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In recent years, nanosatellites have revolutionized the space sector due to their significant economic and time-saving advantages. As a result, they have fostered the testing of advanced instruments intended for larger space science missions. The case of MELISA is presented in this work. MELISA is a magnetic measurement instrument which aims at demonstrating the in-orbit performance of AMR sensors featuring dedicated noise reduction techniques at sub-millihertz frequencies. Such low frequency ranges are relevant for future space-borne gravitational wave detectors, where the local magnetic environment of the satellite might yield a significant contribution to the overall noise budget of the observatory. The demanding magnetic noise levels required for this bandwidth, down to 0.1 mHz, make measurements arduous. To explore sensing solutions within the H2020 European Commission Programme with the involvement of ESA, the functional performance of MELISA-III will be validated in-orbit. During operations, there is the possibility to measure the low-frequency magnetic contribution stemming from orbiting the Earth's magnetic field, impeding the characterization of the intrinsic performance of the sensor. With the objective of minimizing excess noise during the in-flight operations, the present research aims to simulate the environmental magnetic conditions in LEO to identify and subtract undesired contributions to the measurements. The in-orbit long-term magnetic fluctuations are replicated using a Helmholtz coil system. A fluxgate magnetometer allows the correlation of the generated field with the payload measurements, leading to the subsequent subtraction. Proving the effect of this approach will facilitate the noise characterization of magnetic sensors in LEO, paving the way for the in-orbit validation of MELISA-III for use in magnetically demanding missions with long integration times.

High computational power and the availability of large datasets have supported the development of Foundational Models. They are a new emerging technique widely used in Generative Artificial Intelligence, characterized by their scalability and their use in Transfer Learning. The enormous and heterogeneous amounts of data used in their initial training phase, known as pre-training, give them a higher generalization capacity than any other specific model, constituting a solid base that can be adapted or adjusted to a wide range of tasks, increasing their applicability. This study proposes LLIAM, the Llama Lora-Integrated Autorregresive Model. Low-Rank Adaptations are used to enhance the knowledge of the model with diverse time series datasets, known as the fine-tuning phase. To illustrate the capabilities of our proposal, two sets of experiments have been carried out that obtained favorable and promising results with lower training times than other Deep Learning approaches. With this work, we also encourage the use of available resources (such as these pre-trained models) to avoid unnecessary and costly training, narrowing the gap between the goals of traditional Artificial Intelligence and those specified by the definition of Green Artificial Intelligence.

Unique identification of multiple sclerosis (MS) white matter lesions (WMLs) is important to help characterize MS progression. WMLs are routinely identified from magnetic resonance images (MRIs) but the resultant total lesion load does not correlate well with EDSS; whereas mean unique lesion volume has been shown to correlate with EDSS. Our approach builds on prior work by incorporating Hessian matrix computation from lesion probability maps before using the random walker algorithm to estimate the volume of each unique lesion. Synthetic images demonstrate our ability to accurately count the number of lesions present. The takeaways, are: 1) that our method correctly identifies all lesions including many that are missed by previous methods; 2) we can better separate confluent lesions; and 3) we can accurately capture the total volume of WMLs in a given probability map. This work will allow new more meaningful statistics to be computed from WMLs in brain MRIs, Comment: 5 pages, 5 figures, submitted to SPIE medical imaging conference

As the need for large-scale data processing grows, distributed programming frameworks like PySpark have become increasingly popular. However, the task of converting traditional, sequential code to distributed code remains a significant hurdle, often requiring specialized knowledge and substantial time investment. While existing tools have made strides in automating this conversion, they often fall short in terms of speed, flexibility, and overall applicability. In this paper, we introduce ROOP, a groundbreaking tool designed to address these challenges. Utilizing a BERT-based Natural Language Processing (NLP) model, ROOP automates the translation of Python code to its PySpark equivalent, offering a streamlined solution for leveraging distributed computing resources. We evaluated ROOP using a diverse set of 14 Python programs comprising 26 loop fragments. Our results are promising: ROOP achieved a near-perfect translation accuracy rate, successfully converting 25 out of the 26 loop fragments. Notably, for simpler operations, ROOP demonstrated remarkable efficiency, completing translations in as little as 44 seconds. Moreover, ROOP incorporates a built-in testing mechanism to ensure the functional equivalence of the original and translated code, adding an extra layer of reliability. This research opens up new avenues for automating the transition from sequential to distributed programming, making the process more accessible and efficient for developers.

As cities adopt sustainable mobility solutions, electric scooters (e-scooters) offer both challenges and opportunities for public transportation systems. This study, the first in Latin America, examines the effects of e-scooters on public transport demand in Santiago, Chile, focusing on two scenarios: "generation" of trips (trips starting in study zones) and "attraction" of trips (trips ending in study zones). A negative binomial regression model was applied to data from public transport smart cards and e-scooter GPS. The methodology included urban area clustering and a differences-in-differences approach. The findings reveal significant regional differences: in the Central Region, public transport trips decreased by 21.38% in the generation scenario, while bus trips increased by 76.39%. In the Intermediate Region, metro trips increased by 70.05%, and in the Peripheral Region, bus trips increased by 84.64%. These results suggest that e-scooters reduce public transport usage in highly accessible areas but increase it in less accessible regions., Comment: 62 pages, 12 figures. Submitted to Transport Research Part D

In an effort to shift the paradigm of power conversion, acoustic resonators pose as compact alternatives for lossy magnetic inductors. Currently, the acoustic resonator's restricted inductive region between its series and parallel resonances constitutes a major bottleneck, which is further diminished due to spurious modes. Prior work has partially addressed this issue by the introduction of various design guidelines tailored to the material and the mode of interest, but can only provide a limited spurious-free region. Alternatively, a separated grounded ring on LN operating in the first order symmetric lamb mode (S1), maintains optimal device performance with a large fractional spurious mode suppressed region, but has been shown to experience voltage breakdown at high power near the ring at different potentials. Hence, we propose a new spurious mode suppressing design leveraging a thickened active ring in lithium niobate (LN), maintaining high Q and k2 while also reducing resistance at resonance (Rr), and mitigating breakdown effects., Comment: 4 pages, 5 figures, accepted by IEEE UFFC-JS

k-Contact geometry appeared as a generalisation of contact geometry to analyse field theories. This work provides a new insightful approach to k-contact geometry by devising a theory of k-contact forms and proving that the kernel of a k-contact form is locally equivalent to a distribution of corank k that is distributionally maximally non-integrable and admits k commuting Lie symmetries: a so-called k-contact distribution. Compact manifolds admitting a global k-contact form are analysed, we give necessary topological conditions for their existence, k-contact Lie groups are defined and studied, we extend the Weinstein conjecture for the existence of closed orbits of Reeb vector fields in compact manifolds to the k-contact setting after studying compact low-dimensional manifolds endowed with a global k-contact form, and we provide some physical applications of some of our results. Polarisations for k-contact distributions are introduced and it is shown that a polarised k-contact distribution is locally diffeomorphic to the Cartan distribution of the first-order jet bundle over a fibre bundle of order k, which is a globally defined polarised k-contact distribution. Then, we relate k-contact manifolds to presymplectic and k-symplectic manifolds on fibre bundles of larger dimension and define for the first time types of submanifolds in k-contact geometry. We also review the theory of Hamiltonian k-vector fields, studying Hamilton-De Donder-Weyl equations in general and in Lie groups, which are here studied in an unprecedented manner. A theory of k-contact Hamiltonian vector fields is developed, which describes the theory of characteristics for Lie symmetries for first-order partial differential equations in a k-contact Hamiltonian manner. Our new Hamiltonian k-contact techniques are illustrated by analysing Hamilton-Jacobi and Dirac equations., Comment: 59 pp

Action-dependent field theories are systems where the Lagrangian or Hamiltonian depends on new variables that encode the action. After a friendly introduction, we make a quick presentation of a new mathematical framework for action-dependent field theory: multicontact geometry. The formalism is illustrated in a variety of action-dependent Lagrangians, some of which are regular and other singular, that come from some well-known theories whose Lagrangians have been modified to incorporate action-dependent terms. Detailed computations are provided, including the constraint algorithm, in both the Lagrangian and Hamiltonian formalisms. They include the Klein-Gordon equation, the Bosonic string theory, Metric-affine gravity, Maxwell's electromagnetism, (2+1)-dimensional gravity and Chern-Simons equation, and the heat equation and Burgers' equation., Comment: 37 pages, plus appendix and references

Quantum Machine Learning (QML) has emerged as a promising field that combines the power of quantum computing with the principles of machine learning. One of the significant challenges in QML is dealing with noise in quantum systems, especially in the Noisy Intermediate-Scale Quantum (NISQ) era. Noise in quantum systems can introduce errors in quantum computations and degrade the performance of quantum algorithms. In this paper, we propose a framework for learning observables that are robust against noisy channels in quantum systems. We demonstrate that it is possible to learn observables that remain invariant under the effects of noise and show that this can be achieved through a machine-learning approach. We present a toy example using a Bell state under a depolarization channel to illustrate the concept of robust observables. We then describe a machine-learning framework for learning such observables across six two-qubit quantum circuits and five noisy channels. Our results show that it is possible to learn observables that are more robust to noise than conventional observables. We discuss the implications of this finding for quantum machine learning, including potential applications in enhancing the stability of QML models in noisy environments. By developing techniques for learning robust observables, we can improve the performance and reliability of quantum machine learning models in the presence of noise, contributing to the advancement of practical QML applications in the NISQ era.

Despite the mounting anticipation for the quantum revolution, the success of Quantum Machine Learning (QML) in the Noisy Intermediate-Scale Quantum (NISQ) era hinges on a largely unexplored factor: the generalization error bound, a cornerstone of robust and reliable machine learning models. Current QML research, while exploring novel algorithms and applications extensively, is predominantly situated in the context of noise-free, ideal quantum computers. However, Quantum Circuit (QC) operations in NISQ-era devices are susceptible to various noise sources and errors. In this article, we conduct a Systematic Mapping Study (SMS) to explore the state-of-the-art generalization bound for supervised QML in NISQ-era and analyze the latest practices in the field. Our study systematically summarizes the existing computational platforms with quantum hardware, datasets, optimization techniques, and the common properties of the bounds found in the literature. We further present the performance accuracy of various approaches in classical benchmark datasets like the MNIST and IRIS datasets. The SMS also highlights the limitations and challenges in QML in the NISQ era and discusses future research directions to advance the field. Using a detailed Boolean operators query in five reliable indexers, we collected 544 papers and filtered them to a small set of 37 relevant articles. This filtration was done following the best practice of SMS with well-defined research questions and inclusion and exclusion criteria.

In pursuit of a full-fledged theory of quantum gravity, operational approaches offer insights into quantum-gravitational effects produced by quantum superposition of different spacetimes not diffeomorphic to one another. Recent work applies this approach to superpose cylindrically identified Minkowski spacetimes (i.e. periodic boundary conditions) with different characteristic circumferences, where a two-level detector coupled to a quantum field residing in the spacetime exhibits resonance peaks in response at certain values of the superposed length ratios. Here, we extend this analysis to a superposition of cylindrically identified Rindler spacetimes, considering a two-level detector constantly accelerated in the direction orthogonal to the compact dimension. Similarly to previous work, we find resonance peaks in the detector response at rational ratios of the superposed compactified lengths, which we observe to be accentuated by the acceleration of the detector. Furthermore, for the first time we confirm the detailed balance condition due to acceleration in a superposition of spacetimes, commensurate with the Unruh effect in a single spacetime state. The resonant structure of detector response in the presence of event horizons, for the first time observed in 3+1 dimensions, may offer clues to the nature of black hole entropy in the full theory of quantum gravity., Comment: 8 + 12 pages, 7 + 2 figures

Unbiased representation learning is still an object of study under specific applications and contexts. Novel architectures are usually crafted to resolve particular problems using mixtures of fundamental pieces. This paper presents different image feature extraction mechanisms that work together with residual connections to encode perceptual image information in an autoencoder configuration. We use image data that aims to support a larger research agenda dealing with issues regarding criminal activity in consumer-to-consumer online platforms. Preliminary results suggest that the proposed architecture can learn rich spaces using ours and other image datasets resolving important challenges that are identified., Comment: 2022 LXAI Workshop at the 39th International Conference on Machine Learning (ICML), Baltimore, Maryland

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

This study investigates ImageBind's ability to generate meaningful fused multimodal embeddings for online auto parts listings. We propose a simplistic embedding fusion workflow that aims to capture the overlapping information of image/text pairs, ultimately combining the semantics of a post into a joint embedding. After storing such fused embeddings in a vector database, we experiment with dimensionality reduction and provide empirical evidence to convey the semantic quality of the joint embeddings by clustering and examining the posts nearest to each cluster centroid. Additionally, our initial findings with ImageBind's emergent zero-shot cross-modal retrieval suggest that pure audio embeddings can correlate with semantically similar marketplace listings, indicating potential avenues for future research., Comment: The 26th International Conference on Artificial Intelligence (ICAI'24)

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, the 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.

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.