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We report on the results of performing computational gravitational backreaction on cosmic string loops taken from a network simulation. The principal effect of backreaction is to smooth out small-scale structure on loops, which we demonstrate by various measures including the average loop power spectrum and the distribution of kink angles on the loops. Backreaction does lead to self-intersections in most cases, but these are typically small. An important effect discussed in prior work is the rounding off of kinks to form cusps, but we find that the cusps produced by that process are very weak and do not significantly contribute to the total gravitational-wave radiation of the loop. We comment briefly on extrapolating our results to loops as they would be found in nature., Comment: 26 pages, 11 figures. Power spectrum data available via Zenodo: https://doi.org/10.5281/zenodo.14037540

The distribution of chemical elements in the Galactic disc can reveal fundamental clues on the physical processes that led to the current configuration of our Galaxy. We map chemical azimuthal variations in the disc using individual stellar chemical abundances and discuss their possible connection with the spiral arms and other perturbing mechanisms. Using Gaia Data Release 3, we examine [Ca/Fe] and [Mg/Fe] fluctuations in a ~4 kpc region around the Sun, focusing on bright giant stars. We implemented a kernel density estimator technique to enhance the chemical inhomogeneities. We observe radial gradients and azimuthal fluctuations in [alpha/Fe] for young (<150 Myr) and old (>2 Gyr) stars, with amplitudes varying according to the studied element. In young stars, those within spiral arms (e.g., Sagittarius-Carina and Local arms) are generally more metal and calcium-rich (~0-0.19 dex) but show lower [Ca/Fe] (~0.06 dex) and [Mg/Fe] (~0.05 dex) compared to inter-arm regions, suggesting enhanced iron production in spiral arms. These [alpha/Fe] depletions are analysed in light of theoretical scenarios and compared to a 2D chemical evolution model with multiple spiral patterns. For the old sample, [Ca/Fe] maps reveal deficiencies along a segment of the Local arm identified by young stars. We caution that, for this old sample, the quality of the obtained maps might be limited along a specific line-of-sight, due to the Gaia scanning law. This study transitions our understanding of disc chemical evolution from a 1D radial view to a more detailed 2D framework incorporating radial, azimuthal, and small-scale variations. Individual chemical abundances prove essential for tracing spiral arms in disc galaxies. We recommend models and simulations incorporate alpha-abundance trends to better address spiral arm lifetimes.

Understanding the interplay of non-Hermiticity and topology is crucial given the intrinsic openness of most natural and engineered systems and it has important ramifications in topological lasers and sensors. Intense efforts have been devoted to unveiling how non-Hermiticity may impact the most significant features of topological systems, but only recently it has been theoretically proposed that topological features could originate solely from the system's non-Hermiticity in photonic systems. In this work, we experimentally demonstrate the appearance of non-Hermitian topology exclusively from loss modulation in a photonic system that is topologically trivial in the absence of loss. We do this by implementing a non-Hermitian generalization of an Aubry-Andre-Harper model with purely imaginary potential in a programmable integrated photonics platform, which allows us to investigate different periodic and quasi-periodic configurations of the model. In both cases, we show the emergence of topological edge modes and explore their resilience to different kinds of disorder. Our work highlights loss engineering as a mechanism to generate topological properties.

In recent years, it has become increasingly clear that space weather disturbances can be triggered by transient upstream mesoscale structures (TUMS), independently of the occurrence of large-scale solar wind (SW) structures, such as interplanetary coronal mass ejections and stream interaction regions. Different types of magnetospheric pulsations, transient perturbations of the geomagnetic field and auroral structures are often observed during times when SW monitors indicate quiet conditions, and have been found to be associated to TUMS. In this mini-review we describe the space weather phenomena that have been associated with four of the largest-scale and the most energetic TUMS, namely hot flow anomalies, foreshock bubbles, travelling foreshocks and foreshock compressional boundaries. The space weather phenomena associated with TUMS tend to be more localized and less intense compared to geomagnetic storms. However, the quiet time space weather may occur more often since, especially during solar minima, quiet SW periods prevail over the perturbed times., Comment: 1 figure, 1 table

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

Modern digital applications extensively integrate Artificial Intelligence models into their core systems, offering significant advantages for automated decision-making. However, these AI-based systems encounter reliability and safety challenges when handling continuously generated data streams in complex and dynamic scenarios. This work explores the concept of resilient AI systems, which must operate in the face of unexpected events, including instances that belong to patterns that have not been seen during the training process. This is an issue that regular closed-set classifiers commonly encounter in streaming scenarios, as they are designed to compulsory classify any new observation into one of the training patterns (i.e., the so-called \textit{over-occupied space} problem). In batch learning, the Open Set Recognition research area has consistently confronted this issue by requiring models to robustly uphold their classification performance when processing query instances from unknown patterns. In this context, this work investigates the application of an Open Set Recognition framework that combines classification and clustering to address the \textit{over-occupied space} problem in streaming scenarios. Specifically, we systematically devise a benchmark comprising different classification datasets with varying ratios of known to unknown classes. Experiments are presented on this benchmark to compare the performance of the proposed hybrid framework with that of individual incremental classifiers. Discussions held over the obtained results highlight situations where the proposed framework performs best, and delineate the limitations and hurdles encountered by incremental classifiers in effectively resolving the challenges posed by open-world streaming environments., Comment: 12 pages, 3 figures, an updated version of this article is published in LNAI,volume 14857 as part of the conference proceedings HAIS 2024

We study the formation of gravitating global strings through quantum mechanical tunneling. The instantons that describe the nucleation process are characterized by two parameters: the string core thickness and its gravitational backreaction controlled by the string core energy density. We obtain solutions across a wide range of these parameters by carrying out numerical integration via multiple shooting methods. Our results are in agreement with previous findings on the nucleation of other topological defects; specifically, after reaching a certain threshold for the string core thickness or its gravitational backreaction, the configuration becomes homogeneous in a manner akin to Hawking-Moss solutions. Additionally, we analyze the global structure of the analytical continuation of the solutions to Lorentzian signature, revealing the emergence of a region of spacetime that describes an anisotropic universe. Finally, we also discuss the relevance of these instantons in the context of quantum cosmology., Comment: 36 pages, 17 figures

Interplanetary shocks are fundamental constituents of the heliosphere, where they form as a result of solar activity. We use previously unavailable measurements of interplanetary shocks in the inner heliosphere provided by Solar Orbiter, and present a survey of the first 100 shocks observed in situ at different heliocentric distances during the rising phase of solar cycle 25. The fundamental shock parameters (shock normals, shock normal angles, shock speeds, compression ratios, Mach numbers) have been estimated and studied as a function of heliocentric distance, revealing a rich scenario of configurations. Comparison with large surveys of shocks at 1~au show that shocks in the quasi-parallel regime and with high speed are more commonly observed in the inner heliosphere. The wave environment of the shocks has also been addressed, with about 50\% of the events exhibiting clear shock-induced upstream fluctuations. We characterize energetic particle responses to the passage of IP shocks at different energies, often revealing complex features arising from the interaction between IP shocks and pre-existing fluctuations, including solar wind structures being processed upon shock crossing. Finally, we give details and guidance on the access use of the present survey, available on the EU-project ``solar energetic particle analysis platform for the inner heliosphere'' (SERPENTINE) website. The algorithm used to identify shocks in large datasets, now publicly available, is also described.

Blockchain has been widely criticized due to the use of inefficient consensus protocols and energy-intensive mechanisms that derived into a global enormous power consumption. Fortunately, since the first blockchain was conceived in 2008 (the one that supports Bitcoin), hardware and consensus protocols have evolved, decreasing energy consumption significantly. This article describes a green blockchain solution and quantifies energy savings when deploying the system on traditional computers and embedded Single-Board Computers (SBCs). To illustrate such savings, it is proposed a solution for tackling the problem of academic certificate forgery, which has a significant cost to society, since it harms the trustworthiness of certificates and academic institutions. The proposed solution is aimed at recording and verifying academic records (ARs) through a decentralized application (DApp) that is supported by a smart contract deployed in the Ethereum blockchain. The application stores the raw data (i.e., the data that are not managed by the blockchain) on a decentralized storage system based on Inter-Planetary File System (IPFS). To demonstrate the efficiency of the developed solution, it is evaluated in terms of performance (transaction latency and throughput) and efficiency (CPU usage and energy consumption), comparing the results obtained with a traditional Proof-of-Work (PoW) consensus protocol and the new Proof-of-Authority (PoA) protocol. The results shown in this paper indicate that the latter is clearly greener and demands less CPU load. Moreover, this article compares the performance of a traditional computer and two SBCs (a Raspberry Pi 4 and an Orange Pi One), showing that is possible to make use of the latter low-power devices to implement blockchain nodes for proposed DApp, but at the cost of higher response latency that varies greatly depending on the used SBCs [...]

Counterspeech is a targeted response to counteract and challenge abusive or hateful content. It can effectively curb the spread of hatred and foster constructive online communication. Previous studies have proposed different strategies for automatically generated counterspeech. Evaluations, however, focus on the relevance, surface form, and other shallow linguistic characteristics. In this paper, we investigate the human likeness of AI-generated counterspeech, a critical factor influencing effectiveness. We implement and evaluate several LLM-based generation strategies, and discover that AI-generated and human-written counterspeech can be easily distinguished by both simple classifiers and humans. Further, we reveal differences in linguistic characteristics, politeness, and specificity.

We implement a simulation environment on top of NetSquid that is specifically designed for estimating the end-to-end fidelity across a path of quantum repeaters or quantum switches. The switch model includes several generalizations which are not currently available in other tools, and are useful for gaining insight into practical and realistic quantum network engineering problems: an arbitrary number of memory registers at the switches, simplicity in including entanglement distillation mechanisms, arbitrary switching topologies, and more accurate models for the depolarization noise. An illustrative case study is presented, namely a comparison in terms of performance between a repeater chain where repeaters can only swap sequentially, and a single switch equipped with multiple memory registers, able to handle multiple swapping requests.

The relationship between UV Bursts and solar surges is complex, with these events sometimes being observed together and sometimes being observed independently. Why this sporadic association exists is unknown, however, it likely relates to the physical conditions at the site of the energy release that drives these events. Here, we aim to better understand the relationship between UV Bursts and solar surges through a multi-instrument analysis of several associated events that occurred around the trailing sunspot in AR 12957. We use data from Solar Orbiter, the Solar Dynamics Observatory (SDO), and the Interface Region Imaging Spectrograph (IRIS) to achieve our aims. These data were sampled on 3rd March 2022 between 09:30:30 UT and 11:00:00 UT, during which time a coordinated observing campaign associated with the Slow Solar Wind Connection Solar Orbiter Observing Plan took place. Numerous small-scale negative polarity magnetic magnetic features (MMFs) are observed to move quickly (potentially up to 3.3 km/s) away from a sunspot until they collide with a more stable positive polarity plage region around 7 Mm away. Several UV Bursts are identified in IRIS slit-jaw imager (SJI) 1400 {\AA} data co-spatial to where these opposite polarity fields interact, with spatial scales (2 Mm<) and lifetimes (20< min) larger than typical values for such events. Two surges are also observed to occur at these locations, with one being short (5 Mm) and hot (bright in IRIS SJI images), whilst the other is a cooler (dark in coronal imaging channels), longer surge that appears to fill an active region loop. Magnetic reconnection between the negative polarity MMFs around the sunspot and the positive polarity plage region appears to be the driver of these events. Both the speed of the MMFs and the locally open magnetic topology of the plage region could possibly be important for forming the surges., Comment: 13 pages, 9 figures, accepted in A&A

We present the Merian Survey, an optical imaging survey optimized for studying the physical properties of bright star-forming dwarf galaxies. Merian is carried out with two medium-band filters ($N708$ and $N540$, centered at $708$ and $540$ nm), custom-built for the Dark Energy Camera (DECam) on the Blanco telescope. Merian covers $\sim 750\,\mathrm{deg}^2$ of equatorial fields, overlapping with the Hyper Suprime-Cam Subaru Strategic Program (HSC-SSP) wide, deep, and ultra-deep fields. When combined with the HSC-SSP imaging data ($grizy$), the new Merian DECam medium-band imaging allows for photometric redshift measurements via the detection of H$\rm\alpha$ and [OIII] line emission flux excess in the $N708$ and $N540$ filters, respectively, at $0.06

The Polynomial Learning With Errors problem (PLWE) serves as the background of two of the three cryptosystems standardized in August 2024 by the National Institute of Standards and Technology to replace non-quantum resistant current primitives like those based on RSA, Diffie-Hellman or its elliptic curve analogue. Although PLWE is highly believed to be quantum resistant, this fact has not yet been established, contrariwise to other post-quantum proposals like multivariate and some code based ones. Moreover, several vulnerabilities have been encountered for a number of specific instances. In a search for more flexibility, it becomes fully relevant to study the robustness of PLWE based on other polynomials, not necessarily cyclotomic. In 2015, Elias et al found a good number of attacks based on different features of the roots of the polynomial. In the present work we present an overview of the approximations made against PLWE derived from this and subsequent works, along with several new attacks which refine those by Elias et al. exploiting the order of the trace of roots over finite extensions of the finite field under the three scenarios laid out by Elias et al., allowing to generalize the setting in which the attacks can be carried out.

We prove the equivalence between the Ring Learning With Errors (RLWE) and the Polynomial Learning With Errors (PLWE) problems for the maximal totally real subfield of the $2^r 3^s$-th cyclotomic field for $r \geq 3$ and $s \geq 1$. Moreover, we describe a fast algorithm for computing the product of two elements in the ring of integers of these subfields. This multiplication algorithm has quasilinear complexity in the dimension of the field, as it makes use of the fast Discrete Cosine Transform (DCT). Our approach assumes that the two input polynomials are given in a basis of Chebyshev-like polynomials, in contrast to the customary power basis. To validate this assumption, we prove that the change of basis from the power basis to the Chebyshev-like basis can be computed with $\mathcal{O}(n \log n)$ arithmetic operations, where $n$ is the problem dimension. Finally, we provide a heuristic and theoretical comparison of the vulnerability to some attacks for the $p$-th cyclotomic field versus the maximal totally real subextension of the $4p$-th cyclotomic field for a reasonable set of parameters of cryptographic size.

Even though a few initial works have shown on small sets of data some level of bias in the performance of fingerprint recognition technology with respect to certain demographic groups, there is still not sufficient evidence to understand the impact that certain factors such as gender, age or finger-type may have on fingerprint quality and, in turn, also on fingerprint matching accuracy. The present work addresses this still under researched topic, on a large-scale database of operational data containing 10-print impressions of almost 16,000 subjects. The results reached provide further insight into the dependency of fingerprint quality and demographics, and show that there in fact exists a certain degree of performance variability in fingerprint-based recognition systems for different segments of the population. Based on the experimental evaluation, the work points out new observations based on data-driven evidence, provides plausible hypotheses to explain such observations, and concludes with potential follow-up actions that can help to reduce the observed fingerprint quality differences. This way, the current paper can be considered as a contribution to further increase the algorithmic fairness and equality of biometric technology., Comment: Extended journal version submitted to IET Biometrics. 10 pages, 5 figures Reference conference paper: J. Galbally, A. Cepilovs, R. Blanco-Gonzalo, G. Ormiston, O. Miguel-Hurtado, and I. S. Racz, 'Fingerprint quality per individual finger type: A large-scale study on real operational data' in Proc. IEEE Intl. Workshop on Biometrics and Forensics 2023 (IWBF 2023)

Frequency domain sweeps of array antennas are well-known to be time-intensive, and different surrogate models have been used to improve the performance. Data-driven model order reduction algorithms, such as the Loewner framework and vector fitting, can be integrated with these adaptive error estimates, in an iterative algorithm, to reduce the number of full-wave simulations required to accurately capture the requested frequency behavior of multiport array antennas. In this work, we propose two novel adaptive methods exploiting a block matrix function which is a key part of the Loewner framework generating system approach. The first algorithm leverages an inherent matrix parameter freedom in the block matrix function to identify frequency points with large errors, whereas the second utilizes the condition number of the block matrix function. Both methods effectively provide frequency domain error estimates, which are essential for improved performance. Numerical experiments on multiport array antenna S-parameters demonstrate the effectiveness of our proposed algorithms within the Loewner framework., Comment: 10 pages, 12 figures

For years, the cosmological constant $\Lambda$ and cold dark matter (CDM) model ($\Lambda\text{CDM}$) has stood as a cornerstone in modern cosmology and serves as the predominant theoretical framework for current and forthcoming surveys. However, the latest results shown by the Dark Energy Spectroscopic Instrument (DESI), along other cosmological data, show hints in favor of an evolving dark energy. Given the elusive nature of dark energy and the imperative to circumvent model bias, we introduce a novel null test, derived from Noether's theorem, that uses measurements of the Weyl potential (the sum of the spatial and temporal distortion) at different redshifts. In order to assess the consistency of the concordance model we quantify the precision of this null test through the reconstruction of mock catalogs based on $\Lambda\text{CDM}$ using forthcoming survey data, employing Genetic Algorithms, a machine learning technique. Our analysis indicates that with forthcoming LSST-like and DESI-like mock data our consistency test will be able to rule out several cosmological models at around 4$\sigma$ and help to check for tensions in the data., Comment: 11 pages, 1 table and 3 figures. Comments are welcome!

Principal component analysis (PCA) is often used to analyze multivariate data together with cluster analysis, which depends on the number of principal components used. It is therefore important to determine the number of significant principal components (PCs) extracted from a data set. Here we use a variational Bayesian version of classical PCA, to develop a new method for estimating the number of significant PCs in contexts where the number of samples is of a similar to or greater than the number of features. This eliminates guesswork and potential bias in manually determining the number of principal components and avoids overestimation of variance by filtering noise. This framework can be applied to datasets of different shapes (number of rows and columns), different data types (binary, ordinal, categorical, continuous), and with noisy and missing data. Therefore, it is especially useful for data with arbitrary encodings and similar numbers of rows and columns, such as cultural, ecological, morphological, and behavioral datasets. We tested our method on both synthetic data and empirical datasets and found that it may underestimate but not overestimate the number of principal components for the synthetic data. A small number of components was found for each empirical dataset. These results suggest that it is broadly applicable across the life sciences., Comment: 9 pages, 2 figures

Exploiting symmetries in the numerical renormalization group (NRG) method significantly enhances performance by improving accuracy, increasing computational speed, and optimizing memory efficiency. Published codes focus on continuous rotations and unitary groups, which generally are not applicable to systems with strong crystal-field effects. The PointGroupNRG code implements symmetries related to discrete rotation groups, which are defined by the user in terms of Clebsch-Gordan coefficients, together with particle conservation and spin rotation symmetries. In this paper we present a new version of the code that extends the available finite groups, previously limited to simply reducible point groups, in a way that all point and double groups become accessible. It also includes the full spin-orbital rotation group. Moreover, to improve the code's flexibility for impurities with complex interactions, this new version allows to choose between a standard Anderson Hamiltonian for the impurity or, as another novel feature, an ionic model that requires only the spectrum and the impurity Lehmann amplitudes., Comment: 19 pages, 2 figures

Accurate modeling of tidal interactions is crucial for interpreting recent JWST observations of the thermal emissions of TRAPPIST-1~b and c and for characterizing the surface conditions and potential habitability of the other planets in the system. Indeed, the rotation state of the planets, driven by tidal forces, significantly influences the heat redistribution regime. Due to their proximity to their host star and the estimated age of the system, the TRAPPIST-1 planets are commonly assumed to be in a synchronization state. In this work, we present the recent implementation of the co-planar tidal torque and forces equations within the formalism of Kaula in the N-body code Posidonius. This enables us to explore the hypothesis of synchronization using a tidal model well suited to rocky planets. We studied the rotational state of each planet by taking into account their multi-layer internal structure computed with the code Burnman. Simulations show that the TRAPPIST-1 planets are not perfectly synchronized but oscillate around the synchronization state. Planet-planet interactions lead to strong variations on the mean motion and tides fail to keep the spin synchronized with respect to the mean motion. As a result, the sub-stellar point of each planet experiences short oscillations and long-timescale drifts that lead the planets to achieve a synodic day with periods varying from $55$~years to $290$~years depending on the planet., Comment: 7 pages, 6 figures

Kagome materials manifest rich physical properties due to the emergence of abundant electronic phases. Here, we carry out a high-throughput first-principles study of the kagome 1:6:6 family MT$_6$Z$_6$ materials in space group 191, focusing on their phonon instability and electronic flat bands. Different MT$_6$Z$_6$ kagome candidates reveal a remarkable variety of kagome flat bands ranging from unfilled, partially filled, to fully filled. Notably, the Mn/Fe-166 compounds exhibit partially filled flat bands with a pronounced sharp peak in the density of states near the Fermi level, leading to magnetic orders that polarize the bands and stabilize the otherwise unstable phonon. When the flat bands are located away from the Fermi level, we find a large number of phonon instabilities, which can be classified into three types, based on the phonon dispersion and vibrational modes. Type-I instabilities involve the in-plane distortion of kagome nets, while type-II and type-III present out-of-plane distortion of trigonal M and Z atoms. We take MgNi$_6$Ge$_6$ and HfNi$_6$In$_6$ as examples to illustrate the possible CDW structures derived from the emergent type-I and type-II instabilities. The type-I instability in MgNi$_6$Ge$_6$ suggests a nematic phase transition, governed by the local twisting of kagome nets. The type-II instability in HfNi$_6$In$_6$ may result in a hexagonal-to-orthorhombic transition, offering insight into the formation of MT$_6$Z$_6$ in other space groups. Additionally, the predicted ScNb$_6$Sn$_6$ is analyzed as an example of the type-III instability. Our predictions suggest a vast kagome family with rich properties induced by the flat bands, possible CDW transitions, and their interplay with magnetism., Comment: 14 pages, 7 figures, with 1000 pages of additional supplemental materials

Currently, the Weather Research and Forecasting model (WRF) utilizes shared memory (OpenMP) and distributed memory (MPI) parallelisms. To take advantage of GPU resources on the Perlmutter supercomputer at NERSC, we port parts of the computationally expensive routines of the Fast Spectral Bin Microphysics (FSBM) microphysical scheme to NVIDIA GPUs using OpenMP device offloading directives. To facilitate this process, we explore a workflow for optimization which uses both runtime profilers and a static code inspection tool Codee to refactor the subroutine. We observe a 2.08x overall speedup for the CONUS-12km thunderstorm test case.

In this review, automatic defect inspection algorithms that analyze Electron Microscope (EM) images of Semiconductor Manufacturing (SM) products are identified, categorized, and discussed. This is a topic of critical importance for the SM industry as the continuous shrinking of device patterns has led to increasing defectivity and a greater prevalence of higher-resolution imaging tools such as EM. These aspects among others threaten to increase costs as a result of increased inspection time-to-solution and decreased yield, respectively. Relevant research papers were systematically identified in four popular publication databases in January 2024. A total of 103 papers were selected after screening for novel contributions relating to automatic EM image analysis algorithms for semiconductor defect inspection. These papers were then categorized based on the inspection tasks they addressed, their evaluation metrics, and the type of algorithms used. A notable finding from this categorization is that reference-based defect detection algorithms were the most popular algorithm type until 2020 when deep learning-based inspection algorithms became more popular, especially for defect classification. Furthermore, four broader research questions were discussed to come to the following conclusions: (i) the key components of inspection algorithms are set up, pre-processing, feature extraction, and final prediction; (ii) the maturity of the manufacturing process affects the data availability and required sensitivity of inspection algorithms; (iii) key challenges for these algorithms relate to the desiderata of minimizing time-to-solution which pushes for high imaging throughput, reducing manual input during algorithm setup, and higher processing throughput; and (iv) three promising directions for future work are suggested based on gaps in the reviewed literature that address key remaining limitations.

Electronic ordering is prevalent in correlated systems, which commonly exhibit competing interactions. Here, we use x-ray diffraction to show that the charge density wave transition temperature of FeGe increases with pressure and evolves towards a $\sqrt{3}\times\sqrt{3}$ periodic lattice modulation, $\mathbf{q}$$^*$=$\left(\frac{1}{3}\ \frac{1}{3}\ \frac{1}{2}\right)$. In the pressure interval between 4$<$$p$$<$12 GPa both orders coexist and the spatial extent of the $\sqrt{3}\times\sqrt{3}$ order at high pressure becomes nearly long-range, $\sim$30 unit cells, while the correlation length of the 2$\times$2 phase remains shorter-ranged. The $\sqrt{3}\times\sqrt{3}$ phase is the ground state above 15 GPa, consistent with harmonic DFT calculations that predict a dimerization induced $\sqrt{3}\times\sqrt{3}$ order without phonon softening. The pressure dependence of the integrated intensities of $\mathbf{q}$$_\mathrm{CDW}=\left(\frac{1}{2}\ 0\ \frac{1}{2}\right)$ and $\mathbf{q}$$^*$ indicates a competition between the 2$\times$2 and $\sqrt{3}\times\sqrt{3}$ and demonstrates that the ground state of FeGe is characterized by a rich landscape of metastable/fragile phases. We discuss possible scenarios based on an order-disorder transformation and the formation of Friedel oscillations.

In this research, we introduce a unified end-to-end Automated Defect Classification-Detection-Segmentation (ADCDS) framework for classifying, detecting, and segmenting multiple instances of semiconductor defects for advanced nodes. This framework consists of two modules: (a) a defect detection module, followed by (b) a defect segmentation module. The defect detection module employs Deformable DETR to aid in the classification and detection of nano-scale defects, while the segmentation module utilizes BoxSnake. BoxSnake facilitates box-supervised instance segmentation of nano-scale defects, supported by the former module. This simplifies the process by eliminating the laborious requirement for ground-truth pixel-wise mask annotation by human experts, which is typically associated with training conventional segmentation models. We have evaluated the performance of our ADCDS framework using two distinct process datasets from real wafers, as ADI and AEI, specifically focusing on Line-space patterns. We have demonstrated the applicability and significance of our proposed methodology, particularly in the nano-scale segmentation and generation of binary defect masks, using the challenging ADI SEM dataset where ground-truth pixelwise segmentation annotations were unavailable. Furthermore, we have presented a comparative analysis of our proposed framework against previous approaches to demonstrate its effectiveness. Our proposed framework achieved an overall mAP@IoU0.5 of 72.19 for detection and 78.86 for segmentation on the ADI dataset. Similarly, for the AEI dataset, these metrics were 90.38 for detection and 95.48 for segmentation. Thus, our proposed framework effectively fulfils the requirements of advanced defect analysis while addressing significant constraints., Comment: Accepted in ECCV 2024 2nd workshop on Vision-based InduStrial InspectiON (VISION)

In this paper, we introduce the Fixed Topology Minimum-Length Tree with Neighborhood Problem, which aims to embed a rooted tree-shaped graph into a $d$-dimensional metric space while minimizing its total length provided that the nodes must be embedded to some restricted areas. This problem has significant applications in efficiently routing cables or pipelines in engineering designs. We propose novel mathematical optimization-based approaches to solve different versions of the problem based on the domain for the embedding. In cases where the embedding maps to a continuous space, we provide several Mixed Integer Nonlinear Optimization formulations. If the embedding is to a network, we derive a mixed integer linear programming formulation as well as a dimensionality reduction methodology that allows for solving larger problems in less CPU time. A data-driven methodology is also proposed to construct a proper network based on the instance of the problem. We report the results of a battery of computational experiments that validate our proposal., Comment: 18 pages, 10 figures

This paper deals with the problem of energy consumption minimization in Open RAN cell-free (CF) massive Multiple-Input Multiple-Output (mMIMO) systems under minimum per-user signal-to-noise-plus-interference ratio (SINR) constraints. Considering that several access points (APs) are deployed with multiple antennas, and they jointly serve multiple users on the same time-frequency resources, we design the precoding vectors that minimize the system power consumption, while preserving a minimum SINR for each user. We use a simple, yet representative, power consumption model, which consists of a fixed term that models the power consumption due to activation of the AP and a variable one that depends on the transmitted power. The mentioned problem boils down to a binary-constrained quadratic optimization problem, which is strongly non-convex. In order to solve this problem, we resort to a novel approach, which is based on the penalized convex-concave procedure. The proposed approach can be implemented in an O-RAN cell-free mMIMO system as an xApp in the near-real time RIC (RAN intelligent Controller). Numerical results show the potential of this approach for dealing with joint precoding optimization and AP selection.

Gaia/DR3 has provided a large sample of new g-mode pulsators, among which~11,600 are Gam Dor stars. This work present the spectroscopic parameters of these Gam Dor pulsators estimated by the GSP-spec module that analysed millions of Gaia spectra. The Galactic positions, kinematics, and orbital properties of these new Gaia pulsators were examined in order to define a sub-sample belonging to the Milky Way thin disc, in which these young stars should preferentially be found. The stellar luminosities, radii, and astrometric surface gravities were estimated without adopting any priors from uncertain stellar evolution models. These parameters, combined with the GSP-spec effective temperatures, spectroscopic gravities, and metallicities were then validated by comparison with recent literature studies. Most stars are found to belong to the Galactic thin disc, as expected. It is also found that the derived luminosities, radii, and astrometric surface gravities are of high quality and have values typical of genuine Gam Dor pulsators. Moreover, we have shown that Teff and [M/H] of pulsators with high enough SNR spectra or slow to moderate rotation rates are robust. This allowed to define a sub-sample of genuine slow-rotating Gaia Gam Dor pulsators. Their Teff are found between ~6500 and ~7800K, log(g) around 4.2 and luminosities and stellar radii peak at ~5Lsun and ~1.7Rsun. [M/H] is close to the Solar value, although 0.5dex more metal-poor and metal-rich Gam Dor are identified. The [alpha/Fe] content is fully consistent with the chemical properties of the Galactic disc. Gaia/DR3 spectroscopic properties of Gam Dor stars therefore confirm the nature of these pulsators and allow to chemo-physically parametrise a new large sample of such stars. Moreover, future Gaia DR should drastically increase the number of Gam Dor stars with good-precision spectroscopically derived parameters., Comment: A&A, revised version

The chemical evolution of neutron capture elements in the Milky Way is still a matter of debate. Although more and more studies investigate their chemical behaviour, there is still a lack of a significant large sample of abundances of a key heavy element: lead. Lead is the final product of the s-process nucleosynthesis channel and is one of the most stable heavy elements. We analysed high-resolution spectra from the ESO UVES and FEROS archives. Atmospheric parameters were taken from the AMBRE parametrisation. We used the automated abundance method GAUGUIN to derive lead abundances in 653 slow-rotating FGK-type stars from the 368.34nm Pb I line. We present the largest catalogue of homogeneous LTE and non-LTE lead abundances ever published with metallicities ranging from -2.9 to 0.6dex and [Pb/Fe] from -0.7 to 3.3dex. Within this sample, no lead-enhanced Asymptotic Giant Branch (AGB) stars were found, but nine lead-enhanced metal-poor stars ([Pb/Fe] > 1.5) were detected. Most of them were already identified as carbon-enhanced metal-poor stars with enrichments in other s-process species. The lead abundance of 13 Gaia Benchmark Stars are also provided. We then investigated the Pb content of the Milky Way disc by computing vertical and radial gradients and found a slightly decreasing [Pb/Fe] radial trend with metallicity. This trend together with other related ratios ([Pb/Eu], [Pb/Ba], and [Pb/alpha]) are interpreted thanks to chemical evolution models. The two-infall model closely reproduces the observed trends with respect to the metallicity. It is also found that the AGB contribution to the Pb Galactic enrichment has to be strongly reduced. Moreover, the contribution of massive stars with rather high rotational velocities should be favoured in the low-metallicity regime., Comment: A&A, accepted

We point out that dark matter can illuminate planetary skies via ultraviolet airglow. Dark matter annihilation products can excite molecular hydrogen, which then deexcites to produce ultraviolet emission in the Lyman and Werner bands. We search for this new effect by analyzing nightside ultraviolet radiation data from Voyager 1, Voyager 2, and New Horizons flybys of Neptune, Uranus, Saturn, and Jupiter. Our findings set new constraints on the dark matter-nucleon scattering cross section down to about $10^{-40}~$cm$^2$. We highlight that future ultraviolet airglow measurements of Solar System planets or other worlds provide a new dark matter discovery avenue., Comment: 5+6 pages, 3+2 figures

This paper presents PROB-IRM, an approach that learns robust reward machines (RMs) for reinforcement learning (RL) agents from noisy execution traces. The key aspect of RM-driven RL is the exploitation of a finite-state machine that decomposes the agent's task into different subtasks. PROB-IRM uses a state-of-the-art inductive logic programming framework robust to noisy examples to learn RMs from noisy traces using the Bayesian posterior degree of beliefs, thus ensuring robustness against inconsistencies. Pivotal for the results is the interleaving between RM learning and policy learning: a new RM is learned whenever the RL agent generates a trace that is believed not to be accepted by the current RM. To speed up the training of the RL agent, PROB-IRM employs a probabilistic formulation of reward shaping that uses the posterior Bayesian beliefs derived from the traces. Our experimental analysis shows that PROB-IRM can learn (potentially imperfect) RMs from noisy traces and exploit them to train an RL agent to solve its tasks successfully. Despite the complexity of learning the RM from noisy traces, agents trained with PROB-IRM perform comparably to agents provided with handcrafted RMs., Comment: Preprint accepted for publication to the 21st International Conference on Principles of Knowledge Representation and Reasoning (KR 2024)

In-context learning (ICL) has proven to be an effective strategy for improving the performance of large language models (LLMs) with no additional training. However, the exact mechanism behind this performance improvement remains unclear. This study is the first to show how ICL surfaces memorized training data and to explore the correlation between this memorization and performance on downstream tasks across various ICL regimes: zero-shot, few-shot, and many-shot. Our most notable findings include: (1) ICL significantly surfaces memorization compared to zero-shot learning in most cases; (2) demonstrations, without their labels, are the most effective element in surfacing memorization; (3) ICL improves performance when the surfaced memorization in few-shot regimes reaches a high level (about 40%); and (4) there is a very strong correlation between performance and memorization in ICL when it outperforms zero-shot learning. Overall, our study uncovers memorization as a new factor impacting ICL, raising an important question: to what extent do LLMs truly generalize from demonstrations in ICL, and how much of their success is due to memorization?, Comment: v2

Massive stars produce strong stellar winds that consist of continuous outflows of material at speeds of thousands of km/s. These winds convey large amounts of kinetic power, especially in the case of Wolf-Rayet (WR) stars. When these winds interact with nearby material, they will likely produce shocks. Among other processes, particle acceleration is expected to occur. This is particularly well established in the case of massive binary systems, where the stellar winds collide, allowing these systems to be identified thanks to the detection of synchrotron radio emission, produced by a population of relativistic particles accelerated in the shocks. Our goal is to investigate the occurrence of particle acceleration among massive stars in their pre-supernova evolution phases. To this end, we observed a subset of five WR stars in the radio domain using the upgraded Giant Metrewave Radio Telescope (uGMRT), located in India. The observations were carried out in bands 4 (550-950 MHz) and 5 (1050-1450 MHz) for all the targets. We detected radio emission for only WR 110 in bands 4 and 5. Its thermal spectrum displays a consistent index of +0.74 down to uGMRT bands. The four other targets were not detected and we derived 3$\sigma$ upper limits. Our upper limits in Band 4 are the first provided for these targets below 1 GHz. None of the targets was identified as a synchrotron radio emitter in these radio bands. If some synchrotron emission is produced in these systems, the non-detection with uGMRT can be most likely attributed to strong free-free absorption (FFA). This is especially relevant for WR 98a, which is catalogued as a particle accelerator based on previous measurements at higher radio frequencies. We discuss how the prominence of FFA constitutes a severe obstacle to identifying particle accelerators in the radio domain.

The burnt pancake graph, denoted by $\mathbb{BP}_n$, is formed by connecting signed permutations via prefix reversals. Here, we discuss some spectral properties of $\mathbb{BP}_n$. More precisely, we prove that the adjacency spectrum of $\mathbb{BP}_n$ contains all integer values in the set $\{0, 1, \ldots, n\}\setminus\{\left\lfloor n/2 \right\rfloor\}$., Comment: Fixed typos and other small changes. Final version to appear in Linear Algebra and its Applications

The intrinsic frustrated nature of a kagome lattice is amenable to the realization of exotic phases of matter, such as quantum spin liquids or spin ices, and more recently the multiple-$\mathrm{\textbf{q}}$ charge density waves (CDW) in the kagome metals. Despite intense efforts to understand the mechanism driving the electronic modulations, its origin is still unknown and hindered by competing interactions and intertwined orders. Here, we identify a dimerization-driven 2D hexagonal charge-diffuse precursor in the antiferromagnetic kagome metal FeGe and demonstrate that the fraction of dimerized/undimerized states is the relevant order parameter of the multiple-$\mathrm{\textbf{q}}$ CDW of a continuous phase transition. The pretransitional charge fluctuations with propagation vector $\mathrm{\textbf{q}=\textbf{q}_M}$ at T$_{\mathrm{CDW}}$$<$T$<$T$^*$(125 K) are anisotropic, hence holding a quasi-long-range bond-orientational order. The broken translational symmetry emerges from the anisotropic diffuse precursor, akin to the Ising scenario of antiferromagnetic triangular lattices. The temperature and momentum dependence of the critical scattering show parallels to the stacked hexatic $\mathrm{B}$-phases reported in liquid crystals and transient states of CDWs and highlight the key role of the topological defect-mediated melting of the CDW in FeGe.

Since the universe is not transparent to gamma rays with energies above around one hundred GeV, it is necessary to account for the interaction of high-energy photons with intergalactic radiation fields in order to model gamma-ray propagation. Here, we present a public numerical software for the modeling of gamma-ray observables. This code computes the effects on gamma-ray spectra from the development of electromagnetic cascades and cosmological redshifting. The code introduced here is based on the original $\gamma$-Cascade, and builds on it by improving its performance at high redshifts, introducing new propagation modules, and adding many more extragalactic radiation field models, which enables the ability to estimate the uncertainties inherent to EBL modeling. We compare the results of this new code to existing Monte Carlo electromagnetic transport models, finding good agreement within EBL uncertainties., Comment: 15 pages, 11 figures; v2: typos corrected, matches version published in CPC

We extend Ravagnani's MacWilliams duality theory to the settings of rank metric codes over finite chain rings, relating the sequences of $q$-binomial moments of a rank metric code over this class of rings with those of its dual.