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The rise of non-magnetic topological semimetals, which provide a promising platform for observing and controlling various spin-orbit effects, has led to significant advancements in the field of topological spintronics. RhSi exists in two distinct polymorphs: cubic and orthorhombic crystal structures. The noncentrosymmetric B20 cubic structure has been extensively studied for hosting unconventional multifold fermions. In contrast, the orthorhombic structure, which crystallizes in the Pnma space group (No. 62), remains less explored and belongs to the family of topological Dirac semimetals. In this work, we investigate the structural, magnetic, and electrical properties of RhSi textured-epitaxial films grown on Si(111) substrates, which crystallize in the orthorhombic structure. We investigate the efficiency of pure spin current transport across RhSi/permalloy interfaces and the subsequent spin-to-charge current conversion via inverse spin Hall effect measurements. The xperimentally determined spin Hall conductivity in orthorhombic RhSi reaches a maximum value of 126 ($\hbar$/e)($\Omega$.cm)$^{-1}$ at 10 K, which aligns reasonably well with first-principles calculations that attribute the spin Hall effect in RhSi to the spin Berry curvature mechanism. Additionally, we demonstrate the ability to achieve a sizable spin-mixing conductance (34.7 nm$^{-2}$) and an exceptionally high interfacial spin transparency of 88$%$ in this heterostructure, underlining its potential for spin-orbit torque switching applications. Overall, this study broadens the scope of topological spintronics, emphasizing the controlled interfacial spin-transport processes and subsequent spin-to-charge conversion in a previously unexplored topological Dirac semimetal RhSi/ferromagnet heterostructure.

Deep learning has made significant strides in medical imaging, leveraging the use of large datasets to improve diagnostics and prognostics. However, large datasets often come with inherent errors through subject selection and acquisition. In this paper, we investigate the use of Diffusion Autoencoder (DAE) embeddings for uncovering and understanding data characteristics and biases, including biases for protected variables like sex and data abnormalities indicative of unwanted protocol variations. We use sagittal T2-weighted magnetic resonance (MR) images of the neck, chest, and lumbar region from 11186 German National Cohort (NAKO) participants. We compare DAE embeddings with existing generative models like StyleGAN and Variational Autoencoder. Evaluations on a large-scale dataset consisting of sagittal T2-weighted MR images of three spine regions show that DAE embeddings effectively separate protected variables such as sex and age. Furthermore, we used t-SNE visualization to identify unwanted variations in imaging protocols, revealing differences in head positioning. Our embedding can identify samples where a sex predictor will have issues learning the correct sex. Our findings highlight the potential of using advanced embedding techniques like DAEs to detect data quality issues and biases in medical imaging datasets. Identifying such hidden relations can enhance the reliability and fairness of deep learning models in healthcare applications, ultimately improving patient care and outcomes., Comment: This paper was accepted in the "Workshop on Interpretability of Machine Intelligence in Medical Image Computing" (iMIMIC) at MICCAI 2024

Moir\'e lattices created in two-dimensional heterostructures exhibit rich many-body physics of interacting electrons and excitons and, at the same time, suggest promising optoelectronic applications. Here, we study the cooperative radiance of moir\'e excitons that is demonstrated to emerge from the deep subwavelength nature of the moir\'e lattice and the strong excitonic onsite interaction. In particular, we show that the static dipole-dipole interaction between interlayer excitons can strongly affect their cooperative optical properties, suppressing superradiance of disordered states while enhancing superradiance of ordered phases of moir\'e excitons. Moreover, we show that doping permits direct control of optical cooperativity, e.g., by generating supperradiant dynamics of otherwise subradiant states of excitons. Our results show that interlayer moir\'e excitons offer a unique platform for exploring cooperative optical phenomena in strongly interacting many-body systems, thus, holding promise for applications in quantum nonlinear optics., Comment: 8 pages (including references), 4 figures

Lung radiotherapy treatment systems are subject to a latency that leads to uncertainty in the estimated tumor location and high irradiation of healthy tissue. This work addresses future frame prediction in chest dynamic MRI sequences to compensate for that delay using RNNs trained with online learning algorithms. The latter enable networks to mitigate irregular movements, as they update synaptic weights with each new training example. Experiments were conducted using four publicly available 2D thoracic cine-MRI sequences. PCA decomposes the time-varying deformation vector field (DVF), computed with the Lucas-Kanade optical flow algorithm, into static deformation fields and low-dimensional time-dependent weights. We compare various algorithms to forecast the latter: linear regression, least mean squares (LMS), and RNNs trained with real-time recurrent learning (RTRL), unbiased online recurrent optimization, decoupled neural interfaces and sparse 1-step approximation (SnAp-1). That enables estimating the future DVFs and, in turn, the next frames by warping the initial image. Linear regression led to the lowest mean DVF error at a horizon h = 0.32s (the time interval in advance for which the prediction is made), equal to 1.30mm, followed by SnAp-1 and RTRL, whose error increased from 1.37mm to 1.44mm as h increased from 0.62s to 2.20s. Similarly, the structural similarity index measure (SSIM) of LMS decreased from 0.904 to 0.898 as h increased from 0.31s to 1.57s and was the highest among the algorithms compared for the latter horizons. SnAp-1 attained the highest SSIM for h $\geq$ 1.88s, with values of less than 0.898. The predicted images look similar to the original ones, and the highest errors occurred at challenging areas such as the diaphragm boundary at the end-of-inhale phase, where motion variability is more prominent, and regions where out-of-plane motion was more prevalent., Comment: 28 pages, 16 figures

We study the equivariant edge measure: a measure on partitions which arises implicitly in the edge term in the localization computation of the Donaldson-Thomas invariants of a toric threefold. We combinatorially show that the equivariant edge measure is, up to choices of convention, equal to the Jack-Plancherel measure., Comment: 12 pages

Recent advancements in medicine have confirmed that brain disorders often comprise multiple subtypes of mechanisms, developmental trajectories, or severity levels. Such heterogeneity is often associated with demographic aspects (e.g., sex) or disease-related contributors (e.g., genetics). Thus, the predictive power of machine learning models used for symptom prediction varies across subjects based on such factors. To model this heterogeneity, one can assign each training sample a factor-dependent weight, which modulates the subject's contribution to the overall objective loss function. To this end, we propose to model the subject weights as a linear combination of the eigenbases of a spectral population graph that captures the similarity of factors across subjects. In doing so, the learned weights smoothly vary across the graph, highlighting sub-cohorts with high and low predictability. Our proposed sample weighting scheme is evaluated on two tasks. First, we predict initiation of heavy alcohol drinking in young adulthood from imaging and neuropsychological measures from the National Consortium on Alcohol and NeuroDevelopment in Adolescence (NCANDA). Next, we detect Dementia vs. Mild Cognitive Impairment (MCI) using imaging and demographic measurements in subjects from the Alzheimer's Disease Neuroimaging Initiative (ADNI). Compared to existing sample weighting schemes, our sample weights improve interpretability and highlight sub-cohorts with distinct characteristics and varying model accuracy.

Emerging magnetic fields related to the presence of topologically protected spin textures such as skyrmions are expected to give rise to additional, topology-related contributions to the Hall effect. In order to doubtlessly identify this so-called topological Hall effect, it is crucial to disentangle such contributions from the anomalous Hall effect. This necessitates a direct correlation of the transversal Hall voltage with the underlying magnetic textures. We utilize a novel measurement platform that allows to acquire high-resolution Lorentz transmission electron microscopy images of magnetic textures as a function of an external magnetic field and to concurrently measure the (anomalous) Hall voltage in-situ in the microscope on one and the same specimen. We use this approach to investigate the transport signatures of the chiral soliton lattice and antiskyrmions in Mn$_{1.4}$PtSn. Notably, the observed textures allow to fully understand the measured Hall voltage without the need of any additional contributions due to a topological Hall effect, and the field-controlled formation and annihilation of anstiskyrmions are found to have no effect on the measurend Hall voltage.

Deep learning can help uncover patterns in resting-state functional Magnetic Resonance Imaging (rs-fMRI) associated with psychiatric disorders and personal traits. Yet the problem of interpreting deep learning findings is rarely more evident than in fMRI analyses, as the data is sensitive to scanning effects and inherently difficult to visualize. We propose a simple approach to mitigate these challenges grounded on sparsification and self-supervision. Instead of extracting post-hoc feature attributions to uncover functional connections that are important to the target task, we identify a small subset of highly informative connections during training and occlude the rest. To this end, we jointly train a (1) sparse input mask, (2) variational autoencoder (VAE), and (3) downstream classifier in an end-to-end fashion. While we need a portion of labeled samples to train the classifier, we optimize the sparse mask and VAE with unlabeled data from additional acquisition sites, retaining only the input features that generalize well. We evaluate our method - Sparsely Reconstructed Graphs (SpaRG) - on the public ABIDE dataset for the task of sex classification, training with labeled cases from 18 sites and adapting the model to two additional out-of-distribution sites with a portion of unlabeled samples. For a relatively coarse parcellation (64 regions), SpaRG utilizes only 1% of the original connections while improving the classification accuracy across domains. Our code can be found at github.com/yanismiraoui/SpaRG.

Many longitudinal neuroimaging studies aim to improve the understanding of brain aging and diseases by studying the dynamic interactions between brain function and cognition. Doing so requires accurate encoding of their multidimensional relationship while accounting for individual variability over time. For this purpose, we propose an unsupervised learning model (called \underline{\textbf{Co}}ntrastive Learning-based \underline{\textbf{Gra}}ph Generalized \underline{\textbf{Ca}}nonical Correlation Analysis (CoGraCa)) that encodes their relationship via Graph Attention Networks and generalized Canonical Correlational Analysis. To create brain-cognition fingerprints reflecting unique neural and cognitive phenotype of each person, the model also relies on individualized and multimodal contrastive learning. We apply CoGraCa to longitudinal dataset of healthy individuals consisting of resting-state functional MRI and cognitive measures acquired at multiple visits for each participant. The generated fingerprints effectively capture significant individual differences and outperform current single-modal and CCA-based multimodal models in identifying sex and age. More importantly, our encoding provides interpretable interactions between those two modalities.

Deep learning models generating structural brain MRIs have the potential to significantly accelerate discovery of neuroscience studies. However, their use has been limited in part by the way their quality is evaluated. Most evaluations of generative models focus on metrics originally designed for natural images (such as structural similarity index and Frechet inception distance). As we show in a comparison of 6 state-of-the-art generative models trained and tested on over 3000 MRIs, these metrics are sensitive to the experimental setup and inadequately assess how well brain MRIs capture macrostructural properties of brain regions (i.e., anatomical plausibility). This shortcoming of the metrics results in inconclusive findings even when qualitative differences between the outputs of models are evident. We therefore propose a framework for evaluating models generating brain MRIs, which requires uniform processing of the real MRIs, standardizing the implementation of the models, and automatically segmenting the MRIs generated by the models. The segmentations are used for quantifying the plausibility of anatomy displayed in the MRIs. To ensure meaningful quantification, it is crucial that the segmentations are highly reliable. Our framework rigorously checks this reliability, a step often overlooked by prior work. Only 3 of the 6 generative models produced MRIs, of which at least 95% had highly reliable segmentations. More importantly, the assessment of each model by our framework is in line with qualitative assessments, reinforcing the validity of our approach., Comment: Accepted MICCAI 20224

The number of samples in structural brain MRI studies is often too small to properly train deep learning models. Generative models show promise in addressing this issue by effectively learning the data distribution and generating high-fidelity MRI. However, they struggle to produce diverse, high-quality data outside the distribution defined by the training data. One way to address the issue is using causal models developed for 3D volume counterfactuals. However, accurately modeling causality in high-dimensional spaces is a challenge so that these models generally generate 3D brain MRIS of lower quality. To address these challenges, we propose a two-stage method that constructs a Structural Causal Model (SCM) within the latent space. In the first stage, we employ a VQ-VAE to learn a compact embedding of the MRI volume. Subsequently, we integrate our causal model into this latent space and execute a three-step counterfactual procedure using a closed-form Generalized Linear Model (GLM). Our experiments conducted on real-world high-resolution MRI data (1mm) demonstrate that our method can generate high-quality 3D MRI counterfactuals.

In the core-collapse scenario, the supernova remnants evolve inside the complex wind-blown bubbles, structured by massive progenitors during their lifetime. Therefore, particle acceleration and the emissions from these SNRs can carry the fingerprints of the evolutionary sequences of the progenitor stars. We time-dependently investigate the impact of the ambient environment of core-collapse SNRs on particle spectra and the emissions. We use the RATPaC code to model the particle acceleration at the SNRs with progenitors having ZAMS masses of 20 Msol and 60 Msol. We have constructed the pre-supernova circumstellar medium by solving the hydrodynamic equations for the lifetime of the progenitor stars. Then, the transport equation for cosmic rays, and magnetic turbulence in test-particle approximation along with the induction equation for the evolution of large-scale magnetic field have been solved simultaneously with the hydrodynamic equations for the expansion of SNRs inside the pre-supernova CSM. The structure of the wind bubbles along with the magnetic field and the scattering turbulence regulate the spectra of accelerated particles for both SNRs. For the 60 Msol progenitor the spectral index reaches 2.4 even below 10 GeV during the propagation of the SNR shock inside the hot shocked wind. In contrast, we have not observed persistent soft spectra at earlier evolutionary stages of the SNR with 20 Msol progenitor, for which the spectral index becomes 2.2 only for a brief period. Later, the spectra become soft above ~10 GeV for both SNRs, as weak driving of turbulence permits the escape of high-energy particles from the remnants. The emission morphology of the SNRs strongly depends on the type of progenitors. For instance, the radio morphology of the SNR with 20 Msol progenitor is centre-filled at early stages whereas that for the more massive progenitor is shell-like.

Knowledge probing evaluates the extent to which a language model (LM) has acquired relational knowledge during its pre-training phase. It provides a cost-effective means of comparing LMs of different sizes and training setups and is useful for monitoring knowledge gained or lost during continual learning (CL). In prior work, we presented an improved knowledge probe called BEAR (Wiland et al., 2024), which enables the comparison of LMs trained with different pre-training objectives (causal and masked LMs) and addresses issues of skewed distributions in previous probes to deliver a more unbiased reading of LM knowledge. With this paper, we present LM-PUB- QUIZ, a Python framework and leaderboard built around the BEAR probing mechanism that enables researchers and practitioners to apply it in their work. It provides options for standalone evaluation and direct integration into the widely-used training pipeline of the Hugging Face TRANSFORMERS library. Further, it provides a fine-grained analysis of different knowledge types to assist users in better understanding the knowledge in each evaluated LM. We publicly release LM-PUB-QUIZ as an open-source project.

The competition between the Kondo spin-screening effect and the Ruderman-Kittel-Kasuya-Yosida (RKKY) interaction in heavy-fermion systems drives the quantum phase transition between the magnetically ordered and the heavy-Fermi-liquid ground states. Despite intensive investigations of heavy quasiparticles on the Kondo-screened side of the quantum phase transition and of their breakdown at the quantum critical point, studies on the magnetically ordering side are scarce. Using terahertz time-domain spectroscopy, we report a suppression of the Kondo quasiparticle weight in CeCu6-xAux samples on the antiferromagnetic side of the quantum phase transition at temperatures as much as two orders of magnitude above the Neel temperature TN. The suppression results from a quantum frustration effect induced by the temperature-independent RKKY interaction. Hence, our results emphasize that besides critical fluctuations, the RKKY interaction may play an important role in the quantum-critical scenario., Comment: 6 pages, 4 figures

In silico testing of implant materials is a research area of high interest, as cost- and labour-intensive experiments may be omitted. However, assessing the tissue-material interaction mathematically and computationally can be very complex, in particular when functional, such as biodegradable, implant materials are investigated. In this work, we expand and refine suitable existing mathematical models of bone growth and magnesium-based implant degradation based on ordinary differential equations. We show that we can simulate the implant degradation, as well as the osseointegration in terms of relative bone volume fraction and changes in bone ultrastructure when applying the model to experimental data from titanium and magnesium-gadolinium implants for healing times up to 32 weeks. By conducting a parameter study we further show that a lack of data at early time points has little influence on the simulation outcome. Moreover, we show that the model is predictive in terms of relative bone volume fraction with mean absolute errors below 6%

For vector-valued Maass cusp forms for~$SL_2(\mathbb{Z})$ with real weight~$k\in\mathbb{R}$ and spectral parameter $s\in\mathbb{C}$, $\mathrm{Re} s\in (0,1)$, $s\not\equiv \pm k/2$ mod $1$, we propose a notion of vector-valued period functions, and we establish a linear isomorphism between the spaces of Maass cusp forms and period functions by means of a cohomological approach. The period functions are a generalization of those for the classical Maass cusp forms, being solutions of a finite-term functional equation or, equivalently, eigenfunctions with eigenvalue $1$ of a transfer operator deduced from the geodesic flow on the modular surface. We apply this result to deduce a notion of period functions and related linear isomorphism for Jacobi Maass forms of weight $k+1/2$ for the semi-direct product of $SL_2(\mathbb{Z})$ with the integer points $Hei(\mathbb{Z})$ of the Heisenberg group., Comment: 58 pages, 7 figure

Understanding the nature and identity of dark matter is a key goal in the physics community. In the case that TeV-scale dark matter particles decay or annihilate into standard model particles, very-high-energy (VHE) gamma rays (greater than 100 GeV) will be present in the final state. The Very Energetic Radiation Imaging Telescope Array System (VERITAS) is an imaging atmospheric Cherenkov telescope array that can indirectly detect VHE gamma rays in an energy range of 100 GeV to > 30 TeV. Dwarf spheroidal galaxies (dSphs) are ideal candidates in the search for dark matter due to their high dark matter content, high mass-to-light ratios, and their low gamma-ray fluxes from astrophysical processes. This study uses a legacy data set of 638 hours collected on 17 dSphs, built over 11 years with an observing strategy optimized according to the dark matter content of the targets. The study addresses a broad dark matter particle mass range, extending from 200 GeV to 30 PeV. In the absence of a detection, we set the upper limits on the dark matter velocity-weighted annihilation cross section., Comment: 18 pages, 7 figures, 3 tables, accepted in PRD

In February 2017, the blazar OJ~287 underwent a period of intense multiwavelength activity. It reached a new historic peak in the soft X-ray (0.3-10 keV) band, as measured by Swift-XRT. This event coincides with a very-high-energy (VHE) $\gamma$-ray outburst that led VERITAS to detect emission above 100 GeV, with a detection significance of $10\sigma$ (from 2016 December 9 to 2017 March 31). The time-averaged VHE $\gamma$-ray spectrum was consistent with a soft power law ($\Gamma = -3.81 \pm 0.26$) and an integral flux corresponding to $\sim2.4\%$ that of the Crab Nebula above the same energy. Contemporaneous data from multiple instruments across the electromagnetic spectrum reveal complex flaring behavior, primarily in the soft X-ray and VHE bands. To investigate the possible origin of such an event, our study focuses on three distinct activity states: before, during, and after the February 2017 peak. The spectral energy distributions during these periods suggest the presence of at least two non-thermal emission zones, with the more compact one responsible for the observed flare. Broadband modeling results and observations of a new radio knot in the jet of OJ~287 in 2017 are consistent with a flare originating from a strong recollimation shock outside the radio core., Comment: 21 pages, 7 figures, accepted for publication in The Astrophysical Journal

A detailed and systematic investigation of polytropic gas effects in Parker's solar wind model and coronal-hole flows is given. We present a viable equation governing the acceleration of solar wind of a polytropic gas and give its exact analytical and numerical solutions and deduce its asymptotic analytic properties (i) near the sun, (ii) far away from the sun, (iii) near the Parker sonic critical point (where the wind speed is equal to the speed of sound in the wind). We proceed to give a detailed and systematic investigation of coronal-hole polytropic gas outflows which contribute to bulk of the solar wind. We will model coronal-hole outflow by considering a single radial stream tube and use phenomenological considerations to represent its rapidly-diverging flow geometry. We give analytical and numerical solutions for this outflow and deduce its asymptotic analytic properties in the three flow regimes above. We find that, in general, the polytropic effects cause the Parker sonic critical point to move closer to the sun than that for the case with isothermal gas. Furthermore, the flow acceleration is found to exhibit (even for an infinitesimal deviation from isothermality of the gas) a power-law behavior rather than an exponential-law behavior near the sun or a logarithmic-law behavior far away from the sun, thus implying a certain robustness of the power-law behavior. The Parker sonic critical point is shown to continue to be of X-type, hence facilitating a smooth transition from subsonic to supersonic wind flow through the transonic regime. Our analytical and numerical solutions show that the super-radiality of the stream tube causes the Parker sonic critical point to move further down in the corona, and the gas to become more diabatic (the polytropic exponent gamma drops further below 5/3), and the flow acceleration to be enhanced further., Comment: 16 pages, 2 figures

We describe an approach to achieve near-perfect unidirectional light-matter coupling to an effective quantum emitter that is formed by a subwavelength array of atoms in the Rydberg-blockade regime. The nonlinear reflection and transmission of such two-dimensional superatoms are exploited in different interferometric setups for the deterministic generation of tunable single photons and entangling two-photon operations with high fidelities, $\mathcal{F}\gtrsim0.999$. The described setup can function as a versatile nonlinear optical element in a free-space photonic quantum network with simple linear elements and without the need of additional mode confinement, optical resonators, or optical isolators.

This study introduces the eFontes models for automatic linguistic annotation of Medieval Latin texts, focusing on lemmatization, part-of-speech tagging, and morphological feature determination. Using the Transformers library, these models were trained on Universal Dependencies (UD) corpora and the newly developed eFontes corpus of Polish Medieval Latin. The research evaluates the models' performance, addressing challenges such as orthographic variations and the integration of Latinized vernacular terms. The models achieved high accuracy rates: lemmatization at 92.60%, part-of-speech tagging at 83.29%, and morphological feature determination at 88.57%. The findings underscore the importance of high-quality annotated corpora and propose future enhancements, including extending the models to Named Entity Recognition.

Sub-wavelength arrays of quantum emitters offer an efficient free-space approach to coherent light-matter interfacing, using ultracold atoms or two-dimensional solid-state quantum materials. The combination of collectively suppressed photon-losses and emerging optical nonlinearities due to strong photon-coupling to mesoscopic numbers of emitters holds promise for generating nonclassical light and engineering effective interactions between freely propagating photons. While most studies have thus far relied on numerical simulations, we describe here a diagrammatic Green's function approach that permits analytical investigations of nonlinear processes. We illustrate the method by deriving a simple expression for the scattering matrix that describes photon-photon interactions in an extended two-dimensional array of quantum emitters, and reproduces the results of numerical simulations of coherently driven arrays. The approach yields intuitive insights into the nonlinear response of the system and offers a promising framework for a systematic development of a theory for interacting photons and many-body effects on collective radiance in two-dimensional arrays of quantum emitters., Comment: 13 pages, 8 figures

Creating graph visualizations involves many decisions, such as layout, node and edge appearance, and color choices. These decisions are challenging due to the multitude of options available. For instance, graph layout can be force-directed or orthogonal, and edges can be curved, tapered, partially drawn, or animated. Thus, research offers a multitude of guidelines to optimize graph visualizations for human perception and usability. Guidelines can be actionable, providing direct instructions, or non-actionable, specifying what to avoid. This work focuses on actionable guidelines for node-link diagrams, aiding designers in making better decisions. Given the abundance of graph visualization research and the difficulty in navigating it, this work aims to collect and structure actionable guidelines for node-linkvisualizations. To demonstrate the general applicability of our approach to structuring actionable guidelines for node-link diagrams, we also included guidelines for visualizing graphs as matrices. It also proposes a visual interactive system, GuidelineExplorer, to apply guidelines directly to graphs, streamlining the design process and promoting collaboration within the research community.

Prior research has shown that human perception of similarity differs from mathematical measures in visual comparison tasks, including those involving directed acyclic graphs. This divergence can lead to missed differences and skepticism about algorithmic results. To address this, we aim to learn the structural differences humans detect in graphs visually. We want to visualize these human-detected differences alongside actual changes, enhancing credibility and aiding users in spotting overlooked differences. Our approach aligns with recent research in machine learning capturing human behavior. We provide a data augmentation algorithm, a dataset, and a machine learning model to support this task. This work fills a gap in learning differences in directed acyclic graphs and contributes to better comparative visualizations.

Comparing directed acyclic graphs is essential in various fields such as healthcare, social media, finance, biology, and marketing. DAGs often result from contagion processes over networks, including information spreading, retweet activity, disease transmission, financial crisis propagation, malware spread, and gene mutations. For instance, in disease spreading, an infected patient can transmit the disease to contacts, making it crucial to analyze and predict scenarios. Similarly, in finance, understanding the effects of saving or not saving specific banks during a crisis is vital. Experts often need to identify small differences between DAGs, such as changes in a few nodes or edges. Even the presence or absence of a single edge can be significant. Visualization plays a crucial role in facilitating these comparisons. However, standard hierarchical layout algorithms struggle to visualize subtle changes effectively. The typical hierarchical layout, with the root on top, is preferred due to its performance in comparison to other layouts. Nevertheless, these standard algorithms prioritize single-graph aesthetics over comparison suitability, making it challenging for users to spot changes. To address this issue, we propose a layout that enhances shape changes in DAGs while minimizing the impact on aesthetics. Our approach involves outwardly swapping changes, altering the DAG's shape. We introduce new drawing criteria. Our layout builds upon a Sugiyama-like hierarchical layout and implements these criteria through two extensions. We designed it this way to maintain interchangeability and accommodate future optimizations, such as pseudo-nodes for edge crossing minimization. In our evaluations, our layout achieves excellent results, with edge crossing aesthetics averaging around 0.8 (on a scale of 0 to 1). Additionally, our layout outperforms the base implementation by an average of 60-75\%.

Pair cascades in polar cap regions of neutron stars are considered to be an essential process in various models of coherent radio emissions of pulsars. The cascades produce pair plasma bunch discharges in quasi-periodic spark events. The cascade properties, and therefore also the coherent radiation, depend strongly on the magnetospheric plasma properties and vary significantly across and along the polar cap. Importantly, where the radio emission emanates from in the polar cap region is still uncertain. We investigate the generation of electromagnetic waves by pair cascades and their propagation in the polar cap for three representative inclination angles of a magnetic dipole, $0^\circ$, $45^\circ$, and $90^\circ$. We use two-dimensional particle-in-cell simulations that include quantum-electrodynamic pair cascades in a charge-limited flow from the star surface. We find that the discharge properties are strongly dependent on the magnetospheric current profile in the polar cap and that transport channels for high intensity Poynting flux are formed along magnetic field lines where the magnetospheric currents approach zero and where the plasma cannot carry the magnetospheric currents. There, the parallel Poynting flux component is efficiently transported away from the star and may eventually escape the magnetosphere as coherent radio waves. The Poynting flux decreases with increasing distance from the star in regions of high magnetospheric currents. Our model shows that no process of energy conversion from particles to waves is necessary for the coherent radio wave emission. Moreover, the pulsar radio beam does not have a cone structure; rather, the radiation generated by the oscillating electric gap fields directly escapes along open magnetic field lines in which no pair creation occurs., Comment: 18 pages, 11 figures, 2 tables

Fiber Bragg Grating (FBG) sensors offer multiple benefits in comparison with electronic sensors due to their compactness, electromagnetic immunity as well as their resistance to harsh environments and their multiplexing capabilities. Structural Health Monitoring (SHM) is one of the various potential industrial applications that could take full advantage of those sensors. However, there is a need for a low size, weight, power and cost interrogation unit for certain application areas such as aerospace or aeronautics. That is the reason why recent efforts have been made to use integrated components and circuits for interrogation of FBGs. Among different techniques, interrogation with a swept laser source is of high interest since it has a high multiplexing capability and could reach a high level of integration using other integrated components such as photodetectors, grating couplers or directional couplers to form a compact interrogation unit. In this paper, we present characterization results of a fully-packaged hybrid III-V on silicon tunable laser diode operating in the C and L bands. Wavelength maps are produced and analyzed and modulation of emitted wavelength is discussed. Preliminary results corresponding to a moderate frequency (10-Hz sweep rate) were obtained and FBG reflection spectra acquired with a broadband source (BBS) and a swept laser diode are compared. Finally, we discuss potential design improvements in order to reach high scan rates (> 10 kHz) and a large tuning range.

In the course of the energy transition, the expansion of generation and consumption will change, and many of these technologies, such as PV systems, electric cars and heat pumps, will influence the power flow, especially in the distribution grids. Scalable methods that can make decisions for each grid connection are needed to enable congestion-free grid operation in the distribution grids. This paper presents a novel end-to-end approach to resolving congestion in distribution grids with deep reinforcement learning. Our architecture learns to curtail power and set appropriate reactive power to determine a non-congested and, thus, feasible grid state. State-of-the-art methods such as the optimal power flow (OPF) demand high computational costs and detailed measurements of every bus in a grid. In contrast, the presented method enables decisions under sparse information with just some buses observable in the grid. Distribution grids are generally not yet fully digitized and observable, so this method can be used for decision-making on the majority of low-voltage grids. On a real low-voltage grid the approach resolves 100\% of violations in the voltage band and 98.8\% of asset overloads. The results show that decisions can also be made on real grids that guarantee sufficient quality for congestion-free grid operation.

Conductive metal-organic frameworks (MOFs) are emerging electroactive materials for (opto-)electronics. However, it remains a great challenge to achieve reliable MOF-based devices via the existing synthesis methods that are compatible with the complementary metal-oxide-semiconductor technology, as the surface roughness of thus-far synthetic MOF films or pellets is rather high for efficient electrode contact. Here, we develop an on-liquid-gallium surface synthesis (OLGSS) strategy under chemical vapor deposition (CVD) conditions for the controlled growth of two-dimensional conjugated MOF (2D c-MOF) thin films with ten-fold improvement of surface flatness (surface roughness can reach as low as ~2 {\AA}) compared with MOF films grown by the traditional methods. Supported by theoretical modeling, we unveil a layer-by-layer CVD growth mode for constructing flattening surfaces, that is triggered by the high adhesion energy between gallium (Ga) and planar aromatic ligands. We further demonstrate the generality of the as-proposed OLGSS strategy by reproducing such a flat surface over nine different 2D c-MOF films with variable thicknesses (~2 to 208 nm) and large lateral sizes (over 1 cm2). The resultant ultra-smooth 2D c-MOF films enable the formation of high-quality electrical contacts with gold (Au) electrodes, leading to a reduction of contact resistance by over ten orders of magnitude compared to the traditional uneven MOF films. Furthermore, due to the efficient interfacial interaction benifited from the high-quality contacts, the prepared van der Waals heterostructure (vdWH) of OLGSS c-MOF and MoS2 exhibits intriguing photoluminescence (PL) enhancement, PL peak shift and large work function modulation. The establishment of the reliable OLGSS method provides the chances to push the development of MOF electronics and the construction of multicomponent MOF-based heterostructure materials.

Metal oxide (MO) semiconductors are widely used in electronic devices due to their high optical transmittance and promising electrical performance. This work describes the advancement toward an eco-friendly, streamlined method for preparing thin-film transistors (TFTs) via a pure water-solution blade-coating process with focus on a low thermal budget. Low temperature and rapid annealing of triple-coated indium oxide thin-film transistors (3C-TFTs) and indium oxide/zinc oxide/indium oxide thin-film transistors (IZI-TFTs) on a 300 nm SiO2 gate dielectric at 300 $^{\circ}$C for only 60 s yields devices with an average field effect mobility of 10.7 and 13.8 cm2/Vs, respectively. The devices show an excellent on/off ratio (>10^6), and a threshold voltage close to 0 V when measured in air. Flexible MO-TFTs on polyimide substrates with AlOx dielectrics fabricated by rapid annealing treatment can achieve a remarkable mobility of over 10 cm2/Vs at low operating voltage. When using a longer post-coating annealing period of 20 min, high-performance 3C-TFTs (over 18 cm2/Vs) and IZI-TFTs (over 38 cm2/Vs) using MO semiconductor layers annealed at 300 $^{\circ}$C are achieved.

Core-collapse supernova remnants are the nebular leftover of defunct massive stars which have died during a supernova explosion, mostly while undergoing the red supergiant phase of their evolution. The morphology and emission properties of those remnants are a function of the distribution of circumstellar material at the moment of the supernova, the intrisic properties of the explosion, as well as those of the ambient medium. By means of 2.5 dimensional numerical magnetohydrodynamics simulations, we model the long term evolution of supernova remnants generated by runaway rotating massive stars moving into a magnetised interstellar medium. Radiative transfer calculations reveal that the projected non-thermal emission of the supernova remnants decreases with time, i.e. older remnants are fainter than younger ones. Older (80 kyr) supernova remnants whose progenitors were moving with space velocity corresponding to a Mach number M = 1 (v_star = 20 km/s ) in the Galactic plane of the ISM (nISM = 1/cm3 ) are brighter in synchrotron than when moving with a Mach number M = 2 (v_star = 40 km/s ). We show that runaway red supergiant progenitors first induce an asymmetric non thermal 1.4 GHz barrel like synchrotron supernova remnants (at the age of about 8 kyr), before further evolving to adopt a Cygnus loop like shape (at about 80 kyr). It is conjectured that a significative fraction of supernova remnants are currently in this bilateral-to-Cygnus-loop evolutionary sequence, and that this should be taken into account in the data interpretation of the forthcoming Cherenkov Telescope Array (CTA) observatory., Comment: Accepted at Astronomie and Astrophysics

This paper develops a systematic framework for the evidence neuroscientists use to establish whether a neural response represents a feature. Researchers try to establish that the neural response is (1) sensitive and (2) specific to the feature, (3) invariant to other features, and (4) functional, which means that it is used downstream in the brain. We formalize these desiderata in information-theoretic terms. This formalism allows us to precisely state the desiderata while unifying the different analysis methods used in neuroscience under one framework. We discuss how common methods such as correlational analyses, decoding and encoding models, representational similarity analysis, and tests of statistical dependence are used to evaluate the desiderata. In doing so, we provide a common terminology to researchers that helps to clarify disagreements, to compare and integrate results across studies and research groups, and to identify when evidence might be missing and when evidence for some representational conclusion is strong. We illustrate the framework with several canonical examples, including the representation of orientation, numerosity, faces, and spatial location. We end by discussing how the framework can be extended to cover models of the neural code, multi-stage models, and other domains., Comment: 50 pages, 11 figures

Task-oriented object grasping and rearrangement are critical skills for robots to accomplish different real-world manipulation tasks. However, they remain challenging due to partial observations of the objects and shape variations in categorical objects. In this paper, we propose the Multi-feature Implicit Model (MIMO), a novel object representation that encodes multiple spatial features between a point and an object in an implicit neural field. Training such a model on multiple features ensures that it embeds the object shapes consistently in different aspects, thus improving its performance in object shape reconstruction from partial observation, shape similarity measure, and modeling spatial relations between objects. Based on MIMO, we propose a framework to learn task-oriented object grasping and rearrangement from single or multiple human demonstration videos. The evaluations in simulation show that our approach outperforms the state-of-the-art methods for multi- and single-view observations. Real-world experiments demonstrate the efficacy of our approach in one- and few-shot imitation learning of manipulation tasks.

Monochromatic gamma-ray signals constitute a potential smoking gun signature for annihilating or decaying dark matter particles that could relatively easily be distinguished from astrophysical or instrumental backgrounds. We provide an updated assessment of the sensitivity of the Cherenkov Telescope Array (CTA) to such signals, based on observations of the Galactic centre region as well as of selected dwarf spheroidal galaxies. We find that current limits and detection prospects for dark matter masses above 300 GeV will be significantly improved, by up to an order of magnitude in the multi-TeV range. This demonstrates that CTA will set a new standard for gamma-ray astronomy also in this respect, as the world's largest and most sensitive high-energy gamma-ray observatory, in particular due to its exquisite energy resolution at TeV energies and the adopted observational strategy focussing on regions with large dark matter densities. Throughout our analysis, we use up-to-date instrument response functions, and we thoroughly model the effect of instrumental systematic uncertainties in our statistical treatment. We further present results for other potential signatures with sharp spectral features, e.g.~box-shaped spectra, that would likewise very clearly point to a particle dark matter origin., Comment: 44 pages JCAP style (excluding author list and references), 19 figures; minor changes to match published version

In lung radiotherapy, infrared cameras can record the location of reflective objects on the chest to infer the position of the tumor moving due to breathing, but treatment system latencies hinder radiation beam precision. Real-time recurrent learning (RTRL), is a potential solution as it can learn patterns within non-stationary respiratory data but has high complexity. This study assesses the capabilities of resource-efficient online RNN algorithms, namely unbiased online recurrent optimization (UORO), sparse-1 step approximation (SnAp-1), and decoupled neural interfaces (DNI) to forecast respiratory motion during radiotherapy treatment accurately. We use time series containing the 3D position of external markers on the chest of healthy subjects. We propose efficient implementations for SnAp-1 and DNI based on compression of the influence and immediate Jacobian matrices and an accurate update of the linear coefficients used in credit assignment estimation, respectively. The original sampling frequency was 10Hz; we performed resampling at 3.33Hz and 30Hz. We use UORO, SnAp-1, and DNI to forecast each marker's 3D position with horizons (the time interval in advance for which the prediction is made) h<=2.1s and compare them with RTRL, least mean squares, and linear regression. RNNs trained online achieved similar or better accuracy than most previous works using larger training databases and deep learning, even though we used only the first minute of each sequence to predict motion within that exact sequence. SnAp-1 had the lowest normalized root mean square errors (nRMSE) averaged over the horizon values considered, equal to 0.335 and 0.157, at 3.33Hz and 10.0Hz, respectively. Similarly, UORO had the highest accuracy at 30Hz, with an nRMSE of 0.0897. DNI's inference time, equal to 6.8ms per time step at 30Hz (Intel Core i7-13700 CPU), was the lowest among the RNN methods examined., Comment: 34 pages, 11 figures

For multi-level systems in hot atomic vapors the interplay between the Doppler shift due to atom velocity and the wavenubmer mismatch between driving laser fields strongly influences transmission and absorption properties of the atomic medium. In a three-level atomic ladder-system, Doppler broadening limits the visibility of electromagnetically-induced transparency (EIT) when the probe and control fields are co-propagating, while EIT is recovered under the opposite condition of counter-propagating geometry and $k_{p} < k_{c}$, with $k_{p}$ and $k_{c}$ being the wavenumbers of the probe and control fields, respectively. This effect has been studied and experimentally demonstrated as an efficient mechanism to realize non-reciprocal probe light transmission, opening promising avenues for example for realization of magnetic-field free optical isolators. In this tutorial we discuss the theoretical derivation of this effect and show the underlying mechanism to be an avoided crossing of the states dressed by the coupling laser as a function of atomic velocities when $k_{p}