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Generative diffusion models are becoming one of the most popular prior in image restoration (IR) tasks due to their remarkable ability to generate realistic natural images. Despite achieving satisfactory results, IR methods based on diffusion models present several limitations. First of all, most non-blind approaches require an analytical expression of the degradation model to guide the sampling process. Secondly, most existing blind approaches rely on families of pre-defined degradation models for training their deep networks. The above issues limit the flexibility of these approaches and so their ability to handle real-world degradation tasks. In this paper, we propose a novel INN-guided probabilistic diffusion algorithm for non-blind and blind image restoration, namely INDIGO and BlindINDIGO, which combines the merits of the perfect reconstruction property of invertible neural networks (INN) with the strong generative capabilities of pre-trained diffusion models. Specifically, we train the forward process of the INN to simulate an arbitrary degradation process and use the inverse to obtain an intermediate image that we use to guide the reverse diffusion sampling process through a gradient step. We also introduce an initialization strategy, to further improve the performance and inference speed of our algorithm. Experiments demonstrate that our algorithm obtains competitive results compared with recently leading methods both quantitatively and visually on synthetic and real-world low-quality images., Comment: Accepted by IEEE Journal of Selected Topics in Signal Processing (JSTSP)

Understanding the transition from atomic gas to molecular gas is critical to explain the formation and evolution of molecular clouds. However, the gas phases involved, cold HI and CO-dark molecular gas, are challenging to directly observe and physically characterize. We observed the Cygnus X star-forming complex in carbon radio recombination lines (CRRLs) at 274--399 MHz with the Green Bank Telescope at 21 pc (48') resolution. Of the 30 deg^2 surveyed, we detect line-synthesized C273alpha emission from 24 deg^2 and produce the first large-area maps of low-frequency CRRLs. The morphology of the C273alpha emission reveals arcs, ridges, and extended possibly sheet-like gas which are often on the outskirts of CO emission and likely transitioning from HI-to-H_2. The typical angular separation of C273alpha and 13CO emission is 12 pc, and we estimate C273alpha gas densities of n_H ~ 40 - 400 cm^3. The C273alpha line profiles are Gaussian and likely turbulent broadened, spanning a large range of FWHM from 2 to 20 km/s with a median of 10.6 km/s. Mach numbers fall within 10--30. The turbulent timescale is relatively short, 2.6 Myr, and we deduce that the turbulent pressure likely dominates the evolution of the C273alpha gas. Velocity offsets between C273alpha and 13CO components are apparent throughout the region and have a typical value of 2.9 km/s. Two regimes have emerged from the data: one regime in which C273alpha and 13CO are strongly related (at N_H ~ 4 x 10^21 cm^-2), and a second, in which C273alpha emits independently of the 13CO intensity. In the former regime, C273alpha may arise from the the envelopes of massive clouds (filaments), and in the latter, C273alpha emits from cold clumps in a more-diffuse mix of HI and H_2 gas., Comment: 26 pages. 13 figures. submitted to ApJ. Comments welcome!

Body composition analysis provides valuable insights into aging, disease progression, and overall health conditions. Due to concerns of radiation exposure, two-dimensional (2D) single-slice computed tomography (CT) imaging has been used repeatedly for body composition analysis. However, this approach introduces significant spatial variability that can impact the accuracy and robustness of the analysis. To mitigate this issue and facilitate body composition analysis, this paper presents a novel method to generate 3D CT volumes from limited number of 2D slices using a latent diffusion model (LDM). Our approach first maps 2D slices into a latent representation space using a variational autoencoder. An LDM is then trained to capture the 3D context of a stack of these latent representations. To accurately interpolate intermediateslices and construct a full 3D volume, we utilize body part regression to determine the spatial location and distance between the acquired slices. Experiments on both in-house and public 3D abdominal CT datasets demonstrate that the proposed method significantly enhances body composition analysis compared to traditional 2D-based analysis, with a reduced error rate from 23.3% to 15.2%.

The Cryogenic AntiCoincidence Detector (CryoAC) is a key element of the X-ray Integral Field Unit (X-IFU) on board the future ATHENA X-ray observatory. It is a TES-based detector designed to reduce the particle background of the instrument, thereby increasing its sensitivity. The detector design is driven by an end-to-end simulator which includes the electro-thermal modelling of the detector and the dynamics of its readout chain. Here, we present the measurements carried out on the last CryoAC single pixel prototype, namely DM127, in order to evaluate the critical thermal parameters of the detector and consequently to tune and validate the CryoAC end-to-end simulator., Comment: Accepted for publication in IEEE Transactions on Applied Superconductivity for ASC2024 special issue

Using unfolded top-decay data we can measure the top mass, as well as search for unexpected kinematic effects. We show how generative unfolding enables both tasks and how both benefit from unbinned,high-dimensional unfolding. Our method includes an unbiasing step with respect to the training data and promises significant advantages over standard methods, in terms of flexibility and precision.

Suppressing of an ordered state that competes with superconductivity is one route to enhance superconducting transition temperatures. Whereas the effect of suppressing magnetic states is still not fully understood, materials featuring charge-density waves and superconductivity offer a clearer scenario as both states can be associated with electron-phonon coupling. Metallic transition-metal dichalcogenides are prime examples for such intertwined electron-phonon-driven phases, yet, various compounds do not show the expected interrelation or feature additional mechanisms which makes an unambiguous interpretation difficult. Here, we report high-pressure X-ray diffraction and inelastic X-ray scattering measurements of the prototypical transition-metal dichalcogenide 2$H$-TaSe$_2$ and determine the evolution of the charge-density-wave state and its lattice dynamics up to and beyond its suppression at the critical pressure $p_c = 19.9(1)\,\rm{GPa}$ and at low temperatures. The high quality of our data allows the full refinement of the commensurate charge-density-wave superstructure at low pressure and we find the quantum critical point of the charge-density-wave to be in close vicinity to the reported maximum superconducting transition temperature $T_{sc} = 8.2\,\rm{K}$. $Ab-initio$ calculations corroborate that 2$H$-TaSe$_2$ is a reference example of order-suppressed enhanced superconductivity and can serve as a textbook case to investigate superconductivity near a charge-density-wave quantum critical point.

This expository paper introduces a simplified approach to image-based quality inspection in manufacturing using OpenAI's CLIP (Contrastive Language-Image Pretraining) model adapted for few-shot learning. While CLIP has demonstrated impressive capabilities in general computer vision tasks, its direct application to manufacturing inspection presents challenges due to the domain gap between its training data and industrial applications. We evaluate CLIP's effectiveness through five case studies: metallic pan surface inspection, 3D printing extrusion profile analysis, stochastic textured surface evaluation, automotive assembly inspection, and microstructure image classification. Our results show that CLIP can achieve high classification accuracy with relatively small learning sets (50-100 examples per class) for single-component and texture-based applications. However, the performance degrades with complex multi-component scenes. We provide a practical implementation framework that enables quality engineers to quickly assess CLIP's suitability for their specific applications before pursuing more complex solutions. This work establishes CLIP-based few-shot learning as an effective baseline approach that balances implementation simplicity with robust performance, demonstrated in several manufacturing quality control applications., Comment: 31 pages, 13 figures

The fast fashion industry's insatiable demand for new styles and rapid production cycles has led to a significant environmental burden. Overproduction, excessive waste, and harmful chemicals have contributed to the negative environmental impact of the industry. To mitigate these issues, a paradigm shift that prioritizes sustainability and efficiency is urgently needed. Integrating learning-based predictive analytics into the fashion industry represents a significant opportunity to address environmental challenges and drive sustainable practices. By forecasting fashion trends and optimizing production, brands can reduce their ecological footprint while remaining competitive in a rapidly changing market. However, one of the key challenges in forecasting fashion sales is the dynamic nature of consumer preferences. Fashion is acyclical, with trends constantly evolving and resurfacing. In addition, cultural changes and unexpected events can disrupt established patterns. This problem is also known as New Fashion Products Performance Forecasting (NFPPF), and it has recently gained more and more interest in the global research landscape. Given its multidisciplinary nature, the field of NFPPF has been approached from many different angles. This comprehensive survey wishes to provide an up-to-date overview that focuses on learning-based NFPPF strategies. The survey is based on the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) methodological flow, allowing for a systematic and complete literature review. In particular, we propose the first taxonomy that covers the learning panorama for NFPPF, examining in detail the different methodologies used to increase the amount of multimodal information, as well as the state-of-the-art available datasets. Finally, we discuss the challenges and future directions., Comment: Accepted at the Springer Nature Computer Science journal

We present the discovery of 11 new transiting brown dwarfs and low-mass M-dwarfs from NASA's TESS mission: TOI-2844, TOI-3122, TOI-3577, TOI-3755, TOI-4462, TOI-4635, TOI-4737, TOI-4759, TOI-5240, TOI-5467, and TOI-5882. They consist of 5 brown dwarf companions and 6 very low mass stellar companions ranging in mass from $25 M_{\rm J}$ to $128 M_{\rm J}$. We used a combination of photometric time-series, spectroscopic, and high resolution imaging follow-up as a part of the TESS Follow-up Observing Program (TFOP) in order to characterize each system. With over 50 transiting brown dwarfs confirmed, we now have a large enough sample to directly test different formation and evolutionary scenarios. We provide a renewed perspective on the transiting brown dwarf desert and its role in differentiating between planetary and stellar formation mechanisms. Our analysis of the eccentricity distribution for the transiting brown dwarf sample does not support previous claims of a transition between planetary and stellar formation at $\sim42$ $M_{\rm J}$. We also contribute a first look into the metallicity distribution of transiting companions in the range $7 - 150$ $M_{\rm J}$, showing that this too does not support a $\sim42$ $M_{\rm J}$ transition. Finally, we also detect a significant lithium absorption feature in one of the brown dwarf hosts (TOI-5882) but determine that the host star is likely old based on rotation, kinematic, and photometric measurements. We therefore claim that TOI-5882 may be a candidate for planetary engulfment., Comment: Submitted, 32 pages, 16 figures

Despite the prominence of tensor mesons in photon-photon collisions, until recently their contribution to the hadronic light-by-light scattering part of the anomalous magnetic moment of the muon has been estimated at the level of only a few $10^{-12}$, with an almost negligible contribution to the error budget of the standard model prediction. A recent reanalysis within the dispersive approach has found that after resolving the issue of kinematic singularities in previous approaches, a larger result is obtained, a few $10^{-11}$, and with opposite sign as in previous results, when a simple quark model for the transition form factors is employed. In this paper, we present the first complete evaluation of tensor meson contributions within a hard-wall model in holographic QCD, which reproduces surprisingly well mass, two-photon width, and the observed singly virtual transition form factors of the dominant $f_2(1270)$, requiring only that the energy-momentum tensor correlator is matched to the leading OPE result of QCD. Due to a second structure function that is absent in the quark model, the result for $a_\mu$ turns out to be positive instead of negative, and also with a magnitude of a few $10^{-11}$. We discuss both pole and non-pole contributions arising from tensor meson exchanges in the holographic light-by-light scattering amplitude, finding that keeping all contributions improves dramatically the convergence of a sum over excited tensor mesons and avoids unnaturally large contributions from the first few excited modes at low energies. Doing so, the contribution from the lightest tensor mode becomes $a_\mu^T=5.9\times 10^{-11}$ ($5.4\times 10^{-11}$ from virtualities less than 1.5 GeV)., Comment: REVTEX, 18 pages, 1 figure, 3 tables

Cerberus will be a new scientific instrument for the alt-az, 1m-class OARPAF telescope in Northern Italy. Currently, the telescope operates with a CCD camera used for imaging and photometry. One of the objectives of the project is to improve this observing mode with a tip-tilt lens for image stabilization up to 10Hz. Moreover, a long-slit spectroscopy at R 5900 and an optical fiber \'echelle spectroscopy at R 9300 observing modes will be included. These addtional two "heads" of Cerberus will be exclusively selected by moving flat 45 degree mirrors by means of a linear stage placed in a new custom interface flange. The flange will replace the existing one, recovering the included field flattener lens, to ensure optical correction of the imaging channel. We present the already procured COTS hardware, the opto-mechanical design of the interface flange, the results of the Zemax ray tracing and the design of the web-based instrument control software., Comment: 5 pages, 2 figures

Quantum computation will rely on quantum error correction to counteract decoherence. Successfully implementing an error correction protocol requires the fidelity of qubit operations to be well-above error correction thresholds. In superconducting quantum computers, measurement of the qubit state remains the lowest-fidelity operation. For the transmon, a prototypical superconducting qubit, measurement is carried out by scattering a microwave tone off the qubit. Conventionally, the frequency of this tone is of the same order as the transmon frequency. The measurement fidelity in this approach is limited by multi-excitation resonances in the transmon spectrum which are activated at high readout power. These resonances excite the qubit outside of the computational basis, violating the desired quantum non-demolition character of the measurement. Here, we find that strongly detuning the readout frequency from that of the transmon exponentially suppresses the strength of spurious multi-excitation resonances. By increasing the readout frequency up to twelve times the transmon frequency, we achieve a quantum non-demolition measurement fidelity of 99.93% with a residual probability of leakage to non-computational states of only 0.02%., Comment: 27 pages, 14 figures

This roadmap consolidates recent advances while exploring emerging applications, reflecting the remarkable diversity of hardware platforms, neuromorphic concepts, and implementation philosophies reported in the field. It emphasizes the critical role of cross-disciplinary collaboration in this rapidly evolving field.

Despite their simplicity, quantum harmonic oscillators are ubiquitous in the modeling of physical systems. They are able to capture universal properties that serve as reference for the more complex systems found in nature. In this spirit, we apply a model of a Hamiltonian for open quantum systems in equilibrium with a particle reservoir to ensembles of quantum oscillators. By treating (i) a dilute gas of vibrating particles and (ii) a chain of coupled oscillators as showcases, we demonstrate that the property of varying number of particles leads to a mandatory condition on the energy of the system. In particular, the chemical potential plays the role of a parameter of control that can externally manipulate the spectrum of a system and the corresponding accessible quantum states., Comment: 10 pages

The Einstein Probe (EP) is an interdisciplinary mission of time-domain and X-ray astronomy. Equipped with a wide-field lobster-eye X-ray focusing imager, EP will discover cosmic X-ray transients and monitor the X-ray variability of known sources in 0.5-4 keV, at a combination of detecting sensitivity and cadence that is not accessible to the previous and current wide-field monitoring missions. EP can perform quick characterisation of transients or outbursts with a Wolter-I X-ray telescope onboard. In this paper, the science objectives of the Einstein Probe mission are presented. EP is expected to enlarge the sample of previously known or predicted but rare types of transients with a wide range of timescales. Among them, fast extragalactic transients will be surveyed systematically in soft X-rays, which include {\gamma}-ray bursts and their variants, supernova shock breakouts, and the predicted X-ray transients associated with binary neutron star mergers. EP will detect X-ray tidal disruption events and outbursts from active galactic nuclei, possibly at an early phase of the flares for some. EP will monitor the variability and outbursts of X-rays from white dwarfs, neutron stars and black holes in our and neighbouring galaxies at flux levels fainter than those detectable by the current instruments, and is expected to discover new objects. A large sample of stellar X-ray flares will also be detected and characterised. In the era of multi-messenger astronomy, EP has the potential of detecting the possible X-ray counterparts of gravitational wave events, neutrino sources, and ultra-high energy {\gamma}-ray and cosmic ray sources. EP is expected to help advance the studies of extreme objects/phenomena and their underlying physical processes revealed in the dynamic X-ray universe, as well as studies in other areas of X-ray astronomy., Comment: 67 pages, 24 figures, accepted for publication in SCIENCE CHINA Physics, Mechanics & Astronomy

We present VIRAC2, a catalogue of positions, proper motions, parallaxes and $Z$, $Y$, $J$, $H$, and $K_s$ near-infrared photometric time series of 545 346 537 unique stars. The catalogue is based on a point spread function fitting reduction of nearly a decade of VISTA VVV and VVVX images, which cover $560~{\rm deg}^2$ of the Southern Galactic plane and bulge. The catalogue is complete at the $>90$ per cent level for $11

We present the result from the April 2024 observation of the low-mass X-ray binary GX 13+1 with the Imaging X-ray Polarimetry Explorer (IXPE), together with NICER and Swift-XRT coordinated observations. Two light curve dips were observed; during them, the harder Comptonized spectral component was dominant and the polarization degree higher than in the softer, off-dip intervals. Through a joint analysis of the three IXPE observations, which also included the dip from the first observation, we demonstrate that the polarization properties varied in response to the intensity and spectral hardness changes associated with the dips. The polarization degree attained values up to ~4%. The polarization angle showed a swing of ~70{\deg} across the dip and off-dip states, comparable to the continuous rotation seen during the first IXPE observation. We discuss these results in the context of models for polarized emission from the accretion disk and the boundary/spreading layer on the neutron star surface. We also draw attention to the role that an extended accretion disk corona or disk wind can play in generating high polarization degrees and, possibly, swings of the polarization angle., Comment: Accepted on ApJL

Telecommunication technologies are important enablers for both digital and ecological transitions. By offering digital alternatives to traditional modes of transportation and communication, they help reduce carbon footprints while improving access to fundamental services. Particularly in rural and remote areas, telecommunications facilitate access to education, healthcare, and employment, helping to bridge the digital divide. Additionally, telecommunications can promote sustainability by supporting renewable energy usage, gender equality, and circular economies. However, defining the role of telecommunications in sustainability remains complex due to the historical focus on performance rather than long-term societal goals. Given the significance of this theme, this paper aims to provide the reader with a deeper look at the concept of sustainability within the telecommunications sector by examining relevant initiatives and projects. It reviews the major approaches for measuring sustainability and outlines practical approaches for implementing these assessments. Furthermore, the paper explores the proposed network architectures that incorporate Key Value Indicators and discusses major technologies in this area, such as Network Digital Twins and Intent-Based Networking. Through this analysis, the paper aims to contribute to creating sustainable telecommunication networks and broader industries., Comment: This paper has been sent for peer-reviewing at Elsevier's Computer Networks on November 21st, 2024

We study the entropic regularizations of optimal transport problems under suitable summability assumptions on the point-wise transport cost. These summability assumptions already appear in the literature. However, we show that the weakest compactness conditions that can be derived are already enough to obtain the convergence of the regularized functionals. This approach allows us to characterize the variational limit of the regularization even when it does not converge to the original problem. The results apply also to problems with more than two marginals.

We present results from a high cadence multi-wavelength observational campaign of the enigmatic changing look AGN 1ES 1927+654 from May 2022- April 2024, coincident with an unprecedented radio flare (an increase in flux by a factor of $\sim 60$ over a few months) and the emergence of a spatially resolved jet at $0.1-0.3$ pc scales (Meyer et al. 2024). Companion work has also detected a recurrent quasi-periodic oscillation (QPO) in the $2-10$ keV band with an increasing frequency ($1-2$ mHz) over the same period (Masterson et al., 2025). During this time, the soft X-rays ($0.3-2$ keV) monotonically increased by a factor of $\sim 8$, while the UV emission remained near-steady with $<30\%$ variation and the $2-10$ keV flux showed variation by a factor $\lesssim 2$. The weak variation of the $2-10$ keV X-ray emission and the stability of the UV emission suggest that the magnetic energy density and accretion rate are relatively unchanged, and that the jet could be launched due to a reconfiguration of the magnetic field (toroidal to poloidal) close to the black hole. Advecting poloidal flux onto the event horizon would trigger the Blandford-Znajek (BZ) mechanism, leading to the onset of the jet. The concurrent softening of the coronal slope (from $\Gamma= 2.70\pm 0.04$ to $\Gamma=3.27\pm 0.04$), the appearance of a QPO, and low coronal temperature ($kT_{e}=8_{-3}^{+8}$ keV) during the radio outburst suggest that the poloidal field reconfiguration can significantly impact coronal properties and thus influence jet dynamics. These extraordinary findings in real time are crucial for coronal and jet plasma studies, particularly as our results are independent of coronal geometry., Comment: Submitted to ApJ after minor referee comments

Counterfeiting poses an evergrowing challenge, driving the need for innovative and sophisticated anti-counterfeiting strategies and technologies. Many solutions focus on tags characterized by optical features that are partially or completely camouflaged to the human eye, thus discouraging scammers. In this paper, a QR code is laser printed on a thin plastic foil previously coated by a specific nanocavity consisting of a metal/insulator/metal/insulator (MIMI) multilayer. This metamaterial possesses unique features in terms of light transmission that are due to the specific design. A thin layer of polymer dispersed liquid crystals, fabricated incorporating specific nematic liquid crystals in a polymer matrix, is able to camouflage the QR code that becomes, then, readable only under specific thermal conditions. Three anti-counterfeiting tags were fabricated, each using a distinct LC with its own nematic-isotropic transition temperature. The peculiar combination of the unique optical properties of nematic liquid crystals and optical nanocavities results in the creation of a novel type of tags showing two different encoding levels. Stress tests including water immersion, bending test, and prolonged heating have been performed ensuring the long-term stability of the tags. The realized two security-level anti-counterfeiting tags are cost-effective, straightforward to manufacture and, thanks to their flexibility, can be easily integrated into packaging and products.

In this paper, we examine a fully-discrete finite element approximation of the unsteady $p(\cdot,\cdot)$-Stokes equations ($i.e.$, $p(\cdot,\cdot)$ is time- and space-dependent), employing a backward Euler step in time and conforming, discretely inf-sup stable finite elements in space. More precisely, we derive error decay rates for the vector-valued velocity field imposing fractional regularity assumptions on the velocity and the kinematic pressure. In addition, we carry out numerical experiments that confirm the optimality of the derived error decay rates in the case $p(\cdot,\cdot)\ge 2$., Comment: 27 pages, 4 tables

Epitaxially grown InAs NWs are relevant for electrical sensing applications due to Fermi level pinning at NW surface, thus very sensitive to surrounding environment. While a single NW growth batch consists of millions virtually identical replicas of the same NW, real samples display subtle differences in NW size, shape, structure which may affect the detection performance. Here, electrical gas detection is investigated the in two NW-based nominally identical or twin devices fabricated starting from the same NW growth batch. Two individual wurtzite InAs NWs are placed onto a fabrication substrate at 2 micrometers distance with 90 degrees relative orientation, each NW is electrically contacted, and the nanodevices are exposed to humidity and NO$_2$ flux diluted in synthetic air. Electrical signal versus time is measured simultaneously in each nanodevice, upon different gases and concentrations. Observed detection limit is 2 ppm for NO$_2$, 20% for relative humidity. Correlation analysis method is exploited by calculating auto- and cross-correlation functions for the experimental signal pairs, indicating lack of cross-correlation in the signal noise of the two nanodevices, suggesting that signal differences could be ascribed mainly to nonidealities of fabrication protocol and nanoscopic differences in the two nanostructures, rather than different environmental conditions., Comment: 25 pages

We present astrometric results from a Hubble Space Telescope (HST) campaign aimed at determining precise distances for cold Y-type brown dwarfs. Combining observations from a dedicated HST/WFC3 program with archival data, we derive astrometric solutions for 15 nearby Y dwarfs, by linking the high-precision relative astrometry from Hubble to the high-accuracy Gaia DR3 absolute reference system, using stars present in both to anchor the two frames of reference. We reach uncertainties on parallaxes below the 1-mas level for half of the sample, and down to 3 mas for two thirds of the targets, or relative precisions <1% in most cases and 2-5x improvements over previous measurements. For the remaining targets, we achieved slightly lower precisions on parallaxes (5-12 mas, 5-10%), correlated with the lower signal-to-noise of the faintest targets. The precision reached in our derived proper motions is around 0.1-0.4 mas/yr for most targets, and up to 1-2 mas/yr for less precise cases. Our estimated parallaxes and proper motions are generally in good agreement with literature values, and consistent to 1-2 sigma with recent Spitzer-derived parallaxes in most cases. These new astrometric solutions provide important validation of these objects' distances and sky motions, especially given the large disparities seen in previous estimates. Our results demonstrate the power of HST combined with Gaia to measure highly-precise absolute astrometry for faint brown dwarfs, and highlights the limitations reached for the reddest and coldest objects, for which JWST will certainly provide a favourable platform to improve these results., Comment: Accepted in MNRAS, 17 pages, 19 figures

Magnetic fields are ubiquitous across different physical systems of current interest; from the early Universe, compact astrophysical objects and heavy-ion collisions to condensed matter systems. A proper treatment of the effects produced by magnetic fields during the dynamical evolution of these systems, can help to understand observables that otherwise show a puzzling behavior. Furthermore, when these fields are comparable to or stronger than \Lambda_QCD, they serve as excellent probes to help elucidate the physics of strongly interacting matter under extreme conditions of temperature and density. In this work we provide a comprehensive review of recent developments on the description of QED and QCD systems where magnetic field driven effects are important. These include the modification of meson static properties such as masses and form factors, the chiral magnetic effect, the description of anomalous transport coefficients, superconductivity in extreme magnetic fields, the properties of neutron stars, the evolution of heavy-ion collisions, as well as effects on the QCD phase diagram. We describe recent theory and phenomenological developments using effective models as well as LQCD methods. The work represents a state-of-the-art review of the field, motivated by presentations and discussions during the "Workshop on Strongly Interacting Matter in Strong Electromagnetic Fields" that took place in the European Centre for Theoretical Studies in Nuclear Physics and Related Areas (ECT*) in the city of Trento, Italy, September 25-29, 2023., Comment: 325 pages-long review of recent topics of interest in the field of magnetic field effects on QED and QCD matter. To be susbmitted to PNPP

Thermal runaway in lithium ion batteries is a critical safety concern for the battery industry due to its potential to cause uncontrolled temperature rises and subsequent fires that can engulf the battery pack and its surroundings. Modeling and simulation offer cost effective tools for designing strategies to mitigate thermal runaway. Accurately simulating the chemical kinetics of thermal runaway,commonly represented by systems of Arrhenius based Ordinary Differential Equations (ODEs), requires fitting kinetic parameters to experimental calorimetry data, such as Accelerating Rate Calorimetry (ARC) measurements. However, existing fitting methods often rely on empirical assumptions and simplifications that compromise generality or require manual tuning during the fitting process. Particle Swarm Optimization (PSO) offers a promising approach for directly fitting kinetic parameters to experimental data. Yet, for systems involving large search spaces, such as those created by multiple Arrhenius ODEs, the computational cost of fitting can become prohibitive. This work introduces a divide and conquer approach based on PSO to fit N equation Arrhenius ODE models to ARC data. The proposed method achieves accurate parameter fitting while maintaining low computational costs. The resulting fit is analyzed using two distinct ARC datasets, highlighting the methods flexibility. The resulting models are further validated through simulations of 3D ARC and oven tests, showing excellent agreement with experimental data and alignment with expected trends.

The $L_\infty$-algebra approach to scattering amplitudes elegantly describes the nontrivial part of the $S$-matrix but fails to take into account the trivial part. We argue that the trivial contribution to the $S$-matrix should be accounted for by another, complementary $L_\infty$-algebra, such that a perturbative field theory is described by a cyclic relative $L_\infty$-algebra. We further demonstrate that this construction reproduces Witten diagrams that arise in AdS/CFT including, in particular, the trivial Witten diagrams corresponding to CFT two-point functions. We also discuss Chern-Simons theory and Yang-Mills theory on manifolds with boundaries using this approach., Comment: 35 pages

In Embodied Question Answering (EQA), agents must explore and develop a semantic understanding of an unseen environment in order to answer a situated question with confidence. This remains a challenging problem in robotics, due to the difficulties in obtaining useful semantic representations, updating these representations online, and leveraging prior world knowledge for efficient exploration and planning. Aiming to address these limitations, we propose GraphEQA, a novel approach that utilizes real-time 3D metric-semantic scene graphs (3DSGs) and task relevant images as multi-modal memory for grounding Vision-Language Models (VLMs) to perform EQA tasks in unseen environments. We employ a hierarchical planning approach that exploits the hierarchical nature of 3DSGs for structured planning and semantic-guided exploration. Through experiments in simulation on the HM-EQA dataset and in the real world in home and office environments, we demonstrate that our method outperforms key baselines by completing EQA tasks with higher success rates and fewer planning steps., Comment: Project website: https://saumyasaxena.github.io/grapheqa

Accurate human activity and trajectory prediction are crucial for ensuring safe and reliable human-robot interactions in dynamic environments, such as industrial settings, with mobile robots. Datasets with fine-grained action labels for moving people in industrial environments with mobile robots are scarce, as most existing datasets focus on social navigation in public spaces. This paper introduces the TH\"OR-MAGNI Act dataset, a substantial extension of the TH\"OR-MAGNI dataset, which captures participant movements alongside robots in diverse semantic and spatial contexts. TH\"OR-MAGNI Act provides 8.3 hours of manually labeled participant actions derived from egocentric videos recorded via eye-tracking glasses. These actions, aligned with the provided TH\"OR-MAGNI motion cues, follow a long-tailed distribution with diversified acceleration, velocity, and navigation distance profiles. We demonstrate the utility of TH\"OR-MAGNI Act for two tasks: action-conditioned trajectory prediction and joint action and trajectory prediction. We propose two efficient transformer-based models that outperform the baselines to address these tasks. These results underscore the potential of TH\"OR-MAGNI Act to develop predictive models for enhanced human-robot interaction in complex environments., Comment: This paper has been accepted to the the 20th edition of the IEEE/ACM International Conference on Human-Robot Interaction (HRI'25), which will be held in Melbourne, Australia on March 4-6, 2025. Code: https://github.com/tmralmeida/thor-magni-actions

In this work we investigate some non-Newtonian effects in exact solutions of the Einstein equations, which describe stationary and axisymmetric configurations of self-gravitating dust. A distinctive feature of these solutions is the potential presence of conical singularities along the rotation axis, manifesting as angular deficits. While such singularities can be removed by imposing suitable boundary conditions along the axis, asymptotically far away from it the geometry becomes locally flat, in the sense that the Riemann tensor vanishes, but globally, instead of reducing to Minkowski space, it takes the form of a cone. We refer to these spacetimes as Asymptotically Conically Minkowskian (ACM). We show that such conical structure can originate some interesting effects as seen by asymptotic local observers. These include modifications to the gravitational lensing and the misinterpretation of the vacuum state of a scalar field as a distribution of scalar particles., Comment: 27 pages, 1 figure

In the wake of the recent resurgence of the Datalog language of databases, together with its extensions for ontological reasoning settings, this work aims to bridge the gap between the theoretical studies of DatalogMTL (Datalog extended with metric temporal logic) and the development of production-ready reasoning systems. In particular, we lay out the functional and architectural desiderata of a modern reasoner and propose our system, Temporal Vadalog. Leveraging the vast amount of experience from the database community, we go beyond the typical chase-based implementations of reasoners, and propose a set of novel techniques and a system that adopts a modern data pipeline architecture. We discuss crucial architectural choices, such as how to guarantee termination when infinitely many time intervals are possibly generated, how to merge intervals, and how to sustain a limited memory footprint. We discuss advanced features of the system, such as the support for time series, and present an extensive experimental evaluation. This paper is a substantially extended version of "The Temporal Vadalog System" as presented at RuleML+RR '22. Under consideration in Theory and Practice of Logic Programming (TPLP).

The European project SCARLET aims to study and realize a demonstrator of a MVDC (Medium Voltage Direct Current) high-power superconducting cable. This device might be employed to connect offshore wind farms with land, expecting to significantly simplify the offshore platform by eliminating the need for its conversion function. For this purpose, windmill conversion chain must be modified to directly produce the MVDC export voltage. In this scenario, this paper presents the case of a 1GW offshore windmill superconducting link and outlines the design consideration for a 1 GW onshore converter. For this cable, a protection strategy that combines DC circuit breakers with a Resistive Superconducting Fault Current Limiter is proposed. Moreover, this works demonstrates how a superconducting cable can be modelled as an electrical circuit to be integrated into a network simulation tool, enabling the investigation of various fault scenarios and protection strategies. Finally, a specific result is discussed to exemplify how the proposed approach can benefit the design of both the electrical network and the superconducting cable itself.

We study the finite-density phases of a $\mathbb{Z}_2$ lattice gauge theory (LGT) of interconnected loops and dynamical $\mathbb{Z}_2$ charges. The gauge-invariant Wilson terms, accounting for the magnetic flux threading each loop, correspond to simple two-body Ising interactions in this setting. Such terms control the interference of charges tunneling around the loops, leading to dynamical Aharonov-Bohm (AB) cages that are delimited by loops threaded by a $\pi$-flux. The latter can be understood as $\mathbb{Z}_2$ vortices, the analog of visons in two dimensional LGTs, which become mobile by adding quantum fluctuations through an external electric field. In contrast to a semi-classical regime of static and homogeneous AB cages, the mobile visons can self-assemble leading to AB cages of different lengths depending on the density of $\mathbb{Z}_2$ charges and the interplay of magnetic and electric terms. Inside these cages, the individual charges get confined into tightly-bound charge-neutral pairs, the $\mathbb{Z}_2$ analogue of mesons. Depending on the region of parameter space, these tightly-bound mesons can propagate within dilute AB-dimers that virtually expand and contract, or else move by virtually stretching and compressing an electric field string. Both limits lead to a Luttinger liquid described by a constrained integrable model. This phase is separated from an incompressible Mott insulator where mesons belong to closely-packed AB-trimers. In light of recent trapped-ion experiments for a single $\mathbb{Z}_2$ loop, these phases could be explored in future experiments., Comment: Main text: 10 pages, 12 figures

Neural networks for LHC physics have to be accurate, reliable, and controlled. Using surrogate loop amplitudes as a use case, we first show how activation functions can be systematically tested with KANs. For reliability and control, we learn uncertainties together with the target amplitude over phase space. Systematic uncertainties can be learned by a heteroscedastic loss, but a comprehensive learned uncertainty requires Bayesian networks or repulsive ensembles. We compute pull distributions to show to what level learned uncertainties are calibrated correctly for cutting-edge precision surrogates., Comment: 32 pages, 14 figures

Human perception integrates multiple modalities, such as vision, hearing, and language, into a unified understanding of the surrounding reality. While recent multimodal models have achieved significant progress by aligning pairs of modalities via contrastive learning, their solutions are unsuitable when scaling to multiple modalities. These models typically align each modality to a designated anchor without ensuring the alignment of all modalities with each other, leading to suboptimal performance in tasks requiring a joint understanding of multiple modalities. In this paper, we structurally rethink the pairwise conventional approach to multimodal learning and we present the novel Gramian Representation Alignment Measure (GRAM), which overcomes the above-mentioned limitations. GRAM learns and then aligns $n$ modalities directly in the higher-dimensional space in which modality embeddings lie by minimizing the Gramian volume of the $k$-dimensional parallelotope spanned by the modality vectors, ensuring the geometric alignment of all modalities simultaneously. GRAM can replace cosine similarity in any downstream method, holding for 2 to $n$ modality and providing more meaningful alignment with respect to previous similarity measures. The novel GRAM-based contrastive loss function enhances the alignment of multimodal models in the higher-dimensional embedding space, leading to new state-of-the-art performance in downstream tasks such as video-audio-text retrieval and audio-video classification. The project page, the code, and the pretrained models are available at https://ispamm.github.io/GRAM/.

Quantum computation requires high-fidelity qubit readout, preserving the quantum state. In the case of superconducting (SC) qubits, readout is typically performed using a complex analog experimental setup operated at room temperature, which poses significant technological and economic barriers to large system scalability. An alternative approach is to perform a cryogenic on-chip qubit readout based on a Josephson Digital Phase Detector (JDPD): a flux switchable device capable of digitizing the phase sign of a coherent input. The readout operation includes the flux excitation of the JDPD to evolve from a single to a double-minima potential. In this work, the effect of the flux bias characteristics on the JDPD performances is studied numerically. To meet the identified requirements that maximize detection fidelity and tackle the engineering challenges, a cryogenic on-chip Single Flux Quantum based flux bias driver is proposed and discussed., Comment: 10 pages, 7 figures, under review for IEEE Transaction on Quantum Engineering

We present an experimental and numerical study of a piezoelectric energy harvester driven by broadband vibrations. This device can extract power from random fluctuations and can be described by a stochastic model, based on an underdamped Langevin equation with white noise, which mimics the dynamics of the piezoelectric material. A crucial point in the modelisation is represented by the appropriate description of the coupled load circuit that is necessary to harvest electrical energy. We consider a linear load (resistance) and a nonlinear load (diode bridge rectifier connected to the parallel of a capacitance and a load resistance), and focus on the characteristic curve of the extracted power as a function of the load resistance, in order to estimate the optimal values of the parameters that maximise the collected energy. In both cases, we find good agreement between the numerical simulations of the theoretical model and the results obtained in experiments. In particular, we observe a non-monotonic behaviour of the characteristic curve which signals the presence of an optimal value for the load resistance at which the extracted power is maximised. We also address a more theoretical issue, related to the inference of the non-equilibrium features of the system from data: we show that the analysis of high-order correlation functions of the relevant variables, when in the presence of nonlinearities, can represent a simple and effective tool to check the irreversible dynamics., Comment: 11 pages, 13 figures

Active galactic nuclei (AGN) are powerful sources of panchromatic radiation. All AGN emit in X-rays, contributing around $\sim 5-10\%$ of the AGN bolometric luminosity. The X-ray emitting region, popularly known as the corona, is geometrically and radiatively compact with a size typically $\lesssim 10 \, R_{\rm G}$ (gravitational radii). The rapid and extreme variability in X-rays also suggest that the corona must be a dynamic structure. Decades of X-ray studies have shed much light on the topic, but the nature and origin of AGN corona are still not clearly understood. This is mostly due to the complexities involved in several physical processes at play in the high-gravity, high-density and high-temperature region in the vicinity of the supermassive black hole (SMBH). It is still not clear how exactly the corona is energetically and physically sustained near a SMBH. The ubiquity of coronal emission in AGN points to their fundamental role in black hole accretion processes. In this review we discuss the X-ray observational properties of corona in radio quiet AGN., Comment: Review article, accepted for publication in Frontiers in Astronomy and Space Sciences

This paper continues the program of applying beyond physics the technique of \textbf{probabilistic quantization} and extending to the quantum mechanics associated with the arc--sine distributions our previous results on the semi--circle distribution. We derive analytical expressions for the momentum and kinetic energy operators using the arc--sine weighted Hilbert transform and express corresponding evolutions as Neumann series of Bessel functions. These series are applicable in various physical problems and in solving certain mixed difference equations and differential equations. Moreover, exploiting the similarity between the Jacobi sequences of the semi-circle and arc-sine measures, we establish a unified formulation of their quantum mechanics. We introduce the semicircle and arc--sine exponential vectors and the corresponding coherent states and prove that, for both measures, the vacuum distributions of the number operator in these states (arc--sine photon statistics) are a \textit{perturbation} of the geometric distribution (Gibbs states in Boson physics: see the Introduction below for a discussion of the physical meaning of this perturbation). The $*$--Lie algebra generated by canonical creation and annihilation operators of both probability measures is isomorphic to the $*$--Lie algebra generated by all rank-one operators in corresponding $L^2$-spaces. The paper concludes with appendices that discuss the integral and Neumann series representations of the $1$--parameter unitary groups generated by momentum in semi-circle and arc-sine cases.

Describing electron-phonon interactions in a solid requires knowledge of the electron-phonon matrix elements in the Hamiltonian. State-of-the-art first-principles calculations for the electron-phonon interaction are limited to the 1-electron-1-phonon matrix element, which is suitable for harmonic materials. However, there is no first-principles theory for 1-electron-2-phonon interactions, which occur in anharmonic materials with significant electron-phonon interaction such as halide perovskites and quantum paraelectrics. Here, we derive an analytical expression for the long-range part of the 1-electron-2-phonon matrix element, written in terms of microscopic quantities that can be calculated from first principles. We show that the long-range 1-electron-2-phonon interaction is described by the derivative of the phonon dynamical matrix with respect to an external electric field. We calculate the quasiparticle energy of a large polaron including 1-electron-2-phonon interaction, and show that it can be written in terms of a 1-electron-2-phonon spectral function $\mathcal{T}_{\alpha \beta}(\omega)$. We demonstrate how to calculate this spectral function for the benchmark material LiF. The first-principles framework developed in this article is general, paving the way for future calculations of 1-electron-2-phonon interactions from first principles., Comment: 62 pages, 9 figures. Under review in Physical Review B

Electron-phonon interactions in solids are crucial for understanding many interesting phenomena, such as conventional superconductivity, temperature-dependent band-gap renormalization, and polarons. For harmonic materials, the linear interaction of one electron with one phonon is sufficient to quantitatively describe these properties. However, in anharmonic materials such as quantum paraelectrics, halide perovskites, and high-pressure hydrides, the nonlinear electron-phonon interactions may play an important role. Currently, the only available Hamiltonians for nonlinear electron-phonon interaction are model Hamiltonians, written in terms of phenomenological parameters. Here, we present a microscopic theory for long-range nonlinear electron-phonon interactions, which can be combined with first-principles calculations. We provide a semi-analytical expression for the long-range part of the 1-electron-2-phonon matrix element. We show that in contrast to the long-range 1-electron-1-phonon interaction, the continuum approximation is not sufficient and the entire phonon dispersion must be taken into account. Additionally, we show that the quasiparticle energies can be written in terms of a 1-electron-2-phonon spectral function. To demonstrate the method, we calculate the 1-electron-2-phonon spectral function for LiF and KTaO$_3$ from first principles. Our framework is a step forward toward complete first-principles calculations of nonlinear electron-phonon interactions in solids., Comment: 15 pages, 3 figures. Under review in Physical Review Letters

Word Sense Disambiguation (WSD) is the task of associating a word in a given context with its most suitable meaning among a set of possible candidates. While the task has recently witnessed renewed interest, with systems achieving performances above the estimated inter-annotator agreement, at the time of writing it still struggles to find downstream applications. We argue that one of the reasons behind this is the difficulty of applying WSD to plain text. Indeed, in the standard formulation, models work under the assumptions that a) all the spans to disambiguate have already been identified, and b) all the possible candidate senses of each span are provided, both of which are requirements that are far from trivial. In this work, we present a new task called Word Sense Linking (WSL) where, given an input text and a reference sense inventory, systems have to both identify which spans to disambiguate and then link them to their most suitable meaning.We put forward a transformer-based architecture for the task and thoroughly evaluate both its performance and those of state-of-the-art WSD systems scaled to WSL, iteratively relaxing the assumptions of WSD. We hope that our work will foster easier integration of lexical semantics into downstream applications.

We consider weak solutions to the incompressible Euler equations. It is shown that energy conservation holds in any Onsager critical class in which smooth functions are dense. The argument is independent of the specific critical regularity and the underling PDE. This groups several energy conservation results and it suggests that critical spaces where smooth functions are dense are not at all different from subcritical ones, although possessing the "minimal" regularity index. Then, we study properties of the dissipation $D$ in the case of bounded solutions that are allowed to jump on $\mathcal{H}^d$-rectifiable space-time sets $\Sigma$, which are the natural dissipative regions in the compressible setting. As soon as both the velocity and the pressure posses traces on $\Sigma$, it is shown that $\Sigma$ is $D$-negligible. The argument makes the role of the incompressibility very apparent, and it prevents dissipation on codimension 1 sets even if they happen to be densely distributed. As a corollary, we deduce energy conservation for bounded solutions of "special bounded deformation", providing the first energy conservation criterion in a critical class where only an assumption on the "longitudinal" increment is made, while the energy flux does not vanish for kinematic reasons.

This study investigates the temperature-induced hysteretic behavior of resistivity and magnetoresistance in electrodeposited bismuth films, with a focus on their application as absorbers in transition-edge sensors (TESs) for X-ray detection. Through a series of resistance versus temperature measurements from room temperature to a few Kelvins, we explore the change in the conductive behavior of bismuth electrodeposited on various substrates at the various temperatures. Our findings show for the first time both hysteretic and irreversible changes in resistivity as a function of temperature. Further, magnetoresistance measurements reveal notable variations in resistance behavior under different magnetic fields, highlighting the impact of magnetic fields on these films' electronic transport properties, with an indication of potential weak anti-localization effects at the lowest temperatures. This study not only provides a deeper understanding of bismuth's conductivity characteristics at low temperatures but also sheds light on the practical implications for developing more effective TESs for synchrotron X-ray facilities.

We study initial value problems for various geometric equations on a cohomogeneity manifold near a singular orbit. We show that when prescribing the Ricci curvature, or finding solutions to the Einstein and soliton equations, there exist solutions near the singular orbit, unique up to a finite number of constants. In part I we make a special assumption that significantly simplifies the proof, and will solve the general case in Part II., Comment: 20 pages

The fast fashion industry suffers from significant environmental impacts due to overproduction and unsold inventory. Accurately predicting sales volumes for unreleased products could significantly improve efficiency and resource utilization. However, predicting performance for entirely new items is challenging due to the lack of historical data and rapidly changing trends, and existing deterministic models often struggle with domain shifts when encountering items outside the training data distribution. The recently proposed diffusion models address this issue using a continuous-time diffusion process. This allows us to simulate how new items are adopted, reducing the impact of domain shift challenges faced by deterministic models. As a result, in this paper, we propose MDiFF: a novel two-step multimodal diffusion models-based pipeline for New Fashion Product Performance Forecasting (NFPPF). First, we use a score-based diffusion model to predict multiple future sales for different clothes over time. Then, we refine these multiple predictions with a lightweight Multi-layer Perceptron (MLP) to get the final forecast. MDiFF leverages the strengths of both architectures, resulting in the most accurate and efficient forecasting system for the fast-fashion industry at the state-of-the-art. The code can be found at https://github.com/intelligolabs/MDiFF., Comment: Accepted at the FashionAI workshop @ the European Conference on Computer Vision (ECCV) 2024. arXiv admin note: substantial text overlap with arXiv:2412.05566

We obtain sharp estimates for heat kernels and Green's functions on complete noncompact Riemannian manifolds with Euclidean volume growth and nonnegative Ricci curvature. We will then apply these estimates to obtain sharp Moser-Trudinger inequalities on such manifolds., Comment: 36 pages

In the fast-fashion industry, overproduction and unsold inventory create significant environmental problems. Precise sales forecasts for unreleased items could drastically improve the efficiency and profits of industries. However, predicting the success of entirely new styles is difficult due to the absence of past data and ever-changing trends. Specifically, currently used deterministic models struggle with domain shifts when encountering items outside their training data. The recently proposed diffusion models address this issue using a continuous-time diffusion process. Specifically, these models enable us to predict the sales of new items, mitigating the domain shift challenges encountered by deterministic models. As a result, this paper proposes Dif4FF, a novel two-stage pipeline for New Fashion Product Performance Forecasting (NFPPF) that leverages the power of diffusion models conditioned on multimodal data related to specific clothes. Dif4FF first utilizes a multimodal score-based diffusion model to forecast multiple sales trajectories for various garments over time. The forecasts are refined using a powerful Graph Convolutional Network (GCN) architecture. By leveraging the GCN's capability to capture long-range dependencies within both the temporal and spatial data and seeking the optimal solution between these two dimensions, Dif4FF offers the most accurate and efficient forecasting system available in the literature for predicting the sales of new items. We tested Dif4FF on VISUELLE, the de facto standard for NFPPF, achieving new state-of-the-art results., Comment: Accepted at the 27th International Conference on Pattern Recognition (ICPR 2024)

Recently, dynamical edge modes (DEM) in Maxwell theory have been constructed using a specific local boundary condition on the horizon. We discuss how to enforce this boundary condition on an infinite parallel plate in the QED vacuum by introducing Lagrange multiplier fields into the action. We carefully introduce appropriate boundary ghosts to maintain BRST invariance. Explicit correspondence of this BRST extended theory with the original DEM formulation is discussed, both directly, and through the correspondence between edge modes and Wilson lines attached to the boundary surface. We then use functional methods to calculate the Casimir energy for the first time with DEM boundary conditions imposed on two infinite parallel plates, both in generalized Coulomb and linear covariant gauge. Depending on the gauge, different fields are contributing, but, after correctly implementing the BRST symmetry, we retrieve the exact same Casimir energy as for two perfectly conducting parallel plates., Comment: 21 pages, 3 figures

We consider the renormalization group equations of axion-like particle effective field theories and determine the corresponding anomalous dimensions at one loop via on-shell and unitarity-based methods. The calculation of the phase-space cut-integrals is carried out using different integration methods, among which the double-cut integration via Stokes' theorem proves to be technically simpler. A close comparison between the standard Feynman diagrammatic approach and the unitarity-based method enables us to explicitly verify the reduction of complexity in the latter case, along with a more direct and elegant way to establish a connection among anomalous dimensions of operators that are dual under the CP symmetry., Comment: 46 pages, 16 figures