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The X-ray free-electron lasers (XFELs) are cutting-edge instruments pivotal in a broad range of fields, providing high-power X-ray pulses with durations spanning from femtoseconds to attoseconds. One of the critical challenges in XFEL facilities is the simultaneous accommodation of diverse requirements for XFEL operation modes and photon properties across different undulator lines. This paper proposes a dipole-kicker combination in the bunch compressors to vary the electron bunch length for the continuous-wave XFEL facilities driven by a superconducting linac. This method enables optimization of the electron bunch length on a per-bunch basis, tailored to each specific needs of each undulator. Through start-to-end simulations based on the parameters of the Shanghai high-repetition-rate XFEL and extreme light facility, we demonstrate the feasibility of this technique. The results show its effectiveness in enabling simultaneous operations of self-amplified spontaneous emission and externally seeded FEL across different undulator lines, ensuring optimal electron bunch compression for each undulator line.

A necessary capability for humanoid robots is the ability to stand and walk while rejecting natural disturbances. Recent progress has been made using sim-to-real reinforcement learning (RL) to train such locomotion controllers, with approaches differing mainly in their reward functions. However, prior works lack a clear method to systematically test new reward functions and compare controller performance through repeatable experiments. This limits our understanding of the trade-offs between approaches and hinders progress. To address this, we propose a low-cost, quantitative benchmarking method to evaluate and compare the real-world performance of standing and walking (SaW) controllers on metrics like command following, disturbance recovery, and energy efficiency. We also revisit reward function design and construct a minimally constraining reward function to train SaW controllers. We experimentally verify that our benchmarking framework can identify areas for improvement, which can be systematically addressed to enhance the policies. We also compare our new controller to state-of-the-art controllers on the Digit humanoid robot. The results provide clear quantitative trade-offs among the controllers and suggest directions for future improvements to the reward functions and expansion of the benchmarks., Comment: 8 pages, 5 figs

In programming education, providing manual feedback is essential but labour-intensive, posing challenges in consistency and timeliness. We introduce ECHO, a machine learning method to automate the reuse of feedback in educational code reviews by analysing patterns in abstract syntax trees. This study investigates two primary questions: whether ECHO can predict feedback annotations to specific lines of student code based on previously added annotations by human reviewers (RQ1), and whether its training and prediction speeds are suitable for using ECHO for real-time feedback during live code reviews by human reviewers (RQ2). Our results, based on annotations from both automated linting tools and human reviewers, show that ECHO can accurately and quickly predict appropriate feedback annotations. Its efficiency in processing and its flexibility in adapting to feedback patterns can significantly reduce the time and effort required for manual feedback provisioning in educational settings.

Machine learning has the potential to revolutionize passive acoustic monitoring (PAM) for ecological assessments. However, high annotation and compute costs limit the field's efficacy. Generalizable pretrained networks can overcome these costs, but high-quality pretraining requires vast annotated libraries, limiting its current applicability primarily to bird taxa. Here, we identify the optimum pretraining strategy for a data-deficient domain using coral reef bioacoustics. We assemble ReefSet, a large annotated library of reef sounds, though modest compared to bird libraries at 2% of the sample count. Through testing few-shot transfer learning performance, we observe that pretraining on bird audio provides notably superior generalizability compared to pretraining on ReefSet or unrelated audio alone. However, our key findings show that cross-domain mixing which leverages bird, reef and unrelated audio during pretraining maximizes reef generalizability. SurfPerch, our pretrained network, provides a strong foundation for automated analysis of marine PAM data with minimal annotation and compute costs., Comment: 18 pages, 5 figures

This paper presents a novel approach for distributed model predictive control (MPC) for piecewise affine (PWA) systems. Existing approaches rely on solving mixed-integer optimization problems, requiring significant computation power or time. We propose a distributed MPC scheme that requires solving only convex optimization problems. The key contribution is a novel method, based on the alternating direction method of multipliers, for solving the non-convex optimal control problem that arises due to the PWA dynamics. We present a distributed MPC scheme, leveraging this method, that explicitly accounts for the coupling between subsystems by reaching agreement on the values of coupled states. Stability and recursive feasibility are shown under additional assumptions on the underlying system. Two numerical examples are provided, in which the proposed controller is shown to significantly improve the CPU time and closed-loop performance over existing state-of-the-art approaches., Comment: 15 pages, 9 figures, submitted to IEEE Transactions on Automatic Control, code available at https://github.com/SamuelMallick/stable-dmpc-pwa/tree/paper_2024 and https://github.com/SamuelMallick/hybrid-vehicle-platoon/tree/paper-2024

For a fixed graph $H$, the $H$-SUBGRAPH HITTING problem consists in deleting the minimum number of vertices from an input graph to obtain a graph without any occurrence of $H$ as a subgraph. This problem can be seen as a generalization of VERTEX COVER, which corresponds to the case $H = K_2$. We initiate a study of $H$-SUBGRAPH HITTING from the point of view of characterizing structural parameterizations that allow for polynomial kernels, within the recently active framework of taking as the parameter the number of vertex deletions to obtain a graph in a "simple" class $C$. Our main contribution is to identify graph parameters that, when $H$-SUBGRAPH HITTING is parameterized by the vertex-deletion distance to a class $C$ where any of these parameters is bounded, and assuming standard complexity assumptions and that $H$ is biconnected, allow us to prove the following sharp dichotomy: the problem admits a polynomial kernel if and only if $H$ is a clique. These new graph parameters are inspired by the notion of $C$-elimination distance introduced by Bulian and Dawar [Algorithmica 2016], and generalize it in two directions. Our results also apply to the version of the problem where one wants to hit $H$ as an induced subgraph, and imply in particular, that the problems of hitting minors and hitting (induced) subgraphs have a substantially different behavior with respect to the existence of polynomial kernels under structural parameterizations., Comment: 58 pages, 7 figures

This work aims to measure the mass accretion rate, the accretion luminosity, and more generally the physical conditions of the warm emitting gas in the inner disk of the very low-mass star 2MASS-J16053215-1933159. We investigate the source mid-infrared spectrum for atomic and molecular hydrogen line emission. We present the full James Webb Space Telescope (JWST) Mid-InfraRed Instrument (MIRI) Medium Resolution Spectrometer (MRS) spectrum of the protoplanetary disk around the very low-mass star 2MASS-J16053215-1933159 from the MINDS GTO program, previously shown to be abundant in hydrocarbon molecules. We analyzed the atomic and molecular hydrogen lines in this source by fitting one or multiple Gaussian profiles. We then built a rotational diagram for the H2 lines to constrain the rotational temperature and column density of the gas. Finally, we compared the observed atomic line fluxes to predictions from two standard emission models. We identify five molecular hydrogen pure rotational lines and 16 atomic hydrogen recombination lines. The spectrum indicates optically thin emission for both species. We use the molecular hydrogen lines to constrain the mass and temperature of the warm emitting gas. The HI (7-6) recombination line is used to measure the mass accretion rate and luminosity onto the central source. HI recombination lines can also be used to derive the physical properties of the gas using atomic recombination models. The JWST-MIRI MRS observations for the very low-mass star 2MASS-J16053215-1933159 reveal a large number of emission lines, many originating from atomic and molecular hydrogen because we are able to look into the disk warm molecular layer. Their analysis constrains the physical properties of the emitting gas and showcases the potential of JWST to deepen our understanding of the physical and chemical structure of protoplanetary disks, Comment: 11 pages, 9 figures, 2 tables

For an optimization problem $\Pi$ on graphs whose solutions are vertex sets, a vertex $v$ is called $c$-essential for $\Pi$ if all solutions of size at most $c \cdot OPT$ contain $v$. Recent work showed that polynomial-time algorithms to detect $c$-essential vertices can be used to reduce the search space of fixed-parameter tractable algorithms solving such problems parameterized by the size $k$ of the solution. We provide several new upper- and lower bounds for detecting essential vertices. For example, we give a polynomial-time algorithm for $3$-Essential detection for Vertex Multicut, which translates into an algorithm that finds a minimum multicut of an undirected $n$-vertex graph $G$ in time $2^{O(\ell^3)} \cdot n^{O(1)}$, where $\ell$ is the number of vertices in an optimal solution that are not $3$-essential. Our positive results are obtained by analyzing the integrality gaps of certain linear programs. Our lower bounds show that for sufficiently small values of $c$, the detection task becomes NP-hard assuming the Unique Games Conjecture. For example, we show that ($2-\varepsilon$)-Essential detection for Directed Feedback Vertex Set is NP-hard under this conjecture, thereby proving that the existing algorithm that detects $2$-essential vertices is best-possible., Comment: Conference version to appear at the 19th Scandinavian Symposium on Algorithm Theory (SWAT 2024)

A divisible design graph is a graph whose adjacency matrix is an incidence matrix of a (group) divisible design. Divisible design graphs were introduced in 2011 as a generalization of $(v,k,\lambda)$-graphs. Here we describe four new infinite families that can be obtained from the symplectic strongly regular graph $Sp(2e,q)$ ($q$ odd, $e\geq 2$) by modifying the set of edges. To achieve this we need two kinds of spreads in $PG(2e-1,q)$ with respect to the associated symplectic form: the symplectic spread consisting of totally isotropic subspaces and, when $e=2$, a special spread consisting of lines which are not totally isotropic. Existence of symplectic spreads is known, but the construction of a special spread for every odd prime power $q$ is a major result of this paper. We have included relevant back ground from finite geometry, and when $q=3,5$ and $7$ we worked out all possible special spreads.

Systematic in vivo validations of computational models of the aortic valve remain scarce, despite successful validation against in vitro data. Utilizing a combination of computed tomography and 4D flow magnetic resonance imaging data, we developed patient-specific fluid-structure interaction models of the aortic valve immersed in the aorta for five patients in the pre-transcatheter aortic valve replacement configuration. Incorporating also an in vitro setup of the valve, our computational models are subjected to rigorous validation against 4D flow measurements. Our results demonstrate the models' capacity to accurately replicate flow dynamics within established ranges of uncertainties mainly arising from 4D flow noise. In addition, we illustrate how computational models can serve as valuable cross-checks to reduce noise and erratic behaviour of in vivo data. This study represents a significant step towards integrating in silico technologies into real clinical contexts, providing a robust framework for improving aortic stenosis diagnosis and the design of next-generation aortic valve bioprostheses.

Scenario reduction algorithms can be an effective means to provide a tractable description of the uncertainty in optimal control problems. However, they might significantly compromise the performance of the controlled system. In this paper, we propose a method to compensate for the effect of scenario reduction on stochastic optimal control problems for chance-constrained linear systems with additive uncertainty. We consider a setting in which the uncertainty has a discrete distribution, where the number of possible realizations is large. We then propose a reduction algorithm with a problem-dependent loss function, and we define sufficient conditions on the stochastic optimal control problem to ensure out-of-sample guarantees (i.e., against the original distribution of the uncertainty) for the controlled system in terms of performance and chance constraint satisfaction. Finally, we demonstrate the effectiveness of the approach on a numerical example., Comment: 7 pages, 1 figure, submitted to the European Control Conference

We introduce General Lotto games with Scouts: a General Lotto game with asymmetric information. There are two players, Red and Blue, who both allocate resources to a field. However, scouting capabilities afford Blue to gain information, with some probability, on the number of Red's resources before allocating his own. We derive optimal strategies for this game in the case of a single field. In addition we provide upper and lower bounds of the value of the game in a multi-stage case with multiple battlefields. We devise several ways to characterise the influence of information versus strength. We conclude by drawing qualitative insights from these characterisations and the game values, and draw parallels with military practice.

Time series anomaly detection (TSAD) finds many applications such as monitoring environmental sensors, industry KPIs, patient biomarkers, etc. A two-fold challenge for TSAD is a versatile and unsupervised model that can detect various different types of time series anomalies (spikes, discontinuities, trend shifts, etc.) without any labeled data. Modern neural networks have outstanding ability in modeling complex time series. Self-supervised models in particular tackle unsupervised TSAD by transforming the input via various augmentations to create pseudo anomalies for training. However, their performance is sensitive to the choice of augmentation, which is hard to choose in practice, while there exists no effort in the literature on data augmentation tuning for TSAD without labels. Our work aims to fill this gap. We introduce TSAP for TSA "on autoPilot", which can (self-)tune augmentation hyperparameters end-to-end. It stands on two key components: a differentiable augmentation architecture and an unsupervised validation loss to effectively assess the alignment between augmentation type and anomaly type. Case studies show TSAP's ability to effectively select the (discrete) augmentation type and associated (continuous) hyperparameters. In turn, it outperforms established baselines, including SOTA self-supervised models, on diverse TSAD tasks exhibiting different anomaly types.

Optimizing deep learning models requires large amounts of annotated images, a process that is both time-intensive and costly. Especially for semantic segmentation models in which every pixel must be annotated. A potential strategy to mitigate annotation effort is active learning. Active learning facilitates the identification and selection of the most informative images from a large unlabelled pool. The underlying premise is that these selected images can improve the model's performance faster than random selection to reduce annotation effort. While active learning has demonstrated promising results on benchmark datasets like Cityscapes, its performance in the agricultural domain remains largely unexplored. This study addresses this research gap by conducting a comparative study of three active learning-based acquisition functions: Bayesian Active Learning by Disagreement (BALD), stochastic-based BALD (PowerBALD), and Random. The acquisition functions were tested on two agricultural datasets: Sugarbeet and Corn-Weed, both containing three semantic classes: background, crop and weed. Our results indicated that active learning, especially PowerBALD, yields a higher performance than Random sampling on both datasets. But due to the relatively large standard deviations, the differences observed were minimal; this was partly caused by high image redundancy and imbalanced classes. Specifically, more than 89\% of the pixels belonged to the background class on both datasets. The absence of significant results on both datasets indicates that further research is required for applying active learning on agricultural datasets, especially if they contain a high-class imbalance and redundant images. Recommendations and insights are provided in this paper to potentially resolve such issues.

Understanding the explosion mechanism and hydrodynamic evolution of core-collapse supernovae is a long-standing quest in astronomy. The asymmetries caused by the explosion are encoded into the line profiles which appear in the nebular phase of the SN evolution -- with particularly clean imprints in He star explosions. Here, we carry out nine different supernova simulations of He-core progenitors, exploding them in 3D with parametrically varied neutrino luminosities using the $\texttt{Prometheus-HotB}$ code, hydrodynamically evolving the models to the homologeous phase. We then compute nebular phase spectra with the 3D NLTE spectral synthesis code $\texttt{ExTraSS}$ (EXplosive TRAnsient Spectral Simulator). We study how line widths and shifts depend on progenitor mass, explosion energy, and viewing angle. We compare the predicted line profile properties against a large set of Type Ib observations, and discuss the degree to which current neutrino-driven explosions can match observationally inferred asymmetries. With self-consistent 3D modelling -- circumventing the difficulties of representing $^{56}$Ni mixing and clumping accurately in 1D models -- we find that neither low-mass He cores exploding with high energies nor high-mass cores exploding with low energies contribute to the Type Ib SN population. Models which have line profile widths in agreement with this population give sufficiently large centroid shifts for calcium emission lines. Calcium is more strongly affected by explosion asymmetries connected to the neutron star kicks than oxygen and magnesium. Lastly, we turn to the NIR spectra from our models to investigate the potential of using this regime to look for the presence of He in the nebular phase., Comment: Submitted to MNRAS; 20+6 pages, 10+6 figures

Perturber objects interacting with supermassive black hole accretion disks are often invoked to explain observed quasi-periodic behavior in active galactic nuclei (AGN). We present global, 3D general relativistic magnetohydrodynamic (GRMHD) simulations of black holes on inclined orbits colliding with magnetically arrested thick AGN disks using a binary black hole spacetime with mass ratio $0.1$. We do this by implementing an approximate time-dependent binary black hole metric into the GRMHD code Athena++. The secondary enhances the unbound mass outflow rate 2-4 times above that provided by the disk in quasi-periodic outbursts, eventually merging into a more continuous outflow at larger distances. We present a simple analytic model that qualitatively agrees well with this result and can be used to extrapolate to unexplored regions of parameter space. We show self-consistently for the first time that spin-orbit coupling between the primary black hole spin and the binary orbital angular momentum causes the accretion disk and jet directions to precess significantly (by 60$^\circ$-80$^\circ$) on long time-scales (e.g., $\sim$ 20 times the binary orbital period). Because this effect may be the only way for thick AGN disks to consistently precess, it could provide strong evidence of a secondary black hole companion if observed in such a system. Besides this new phenomenology, the time-average properties of the disk and accretion rates onto the primary are only marginally altered by the presence of the secondary, consistent with our estimate for a perturbed thick disk. This situation might drastically change in cooled thin disks., Comment: Accepted by ApJ. Animations are available at https://www.youtube.com/playlist?list=PL3pLmTeUPcqSBQM0OvfkR47opm8ucjA0_

We perform the first magnetohydrodynamic simulation tracking the magnetosphere of a collapsing magnetar. The collapse is expected for massive rotating magnetars formed in merger events, and may occur many hours after the merger. Our simulation suggests a novel mechanism for a gamma-ray burst (GRB) which is uncollimated and forms a delayed high-energy counterpart of the merger gravitational waves. The simulation shows that the collapse launches an outgoing magnetospheric shock, and a hot magnetized outflow forms behind the shock. The outflow is baryon-free and uncollimated, and its power peaks on a millisecond timescale. Then, the outflow becomes modulated by the ring-down of the nascent black hole, imprinting its kilohertz quasi-normal modes on the GRB tail., Comment: 9 pages, 4 figures; accepted version; to appear in ApJL

Race conditions are a class of bugs in software where concurrent accesses to shared resources are not protected from each other. Consequences of race conditions include privilege escalation, denial of service, and memory corruption which can potentially lead to arbitrary code execution. However, in large code bases the exact rules as to which fields should be accessed under which locks are not always clear. We propose a novel static technique that infers rules for how field accesses should be locked, and then checks the code against these rules. Traditional static analysers for detecting race conditions are based on lockset analysis. Instead, we propose an outlier-based technique enhanced with a context-sensitive mechanism that scales well. We have implemented this analysis in LLIF, and evaluated it to find incorrectly protected field accesses in Linux v5.14.11. We thoroughly evaluate its ability to find race conditions, and study the causes for false positive reports. In addition, we reported a subset of the issues and submitted patches. The maintainers confirmed 24 bugs.

Next-generation low-cost semiconductors such as halide perovskites exhibit optoelectronic properties dominated by nanoscale variations in their structure, composition and photophysics. While microscopy provides a proxy for ultimate device function, past works have focused on neat thin-films on insulating substrates, missing crucial information about charge extraction losses and recombination losses introduced by transport layers. Here we use a multimodal operando microscopy toolkit to measure nanoscale current-voltage curves, recombination losses and chemical composition in an array of state-of-the-art perovskite solar cells before and after extended operational stress. We apply this toolkit to the same scan areas before and after extended operation to reveal that devices with the highest performance have the lowest initial performance spatial heterogeneity - a crucial link that is missed in conventional microscopy. We find that subtle compositional engineering of the perovskite has surprising effects on local disorder and resilience to operational stress. Minimising variations in local efficiency, rather than compositional disorder, is predictive of improved performance and stability. Modulating the interfaces with different contact layers or passivation treatments can increase initial performance but can also lead to dramatic nanoscale, interface-dominated degradation even in the presence of local performance homogeneity, inducing spatially varying transport, recombination, and electrical losses. These operando measurements of full devices act as screenable diagnostic tools, uniquely unveiling the microscopic mechanistic origins of device performance losses and degradation in an array of halide perovskite devices and treatments. This information in turn reveals guidelines for future improvements to both performance and stability., Comment: Main text and supplementary information. Main text 26 pages, 4 figures. Supplementary information 79 pages, 76 figures. Kyle Frohna and Cullen Chosy contributed equally

PDE-based Group Convolutional Neural Networks (PDE-G-CNNs) utilize solvers of geometrically meaningful evolution PDEs as substitutes for the conventional components in G-CNNs. PDE-G-CNNs offer several key benefits all at once: fewer parameters, inherent equivariance, better performance, data efficiency, and geometric interpretability. In this article we focus on Euclidean equivariant PDE-G-CNNs where the feature maps are two dimensional throughout. We call this variant of the framework a PDE-CNN. From a machine learning perspective, we list several practically desirable axioms and derive from these which PDEs should be used in a PDE-CNN. Here our approach to geometric learning via PDEs is inspired by the axioms of classical linear and morphological scale-space theory, which we generalize by introducing semifield-valued signals. Furthermore, we experimentally confirm for small networks that PDE-CNNs offer fewer parameters, increased performance, and better data efficiency when compared to CNNs. We also investigate what effect the use of different semifields has on the performance of the models.

The MRS mode of the JWST-MIRI instrument has been shown to be a powerful tool to characterise the molecular gas emission of the inner region of planet-forming disks. Here, we analyse the spectrum of the compact T-Tauri disk DR Tau, which is complemented by high spectral resolution (R~60000-90000) CO ro-vibrational observations. Various molecular species, including CO, CO$_2$, HCN, and C$_2$H$_2$ are detected in the JWST-MIRI spectrum, for which excitation temperatures of T~325-900 K are retrieved using LTE slab models. The high-resolution CO observations allow for a full treatment of the line profiles, which show evidence for two components of the main isotopologue, $^{12}$CO: a broad component tracing the Keplerian disk and a narrow component tracing a slow disk wind. Rotational diagrams yield excitation temperatures of T>725 K for CO, with consistently lower temperatures found for the narrow components, suggesting that the disk wind is launched from a larger distance. The inferred excitation temperatures for all molecules suggest that CO originates from the highest atmospheric layers close to the host star, followed by HCN and C$_2$H$_2$, which emit, together with $^{13}$CO, from slightly deeper layers, whereas the CO$_2$ originates from even deeper inside or further out in the disk. Additional analysis of the $^{12}$CO line wings hint at a misalignment between the inner (i~20 degrees) and outer disk (i~5 degrees). Finally, we emphasise the need for complementary high-resolution CO observations, as in combination with the JWST-MIRI observations they can be used to characterise the CO kinematics and the physical and chemical conditions of the other observed molecules with respect to CO., Comment: Accepted for publication in Astronomy & Astrophysics on 20/03/2024

High-speed water entry of projectiles and diving systems induces high forces and jerk to the entering bodies due to the development of large hydrodynamic pressure. Previous research has shown separately that the peak forces can be reduced by improving the aerodynamic shape of the head (nose) or, recently, by introducing a spring element between the head and body. This study seeks to understand whether the aerodynamic shape or spring stiffness coupling is most important for force reduction by combining both in one study. The experiment combines the nose cone aerodynamics and spring stiffness with a rear body and examines the forces acting on the nose and body. Three parameters are varied: the nose angle, spring stiffness, and impact velocity. An unsteady semi-analytical formulation is developed to estimate the water entry forces and coupled body dynamics. We find that the peak force reduction due to the spring is highest when the slamming force is most significant, particularly at higher impact velocities and with blunter nose angles. The spring coupling enables periodic fluctuations between the kinetic and potential energy throughout the duration of impact, which can be tuned by varying the stiffness. These findings can allow engineers to control the dynamic response of water entry.

The study of protoplanetary disks has become increasingly important with the Kepler satellite finding that exoplanets are ubiquitous around stars in our galaxy and the discovery of enormous diversity in planetary system architectures and planet properties. High-resolution near-IR and ALMA images show strong evidence for ongoing planet formation in young disks. The JWST MIRI mid-INfrared Disk Survey (MINDS) aims to (1) investigate the chemical inventory in the terrestrial planet-forming zone across stellar spectral type, (2) follow the gas evolution into the disk dispersal stage, and (3) study the structure of protoplanetary and debris disks in the thermal mid-IR. The MINDS survey will thus build a bridge between the chemical inventory of disks and the properties of exoplanets. The survey comprises 52 targets (Herbig Ae stars, T Tauri stars, very low-mass stars and young debris disks). We primarily obtain MIRI/MRS spectra with high S/N (~100-500) covering the complete wavelength range from 4.9 to 27.9 {\mu}m. For a handful of selected targets we also obtain NIRSpec IFU high resolution spectroscopy (2.87-5.27 {\mu}m). We will search for signposts of planet formation in thermal emission of micron-sized dust - information complementary to near-IR scattered light emission from small dust grains and emission from large dust in the submillimeter wavelength domain. We will also study the spatial structure of disks in three key systems that have shown signposts for planet formation, TW Hya and HD 169142 using the MIRI coronagraph at 15.5 {\mu}m and 10.65 {\mu}m respectively and PDS70 using NIRCam imaging in the 1.87 {\mu}m narrow and the 4.8 {\mu}m medium band filter. ..., Comment: accepted for publication in PASP

The pervasive presence of warm gas in galaxy halos suggests that the circumgalactic medium (CGM) is multiphase in its ionization structure and complex in its kinematics. Some recent state-of-the-art cosmological galaxy simulations predict an azimuthal dependence of CGM metallicities. We investigate the presence of such a trend by analyzing the distribution of gas properties in the CGM around 47 $z <$ 0.7 galaxies from the Multiphase Galaxy Halos Survey determined using a cloud-by-cloud, multiphase, ionization modelling approach. We identify three distinct populations of absorbers: cool clouds ($T \sim$ 10$^{4.1}$ K) in photoionization equilibrium, warm-hot collisionally ionized clouds ($T \sim$ 10$^{4.5-5}$ K) affected by time-dependent photoionization, and hotter clouds ($T \sim$ 10$^{5.4-6}$ K) with broad OVI and Lya absorption consistent with collisional ionization. We find that fragmentation can play a role in the origin of cool clouds, that warm-hot clouds are out of equilibrium due to rapid cooling, and that hotter clouds are representative of virialized halo gas in all but the lowest mass galaxies. The metallicities of clouds do not depend on the azimuthal angle or other galaxy properties for any of these populations. At face value, this disagrees with the simplistic model of the CGM with bipolar outflows and cold-mode planar accretion. However, the number of clouds per sightline is significantly larger close to the minor and major axes. This implies that the processes of outflows and accretion are contributing to these CGM cloud populations, and our sightlines are probing gas of mixed origins at all azimuthal angles in these low redshift galaxies., Comment: Accepted for publication in MNRAS

These are the lecture notes that accompanied the course of the same name that I taught at the Eindhoven University of Technology from 2021 to 2023. The course is intended as an introduction to neural networks for mathematics students at the graduate level and aims to make mathematics students interested in further researching neural networks. It consists of two parts: first a general introduction to deep learning that focuses on introducing the field in a formal mathematical way. The second part provides an introduction to the theory of Lie groups and homogeneous spaces and how it can be applied to design neural networks with desirable geometric equivariances. The lecture notes were made to be as self-contained as possible so as to accessible for any student with a moderate mathematics background. The course also included coding tutorials and assignments in the form of a set of Jupyter notebooks that are publicly available at https://gitlab.com/bsmetsjr/mathematics_of_neural_networks., Comment: Lecture notes of the graduate course 2MMA80 Mathematics of Neural Networks as thought at the Eindhoven University of Technology from 2021 to 2023

We demonstrate the opportunities of first-principles density functional theory (DFT) calculations for the development of new metallurgical refining processes. As such, a methodology based on DFT calculations is developed to discover new pyrometallurgical refining processes that use the addition of a third element to remove an impurity from a molten host material. As a case study, this methodology is applied to the refining of lead. The proposed method predicts the existing refining routes as well as alternative processes. The most interesting candidate for the removal of arsenic from lead is experimentally verified, which confirms the suitability of the remover element. The method is therefore considered as a useful approach to speed up the discovery of new pyrometallurgical refining processes, as it provides an ordered set of interesting candidate remover elements

A potential field solution is widely used to extrapolate the coronal magnetic field above the Sun's surface to a certain height. This model applies the current-free approximation and assumes that the magnetic field is entirely radial beyond the source surface height, which is defined as the radial distance from the center of the Sun. Even though the source surface is commonly specified at 2.5 Rs (solar radii), previous studies have suggested that this value is not optimal in all cases. In this study, we propose a novel approach to specify the source surface height, by comparing the areas of the open magnetic field regions from the potential field solution with predictions made by a magnetohydrodynamics model, in our case the Alfven Wave Solar atmosphere Model. We find that the adjusted source surface height is significantly less than 2.5 Rs near solar minimum, and slightly larger than 2.5 Rs near solar maximum. We also report that the adjusted source surface height can provide a better open flux agreement with the observations near the solar minimum, while the comparison near the solar maximum is slightly worse., Comment: 15 pages, 5 figures, 1 table, submitted ApjL

In a previous study, Huang et al. (2023) used the Alfven Wave Solar atmosphere Model (AWSoM), one of the widely used solar wind models in the community, driven by ADAPT-GONG magnetograms to simulate the solar wind in the last solar cycle and found that the optimal Poynting flux parameter can be estimated from either the open field area or the average unsigned radial component of the magnetic field in the open field regions. It was also found that the average energy deposition rate (Poynting flux) in the open field regions is approximately constant. In the current study, we expand the previous work by using GONG magnetograms to simulate the solar wind for the same Carrington rotations and determine if the results are similar to the ones obtained with ADAPT-GONG magnetograms. Our results indicate that similar correlations can be obtained from the GONG maps. Moreover, we report that ADAPT-GONG magnetograms can consistently provide better comparisons with 1 AU solar wind observations than GONG magnetograms, based on the best simulations selected by the minimum of the average curve distance for the solar wind speed and density., Comment: 17 pages, 5 figures, 2 tables, submitted to ApJ

Piezoelectric transducers applied on or integrated in structures, combined with appropriate circuits have been extensively investigated as a smart approach to the mitigation of resonant vibrations with high relative amplitudes. \cmt{A resonant shunt circuit consisting of the capacitive piezoelectric transducer and an inductance can be configured to target specific eigenmodes of a structure, if appropriately placed and tuned. Their effect is expressed in terms of mechanical impedance of the host structure, allowing for the exchange of energy between the mechanical and electrical domain, to dramatically affect the dynamic response of the structure. By re-framing the function of resonant shunted piezoelectric transducers as frequency dependent variable stiffness elements, this paper investigates their capability to realize a frequency dependent structural mechanical connectivity, where the load path within a lattice structure can be interrupted at will for specific frequencies by tunable null-stiffness components. Here, we offer the numerical and experimental verification of this idea, by demonstrating the ability to significantly affect the dynamic response of a unit cell of an adaptive lattice metamaterial, even away from a structural resonance. In the latter case, the null-stiffness shunt leads to an additional resonance peak in the truss' dynamic response.} Its realization as additively manufactured component points to the feasibility of such structures in real life.

Kron reduction is concerned with the elimination of interior nodes of physical network systems such as linear resistor electrical circuits. In this paper it is shown how this can be extended to networks with nonlinear static relations between the variables associated to the edges of the underlying directed graph.

In this study, we explore the effectiveness of persuasive messages endorsing the adoption of a privacy protection technology (IoT Inspector) tailored to individuals' regulatory focus (promotion or prevention). We explore if and how regulatory fit (i.e., tuning the goal-pursuit mechanism to individuals' internal regulatory focus) can increase persuasion and adoption. We conducted a between-subject experiment (N = 236) presenting participants with the IoT Inspector in gain ("Privacy Enhancing Technology" -- PET) or loss ("Privacy Preserving Technology" -- PPT) framing. Results show that the effect of regulatory fit on adoption is mediated by trust and privacy calculus processes: prevention-focused users who read the PPT message trust the tool more. Furthermore, privacy calculus favors using the tool when promotion-focused individuals read the PET message. We discuss the contribution of understanding the cognitive mechanisms behind regulatory fit in privacy decision-making to support privacy protection.

Let $\mathfrak{g}$ be a symmetrizable Kac-Moody algebra, $U_q(\mathfrak{g})$ its quantum group, and $U_q(\mathfrak{k}) \subset U_q(\mathfrak{g})$ a quantum symmetric pair subalgebra determined by a Lie algebra automorphism $\theta$. We introduce a category $W_\theta$ of weight $U_q(\mathfrak{k})$-modules, which is acted upon by the category of weight $U_q(\mathfrak{g})$-modules via tensor products. We construct a universal tensor K-matrix $\mathbb{K}$ (that is, a solution of a reflection equation) in a completion of $U_q(\mathfrak{k}) \otimes U_q(\mathfrak{g})$. This yields a natural operator on any tensor product $M \otimes V$, where $M\in W_\theta$ and $V\in \mathcal{O}_\theta$, that is $V$ is a $U_q(\mathfrak{g})$-module in category $\mathcal{O}$ satisfying an integrability property determined by $\theta$. $W_\theta$ is equipped with a structure of a bimodule category over $\mathcal{O}_\theta$ and the action of $\mathbb{K}$ is encoded by a new categorical structure, which we call a boundary structure on $W_\theta$. This generalizes a result of Kolb which describes a braided module structure on finite-dimensional $U_q(\mathfrak{k})$-modules when $\mathfrak{g}$ is finite-dimensional. We apply our construction to the case of an affine Lie algebra, where it yields a formal tensor K-matrix valued in the endomorphisms of the tensor product of any module in $W_\theta$ and any finite-dimensional module over the corresponding quantum affine algebra. This formal series is normalized to a trigonometric K-matrix if the factors in the tensor product are both finite-dimensional irreducible modules over the quantum affine algebra., Comment: 52 pages. Minor revision: Appendix A added; bibliography updated and expanded; minor changes to the introduction

The bottom-left algorithm is a simple heuristic for the Strip Packing Problem. It places the rectangles in the given order at the lowest free position in the strip, using the left most position in case of ties. Despite its simplicity, the exact approximation ratio of the bottom-left algorithm remains unknown. We will improve the more-than-40-year-old value for the lower bound from $5/4$ to $4/3 - \varepsilon$. Additionally, we will show that this lower bound holds even in the special case of squares, where the previously known lower bound was $12/11 -\varepsilon$. These lower bounds apply regardless of the ordering of the rectangles. When squares are arranged in the worst possible order, we establish a constant upper bound and a $10/3-\varepsilon$ lower bound for the approximation ratio of the bottom-left algorithm. This bound also applies to some online setting and yields an almost tight result there. Finally, we show that the approximation ratio of a local search algorithm based on permuting rectangles in the ordering of the bottom-left algorithm is at least~$2$ and that such an algorithm may need an exponential number of improvement steps to reach a local optimum., Comment: 42 pages

We study the magnetospheric evolution of a non-accreting spinning black hole (BH) with an initially inclined split monopole magnetic field by means of three-dimensional general relativistic magnetohydrodynamics simulations. This serves as a model for a neutron star (NS) collapse or a BH-NS merger remnant after the inherited magnetosphere has settled into a split monopole field creating a striped wind. We show that the initially inclined split monopolar current sheet aligns over time with the BH equatorial plane. The inclination angle evolves exponentially towards alignment, with an alignment timescale that is inversely proportional to the square of the BH angular velocity, where higher spin results in faster alignment. Furthermore, magnetic reconnection in the current sheet leads to exponential decay of event horizon penetrating magnetic flux with nearly the same timescale for all considered BH spins. In addition, we present relations for the BH mass and spin in terms of the period and alignment timescale of the striped wind. The explored scenario of a rotating, aligning and reconnecting current sheet can potentially lead to multimessenger electromagnetic counterparts to a gravitational wave event due to the acceleration of particles powering high-energy radiation, plasmoid mergers resulting in coherent radio signals, and pulsating emission due to the initial misalignment of the BH magnetosphere., Comment: 16 pages, 9 figures

To support the ever-increasing performance requirements of lithography systems in terms of throughput and accuracy, in this paper, we introduce a design framework for partial state-feedback reduced-order switching predictive models. By combining measurements and predictions, this method aims to: 1) improve overall system performance by reducing the placement errors in a die within and across the full-wafer and 2) eliminate redundant measurements by using predictions to improve system throughput. We primarily focus on well-known trade-off introduced by measurement time, which can correct errors at a cost of throughput, noise and not being robust to uncertainties. The proposed approach consists of a reduced-order model with a switching logic that acts a scheduler to deal with uncertain operating conditions. It also utilizes linear predictive models as a basis for the control design which is appealing to the ease and cost of implementation, enhancing the applicability. For the add-on part, the scheduler logic is adapted based on expected operating conditions of the system while guaranteeing global uniform ultimate bounded asymptotic stability. Lastly, to deal with measurement layouts, the predictor combines the measurement into model using partial state-feedback. Effectiveness of the proposed strategy is demonstrated in practice on a high-precision industrial scanner., Comment: 9 pages, 9 figures, to be submitted in IEEE-TIE

Graph augmentation methods play a crucial role in improving the performance and enhancing generalisation capabilities in Graph Neural Networks (GNNs). Existing graph augmentation methods mainly perturb the graph structures, and are usually limited to pairwise node relations. These methods cannot fully address the complexities of real-world large-scale networks, which often involve higher-order node relations beyond only being pairwise. Meanwhile, real-world graph datasets are predominantly modelled as simple graphs, due to the scarcity of data that can be used to form higher-order edges. Therefore, reconfiguring the higher-order edges as an integration into graph augmentation strategies lights up a promising research path to address the aforementioned issues. In this paper, we present Topological Augmentation (TopoAug), a novel graph augmentation method that builds a combinatorial complex from the original graph by constructing virtual hyperedges directly from the raw data. TopoAug then produces auxiliary node features by extracting information from the combinatorial complex, which are used for enhancing GNN performances on downstream tasks. We design three diverse virtual hyperedge construction strategies to accompany the construction of combinatorial complexes: (1) via graph statistics, (2) from multiple data perspectives, and (3) utilising multi-modality. Furthermore, to facilitate TopoAug evaluation, we provide 23 novel real-world graph datasets across various domains including social media, biology, and e-commerce. Our empirical study shows that TopoAug consistently and significantly outperforms GNN baselines and other graph augmentation methods, across a variety of application contexts, which clearly indicates that it can effectively incorporate higher-order node relations into the graph augmentation for real-world complex networks., Comment: In 27th European Conference on Artificial Intelligence (ECAI 2024). 13 pages, 2 figures, 13 tables

With the release of Generative AI systems such as ChatGPT, an increasing interest in using Artificial Intelligence (AI) has been observed across domains, including higher education. While emerging statistics show the popularity of using AI amongst undergraduate students, little is yet known about students' perceptions regarding AI including self-reported benefits and concerns from their actual usage, in particular in distance learning contexts. Using a two-step, mixed-methods approach, we examined the perceptions of ten online and distance learning students from diverse disciplines regarding the design of a hypothetical AI Digital Assistant (AIDA). In the first step, we captured students' perceptions via interviews, while the second step supported the triangulation of data by enabling students to share, compare, and contrast perceptions with those of peers. All participants agreed on the usefulness of such an AI tool while studying and reported benefits from using it for real-time assistance and query resolution, support for academic tasks, personalisation and accessibility, together with emotional and social support. Students' concerns related to the ethical and social implications of implementing AIDA, data privacy and data use, operational challenges, academic integrity and misuse, and the future of education. Implications for the design of AI-tailored systems are also discussed., Comment: 23 pages, 1 figure, submitted to Distance Education

Source code plagiarism is a significant issue in educational practice, and educators need user-friendly tools to cope with such academic dishonesty. This article introduces the latest version of Dolos, a state-of-the-art ecosystem of tools for detecting and preventing plagiarism in educational source code. In this new version, the primary focus has been on enhancing the user experience. Educators can now run the entire plagiarism detection pipeline from a new web app in their browser, eliminating the need for any installation or configuration. Completely redesigned analytics dashboards provide an instant assessment of whether a collection of source files contains suspected cases of plagiarism and how widespread plagiarism is within the collection. The dashboards support hierarchically structured navigation to facilitate zooming in and out of suspect cases. Clusters are an essential new component of the dashboard design, reflecting the observation that plagiarism can occur among larger groups of students. To meet various user needs, the Dolos software stack for source code plagiarism detections now includes a web interface, a JSON application programming interface (API), a command line interface (CLI), a JavaScript library and a preconfigured Docker container. Clear documentation and a free-to-use instance of the web app can be found at https://dolos.ugent.be. The source code is also available on GitHub., Comment: 20 pages, 5 figures; minor corrections, reference added for section 2

Binaries containing a compact object orbiting a supermassive black hole are thought to be precursors of gravitational wave events, but their identification has been extremely challenging. Here, we report quasi-periodic variability in X-ray absorption which we interpret as quasi-periodic outflows (QPOuts) from a previously low-luminosity active galactic nucleus after an outburst, likely caused by a stellar tidal disruption. We rule out several models based on observed properties and instead show using general relativistic magnetohydrodynamic simulations that QPOuts, separated by roughly 8.3 days, can be explained with an intermediate-mass black hole secondary on a mildly eccentric orbit at a mean distance of about 100 gravitational radii from the primary. Our work suggests that QPOuts could be a new way to identify intermediate/extreme-mass ratio binary candidates., Comment: Accepted for publication in Science Advances. We report a new supermassive black hole phenomenon that we call quasi-periodic outflows (QPOuts)

We consider numerical approaches for deterministic, finite-dimensional optimal control problems whose dynamics depend on unknown or uncertain parameters. We seek to amortize the solution over a set of relevant parameters in an offline stage to enable rapid decision-making and be able to react to changes in the parameter in the online stage. To tackle the curse of dimensionality arising when the state and/or parameter are high-dimensional, we represent the policy using neural networks. We compare two training paradigms: First, our model-based approach leverages the dynamics and definition of the objective function to learn the value function of the parameterized optimal control problem and obtain the policy using a feedback form. Second, we use actor-critic reinforcement learning to approximate the policy in a data-driven way. Using an example involving a two-dimensional convection-diffusion equation, which features high-dimensional state and parameter spaces, we investigate the accuracy and efficiency of both training paradigms. While both paradigms lead to a reasonable approximation of the policy, the model-based approach is more accurate and considerably reduces the number of PDE solves., Comment: 19 pages, 5 figures

Semiconducting transition metal dichalcogenides (TMDs) are promising for high-specific-power photovoltaics due to desirable band gaps, high absorption coefficients, and ideally dangling-bond-free surfaces. Despite their potential, the majority of TMD solar cells are fabricated in a non-scalable fashion using exfoliated materials due to the absence of high-quality, large-area, multilayer TMDs. Here, we present the scalable, thickness-tunable synthesis of multilayer tungsten diselenide (WSe$_{2}$) films by selenizing pre-patterned tungsten with either solid source selenium or H$_{2}$Se precursors, which leads to smooth, wafer-scale WSe$_{2}$ films with a layered van der Waals structure. The films have charge carrier lifetimes up to 144 ns, over 14x higher than large-area TMD films previously demonstrated. Such high carrier lifetimes correspond to power conversion efficiency of ~22% and specific power of ~64 W g$^{-1}$ in a packaged solar cell, or ~3 W g$^{-1}$ in a fully-packaged solar module. This paves the way for the mass-production of high-efficiency multilayer WSe$_{2}$ solar cells at low cost.

Bounded rationality in mechanism design aims to ensure incentive-compatibility for agents who are cognitively limited. These agents lack the contingent reasoning skills that traditional mechanism design assumes, and depending on how these cognitive limitations are modelled this alters the class of incentive-compatible mechanisms. In this work we design mechanisms without any "obvious" manipulations for several auction settings that aim to either maximise revenue or minimise the compensation paid to the agents. A mechanism without obvious manipulations is said to be "not obviously manipulable" (NOM), and assumes agents act truthfully as long as the maximum and minimum utilities from doing so are no worse than the maximum and minimum utilities from lying, with the extremes taken over all possible actions of the other agents. We exploit the definition of NOM by introducing the concept of "golden tickets" and "wooden spoons", which designate bid profiles ensuring the mechanism's incentive-compatibility for each agent. We then characterise these "Willy Wonka" mechanisms, and by carefully choosing the golden tickets and wooden spoons we use this to design revenue-maximising auctions and frugal procurement auctions., Comment: 12 pages

The switching and optical properties of phase-change thin films are actively investigated for future smart optical devices. The possibility of having more than one stable state, the large optical contrast between phases, and the fast and reversible switching are some attractive properties driving the research interest. Optical devices based on phase change alloys are considered the frontier contenders for tunable photonics. The combination of vivid structural color formation, with partial amorphization/crystallization of phase change alloys, and the associated optical tunability could be integrated into an energy-efficient reflective display device with high pixel density. This work demonstrates a contrast formation due to relative height differences from isolated pixelated structures. A reflective heterostructure device consisting of a low-loss Sb2Se3 alloy on a gold substrate was produced. With a focused ion beam, a pixelated metasurface structure was produced. Moreover, the ability to create local height differences using an ion beam was employed to create a structural color combination mimicking traditional LED like RGB pixels. We believe our approach in creating metapixels on phase change thin film surfaces could open up research interest in phase change alloys and moving away from semi/static plasmonic systems into truly dynamic display devices.

Optimization constrained by high-fidelity computational models has potential for transformative impact. However, such optimization is frequently unattainable in practice due to the complexity and computational intensity of the model. An alternative is to optimize a low-fidelity model and use limited evaluations of the high-fidelity model to assess the quality of the solution. This article develops a framework to use limited high-fidelity simulations to update the optimization solution computed using the low-fidelity model. Building off a previous article [22], which introduced hyper-differential sensitivity analysis with respect to model discrepancy, this article provides novel extensions of the algorithm to enable uncertainty quantification of the optimal solution update via a Bayesian framework. Specifically, we formulate a Bayesian inverse problem to estimate the model discrepancy and propagate the posterior model discrepancy distribution through the post-optimality sensitivity operator for the low-fidelity optimization problem. We provide a rigorous treatment of the Bayesian formulation, a computationally efficient algorithm to compute posterior samples, a guide to specify and interpret the algorithm hyper-parameters, and a demonstration of the approach on three examples which highlight various types of discrepancy between low and high-fidelity models.

The use of partially automated driving systems raises concerns about potential responsibility issues, posing risk to the system safety, acceptance, and adoption of these technologies. The concept of meaningful human control has emerged in response to the responsibility gap problem, requiring the fulfillment of two conditions, tracking and tracing. While this concept has provided important philosophical and design insights on automated driving systems, there is currently little knowledge on how meaningful human control relates to subjective experiences of actual users of these systems. To address this gap, our study aimed to investigate the alignment between the degree of meaningful human control and drivers' perceptions of safety and trust in a real-world partially automated driving system. We utilized previously collected data from interviews with Tesla "Full Self-Driving" (FSD) Beta users, investigating the alignment between the user perception and how well the system was tracking the users' reasons. We found that tracking of users' reasons for driving tasks (such as safe maneuvers) correlated with perceived safety and trust, albeit with notable exceptions. Surprisingly, failure to track lane changing and braking reasons was not necessarily associated with negative perceptions of safety. However, the failure of the system to track expected maneuvers in dangerous situations always resulted in low trust and perceived lack of safety. Overall, our analyses highlight alignment points but also possible discrepancies between perceived safety and trust on the one hand, and meaningful human control on the other hand. Our results can help the developers of automated driving technology to design systems under meaningful human control and are perceived as safe and trustworthy., Comment: 8 pages

The non-linear and tunable optical constants of phase-change materials associated with their phase-switching have been utilized in reconfigurable optical devices. For example, one possible application of phase-change thin films is for tunable perfect absorption designs, where p- polarized light reflectance vanishes at a specific incidence angle known as the Brewster angle. This work demonstrates a generalized Brewster effect (s- and p- polarized light absorption) for a multilayered heterostructure design based on the strong interference effect. The proposed design comprises a low-loss phase-change material, Sb2Se3, coated on a gold substrate. We experimentally and theoretically show the coexistence of vanishing reflectance values for both s- and p- polarized lights in the visible and near IR wavelength range at a single Brewster angle. Such vanishing reflectance values (points of darkness) are associated with phase singularities with abrupt changes. Moreover, we show that additional phase singularities can be realized by switching the active Sb2Se3 layer between amorphous and crystalline structures, extending the functionality of our design. The realized phase singularities are susceptible to small optical constant changes and can be utilized for ultra-sensing applications. As a proof of concept, we demonstrate our design's CO2 gas sensing capabilities, showing a linearly dependent optical response with gas flow rate. We believe our lithography-free design with multiple phase singularities, from the coexisting generalized Brewster effect and from the additional structural switching, is highly promising for tunable optical sensing applications.

Complex mechatronic systems are typically composed of interconnected modules, often developed by independent teams. This development process challenges the verification of system specifications before all modules are integrated. To address this challenge, a modular redesign framework is proposed in this paper. Herein, first, allowed changes in the dynamics (represented by frequency response functions (FRFs)) of the redesigned system are defined with respect to the original system model, which already satisfies system specifications. Second, these allowed changes in the overall system dynamics (or system redesign specifications) are automatically translated to dynamics (FRF) specifications on module level that, when satisfied, guarantee overall system dynamics (FRF) specifications. This modularity in specification management supports local analysis and verification of module design changes, enabling design teams to work in parallel without the need to iteratively rebuild the system model to check fulfilment of system FRF specifications. A modular redesign process results that shortens time-to-market and decreases redesign costs. The framework's effectiveness is demonstrated through three examples of increasing complexity, highlighting its potential to enable modular mechatronic system (re)design.

In this paper, we characterise ovoidal cones by their intersection numbers. We first show that a set of points of $\mathrm{PG}(4,q)$ which intersects planes in $1$, $q+1$ or $2q+1$ points is either an ovoidal cone or a parabolic quadric, unless $q=3$, in which case also a sporadic example with automorphism group $M_{11}$ exists. We then show that a set of points of $\mathrm{PG}(4,q)$ which blocks all planes and intersects solids in $q+1$, $q^2+1$ or $q^2+q+1$ points is a plane or an ovoidal cone, and determine all examples that arise when the blocking condition is omitted., Comment: We were informed that Theorem 1.1 in the paper was already proved by Innamorati and Zuanni. Theorems 1.2 and 1.3 remain new results

This paper introduces a novel framework for DNA sequence generation, comprising two key components: DiscDiff, a Latent Diffusion Model (LDM) tailored for generating discrete DNA sequences, and Absorb-Escape, a post-training algorithm designed to refine these sequences. Absorb-Escape enhances the realism of the generated sequences by correcting `round errors' inherent in the conversion process between latent and input spaces. Our approach not only sets new standards in DNA sequence generation but also demonstrates superior performance over existing diffusion models, in generating both short and long DNA sequences. Additionally, we introduce EPD-GenDNA, the first comprehensive, multi-species dataset for DNA generation, encompassing 160,000 unique sequences from 15 species. We hope this study will advance the generative modelling of DNA, with potential implications for gene therapy and protein production., Comment: Different from the prior work "Latent Diffusion Model for DNA Sequence Generation" (arXiv:2310.06150), we updated the evaluation framework and compared the DiscDiff with other methods comprehensively. In addition, a post-training framework is proposed to increase the quality of generated sequences

In the current environment of data generation and publication, there is an ever-growing number of datasets available for download. This growth precipitates an existing challenge: sourcing and integrating relevant datasets for analysis is becoming more complex. Despite efforts by open data platforms, obstacles remain, predominantly rooted in inadequate metadata, unsuitable data presentation, complications in pinpointing desired data, and data integration. This paper delves into the intricacies of dataset retrieval, emphasizing the pivotal role of metadata in aligning datasets with user queries. Through an exploration of existing literature, it underscores prevailing issues such as the identification of valuable metadata and the development of tools to maintain and annotate them effectively. The central contribution of this research is the proposition of the Urban Dataset Metadata Requirements. Deriving inspiration from software engineering maturity models, this framework delineates a progression from rudimentary metadata documentation to advanced levels, aiding dataset creators in their documentation efforts. The requirements encompass seven pivotal dimensions, spanning content to quality information, each stratified across six levels to guide the optimal documentation of datasets, ensuring ease of discovery, relevance assessment, and comprehensive dataset understanding. This paper also incorporates the metadata properties into a data cataloguing tool called CKAN through a custom plugin, CKANext-udc. The plugin introduces custom fields based on different metadata levels, allows for user interface customisation, and integrates with a graph database, converting catalogue data into a knowledge graph based on the Urban Data Metadata Requirements ontology.