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The vibrational behavior of molecules serves as a crucial fingerprint of their structure, chemical state, and surrounding environment. Neutron vibrational spectroscopy provides comprehensive measurements of vibrational modes without selection rule restrictions. However, analyzing and interpreting the resulting spectra remains a computationally formidable task. Here, we introduce a symmetry-aware neural network that directly predicts Hessian matrices from molecular structures, thereby enabling rapid vibrational spectral reconstruction. Unlike traditional approaches that focus on eigenvalue prediction, the Hessian matrix provides richer, more fundamental information with broader applications and superior extrapolation. This approach also paves the way for predicting other properties, such as reaction pathways. Trained on small molecules, our model achieves spectroscopic-level accuracy, allowing real-time, unambiguous peak assignment. Moreover, it maintains high accuracy for larger molecules, demonstrating strong transferability. This adaptability unlocks new capabilities, including on-the-fly spectral interpretation for future autonomous laboratories, and offers insights into molecular design for targeted chemical pathways.

Given a field $\mathbb K$, for any $n\geq 3$ the first cohomology group $H^1(G_n,A^*_n)$ of the special linear group $G_n = \mathrm{SL}(n,{\mathbb K})$ over the dual $A^*_n$ of its adjoint module $A_n$ is isomorphic to the space $\mathrm{Der}({\mathbb K})$ of the derivations of $\mathbb K$, except possibly when $|{\mathbb K}| \in \{2, 4\}$ and $n$ is even. This fact is stated by S. Smith and H. V\"{o}lklein in their paper "A geometric presentation for the adjont module of $\mathrm{SL}_3(k)$" (J. Algebra 127 (1989), 127--138). They claim that when $|{\mathbb K}| > 9$ this fact follows from the main result of V\"{o}lklein's paper "The 1-cohomology of the adjoint module of a Chevalley group" (Forum Math. 1 (1989), 1--13), but say nothing that can help the reader to deduce it from that result. When $|{\mathbb K}| \leq 9$ they obtain the isomorphism $H^1(G_n,A^*_n) \cong \mathrm{Der}({\mathbb K})$ by means of other results from homological algebra, which however miss the case $|{\mathbb K}| \in\{2, 4\}$ with $n $ even. In the present paper we shall provide a straightforward proof of the isomorphism $H^1(G_n,A^*_n) \cong \mathrm{Der}({\mathbb K})$ under the hypothesis $n > 3$. Our proof also covers the above mentioned missing case., Comment: 25 pages

In galaxies, the flattening of the spectrum at low radio frequencies below 300 MHz has been the subject of some debate. A turnover at low frequencies could be caused by multiple physical processes, which can yield new insights into the properties of the ionised gas in the interstellar medium. We investigate the existence and nature of the low-frequency turnover in the HII regions of M 101. We study the nearby galaxy M 101 using the LOw Frequency ARray (LOFAR) at frequencies of 54 and 144 MHz, Apertif at 1370 MHz, and published combined map from the Very Large Array (VLA) and Effelesberg telescope at 4850 MHz. The spectral index between 54 and 144 MHz is inverted at the centres of HII regions. We find a significant low-frequency flattening at the centres of five out of six HII regions that we selected for this study. The low frequency flattening in HII regions of M 101 can be explained with two different free-free absorption models. The flattening is localised in a region smaller than 1.5 kpc and can only be detected with high resolution (better than 45''). The detection of low frequency flattening has important consequences for using radio continuum observations below 100 MHz to measure extinction-free star-formation rates., Comment: 15 pages, 13 figures, 5 tables, accepted to A&A

Efficiently compressing high-dimensional audio signals into a compact and informative latent space is crucial for various tasks, including generative modeling and music information retrieval (MIR). Existing audio autoencoders, however, often struggle to achieve high compression ratios while preserving audio fidelity and facilitating efficient downstream applications. We introduce Music2Latent2, a novel audio autoencoder that addresses these limitations by leveraging consistency models and a novel approach to representation learning based on unordered latent embeddings, which we call summary embeddings. Unlike conventional methods that encode local audio features into ordered sequences, Music2Latent2 compresses audio signals into sets of summary embeddings, where each embedding can capture distinct global features of the input sample. This enables to achieve higher reconstruction quality at the same compression ratio. To handle arbitrary audio lengths, Music2Latent2 employs an autoregressive consistency model trained on two consecutive audio chunks with causal masking, ensuring coherent reconstruction across segment boundaries. Additionally, we propose a novel two-step decoding procedure that leverages the denoising capabilities of consistency models to further refine the generated audio at no additional cost. Our experiments demonstrate that Music2Latent2 outperforms existing continuous audio autoencoders regarding audio quality and performance on downstream tasks. Music2Latent2 paves the way for new possibilities in audio compression., Comment: Accepted to ICASSP 2025

Lattice structures have great potential for several application fields ranging from medical and tissue engineering to aeronautical one. Their development is further speeded up by the continuing advances in additive manufacturing technologies that allow to overcome issues typical of standard processes and to propose tailored designs. However, the design of lattice structures is still challenging since their properties are considerably affected by numerous factors. The present paper aims to propose, discuss, and compare various modeling approaches to describe, understand, and predict the correlations between the mechanical properties and the void volume fraction of different types of lattice structures fabricated by fused deposition modeling 3D printing. Particularly, four approaches are proposed: (i) a simplified analytical model; (ii) a semi-empirical model combining analytical equations with experimental correction factors; (iii) an artificial neural network trained on experimental data; (iv) numerical simulations by finite element analyses. The comparison among the various approaches, and with experimental data, allows to identify the performances, advantages, and disadvantages of each approach, thus giving important guidelines for choosing the right design methodology based on the needs and available data., Comment: This work was funded by the European Union ERC CoDe4Bio Grant ID 101039467 under the funding programme Horizon Europe

Recent advances in neural recording technology allow simultaneously recording action potentials from hundreds to thousands of neurons in awake, behaving animals. However, characterizing spike patterns in the resulting data, and linking these patterns to behaviour, remains a challenging task. The lack of a rigorous mathematical language for variable numbers of events (spikes) emitted by multiple agents (neurons) is an important limiting factor. We introduce a new mathematical operation to decompose complex spike patterns into a set of simple, structured elements. This creates a mathematical language that allows comparing spike patterns across trials, detecting sub-patterns, and making links to behaviour via a clear distance measure. We apply the method to dual Utah array recordings from macaque prefrontal cortex, where this technique reveals previously unseen structure that can predict both memory-guided decisions and errors in a virtual-reality working memory task. These results demonstrate that this technique provides a powerful new approach to understand structure in the spike times of neural populations, at a scale that will continue to grow more and more rapidly in upcoming years.

Large Language Models (LLMs) are increasingly used in software development to generate functions, such as attack detectors, that implement security requirements. However, LLMs struggle to generate accurate code, resulting, e.g., in attack detectors that miss well-known attacks when used in practice. This is most likely due to the LLM lacking knowledge about some existing attacks and to the generated code being not evaluated in real usage scenarios. We propose a novel approach integrating Retrieval Augmented Generation (RAG) and Self-Ranking into the LLM pipeline. RAG enhances the robustness of the output by incorporating external knowledge sources, while the Self-Ranking technique, inspired to the concept of Self-Consistency, generates multiple reasoning paths and creates ranks to select the most robust detector. Our extensive empirical study targets code generated by LLMs to detect two prevalent injection attacks in web security: Cross-Site Scripting (XSS) and SQL injection (SQLi). Results show a significant improvement in detection performance compared to baselines, with an increase of up to 71%pt and 37%pt in the F2-Score for XSS and SQLi detection, respectively.

Autoregressive models are typically applied to sequences of discrete tokens, but recent research indicates that generating sequences of continuous embeddings in an autoregressive manner is also feasible. However, such Continuous Autoregressive Models (CAMs) can suffer from a decline in generation quality over extended sequences due to error accumulation during inference. We introduce a novel method to address this issue by injecting random noise into the input embeddings during training. This procedure makes the model robust against varying error levels at inference. We further reduce error accumulation through an inference procedure that introduces low-level noise. Experiments on musical audio generation show that CAM substantially outperforms existing autoregressive and non-autoregressive approaches while preserving audio quality over extended sequences. This work paves the way for generating continuous embeddings in a purely autoregressive setting, opening new possibilities for real-time and interactive generative applications., Comment: Accepted to NeurIPS 2024 - Audio Imagination Workshop

Galaxy clusters and groups are the last link in the chain of hierarchical structure formation. Their environments can be significantly affected by outbursts from AGN, especially in groups where the medium density is lower and the gravitational potential shallower. The interaction between AGN and group weather can therefore greatly impact their evolution. We investigate the non-thermal radio emission in Abell 1213, a galaxy group which is part of a larger sample of ~50 systems (X-GAP) recently granted XMM-Newton observations. We exploit proprietary LOFAR 54 MHz and uGMRT 380 MHz observations, complementing them with 144 MHz LOFAR survey and XMM-Newton archival data. A1213 hosts a bright AGN associated with one of the central members, 4C 29.41, which was previously optically identified as a dumb-bell galaxy. Observations at 144 MHz at a resolution of 0.3'' allow us to resolve the central radio galaxy. From this source, a ~500 kpc-long tail extends North-East. Our analysis suggests that the tail likely originated from a past outburst of 4C 29.41, and its current state might be the result of the interaction with the surrounding environment. The plateau of the spectral index distribution in the Easternmost part of the tail suggests mild particle re-acceleration, that could have re-energised seed electrons from the past activity of the AGN. While we observe a spatial and physical correlation of the extended, central emission with the thermal plasma, which might hint at a mini-halo, current evidence cannot conclusively prove this. A1213 is only the first group, among the X-GAP sample, that we are able to investigate through low-frequency radio observations. Its complex environment once again demonstrates the significant impact that the interplay between thermal and non-thermal processes can have on galaxy groups., Comment: 15 pages, 10 figures

Building upon Diff-A-Riff, a latent diffusion model for musical instrument accompaniment generation, we present a series of improvements targeting quality, diversity, inference speed, and text-driven control. First, we upgrade the underlying autoencoder to a stereo-capable model with superior fidelity and replace the latent U-Net with a Diffusion Transformer. Additionally, we refine text prompting by training a cross-modality predictive network to translate text-derived CLAP embeddings to audio-derived CLAP embeddings. Finally, we improve inference speed by training the latent model using a consistency framework, achieving competitive quality with fewer denoising steps. Our model is evaluated against the original Diff-A-Riff variant using objective metrics in ablation experiments, demonstrating promising advancements in all targeted areas. Sound examples are available at: https://sonycslparis.github.io/improved_dar/., Comment: 5 pages; 1 table

The long root geometry $A_{n,\{1,n\}}(\mathbb{K})$ for the special linear group $\mathrm{SL}(n+1,\mathbb{K})$ admits an embedding in the (projective space of) the vector space of the traceless square matrices of order $n+1$ with entries in the field $\mathbb{K}$, usually regarded as the {\em natural} embedding of $A_{n,\{1,n\}}(\mathbb{K})$. S. Smith and H. V\"{o}lklein (A geometric presentation for the adjoint module of $\mathrm{SL}_3(\mathbb{K})$, {\em J. Algebra}, vol. 127) have proved that the natural embedding of $A_{2,\{1,2\}}(\mathbb{K})$ is relatively universal if and only if $\mathbb{K}$ is either algebraic over its minimal subfield or perfect with positive characteristic. They also give some information on the relatively universal embedding of $A_{2,\{1,2\}}(\mathbb{K})$ which covers the natural one, but that information is not sufficient to exhaustively describe it. The ``if" part of Smith-V\"{o}lklein's result also holds true for any $n$, as proved by V\"{o}lklein in his investigation of the adjoint modules of Chevalley groups (H. V\"{o}lklein, On the geometry of the adjoint representation of a Chevalley group, {\em J. Algebra}, vol. 127). In this paper we give an explicit description of the relatively universal embedding of $A_{n,\{1,n\}}(\mathbb{K})$ which covers the natural one. In particular, we prove that this relatively universal embedding has (vector) dimension equal to $\mathfrak{d}+n^2+2n$ where $\mathfrak{d}$ is the transcendence degree of $\mathbb{K}$ over its minimal subfield (if $\mathrm{char}(\mathbb{K}) = 0$) or the generating rank of $\mathbb{K}$ over ${\mathbb K}^p$ (if $\mathrm{char}(\mathbb{K}) = p > 0$). Accordingly, both the ``if" and the ``only if" part of Smith-V\"{o}lklein's result hold true for every $n \geq 2$., Comment: 23 pages

Solid-state quantum registers consisting of optically active electron spins with nearby nuclear spins are promising building blocks for future quantum technologies. For electron spin-1 registers, dynamical decoupling (DD) quantum gates have been developed that enable the precise control of multiple nuclear spin qubits. However, for the important class of electron spin-1/2 systems, this control method suffers from intrinsic selectivity limitations, resulting in reduced nuclear spin gate fidelities. Here we demonstrate improved control of single nuclear spins by an electron spin-1/2 using Dynamically Decoupled Radio Frequency (DDRF) gates. We make use of the electron spin-1/2 of a diamond tin-vacancy center, showing high-fidelity single-qubit gates, single-shot readout, and spin coherence beyond a millisecond. The DD control is used as a benchmark to observe and control a single carbon-13 nuclear spin. Using the DDRF control method, we demonstrate improved control on that spin. In addition, we find and control an additional nuclear spin that is insensitive to the DD control method. Using these DDRF gates, we show entanglement between the electron and the nuclear spin with 72(3)% state fidelity. Our extensive simulations indicate that DDRF gate fidelities well in excess are feasible. Finally, we employ time-resolved photon detection during readout to quantify the hyperfine coupling for the electron's optically excited state. Our work provides key insights into the challenges and opportunities for nuclear spin control in electron spin-1/2 systems, opening the door to multi-qubit experiments on these promising qubit platforms.

Objective. In this work, we propose a new computational framework to assist neurophysiologists in Stereoelectroencephalography (SEEG) analysis, with the final aim of improving the definition of the Epileptogenic Zone (EZ) in patients with drug-resistant epilepsy. Method. We consider the Phase Transfer Entropy (PTE) model to estimate the effective connectivity between SEEG channels. Hence, we design an algorithm, named the Desynchronization Index (DI), that classifies as epileptogenic the channels that show independent behavior with respect to the rest of the network during the seconds preceding the seizure propagation. Results. We test the proposed DI algorithm against Epileptogenic Index (EI) on a clinical dataset of 11 patients, considering the neurophysiological evaluation of the EZ as the clinical ground truth. Our results denote that DI overcomes EI in terms of area under the ROC curve (AUC=0.80 vs AUC=0.74), while combining the two algorithms as a unique tool leads to the best performance (AUC=0.87). Conclusions. The DI algorithm underscores connectivity dynamics that can hardly be identified with a pure visual analysis, increasing the accuracy in the EZ definition compared to traditional methods. Significance. The integration of connectivity- and energy-based metrics, as the one we propose, can lead to the definition of a new effective biomarker of the EZ, reducing the burden required by the SEEG review in the case of extensive implants and improving our understanding of the dynamics leading to the generation of seizures.

Superclusters are the largest-scale environments where a number of galaxy clusters interact with each other through minor/major mergers and grow via accretion along cosmic filaments. We focus on the A3528 complex in the core of the Shapley Supercluster. This system includes three clusters, A3528 (composed itself by two sub-clusters, namely A3528N and A3528S), A3532 and A3530, and presents a mildly active dynamical state. We study how minor mergers affect the evolution of radio galaxies and whether they are able to re-accelerate relativistic electrons in the ICM. We used observations from the uGMRT (Band 3, 4 and 5) and MeerKAT (L-band) telescopes to obtain images and spectral index maps over a wide frequency band and spatial resolutions. We compare these data with those from the SRG/eROSITA X-ray telescope. We detect faint diffuse radio emission associated with the radio galaxies. The BCGs in A3528S and A3532 show filaments of diffuse radio emission which extend for $\sim200-400$ kpc out of the radio galaxy. The spectral index of these filaments is extremely steep and almost constant ($\alpha\sim -2, -2.5$). Contrary to the radio tails in A3528N, the spectral properties of these radio filaments are not consistent with standard models of plasma ageing. We also detect roundish diffuse radio emission around the BCG in A3528S which could be classified as a radio mini-halo. The radio tail in this cluster appears longer that in earlier detections, being $\sim300$ kpc long at all frequencies. We linked the presence of extended radio emission in the form of filaments and threads in the A3528 complex with the effect of minor mergers. This is reinforced by the increasing X-ray fluctuations in correspondence with the radio extended emission in A3528S. Despite the less energy involved, our findings support the hypothesis that these events can re-energise plasma originating from radio galaxies., Comment: 28 pages, 23 figures and 5 tables; accepted for publication in Astronomy & Astrophysics

Efficient audio representations in a compressed continuous latent space are critical for generative audio modeling and Music Information Retrieval (MIR) tasks. However, some existing audio autoencoders have limitations, such as multi-stage training procedures, slow iterative sampling, or low reconstruction quality. We introduce Music2Latent, an audio autoencoder that overcomes these limitations by leveraging consistency models. Music2Latent encodes samples into a compressed continuous latent space in a single end-to-end training process while enabling high-fidelity single-step reconstruction. Key innovations include conditioning the consistency model on upsampled encoder outputs at all levels through cross connections, using frequency-wise self-attention to capture long-range frequency dependencies, and employing frequency-wise learned scaling to handle varying value distributions across frequencies at different noise levels. We demonstrate that Music2Latent outperforms existing continuous audio autoencoders in sound quality and reconstruction accuracy while achieving competitive performance on downstream MIR tasks using its latent representations. To our knowledge, this represents the first successful attempt at training an end-to-end consistency autoencoder model., Comment: Accepted to ISMIR 2024

A fraction of galaxy clusters harbor diffuse radio sources known as radio halos. The currently adopted scenario for their formation is based on second-order Fermi re-acceleration of seed electrons that is driven by merger-driven turbulence in the intra-cluster medium. This mechanism is expected to be inefficient, which implies that a significant fraction of halos should have very steep ($\alpha < -1.5$) energy spectra. We start investigating the potential and current limitations of the combination of the two surveys conducted by LOFAR, LoTSS (144 MHz) and LoLSS (54 MHz), to probe the origin of radio halos. We follow up the 20 radio halos detected in the DR1 of LoTSS, which covers the HETDEX field, with the LoLSS survey, and we study their spectral properties between 54 and 144 MHz. After the removal of compact sources, 9 halos were excluded due to unreliable halo flux density measurements at 54 MHz. Our main finding is that 7 out of 11 ($\sim$ 64%) exhibit an ultra-steep spectrum ($\alpha < -1.5$), which is a key prediction of turbulent re-acceleration models. We also note a tentative trend for more massive systems to host flatter halos, although the currently poor statistics does not allow for a deeper analysis. Our sample suffers from low angular resolution at 54 MHz, which limits the accuracy of the compact-sources subtraction. Nevertheless, this study is the first step towards providing compelling evidence for the existence of a large fraction of radio halos with very steep spectrum, which is a fundamental prediction of turbulent re-acceleration models. In this regard, the forthcoming second data release of LoLSS, along with the integration of LOFAR international stations and the instrumental upgrade to LOFAR2.0, will improve both the statistics and the low-frequency angular resolution, allowing to conclusively determine the origin of radio halos in galaxy clusters., Comment: A&A Letter; 11 pages, 12 figures

We present our work on developing and training scalable, trustworthy, and energy-efficient predictive graph foundation models (GFMs) using HydraGNN, a multi-headed graph convolutional neural network architecture. HydraGNN expands the boundaries of graph neural network (GNN) computations in both training scale and data diversity. It abstracts over message passing algorithms, allowing both reproduction of and comparison across algorithmic innovations that define nearest-neighbor convolution in GNNs. This work discusses a series of optimizations that have allowed scaling up the GFMs training to tens of thousands of GPUs on datasets consisting of hundreds of millions of graphs. Our GFMs use multi-task learning (MTL) to simultaneously learn graph-level and node-level properties of atomistic structures, such as energy and atomic forces. Using over 154 million atomistic structures for training, we illustrate the performance of our approach along with the lessons learned on two state-of-the-art United States Department of Energy (US-DOE) supercomputers, namely the Perlmutter petascale system at the National Energy Research Scientific Computing Center and the Frontier exascale system at Oak Ridge Leadership Computing Facility. The HydraGNN architecture enables the GFM to achieve near-linear strong scaling performance using more than 2,000 GPUs on Perlmutter and 16,000 GPUs on Frontier., Comment: 51 pages, 32 figures

Recent advancements in deep generative models present new opportunities for music production but also pose challenges, such as high computational demands and limited audio quality. Moreover, current systems frequently rely solely on text input and typically focus on producing complete musical pieces, which is incompatible with existing workflows in music production. To address these issues, we introduce "Diff-A-Riff," a Latent Diffusion Model designed to generate high-quality instrumental accompaniments adaptable to any musical context. This model offers control through either audio references, text prompts, or both, and produces 48kHz pseudo-stereo audio while significantly reducing inference time and memory usage. We demonstrate the model's capabilities through objective metrics and subjective listening tests, with extensive examples available on the accompanying website: sonycslparis.github.io/diffariff-companion/, Comment: 8 pages, 2 figures, 3 tables

The evolution of Active Galactic Nuclei (AGN) is closely connected to their host galaxies and surroundings. Via feedback processes, AGN can counteract the cooling of the intracluster medium (ICM) and suppress star formation in their host galaxies. Radio observations at low frequencies provide a glimpse into the history of AGN activity. The Virgo cluster is a substantial reservoir of nearby galaxies and provides an ideal laboratory for the study of AGN as well as their feedback mechanisms. The aim of our work is to characterise the AGN population within the Virgo cluster down to low radio luminosities, constrain the AGN duty cycle and investigate environmental feedback in cluster member galaxies. We analyse 144 MHz and 1.3 GHz radio observations of early-type galaxies from the ACS Virgo Cluster Survey (ACSVCS) taken with LOFAR and MeerKAT. We detect 12 of these galaxies at 144 MHz, 5 of which show clearly extended radio emission. The radio luminosity shows a strong dependence on the stellar mass of the host galaxy, in agreement with previous results. As a notable outlier, the massive elliptical galaxy NGC 4365 ($M_* = 2.2 \times 10^{11} M_\odot$) is not detected as a compact source in the LOFAR observations. Instead, it is surrounded by diffuse, low-surface brightness emission, which hints towards a past phase of stronger nuclear activity. Furthermore, we find a cavity in NGC 4472 (= M 49) inflated by the wide-angle tail only visible in the LOFAR data, which implies that the cavity was created by a past outburst. The corresponding cavity power is of the same order of magnitude as the jet power in the present duty cycle of the AGN., Comment: 17 pages, 10 figures

Composing poetry or lyrics involves several creative factors, but a challenging aspect of generation is the adherence to a more or less strict metric and rhyming pattern. To address this challenge specifically, previous work on the task has mainly focused on reverse language modeling, which brings the critical selection of each rhyming word to the forefront of each verse. On the other hand, reversing the word order requires that models be trained from scratch with this task-specific goal and cannot take advantage of transfer learning from a Pretrained Language Model (PLM). We propose a novel fine-tuning approach that prepends the rhyming word at the start of each lyric, which allows the critical rhyming decision to be made before the model commits to the content of the lyric (as during reverse language modeling), but maintains compatibility with the word order of regular PLMs as the lyric itself is still generated in left-to-right order. We conducted extensive experiments to compare this fine-tuning against the current state-of-the-art strategies for rhyming, finding that our approach generates more readable text and better rhyming capabilities. Furthermore, we furnish a high-quality dataset in English and 12 other languages, analyse the approach's feasibility in a multilingual context, provide extensive experimental results shedding light on good and bad practices for lyrics generation, and propose metrics to compare methods in the future., Comment: 18 pages, 1 figure

Recent low-frequency radio observations at 140 MHz discovered a 3 Mpc-long bridge of diffuse emission connecting the galaxy clusters Abell 0399 and Abell 0401. We present follow-up observations at 60 MHz to constrain the spectral index of the bridge, which so far has only been detected at 140 and 144 MHz. We analysed deep (~18 hours) LOw Frequency ARray (LOFAR) Low Band Antenna (LBA) data at 60 MHz to detect the bridge at very low frequencies. We then conducted a multi-frequency study with LOFAR HBA data at 144 MHz and uGMRT data at 400 MHz. Assuming second-order Fermi mechanisms for the re-acceleration of relativistic electrons driven by turbulence in the radio bridge regions, we compare the observed radio spectrum with theoretical synchrotron models. The bridge is detected in the 75'' resolution LOFAR image at 60 MHz and its emission fully connects the region between the two galaxy clusters. Between 60 MHz and 144 MHz we found an integrated spectral index value of -1.44 +\- 0.16 for the bridge emission. For the first time, we produced spectral index and related uncertainties maps for a radio bridge. We produce a radio spectrum, which show significant steepening between 144 and 400 MHz. This detection at low frequencies provides important information on the models of particle acceleration and magnetic field structure on very extended scales. The spectral index gives important clues to the origin of inter-cluster diffuse emission. The steepening of the spectrum above 144 MHz can be explained in a turbulent re-acceleration framework, assuming that the acceleration timescales are longer than ~200 Myr., Comment: Letter to the Editor: 7 pages, 4 figures, 2 tables; accepted for publication in Astronomy & Astrophysics (A&A)

Radio jets are present in a diverse sample of AGN. However, the mechanisms of jet powering are not fully understood, and it is yet unclear to what extent they obey mass-invariant scaling relations, similar to those found for the triggering and fuelling of X-ray selected AGN. We study the incidence of eROSITA/eFEDS X-ray and LOFAR radio AGN as a function of several stellar mass normalised AGN power indicators. A new sample of radio AGN from the LOFAR-eFEDS survey is defined and we publicly release this catalogue, including host galaxy counterparts from the Legacy Survey DR9, LOFAR radio morphologies and host galaxy properties from the complete, spectroscopic (z<0.4) GAMA09 survey. The fraction of GAMA09 galaxies hosting radio, X-ray and both radio and X-ray AGN are calculated as a function of the specific black hole kinetic ($\lambda_{\rm Jet}$) and radiative ($\lambda_{\rm Edd}$) power. The incidence of eFEDS X-ray AGN as a function of $\lambda_{\rm Edd}$ shows the same mass-invariance as found in past studies. Meanwhile, radio AGN, regardless of their morphology, are more likely to be hosted in more massive galaxies, at all $\lambda_{\rm Jet}$. Across the stellar mass range, the compact radio AGN incidence follows the same power-law distribution, showing that it is not only high mass galaxies that host high power radio AGN and vice versa. On the other hand, the incidence of compact and complex radio AGN is boosted at the highest jet powers, diverging from a simple power-law. Interestingly, this increased incidence cannot be explained by more powerful radio AGN lying in more dense environments which could naturally boost their radio luminosity. Overall, we show that statistical incidence studies are a powerful method to probe disk-jet coupling for different AGN accretion modes, although future work on a more reliable determination of jet power for diverse samples of radio AGN is needed., Comment: 30 pages, 32 figures, accepted for publication in A&A

The ability to automatically generate music that appropriately matches an arbitrary input track is a challenging task. We present a novel controllable system for generating single stems to accompany musical mixes of arbitrary length. At the core of our method are audio autoencoders that efficiently compress audio waveform samples into invertible latent representations, and a conditional latent diffusion model that takes as input the latent encoding of a mix and generates the latent encoding of a corresponding stem. To provide control over the timbre of generated samples, we introduce a technique to ground the latent space to a user-provided reference style during diffusion sampling. For further improving audio quality, we adapt classifier-free guidance to avoid distortions at high guidance strengths when generating an unbounded latent space. We train our model on a dataset of pairs of mixes and matching bass stems. Quantitative experiments demonstrate that, given an input mix, the proposed system can generate basslines with user-specified timbres. Our controllable conditional audio generation framework represents a significant step forward in creating generative AI tools to assist musicians in music production., Comment: ICASSP 2024

The eROSITA telescope array aboard the Spektrum Roentgen Gamma (SRG) satellite began surveying the sky in December 2019, with the aim of producing all-sky X-ray source lists and sky maps of an unprecedented depth. Here we present catalogues of both point-like and extended sources using the data acquired in the first six months of survey operations (eRASS1; completed June 2020) over the half sky whose proprietary data rights lie with the German eROSITA Consortium. We describe the observation process, the data analysis pipelines, and the characteristics of the X-ray sources. With nearly 930000 entries detected in the most sensitive 0.2-2.3 keV energy range, the eRASS1 main catalogue presented here increases the number of known X-ray sources in the published literature by more than 60%, and provides a comprehensive inventory of all classes of X-ray celestial objects, covering a wide range of physical processes. A smaller catalogue of 5466 sources detected in the less sensitive but harder 2.3-5 keV band is the result of the first true imaging survey of the entire sky above 2 keV. We show that the number counts of X-ray sources in eRASS1 are consistent with those derived over narrower fields by past X-ray surveys of a similar depth, and we explore the number counts variation as a function of the location in the sky. Adopting a uniform all-sky flux limit (at 50% completeness) of F_{0.5-2 keV} > 5 \times 10^{-14}$ erg\,s$^{-1}$\,cm$^{-2}$, we estimate that the eROSITA all-sky survey resolves into individual sources about 20% of the cosmic X-ray background in the 1-2 keV range. The catalogues presented here form part of the first data release (DR1) of the SRG/eROSITA all-sky survey. Beyond the X-ray catalogues, DR1 contains all detected and calibrated event files, source products (light curves and spectra), and all-sky maps. Illustrative examples of these are provided., Comment: 39 pages, 23 figures. Accepted for publication in A&A. Accompanying eROSITA-DE Data Release 1

We reassess the problem of symmetry restoration induced by observers' acceleration within the context of interacting quantum field theories in Minkowski spacetime. We argue that the imposition of a frame-independent renormalization condition negates any observed symmetry restoration by a Rindler observer. Technically, we compute the one-loop effective potential of a $\lambda\varphi^4$ theory for an accelerated observer, employing a distinct methodology from prior investigations. Emphasizing the intricacies of the model's renormalization, the analysis offers novel insights into the interplay between acceleration and spontaneous symmetry breaking in quantum field theory., Comment: 16 pages, 1 figure

We present the analysis of new, deep $Chandra$ observations (130~ks) of the galaxy cluster Abell~2495. This object is known for the presence of a triple offset between the peaks of the intracluster medium (ICM), the brightest cluster galaxy (BCG), and the warm gas glowing in H$\alpha$ line. The new $Chandra$ data confirm that the X-ray emission peak is located at a distance of $\sim$6.2 kpc from the BCG, and at $\sim$3.9 kpc from the H$\alpha$ emission peak. Moreover, we identify two generations of X-ray cavities in the ICM, likely inflated by the central radio galaxy activity. Through a detailed morphological and spectral analysis we determine that the power of the AGN outbursts ($P_{cav} = 4.7\pm1.3\times10^{43}$~erg~s$^{-1}$) is enough to counterbalance the radiative losses from ICM cooling ($L_{cool} = 5.7\pm0.1\times10^{43}$~erg~s$^{-1}$). This indicates that, despite a fragmented cooling core, Abell~2495 still harbors an effective feedback cycle. We argue that the offsets are most likely caused by sloshing of the ICM, supported by the presence of spiral structures and a probable cold front in the gas at $\sim$58 kpc east of the center. Ultimately, we find that the outburst interval between the two generations of X-ray cavities is of the order of the dynamical sloshing timescale, as already hinted from the previous $Chandra$ snapshot. We thus speculate that sloshing may be able to regulate the timescales of AGN feedback in Abell~2495, by periodically fuelling the central AGN.

We present the discovery of a very extended (550 kpc) and low-surface-brightness ($ 3.3 \mu \mathrm{Jy} \, arcsec^{-2} $ at 144 MHz) radio emission region in Abell 1318. These properties are consistent with its characterisation as an active galactic nucleus (AGN) remnant radio plasma, based on its morphology and radio spectral properties. We performed a broad-band (54 - 1400 MHz) radio spectral index and curvature analysis using LOFAR, uGMRT, and WSRT-APERTIF data. We also derived the radiative age of the detected emission, estimating a maximum age of 250 Myr. The morphology of the source is remarkably intriguing, with two larger, oval-shaped components and a thinner, elongated, and filamentary structure in between, plausibly reminiscent of two aged lobes and a jet. Based on archival {\it Swift} as well as SDSS data we performed an X-ray and optical characterisation of the system, whose virial mass was estimated to be $ \sim 7.4 \times 10^{13} \, \mathrm{M} _{\odot}$. This places A1318 in the galaxy group regime. Interestingly, the radio source does not have a clear optical counterpart embedded in it, thus, we propose that it is most likely an unusual AGN remnant of previous episode(s) of activity of the AGN hosted by the brightest group galaxy ($ \sim 2.6 \times 10^{12} \, \mathrm{M} _{\odot}$), which is located at a projected distance of $\sim$170 kpc in the current epoch. This relatively high offset may be a result of IGrM sloshing sourced by a minor merger. The filamentary morphology of the source may suggest that the remnant plasma has been perturbed by the system dynamics, however, only future deeper X-ray observations will be able to address this question., Comment: 16 pages, 9 figures, accepted by A&A; edited some affiliations