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Quantum Communications is a field that promises advances in cryptography, quantum computing and clock synchronisation, among other potential applications. However, communication based on quantum phenomena requires an extreme level of isolation from external disturbances, making the transmission of quantum signals together with classical ones difficult. A range of techniques has been tested to introduce quantum communications in already deployed optical networks which also carry legacy traffic. This comes with challenges, not only at the physical layer but also at the operations and management layer. To achieve a broad acceptance among network operators, the joint management and operation of quantum and classical resources, compliance with standards, and quality and legal assurance need to be addressed. This article presents a detailed account of solutions to the above issues, deployed and evaluated in the MadQCI (Madrid Quantum Communication Infrastructure) testbed. This network is designed to integrate quantum communications in the telecommunications ecosystem by installing quantum-key-distribution modules from multiple providers in production nodes of two different operators. The modules were connected through an optical-switched network with more than 130 km of deployed optical fibre. The tests were done in compliance with strict service level agreements that protected the legacy traffic of the pre-existing classical network. The goal was to achieve full quantum-classical compatibility at all levels, while limiting the modifications of optical transport and encryption and complying with as many standards as possible. This effort was intended to serve as a blueprint, which can be used as the foundation of large-scale quantum network deployments. To demonstrate the capabilities of MadQCI, end-to-end encryption services were deployed and a variety of use-cases were showcased., Comment: 16 pages

Context: On 2022 May 4, an M5.7 flare erupted in the active region NOAA 13004, which was the target of a coordinated campaign between GREGOR, IRIS, Hinode, and ground-based instruments at the Ond\v{r}ejov observatory. A flare kernel located at the edge of a pore was co-observed by the IRIS slit and GREGOR HiFI+ imagers. Aims: We investigated the flare continuum enhancement at different wavelength ranges in order to derive the temperature of the chromospheric layer heated during the flare. Methods: All datasets were aligned to IRIS slit-jaw images. We selected a pixel along the IRIS slit where the flare kernel was captured and evaluated multi-wavelength light curves within it. We defined a narrow IRIS near-UV band that comprises only continuum emission. The method, which assumes that the flare continuum enhancement is due to optically thin emission from hydrogen recombination processes, was applied to obtain a lower limit on the temperature in the layer where the continuum enhancement was formed. Results: We determined a lower limit for the temperature and its time evolution in the chromospheric layer heated during the flare in the range of (3-15).10^3 K. The mean electron density in that layer was estimated to be about 1.10^(13) cm^(-3). Conclusions: Multi-wavelength flare co-observations are a rich source of diagnostics. Due to the rapidly evolving nature of flares, the sit-and-stare mode is key to achieving a high temporal cadence that allows one to thoroughly analyse the same flare structure., Comment: 14 pages, 10 figures

Variable sharing is a fundamental property in the static analysis of logic programs, since it is instrumental for ensuring correctness and increasing precision while inferring many useful program properties. Such properties include modes, determinacy, non-failure, cost, etc. This has motivated significant work on developing abstract domains to improve the precision and performance of sharing analyses. Much of this work has centered around the family of set-sharing domains, because of the high precision they offer. However, this comes at a price: their scalability to a wide set of realistic programs remains challenging and this hinders their wider adoption. In this work, rather than defining new sharing abstract domains, we focus instead on developing techniques which can be incorporated in the analyzers to address aspects that are known to affect the efficiency of these domains, such as the number of variables, without affecting precision. These techniques are inspired in others used in the context of compiler optimizations, such as expression reassociation and variable trimming. We present several such techniques and provide an extensive experimental evaluation of over 1100 program modules taken from both production code and classical benchmarks. This includes the Spectector cache analyzer, the s(CASP) system, the libraries of the Ciao system, the LPdoc documenter, the PLAI analyzer itself, etc. The experimental results are quite encouraging: we have obtained significant speed-ups, and, more importantly, the number of modules that require a timeout was cut in half. As a result, many more programs can be analyzed precisely in reasonable times., Comment: 61 pages, 10 figures, 7 tables, submitted to ICLP 2024

This paper examines the challenges and advancements in recognizing seals within their natural habitats using conventional photography, underscored by the emergence of machine learning technologies. We used the leopard seal, \emph{Hydrurga leptonyx}, a key species within Antarctic ecosystems, to review the different available methods found. As apex predators, Leopard seals are characterized by their significant ecological role and elusive nature so studying them is crucial to understand the health of their ecosystem. Traditional methods of monitoring seal species are often constrained by the labor-intensive and time-consuming processes required for collecting data, compounded by the limited insights these methods provide. The advent of machine learning, particularly through the application of vision transformers, heralds a new era of efficiency and precision in species monitoring. By leveraging state-of-the-art approaches in detection, segmentation, and recognition within digital imaging, this paper presents a synthesis of the current landscape, highlighting both the cutting-edge methodologies and the predominant challenges faced in accurately identifying seals through photographic data., Comment: 28th International Conference on Image Processing, Computer Vision, & Pattern Recognition (IPCV'24), Las Vegas, USA

Attosecond X-ray pulses are the key to studying electron dynamics at their natural time scale involving specific electronic states. They are promising to build the conceptual bridge between physical and chemical photo-reaction processes. Free-electron lasers have demonstrated their capability of generating intense attosecond X-ray pulses. However, harnessing them for time-resolving experiments and investigations of nonlinear X-ray absorption mechanisms remains a cutting-edge challenge. We have characterised X-ray pulses with durations of down to 700$\,$attoseconds and peak powers up to 200$\,$GW at $\sim$ 1$\,$keV photon energy via angular streaking at the SQS instrument of the European XFEL. As direct application, we present results of nonlinear X-ray-matter interaction via state-specific spectroscopy on a transient system. Using the derived spectral and temporal information of each pulse, we deliberately steer the probability for formation of double-core vacancies in neon gas atoms through excitation or ionisation of the second inner-shell electron after K-shell ionisation. Our results advance the field of attosecond science with highly intense and fully characterised X-ray pulses to the site-specific investigation of electronic motion in transient media.

Model networks and their underlying topologies have been used as a reference for techno-economic studies for several decades. Existing reference topologies for optical networks may cover different network segments such as backbone, metro core, metro aggregation, access and/or data center. While telco operators work on the optimization of their own existing deployed optical networks, the availability of different topologies is useful for researchers and technology developers to test their solutions in a variety of scenarios and validate the performance in terms of energy efficiency or cost reduction. This paper presents an open-source tool, MoleNetwork, to generate graphs inspired by real network topologies of telecommunication operators that can be used as benchmarks for techno-economic studies.

Multimodal machine learning models that combine visual and textual data are increasingly being deployed in critical applications, raising significant safety and security concerns due to their vulnerability to adversarial attacks. This paper presents an effective strategy to enhance the robustness of multimodal image captioning models against such attacks. By leveraging the Fast Gradient Sign Method (FGSM) to generate adversarial examples and incorporating adversarial training techniques, we demonstrate improved model robustness on two benchmark datasets: Flickr8k and COCO. Our findings indicate that selectively training only the text decoder of the multimodal architecture shows performance comparable to full adversarial training while offering increased computational efficiency. This targeted approach suggests a balance between robustness and training costs, facilitating the ethical deployment of multimodal AI systems across various domains., Comment: Accepted into KDD 2024 workshop on Ethical AI

We study the effect of random perturbations in the Human and Nature Dynamics (HANDY) model. HANDY models the interactions between human population, depletion, and consumption of natural resources. HANDY explains how endogenous human--nature interactions could lead to sustainability or collapse in past societies. We introduce a Gaussian random noise perturbation on the population change to represent generic external perturbations. The robustness of the results is investigated with statistical analysis based on probability distributions of specific events. Our study shows that the results of the unperturbed HANDY model are robust under small perturbations of $\lesssim$ 10\% of the Human population. Our results confirm that endogenous dynamics drive the societal cycles. However, exogenous perturbations, such as floods, droughts, earthquakes, volcanic eruptions, infectious disease, epidemics, and wars, can accelerate or delay a collapse cycle., Comment: 26 pages 12 figures

The circular dichroism (CD) of photoelectrons generated by near-infrared (NIR) laser pulses using multiphoton ionization of excited He$^+$ ions in the 3p(m=+1) state. The ions were prepared by circularly polarized extreme ultraviolet (XUV) pulses. For circularly polarized NIR pulses co- and counter-rotating relative to the polarization of the XUV pulse, a complex variation of the CD is observed as a result of intensity- and polarization-dependent Freeman resonances, with and without additional dichroic AC-Stark shifts. The experimental results are compared with numerical solutions of the time-dependent Schr\"odinger equation to identify and interpret the pronounced variation of the experimentally observed CD., Comment: 9 pages, 8 figures

With the advent of next-generation AR/VR headsets, many of them with affordable prices, telecom operators have forecasted an explosive growth of traffic in their networks. Penetration of AR/VR services and applications is estimated to grow exponentially in the next few years. This work attempts to shed light on the bandwidth capacity requirements and latency of popular AR/VR applications with four different real experimental settings on the Meta Quest 3 headsets, and their potential impact on the network.

The tilt-to-length coupling during the LISA Pathfinder mission has been numerically and analytically modeled for particular timespans. In this work, we investigate the long-term stability of the coupling coefficients of this noise. We show that they drifted slowly (by 1\,$\mu$m/rad and 6$\times10^{-6}$ in 100 days) and strongly correlated to temperature changes within the satellite (8\,$\mu$m/rad/K and 30$\times10^{-6}$/K). Based on analytical TTL coupling models, we attribute the temperature-driven coupling changes to rotations of the test masses and small distortions in the optical setup. Particularly, we show that LISA Pathfinder's optical baseplate was bent during the cooldown experiment, which started in late 2016 and lasted several months.

High entropy materials are often entropy stabilized, meaning that the configurational entropy from multiple elements sharing a single lattice site stabilizes the structure. In this work, we study how high-pressure synthesis conditions can stabilize or destabilize a high entropy oxide (HEO). We study the high-pressure and high-temperature phase equilibria of two well-known families of HEOs: the rock-salt structured compound (Mg,Co,Ni,Cu,Zn)O including some cation substitutions and the spinel structured (Cr,Mn,Fe,Co,Ni)$_3$O$_4$. Syntheses were performed at various temperatures, pressures, and oxygen activity levels resulting in dramatically different synthesis outcomes. In particular, in the rock salt HEO we observe the competing tenorite and wurtzite phases and the possible formation of a layered rock salt phase, while the spinel HEO is highly susceptible to decomposition into a mixture of rock-salt and corundum phases. At the highest tested pressures, 15 GPa, we discover the transformation of the spinel HEO into a metastable modified ludwigite-type structure with nominal formula (Cr,Mn,Fe,Co,Ni)$_4$O$_5$. The relationship between the synthesis conditions and the final reaction product is not straightforward. Nonetheless, we conclude that high-pressure conditions provide an important opportunity to synthesize high entropy phases that cannot be formed any other way., Comment: This article may be downloaded for personal use only. Any other use requires prior permission of the author and AIP Publishing

A precise characterization of the magnetic properties of LISA Pathfinder free falling test-masses is of special interest for future gravitational wave observatory in space. Magnetic forces have an important impact on the instrument sensitivity in the low frequency regime below the millihertz. In this paper we report on the magnetic injection experiments performed throughout LISA Pathfinder operations. We show how these experiments allowed a high precision estimate of the instrument magnetic parameters. The remanent magnetic moment was found to have a modulus of $(0.245\pm0.081)\,\rm{nAm}^2$, the x-component of the background magnetic field within the test masses position was measured to be $(414 \pm 74)$ nT and its gradient had a value of $(-7.4\pm 2.1)\,\mu$T/m. Finally, we also measured the test mass magnetic susceptibility to be $(-3.35\pm0.15)\times$10$^{-5}$ in the low frequency regime. All results are in agreement with on-ground estimates.

LISA Pathfinder was a mission designed to test key technologies required for gravitational wave detection in space. Magnetically driven forces play a key role in the instrument sensitivity in the low-frequency regime, which corresponds to the measurement band of interest for future space-borne gravitational wave observatories. Magnetic-induced forces couple to the test mass motion, introducing a contribution to the relative acceleration noise between the free falling test masses. In this Letter we present the first complete estimate of this term of the instrument performance model. Our results set the magnetic-induced acceleration noise during the February 2017 noise run of $\rm 0.25_{-0.08}^{+0.15}\,fm\,s^{-2}/\sqrt{Hz}$ at 1 mHz and $\rm 1.01_{-0.24}^{+0.73}\, fm\,s^{-2}/\sqrt{Hz}$ at 0.1 mHz. We also discuss how the non-stationarities of the interplanetary magnetic field can affect these values during extreme space weather conditions.

The complexation of plasmonic nanoparticles (NPs) and thermoresponsive microgels is widely exploited for applications. Yet, a microscopic description of the mechanisms governing spatial organization of the NPs is still lacking. Combining small angle X-ray scattering, state-of-the-art numerical simulations and a simple toy model, we uncover how the volume phase transition of microgels drives NP-NP interactions, inducing NP progressive rearrangement with temperature. These results are directly compared to the extinction spectra of microgel-NPs complexes, allowing us to establish for the first time a microscopic link between plasmon coupling and NP local structure.

We study the quantum thermal transport of elastic excitations through a two-dimensional elastic waveguide between two thermal reservoirs. We solve the classical Kirchhoff-Love equation for rectangular wires and explore the dispersion relation for both the symmetric and antisymmetric solutions. Then, we study the phonon transport of these modes within the second quantization framework by analyzing the mean quadratic displacement, from which the energy density current, the temperature field, and conductance are determined. We identify the relevant modes contributing to thermal transport and explore the average temperature difference to reach the high-temperature limit. We expect our results to pave the way for understanding phonon-mediated thermal transport in two-dimensional mesoscopic quantum devices., Comment: Submitted to Journal of Applied Physics

Based on the PubMed database, there are around 260 manuscripts describing nanothermometers. These research articles detail the synthesis, performance, and application of intracellular nanothermometers. This intracellular prevalence is due to the significant importance, complexity, and utility of the intracellular compartments for understanding cell metabolism and disease treatment. However, in recent years, the extracellular environment of the cell has emerged as a crucial factor in medicine, particularly in hyperthermia and immunotherapy. Despite this, we have not seen evidence in the literature describing the utilization or performance of a nanothermometer designed for extracellular temperature measurements. This oversight not only neglects the potential for measuring extracellular temperature but also fails to address the extracellular environment of the cell. Here, we introduce a nanothermometer designed specifically for measuring extracellular temperature by directly converting serum proteins into nanothermometers (either unmodified or labeled with the clinically approved dye Fluorescein). Additionally, leveraging the extracellular localization of these nanothermometers, we demonstrate (1) the enhancement of their temperature sensitivity by combining them with gold nanorods, and (2) their capability to generate damage and disrupt the plasma membrane, thus opening the door to their use as photodynamic therapy agents. We firmly believe that these advancements represent not only a broadening of the applications of nanothermometry but also a pioneering step in showcasing the ability of nanothermometers to induce cell death., Comment: 18 pages, 5 figures

Previous studies have shown that an Unruh-DeWitt (UDW) detector, when coupled linearly to a massless scalar field and permitted to fall radially into certain black holes, will exhibit non-monotonicity in its transition properties near the horizon. Specifically, the transition probability of a detector freely falling into a (3+1)-dimensional Schawrzschild black hole, when considering the Unruh and Hartle-Hawking vacuum states, was shown to possess a local extremum at horizon crossing [K.K. Ng et al., New J. Phys. 24 (2022) 103018]. The transition rate of a detector falling into a static (2+1)-dimensional Ba\~nados-Teitelboim-Zanelli (BTZ) black hole, for the Hartle-Hawking state, was also found to have multiple local extrema near the horizon under certain parameter settings [M.R. Preciado-Rivas et al., arXiv:2402.14908v1]. These discoveries are of interest, as they suggest that the event horizon of a black hole may be distinguishable to a local probe when QFT effects are included. In this paper, we explore the problem of a UDW detector falling freely into a rotating BTZ black hole. We numerically compute the detector's transition rate for different values of black hole mass, black hole angular momentum, detector energy gap, and field boundary conditions at infinity. Our results lead to a more generalized description of the behaviour of particle detectors in BTZ black hole spacetime, from which the previous non-rotating BTZ case can be retrieved in the limit as angular momentum vanishes.

Continuous-wave lasers generated bubbles in microfluidic channels are proposed for applications such as needle-free jet injection due to their small size and affordable price of these lasers. However, water is transparent in the visible and near-IR regime, where the affordable diode lasers operate. Therefore a dye is required for absorption, which is often unwanted in thermocavitation applications such as vaccines or cosmetics. In this work we explore a different mechanism of the absorption of optical energy. The microfluidic channel wall is partially covered with a thin gold layer which absorbs light from a blue laser diode. This surface absorption is compared with the conventional volumetric absorption by a red dye. The results show that this surface absorption can be used to generate bubbles without the requirement of a dye. However, the generated bubbles are smaller and grow slower when compared to the dye-generated bubbles. Furthermore, heat dissipation in the glass channel walls affect the overall efficiency. Finally, degradation of the gold layer over time reduces the reproducibility and limits its lifetime. Further experiments and simulations are proposed to potentially solve these problems and optimize the bubble formation. Our findings can inform the design and operation of microfluidic devices used in phase transition experiments and other cavitation phenomena, such as jet injectors or liquid dispensing for bio-engineering.

Grading SQL queries can be a time-consuming, tedious and challenging task, especially as the number of student submissions increases. Several systems have been introduced in an attempt to mitigate these challenges, but those systems have their own limitations. This paper describes our novel approach to automating the process of grading SQL queries. Unlike previous approaches, we employ a unique convolutional neural network architecture that employs a parameter-sharing approach for different machine learning tasks that enables the architecture to induce different knowledge representations of the data to increase its potential for understanding SQL statements., Comment: 12 pages, 8 figures, paper accepted at "The 18th International Conference on Frontiers in Education: Computer Science and Computer Engineering"

The magnetic properties of the umbra-penumbra (UP) boundary of sunspots and the boundary of pores at various evolutionary stages have been characterised using datasets from different instruments. We aim to study the differences between the intensity and vector magnetic field properties derived from Hinode/SP and SDO/HMI observations of a decaying sunspot. We analysed the sunspot embedded in AR 12797 during six days in 30 SP/Hinode scans and 704 HMI/SDO for both regular maps and HMI_dcon. We studied the correlation of the magnetic properties and continuum intensity in the datasets within the spot, and at the UP boundary. We examined the decaying process using the full temporal resolution of the HMI_dcon maps. We find a good correspondence between the magnetic properties in the SP and HMI_dcon maps, but the continuum intensity of the spots in the SP maps is 0.04I_qs brighter than in the HMI_dcon maps. The influence of scattered light in the HMI maps makes it the least ideal dataset for studying the boundary of spots without a penumbra. The properties at the UP boundary evolve slowly during the sunspot decay stage, while the penumbra still provides some stability. In contrast, they respond more abruptly to areal changes in the naked-spot stage. During the sunspot decay, we find linear decay in the area and in the magnetic flux. Moreover, the umbra shows two characteristic decaying processes: a slow decay during the first three days, and a sudden fast decay during the final dissipation of the penumbra. We find indications of a 3.5h lag between the dissipation of the vertical fields in the umbral region and the photometric decay of the umbral area. The differences found in the continuum intensity and in the vertical component of the magnetic field, Bver, between the analysed datasets explain the discrepancies among the Bver values found at the boundaries of umbrae in previous studies., Comment: 12 pages, 8 figures

We consider the problem of the general assessment of non-Markovianity in multipartite open quantum systems. To this end, we elaborate on a previously introduced non-Markovian measure to formulate a finite and additive quantifier with appropriate asymptotic behavior. We then study the dynamics of two two-level atoms in a resonant cavity as a function of their interatomic distance using an exact treatment. We observe a critical behavior in the non-Markovianity in the form of a Markovian to non-Markovian transition at a finite distance and a discontinuity in the limit of infinitely close atoms. Additionally, in this limit of highly correlated dynamics, non-Markovianity can emerge when the size of the system exceeds a critical threshold., Comment: RevTex4 file, color figures

The goal of the measurement of the magnetic moment of the electron $\mu$, is to experimentaly determine the gyromagnetic ratio. The factor $g/2$ is computed by the accurate measurement of two frequencies, the spin precession frequency $\nu_s$, and the cyclotron frequency $\nu_c$, and is defined as $\nu_s/\nu_c=g/2$. These experiments are performed with a single electron confined inside a Penning trap. The existence of the electric dipole moment ${d}_e$, involves the idea of an asymmetric charge distribution along the spin direction such that ${ d}_e=d_e{ S}/(\hbar/2)$. The energy shift $\Delta U=2{d}_eE_{eff}$ of the interaction of the electric dipole of electrons with a huge effective electric field ${E}_{eff}$, close to the nucleus of heavy neutral atoms or molecules, is determined by a spin precession measurement. By using a classical model of a spinning electron, which satisfies Dirac's equation when quantized, we determine classically the time average value of the electric and magnetic dipole moments of this electron model when moving in a uniform magnetic field and in a Penning trap, with the same fields as in the real experiments, and obtain an estimated value of these dipoles. We compare these results with the experimental data and make some interpretation of the measured dipoles., Comment: 29 pages, 9 figures, 21 Refrences

This article provides a methodology and open-source implementation of Reinforcement Learning algorithms for finding optimal routes in a packet-optical network scenario. The algorithm uses measurements provided by the physical layer (pre-FEC bit error rate and propagation delay) and the link layer (link load) to configure a set of latency-based rewards and penalties based on such measurements. Then, the algorithm executes Q-learning based on this set of rewards for finding the optimal routing strategies. It is further shown that the algorithm dynamically adapts to changing network conditions by re-calculating optimal policies upon either link load changes or link degradation as measured by pre-FEC BER.

Docking is a crucial component in drug discovery aimed at predicting the binding conformation and affinity between small molecules and target proteins. ML-based docking has recently emerged as a prominent approach, outpacing traditional methods like DOCK and AutoDock Vina in handling the growing scale and complexity of molecular libraries. However, the availability of comprehensive and user-friendly datasets for training and benchmarking ML-based docking algorithms remains limited. We introduce Smiles2Dock, an open large-scale multi-task dataset for molecular docking. We created a framework combining P2Rank and AutoDock Vina to dock 1.7 million ligands from the ChEMBL database against 15 AlphaFold proteins, giving us more than 25 million protein-ligand binding scores. The dataset leverages a wide range of high-accuracy AlphaFold protein models, encompasses a diverse set of biologically relevant compounds and enables researchers to benchmark all major approaches for ML-based docking such as Graph, Transformer and CNN-based methods. We also introduce a novel Transformer-based architecture for docking scores prediction and set it as an initial benchmark for our dataset. Our dataset and code are publicly available to support the development of novel ML-based methods for molecular docking to advance scientific research in this field.

An idealized high entropy oxide is characterized by perfect chemical disorder and perfect positional order. In this work, we investigate the extent to which short-range order (SRO) and local structural distortions impede that idealized scenario. Working in the entropy stabilized $\alpha$-PbO$_2$ structure, we compare a two-component system, (Ti,Zr)O$_2$, with a four-component system, (Ti,Zr,Hf,Sn)O$_2$, using a combination of experimental and computational approaches. Special quasi-random structures are used in conjunction with density functional theory calculations to investigate the local distortions around specific elements revealing significant local distortions that are relatively insensitive to the number of chemical constituents. Using finite temperature Monte Carlo simulations, we are able to reproduce the previously experimentally observed SRO and transition temperature for the two-component system. However, ideal configurational entropy is never reached, so SRO is expected even at synthesis temperatures. On the other hand, the order-disorder transition temperature is dramatically lower and experimentally inaccessible for the four-component system, while the configurational entropy is closer to ideal and less sensitive to temperature. Total scattering measurements and pair distribution function analysis of slow-cooled and quenched samples support this view. In general, we demonstrate that SRO effects in high entropy materials are less prevalent as more components are added in, provided the pairwise interaction strengths remain comparable, while local distortions are less affected by the number of components.

We introduce an efficient neural network (NN) architecture for classifying wave functions in terms of their localization. Our approach integrates a versatile quantum phase space parametrization leading to a custom 'quantum' NN, with the pattern recognition capabilities of a modified convolutional model. This design accepts wave functions of any dimension as inputs and makes accurate predictions at an affordable computational cost. This scalability becomes crucial to explore the localization rate at the semiclassical limit, a long standing question in the quantum scattering field. Moreover, the physical meaning built in the model allows for the interpretation of the learning process, Comment: 15 pages, 12 figures

Research in neural models inspired by mammal's visual cortex has led to many spiking neural networks such as pulse-coupled neural networks (PCNNs). These models are oscillating, spatio-temporal models stimulated with images to produce several time-based responses. This paper reviews PCNN's state of the art, covering its mathematical formulation, variants, and other simplifications found in the literature. We present several applications in which PCNN architectures have successfully addressed some fundamental image processing and computer vision challenges, including image segmentation, edge detection, medical imaging, image fusion, image compression, object recognition, and remote sensing. Results achieved in these applications suggest that the PCNN architecture generates useful perceptual information relevant to a wide variety of computer vision tasks., Comment: The 25th International Conference on Image Processing, Computer Vision, and Pattern Recognition (IPCV 2021)

Advances in NERFs have allowed for 3D scene reconstructions and novel view synthesis. Yet, efficiently editing these representations while retaining photorealism is an emerging challenge. Recent methods face three primary limitations: they're slow for interactive use, lack precision at object boundaries, and struggle to ensure multi-view consistency. We introduce IReNe to address these limitations, enabling swift, near real-time color editing in NeRF. Leveraging a pre-trained NeRF model and a single training image with user-applied color edits, IReNe swiftly adjusts network parameters in seconds. This adjustment allows the model to generate new scene views, accurately representing the color changes from the training image while also controlling object boundaries and view-specific effects. Object boundary control is achieved by integrating a trainable segmentation module into the model. The process gains efficiency by retraining only the weights of the last network layer. We observed that neurons in this layer can be classified into those responsible for view-dependent appearance and those contributing to diffuse appearance. We introduce an automated classification approach to identify these neuron types and exclusively fine-tune the weights of the diffuse neurons. This further accelerates training and ensures consistent color edits across different views. A thorough validation on a new dataset, with edited object colors, shows significant quantitative and qualitative advancements over competitors, accelerating speeds by 5x to 500x.

Concerns regarding the propensity of Large Language Models (LLMs) to produce inaccurate outputs, also known as hallucinations, have escalated. Detecting them is vital for ensuring the reliability of applications relying on LLM-generated content. Current methods often demand substantial resources and rely on extensive LLMs or employ supervised learning with multidimensional features or intricate linguistic and semantic analyses difficult to reproduce and largely depend on using the same LLM that hallucinated. This paper introduces a supervised learning approach employing two simple classifiers utilizing only four numerical features derived from tokens and vocabulary probabilities obtained from other LLM evaluators, which are not necessarily the same. The method yields promising results, surpassing state-of-the-art outcomes in multiple tasks across three different benchmarks. Additionally, we provide a comprehensive examination of the strengths and weaknesses of our approach, highlighting the significance of the features utilized and the LLM employed as an evaluator. We have released our code publicly at https://github.com/Baylor-AI/HalluDetect., Comment: ICAI'24 - The 26th Int'l Conf on Artificial Intelligence

Video game localisation, a field highly impacted by the lack of visual environment and text linearity, forces translators to create inclusive solutions in terms of gender to overcome the hurdles created by variables. This paper will introduce the specificities of this sector and present an analysis of some of those techniques extracted from parallel corpora compiled from video games that include female, transgender, non-binary, and non-sexualised characters.\  LE GENRE DANS LES JEUX VID\ÉO : LA CR\ÉATIVIT\É POUR L\&\#39;INCLUSIVIT\É\  La localisation de jeux vid\éo, un domaine tr\ès impact\é par l\’absence d\’acc\ès au jeu ainsi que par le manque de lin\éarit\é textuelle, oblige les traducteurs \à trouver des solutions inclusives pour faire face aux probl\èmes cr\é\és par les variables. Cet article se propose d\’introduire les caract\éristiques du secteur ainsi que de pr\ésenter l\’analyse des techniques extraites des corpus parall\èles compil\és \à partir des jeux ayant des personnages f\éminins, transgenres, non-binaires, et non-sexualis\és. \

This study presents the development and evaluation of a ByT5-based multilingual translation model tailored for translating the Bible into underrepresented languages. Utilizing the comprehensive Johns Hopkins University Bible Corpus, we trained the model to capture the intricate nuances of character-based and morphologically rich languages. Our results, measured by the BLEU score and supplemented with sample translations, suggest the model can improve accessibility to sacred texts. It effectively handles the distinctive biblical lexicon and structure, thus bridging the linguistic divide. The study also discusses the model's limitations and suggests pathways for future enhancements, focusing on expanding access to sacred literature across linguistic boundaries., Comment: LXAI Workshop at the 2024 Annual Conference of the North American Chapter of the Association for Computational Linguistics (NAACL 2024)

Our study addresses the challenges of building datasets to understand the risks associated with organized activities and human trafficking through commercial sex advertisements. These challenges include data scarcity, rapid obsolescence, and privacy concerns. Traditional approaches, which are not automated and are difficult to reproduce, fall short in addressing these issues. We have developed a reproducible and automated methodology to analyze five million advertisements. In the process, we identified further challenges in dataset creation within this sensitive domain. This paper presents a streamlined methodology to assist researchers in constructing effective datasets for combating organized crime, allowing them to focus on advancing detection technologies., Comment: LXAI Workshop at the 2024 Annual Conference of the North American Chapter of the Association for Computational Linguistics (NAACL 2024)

A permanently available molecular-beam injection setup for controlled molecules (COMO) was installed and commissioned at the small quantum systems (SQS) instrument at the European x-ray free-electron laser (EuXFEL). A $b$-type electrostatic deflector allows for pure state-, size-, and isomer-selected samples of polar molecules and clusters. The source provides a rotationally cold ($T\approx1$~K) and dense ($\rho\approx10^8$~cm$^{-3}$) molecular beam with pulse durations up to 100~\us generated by a new version of the Even-Lavie valve. Here, a performance overview of the COMO setup is presented along with characterization experiments performed both, with an optical laser at the Center for Free-Electron-Laser Science and with x-rays at EuXFEL under burst-mode operation. COMO was designed to be attached to different instruments at the EuXFEL, in particular at the small quantum systems (SQS) and single particles, clusters, and biomolecules (SPB) instruments. This advanced controlled-molecules injection setup enables XFEL studies using highly defined samples with soft and hard x-ray FEL radiation for applications ranging from atomic, molecular, and cluster physics to elementary processes in chemistry and biology.

We present an in-depth analysis of the LISA Pathfinder differential acceleration performance over the entire course of its science operations, spanning approximately 500 days. We find that: 1) the evolution of the Brownian noise that dominates the acceleration amplitude spectral density (ASD), for frequencies $f\gtrsim 1\,\text{mHz}$, is consistent with the decaying pressure due to the outgassing of a single gaseous species. 2) between $f=36\,\mu\text{Hz}$ and $1\,\text{mHz}$, the acceleration ASD shows a $1/f$ tail in excess of the Brownian noise of almost constant amplitude, with $\simeq 20\%$ fluctuations over a period of a few days, with no particular time pattern over the course of the mission; 3) at the lowest considered frequency of $f=18\,\mu\text{Hz}$, the ASD significantly deviates from the $1/f$ behavior, because of temperature fluctuations that appear to modulate a quasi-static pressure gradient, sustained by the asymmetries of the outgassing pattern. We also present the results of a projection of the observed acceleration noise on the potential sources for which we had either a direct correlation measurement, or a quantitative estimate from dedicated experiments. These sources account for approximately $40\%$ of the noise power in the $1/f$ tail. Finally, we analyze the possible sources of the remaining unexplained fraction, and identify the possible measures that may be taken to keep those under control in LISA.

Recently, with the advancement of deep learning, several applications in text classification have advanced significantly. However, this improvement comes with a cost because deep learning is vulnerable to adversarial examples. This weakness indicates that deep learning is not very robust. Fortunately, the input of a text classifier is discrete. Hence, it can prevent the classifier from state-of-the-art attacks. Nonetheless, previous works have generated black-box attacks that successfully manipulate the discrete values of the input to find adversarial examples. Therefore, instead of changing the discrete values, we transform the input into its embedding vector containing real values to perform the state-of-the-art white-box attacks. Then, we convert the perturbed embedding vector back into a text and name it an adversarial example. In summary, we create a framework that measures the robustness of a text classifier by using the gradients of the classifier., Comment: 7 pages

The efficacy of deep learning models has been called into question by the presence of adversarial examples. Addressing the vulnerability of deep learning models to adversarial examples is crucial for ensuring their continued development and deployment. In this work, we focus on the role of rectified linear unit (ReLU) activation functions in the generation of adversarial examples. ReLU functions are commonly used in deep learning models because they facilitate the training process. However, our empirical analysis demonstrates that ReLU functions are not robust against adversarial examples. We propose a modified version of the ReLU function, which improves robustness against adversarial examples. Our results are supported by an experiment, which confirms the effectiveness of our proposed modification. Additionally, we demonstrate that applying adversarial training to our customized model further enhances its robustness compared to a general model., Comment: 10 pages

Recent genome-wide association studies (GWAS) have uncovered the genetic basis of complex traits, but show an under-representation of non-European descent individuals, underscoring a critical gap in genetic research. Here, we assess whether we can improve disease prediction across diverse ancestries using multiomic data. We evaluate the performance of Group-LASSO INTERaction-NET (glinternet) and pretrained lasso in disease prediction focusing on diverse ancestries in the UK Biobank. Models were trained on data from White British and other ancestries and validated across a cohort of over 96,000 individuals for 8 diseases. Out of 96 models trained, we report 16 with statistically significant incremental predictive performance in terms of ROC-AUC scores (p-value < 0.05), found for diabetes, arthritis, gall stones, cystitis, asthma and osteoarthritis. For the interaction and pretrained models that outperformed the baseline, the PRS score was the primary driver behind prediction. Our findings indicate that both interaction terms and pre-training can enhance prediction accuracy but for a limited set of diseases and moderate improvements in accuracy

Self-supervised contrastive learning has emerged as one of the most successful deep learning paradigms. In this regard, it has seen extensive use in image registration and, more recently, in the particular field of medical image registration. In this work, we propose to test and extend and improve a state-of-the-art framework for color fundus image registration, ConKeD. Using the ConKeD framework we test multiple loss functions, adapting them to the framework and the application domain. Furthermore, we evaluate our models using the standarized benchmark dataset FIRE as well as several datasets that have never been used before for color fundus registration, for which we are releasing the pairing data as well as a standardized evaluation approach. Our work demonstrates state-of-the-art performance across all datasets and metrics demonstrating several advantages over current SOTA color fundus registration methods

We study the diffraction of a particular class of beams, composed only by a combination of azimuthally invariant guided modes of an optical fiber. We demonstrate that such beams can be obtained by injecting a Gaussian beam in a small piece of silica graded-index multimode fiber. This minimalistic low-cost method is applied for improving the axial resolution of a two-photon microscope., Comment: 9 pages, 5 figures

The FitzHugh-Nagumo equation, originally conceived in neuroscience during the 1960s, became a key model providing a simplified view of excitable neuron cell behavior. Its applicability, however, extends beyond neuroscience into fields like cardiac physiology, cell division, population dynamics, electronics, and other natural phenomena. In this review spanning six decades of research, we discuss the diverse spatio-temporal dynamical behaviors described by the FitzHugh-Nagumo equation. These include dynamics like bistability, oscillations, and excitability, but it also addresses more complex phenomena such as traveling waves and extended patterns in coupled systems. The review serves as a guide for modelers aiming to utilize the strengths of the FitzHugh-Nagumo model to capture generic dynamical behavior. It not only catalogs known dynamical states and bifurcations, but also extends previous studies by providing stability and bifurcation analyses for coupled spatial systems., Comment: 39 pages, 18 figures

Dust storms are associated with certain respiratory illnesses across different areas in the world. Researchers have devoted time and resources to study the elements surrounding dust storm phenomena. This paper reviews the efforts of those who have investigated dust aerosols using sensors onboard of satellites using machine learning-based approaches. We have reviewed the most common issues revolving dust aerosol modeling using different datasets and different sensors from a historical perspective. Our findings suggest that multi-spectral approaches based on linear and non-linear combinations of spectral bands are some of the most successful for visualization and quantitative analysis; however, when researchers have leveraged machine learning, performance has been improved and new opportunities to solve unique problems arise., Comment: The 23rd International Conference on Artificial Intelligence (ICAI 2021)

Quantum Computing in the Noisy Intermediate-Scale Quantum (NISQ) era has shown promising applications in machine learning, optimization, and cryptography. Despite the progress, challenges persist due to system noise, errors, and decoherence that complicate the simulation of quantum systems. The depolarization channel is a standard tool for simulating a quantum system's noise. However, modeling such noise for practical applications is computationally expensive when we have limited hardware resources, as is the case in the NISQ era. We propose a modified representation for a single-qubit depolarization channel with two Kraus operators based only on X and Z Pauli matrices. Our approach reduces the computational complexity from six to four matrix multiplications per execution of a channel. Experiments on a Quantum Machine Learning (QML) model on the Iris dataset across various circuit depths and depolarization rates validate that our approach maintains the model's accuracy while improving efficiency. This simplified noise model enables more scalable simulations of quantum circuits under depolarization, advancing capabilities in the NISQ era.

In the statistical mechanics approach to liquid-state theory, understanding the role of the intermolecular potential in determining thermodynamic and structural properties is crucial. The Fisher--Widom (FW) line, which separates regions in the temperature vs density plane where the decay of the total correlation function is monotonic or oscillatory, provides insights into the dominance of the attractive or repulsive part of the interactions. Stopper et al. have recently conjectured [J. Chem. Phys. \textbf{151}, 014501 (2019)] that the line of vanishing excess isothermal compressibility approximates the FW line in simple fluids. Here, we investigate this conjecture using the Jagla potential and also explore the line of vanishing excess pressure. We employ theoretical approximations and Monte Carlo simulations to study one-dimensional and three-dimensional systems. While exact results for the one-dimensional case do not support the conjecture, our Monte Carlo simulations for the three-dimensional fluid validate it. Our findings not only contribute to the understanding of the relationship between the three transition lines but also provide valuable insights into the thermodynamic and structural behaviour of simple fluids., Comment: 17 pages, 5 figures; v2: addition of the Widom line plus other minor changes. Rull-Abascal Special Issue for Statistical Physics in Spain

We construct weak solutions of the anisotropic inverse mean curvature flow (A-IMCF) under very mild assumptions both on the anisotropy (which is simply a norm in $\mathbb R^N$ with no ellip\-ticity nor smoothness requirements, in order to include the crystalline case) and on the initial data. By means of an approximation procedure introduced by Moser, our solutions are limits of anisotropic $p$-harmonic functions or $p$-capacitary functions (after a change of variable), and we get uniqueness both for the approximating solutions (i.e., uniqueness of $p$-capacitary functions) and the limiting ones. Our notion of weak solution still recovers variational and geometric definitions similar to those introduced by Huisken-Ilmanen, but requires to work within the broader setting of $BV$-functions. Despite of this, we still reach classical results like the continuity and exponential growth of perimeter, as well as outward minimizing properties of the sublevel sets. Moreover, by assuming the extra regularity given by an interior rolling ball condition (where a sliding Wulff shape plays the role of a ball), the solutions are shown to be continuous and satisfy Harnack inequalities. Finally, examples of explicit solutions are built.