 3D point cloud extraction and surface reconstruction. Motivated by its potential, we address the domain of 3D scene reconstruction, aiming to leverage the capabilities of the Microsoft HoloLens 2 for instant 3D Gaussian Splatting. We present HoloGS, a novel workflow utilizing HoloLens sensor data, which bypasses the need for pre-processing steps like Structure from Motion by instantly accessing the required input data i.e. the images, camera poses and the point cloud from depth sensing. We provide comprehensive investigations, including the training process and the rendering quality, assessed through the Peak Signal-to-Noise Ratio, and the geometric 3D accuracy of the densified point cloud from Gaussian centers, measured by Chamfer Distance. We evaluate our approach on two self-captured scenes: An outdoor scene of a cultural heritage statue and an indoor scene of a fine-structured plant. Our results show that the HoloLens data, including RGB images, corresponding camera poses, and depth sensing based point clouds to initialize the Gaussians, are suitable as input for 3D Gaussian Splatting., Comment: 8 pages, 9 figures, 2 tables. Will be published in the ISPRS The International Archives of Photogrammetry, Remote Sensing and Spatial Information Sciences

Working with cognate data involves handling synonyms, that is, multiple words that describe the same concept in a language. In the early days of language phylogenetics it was recommended to select one synonym only. However, as we show here, binary character matrices, which are used as input for computational methods, do allow for representing the entire dataset including all synonyms. Here we address the question how one can and if one should include all synonyms or whether it is preferable to select synonyms a priori. To this end, we perform maximum likelihood tree inferences with the widely used RAxML-NG tool and show that it yields plausible trees when all synonyms are used as input. Furthermore, we show that a priori synonym selection can yield topologically substantially different trees and we therefore advise against doing so. To represent cognate data including all synonyms, we introduce two types of character matrices beyond the standard binary ones: probabilistic binary and probabilistic multi-valued character matrices. We further show that it is dataset-dependent for which character matrix type the inferred RAxML-NG tree is topologically closest to the gold standard. We also make available a Python interface for generating all of the above character matrix types for cognate data provided in CLDF format., Comment: Experiments available on GitHub (https://github.com/luisevonderwiese/synonyms, https://github.com/luisevonderwiese/lingdata)

We present the second and final release of optical spectroscopy of Type Ia Supernovae (SNe Ia) obtained during the first and second phases of the Carnegie Supernova Project (CSP-I and CSP-II). The newly released data consist of 148 spectra of 30 SNe Ia observed in the course of the CSP-I, and 234 spectra of 127 SNe Ia obtained during the CSP-II. We also present 216 optical spectra of 46 historical SNe Ia, including 53 spectra of 30 SNe Ia observed by the Cal\'an/Tololo Supernova Survey. We combine these observations with previously published CSP data and publicly-available spectra to compile a large sample of measurements of spectroscopic parameters at maximum light, consisting of pseudo-equivalent widths and expansion velocities of selected features, for 232 CSP and historical SNe Ia (including more than 1000 spectra). Finally, we review some of the strongest correlations between spectroscopic and photometric properties of SNe Ia. Specifically, we define two samples: one consisting of SNe Ia discovered by targeted searches (most of them CSP-I objects) and the other composed of SNe Ia discovered by untargeted searches, which includes most of the CSP-II objects. The analysed correlations are similar for both samples. We find a larger incidence of SNe Ia belonging to the Cool (CL)and Broad Line (BL) Branch subtypes among the events discovered by targeted searches, Shallow Silicon (SS) SNe Ia are present with similar frequencies in both samples, while Core Normal (CN) SNe Ia are more frequent in untargeted searches., Comment: 59 pages, 16 figures, accepted for publication in The Astrophysical Journal. May 7, 2024: LaTex file updated: corrected one missing comma and an extraneous space in Table 2

We present a JWST MIRI/MRS spectrum (5-27 $\mathrm{\mu}$m) of the Type Ia supernova (SN Ia), SN 2021aefx at $+415$ days past $B$-band maximum. The spectrum, which was obtained during the iron-dominated nebular phase, has been analyzed in combination with previous JWST observations of SN 2021aefx, to provide the first JWST time series analysis of an SN Ia. We find the temporal evolution of the [Co III] 11.888 $\mathrm{\mu}$m feature directly traces the decay of $^{56}$Co. The spectra, line profiles, and their evolution are analyzed with off-center delayed-detonation models. Best fits were obtained with White Dwarf (WD) central densities of $\rho_c=0.9-1.1\times 10^9$g cm$^{-3}$, a WD mass of M$_{\mathrm{WD}}$=1.33-1.35M$_\odot$, a WD magnetic field of $\approx10^6$G, and an off-center deflagration-to-detonation transition at $\approx$ 0.5 $M_\odot$ seen opposite to the line of sight of the observer (-30). The inner electron capture core is dominated by energy deposition from $\gamma$-rays whereas a broader region is dominated by positron deposition, placing SN 2021aefx at +415 d in the transitional phase of the evolution to the positron-dominated regime. The formerly `flat-tilted' profile at 9 $\mathrm{\mu}$m now has significant contribution from [Ni IV], [Fe II], and [Fe III] and less from [Ar III], which alters the shape of the feature as positrons excite mostly the low-velocity Ar. Overall, the strength of the stable Ni features in the spectrum is dominated by positron transport rather than the Ni mass. Based on multi-dimensional models, our analysis is consistent with a single-spot, close-to-central ignition with an indication for a pre-existing turbulent velocity field, and excludes a multiple-spot, off-center ignition., Comment: Accepted for publication in ApJ

We investigate properties of the lasing action observed on the 1S0--3P1 intercombination transition of ytterbium atoms that are laser-cooled and -trapped inside a high-finesse cavity. The dressing of the atomic states on the 1S0--1P1 transition by the magneto-optical trap (MOT) laser light allows the coupled atom-cavity system to lase, via a two-photon transition, on the same line on which it is pumped. The observation and basic description of this phenomenon was presented earlier by Gothe et al. [Phys. Rev. A 99, 013415 (2019)]. In the current work, we focus on a detailed analysis of the lasing threshold and frequency properties and perform a comparison to our theoretical models.

We experimentally and theoretically study the formation of dressed states emerging from strong collective coupling of the narrow intercombination line of Yb atoms to a single mode of a high-finesse optical cavity. By permanently trapping and cooling the Yb atoms during their interaction with the cavity, we gain continuous experimental access to the dressed states. This allows us to detect both their field and their atomic properties, by simultaneously measuring the steady-state cavity transmission and free-space fluorescence. By varying the cavity and probe frequencies, we observe coupled atom-cavity states with atom number-dependent splitting, the hallmark of collective strong coupling of the atoms with the single cavity mode. We find additional fluorescence output at atomic resonance, which we explain by the effects of dephasing and inhomogeneous broadening. We compare our experimental results with a theoretical model and find good qualitative agreement.

The release of nnU-Net marked a paradigm shift in 3D medical image segmentation, demonstrating that a properly configured U-Net architecture could still achieve state-of-the-art results. Despite this, the pursuit of novel architectures, and the respective claims of superior performance over the U-Net baseline, continued. In this study, we demonstrate that many of these recent claims fail to hold up when scrutinized for common validation shortcomings, such as the use of inadequate baselines, insufficient datasets, and neglected computational resources. By meticulously avoiding these pitfalls, we conduct a thorough and comprehensive benchmarking of current segmentation methods including CNN-based, Transformer-based, and Mamba-based approaches. In contrast to current beliefs, we find that the recipe for state-of-the-art performance is 1) employing CNN-based U-Net models, including ResNet and ConvNeXt variants, 2) using the nnU-Net framework, and 3) scaling models to modern hardware resources. These results indicate an ongoing innovation bias towards novel architectures in the field and underscore the need for more stringent validation standards in the quest for scientific progress., Comment: Accepted at MICCAI 2024

We demonstrate efficient sub-thermal cooling of the modified cyclotron mode of a single trapped antiproton and reach particle temperatures $T_+=E_+/k_\text{B}$ below $200\,$mK in preparation times shorter than $500\,$s. This corresponds to the fastest resistive single-particle cyclotron cooling to sub-thermal temperatures ever demonstrated. By cooling trapped particles to such low energies, we demonstrate the detection of antiproton spin transitions with an error-rate $<0.000025$, more than three orders of magnitude better than in previous best experiments. This method will have enormous impact on multi-Penning-trap experiments that measure magnetic moments with single nuclear spins for tests of matter/antimatter symmetry, high-precision mass-spectrometry, and measurements of electron $g$-factors bound to highly-charged ions that test quantum electrodynamics.

We present a Monte-Carlo program for the simulation of polarized deep inelastic scattering at next-to-leading order in QCD matched to parton shower programs building on an existing implementation of the unpolarized case in the POWHEG BOX package. We discuss extensions of the POWHEG BOX framework necessary to account for polarized initial states and validate the code by detailed comparisons to existing fixed-order results. We then use the new tool to make predictions for the upcoming Electron Ion Collider. We find that parton-shower effects do have an impact on experimentally accessible distributions and improve the agreement with the next-to-next-to-leading order results., Comment: 26 pages, 8 figures; v2: published version

The Born cross sections for the process $e^{+}e^{-}\to \omega\eta^{\prime}$ are measured at 22 center-of-mass energies from 2.000 to 3.080 GeV using data collected with the BESIII detector at the BEPCII collider. A resonant structure is observed with a statistical significance of 9.6$\sigma$. A Breit-Wigner fit determines its mass to be $M_R=(2153\pm30\pm31)~{\rm{MeV}}/c^{2}$ and its width to be $\Gamma_{R}=(167\pm77\pm7)~\rm{MeV}$, where the first uncertainties are statistical and the second are systematic.

We search for the di-photon decay of a light pseudoscalar axion-like particle, $a$, in radiative $J/\psi$ decays, using 10 billion $J/\psi$ events collected with the BESIII detector. We find no evidence of a signal and set upper limits at the $95\%$ confidence level on the product branching fraction $\mathcal{B}(J/\psi \to \gamma a) \times \mathcal{B}(a \to \gamma \gamma)$ and the axion-like particle photon coupling constant $g_{a \gamma \gamma}$ in the ranges of $(3.7-48.5) \times 10^{-8}$ and $(2.2 -101.8)\times 10^{-4}$ GeV$^{-1}$, respectively, for $0.18 \le m_a \le 2.85$ GeV/$c^2$. These are the most stringent limits to date in this mass region., Comment: 9 pages, 5 figures, Published in Phys. Rev. D 110, L031101 (2024)

Multimodal artificial intelligence (AI) systems have the potential to enhance clinical decision-making by interpreting various types of medical data. However, the effectiveness of these models across all medical fields is uncertain. Each discipline presents unique challenges that need to be addressed for optimal performance. This complexity is further increased when attempting to integrate different fields into a single model. Here, we introduce an alternative approach to multimodal medical AI that utilizes the generalist capabilities of a large language model (LLM) as a central reasoning engine. This engine autonomously coordinates and deploys a set of specialized medical AI tools. These tools include text, radiology and histopathology image interpretation, genomic data processing, web searches, and document retrieval from medical guidelines. We validate our system across a series of clinical oncology scenarios that closely resemble typical patient care workflows. We show that the system has a high capability in employing appropriate tools (97%), drawing correct conclusions (93.6%), and providing complete (94%), and helpful (89.2%) recommendations for individual patient cases while consistently referencing relevant literature (82.5%) upon instruction. This work provides evidence that LLMs can effectively plan and execute domain-specific models to retrieve or synthesize new information when used as autonomous agents. This enables them to function as specialist, patient-tailored clinical assistants. It also simplifies regulatory compliance by allowing each component tool to be individually validated and approved. We believe, that our work can serve as a proof-of-concept for more advanced LLM-agents in the medical domain., Comment: 91 pages, 2 Figures

Mid-infrared emission features probe the properties of ionized gas, and hot or warm molecular gas. The Orion Bar is a frequently studied photodissociation region (PDR) containing large amounts of gas under these conditions, and was observed with the MIRI IFU aboard JWST as part of the "PDRs4All" program. The resulting IR spectroscopic images of high angular resolution (0.2") reveal a rich observational inventory of mid-IR emission lines, and spatially resolve the substructure of the PDR, with a mosaic cutting perpendicularly across the ionization front and three dissociation fronts. We extracted five spectra that represent the ionized, atomic, and molecular gas layers, and measured the most prominent gas emission lines. An initial analysis summarizes the physical conditions of the gas and the potential of these data. We identified around 100 lines, report an additional 18 lines that remain unidentified, and measured the line intensities and central wavelengths. The H I recombination lines originating from the ionized gas layer bordering the PDR, have intensity ratios that are well matched by emissivity coefficients from H recombination theory, but deviate up to 10% due contamination by He I lines. We report the observed emission lines of various ionization stages of Ne, P, S, Cl, Ar, Fe, and Ni, and show how certain line ratios vary between the five regions. We observe the pure-rotational H$_2$ lines in the vibrational ground state from 0-0 S(1) to 0-0 S(8), and in the first vibrationally excited state from 1-1 S(5) to 1-1 S(9). We derive H$_2$ excitation diagrams, and approximate the excitation with one thermal (~700 K) component representative of an average gas temperature, and one non-thermal component (~2700 K) probing the effect of UV pumping. We compare these results to an existing model for the Orion Bar PDR and highlight the differences with the observations., Comment: 26 pages, 12 figures, 3 tables. Submitted to A&A, under review (1st revision)

We derive, through the deterministic homogenization theory in thin domains, a new model consisting of Hele-Shaw equation with memory coupled with the convective Cahn-Hilliard equation. The obtained system, which models in particular tumor growth, is then analyzed and we prove its well-posedness in dimension 2. To achieve our goal, we develop and use the new concept of sigma-convergence in thin heterogeneous media, and we prove some regularity results for the upscaled model., Comment: 52 pages

Eye-tracking datasets are often shared in the format used by their creators for their original analyses, usually resulting in the exclusion of data considered irrelevant to the primary purpose. In order to increase re-usability of existing eye-tracking datasets for more diverse and initially not considered use cases, this work advocates a new approach of sharing eye-tracking data. Instead of publishing filtered and pre-processed datasets, the eye-tracking data at all pre-processing stages should be published together with data quality reports. In order to transparently report data quality and enable cross-dataset comparisons, we develop data quality reporting standards and metrics that can be automatically applied to a dataset, and integrate them into the open-source Python package pymovements (https://github.com/aeye-lab/pymovements).

By analyzing $e^+e^-$ annihilation data corresponding to an integrated luminosity of 2.93 $\rm fb^{-1}$ collected at a center-of-mass energy of 3.773 GeV with the \text{BESIII} detector, the first observation of the semileptonic decays $D^0\rightarrow K_S^0\pi^-\pi^0 e^+ \nu_e$ and $D^+\rightarrow K_S^0\pi^+\pi^- e^+ \nu_e$ is reported. With a dominant hadronic contribution from $K_1(1270)$, the branching fractions are measured to be $\mathcal{B}(D^0\rightarrow {K}_1(1270)^-(\to K^0_S\pi^-\pi^0)e^+\nu_e)=(1.69^{+0.53}_{-0.46}\pm0.15)\times10^{-4}$ and $\mathcal{B}(D^+\to \bar{K}_1(1270)^0(\to K^0_S\pi^+\pi^-)e^+\nu_e)=(1.47^{+0.45}_{-0.40}\pm0.20)\times10^{-4}$ with statistical significance of 5.4$\sigma$ and 5.6$\sigma$, respectively. When combined with measurements of the $K_1(1270)\to K^+\pi^-\pi$ decays, the absolute branching fractions are determined to be $\mathcal{B}(D^0\to K_1(1270)^-e^+\nu_e)=(1.05^{+0.33}_{-0.28}\pm0.12\pm0.12)\times10^{-3}$ and $\mathcal{B}(D^+\to \bar{K}_1(1270)^0e^+\nu_e)=(1.29^{+0.40}_{-0.35}\pm0.18\pm0.15)\times10^{-3}$. The first and second uncertainties are statistical and systematic, respectively, and the third uncertainties originate from the assumed branching fractions of the $K_1(1270)\to K\pi\pi$ decays., Comment: 19pages

Reports on the detection of carbonates in planetary nebulae (PNe) and protostars suggested the existence of a mechanism that produce these compounds in stellar winds and outflows. A consecutive laboratory study reported a possible mechanism by observing the non-thermodynamic equilibrium (TE), gas-phase condensation of amorphous silicate grains with amorphous calcium carbonate inclusions. It concluded that water vapor was necessary to the formation of the carbonates. We present a laboratory study with pulsed laser ablation of an MgSi target in O$_2$ and CO$_2$ gases and report, in the absence of water vapor, the non-TE, gas-phase condensation of amorphous carbonated magnesium silicate dust. It consists of amorphous silicate grains with formula MgSiO$_3$ that comprise carbonate groups homogeneously dispersed in their structure. The infrared spectra of the grains show the characteristic bands of amorphous silicates and two bands at $\sim$6.3 and $\sim$7.0 $\mu$m that we assign to the carbonate groups. The silicate bands are not significantly affected at an estimated Si:C ratio of 9:1 to 9:2. Such grains could form in winds and outflows of evolved stars and PNe if C atoms are present during silicate condensation. Additionally, we find that Lyman-$\alpha$ radiation dissociates the carbonate groups at the surface of the carbonated silicate grains and we estimate the corresponding photodissociation cross section of (0.04 $\pm$ 0.02) $\times$ 10$^{-16}$ cm$^2$. Therefore, photodissociation would limit the formation of carbonate groups on grains in winds and outflows of stars emitting VUV photons and the carbonates observed in protostars have not formed by gas-phase condensation., Comment: 19 pages, 16 figures, accepted for publication in The Astrophysical Journal

Successive interference cancellation (SIC) is used to approach the achievable information rates (AIRs) of joint detection and decoding for long-haul optical fiber links. The AIRs of memoryless ring constellations are compared to those of circularly symmetric complex Gaussian modulation for surrogate channel models with correlated phase noise. Simulations are performed for 1000 km of standard single-mode fiber with ideal Raman amplification. In this setup, 32 rings and 16 SIC-stages with Gaussian message-passing receivers achieve the AIR peaks of previous work. The computational complexity scales in proportion to the number of SIC-stages, where one stage has the complexity of separate detection and decoding., Comment: This work has been submitted to the IEEE for possible publication

Traditionally, segmentation algorithms require dense annotations for training, demanding significant annotation efforts, particularly within the 3D medical imaging field. Scribble-supervised learning emerges as a possible solution to this challenge, promising a reduction in annotation efforts when creating large-scale datasets. Recently, a plethora of methods for optimized learning from scribbles have been proposed, but have so far failed to position scribble annotation as a beneficial alternative. We relate this shortcoming to two major issues: 1) the complex nature of many methods which deeply ties them to the underlying segmentation model, thus preventing a migration to more powerful state-of-the-art models as the field progresses and 2) the lack of a systematic evaluation to validate consistent performance across the broader medical domain, resulting in a lack of trust when applying these methods to new segmentation problems. To address these issues, we propose a comprehensive scribble supervision benchmark consisting of seven datasets covering a diverse set of anatomies and pathologies imaged with varying modalities. We furthermore propose the systematic use of partial losses, i.e. losses that are only computed on annotated voxels. Contrary to most existing methods, these losses can be seamlessly integrated into state-of-the-art segmentation methods, enabling them to learn from scribble annotations while preserving their original loss formulations. Our evaluation using nnU-Net reveals that while most existing methods suffer from a lack of generalization, the proposed approach consistently delivers state-of-the-art performance. Thanks to its simplicity, our approach presents an embarrassingly simple yet effective solution to the challenges of scribble supervision. Source code as well as our extensive scribble benchmarking suite will be made publicly available upon publication.

We present an update of an existing implementation of $WZjj$ production via vector-boson scattering in the framework of the POWHEG BOX program. In particular, previously unavailable semi-leptonic and fully hadronic decay modes of the intermediate vector bosons are provided, and operators of dimension six in an effective-field theory approach to account for physics beyond the Standard Model in the electroweak sector are included. For selected applications phenomenological results are provided to illustrate the capabilities of the new program. The impact of the considered dimension-six operators on experimentally accessible distributions is found to be small for current LHC energies, but enhanced in the kinematic reach of a potential future hadron collider with an energy of 100 TeV. The relevance of fully accounting for spin correlations and off-shell effects in the decay system is explored by a comparison with results obtained with the MadSpin tool that are based on an approximate treatment of the leptonic final state resulting from vector boson scattering processes. For selected semi-leptonic and hadronic decay modes we demonstrate the sensitivity of realistic signal selection procedures on QCD corrections and parton-shower effects., Comment: 22 pages, 10 figures. Code can be obtained from: https://powhegbox.mib.infn.it/. V2: Version published in EPJC

We present the first fully two-dimensional attenuation imaging technique developed for pulse-echo ultrasound systems. Unlike state-of-the-art techniques, which use line-by-line acquisitions, our method uses steered emissions to constrain attenuation values at each location with multiple crossing wave paths, essential to resolve the spatial variations of this tissue property. At every location, we compute normalized cross-correlations between the beamformed images that are obtained from emissions at different steering angles. We demonstrate that their log-amplitudes provide the changes between attenuation-induced amplitude losses undergone by the different incident waves. This allows us to formulate a linear tomographic problem, which we efficiently solve via a Tikhonov-regularized least-squares approach. The performance of our tomography technique is first validated in numerical examples and then experimentally demonstrated in custom-made tissue-mimicking phantoms with inclusions of varying size, echogenicity, and attenuation. We show that this technique is particularly good at resolving lateral variations in tissue attenuation and remains accurate in media with varying echogenicity. Based on a similar principle, this method can be easily combined with computed ultrasound tomography in echo mode (CUTE) for speed-of-sound imaging, paving the way towards a multi-modal ultrasound tomography framework characterizing multiple acoustic tissue properties simultaneously.

Medical image classification requires labeled, task-specific datasets which are used to train deep learning networks de novo, or to fine-tune foundation models. However, this process is computationally and technically demanding. In language processing, in-context learning provides an alternative, where models learn from within prompts, bypassing the need for parameter updates. Yet, in-context learning remains underexplored in medical image analysis. Here, we systematically evaluate the model Generative Pretrained Transformer 4 with Vision capabilities (GPT-4V) on cancer image processing with in-context learning on three cancer histopathology tasks of high importance: Classification of tissue subtypes in colorectal cancer, colon polyp subtyping and breast tumor detection in lymph node sections. Our results show that in-context learning is sufficient to match or even outperform specialized neural networks trained for particular tasks, while only requiring a minimal number of samples. In summary, this study demonstrates that large vision language models trained on non-domain specific data can be applied out-of-the box to solve medical image-processing tasks in histopathology. This democratizes access of generalist AI models to medical experts without technical background especially for areas where annotated data is scarce., Comment: 40 pages, 5 figures