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The SOXS spectrograph, designed for the ESO NTT telescope, operates in both the optical (UV-VIS: 350-850 nm) and NIR (800-2000 nm) bands. This article provides an overview of the final tests conducted on the UV-VIS camera system using a telescope simulator. It details the system's performance evaluation, including key metrics such as gain, readout noise, and linearity, and highlights the advancements made in the upgraded acquisition system. The testing process, conducted in the Padua laboratory, involved comprehensive simulations of the telescope environment to ensure the results closely resemble those expected at the ESO-NTT telescope. The successful completion of these tests confirms the system's readiness for deployment to Chile, where it will be installed on the NTT telescope, marking a significant milestone in the SOXS project., Comment: SPIE Proceedings Volume 13096, Ground-based and Airborne Instrumentation for Astronomy X; 130962U (2024)

This paper presents the opto-mechanical integration and alignment, functional and optical performance verification of the NIR arm of Son Of X-Shooter (SOXS) instrument. SOXS will be a single object spectroscopic facility for the ESO-NTT 3.6-m telescope, made by two arms high efficiency spectrographs, able to cover the spectral range 350 2050 nm with a mean resolving power R~4500. In particular the NIR arm is a cryogenic echelle cross-dispersed spectrograph spanning the 780-2050 nm range. We describe the integration and alignment method performed to assemble the different opto-mechanical elements and their installation on the NIR vacuum vessel, which mostly relies on mechanical characterization. The tests done to assess the image quality, linear dispersion and orders trace in laboratory conditions are summarized. The full optical performance verification, namely echellogram format, image quality and resulting spectral resolving power in the whole NIR arm (optical path and science detector) is detailed. Such verification is one of the most relevant prerequisites for the subsequent full instrument assembly and provisional acceptance in Europe milestone, foreseen in 2024., Comment: SPIE Proceedings Volume 13096, Ground-based and Airborne Instrumentation for Astronomy X; 130962T (2024)

SOXS (Son Of X-Shooter) is the new ESO instrument that is going to be installed on the 3.58-m New Technology Telescope at the La Silla Observatory. SOXS is a single object spectrograph offering a wide simultaneous spectral coverage from U- to H-band. Although such an instrument may have potentially a large variety of applications, the consortium designed it with a clear science case: it is going to provide the spectroscopic counterparts to the ongoing and upcoming imaging surveys, becoming one of the main follow-up instruments in the Southern hemisphere for the classification and characterization of transients. The NTT+SOXS system is specialized to observe all transients and variable sources discovered by imaging surveys with a flexible schedule maintained by the consortium, based on a remote scheduler which will interface with the observatory software infrastructure. SOXS is realized timely to be highly synergic with transients discovery machines like the Vera C. Rubin Observatory. The instrument has been integrated and tested in Italy, collecting and assembling subsystems coming from all partners spread over six countries in three continents. The first preparatory activities in Chile have been completed at the telescope. This article gives an updated status of the project before the shipping of the instrument to Chile., Comment: SPIE Astronomical Telescopes + Instrumentation, 2024, Yokohama, Japan

The Instrument Control Software of SOXS (Son Of X-Shooter), the forthcoming spectrograph for the ESO New Technology Telescope at the La Silla Observatory, has reached a mature state of development and is approaching the crucial Preliminary Acceptance in Europe phase. Now that all the subsystems have been integrated in the laboratories of the Padova Astronomical Observatory, the team operates for testing purposes with the whole instrument at both engineering and scientific level. These activities will make use of a set of software peculiarities that will be discussed in this contribution. In particular, we focus on the synoptic panel, the co-rotator system special device, on the Active Flexure Compensation system which controls two separate piezo tip-tilt devices., Comment: 6 pages, 3 figures, SPIE conference

Responding to the rapid roll-out and large-scale commercialization of foundation models, large language models, and generative AI, an emerging body of work is shedding light on the myriad impacts these technologies are having across society. Such research is expansive, ranging from the production of discriminatory, fake and toxic outputs, and privacy and copyright violations, to the unjust extraction of labor and natural resources. The same has not been the case in some of the most prominent AI governance initiatives in the global north like the UK's AI Safety Summit and the G7's Hiroshima process, which have influenced much of the international dialogue around AI governance. Despite the wealth of cautionary tales and evidence of algorithmic harm, there has been an ongoing over-emphasis within the AI governance discourse on technical matters of safety and global catastrophic or existential risks. This narrowed focus has tended to draw attention away from very pressing social and ethical challenges posed by the current brute-force industrialization of AI applications. To address such a visibility gap between real-world consequences and speculative risks, this paper offers a critical framework to account for the social, political, and environmental dimensions of foundation models and generative AI. We identify 14 categories of risks and harms and map them according to their individual, social, and biospheric impacts. We argue that this novel typology offers an integrative perspective to address the most urgent negative impacts of foundation models and their downstream applications. We conclude with recommendations on how this typology could be used to inform technical and normative interventions to advance responsible AI., Comment: ACM Conference on Fairness, Accountability, and Transparency (FAccT '24). Association for Computing Machinery, New York, NY, USA, 776-796

This letter illustrates the opinion of the molecular dynamics (MD) community on the need to adopt a new FAIR paradigm for the use of molecular simulations. It highlights the necessity of a collaborative effort to create, establish, and sustain a database that allows findability, accessibility, interoperability, and reusability of molecular dynamics simulation data. Such a development would democratize the field and significantly improve the impact of MD simulations on life science research. This will transform our working paradigm, pushing the field to a new frontier. We invite you to support our initiative at the MDDB community (https://mddbr.eu/community/)

Quantitative MRI (qMRI) offers significant advantages over weighted images by providing objective parameters related to tissue properties. Deep learning-based methods have demonstrated effectiveness in estimating quantitative maps from series of weighted images. In this study, we present qMRI Diffusor, a novel approach to qMRI utilising deep generative models. Specifically, we implemented denoising diffusion probabilistic models (DDPM) for T1 quantification in the brain, framing the estimation of quantitative maps as a conditional generation task. The proposed method is compared with the residual neural network (ResNet) and the recurrent inference machine (RIM) on both phantom and in vivo data. The results indicate that our method achieves improved accuracy and precision in parameter estimation, along with superior visual performance. Moreover, our method inherently incorporates stochasticity, enabling straightforward quantification of uncertainty. Hence, the proposed method holds significant promise for quantitative MR mapping., Comment: Accepted by Deep Generative Models workshop at MICCAI 2024

Stochastic optimization algorithms are widely used for large-scale data analysis due to their low per-iteration costs, but they often suffer from slow asymptotic convergence caused by inherent variance. Variance-reduced techniques have been therefore used to address this issue in structured sparse models utilizing sparsity-inducing norms or $\ell_0$-norms. However, these techniques are not directly applicable to complex (non-convex) graph sparsity models, which are essential in applications like disease outbreak monitoring and social network analysis. In this paper, we introduce two stochastic variance-reduced gradient-based methods to solve graph sparsity optimization: GraphSVRG-IHT and GraphSCSG-IHT. We provide a general framework for theoretical analysis, demonstrating that our methods enjoy a linear convergence speed. Extensive experiments validate

Lexicase selection is a successful parent selection method in genetic programming that has outperformed other methods across multiple benchmark suites. Unlike other selection methods that require explicit parameters to function, such as tournament size in tournament selection, lexicase selection does not. However, if evolutionary parameters like population size and number of generations affect the effectiveness of a selection method, then lexicase's performance may also be impacted by these `hidden' parameters. Here, we study how these hidden parameters affect lexicase's ability to exploit gradients and maintain specialists using diagnostic metrics. By varying the population size with a fixed evaluation budget, we show that smaller populations tend to have greater exploitation capabilities, whereas larger populations tend to maintain more specialists. We also consider the effect redundant test cases have on specialist maintenance, and find that high redundancy may hinder the ability to optimize and maintain specialists, even for larger populations. Ultimately, we highlight that population size, evaluation budget, and test cases must be carefully considered for the characteristics of the problem being solved., Comment: Pre-submission

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.

The first generation of ELT instruments includes an optical-infrared high-resolution spectrograph, indicated as ELT-HIRES and recently christened ANDES (ArmazoNes high Dispersion Echelle Spectrograph). ANDES consists of three fibre-fed spectrographs ([U]BV, RIZ, YJH) providing a spectral resolution of $\sim$100,000 with a minimum simultaneous wavelength coverage of 0.4-1.8 $\mu$m with the goal of extending it to 0.35-2.4 $\mu$m with the addition of a U arm to the BV spectrograph and a separate K band spectrograph. It operates both in seeing- and diffraction-limited conditions and the fibre feeding allows several, interchangeable observing modes including a single conjugated adaptive optics module and a small diffraction-limited integral field unit in the NIR. Modularity and fibre-feeding allow ANDES to be placed partly on the ELT Nasmyth platform and partly in the Coud\'e room. ANDES has a wide range of groundbreaking science cases spanning nearly all areas of research in astrophysics and even fundamental physics. Among the top science cases, there are the detection of biosignatures from exoplanet atmospheres, finding the fingerprints of the first generation of stars, tests on the stability of Nature's fundamental couplings, and the direct detection of the cosmic acceleration. The ANDES project is carried forward by a large international consortium, composed of 35 Institutes from 13 countries, forming a team of almost 300 scientists and engineers which include the majority of the scientific and technical expertise in the field that can be found in ESO member states., Comment: SPIE astronomical telescope and instrumentation 2024, in press

We use the high-resolution HR-DEMNUni simulations to compute cross-correlations of the Cosmic Neutrino Background C$_{\nu}$B with other observables, namely: cold dark matter density, effective weak lensing convergence, neutrino velocity and neutrino deflection angle. We provide, for the first time, this high-fidelity cross-correlation maps in order to illustrate how much can be learned from them once the cosmic neutrino background is measured. As cross-correlations will have more signal than auto-correlations of the C$_{\nu}$B itself, these might be the ones to be measured first by Ptolemy-like experiments. Our predictions thus provide the imprint of what massive neutrinos should look like from cosmological observations.

Using the high-resolution HR-DEMNUni simulations, we computed neutrino profiles within virialized dark matter haloes. These new high-resolution simulations allowed us to revisit fitting formulas proposed in the literature and provided updated fitting parameters that extend to less massive haloes and lower neutrino masses than previously in the literature, in accordance with new cosmological limits. The trend we observe for low neutrino masses is that, for dark matter halo masses below $\sim 4\times10^{14}$$h^{-1}M_\odot$, the presence of the core becomes weaker and the profile over the whole radius is closer to a simple power law. We also characterized the neutrino density profile dependence on the solid angle within clustered structures: a forward-backward asymmetry larger than 10% was found when comparing the density profiles from neutrinos along the direction of motion of cold dark matter particles within the same halo. In addition, we looked for neutrino wakes around halo centres produced by the peculiar motion of the halo itself. Our results suggest that the wakes effect is observable in haloes with masses greater than $3\times10^{14}$ $h^{-1}M_\odot$ where a mean displacement of $0.06$\hmpc was found.

Fiat-pegged stablecoins are by nature exposed to spillover effects during market turmoil in Traditional Finance (TradFi). We observe a difference in TradFi market shocks impact between various stablecoins, in particular, USD Coin (USDC) and Tether USDT (USDT), the former with a higher reporting frequency and transparency than the latter. We investigate this, using top USDC and USDT liquidity pools in Uniswap, by adapting the Marginal Cost of Immediacy (MCI) measure to Uniswap's Automated Market Maker, and then conducting Difference-in-Differences analysis on MCI and Total Value Locked (TVL) in USD, as well as measuring liquidity concentration across different providers. Results show that the Silicon Valley Bank (SVB) event reduced USDC's TVL dominance over USDT, increased USDT's liquidity cost relative to USDC, and liquidity provision remained concentrated with pool-specific trends. These findings reveal a flight-to-safety behavior and counterintuitive effects of stablecoin transparency: USDC's frequent and detailed disclosures led to swift market reactions, while USDT's opacity and less frequent reporting provided a safety net against immediate impacts.

Despite their success, General Relativity (GR) and the Standard Model (SM) are currently understood as effective field theories only valid up to some energy (length) scale, where new physics is expected to appear. We review the framework of extended relativity (ER) in Clifford spaces (C-spaces), summarizing some of its concepts, methods and results. We also discuss likely links of this approach with other relativities (OR), beyond GR and SM theories, recent ideas from emergent spacetime (ES) and quantum entanglement (QE), in search for quantum gravity (QG) and unification (U). Finally, we explore and expose the need to go even beyond ER, and the challenges it poses, from both experimental and theoretical sides., Comment: Second version. Minor corrections, typos in abstract and the whole paper revised and fixed

This paper provides some new characterizations of the diamond partial order for rectangular matrices by using properties of inner inverses, minus order, and SVD decompositions. In addition, the recently introduced 1MP generalized inverse and its dual are used to characterize the diamond partial order as a ${\cal G}$-based one. Finally, the one-sided star partial order is investigated by using 1MP- and MP1-inverses., Comment: 12 pages

This paper posits the decline in market capitalization following a monopoly breakup serves as a means to gauge how financial markets assess market power. Our research, which employs univariate structural time series models to estimate the firm's value without the breakup and juxtapose it with actual post-divestiture values, reveals a staggering drop in AT&T's value by 65% and AMX's by 32% from their pre-breakup levels. These findings underscore the contemporary valuation of monopoly rents as perceived by financial markets, highlighting the significant impact of monopoly breakup on market capitalization and the need for a deeper understanding of these dynamics., Comment: 25 pages, 5 figures

This manuscript revisits the phenomenological emergent dark energy model (PEDE) by confronting it with recent cosmological data from early and late times. In particular we analyze PEDE model by using the baryon acoustic oscillation (BAO) measurements coming from both Dark Energy Spectroscopy Instrument (DESI) data release 1 and Sloan Digital Sky Survey (SDSS). Additionally, the measurements from cosmic chronometers, supernovae type Ia (Pantheon+), quasars, hydrogen II galaxies and cosmic background radiation distance priors are considered. By performing a Bayesian analysis based on Monte Carlo Markov Chain, we find consistent results on the constraints when SDSS and DESI are considered. However, we find higher values on the Hubble constant than Supernova $H_0$ for the Equation of State (SH0ES) does although it is still in agreement, within $1\sigma$ confidence level, when BAO measurements are added. Furthermore, we estimate the age of the Universe younger $\sim3\%$ than the one predicted by the standard cosmology. Additionally, we report values of $q_0 = -0.771^{+0.007}_{-0.007}$, $z_T = 0.764^{+0.011}_{-0.011}$ for the deceleration parameter today and the deceleration-acceleration transition redshift, respectively., Comment: comments are welcome

We investigate the $\Lambda_c \to \Lambda \ell^{+} \nu_\ell$ decay with a focus on potential new physics (NP) effects in the $\ell = \mu$ channel. We employ an effective Hamiltonian within the framework of the Standard Model Effective Field Theory (SMEFT) to consider generalized dimension-6 semileptonic $c\to s$ operators of scalar, pseudoscalar, vector, axial-vector and tensor types. We rely on Lattice QCD (LQCD) for the hadronic transition form factors, using heavy quark spin symmetry (HQSS) to determine those that have not yet been obtained on the lattice. Uncertainties due to the truncation of the NP Hamiltonian and different implementations of HQSS are taken into account. As a result, we unravel the NP discovery potential of the $\Lambda_c\to \Lambda$ semileptonic decay in different observables. Our findings indicate high sensitivity to NP in lepton flavour universality ratios, probing multi-TeV scales in some cases. On the theoretical side, we identify LQCD uncertainties in axial and vector form factors as critical for improving NP sensitivity, alongside better SMEFT uncertainty estimations.

Query embedding approaches answer complex logical queries over incomplete knowledge graphs (KGs) by computing and operating on low-dimensional vector representations of entities, relations, and queries. However, current query embedding models heavily rely on excessively parameterized neural networks and cannot explain the knowledge learned from the graph. We propose a novel query embedding method, AConE, which explains the knowledge learned from the graph in the form of $SROI^-$ description logic axioms while being more parameter-efficient than most existing approaches. AConE associates queries to a $SROI^-$ description logic concept. Every $SROI^-$ concept is embedded as a cone in complex vector space, and each $SROI^-$ relation is embedded as a transformation that rotates and scales cones. We show theoretically that AConE can learn $SROI^-$ axioms, and defines an algebra whose operations correspond one to one to $SROI^-$ description logic concept constructs. Our empirical study on multiple query datasets shows that AConE achieves superior results over previous baselines with fewer parameters. Notably on the WN18RR dataset, AConE achieves significant improvement over baseline models. We provide comprehensive analyses showing that the capability to represent axioms positively impacts the results of query answering., Comment: Accepted by ECAI 2024

Using Darmois-Israel-Sen junction conditions, and with help of Visser's cut-and-paste method, we study the dynamics of thin-shell wormholes that are made of two conformally Killing gravity (a.k.a Harada gravity) black holes. We check the energy conditions for different values of the new parameter that Harada introduced, as alternative for dark energy. We examine the radial acceleration to reveal the attractive and repulsive characteristics of the thin-shell wormhole throat. We consider the dynamics and stability of the wormhole around the static solutions of the linearized radial perturbations at the wormhole throat. Finally, we determine the regions of stability by applying the concavity test on the ''speed of sound'' as a function in the throat radius and other spacetime parameters, particularly the new Harada parameter., Comment: 6 pages, 4 figures

Let $\omega$ be a radial weight, $0

Accurate vision-based speed estimation is much more cost-effective than traditional methods based on radar or LiDAR. However, it is also challenging due to the limitations of perspective projection on a discrete sensor, as well as the high sensitivity to calibration, lighting and weather conditions. Interestingly, deep learning approaches (which dominate the field of computer vision) are very limited in this context due to the lack of available data. Indeed, obtaining video sequences of real road traffic with accurate speed values associated with each vehicle is very complex and costly, and the number of available datasets is very limited. Recently, some approaches are focusing on the use of synthetic data. However, it is still unclear how models trained on synthetic data can be effectively applied to real world conditions. In this work, we propose the use of digital-twins using CARLA simulator to generate a large dataset representative of a specific real-world camera. The synthetic dataset contains a large variability of vehicle types, colours, speeds, lighting and weather conditions. A 3D CNN model is trained on the digital twin and tested on the real sequences. Unlike previous approaches that generate multi-camera sequences, we found that the gap between the the real and the virtual conditions is a key factor in obtaining low speed estimation errors. Even with a preliminary approach, the mean absolute error obtained remains below 3km/h., Comment: Paper accepted at the 27th IEEE International Conference on Intelligent Transportation Systems (ITSC 2024)

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.

We explore the interaction between a three-level atom and a single-mode quantized cavity, known as the three-level ladder-type Jaynes-Cummings model. By employing the exact solution of the Schr\"odinger equation, we investigate how the initial conditions of the atom influence the occupation probabilities of the atomic energy levels, average photon number, and the nonclassicality of light, assessed through the Mandel $\mathcal{Q} (t)$ parameter and the Wigner function. Our findings are rigorously validated through comprehensive numerical simulations, ensuring robust and consistent outcomes.

This paper investigates the application of mini-batch gradient descent to semiflows. Given a loss function, we introduce a continuous version of mini-batch gradient descent by randomly selecting sub-loss functions over time, defining a piecewise flow. We prove that, under suitable assumptions on the gradient flow, the mini-batch descent flow trajectory closely approximates the original gradient flow trajectory on average. Additionally, we propose a randomized minimizing movement scheme that also approximates the gradient flow of the loss function. We illustrate the versatility of this approach across various problems, including constrained optimization, sparse inversion, and domain decomposition. Finally, we validate our results with several numerical examples., Comment: 25 pages, 14 figures

Raman scattering is the inelastic process where photons bounce off molecules, losing energy and becoming red-shifted. This weak effect is unique to each molecular species, making it an essential tool in e.g. spectroscopy and label-free microscopy. The invention of the laser enabled a regime of stimulated Raman scattering (SRS), where the efficiency is greatly increased by inducing coherent molecular oscillations. However, this phenomenon required high intensities due to the limited interaction volumes, and this limitation was overcome by the emergence of anti-resonant fibres (ARFs) guiding light in a small hollow channel over long distances. Based on their unique properties, this Perspective reviews the transformative impact of ARFs on modern SRS-based applications ranging from development of light sources and convertors for spectroscopy and materials science, to quantum technologies for the future quantum networks, providing insights into future trends and the expanding horizons of the field.

We compute electromagnetic and two-photon transition form factors of ground-state pseudoscalar mesons: $\pi,\,K,\,\eta_c,\,\eta_b$. To this end, we employ an algebraic model based upon the coupled formalism of Schwinger-Dyson and Bethe-Salpeter equations. Within this approach, the dressed quark propagator and the relevant Bethe-Salpeter amplitude encode the internal structure of the corresponding meson. Electromagnetic properties of the meson are probed via the quark-photon interaction. The algebraic model employed by us unifies the treatment of all ground-state pseudoscalar mesons. Its parameters are carefully fitted performing a global analysis of existing experimental data including the knowledge of the charge radii of the mesons studied. We then compute and predict electromagnetic and two-photon transition form factors for a wide range of probing photon momentum-squared which is of direct relevance to the experimental observations carried out thus far or planned at different hadron physics facilities such as the Thomas Jefferson National Accelerator Facility (JLab) and the forthcoming Electron-Ion Collider. We also present comparisons with other theoretical models and approaches and lattice quantum chromodynamics., Comment: 18 pages, 8 Figures

Large language models (LLMs) sometimes exhibit dangerous unintended behaviors. Finding and fixing these is challenging because the attack surface is massive -- it is not tractable to exhaustively search for all possible inputs that may elicit such behavior. One specific and particularly challenging case is that if data-poisoning-injected trojans, since there is no way to know what they are to search for them. To our knowledge, there is no generally applicable method to unlearn unknown trojans injected during pre-training. This work seeks to provide a general purpose recipe (filters) and a specific implementation (LoRA) filters that work in practice on small to medium sized models. The focus is primarily empirical, though some perplexing behavior opens the door to the fundamental question of how LLMs store and process information. Not unexpectedly, we find that our filters work best on the residual stream and the latest layers., Comment: 11 pages, 6 figures

In this study, we investigate the problem of comparing gene trees reconciled with the same species tree using a novel semi-metric, called the Path-Label Reconciliation (PLR) dissimilarity measure. This approach not only quantifies differences in the topology of reconciled gene trees, but also considers discrepancies in predicted ancestral gene-species maps and speciation/duplication events, offering a refinement of existing metrics such as Robinson-Foulds (RF) and their labeled extensions LRF and ELRF. A tunable parameter {\alpha} also allows users to adjust the balance between its species map and event labeling components. We show that PLR can be computed in linear time and that it is a semi-metric. We also discuss the diameters of reconciled gene tree measures, which are important in practice for normalization, and provide initial bounds on PLR, LRF, and ELRF. To validate PLR, we simulate reconciliations and perform comparisons with LRF and ELRF. The results show that PLR provides a more evenly distributed range of distances, making it less susceptible to overestimating differences in the presence of small topological changes, while at the same time being computationally efficient. Our findings suggest that the theoretical diameter is rarely reached in practice. The PLR measure advances phylogenetic reconciliation by combining theoretical rigor with practical applicability. Future research will refine its mathematical properties, explore its performance on different tree types, and integrate it with existing bioinformatics tools for large-scale evolutionary analyses. The open source code is available at: https://pypi.org/project/parle/., Comment: Full version of WABI 2024 conference paper

A possible spintronic route to hardware implementation for decision making involves injecting a domain wall into a bifurcated magnetic nanostrip resembling a Y-shaped junction. A decision is made when the domain wall chooses a particular path through the bifurcation. Recently, it was shown that a structure like a nanomagnetic Galton Board, which is essentially an array of interconnected Y-shaped junctions, produces outcomes that are stochastic and therefore relevant to artificial neural networks. However, the exact mechanism leading to the robust nature of randomness is unknown. Here, we directly image the decision-making process in nanomagnetic Galton Boards using Lorentz transmission electron microscopy. We identify that the stochasticity in nanomagnetic Galton Boards arises as a culmination of: (1) topology of the injected domain wall, (2) local disorder, and (3) strength of the applied field. Our results pave the way to a detailed understanding of stochasticity in nanomagnetic networks.

Double beta plus decay is a rare nuclear disintegration process. Difficulties in its measurement arise from suppressed decay probabilities, experimentally challenging decay signatures and low natural abundances of suitable candidate nuclei. In this article, we propose a new detector concept to overcome these challenges. It is based on the first-time combination of hybrid and opaque scintillation detector technology paired with novel light read-out techniques. This approach is particularly suitable detecting positrons (beta plus) signatures. We expect to discover two-neutrino double beta plus decay modes within 1 tonne-week exposure and are able to probe neutrinoless double beta plus decays at several orders of magnitude improved significance compared to current experimental limits., Comment: 23 pages, 9 figures, 3 tables

Introduction: Artificial intelligence (AI) is exhibiting tremendous potential to reduce the massive costs and long timescales of drug discovery. There are however important challenges currently limiting the impact and scope of AI models. Areas covered: Such models excel on benchmarks unlikely to anticipate their prospective performance, which inadvertently misguides their development. In practice, only a few are ever reported to have prospective value (e.g. by discovering potent and innovative drug leads for a therapeutic target). Here we discuss a range of data issues (bias, inconsistency, skewness, irrelevance, small size, high dimensionality), how they challenge AI models and which issue-specific mitigations have been effective. Next, we point out the challenges faced by uncertainty quantification techniques aimed at enhancing these AI models. We also discuss how conceptual errors, unrealistic benchmarks and performance misestimation can confound the evaluation of models and thus their development. Lastly, we explain how human bias, whether from AI experts or drug discovery experts, constitutes another challenge that can be alleviated with prospective studies. Expert opinion: We have discussed what can go wrong in practice with AI for drug discovery. We hope that this will help inform the decisions of editors, funders investors and researchers working in this area.

We study the existence and concentration of positive and nodal solutions to a Schr\"odinger equation in the presence of a shrinking self-focusing core of arbitrary shape. Via a suitable rescaling, the concentration gives rise to a limiting profile that solves a nonautonomous elliptic semilinear equation with a sharp sign change in the nonlinearity. We characterize the (radial or foliated Schwarz) symmetries and the (polynomial) decay of the least-energy positive and nodal limiting profiles., Comment: 19 pages

A detailed study of thermalization is conducted on experimentally accessible states in a system of bosonic atoms trapped in an open linear chain with disorder. When the disorder parameter is large, the system exhibits regularity and localization. In contrast, weak disorder introduces chaos and raises questions about the validity of the Eigenstate Thermalization Hypothesis (ETH), especially for states at the extremes of the energy spectrum which remain regular and non-thermalizing. The validity of ETH is assessed by examining the dispersion of entanglement entropy and the number of bosons on the first site across various dimensions, while maintaining a constant particle density of one. Experimentally accessible states in the occupation basis are categorized using a crowding parameter that linearly correlates with their mean energy. Using full exact diagonalization to simulate temporal evolution, we study the equilibration of entanglement entropy, the number of bosons, and the reduced density matrix of the first site for all states in the occupation basis. Comparing equilibrium values of these observables with those predicted by microcanonical ensembles, we find that, within certain tolerances, most states in the chaotic region thermalize. However, states with low participation ratios in the energy eigenstate basis show greater deviations from thermal equilibrium values.

Synthetic Jet (SJ) actuators are an intrinsically complex combination of electronics, electric and mechanical systems. When studied theoretically, these elements are often simplified to coupled damped harmonic oscillators (DHO) that induce a pressure field within the cavity and drive momentum exchange. Thus, the performance of an SJ actuator results from coupling these DHOs, naturally leading to a few resonant modes. There is good evidence in the specialized literature of two resonant modes developing in SJ actuators: the membrane/piezoelectric mode and the Helmholtz resonance. In this work, we report on the effect of a third resonant mode that develops at very low frequencies due to a cavity much larger than the volume displaced by the actuator. We present evidence that the large-cavity dynamics determine the SJ performance in combination with the well-described formation criteria. We compare the intensity of this resonant mode with the first room modes using standard frequency analysis. Unlike typical room modes, the distribution of this resonant mode is very biased to lower frequencies. We also show that the resonant mode may be dimmed and focused by adding an obstacle in different cavity positions for the lower sound intensities. This mode overcomes the Helmholtz resonance, dominating the dynamics for higher sound intensities. We show that jet and vortex velocities mimic the sound pressure curve for the low-frequency range. Its effect mitigates for the higher range due to a delve through smaller stroke lengths, characterized as a fixed relation between the Reynolds and the Stokes numbers. We consider that the large-cavity dynamics is an additional element that, if integrated as design criteria, could extend the optimum frequency response of SJs., Comment: 21 pages, 10 figures, to be submitted

We present the results of muon-spin relaxation ($\mu^{+}$SR) measurements of the van der Waals magnet NiI$_2$, which probe magnetic phase transitions at $T_{\mathrm{N1}}=73$K and $T_{\mathrm{N2}}=60$K. Supporting density functional theory (DFT) calculations allow the determination of a single muon stopping site whose magnetic environment is consistent with the proposed ground-state magnetic structure. $\mu^{+}$SR measurements of the dynamics reveal behavior consistent with spin-wave excitations below $T_{\mathrm{N2}}$, with an additional contribution from a nonmagnetic component of the material. In the region $T_{\mathrm{N1}}

Language models of code have demonstrated state-of-the-art performance across various software engineering and source code analysis tasks. However, their demanding computational resource requirements and consequential environmental footprint remain as significant challenges. This work introduces ALPINE, an adaptive programming language-agnostic pruning technique designed to substantially reduce these models' computational overhead. The proposed method offers a pluggable layer that can be integrated with all Transformer-based models. With ALPINE, input sequences undergo adaptive compression throughout the pipeline, reaching a size up to $\times 3$ less their initial size, resulting in significantly reduced computational load. Our experiments on two software engineering tasks, defect prediction and code clone detection across three language models CodeBERT, GraphCodeBERT and UniXCoder show that ALPINE achieves up to a 50% reduction in FLOPs, a 58.1% decrease in memory footprint, and a 28.1% improvement in throughput on average. This led to a reduction in CO2 by up to $44.85$%. Importantly, it achieves the reduction in computation resources while maintaining up to 98.1% of the original predictive performance. These findings highlight the potential of ALPINE in making language models of code more resource-efficient and accessible while preserving their performance, contributing to the overall sustainability of adopting language models in software development. Also, it sheds light on redundant and noisy information in source code analysis corpora, as shown by the substantial sequence compression achieved by ALPINE.

Circular Rydberg atoms (CRAs), i.e., Rydberg atoms with maximal orbital momentum, ideally combine long coherence times and strong interactions, a key property of quantum systems, in particular for the development of quantum technologies. However, the dipole-dipole interaction between CRAs has not been observed so far. We report the measurement and characterization of the resonant dipole-dipole interaction between two CRAs, individually trapped in optical tweezers, and find excellent agreement with theoretical predictions. We demonstrate a dynamic control over the strength of the interaction by tuning the orientation of an electric field. We use the interaction between the CRAs as a meter for the interatomic distance, and record the relative motion between two atoms in their traps. This motion, that we induce through the interaction between Rydberg levels with permanent electric dipoles, transiently populated during the preparation of the circular states, is a signature of spin-motion coupling.

The structures and properties of organic-inorganic perovskites are influenced by the hydrogen bonding between the organic cations and the inorganic octahedral networks. This study explores the dynamics of hydrogen bonds in CH$_3$NH$_3$PbBr$_3$ across a temperature range from 70 K to 350 K, using molecular dynamics simulations with machine-learning force fields. The results indicate that the lifetime of hydrogen bonds decreases with increasing temperature from 7.6 ps (70 K) to 0.16 ps (350 K), exhibiting Arrhenius-type behaviour. The geometric conditions for hydrogen bonding, which include bond lengths and angles, maintain consistency across the full temperature range. The relevance of hydrogen bonds for the vibrational states of the material is also evidenced through a detailed analysis of the vibrational power spectra, demonstrating their significant effect on the physical properties for this class of perovskites., Comment: 26 pages, 14 figures

We show how to obtain the Zariski invariant of a plane branch employing the contact order or the intersection multiplicity with elements in a particular family of curves and we present some consequences of this result.

Purpose: Apparent diffusion coefficient (ADC) maps derived from diffusion weighted (DWI) MRI provides functional measurements about the water molecules in tissues. However, DWI is time consuming and very susceptible to image artifacts, leading to inaccurate ADC measurements. This study aims to develop a deep learning framework to synthesize ADC maps from multi-parametric MR images. Methods: We proposed the multiparametric residual vision transformer model (MPR-ViT) that leverages the long-range context of ViT layers along with the precision of convolutional operators. Residual blocks throughout the network significantly increasing the representational power of the model. The MPR-ViT model was applied to T1w and T2- fluid attenuated inversion recovery images of 501 glioma cases from a publicly available dataset including preprocessed ADC maps. Selected patients were divided into training (N=400), validation (N=50) and test (N=51) sets, respectively. Using the preprocessed ADC maps as ground truth, model performance was evaluated and compared against the Vision Convolutional Transformer (VCT) and residual vision transformer (ResViT) models. Results: The results are as follows using T1w + T2-FLAIR MRI as inputs: MPR-ViT - PSNR: 31.0 +/- 2.1, MSE: 0.009 +/- 0.0005, SSIM: 0.950 +/- 0.015. In addition, ablation studies showed the relative impact on performance of each input sequence. Both qualitative and quantitative results indicate that the proposed MR- ViT model performs favorably against the ground truth data. Conclusion: We show that high-quality ADC maps can be synthesized from structural MRI using a MPR- VCT model. Our predicted images show better conformality to the ground truth volume than ResViT and VCT predictions. These high-quality synthetic ADC maps would be particularly useful for disease diagnosis and intervention, especially when ADC maps have artifacts or are unavailable., Comment: arXiv admin note: text overlap with arXiv:2311.15044

The statistical analysis of cosmic large-scale structure is most often based on simple two-point summary statistics, like the power spectrum or the two-point correlation function of a sample of galaxies or other types of tracers. In contrast, topological measures of clustering are also sensitive to higher-order correlations, and thus offer the prospect to access additional information that may harbor important constraining power. We here revisit one such geometric measure of the cosmic web in the form of the so-called percolation analysis, using the recent MillenniumTNG simulation suite of the LCDM paradigm. We analyze continuum percolation statistics both for high resolution dark matter particle distributions, as well as for galaxy mock catalogues from a semi-analytic galaxy formation model within a periodic simulation volume of 3000 Mpc on a side. For comparison, we also investigate the percolation statistics of random particle sets and neutrino distributions with two different summed particle masses. We find that the percolation statistics of the dark matter distribution evolves strongly with redshift and thus clustering strength, yielding progressively lower percolation threshold towards later times. However, there is a sizable residual dependence on numerical resolution which we interpret as a residual influence of different levels of shot noise. This is corroborated by our analysis of galaxy mock catalogues whose results depend on sampling density more strongly than on galaxy selection criteria. While this limits the discriminative power of percolation statistics, our results suggest that it still remains useful as a complementary cosmological test when controlled for sampling density., Comment: 14 pages, 12 figures, accepted by ApJ, updated to match published version

We analyze the confident binary black hole (BBH) detections from the third Gravitational-Wave Transient Catalog (GWTC-3) with an alternative mass population model in order to capture features in the mass distribution beyond the Powerlaw + Peak model. We find that the peak of a second power law characterizes the $\sim 30-35~ M_\odot$ bump, such that the data marginally prefers a mixture of two power laws for the mass distribution of binary components over a Powerlaw + Peak model with a Bayes Factor $\log_{10}\mathcal{B}$ of 0.1. This result may imply that the $\sim 30-35~ M_\odot$ feature represents the onset of a second population of BBH mergers (e.g. from a dynamical formation channel) rather than a specific mass feature over a broader distribution. When an additional Gaussian bump is allowed within our power law mixture model, we find a new feature in the BH mass spectrum at $\sim65-70~M_\odot$. This new feature may be consistent with hierarchical mergers, and constitute $\sim2\%$ of the BBH population. This model also recovers a maximum mass of $58^{+30}_{-14}~M_\odot$ for the second power law, consistent with the onset of a pair-instability supernova mass gap., Comment: Comments are welcomed

Nonlinear optical materials of atomic thickness--such as non-centrosymmetric 2H transition metal dichalcogenide monolayers--have a second order nonlinear susceptibility ($\chi^{(2)}$) whose intensity can be tuned by strain. However, whether $\chi^{(2)}$ is enhanced or reduced by tensile strain is a subject of conflicting reports. Here, we grow high-quality MoSe$_2$ monolayers under controlled biaxial strain created by two different substrates, and study their linear and non-linear optical responses with a combination of experimental and theoretical approaches. A 15-fold overall enhancement in second harmonic generation (SHG) intensity is observed on MoSe$_2$ monolayers grown on SiO$_2$ when compared to its value when on a Si$_3$N$_4$ substrate. A seven-fold enhancement was ascertained to substrate interference, and a factor of two to the enhancement of $\chi^{(2)}$ arising from biaxial strain: substrate interference and strain are independent handles to engineer the SHG strength of non-centrosymmetric 2D materials.

This paper introduces a collaborative, human-centered taxonomy of AI, algorithmic and automation harms. We argue that existing taxonomies, while valuable, can be narrow, unclear, typically cater to practitioners and government, and often overlook the needs of the wider public. Drawing on existing taxonomies and a large repository of documented incidents, we propose a taxonomy that is clear and understandable to a broad set of audiences, as well as being flexible, extensible, and interoperable. Through iterative refinement with topic experts and crowdsourced annotation testing, we propose a taxonomy that can serve as a powerful tool for civil society organisations, educators, policymakers, product teams and the general public. By fostering a greater understanding of the real-world harms of AI and related technologies, we aim to increase understanding, empower NGOs and individuals to identify and report violations, inform policy discussions, and encourage responsible technology development and deployment.

Class\,I methanol masers provide sensitive information about the shocked environment around star-forming regions. Among the brightest Class~I methanol masers, we have those in the $J_{-1}\rightarrow(J-$ 1)$_{0}-E$ line series, currently reported for the $J=4-9$ transitions, with the only exception being the $J=7$ one at 181.295~GHz, and never expanded to higher $J$ transitions. We aim to search for population inversion in the $7_{-1}\rightarrow6_{0}-E$ and $10_{-1}\rightarrow9_{0}-E$ methanol transition lines at 181.295 and 326.961\,GHz, respectively, and also extend the number of known low-mass star-forming sources harboring Class\,I methanol masers. We employed the Atacama Pathfinder Experiment (APEX) 12\,m telescope to survey low-mass Galactic sources, focusing on methanol emission lines. We conducted rotation diagrams for all sources with detected $J=7$ methanol line transitions, while employing radiative transfer modeling (both in and out of local thermodynamic equilibrium) to characterize methanol excitation conditions in detail for one specific source with detected masers. We have detected the $7_{-1}\rightarrow6_{0}-E$ and $10_{-1}\rightarrow9_{0}-E$ methanol transitions in six out of nineteen sources. Among them, we firmly determined the $10_{-1}\rightarrow9_{0}-E$ maser nature in CARMA\,7, L1641N, NGC\,2024, and Serpens FIRS, and we claim for the presence of inverted population emission in the $7_{-1}\rightarrow6_{0}-E$ line toward CARMA\,7 and L1641N. This represents the first report of methanol maser emission in these particular transitions. Our study supports previous works indicating that conditions for Class\,I methanol maser emission are satisfied in low-mass star-forming regions and expands the range of detectable frequencies toward higher values.

In this paper, we provide a general approach to reformulating any continuous-time stochastic Stackelberg differential game under closed-loop strategies as a single-level optimisation problem with target constraints. More precisely, we consider a Stackelberg game in which the leader and the follower can both control the drift and the volatility of a stochastic output process, in order to maximise their respective expected utility. The aim is to characterise the Stackelberg equilibrium when the players adopt `closed-loop strategies', i.e. their decisions are based solely on the historical information of the output process, excluding especially any direct dependence on the underlying driving noise, often unobservable in real-world applications. We first show that, by considering the--second-order--backward stochastic differential equation associated with the continuation utility of the follower as a controlled state variable for the leader, the latter's unconventional optimisation problem can be reformulated as a more standard stochastic control problem with stochastic target constraints. Thereafter, adapting the methodology developed by Soner and Touzi [67] or Bouchard, \'Elie, and Imbert [14], the optimal strategies, as well as the corresponding value of the Stackelberg equilibrium, can be characterised through the solution of a well-specified system of Hamilton--Jacobi--Bellman equations. For a more comprehensive insight, we illustrate our approach through a simple example, facilitating both theoretical and numerical detailed comparisons with the solutions under different information structures studied in the literature.

We measure the spin-density matrix elements (SDMEs) of the $\Delta^{++}(1232)$ in the photoproduction reaction $\gamma p \to \pi^-\Delta^{++}(1232)$ with the GlueX experiment in Hall D at Jefferson Lab. The measurement uses a linearly--polarized photon beam with energies from $8.2$ to $8.8$~GeV and the statistical precision of the SDMEs exceeds the previous measurement by three orders of magnitude for the momentum transfer squared region below $1.4$ GeV$^2$. The data are sensitive to the previously undetermined relative sign between couplings in existing Regge-exchange models. Linear combinations of the extracted SDMEs allow for a decomposition into natural and unnatural--exchange amplitudes. We find that the unnatural exchange plays an important role in the low momentum transfer region.

The Near-InfraRed Planet Searcher or NIRPS is a precision radial velocity spectrograph developed through collaborative efforts among laboratories in Switzerland, Canada, Brazil, France, Portugal and Spain. NIRPS extends to the 0.98-1.8 $\mu$m domain of the pioneering HARPS instrument at the La Silla 3.6-m telescope in Chile and it has achieved unparalleled precision, measuring stellar radial velocities in the infrared with accuracy better than 1 m/s. NIRPS can be used either stand-alone or simultaneously with HARPS. Commissioned in late 2022 and early 2023, NIRPS embarked on a 5-year Guaranteed Time Observation (GTO) program in April 2023, spanning 720 observing nights. This program focuses on planetary systems around M dwarfs, encompassing both the immediate solar vicinity and transit follow-ups, alongside transit and emission spectroscopy observations. We highlight NIRPS's current performances and the insights gained during its deployment at the telescope. The lessons learned and successes achieved contribute to the ongoing advancement of precision radial velocity measurements and high spectral fidelity, further solidifying NIRPS' role in the forefront of the field of exoplanets., Comment: Proceeding at the SPIE Astronomical Telescopes + Instrumentation conference [Yokohama,Japan; June 2024]