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Pattern formation in diffusive-chemotaxis models has become increasingly important for understanding spatial structures in biological, ecological, and chemical systems. In soil, certain bacteria involved in the degradation of soil organic carbon (SOC) exhibit both motility and chemotactic behavior, as observed in experiments and field studies. To capture the formation of hot-spot soil aggregations resulting from bacterial and microorganism spatial organization, the spatially explicit MOMOS model incorporates chemotaxis into soil carbon dynamics, effectively describing the emergence of microbial aggregations and their impact on SOC dynamics. While classical studies on pattern formation primarily rely on asymptotic stability analysis, transient instability has emerged as a key mechanism for generating patterns in asymptotically stable systems. In this work, we investigate pattern formation outside the classical diffusive-chemotaxis-driven instability conditions by focusing on regions of parameter space associated with reactive equilibria. The MOMOS model for soil carbon dynamics serves as a case study to illustrate how transient behaviors can influence environmental dynamical systems. Using Klika's indicator, we estimate the maximum amplification and show that it provides a more accurate lower bound compared to the Kreiss constant. Furthermore, we explore the critical role of return time, showing that short return times suppress the emergence of stable reactive patterns. By employing the determinant of the linearized Jacobian as a proxy for return time, we identify a critical region near the instability boundary where multiple stable reactive patterns emerge.

This paper presents the Byzantine fault-tolerant agreement protocols Flutter and Blink. Both algorithms are deterministic, leaderless and signature-free; both assume partial synchrony and at least $(5f + 1)$ servers, where $f$ bounds the number of faults. The main contribution, Flutter, is a Total-Order Broadcast implementation that achieves faster broadcast-to-delivery latency by removing the extra message delay associated with serializing messages through a leader. In the "good case" where all processes are correct, the network is synchronous, and local clocks are well-synchronized, Flutter delivers client requests in $(2\Delta + \epsilon)$ time units, $\Delta$ being the message delay and $\epsilon$ an arbitrarily small constant. Under the same conditions, state-of-the-art protocols require $3\Delta$ time units. Flutter's good-case latency is quasi-optimal, meaning it cannot be improved upon by any finite amount. Under the hood, Flutter builds upon Blink, a (Representative) Binary Consensus implementation whose fast path enables decisions in $\Delta$ time units when all correct servers propose the same value. Blink generalizes the existing Binary Consensus solution Bosco from the $(7f + 1)$ to the $(5f + 1)$ setting.

Gaussian process (GP) is a powerful modeling method with applications in machine learning for various engineering and non-engineering fields. Despite numerous benefits of modeling using GPs, the computational complexity associated with GPs demanding immense resources make their practical usage highly challenging. In this article, we develop a quantum version of multi-output Gaussian Process (QGP) by implementing a well-known quantum algorithm called HHL, to perform the Kernel matrix inversion within the Gaussian Process. To reduce the large circuit depth of HHL a circuit optimization technique called Approximate Quantum Compiling (AQC) has been implemented. We further showcase the application of QGP for a real-world problem to estimate line parameters of an electrical grid. Using AQC, up to 13-qubit HHL circuit has been implemented for a 32x32 kernel matrix inversion on IBM Quantum hardware for demonstrating QGP based line parameter estimation experimentally. Finally, we compare its performance against noise-less quantum simulators and classical computation results., Comment: 9 pages, 7 figures

The recent detection of extended $\gamma$-ray emission around middle-aged pulsars is interpreted as inverse-Compton scattering of ambient photons by electron-positron pairs escaping the pulsar wind nebula, which are confined near the system by unclear mechanisms. This emerging population of $\gamma$-ray sources was first discovered at TeV energies and remains underexplored in the GeV range. To address this, we conducted a systematic search for extended sources along the Galactic plane using 14 years of Fermi-LAT data above 10 GeV, aiming to identify a number of pulsar halo candidates and extend our view to lower energies. The search covered the inner Galactic plane ($\lvert l\rvert\leq$ 100$^{\circ}$, $\lvert b\rvert\leq$ 1$^{\circ}$) and the positions of known TeV sources and bright pulsars, yielding broader astrophysical interest. We found 40 such sources, forming the Second Fermi Galactic Extended Sources Catalog (2FGES), most with 68% containment radii smaller than 1.0$^{\circ}$ and relatively hard spectra with photon indices below 2.5. We assessed detection robustness using field-specific alternative interstellar emission models and by inspecting significance maps. Noting 13 sources previously known as extended in the 4FGL-DR3 catalog and five dubious sources from complex regions, we report 22 newly detected extended sources above 10 GeV. Of these, 13 coincide with H.E.S.S., HAWC, or LHAASO sources; six coincide with bright pulsars (including four also coincident with TeV sources); six are associated with 4FGL point sources only; and one has no association in the scanned catalogs. Notably, six to eight sources may be related to pulsars as classical pulsar wind nebulae or pulsar halos., Comment: 43 pages, 17 figures, 11 tables

Let $S$ be a closed orientable surface of genus at least two. We introduce a bordification of the moduli space $\mathcal{PT}(S)$ of complex projective structures, with a boundary consisting of projective classes of half-translation surfaces. Thurston established an equivalence between complex projective structures and hyperbolic surfaces grafted along a measured lamination, leading to a homeomorphism $\mathcal{PT}(S) \cong \mathcal{T}(S) \times \mathcal{ML}(S)$. Our bordification is geometric in the sense that convergence to points on the boundary corresponds to the geometric convergence of grafted surfaces to half-translation surfaces (up to rescaling). This result relies on recent work by Calderon and Farre on the orthogeodesic foliation construction. Finally, we introduce a change of perspective, viewing grafted surfaces as a deformation (which we term "inflation") of half-translation surfaces, consisting of inserting negatively curved regions., Comment: 50 pages, 8 figures. Comments welcome!

In the ultra-low inter-core crosstalk working zone of terrestrial multi-band and multi-core fiber (MCF) elastic optical networks (EONs), the ICXT in all channels of all cores remains below the ICXT threshold of the highest modulation format level (64QAM) for long-haul distances (10000 km). This paper analyzes the performance of this type of MCF in multi-band EONs (MB-EONs). We investigate two band and space division multiplexing (BSDM) scenarios: MCF and a bundle of multi-fiber pairs (BuMFP). Furthermore, the UL-ICXT performance of two MCFs, one with the standard cladding diameter (CD = 125 micrometers) with 4 cores and another with a nonstandard larger CD with 7 cores, is evaluated in the US backbone network. Our findings show that, with careful design of the MCFs physical structure, even with a standard CD, it is possible to achieve UL-ICXT in C, L, and S-band long-haul BSDM EONs. Additionally, the simulation results show that network throughput for BSDM EONs with MCFs in the UL-ICXT regime is up to 12 percent higher than the BuMFP scenario, with capacity increasing linearly with the number of cores.

The extension of elastic optical networks (EON) to multi-band transmission (MB-EON) shows promise in enhancing spectral efficiency, throughput, and long-term cost-effectiveness for telecom operators. However, designing MB-EON networks introduces complex challenges, notably the optimization of physical parameters like optical power and quality of transmission (QoT). Frequency-dependent characteristics of fiber, such as loss, dispersion, and nonlinear effects, alongside inter-channel stimulated Raman scattering, pose significant hurdles when extending beyond the L+C (LC) band to a continuous spectrum over 100 nm. In this study, we propose a span-by-span methodology for optimal power allocation, introducing two hyper-accelerated power optimization (HPO) strategies: flat launch power (FLP) and flat received power (FRP). These approaches significantly expedite network power optimization while preserving the stability of running services. Our comparative analysis of FLP and FRP models reveals that while FRP has a minimal effect on capacity (increasing less than 10 Tbps for an L+C+S (LCS) system over 100 km), it improves flatness and GSNR/OSNR metrics in the S-band by approximately 2/0 dB and 2.5/6 dB, respectively. A network-wide analysis across various topologies shows that the FRP technique enhances minimum GSNR, contributing to a throughput increase of 12% to 75%, depending on network scale, at a 1% bandwidth blocking rate. Lastly, our application of HPO in MB-EON for both local and global power optimization demonstrates that while both approaches offer comparable performance, global optimization is simpler and more cost-effective for large-scale networks.

We prove endpoint and sparse-like bounds for Bergman projectors on nonhomogeneous, radial trees $X$ that model manifolds with possibly unbounded geometry. The natural Bergman measures on $X$ may fail to be doubling, and even locally doubling, with respect to the right metric in our setting. Weighted consequences of our sparse domination results are also considered, and are in line with the known results in the disk. Our endpoint results are partly a consequence of a new Calder\'on-Zygmund theory for discrete, non-locally doubling metric spaces., Comment: 38 pages

The spinterface mechanism was suggested as a possible origin for the chirality induced spin-selectivity (CISS) effect, and was used to explain and reproduce, with remarkable accuracy, experimental data from transport experiments showing the CISS effect. Here, we apply the spinterface mechanism to explain the appearance of magnetization at the interface between non-magnetic metals and chiral molecules, through the stabilization of other-wise fluctuating magnetic moments. We show that the stabilization of surface magnetic moments occurs for a wide range of realistic parameters and is robust against dephasing. Importantly, we show that the direction of the surface magnetic moments is determined by the chiral axis of the chiral molecules. Armed with the concept of stable surface magnetic moments, we then formulated a theory for the photoemission CISS effect. The theory, based on spin-dependent scattering, leads to direct predictions regarding the relation between the photoemission CISS effect, the chiral axis direction, the spinterface "size", and the tilt angle of the detector with respect to the surface. These predictions are within reach of current experimental capabilities, and may shed new light on the origin of the CISS effect., Comment: 32 pages

We perform a structural analysis of large scale jammed packings of monodisperse, frictionless and frictional spheres to elucidate structural signatures of the static structure factor in the low-to-intermediate wavenumber (q) region. We employ discrete element method simulations containing up to eighty million particles, in which the particle friction coefficient(s), including sliding, rolling, and twisting interactions, are varied. At intermediate q values, corresponding to length scales that lie between that of the nearest neighbor primary peak and the system size, we find the emergence of a prepeak - a signature of intermediate range order - that grows with increasing friction. We correlate the emergence of this peak to real space fluctuations in the local particle coordination number, which exhibits a grainy-like fluctuating field throughout the packing process that is retained in the final, mechanically stable state. While the formation of the prepeak shows varying degrees of robustness to packing protocol changes, our results suggest that preparation history may be used to construct packings with variable large length scale structural properties.

Astronomy is entering an unprecedented era of Big Data science, driven by missions like the ESA's Gaia telescope, which aims to map the Milky Way in three dimensions. Gaia's vast dataset presents a monumental challenge for traditional analysis methods. The sheer scale of this data exceeds the capabilities of manual exploration, necessitating the utilization of advanced computational techniques. In response to this challenge, we developed a novel approach leveraging deep learning to estimate the metallicity of fundamental mode (ab-type) RR Lyrae stars from their light curves in the Gaia optical G-band. Our study explores applying deep learning techniques, particularly advanced neural network architectures, in predicting photometric metallicity from time-series data. Our deep learning models demonstrated notable predictive performance, with a low mean absolute error (MAE) of 0.0565, the root mean square error (RMSE) achieved is 0.0765 and a high $R^2$ regression performance of 0.9401 measured by cross-validation. The weighted mean absolute error (wMAE) is 0.0563, while the weighted root mean square error (wRMSE) is 0.0763. These results showcase the effectiveness of our approach in accurately estimating metallicity values. Our work underscores the importance of deep learning in astronomical research, particularly with large datasets from missions like Gaia. By harnessing the power of deep learning methods, we can provide precision in analyzing vast datasets, contributing to more precise and comprehensive insights into complex astronomical phenomena., Comment: Sensors 2024, 24(16), 5203; (23 pages)

A graph $G=(V,E)$ is defined as a star-$k$-PCG when it is possible to assign a positive real number weight $w$ to each vertex $V$, and define $k$ distinct intervals $I_1, I_2, \ldots I_k$, in such a way that there is an edge $uv$ in $E$ if and only if the sum of the weights of vertices $u$ and $v$ falls within the union of these intervals. The star-$k$-PCG class is connected to two significant categories of graphs, namely PCGs and multithreshold graphs. The star number of a graph $G$, is the smallest $k$ for which $G$ is a star-$k$-PCG. In this paper, we study the effects of various graph operations, such as the addition of twins, pendant vertices, universal vertices, or isolated vertices, on the star number of the graph resulting from these operations. As a direct application of our results, we determine the star number of lobster graphs and provide an upper bound for the star number of acyclic graphs.

We use RR Lyrae stars identified by the Gaia third data release (DR3) to explore the outskirts of the Ursa Minor (UMi) dwarf spheroidal galaxy (dSph) and update the census of its variable star population. We adopted different tools based on the Gaia DR3 astrometric and photometric data (proper motions, Period-Wesenheit-Metallicity relations, spatial distribution, colour-magnitude diagram and stellar isochrone fitting) to discriminate between different types of variable stars, and to identify UMi members. We find a total of 129 RR Lyrae stars and Anomalous Cepheids (ACs) that belong to UMi. We report 47 new RR Lyrae stars (46 bona fide and 1 candidate) and 5 new ACs (4 bona fide and 1 candidate), including new possible members in the extreme periphery of the galaxy at a distance of $\sim$ 12 half-light radii. We reclassified 13 RR Lyrae stars identified by the Gaia DR3 Specific Object Study pipeline for Cepheids and RR Lyrae stars (SOS Cep$\&$RRL), using data from the literature and Gaia astrometry and photometry. Specifically, we assigned these 13 DR3 RR Lyrae stars to 10 Anomalous Cepheids and 3 double-mode RR Lyrae (RRd), respectively. From the average luminosity of the RR Lyrae stars we derive for UMi a distance modulus of $(m-M)_{0}=$ 19.23 $\pm$ 0.09 mag in excellent agreement with the literature. Finally, we investigated whether some of UMi's variable stars might be members of the ultra-faint stellar cluster Mu{\~n}oz~1, that lies at a projected distance of 45 arcmin from UMi's centre. Based on the properties of the variable stars (distances, colours and metallicities), we find unlikely that they belong to the cluster., Comment: 25 pages, 23 figures and 6 tables. Submitted to Astronomy & Astrophysics

In this work, we propose to use the Reduced-Basis Method (RBM) as a model order reduction approach to solve Maxwell's equations in electromagnetic (EM) scatterers based on plasma to build a metasurface, taking into account a parameter, namely, the plasma frequency. We build up the reduced-order model in an adaptive fashion following a greedy algorithm. This method enables a fast sweep over a wide range of plasma frequencies, thus providing an efficient way to characterize electromagnetic structures based on Drude-like plasma scatterers. We validate and test the proposed technique on several plasma metasurfaces and compare it with the finite element method (FEM) approach.

Climate activism is crucial in stimulating collective societal and behavioral change towards sustainable practices through political pressure. Although multiple factors contribute to the participation in activism, their complex relationships and the scarcity of data on their interactions have restricted most prior research to studying them in isolation, thus preventing the development of a quantitative, causal understanding of why people approach activism. In this work, we develop a comprehensive causal model of how and why Reddit users engage with activist communities driving mass climate protests (mainly the 2019 Earth Strike, Fridays for Future, and Extinction Rebellion). Our framework, based on Stochastic Variational Inference applied to Bayesian Networks, learns the causal pathways over multiple time periods. Distinct from previous studies, our approach uses large-scale and fine-grained longitudinal data (2016 to 2022) to jointly model the roles of sociodemographic makeup, experience of extreme weather events, exposure to climate-related news, and social influence through online interactions. We find that among users interested in climate change, participation in online activist communities is indeed influenced by direct interactions with activists and largely by recent exposure to media coverage of climate protests. Among people aware of climate change, left-leaning people from lower socioeconomic backgrounds are particularly represented in online activist groups. Our findings offer empirical validation for theories of media influence and critical mass, and lay the foundations to inform interventions and future studies to foster public participation in collective action.

It has been a widely shared concern that social media reinforces echo chambers of like-minded users and exacerbate political polarization. While fostering interactions across party lines is recognized as an important strategy to break echo chambers, there is a lack of empirical evidence on whether users will actually become more integrated or instead more segregated following such interactions on real social media platforms. We fill this gap by inspecting how users change their community engagement after receiving a cross-party reply in the U.S. politics discussion on Reddit. More specifically, we investigate if they increase their activity in communities of the opposing party, or in communities of their own party. We find that receiving a cross-party reply to a comment in a non-partisan discussion space is not significantly associated with increased out-party subreddit activity, unless the comment itself is already a reply to another comment. Meanwhile, receiving a cross-party reply is significantly associated with increased in-party subreddit activity, but the effect is comparable to that of receiving a same-party reply. Our results reveal a highly conditional depolarization effect following cross-party interactions in spurring activity in out-party communities, which is likely part of a more general dynamic of feedback-boosted engagement., Comment: To appear in Proceedings of the International AAAI Conference on Web and Social Media (ICWSM 2025)

Neural Architecture Search (NAS) automates neural network design, reducing dependence on human expertise. While NAS methods are computationally intensive and dataset-specific, auxiliary predictors reduce the models needing training, decreasing search time. This strategy is used to generate architectures satisfying multiple computational constraints. Recently, Transferable NAS has emerged, generalizing the search process from dataset-dependent to task-dependent. In this field, DiffusionNAG is a state-of-the-art method. This diffusion-based approach streamlines computation, generating architectures optimized for accuracy on unseen datasets without further adaptation. However, by focusing solely on accuracy, DiffusionNAG overlooks other crucial objectives like model complexity, computational efficiency, and inference latency -- factors essential for deploying models in resource-constrained environments. This paper introduces the Pareto-Optimal Many-Objective Neural Architecture Generator (POMONAG), extending DiffusionNAG via a many-objective diffusion process. POMONAG simultaneously considers accuracy, number of parameters, multiply-accumulate operations (MACs), and inference latency. It integrates Performance Predictor models to estimate these metrics and guide diffusion gradients. POMONAG's optimization is enhanced by expanding its training Meta-Dataset, applying Pareto Front Filtering, and refining embeddings for conditional generation. These enhancements enable POMONAG to generate Pareto-optimal architectures that outperform the previous state-of-the-art in performance and efficiency. Results were validated on two search spaces -- NASBench201 and MobileNetV3 -- and evaluated across 15 image classification datasets.

For a positive integer $s$, an $s$-club in a graph $G$ is a set of vertices inducing a subgraph with diameter at most $s$. As generalizations of cliques, $s$-clubs offer a flexible model for real-world networks. This paper addresses the problems of partitioning and disjoint covering of vertices with $s$-clubs on bipartite graphs. First we prove that for any fixed $s \geq 3$ and fixed $k \geq 3$, determining whether the vertices of $G$ can be partitioned into at most $k$ disjoint $s$-clubs is NP-complete even for bipartite graphs. Additionally, we study the Maximum Disjoint $(t,s)$-Club Covering problem (MAX-DCC($t,s$)), which aims to find a collection of vertex-disjoint $(t,s)$-clubs (i.e. $s$-clubs with at least $t$ vertices) that covers the maximum number of vertices in $G$. We prove that it is NP-hard to achieve an approximation factor of $\frac{95}{94} $ for MAX-DCC($t,3$) for any fixed $t\geq 8$ and for MAX-DCC($t,2$) for any fixed $t\geq 5$ even for bipartite graphs. Previously, results were known only for MAX-DCC($3,2$). Finally, we provide a polynomial-time algorithm for MAX-DCC($2,2$).

Non-adiabatic molecular dynamics (NAMD) has become an essential computational technique for studying the photophysical relaxation of molecular systems after light absorption. These phenomena require approximations that go beyond the Born-Oppenheimer approximation, and the accuracy of the results heavily depends on the electronic structure theory employed. Sophisticated electronic methods, however, make these techniques computationally expensive, even for medium size systems. Consequently, simulations are often performed on simplified models to interpret experimental results. In this context, a variety of techniques have been developed to perform NAMD using approximate methods, particularly Density Functional Tight Binding (DFTB). Despite the use of these techniques on large systems where ab initio methods are computationally prohibitive, a comprehensive validation has been lacking. In this work, we present a new implementation of trajectory surface hopping (TSH) combined with DFTB, utilizing non-adiabatic coupling vectors (NACVs). We selected two different systems for validation, providing an exhaustive comparison with higher-level electronic structure methods. As a case study, we simulated a system from the class of molecular motors, which has been extensively studied experimentally but remains challenging to simulate with ab initio methods due to its inherent complexity. Our approach effectively captures the key photophysical mechanism of dihedral rotation after absorption of light. Additionally, we successfully reproduce the transition from the bright to dark states observed in the time dependent fluorescence experiments, providing valuable insights into this critical part of the photophysical behavior in molecular motors.

We present a complete analysis of Fermi Large Area Telescope (LAT) data of GRB 221009A, the brightest Gamma-Ray Burst (GRB) ever detected. The burst emission above 30 MeV detected by the LAT preceded by 1 s the low-energy (< 10 MeV) pulse that triggered the Fermi Gamma-Ray Burst Monitor (GBM), as has been observed in other GRBs. The prompt phase of GRB 221009A lasted a few hundred seconds. It was so bright that we identify a Bad Time Interval (BTI) of 64 seconds caused by the extremely high flux of hard X-rays and soft gamma rays, during which the event reconstruction efficiency was poor and the dead time fraction quite high. The late-time emission decayed as a power law, but the extrapolation of the late-time emission during the first 450 seconds suggests that the afterglow started during the prompt emission. We also found that high-energy events observed by the LAT are incompatible with synchrotron origin, and, during the prompt emission, are more likely related to an extra component identified as synchrotron self-Compton (SSC). A remarkable 400 GeV photon, detected by the LAT 33 ks after the GBM trigger and directionally consistent with the location of GRB 221009A, is hard to explain as a product of SSC or TeV electromagnetic cascades, and the process responsible for its origin is uncertain. Because of its proximity and energetic nature, GRB 221009A is an extremely rare event., Comment: 60 pages, 38 figures, 9 tables

We propose a DRL-assisted approach for service provisioning in multi-band elastic optical networks. Our simulation environment uses an accurate QoT estimator based on the GN/EGN model. Results show that the proposed approach reduces request blocking by 50% compared with heuristics from the literature., Comment: This paper has been accepted by 50th European Conference on Optical Communications (ECOC 2O24)

Models of soil organic carbon (SOC) frequently overlook the effects of spatial dimensions and microbiological activities. In this paper, we focus on two reaction-diffusion chemotaxis models for SOC dynamics, both supporting chemotaxis-driven instability and exhibiting a variety of spatial patterns as stripes, spots and hexagons when the microbial chemotactic sensitivity is above a critical threshold. We use symplectic techniques to numerically approximate chemotaxis-driven spatial patterns and explore the effectiveness of the piecewice dynamic mode decomposition (pDMD) to reconstruct them. Our findings show that pDMD is effective at precisely recreating chemotaxis-driven spatial patterns, therefore broadening the range of application of the method to classes of solutions different than Turing patterns. By validating its efficacy across a wider range of models, this research lays the groundwork for applying pDMD to experimental spatiotemporal data, advancing predictions crucial for soil microbial ecology and agricultural sustainability.

We present circuit-loaded metasurfaces that behave differently in a passive manner even at the same frequency in accordance with the incoming waveform, specifically, its pulse width. Importantly, the time-varying waveform-selective metasurfaces reported thus far were mostly able to change their electric properties but not their magnetic properties; this severely limited the design range of their corresponding wave impedances and refractive indices and thus hindered the development of potential applications in antennas, sensors, imagers, signal processing, and wireless communications. In this study, passive time-varying waveform-selective metasurfaces were found to attain magnetic property control by introducing an additional circuit-loaded layer that generated an artificial magnetic dipole moment; this magnetic moment only occurred during the designed pulse duration in the time domain. Our proposed concept and structures were validated numerically and experimentally; thus, our results could be used to address electromagnetic and related issues sharing the same frequency component via the variation of the pulse width as an additional degree of freedom., Comment: 8 pages, 7 figures

Irradiations at Ultra High Dose Rate (UHDR) regimes, exceeding 40 Gy/s in single fractions lasting less than 200 ms, have shown an equivalent antitumor effect compared to conventional radio-therapy with reduced harm to normal tissues. This work details the hardware and software modi-fications implemented to deliver 10 MeV UHDR electron beams with a Linear Accelerator Elekta SL 18 MV and the beam characteristics obtained. GafChromic EBT XD films and an Advanced Markus chamber were used for the dosimetry characterization, while a silicon sensor assessed the machine's beam pulses stability and repeatability. Dose per pulse, average dose rate and instantaneous dose rate in the pulse were evaluated for four experimental settings, varying the source-to-surface dis-tance and the beam collimation, i.e. with and without the use of a cylindrical applicator. Results showed dose per pulse from 0.6 Gy to a few tens of Gy and average dose rate up to 300 Gy/s. The obtained results demonstrate the possibility to perform in-vitro radiobiology experiments and test of new technologies for beam monitoring and dosimetry at the upgraded LINAC, thus contributing to the electron UHDR research field.

Semiconductor nanocrystals (quantum dots) are light emitters with high quantum yield that are relatively easy to manufacture. There is therefore much interest in their possible application for the development of high-performance scintillators for use in high-energy physics. However, few previous studies have focused on the response of these materials to high-energy particles. To evaluate the potential for the use of nanocomposite scintillators in calorimetry, we are performing side-by-side tests of fine-sampling shashlyk calorimeter prototypes with both conventional and nanocomposite scintillators using electron and minimum-ionizing particle beams, allowing direct comparison of the performance obtained., Comment: 3 pages, 2 figures, 1 table

The Hitchin component of the character variety of representations of a surface group $\pi_1(S)$ into $\mathrm{PSL}_d(\mathbb R)$ for some $d\geq 3$ can be equipped with a pressure metric whose restriction to the Fuchsian locus equals the Weil-Petersson metric up to a constant factor. We show that if the genus of $S$ is at least $3$, then the Fuchsian locus contains quasi-convex subsets of infinite diameter for the Weil--Petersson metric whose diameter for the path metric of the pressure metric is finite. This is established through showing that biinfinite paths of bending deformations have controlled bounded length. To this end we give a geometric interpretation of Fock--Goncharov positivity and show that bending deformations of Fuchsian representations stabilize a uniform Finsler quasi-convex disk in the symmetric space $\mathrm{PSL}_d(\mathbb R)/\mathrm{PSO}(d)$., Comment: 119 pages, 6 figures, comments welcome!

We present new $P -\phi_{31}-{\rm [Fe/H]}$ and $P -\phi_{31}- A_2 - {\rm [Fe/H]}$ relations for fundamental-mode (RRab) and first-overtone mode (RRc) RR Lyrae stars (RRLs), respectively. The relations were calibrated based on pulsation periods and Fourier parameters of the RRL light curves in the Gaia $G$-band published in the Gaia Data Release 3 (DR3), and accurate spectroscopically measured metallicities available in the literature. We apply the feature selection algorithm to identify the most relevant parameters for the determination of metallicity. To fit the relations, we used the Bayesian approach, which allowed us to carefully take into account uncertainties in various parameters and the intrinsic scatter of the relations. The root mean squared errors of the predicted metallicity values in the training samples are 0.28 dex and 0.21 dex for RRab and RRc stars, respectively, comparable with the typical uncertainty of low/intermediate resolution spectroscopic metallicity measurements. We applied the new relations to measure individual metallicities and distances to $\sim$ 134,000 RRLs from the Gaia DR3 catalogue, as well as mean metallicities and distances to 38 Milky Way globular clusters. We also estimate the mean metallicity and distance to the Large Magellanic Cloud (LMC) and Small Magellanic Cloud (SMC): ${\rm [Fe/H]_{LMC} = -1.63\pm0.36}$ and $\mu_{\rm LMC}=18.55\pm0.18$~mag, ${\rm [Fe/H]_{SMC}=-1.86\pm0.36}$~dex and $\mu_{\rm SMC}=19.01\pm 0.17$~mag, respectively, in excellent agreement with previous measurements., Comment: 21 pages, 20 figure, submitted to MNRAS on May 13

Understanding how light modifies long-range order is key to improve our ability to control material functionality on an ultrafast timescale. Transient spatial heterogeneity has been proposed in many materials, but isolating the dynamics of different regions experimentally has been challenging. Here we address this issue and measure the dynamics of orbital order melting in the layered manganite, La0.5Sr1.5MnO4, and isolate the surface dynamics from the bulk for the first time. Bulk measurements show orbital order is rapidly suppressed, but the correlation length surprisingly increases. However, the surface dynamics, show a stronger suppression and a significant decrease in correlation length. By isolating the surface changes, we find that light preferentially melts a less ordered surface and the loss of long-range order is likely driven by the formation of local and disordered polarons. Melting the disordered surface effectively increases the average correlation of the bulk probed volume, resolving the contradictory response. These results show that surface scattering methods are necessary to understand both surface and bulk dynamics in heterogeneous materials., Comment: 22 pages, 8 figures

The majority of Neural Semantic Parsing (NSP) models are developed with the assumption that there are no concepts outside the ones such models can represent with their target symbols (closed-world assumption). This assumption leads to generate hallucinated outputs rather than admitting their lack of knowledge. Hallucinations can lead to wrong or potentially offensive responses to users. Hence, a mechanism to prevent this behavior is crucial to build trusted NSP-based Question Answering agents. To that end, we propose the Hallucination Simulation Framework (HSF), a general setting for stimulating and analyzing NSP model hallucinations. The framework can be applied to any NSP task with a closed-ontology. Using the proposed framework and KQA Pro as the benchmark dataset, we assess state-of-the-art techniques for hallucination detection. We then present a novel hallucination detection strategy that exploits the computational graph of the NSP model to detect the NSP hallucinations in the presence of ontology gaps, out-of-domain utterances, and to recognize NSP errors, improving the F1-Score respectively by ~21, ~24% and ~1%. This is the first work in closed-ontology NSP that addresses the problem of recognizing ontology gaps. We release our code and checkpoints at https://github.com/amazon-science/handling-ontology-gaps-in-semantic-parsing.

Oxygen vacancies (VO's) are of paramount importance in influencing the properties and applications of ceria (CeO2). Yet, comprehending the distribution and nature of the VO's poses a significant challenge due to the vast number of electronic configurations and intricate many-body interactions among VO's and polarons (Ce3+'s). In this study, we employed a combination of LASSO regression in machine learning, in conjunction with a cluster expansion model and first-principles calculations to decouple the interactions among the Ce3+'s and VO's, thereby circumventing the limitations associated with sampling electronic configurations. By separating these interactions, we identified specific electronic configurations characterized by the most favorable VO-Ce3+ attractions and the least Ce3+-Ce3+/VO-VO repulsions, which are crucial in determining the stability of vacancy structures. Through more than 10^8 Metropolis Monte Carlo samplings of Vo's and Ce3+ in the near-surface of CeO2(111), we explored potential configurations within an 8x8 supercell. Our findings revealed that oxygen vacancies tend to aggregate and are most abundant in the third oxygen layer, primarily due to extensive geometric relaxation-an aspect previously overlooked. This behavior is notably dependent on the concentration of Vo. This work introduces a novel theoretical framework for unraveling the complex vacancy structures in metal oxides, with potential applications in redox and catalytic chemistry., Comment: 22 pages, 1 scheme, 5 figures

A novel method to reconstruct the energy of hadronic showers in the CMS High Granularity Calorimeter (HGCAL) is presented. The HGCAL is a sampling calorimeter with very fine transverse and longitudinal granularity. The active media are silicon sensors and scintillator tiles readout by SiPMs and the absorbers are a combination of lead and Cu/CuW in the electromagnetic section, and steel in the hadronic section. The shower reconstruction method is based on graph neural networks and it makes use of a dynamic reduction network architecture. It is shown that the algorithm is able to capture and mitigate the main effects that normally hinder the reconstruction of hadronic showers using classical reconstruction methods, by compensating for fluctuations in the multiplicity, energy, and spatial distributions of the shower's constituents. The performance of the algorithm is evaluated using test beam data collected in 2018 prototype of the CMS HGCAL accompanied by a section of the CALICE AHCAL prototype. The capability of the method to mitigate the impact of energy leakage from the calorimeter is also demonstrated.

Abstract Meaning Representation (AMR) is a semantic formalism that captures the core meaning of an utterance. There has been substantial work developing AMR corpora in English and more recently across languages, though the limited size of existing datasets and the cost of collecting more annotations are prohibitive. With both engineering and scientific questions in mind, we introduce MASSIVE-AMR, a dataset with more than 84,000 text-to-graph annotations, currently the largest and most diverse of its kind: AMR graphs for 1,685 information-seeking utterances mapped to 50+ typologically diverse languages. We describe how we built our resource and its unique features before reporting on experiments using large language models for multilingual AMR and SPARQL parsing as well as applying AMRs for hallucination detection in the context of knowledge base question answering, with results shedding light on persistent issues using LLMs for structured parsing.

With the development of high-throughput technologies, genomics datasets rapidly grow in size, including functional genomics data. This has allowed the training of large Deep Learning (DL) models to predict epigenetic readouts, such as protein binding or histone modifications, from genome sequences. However, large dataset sizes come at a price of data consistency, often aggregating results from a large number of studies, conducted under varying experimental conditions. While data from large-scale consortia are useful as they allow studying the effects of different biological conditions, they can also contain unwanted biases from confounding experimental factors. Here, we introduce Metadata-guided Feature Disentanglement (MFD) - an approach that allows disentangling biologically relevant features from potential technical biases. MFD incorporates target metadata into model training, by conditioning weights of the model output layer on different experimental factors. It then separates the factors into disjoint groups and enforces independence of the corresponding feature subspaces with an adversarially learned penalty. We show that the metadata-driven disentanglement approach allows for better model introspection, by connecting latent features to experimental factors, without compromising, or even improving performance in downstream tasks, such as enhancer prediction, or genetic variant discovery. The code for our implemementation is available at https://github.com/HealthML/MFD

Recent studies have shown that the electromagnetic shower induced by a high-energy electron, positron or photon incident along the axis of an oriented crystal develops in a space more compact than the ordinary. On the other hand, the properties of the hadronic interactions are not affected by the lattice structure. This means that, inside an oriented crystal, the natural difference between the hadronic and the electromagnetic shower profile is strongly accentuated. Thus, a calorimeter composed of oriented crystals could be intrinsically capable of identifying more accurately the nature of the incident particles, with respect to a detector composed only of non-aligned crystals. Since no oriented calorimeter has ever been developed, this possibility remains largely unexplored and can be investigated only by mean of numerical simulations. In this work, we report the first quantitative evaluation of the particle identification capability of such a calorimeter, focusing on the case of neutron-gamma discrimination. We demonstrate through Geant4 simulations that the use of oriented crystals significantly improves the performance of a Random Forest classifier trained on the detector data. This work is a proof that oriented calorimeters could be a viable option for all the environments where particle identification must be performed with a very high accuracy, such as future high-intensity particle physics experiments and satellite-based gamma-ray telescopes., Comment: 13 pages, 9 figures

Large language models (LLMs) tend to inadequately integrate input context during text generation, relying excessively on encoded prior knowledge in model parameters, potentially resulting in generated text with factual inconsistencies or contextually unfaithful content. LLMs utilize two primary knowledge sources: 1) prior (parametric) knowledge from pretraining, and 2) contextual (non-parametric) knowledge from input prompts. The study addresses the open question of how LLMs effectively balance these knowledge sources during the generation process, specifically in the context of open-domain question answering. To address this issue, we introduce a novel approach integrating contrastive decoding with adversarial irrelevant passages as negative samples to enhance robust context grounding during generation. Notably, our method operates at inference time without requiring further training. We conduct comprehensive experiments to demonstrate its applicability and effectiveness, providing empirical evidence showcasing its superiority over existing methodologies. Our code is publicly available at: https://github.com/amazon-science/ContextualUnderstanding-ContrastiveDecoding., Comment: Accepted to NAACL 2024