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This study demonstrates that two-dimensional talc, a naturally abundant mineral, supports hyperbolic phonon-polaritons (HPhPs) at mid-infrared wavelengths, thus offering a low-cost alternative to synthetic polaritonic materials. Using scattering scanning near-field optical microscopy (s-SNOM) and synchrotron infrared nano spectroscopy (SINS), we reveal tunable HPhP modes in talc flakes of a long lifetime. These results highlight the potential of natural 2D talc crystals to constituting an effective platform for establishing scalable optoelectronic and photonic devices., Comment: 8 pages, 4 figures

We introduce a generalization of the Kelvin-Voigt model in order to include and characterize heterogeneities in viscoelastic semisolid materials. By considering a microrheological approach, we present analytical expressions for the mean square displacement and for the time-dependent diffusion coefficient of probe particles immersed in a viscoelastic material described by this model. Besides validating our theoretical approach through Brownian dynamics simulations, we show how the model can be used to describe experimental data obtained for polyacrylamide and laponite gels., Comment: 10 pages, 6 figures, 44 references. This manuscript corresponds to the submitted version of the article published as part of the themed collection: Soft Matter Emerging Investigators Series in Soft Matter Journal

In this paper, we present a surrogate-based multiscale approach to model constant strain-rate and creep experiments on unidirectional thermoplastic composites under off-axis loading. In previous contributions, these experiments were modeled through a single-scale micromechanical simulation under the assumption of macroscopic homogeneity. Although efficient and accurate in many scenarios, simulations with low-off axis angles showed significant discrepancies with the experiments. It was hypothesized that the mismatch was caused by macroscopic inhomogeneity, which would require a multiscale approach to capture it. However, full-field multiscale simulations remain computationally prohibitive. To address this issue, we replace the micromodel with a Physically Recurrent Neural Network (PRNN), a surrogate model that combines data-driven components with embedded constitutive models to capture history-dependent behavior naturally. The explainability of the latent space of this network is also explored in a transfer learning strategy that requires no re-training. With the surrogate-based simulations, we confirm the hypothesis raised on the inhomogeneity of the macroscopic strain field and gain insights into the influence of adjustment of the experimental setup with oblique end-tabs. Results from the surrogate-based multiscale approach show better agreement with experiments than the single-scale micromechanical approach over a wide range of settings, although with limited accuracy on the creep experiments, where macroscopic test effects were implicitly taken into account in the material properties calibration., Comment: 21 pages. 31 figures

Cybersecurity threats highlight the need for robust network intrusion detection systems to identify malicious behaviour. These systems rely heavily on large datasets to train machine learning models capable of detecting patterns and predicting threats. In the past two decades, researchers have produced a multitude of datasets, however, some widely utilised recent datasets generated with CICFlowMeter contain inaccuracies. These result in flow generation and feature extraction inconsistencies, leading to skewed results and reduced system effectiveness. Other tools in this context lack ease of use, customizable feature sets, and flow labelling options. In this work, we introduce HERA, a new open-source tool that generates flow files and labelled or unlabelled datasets with user-defined features. Validated and tested with the UNSW-NB15 dataset, HERA demonstrated accurate flow and label generation., Comment: 7 pages, 4 figures, 4 tables, TrustCom-2024 Conference

Effective error detection is crucial to prevent task disruption and maintain user trust. Traditional methods often rely on task-specific models or user reporting, which can be inflexible or slow. Recent research suggests social signals, naturally exhibited by users in response to robot errors, can enable more flexible, timely error detection. However, most studies rely on post hoc analysis, leaving their real-time effectiveness uncertain and lacking user-centric evaluation. In this work, we developed a proactive error detection system that combines user behavioral signals (facial action units and speech), user feedback, and error context for automatic error detection. In a study (N = 28), we compared our proactive system to a status quo reactive approach. Results show our system 1) reliably and flexibly detects error, 2) detects errors faster than the reactive approach, and 3) is perceived more favorably by users than the reactive one. We discuss recommendations for enabling robot error awareness in future HRI systems.

We consider the long-range random field Ising model in dimension $d = 1, 2$, whereas the long-range interaction is of the form $J_{xy} = |x-y|^{-\alpha}$ with $1< \alpha < 3/2$ for $d=1$ and with $2 < \alpha \leq 3$ for $d = 2$. Our main results establish phase transitions in these regimes. In one dimension, we employ a Peierls argument with some novel modification, suitable for dealing with the randomness coming from the external field; in two dimensions, our proof follows that of Affonso, Bissacot, and Maia (2023) with some adaptations, but new ideas are required in the critical case of $\alpha=3$., Comment: 42 pages, 7 figures

High-quality datasets are critical for training machine learning models, as inconsistencies in feature generation can hinder the accuracy and reliability of threat detection. For this reason, ensuring the quality of the data in network intrusion detection datasets is important. A key component of this is using reliable tools to generate the flows and features present in the datasets. This paper investigates the impact of flow exporters on the performance and reliability of machine learning models for intrusion detection. Using HERA, a tool designed to export flows and extract features, the raw network packets of two widely used datasets, UNSW-NB15 and CIC-IDS2017, were processed from PCAP files to generate new versions of these datasets. These were compared to the original ones in terms of their influence on the performance of several models, including Random Forest, XGBoost, LightGBM, and Explainable Boosting Machine. The results obtained were significant. Models trained on the HERA version of the datasets consistently outperformed those trained on the original dataset, showing improvements in accuracy and indicating a better generalisation. This highlighted the importance of flow generation in the model's ability to differentiate between benign and malicious traffic., Comment: 9 pages, 10 tables, ICISSP 2025 conference

Software engineering agents (swe-agents), as key innovations in intelligent software engineering, are poised in the industry's end-of-programming debate to transcend from assistance to primary roles. we argue the importance of swe-agents' economic viability to their transcendence -- defined as their capacity to maintain efficient operations in constrained environments -- and propose its exploration via software engineering economics experimentation.we introduce ghissuemarket sandbox, a controlled virtual environment for swe-agents' economic experimentation, simulating the environment of an envisioned peer-to-peer multiagent system for github issues outsourcing auctions. in this controlled setting, autonomous swe-agents auction and bid on github issues, leveraging real-time communication, a built-in retrieval-augmented generation (rag) interface for effective decision-making, and instant cryptocurrency micropayments. we open-source our software artifacts, discuss our sandbox engineering decisions, and advocate towards swe-agents' economic exploration -- an emerging field we intend to pursue under the term intelligent software engineering economics (isee)., Comment: 2 figures

The increased capabilities of generative AI have dramatically expanded its possible use cases in medicine. We provide a comprehensive overview of generative AI use cases for clinicians, patients, clinical trial organizers, researchers, and trainees. We then discuss the many challenges -- including maintaining privacy and security, improving transparency and interpretability, upholding equity, and rigorously evaluating models -- which must be overcome to realize this potential, and the open research directions they give rise to., Comment: To appear in the Annual Review of Biomedical Data Science, August 2025

We prove that any lower triangular and totally nonnegative matrix whose diagonal entries are all equal to one gives rise to a family real-rooted polynomials. This is used to develop a general theory for proving that chain polynomials of rank uniform posets are real-rooted. The results obtained extend and unify results of the first author, Brenti, Welker and Athanasiadis. In the process we define a notion of $h$-vectors for a large class of posets which generalize the $h$-vectors commonly associated to simplicial and cubical complexes. This answers a question of Alder on defining a $h$-vectors for $q$-posets. A consequence of our methods is a characterization of the convex hull of all characteristic polynomials of hyperplane arrangements of fixed dimension and over a fixed finite field. This may be seen as a refinement of the critical problem of Crapo and Rota. We also use the methods developed to answer an open problem posed by Forg\'acs and Tran on the real-rootedness of polynomials arising from certain bivariate rational functions.

Scaling laws for large language models (LLMs) predict model performance based on parameters like size and training data. However, differences in training configurations and data processing across model families lead to significant variations in benchmark performance, making it difficult for a single scaling law to generalize across all LLMs. On the other hand, training family-specific scaling laws requires training models of varying sizes for every family. In this work, we propose Skills Scaling Laws (SSLaws, pronounced as Sloth), a novel scaling law that leverages publicly available benchmark data and assumes LLM performance is driven by low-dimensional latent skills, such as reasoning and instruction following. These latent skills are influenced by computational resources like model size and training tokens but with varying efficiencies across model families. Sloth exploits correlations across benchmarks to provide more accurate and interpretable predictions while alleviating the need to train multiple LLMs per family. We present both theoretical results on parameter identification and empirical evaluations on 12 prominent benchmarks, from Open LLM Leaderboard v1/v2, demonstrating that Sloth predicts LLM performance efficiently and offers insights into scaling behaviors for downstream tasks such as coding and emotional intelligence applications.

The principles of data spaces for sovereign data exchange across trusted organizations have so far mainly been adopted in business-to-business settings, and recently scaled to cloud environments. Meanwhile, research organizations have established distributed research data infrastructures, respecting the principle that data must be FAIR, i.e., findable, accessible, interoperable and reusable. For mutual benefit of these two communities, the FAIR Data Spaces project aims to connect them towards the vision of a common, cloud-based data space for industry and research. Thus, the project establishes a common legal and ethical framework, common technical building blocks, and it demonstrates the orchestration of multiple building blocks in self-contained settings addressing a diverse range of use cases in domains including health, biodiversity, and engineering. This paper gives a summary of all demonstrators, ranging from research data infrastructures scaled to industry-ready cloud environments to work in progress on building bridges between operational business-to-business data spaces and research data infrastructures., Comment: Accepted at the 40th ACM/SIGAPP Symposium On Applied Computing

Synthetic data generation with Large Language Models is a promising paradigm for augmenting natural data over a nearly infinite range of tasks. Given this variety, direct comparisons among synthetic data generation algorithms are scarce, making it difficult to understand where improvement comes from and what bottlenecks exist. We propose to evaluate algorithms via the makeup of synthetic data generated by each algorithm in terms of data quality, diversity, and complexity. We choose these three characteristics for their significance in open-ended processes and the impact each has on the capabilities of downstream models. We find quality to be essential for in-distribution model generalization, diversity to be essential for out-of-distribution generalization, and complexity to be beneficial for both. Further, we emphasize the existence of Quality-Diversity trade-offs in training data and the downstream effects on model performance. We then examine the effect of various components in the synthetic data pipeline on each data characteristic. This examination allows us to taxonomize and compare synthetic data generation algorithms through the components they utilize and the resulting effects on data QDC composition. This analysis extends into a discussion on the importance of balancing QDC in synthetic data for efficient reinforcement learning and self-improvement algorithms. Analogous to the QD trade-offs in training data, often there exist trade-offs between model output quality and output diversity which impact the composition of synthetic data. We observe that many models are currently evaluated and optimized only for output quality, thereby limiting output diversity and the potential for self-improvement. We argue that balancing these trade-offs is essential to the development of future self-improvement algorithms and highlight a number of works making progress in this direction.

Network Intrusion Detection (NID) systems can benefit from Machine Learning (ML) models to detect complex cyber-attacks. However, to train them with a great amount of high-quality data, it is necessary to perform reliable simulations of multiple interacting machines. This paper presents a network simulation solution for the creation of NID datasets with complex attack scenarios. This solution was integrated in the Airbus CyberRange platform to benefit from its simulation capabilities of generating benign and malicious traffic patterns that represent realistic cyber-attacks targeting a computer network. A realistic vulnerable network topology was configured in the CyberRange and three different attack scenarios were implemented: Man-in-the-Middle (MitM), Denial-of-Service (DoS), and Brute-Force (BF)., Comment: 6 pages, 4 figures, CRITIS 2024 conference

We introduce a new class of moir\'e systems and materials based on monolayers with triangular lattices and low-energy states at the M points of the Brillouin zone. These M-point moir\'e materials are fundamentally distinct from those derived from $\Gamma$- or K-point monolayers, featuring three time-reversal-preserving valleys related by three-fold rotational symmetry. We propose twisted bilayers of experimentally exfoliable 1T-SnSe$_2$ and 1T-ZrS$_2$ as realizations of this new class. Using extensive ab initio simulations, we develop quantitative continuum models and analytically show that the corresponding M-point moir\'e Hamiltonians exhibit emergent momentum-space non-symmorphic symmetries and a kagome plane-wave lattice in momentum space. This represents the first experimentally viable realization of a projective representation of crystalline space groups in a non-magnetic system. With interactions, these materials represent six-flavor Hubbard simulators with Mott physics, as can be seen by their flat Wilson loops. Furthermore, the presence of a non-symmorphic momentum-space in-plane mirror symmetry makes some of the M-point moir\'e Hamiltonians quasi-one-dimensional in each valley, suggesting the possibility of realizing Luttinger liquid physics. We predict the twist angles at which a series of (conduction) flat bands appear, provide a faithful continuum Hamiltonian, analyze its topology and charge density and briefly discuss several aspects of the physics of this new platform., Comment: 7+124 pages, 5+132 figures, 1+27 tables. Previously submitted. See also arXiv:2411.08950 and arXiv:2411.09741

Single-particle imaging (SPI) using X-ray free-electron Lasers (XFELs) offers the potential to determine protein structures at high spatial and temporal resolutions without the need for crystallization or vitrification. However, the technique faces challenges due to weak diffraction signals from single proteins and significant background scattering from gases used for sample delivery. A recent observation of a diffraction pattern from an isolated GroEL protein complex had similar numbers of signal and background photons. Ongoing efforts aim to reduce the background created by sample delivery, with one approach replacing most of the used gas with helium. In this study, we investigate the effects of a potentially reduced background on the resolution limits for SPI of isolated proteins under different experiment conditions. As a test case, we used GroEL, and we used experimentally measured parameters for our simulations. We observe that background significantly impacts the achievable resolution, particularly when the signal strength is comparable to the background, and a background reduction would lead to a significant improvement in resolution.

Rocky planets in our Solar System, namely Mercury, Venus, Earth, Mars, and the Moon, which is generally added to this group due to its geological complexity, possess a solid surface and share a common structure divided into major layers, namely a silicate crust, a silicate mantle, and an iron-rich core. However, while all terrestrial planets share a common structure, the thickness of their interior layers, their bulk chemical composition, and surface expressions of geological processes are often unique to each of them. In this chapter we provide an overview of the surfaces and interiors of rocky planets in the Solar System. We list some of the major discoveries in planetary exploration and discuss how they have helped to answer fundamental questions about planetary evolution while at the same time opening new avenues. For each of the major planetary layers, i.e., the surface, the crust and lithosphere, the mantle, and the core, we review key geological and geophysical processes that have shaped the planets that we observe today. Understanding the similarities and differences between the terrestrial planets in the Solar System will teach us about the diversity of evolutionary paths a planet could follow, helping us to better understand our own home, the Earth., Comment: Preprint of a chapter for the 'Encyclopedia of Astrophysics' (Editor-in-Chief Ilya Mandel, Section Editor Dimitri Veras) to be published by Elsevier as a Reference Module

The study of twisted two-dimensional (2D) materials, where twisting layers create moir\'e superlattices, has opened new opportunities for investigating topological phases and strongly correlated physics. While systems such as twisted bilayer graphene (TBG) and twisted transition metal dichalcogenides (TMDs) have been extensively studied, the broader potential of a seemingly infinite set of other twistable 2D materials remains largely unexplored. In this paper, we define "theoretically twistable materials" as single- or multi-layer structures that allow for the construction of simple continuum models of their moir\'e structures. This excludes, for example, materials with a "spaghetti" of bands or those with numerous crossing points at the Fermi level, for which theoretical moir\'e modeling is unfeasible. We present a high-throughput algorithm that systematically searches for theoretically twistable semimetals and insulators based on the Topological 2D Materials Database. By analyzing key electronic properties, we identify thousands of new candidate materials that could host rich topological and strongly correlated phenomena when twisted. We propose representative twistable materials for realizing different types of moir\'e systems, including materials with different Bravais lattices, valleys, and strength of spin-orbital coupling. We provide examples of crystal growth for several of these materials and showcase twisted bilayer band structures along with simplified twisted continuum models. Our results significantly broaden the scope of moir\'e heterostructures and provide a valuable resource for future experimental and theoretical studies on novel moir\'e systems., Comment: 15+81 pages, 5+187 figures, 4+104 tables. The Topological 2D Materials Database is available at https://topologicalquantumchemistry.com/topo2d/index.html . See also the accompanying paper "Two-dimensional Topological Quantum Chemistry and Catalog of Topological Materials"

Often in prediction tasks, the predictive model itself can influence the distribution of the target variable, a phenomenon termed performative prediction. Generally, this influence stems from strategic actions taken by stakeholders with a vested interest in predictive models. A key challenge that hinders the widespread adaptation of performative prediction in machine learning is that practitioners are generally unaware of the social impacts of their predictions. To address this gap, we propose a methodology for learning the distribution map that encapsulates the long-term impacts of predictive models on the population. Specifically, we model agents' responses as a cost-adjusted utility maximization problem and propose estimates for said cost. Our approach leverages optimal transport to align pre-model exposure (ex ante) and post-model exposure (ex post) distributions. We provide a rate of convergence for this proposed estimate and assess its quality through empirical demonstrations on a credit-scoring dataset.

The emergence of superconductivity in the octahedrally coordinated (1T) phase of TaS2 is preceded by the intriguing loss of long-range order in the charge density wave (CDW). Such decoherence, attainable by different methods, results in the formation of nm-sized coherent CDW domains bound by a two-dimensional network of domain walls (DW) - mosaic phase -, which has been proposed as the spatial origin of the superconductivity. Here, we report the atomic-scale characterization of the superconducting state of 1T-TaSSe, a model 1T compound exhibiting the CDW mosaic phase. We use high-resolution scanning tunneling spectroscopy and Andreev spectroscopy to probe the microscopic nature of the superconducting state in unambiguous connection with the electronic structure of the mosaic phase. Spatially resolved conductance maps at the Fermi level at the onset of superconductivity reveal that the density of states is mostly localized on the CDW domains compared to the domain walls, which suggests their dominant role in the formation of superconductivity. This scenario is confirmed within the superconducting dome at 340 mK, where superconductivity is fully developed, and the subtle spatial inhomogeneity of the superconducting gap remains unlinked to the domain wall network. Our results provide key new insights into the fundamental interplay between superconductivity and CDW in these relevant strongly correlated systems.

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

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

Altermagnets are a newly discovered class of magnetic phases that combine the spin polarization behavior of ferromagnetic band structures with the vanishing net magnetization characteristic of antiferromagnets. Initially proposed for collinear magnets, the concept has since been extended to include certain non-collinear structures. A recent development in Landau theory for collinear altermagnets incorporates spin-space symmetries, providing a robust framework for identifying this class of materials. Here we expand on that theory to identify altermagnetic multipolar order parameters in non-collinear chiral materials. We demonstrate that the interplay between non-collinear altermagnetism and chirality allows for spatially odd multipole components, leading to non-trivial spin textures on Fermi surfaces and unexpected transport phenomena, even in the absence of SOC. This makes such chiral altermagnets fundamentally different from the well-known SOC-driven Rashba-Edelstein and spin Hall effects used for 2D spintronics. Choosing the chiral topological magnetic material Mn$_3$IrSi as a case study, we apply toy models and first-principles calculations to predict experimental signatures, such as large spin-Hall and Edelstein effects, that have not been previously observed in altermagnets. These findings pave the way for a new realm of spintronics applications based on spin-transport properties of chiral altermagnets., Comment: 22 pages, 3 figures

The growing use of composite materials in engineering applications has accelerated the demand for computational methods to accurately predict their complex behavior. Multiscale modeling based on computational homogenization is a potentially powerful approach for this purpose, but its widespread adoption is prevented by its excessive computational costs. A popular approach to address this computational bottleneck is using surrogate models, which have been used to successfully predict a wide range of constitutive behaviors. However, applications involving microscale damage and fracture remain largely unexplored. This work aims to extend a recent surrogate modeling approach, the Physically Recurrent Neural Network (PRNN), to include the effect of debonding at the fiber-matrix interface while capturing path-dependent behavior. The core idea of the PRNN is to implement the exact material models from the micromodel into one of the layers of the network. In this work, additional material points with a cohesive zone model are integrated within the network, along with the bulk points associated to the fibers and/or matrix. The limitations of the existing architecture are discussed and taken into account for the development of novel architectures that better represent the stress homogenization procedure. In the proposed layout, the history variables of cohesive points act as extra latent features that help determine the local strains of bulk points. Different architectures are evaluated starting with small training datasets. To maximize the predictive accuracy and extrapolation capabilities of the network, various configurations of bulk and cohesive points are explored, along with different training dataset types and sizes.

Accurate characterization of quantum yield is crucial to the reconstruction of energy depositions in silicon at the eV scale. This work presents a new method for experimentally calculating quantum yield using vacuum UV-sensitive silicon photomultipliers (SiPMs), which can be used to determine the probabilities that a UV photon absorbed in a silicon crystal will produce one, two, or three electron-hole pairs. Results are presented which fully constrain the distribution at photon energies up to 7.75eV. This method works by exploiting the saturation of photon detection efficiency which occurs when these devices are biased sufficiently high above their avalanche breakdown voltage. The measured quantum yield values are lower than those that have been previously reported by experimental data and modelling -- this is expected to impact the sensitivity of experiments searching for light dark matter through direct detection in semiconductors, and should also be taken into account when characterizing the performance of UV photodetectors with high quantum efficiency. Measurements have been taken using a Hamamatsu VUV4 and an FBK VUV-HD3 device, showing good agreements between devices, and at a range of temperatures from 163-233K. The validity of the method is assessed using supplementary measurements of absolute photon detection efficiency, and an additional novel method of measuring average quantum yield using DC current-voltage measurements of SiPMs is presented and used for corroboration., Comment: 13 pages, 11 figures

The large-scale training of multi-modal models on data scraped from the web has shown outstanding utility in infusing these models with the required world knowledge to perform effectively on multiple downstream tasks. However, one downside of scraping data from the web can be the potential sacrifice of the benchmarks on which the abilities of these models are often evaluated. To safeguard against test data contamination and to truly test the abilities of these foundation models we propose LiveXiv: A scalable evolving live benchmark based on scientific ArXiv papers. LiveXiv accesses domain-specific manuscripts at any given timestamp and proposes to automatically generate visual question-answer pairs (VQA). This is done without any human-in-the-loop, using the multi-modal content in the manuscripts, like graphs, charts, and tables. Moreover, we introduce an efficient evaluation approach that estimates the performance of all models on the evolving benchmark using evaluations of only a subset of models. This significantly reduces the overall evaluation cost. We benchmark multiple open and proprietary Large Multi-modal Models (LMMs) on the first version of our benchmark, showing its challenging nature and exposing the models true abilities, avoiding contamination. Lastly, in our commitment to high quality, we have collected and evaluated a manually verified subset. By comparing its overall results to our automatic annotations, we have found that the performance variance is indeed minimal (<2.5%). Our dataset is available online on HuggingFace, and our code will be available here.

We explore a reduced-order model (ROM) of plane Couette flow with a view to performing turbulence control. The ROM is derived through Galerkin projections of the incompressible Navier-Stokes (NS) system onto a basis composed of controllability modes, truncated to a few streamwise and spanwise wavenumbers. Such ROMs were found to reproduce key aspects of nonlinear turbulence dynamics in Couette flow with only a few hundreds degrees of freedom. Here we use the ROM to devise a control strategy. For that, we consider a ROM with an extra forcing term, consisting in a steady body force. The spatial structure of the forcing is given by a linear combination of Stokes modes, optimised using a gradient-descent algorithm in order to minimise the total fluctuation energy. The optimisation is performed at different Reynolds numbers, with the optimal forcing leading to laminarisation of the flow in all cases. The forcing mechanism acts by strongly reducing the shear in a large central portion of the channel. This disrupts the dynamics of large-scale streaks and rolls and hinders the main energy input to the system. When the forcing is active, the flow reaches a new laminar state which is linearly stable and whose linear transient growth is substantially reduced with respect to that of laminar Couette flow. These features prompt the flow to return to the laminar Couette state when the forcing is switched off. Body forces optimised in the ROM are subsequently applied to the full NS system in direct numerical simulations (DNS) for the same flow configurations. The same control mechanisms are observed in the DNS, where laminarisation is also achieved. The present work opens up interesting possibilities for turbulence control. We show that the ROMs provide an effective framework to design turbulence control strategies, despite the high degree of truncation with respect to the full system.

A single material can exist in different states, with solids, liquids, and gases being the most familiar examples. In materials, these states can exhibit periodic structures spanning from atomic to macroscopic length scales. The conventional wisdom is that a low-symmetry periodic structure transitions into a high-symmetry structure as temperature increases beyond the critical point, which is defined by a thermal limit. In this work, we demonstrate an unforeseen emergence of low-symmetry modulated structures with a reentrant phase in nanoflakes of two-dimensional TaCo2Te2 far beyond their thermal limit, using in-situ heating transmission electron microscopy. We contend that entropy can drive the reappearance of structural modulations, consistent with predicted dynamic structural instabilities in undistorted TaCo2Te2, and further supported by Raman measurements. These findings not only reveal unexpected phase transitions in a crystalline material but also present a new pathway for creating novel ordered phases in low-dimensional systems., Comment: 36 pages, 19 figures

Despite significant advancements and pervasive use of vision-language models, a paucity of studies has addressed their ethical implications. These models typically require extensive training data, often from hastily reviewed text and image datasets, leading to highly imbalanced datasets and ethical concerns. Additionally, models initially trained in English are frequently fine-tuned for other languages, such as the CLIP model, which can be expanded with more data to enhance capabilities but can add new biases. The CAPIVARA, a CLIP-based model adapted to Portuguese, has shown strong performance in zero-shot tasks. In this paper, we evaluate four different types of discriminatory practices within visual-language models and introduce FairPIVARA, a method to reduce them by removing the most affected dimensions of feature embeddings. The application of FairPIVARA has led to a significant reduction of up to 98% in observed biases while promoting a more balanced word distribution within the model. Our model and code are available at: https://github.com/hiaac-nlp/FairPIVARA., Comment: 14 pages, 10 figures. Accepted to 35th British Machine Vision Conference (BMVC 2024), Workshop on Privacy, Fairness, Accountability and Transparency in Computer Vision

Supporting older adults in health self-management is crucial for promoting independent aging, particularly given the growing strain on healthcare systems. While voice assistants (VAs) hold the potential to support aging in place, they often lack tailored assistance and present usability challenges. We addressed these issues through a five-stage design process with older adults to develop a personal health assistant. Starting with in-home interviews (N=17), we identified two primary challenges in older adult's health self-management: health awareness and medical adherence. To address these challenges, we developed a high-fidelity LLM-powered VA prototype to debrief doctor's visit notes and generate tailored medication reminders. We refined our prototype with feedback from co-design workshops (N=10) and validated its usability through in-home studies (N=5). Our work highlights key design features for personal health assistants and provides broader insights into desirable VA characteristics, including personalization, adapting to user context, and respect for user autonomy.

Dense suspensions often exhibit shear thickening, characterized by a dramatic increase in viscosity under large external forcing. This behavior has recently been linked to the formation of a system-spanning force chain network (FCN), which contributes to increased resistance during deformation. However, identifying these force chains poses experimental challenges and is computationally expensive. This study introduces a Graph Neural Network (GNN) model designed to accurately predict FCNs in two dimensional simulations of dense shear thickening suspensions. The results demonstrate the GNN model's robustness and scalability across various stress levels $(\sigma)$, packing fractions$(\phi)$, system sizes, particle size ratios$(\Delta)$, and amount of smaller particles. The model is further able to predict both the occurrence and structure of the FCN. The presented model is accurate and interpolates and extrapolates to conditions far from its control parameters. This machine learning approach provides more accurate, cheaper, and faster predictions of suspension properties as compared to conventional methods, while using only small systems. Ultimately, our findings pave the way for predicting force chain networks in real-life large-scale polydisperse suspensions, for which theoretical models are largely limited due to computational challenges.

This paper presents a Digital Twin (DT) framework for the remote control of an Autonomous Guided Vehicle (AGV) within a Network Control System (NCS). The AGV is monitored and controlled using Integrated Sensing and Communications (ISAC). In order to meet the real-time requirements, the DT computes the control signals and dynamically allocates resources for sensing and communication. A Reinforcement Learning (RL) algorithm is derived to learn and provide suitable actions while adjusting for the uncertainty in the AGV's position. We present closed-form expressions for the achievable communication rate and the Cramer-Rao bound (CRB) to determine the required number of Orthogonal Frequency-Division Multiplexing (OFDM) subcarriers, meeting the needs of both sensing and communication. The proposed algorithm is validated through a millimeter-Wave (mmWave) simulation, demonstrating significant improvements in both control precision and communication efficiency.

Coherent Diffraction Imaging (CDI) is an experimental technique to gain images of isolated structures by recording the light scattered off the sample. In principle, the sample density can be recovered from the scattered light field through a straightforward Fourier Transform operation. However, only the amplitude of the field is recorded, while the phase is lost during the measurement process and has to be retrieved by means of suitable, well-established, phase retrieval algorithms. In this work we present SPRING, an analysis framework tailored on X-ray Free Electron Laser (XFEL) diffraction data that implements the Memetic Phase Retrieval method to mitigate the shortcomings of conventional algorithms. We benchmark the approach on experimental data acquired in two experimental campaigns at SwissFEL and European XFEL. Imaging results on isolated nanostructures reveal unprecedented stability and resilience of the algorithm's behavior on the input parameters, as well as the capability of identifying the solution in conditions hardly treatable so far with conventional methods. A user-friendly implementation of SPRING is released as open-source software, aiming at being a reference tool for the coherent diffraction imaging community at XFEL and synchrotron facilities., Comment: 30 pages, 13 figures. Authors list updated

Chirality pervades natural processes from the atomic to the cosmic scales, crucially impacting molecular chemistry and pharmaceutics. Traditional chirality sensing methods face challenges in sensitivity and efficiency, prompting the quest of novel chiral recognition solutions based on nanophotonics. In this work we theoretically investigate the possibility to carry out enantiomeric discrimination by measuring the spontaneous emission rate of chiral molecules on twisted two-dimensional materials. We first present a general theoretical framework based on dyadic Green's functions to calculate the chiral contribution to the decay rate in the presence of a generic chiral bilayer interface. We then combine this theory with density functional theory to obtain numerical estimates of the decay rate of helical bilayer nanographene molecules placed on top of twisted bilayer graphene., Comment: 9 pages, 2 figures, 6 appendices. Draft version. Comments are welcome!

Selective sensing of chiral molecules is a key aspect in fields spanning biology, chemistry, and pharmacology. However, conventional optical methods, such as circular dichroism (CD), encounter limitations owing to weak chiral light-matter interactions. Several strategies have been investigated to enhance CD or circularly polarised luminescence (CPL), including superchiral light, plasmonic nanoresonators and dielectric nanostructures. However, a compromise between spatial uniformity and high sensitivity, without requiring specific molecular functionalization, remains a challenge. In this work, we propose a novel approach using twisted bilayer graphene (TBG), a chiral 2D material with a strong CD peak which energy is tunable through the twist angle. By matching the CD resonance of TBG with the optical transition energy of the molecule, we achieve a decay rate enhancement mediated by resonant energy transfer that depends on the electric-magnetic interaction, that is, on the chirality of both the molecules and TBG. This leads to an enantioselective quenching of the molecule fluorescence, allowing to retrieve the molecule chirality from time-resolved photoluminescence measurements. This method demonstrates high sensitivity down to single layer of molecules, with the potential to achieve the ultimate goal of single-molecule chirality sensing, while preserving the spatial uniformity and integrability of 2D heterostructures.

Orbiting space objects have become in the last decade a major nuisance impacting ground astronomy and orbiting space assets, from observatories to satellites and space stations. In particular with the rise of the satellite population in Low Earth Orbits (LEOs), space objects are becoming an even bigger threat and a strong problem to astronomical observations. To tackle these threats several coordinated surveillance networks composed of dedicated sensors (telescopes, radars and laser ranging facilities) track and survey space objects, from debris to active satellites. As part of the European Space Surveillance \& Tracking (EU-SST) network, Portugal is developing the PAmpilhosa da Serra Space Observatory (PASO), with both radio and optical telescopes dedicated to the Space Situational Awareness (SSA) domain, deployed at a Dark Sky destination. To optimize telescope survey time, we developed {\tt{CLOWN}} (CLOud Watcher at Night), an application interface that automatically monitors clouds in real time. This software can correctly trace clouds positions in the sky, provides accurate pointing information to the observation planning of the optical telescope to avoid cloudy areas. {\tt{CLOWN}} only requires the use of an all-sky camera, which is already a norm in observatories with optical telescopes and can be used with any camera, including those for which no information about its model specification do exist. {\tt{CLOWN}} does not require great computing power and it does not require the installation of additional equipment. {\tt{CLOWN}} results are very promising and confirm that the app can correctly identify clouds in a variety of different conditions and cloud types., Comment: 15 pages, 22 figures. This is the Accepted Manuscript version of an article accepted for publication in Astromomical Journal. IOP Publishing Ltd is not responsible for any errors or omissions in this version of the manuscript or any version derived from it. The Version of Record will be available online at https://iopscience.iop.org/journal/1538-3881

When microspheres are illuminated by tightly focused vortex beams, they can be trapped in a non-equilibrium steady state where they orbit around the optical axis. By using the Mie-Debye theory for optical tweezers, we demonstrate that the orbital period strongly depends on the particle's chirality index. Taking advantage of such sensitivity, we put forth a method to experimentally characterize with high precision the chiroptical response of individual optically trapped particles. The method allows for an enhanced precision at least one order of magnitude larger than that of similar existing enantioselective approaches. It is particularly suited to probe the chiroptical response of individual particles, for which light-chiral matter interactions are typically weak.

In this article, we introduce Lyapunov-type results to investigate the stability of the trivial solution of a Stieltjes dynamical system. We utilize prolongation results to establish the global existence of the maximal solution. Using Lyapunov's second method, we establish results of (uniform) stability and (uniform) asymptotic stability by employing a Lyapunov function. Additionally, we present examples and real-life applications to study asymptotic stability of equilibria in two population dynamics models., Comment: 37 pages, 10 figures