Search

This paper addresses the design and analysis of a multivariable gradient-based stochastic extremum-seeking control method for multi-input systems with arbitrary input delays. The approach accommodates systems with distinct time delays across input channels and achieves local exponential stability of the closed-loop system, guaranteeing convergence to a small neighborhood around the extremum point. By incorporating phase compensation for dither signals and a novel predictor-feedback mechanism with averaging-based estimates of the unknown gradient and Hessian, the proposed method overcomes traditional challenges associated with arbitrary, distinct input delays. Unlike previous work on deterministic multiparameter extremum-seeking with distinct input delays, this stability analysis is achieved without using backstepping transformations, simplifying the predictor design and enabling a more straightforward implementation. Specifically, the direct application of Artstein's reduction approach results in delay- and system-dimension-independent convergence rates, enhancing practical applicability. A numerical example illustrates the robust performance and advantages of the proposed delay-compensated stochastic extremum-seeking method., Comment: 8 pages, 8 figures

When deploying large-scale machine learning models for smart city applications, such as image-based parking lot monitoring, data often must be sent to a central server to perform classification tasks. This is challenging for the city's infrastructure, where image-based applications require transmitting large volumes of data, necessitating complex network and hardware infrastructures to process the data. To address this issue in image-based parking space classification, we propose creating a robust ensemble of classifiers to serve as Teacher models. These Teacher models are distilled into lightweight and specialized Student models that can be deployed directly on edge devices. The knowledge is distilled to the Student models through pseudo-labeled samples generated by the Teacher model, which are utilized to fine-tune the Student models on the target scenario. Our results show that the Student models, with 26 times fewer parameters than the Teacher models, achieved an average accuracy of 96.6% on the target test datasets, surpassing the Teacher models, which attained an average accuracy of 95.3%., Comment: Accepted for presentation at the International Conference on Machine Learning and Applications (ICMLA) 2024

Stochastic dominance of a random variable by a convex combination of its independent copies has recently been shown to hold within the relatively narrow class of distributions with concave odds function. We show that a key property for this stochastic dominance result to hold is the subadditivity of the cumulative distribution function of the reciprocal of the random variable of interest, referred to as the inverted distribution. This enlarges significantly the family of distributions for which the dominance is verified. Moreover, we study the relation between the class of distributions with concave odds function and the class we introduce showing conditions under which the concavity of the odds function implies the subadditivity of inverted distribution-

Large Language Models (LLMs) have become integral to various software engineering tasks, including code generation, bug detection, and repair. To evaluate model performance in these domains, numerous bug benchmarks containing real-world bugs from software projects have been developed. However, a growing concern within the software engineering community is that these benchmarks may not reliably reflect true LLM performance due to the risk of data leakage. Despite this concern, limited research has been conducted to quantify the impact of potential leakage. In this paper, we systematically evaluate popular LLMs to assess their susceptibility to data leakage from widely used bug benchmarks. To identify potential leakage, we use multiple metrics, including a study of benchmark membership within commonly used training datasets, as well as analyses of negative log-likelihood and n-gram accuracy. Our findings show that certain models, in particular codegen-multi, exhibit significant evidence of memorization in widely used benchmarks like Defects4J, while newer models trained on larger datasets like LLaMa 3.1 exhibit limited signs of leakage. These results highlight the need for careful benchmark selection and the adoption of robust metrics to adequately assess models capabilities., Comment: pre-print

Among the challenges of efficiently managing a factory, inventory management is essential for minimizing operational costs and delivery times. In this paper, we focus on optimizing item allocation in warehouses that use gravity flow racks, which are designed for First In, First Out (FIFO) logistics but present challenges due to the need for frequent item reinsertions during picking operations. We introduce a novel strategy formulated as a QUBO problem, suitable for classical, quantum, and hybrid hardware implementations. By leveraging advances in Adiabatic Quantum Computing and Quantum Annealing, we demonstrate the effectiveness of our strategy through simulations and real-world scenarios. The results highlight the potential of quantum-hybrid approaches to significantly enhance operational efficiency in warehouse management.

We aim to apply a quantum computing technique to compose artworks. The main idea is to revisit three paintings of different styles and historical periods: ''Narciso'', painted circa 1597-1599 by Michelangelo Merisi (Caravaggio), ''Les fils de l'homme'', painted in 1964 by Rene Magritte and ''192 Farben'', painted in 1966 by Gerard Richter. We utilize the output of a quantum computation to change the composition in the paintings, leading to a paintings series titled ''Quantum Transformation I, II, III''. In particular, the figures are discretized into square lattices and the order of the pieces is changed according to the result of the quantum simulation. We consider an Ising Hamiltonian as the observable in the quantum computation and its time evolution as the final outcome. From a classical subject to abstract forms, we seek to combine classical and quantum aesthetics through these three art pieces. Besides experimenting with hardware runs and circuit noise, our goal is to reproduce these works as physical oil paintings on wooden panels. With this process, we complete a full circle between classical and quantum techniques and contribute to rethinking Art practice in the era of quantum computing technologies., Comment: 10 pages, 8 figures

The use of 3D modelling in medical education is a revolutionary tool during the learning process. In fact, this type of technology enables a more interactive teaching approach, making information retention more effective and enhancing students' understanding. 3D modelling allows for the creation of precise representations of the human body, as well as interaction with three-dimensional models, giving students a better spatial understanding of the different organs and systems and enabling simulations of surgical and technical procedures. This way, medical education is enriched with a more realistic and safe educational experience. The goal is to understand whether, when students and schools are challenged, they play an important role in addressing health issues in their community. School-led projects are directed towards educational scenarios that emphasize STEM education, tackling relevant public health problems through open-school initiatives. By implementing an educational scenario focused on 3D modelling and leveraging technology, we aim to raise community awareness on public health issues.

When turbulent boundary layer flows encounter abrupt roughness changes, an Internal Boundary Layer (IBL) forms. Equilibrium theory breaks down in the nonequilibrium IBL, which may extend O(10) km for natural atmospheric flows. Here, we find that the IBL possesses a characteristic time-scale associated with the IBL height, ${\delta}_i$. We show that ${\delta}_i$ and the edge velocity set the scales of the mean and defect velocity profiles within the IBL, for simulation and experimental data covering a multitude of roughness transition types. We present a nontrivial extension of equilibrium theory to the dynamically adjusting IBL, which can be useful for modeling a range of environmental and industrial flows., Comment: 6 Pages, 4 Figures

The MUltiplexed Survey Telescope (MUST) is a 6.5-meter telescope under development. Dedicated to highly-multiplexed, wide-field spectroscopic surveys, MUST observes over 20,000 targets simultaneously using 6.2-mm pitch positioning robots within a ~5 deg2 field of view. MUST aims to carry out the first Stage-V spectroscopic survey in the 2030s to map the 3D Universe with over 100 million galaxies and quasars, spanning from the nearby Universe to redshift z~5.5, corresponding to around 1 billion years after the Big Bang. To cover this extensive redshift range, we present an initial conceptual target selection algorithm for different types of galaxies, from local bright galaxies, luminous red galaxies, and emission line galaxies to high-redshift (2 < z < 5.5) Lyman-break galaxies. Using Fisher forecasts, we demonstrate that MUST can address fundamental questions in cosmology, including the nature of dark energy, test of gravity theories, and investigations into primordial physics. This is the first paper in the series of science white papers for MUST, with subsequent developments focusing on additional scientific cases such as galaxy and quasar evolution, Milky Way physics, and dynamic phenomena in the time-domain Universe., Comment: To be submitted to SCPMA

We demonstrate numerical refocusing in coherent confocal laser scanning microscopy based on synthetic optical holography. This physics-based approach implements a computational propagation on the complex signal recovered in synthetic holography consistent with the wave physics and the parameters of the microscope. An experimental demonstration is shown to restore an in-focus image of a test object from data acquired at several focal plane off-sets. Numerical refocusing can provide focused views on samples with large height variation with a potential application in confocal optical surface profiling., Comment: 5 pages, 4 figures. \c{opyright} 2023 Optica Publishing Group. One print or electronic copy may be made for personal use only. Systematic reproduction and distribution, duplication of any material in this paper for a fee or for commercial purposes, or modifications of the content of this paper are prohibited

This paper reports on a comprehensive study of an ultra-relativistic positron beam deflection by an oriented quasi-mosaic crystal. The analysis was carried out for the positron energy of 530 MeV incident on the quasi-mosaic bent Si(111) crystal. This particular case was chosen because it has recently been studied experimentally at the Mainz Microtron MAMI. The results of the relativistic molecular dynamics simulations were compared with the experimental observations and a good agreement was found. The presence of planar channeling, de-channeling, volume reflection and volume capture processes in the angular distribution of deflected positrons for different beam-crystal alignments has been studied. Predictions have been made for certain crystal orientations for which experimental data are lacking.

Artificial Intelligence (AI) has emerged in popularity recently, recording great progress in various industries. However, the environmental impact of AI is a growing concern, in terms of the energy consumption and carbon footprint of Machine Learning (ML) and Deep Learning (DL) models, making essential investigate Green AI, an attempt to reduce the climate impact of AI systems. This paper presents an assessment of different programming languages and Feature Selection (FS) methods to improve computation performance of AI focusing on Network Intrusion Detection (NID) and cyber-attack classification tasks. Experiments were conducted using five ML models - Random Forest, XGBoost, LightGBM, Multi-Layer Perceptron, and Long Short-Term Memory - implemented in four programming languages - Python, Java, R, and Rust - along with three FS methods - Information Gain, Recursive Feature Elimination, and Chi-Square. The obtained results demonstrated that FS plays an important role enhancing the computational efficiency of AI models without compromising detection accuracy, highlighting languages like Python and R, that benefit from a rich AI libraries environment. These conclusions can be useful to design efficient and sustainable AI systems that still provide a good generalization and a reliable detection., Comment: 16 pages, 9 tables, FPS 2024 conference

This paper proposes the Mobile Cell (MC) concept for on-demand 5G private networks. The MC is designed to extend, restore, and reinforce 5G wireless coverage and network capacity on-demand, especially in areas with temporary communications needs or where it is costly or not possible to deploy a permanent fixed infrastructure. The design of the MC as well as the development, integration, and deployment in 5G private networks are discussed. The Mobile Cell concept can be applied in multiple real-world environments, including seaports and application scenarios. Similarly to critical hubs in the global supply chain, seaports require reliable, high-performance wireless communications to increase efficiency and manage dynamic operations in real-time. Current communications solutions in seaports typically rely on Wi-Fi and wired-based technologies. Wired-based technologies lack the necessary flexibility for dynamic environments. Wi-Fi is susceptible to interference from other systems operating in the same frequency bands. An MC operating in a licensed, interference-free spectrum is a promising solution to overcome these limitations and provide improved Quality of Service when using the 5G technology., Comment: Manuscript accepted at the NEXUS International Conference - DGTMP 2024

We prove that if $ T $ is a semi-special tree that is not special, then there exists a graph $ G $, formed as an inflation of a sparse $ T $-graph, such that for any special tree $ S $, $ G $ is not a subdivision of an inflation of an sparse $ S $-graph. Furthermore $G$ has an end of uncountable degree that has no ray graph. This result provides a consistent negative answer to a problem posed by Stefan Geschke et al. in 2023. Additionally, we introduce and explore a property that generalizes Halin's grid theorem, extending it to ends of degree $ \aleph_1 $, which was originally established for ends of countable degree.

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

We investigate the response function of an inertial Unruh-deWitt detector in an impulsive plane wave spacetime. Through symmetry considerations applied to the Wightman function, we demonstrate that the response function remains invariant for any inertial detector, even for those experiencing a discontinuous lightcone coordinate shift after interacting with the shockwave. This implies that the vacuum state in an impulsive plane wave spacetime is preserved under the associated spacetime symmetries. Additionally, we confirm that the quantum imprint of the shockwave, as discussed in [J. High Energ. Phys. 2021, 54 (2021)], is not an artifact and exhibits a distinct characteristic form. We identify this form by defining a "renormalized" response function for an eternally inertial detector, with Minkowski spacetime as a reference., Comment: 8 pages, 2 figures

The aerodynamic influence of a wing-equipped motorcycle on a pursuing motorcycle presents critical implications for stability and performance. This study investigates the induced flow dynamics, characterized by a turbulent and complex wake that significantly affects the aerodynamic forces and moments experienced by the following motorcycle. The presence of aerodynamic appendices on the leading motorcycle intensifies these effects, generating coherent wingtip vortices that propagate downstream-a typical behavior of lift-generating devices. In this work, numerical simulations reveal that the aerodynamic consequences vary with the relative positioning of the pursuing motorcycle. A lateral offset can reduce wheelie tendencies due to beneficial flow interactions, while lateral alignment and longitudinal positioning variation may exacerbate negative aerodynamic impacts, compromising stability. Contrary to initial expectations, the simulations in this work demonstrate that the turbulent wake and the coherent vortex pair influence the pursuing motorcycle's behavior independently, persisting across all tested relative distances. While the turbulent wake usually creates a low-pressure region that facilitates drafting, the presence of coherent vortices reduces this advantage by introducing additional lift through the upwash velocity component. Conversely, specific lateral deviations can lessen wheelie effects, with downwash becoming the dominant flow component. Although difficult to be removed promptly due to being part of the overall high performance motorcycle design, it is suggested that the downforce generating aerodynamic appendices removal should be considered by the motorcycling competition governing bodies to provide better safety and racing conditions at all categories that make use of it.

Building on recent developments in models focused on the shape properties of odds ratios, this paper introduces two new models that expand the class of available distributions while preserving specific shape characteristics of an underlying baseline distribution. The first model offers enhanced control over odds and logodds functions, facilitating adjustments to skewness, tail behavior, and hazard rates. The second model, with even greater flexibility, describes odds ratios as quantile distortions. This approach leads to an enlarged log-logistic family capable of capturing these quantile transformations and diverse hazard behaviors, including non-monotonic and bathtub-shaped rates. Central to our study are the shape relations described through stochastic orders; we establish conditions that ensure stochastic ordering both within each family and across models under various ordering concepts, such as hazard rate, likelihood ratio, and convex transform orders.

In this paper we study geometrical and dynamical properties of codimension one foliations, by exploring a relation between length averages and ball averages of certain group actions. We introduce a new mechanism, which relies on the group structure itself, to obtain irregular behavior of ball averages for certain non-amenable group actions. Several geometric realization results show that any such groups can appear connected with the topology of leaves which are connected sums of plugs with a special geometry, namely nearly equidistant boundary components. This is used to produce the first examples of codimension one $\mathcal C^\infty$ regular foliations on a compact Riemannian manifold $M$ for which the length average of some continuous function does not exist on a non-empty open subset of $M$., Comment: 34 pages, 13 figures

The demand for real-time Indoor Air Quality (IAQ) management has increased recently, since low-cost and modern sensors such as Particulate Matter (PM), Volatile Organic Compounds (VOCs), Carbon Monoxide (CO), Carbon Dioxide (CO2), Radon (Rn), among others, have been put forward with considerable accuracy. Although these low-cost sensors cannot be considered measurement instruments, they are very useful for a vast number of application domains, such as home automation, smart building management, IAQ management, risk exposure assessment, to name a few. This paper presents a literature review and a prospective analysis and discussion regarding Internet of Things (IoT) technologies adopted to deal with scenarios that present known indoor Radon gas problems. Specifically, the main requirements for developing IoT-enabled radon management solutions are reviewed. Thus, a traditional IoT architecture is described, its main components are analyzed and some of the most recent academic solutions are reviewed. Finally, novel approaches for deploying IoT radon management architectures are presented together with the most relevant open challenges. In this way, this article presents a holistic review of the past, present, and future of indoor radon management in order to provide guidelines for future designers and developers., Comment: Paper accepted in Edge-IoT 2022

In smart cities, it is common practice to define a maximum length of stay for a given parking space to increase the space's rotativity and discourage the usage of individual transportation solutions. However, automatically determining individual car dwell times from images faces challenges, such as images collected from low-resolution cameras, lighting variations, and weather effects. In this work, we propose a method that combines two deep neural networks to compute the dwell time of each car in a parking lot. The proposed method first defines the parking space status between occupied and empty using a deep classification network. Then, it uses a Siamese network to check if the parked car is the same as the previous image. Using an experimental protocol that focuses on a cross-dataset scenario, we show that if a perfect classifier is used, the proposed system generates 75% of perfect dwell time predictions, where the predicted value matched exactly the time the car stayed parked. Nevertheless, our experiments show a drop in prediction quality when a real-world classifier is used to predict the parking space statuses, reaching 49% of perfect predictions, showing that the proposed Siamese network is promising but impacted by the quality of the classifier used at the beginning of the pipeline., Comment: Paper accepted to the 2024 International Conference on Machine Learning and Applications

Split conformal prediction techniques are applied to regression problems with circular responses by introducing a suitable conformity score, leading to prediction sets with adaptive arc length and finite-sample coverage guarantees for any circular predictive model under exchangeable data. Leveraging the high performance of existing predictive models designed for linear responses, we analyze a general projection procedure that converts any linear response regression model into one suitable for circular responses. When random forests serve as basis models in this projection procedure, we harness the out-of-bag dynamics to eliminate the necessity for a separate calibration sample in the construction of prediction sets. For synthetic and real datasets the resulting projected random forests model produces more efficient out-of-bag conformal prediction sets, with shorter median arc length, when compared to the split conformal prediction sets generated by two existing alternative models., Comment: 7 pages; 4 figures

This work introduces JEMA (Joint Embedding with Multimodal Alignment), a novel co-learning framework tailored for laser metal deposition (LMD), a pivotal process in metal additive manufacturing. As Industry 5.0 gains traction in industrial applications, efficient process monitoring becomes increasingly crucial. However, limited data and the opaque nature of AI present challenges for its application in an industrial setting. JEMA addresses this challenges by leveraging multimodal data, including multi-view images and metadata such as process parameters, to learn transferable semantic representations. By applying a supervised contrastive loss function, JEMA enables robust learning and subsequent process monitoring using only the primary modality, simplifying hardware requirements and computational overhead. We investigate the effectiveness of JEMA in LMD process monitoring, focusing specifically on its generalization to downstream tasks such as melt pool geometry prediction, achieved without extensive fine-tuning. Our empirical evaluation demonstrates the high scalability and performance of JEMA, particularly when combined with Vision Transformer models. We report an 8% increase in performance in multimodal settings and a 1% improvement in unimodal settings compared to supervised contrastive learning. Additionally, the learned embedding representation enables the prediction of metadata, enhancing interpretability and making possible the assessment of the added metadata's contributions. Our framework lays the foundation for integrating multisensor data with metadata, enabling diverse downstream tasks within the LMD domain and beyond., Comment: 26 pages, 14 figures

In inflationary cosmology, the form of the potential is still an open problem. In this work, second-order effects of the inflationary potential are evaluated and related to the known formula for the primordial perturbations at a wide range of scales. We found effects that may help to unravel the unknown inflationary potential form and impose new constraints on the parameters that define this potential. In particular, we demonstrate that even slight deviations in the inflationary potential can lead to significant differences in the calculated spectra if inflation persists sufficiently long and the normal modes of perturbations are affected by these variations., Comment: 14 pages

After over three decades of unsuccessful attempts, we report the first detection of molecular gas emission in Malin 1, the largest spiral galaxy observed to date, and one of the most iconic giant low surface brightness galaxies. Using ALMA, we detect significant $^{12}$CO(J=1-0) emission in the galaxy's central region and tentatively identify CO emission across three regions on the disc. These observations allow for a better estimate of the H$_2$ mass and molecular gas mass surface density, both of which are remarkably low given the galaxy's scale. By integrating data on its HI mass, we derive a very low molecular-to-atomic gas mass ratio. Overall, our results highlight the minimal presence of molecular gas in Malin 1, contrasting sharply with its extensive, homogeneous atomic gas reservoir. For the first time, we position Malin 1 on the Kennicutt-Schmidt (K-S) diagram, where it falls below the main sequence for normal spirals, consistent with previous upper limits but now with more accurate figures. These findings are crucial for constraining our understanding of star formation processes in environments characterized by extremely low molecular gas densities and for refining models of galaxy formation, thereby improving predictions concerning the formation, evolution, and distribution of these giant, elusive galaxies., Comment: Accepted for publication in The Astrophysical Journal Letters. Published

Recently published work on rephrasing natural text data for pre-training LLMs has shown promising results when combining the original dataset with the synthetically rephrased data. We build upon previous work by replicating existing results on C4 and extending them with our optimized rephrasing pipeline to the English, German, Italian, and Spanish Oscar subsets of CulturaX. Our pipeline leads to increased performance on standard evaluation benchmarks in both the mono- and multilingual setup. In addition, we provide a detailed study of our pipeline, investigating the choice of the base dataset and LLM for the rephrasing, as well as the relationship between the model size and the performance after pre-training. By exploring data with different perceived quality levels, we show that gains decrease with higher quality. Furthermore, we find the difference in performance between model families to be bigger than between different model sizes. This highlights the necessity for detailed tests before choosing an LLM to rephrase large amounts of data. Moreover, we investigate the effect of pre-training with synthetic data on supervised fine-tuning. Here, we find increasing but inconclusive results that highly depend on the used benchmark. These results (again) highlight the need for better benchmarking setups. In summary, we show that rephrasing multilingual and low-quality data is a very promising direction to extend LLM pre-training data., Comment: 21 pages, 4 figures, 12 tables

We present the results of the "Fast Calorimeter Simulation Challenge 2022" - the CaloChallenge. We study state-of-the-art generative models on four calorimeter shower datasets of increasing dimensionality, ranging from a few hundred voxels to a few tens of thousand voxels. The 31 individual submissions span a wide range of current popular generative architectures, including Variational AutoEncoders (VAEs), Generative Adversarial Networks (GANs), Normalizing Flows, Diffusion models, and models based on Conditional Flow Matching. We compare all submissions in terms of quality of generated calorimeter showers, as well as shower generation time and model size. To assess the quality we use a broad range of different metrics including differences in 1-dimensional histograms of observables, KPD/FPD scores, AUCs of binary classifiers, and the log-posterior of a multiclass classifier. The results of the CaloChallenge provide the most complete and comprehensive survey of cutting-edge approaches to calorimeter fast simulation to date. In addition, our work provides a uniquely detailed perspective on the important problem of how to evaluate generative models. As such, the results presented here should be applicable for other domains that use generative AI and require fast and faithful generation of samples in a large phase space., Comment: 204 pages, 100+ figures, 30+ tables

Obtaining well-calibrated photometric redshift probability densities for galaxies without a spectroscopic measurement remains a challenge. Deep learning discriminative models, typically fed with multi-band galaxy images, can produce outputs that mimic probability densities and achieve state-of-the-art accuracy. However, such models may be affected by miscalibration that would result in discrepancies between the model outputs and the actual distributions of true redshifts. Our work develops a novel method called the Contrastive Learning and Adaptive KNN for Photometric Redshift (CLAP) that resolves this issue. It leverages supervised contrastive learning (SCL) and k-nearest neighbours (KNN) to construct and calibrate raw probability density estimates, and implements a refitting procedure to resume end-to-end discriminative models ready to produce final estimates for large-scale imaging data. The harmonic mean is adopted to combine an ensemble of estimates from multiple realisations for improving accuracy. Our experiments demonstrate that CLAP takes advantage of both deep learning and KNN, outperforming benchmark methods on the calibration of probability density estimates and retaining high accuracy and computational efficiency. With reference to CLAP, we point out that miscalibration is particularly sensitive to the method-induced excessive correlations among data instances in addition to the unaccounted-for epistemic uncertainties. Reducing the uncertainties may not guarantee the removal of miscalibration due to the presence of such excessive correlations, yet this is a problem for conventional deep learning methods rather than CLAP. These discussions underscore the robustness of CLAP for obtaining photometric redshift probability densities required by astrophysical and cosmological applications. This is the first paper in our series on CLAP., Comment: 22 + 6 pages, 9 + 5 figures

The Dzyaloshinskii-Lifshitz-Pitaevskii (DLP) theory of Casimir forces predicts a repulsion between two material surfaces separated by a third medium with an intermediate dielectric function. This DLP repulsion paradigm constitutes an important example with many applications. We show here that it is broken when the surfaces interact across salted water due to the nonlocal response of the ions in solution. We consider the interaction between silica and gold and show that the zero-frequency contribution, which is the only one affected by the nonlocal response, is dominant for distances in the range near $100\,{\rm nm}$ and beyond. As a result, the total Casimir force between gold and silica surfaces in salted water is always attractive in spite of the DLP repulsion paradigm., Comment: 9 pages, 4 figures

We report here the results obtained from a systematic optical photometric survey aimed at finding new compact binary millisecond pulsars (also known as "spiders"): the COmpact BInary PULsar SEarch (COBIPULSE). We acquired multi-band optical images over one year around $33$ unidentified Fermi-LAT sources, selected as pulsar candidates based on their curved GeV spectra and steady $\gamma$-ray emission. We present the discovery of four optical variables coinciding with the Fermi sources 3FGL J0737.2$-$3233, 3FGL J2117.6$+$3725 (two systems in this field) and 3FGL J2221.6$+$6507, which we propose as new candidate spider systems. Indeed, they all show optical flux modulation consistent with orbital periods of $0.3548(5) \ \mathrm{d}$, $0.25328(6) \ \mathrm{d}$, $0.441961(2) \ \mathrm{d}$, and $0.165(4) \ \mathrm{d}$, respectively, with amplitudes $\gtrsim 0.3 \ \mathrm{mag}$ and colors compatible with companion star temperatures of $5000$--$6000 \ \mathrm{K}$. These properties are consistent with the "redback" sub-class of spider pulsars. If confirmed as a millisecond pulsar, 3FGL J0737.2$-$3233 will be the closest known spider to Earth ($D=659_{-20}^{+16} \ \mathrm{pc}$, from Gaia-DR3 parallax). We searched and did not find any X-ray sources matching our four candidates, placing $3\sigma$ upper limits of $\sim10^{31}$--$10^{32} \ \mathrm{erg} \ \mathrm{s}^{-1}$ ($0.3$--$10 \ \mathrm{keV}$) on their soft X-ray luminosities. We also present and discuss other multi-wavelength information on our spider candidates, from infrared to X-rays., Comment: 38 pages, 93 figures, 4 tables. This paper has been accepted for publication in ApJ

Controlling the symmetry of optical and mechanical waves is pivotal to their full exploitation in technological applications and topology-linked fundamental physics experiments. Leveraging on the control of orbital angular momentum, we introduce here a device forming super-high-frequency-modulated optical vortex beams via acousto-optic interaction of light with acoustic vortices. Originated by shape-engineering of a single-contact bulk acoustic wave resonator, we generate vortices in a wide band of frequencies around 4 GHz and with frequency/geometrically tunable topological charge from 1 to 13 and beyond. With all electrical control and on-chip integration our device offers new solutions for angular-momentum-based light communication, three-dimensional particle manipulation, as well as novel interaction schemes for optomechanical devices., Comment: 6 pages, 4 figures

This booklet on Active Assisted Living (AAL) technologies has been created as part of the GoodBrother COST Action, which has run from 2020 to 2024. COST Actions are European research programs that promote collaboration across borders, uniting researchers, professionals, and institutions to address key societal challenges. GoodBrother focused on ethical and privacy concerns surrounding video and audio monitoring in care settings. The aim was to ensure that while AAL technologies help older adults and vulnerable individuals, their privacy and data protection rights remain a top priority. This booklet is designed to guide you through the role that AAL technologies play in improving the quality of life for older adults, caregivers, and people with disabilities. AAL technologies offer tools for those facing cognitive or physical challenges. They can enhance independence, assist with daily routines, and promote a safer living environment. However, the rise of these technologies also brings important questions about data protection and user autonomy. This resource is intended for a wide audience, including end users, caregivers, healthcare professionals, and policymakers. It provides practical guidance on integrating AAL technologies into care settings while safeguarding privacy and ensuring ethical use. The insights offered here aim to empower users and caregivers to make informed choices that enhance both the quality of care and respect for personal autonomy.

We apply a variational Ansatz based on neural networks to the problem of spin-$1/2$ fermions in a harmonic trap interacting through a short distance potential. We showed that standard machine learning techniques lead to a quick convergence to the ground state, especially in weakly coupled cases. Higher couplings can be handled efficiently by increasing the strength of interactions during "training"., Comment: 8 pages, 5 figures

Scanning tunneling microscope data from a dinuclear Co(II) complex adsorbed on Au(111) are analysed using density functional theory calculations. We find that the interaction with the substrate substantially changes the geometry of the non-planar molecule. Its electronic states, however, remain fairly similar to those calculated for a gas-phase molecule. The calculations reproduce intriguing contrasts observed in experimental maps of the differential conductance dI/dV and reveal the relative importance of geometric and electronic factors that impinge on the image contrasts. For a meaningful comparison, it is important that the calculations closely mimic the experimental mode of measurement., Comment: 9 pages, 7 figures

This paper studies the number of centers and limit cycles of the family of planar quartic polynomial vector fields that has the invariant algebraic curve $(4x^2-1)(4y^2-1)=0.$ The main interest for this type of vector fields comes from their appearance in some mathematical models in Game Theory composed by two players. In particular, we find examples with five nested limit cycles surrounding the same singularity, as well as examples with four limit cycles formed by two disjoint nests, each one of them with two limit cycles. We also prove a Berlinski\u \i's type result for this family of vector fields.

Robots performing navigation tasks in complex environments face significant challenges due to uncertainty in state estimation. Effectively managing this uncertainty is crucial, but the optimal approach varies depending on the specific details of the task: different tasks require varying levels of precision in different regions of the environment. For instance, a robot navigating a crowded space might need precise localization near obstacles but can operate effectively with less precise state estimates in open areas. This varying need for certainty in different parts of the environment, depending on the task, calls for policies that can adapt their uncertainty management strategies based on task-specific requirements. In this paper, we present a framework for integrating task-specific uncertainty requirements directly into navigation policies. We introduce Task-Specific Uncertainty Map (TSUM), which represents acceptable levels of state estimation uncertainty across different regions of the operating environment for a given task. Using TSUM, we propose Generalized Uncertainty Integration for Decision-Making and Execution (GUIDE), a policy conditioning framework that incorporates these uncertainty requirements into the robot's decision-making process. We find that conditioning policies on TSUMs provides an effective way to express task-specific uncertainty requirements and enables the robot to reason about the context-dependent value of certainty. We show how integrating GUIDE into reinforcement learning frameworks allows the agent to learn navigation policies without the need for explicit reward engineering to balance task completion and uncertainty management. We evaluate GUIDE on a variety of real-world navigation tasks and find that it demonstrates significant improvements in task completion rates compared to baselines. Evaluation videos can be found at https://guided-agents.github.io.

High penetration of distributed energy resources in distribution systems, such as rooftop solar PVs, has caused voltage fluctuations which are much faster than typical voltage control devices can react to, leading to increased operation cost and reduced equipment life. Residential buildings consume about 35% of the electricity in U.S. and are co-located with rooftop solar PV. Thus, they present an opportunity to mitigate these fluctuations locally, while benefiting both the grid and building owners. Previous works on DER-aware localized building energy management mostly focus on commercial buildings and analyzing impacts either on buildings or the grid. To fill the gaps, this paper proposes a distributed, differential predictive control scheme for residential HVAC systems for maximizing renewable local consumption. In addition, a detailed controller-building-grid co-simulation platform is developed and utilized for analyzing the potential impacts of the proposed control scheme on both the buildings and distribution system. Our studies show that the proposed method can provide benefits to both the buildings' owners and the distribution system by reducing energy draw from the grid by 12%, voltage violations and fast fluctuations by 20%, and the number of tap changes in voltage regulators by 14%.

While the activations of neurons in deep neural networks usually do not have a simple human-understandable interpretation, sparse autoencoders (SAEs) can be used to transform these activations into a higher-dimensional latent space which may be more easily interpretable. However, these SAEs can have millions of distinct latent features, making it infeasible for humans to manually interpret each one. In this work, we build an open-source automated pipeline to generate and evaluate natural language explanations for SAE features using LLMs. We test our framework on SAEs of varying sizes, activation functions, and losses, trained on two different open-weight LLMs. We introduce five new techniques to score the quality of explanations that are cheaper to run than the previous state of the art. One of these techniques, intervention scoring, evaluates the interpretability of the effects of intervening on a feature, which we find explains features that are not recalled by existing methods. We propose guidelines for generating better explanations that remain valid for a broader set of activating contexts, and discuss pitfalls with existing scoring techniques. We use our explanations to measure the semantic similarity of independently trained SAEs, and find that SAEs trained on nearby layers of the residual stream are highly similar. Our large-scale analysis confirms that SAE latents are indeed much more interpretable than neurons, even when neurons are sparsified using top-$k$ postprocessing. Our code is available at https://github.com/EleutherAI/sae-auto-interp, and our explanations are available at https://huggingface.co/datasets/EleutherAI/auto_interp_explanations.

Lack of data on which to perform experimentation is a recurring issue in many areas of research, particularly in machine learning. The inability of most automated data mining techniques to be generalized to all types of data is inherently related with their dependency on those types which deems them ineffective against anything slightly different. Meta-heuristics are algorithms which attempt to optimize some solution independently of the type of data used, whilst classifiers or neural networks focus on feature extrapolation and dimensionality reduction to fit some model onto data arranged in a particular way. These two algorithmic fields encompass a group of characteristics which when combined are seemingly capable of achieving data mining regardless of how it is arranged. To this end, this work proposes a means of combining meta-heuristic methods with conventional classifiers and neural networks in order to perform automated data mining. Experiments on the MNIST dataset for handwritten digit recognition were performed and it was empirically observed that using a ground truth labeled dataset's validation accuracy is inadequate for correcting labels of other previously unseen data instances., Comment: Tentative submission for data mining and knowledge discovery

Predicting price spikes in critical metals such as Cobalt, Copper, Magnesium, and Nickel is crucial for mitigating economic risks associated with global trends like the energy transition and reshoring of manufacturing. While traditional models have focused on regression-based approaches, our work introduces a neurosymbolic ensemble framework that integrates multiple neural models with symbolic error detection and correction rules. This framework is designed to enhance predictive accuracy by correcting individual model errors and offering interpretability through rule-based explanations. We show that our method provides up to 6.42% improvement in precision, 29.41% increase in recall at 13.24% increase in F1 over the best performing neural models. Further, our method, as it is based on logical rules, has the benefit of affording an explanation as to which combination of neural models directly contribute to a given prediction.

We establish a compactness result for solutions of a certain class of hypoelliptic equations. This result allows us to show the existence of global weak solutions to the non-homogeneous Landau-Fermi-Dirac equation with Coulomb potential.

The quantum entanglement phenomenon was demonstrated to operate on a bipartite entangled system composed of two single layers of graphene embedded in an electrolytic medium (which did not permit the transport of electrons) and subjected to an external time-dependent electric perturbation driven by a potentiostat equipped with a frequency response analyser. Time-dependent perturbation-mediating entanglement was hypothesised because of the equivalent quantum resistive-capacitive circuit frequency of each single-layer graphene system that obeys quantum electrodynamics principles. \textit{De facto}, quantum electrodynamics, associated with the massless fermionic characteristics in graphene sheets, was observed at room temperature and electrolyte medium under a time-dependent modulation, and the entanglement between the two sheets is consistent with a Hilbertian subspace mathematical examination of the phenomenon. Remarkably, this experiment, among numerous other quantum electrochemical experiments conducted by this research group that follow quantum electrodynamics principles, highlights the generality of the methodology for studying entanglement phenomena, leading to alternative methods for investigating Weyl semi-metal structures and designing room-temperature quantum applications., Comment

We propose an inflation scenario with three independent stages of cold, warm and thermal inflation, respectively, driven by different scalar fields, motivated by the large number of such fields predicted in most extensions of the Standard Model. We show, in particular, that the intermediate period of warm inflation naturally leads to large density fluctuations on small scales, which can lead to primordial black hole formation in the mass window where they may account for all dark matter. This type of scenario yields a distinctive primordial black hole mass function with a mass gap, with the final period of thermal inflation diluting the abundance of very light black holes., Comment: 26 pages, 5 figures

We consider the problem of estimating the physical resources required for routing entanglement along an arbitrary path in a quantum bipartite entanglement network based on first-generation quantum repeaters. This resource consumption is intimately linked with the purification protocol and the errors that it introduces due to experimental imperfections. We propose a novel way of accounting for experimental errors in the purification process, which offers the flexibility of accounting for a non uniform probability distribution over different kinds of errors. Moreover, we introduce a novel approach for computing a non-recursive estimation of the resource consumption and illustrate it specifically for our error treatment on a nested repeater protocol. This allows for a reduction in the time complexity of the computation required for the resource estimation, from linear in the required number of purification steps, to constant. Given the fragility and ultra-short lifespans of quantum information, this is especially crucial for an effective operation of a quantum network. The results demonstrate that the approximation works reasonably well over a wide-range of errors., Comment: 18 pages, 7 figures, 2 tables

This paper introduces a novel Graph Neural Network (GNN) architecture for time series classification, based on visibility graph representations. Traditional time series classification methods often struggle with high computational complexity and inadequate capture of spatio-temporal dynamics. By representing time series as visibility graphs, it is possible to encode both spatial and temporal dependencies inherent to time series data, while being computationally efficient. Our architecture is fully modular, enabling flexible experimentation with different models and representations. We employ directed visibility graphs encoded with in-degree and PageRank features to improve the representation of time series, ensuring efficient computation while enhancing the model's ability to capture long-range dependencies in the data. We show the robustness and generalization capability of the proposed architecture across a diverse set of classification tasks and against a traditional model. Our work represents a significant advancement in the application of GNNs for time series analysis, offering a powerful and flexible framework for future research and practical implementations.

We study the formalized v statement by allowing the occurrence of different arrays of quantifiers in it. We prove that for some specific arrays of quantifiers we get consistency statements that are S-equivalent to the original $\omega$-consistency statement (S denotes the basis theory to develop metamathematics). We end our paper by creating a theory of truth that proves each $\omega$-consistency-statement.

We measured the complex Young's modulus of BaTiO3-d, BaxSr1-xTi3-d (BST) and Ba0.85Ca0.15Zr0.1Ti0.9O3-d (BCTZ) during heating and cooling runs at various O deficiencies and aging times. The elastic energy loss has peaks due to the jumps of isolated O vacancies (VO) and reorientations of pairs of VO in the paraelectric phase, from which the respective rates and activation energies are measured. These rates control the mechanisms of domain clamping, pinning, fatigue, and anything related to the VO mobility. In the ferroelectric (FE) phase, the drop of the losses due to the domain wall motion upon introduction of VO monitors the degree of pinning. In addition, large shifts of Tc are observed at the same value of d upon varying the permanence time in the FE state, up to DTc = 21K in BST, while no aging effect is found in BCTZ. The phenomenology is explained by considering that Tc is depressed mainly by the mobile electrons doped by VO. Each isolated VO dopes two electrons as itinerant Ti^3+ ions, but, when it forms a stable linear VO-Ti^2+-VO pair, the two electrons of the Ti^2+ are subtracted from the mobile ones, halving doping. The rise of Tc during the initial aging is then explained in terms of the progressive aggregation of the VO. Prolonging aging for years leads to a decrease of Tc, explained assuming that the most stable position of a VO is at 90^o domain walls, whose geometry is incompatible with the pairs. Then, after enough time the initially aggregated VO within the domains dissociate to decorate the 90^o walls, increasing doping and lowering Tc. The absence of such effects in BCTZ is due to larger activation energy for pair reorientation and pair binding energy. Then, at room temperature practically all VO are paired and static over a time scale of hundreds of years, explaining the superior resistance of BCTZ to fatigue.

This work tackles the challenge of efficiently selecting high-performance pre-trained vision backbones for specific target tasks. Although exhaustive search within a finite set of backbones can solve this problem, it becomes impractical for large datasets and backbone pools. To address this, we introduce Vision Backbone Efficient Selection (VIBES), which aims to quickly find well-suited backbones, potentially trading off optimality for efficiency. We propose several simple yet effective heuristics to address VIBES and evaluate them across four diverse computer vision datasets. Our results show that these approaches can identify backbones that outperform those selected from generic benchmarks, even within a limited search budget of one hour on a single GPU. We reckon VIBES marks a paradigm shift from benchmarks to task-specific optimization., Comment: 9 pages, 4 figures, under review at WACV 2025

In the context of the GRAVITY+ upgrade, the adaptive optics (AO) systems of the GRAVITY interferometer are undergoing a major lifting. The current CILAS deformable mirrors (DM, 90 actuators) will be replaced by ALPAO kilo-DMs (43x43, 1432 actuators). On top of the already existing 9x9 Shack-Hartmann wavefront sensors (SH-WFS) for infrared (IR) natural guide star (NGS), new 40x40 SH-WFSs for visible (VIS) NGS will be deployed. Lasers will also be installed on the four units of the Very Large Telescope to provide a laser guide star (LGS) option with 30x30 SH-WFSs and with the choice to either use the 9x9 IR-WFSs or 2x2 VIS-WFSs for low order sensing. Thus, four modes will be available for the GRAVITY+ AO system (GPAO): IR-NGS, IR-LGS, VIS-NGS and VIS-LGS. To prepare the instrument commissioning and help the observers to plan their observations, a tool is needed to predict the performances of the different modes and for different observing conditions (NGS magnitude, science object magnitude, turbulence conditions...) We developed models based on a Mar{\'e}chal approximation to predict the Strehl ratio of the four GPAO modes in order to feed the already existing tool that simulates the GRAVITY performances. Waiting for commissioning data, our model was validated and calibrated using the TIPTOP toolbox, a Point Spread Function simulator based on the computation of Power Spectrum Densities. In this work, we present our models of the NGS modes of GPAO and their calibration with TIPTOP.

The Su-Schrieffer-Heeger (SSH) model describes the dynamics of spinless fermions in a one-dimensional lattice, with sublattices $A$ and $B$, and governed by staggered hopping potentials $v$ and $w$ representing the intracell and intercell hopping energies, respectively. In this study, we extend the SSH model into three distinct types: a trimer chain, the generalized trimer chain, and a hexagonal chain. The trimer chain involves three sublattices with intracell and intercell hopping potentials $v$ and $w$, respectively. The generalized trimer chain incorporates the intracell hopping $v_1$ and $v_2$ and intercell hopping $w_1$ and $w_2$ to differentiate the hopping energies between different sublattices in the chain. The hexagonal chain is composed of six sublattices with intracell hopping potential $v$ and intercell hopping potential $w$. We utilize exact diagonalization to determine the bulk eigenvalues of the different models. We find that in the trimer and generalized trimer chain, the bulk eigenvalues exhibit conducting characteristics, independent of the hopping parameter, owing to the presence of a flat band situated along the Fermi energy. In the hexagonal chain, the bulk eigenvalues display semi-metallic characteristics in the region $v