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Semantic communication is a new paradigm that considers the meaning of transmitted information to optimize communication. One possible application is the remote monitoring of a process under communication costs: scheduling updates based on semantic considerations can significantly reduce transmission frequency while maintaining high-quality tracking performance. However, semantic scheduling also opens a timing-based side-channel that an eavesdropper may exploit to obtain information about the state of the remote process, even if the content of updates is perfectly secure. In this work, we study an eavesdropping attack against pull-based semantic scheduling for the tracking of remote Markov processes. We provide a theoretical framework for defining the effectiveness of the attack and of possible countermeasures, as well as a practical heuristic that can provide a balance between the performance gains offered by semantic communication and the information leakage.

Artificial Intelligence (AI) is a key component of 6G networks, as it enables communication and computing services to adapt to end users' requirements and demand patterns. The management of Mobile Edge Computing (MEC) is a meaningful example of AI application: computational resources available at the network edge need to be carefully allocated to users, whose jobs may have different priorities and latency requirements. The research community has developed several AI algorithms to perform this resource allocation, but it has neglected a key aspect: learning is itself a computationally demanding task, and considering free training results in idealized conditions and performance in simulations. In this work, we consider a more realistic case in which the cost of learning is specifically accounted for, presenting a new algorithm to dynamically select when to train a Deep Reinforcement Learning (DRL) agent that allocates resources. Our method is highly general, as it can be directly applied to any scenario involving a training overhead, and it can approach the same performance as an ideal learning agent even under realistic training conditions.

This document is comprised of a collection of updated preliminary parameters for the key parts of the muon collider. The updated preliminary parameters follow on from the October 2023 Tentative Parameters Report. Particular attention has been given to regions of the facility that are believed to hold greater technical uncertainty in their design and that have a strong impact on the cost and power consumption of the facility. The data is collected from a collaborative spreadsheet and transferred to overleaf.

The energy cost of erasing a bit of information was fundamentally lower bounded by Landauer, in terms of the temperature of its environment: $W\geq k_\mathrm{B} T \ln 2$. However, in real electronic devices, the information-bearing system is usually in contact with two or more electrodes, with different temperatures and chemical potentials. It is not clear what sets the cost of erasure in such nonequilibrium situations. One promising technology for testing the thermodynamic limits of information processing is quantum dots, in which a bit is encoded in the presence or absence of a single electron. We here develop a thermodynamic description of devices of this type and find that, in addition to the electrode temperatures, the potential difference across the quantum dot and lifetime broadening of its energy level contribute to the minimum work cost of erasure. In practical contexts, these contributions may significantly outweigh the cost due to temperature alone., Comment: 6 pages plus 8-page supplemental material. 3 figures

The dynamics and processes involved in particle-molecule scattering, including nuclear dynamics, is described and analyzed by different quantum information quantities along the different stages of the scattering. The main process studied and characterized with the information quantities is the interatomic coulombic electronic capture (ICEC), an inelastic process that can lead to dissociation of the target molecule. The analysis is focused in a one-dimensional transversely confined $\text{NeHe}$ molecule model used to simulate the scattering between an electron $\text{e}^-$(particle) and a $\text{NeHe}^+$ ion (molecule). The time-independent Schr\"odinger equation (TISE) is solved using the Finite Element Method (FEM) with a self-developed Julia package FEMTISE to compute potential energy curves (PECs) and the parameters of the interactions between particles. The time-dependent Schr\"odinger equation (TDSE) is solved using the Multi-configuration time-dependent Hartree (MCTDH) algorithm. The time dependent electronic and nuclear probability densities are calculated for different electron incoming energies, evidencing elastic and inelastic processes that can be correlated to changes in von Neumann entropy, conditional mutual information and Shannon entropies. The expectation value of the position of the particles, as well as their standard deviations, are analyzed along the whole dynamics and related to the entanglement during the collision and after the process is over, hence evidencing the dynamics of entanglement generation. It is shown that the correlations generated in the collision is partially retained only when the inelastic process is active.

Transformer-based Super-Resolution (SR) models have recently advanced image reconstruction quality, yet challenges remain due to computational complexity and an over-reliance on large patch sizes, which constrain fine-grained detail enhancement. In this work, we propose TaylorIR to address these limitations by utilizing a patch size of 1x1, enabling pixel-level processing in any transformer-based SR model. To address the significant computational demands under the traditional self-attention mechanism, we employ the TaylorShift attention mechanism, a memory-efficient alternative based on Taylor series expansion, achieving full token-to-token interactions with linear complexity. Experimental results demonstrate that our approach achieves new state-of-the-art SR performance while reducing memory consumption by up to 60% compared to traditional self-attention-based transformers.

Thermal comfort is essential for well-being in urban spaces, especially as cities face increasing heat from urbanization and climate change. Existing thermal comfort models usually overlook temporal dynamics alongside spatial dependencies. We address this problem by introducing a spatio-temporal jump model that clusters data with persistence across both spatial and temporal dimensions. This framework enhances interpretability, minimizes abrupt state changes, and easily handles missing data. We validate our approach through extensive simulations, demonstrating its accuracy in recovering the true underlying partition. When applied to hourly environmental data gathered from a set of weather stations located across the city of Singapore, our proposal identifies meaningful thermal comfort regimes, demonstrating its effectiveness in dynamic urban settings and suitability for real-world monitoring. The comparison of these regimes with feedback on thermal preference indicates the potential of an unsupervised approach to avoid extensive surveys.

A graph $G$ has the \emph{Perfect Matching Hamiltonian property} (or for short, $G$ is $PMH$) if, for each one of its perfect matchings, there is another perfect matching of $G$ such that the union of the two perfect matchings yields a Hamiltonian cycle of $G$. In this note, we show that \emph{Prism graphs} $\cP_n$ are not $PMH$, except for the $Cube\ graph$, and indicate for which values of $n$ the \emph{Crossed Prism graphs} $\cCP_n$ are $PMH$., Comment: 19 pages, 9 figures. arXiv admin note: substantial text overlap with arXiv:2106.00513

The VLT Survey Telescope Survey of Mass Assembly and Structural Hierarchy (VST-SMASH) aims to detect tidal features and remnants around very nearby galaxies, a unique and essential diagnostic of the hierarchical nature of galaxy formation. Leveraging optimal sky conditions at ESO's Paranal Observatory, combined with the VST's multi-band optical filters, VST-SMASH aims to be the definitive survey of stellar streams and tidal remnants in the Local Volume, targeting a low surface-brightness limit of $\mu \sim$ 30 mag arcsec$^{-2}$ in the g and r bands, and $\mu \sim$ 28 mag arcsec$^{-2}$ in the i band, in a volume-limited sample of local galaxies within 11 Mpc and the Euclid footprint., Comment: 4 pages, 2 figures, 1 table, published in the ESO Messenger 193

Motion Magnification (MM) is a collection of relative recent techniques within the realm of Image Processing. The main motivation of introducing these techniques in to support the human visual system to capture relevant displacements of an object of interest; these motions can be in object color and in object location. In fact, the goal is to opportunely process a video sequence to obtain as output a new video in which motions are magnified and visible to the viewer. We propose a numerical technique using the Phase-Based Motion Magnification which analyses the video sequence in the Fourier Domain and rely on the Fourier Shifting Property. We describe the mathematical foundation of this method and the corresponding implementation in a numerical algorithm. We present preliminary experiments, focusing on some basic test made up using synthetic images.

A novel concept to enhance the photo-detection efficiency (PDE) of silicon photomultipliers (SiPMs) has been applied and remarkable positive results can be reported. This concept uses arrays of microlenses to cover every second SiPM pixel in a checkerboard arrangement and aims to deflect the light from the dead region of the pixelised structure towards the active region in the center of the pixel. The PDE is improved up to 24%, external cross-talk is reduced by 40% compared to a flat epoxy layer, and single photon time resolution is improved. This detector development is conducted in the context of the next generation LHCb scintillating fibre tracker located in a high radiation environment with a total of 700'000 detector channels. The simulation and measurement results are in good agreement and will be discussed in this work., Comment: 8 pages, 11 figures

Multimedia streaming protocols are becoming increasingly popular in Crowdsensing due to their ability to deliver high-quality video content over the internet in real-time. Streaming multimedia content, as in the context of live video streaming, requires high bandwidth and large storage capacity to ensure a sufficient throughput. Crowdsensing can distribute information about shared video contents among multiple users in network, reducing storage capacity and computational and bandwidth requirements. However, Crowdsensing introduces several security constraints that must be taken into account to ensure the confidentiality, integrity, and availability of the data. In the specific case of video streaming, commonly named as visual crowdsensing (VCS) within this context, data is transmitted over wireless networks, making it vulnerable to security breaches and susceptible to eavesdropping and interception by attackers. Multimedias often contains sensitive user data and may be subject to various privacy laws, including data protection laws and laws related to photography and video recording, based on local GDPR (General Data Protection Regulation). For this reason the realization of a secure protocol optimized for a distributed data streaming in real-time becomes increasingly important in crowdsensing and smart-enviroment context. In this article, we will discuss the use of a symmetric AES-CTR encryption based protocol for securing data streaming over a crowd-sensed network.

Coupled cluster theory in the standard formulation is unable to correctly describe conical intersections among states of the same symmetry. This limitation has restricted the practical application of an otherwise highly accurate electronic structure model, particularly in nonadiabatic dynamics. Recently, the intersection problem among the excited states was fully characterized and resolved. However, intersections with the ground state remain an open challenge, and addressing this problem is our objective here. We present a generalized coupled cluster framework that correctly accounts for the geometric phase effect and avoids bifurcations of the solutions to the ground state equations. Several applications are presented that demonstrate the correct description of ground state conical intersections. We also propose how the framework can be used for other electronic-structure methods., Comment: 45 pages, 9 figures

We present a novel reduced-order Model (ROM) that leverages optimal transport (OT) theory and displacement interpolation to enhance the representation of nonlinear dynamics in complex systems. While traditional ROM techniques face challenges in this scenario, especially when data (i.e., observational snapshots) is limited, our method addresses these issues by introducing a data augmentation strategy based on OT principles. The proposed framework generates interpolated solutions tracing geodesic paths in the space of probability distributions, enriching the training dataset for the ROM. A key feature of our approach is its ability to provide a continuous representation of the solution's dynamics by exploiting a virtual-to-real time mapping. This enables the reconstruction of solutions at finer temporal scales than those provided by the original data. To further improve prediction accuracy, we employ Gaussian Process Regression to learn the residual and correct the representation between the interpolated snapshots and the physical solution. We demonstrate the effectiveness of our methodology with atmospheric mesoscale benchmarks characterized by highly nonlinear, advection-dominated dynamics. Our results show improved accuracy and efficiency in predicting complex system behaviors, indicating the potential of this approach for a wide range of applications in computational physics and engineering.

Age of Incorrect Information (AoII) is particularly relevant in systems where real time responses to anomalies are required, such as natural disaster alerts, cybersecurity warnings, or medical emergency notifications. Keeping system control with wrong information for too long can lead to inappropriate responses. In this paper, we study the Peak AoII (PAoII) for multi-source status reporting by independent devices over a collision channel, following a zero-threshold ALOHA access where nodes observing an anomaly immediately start transmitting about it. If a collision occurs, nodes reduce the transmission probability to allow for a resolution. Finally, wrong or lost feedback messages may lead a node that successfully updated the destination to believe a collision happened. The PAoII for this scenario is computed in closed-form. We are eventually able to derive interesting results concerning the minimization of PAoII, which can be traded against the overall goodput and energy efficiency, but may push the system to the edge of congestion collapse., Comment: Submitted for publication to the IEEE International Conference on Communications (ICC) 2025

Artificial intelligence (AI) advancements over the past few years have had an unprecedented and revolutionary impact across everyday application areas. Its significance also extends to technical challenges within science and engineering, including the nascent field of quantum computing (QC). The counterintuitive nature and high-dimensional mathematics of QC make it a prime candidate for AI's data-driven learning capabilities, and in fact, many of QC's biggest scaling challenges may ultimately rest on developments in AI. However, bringing leading techniques from AI to QC requires drawing on disparate expertise from arguably two of the most advanced and esoteric areas of computer science. Here we aim to encourage this cross-pollination by reviewing how state-of-the-art AI techniques are already advancing challenges across the hardware and software stack needed to develop useful QC - from device design to applications. We then close by examining its future opportunities and obstacles in this space., Comment: 42 pages, 7 figures, 2 tables

The gas dynamics of galaxies provide critical insights into the evolution of both baryons and dark matter (DM) across cosmic time. In this context, galaxies at cosmic noon -- the period characterized by the most intense star formation and black hole activities -- are particularly significant. In this work, we present an analysis of the gas dynamics of PKS 0529-549: a galaxy at $z\simeq2.6$, hosting a radio-loud active galactic nucleus (AGN). We use new ALMA observations of the [CI] (2-1) line at a spatial resolution of 0.18$''$ ($\sim$1.5 kpc). We find that (1) the molecular gas forms a rotation-supported disk with $V_{\rm rot}/\sigma_{\rm v}=6\pm3$ and displays a flat rotation curve out to 3.3 kpc; (2) there are several non-circular components including a kinematically anomalous structure near the galaxy center, a gas tail to the South-West, and possibly a second weaker tail to the East; (3) dynamical estimates of gas and stellar masses from fitting the rotation curve are inconsistent with photometric estimates using standard gas conversion factors and stellar population models, respectively; these discrepancies may be due to systematic uncertainties in the photometric masses, in the dynamical masses, or in the case a more massive radio-loud AGN-host galaxy is hidden behind the gas-rich [CI] emitting starburst galaxy along the line of sight. Our work shows that in-depth investigations of 3D line cubes are crucial for revealing the complexity of gas dynamics in high-$z$ galaxies, in which regular rotation may coexist with non-circular motions and possibly tidal structures., Comment: 17 pages, 15 figures, 4 tables. Accepted for publication in Astronomy & Astrophysics. A companion paper (arXiv:2411.04290) is accepted in ApJ. Comments are welcome

Robotic fruit monitoring is a key step toward automated agricultural production systems. Robots can significantly enhance plant and temporal fruit monitoring by providing precise, high-throughput assessments that overcome the limitations of traditional manual methods. Fruit monitoring is a challenging task due to the significant variation in size, shape, orientation, and occlusion of fruits. Also, fruits may be harvested or newly grown between recording sessions. Most methods are 2D image-based and they lack the 3D structure, depth, and spatial information, which represent key aspects of fruit monitoring. 3D colored point clouds, instead, can offer this information but they introduce challenges such as their sparsity and irregularity. In this paper, we present a novel approach for temporal fruit monitoring that addresses point clouds collected in a greenhouse over time. Our method segments fruits using a learning-based instance segmentation approach directly on the point cloud. Each segmented fruit is processed by a 3D sparse convolutional neural network to extract descriptors, which are used in an attention-based matching network to associate fruits with their instances from previous data collections. Experimental results on a real dataset of strawberries demonstrate that our approach outperforms other methods for fruits re-identification over time, allowing for precise temporal fruit monitoring in real and complex scenarios., Comment: Submitted to IEEE Robotics and Automation Letters

Recommender Systems (RSs) are pivotal in diverse domains such as e-commerce, music streaming, and social media. This paper conducts a comparative analysis of prevalent loss functions in RSs: Binary Cross-Entropy (BCE), Categorical Cross-Entropy (CCE), and Bayesian Personalized Ranking (BPR). Exploring the behaviour of these loss functions across varying negative sampling settings, we reveal that BPR and CCE are equivalent when one negative sample is used. Additionally, we demonstrate that all losses share a common global minimum. Evaluation of RSs mainly relies on ranking metrics known as Normalized Discounted Cumulative Gain (NDCG) and Mean Reciprocal Rank (MRR). We produce bounds of the different losses for negative sampling settings to establish a probabilistic lower bound for NDCG. We show that the BPR bound on NDCG is weaker than that of BCE, contradicting the common assumption that BPR is superior to BCE in RSs training. Experiments on five datasets and four models empirically support these theoretical findings. Our code is available at \url{https://anonymous.4open.science/r/recsys_losses} ., Comment: main paper 8 pages, 4 figures

We present a comprehensive set of harmonic resistance measurements of the dampinglike (DL) and fieldlike (FL) torques in Pt/CoFeB, Pt/Co, W/CoFeB, W/Co, and YIG/Pt bilayers complemented by measurements of the DL torque using the magneto-optical Kerr effect and calibrated by nitrogen vacancy magnetometry on the same devices. The magnon creation-annihilation magnetoresistances depend strongly on temperature and on the magnetic and transport properties of each bilayer, affecting the estimate of both the DL and FL torque. The DL torque, the most important parameter for applications, is overestimated by a factor of 2 in W/CoFeB and by one order of magnitude in YIG/Pt when not accounting for the magnonic contribution to the planar Hall resistance. We further show that the magnonic contribution can be quantified by combining measurements of the nonlinear longitudinal and transverse magnetoresistances, thus providing a reliable method to measure the spin-orbit torques in different material systems.

Charge-spin conversion phenomena such as the spin Hall effect allow for the excitation of magnons in a magnetic layer by passing an electric current in an adjacent nonmagnetic conductor. We demonstrate that this current-induced modification of the magnon density generates an additional nonlinear longitudinal and transverse magnetoresistance for every magnetoresistance that depends on the magnetization. Using harmonic measurements, we evidence that these magnon creation-annihilation magnetoresistances dominate the second harmonic longitudinal and transverse resistance of thin Y$_{3}$Fe$_{5}$O$_{12}$/Pt bilayers. Our results apply to both insulating and metallic magnetic layers, elucidating the dependence of the magnetoresistance on applied current and magnetic field for a broad variety of systems excited by spin currents.

Hate speech relies heavily on cultural influences, leading to varying individual interpretations. For that reason, we propose a Semantic Componential Analysis (SCA) framework for a cross-cultural and cross-domain analysis of hate speech definitions. We create the first dataset of definitions derived from five domains: online dictionaries, research papers, Wikipedia articles, legislation, and online platforms, which are later analyzed into semantic components. Our analysis reveals that the components differ from definition to definition, yet many domains borrow definitions from one another without taking into account the target culture. We conduct zero-shot model experiments using our proposed dataset, employing three popular open-sourced LLMs to understand the impact of different definitions on hate speech detection. Our findings indicate that LLMs are sensitive to definitions: responses for hate speech detection change according to the complexity of definitions used in the prompt.

We study a deterministic particle scheme to solve a scalar balance equation with nonlocal interaction and nonlinear mobility used to model congested dynamics. The main novelty with respect to "Radici-Stra [SIAM J. Math. Anal. 55.3 (2023)]" is the presence of a source term; this causes the solutions to no longer be probability measures, thus requiring a suitable adaptation of the numerical scheme and of the estimates leading to compactness., Comment: 26 pages, 1 figure

Single-layer semiconducting transition-metal dichalcogenides, lacking point inversion symmetry, provide an efficient platform for valleytronics, where the electronic, magnetic, valley and lattice degrees of freedom can be selectively manipulated by using polarized light. This task is however thought to be limited in parent bulk compounds where the point inversion symmetry is restored. Exploiting the underlying quantum physics in bulk materials is thus one of the biggest paradigmatic challenges. Here we show that a sizable optical Kerr rotation can be efficiently generated in a wide energy range on ultrafast timescales in bulk WSe$_2$, by means of circularly-polarized light. We rationalize these findings as a result of the hidden spin/layer/valley quantum entanglement. The spectral analysis reveals clear features at the three characteristic frequencies corresponding to the A-, B- and C-exciton edges. The origin and the relative sign of all these features is shown to stem from the selective Pauli blocking of intralayer and interlayer optical transitions. The long lifetime of the broadband Kerr response ($\tau \sim 500$ fs) provides a strong indication that coupled photo-induced electron and hole densities survive in bulk compounds longer than previously expected. The present report demonstrates that a hidden quantum entanglement is operative also in bulk centrosymmetric layered materials, opening the way for an effective exploitation of bulk WSe$_2$ in optoelectronic applications., Comment: 14+3 pages, 3+3 figures

We study mathematical aspects concerning two site tree-level cosmological correlators with massive internal and external states in a de Sitter universe. We employ integration by parts identities, (relative) twisted cohomology and the method of differential equations. We explicitly express the internally massive, externally conformally coupled correlator as a power series with respect to a small mass parameter, where the various terms in the series are given by multiple polylogarithms., Comment: 43 pages, 6 figures, 4 ancillary files

We experimentally demonstrate the emergence of flat-band-induced compact-localized modes in acoustic Kagome lattices. Compact localized states populate singular dispersion bands characterized by band crossing, where a quadratic and a flat-band dispersion coalesce into a singularity. These conditions enable intriguing wave phenomena when the Hilbert Schmidt quantum distance, measuring the strength of the singularity, is nonzero. We report numerically and experimentally the formation of compact localized states (CLS), extremely localized in space and protected by dispersion flatness. In our system of coupled acoustic waveguides, sound waves are confined to propagate within tightly localized sites positioned both at the boundaries and within the interior of the lattice, achieving broadband and sustained confinement over time. This framework opens new avenues for the manipulation and transport of information through sound waves, with potential application in mechanics and acoustics, including communication, signal processing, and sound isolation. This work also expands the exploration of flat-band lattice physics within the realm of acoustics.

In [1] the asymptotic charges of p-form gauge theories in any dimension are studied. Here we prove an existence and uniqueness theorem for the duality map linking asymptotic electric-like charges of the dual descriptions and we give it an algebraic topology interpretation. As a result the duality map has a topological nature and ensures the charge of a description has information of the dual description. The result of the theorem could be generalized to more generic gauge theories where the gauge field is a mixed symmetry tensor leading to a deeper understanding of gauge theories, of the non-trivial charges associated to them and of the duality of their observable.

Algorithms that use derivatives of governing equations have accelerated rigid robot simulations and improved their accuracy, enabling the modeling of complex, real-world capabilities. However, extending these methods to soft and hybrid soft-rigid robots is significantly more challenging due to the complexities in modeling continuous deformations inherent in soft bodies. A considerable number of soft robots and the deformable links of hybrid robots can be effectively modeled as slender rods. The Geometric Variable Strain (GVS) model, which employs the screw theory and the strain parameterization of the Cosserat rod, extends the rod theory to model hybrid soft-rigid robots within the same mathematical framework. Using the Recursive Newton-Euler Algorithm, we developed the analytical derivatives of the governing equations of the GVS model. These derivatives facilitate the implicit integration of dynamics and provide the analytical Jacobian of the statics residue, ensuring fast and accurate computations. We applied these derivatives to the mechanical simulations of six common robotic systems: a soft cable-driven manipulator, a hybrid serial robot, a fin-ray finger, a hybrid parallel robot, a contact scenario, and an underwater hybrid mobile robot. Simulation results demonstrate substantial improvements in computational efficiency, with speed-ups of up to three orders of magnitude. We validate the model by comparing simulations done with and without analytical derivatives. Beyond static and dynamic simulations, the techniques discussed in this paper hold the potential to revolutionize the analysis, control, and optimization of hybrid robotic systems for real-world applications., Comment: 27 pages including appendix, 17 figures

This paper reports on the shared tasks organized by the 21st IWSLT Conference. The shared tasks address 7 scientific challenges in spoken language translation: simultaneous and offline translation, automatic subtitling and dubbing, speech-to-speech translation, dialect and low-resource speech translation, and Indic languages. The shared tasks attracted 18 teams whose submissions are documented in 26 system papers. The growing interest towards spoken language translation is also witnessed by the constantly increasing number of shared task organizers and contributors to the overview paper, almost evenly distributed across industry and academia., Comment: IWSLT 2024; 59 pages

We compute the surface charges associated to $p-$form gauge fields in arbitrary spacetime dimension for large values of the radial coordinate. In the critical dimension where radiation and Coulomb falloff coincide we find asymptotic charges involving asymptotic parameters, i.e. parameters with a component of order zero in the radial coordinate. However, in different dimensions we still find nontrivial asymptotic charges now involving parameters that are not asymptotic times the radiation-order fields. For $p$=1 and $D>4$, our charges thus differ from those presented in the literature. We then show that under Hodge duality electric charges for $p-$forms are mapped to magnetic charges for the dual $q-$forms, with $q = D-p-2$. For charges involving fields with radiation falloffs the duality relates charges that are finite and nonvanishing. For the case of Coulomb falloffs, above or below the critical dimension, Hodge duality exchanges overleading charges in one theory with subleading ones in its dual counterpart., Comment: 30 pages

The recently derived Fourier--Matsubara expansion of imaginary--time correlation functions comprises an exact result of linear response theory for finite-temperature quantum many-body systems. In its density--density version, the expansion facilitates systematic comparisons between quasi-exact \emph{ab initio} path integral Monte Carlo simulations and approximate dielectric formalism schemes at the level of the imaginary--time (density--density) correlation functions and the dynamic Matsubara local field corrections. On this theoretical basis, the dynamic properties of the quantum version of the Singwi--Tosi--Land--Sj\"olander scheme are analyzed for the paramagnetic warm dense uniform electron gas. The marginal improvement compared to the semi-classical version of the Singwi--Tosi--Land--Sj\"olander scheme is attributed to the weak Matsubara order dependence of the approximate dynamic Matsubara local field correction. The evaluation of the ideal density response function at the non-interacting occupation numbers is identified to constitute a general deficiency of the dielectric formalism, which calls for a reformulation in future works., Comment: 26 pages, 4 figures

Open many-body quantum systems can exhibit intriguing nonequilibrium phases of matter, such as time crystals. In these phases, the state of the system spontaneously breaks the time-translation symmetry of the dynamical generator, which typically manifests through persistent oscillations of an order parameter. A paradigmatic model displaying such a symmetry breaking is the boundary time crystal, which has been extensively analyzed experimentally and theoretically. Despite the broad interest in these nonequilibrium phases, their thermodynamics and their fluctuating behavior remain largely unexplored, in particular for the case of coupled time crystals. In this work, we consider two interacting boundary time crystals and derive a consistent interpretation of their thermodynamic behavior. We fully characterize their average dynamics and the behavior of their quantum fluctuations, which allows us to demonstrate the presence of quantum and classical correlations in both the stationary and the time-crystal phases displayed by the system. We furthermore exploit our theoretical derivation to explore possible applications of time crystals as quantum batteries, demonstrating their ability to efficiently store energy., Comment: 15 + 18 pages, 8 figures

We present new ALMA observations of a starburst galaxy at cosmic noon hosting a radio-loud active galactic nucleus: PKS 0529-549 at $z=2.57$. To investigate the conditions of its cold interstellar medium, we use ALMA observations which spatially resolve the [CI] fine-structure lines, [CI] (2-1) and [CI] (1-0), CO rotational lines, CO (7-6) and CO (4-3), and the rest-frame continuum emission at 461 and 809 GHz. The four emission lines display different morphologies, suggesting spatial variation in the gas excitation conditions. The radio jets have just broken out of the molecular gas but not through the more extended ionized gas halo. The [CI] (2-1) emission is more extended ($\approx8\,{\rm kpc}\times5\,{\rm kpc}$) than detected in previous shallower ALMA observations. The [CI] luminosity ratio implies an excitation temperature of $44\pm16$ K, similar to the dust temperature. Using the [CI] lines, CO (4-3), and 227 GHz dust continuum, we infer the mass of molecular gas $M_{\mathrm{mol}}$ using three independent approaches and typical assumptions in the literature. All approaches point to a massive molecular gas reservoir of about $10^{11}$ $M_{\odot}$, but the exact values differ by up to a factor of 4. Deep observations are critical in correctly characterizing the distribution of cold gas in high-redshift galaxies, and highlight the need to improve systematic uncertainties in inferring accurate molecular gas masses., Comment: 15 pages, 4 figures, 5 tables, accepted in ApJ

This paper investigates marginal and dipole TsT transformations of a seed type IIB supergravity solution dual to a supersymmetry-preserving deformation of the Klebanov-Witten 4d SCFT. To explore key properties of the deformed theories, we holographically analyze various observables, including Wilson loops, 't Hooft loops, Entanglement Entropy, and holographic central charge flow. Moreover, we focus on detecting which of these observables are affected by the dynamics of the Kaluza-Klein (KK) modes resulting from the circle compactification., Comment: 18 pages, no figures

Despite the successful application of Temporal Graph Networks (TGNs) for tasks such as dynamic node classification and link prediction, they still perform poorly on the task of dynamic node affinity prediction -- where the goal is to predict 'how much' two nodes will interact in the future. In fact, simple heuristic approaches such as persistent forecasts and moving averages over ground-truth labels significantly and consistently outperform TGNs. Building on this observation, we find that computing heuristics over messages is an equally competitive approach, outperforming TGN and all current temporal graph (TG) models on dynamic node affinity prediction. In this paper, we prove that no formulation of TGN can represent persistent forecasting or moving averages over messages, and propose to enhance the expressivity of TGNs by adding source-target identification to each interaction event message. We show that this modification is required to represent persistent forecasting, moving averages, and the broader class of autoregressive models over messages. Our proposed method, TGNv2, significantly outperforms TGN and all current TG models on all Temporal Graph Benchmark (TGB) dynamic node affinity prediction datasets., Comment: Accepted to NeurIPS Symmetry and Geometry in Neural Representations Workshop 2024

In this paper, we consider Dirichlet boundary value problem involving the anisotropic $p(x)$-Laplacian, where $p(x)= (p_1(x), ..., p_n(x))$, with $p_i(x)> 1$ in $\overline{\Omega}$. Using the topological degree constructed by Berkovits, we prove, under appropriate assumptions on the data, the existence of weak solutions for the given problem. An important contribution is that we are considering the degenerate and the singular cases in the discussion. Finally, according to the compact embedding for anisotropic Sobolev spaces, we point out that the considered boundaru value problem may be critical in some region of $\Omega$.

In clinical practice, decision-making relies heavily on established protocols, often formalised as rules. Concurrently, Machine Learning (ML) models, trained on clinical data, aspire to integrate into medical decision-making processes. However, despite the growing number of ML applications, their adoption into clinical practice remains limited. Two critical concerns arise, relevant to the notions of consistency and continuity of care: (a) accuracy - the ML model, albeit more accurate, might introduce errors that would not have occurred by applying the protocol; (b) interpretability - ML models operating as black boxes might make predictions based on relationships that contradict established clinical knowledge. In this context, the literature suggests using ML models integrating domain knowledge for improved accuracy and interpretability. However, there is a lack of appropriate metrics for comparing ML models with clinical rules in addressing these challenges. Accordingly, in this article, we first propose metrics to assess the accuracy of ML models with respect to the established protocol. Secondly, we propose an approach to measure the distance of explanations provided by two rule sets, with the goal of comparing the explanation similarity between clinical rule-based systems and rules extracted from ML models. The approach is validated on the Pima Indians Diabetes dataset by training two neural networks - one exclusively on data, and the other integrating a clinical protocol. Our findings demonstrate that the integrated ML model achieves comparable performance to that of a fully data-driven model while exhibiting superior accuracy relative to the clinical protocol, ensuring enhanced continuity of care. Furthermore, we show that our integrated model provides explanations for predictions that align more closely with the clinical protocol compared to the data-driven model.

Galaxies obey a set of strict dynamical laws, which imply a close coupling between the visible matter (stars and gas) and the observed dynamics (set by dark matter in the standard cosmological context). Here we review recent results from weak gravitational lensing, which allows studying the empirical laws of galaxy dynamics out to exceedingly large radii in both late-type galaxies (LTGs) and early-type galaxies (ETGs). We focus on three laws: (1) the circular velocity curves of both LTGs and ETGs remain indefinitely flat out to several hundreds of kpc; (2) the same baryonic Tully-Fisher relation is followed by LTGs and ETGs; (3) the same radial acceleration relation (RAR) is followed by LTGs and ETGs. Combining galaxy data with Solar System data, the RAR covers about 16 orders of magnitude in the Newtonian baryonic acceleration. Remarkably, these empirical facts were predicted a priori by MOND., Comment: Invited review for the IAU General Assembly 2024, Focus Meeting 9 (6 pages, 3 figures)

We study Higgs branches of field theories with 8 supercharges in 5 and 6 dimensions, focusing on theories realised on 5-brane webs in Type IIB with an O$7^+$ plane, or a D6-D8-NS5 brane system in Type IIA in the presence of an O$8^+$ plane. We find magnetic quivers for the Higgs branches of these theories. The main consequence of the presence of the orientifold is that it renders the magnetic quiver to be non-simply-laced. We propose a contribution of the O$7^+$ to the usual stable intersection number of 5-branes from tropical geometry, and show that it is consistent with Fayet-Iliopoulos deformations of magnetic quivers which represent mass deformations of 5d SQFTs. From the magnetic quivers, we compute phase diagrams and highest weight generating functions for the Higgs branches, enabling us to identify the global form of the flavour symmetry for several families of 5d SQFTs; among them Bhardwaj's rank-1 theory. For 6d theories realised on a $-4$ curve, we observe the appearance of an additional $D_4$ slice on top of the phase diagram as one goes to the tensionless limit.

Matrix-vector multiplications (MVMs) are essential for a wide range of applications, particularly in modern machine learning and quantum computing. In photonics, there is growing interest in developing architectures capable of performing linear operations with high speed, low latency, and minimal loss. Traditional interferometric photonic architectures, such as the Clements design, have been extensively used for MVM operations. However, as these architectures scale, improving stability and robustness becomes critical. In this paper, we introduce a novel photonic braid interferometer architecture that outperforms both the Clements and Fldzhyan designs in these aspects. Using numerical simulations, we evaluate the performance of these architectures under ideal conditions and systematically introduce non-idealities such as insertion losses, beam splitter imbalances, and crosstalk. The results demonstrate that the braid architecture offers superior robustness due to its symmetrical design and reduced layer count. Further analysis shows that the braid architecture is particularly advantageous in large-scale implementations, delivering better performance as the size of the interferometer increases. We also assess the footprint and total insertion losses of each architecture. Although waveguide crossings in the braid architecture slightly increase the footprint and insertion loss, recent advances in crossing technology significantly minimize these effects. Our study suggests that the braid architecture is a robust solution for photonic neuromorphic computing, maintaining high fidelity in realistic conditions where imperfections are inevitable.

In the context of safety-critical control, we propose and analyse the use of Control Barrier Functions (CBFs) to limit the kinetic energy of torque-controlled robots. The proposed scheme is able to modify a nominal control action in a minimally invasive manner to achieve the desired kinetic energy limit. We show how this safety condition is achieved by appropriately injecting damping in the underlying robot dynamics independently of the nominal controller structure. We present an extensive experimental validation of the approach on a 7-Degree of Freedom (DoF) Franka Emika Panda robot. The results demonstrate that this approach provides an effective, minimally invasive safety layer that is straightforward to implement and is robust in real experiments.

In spite of the popularity of end-to-end diarization systems nowadays, modular systems comprised of voice activity detection (VAD), speaker embedding extraction plus clustering, and overlapped speech detection (OSD) plus handling still attain competitive performance in many conditions. However, one of the main drawbacks of modular systems is the need to run (and train) different modules independently. In this work, we propose an approach to jointly train a model to produce speaker embeddings, VAD and OSD simultaneously and reach competitive performance at a fraction of the inference time of a standard approach. Furthermore, the joint inference leads to a simplified overall pipeline which brings us one step closer to a unified clustering-based method that can be trained end-to-end towards a diarization-specific objective.

Transition Edge Sensors (TESs) are amongst the most sensitive cryogenic detectors and can be easily optimized for the detection of massive particles or photons ranging from X-rays all the way down to millimetre radiation. Furthermore, TESs exhibit unmatched energy resolution while being easily frequency domain multiplexed in arrays of several hundred pixels. Such great performance, along with rather simple and sturdy readout and amplification chains make TESs extremely compelling for applications in many fields of scientific endeavour. While the first part of this article is an in-depth discussion on the working principles of Transition Edge Sensors, the remainder of this review article focuses on the applications of Transition Edge Sensors in advanced scientific instrumentation serving as an accessible and thorough list of possible starting points for more comprehensive literature research.

We study connection probabilities between vertices of the square lattice for the critical random-cluster (FK) model with cluster weight $2$, which is related to the critical Ising model. We consider the model on the plane and on domains conformally equivalent to the upper half-plane. We prove that, when appropriately rescaled, the connection probabilities between vertices in the domain or on the boundary have nontrivial limits, as the mesh size of the square lattice is sent to zero, and that those limits are conformally covariant. Because of the relation between the FK and Ising models, this provides an alternative proof of the conformal covariance of the Ising spin correlation functions. In an appendix, we also derive exact formulas for some Ising boundary spin correlation functions., Comment: 33 pages, 2 figures

Harsanyi (1955) showed that the only way to aggregate individual preferences into a social preference which satisfies certain desirable properties is ``utilitarianism'', whereby the social utility function is a weighted average of individual utilities. This representation forms the basis for welfare analysis in most applied work. We argue, however, that welfare analysis based on Harsanyi's version of utilitarianism may overlook important distributional considerations. We therefore introduce a notion of utilitarianism for discrete-choice settings which applies to \textit{social choice functions}, which describe the actions of society, rather than social welfare functions which describe society's preferences (as in Harsanyi). We characterize a representation of utilitarian social choice, and show that it provides a foundation for a family of \textit{distributional welfare measures} based on quantiles of the distribution of individual welfare effects, rather than averages.

The nature of the gravitational wave background (GWB) is a key question in modern astrophysics and cosmology, with significant implications for understanding of the structure and evolution of the Universe. We demonstrate how cross-correlating large-scale structure (LSS) tracers with the GWB spatial anisotropies can extract a clear astrophysical imprint from the GWB signal. Focusing on the unresolved population of supermassive black hole binaries (SMBHBs) as the primary source for the GWB at nanohertz frequencies, we construct full-sky maps of galaxy distributions and characteristic strain of the GWB to explore the relationship between GWB anisotropies and the LSS. We find that at current pulsar timing array (PTA) sensitivities, very few loud SMBHBs act as Poisson-like noise. This results in anisotropies dominated by a small number of sources, making GWB maps where SMBHBs trace the LSS indistinguishable from a GWBs from a uniform distribution of SMBHBs. In contrast, we find that the bulk of the unresolved SMBHBs produce anisotropies which mirror the spatial distribution of galaxies, and thus trace the LSS. Importantly, we show that cross-correlations are required to retrieve a clear LSS imprint in the GWB. Specifically, we find this LSS signature can me measured at a $3\sigma$ level in near-future PTA experiments that probe angular scales of $\ell_{\text{max}} \geq 42$, and $5\sigma$ for $\ell_{\text{max}} \geq 72$. Our approach opens new avenues to employ the GWB as an LSS tracer, providing unique insights into SMBHB population models and the nature of the GWB itself. Our results motivate further exploration of potential synergies between next-generation PTA experiments and cosmological tracers of the LSS., Comment: 9 pages, 7 figures. Submitted

Instruction tuning is a supervised fine-tuning approach that significantly improves the ability of large language models (LLMs) to follow human instructions. We propose SelfCodeAlign, the first fully transparent and permissive pipeline for self-aligning code LLMs without extensive human annotations or distillation. SelfCodeAlign employs the same base model for inference throughout the data generation process. It first extracts diverse coding concepts from high-quality seed snippets to generate new tasks. It then samples multiple responses per task, pairs each with test cases, and validates them in a sandbox environment. Finally, passing examples are selected for instruction tuning. In our primary experiments, we use SelfCodeAlign with CodeQwen1.5-7B to generate a dataset of 74k instruction-response pairs. Finetuning on this dataset leads to a model that achieves a 67.1 pass@1 on HumanEval+, surpassing CodeLlama-70B-Instruct despite being ten times smaller. Across all benchmarks, this finetuned model consistently outperforms the original version trained with OctoPack, the previous state-of-the-art method for instruction tuning without human annotations or distillation. Additionally, we show that SelfCodeAlign is effective across LLMs of various sizes, from 3B to 33B, and that the base models can benefit more from alignment with their own data distribution. We further validate each component's effectiveness in our pipeline, showing that SelfCodeAlign outperforms both direct distillation from GPT-4o and leading GPT-3.5-based distillation methods, such as OSS-Instruct and Evol-Instruct. SelfCodeAlign has also led to the creation of StarCoder2-Instruct, the first fully transparent, permissively licensed, and self-aligned code LLM that achieves state-of-the-art coding performance., Comment: Accepted to NeurIPS 2024

The thesis explores novel methods for Human Activity Recognition (HAR) using passive radar with a focus on non-intrusive Wi-Fi Channel State Information (CSI) data. Traditional HAR approaches often use invasive sensors like cameras or wearables, raising privacy issues. This study leverages the non-intrusive nature of CSI, using Spiking Neural Networks (SNN) to interpret signal variations caused by human movements. These networks, integrated with symbolic reasoning frameworks such as DeepProbLog, enhance the adaptability and interpretability of HAR systems. SNNs offer reduced power consumption, ideal for privacy-sensitive applications. Experimental results demonstrate SNN-based neurosymbolic models achieve high accuracy making them a promising alternative for HAR across various domains.

The wave-like nature of electrons is evident from quantum interference effects observed during the photoemission process. When there are different nuclei in the unit cell of a crystal and/or structural distortions, photo-electron wavefunctions can interfere, giving rise to peculiar intensity modulation of the spectrum, which can also hide energy states in a photoemission experiment. The 2H phase of transition metal dichalcogenides, with two nonequivalent layers per unit cell and charge density wave distortion, is an optimal platform for such effects to be observed. Here, we discover undetectable states in 2H-TaS2, interpreting high-resolution angular resolved photoemission spectroscopy considering interference effects of the correlated electron wave functions. In addition, phase mismatching induced by the charge density wave distortion, results in evident signature of the phase transition in the photoemission spectrum. Our results highlight the importance of quantum interference, electronic correlations and structural distortion to understand the physics of layered materials., Comment: 7 pages, 3 figures