Search

The International LOFAR Telescope (ILT) is a pan-European radio interferometer with baselines up to 2,000 km. This provides sub-arcsecond resolution at frequencies of <200 MHz. Since starting science operations in 2012, the ILT has carried out observations for the state-of-the-art LOFAR Two-metre Sky Survey, which has 6 arcsec resolution at 144 MHz. Wide-area surveys at low frequencies, while scientifically productive, have to compromise on resolution. Sub-arcsecond imaging with the ILT has become more accessible over the last decade, thanks to efforts to build a publicly available pipeline using LOFAR-specific tools, which has resulted in a dramatic increase in the number of publications. The ILT's combination of resolution, field of view, and low observing frequency make it a unique instrument for a wide range of scientific applications, and it will remain unparalleled even in the era of the Square Kilometre Array Observatory. Here we provide an overview of the technical considerations and calibration methods sub-arcsecond imaging with the ILT. This is followed by a review of the unique capabilities unlocked by sub-arcsecond imaging with the ILT, using examples from the literature for demonstration. Finally we describe ongoing work including: surveying large areas of the sky at high resolution, going deeper in fields with excellent ancillary information, producing images of polarisation, and extending to lower frequencies (<100 MHz)., Comment: Review paper, 37 pages, 13 Figures, Acceped for publication in Astrophysics and Space Science

Books, while often rich in cultural insights, can also mirror societal biases of their eras - biases that Large Language Models (LLMs) may learn and perpetuate during training. We introduce a novel method to trace and quantify these biases using fine-tuned LLMs. We develop BookPAGE, a corpus comprising 593 fictional books across seven decades (1950-2019), to track bias evolution. By fine-tuning LLMs on books from each decade and using targeted prompts, we examine shifts in biases related to gender, sexual orientation, race, and religion. Our findings indicate that LLMs trained on decade-specific books manifest biases reflective of their times, with both gradual trends and notable shifts. For example, model responses showed a progressive increase in the portrayal of women in leadership roles (from 8% to 22%) from the 1950s to 2010s, with a significant uptick in the 1990s (from 4% to 12%), possibly aligning with third-wave feminism. Same-sex relationship references increased markedly from the 1980s to 2000s (from 0% to 10%), mirroring growing LGBTQ+ visibility. Concerningly, negative portrayals of Islam rose sharply in the 2000s (26% to 38%), likely reflecting post-9/11 sentiments. Importantly, we demonstrate that these biases stem mainly from the books' content and not the models' architecture or initial training. Our study offers a new perspective on societal bias trends by bridging AI, literary studies, and social science research., Comment: 9 pages (excluding references), accepted to NAACL 2025

The evolving requirements of Internet of Things (IoT) applications are driving an increasing shift toward bringing intelligence to the edge, enabling real-time insights and decision-making within resource-constrained environments. Tiny Machine Learning (TinyML) has emerged as a key enabler of this evolution, facilitating the deployment of ML models on devices such as microcontrollers and embedded systems. However, the complexity of managing the TinyML lifecycle, including stages such as data processing, model optimization and conversion, and device deployment, presents significant challenges and often requires substantial human intervention. Motivated by these challenges, we began exploring whether Large Language Models (LLMs) could help automate and streamline the TinyML lifecycle. We developed a framework that leverages the natural language processing (NLP) and code generation capabilities of LLMs to reduce development time and lower the barriers to entry for TinyML deployment. Through a case study involving a computer vision classification model, we demonstrate the framework's ability to automate key stages of the TinyML lifecycle. Our findings suggest that LLM-powered automation holds potential for improving the lifecycle development process and adapting to diverse requirements. However, while this approach shows promise, there remain obstacles and limitations, particularly in achieving fully automated solutions. This paper sheds light on both the challenges and opportunities of integrating LLMs into TinyML workflows, providing insights into the path forward for efficient, AI-assisted embedded system development., Comment: This paper is currently under review for publication in an IEEE magazine. If accepted, the copyright will be transferred to IEEE. This work was presented at the TinyML Foundation (now Edge AI Foundation) event, titled Beyond LLMs and Chatbots: The Journey to Generative AI at the Edge. For more details, the presentation is available at https://youtu.be/aFWfisdjQIs

Recent studies have found a striking positive correlation between the amount of dust obscuration and enhanced radio emission in quasi-stellar objects (QSOs). However, what causes this connection remains unclear. In this paper we analyse uGMRT Band-3 (400 MHz) and Band-4 (650 MHz) data of a sample of 38 $1.0 < z < 1.5$ QSOs with existing high-resolution $0.2''$ e-MERLIN 1.4 GHz imaging. In combination with archival radio data, we have constructed sensitive 4-5 band radio SEDs across 0.144-3 GHz to further characterize the radio emission in dusty QSOs. We find that the dusty QSOs (those with E(B-V) $> 0.1$ mag) are more likely to exhibit steep spectral slopes ($\alpha < -0.5$; $S_{\nu} \propto \nu^{\alpha}$) than the non-dusty QSOs (E(B-V) $< 0.1$ mag), with fractions of 46$\pm$12 and 12$\pm$4 per cent, respectively. A higher fraction of the non-dusty QSOs have peaked radio SEDs (48$\pm$9 per cent) compared to the dusty QSOs (23$\pm$8 per cent). We discuss the origin of the radio emission, finding that the majority of the peaked, predominantly non-dusty, QSOs have consistent sizes and luminosities with compact jetted radio galaxies. However, the connection between steepness and dust obscuration implies an outflow-driven shock origin for the enhanced radio more commonly found in dusty QSOs. These results add to the emerging picture whereby dusty QSOs are in an earlier blow-out phase, with shocks that heat and destroy the surrounding dust, eventually revealing a typical non-dusty QSO., Comment: 31 pages, 14 figures, 4 tables, accepted by MNRAS

Whether the mass of supermassive black hole ($M_\mathrm{BH}$) is directly linked to the quasar radio luminosity remains a long-debated issue, and understanding the role of $M_\mathrm{BH}$ in the evolution of quasars is pivotal to unveiling the mechanism of AGN feedback. In this work, based on a two-component Bayesian model, we examine how $M_\mathrm{BH}$ affects the radio emission from quasars, separating the contributions from host galaxy star formation (SF) and AGN activity. By modelling the radio flux density distribution of Sloan Digital Sky Survey (SDSS) quasars from the LOFAR Two-metre Sky Survey Data Release 2, we find no correlation between $M_\mathrm{BH}$ and SF rate (SFR) at any mass for quasars at a given redshift and bolometric luminosity. The same holds for AGN activity across most $M_\mathrm{BH}$ values; however, quasars with the top 20\% most massive SMBHs are 2 to 3 times more likely to host strong radio jets than those with lower-mass SMBHs at similar redshift and luminosity. We suggest defining radio quasar populations by their AGN and SF contributions instead of radio loudness; our new definition unifies previously divergent observational results on the role of $M_\mathrm{BH}$ in quasar radio emissions. We further demonstrate that this radio enhancement in quasars with the 20\% most massive SMBHs affects only the $\sim5\%$ most radio bright quasars at a given redshift and bolometric luminosity. We discuss possible physical origins of this radio excess in the most massive and radio-bright quasar population, which remains an interest for future study., Comment: 18 pages, 13 figures, accepted for publication in MNRAS

We present the final 6'' resolution data release of the ELAIS-N1 field from the LOw-Frequency ARray (LOFAR) Two-metre Sky Survey Deep Fields project (LoTSS Deep). The 144MHz images are the most sensitive achieved to date at this frequency and were created from 290 TB of data obtained from 505 hrs on-source observations taken over 7.5 years. The data were processed following the strategies developed for previous LoTSS and LoTSS Deep data releases. The resulting images span 24.53 square degrees and, using a refined source detection approach, we identified 154,952 radio sources formed from 182,184 Gaussian components within this area. The maps reach a noise level of 10.7 $\mu$Jy/beam at 6'' resolution where approximately half of the noise is due to source confusion. In about 7.4% of the image our limited dynamic range around bright sources results in a further > 5% increase in the noise. The images have a flux density scale accuracy of about 9% and the standard deviation of offsets between our source positions and those from Pan-STARRS is 0.2'' in RA and Dec for high significance detections. We searched individual epoch images for variable sources, identifying 39 objects with considerable variation. We also searched for circularly polarised sources achieving three detections of previously known emitters (two stars and one pulsar) whilst constraining the typical polarisation fraction plus leakage to be less than 0.045%., Comment: Accepted for publication in A&A. 16 figures, 1 table and 20 pages. The catalogues and images associated with this data release are publicly available via https://lofar-surveys.org/

We present a qualitative comparison between the host and black hole properties of radio galaxies in the MeerKAT GigaHertz Tiered Extragalactic Exploration~(MIGHTEE) survey with the radio galaxy population in the SIMBA suite of cosmological hydrodynamical simulations. The MIGHTEE data includes a $\sim$1deg$^{2}$ pointing of the COSMOS field observed at 1.28GHz with the MeerKAT radio telescope and cross-matched with multi-wavelength counterparts to provide classifications of high and low excitation radio galaxies (HERGs and LERGs) along with their corresponding host properties. We compare the properties of the MIGHTEE HERGs and LERGs with that predicted by the SIMBA simulations where HERGs and LERGs are defined as radio galaxies dominated by cold or hot mode accretion respectively. We consider stellar masses $\;{M}_{*}$, star formation rates SFR, AGN bolometric luminosity $L_{\rm bol}$, and Eddington fraction $f_{\rm Edd}$, as a function of 1.4GHz radio luminosity and redshift. In both MIGHTEE and SIMBA, the properties of HERGs and LERGs are similar across all properties apart from SFRs due to differences in host cold gas content in SIMBA. We predict a population of HERGs with low $f_{\rm Edd}$ in SIMBA that are confirmed in the MIGHTEE observations and tied to the faint population at low $z$. The predictions from SIMBA with the MIGHTEE observations describe a regime where our understanding of the radio galaxy dichotomy breaks down, challenging our understanding of the role of AGN accretion and feedback in the faint population of radio galaxies., Comment: 18 pages; Accepted for publication in MNRAS

Some Active Galactic Nuclei (AGN) host outflows which have the potential to alter the host galaxy's evolution (AGN feedback). These outflows have been linked to enhanced radio emission. Here we investigate the connection between low-frequency radio emission using the International LOFAR Telescope and [O III] $\lambda$5007 ionised gas outflows using the Sloan Digital Sky Survey. Using the LOFAR Two-metre Sky Survey (LoTSS) Deep Fields, we select 198 AGN with optical spectra, 115 of which are detected at 144 MHz, and investigate their low-frequency radio emission properties. The majority of our sample do not show a radio excess when considering radio luminosity - SFR relationship, and are therefore not driven by powerful jets. We extract the [O III] $\lambda$5007 kinematics and remove AGN luminosity dependencies by matching the radio detected and non-detected AGN in $L_{\mathrm{6\mu m}}$ and redshift. Using both spectral fitting and $W_{80}$ measurements, we find radio detected AGN have a higher outflow rate (67.2$\pm$3.4 percent) than the radio non-detected AGN (44.6$\pm$2.7 percent), indicating a connection between ionised outflows and the presence of radio emission. For spectra where there are two components of the [O III] emission line present, we normalise all spectra by the narrow component and find that the average broad component in radio detected AGN is enhanced compared to the radio non-detected AGN. This could be a sign of higher gas content, which is suggestive of a spatial relationship between [O III] outflows and radio emission in the form of either low-powered jets or shocks from AGN winds., Comment: Accepted for publication in MNRAS, 15 pages, 11 figures, 1 table

We present the discovery of a large extended radio jet associated with the extremely radio-loud quasar J1601+3102 at $z\sim5$ from sub-arcsecond resolution imaging at 144 MHz with the LOFAR International Telescope. These large radio lobes have been argued to remain elusive at $z>4$ due to energy losses in the synchrotron emitting plasma as a result of scattering of the strong CMB at these high redshifts. Nonetheless, the 0.3" resolution radio image of J1601+3102 reveals a Northern and Southern radio lobe located at 9 and 57 kpc from the optical quasar, respectively. The measured jet size of 66 kpc makes J1601+3102 the largest extended radio jet at $z>4$ to date. However, it is expected to have an even larger physical size in reality due to projection effects brought about by the viewing angle. Furthermore, we observe the rest-frame UV spectrum of J1601+3102 with Gemini/GNIRS to examine its black hole properties, which results in a mass of 4.5$\times$10$^{8}$ M$_{\odot}$ with an Eddington luminosity ratio of 0.45. The BH mass is relatively low compared to the known high-$z$ quasar population, which suggests that a high BH mass is not strictly necessary to generate a powerful jet. This discovery of the first $\sim100$ kpc radio jet at $z>4$ shows that these objects exist despite energy losses from Inverse Compton scattering and can put invaluable constraints on the formation of the first radio-loud sources in the early Universe., Comment: Accepted for publication in ApJL. 15 pages, 5 figures

The molecular gas in the interstellar medium (ISM) of star-forming galaxy populations exhibits diverse physical properties. We investigate the $^{12}$CO excitation of twelve dusty, luminous star-forming galaxies at $z \sim 2-4$ by combining observations of the $^{12}$CO from $J_{\rm up} = 1$ to $J_{\rm up} = 8$. The spectral line energy distribution (SLED) has a similar shape to NGC 253, M82, and local ULIRGs, with much stronger excitation than the Milky Way inner disc. By combining with resolved dust continuum sizes from high-resolution $870$-$\mu$m ALMA observations and dust mass measurements determined from multi-wavelength SED fitting, we measure the relationship between the $^{12}$CO SLED and probable physical drivers of excitation: star-formation efficiency, the average intensity of the radiation field $\langle U\rangle$, and the star-formation rate surface density. The primary driver of high-$J_{\rm up}$ $^{12}$CO excitation in star-forming galaxies is star-formation rate surface density. We use the ratio of the CO($3-2$) and CO($6-5$) line fluxes to infer the CO excitation in each source and find that the average ratios for our sample are elevated compared to observations of low-redshift, less actively star-forming galaxies and agree well with predictions from numerical models that relate the ISM excitation to the star-formation rate surface density. The significant scatter in the line ratios of a factor $\approx 3$ within our sample likely reflects intrinsic variations in the ISM properties which may be caused by other effects on the excitation of the molecular gas, such as cosmic ray ionization rates and mechanical heating through turbulence dissipation., Comment: Accepted for publication in MNRAS; 17 pages, 7 figures

We report on an international scientific conference, where we brought together the African and European academic astronomy communities. This conference aimed to bridge the gap between those in position of privilege, with ease of access to international networking events (i.e., the typical experience of those affiliated with Western institutions), with those who have been historically excluded (affecting the majority of African scientists/institutions). We describe how we designed the conference around cutting-edge problems in the research field, but with a large focus on building networking and professional relationships. Significant effort went into: (1) ensuring a diverse representation of participants; (2) practically and financially supporting those who may have never attended an international conference and; (3) creating an inclusive and supportive environment through a careful programme of activities, both before and during the event. Throughout this process maintaining scientific integrity was a core commitment. We summarise some of the successes, challenges, and lessons learnt from organising this conference. We also present feedback obtained from participants, which demonstrates an overall achievement of our objectives. This is all combined to provide some key recommendations for any groups, from any research field, who wishes to lead similar initiatives.

We combine the power of blind integral field spectroscopy from the Hobby-Eberly Telescope (HET) Dark Energy Experiment (HETDEX) with sources detected by the Low Frequency Array (LOFAR) to construct the HETDEX-LOFAR Spectroscopic Redshift Catalog. Starting from the first data release of the LOFAR Two-metre Sky Survey (LoTSS), including a value-added catalog with photometric redshifts, we extracted 28,705 HETDEX spectra. Using an automatic classifying algorithm, we assigned each object a star, galaxy, or quasar label along with a velocity/redshift, with supplemental classifications coming from the continuum and emission line catalogs of the internal, fourth data release from HETDEX (HDR4). We measured 9,087 new redshifts; in combination with the value-added catalog, our final spectroscopic redshift sample is 9,710 sources. This new catalog contains the highest substantial fraction of LOFAR galaxies with spectroscopic redshift information; it improves archival spectroscopic redshifts, and facilitates research to determine the [O II] emission properties of radio galaxies from $0.0 < z < 0.5$, and the Ly$\alpha$ emission characteristics of both radio galaxies and quasars from $1.9 < z < 3.5$. Additionally, by combining the unique properties of LOFAR and HETDEX, we are able to measure star formation rates (SFR) and stellar masses. Using the Visible Integral-field Replicable Unit Spectrograph (VIRUS), we measure the emission lines of [O III], [Ne III], and [O II] and evaluate line-ratio diagnostics to determine whether the emission from these galaxies is dominated by AGN or star formation and fit a new SFR-L$_{150MHz}$ relationship., Comment: 21 pages, 17 figures, submitted to ApJ

In this study we report spatially resolved, wideband spectral properties of three giant radio galaxies (GRGs) in the COSMOS field: MGTC J095959.63+024608.6 , MGTC J100016.84+015133.0 and MGTC J100022.85+031520.4. One such galaxy MGTC J100022.85+031520.4 is reported here for the first time with a projected linear size of 1.29 Mpc at a redshift of 0.1034. Unlike the other two, it is associated with a brightest cluster galaxy (BCG), making it one of the few GRGs known to inhabit cluster environments. We examine the spectral age distributions of the three GRGs using new MeerKAT UHF-band (544-1088 MHz) observations, and $L$-band (900-1670 MHz) data from the MeerKAT International GHz Tiered Extragalactic Exploration (MIGHTEE) survey. We test two different models of spectral ageing, the Jaffe-Perola and Tribble models, using the Broadband Radio Astronomy Tools (\textsc{brats}) software which we find agree well with each other. We estimate the Tribble spectral age for MGTC J095959.63+024608.6 as 68 Myr, MGTC J100016.84+015133.0 as 47 Myr and MGTC J100022.85+031520.4 as 67 Myr. We find significant disagreements between these spectral age estimates and the estimates of the dynamical ages of these GRGs, modelled in cluster and group environments. Our results highlight the need for additional processes which are not accounted for in either the dynamic age or spectral age estimations., Comment: 13 pages, 9 figures. Accepted for publication in MNRAS

Both star formation (SF) and Active Galactic Nuclei (AGN) play an important role in galaxy evolution. Statistically quantifying their relative importance can be done using radio luminosity functions. Until now these relied on galaxy classifications, where sources with a mixture of radio emission from SF and AGN are labelled as either a star-forming galaxy or an AGN. This can cause the misestimation of the relevance of AGN. Brightness temperature measurements at 144 MHz with the International LOFAR telescope can separate radio emission from AGN and SF. We use the combination of sub-arcsec and arcsec resolution imaging of 7,497 sources in the Lockman Hole and ELAIS-N1 fields to identify AGN components in the sub-arcsec resolution images and subtract them from the total flux density, leaving flux density from SF only. We construct, for the first time, radio luminosity functions by physical process, either SF or AGN activity, revealing a hidden AGN population at $L_{\textrm{144MHz}}$$<10^{24}$ W$\,$Hz$^{-1}$ . This population is 1.56$\pm$0.06 more than expected for $0.5

Given a compactly supported Hamiltonian diffeomorphism of the plane, one can define a generating function for it. In this paper, we show how generating functions retain information about the braid type of collections of fixed points of Hamiltonian diffeomorphisms. One the one hand, we show that it is possible to define a filtration keeping track of linking numbers of pairs of fixed points on the Morse complex of the generating function. On the other, we provide a finite-dimensional proof of a Theorem by Alves and Meiwes about the lower-semicontinuity of the topological entropy with respect to the Hofer norm. The technical tools come from work by Le Calvez which was developed in the 90s. In particular, we apply a version of positivity of intersections for generating functions., Comment: 44 pages, 1 figure. Comments welcome!

Mitigating explicit and implicit biases in Large Language Models (LLMs) has become a critical focus in the field of natural language processing. However, many current methodologies evaluate scenarios in isolation, without considering the broader context or the spectrum of potential biases within each situation. To address this, we introduce the Sensitivity Testing on Offensive Progressions (STOP) dataset, which includes 450 offensive progressions containing 2,700 unique sentences of varying severity that progressively escalate from less to more explicitly offensive. Covering a broad spectrum of 9 demographics and 46 sub-demographics, STOP ensures inclusivity and comprehensive coverage. We evaluate several leading closed- and open-source models, including GPT-4, Mixtral, and Llama 3. Our findings reveal that even the best-performing models detect bias inconsistently, with success rates ranging from 19.3% to 69.8%. We also demonstrate how aligning models with human judgments on STOP can improve model answer rates on sensitive tasks such as BBQ, StereoSet, and CrowS-Pairs by up to 191%, while maintaining or even improving performance. STOP presents a novel framework for assessing the complex nature of biases in LLMs, which will enable more effective bias mitigation strategies and facilitates the creation of fairer language models., Comment: 9 pages (excluding references), accepted to EMNLP 2024 Main Conference

Electroencephalogram (EEG) has emerged as a cost-effective and efficient method for supporting neurologists in assessing Alzheimer's disease (AD). Existing approaches predominantly utilize handcrafted features or Convolutional Neural Network (CNN)-based methods. However, the potential of the transformer architecture, which has shown promising results in various time series analysis tasks, remains underexplored in interpreting EEG for AD assessment. Furthermore, most studies are evaluated on the subject-dependent setup but often overlook the significance of the subject-independent setup. To address these gaps, we present ADformer, a novel multi-granularity transformer designed to capture temporal and spatial features to learn effective EEG representations. We employ multi-granularity data embedding across both dimensions and utilize self-attention to learn local features within each granularity and global features among different granularities. We conduct experiments across 5 datasets with a total of 525 subjects in setups including subject-dependent, subject-independent, and leave-subjects-out. Our results show that ADformer outperforms existing methods in most evaluations, achieving F1 scores of 75.19% and 93.58% on two large datasets with 65 subjects and 126 subjects, respectively, in distinguishing AD and healthy control (HC) subjects under the challenging subject-independent setup., Comment: 17 pages main paper + 3 pages supplementary materials. This work will submit to the IEEE for possible publication

Coupling between relativistic jets launched by accreting supermassive black holes and the surrounding gaseous media is a vital ingredient in galaxy evolution models. To constrain the environments in which this feedback takes place over cosmic time, we study the host halo properties of luminous low-frequency radio galaxies ($L_{150 \ \mathrm{MHz}} \gtrsim$ 25.25 W/Hz) selected with the International LOFAR Telescope out to $z \sim 2$ through tomographic clustering and cosmic microwave background lensing measurements. We find that these systems occupy halos characteristic of galaxy groups ($M_h = 10^{13} - 10^{14} h^{-1} M_{\odot}$), evolving at a rate consistent with the mean growth rate of halos over the past $\sim$10 Gyr. The coevolution of the clustering and the luminosity function reveals that the duty cycle of these systems is of order $\sim 10\%$ but has been mildly increasing since $z\sim 2$, while the duty cycle of quasars has been declining. We estimate the characteristic kinetic heating power injected by powerful jets per halo as a function of mass, and compare to the same quantity injected by quasar winds. We find that powerful jet heating dominates over quasar winds in halos $M_h \gtrsim 10^{13} h^{-1} M_{\odot}$ at $z < 2$. These results conform to the paradigm of galaxy evolution in which mechanical jet power feedback is the dominant heating mechanism of the gas content of groups and clusters., Comment: Accepted to ApJ

Digital Twins (DTs) are set to become a key enabling technology in future wireless networks, with their use in network management increasing significantly. We developed a DT framework that leverages the heterogeneity of network access technologies as a resource for enhanced network performance and management, enabling smart data handling in the physical network. Tested in a Campus Area Network environment, our framework integrates diverse data sources to provide real-time, holistic insights into network performance and environmental sensing. We also envision that traditional analytics will evolve to rely on emerging AI models, such as Generative AI (GenAI), while leveraging current analytics capabilities. This capacity can simplify analytics processes through advanced ML models, enabling descriptive, diagnostic, predictive, and prescriptive analytics in a unified fashion. Finally, we present specific research opportunities concerning interoperability aspects and envision aligning advancements in DT technology with evolved AI integration., Comment: A shortened version of this paper is currently under review for publication in an IEEE magazine. If accepted, the copyright will be transferred to IEEE

We present the deepest wide-field 115-166 MHz image at sub-arcsecond resolution spanning an area of 2.5 by 2.5 degrees centred at the ELAIS-N1 deep field. To achieve this, we improved the calibration for the International LOFAR Telescope. This enhancement enabled us to efficiently process 32 hrs of data from four different 8-hr observations using the high-band antennas (HBAs) of all 52 stations, covering baselines up to approximately 2,000 km across Europe. The DI calibration was improved by using an accurate sky model and refining the series of calibration steps on the in-field calibrator, while the DD calibration was improved by adopting a more automated approach for selecting the DD calibrators and inspecting the self-calibration on these sources. We also added an additional round of self-calibration for the Dutch core and remote stations in order to refine the solutions for shorter baselines. To complement our highest resolution at 0.3", we also made intermediate resolution wide-field images at 0.6" and 1.2". Our resulting wide-field images achieve a central noise level of 14 muJy/beam at 0.3", doubling the depth and uncovering four times more objects than the Lockman Hole deep field image at comparable resolution but with only 8 hrs of data. Compared to LOFAR imaging without the international stations, we note that due to the increased collecting area and the absence of confusion noise, we reached a point-source sensitivity comparable to a 500-hr ELAIS-N1 6" image with 16 times less observing time. Importantly, we have found that the computing costs for the same amount of data are almost halved (to about 139,000 CPU hrs per 8 hrs of data) compared to previous efforts, though they remain high. Our work underscores the value and feasibility of exploiting all Dutch and international LOFAR stations to make deep wide-field images at sub-arcsecond resolution., Comment: Images and catalogues will be available on https://lofar-surveys.org/hd-en1.html

On-device inference holds great potential for increased energy efficiency, responsiveness, and privacy in edge ML systems. However, due to less capable ML models that can be embedded in resource-limited devices, use cases are limited to simple inference tasks such as visual keyword spotting, gesture recognition, and predictive analytics. In this context, the Hierarchical Inference (HI) system has emerged as a promising solution that augments the capabilities of the local ML by offloading selected samples to an edge server or cloud for remote ML inference. Existing works demonstrate through simulation that HI improves accuracy. However, they do not account for the latency and energy consumption on the device, nor do they consider three key heterogeneous dimensions that characterize ML systems: hardware, network connectivity, and models. In contrast, this paper systematically compares the performance of HI with on-device inference based on measurements of accuracy, latency, and energy for running embedded ML models on five devices with different capabilities and three image classification datasets. For a given accuracy requirement, the HI systems we designed achieved up to 73% lower latency and up to 77% lower device energy consumption than an on-device inference system. The key to building an efficient HI system is the availability of small-size, reasonably accurate on-device models whose outputs can be effectively differentiated for samples that require remote inference. Despite the performance gains, HI requires on-device inference for all samples, which adds a fixed overhead to its latency and energy consumption. Therefore, we design a hybrid system, Early Exit with HI (EE-HI), and demonstrate that compared to HI, EE-HI reduces the latency by up to 59.7% and lowers the device's energy consumption by up to 60.4%.

As the use of Large Language Models (LLMs) becomes more widespread, understanding their self-evaluation of confidence in generated responses becomes increasingly important as it is integral to the reliability of the output of these models. We introduce the concept of Confidence-Probability Alignment, that connects an LLM's internal confidence, quantified by token probabilities, to the confidence conveyed in the model's response when explicitly asked about its certainty. Using various datasets and prompting techniques that encourage model introspection, we probe the alignment between models' internal and expressed confidence. These techniques encompass using structured evaluation scales to rate confidence, including answer options when prompting, and eliciting the model's confidence level for outputs it does not recognize as its own. Notably, among the models analyzed, OpenAI's GPT-4 showed the strongest confidence-probability alignment, with an average Spearman's $\hat{\rho}$ of 0.42, across a wide range of tasks. Our work contributes to the ongoing efforts to facilitate risk assessment in the application of LLMs and to further our understanding of model trustworthiness., Comment: 9 pages (excluding references), accepted to ACL 2024 Main Conference

Spectral energy distribution (SED) fitting has been extensively used to determine the nature of the faint radio source population. Recent efforts have combined fits from multiple SED-fitting codes to account for the host galaxy and any active nucleus that may be present. We show that it is possible to produce similar-quality classifications using a single energy-balance SED fitting code, Prospector, to model up to 26 bands of UV$-$far-infrared aperture-matched photometry for $\sim$31,000 sources in the ELAIS-N1 field from the LOFAR Two-Metre Sky Survey (LoTSS) Deep fields first data release. One of a new generation of SED-fitting codes, Prospector accounts for potential contributions from radiative active galactic nuclei (AGN) when estimating galaxy properties, including star formation rates (SFRs) derived using non-parametric star formation histories. Combining this information with radio luminosities, we classify 92 per cent of the radio sources as a star-forming galaxy, high/low-excitation radio galaxy, or radio-quiet AGN and study the population demographics as a function of 150 MHz flux density, luminosity, SFR, stellar mass, redshift and apparent $r$-band magnitude. Finally, we use Prospector SED fits to investigate the SFR$-$150 MHz luminosity relation for a sample of $\sim$$133,000~3.6~\mu$m-selected $z<1$ sources, finding that the stellar mass dependence is significantly weaker than previously reported, and may disappear altogether at $\log_{10} (\mathrm{SFR}/M_\odot~\mathrm{yr}^{-1}) > 0.5$. This approach makes it significantly easier to classify radio sources from LoTSS and elsewhere, and may have important implications for future studies of star-forming galaxies at radio wavelengths., Comment: 20 pages, 13 figures, accepted for publication in MNRAS

This article introduces Follow-Me AI, a concept designed to enhance user interactions with smart environments, optimize energy use, and provide better control over data captured by these environments. Through AI agents that accompany users, Follow-Me AI negotiates data management based on user consent, aligns environmental controls as well as user communication and computes resources available in the environment with user preferences, and predicts user behavior to proactively adjust the smart environment. The manuscript illustrates this concept with a detailed example of Follow-Me AI in a smart campus setting, detailing the interactions with the building's management system for optimal comfort and efficiency. Finally, this article looks into the challenges and opportunities related to Follow-Me AI.

The Hierarchical Inference (HI) paradigm employs a tiered processing: the inference from simple data samples are accepted at the end device, while complex data samples are offloaded to the central servers. HI has recently emerged as an effective method for balancing inference accuracy, data processing, transmission throughput, and offloading cost. This approach proves particularly efficient in scenarios involving resource-constrained edge devices, such as IoT sensors and micro controller units (MCUs), tasked with executing tinyML inference. Notably, it outperforms strategies such as local inference execution, inference offloading to edge servers or cloud facilities, and split inference (i.e., inference execution distributed between two endpoints). Building upon the HI paradigm, this work explores different techniques aimed at further optimizing inference task execution. We propose and discuss three distinct HI approaches and evaluate their utility for image classification.

Given a pre-monotone Lagrangian link, we obtain Hofer energy estimates for Hamiltonian diffeomorphisms preserving it. Such estimates depend on the braid type of the Hamiltonian diffeomorphism only, and the natural language to talk about this phenomenon is provided by a family of norms on braid groups for surfaces with boundary. This generalises the results obtained by the first author to higher genus surfaces with boundary., Comment: Exposition has been improved and typos have been corrected. No changes to the main result have been made

We present a novel scheme for rapid quantitative analysis of debris generated during experiments with solid targets following relativistic laser-plasma interaction at high-power laser facilities. Experimental data indicates that predictions by available modelling for non-mass-limited targets are reasonable, with debris on the order of hundreds ug-per-shot. We detect for the first time that several % of the debris is ejected directional following the target normal (rear- and interaction side); and confirm previous work that found the debris ejection in direction of the interaction side to be larger than on the side of the target rear.

Studies show that both radio jets from the active galactic nuclei (AGN) and the star formation (SF) activity in quasar host galaxies contribute to the quasar radio emission; yet their relative contributions across the population remain unclear. Here, we present an improved parametric model that allows us to statistically separate the SF and AGN components in observed quasar radio flux density distributions, and investigate how their relative contributions evolve with AGN bolometric luminosity ($L_\mathrm{bol}$) and redshift ($z$) using a fully Bayesian method. Based on the newest data from LOFAR Two-Metre Sky Survey Data Release 2, our model gives robust fitting results out to $z\sim4$, showing a quasar host galaxy SFR evolution that increases with bolometric luminosity and with redshift out to $z\sim4$. This differs from the global cosmic SFR density, perhaps due to the importance of galaxy mergers. The prevalence of radio AGN emissions increases with quasar luminosity, but has little dependence on redshift. Furthermore, our new methodology and large sample size allow us to subdivide our dataset to investigate the role of other parameters. Specifically, in this paper, we explore quasar colour and demonstrate that the radio excess in red quasars is due to an enhancement in AGN-related emission, since the host galaxy SF contribution to the total radio emission is independent of quasar colour. We also find evidence that this radio enhancement occurs mostly in quasars with weak or intermediate radio power., Comment: Accepted for publication in MNRAS. 19 pages, 13 figures, 1 table

Radio-mode feedback associated with the active galactic nuclei (AGN) at the cores of galaxy clusters injects large amount of energy into the intracluster medium (ICM), offsetting radiative losses through X-ray emission. This mechanism prevents the ICM from rapidly cooling down and fueling extreme starburst activity as it accretes onto the central galaxies, and is therefore a key ingredient in the evolution of galaxy clusters. However, the influence and mode of feedback at high redshifts (z~1) remains largely unknown. Low-frequency sub-arcsecond resolution radio observations taken with the International LOFAR Telescope have demonstrated their ability to assist X-ray observations with constraining the energy output from the AGNs (or "cavity power") in galaxy clusters, thereby enabling research at higher redshifts than before. In this pilot project, we test this hybrid method on a high redshift (0.6

The origin of radio emission in different populations of radio-quiet quasars is relatively unknown, but recent work has uncovered various drivers of increased radio-detection fraction. In this work, we pull together three known factors: optical colour ($g-i$), \CIV Distance (a proxy for $L/L_{Edd}$) and whether or not the quasar contains broad absorption lines (BALQSOs) which signify an outflow. We use SDSS DR14 spectra along with the LOFAR Two Metre Sky Survey Data Release 2 and find that each of these properties have an independent effect. BALQSOs are marginally more likely to be radio-detected than non-BALQSOs at similar colours and $L/L_{Edd}$, moderate reddening significantly increases the radio-detection fraction and the radio-detection increases with $L/L_{Edd}$ above a threshold for all populations. We test a widely used simple model for radio wind shock emission and calculate energetic efficiencies that would be required to reproduce the observed radio properties. We discuss interpretations of these results concerning radio-quiet quasars more generally. We suggest that radio emission in BALQSOs is connected to a different physical origin than the general quasar population since they show different radio properties independent of colour and \CIV distance., Comment: 14 pages, 8 figures, 2 tables

Optogenetic modulation of adenosine triphosphatase (ATPase) expression represents a novel approach to maximize bioprocess efficiency by leveraging enforced adenosine triphosphate (ATP) turnover. In this study, we experimentally implement a model-based open-loop optimization scheme for optogenetic modulation of the expression of ATPase. Increasing the intracellular concentration of ATPase, and thus the level of ATP turnover, in bioprocesses with product synthesis coupled with ATP generation, can lead to increased product formation and substrate uptake. Previous simulation studies formulated optimal control problems using dynamic constraint-based models to find optimal light inputs in fermentations with optogenetically mediated ATPase expression. However, using these models poses challenges due to resulting bilevel optimizations and complex parameterization. Here, we outline a simplified unsegregated and quasi-unstructured kinetic modeling approach that reduces the number of dynamic states and leads to single-level optimizations. The models can be augmented with Gaussian processes to compensate for model uncertainties. We implement optimal control constrained by knowledge-based and hybrid models for optogenetic ATPase expression in $\textit{Escherichia coli}$ with lactate as the main product. To do so, we genetically engineer $\textit{E. coli}$ to obtain optogenetic expression of ATPase using the CcaS/CcaR system. This represents the first experimental implementation of model-based optimization of ATPase expression in bioprocesses., Comment: 24 pages, 5 figures, journal submission

In today's digital world, Generative Artificial Intelligence (GenAI) such as Large Language Models (LLMs) is becoming increasingly prevalent, extending its reach across diverse applications. This surge in adoption has sparked a significant increase in demand for data-centric GenAI models, highlighting the necessity for robust data communication infrastructures. Central to this need are message brokers, which serve as essential channels for data transfer within various system components. This survey aims to delve into a comprehensive analysis of traditional and modern message brokers, offering a comparative study of prevalent platforms. Our study considers numerous criteria including, but not limited to, open-source availability, integrated monitoring tools, message prioritization mechanisms, capabilities for parallel processing, reliability, distribution and clustering functionalities, authentication processes, data persistence strategies, fault tolerance, and scalability. Furthermore, we explore the intrinsic constraints that the design and operation of each message broker might impose, recognizing that these limitations are crucial in understanding their real-world applicability. Finally, this study examines the enhancement of message broker mechanisms specifically for GenAI contexts, emphasizing the criticality of developing a versatile message broker framework. Such a framework would be poised for quick adaptation, catering to the dynamic and growing demands of GenAI in the foreseeable future. Through this dual-pronged approach, we intend to contribute a foundational compendium that can guide future innovations and infrastructural advancements in the realm of GenAI data communication., Comment: 20 pages, 181 references, 7 figures, 5 tables

We present a comprehensive study of the physical origin of radio emission in optical quasars at redshifts z < 2.5. We focus particularly on the associations between compact radio emission, dust reddening, and outflows identified in our earlier work. Leveraging the deepest low-frequency radio data available to date (LoTSS Deep DR1), we achieve radio detection fractions of up to 94%, demonstrating the virtual ubiquity of radio emission in quasars, and a continuous distribution in radio loudness. Through our analysis of radio properties, combined with spectral energy distribution modeling of multiwavelength photometry, we establish that the primary source of radio emission in quasars is the AGN, rather than star formation. Modeling the dust reddening of the accretion disk emission shows a continuous increase in radio detection in quasars as a function of the reddening parameter E(B-V), suggesting a causal link between radio emission and dust reddening. Confirming previous findings, we observe that the radio excess in red quasars is most pronounced for sources with compact radio morphologies and intermediate radio loudness. We find a significant increase in [Oiii] and Civ outflow velocities for red quasars not seen in our control sample, with particularly powerful [Oiii] winds in those around the radio-quiet/radio-loud threshold. Based on the combined characterisation of radio, reddening, and wind properties in our sample, we favor a model in which the compact radio emission observed in quasars originates in compact radio jets and their interaction with a dusty, circumnuclear environment. Our results align with the theory that jet-induced winds and shocks resulting from this interaction are the origin of the enhanced radio emission in red quasars. Further investigation of this model is crucial for advancing our understanding of quasar feedback mechanisms and their role in galaxy evolution., Comment: 11 pages, 8 Figures, submitted to A&A

We present the first deep (72 hours of observations) radio image of the Euclid Deep Field North (EDFN) obtained with the LOw-Frequency ARray (LOFAR) High Band Antenna (HBA) at 144 MHz. The EDFN is the latest addition to the LOFAR Two-Metre Sky Survey (LoTSS) Deep Fields and these observations represent the first data release for this field. The observations produced a 6" resolution image with a central r.m.s. noise of $32\,\mu$Jy\,beam$^{-1}$. A catalogue of $\sim 23,000$ radio sources above a signal-to-noise ratio (SNR) threshold of 5 is extracted from the inner circular 10 deg$^2$ region. We discuss the data analysis and we provide a detailed description of how we derived the catalogue of radio sources and on the issues related to direction-dependent calibration and their effects on the final products. Finally, we derive the radio source counts at 144 MHz in the EDFN using catalogues of mock radio sources to derive the completeness correction factors. The source counts in the EDFN are consistent with those obtained from the first data release of the other LoTSS Deep Fields (ELAIS-N1, Lockman Hole and Bootes), despite the different method adopted to construct the final catalogue and to assess its completeness., Comment: accepted for publication in A&A