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Case law is instrumental in shaping our understanding of human rights, including the right to adequate housing. The HUDOC database provides access to the textual content of case law from the European Court of Human Rights (ECtHR), along with some metadata. While this metadata includes valuable information, such as the application number and the articles addressed in a case, it often lacks detailed substantive insights, such as the specific issues a case covers. This underscores the need for detailed analysis to extract such information. However, given the size of the database - containing over 40,000 cases - an automated solution is essential. In this study, we focus on the right to adequate housing and aim to build models to detect cases related to housing and eviction issues. Our experiments show that the resulting models not only provide performance comparable to more sophisticated approaches but are also interpretable, offering explanations for their decisions by highlighting the most influential words. The application of these models led to the identification of new cases that were initially overlooked during data collection. This suggests that NLP approaches can be effectively applied to categorise case law based on the specific issues they address.

Sulfur is depleted with respect to its cosmic standard abundance in dense star-forming regions. It has been suggested that this depletion is caused by the freeze-out of sulfur on interstellar dust grains, but the observed abundances and upper limits of sulfur-bearing ices remain too low to account for all of the missing sulfur. Toward the same environments, a strong absorption feature at 6.85 $\mu$m is observed, but its long-standing assignment to the NH4+ cation remains tentative. We investigate the plausibility of NH4SH salt serving as a sulfur reservoir and a carrier of the 6.85 $\mu$m band in interstellar ices by characterizing its IR signatures and apparent band strengths in water-rich laboratory ice mixtures and using this laboratory data to constrain NH4SH abundances in observations of 4 protostars and 2 cold dense clouds. The observed 6.85 $\mu$m feature is fit well with the laboratory NH4SH:H2O ice spectra. NH4+ column densities obtained from the 6.85 $\mu$m band range from 8-23% with respect to H2O toward the sample of protostars and dense clouds. The redshift of the 6.85 $\mu$m feature correlates with higher abundances of NH4+ with respect to H2O in both the laboratory data presented here and observational data of dense clouds and protostars. The apparent band strength of the SH- feature is likely too low for the feature to be detectable in the spectrally busy 3.9 $\mu$m region, but the 5.3 $\mu$m NH4+ $\nu_{4}$ + SH- R combination mode may be an alternative means of detection. Its tentative assignment adds to mounting evidence supporting the presence of NH4+ salts in ices and is the first tentative observation of the SH- anion toward interstellar ices. If the majority ($\gtrsim$80-85%) of the NH4+ cations quantified toward the investigated sources in this work are bound to SH- anions, then NH4SH salts could account for up to 17-18% of their sulfur budgets., Comment: Accepted for publication in A&A. 20 pages, 14 figures, and 7 tables in the main text; 15 pages, 17 figures, and 10 tables in the appendix

Decentralized learning (DL) is an emerging approach that enables nodes to collaboratively train a machine learning model without sharing raw data. In many application domains, such as healthcare, this approach faces challenges due to the high level of heterogeneity in the training data's feature space. Such feature heterogeneity lowers model utility and negatively impacts fairness, particularly for nodes with under-represented training data. In this paper, we introduce \textsc{Facade}, a clustering-based DL algorithm specifically designed for fair model training when the training data exhibits several distinct features. The challenge of \textsc{Facade} is to assign nodes to clusters, one for each feature, based on the similarity in the features of their local data, without requiring individual nodes to know apriori which cluster they belong to. \textsc{Facade} (1) dynamically assigns nodes to their appropriate clusters over time, and (2) enables nodes to collaboratively train a specialized model for each cluster in a fully decentralized manner. We theoretically prove the convergence of \textsc{Facade}, implement our algorithm, and compare it against three state-of-the-art baselines. Our experimental results on three datasets demonstrate the superiority of our approach in terms of model accuracy and fairness compared to all three competitors. Compared to the best-performing baseline, \textsc{Facade} on the CIFAR-10 dataset also reduces communication costs by 32.3\% to reach a target accuracy when cluster sizes are imbalanced.

Integrated microwave photonic (MWP) front-ends are capable of ultra-broadband signal reception and processing. However, state-of-the-art demonstrations are limited to performing only one specific functionality at any given time, which fails to meet the demands of advanced radio frequency applications in real-world electromagnetic environments. In this paper, we present a major departure from the current trend, which is a novel integrated MWP front-end capable of simultaneous cascaded functions with enhanced performances. Our integrated MWP front-end can delay or phase-shift signals within the selected frequency band while simultaneously suppressing noise signals in other frequency bands, resembling the function of a conventional RF front-end chain. Moreover, we implement an on-chip linearization technique to improve the spurious-free dynamic range of the system. Our work represents a paradigm shift in designing RF photonic front-ends and advancing their practical applications.

We study DC magnetotransport in a quantum critical metal in the presence of a lattice. In the regime where the transport is hydrodynamical the interplay of the Lorentz force and the lattice gives rise to a natural anomalous contribution to the cyclotron frequency that changes it from its canonical charge-to-mass ratio. The size of this effect is universal as it is determined only by thermodynamic quantities. Remarkably the Drude weight changes in such a way that to first subleading order in the lattice strength the Hall resistivity and Hall coefficient do not change, though the Hall angle does change. We confirm our results with numerical simulations in a holographic model of a strange metal. For weak lattice strength these hydrodynamic effects are shown to be present. The numerical simulations also suggest that strong lattice effects beyond a hydrodynamic regime may provide a resolution to the experimentally observed anomalous Hall response of cuprate strange metals., Comment: 29+22 pages, 10+3 figures. v2 references corrected and added

We report the discovery of jets emanating from pre-main-sequence objects exclusively at mid-infrared wavelengths, enabled by the superb sensitivity of JWST's Mid-InfraRed Medium-Resolution Spectrometer (MIRI MRS) instrument. These jets are observed only in lines of [NiII], [FeII], [ArII], and [NeII]. The H$_2$ emission, imaged in eight distinct transitions, has a completely different morphology, exhibiting a wide-angled, biconical shape, symmetrically distributed about the jet axes. Synergistic high-resolution Atacama Large Millimeter/submillimeter Array (ALMA) observations resolve a pair of side-by-side edge-on accretion disks lying at the origin of the twin mid-infrared jets. Assuming coevality of the components of the young multiple system under investigation, the system age is at least (2 $-$ 2.5) $\times$ 10$^6$ yr, despite the discrepantly younger age inferred from the spectral energy distribution of the combined edge-on disk sources. The later system evolutionary stage is corroborated by ALMA observations of CO(2$-$1), $^{13}$CO(2$-1$), and C$^{18}$O(2$-$1), which show no traces of molecular outflows or remnant cavity walls. Consequently, the observed H$_2$ structures must have their origins in wide-angled disk winds, in the absence of any ambient, swept-up gas. In the context of recent studies of protostars, we propose an outflow evolutionary scenario in which the molecular gas component dominates in the youngest sources, whereas the fast, ionized jets dominate in the oldest sources, as is the case for the twin jets discovered in the WL 20 system., Comment: 25 pages, 19 figures; Accepted for publication in ApJ

We present a 3D food printing (3DFP) method to create coiled structures, harnessing the liquid rope coiling effect as a rapid method of food printing with tunable fractural properties. By studying the printability and coil-forming ability of pea, carrot, and cookie dough inks, we identified optimal printing parameters to induce steady and controlled coiling, enabling the creation of coiled structures with tunable porosities using a single nozzle. Fracture profiles from post-processed coiled structures showed complex responses but presented direct correlations between the porosity and textural parameters, including hardness, brittleness, and initial stiffness. This study provides a foundation for the fabrication of coiled food structures using 3DFP and highlights its potential application in designing textural properties and a range of unique sensory experiences., Comment: Main text: 18 pages, 9 figures. SI: 9 pages, 10 figures

To test the hypothesis that megaparsec-scale giant radio galaxies (GRGs) have multiple epochs of recurrent activity which lead to their giant sizes and to understand the nature of double-double radio galaxies (DDRGs), we have built the largest sample of giant DDRGs from the LOFAR Two Metre Sky Survey (LoTSS). The sample consists of 111 sources, of which 76 are newly identified. Their redshift and total projected size ranges are between 0.06 to 1.6 and 0.7 Mpc to 3.2 Mpc, respectively. We conducted a detailed analysis to characterise their properties, including arm-length ratios, flux density ratios of pairs of lobes, and misalignment angles. These measurements allow us to study the symmetry parameters, which are influenced by the immediate and large-scale environments of DDRGs. Our study conclusively shows that the cocoons in which the inner lobes of DDRGs grow are often contaminated with surrounding material from the external medium, leading to observed asymmetries in the inner lobes. Our analysis also reveals highly misaligned DDRGs, which could be due to environmental factors and/or changes in the supermassive black hole jet ejection axes. By studying the misalignment angles, we assess the stability of the jets in these systems in relation to their environment. Additionally, we have also characterised the large-scale environments of DDRGs, identifying them within dense galaxy clusters and studying how cluster weather significantly affects their morphologies. Interestingly, we have identified two gigahertz peaked-spectrum (GPS) candidates in the unresolved cores of the DDRGs, as well as one triple-double candidate, which, if confirmed, would be only the fifth known case. Additionally, we have discovered a DDRG in a distant galaxy cluster at $z\sim$\,1.4. Abridged., Comment: Submitted to A&A journal. 24 Pages. Comments are welcome

Soft-tissue and bone tumours (STBT) are rare, diagnostically challenging lesions with variable clinical behaviours and treatment approaches. This systematic review provides an overview of Artificial Intelligence (AI) methods using radiological imaging for diagnosis and prognosis of these tumours, highlighting challenges in clinical translation, and evaluating study alignment with the Checklist for AI in Medical Imaging (CLAIM) and the FUTURE-AI international consensus guidelines for trustworthy and deployable AI to promote the clinical translation of AI methods. The review covered literature from several bibliographic databases, including papers published before 17/07/2024. Original research in peer-reviewed journals focused on radiology-based AI for diagnosing or prognosing primary STBT was included. Exclusion criteria were animal, cadaveric, or laboratory studies, and non-English papers. Abstracts were screened by two of three independent reviewers for eligibility. Eligible papers were assessed against guidelines by one of three independent reviewers. The search identified 15,015 abstracts, from which 325 articles were included for evaluation. Most studies performed moderately on CLAIM, averaging a score of 28.9$\pm$7.5 out of 53, but poorly on FUTURE-AI, averaging 5.1$\pm$2.1 out of 30. Imaging-AI tools for STBT remain at the proof-of-concept stage, indicating significant room for improvement. Future efforts by AI developers should focus on design (e.g. define unmet clinical need, intended clinical setting and how AI would be integrated in clinical workflow), development (e.g. build on previous work, explainability), evaluation (e.g. evaluating and addressing biases, evaluating AI against best practices), and data reproducibility and availability (making documented code and data publicly available). Following these recommendations could improve clinical translation of AI methods., Comment: 23 pages, 6 figures, 6 supplementary figures

Lightsails are a promising spacecraft concept that can reach relativistic speeds via propulsion by laser light, allowing travel to nearby stars within a human lifetime. The success of a lightsail mission requires that any motion in the plane transverse to the propagation direction is bounded and damped for the entire acceleration phase. Here, we demonstrate that a previously unappreciated relativistic force, which generalizes the Poynting-Robertson effect, can passively damp this transverse motion. We show that this purely optical effect can be enhanced by two orders of magnitude compared to plane mirror sails by judicious design of the scattering response. We thus demonstrate that exploiting relativistic effects may be a practical means to control the motion of lightsails.

Microscopic levitated objects are a promising platform for inertial sensing, testing gravity at small scales, optomechanics in the quantum regime, and large-mass superpositions. However, existing levitation techniques harnessing optical and electrical fields suffer from noise induced by elevated internal temperatures and charge noise, respectively. Meissner-based magnetic levitation circumvents both sources of decoherence but requires cryogenic environments. Here we characterize a sub-mg ferromagnetic cube levitated in an alternating-current planar magnetic Paul trap at room temperature. We show behavior in line with the Mathieu equations and quality factors of up to 2500 for the librational modes. Besides technological sensing applications, this technique sets out a path for MHz librational modes in the micron-sized particle limit, allowing for magnetic coupling to superconducting circuits and spin-based quantum systems., Comment: 12 pages, 7 figures

We prove rigidity properties for von Neumann algebraic graph products. We introduce the notion of rigid graphs and define a class of II$_1$-factors named $\mathcal{C}_{\rm Rigid}$. For von Neumann algebras in this class we show a unique rigid graph product decomposition. In particular, we obtain unique prime factorization results and unique free product decomposition results for new classes of von Neumann algebras. Furthermore, we show that for many graph products of II$_1$-factors, including the hyperfinite II$_1$-factor, we can, up to a constant 2, retrieve the radius of the graph from the graph product. We also prove several technical results concerning relative amenability and embeddings of (quasi)-normalizers in graph products. Furthermore, we give sufficient conditions for a graph product to be nuclear and characterize strong solidity, primeness and free-indecomposability for graph products., Comment: Added a new result (Theorem F) and shortened section 5.1

The ability of a peer-to-peer (P2P) system to effectively host decentralized applications often relies on the availability of a peer-sampling service, which provides each participant with a random sample of other peers. Despite the practical effectiveness of existing peer samplers, their ability to produce random samples within a reasonable time frame remains poorly understood from a theoretical standpoint. This paper contributes to bridging this gap by introducing PeerSwap, a peer-sampling protocol with provable randomness guarantees. We establish execution time bounds for PeerSwap, demonstrating its ability to scale effectively with the network size. We prove that PeerSwap maintains the fixed structure of the communication graph while allowing sequential peer position swaps within this graph. We do so by showing that PeerSwap is a specific instance of an interchange process, a renowned model for particle movement analysis. Leveraging this mapping, we derive execution time bounds, expressed as a function of the network size N. Depending on the network structure, this time can be as low as a polylogarithmic function of N, highlighting the efficiency of PeerSwap. We implement PeerSwap and conduct numerical evaluations using regular graphs with varying connectivity and containing up to 32768 (2^15) peers. Our evaluation demonstrates that PeerSwap quickly provides peers with uniform random samples of other peers.

We investigate the chemical history of interstellar OCS and SO2 by deriving a statistically-significant sample of gas-phase column densities towards massive protostars and comparing to observations of gas and ices towards other sources spanning from dark clouds to comets. We analyze a subset of 26 line-rich massive protostars observed by ALMA as part of the ALMAGAL survey. Column densities are derived for OCS and SO2 from their rare isotopologues O13CS and 34SO2 towards the compact gas around the hot core. We find that gas-phase column density ratios of OCS and SO2 with respect to methanol remain fairly constant as a function of luminosity between low- and high-mass sources, despite their very different physical conditions. The derived gaseous OCS and SO2 abundances relative to CH3OH are overall similar to protostellar ice values, with a significantly larger scatter for SO2 than for OCS. Cometary and dark-cloud ice values agree well with protostellar gas-phase ratios for OCS, whereas higher abundances of SO2 are generally seen in comets compared to the other sources. Gaseous SO2/OCS ratios are consistent with ices toward dark clouds, protostars, and comets, albeit with some scatter. The constant gas-phase column density ratios throughout low and high-mass sources indicate an early stage formation before intense environmental differentiation begins. Icy protostellar values are similar to the gas phase medians, compatible with an icy origin of these species followed by thermal sublimation. The larger spread in SO2 compared to OCS ratios w.r.t. CH3OH is likely due to a more water-rich chemical environment associated with the former, as opposed to a CO-rich origin of the latter. Post-sublimation gas-phase processing of SO2 can also contribute to the large spread. Comparisons to ices in dark clouds and comets point to a significant inheritance of OCS from earlier to later evolutionary stages., Comment: Accepted for publication in Astronomy and Astrophysics on July 17th 2024

Owing to the characteristics of high density and unlimited write cycles, skyrmion racetrack memory (SK-RM) has demonstrated great potential as either the next-generation main memory or the last-level cache of processors with non-volatility. Nevertheless, the distinct skyrmion manipulations, such as injecting and shifting, demand a fundamental change in widely-used memory structures to avoid excessive energy and performance overhead. For instance, while B{\epsilon}-trees yield an excellent query and insert performance trade-off between B-trees and Log-Structured Merge (LSM)-trees, the applicability of deploying B{\epsilon}-trees onto SK-RM receives much less attention. In addition, even though optimizing designs have been proposed for B+-trees on SK-RM, those designs are not directly applicable to B{\epsilon}-trees owing to the batch update behaviors between tree nodes of B{\epsilon}-trees. Such an observation motivates us to propose the concept of Sky{\epsilon}-tree to effectively utilize the access port parallelism of SK-RM to embrace the excellent query and insert performance of B{\epsilon}-trees. Experimental results have shown promising improvements in access performance and energy conservation.

Decentralized learning (DL) offers a powerful framework where nodes collaboratively train models without sharing raw data and without the coordination of a central server. In the iterative rounds of DL, models are trained locally, shared with neighbors in the topology, and aggregated with other models received from neighbors. Sharing and merging models contribute to convergence towards a consensus model that generalizes better across the collective data captured at training time. In addition, the energy consumption while sharing and merging model parameters is negligible compared to the energy spent during the training phase. Leveraging this fact, we present SkipTrain, a novel DL algorithm, which minimizes energy consumption in decentralized learning by strategically skipping some training rounds and substituting them with synchronization rounds. These training-silent periods, besides saving energy, also allow models to better mix and finally produce models with superior accuracy than typical DL algorithms that train at every round. Our empirical evaluations with 256 nodes demonstrate that SkipTrain reduces energy consumption by 50% and increases model accuracy by up to 12% compared to D-PSGD, the conventional DL algorithm.

We propose a hybrid quantum system consisting of a magnetic particle inductively coupled to two superconducting transmon qubits, where qubit-qubit interactions are mediated via magnons. We show that the system can be tuned into three different regimes of effective qubit-qubit interactions, namely a transverse ($XX + YY$), a longitudinal ($ZZ$) and a non-trivial $ZX$ interaction. In addition, we show that an enhanced coupling can be achieved by employing an ellipsoidal magnet, carrying anisotropic magnetic fluctuations. We propose a scheme for realizing two-qubit gates, and simulate their performance under realistic experimental conditions. We find that iSWAP and CZ gates can be performed in this setup with an average fidelity $\gtrsim 99 \% $ , while an iCNOT gate can be applied with an average fidelity $\gtrsim 88 \%$. Our proposed hybrid circuit architecture offers an alternative platform for realizing two-qubit gates between superconducting qubits and could be employed for constructing qubit networks using magnons as mediators.

Current research in breast cancer Magnetic Resonance Imaging (MRI), especially with Artificial Intelligence (AI), faces challenges due to the lack of expert segmentations. To address this, we introduce the MAMA-MIA dataset, comprising 1506 multi-center dynamic contrast-enhanced MRI cases with expert segmentations of primary tumors and non-mass enhancement areas. These cases were sourced from four publicly available collections in The Cancer Imaging Archive (TCIA). Initially, we trained a deep learning model to automatically segment the cases, generating preliminary segmentations that significantly reduced expert segmentation time. Sixteen experts, averaging 9 years of experience in breast cancer, then corrected these segmentations, resulting in the final expert segmentations. Additionally, two radiologists conducted a visual inspection of the automatic segmentations to support future quality control studies. Alongside the expert segmentations, we provide 49 harmonized demographic and clinical variables and the pretrained weights of the well-known nnUNet architecture trained using the DCE-MRI full-images and expert segmentations. This dataset aims to accelerate the development and benchmarking of deep learning models and foster innovation in breast cancer diagnostics and treatment planning., Comment: 15 paes, 7 figures, 3 tables

ML-SUPERB evaluates self-supervised learning (SSL) models on the tasks of language identification and automatic speech recognition (ASR). This benchmark treats the models as feature extractors and uses a single shallow downstream model, which can be fine-tuned for a downstream task. However, real-world use cases may require different configurations. This paper presents ML-SUPERB~2.0, which is a new benchmark for evaluating pre-trained SSL and supervised speech models across downstream models, fine-tuning setups, and efficient model adaptation approaches. We find performance improvements over the setup of ML-SUPERB. However, performance depends on the downstream model design. Also, we find large performance differences between languages and datasets, suggesting the need for more targeted approaches to improve multilingual ASR performance., Comment: Accepted by Interspeech 2024

This study investigates how surgical intervention for speech pathology (specifically, as a result of oral cancer surgery) impacts the performance of an automatic speaker verification (ASV) system. Using two recently collected Dutch datasets with parallel pre and post-surgery audio from the same speaker, NKI-OC-VC and SPOKE, we assess the extent to which speech pathology influences ASV performance, and whether objective/subjective measures of speech severity are correlated with the performance. Finally, we carry out a perceptual study to compare judgements of ASV and human listeners. Our findings reveal that pathological speech negatively affects ASV performance, and the severity of the speech is negatively correlated with the performance. There is a moderate agreement in perceptual and objective scores of speaker similarity and severity, however, we could not clearly establish in the perceptual study, whether the same phenomenon also exists in human perception., Comment: 5 pages, 2 figures, 2 tables. Accepted to Interspeech 2024

Zeta generators are derivations associated with odd Riemann zeta values that act freely on the Lie algebra of the fundamental group of Riemann surfaces with marked points. The genus-zero incarnation of zeta generators are Ihara derivations of certain Lie polynomials in two generators that can be obtained from the Drinfeld associator. We characterize a canonical choice of these polynomials, together with their non-Lie counterparts at even degrees $w\geq 2$, through the action of the dual space of formal and motivic multizeta values. Based on these canonical polynomials, we propose a canonical isomorphism that maps motivic multizeta values into the $f$-alphabet. The canonical Lie polynomials from the genus-zero setup determine canonical zeta generators in genus one that act on the two generators of Enriquez' elliptic associators. Up to a single contribution at fixed degree, the zeta generators in genus one are systematically expanded in terms of Tsunogai's geometric derivations dual to holomorphic Eisenstein series, leading to a wealth of explicit high-order computations. Earlier ambiguities in defining the non-geometric part of genus-one zeta generators are resolved by imposing a new representation-theoretic condition. The tight interplay between zeta generators in genus zero and genus one unravelled in this work connects the construction of single-valued multiple polylogarithms on the sphere with iterated-Eisenstein-integral representations of modular graph forms., Comment: 92 pages. Submission includes ancillary data files. v2: Typos corrected

Federated Learning (FL) is a machine learning approach where nodes collaboratively train a global model. As more nodes participate in a round of FL, the effectiveness of individual model updates by nodes also diminishes. In this study, we increase the effectiveness of client updates by dividing the network into smaller partitions, or cohorts. We introduce Cohort-Parallel Federated Learning (CPFL): a novel learning approach where each cohort independently trains a global model using FL, until convergence, and the produced models by each cohort are then unified using one-shot Knowledge Distillation (KD) and a cross-domain, unlabeled dataset. The insight behind CPFL is that smaller, isolated networks converge quicker than in a one-network setting where all nodes participate. Through exhaustive experiments involving realistic traces and non-IID data distributions on the CIFAR-10 and FEMNIST image classification tasks, we investigate the balance between the number of cohorts, model accuracy, training time, and compute and communication resources. Compared to traditional FL, CPFL with four cohorts, non-IID data distribution, and CIFAR-10 yields a 1.9$\times$ reduction in train time and a 1.3$\times$ reduction in resource usage, with a minimal drop in test accuracy.

We consider an Erd\H{o}s-R\'enyi random graph, conditioned on the rare event that all connected components are fully connected. Such graphs can be considered as partitions of vertices into cliques. Hence, this conditional distribution defines a distribution over partitions. Using tools from analytic combinatorics, we prove limit theorems for several graph observables: the number of cliques; the number of edges; and the degree distribution. We consider several regimes of the connection probability $p$ as the number of vertices $n$ diverges. For $p=\frac{1}{2}$, the conditioning yields the uniform distribution over set partitions, which is well-studied, but has not been studied as a graph distribution before. For $p < \frac{1}{2}$, we show that the number of cliques is of the order $n/\sqrt{\log n}$, while for $p > \frac{1}{2}$, we prove that the graph consists of a single clique with high probability. This shows that there is a phase transition at $p=\frac{1}{2}$. We additionally study the near-critical regime $p_n\downarrow\frac{1}{2}$, as well as the sparse regime $p_n\downarrow0$.

We propose a novel mechanism for generating single photons in the mid-Infrared (MIR) using a solid-state or molecular quantum emitter. The scheme utilises cavity QED effects to selectively enhance a Frank-Condon transition, deterministically preparing a single Fock state of a polar phonon mode. By coupling the phonon mode to an antenna, the resulting excitation is then radiated to the far field as a single photon with a frequency matching the phonon mode. By combining macroscopic QED calculations with methods from open quantum system theory, we show that optimal parameters to generate these MIR photons occur for modest light-matter coupling strengths, which are achievable with state-of-the-art technologies. Combined, the cascaded system we propose provides a new quasi-deterministic source of heralded single photons in a regime of the electromagnetic spectrum where this previously was not possible., Comment: Emails: jake.iles-smith@manchester.ac.uk, mark-kamper.svendsen@mpsd.mpg.de, niste@dtu.dk

Polarization imaging can yield crucial information in multiple applications of remote sensing, such as characterization of clouds, aerosols, and the Aurora Borealis. Some applications require sub-percent polarimetric sensitivity and accuracy in determining the Stokes parameters, which can be a challenge to attain. In 2018, Sony released a low-cost CMOS-based imaging chip with integrated micro-polarizer array for general polarization measurements. We implement the calibration steps required for these Sony chips to reach sub-percent polarimetric accuracies. To analyze their performances, we have compared the characteristics of four different detector packages by three manufacturers housing either the monochromatic version or the RGB color variant. We present a comprehensive overview of the effects that these characteristics have on the polarimetric performance of the camera. They include dark noise, behavior over different gain settings, detector/pixel artifacts, and polarimetric effects determined by polarizer extinction ratios, polarizer orientations, and accuracy of polarimetric zero points due to differential pixel gains. In addition to calibrations using unpolarized light and fully linearly polarized light, we assess the polarimetric sensitivity within a tilting and rotating glass-plate set-up. We discuss the benefits of adding a rotating half-wave plate as an additional temporal modulator to generically mitigate some of the detector effects, and achieve better polarimetric sensitivity/accuracy albeit at the expense of lower temporal resolution. We conclude by presenting and discussing the polarimetric limits to which we were able to calibrate the detector effects for practical purposes. By reaching a compound absolute polarimetric uncertainty of less than a percent, these very compact, low-cost detectors are enabled for a multitude of scientific goals., Comment: Conference proceeding for SPIE Polarization: Measurement, Analysis, and Remote Sensing XVI, 31 pages, 21 figures

Unmanned Aerial Systems (UASs) or drones become more and more commercially available and cheap. There has been much emphasis on developing and deploying Counter-UAS systems (UASs) with Detection Tracking and Identification (DTI) solutions. However, the capabilities of these systems are hard to benchmark. Performance claims of these systems are currently not supported by evidence. In addition, no standard test methodologies are available for these DTI systems and different test methodologies make comparison of these systems hard or impossible. We report on the definition, development and verification of an objective-driven test method and corresponding comparative performance evaluation for commercial DTI solutions for C-UASs. The developed methodology is based on end-user scenarios that are operationally relevant. The test methodology is based on a generic DTI system lay-out and is detailed towards detection, tracking and identification, taking into account contextual information and end-user input. The comparative performance evaluation is developed to enable the use of the methodology in a relevant environment, thereby taking into account any potential environmental aspect that might influence DTI system performance. Validation of the work in a relevant environment has been done in three operational trials. The operational trial results show that the method allows for performance evaluation at component level (i.e., detection, tracking or identification component) and at system level (combinations of these components and integrated DTI system of system solutions)., Comment: Published to make content available online, though this is still work in progress and subject to additions and change

We give a new proof of the boundedness of bilinear Schur multipliers of second order divided difference functions, as obtained earlier by Potapov, Skripka and Sukochev in their proof of Koplienko's conjecture on the existence of higher order spectral shift functions. Our proof is based on recent methods involving bilinear transference and the H\"ormander-Mikhlin-Schur multiplier theorem. Our approach provides a significant sharpening of the known asymptotic bounds of bilinear Schur multipliers of second order divided difference functions. Furthermore, we give a new lower bound of these bilinear Schur multipliers, giving again a fundamental improvement on the best known bounds obtained by Coine, Le Merdy, Potapov, Sukochev and Tomskova. More precisely, we prove that for $f \in C^2(\mathbb{R})$ and $1 < p, p_1, p_2 < \infty$ with $\frac{1}{p} = \frac{1}{p_1} + \frac{1}{p_2}$ we have \[ \Vert M_{f^{[2]}}: S_{p_1} \times S_{p_2} \rightarrow S_p \Vert \lesssim \Vert f'' \Vert_\infty D(p, p_1, p_2), \] where the constant $D(p, p_1, p_2)$ is specified in Theorem 7.1 and $D(p, 2p, 2p) \approx p^4 p^\ast$ with $p^\ast$ the H\"older conjugate of $p$. We further show that for $f(\lambda) = \lambda \vert \lambda \vert$, $\lambda \in \mathbb{R}$, for every $1 < p < \infty$ we have \[ p^2 p^\ast \lesssim \Vert M_{f^{[2]}}: S_{2p} \times S_{2p} \rightarrow S_p \Vert. \] Here $f^{[2]}$ is the second order divided difference function of $f$ with $M_{f^{[2]}}$ the associated Schur multiplier. In particular it follows that our estimate $D(p, 2p, 2p)$ is optimal for $p \searrow 1$.

We provide a general condition under which e-variables in the form of a simple-vs.-simple likelihood ratio exist when the null hypothesis is a composite, multivariate exponential family. Such `simple' e-variables are easy to compute and expected-log-optimal with respect to any stopping time. Simple e-variables were previously only known to exist in quite specific settings, but we offer a unifying theorem on their existence for testing exponential families. We start with a simple alternative $Q$ and a regular exponential family null. Together these induce a second exponential family ${\cal Q}$ containing $Q$, with the same sufficient statistic as the null. Our theorem shows that simple e-variables exist whenever the covariance matrices of ${\cal Q}$ and the null are in a certain relation. Examples in which this relation holds include some $k$-sample tests, Gaussian location- and scale tests, and tests for more general classes of natural exponential families.

Large-scale sky surveys at low frequencies, like the LOFAR Two-metre Sky Survey (LoTSS), allow for the detection and characterisation of unprecedented numbers of giant radio galaxies (GRGs, or 'giants'). In this work, by automating the creation of radio--optical catalogues, we aim to significantly expand the census of known giants. We then combine this sample with a forward model to constrain GRG properties of cosmological interest. In particular, we automate radio source component association through machine learning and optical host identification for resolved radio sources. We create a radio--optical catalogue for the full LoTSS Data Release 2 (DR2) and select all possible giants. We combine our candidates with an existing catalogue of LoTSS DR2 crowd-sourced GRG candidates and visually confirm or reject them. To infer intrinsic GRG properties from GRG observations, we develop further a population-based forward model that takes into account selection effects and constrain its parameters using Bayesian inference. We confirm 5,647 previously unknown giants from the crowd-sourced catalogue and 2,597 previously unknown giants from the ML-driven catalogue. Our confirmations and discoveries bring the total number of known giants to at least 11,585. We predict a comoving GRG number density $n_\mathrm{GRG} = 13 \pm 10\ (100\ \mathrm{Mpc})^{-3}$, close to a recent estimate of the number density of luminous non-giant radio galaxies. We derive a current-day GRG lobe volume-filling fraction $V_\mathrm{GRG-CW}(z = 0) = 1.4 \pm 1.1 \cdot 10^{-5}$ in clusters and filaments of the Cosmic Web. Our analysis suggests that giants are more common than previously thought. Moreover, tentative results imply that it is possible that magnetic fields once contained in giants pervade a significant ($\gtrsim 10\%$) fraction of today's Cosmic Web., Comment: 25 pages, 15 figures, 3 tables, accepted for publication in Astronomy & Astrophysics

(abridged) Protostellar outflows exhibit large variations in their structure depending on the observed gas emission. This study analyzes the atomic jet and molecular outflow in the Class I protostar, TMC1A to characterize morphology and identify previously undetected spatial features with JWST's NIRSpec IFU. In addition to identifying a large number of Fe II and H2 lines, we have detected the bipolar Fe jet by revealing, for the first time, the presence of a red-shifted atomic jet. Similarly, the red-shifted component of the H2 slower wide-angle outflow is observed. Both Fe II and H2 red-shifted emission exhibit significantly lower flux densities compared to their blue-shifted counterparts. Additionally, we report the detection of a collimated high-velocity (100 km s-1), blue-shifted H2 outflow, suggesting the presence of a molecular jet in addition to the well-known wider angle low-velocity structure. The Fe II and H2 jets show multiple intensity peaks along the jet axis, which may be associated with ongoing or recent outburst events. In addition to the variation in their intensities, the H2 wide-angle outflow exhibits a "ring"-like structure. The blue-shifted H2 outflow also shows a left-right brightness asymmetry likely due to interactions with the surrounding ambient medium and molecular outflows. Using the Fe II line ratios, the extinction along the atomic jet is estimated to be between Av = 10-30 on the blue-shifted side, with a trend of decreasing extinction with distance from the protostar. A similar Av is found for the red-shifted side, supporting the argument for an intrinsic red-blue outflow lobe asymmetry rather than environmental effects such as extinction. This intrinsic difference revealed by the unprecedented sensitivity of JWST, suggests that younger outflows already exhibit the red-blue side asymmetry more commonly observed towards jets associated with Class II disks., Comment: 16 pages, 11 figures

Giant radio galaxies (GRGs, giant RGs, or giants) are megaparsec-scale, jet-driven outflows from accretion disks of supermassive black holes, and represent the most extreme pathway by which galaxies can impact the Cosmic Web around them. A long-standing but unresolved question is why giants are so much larger than other radio galaxies. It has been proposed that, in addition to having higher jet powers than most RGs, giants might live in especially low-density Cosmic Web environments. In this work, we aim to test this hypothesis by pinpointing Local Universe giants and other RGs in physically principled, Bayesian large-scale structure reconstructions. More specifically, we localised a LOFAR Two-metre Sky Survey (LoTSS) DR2-dominated sample of luminous ($l_\nu(\nu = 150\ \mathrm{MHz}) \geq 10^{24}\ \mathrm{W\ Hz^{-1}}$) giants and a control sample of LoTSS DR1 RGs, both with spectroscopic redshifts up to $z_\mathrm{max} = 0.16$, in the BORG SDSS Cosmic Web reconstructions. We measured the Cosmic Web density for each RG; for the control sample, we then quantified the relation between RG radio luminosity and Cosmic Web density. With the BORG SDSS tidal tensor, we also measured for each RG whether the gravitational dynamics of its Cosmic Web environment resemble those of clusters, filaments, sheets, or voids. Luminous giants populate large-scale environments that tend to be denser than those of general RGs. This shows that -- at least at high jet powers -- low-density environments are no prerequisite for giant growth. This result is corroborated by gravitational dynamics classification and a cluster catalogue crossmatching analysis. This work presents more than a thousand inferred megaparsec-scale densities around radio galaxies. Our findings are consistent with the view that giants are regular, rather than mechanistically special, members of the radio galaxy population., Comment: 28 pages, 21 figures, 4 tables, accepted for publication in Astronomy & Astrophysics

An implicit causal space-time Galerkin scheme applied to the contrast current density volume integral equation gives rise to a marching-on-in-time scheme known as the MOT-JVIE, which is accelerated and stabilized via a fully embedded FIR filter to compute the electromagnetic scattering from high permittivity dielectric objects discretized with over a million voxels. A review of two different acceleration approaches previously developed for two-dimensional time-domain surface integral equations based on fast Fourier transforms (FFTs), leads to an understanding why these schemes obtain the same order of acceleration and the extension of this FFT-acceleration to the three-dimensional MOT-JVIE. The positive definite stability analysis (PDSA) for the MOT-JVIE shows that the number of voxels for a stable MOT-JVIE discretization is restricted by the finite precision of the matrix elements. The application of the PDSA provides the insight that stability can be enforced through regularization, at the cost of accuracy. To minimize the impact in accuracy, FIR-regularization is introduced, which is based on low group-delay linear-phase high-pass FIR-filters. We demonstrate the capabilities of the FFT-accelerated FIR-regularized MOT-JVIE for a number of numerical experiments with high permittivity dielectric scatterers., Comment: Submitted to Progress In Electromagnetic Research

Optical trapping enables precise control of individual particles of different sizes, such as atoms, molecules, or nanospheres. Optical tweezers provide free-space omnidirectional optical trapping of objects in laboratories around the world. As an alternative to standard macroscopic setups based on lenses, which are inherently bound by the diffraction limit, plasmonic and photonic nanostructures promise trapping by near-field optical effects on the extreme nanoscale. However, the practical design of lossless waveguide-coupled nanostructures capable of trapping sub-wavelength-sized particles in all spatial directions has until now proven insurmountable. In this work, we demonstrate an omnidirectional optical trap realized by inverse-designing fabrication-ready integrated dielectric nanocavities. The sub-wavelength optical trap is designed to rely solely on the gradient force and is thus particle-size agnostic. In particular, we show how a trapped particle with a radius of 15 nm experiences a force strong enough to overcome room-temperature thermal fluctuations. Furthermore, through the robust inverse design framework, we tailor manufacturable devices operating at short-wave infrared and near-infrared wavelengths. Our results open a new regime of levitated optical trapping by achieving a deep trapping potential capable of trapping single sub-wavelength particles in all directions using optical gradient forces. We anticipate potentially groundbreaking applications of the optimized optical trapping system for biomolecular analysis in aqueous environments, levitated cavity-optomechanics, and cold atom physics, constituting an important step towards realizing integrated bio-nanophotonics and mesoscopic quantum mechanical experiments.

Hot-carrier solar cells use the photon excess energy, that is, the energy exceeding the absorber bandgap, to do additional work. These devices have the potential to beat the upper limit for the photovoltaic power conversion efficiency set by near-equilibrium thermodynamics. However, since their conceptual inception in 1982, making this concept work under practical conditions has proven a tremendous hurdle, mostly due to the fast thermalization of photo-generated charges in typical semiconductor materials like silicon. Here, we use noise spectroscopy in combination with numerical modelling to show that common bulk heterojunction organic solar cells actually work as hot-carrier devices. Due to static energetic disorder, thermalization of photo-generated electrons and holes in the global density of states is slow compared to the charge carrier lifetime, leading to thermal populations of localized charge carriers that have an electronic temperature exceeding the lattice temperature. Since charge extraction takes place in a high-lying, narrow energy window around the transport energy, the latter takes the role of an energy filter. For common disorder values, this leads to substantial enhancements in open circuit voltage. We expect these results to inspire new strategies to more efficiently convert solar energy into electricity.

The nuclear magnetic resonance signal from sodium (23Na) nuclei demonstrates a fast bi-exponential T2 decay in biological tissues (T2,short = 0.5-5 ms and T2,long = 10-30 ms). Hence, blurring observed in sodium images acquired with center-out sequences is generally assumed to be dominated by signal attenuation at higher k-space frequencies. Most of the studies in the field primarily focus on the impact of readout duration on blurring but neglect the impact of resolution. In this paper, we examine the blurring effect of short T2 on images at different resolutions. A series of simulations, as well as phantom and in vivo scans were performed at varying resolutions and readout durations in order to evaluate progressive changes in image quality. We demonstrate that, given a fixed readout duration, T2 decay produces distinct blurring effects at different resolutions. Therefore, in addition to voxel size-dependent partial volume effects, the choice of resolution adds additional T2-dependent blurring.

Decentralized learning (DL) enables collaborative learning without a server and without training data leaving the users' devices. However, the models shared in DL can still be used to infer training data. Conventional defenses such as differential privacy and secure aggregation fall short in effectively safeguarding user privacy in DL, either sacrificing model utility or efficiency. We introduce Shatter, a novel DL approach in which nodes create virtual nodes (VNs) to disseminate chunks of their full model on their behalf. This enhances privacy by (i) preventing attackers from collecting full models from other nodes, and (ii) hiding the identity of the original node that produced a given model chunk. We theoretically prove the convergence of Shatter and provide a formal analysis demonstrating how Shatter reduces the efficacy of attacks compared to when exchanging full models between nodes. We evaluate the convergence and attack resilience of Shatter with existing DL algorithms, with heterogeneous datasets, and against three standard privacy attacks. Our evaluation shows that Shatter not only renders these privacy attacks infeasible when each node operates 16 VNs but also exhibits a positive impact on model utility compared to standard DL. In summary, Shatter enhances the privacy of DL while maintaining the utility and efficiency of the model., Comment: Accepted at PETS 2025

In many fact-finding investigations, notably parliamentary inquiries, process chronologies are created to reconstruct how a controversial policy or decision came into existence. Current approaches, like timelines, lack the expressiveness to represent the variety of relations in which historic events may link to the overall chronology. This obfuscates the nature of the interdependence among the events, and the texts from which they are distilled. Based on explorative interviews with expert analysts, we propose an extended, rich set of relationships. We describe how these can be visualized as TimeFlows. We provide an example of such a visualization by illustrating the Childcare Benefits Scandal -- an affair that deeply affected Dutch politics in recent years. This work extends the scope of existing process discovery research into the direction of unveiling non-repetitive processes from unstructured information objects., Comment: 16 pages, accepted at RCIS 2024

Identifying the chemical structure from a graphical representation, or image, of a molecule is a challenging pattern recognition task that would greatly benefit drug development. Yet, existing methods for chemical structure recognition do not typically generalize well, and show diminished effectiveness when confronted with domains where data is sparse, or costly to generate, such as hand-drawn molecule images. To address this limitation, we propose a new chemical structure recognition tool that delivers state-of-the-art performance and can adapt to new domains with a limited number of data samples and supervision. Unlike previous approaches, our method provides atom-level localization, and can therefore segment the image into the different atoms and bonds. Our model is the first model to perform OCSR with atom-level entity detection with only SMILES supervision. Through rigorous and extensive benchmarking, we demonstrate the preeminence of our chemical structure recognition approach in terms of data efficiency, accuracy, and atom-level entity prediction., Comment: Accepted in IEEE/CVF Conference on Computer Vision and Pattern Recognition 2024

Exciton-plasmon polaritons (EPPs) are attractive both for the exploration of fundamental phenomena and applications in nanophotonics. Previous studies of EPPs mainly relied on far-field characterization. Here, using near-field optical microscopy, we quantitatively characterize the dispersion of EPPs existing in 13-nm-thick tungsten diselenide (WSe$_2$) deposited on a monocrystalline gold platelet. We extract from our experimental data a Rabi splitting of 81 meV, and an experimental effective polariton loss of 55 meV, demonstrating that our system is in the strong-coupling regime. Furthermore, we measure for the first time at visible wavelengths the propagation length of these EPPs for each excitation energy of the dispersion relation. To demonstrate the quantitative nature of our near-field method to obtain the full complex-valued wavevector of EPPs, we use our near-field measurements to predict, via the transfer matrix method, the far-field reflectivities across the exciton resonance. These predictions are in excellent agreement with our experimental far-field measurements. Our findings open the door towards the full near-field study of light-manipulating devices at the nanoscale., Comment: 21 pages, 3 figures

We study non-holomorphic modular forms built from iterated integrals of holomorphic modular forms for SL$(2,\mathbb Z)$ known as equivariant iterated Eisenstein integrals. A special subclass of them furnishes an equivalent description of the modular graph forms appearing in the low-energy expansion of string amplitudes at genus one. Notably the Fourier expansion of modular graph forms contains single-valued multiple zeta values. We deduce the appearance of products and higher-depth instances of multiple zeta values in equivariant iterated Eisenstein integrals, and ultimately modular graph forms, from the appearance of simpler odd Riemann zeta values. This analysis relies on so-called zeta generators which act on certain non-commutative variables in the generating series of the iterated integrals. From an extension of these non-commutative variables we incorporate iterated integrals involving holomorphic cusp forms into our setup and use them to construct the modular completion of triple Eisenstein integrals. Our work represents a fully explicit realisation of the modular graph forms within Brown's framework of equivariant iterated Eisenstein integrals and reveals structural analogies between single-valued period functions appearing in genus zero and one string amplitudes., Comment: 102 pages plus appendices; submission includes ancillary data files; v2: minor corrections, published version

In pharmacoepidemiology research, instrumental variables (IVs) are variables that strongly predict treatment but have no causal effect on the outcome of interest except through the treatment. There remain concerns about the inclusion of IVs in propensity score (PS) models amplifying estimation bias and reducing precision. Some PS modeling approaches attempt to address the potential effects of IVs, including selecting only covariates for the PS model that are strongly associated to the outcome of interest, thus screening out IVs. We conduct a study utilizing simulations and negative control experiments to evaluate the effect of IVs on PS model performance and to uncover best PS practices for real-world studies. We find that simulated IVs have a weak effect on bias and precision in both simulations and negative control experiments based on real-world data. In simulation experiments, PS methods that utilize outcome data, including the high-dimensional propensity score, produce the least estimation bias. However, in real-world settings underlying causal structures are unknown, and negative control experiments can illustrate a PS model's ability to minimize systematic bias. We find that large-scale, regularized regression based PS models in this case provide the most centered negative control distributions, suggesting superior performance in real-world scenarios.

A consistent theory describing the dynamics of quantum systems interacting on a classical space-time was recently put forward by Oppenheim et al..[1, 2]. Quantum states may retain their coherence, at the cost of some amount of stochasticity of the spacetime metric, characterized by a spacetime diffusion parameter. Here, we report existing experimental upper bounds on such space-time diffusion, based on a review of several types of experiments with very low force noise over a broad range of test masses from single atoms to several kilograms. We find an upper bound at least 15 orders of magnitude lower as compared to the initial bounds for explicit models presented by Oppenheimn et al. The results presented here provide a path forward for future experiments that can help evaluate classical-quantum theories, Comment: 8 pages, 1 figure, 1 table

Cyber threats continue to evolve in complexity, thereby traditional Cyber Threat Intelligence (CTI) methods struggle to keep pace. AI offers a potential solution, automating and enhancing various tasks, from data ingestion to resilience verification. This paper explores the potential of integrating Artificial Intelligence (AI) into CTI. We provide a blueprint of an AI-enhanced CTI processing pipeline, and detail its components and functionalities. The pipeline highlights the collaboration of AI and human expertise, which is necessary to produce timely and high-fidelity cyber threat intelligence. We also explore the automated generation of mitigation recommendations, harnessing AI's capabilities to provide real-time, contextual, and predictive insights. However, the integration of AI into CTI is not without challenges. Thereby, we discuss ethical dilemmas, potential biases, and the imperative for transparency in AI-driven decisions. We address the need for data privacy, consent mechanisms, and the potential misuse of technology. Moreover, we highlights the importance of addressing biases both during CTI analysis and AI models warranting their transparency and interpretability. Lastly, our work points out future research directions such as the exploration of advanced AI models to augment cyber defences, and the human-AI collaboration optimization. Ultimately, the fusion of AI with CTI appears to hold significant potential in cybersecurity domain.

This manuscript will unify inverse freeform reflector design and surface light scattering to design freeform reflectors with a scattering surface. We use microfacets, which are small, tilted mirrors superimposed on a smooth surface. We form a simple model of surface roughness and light scattering based on the orientations of the microfacets. Using a least-squares solver to compute the smooth reflector as a starting point, we can subsequently alter the surface using an optimization procedure to account for the scattering. After optimization, the resulting reflector surface produces the desired scattered light distribution. We verify the resulting reflector using raytracing.

Scheduling jobs with given processing times on identical parallel machines so as to minimize their total completion time is one of the most basic scheduling problems. We study interesting generalizations of this classical problem involving scenarios. In our model, a scenario is defined as a subset of a predefined and fully specified set of jobs. The aim is to find an assignment of the whole set of jobs to identical parallel machines such that the schedule, obtained for the given scenarios by simply skipping the jobs not in the scenario, optimizes a function of the total completion times over all scenarios. While the underlying scheduling problem without scenarios can be solved efficiently by a simple greedy procedure (SPT rule), scenarios, in general, make the problem NP-hard. We paint an almost complete picture of the evolving complexity landscape, drawing the line between easy and hard. One of our main algorithmic contributions relies on a deep structural result on the maximum imbalance of an optimal schedule, based on a subtle connection to Hilbert bases of a related convex cone.

Existing work in fairness auditing assumes that each audit is performed independently. In this paper, we consider multiple agents working together, each auditing the same platform for different tasks. Agents have two levers: their collaboration strategy, with or without coordination beforehand, and their strategy for sampling appropriate data points. We theoretically compare the interplay of these levers. Our main findings are that (i) collaboration is generally beneficial for accurate audits, (ii) basic sampling methods often prove to be effective, and (iii) counter-intuitively, extensive coordination on queries often deteriorates audits accuracy as the number of agents increases. Experiments on three large datasets confirm our theoretical results. Our findings motivate collaboration during fairness audits of platforms that use ML models for decision-making., Comment: 14 pages, 7 figures, ECAI

Segmentations are crucial in medical imaging to obtain morphological, volumetric, and radiomics biomarkers. Manual segmentation is accurate but not feasible in the radiologist's clinical workflow, while automatic segmentation generally obtains sub-par performance. We therefore developed a minimally interactive deep learning-based segmentation method for soft-tissue tumors (STTs) on CT and MRI. The method requires the user to click six points near the tumor's extreme boundaries. These six points are transformed into a distance map and serve, with the image, as input for a Convolutional Neural Network. For training and validation, a multicenter dataset containing 514 patients and nine STT types in seven anatomical locations was used, resulting in a Dice Similarity Coefficient (DSC) of 0.85$\pm$0.11 (mean $\pm$ standard deviation (SD)) for CT and 0.84$\pm$0.12 for T1-weighted MRI, when compared to manual segmentations made by expert radiologists. Next, the method was externally validated on a dataset including five unseen STT phenotypes in extremities, achieving 0.81$\pm$0.08 for CT, 0.84$\pm$0.09 for T1-weighted MRI, and 0.88\pm0.08 for previously unseen T2-weighted fat-saturated (FS) MRI. In conclusion, our minimally interactive segmentation method effectively segments different types of STTs on CT and MRI, with robust generalization to previously unseen phenotypes and imaging modalities.