Search

We present photometric and spectroscopic data of the type IIb supernova (SN) 2024abfo in NGC 1493 (at 11 Mpc). The ATLAS survey discovered the object just a few hours after the explosion, and observed a fast rise on the first day. Signs of the sharp shock break-out peak and the subsequent cooling phase are observed in the ultraviolet and the bluest optical bands in the first couple of days, while no peak is visible in the reddest filters. Subsequently, in analogy with normal SNe IIb, the light curve of SN 2024abfo rises again in all bands to the broad peak, with the maximum light reached around one month after the explosion. Its absolute magnitude at peak is $M_r=-16.5\pm0.1$ mag, making it a faint SN IIb. The early spectra are dominated by Balmer lines with broad P-Cygni profiles indicating ejecta velocity of 22,500 \kms. One month after the explosion, the spectra display a transition towards being He-dominated, though the H lines do not completely disappear, supporting the classification of SN 2024abfo as a relatively H-rich SN IIb. We identify the progenitor of SN 2024abfo in archival images of the Hubble Space Telescope, the Dark Energy Survey, and the XMM-Newton space telescope, in multiple optical filters. From its spectral energy distribution, the progenitor is consistent with being a white supergiant, Deneb-like star, with a photospheric temperature of 8110 K, a radius of 176 \Rsun, a luminosity of $\log(L/L_{\odot})=5.08$, having an initial mass of 15-16 \Msun. This detection supports an emerging trend of SN IIb progenitors being more luminous and hotter than SN II ones, and being primaries of massive binaries. Within the SN IIb class, fainter events such as SN 2024abfo tend to have cooler and more expanded progenitors than luminous SNe IIb., Comment: 9 pages, 9 figures, 4 tables, submitted to A&A

We characterize global centers (all solutions are periodic) of the piecewise linear equation $x'=a(t)|x| + b(t)$ when the coefficients $a,b$ are trigonometric polynomials, under some generic hypotheses. We prove that the global centers are those determined by the composition condition on $a,b$. That is, the equation has a global center if and only if there exist polynomials $P, Q$ and a trigonometric polynomial $h$ such that $a(t)=P(h(t))h'(t)$, $b(t)=Q(h(t))h'(t)$., Comment: 12 Pages, no figures

Sliding-induced topological transitions in biphenylene bilayers are investigated, considering various stacking configurations which are analyzed from a symmetry perspective and described in detail,highlighting the intricate patterns of type-II Dirac cone crossings. Topological changes in the Fermi surface are assessed via the Euler characteristic, linking each transition to its corresponding symmetry, which can be experimentally tested by conductance measurements. Moreover, the ability to tune these topological properties by sliding the layers provides a simpler and more effective way to observe such phenomena.

Automated Machine Learning (AutoML) has significantly advanced the efficiency of ML-focused software development by automating hyperparameter optimization and pipeline construction, reducing the need for manual intervention. Quantum Machine Learning (QML) offers the potential to surpass classical machine learning (ML) capabilities by utilizing quantum computing. However, the complexity of QML presents substantial entry barriers. We introduce \emph{AutoQML}, a novel framework that adapts the AutoML approach to QML, providing a modular and unified programming interface to facilitate the development of QML pipelines. AutoQML leverages the QML library sQUlearn to support a variety of QML algorithms. The framework is capable of constructing end-to-end pipelines for supervised learning tasks, ensuring accessibility and efficacy. We evaluate AutoQML across four industrial use cases, demonstrating its ability to generate high-performing QML pipelines that are competitive with both classical ML models and manually crafted quantum solutions., Comment: 9 pages, 4 figures

Recent advancements in Large Language Models (LLMs) and Vision-Language Models (VLMs) have made them powerful tools in embodied navigation, enabling agents to leverage commonsense and spatial reasoning for efficient exploration in unfamiliar environments. Existing LLM-based approaches convert global memory, such as semantic or topological maps, into language descriptions to guide navigation. While this improves efficiency and reduces redundant exploration, the loss of geometric information in language-based representations hinders spatial reasoning, especially in intricate environments. To address this, VLM-based approaches directly process ego-centric visual inputs to select optimal directions for exploration. However, relying solely on a first-person perspective makes navigation a partially observed decision-making problem, leading to suboptimal decisions in complex environments. In this paper, we present a novel vision-language model (VLM)-based navigation framework that addresses these challenges by adaptively retrieving task-relevant cues from a global memory module and integrating them with the agent's egocentric observations. By dynamically aligning global contextual information with local perception, our approach enhances spatial reasoning and decision-making in long-horizon tasks. Experimental results demonstrate that the proposed method surpasses previous state-of-the-art approaches in object navigation tasks, providing a more effective and scalable solution for embodied navigation.

Type Ia supernovae (SNe Ia) are a key probe in modern cosmology, as they can be used to measure luminosity distances at gigaparsec scales. Models of their light-curves are used to project heterogeneous observed data onto a common basis for analysis. The SALT model currently used for SN Ia cosmology describes SNe as having two sources of variability, accounted for by a color parameter c, and a "stretch parameter" x1. We extend the model to include an additional parameter we label x2, to investigate the cosmological impact of currently unaddressed light-curve variability. We construct a new SALT model, which we dub "SALT3+". This model was trained by an improved version of the SALTshaker code, using training data combining a selection of the second data release of cosmological SNe Ia from the Zwicky Transient Facility and the existing SALT3 training compilation. We find additional, coherent variability in supernova light-curves beyond SALT3. Most of this variation can be described as phase-dependent variation in g-r and r-i color curves, correlated with a boost in the height of the secondary maximum in i-band. These behaviors correlate with spectral differences, particularly in line velocity. We find that fits with the existing SALT3 model tend to address this excess variation with the color parameter, leading to less informative measurements of supernova color. We find that neglecting the new parameter in light-curve fits leads to a trend in Hubble residuals with x2 of 0.039 +/- 0.005 mag, representing a potential systematic uncertainty. However, we find no evidence of a bias in current cosmological measurements. We conclude that extended SN Ia light-curve models promise mild improvement in the accuracy of color measurements, and corresponding cosmological precision. However, models with more parameters are unlikely to substantially affect current cosmological results.

We assess the local symmetry and crystal environment of trivalent terbium ions embedded in an indium tin oxide (ITO) matrix with bixbyite structure. The \mbox{Tb$^{3+}$} ions tend to substitute \mbox{In$^{3+}$} ions in two different cationic sites ($b$ and $d$). Density Functional Theory (DFT) calculations suggest that the \mbox{Tb$^{3+}$} ions are mainly located at $C_2$ symmetry sites relaxing selection rules and enabling electric dipole transitions, with the $^5\text{D}_4\rightarrow\leftindex^7{\text{F}}_2$ transition being the most intense, providing a red color to the light emission. Photoluminescence emission spectra under UV excitation at \qty{83}{\kelvin} revealed 30 intra-4$f$ transitions, which were assigned to the $\leftindex^7{\text{F}}_J$ ground multiplet of the \mbox{Tb$^{3+}$} ion. Crystal-field analysis shows a strong alignment between calculated and observed energy levels, yielding a standard deviation of $\sigma=\qty{15.1}{\centi\per\metre}$. We believe these results can help to understand the activation mechanisms of \mbox{Tb$^{3+}$} luminescent centers in transparent conductive oxides, as well as the potential to modulate \mbox{Tb$^{3+}$} emission color through its crystalline environment., Comment: 8 pages, 4figures

Cold molecular gas, largely traced by CO emission, is the primary fuel for star formation, making it essential for understanding galaxy evolution. ALMA has made significant progress in the study of the cosmic evolution of cold molecular gas. Here, we exploit the ALMACAL survey to address issues relating to small sample sizes and cosmic variance, utilising calibration data from ALMA to compile a statistically significant and essentially unbiased sample of CO-selected galaxies. By employing a novel statistical approach to emission-line classification using semi-analytical models, we place strong constraints on the CO luminosity function and the cosmic evolution of molecular gas mass density ($\rho_{H_2}$) back to $z \sim 6$. The cosmic molecular gas mass density increases with redshift, peaking around $z \sim 1.5$, then slowly declines towards higher redshifts by $\sim 1$ dex. Our findings confirm the key role of molecular gas in fuelling star formation. The new $\rho_{H_2}$ estimates allow us to revisit the cosmic baryon cycle, showing that the ratio of molecular gas-to-stellar mass density is consistent with the so-called 'bathtub model' of baryons, which implies a continuous replenishment of gas. The cosmic gas depletion timescale, estimated on a global scale, is shown to be fairly constant at all redshifts. We emphasise the importance of surveys using multiple small fields rather than a single contiguous area to mitigate the effects of cosmic variance., Comment: Main text 18 pages, 7 figures. Appendix 7 pages, 4 figures. Accepted for publication in Astronomy & Astrophysics

The Pandora Software Development Kit and algorithm libraries perform reconstruction of neutrino interactions in liquid argon time projection chamber detectors. Pandora is the primary event reconstruction software used at the Deep Underground Neutrino Experiment, which will operate four large-scale liquid argon time projection chambers at the far detector site in South Dakota, producing high-resolution images of charged particles emerging from neutrino interactions. While these high-resolution images provide excellent opportunities for physics, the complex topologies require sophisticated pattern recognition capabilities to interpret signals from the detectors as physically meaningful objects that form the inputs to physics analyses. A critical component is the identification of the neutrino interaction vertex. Subsequent reconstruction algorithms use this location to identify the individual primary particles and ensure they each result in a separate reconstructed particle. A new vertex-finding procedure described in this article integrates a U-ResNet neural network performing hit-level classification into the multi-algorithm approach used by Pandora to identify the neutrino interaction vertex. The machine learning solution is seamlessly integrated into a chain of pattern-recognition algorithms. The technique substantially outperforms the previous BDT-based solution, with a more than 20\% increase in the efficiency of sub-1\,cm vertex reconstruction across all neutrino flavours., Comment: 32 pages, 18 figures

In this paper we generalise a celebrated result of Milnor that characterises whether a rotationally symmetric surface is parabolic or hyperbolic to the case of biharmonic functions., Comment: 11 pages. Criticisms are more than welcome

A dedicated search for upward-going air showers at zenith angles exceeding $110^\circ$ and energies $E>0.1$ EeV has been performed using the Fluorescence Detector of the Pierre Auger Observatory. The search is motivated by two "anomalous" radio pulses observed by the ANITA flights I and III which appear inconsistent with the Standard Model of particle physics. Using simulations of both regular cosmic ray showers and upward-going events, a selection procedure has been defined to separate potential upward-going candidate events and the corresponding exposure has been calculated in the energy range [0.1-33] EeV. One event has been found in the search period between 1 Jan 2004 and 31 Dec 2018, consistent with an expected background of $0.27 \pm 0.12$ events from mis-reconstructed cosmic ray showers. This translates to an upper bound on the integral flux of $(7.2 \pm 0.2) \times 10^{-21}$ cm$^{-2}$ sr$^{-1}$ y$^{-1}$ and $(3.6 \pm 0.2) \times 10^{-20}$ cm$^{-2}$ sr$^{-1}$ y$^{-1}$ for an $E^{-1}$ and $E^{-2}$ spectrum, respectively. An upward-going flux of showers normalized to the ANITA observations is shown to predict over 34 events for an $E^{-3}$ spectrum and over 8.1 events for a conservative $E^{-5}$ spectrum, in strong disagreement with the interpretation of the anomalous events as upward-going showers., Comment: 10+5 Pages of Manuscript plus Supplemental Material, 3+8 figures, Accepted for publication in Physical Review Letters

We study a new model-free algorithm to compute $\varepsilon$-optimal policies for average reward Markov decision processes, in the weakly communicating case. Given a generative model, our procedure combines a recursive sampling technique with Halpern's anchored iteration, and computes an $\varepsilon$-optimal policy with sample and time complexity $\widetilde{O}(|\mathcal{S}||\mathcal{A}|\|h^*\|_{\text{sp}}^{2}/\varepsilon^{2})$ both in high probability and in expectation. To our knowledge, this is the best complexity among model-free algorithms, matching the known lower bound up to a factor $\|h^*\|_{\text{sp}}$. Although the complexity bound involves the span seminorm $\|h^*\|_{\text{sp}}$ of the unknown bias vector, the algorithm requires no prior knowledge and implements a stopping rule which guarantees with probability 1 that the procedure terminates in finite time. We also analyze how these techniques can be adapted for discounted MDPs.

We investigate the flux intensities spanning from radio waves to X-rays across 39 light curves of Quasar 3C 273, utilizing publicly available data collected by the Integral Science Data Centre (ISDC) database. Our results suggest that Quasar 3C 273 exhibits nonextensive behavior. Furthermore, we calculate the $q$ entropic indices for these light curves using the $q$-Gaussian distribution with a predominant observation of cases where $q>1$. Based on this index, we estimate the non-extensive entropy ($S_{q}$) and explore its correlation with the energy (in eV). In this context, we identify two jump-like increases in entropy, particularly evident in the infrared (IR) and X-ray wavebands. The peak in the far-IR band, around 0.34 eV, results from synchrotron flares evolving from higher to lower energies and thermal radiation emitted by hot dust near the sublimation radius. However, the second entropic peak in the hard X-ray range lacks statistical robustness due to limited data or large measurement uncertainties., Comment: 7 pages, 2 figures, 1 table, submitted to EPL

The first few hours of a supernova contain significant information about the progenitor system. The most modern wide-field surveys that scan the sky repeatedly every few days can discover all kinds of transients in those early epochs. At such times, some progenitor footprints may be visible, elucidating critical explosion parameters and helping to distinguish between leading explosion models. A dedicated spectroscopic classification programme using the optical spectrograph OSIRIS mounted to the Gran Telescopio de Canarias was set up to try to obtain observations of supernova at those early epochs. With the time awarded, we obtained spectra for 10 SN candidates, which we present here. Half of them were thermonuclear SNe, while the other half were core-collapse SNe. Most (70\%) were observed within the first six days of the estimated explosion, with two being captured within the first 48 hours. We present a characterization of the spectra, together with other public ancillary photometry from ZTF and ATLAS. This programme shows the need for an accompanying rapid-response spectroscopic programme to existing and future deep photometric wide-field surveys located at the right longitude to be able to trigger observations in a few hours after the discovery of the supernova candidate. Both the future La Silla Southern Supernova Survey (LS4) and the Legacy Survey of Space and Time (LSST) both located in Chile will be providing discovery and follow up of most of the transients in the southern hemisphere. This paper demonstrates that with a rapid spectroscopic programme and stringent triggering criteria, obtaining a sample of SN with spectra within a day of the explosion is possible., Comment: 20 pages, 16 figures. Submitted to A&A

We extend the notion of stability in the non-abelian category of poset representations (introduced by Futorny and Iusenko) to the category of socle-projective representations of a given $r$-peak poset $\P$. When $\P$ is a poset of type $\mathbb{A}$, we demonstrate in two distinct ways that every indecomposable peak $\P$-space is stable. First, this is shown using a bilinear form associated with the poset. Second, we prove it by observing that a stability function derived from a geometric model ensures that all indecomposable objects are stable. Along the way, we provide a new geometric realization of the category of socle-projective representations, inspired by the work of Schiffler and Serna [\textit{J. Pure Appl. Algebra} \textbf{224} (2020), no.~12, 106436, 23 pp.; MR4101480]. Finally, we establish a connection between the geometric perspective and the bilinear form approach., Comment: 28 pages

Technology is currently ubiquitous and is also part of the educational system at all levels. It started with communication technology systems, and later continued with digital competence. Nowadays, although these previous concepts are still in force and are useful for students and workers in general, a new concept has been born that can function as a cross-curricular competence called Computational Thinking. There is currently no consensus on the definition of computational thinking, nor on the classification of its skills, but there is a consensus that it refers to a set of skills necessary for the formulation and resolution of problems. The study of Computational Thinking has been very influential in recent years in research on teaching and learning processes, which has led educational institutions to begin to address these issues during training. In this paper, we try to introduce this new cross-curricular competence and expose a project of implementation of Computational Thinking in engineering careers through Calculus subject.

Maintaining railway axles is critical to preventing severe accidents and financial losses. The railway industry is increasingly interested in advanced condition monitoring techniques to enhance safety and efficiency, moving beyond traditional periodic inspections toward Maintenance 4.0. This study introduces a robust Deep Autoregressive solution that integrates seamlessly with existing systems to avert mechanical failures. Our approach simulates and predicts vibration signals under various conditions and fault scenarios, improving dataset robustness for more effective detection systems. These systems can alert maintenance needs, preventing accidents preemptively. We use experimental vibration signals from accelerometers on train axles. Our primary contributions include a transformer model, ShaftFormer, designed for processing time series data, and an alternative model incorporating spectral methods and enhanced observation models. Simulating vibration signals under diverse conditions mitigates the high cost of obtaining experimental signals for all scenarios. Given the non-stationary nature of railway vibration signals, influenced by speed and load changes, our models address these complexities, offering a powerful tool for predictive maintenance in the rail industry.

The double descent phenomenon challenges traditional statistical learning theory by revealing scenarios where larger models do not necessarily lead to reduced performance on unseen data. While this counterintuitive behavior has been observed in a variety of classical machine learning models, particularly modern neural network architectures, it remains elusive within the context of quantum machine learning. In this work, we analytically demonstrate that quantum learning models can exhibit double descent behavior by drawing on insights from linear regression and random matrix theory. Additionally, our numerical experiments on quantum kernel methods across different real-world datasets and system sizes further confirm the existence of a test error peak, a characteristic feature of double descent. Our findings provide evidence that quantum models can operate in the modern, overparameterized regime without experiencing overfitting, thereby opening pathways to improved learning performance beyond traditional statistical learning theory.

This paper discusses the mutual interactions between a thin flexible aluminum plate and supersonic flow using two-dimensional (2D) numerical simulations. Calculations are performed using an open source library, SU2, that solves partial differential equations governing fluid and structural dynamics. The configuration considered in this research effort is based on an experiment in which a thin flexible panel of 1.02 mm with a 50.8 mm overhang at the outer edge of a backward facing step is exposed to Mach 2 flow. The computational framework was first validated against measurements for both the initial transients of 10 ms and the fully started conditions at 0.4 s. Then, numerical studies were performed to analyze the fluid-structure interactions at 4 different Mach numbers between 0.5 and 3. The flow behavior revealed distinct phenomena, including shear layer separation for subsonic and transonic flows, and a fully enclosed recirculation region under the overhang in supersonic cases. The time-averaged flow field identified potential temperature hotspots during the initial transients, which intensified as time evolved. For Mach 0.50, the amplitude of the thin panel oscillations increased as the flow transitioned from transient to steady-state conditions. In the transonic case (M = 0.95), the oscillation amplitude became significantly larger, potentially leading to resonant behavior and structural failure (we did not model failure). However, in the supersonic cases, the oscillations stabilized and were sustained after the initial transients. The research quantitatively identifies the influence of the Mach number on the fluid-structure interaction phenomena, which affect pressure loads and the development of thermal hotspots, which are crucial elements in engineering design., Comment: 20 pages, 20 figures, 57 references

We present a new photometric pipeline for the detection of pre-supernova (pre-SN) emission in the Young Supernova Experiment (YSE) sky survey. The method described is applied to SN 2020tlf, a type II SN (SN II) with precursor emission in the last ~100 days before first light. We re-analyze the YSE griz-band light curves of SN 2020tlf and provide revised pre-explosion photometry that includes a robust list of confident detection and limiting magnitudes. Compared to the results of Jacobson-Galan et al. 2022a, this new analysis yields fewer total r/i/z-band pre-SN detections at phases > -100 days. Furthermore, we discourage the use of the blackbody modeling of the pre-explosion spectral energy distribution, the pre-SN bolometric light curve and the blackbody model parameters presented in Jacobson-Galan et al. 2022a. Nevertheless, binned photometry of SN 2020tlf confirms a consistent progenitor luminosity of ~10$^{40}$ erg s$^{-1}$ before explosion., Comment: 5 pages, 1 figure. Published in RNAAS

This study explores in depth rotating detonation engines (RDEs) fueled by premixed stoichiometric hydrogen/air mixtures through two-dimensional numerical simulations including a detailed chemical kinetic mechanism. To model the spatial reactant non-uniformities observed in practical RDE combustors, the referred simulations incorporate different numbers of discrete inlet nozzles. The primary focus here is to analyze the influence of reactant non-uniformities on detonation combustion dynamics in RDEs. By systematically varying the number of reactant injection nozzles (from 15 to 240), while maintaining a constant total injection area, the study delves into how this variation influences the behavior of rotating detonation waves (RDWs) and the associated overall flow field structure. The numerical results obtained here reveal significant effects of the number of inlets employed on both RDE stability (self-sustaining detonation wave) and performance. RDE configurations with a lower number of inlets exhibit a detonation front with chaotic behavior (pressure oscillations) due to an increased amount of unburned gas ahead of the detonation wave. This chaotic behavior can lead to the flame extinguishing or decreasing in intensity, ultimately diminishing the engine's overall performance. Conversely, RDE configurations with a higher number of inlets feature smoother detonation propagations without chaotic transients, leading to more stable and reliable performance metrics. This study uses high-fidelity numerical techniques such as adaptive mesh refinement (AMR) and the PeleC compressible reacting flow solver. This comprehensive approach enables a thorough evaluation of critical RDE characteristics including detonation velocity, fuel mass flow rate, impulse, thrust, and reverse pressure waves under varying reactant injection conditions.

Terahertz communications are envisioned as a key enabler for 6G networks. The abundant spectrum available in such ultra high frequencies has the potential to increase network capacity to huge data rates. However, they are extremely affected by blockages, to the point of disrupting ongoing communications. In this paper, we elaborate on the relevance of predicting visibility between users and access points (APs) to improve the performance of THz-based networks by minimizing blockages, that is, maximizing network availability, while at the same time keeping a low reconfiguration overhead. We propose a novel approach to address this problem, by combining a neural network (NN) for predicting future user-AP visibility probability, with a probability threshold for AP reselection to avoid unnecessary reconfigurations. Our experimental results demonstrate that current state-of-the-art handover mechanisms based on received signal strength are not adequate for THz communications, since they are ill-suited to handle hard blockages. Our proposed NN-based solution significantly outperforms them, demonstrating the interest of our strategy as a research line., Comment: Article published in Computer Communications journal

We study the mixing time of the projected Langevin algorithm (LA) and the privacy curve of noisy Stochastic Gradient Descent (SGD), beyond nonexpansive iterations. Specifically, we derive new mixing time bounds for the projected LA which are, in some important cases, dimension-free and poly-logarithmic on the accuracy, closely matching the existing results in the smooth convex case. Additionally, we establish new upper bounds for the privacy curve of the subsampled noisy SGD algorithm. These bounds show a crucial dependency on the regularity of gradients, and are useful for a wide range of convex losses beyond the smooth case. Our analysis relies on a suitable extension of the Privacy Amplification by Iteration (PABI) framework (Feldman et al., 2018; Altschuler and Talwar, 2022, 2023) to noisy iterations whose gradient map is not necessarily nonexpansive. This extension is achieved by designing an optimization problem which accounts for the best possible R\'enyi divergence bound obtained by an application of PABI, where the tractability of the problem is crucially related to the modulus of continuity of the associated gradient mapping. We show that, in several interesting cases -- including the nonsmooth convex, weakly smooth and (strongly) dissipative -- such optimization problem can be solved exactly and explicitly. This yields the tightest possible PABI-based bounds, where our results are either new or substantially sharper than those in previous works., Comment: 40 pages, 2 figures

Systems characterized by evolving interactions, prevalent in social, financial, and biological domains, are effectively modeled as continuous-time dynamic graphs (CTDGs). To manage the scale and complexity of these graph datasets, machine learning (ML) approaches have become essential. However, CTDGs pose challenges for ML because traditional static graph methods do not naturally account for event timings. Newer approaches, such as graph recurrent neural networks (GRNNs), are inherently time-aware and offer advantages over static methods for CTDGs. However, GRNNs face another issue: the short truncation of backpropagation-through-time (BPTT), whose impact has not been properly examined until now. In this work, we demonstrate that this truncation can limit the learning of dependencies beyond a single hop, resulting in reduced performance. Through experiments on a novel synthetic task and real-world datasets, we reveal a performance gap between full backpropagation-through-time (F-BPTT) and the truncated backpropagation-through-time (T-BPTT) commonly used to train GRNN models. We term this gap the "truncation gap" and argue that understanding and addressing it is essential as the importance of CTDGs grows, discussing potential future directions for research in this area., Comment: Published in Transactions on Machine Learning Research (TMLR)

We explore the Mott transition in orthorhombic diamond lattices relevant to (ET)Ag$_4$(CN)$_5$ molecular compounds. The non-interacting phases include nodal line, Dirac and/or Weyl semimetals depending on the strength of spin-orbit coupling and the degree of dimerization of the lattice. Based on an extension of slave-rotor mean-field theory which accounts for magnetic order, we find a transition from a semimetal to a paramagnetic Mott insulator at a critical $U_c$ which becomes N\'eel ordered at a larger Coulomb repulsion, $U_{cm}>U_{c}$. The resulting intermediate Mott phase is a $U(1)$ quantum spin liquid (QSL) consisting on spinon preserving the nodal structure of the nearby semimetallic phases. An analysis of the Green's function in this Mott phase shows how the zeros follow the spinon band dispersions carrying the topology while the poles describe the Hubbard bands. Our results are relevant to recent observations in (ET)Ag$_4$(CN)$_5$ molecular compounds in which the ambient pressure N\'eel ordered Mott insulator is gradually suppressed until semimetallic behavior arises at larger pressures., Comment: 22 pages, 13 figures

In this study, we explore the characteristics of `low-mass' ($\log(M_{\star}/M_{\odot}) \leq 8$) and `intermediate-mass' ($8 \lt \log(M_{\star}/M_{\odot}) \leq 10$) host galaxies of Type Ia supernovae (SNe Ia) from the second data release (DR2) of the Zwicky Transient Facility survey and investigate their correlations with different sub-types of SNe Ia. We use the photospheric velocities measured from the Si II $\lambda$6355 feature, SALT2 light-curve stretch ($x_1$) and host-galaxy properties of SNe Ia to re-investigate the existing relationship between host galaxy mass and Si II $\lambda$6355 velocities. We also investigate sub-type preferences for host populations and show that while the more energetic and brighter 91T-like SNe Ia tends to populate the younger host populations, 91bg-like SNe Ia populate in the older populations. Our findings suggest High Velocity SNe Ia (HV SNe Ia) not only comes from the older populations but they also come from young populations as well. Therefore, while our findings can partially provide support for HV SNe Ia relating to single degenerate progenitor models, they indicate that HV SNe Ia other than being a different population, might be a continued distribution with different explosion mechanisms. We lastly investigate the specific rate of SNe Ia in the volume-limited SN Ia sample of DR2 and compare with other surveys., Comment: 13 pages, 8 figures, accepted for publication in Astronomy & Astrophysics

Obstructed atomic phases, with their realizations in systems of diverse dimensionality, have recently arisen as one of the topological states with greatest potential to show higher-order phenomena. In this work we report a special type of obstruction, known as orbital-mediated atomic obstruction, in monolayers of materials with spatial symmetry described by the space group P-3m1. By means of a minimal tight-binding model and first-principles calculations, we show that this obstructed phase is related to the mismatch of the charge centers coming from the atomic limit with respect to the centers that are obtained from the reciprocal space description. Although we find atomic limits that correspond with occupied atomic sites, orbital-mediated atomic obstruction requires the presence of orbitals that have no support in real space. In order to demonstrate the nontrivial character of the obstruction, we confirm the presence of a filling anomaly for finite geometries that is directly associated with the bulk configuration, and discuss the role of the boundary states and their underlying mechanism. Several material examples are presented to illustrate the ubiquity of these nontrivial responses and, in turn, to discuss the differences related to the particular ground state configuration. In addition, we perform a survey of materials and elaborate a list of candidate systems which will host this obstructed phase in monolayer form., Comment: Supplementary Material in preparation

We conducted a search for new ultracool companions to nearby white dwarfs using multiple methods, including the analysis of colors and examination of images in both the optical and the infrared. Through this process, we identified fifty-one previously unrecognized systems with candidate ultracool companions. Thirty-one of these systems are resolved in at least one catalog, and all but six are confirmed as co-moving companions via common proper motion and consistent parallax measurements (when available). We have followed up four co-moving companions with near-infrared spectroscopy and confirm their ultracool nature. The remaining twenty candidates are unresolved, but show clear signs of infrared excess which is most likely due to the presence of a cold, low-mass companion or a dusty circumstellar disk. Three of these unresolved systems have existing optical spectra that clearly show the presence of a cool stellar companion to the white dwarf primary via spectral decomposition. These new discoveries, along with our age estimates for the primary white dwarfs, will serve as valuable benchmark systems for future characterization of ultracool dwarfs., Comment: Accepted to the Astronomical Journal

Recently, a solution to the long-standing issue of general covariance in canonical quantum gravity has been proposed, leading to the proposal of two black hole solutions. From the above, a fundamental question arises: which solution is superior? Note that one of the solutions possesses a Cauchy horizon. Considering this quantum black hole solution with a Cauchy horizon, in the present letter, we explore whether it exhibits properties similar to those of the Reissner-Nordstr\"{o}m black hole. Given its geometric similarity, by applying the generalized Dray-'t Hooft-Redmond relation, we find evidence of mass inflation and divergence in scalar curvature, indicating that the Cauchy horizon is unstable. While this is consistent with the Strong Cosmic Censorship Conjecture, it suggests that it does not represent a regular black hole. Furthermore, we extend the metric to include a cosmological constant and study the validity of Strong Cosmic Censorship conjecture for the quantum black hole in de Sitter spacetime. The results indicate that the presence of a cosmological constant cannot prevent the violation of the conjecture when the quantum black hole approaches its extreme limit. These reasons suggest that the other black hole solution, which does not have a Cauchy horizon, is more preferable., Comment: 10 pages, 5 figures

Visual framing analysis is a key method in social sciences for determining common themes and concepts in a given discourse. To reduce manual effort, image clustering can significantly speed up the annotation process. In this work, we phrase the clustering task as a Minimum Cost Multicut Problem [MP]. Solutions to the MP have been shown to provide clusterings that maximize the posterior probability, solely from provided local, pairwise probabilities of two images belonging to the same cluster. We discuss the efficacy of numerous embedding spaces to detect visual frames and show its superiority over other clustering methods. To this end, we employ the climate change dataset \textit{ClimateTV} which contains images commonly used for visual frame analysis. For broad visual frames, DINOv2 is a suitable embedding space, while ConvNeXt V2 returns a larger number of clusters which contain fine-grain differences, i.e. speech and protest. Our insights into embedding space differences in combination with the optimal clustering - by definition - advances automated visual frame detection. Our code can be found at https://github.com/KathPra/MP4VisualFrameDetection., Comment: WACV25 applications track

Image classification tasks are among the most prominent examples that can be reliably solved by classical machine learning models. In this study, we compare the performance of randomized classical and quantum neural networks as well as classical and quantum-classical hybrid convolutional neural networks for the task of binary image classification. To this end, we employ various data sets of increasing complexity - (i) an artificial hypercube dataset, (ii) MNIST handwritten digits, and (iii) real-world industrial images from laser cutting machines. We analyze the performance of the employed quantum models with respect to correlations between classification accuracy and various hyperparameters. For the random quantum neural networks, we additionally compare their performance with some known literature models and how top-performing models from one data set perform on the others. In general, we observe fairly similar performances of classical and quantum or hybrid models. Our study provides an industry perspective on the prospects of quantum machine learning for practical image classification tasks., Comment: 23 pages, 12 figures

This study examines the average X-ray properties of massive halos at z< 0.2, covering the largest halo mass range to date, from Milky Way-like halos to massive clusters. The analysis is based on stacking in the eFEDS area of the GAMA galaxy group sample, validated with synthetic data that mimic observed eROSITA X-ray and GAMA optical data using Magneticum lightcones. Stacking was conducted in halo mass bins and tested for AGN and X-ray binary contamination, systematics in the halo mass proxy, and uncertainties in optical group centers. The study provides average X-ray surface brightness profiles in six mass bins, spanning Milky Way-like systems to poor clusters. The scatter in the X-ray luminosity-mass (LX-M) relation is attributed to gas concentration: low X-ray luminosity systems at fixed halo mass exhibit lower central gas concentrations than high-luminosity systems, consistent with Magneticum predictions. However, discrepancies in dark matter concentration arise, with Magneticum predicting undetected groups as older and more relaxed, while observations suggest the opposite. New LX-M relations are presented covering three decades of halo mass. These relations fit a single power law, aligning with previous studies. Magneticum matches observed gas distributions across all masses, whereas IllustrisTNG, EAGLE, Simba, and FLAMINGO exhibit significant discrepancies at various mass scales. Simulations calibrated on local galaxy properties accurately reproduce central galaxies but fail to capture gas properties. Conversely, simulations like Magneticum excel in gas predictions but produce overly massive central galaxies. Further exploration of gas and dark matter distributions and their effects on galaxy properties is critical to comprehending the role of gravitational forces and feedback in shaping large-scale structure and galaxy evolution., Comment: Submitted to A&A, 15 pages, 9 figures. Abstract adapted to arxiv submission. arXiv admin note: text overlap with arXiv:2411.16546

We investigate the internal and external mechanisms that regulate and quench star formation (SF) in galaxies at $z \sim 0.3$ using MAGPI observations and the EAGLE, Magneticum, and IllustrisTNG cosmological simulations. Using SimSpin to generate mock observations of simulated galaxies, we match detection/resolution limits in star formation rates and stellar mass, along with MAGPI observational details including the average point spread function and pixel scale. While we find a good agreement in the slope of the global star-forming main sequence (SFMS) between MAGPI observations and all three simulations, the slope of the resolved SFMS does not agree within 1 $-$ 2$\sigma$. Furthermore, in radial SF trends, good agreement between observations and simulations exists only for galaxies far below the SFMS, where we capture evidence for inside-out quenching. The simulations overall agree with each other between $\sim1.5-4 \ R_{\rm e}$ but show varying central suppression within $R \sim 1.5 \ R_{\rm e}$ for galaxies on and below the SFMS, attributable to different AGN feedback prescriptions. All three simulations show similar dependencies of SF radial trends with environment. Central galaxies are subject to both internal and external mechanisms, showing increased SF suppression in the centre with increasing halo mass, indicating AGN feedback. Satellite galaxies display increasing suppression in the outskirts as halo mass increases, indicative of environmental processes. These results demonstrate the power of spatially resolved studies of galaxies; while global properties align, radial profiles reveal discrepancies between observations and simulations and their underlying physics., Comment: 20 pages, 10 figures, submitted to MNRAS

We study the conditions under which a TTF class in a module category over a ring is silting. Using the correspondence between idempotent ideals over a ring and TTF classes in the module category, we focus on finding the necessary and sufficient conditions for $R/I$ to be a silting $R$-module, and hence for the TTF class $\mathbf{Gen}(R/I)$ to be silting, where $I$ is an idempotent two-sided ideal of $R$. In our main result, we show that $R/I$ is a silting module whenever $I$ is the trace of a projective $R$-module. Furthermore, we demonstrate that the converse holds for a broad class of rings, including semiperfect rings.

By using eROSITA data in the eFEDS area, we provide a measure of the hot gas fraction vs. halo mass relation over the largest halo mass range, from Milky Way-sized halos to massive clusters, and to the largest radii ever probed so far in local systems. To cope with the incompleteness and selection biases of the X-ray selection, we apply the stacking technique in eROSITA data of a highly complete and tested sample of optically selected groups. The method has been extensively tested on mock observations. In massive clusters, the hot gas alone provides a baryon budget within $R_{200}$ consistent with the cosmic value. At the same time, at the group mass scale, it accounts only for 20-40% of it. The hot gas fraction vs. halo mass relation is well-fitted by a power law, with a consistent shape and a normalization varying at maximum by a factor of 2 from $r_{500}$ to $r_{200}$. Such a relation is consistent with other works in the literature that consider X-ray survey data at the same depth as eFEDS. However, it provides a lower average gas fraction in the group regime than works based on X-ray bright group samples. The comparison of the observed relation with the predictions of several hydrodynamical simulations (BAHAMAS, FLAMINGO, SIMBA, Illustris, IllustrisTNG, MillenniumTNG, and Magneticum) shows that all simulations but Magneticum and SIMBA overpredict the gas fraction, with the largest discrepancy (up to a factor of 3) in the massive group-poor cluster halo mass range. We emphasize the need for mechanisms that can effectively expel gas to larger radii in galaxy groups without excessively quenching star formation in their member galaxies. Current hydrodynamical simulations face a significant challenge in balancing their subgrid physics: none can sufficiently evacuate gas from the halo virial region without negatively impacting the properties of the resident galaxy population., Comment: submitted to A&A, 11 pages, 8 figures. Abstract reduced for ArXiv submission

Hydrodynamical simulation predictions are often compared with observational data without fully accounting for systematics and biases specific to observational techniques. Using the magnetohydrodynamical simulation Magneticum, we generate mock eROSITA eRASS:4 data, combined with GAMA-like spectroscopic surveys and optically selected galaxy catalogs from the same light-cone, to analyze hot gas properties in galaxy groups via a stacking technique. This study aims to (i) incorporate observational systematics into predictions and (ii) evaluate the reliability of stacking techniques for determining average X-ray properties of galaxy groups. Our analysis provides X-ray emission predictions from Magneticum, including contributions from AGN, X-ray binaries (XRBs), and the Intra-Group Medium (IGM) as a function of halo mass, covering Milky Way (MW)-like groups to poor clusters. We find that AGN and XRBs dominate the X-ray surface brightness profiles of low-mass halos. The reliability of stacking techniques is tested by reproducing input X-ray surface brightness and electron density profiles, accounting for completeness and contamination of prior samples, miscentering of optical group centers, uncertainties in X-ray emissivity due to gas temperature and metallicity assumption, and systematics in halo mass proxies. The halo mass proxy emerges as the primary source of systematics, affecting X-ray surface brightness and scaling relations. We show that stacked X-ray luminosity-mass relations are flatter than input relations but consistent with observations. Additionally, the retrieved hot gas fraction-mass relation aligns well with observational data. These results highlight the need to account for systematic errors when comparing stacking techniques to other methods using different prior catalogs or predictions., Comment: Submitted to A&A. 18 pages and 19 figures, Abstract reduced for ArXiv submission

Wide-field cosmological surveys provide hundreds of thousands of spectroscopically confirmed galaxy groups and clusters, valuable for tracing baryonic matter distribution. However, controlling systematics in identifying host dark matter halos and estimating their properties is crucial. We evaluate three group detection methods on a simulated dataset replicating the GAMA selection to understand systematics and selection effects. This is key for interpreting data from SDSS, GAMA, DESI, WAVES, and leveraging optical catalogues in the (X-ray) eROSITA era to quantify baryonic mass in galaxy groups. Using a lightcone from the Magneticum hydrodynamical simulation, we simulate a spectroscopic galaxy survey in the local Universe (down to $z<0.2$ and stellar mass completeness $M_{\star}\geq10^{9.8} M_{\odot}$). We assess completeness and contamination of reconstructed halo catalogues, evaluate membership accuracy, and analyse the halo mass recovery rate of group finders. All three group finders achieve high completeness ($>80\%$) at group and cluster scales, confirming optical selection's suitability for dense regions. Contamination at low masses ($M_{200}<10^{13} M_{\odot}$) arises from interlopers and fragmentation. Membership is at least 70\% accurate above the group mass scale, but inaccuracies bias halo mass estimates using galaxy velocity dispersion. Alternative proxies, like total stellar luminosity or mass, yield more accurate halo masses. The cumulative luminosity function of galaxy members matches predictions, showing the group finders' accuracy in identifying galaxy populations. These results confirm the reliability and completeness of spectroscopic catalogues produced by state-of-the-art group finders. This supports studies requiring large spectroscopic samples of galaxy groups and clusters, as well as investigations into galaxy evolution across diverse environments., Comment: submitted to A&A, 17 pages, 9 figures. Abstract reduced for ArXiv submission

To cope with environments with high levels of radiation, non-silicon semiconductors such as silicon carbide detectors are being proposed for instrumentation. 4H-SiC diodes for radiation detection have been fabricated in the IMB-CNM Clean Room, for which different strategies to define the electrical contact of the implants had been implemented, in an attempt to optimise the technology for, e.g., medical applications or low energy radiation detection, as the material choice can affect the sensitivity of the device. Among these technologies, it is included an epitaxially-grown graphene layer as part of the electrical contact. In this paper, a selection of four configurations of the IMB-CNM SiC diodes are characterised in terms of radiation detector response. Photodiode performance under 20 keV X-rays irradiation in the XALOC beam line at ALBA Synchrotron is presented. Over-responses in the range of 12-19% linked to the interaction of the radiation with the metallic layers are observed. A good uniformity response as well as a good linearity at 0~V bias is reported, even in the under-depleted devices. This work exemplifies the good performance of SiC detectors fabricated at IMB-CNM specifically for low-energy X ray characterization at high X-ray intensities., Comment: 12 pages, 5 figures. accepted to JINST

The second data release of Type Ia supernovae (SNe Ia) observed by the Zwicky Transient Facility has provided a homogeneous sample of 3628 SNe Ia with photometric and spectral information. This unprecedented sample size enables us to better explore our currently tentative understanding of the dependence of host environment on SN Ia properties. In this paper, we make use of two-dimensional image decomposition to model the host galaxies of SNe Ia. We model elliptical galaxies as well as disk/spiral galaxies with or without central bulges and bars. This allows for the categorisation of SN Ia based on their morphological host environment, as well as the extraction of intrinsic galaxy properties corrected for both cosmological and atmospheric effects. We find that although this image decomposition technique leads to a significant bias towards elliptical galaxies in our final sample of galaxies, the overall results are robust. By successfully modelling 728 host galaxies, we find that the photometric properties of SNe Ia found in disks and in elliptical galaxies, correlate fundamentally differently with their host environment. We identified strong linear relations between light-curve stretch and our model-derived galaxy colour for both the elliptical (16.8$\sigma$) and disk (5.1$\sigma$) subpopulations of SNe Ia. Lower stretch SNe Ia are found in redder environments, which we identify as an age/metallicity effect. Within the subpopulation of SNe Ia found in disk containing galaxies, we find a significant linear trend (6.1$\sigma$) between light-curve stretch and model-derived local $r$-band surface brightness, which we link to the age/metallicity gradients found in disk galaxies. SN Ia colour shows little correlation with host environment as seen in the literature. We identify a possible dust effect in our model-derived surface brightness (3.3$\sigma$), for SNe Ia in disk galaxies., Comment: ZTF SN Ia DR2 release paper. Submitted to A&A (ZTF DR2 Special Issue)

Quantum machine learning is an emergent field that continues to draw significant interest for its potential to offer improvements over classical algorithms in certain areas. However, training quantum models remains a challenging task, largely because of the difficulty in establishing an effective inductive bias when solving high-dimensional problems. In this work, we propose a training framework that prioritizes informative data points over the entire training set. This approach draws inspiration from classical techniques such as curriculum learning and hard example mining to introduce an additional inductive bias through the training data itself. By selectively focusing on informative samples, we aim to steer the optimization process toward more favorable regions of the parameter space. This data-centric approach complements existing strategies such as warm-start initialization methods, providing an additional pathway to address performance challenges in quantum machine learning. We provide theoretical insights into the benefits of prioritizing informative data for quantum models, and we validate our methodology with numerical experiments on selected recognition tasks of quantum phases of matter. Our findings indicate that this strategy could be a valuable approach for improving the performance of quantum machine learning models., Comment: 10 + 2 pages, 6 figures, 3 tables

Let $\A$ be a finite dimensional vector space and $\Phi$ be a finite root system in $\A$. To this data is associated an affine poly-simplicial complex. Motivated by a forthcoming construction of connectified higher buildings, we study "affine weight polytopes" associated to these data. We prove that these polytopes tesselate $\A$. We also prove a kind of "mixed" tessellation, involving the affine weight polytopes and the poly-simplical structure on $\A$.

Quantum Neuromorphic Computing (QNC) merges quantum computation with neural computation to create scalable, noise-resilient algorithms for quantum machine learning (QML). At the core of QNC is the quantum perceptron (QP), which leverages the analog dynamics of interacting qubits to enable universal quantum computation. Canonically, a QP features $N$ input qubits and one output qubit, and is used to determine whether an input state belongs to a specific class. Rydberg atoms, with their extended coherence times and scalable spatial configurations, provide an ideal platform for implementing QPs. In this work, we explore the implementation of QPs on Rydberg atom arrays, assessing their performance in tasks such as phase classification between Z2, Z3, Z4 and disordered phases, achieving high accuracy, including in the presence of noise. We also perform multi-class entanglement classification by extending the QP model to include multiple output qubits, achieving 95\% accuracy in distinguishing noisy, high-fidelity states based on separability. Additionally, we discuss the experimental realization of QPs on Rydberg platforms using both single-species and dual-species arrays, and examine the error bounds associated with approximating continuous functions.

One way to define a sub-Riemannian metric is as the limit of a Riemannian metric. Consider a Riemannian structure depending on a parameter $s$ such that its limit defines a sub-Riemannian metric when $s \to \infty$, assuming that the Riemannian geodesic flow is integrable for all $s$. An interesting question is: Can we determine the integrability of the sub-Riemannian geodesic flow as the limit of the integrals of motion of the Riemannian geodesic flow? The paper's main contribution is to provide a positive answer to this question in the special orthogonal group. Theorem 1.1 states that the Riemannian geodesic flow is Liuville integrable: The Manakov integrals' limit suggests the existence of a Lax pair formulation of the Riemannian geodesic equations, and the proof of Theorem 1.1 relies on this Lax pair.

Instrumental variables (IVs) are widely used to estimate causal effects in the presence of unobserved confounding between exposure and outcome. An IV must affect the outcome exclusively through the exposure and be unconfounded with the outcome. We present a framework for relaxing either or both of these strong assumptions with tuneable and interpretable budget constraints. Our algorithm returns a feasible set of causal effects that can be identified exactly given relevant covariance parameters. The feasible set may be disconnected but is a finite union of convex subsets. We discuss conditions under which this set is sharp, i.e., contains all and only effects consistent with the background assumptions and the joint distribution of observable variables. Our method applies to a wide class of semiparametric models, and we demonstrate how its ability to select specific subsets of instruments confers an advantage over convex relaxations in both linear and nonlinear settings. We also adapt our algorithm to form confidence sets that are asymptotically valid under a common statistical assumption from the Mendelian randomization literature., Comment: 11 pages, 4 figures

We introduce Concept Bottleneck Protein Language Models (CB-pLM), a generative masked language model with a layer where each neuron corresponds to an interpretable concept. Our architecture offers three key benefits: i) Control: We can intervene on concept values to precisely control the properties of generated proteins, achieving a 3 times larger change in desired concept values compared to baselines. ii) Interpretability: A linear mapping between concept values and predicted tokens allows transparent analysis of the model's decision-making process. iii) Debugging: This transparency facilitates easy debugging of trained models. Our models achieve pre-training perplexity and downstream task performance comparable to traditional masked protein language models, demonstrating that interpretability does not compromise performance. While adaptable to any language model, we focus on masked protein language models due to their importance in drug discovery and the ability to validate our model's capabilities through real-world experiments and expert knowledge. We scale our CB-pLM from 24 million to 3 billion parameters, making them the largest Concept Bottleneck Models trained and the first capable of generative language modeling.

The state of health of solar photovoltaic (PV) systems is assessed by measuring the current-voltage (I-V) curves, which present a collection of three cardinal points: the short-circuit point, the open-circuit point, and the maximum power point. To understand the response of PV systems, the I-V curve is typically modeled using the well-known single-diode model (SDM), which involves five parameters. However, the SDM can be expressed as a function of one parameter when the information of the cardinal points is incorporated into the formulation. This paper presents a methodology to address the uncertainty of the cardinal points on the parameters of the single-diode model based on the mathematical theory. Utilizing the one-dimensional single-diode model as the basis, the study demonstrates that it is possible to include the uncertainty by solving a set of nonlinear equations. The results highlight the feasibility and effectiveness of this approach in accounting for uncertainties in the SDM parameters.

We developed a digital inline holography (DIH) system integrated with deep learning algorithms for real-time detection of particulate matter (PM) and bacterial contamination in peritoneal dialysis (PD) fluids. The system comprises a microfluidic sample delivery module and a DIH imaging module that captures holograms using a pulsed laser and a digital camera with a 40x objective. Our data processing pipeline enhances holograms, reconstructs images, and employs a YOLOv8n-based deep learning model for particle identification and classification, trained on labeled holograms of generic PD particles, Escherichia coli (E. coli), and Pseudomonas aeruginosa (P. aeruginosa). The system effectively detected and classified generic particles in sterile PD fluids, revealing diverse morphologies predominantly sized 1-5 um with an average concentration of 61 particles per microliter. In PD fluid samples spiked with high concentrations of E. coli and P. aeruginosa, our system achieved high sensitivity in detecting and classifying these bacteria at clinically relevant low false positive rates. Further validation against standard colony-forming unit (CFU) methods using PD fluid spiked with bacterial concentrations from approximately 100 to 10,000 bacteria per milliliter demonstrated a clear one-to-one correspondence between our measurements and CFU counts. Our DIH system provides a rapid, accurate alternative to traditional culture-based methods for assessing bacterial contamination in PD fluids. By enabling real-time sterility monitoring, it can significantly improve patient outcomes in PD treatment, facilitate point-of-care fluid production, reduce logistical challenges, and be extended to quality control in pharmaceutical production., Comment: 16 pages, 5 figures

For most health or well-being interventions, the process of evaluation is distinct from the activity itself, both in terms of who is involved, and how the actual data is collected and analyzed. Tangible interaction affords the opportunity to combine direct and embodied collaboration with a holistic approach to data collection and evaluation. We demonstrate this potential by describing our experiences designing and using the Communal Loom, an artifact for art therapy that translates quantitative data to collectively woven artifacts., Comment: Presented at CHI 2022 Tangible Interaction for Supporting Well-being Workshop

CRAFT@Large (C@L) is an initiative launched by the MakerLAB at Cornell Tech to create an inclusive environment for the intercultural and intergenerational exchange of ideas through making. With our approach, we challenge the traditional definition of community outreach performed by academic makerspaces. Existing academic makerspaces often perform community engagement by only offering hourly, one-time workshops or by having community members provide a problem that is then used by students as a project assignment. These approaches position community members as occasional visitors and non-equal contributors, which not only conflict with the core values of co-creation but also limit the makerspaces' impact on connecting the universities and the communities. C@L explored an alternative approach in which we invited community members as long-term and equal co-makers into the academic makerspaces. In this article, we showcase two sets of collaborations that illustrate the continuity of people through co-making. We present how academic makerspaces can function as a hub that connects community members and partner organizations with the campus community in a long-term relationship.

Digital product passports (DPP) have been proposed in the European Ecodesign for Sustainable Products Regulation (ESPR) as a means to keep and provide product information that facilitates product reusage, reparation, and recycling. Thus, DPPs should provide a positive effect on the environmental impact of future manufactured products, preventing waste and promoting a circular economy (CE) model. ESPR settles a set of requirements in collecting and administering product-related data. Decentralised identifiers (DID) and verifiable credentials (VC) are two self-sovereign-identity-related elements that may help in that DPP management since they introduce a decentralised administration of identity that may enhance the overall scalability of the resulting system, improving also its reliability. This paper analyses the ESPR requirements and describes how they may be achieved using DIDs and VCs, assessing their performance in some scenarios., Comment: 22 pages, 8 images