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The most metal-poor stars provide valuable insights into the early chemical enrichment history of a system, carrying the chemical imprints of the first generations of supernovae. The most metal-poor region of the Sagittarius dwarf galaxy remains inadequately observed and characterised. To date, only $\sim4$ stars with [Fe/H]~$<-2.0$ have been chemically analysed with high-resolution spectroscopy. In this study, we present the most extensive chemical abundance analysis of 12 low-metallicity stars with metallicities down to [Fe/H]~$=-3.26$ and located in the main body of Sagittarius. These targets, selected from the Pristine Inner Galaxy Survey, were observed using the MIKE high-resolution spectrograph at the {\it Magellan-Clay} telescope, which allowed us to measure up to 17 chemical species. The chemical composition of these stars reflects the imprint of a variety of type~II supernovae (SNe~II). A combination of low- to intermediate-mass high-energy SNe and hypernovae ($\sim10-70\msun$) is required to account for the abundance patterns of the lighter elements up to the Fe-peak. The trend of the heavy elements suggests the involvement of compact binary merger events and fast-rotating (up to $\sim300\kms$) intermediate-mass to massive metal-poor stars ($\sim25-120\msun$) that are the sources of rapid and slow processes, respectively. Additionally, asymptotic giant branch stars contribute to a wide dispersion of [Ba/Mg] and [Ba/Eu]. The absence of an $\alpha-$knee in our data indicates that type Ia supernovae did not contribute in the very metal-poor region ([Fe/H]~$\leq-2.0$). However, they might have started to pollute the interstellar medium at [Fe/H]~$>-2.0$, given the relatively low [Co/Fe] in this metallicity region., Comment: Accepted for publication in A&A. New plot on [Eu/Mg]. Some refs updated

We report metallicities for three $\sim$Gyr-old stars in the Milky Way nuclear star cluster (NSC) using high-resolution near-infrared spectroscopy. We derive effective temperatures from a calibration with Sc line strength, which yields results in good agreement with other methods, and metallicities from spectral fits to Fe I lines. Our derived metallicities range from -1.2 < [Fe/H] < +0.5, a span of 1.7 dex. In addition we use isochrone projection to obtain masses of 1.6 to 4.3 M$_\odot$, and ages assuming single-star evolution. The oldest of these stars is 1.5 Gyr while the youngest and most metal-rich is only 100 Myr. The wide range in metallicity poses interesting questions concerning the chemical evolution and enrichment of the NSC and adds to the evidence for the presence of a young, metal-rich population in the NSC. We suggest that the candidate intermediate-age, metal-poor ([Fe/H] = -1.2) star may be best explained as a blue straggler from an underlying old population., Comment: 6 pages, 4 figures

In this paper we present a new formulation of the Beurling-Malliavin density (Proposition 1). Then we consider the upper Polya density and show how its existence is connected with the concept of subadditivity; moreover, by means of some quantities introduced for proving Proposition 1, a theorem is presented that clarifies the connection between the upper Polya and the Beurling-Malliavin densities. In the last section we discuss the classical definition of the upper Polya density and we prove a result which seems to be new., Comment: 20 pages

Deep learning-based positron emission tomography (PET) image denoising offers the potential to reduce radiation exposure and scanning time by transforming low-count images into high-count equivalents. However, existing methods typically blur crucial details, leading to inaccurate lesion quantification. This paper proposes a lesion-perceived and quantification-consistent modulation (LpQcM) strategy for enhanced PET image denoising, via employing downstream lesion quantification analysis as auxiliary tools. The LpQcM is a plug-and-play design adaptable to a wide range of model architectures, modulating the sampling and optimization procedures of model training without adding any computational burden to the inference phase. Specifically, the LpQcM consists of two components, the lesion-perceived modulation (LpM) and the multiscale quantification-consistent modulation (QcM). The LpM enhances lesion contrast and visibility by allocating higher sampling weights and stricter loss criteria to lesion-present samples determined by an auxiliary segmentation network than lesion-absent ones. The QcM further emphasizes accuracy of quantification for both the mean and maximum standardized uptake value (SUVmean and SUVmax) across multiscale sub-regions throughout the entire image, thereby enhancing the overall image quality. Experiments conducted on large PET datasets from multiple centers and vendors, and varying noise levels demonstrated the LpQcM efficacy across various denoising frameworks. Compared to frameworks without LpQcM, the integration of LpQcM reduces the lesion SUVmean bias by 2.92% on average and increases the peak signal-to-noise ratio (PSNR) by 0.34 on average, for denoising images of extremely low-count levels below 10%., Comment: 10 pages

The first stars might have been fast rotators. This would have important consequences for their radiative, mechanical and chemical feedback. We discuss the impact of fast initial rotation on the evolution of massive Population III models and on their nitrogen and oxygen stellar yields. We explore the evolution of Population III stars with initial masses in the range of 9Msol < Mini < 120Msol starting with an initial rotation on the Zero Age Main Sequence equal to 70% of the critical one. We find that with the physics of rotation considered here, our rapidly-rotating Population III stellar models do not follow a homogeneous evolution. They lose very little mass in case mechanical winds are switched on when the surface rotation becomes equal or larger than the critical velocity. Impact on the ionising flux appears modest when compared to moderately-rotating models. Fast rotation favours, in models with initial masses above ~20Msol, the appearance of a very extended intermediate convective zone around the H-burning shell during the core He-burning phase. This shell has important consequences on the sizes of the He- and CO-cores and thus impacts the final fate of stars. Moreover, it has a strong impact on nucleosynthesis boosting the production of primary 14N. Fast initial rotation impacts significantly the chemical feedback of Population III stars. Observations of extremely metal-poor stars and/or starbursting regions are essential to provide constraints on the properties of the first stars., Comment: Accepted in A&A. Pages 17. Figures 15. Tables 2

Earth Observation imagery can capture rare and unusual events, such as disasters and major landscape changes, whose visual appearance contrasts with the usual observations. Deep models trained on common remote sensing data will output drastically different features for these out-of-distribution samples, compared to those closer to their training dataset. Detecting them could therefore help anticipate changes in the observations, either geographical or environmental. In this work, we show that the reconstruction error of diffusion models can effectively serve as unsupervised out-of-distribution detectors for remote sensing images, using them as a plausibility score. Moreover, we introduce ODEED, a novel reconstruction-based scorer using the probability-flow ODE of diffusion models. We validate it experimentally on SpaceNet 8 with various scenarios, such as classical OOD detection with geographical shift and near-OOD setups: pre/post-flood and non-flooded/flooded image recognition. We show that our ODEED scorer significantly outperforms other diffusion-based and discriminative baselines on the more challenging near-OOD scenarios of flood image detection, where OOD images are close to the distribution tail. We aim to pave the way towards better use of generative models for anomaly detection in remote sensing., Comment: EARTHVISION 2024 IEEE/CVF CVPR Workshop. Large Scale Computer Vision for Remote Sensing Imagery, Jun 2024, Seattle, United States

We present a method for a non-linear asteroseismic inversion suitable for gravity-mode pulsators and apply it to slowly pulsating B-type (SPB) stars. Our inversion method is based on the iterative improvement of a parameterised static stellar structure model, which in turn is based on constraints from the observed oscillation periods. We present tests to demonstrate that the method is successful in recovering the properties of artificial targets both inside and outside the parameter space. We also present a test of our method on the well-studied SPB star KIC 7760680. We believe that this method is promising for carrying out detailed analyses of observations of SPB and $\gamma$ Dor stars and will provide complementary information to evolutionary models., Comment: Accepted in A&A

Retrieval-augmented language models pose a promising alternative to standard language modeling. During pretraining, these models search in a corpus of documents for contextually relevant information that could aid the language modeling objective. We introduce an 'ideal retrieval' methodology to study these models in a fully controllable setting. We conduct an extensive evaluation to examine how retrieval augmentation affects the behavior of the underlying language model. Among other things, we observe that these models: i) save substantially less world knowledge in their weights, ii) are better at understanding local context and inter-word dependencies, but iii) are worse at comprehending global context., Comment: NAACL 2024

We use Geneva-evolution-code to run evolutionary tracks for stellar masses ranging from $20$ to $85$ $M_\odot$ at SMC metallicity ($Z=0.002$). We upgrade the recipe for stellar winds by adopting our self-consistent m-CAK prescription, which reduces the value of mass-loss rate by a factor between 2 and 6 depending on the mass range. The impact of our new winds is wide, and it can be divided between direct and indirect impact. For the most massive models ($60$ and $85$ $M_\odot$) with $\dot M\gtrsim2\times10^{-7}$ $M_\odot$ yr$^{-1}$, the impact is direct because lower mass loss make stars remove less envelope and therefore remain more massive and less chemically enriched at their surface at the end of their MS phase. For the less massive models ($20$ and $25$ $M_\odot$) with $\dot M\lesssim2\times10^{-8}$ $M_\odot$ yr$^{-1}$, the impact is indirect because lower mass loss make the stars keep high rotational velocities for a longer period of time, then extending the H-core burning lifetime and reaching the end of the MS with higher surface enrichment. Given that the conditions at the H-depletion change, the stars will lose more mass during their He-core burning stages anyways. For $M_\text{zams}=20$ to $40$ $M_\odot$, our models predict stars will evolve through the Hertzsprung gap, from O-type supergiants to BSG and finally RSG, with larger mass fractions of helium compared to old evolution models. New models also set down to $M_\text{zams}=85\,M_\odot$ the minimal initial mass required for a single star to become WR at metallicity $Z=0.002$. New values for $\dot M$ need to be complemented with upgrades in additional features such as convective core overshooting and distribution of rotational velocities, besides more detailed observations from projects such as XShootU, in order to provide a robust framework for the study of massive stars at low metallicity environments., Comment: Accepted for publication in Astronomy & Astrophysics

The spectral and transport properties of strongly correlated metals, such as SrVO$_3$ (SVO), are widely attributed to electron-electron ($e$-$e$) interactions, with lattice vibrations (phonons) playing a secondary role. Here, using first-principles electron-phonon ($e$-ph) and dynamical mean field theory calculations, we show that $e$-ph interactions play an essential role in SVO: they govern the electron scattering and resistivity in a wide temperature range down to 30 K, and induce an experimentally observed kink in the spectral function. In contrast, the $e$-$e$ interactions control quasiparticle renormalizations and low temperature transport, and enhance the $e$-ph coupling. We clarify the origin of the near $T^2$ temperature dependence of the resistivity by analyzing the $e$-$e$ and $e$-ph limited transport regimes. Our work disentangles the electronic and lattice degrees of freedom in a prototypical correlated metal, revealing the dominant role of $e$-ph interactions in SVO., Comment: 7 pages, 4 figures

We present and distribute a parallel finite-element toolbox written in the free software FreeFem for computing the Bogoliubov-de Gennes (BdG) spectrum of stationary solutions to one- and two-component Gross-Pitaevskii (GP) equations, in two or three spatial dimensions. The parallelization of the toolbox relies exclusively upon the recent interfacing of FreeFem with the PETSc library. The latter contains itself a wide palette of state-of-the-art linear algebra libraries, graph partitioners, mesh generation and domain decomposition tools, as well as a suite of eigenvalue solvers that are embodied in the SLEPc library. Within the present toolbox, stationary states of the GP equations are computed by a Newton method. Branches of solutions are constructed using an adaptive step-size continuation algorithm. The combination of mesh adaptivity tools from FreeFem with the parallelization features from PETSc makes the toolbox efficient and reliable for the computation of stationary states. Their BdG spectrum is computed using the SLEPc eigenvalue solver. We perform extensive tests and validate our programs by comparing the toolbox's results with known theoretical and numerical findings that have been reported in the literature., Comment: arXiv admin note: text overlap with arXiv:2303.05350

Let $S$ be a minimal compact complex surface with Betti numbers $b_1(S)=1$ and $b_2(S)\ge 1$ i.e. a compact surface in class VII$_0^+$. We show that if there exists a twisted logarithmic 1-form $\tau\in H^0(S,\Omega^1(\log D)\otimes \mathcal L_\lambda)$, where $D$ is a non zero divisor and $\mathcal L\in H^1(S,\mathbb C^\star)$, then $S$ is a Kato surface. It is known that $\lambda$ is in fact real and we show that $\lambda\ge 1$ and unique if $S$ is not a Inoue-Hirzebruch surface. Moreover $\lambda=1$ if and only if $S$ is a Enoki surface. When $\lambda>1$ these conditions are equivalent to the existence of a negative PSH function $\hat \tau$ on the cyclic covering $p:\hat S\to S$ of $S$ which is PH outside $\hat D:=p^{-1}(D)$ with automorphy constant being the same automorphy constant $\lambda$ for a suitable automorphism of $\hat S$. With previous results obtained with V.Apostolov it suggests a strategy to prove the GSS conjecture., Comment: 23 pages

Let $k = \mathbb{Q}(\sqrt {-m})$ and $p > 2$ split in $k$. We prove new properties of the $\mathbb{Z}_p$-extensions $K/k$, distinct from the cyclotomic; we do not assume $K/k$ totally ramified, nor the triviality of the $p$-class group of $k$. These properties are governed by the $p$-valuation, $\delta_p(k)$, of a suitable Fermat quotient of the fundamental $p$-unit $x$ of $k$, which also yields the order of the logarithmic class group (2.2, App. A), and generalize the Gold-Sands criterion (5.1, 5.3, 5.4, 5.5). These results use the second element $(\mathcal{H}_{K_n}/\mathcal{H}_{K_n}^{G_n})^{G_n}$ of the filtration of the $p$-class groups in $K=\cup_n K_n$, without any argument of Iwasawa's theory, and provide new perspectives. We compute, Sec. 7, the first layer $K_1$, using the Log-function, then give a short proof of a result of Kundu-Washington (5.6), and show (7.4, 7.6) that capitulation is possible in $K$, suggesting Conjecture 5.8. Calculations are gathered App. B., Comment: Addition, Section 7: Computation of the initial layer of the $\mathbb{Z}_p$-extensions of $k = \mathbb{Q}(\sqrt {-m})$ -- New results on the structure of its $p$-class group and capitulation phenomenon - New programs

When writing and talking, people sometimes pause to think. Although reasoning-focused works have often framed reasoning as a method of answering questions or completing agentic tasks, reasoning is implicit in almost all written text. For example, this applies to the steps not stated between the lines of a proof or to the theory of mind underlying a conversation. In the Self-Taught Reasoner (STaR, Zelikman et al. 2022), useful thinking is learned by inferring rationales from few-shot examples in question-answering and learning from those that lead to a correct answer. This is a highly constrained setting -- ideally, a language model could instead learn to infer unstated rationales in arbitrary text. We present Quiet-STaR, a generalization of STaR in which LMs learn to generate rationales at each token to explain future text, improving their predictions. We address key challenges, including 1) the computational cost of generating continuations, 2) the fact that the LM does not initially know how to generate or use internal thoughts, and 3) the need to predict beyond individual next tokens. To resolve these, we propose a tokenwise parallel sampling algorithm, using learnable tokens indicating a thought's start and end, and an extended teacher-forcing technique. Encouragingly, generated rationales disproportionately help model difficult-to-predict tokens and improve the LM's ability to directly answer difficult questions. In particular, after continued pretraining of an LM on a corpus of internet text with Quiet-STaR, we find zero-shot improvements on GSM8K (5.9%$\rightarrow$10.9%) and CommonsenseQA (36.3%$\rightarrow$47.2%) and observe a perplexity improvement of difficult tokens in natural text. Crucially, these improvements require no fine-tuning on these tasks. Quiet-STaR marks a step towards LMs that can learn to reason in a more general and scalable way.

The Wide-field Spectroscopic Telescope (WST) is proposed as a new facility dedicated to the efficient delivery of spectroscopic surveys. This white paper summarises the initial concept as well as the corresponding science cases. WST will feature simultaneous operation of a large field-of-view (3 sq. degree), a high multiplex (20,000) multi-object spectrograph (MOS) and a giant 3x3 sq. arcmin integral field spectrograph (IFS). In scientific capability these requirements place WST far ahead of existing and planned facilities. Given the current investment in deep imaging surveys and noting the diagnostic power of spectroscopy, WST will fill a crucial gap in astronomical capability and work synergistically with future ground and space-based facilities. This white paper shows that WST can address outstanding scientific questions in the areas of cosmology; galaxy assembly, evolution, and enrichment, including our own Milky Way; origin of stars and planets; time domain and multi-messenger astrophysics. WST's uniquely rich dataset will deliver unforeseen discoveries in many of these areas. The WST Science Team (already including more than 500 scientists worldwide) is open to the all astronomical community. To register in the WST Science Team please visit https://www.wstelescope.com/for-scientists/participate, Comment: 194 pages, 66 figures. Comments are welcome (wstelescope@gmail.com)

Changes in the timescales at which complex systems evolve are essential to predicting critical transitions and catastrophic failures. Disentangling the timescales of the dynamics governing complex systems remains a key challenge. With this study, we introduce an integrated Bayesian framework based on temporal network models to address this challenge. We focus on two methodologies: change point detection for identifying shifts in system dynamics, and a spectrum analysis for inferring the distribution of timescales. Applied to synthetic and empirical datasets, these methologies robustly identify critical transitions and comprehensively map the dominant and subsidiaries timescales in complex systems. This dual approach offers a powerful tool for analyzing temporal networks, significantly enhancing our understanding of dynamic behaviors in complex systems., Comment: 24 pages, 6 figures

The increasing use of digital technologies and mobile-based registration procedures highlights the vital role of personal identity documents (IDs) in verifying users and safeguarding sensitive information. However, the rise in counterfeit ID production poses a significant challenge, necessitating the development of reliable and efficient automated verification methods. This paper introduces IDTrust, a deep-learning framework for assessing the quality of IDs. IDTrust is a system that enhances the quality of identification documents by using a deep learning-based approach. This method eliminates the need for relying on original document patterns for quality checks and pre-processing steps for alignment. As a result, it offers significant improvements in terms of dataset applicability. By utilizing a bandpass filtering-based method, the system aims to effectively detect and differentiate ID quality. Comprehensive experiments on the MIDV-2020 and L3i-ID datasets identify optimal parameters, significantly improving discrimination performance and effectively distinguishing between original and scanned ID documents., Comment: The primary reason is confidentiality and the joint ownership between the L3i laboratory and the company IMDS

Making ideal decisions as a product leader in a web-facing company is extremely difficult. In addition to navigating the ambiguity of customer satisfaction and achieving business goals, one must also pave a path forward for ones' products and services to remain relevant, desirable, and profitable. Data and experimentation to test product hypotheses are key to informing product decisions. Online controlled experiments by A/B testing may provide the best data to support such decisions with high confidence, but can be time-consuming and expensive, especially when one wants to understand impact to key business metrics such as retention or long-term value. Offline experimentation allows one to rapidly iterate and test, but often cannot provide the same level of confidence, and cannot easily shine a light on impact on business metrics. We introduce a novel, lightweight, and flexible approach to investigating hypotheses, called scenario analysis, that aims to support product leaders' decisions using data about users and estimates of business metrics. Its strengths are that it can provide guidance on trade-offs that are incurred by growing or shifting consumption, estimate trends in long-term outcomes like retention and other important business metrics, and can generate hypotheses about relationships between metrics at scale.

The spin-fermion (SF) model postulates that the dominant coupling between low-energy fermions in near critical metals is mediated by collective spin fluctuations (paramagnons) peaked at the N\'{e}el wave vector, ${\bf Q}_N$, connecting hot spots on opposite sides of the Fermi surface. It has been argued that strong correlations at hot spots lead to a Fermi surface deformation (FSD) featuring flat regions and increased nesting. This conjecture was confirmed in the perturbative self-consistent calculations when the paramagnon propagator dependence on momentum deviation from ${\bf Q}_N$ is given by $\chi^{-1} \propto |\Delta q|$. Using diagrammatic Monte Carlo (diagMC) technique we show that such a dependence holds only at temperatures orders of magnitude smaller than any other energy scale in the problem, indicating that a different mechanism may be at play. Instead, we find that a $\chi^{-1} \propto |\Delta q|^{2}$ dependence yields a robust finite-$T$ scenario for achieving FSD. To link phenomenological and microscopic descriptions, we applied the connected determinant diagMC method to the $(t-t')$ Hubbard model and found that in this case: (i) the FSD is not very pronounced, and, instead, it is the lines of zeros of the renormalized dispersion relation that deform towards nesting; (ii) this phenomenon appears at large $U/t>5.5$ before the formation of electron and hole pockets; (iii) the static spin susceptibility is well described by $\chi^{-1} \propto |\Delta q|^{2}$. Flat FS regions yield a non-trivial scenario for realizing a non-Fermi liquid state., Comment: 5 pages, 4 figures

Understanding the relationship between tongue motion patterns during speech and their resulting speech acoustic outcomes -- i.e., articulatory-acoustic relation -- is of great importance in assessing speech quality and developing innovative treatment and rehabilitative strategies. This is especially important when evaluating and detecting abnormal articulatory features in patients with speech-related disorders. In this work, we aim to develop a framework for detecting speech motion anomalies in conjunction with their corresponding speech acoustics. This is achieved through the use of a deep cross-modal translator trained on data from healthy individuals only, which bridges the gap between 4D motion fields obtained from tagged MRI and 2D spectrograms derived from speech acoustic data. The trained translator is used as an anomaly detector, by measuring the spectrogram reconstruction quality on healthy individuals or patients. In particular, the cross-modal translator is likely to yield limited generalization capabilities on patient data, which includes unseen out-of-distribution patterns and demonstrates subpar performance, when compared with healthy individuals.~A one-class SVM is then used to distinguish the spectrograms of healthy individuals from those of patients. To validate our framework, we collected a total of 39 paired tagged MRI and speech waveforms, consisting of data from 36 healthy individuals and 3 tongue cancer patients. We used both 3D convolutional and transformer-based deep translation models, training them on the healthy training set and then applying them to both the healthy and patient testing sets. Our framework demonstrates a capability to detect abnormal patient data, thereby illustrating its potential in enhancing the understanding of the articulatory-acoustic relation for both healthy individuals and patients., Comment: SPIE Medical Imaging 2024: Image Processing

In this work, we aim to predict the survival time (ST) of glioblastoma (GBM) patients undergoing different treatments based on preoperative magnetic resonance (MR) scans. The personalized and precise treatment planning can be achieved by comparing the ST of different treatments. It is well established that both the current status of the patient (as represented by the MR scans) and the choice of treatment are the cause of ST. While previous related MR-based glioblastoma ST studies have focused only on the direct mapping of MR scans to ST, they have not included the underlying causal relationship between treatments and ST. To address this limitation, we propose a treatment-conditioned regression model for glioblastoma ST that incorporates treatment information in addition to MR scans. Our approach allows us to effectively utilize the data from all of the treatments in a unified manner, rather than having to train separate models for each of the treatments. Furthermore, treatment can be effectively injected into each convolutional layer through the adaptive instance normalization we employ. We evaluate our framework on the BraTS20 ST prediction task. Three treatment options are considered: Gross Total Resection (GTR), Subtotal Resection (STR), and no resection. The evaluation results demonstrate the effectiveness of injecting the treatment for estimating GBM survival., Comment: SPIE Medical Imaging 2024: Computer-Aided Diagnosis

This paper studies the use of Metropolis-Hastings sampling for training Spiking Neural Network (SNN) hardware subject to strong unknown non-idealities, and compares the proposed approach to the common use of the backpropagation of error (backprop) algorithm and surrogate gradients, widely used to train SNNs in literature. Simulations are conducted within a chip-in-the-loop training context, where an SNN subject to unknown distortion must be trained to detect cancer from measurements, within a biomedical application context. Our results show that the proposed approach strongly outperforms the use of backprop by up to $27\%$ higher accuracy when subject to strong hardware non-idealities. Furthermore, our results also show that the proposed approach outperforms backprop in terms of SNN generalization, needing $>10 \times$ less training data for achieving effective accuracy. These findings make the proposed training approach well-suited for SNN implementations in analog subthreshold circuits and other emerging technologies where unknown hardware non-idealities can jeopardize backprop.

The Anderson overlap catastrophe (AOC) is a many-body effect arising as a result of a shakeup of a Fermi sea due to an abrupt change of a local potential, leading to a power-law dependence of the density of states on energy. Here we demonstrate that a standard quantum-dot detector can be employed as a highly tuneable probe of the AOC, where the power law can be continuously modified by a gate voltage. We show that signatures of the AOC have already appeared in previous experiments, and give explicit predictions allowing to tune and pinpoint their non-perturbative aspects.

Terahertz (THz) communication is envisioned as one of the candidate technologies for future wireless communications to enable achievable data rates of up to several terabits per second (Tbps). However, the high pathloss and molecular absorption in THz band communications often limit the transmission range. To overcome these limitations, this paper proposes intelligent reconfigurable surface (IRS)-aided THz networks with imperfect channel state information (CSI). Specifically, we present an angle-based trigonometric channel model to facilitate the performance evaluation of IRS-aided THz networks. In addition, to maximize the sum rate, we formulate the transmitter (Tx)-IRS-receiver (Rx) matching problem, which is a mixed-integer nonlinear programming (MINLP) problem. To address this non-deterministic polynomial-time hard (NP-hard) problem, we propose a Gale-Shapley algorithm-based solutions to obtain stable matching between transmitters and IRSs, and receivers and IRSs, in the first and second sub-problems, respectively. The impact of the transmission power, the number of IRS elements, and the network area on the sum rate are investigated. Furthermore, the proposed algorithm is compared to an exhaustive search, nearest association, greedy search, and random allocation to validate the proposed solution. The complexity and convergence analysis demonstrate that the computational complexity of our algorithm is lower than that of the ES method., Comment: arXiv admin note: text overlap with arXiv:2401.15028

The chemical enrichment of the early Universe is a crucial element in the formation and evolution of galaxies, and Population III (PopIII) stars must play a vital role in this process. In this study, we examine metal enrichment from massive stars in the early Universe's embryonic galaxies. Using radiation hydrodynamic simulations and stellar evolution modelling, we calculated the expected metal yield from these stars. Specifically, we applied accretion rates from a previous radiation-hydrodynamic simulation to inform our stellar evolution modelling, executed with the Geneva code, across 11 selected datasets, with final stellar masses between 500 and 9000 Msol. Our results demonstrate that the first generation of Pop III stars within a mass range of 2000 to 9000 Msol result in N/O, C/O and O/H ratios compatible with the values observed in very high-z galaxies GN-z11 and CEERS 1019. The ejecta of these Pop III stars are predominantly composed of He, H, and N. Our Pop III chemical enrichment model of the halo can accurately reproduce the observed N/O and C/O ratios, and, by incorporating a hundred times more zero-metallicity interstellar material with the stellar ejecta, it accurately attains the observed O/H ratio. Thus, a sub-population of extremely massive PopIII stars, with masses surpassing approximately 2000 Msol, effectively reproduces the CNO elemental abundances observed in high-z JWST galaxies to date., Comment: Accepted in A&A, 13 pages, 4 figures

Consensus control in multi-agent systems has received significant attention and practical implementation across various domains. However, managing consensus control under unknown dynamics remains a significant challenge for control design due to system uncertainties and environmental disturbances. This paper presents a novel learning-based distributed control law, augmented by an auxiliary dynamics. Gaussian processes are harnessed to compensate for the unknown components of the multi-agent system. For continuous enhancement in predictive performance of Gaussian process model, a data-efficient online learning strategy with a decentralized event-triggered mechanism is proposed. Furthermore, the control performance of the proposed approach is ensured via the Lyapunov theory, based on a probabilistic guarantee for prediction error bounds. To demonstrate the efficacy of the proposed learning-based controller, a comparative analysis is conducted, contrasting it with both conventional distributed control laws and offline learning methodologies.

This work presents an innovative learning-based approach to tackle the tracking control problem of Euler-Lagrange multi-agent systems with partially unknown dynamics operating under switching communication topologies. The approach leverages a correlation-aware cooperative algorithm framework built upon Gaussian process regression, which adeptly captures inter-agent correlations for uncertainty predictions. A standout feature is its exceptional efficiency in deriving the aggregation weights achieved by circumventing the computationally intensive posterior variance calculations. Through Lyapunov stability analysis, the distributed control law ensures bounded tracking errors with high probability. Simulation experiments validate the protocol's efficacy in effectively managing complex scenarios, establishing it as a promising solution for robust tracking control in multi-agent systems characterized by uncertain dynamics and dynamic communication structures., Comment: 8 pages

This paper introduces an innovative approach to enhance distributed cooperative learning using Gaussian process (GP) regression in multi-agent systems (MASs). The key contribution of this work is the development of an elective learning algorithm, namely prior-aware elective distributed GP (Pri-GP), which empowers agents with the capability to selectively request predictions from neighboring agents based on their trustworthiness. The proposed Pri-GP effectively improves individual prediction accuracy, especially in cases where the prior knowledge of an agent is incorrect. Moreover, it eliminates the need for computationally intensive variance calculations for determining aggregation weights in distributed GP. Furthermore, we establish a prediction error bound within the Pri-GP framework, ensuring the reliability of predictions, which is regarded as a crucial property in safety-critical MAS applications., Comment: 9 pages, conference preprint

The emergence of the disc in our Galaxy and the relation of the thick and thin disc formation and evolution is still a matter of debate. The chemo-dynamical characterization of disc stars is key to resolve this question, in particular at parameter regimes where both disc components overlap, such as the region around [Fe/H] $\sim$ $-0.7$ corresponding to the thin disc metal-poor end. In this paper we re-assess the recent detection of a metal-poor extension of stars moving with thin-disc-like rotational velocities between -2 < [Fe/H] < -0.7 that was made based on metallicity estimates obtained from photometric data and their rotational velocity distribution. We explore the chemo-dynamical properties of metal-poor stars within the recent Gaia third data release (DR3), which includes the first catalogue of metallicity estimates from the Radial Velocity Spectrometer (RVS) experiment. We complement them with the two largest high-resolution ($\lambda/d\lambda$ > 20,000) spectroscopic surveys available, the GALAH DR3 and the APOGEE DR17. We confirm that there are high angular-momentum stars moving in thin-disc-like orbits, i.e., with high angular momentum $\rm L_{z}/J_{tot}$ > 0.95, and close to the Galactic plane, $\rm |Z_{max}|$ < 750 pc, reaching metallicity values down to [Fe/H] $\sim-1.5$. We also find tentative evidence of stars moving on such orbits at lower metallicities, down to [Fe/H] $\sim -2.5$, although in smaller numbers. Based on their chemical trends the fast rotators with [Fe/H] < -1 would have formed in a medium less chemically evolved than the bulk of the thick disc. Fast rotators with chemical abundances typical of the thin disc appear at metallicities between -1 < [Fe/H] < -0.7., Comment: Submitted to A&A on December 12th

This paper investigates a deep reinforcement learning (DRL)-based approach for managing channel access in wireless networks. Specifically, we consider a scenario in which an intelligent user device (iUD) shares a time-varying uplink wireless channel with several fixed transmission schedule user devices (fUDs) and an unknown-schedule malicious jammer. The iUD aims to harmoniously coexist with the fUDs, avoid the jammer, and adaptively learn an optimal channel access strategy in the face of dynamic channel conditions, to maximize the network's sum cross-layer achievable rate (SCLAR). Through extensive simulations, we demonstrate that when we appropriately define the state space, action space, and rewards within the DRL framework, the iUD can effectively coexist with other UDs and optimize the network's SCLAR. We show that the proposed algorithm outperforms the tabular Q-learning and a fully connected deep neural network approach.

Neural quantum state (NQS) ans\"atze have shown promise in variational Monte Carlo algorithms by their theoretical capability of representing any quantum state. However, the reason behind the practical improvement in their performance with an increase in the number of parameters is not fully understood. In this work, we systematically study the efficiency of a shallow neural network to represent the ground states in different phases of the spin-1 bilinear-biquadratic chain, as the number of parameters increases. We train our ansatz by a supervised learning procedure, minimizing the infidelity w.r.t. the exact ground state. We observe that the accuracy of our ansatz improves with the network width in most cases, and eventually saturates. We demonstrate that this can be explained by looking at the spectrum of the quantum geometric tensor (QGT), particularly its rank. By introducing an appropriate indicator, we establish that the QGT rank provides a useful diagnostic for the practical representation power of an NQS ansatz.

Multimodal Magnetic Resonance (MR) Imaging plays a crucial role in disease diagnosis due to its ability to provide complementary information by analyzing a relationship between multimodal images on the same subject. Acquiring all MR modalities, however, can be expensive, and, during a scanning session, certain MR images may be missed depending on the study protocol. The typical solution would be to synthesize the missing modalities from the acquired images such as using generative adversarial networks (GANs). Yet, GANs constructed with convolutional neural networks (CNNs) are likely to suffer from a lack of global relationships and mechanisms to condition the desired modality. To address this, in this work, we propose a transformer-based modality infuser designed to synthesize multimodal brain MR images. In our method, we extract modality-agnostic features from the encoder and then transform them into modality-specific features using the modality infuser. Furthermore, the modality infuser captures long-range relationships among all brain structures, leading to the generation of more realistic images. We carried out experiments on the BraTS 2018 dataset, translating between four MR modalities, and our experimental results demonstrate the superiority of our proposed method in terms of synthesis quality. In addition, we conducted experiments on a brain tumor segmentation task and different conditioning methods., Comment: 6 pages

The Industrial Internet of Things (IIoT) enables industries to build large interconnected systems utilizing various technologies that require high data rates. Terahertz (THz) communication is envisioned as a candidate technology for achieving data rates of several terabits-per-second (Tbps). Despite this, establishing a reliable communication link at THz frequencies remains a challenge due to high pathloss and molecular absorption. To overcome these limitations, this paper proposes using intelligent reconfigurable surfaces (IRSs) with THz communications to enable future smart factories for the IIoT. In this paper, we formulate the power allocation and joint IIoT device and IRS association (JIIA) problem, which is a mixed-integer nonlinear programming (MINLP) problem. {Furthermore, the JIIA problem aims to maximize the sum rate with imperfect channel state information (CSI).} To address this non-deterministic polynomial-time hard (NP-hard) problem, we decompose the problem into multiple sub-problems, which we solve iteratively. Specifically, we propose a Gale-Shapley algorithm-based JIIA solution to obtain stable matching between uplink and downlink IRSs. {We validate the proposed solution by comparing the Gale-Shapley-based JIIA algorithm with exhaustive search (ES), greedy search (GS), and random association (RA) with imperfect CSI.} The complexity analysis shows that our algorithm is more efficient than the ES.