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An important and often overlooked aspect of particle filtering methods is the estimation of unknown static parameters. A simple approach for addressing this problem is to augment the unknown static parameters as auxiliary states that are jointly estimated with the time-varying parameters of interest. This can be impractical, especially when the system of interest is high-dimensional. Multiple particle filtering (MPF) methods were introduced to try to overcome the curse of dimensionality by using a divide and conquer approach, where the vector of unknowns is partitioned into a set of subvectors, each estimated by a separate particle filter. Each particle filter weighs its own particles by using predictions and estimates communicated from the other filters. Currently, there is no principled way to implement MPF methods where the particle filters share unknown parameters or states. In this work, we propose a fusion strategy to allow for the sharing of unknown static parameters in the MPF setting. Specifically, we study the systems which are separable in states and observations. It is proved that optimal Bayesian fusion can be obtained for state-space models with non-interacting states and observations. Simulations are performed to show that MPF with fusion strategy can provide more accurate estimates within fewer time steps comparing to existing algorithms.

The Parameterized Post-Newtonian (PPN) formalism offers an agnostic framework for evaluating theories of gravity that extend beyond General Relativity. Departures from General Relativity are represented by a set of dimensionless parameters that, at the first order in the expansion, reduce to $\beta$ and $\gamma$, which describe deviations in spatial curvature and non-linear superposition effects of gravity, respectively. We exploit future observations of stars at the Galactic Center, orbiting the supermassive black hole Sagittarius A*, to forecast the ability to constrain the first-order PPN parameters $\gamma$ and $\beta$. We have generated a mock catalog of astrometric and spectroscopic data for S2, based on the Schwarzschild metric, simulating observations over multiple orbital periods with the GRAVITY and SINFONI instruments. Our analysis includes the effects of relativistic orbital precession and line-of-sight (LOS) velocity gravitational redshift. Since future data for S2 can provide constraints only on a linear combination of the PPN parameters $\beta$ and $\gamma$ we also analyzed the impact of future observations of the gravitational lensing for stars that pass closer in the sky to Sgr A*, like the known star S62, which can potentially provide tight constraints on the parameter $\gamma$, that alone regulates the amplitude of the astrometric deviations due to lensing. When combining lensing observations for S62, and the precise orbital tracking of S2, one obtains independent constraints on both $\gamma$ (with a potential precision as good as $\sim 1\%$) and $\beta$ (with a corresponding precision of $\sim 2\%$), providing a precision test of General Relativity and its extensions., Comment: 9 pages, 7 figures, 1 table. Comments are welcome

Qualitative timeline-based planning models domains as sets of independent, but interacting, components whose behaviors over time, the timelines, are governed by sets of qualitative temporal constraints (ordering relations), called synchronization rules. Its plan-existence problem has been shown to be PSPACE-complete; in particular, PSPACE-membership has been proved via reduction to the nonemptiness problem for nondeterministic finite automata. However, nondeterministic automata cannot be directly used to synthesize planning strategies as a costly determinization step is needed. In this paper, we identify a large fragment of qualitative timeline-based planning whose plan-existence problem can be directly mapped into the nonemptiness problem of deterministic finite automata, which can then be exploited to synthesize strategies. In addition, we identify a maximal subset of Allen's relations that fits into such a deterministic fragment., Comment: In Proceedings GandALF 2024, arXiv:2410.21884

Large Language Models (LLMs) have demonstrated impressive capabilities in answering questions, but they lack domain-specific knowledge and are prone to hallucinations. Retrieval Augmented Generation (RAG) is one approach to address these challenges, while multimodal models are emerging as promising AI assistants for processing both text and images. In this paper we describe a series of experiments aimed at determining how to best integrate multimodal models into RAG systems for the industrial domain. The purpose of the experiments is to determine whether including images alongside text from documents within the industrial domain increases RAG performance and to find the optimal configuration for such a multimodal RAG system. Our experiments include two approaches for image processing and retrieval, as well as two LLMs (GPT4-Vision and LLaVA) for answer synthesis. These image processing strategies involve the use of multimodal embeddings and the generation of textual summaries from images. We evaluate our experiments with an LLM-as-a-Judge approach. Our results reveal that multimodal RAG can outperform single-modality RAG settings, although image retrieval poses a greater challenge than text retrieval. Additionally, leveraging textual summaries from images presents a more promising approach compared to the use of multimodal embeddings, providing more opportunities for future advancements.

We study the Cahn-Hilliard equation with non-degenerate concentration-dependent mobility and logarithmic potential in two dimensions. We show that any weak solution is unique, exhibits propagation of uniform-in-time regularity, and stabilizes towards an equilibrium state of the Ginzburg-Landau free energy for large times. These results improve the state of the art dating back to a work by Barrett and Blowey. Our analysis relies on the combination of enhanced energy estimates, elliptic regularity theory and tools in critical Sobolev spaces.

Fuzzy Dark Matter (FDM) is among the most suitable candidates to replace WIMPs and to resolve the puzzling mystery of dark matter. A galactic dark matter halo made of these ultralight bosonic particles leads to the formation of a solitonic core surrounded by quantum interference patterns that, on average, reproduce a Navarro-Frenk-White-like mass density profile in the outskirts of the halo. The structure of such a core is determined once the boson mass and the total mass of the halo are set. We investigated the capability of future astrometric Theia-like missions to detect the properties of such a halo within the FDM model, namely the boson mass and the core radius. To this aim, we built mock catalogs containing three-dimensional positions and velocities of stars within a target dwarf galaxy. We exploited these catalogs using a Markov Chain Monte Carlo algorithm and found that measuring the proper motion of at least 2000 stars within the target galaxy, with uncertainty $\sigma_v \leq 3$ km/s on the velocity components, will constrain the boson mass and the core radius with 3\% accuracy. Furthermore, the transition between the solitonic core and the outermost NFW-like density profile could be detected with an uncertainty of 7\%. Such results would not only help to confirm the existence of FDM, but they would also be useful for alleviating the current tension between galactic and cosmological estimations of the boson mass, or demonstrating the need for multiple particles with a broad mass spectrum as naturally arise String Axiverse., Comment: 11 pages, 3 Figures, 4 Tables

Aligning Large Language Models (LLM) to address subjectivity and nuanced preference levels requires adequate flexibility and control, which can be a resource-intensive and time-consuming procedure. Existing training-time alignment methods require full re-training when a change is needed and inference-time ones typically require access to the reward model at each inference step. To address these limitations, we introduce inference-time model alignment method that learns encoded representations of preference dimensions, called \textit{Alignment Vectors} (AV). These representations are computed by subtraction of the base model from the aligned model as in model editing enabling dynamically adjusting the model behavior during inference through simple linear operations. Even though the preference dimensions can span various granularity levels, here we focus on three gradual response levels across three specialized domains: medical, legal, and financial, exemplifying its practical potential. This new alignment paradigm introduces adjustable preference knobs during inference, allowing users to tailor their LLM outputs while reducing the inference cost by half compared to the prompt engineering approach. Additionally, we find that AVs are transferable across different fine-tuning stages of the same model, demonstrating their flexibility. AVs also facilitate multidomain, diverse preference alignment, making the process 12x faster than the retraining approach.

This paper addresses the long-time dynamics of solutions to the 2D incompressible Euler equations. We construct solutions with continuous vorticity $\omega_{\varepsilon}(x,t)$ concentrated around points $\xi_{j}(t)$ that converge to a sum of Dirac delta masses as $\varepsilon\to0$. These solutions are associated with the Kirchhoff-Routh point-vortex system, and the points $\xi_{j}(t)$ follow an expanding self similar trajectory of spirals, with the support of the vorticities contained in balls of radius $3\varepsilon$ around each $\xi_{j}$.

The ability for optically active media to rotate the polarization of light is the basis of polarimetry, an illustrious technique responsible for many breakthroughs in fields as varied as astronomy, medicine and material science. Here, we recast the primary mechanism for spin readout in semiconductor-based quantum computers, Pauli spin-blockade (PSB), as the natural extension of polarimetry to the third dimension. We perform polarimetry with spins through a silicon quantum dot exchanging a hole with a boron acceptor, demonstrating the role of spin-orbit coupling in creating spin misalignment. Perfect spin alignment may be recovered by means of rotating the applied magnetic-field orientation. This work shows how spin misalignment sets a fundamental upper limit for the spin readout fidelity in quantum-computing systems based on PSB.

This study investigates the interaction between humanoid robots NAO and Pepper, emphasizing their potential applications in educational settings. NAO, widely used in education, and Pepper, designed for social interactions, of er new opportunities for autonomous communication and collaboration. Through a series of programmed interactions, the robots demonstrated their ability to communicate and coordinate actions autonomously, highlighting their potential as tools for enhancing learning environments. The research also explores the integration of emerging technologies, such as artificial intelligence, into these systems, allowing robots to learn from each other and adapt their behavior. The findings suggest that NAO and Pepper can significantly contribute to both technical learning and the development of social and emotional skills in students, of ering innovative pedagogical approaches through the use of humanoid robotics., Comment: in Portuguese language

We consider the Ginzburg-Landau equation in the plane linearized around the standard degree-one vortex solution $W(x)=w(r)e^{i\theta}$. Using explicit representation formulae for the Fourier modes in $\theta$, we obtain sharp estimates for the inverse of the linearized operator which hold for a large class of right-hand sides. This theory can be applied, for example, to estimate the inverse after dropping the usual orthogonality conditions., Comment: 21 pages

This study aims to enrich and leverage data from the Informatics Europe Higher Education (IEHE) data portal to extract and analyze trends in female participation in Informatics across Europe. The research examines the proportion of female students, first-year enrollments, and degrees awarded to women in the field. The issue of low female participation in Informatics has long been recognized as a persistent challenge and remains a critical area of scholarly inquiry. Furthermore, existing literature indicates that socio-economic factors can unpredictably influence female participation, complicating efforts to address the gender gap. The analysis focuses on participation data from research universities at various academic levels, including Bachelors, Masters, and PhD programs, and seeks to uncover potential correlations between female participation and geographical or economic zones. The dataset was first enriched by integrating additional information, such as each country's GDP and relevant geographical data, sourced from various online repositories. Subsequently, the data was cleaned to ensure consistency and eliminate incomplete time series. A final set of complete time series was selected for further analysis. We then used the data collected from the internet to assign countries to different clusters. Specifically, we employed Economic Zone, Geographical Area, and GDP quartile to cluster countries and compare their temporal trends both within and between clusters. We analyze the results for each classification and derive conclusions based on the available data.

Large Language Models~(LLMs) have demonstrated capabilities across various applications but face challenges such as hallucination, limited reasoning abilities, and factual inconsistencies, especially when tackling complex, domain-specific tasks like question answering~(QA). While Knowledge Graphs~(KGs) have been shown to help mitigate these issues, research on the integration of LLMs with background KGs remains limited. In particular, user accessibility and the flexibility of the underlying KG have not been thoroughly explored. We introduce AGENTiGraph (Adaptive Generative ENgine for Task-based Interaction and Graphical Representation), a platform for knowledge management through natural language interaction. It integrates knowledge extraction, integration, and real-time visualization. AGENTiGraph employs a multi-agent architecture to dynamically interpret user intents, manage tasks, and integrate new knowledge, ensuring adaptability to evolving user requirements and data contexts. Our approach demonstrates superior performance in knowledge graph interactions, particularly for complex domain-specific tasks. Experimental results on a dataset of 3,500 test cases show AGENTiGraph significantly outperforms state-of-the-art zero-shot baselines, achieving 95.12\% accuracy in task classification and 90.45\% success rate in task execution. User studies corroborate its effectiveness in real-world scenarios. To showcase versatility, we extended AGENTiGraph to legislation and healthcare domains, constructing specialized KGs capable of answering complex queries in legal and medical contexts., Comment: 30 pages, 7 figures; Submitted to COLING 2025 System Demonstrations Track

The first FASER search for a light, long-lived particle decaying into a pair of photons is reported. The search uses LHC proton-proton collision data at $\sqrt{s}=13.6~\text{TeV}$ collected in 2022 and 2023, corresponding to an integrated luminosity of $57.7\text{fb}^{-1}$. A model with axion-like particles (ALPs) dominantly coupled to weak gauge bosons is the primary target. Signal events are characterised by high-energy deposits in the electromagnetic calorimeter and no signal in the veto scintillators. One event is observed, compared to a background expectation of $0.44 \pm 0.39$ events, which is entirely dominated by neutrino interactions. World-leading constraints on ALPs are obtained for masses up to $300~\text{MeV}$ and couplings to the Standard Model W gauge boson, $g_{aWW}$, around $10^{-4}$ GeV$^{-1}$, testing a previously unexplored region of parameter space. Other new particle models that lead to the same experimental signature, including ALPs coupled to gluons or photons, U(1)$_B$ gauge bosons, up-philic scalars, and a Type-I two-Higgs doublet model, are also considered for interpretation, and new constraints on previously viable parameter space are presented in this paper., Comment: 37 pages, 22 figures

Advances in modern technologies enable the characterisation of exoplanetary atmospheres, most efficiently exploiting the transmission spectroscopy technique. We performed visible (VIS) and near infrared (nIR) high-resolution spectroscopic observations of one transit of HD 149026b, a close-in orbit sub Saturn exoplanet. We first analysed the radial velocity data, refining the value of the projected spin-orbit obliquity. Then we performed transmission spectroscopy, looking for absorption signals from the planetary atmosphere. We find no evidence for H$\alpha$, NaI D2 - D1, MgI and LiI in the VIS and metastable helium triplet HeI(2$^3$S) in the nIR using a line-by-line approach. The non-detection of HeI is also supported by theoretical simulations. With the use of the cross-correlation technique, we do not detect TiI, VI, CrI, FeI and VO in the visible, and CH$_4$, CO$_2$, H$_2$O, HCN, NH$_3$, VO in the nIR. Our non-detection of TiI in the planetary atmosphere is in contrast with a previous detection. We performed injection-retrieval tests, finding that our dataset is sensitive to our TiI model. The non-detection supports the TiI cold-trap theory, which is valid for planets with $T_{\rm eq} <$ 2200 K like HD 149026b. Even if we do not attribute it directly to the planet, we find a possibly significant TiI signal highly redshifted ($\simeq$+20 km s$^{-1}$) with respect to the planetary restframe. Redshifted signals are also found in the FeI and CrI maps. While we can exclude an eccentric orbit to cause it, we investigated the possibility of material accretion falling onto the star, possibly supported by the presence of strong LiI in the stellar spectrum, without finding conclusive results. The analysis of multiple transits datasets could shed more light on this target.

The quest for radio signals from technologically-advanced extraterrestrial intelligence has traditionally concentrated on the vicinity of 1.4 GHz. In this paper, we extend the search to unprecedented territories, detailing our extensive observations at 6 GHz and initiating the first-ever survey at 18 GHz with the Sardinia Radio Telescope (SRT). Our strategy entailed rigorous observation sessions, totaling 36 hours, directed towards the Galactic Center and 72 selected TESS targets-making this the most comprehensive high-frequency technosignature search to date. Our narrowband signal search found no definitive evidence of drifting signals that could suggest an extraterrestrial origin from the surveyed regions. Nevertheless, our efforts have enabled us to set new constraints on the presence of radio emissions from approximately $5\times 10^{5}$ stars, establishing an isotropic radiated power limit of $1.8\times 10^{19} W$. We also provide a comparative analysis of the 'hits' recorded across both frequencies to highlight the significance of pursuing technosignature searches at higher frequencies, where the spectral landscape is less congested and more conducive to detection., Comment: Submitted to Acta Astronautica

We establish the Gauss-Green formula for extended divergence-measure fields (i.e., vector-valued measures whose distributional divergences are Radon measures) over open sets. We prove that, for almost every open set, the normal trace is a measure supported on the boundary of the set. Moreover, for any open set, we provide a representation of the normal trace of the field over the boundary of the open set as the limit of measure-valued normal traces over the boundaries of approximating sets. Furthermore, using this theory, we extend the balance law from classical continuum physics to a general framework in which the production on any open set is measured with a Radon measure and the associated Cauchy flux is bounded by a Radon measure concentrated on the boundary of the set. We prove that there exists an extended divergence-measure field such that the Cauchy flux can be recovered through the field, locally on almost every open set and globally on every open set. Our results generalize the classical Cauchy's Theorem (that is only valid for continuous vector fields) and extend the previous formulations of the Cauchy flux (that generate vector fields within $L^{p}$). Thereby, we establish the equivalence between entropy solutions of the multidimensional nonlinear partial differential equations of divergence form and of the mathematical formulation of physical balance laws via the Cauchy flux through the constitutive relations in the axiomatic foundation of Continuum Physics., Comment: 70 pages

The Large Magellanic Cloud (LMC) is a Milky Way (MW) satellite that is massive enough to gravitationally attract the MW disc and inner halo, causing significant motion of the inner MW with respect to the outer halo. In this work, we probe this interaction by constructing a sample of 9,866 blue horizontal branch (BHB) stars with radial velocities from the DESI spectroscopic survey out to 120 kpc from the Galactic centre. This is the largest spectroscopic set of BHB stars in the literature to date, and it contains four times more stars with Galactocentric distances beyond 50 kpc than previous BHB catalogues. Using the DESI BHB sample combined with SDSS BHBs, we measure the bulk radial velocity of stars in the outer halo and observe that the velocity in the Southern Galactic hemisphere is different by 3.7$\sigma$ from the North. Modelling the projected velocity field shows that its dipole component is directed at a point 22 degrees away from the LMC along its orbit, which we interpret as the travel direction of the inner MW. The velocity field includes a monopole term that is -24 km/s, which we refer to as compression velocity. This velocity is significantly larger than predicted by the current models of the MW and LMC interaction. This work uses DESI data from its first two years of observations, but we expect that with upcoming DESI data releases, the sample of BHB stars will increase and our ability to measure the MW-LMC interaction will improve significantly., Comment: 22 pages, 19 figures. Submitted to MNRAS

A standard method to reduce the linewidth of semiconductor lasers involves the use of external optical feedback (EOF). However, feedback powers less than 1 % usually trigger coherence collapse (CC), leading to chaotic laser dynamics and linewidth broadening. This paper explores a method to mitigate CC through precise tuning of the feedback polarization depending on the feedback power. We report a semiconductor laser with a sub-kHz linewidth, achieved via EOF. The laser features a U-shaped cavity with two sampled grating distributed Bragg reflectors (SG-DBRs), enabling broad tunability across a 42 nm wavelength range (1513-1555 nm). By injecting optical feedback into both sides of the laser cavity via an external fiber-based cavity, we reduce the linewidth by more than three orders of magnitude, from MHz to sub-kHz across the laser's tuning range. Our approach achieves significant linewidth reduction while maintaining coherence at high feedback levels, marking an improvement over prior studies where CC limited performance. These results pave the way for ultra-narrow linewidth diode lasers with wide tunability, which would benefit fields like coherent optical communications and spectroscopy.

Hydrogels are attractive and versatile gel-based structures consisting of three-dimensional networks of hydrophilic polymers with unique properties that can retain a large amount of water and be tailor-designed according to specific requirements, and thus, suitable for numerous commercial areas (i.e., medicine, agriculture, electronics, cosmetics, etc.). However, since these materials can be very soft and flexible, mechanical characterization is challenging. This study explores the mechanical properties of a cellulose-based hydrogel using the optical, non-contact technique Brillouin light scattering spectroscopy (BLS). Two differently dried hydrogel samples were investigated and pre-characterized for their morphology by scanning electron microscopy (SEM) and their mechanical performance by tensile testing. Then, BLS spectra were recorded to characterize the material's stiffness, indicating that the structural arrangement of the material due to the drying procedure (the density) is the main factor limiting the material's strength. By further analyzing the effect of moisture and the changes during drying, we characterize the mechanical behavior of organic hydrogel films and sponges and their dehydration evolution to understand the liquid-solid transition.

The Calar Alto Void Integral-field Treasury surveY (CAVITY) is a legacy project aimed at characterising the population of galaxies inhabiting voids, which are the most under-dense regions of the cosmic web, located in the Local Universe. This paper describes the first public data release (DR1) of CAVITY, comprising science-grade optical data cubes for the initial 100 out of a total of $\sim$300 galaxies in the Local Universe ($0.005 < z < 0.050$). These data were acquired using the integral-field spectrograph PMAS/PPak mounted on the 3.5m telescope at the Calar Alto observatory. The DR1 galaxy sample encompasses diverse characteristics in the color-magnitude space, morphological type, stellar mass, and gas ionisation conditions, providing a rich resource for addressing key questions in galaxy evolution through spatially resolved spectroscopy. The galaxies in this study were observed with the low-resolution V500 set-up, spanning the wavelength range 3745-7500 \AA, with a spectral resolution of 6.0 \AA\ (FWHM). Here, we describe the data reduction and characteristics and data structure of the CAVITY datasets essential for their scientific utilisation, highlighting such concerns as vignetting effects, as well as the identification of bad pixels and management of spatially correlated noise. We also provide instructions for accessing the CAVITY datasets and associated ancillary data through the project's dedicated database., Comment: 17 pages, 9 figures, 5 tables. Accepted for publication in A&A (replacement after minor language edits)

One of the most challenging problems in the computational study of localization in quantum manybody systems is to capture the effects of rare events, which requires sampling over exponentially many disorder realizations. We implement an efficient procedure on a quantum processor, leveraging quantum parallelism, to efficiently sample over all disorder realizations. We observe localization without disorder in quantum many-body dynamics in one and two dimensions: perturbations do not diffuse even though both the generator of evolution and the initial states are fully translationally invariant. The disorder strength as well as its density can be readily tuned using the initial state. Furthermore, we demonstrate the versatility of our platform by measuring Renyi entropies. Our method could also be extended to higher moments of the physical observables and disorder learning.

We consider differentially private counting when each data point consists of $d$ bits satisfying a partial order. Our main technical contribution is a problem-specific $K$-norm mechanism that runs in time $O(d^2)$. Experiments show that, depending on the partial order in question, our solution dominates existing pure differentially private mechanisms, and can reduce their error by an order of magnitude or more.

We use low-temperature scanning tunneling microscopy (LT-STM) to characterize the early stages of silver fluorination. On Ag(100), we observe only one adsorbate species, which shows a bias-dependent STM topography. Notably, at negative bias voltages, $V_{\rm B}<0$, the apparent shape can be described as a round protrusion surrounded by a moat-like depression (sombrero). As the voltage increases, the apparent shape changes, eventually evolving into a round depression. From the STM images, we determine the adsorption site to be the hollow position. On Ag(110) we find adsorbates with three distinct STM topographies. One type exhibits the same shape change with $V_{\rm B}$ as observed on Ag(100), that is, from a sombrero shape to a round depression as the voltage changes from negative to positive values; the other two types are observed as round depressions regardless of $V_{\rm B}$. From the STM images, we find the three adsorbates to be sitting on the short-bridge, hollow and top position on the Ag(110) surface, with a relative abundance of 60\%, 35\% and 5\%., Comment: 13 pages, 7 figures

We present an approach for the analysis of hybrid visual compositions in animation in the domain of ephemeral film. We combine ideas from semi-supervised and weakly supervised learning to train a model that can segment hybrid compositions without requiring pre-labeled segmentation masks. We evaluate our approach on a set of ephemeral films from 13 film archives. Results demonstrate that the proposed learning strategy yields a performance close to a fully supervised baseline. On a qualitative level the performed analysis provides interesting insights on hybrid compositions in animation film., Comment: Vision for Art (VISART VII) Workshop at the European Conference of Computer Vision (ECCV)

JWST observations of the young Galactic supernova remnant Cassiopeia A revealed an unexpected structure seen as a green emission feature in colored composite MIRI F1130W and F1280W images - hence dubbed the Green Monster - that stretches across the central parts of the remnant in projection. Combining the kinematic information from NIRSpec and MIRI MRS with the multi-wavelength imaging from NIRCam and MIRI, we associate the Green Monster with circumstellar material that was lost during an asymmetric mass-loss phase. MIRI images are dominated by dust emission but its spectra show emission lines from Ne, H and Fe with low radial velocities indicative of a CSM nature. An X-ray analysis of this feature in a companion paper (Vink et al. 2024) supports its CSM nature and detects significant blue shifting, thereby placing the Green Monster on the near side, in front of the Cas A SN remnant. The most striking features of the Green Monster are dozens of almost perfectly circular 1" - 3" sized holes, most likely created by interaction between high-velocity SN ejecta material and the CSM. Further investigation is needed to understand whether these holes were formed by small 8000-10500 km/s N-rich ejecta knots that penetrated and advanced out ahead of the remnant's 5000 - 6000 km/s outer blastwave, or by narrow ejecta fingers that protrude into the forward-shocked CSM. The detection of the Green Monster provides further evidence of the highly asymmetric mass-loss that Cas A's progenitor star underwent prior to explosion., Comment: 28 pages, 12 figures, resubmitted to ApJL after minor revision, comments welcome

We report a comprehensive study of electronic band structure for single-layer (SL) and bilayer (BL) RP-nickelates probed by in-situ HP X-ray absorption near edge spectroscopy (XANES). At ambient pressure (AP), the energy splitting delta_E of d_3z^2-r^2 and d_x^2-y^2 bands are directly observed in La3Ni2O7 (BL-La327) but not in La2NiO4 (SL-La214) above E_F, underlining the critical role of inner apical O atoms. A combination of DFT-based electronic band structure and projected density of states (PDOS) calculations with simulated XANES enables us to explain the observed main XANES features labelled by a, A, B', B and C when considering the orbital hybridizations, crystal field splitting (CFS) and core-hole screening of different 3d configurations for SL-La214 and BL-La327 nickelates. At high pressure (HP), the delta_E values of pre-edge peak form a dome-like evolution above 7.7 GPa with the maximum locating at around 20 GPa for metallic BL-La327. Analysis of its integrated area and FWHM provides strong evidence that the bonding d_3z^2-r^2 band crosses E_F above about 7.7 GPa for the metallic BL-La327. Growth of integrated area of pre-edge peak and C peak further evidences pressure-induced hole doping effect. Meanwhile, the pressure dependent FWHM of pre-edge peak implies a nonmonotonic evolution of orbital-selective electronic correlation above 7.7 GPa with extrema emerging at about 20 GPa. Moreover, we estimate the relative hole doping level using the energy shift of pre-edge peak, yielding 0.074 hole per Ni site or equivalently 1.1*10^21 cm^-3 at 20 GPa for the metallic BL-La327, which is comparable to cuprates. Our results have timely examined the electronic band structures as obtained from theoretical calculations, emphasizing the essential role of both d_3z^2-r^2 and d_x^2-y^2 bands as well as the electronic correlation in superconducting pairing for pressurized La3Ni2O7.

The rapid evolution of artificial intelligence (AI) technologies holds transformative potential for the healthcare sector. In critical situations requiring immediate decision-making, healthcare professionals can leverage machine learning (ML) algorithms to prioritize and optimize treatment options, thereby reducing costs and improving patient outcomes. However, the sensitive nature of healthcare data presents significant challenges in terms of privacy and data ownership, hindering data availability and the development of robust algorithms. Federated Learning (FL) addresses these challenges by enabling collaborative training of ML models without the exchange of local data. This paper introduces a novel FL platform designed to support the configuration, monitoring, and management of FL processes. This platform operates on Platform-as-a-Service (PaaS) principles and utilizes the Message Queuing Telemetry Transport (MQTT) publish-subscribe protocol. Considering the production readiness and data sensitivity inherent in clinical environments, we emphasize the security of the proposed FL architecture, addressing potential threats and proposing mitigation strategies to enhance the platform's trustworthiness. The platform has been successfully tested in various operational environments using a publicly available dataset, highlighting its benefits and confirming its efficacy.

The extended ultraviolet (XUV) disks of nearby galaxies show ongoing massive star formation, but their parental molecular clouds remain mostly undetected despite searches in CO(1-0) and CO(2-1). The recent detection of 23 clouds in the higher excitation transition CO(3-2) within the XUV disk of M83 requires an explanation. We test the hypothesis: the clouds in XUV disks have a clump-envelope structure similar to those in Galactic star-forming clouds, having star-forming dense clumps (or concentrations of multiple clumps) at their centers, which predominantly contribute to the CO(3-2) emission, surrounded by less-dense envelopes, where CO molecules are photo-dissociated due to the low-metallicity environment there. We utilize new high-resolution ALMA CO(3-2) observations of a subset (11) of the 23 clouds in the XUV disk. We confirm the compactness of the CO(3-2)-emitting dense clumps (or their concentrations), finding clump diameters below the spatial resolution of 6-9~pc. This is similar to the size of the dense gas region in the Orion A molecular cloud, the local star-forming cloud with massive star formation. The dense star-forming clumps are common between normal and XUV disks. This may also indicate that once the cloud structure is set, the process of star formation is governed by the cloud internal physics rather than by external triggers. This simple model explains the current observations of the clouds with ongoing massive star formation, although it may require some adjustment, e.g., including an effect of cloud evolution, for a general scenario of star formation in molecular clouds., Comment: A&A accepted; 15 pages, 8 figures - after proof

This work describes the rescue and enhancement of several thousand spectrophotometric plates from the La Plata Observatory. These plates and their attached handwritten documents, date from the beginning of the 20th century and contain information on observations made at different observatories around the world. Due to their fragility and the inevitable deterioration produced by the passage of time, they are being cleaned up, recovered and digitized. The information contained in the plates has a high scientific value and, based on a comparison of an old spectrum with a current one, part of the history of the star can be reconstructed. Through the ReTrOH project, we seek to translate the document into an appropriate format, preserve and disseminate that knowledge, and also recover the scientific project and the historical and personal context of the observers. The digitized spectra are available in the virtual repository (SEDICI) of the UNLP., Comment: in Spanish language. https://ffyh.unc.edu.ar/publicaciones/wp-content/uploads/sites/35/2023/04/HistoriadelaAstronomia_compressed.pdf

Lattice gauge theories (LGTs) can be employed to understand a wide range of phenomena, from elementary particle scattering in high-energy physics to effective descriptions of many-body interactions in materials. Studying dynamical properties of emergent phases can be challenging as it requires solving many-body problems that are generally beyond perturbative limits. We investigate the dynamics of local excitations in a $\mathbb{Z}_2$ LGT using a two-dimensional lattice of superconducting qubits. We first construct a simple variational circuit which prepares low-energy states that have a large overlap with the ground state; then we create particles with local gates and simulate their quantum dynamics via a discretized time evolution. As the effective magnetic field is increased, our measurements show signatures of transitioning from deconfined to confined dynamics. For confined excitations, the magnetic field induces a tension in the string connecting them. Our method allows us to experimentally image string dynamics in a (2+1)D LGT from which we uncover two distinct regimes inside the confining phase: for weak confinement the string fluctuates strongly in the transverse direction, while for strong confinement transverse fluctuations are effectively frozen. In addition, we demonstrate a resonance condition at which dynamical string breaking is facilitated. Our LGT implementation on a quantum processor presents a novel set of techniques for investigating emergent particle and string dynamics.

Product images (e.g., a phone) can be used to elicit a diverse set of consumer-reported features expressed through language, including surface-level perceptual attributes (e.g., "white") and more complex ones, like perceived utility (e.g., "battery"). The cognitive complexity of elicited language reveals the nature of cognitive processes and the context required to understand them; cognitive complexity also predicts consumers' subsequent choices. This work offers an approach for measuring and validating the cognitive complexity of human language elicited by product images, providing a tool for understanding the cognitive processes of human as well as virtual respondents simulated by Large Language Models (LLMs). We also introduce a large dataset that includes diverse descriptive labels for product images, including human-rated complexity. We demonstrate that human-rated cognitive complexity can be approximated using a set of natural language models that, combined, roughly capture the complexity construct. Moreover, this approach is minimally supervised and scalable, even in use cases with limited human assessment of complexity.

Three-dimensional reconstruction is a fundamental problem in robotics perception. We examine the problem of active view selection to perform 3D Gaussian Splatting reconstructions with as few input images as possible. Although 3D Gaussian Splatting has made significant progress in image rendering and 3D reconstruction, the quality of the reconstruction is strongly impacted by the selection of 2D images and the estimation of camera poses through Structure-from-Motion (SfM) algorithms. Current methods to select views that rely on uncertainties from occlusions, depth ambiguities, or neural network predictions directly are insufficient to handle the issue and struggle to generalize to new scenes. By ranking the potential views in the frequency domain, we are able to effectively estimate the potential information gain of new viewpoints without ground truth data. By overcoming current constraints on model architecture and efficacy, our method achieves state-of-the-art results in view selection, demonstrating its potential for efficient image-based 3D reconstruction., Comment: 8 pages, 4 figures

Optical microscopy has long been the standard method for diagnosis in cytopathology. Whole slide scanners can image and digitize large sample areas automatically, but are slow, expensive and therefore not widely available. Clinical diagnosis of cytology specimens is especially challenging since these samples are both spread over large areas and thick, which requires 3D capture. Here, we introduce a new parallelized microscope for scanning thick specimens across extremely wide fields-of-view (54x72 mm^2) at 1.2 and 0.6 {\mu}m resolutions, accompanied by machine learning software to rapidly assess these 16 gigapixel scans. This Multi-Camera Array Scanner (MCAS) comprises 48 micro-cameras closely arranged to simultaneously image different areas. By capturing 624 megapixels per snapshot, the MCAS is significantly faster than most conventional whole slide scanners. We used this system to digitize entire cytology samples (scanning three entire slides in 3D in just several minutes) and demonstrate two machine learning techniques to assist pathologists: first, an adenocarcinoma detection model in lung specimens (0.73 recall); second, a slide-level classification model of lung smears (0.969 AUC).

This paper presents a Multimodal Ambient Context-enriched Intelligence Platform (MACeIP) for Smart Cities, a comprehensive system designed to enhance urban management and citizen engagement. Our platform integrates advanced technologies, including Internet of Things (IoT) sensors, edge and cloud computing, and Multimodal AI, to create a responsive and intelligent urban ecosystem. Key components include Interactive Hubs for citizen interaction, an extensive IoT sensor network, intelligent public asset management, a pedestrian monitoring system, a City Planning Portal, and a Cloud Computing System. We demonstrate the prototype of MACeIP in several cities, focusing on Fredericton, New Brunswick. This work contributes to innovative city development by offering a scalable, efficient, and user-centric approach to urban intelligence and management., Comment: 4 pages, 6 figures, IEEE/IEIE ICCE-Asia 2024

The liquid drop model was introduced by Gamow in 1928 and Bohr-Wheeler in 1938 to model atomic nuclei. The model describes the competition between the surface tension, which keeps the nuclei together, and the Coulomb force, corresponding to repulsion among protons. More precisely, the problem consists of finding a surface $\Sigma =\partial \Omega$ in $\mathbb{R}^3$ that is critical for the energy $$ E(\Omega) = {\rm Per\,} (\Omega ) + \frac 12 \int_\Omega\int_\Omega \frac {dxdy}{|x-y|} $$ under the volume constraint $|\Omega| = m$. The term ${\rm Per\,} (\Omega ) $ corresponds to the surface area of $\Sigma$. The associated Euler-Lagrange equation is $$ H_\Sigma (x) + \int_{\Omega } \frac {dy}{|x-y|} = \lambda \quad \hbox{ for all } x\in \Sigma, \quad $$ where $H_\Sigma$ stands for the mean curvature of the surface, and where $\lambda\in\mathbb{R}$ is the Lagrange multiplier associated to the constraint $|\Omega|=m$. Round spheres enclosing balls of volume $m$ are always solutions. They are minimizers for sufficiently small $m$. Since the two terms in the energy compete, finding non-minimizing solutions can be challenging. We find a new class of compact, embedded solutions with large volumes, whose geometry resembles a "pearl necklace" with an axis located on a large circle, with a shape close to a Delaunay's unduloid surface of constant mean curvature. The existence of such equilibria is not at all obvious, since for the closely related constant mean curvature problem $H_\Sigma = \lambda$, the only compact embedded solutions are spheres, as stated by the classical Alexandrov result.

Resilience refers to the ability of systems to withstand, adapt to, and recover from disruptive events. While studies on resilience have attracted significant attention across various research domains, the precise definition of this concept within the field of cooperative artificial intelligence remains unclear. This paper addresses this gap by proposing a clear definition of `cooperative resilience' and outlining a methodology for its quantitative measurement. The methodology is validated in an environment with RL-based and LLM-augmented autonomous agents, subjected to environmental changes and the introduction of agents with unsustainable behaviors. These events are parameterized to create various scenarios for measuring cooperative resilience. The results highlight the crucial role of resilience metrics in analyzing how the collective system prepares for, resists, recovers from, sustains well-being, and transforms in the face of disruptions. These findings provide foundational insights into the definition, measurement, and preliminary analysis of cooperative resilience, offering significant implications for the broader field of AI. Moreover, the methodology and metrics developed here can be adapted to a wide range of AI applications, enhancing the reliability and effectiveness of AI in dynamic and unpredictable environments., Comment: Supplementary material in https://github.com/mavivi95/resilience/blob/main/Supplementary_File.pdf

Using a combination of HST, JWST, and ALMA data, we perform spatially resolved spectral energy distributions (SED) fitting of fourteen 4

Electrical resistivity tomography is a suitable technique for non-invasive monitoring of municipal solid waste landfills, but accurate sensitivity analysis is necessary to evaluate the effectiveness and reliability of geoelectrical investigations and to properly design data acquisition. Commonly, a thin high-resistivity membrane in placed underneath the waste to prevent leachate leakage. In the construction of a numerical framework for sensitivity computation, taking into account the actual dimensions of the electrodes and, in particular, of the membrane, can lead to extremely high computational costs. In this work, we present a novel approach for numerically computing sensitivity effectively by adopting a mixed-dimensional framework, where the membrane is approximated as a 2D object and the electrodes as 1D objects. The code is first validated against analytical expressions for simple 4-electrode arrays and a homogeneous medium. It is then tested in simplified landfill models, where a 2D box-shaped liner separates the landfill body from the surrounding media, and 48 electrodes are used. The results show that electrodes arranged linearly along both sides of the perimeter edges of the box-shaped liner are promising for detecting liner damage, with sensitivity increasing by 2-3 orders of magnitude, even for damage as small as one-sixth of the electrode spacing in diameter. Good results are also obtained when simulating an electrical connection between the landfill and the surrounding media that is not due to liner damage. The next steps involve evaluating the minimum number of configurations needed to achieve suitable sensitivity with a manageable field effort and validating the modeling results with downscaled laboratory tests.

While systems analysis has been studied for decades in the context of control theory, it has only been recently used to improve the convergence of Denoising Diffusion Probabilistic Models. This work describes a novel improvement to Third- Order Langevin Dynamics (TOLD), a recent diffusion method that performs better than its predecessors. This improvement, abbreviated TOLD++, is carried out by critically damping the TOLD forward transition matrix similarly to Dockhorn's Critically-Damped Langevin Dynamics (CLD). Specifically, it exploits eigen-analysis of the forward transition matrix to derive the optimal set of dynamics under the original TOLD scheme. TOLD++ is theoretically guaranteed to converge faster than TOLD, and its faster convergence is verified on the Swiss Roll toy dataset and CIFAR-10 dataset according to the FID metric., Comment: 5 pages, 2 figures, 2 tables, submitted to ICASSP 2025

In this work we introduce the broken line construction, which is a geometric and combinatorial algorithm that computes periodic Sturmian angles of a given period, yielding the locations of their landing parameters in the Mandelbrot set. An easy to implement method to compute the conjugated angle of a periodic Sturmian angle is also provided. Furthermore, if $\theta$ is a periodic Sturmian angle computed by the broken line construction, then we show the existence of a one-to-one correspondence between its binary expansion and its associated kneading sequence.

Generative models for level generation have shown great potential in game production. However, they often provide limited control over the generation, and the validity of the generated levels is unreliable. Despite this fact, only a few approaches that learn from existing data provide the users with ways of controlling the generation, simultaneously addressing the generation of unsolvable levels. %One of the main challenges it faces is that levels generated through automation may not be solvable thus requiring validation. are not always engaging, challenging, or even solvable. This paper proposes Avalon, a novel method to improve models that learn from existing level designs using difficulty statistics extracted from gameplay. In particular, we use a conditional variational autoencoder to generate layouts for match-3 levels, conditioning the model on pre-collected statistics such as game mechanics like difficulty and relevant visual features like size and symmetry. Our method is general enough that multiple approaches could potentially be used to generate these statistics. We quantitatively evaluate our approach by comparing it to an ablated model without difficulty conditioning. Additionally, we analyze both quantitatively and qualitatively whether the style of the dataset is preserved in the generated levels. Our approach generates more valid levels than the same method without difficulty conditioning., Comment: 10 pages, 5 figures, 2 tables

We analyze the $G$-skein theory invariants of the 3-torus $T^3$ and the two-torus $T^2$, for the groups $G = GL_N, SL_N$ and for generic quantum parameter. We obtain formulas for the dimension of the skein module of $T^3$, and we describe the algebraic structure of the skein category of $T^2$ -- namely of the $n$-point relative skein algebras. The case $n=N$ (the Schur-Weyl case) is special in our analysis. We construct an isomorphism between the $N$-point relative skein algebra and the double affine Hecke algebra at specialized parameters. As a consequence, we prove that all tangles in the relative $N$-point skein algebra are in fact equivalent to linear combinations of braids, modulo skein relations. More generally for $n$ an integer multiple of $N$, we construct a surjective homomorphism from an appropriate DAHA to the $n$-point relative skein algebra. In the case $G=SL_2$ corresponding to the Kauffman bracket we give proofs directly using skein relations. Our analysis of skein categories in higher rank hinges instead on the combinatorics of multisegment representations when restricting from DAHA to AHA and nonvanishing properties of parabolic sign idempotents upon them.

The increasing demand for energy storage solutions has spurred intensive research into next-generation battery technologies. Oxygen-ion batteries (OIBs), which leverage mixed ionic-electronic conducting (MIEC) oxides, have emerged as promising candidates due to their solid, non-flammable nature and potential for high power densities. This study investigates the use of over-stoichiometric La2NiO4+delta (L2NO4) as a cathode material for OIBs, exploring its capacity for electrochemical energy storage. Half-cell measurements reveal that L2NO4 with a closed-pore microstructure can store oxygen, achieving a volumetric charge of 63 mA.h.cm-3 at 400 {\deg}C with a current density of 3.6 uA.cm-2 and potentials up to 0.75 V vs. 1 bar O2. Additionally, a functional full cell combining over-stoichiometric L2NO4 and under-stoichiometric La0.5Sr0.5Cr0.2Mn0.8O3-delta (LSCrMn) has been successfully developed, demonstrating excellent cyclability and coulomb efficiency. The full cell reaches a maximum volumetric charge of 90 mA.h.cm-3 at 400 {\deg}C, 17.8 uA.cm-2, and a cut-off voltage of 1.8 V. This proof of concept underscores the viability of combining over- and under-stoichiometric MIEC materials in OIBs and provides critical insights into optimizing electrode materials and tuning oxygen content for improved performance. This research lays the groundwork for future advancements in OIB technology, aiming to develop materials with lower resistance and higher efficiency.

Predicting traffic flow in data-scarce cities is challenging due to limited historical data. To address this, we leverage transfer learning by identifying periodic patterns common to data-rich cities using a customized variant of Dynamic Mode Decomposition (DMD): constrained Hankelized DMD (TrHDMD). This method uncovers common eigenmodes (urban heartbeats) in traffic patterns and transfers them to data-scarce cities, significantly enhancing prediction performance. TrHDMD reduces the need for extensive training datasets by utilizing prior knowledge from other cities. By applying Koopman operator theory to multi-city loop detector data, we identify stable, interpretable, and time-invariant traffic modes. Injecting ``urban heartbeats'' into forecasting tasks improves prediction accuracy and has the potential to enhance traffic management strategies for cities with varying data infrastructures. Our work introduces cross-city knowledge transfer via shared Koopman eigenmodes, offering actionable insights and reliable forecasts for data-scarce urban environments.

Barred galaxies exhibit boxy/peanut or X-shapes (BP/X) protruding from their disks in edge-on views. Two types of BP/X morphologies exist depending on whether the X-wings meet at the center (CX) or are off-centered (OX). Orbital studies indicate that various orbital types can generate X-shaped structures. Here, we provide a classification approach that identifies the specific orbit families responsible for generating OX and CX-shaped structures. Applying this approach to three different N-body bar models, we show that both OX and CX structures are associated with the x1 orbit family, but OX-supporting orbits possess higher angular momentum (closer to x1 orbits) than orbits in CX structures. Consequently, as the bar slows down the contribution of higher angular momentum OX-supporting orbits decreases and that of lower angular momentum orbits increases resulting in an evolution of the morphology from OX to CX. If the bar does not slow down, the shape of the BP/X structure and the fractions of OX/CX supporting orbits remain substantially unchanged. Bars that do not undergo buckling but that do slow down initially show the OX structure and are dominated by high angular momentum orbits, transitioning to a CX morphology. Bars that buckle exhibit a combination of both OX and CX supporting orbits immediately after the buckling, but become more CX dominated as their pattern speed decreases. This study demonstrates that the evolution of BP/X morphology and orbit populations strongly depends on the evolution of the bar angular momentum., Comment: 16 pages, 12 figures, Accepted for publication in ApJ

We provide the first construction of overhanging gravity water waves having the approximate form of a disk joined to a strip by a thin neck. The waves are solitary with constant vorticity, and exist when an appropriate dimensionless gravitational constant $g>0$ is sufficiently small. Our construction involves combining three explicit solutions to related problems: a disk of fluid in rigid rotation, a linear shear flow in a strip, and a rescaled version of an exceptional domain discovered by Hauswirth, H\'elein, and Pacard \cite{hauswirth-helein-pacard}. The method developed here is related to the construction of constant mean curvature surfaces through gluing., Comment: 93 pages