Search

Python has gained widespread popularity in the fields of machine learning, artificial intelligence, and data engineering due to its effectiveness and extensive libraries. R, on its side, remains a dominant language for statistical analysis and visualization. However, certain libraries have become outdated, limiting their functionality and performance. Users can use Python's advanced machine learning and AI capabilities alongside R's robust statistical packages by combining these two programming languages. This paper explores using R's reticulate package to call Python from R, providing practical examples and highlighting scenarios where this integration enhances productivity and analytical capabilities. With a few hello-world code snippets, we demonstrate how to run Python's scikit-learn, pytorch and OpenAI gym libraries for building Machine Learning, Deep Learning, and Reinforcement Learning projects easily.

A critical element of the LHC physics program is the search for an additional source of CP violation. This is largely unexplored in the context of non-linear Higgs physics, which is naturally described in Higgs Effective Field Theory (HEFT). Relevant new higher-dimensional operators modify the production rate and branching ratios of the Higgs boson, de-correlating different Higgs multiplicities. In this work, we consider single Higgs and Higgs pair production via weak boson fusion from the perspective of gauge-Higgs CP violation through the lens of Higgs non-linearity. This generalizes existing rate-based searches and analyses by the ATLAS and CMS experiments. Particular focus is given to the phenomenological differences in the expected BSM sensitivity pattern when comparing HEFT constraints with Standard Model Effective Field Theory (SMEFT) limits., Comment: 8 pages, 3 figures

Making use of JWST NIRSpec and NIRCam data, we conduct a detailed analysis of the spectroscopic and photometric properties of GN-z8-LAE, a strong Lya emitter at z=8.279. Our goal is to investigate the interstellar medium (ISM) physical conditions that enable the Lya detection in this source at the Epoch of Reionization (EoR) and scrutinize GN-z8-LAE as an early reionizer. In broad agreement with previous results, we find that GN-z8-LAE is a young galaxy (age ~ 10 Myr) with a low stellar mass (M* ~ 10^7.66 Msun), significantly lower than those of most Lya emitters known at similarly high redshifts. The derived stellar mass and star formation rate surface densities are 355 Msun/pc^2 and 88 Msun/yr/kpc^2, respectively. Our spectral analysis indicates that: the Lya line peak has a small velocity offset 133+-72 km/s with respect to the galaxy systemic redshift; CIV] / CIII] ~ 3.3; the ISM is characterized by a hard ionization field, although no signature of AGN is present. Moreover, we report the presence of NIII]1750 emission with super-solar N abundance, which makes GN-z8-LAE one of the first known cases of a simultaneous strong Lya and nitrogen emitter at the EoR. Based on all these properties, we apply a wide range of methods to constrain the absolute Lyman continuum escape fraction of GN-z8-LAE, and find that it is >14% in all cases. Therefore, we conclude that GN-z8-LAE is a robust candidate for a Lyman continuum (LyC) leaker at the EoR which is being caught at the moment of efficiently reionizing its surrounding medium., Comment: Submitted to ApJ. 22 pages, 8 Figures

We present a catalog of optical and infrared identifications (ID) of X-ray sources in the AKARI North Ecliptic Pole (NEP) Deep field detected with Chandra covering $\sim 0.34\,{\rm deg^{2}}$ with 0.5-2 keV flux limits ranging $\sim 2 \mathrm{-} 20\times 10^{-16}\,{\rm erg\,s^{-1}\,cm^{-2}}$. The optical/near-infrared counterparts of the X-ray sources are taken from our Hyper Suprime Cam (HSC)/Subaru and Wide-Field InfraRed Camera (WIRCam)/Canada-France-Hawaii Telescope (CFHT) data because these have much more accurate source positions due to their spatial resolution than that of {Chandra} and longer wavelength infrared data. We concentrate our identifications in the HSC $g$ band and WIRCam $K_{\rm s}$ band-based catalogs. To select the best counterpart, we utilize a novel extension of the likelihood-ratio (LR) analysis, where we use the X-ray flux as well as $g - K_{\rm s}$ colors to calculate the likelihood ratio. Spectroscopic and photometric redshifts of the counterparts are summarized. Also, simple X-ray spectroscopy is made on the sources with sufficient source counts. We present the resulting catalog in an electronic form. The main ID catalog contains 403 X-ray sources and includes X-ray fluxes, luminosities, $g$ and $K_{\rm s}$ band magnitudes, redshifts, and their sources, optical spectroscopic properties, as well as intrinsic absorption column densities and power-law indices from simple X-ray spectroscopy. The identified X-ray sources include 27 Milky-Way objects, 57 type I AGNs, 131 other AGNs, and 15 galaxies. The catalog serves as a basis for further investigations of the properties of the X-ray and near-infrared sources in this field. (Abridged), Comment: 16 pages, 9 figures, Three electronic (fits) tables are included in src. Accepted to Astronomy and Astrophysics

Missing values are a major challenge in most data science projects working on real data. To avoid losing valuable information, imputation methods are used to fill in missing values with estimates, allowing the preservation of samples or variables that would otherwise be discarded. However, if the process is not well controlled, imputation can generate spurious values that introduce uncertainty and bias into the learning process. The abundance of univariate and multivariate imputation techniques, along with the complex trade-off between data reliability and preservation, makes it difficult to determine the best course of action to tackle missing values. In this work, we present ITI-IQA (Imputation Quality Assessment), a set of utilities designed to assess the reliability of various imputation methods, select the best imputer for any feature or group of features, and filter out features that do not meet quality criteria. Statistical tests are conducted to evaluate the suitability of every tested imputer, ensuring that no new biases are introduced during the imputation phase. The result is a trainable pipeline of filters and imputation methods that streamlines the process of dealing with missing data, supporting different data types: continuous, discrete, binary, and categorical. The toolbox also includes a suite of diagnosing methods and graphical tools to check measurements and results during and after handling missing data., Comment: 28 pages, 8 figures

Binding affinity optimization is crucial in early-stage drug discovery. While numerous machine learning methods exist for predicting ligand potency, their comparative efficacy remains unclear. This study evaluates the performance of classical tree-based models and advanced neural networks in protein-ligand binding affinity prediction. Our comprehensive benchmarking encompasses 2D models utilizing ligand-only RDKit embeddings and Large Language Model (LLM) ligand representations, as well as 3D neural networks incorporating bound protein-ligand conformations. We assess these models across multiple standard datasets, examining various predictive scenarios including classification, ranking, regression, and active learning. Results indicate that simpler models can surpass more complex ones in specific tasks, while 3D models leveraging structural information become increasingly competitive with larger training datasets containing compounds with labelled affinity data against multiple targets. Pre-trained 3D models, by incorporating protein pocket environments, demonstrate significant advantages in data-scarce scenarios for specific binding pockets. Additionally, LLM pretraining on 2D ligand data enhances complex model performance, providing versatile embeddings that outperform traditional RDKit features in computational efficiency. Finally, we show that combining 2D and 3D model strengths improves active learning outcomes beyond current state-of-the-art approaches. These findings offer valuable insights for optimizing machine learning strategies in drug discovery pipelines., Comment: 20 pages, 14 figures, 1 table

Generative Artificial Intelligence has grown exponentially as a result of Large Language Models (LLMs). This has been possible because of the impressive performance of deep learning methods created within the field of Natural Language Processing (NLP) and its subfield Natural Language Generation (NLG), which is the focus of this paper. Within the growing LLM family are the popular GPT-4, Bard and more specifically, tools such as ChatGPT have become a benchmark for other LLMs when solving most of the tasks involved in NLG research. This scenario poses new questions about the next steps for NLG and how the field can adapt and evolve to deal with new challenges in the era of LLMs. To address this, the present paper conducts a review of a representative sample of surveys recently published in NLG. By doing so, we aim to provide the scientific community with a research roadmap to identify which NLG aspects are still not suitably addressed by LLMs, as well as suggest future lines of research that should be addressed going forward.

Context. Observational evidence has accumulated in the past years, showing that the Galactic bulge includes two populations, a metal poor and a metal rich one that, in addition to a different metallicity, show different alpha over iron abundances, spatial distribution, and kinematics. While the metal rich, barred component has been fairly well characterized, the metal poor, spheroidal component has been more elusive and harder to describe. RR Lyrae variables are clean tracers of the old bulge component, and they are, on average, more metal poor than red clump stars. Aims. In the present paper, we provide a new catalog of 16488 ab type RR Lyrae variables in the bulge region within -10

Purpose: Analyze the diversity of citation distributions to publications in different research topics to investigate the accuracy of size-independent, rank-based indicators. Top percentile-based indicators are the most common indicators of this type, and the evaluations of Japan are the most evident misjudgments. Design/methodology/approach: The distributions of citations to publications from countries and in journals in several research topics were analyzed along with the corresponding global publications using histograms with logarithmic binning, double rank plots, and normal probability plots of log-transformed numbers of citations. Findings: Size-independent, top percentile-based indicators are accurate when the global ranks of local publications fit a power law, but deviations in the least cited papers are frequent in countries and occur in all journals with high impact factors. In these cases, a single indicator is misleading. Comparisons of proportions of uncited papers are the best way to predict these deviations. Research limitations: The study is fundamentally analytical; its results describe mathematical facts that are self-evident. Practical implications: Respectable institutions, such as the OECD, European Commission, US National Science Board, and others, produce research country rankings and individual evaluations using size-independent percentile indicators that are misleading in many countries. These misleading evaluations should be discontinued because they cause confusion among research policymakers and lead to incorrect research policies. Originality/value: Studies linking the lower tail of citation distribution, including uncited papers, to percentile research indicators have not been performed previously. The present results demonstrate that studies of this type are necessary to find reliable procedures for research assessments., Comment: 30 pages, 6 figures, 7 tables

Nonlinear model predictive control (NMPC) has proven to be an effective control method, but it is expensive to compute. This work demonstrates the use of hardware FPGA neural network controllers trained to imitate NMPC with supervised learning. We use these Neural Controllers (NCs) implemented on inexpensive embedded FPGA hardware for high frequency control on physical cartpole and F1TENTH race car. Our results show that the NCs match the control performance of the NMPCs in simulation and outperform it in reality, due to the faster control rate that is afforded by the quick FPGA NC inference. We demonstrate kHz control rates for a physical cartpole and offloading control to the FPGA hardware on the F1TENTH car. Code and hardware implementation for this paper are available at https:// github.com/SensorsINI/Neural-Control-Tools.

With the advent of next-generation AR/VR headsets, many of them with affordable prices, telecom operators have forecasted an explosive growth of traffic in their networks. Penetration of AR/VR services and applications is estimated to grow exponentially in the next few years. This work attempts to shed light on the bandwidth capacity requirements and latency of popular AR/VR applications with four different real experimental settings on the Meta Quest 3 headsets, and their potential impact on the network.

Federated learning is one popular paradigm to train a joint model in a distributed, privacy-preserving environment. But partial annotations pose an obstacle meaning that categories of labels are heterogeneous over clients. We propose to learn a joint backbone in a federated manner, while each site receives its own multi-label segmentation head. By using Bayesian techniques we observe that the different segmentation heads although only trained on the individual client's labels also learn information about the other labels not present at the respective site. This information is encoded in their predictive uncertainty. To obtain a final prediction we leverage this uncertainty and perform a weighted averaging of the ensemble of distributed segmentation heads, which allows us to segment "locally unknown" structures. With our method, which we refer to as FUNAvg, we are even on-par with the models trained and tested on the same dataset on average. The code is publicly available at https://github.com/Cardio-AI/FUNAvg., Comment: Accepted at MICCAI24

We present the results of a set of cosmological N-body simulations with standard $\Lambda$CDM cosmology but characterized by a scale-dependent primordial non-Gaussianity of the local type featuring a power-law dependence of the $f_{\rm NL}^{\rm loc}(k)$ at large scales followed by a saturation to a constant value at smaller scales where non-linear growth leads to the formation of collapsed cosmic structures. Such models are built to ensure consistency with current Cosmic Microwave Background bounds on primordial non-Gaussianity yet allowing for large effects of the non-Gaussian statistics on the properties of non-linear structure formation. We show the impact of such scale-dependent non-Gaussian scenarios on a wide range of properties of the resulting cosmic structures, such as the non-linear matter power spectrum, the halo and sub-halo mass functions, the concentration-mass relation, the halo and void density profiles, and we highlight for the first time that some of these models might mimic the effects of Warm Dark Matter for several of such observables, Comment: 21 pages, 9 figures, 2 tables; to be submitted to JCAP

Top quark polarization provides an important tool for studying its production mechanisms, spin correlations, top quark properties, and new physics searches. Unlike lighter quarks, the top quark's polarization remains intact until its decay, enabling precise spin measurements. While the down-type fermions from $W$ boson decay are known to be effective spin analyzers, charged leptons have typically been the main target for most analyses. In this paper, we investigate the relevance of global jet dynamics -- considering kinematics, jet charges, and particle multiplicity -- for hadronic top quark polarimetry. The formalism used allows for analytical derivations obtained throughout the manuscript, offering deeper insights into the corresponding phenomenology., Comment: 14 pages and 3 figures

Pre-trained large language models (LLM) are starting to be widely used in many applications. In this work, we explore the use of these models in interactive machine translation (IMT) environments. In particular, we have chosen mBART (multilingual Bidirectional and Auto-Regressive Transformer) and mT5 (multilingual Text-to-Text Transfer Transformer) as the LLMs to perform our experiments. The system generates perfect translations interactively using the feedback provided by the user at each iteration. The Neural Machine Translation (NMT) model generates a preliminary hypothesis with the feedback, and the user validates new correct segments and performs a word correction--repeating the process until the sentence is correctly translated. We compared the performance of mBART, mT5, and a state-of-the-art (SoTA) machine translation model on a benchmark dataset regarding user effort, Word Stroke Ratio (WSR), Key Stroke Ratio (KSR), and Mouse Action Ratio (MAR). The experimental results indicate that mBART performed comparably with SoTA models, suggesting that it is a viable option for this field of IMT. The implications of this finding extend to the development of new machine translation models for interactive environments, as it indicates that some novel pre-trained models exhibit SoTA performance in this domain, highlighting the potential benefits of adapting these models to specific needs., Comment: 10 pages, 4 figures

We investigate the electronic structure and transport properties of twisted bilayer graphene (TBLG) at a twist angle of $\theta\approx 1.696\text{{\deg}}$. Using a combination of molecular dynamics and tight-binding calculations, we find two superlattice gaps in the energy spectrum of the bulk, which emerge close to the Fermi level from the atomic rearrangement of the carbon atoms leading to a corrugation of the graphene sheets. Nanoribbons made from 1.696{\deg}-TBLG show edge-localized states inside the superlattice gaps. Applying the Green's function method, we demonstrate that the edge states carry electronic current with conductance values close to the conductance quantum. The edge states can generate a non-local resistance, which is not due to one-way transport at the edges but due to the fact that these states are localized only at certain edges of the system, depending on how the nanoribbon has been cut from the bulk., Comment: 9 pages, 6 figures

This paper examines the incidence and persistence of overeducation in the early careers of Spanish university graduates. We investigate the role played by the business cycle and field of study and their interaction in shaping both phenomena. We also analyse the relevance of specific types of knowledge and skills as driving factors in reducing overeducation risk. We use data from the Survey on the Labour Insertion of University Graduates (EILU) conducted by the Spanish National Statistics Institute in 2014 and 2019. The survey collects rich information on cohorts that graduated in the 2009/2010 and 2014/2015 academic years during the Great Recession and the subsequent economic recovery, respectively. Our results show, first, the relevance of the economic scenario when graduates enter the labour market. Graduation during a recession increased overeducation risk and persistence. Second, a clear heterogeneous pattern occurs across fields of study, with health sciences graduates displaying better performance in terms of both overeducation incidence and persistence and less impact of the business cycle. Third, we find evidence that some transversal skills (language, IT, management) can help to reduce overeducation risk in the absence of specific knowledge required for the job, thus indicating some kind of compensatory role. Finally, our findings have important policy implications. Overeducation, and more importantly overeducation persistence, imply a non-neglectable misallocation of resources. Therefore, policymakers need to address this issue in the design of education and labour market policies., Comment: 37 pages, 4 figures and 4 tables. Submitted to Journal of Policy Modeling

We propose an inertial variant of the strongly convergent inexact proximal-point (PP) method of Solodov and Svaiter (2000) for monotone inclusions. We prove strong convergence of our main algorithm under less restrictive assumptions on the inertial parameters when compared to previous analysis of inertial PP-type algorithms, which makes our method of interest even in finite-dimensional settings. We also performed an iteration-complexity analysis and applied our main algorithm to variational inequalities for monotone operators, obtaining strongly convergent (inertial) variants of Korpolevich's extragradient, forward-backward and Tseng's modified forward-backward methods.

Precision studies for top quark physics are a cornerstone of the Large Hadron Collider program. Polarization, probed through decay kinematics, provides a unique tool to scrutinize the top quark across its various production modes and to explore potential new physics effects. However, the top quark most often decays hadronically, for which unambiguous identification of its decay products sensitive to top quark polarization is not possible. In this Letter, we introduce a jet flavor tagging method to significantly improve spin analyzing power in hadronic decays, going beyond exclusive kinematic information employed in previous studies. We provide parametric estimates of the improvement from flavor tagging with any set of measured observables and demonstrate this in practice on simulated data using a Graph Neural Network (GNN). We find that the spin analyzing power in hadronic decays can improve by approximately 20% (40%) compared to the kinematic approach, assuming an efficiency of 0.5 (0.2) for the network., Comment: 7 pages and 3 figures

In this work, we present a novel approach to transform supervised classifiers into effective unsupervised anomaly detectors. The method we have developed, termed Discriminatory Detection of Distortions (DDD), enhances anomaly detection by training a discriminator model on both original and artificially modified datasets. We conducted a comprehensive evaluation of our models on the Dark Machines Anomaly Score Challenge channels and a search for 4-top quark events, demonstrating the effectiveness of our approach across various final states and beyond the Standard Model scenarios. We compare the performance of the DDD method with the Deep Robust One-Class Classification method (DROCC), which incorporates signals in the training process, and the Deep Support Vector Data Description (DeepSVDD) method, a well established and well performing method for anomaly detection. Results show that the effectiveness of each model varies by signal and channel, with DDD proving to be a very effective anomaly detector. We recommend the combined use of DeepSVDD and DDD for purely unsupervised applications, with the addition of flow models for improved performance when resources allow. Findings suggest that network architectures that excel in supervised contexts, such as the particle transformer with standard model interactions, also perform well as unsupervised anomaly detectors. We also show that with these methods, it is likely possible to recognize 4-top quark production as an anomaly without prior knowledge of the process. We argue that the Large Hadron Collider community can transform supervised classifiers into anomaly detectors to uncover potential new physical phenomena in each search.

The flavour puzzle remains as one of the most intriguing enigmas of particle physics. In this thesis, we propose and study theories of flavour which generically hint to a multi-scale origin of flavour. First we explore the idea of fermiophobic models and messenger dominance, where the chiral fermions of the Standard Model are uncharged under (part of) an extended gauge symmetry, which also forbids Yukawa couplings for chiral fermions. These are generated effectively due to the presence of hierarchically heavy messengers, including vector-like fermions and/or heavy Higgs doublets. Simultaneously, this mechanism generates effective couplings of chiral fermions to the gauge bosons of the extended symmetry, leading to a predictive phenomenology connected to the origin of flavour hierarchies. We present two models of this kind, based on gauge $U(1)$ and twin Pati-Salam symmetries, that have implications for low energy flavour anomalies. Secondly, we study the framework of flavour deconstruction, where the Standard Model emerges from a non-universal gauge theory that contains a separate gauge factor for each fermion family. We argue that one of the most simple ways to achieve this is by assigning a separate gauge hypercharge to each fermion family, denoted as the "tri-hypercharge" theory. If the SM Higgs only carries third family hypercharge, then only the third family is massive at renormalisable level, and light families get their mass from higher dimensional operators. Finally, we propose a gauge unified origin for gauge non-universal frameworks such as the aforementioned tri-hypercharge theory. The model assigns a separate $SU(5)$ group to each fermion family. However, assuming that the three $SU(5)$ groups are related by a cyclic permutation symmetry $\mathbb{Z}_{3}$, then the model is described by a single gauge coupling in the ultraviolet, despite $SU(5)^{3}$ being a non-simple group., Comment: PhD thesis: https://eprints.soton.ac.uk/488650 . Comments and typos spotted are welcome

Observations with FAST recently detected HI 21-cm emission near M94, revealing an intriguing object, Cloud-9, without an optical counterpart. Subsequent analysis suggests Cloud-9 is consistent with a gas-rich ($M_{\rm HI} \approx 10^{6} \ M_{\odot}$), starless dark matter (DM) halo of mass $M_{200} \approx 5 \times 10^{9} \ M_{\odot}$. Using VLA in D-array configuration, we present interferometric observations of Cloud-9 revealing it as a dynamically cold ($W_{50} \approx 12 \rm \ km \ s^{-1}$), non-rotating, and spatially-asymmetric system, exhibiting gas compression on one side and a tail-like structure towards the other, features likely originating from ram pressure. Our observations suggest Cloud-9 is consistent with a starless $\Lambda$CDM dark matter halo if the gas is largely isothermal. If interpreted as a faint dwarf, Cloud-9 is similar to Leo T, a nearby gas-rich galaxy that would fall below current optical detection limits at Cloud-9's distance ($d\approx 5 \rm \ Mpc$). Further observations with HST reaching magnitudes $m_{g} \approx 30$ would help identify such a galaxy or dramatically lower current limits to its stellar mass ($M_{\rm gal} \lesssim 10^{5} \ M_{\odot}$). Cloud-9 thus stands as the firmest starless DM halo candidate to date or the faintest galaxy known at its distance., Comment: Submitted to ApJ

We aim to constrain the chemo-dynamical properties of the Sagittarius (Sgr) dwarf galaxy using carbon abundances. Our sample from the \textit{Pristine} Inner Galaxy Survey (PIGS) includes $\sim 350$ metal-poor ([Fe/H]~$<-1.5$) stars in the main body of Sgr with good quality spectroscopic observations. Our metal-poor Sgr population has a larger velocity dispersion than metal-rich Sgr from the literature, which could be explained by outside-in star formation, extreme Galactic tidal perturbations and/or the presence of a metal-rich disc/bar $+$ a metal-poor halo. The average carbon abundance [C/Fe] in Sgr is similar to that of other classical dwarf galaxies (DGs) and consistently lower than in the Milky Way by $\sim0.2-0.3$~dex at low metallicity. The interstellar medium in DGs, including Sgr, may have retained yields from more energetic Population~III~and~II supernovae (SNe), thereby reducing the average [C/Fe]. Additionally, SNe~Ia, producing more Fe than C, would start to contribute at lower metallicity in DGs/Sgr than in the Galaxy. The presence of a [C/Fe] gradient for Sgr stars with [Fe/H]~$\gtrsim-2.0$ ($\sim 6.8\times 10^{-4}\ \rm{dex \ arcmin^{-1}}$) suggests that SNe~Ia contributed in the system at those metallicities, especially in its inner regions. There is a low frequency of carbon-enhanced metal-poor (CEMP) stars in our Sgr sample. At higher metallicity/carbon abundance (mostly CEMP-s) this may be due to photometric selection effects, but those are less likely to affect CEMP-no stars. We propose that, given the lower average [C/Fe] in DGs, using the same CEMP definition ([C/Fe]~$>+0.7$) as in the Galaxy under-predicts the number of CEMP stars in DGs, and for Sgr a cut at [C/Fe]$~\sim +0.35$ may be more appropriate, which brings the frequency of CEMP stars in agreement with that in the Galaxy., Comment: Accepted for publication in A&A. The abstract in the metadata is a shorter version than the actual one

Cellular automata (CA) are simulation models that can produce complex emergent behaviors from simple local rules. Although state-of-the-art GPU solutions are already fast due to their data-parallel nature, their performance can rapidly degrade in CA with a large neighborhood radius. With the inclusion of tensor cores across the entire GPU ecosystem, interest has grown in finding ways to leverage these fast units outside the field of artificial intelligence, which was their original purpose. In this work, we present CAT, a GPU tensor core approach that can accelerate CA in which the cell transition function acts on a weighted summation of its neighborhood. CAT is evaluated theoretically, using an extended PRAM cost model, as well as empirically using the Larger Than Life (LTL) family of CA as case studies. The results confirm that the cost model is accurate, showing that CAT exhibits constant time throughout the entire radius range $1 \le r \le 16$, and its theoretical speedups agree with the empirical results. At low radius $r=1,2$, CAT is competitive and is only surpassed by the fastest state-of-the-art GPU solution. Starting from $r=3$, CAT progressively outperforms all other approaches, reaching speedups of up to $101\times$ over a GPU baseline and up to $\sim 14\times$ over the fastest state-of-the-art GPU approach. In terms of energy efficiency, CAT is competitive in the range $1 \le r \le 4$ and from $r \ge 5$ it is the most energy efficient approach. As for performance scaling across GPU architectures, CAT shows a promising trend that if continues for future generations, it would increase its performance at a higher rate than classical GPU solutions. The results obtained in this work put CAT as an attractive GPU approach for scientists that need to study emerging phenomena on CA with large neighborhood radius., Comment: 15 pages

Hypothetical axions provide a compelling explanation for dark matter and could be emitted from the hot solar interior. The CERN Axion Solar Telescope (CAST) has been searching for solar axions via their back conversion to X-ray photons in a 9-T 10-m long magnet directed towards the Sun. We report on an extended run with the IAXO (International Axion Observatory) pathfinder detector, doubling the previous exposure time. The detector was operated with a xenon-based gas mixture for part of the new run, providing technical insights for future detector configurations in IAXO. No counts are detected in the 95% signal-encircling region during the new run, while 0.75 are expected. The new data improve the axion-photon coupling limit to 5.8$\times 10^{-11}\,$GeV$^{-1}$ at 95% C.L. (for $m_a \lesssim 0.02$ eV), the most restrictive experimental limit to date., Comment: 9 pages, 4 figures (plus 8 pages, 10 figures of supplemental material) Corresponding authors: C. Margalejo (cmargalejo@unizar.es) and J. Ruz (Jaime.Ruz@cern.ch)

If $H$ is a Hall subgroup of a finite group $G$, it was proven in 1989 using the classification of finite simple groups that all the irreducible complex characters of $H$ extend to $G$ if and only if there is $N\trianglelefteq G$ such that $HN=G$ and $H\cap N=1$. In this note we offer a modular version of this result under more general hypothesis.

Whilst current observational evidence favors a close-to-Gaussian spectrum of primordial perturbations, there exist many models of the early Universe that predict this distribution to have exponentially enhanced or suppressed tails. In this work, we generate realizations of the primordial potential with non-Gaussian tails via a phenomenological model; these are then evolved numerically to obtain maps of the cosmic microwave background (CMB) and large-scale structure (LSS). In the CMB maps, our added non-Gaussianity manifests as a localized enhancement of hot and cold spots, which would be expected to contribute to $N$-point functions up to large $N$. Such models are indirectly constrained by \textit{Planck} trispectrum bounds, which restrict the changes in the temperature fluctuations to $O(10\mu\mathrm{K})$. In the late-time Universe, we find that tailed cosmologies lead to a halo mass function enhanced at high masses, as expected. Furthermore, significant scale-dependent bias in the halo-halo and halo-matter power spectrum is also sourced, which arises from the squeezed limit of large $N$-point functions that are implicitly generated through the enhancement of the tails. These results underscore that a detection of scale-dependent bias alone cannot be used to rule out single field inflation, but can be used together with other statistics to probe a wide range of primordial processes., Comment: Comments welcome!

We investigate the correlation between stellar mass (M*) and star formation rate (SFR) across the stellar mass range log10(M*/Msun)~6-11. We consider almost 50,000 star-forming galaxies at z~3-7, leveraging data from COSMOS/SMUVS, JADES/GOODS-SOUTH, and MIDIS/XDF. This is the first study spanning such a wide stellar mass range without relying on gravitational lensing effects. We locate our galaxies on the SFR-M* plane to assess how the location of galaxies in the star-formation main sequence (MS) and starburst (SB) region evolves with stellar mass and redshift. We find that the two star-forming modes tend to converge at log10(M*/Msun) < 7, with all galaxies found in the SB mode. By dissecting our galaxy sample in stellar mass and redshift, we show that the emergence of the star-formation MS is stellar-mass dependent: while in galaxies with log10(M*/Msun) > 9 the MS is already well in place at z = 5-7, for galaxies with log10(M*/Msun)~7-8 it only becomes significant at z<4. Overall, our results are in line with previous findings that the SB mode dominates amongst low stellar-mass galaxies. The earlier emergence of the MS for massive galaxies is consistent with galaxy downsizing., Comment: 15 pages, 4 figures. Submitted to ApJ

Insects represent half of all global biodiversity, yet many of the world's insects are disappearing, with severe implications for ecosystems and agriculture. Despite this crisis, data on insect diversity and abundance remain woefully inadequate, due to the scarcity of human experts and the lack of scalable tools for monitoring. Ecologists have started to adopt camera traps to record and study insects, and have proposed computer vision algorithms as an answer for scalable data processing. However, insect monitoring in the wild poses unique challenges that have not yet been addressed within computer vision, including the combination of long-tailed data, extremely similar classes, and significant distribution shifts. We provide the first large-scale machine learning benchmarks for fine-grained insect recognition, designed to match real-world tasks faced by ecologists. Our contributions include a curated dataset of images from citizen science platforms and museums, and an expert-annotated dataset drawn from automated camera traps across multiple continents, designed to test out-of-distribution generalization under field conditions. We train and evaluate a variety of baseline algorithms and introduce a combination of data augmentation techniques that enhance generalization across geographies and hardware setups., Comment: Published at ECCV 2024. The dataset is publicly available at https://github.com/RolnickLab/ami-dataset

The need for regression models to predict circular values arises in many scientific fields. In this work we explore a family of expressive and interpretable distributions over circle-valued random functions related to Gaussian processes targeting two Euclidean dimensions conditioned on the unit circle. The resulting probability model has connections with continuous spin models in statistical physics. Moreover, its density is very simple and has maximum-entropy, unlike previous Gaussian process-based approaches, which use wrapping or radial marginalization. For posterior inference, we introduce a new Stratonovich-like augmentation that lends itself to fast Markov Chain Monte Carlo sampling. We argue that transductive learning in these models favors a Bayesian approach to the parameters. We present experiments applying this model to the prediction of (i) wind directions and (ii) the percentage of the running gait cycle as a function of joint angles., Comment: Contribution to the Structured Probabilistic Inference & Generative Modeling workshop of ICML 2024

We present the joint tomographic analysis of galaxy-galaxy lensing and galaxy clustering in harmonic space, using galaxy catalogues from the first three years of observations by the Dark Energy Survey (DES Y3). We utilise the redMaGiC and MagLim catalogues as lens galaxies and the METACALIBRATION catalogue as source galaxies. The measurements of angular power spectra are performed using the pseudo-$C_\ell$ method, and our theoretical modelling follows the fiducial analyses performed by DES Y3 in configuration space, accounting for galaxy bias, intrinsic alignments, magnification bias, shear magnification bias and photometric redshift uncertainties. We explore different approaches for scale cuts based on non-linear galaxy bias and baryonic effects contamination. Our fiducial covariance matrix is computed analytically, accounting for mask geometry in the Gaussian term, and including non-Gaussian contributions and super-sample covariance terms. To validate our harmonic space pipelines and covariance matrix, we used a suite of 1800 log-normal simulations. We also perform a series of stress tests to gauge the robustness of our harmonic space analysis. In the $\Lambda$CDM model, the clustering amplitude $S_8 =\sigma_8(\Omega_m/0.3)^{0.5}$ is constrained to $S_8 = 0.704\pm 0.029$ and $S_8 = 0.753\pm 0.024$ ($68\%$ C.L.) for the redMaGiC and MagLim catalogues, respectively. For the $w$CDM, the dark energy equation of state is constrained to $w = -1.28 \pm 0.29$ and $w = -1.26^{+0.34}_{-0.27}$, for redMaGiC and MagLim catalogues, respectively. These results are compatible with the corresponding DES Y3 results in configuration space and pave the way for harmonic space analyses using the DES Y6 data., Comment: To be submitted to MNRAS

This article provides a methodology and open-source implementation of Reinforcement Learning algorithms for finding optimal routes in a packet-optical network scenario. The algorithm uses measurements provided by the physical layer (pre-FEC bit error rate and propagation delay) and the link layer (link load) to configure a set of latency-based rewards and penalties based on such measurements. Then, the algorithm executes Q-learning based on this set of rewards for finding the optimal routing strategies. It is further shown that the algorithm dynamically adapts to changing network conditions by re-calculating optimal policies upon either link load changes or link degradation as measured by pre-FEC BER.

We release the Nemotron-4 340B model family, including Nemotron-4-340B-Base, Nemotron-4-340B-Instruct, and Nemotron-4-340B-Reward. Our models are open access under the NVIDIA Open Model License Agreement, a permissive model license that allows distribution, modification, and use of the models and its outputs. These models perform competitively to open access models on a wide range of evaluation benchmarks, and were sized to fit on a single DGX H100 with 8 GPUs when deployed in FP8 precision. We believe that the community can benefit from these models in various research studies and commercial applications, especially for generating synthetic data to train smaller language models. Notably, over 98% of data used in our model alignment process is synthetically generated, showcasing the effectiveness of these models in generating synthetic data. To further support open research and facilitate model development, we are also open-sourcing the synthetic data generation pipeline used in our model alignment process.

The evidence indicates that intracranial EEG connectivity, as estimated from daily resting state recordings from epileptic patients, may be capable of identifying preictal states. In this study, we employed hyperbolic embedding of brain networks to capture non-trivial patterns that discriminate between connectivity networks from days with (preictal) and without (interictal) seizure. A statistical model was constructed by combining hyperbolic geometry and machine learning tools, which allowed for the estimation of the probability of an upcoming seizure. The results demonstrated that representing brain networks in a hyperbolic space enabled an accurate discrimination (85%) between interictal (no-seizure) and preictal (seizure within the next 24 hours) states. The proposed method also demonstrated excellent prediction performances, with an overall accuracy of 87% and an F1-score of 89% (mean Brier score and Brier skill score of 0.12 and 0.37, respectively). In conclusion, our findings indicate that representations of brain connectivity in a latent geometry space can reveal a daily and reliable signature of the upcoming seizure(s), thus providing a promising biomarker for seizure forecasting.

The diffusionless martensitic phase transition from a hexagonal close-packed (hcp) arrangement to the face-centered close-packed (fcc) and subsequently the body-centered cubic (bcc) lattice is discussed for a Lennard-Jones solid. The associated lattice vectors to construct the underlying bi-lattice for a Burgers-Bain-type of transformation require a minimum of four parameters $(a,\alpha,\beta,\gamma=c/a)$ describing, beside the change in the base lattice parameters $a$ and $c$, the shear force acting on the hexagonal base plane through the parameter $\alpha$, and the sliding force of the middle layer in the original AB hexagonal packing arrangement through the parameter $\beta$. By optimizing the lattice parameters $a$ and $\beta$ for a $(n,m)$-Lennard-Jones potential, we obtain a simple two-dimensional picture for the complete Burgers-Bain-type hcp$\leftrightarrow$fcc$\leftrightarrow$bcc phase transition. From the generalized lattice vectors we were able to construct the corresponding lattice sums in terms of inverse power potentials applying fast converging Bessel function expansions using a Terras decomposition of the Epstein zeta function combined with a Van der Hoff-Benson expansion for the lattice sums. This allows the cohesive energy to be determined to computer precision for a Lennard-Jones solid. For six different combinations of $(n,m)$-Lennard-Jones potentials the energy $(\alpha,\gamma)$ hypersurface was then mapped out and studied in more detail. We show that for a Lennard-Jones model the minimum energy path is found to be a two-step hcp$\rightarrow$fcc$\rightarrow$bcc transition process. The lowest transition state in each case can be regarded as an upper limit to a hypothetical true minimum energy path out of the many possibilities in a hcp$\leftrightarrow$fcc$\leftrightarrow$bcc phase transition.

We propose estimating Gaussian graphical models (GGMs) that are fair with respect to sensitive nodal attributes. Many real-world models exhibit unfair discriminatory behavior due to biases in data. Such discrimination is known to be exacerbated when data is equipped with pairwise relationships encoded in a graph. Additionally, the effect of biased data on graphical models is largely underexplored. We thus introduce fairness for graphical models in the form of two bias metrics to promote balance in statistical similarities across nodal groups with different sensitive attributes. Leveraging these metrics, we present Fair GLASSO, a regularized graphical lasso approach to obtain sparse Gaussian precision matrices with unbiased statistical dependencies across groups. We also propose an efficient proximal gradient algorithm to obtain the estimates. Theoretically, we express the tradeoff between fair and accurate estimated precision matrices. Critically, this includes demonstrating when accuracy can be preserved in the presence of a fairness regularizer. On top of this, we study the complexity of Fair GLASSO and demonstrate that our algorithm enjoys a fast convergence rate. Our empirical validation includes synthetic and real-world simulations that illustrate the value and effectiveness of our proposed optimization problem and iterative algorithm.

Optomechanical oscillators stand out as high-performance and versatile candidates for serving as reference clocks in sequential photonic integrated circuits. Indeed, they have the unique capability of simultaneously generating mechanical tones and optical signal modulations at frequencies determined by their geometrical design. In this context, the concept of synchronization introduces a powerful means to precisely coordinate the dynamics of multiple oscillators in a controlled manner, thus increasing efficiency and preventing errors in signal processing photonic systems or communication interfaces. In this work, we demonstrate the cascaded injection locking of a pair of silicon-based optomechanical crystal cavities to an external reference signal that subtly modulates the laser driving one of the oscillators. Both cavities interact solely through a weak mechanical link, making the extension of this synchronization mechanism to an increased number of optomechanical oscillators within a common chip more feasible than relying solely on optical interactions. Thus, the combination of the obtained results, supported by a numerical model, with remote optical injection locking schemes discussed in the literature, lays the groundwork for the distribution of reference signals within large networks of processing elements in future phonon-photon hybrid circuits.

Conformal phased arrays provide multiple degrees of freedom to the scan angle, which is typically limited by antenna aperture in rigid arrays. Silicon-based RF signal processing offers reliable, reconfigurable, multi-functional, and compact control for conformal phased arrays that can be used for on-the-move communication. While the lightweight, compactness, and shape-changing properties of the conformal phased arrays are attractive, these features result in dynamic deformation of the array during motion leading to significant dynamic beam pointing errors. We propose a silicon-based, compact, reconfigurable solution to self-correct these dynamic deformation-induced beam pointing errors. Furthermore, additive printing is leveraged to enhance the flexibility of the conformal phased arrays, as the printed conductive ink is more flexible than bulk copper and can be easily deposited on flexible sheets using different printing tools, providing an environmentally-friendly solution for large-scale production. The inks such as conventional silver inks are expensive and copper-based printable inks suffer from spontaneous metal oxidation that alters trace impedance and degrades beamforming performance. This work uses a low-cost molecular copper decomposition ink with reliable RF properties at different temperature and strain to print the proposed intelligent conformal phased array operating at 2.1 GHz. Proof-of-concept prototype $2\times2$ array self-corrects the deformation induces beampointing error with an error $<1.25^\circ$. The silicon based array processing part occupying only 2.58 mm$^2$ area and 83 mW power per tile.

In a hierarchical, dark matter-dominated Universe, stellar mass functions (SMFs), galaxy merger rates, star formation histories (SFHs), satellite abundances, and intracluster light, should all be intimately connected observables. However, the systematics affecting observations still prevent universal and uniform measurements of, for example, the SMF and the SFHs, inevitably preventing theoretical models to compare with multiple data sets robustly and simultaneously. We here present our holistic semi-empirical model DECODE (Discrete statistical sEmi-empiriCal mODEl) that converts via abundance matching dark matter merger trees into galaxy assembly histories, using different SMFs in input and predicting all other observables in output in a fully data-driven and self-consistent fashion with minimal assumptions. We find that: 1) weakly evolving or nearly constant SMFs below the knee ($M_\star \lesssim 10^{11} \, M_\odot$) are the best suited to generate star formation histories aligned with those inferred from MaNGA, SDSS, GAMA, and, more recently, JWST; 2) the evolution of satellites after infall only affects the satellite abundances and star formation histories of massive central galaxies but not their merger histories; 3) the resulting SFR-$M_\star$ relation is lower in normalization by a factor of $\sim 2$ with respect to observations, with a flattening at high masses more pronounced in the presence of mergers; 4) the latest data on intracluster light can be reproduced if mass loss from mergers is included in the models. Our findings are pivotal in acting as pathfinder to test the self-consistency of the high-quality data from, e.g., JWST and Euclid., Comment: 21 pages, 12 figures, MNRAS accepted

The capability of the global, electromagnetic gyrokinetic GENE code interfaced with the transport Tango solver is exploited to address the impact of fusion alpha particles (in their dual role of fast particles and heating source) on plasma profiles and performance at JET in the discharges with the highest quasi-stationary peak fusion power during the DTE2 experimental campaigns. Employing radially global nonlinear electromagnetic GENE-Tango simulations, we compare results with/without alpha particles and alpha heating. Our findings reveal that alpha particles have a negligible impact on turbulent transport, with GENE-Tango converging to similar plasma profiles regardless of their inclusion as a kinetic species in GENE. On the other hand, alpha heating is found to contribute to the peaking of the electron temperature profiles, leading to a 1keV drop on the on-axis electron temperature when alpha heating is neglected in Tango. The minimal impact of alpha particles on turbulent transport in this JET discharge - despite this being the shot with the highest fusion output - is attributed to the low content of fusion alpha in this discharge. To assess the potential impact of alpha particles on turbulent transport in regimes with higher alpha particle density, as expected in ITER and fusion reactors, we artificially increased the alpha particle concentration to levels expected for ITER. By performing global nonlinear GENE standalone simulations, we found that increasing the alpha particle density beyond five times the nominal value lead to significant overall turbulence destabilization.

The Physics of the Accelerating Universe (PAU) camera is an optical narrow band and broad band imaging instrument mounted at the prime focus of the William Herschel Telescope. We describe the image calibration procedure of the PAU Survey data. We rely on an external photometric catalogue to calibrate our narrow band data using stars that have been observed by both datasets. We fit stellar templates to the stellar broad band photometry of the Sloan Digital Sky Survey and synthesise narrow band photometry that we compare to the PAUS narrow band data to determine their calibration. Consequently, the PAUS data are in the AB system as inherited from its reference calibrator. We do several tests to check the performance of the calibration. We find it self-consistent when comparing repeated observations of the same objects, with a good overall accuracy to the AB system which we estimate to be at the 2\% precision level and no significant trends as a function of narrow band filter or wavelength. Repeated observations allow us to build a spatial map of the illumination pattern of the system. We also check the wavelength dependence of the calibration comparing to stellar spectra. We find that using only blue stars reduces the effects of variations in the stellar template fitting to broad-band colours, improving the overall precision of the calibration to around 1\% and its wavelength uniformity. The photometric redshift performance obtained with the PAUS data attests to the validity of our calibration to reach the PAUS science goals., Comment: 18 pages, 20 figures, accepted for publication in MNRAS

We study the sphaleron solutions in two deformations of the $\phi^6$ model and analyze the oscillons originated from them. We find that the presence of internal modes plays a crucial role in the sphaleron collapse. The positive internal modes triggered by a squeezing of the sphaleron are able to change the direction of collapse. We provide an analytical understanding behind this phenomenon., Comment: 15 pages, 18 figures

Observations of nearby massive galaxies have revealed that they are older and richer in metals and magnesium than their low-mass counterparts. In particular, the overabundance of magnesium compared to iron, [Mg/Fe], is interpreted to reflect the short star formation history that the current massive galaxies underwent early in the Universe. We present a systematic revision of the [Mg/Fe] - velocity dispersion ($\sigma$) relation based on stacked spectra of early-type galaxies with a high signal-to-noise ratio from the Sloan Digital Sky Survey (SDSS). Using the penalized pixel-fitting (pPXF) method of Cappellari & Emsellem 2004 and the Vazdekis et al. 2015 MILES single stellar population (SSP) models, we fit a wide optical wavelength range to measure the net $\alpha$-abundance. The combination of pPXF and $\alpha$-enhanced MILES models incorrectly leads to an apparently decreasing trend of [$\alpha$/Fe] with velocity dispersion. We interpret this result as a consequence of variations in the individual abundances of the different $\alpha$-elements. This warrants caution for a na\"ive use of full spectral fitting algorithms paired with stellar population models that do not take individual elemental abundance variations into account, especially when deriving averaged quantities such as the mean [$\alpha$/Fe] of a stellar population. In addition, and based on line-strength measurements, we quantify the impact of a non-universal initial mass function on the recovered abundance pattern of galaxies. In particular, we find that a simultaneous fit of the slope of the initial mass function and the [Mg/Fe] results in a shallower [Mg/Fe]-$\sigma$ relation. Therefore, our results suggest that star formation in massive galaxies lasted longer than what has been reported previously, although it still occurred significantly faster than in the solar neighbourhood., Comment: 7 pages, 5 figures, accepted for publication in A&A

In this conceptual paper, we review existing literature on artificial intelligence/machine learning (AI/ML) education to identify three approaches to how learning and teaching ML could be conceptualized. One of them, a data-driven approach, emphasizes providing young people with opportunities to create data sets, train, and test models. A second approach, learning algorithm-driven, prioritizes learning about how the learning algorithms or engines behind how ML models work. In addition, we identify efforts within a third approach that integrates the previous two. In our review, we focus on how the approaches: (1) glassbox and blackbox different aspects of ML, (2) build on learner interests and provide opportunities for designing applications, (3) integrate ethics and justice. In the discussion, we address the challenges and opportunities of current approaches and suggest future directions for the design of learning activities.

With the recent advances of large language models (LLMs), it is no longer infeasible to build an automated debate system that helps people to synthesise persuasive arguments. Previous work attempted this task by integrating multiple components. In our work, we introduce an argument mining dataset that captures the end-to-end process of preparing an argumentative essay for a debate, which covers the tasks of claim and evidence identification (Task 1 ED), evidence convincingness ranking (Task 2 ECR), argumentative essay summarisation and human preference ranking (Task 3 ASR) and metric learning for automated evaluation of resulting essays, based on human feedback along argument quality dimensions (Task 4 SQE). Our dataset contains 14k examples of claims that are fully annotated with the various properties supporting the aforementioned tasks. We evaluate multiple generative baselines for each of these tasks, including representative LLMs. We find, that while they show promising results on individual tasks in our benchmark, their end-to-end performance on all four tasks in succession deteriorates significantly, both in automated measures as well as in human-centred evaluation. This challenge presented by our proposed dataset motivates future research on end-to-end argument mining and summarisation. The repository of this project is available at https://github.com/HaoBytes/ArgSum-Datatset, Comment: Published on ACL 2024 Findings

Astrochemical models are important tools to interpret observations of molecular and atomic species in different environments. However, these models are time-consuming, precluding a thorough exploration of the parameter space, leading to uncertainties and biased results. Using neural networks to simulate the behavior of astrochemical models is a way to circumvent this problem, providing fast calculations that are based on real astrochemical models. In this paper, we present a fast neural emulator of the astrochemical code Nautilus based on conditional neural fields. The resulting model produces the abundance of 192 species for arbitrary times between 1 and 10$^7$ years. Uncertainties well below 0.2 dex are found for all species, while the computing time is of the order of 10$^4$ smaller than Nautilus. This will open up the possibility of performing much more complex forward models to better understand the physical properties of the interstellar medium. As an example of the power of these models, we ran a feature importance analysis on the electron abundance predicted by Nautilus. We found that the electron density is coupled to the initial sulphur abundance in a low density gas. Increasing the initial sulphur abundance from a depleted scenario to the cosmic abundance leads to an enhancement of an order of magnitude of the electron density. This enhancement can potentially influence the dynamics of the gas in star formation sites., Comment: 12 pages, 8 figures, accepted for publication in MNRAS