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This work explores the effectiveness of masked image modelling for learning representations of retinal OCT images. To this end, we leverage Masked Autoencoders (MAE), a simple and scalable method for self-supervised learning, to obtain a powerful and general representation for OCT images by training on 700K OCT images from 41K patients collected under real world clinical settings. We also provide the first extensive evaluation for a model of OCT on a challenging battery of 6 downstream tasks. Our model achieves strong performance when fully finetuned but can also serve as a versatile frozen feature extractor for many tasks using lightweight adapters. Furthermore, we propose an extension of the MAE pretraining to fuse OCT with an auxiliary modality, namely, IR fundus images and learn a joint model for both. We demonstrate our approach improves performance on a multimodal downstream application. Our experiments utilize most publicly available OCT datasets, thus enabling future comparisons. Our code and model weights are publicly available https://github.com/TheoPis/MIM_OCT.

Live action roleplay (larp) has a wide range of applications, and can be relevant in relation to HCI. While there has been research about larp in relation to topics such as embodied interaction, playfulness and futuring published in HCI venues since the early 2000s, there is not yet a compilation of this knowledge. In this paper, we synthesise knowledge about larp and larp-adjacent work within the domain of HCI. We present a practitioner overview from an expert group of larp researchers, the results of a literature review, and highlight particular larp research exemplars which all work together to showcase the diverse set of ways that larp can be utilised in relation to HCI topics and research. This paper identifies the need for further discussions toward establishing best practices for utilising larp in relation to HCI research, as well as advocating for increased engagement with larps outside academia.

We construct a new invariant of singular links through representations of the singular braid monoid into the two parameters bt-algebra. Additionally, we recover this invariant by using the approach of Paris and Rabenda. Hence, we introduce the so called two parameter Singular bt-algebra. Finally, we provide the skein relations that define our invariant, and we prove that this invariant is more powerful than previous invariants of singular links in literature.

We study the impact of black hole nuclear activity on both the global and radial star formation rate (SFR) profiles in X-ray-selected active galactic nuclei (AGN) in the field of miniJPAS, the precursor of the much wider J-PAS project. Our sample includes 32 AGN with z < 0.3 detected via the XMM-Newton and Chandra surveys. For comparison, we assembled a control sample of 71 star-forming (SF) galaxies with similar magnitudes, sizes, and redshifts. To derive the global properties of both the AGN and the control SF sample, we used CIGALE to fit the spectral energy distributions derived from the 56 narrowband and 4 broadband filters from miniJPAS. We find that AGN tend to reside in more massive galaxies than their SF counterparts. After matching samples based on stellar mass and comparing their SFRs and specific SFRs (sSFRs), no significant differences appear. This suggests that the presence of AGN does not strongly influence overall star formation. However, when we used miniJPAS as an integral field unit (IFU) to dissect galaxies along their position angle, a different picture emerges. We find that AGN tend to be more centrally concentrated in mass with respect to SF galaxies. Moreover, we find a suppression of the sSFR up to 1Re and then an enhancement beyond 1Re , strongly contrasting with the decreasing radial profile of sSFRs in SF galaxies. This could point to an inside-out quenching of AGN host galaxies. These findings suggest that the reason we do not see differences on a global scale is because star formation is suppressed in the central regions and enhanced in the outer regions of AGN host galaxies. While limited in terms of sample size, this work highlights the potential of the upcoming J-PAS as a wide-field low-resolution IFU for thousands of nearby galaxies and AGN., Comment: 21 pages, 20 figures, Accepted for Publication in A&A

We present JWST/NIRSpec high-resolution spectroscopy G395H/F290LP of MACS0647-JD, a gravitationally lensed galaxy merger at $z=10.167$. The new spectroscopy, which is acquired for the two lensed images (JD1 and JD2), detects and resolves emission lines in the rest-frame ultraviolet (UV) and blue optical, including the resolved [OII]3726,3729 doublet, [NeIII]3870, [HeI]3890, H$\delta$, H$\gamma$, and [OIII]4363. This is the first observation of the resolved [OII]3726,3729 doublet for a galaxy at $z>8$. We measure a line flux ratio [OII]3729/3726 $= 0.9 \pm 0.3$, which corresponds to an estimated electron density of $\log(n_{e} / \rm{cm}^{-3}) = 2.9 \pm 0.5$. This is significantly higher than the electron densities of local galaxies reported in the literature. We compile the measurements from the literature and further analyze the redshift evolution of $n_{e}$. We find that the redshift evolution follows the power-law form of $n_{e} = A\times (1+z)^{p}$ with $A=54^{+31}_{-23}$ cm$^{-3}$ and $p=1.2^{+0.4}_{-0.4}$. This power-law form may be explained by a combination of metallicity and morphological evolution of galaxies, which become, on average, more metal-poor and more compact with increasing redshift., Comment: 20 pages, 11 figures. Accepted for publication in ApJ

JWST spectroscopy has revolutionized our understanding of galaxies in the early universe. Covering wavelengths up to $5.3\,{\rm \mu m}$, NIRSpec can detect rest-frame optical emission lines H$\alpha$ out to $z = 7$ and [O III] to $z = 9.5$. Observing these lines in more distant galaxies requires longer wavelength spectroscopy with MIRI. Here we present MIRI MRS IFU observations of the lensed galaxy merger MACS0647$-$JD at $z = 10.165$. With exposure times of 4.2 hours in each of two bands, we detect H$\alpha$ at $9\sigma$, [O III]$\,\lambda5008$ at $11\sigma$, and [O III]$\,\lambda4960$ at $3\sigma$. Combined with previously reported NIRSpec spectroscopy that yields seven emission lines including the auroral line [O III]$\,\lambda4363$, we present the first direct metallicity measurement of a $z > 10$ galaxy: $12+{\rm log(O/H)}= 7.79\pm0.09$, or $0.13^{+0.02}_{-0.03}\,Z_{\odot}$. This is similar to galaxies at $z \sim 4 - 9$ with direct metallicity measurements, though higher than expected given the high specific star formation rate ${\rm log(sSFR / yr^{-1})} = -7.4 \pm 0.3$. We further constrain the ionization parameter ${\rm log}(U)$ = $-1.9 \pm 0.1$, ionizing photon production efficiency ${\rm log}(\xi_{\rm ion})$ = $25.3\pm0.1$, and star formation rate $5.0\pm0.6\,M_{\odot}/{\rm yr}$ within the past $10\,{\rm Myr}$. These observations demonstrate the combined power of JWST NIRSpec and MIRI for studying galaxies in the first $500$ million years., Comment: 16 pages, 8 figures, accepted for publication in ApJ

Multiple visions of 6G networks elicit Artificial Intelligence (AI) as a central, native element. When 6G systems are deployed at a large scale, end-to-end AI-based solutions will necessarily have to encompass both the radio and the fiber-optical domain. This paper introduces the Decentralized Multi-Party, Multi-Network AI (DMMAI) framework for integrating AI into 6G networks deployed at scale. DMMAI harmonizes AI-driven controls across diverse network platforms and thus facilitates networks that autonomously configure, monitor, and repair themselves. This is particularly crucial at the network edge, where advanced applications meet heightened functionality and security demands. The radio/optical integration is vital due to the current compartmentalization of AI research within these domains, which lacks a comprehensive understanding of their interaction. Our approach explores multi-network orchestration and AI control integration, filling a critical gap in standardized frameworks for AI-driven coordination in 6G networks. The DMMAI framework is a step towards a global standard for AI in 6G, aiming to establish reference use cases, data and model management methods, and benchmarking platforms for future AI/ML solutions.

Discrete-time Quantum Walks (QWs) are transportation models of single quantum particles over a lattice. Their evolution is driven through causal and local unitary operators. QWs are a powerful tool for quantum simulation of fundamental physics as some of them have a continuum limit converging to well-known physics partial differential equations, such as the Dirac or the Schr\"odinger equation. In this work, we show how to recover the Dirac equation in (3+1)-dimensions with a QW evolving in a tetrahedral space. This paves the way to simulate the Dirac equation on a curved spacetime. This also suggests an ordered scheme for propagating matter over a spin network, of interest in Loop Quantum Gravity where matter propagation has remained an open problem.

The ICLR conference is unique among the top machine learning conferences in that all submitted papers are openly available. Here we present the ICLR dataset consisting of abstracts of all 24 thousand ICLR submissions from 2017-2024 with meta-data, decision scores, and custom keyword-based labels. We find that on this dataset, bag-of-words representation outperforms most dedicated sentence transformer models in terms of $k$NN classification accuracy, and the top performing language models barely outperform TF-IDF. We see this as a challenge for the NLP community. Furthermore, we use the ICLR dataset to study how the field of machine learning has changed over the last seven years, finding some improvement in gender balance. Using a 2D embedding of the abstracts' texts, we describe a shift in research topics from 2017 to 2024 and identify hedgehogs and foxes among the authors with the highest number of ICLR submissions.

A common and fundamental limitation of Generative AI (GenAI) is its propensity to hallucinate. While large language models (LLM) have taken the world by storm, without eliminating or at least reducing hallucinations, real-world GenAI systems may face challenges in user adoption. In the process of deploying an enterprise application that produces workflows based on natural language requirements, we devised a system leveraging Retrieval Augmented Generation (RAG) to greatly improve the quality of the structured output that represents such workflows. Thanks to our implementation of RAG, our proposed system significantly reduces hallucinations in the output and improves the generalization of our LLM in out-of-domain settings. In addition, we show that using a small, well-trained retriever encoder can reduce the size of the accompanying LLM, thereby making deployments of LLM-based systems less resource-intensive., Comment: To be presented at NAACL 2024. 11 pages and 4 figures

In the realm of Artificial Intelligence (AI), the need for privacy and security in data processing has become paramount. As AI applications continue to expand, the collection and handling of sensitive data raise concerns about individual privacy protection. Federated Learning (FL) emerges as a promising solution to address these challenges by enabling decentralized model training on local devices, thus preserving data privacy. This paper introduces FLEX: a FLEXible Federated Learning Framework designed to provide maximum flexibility in FL research experiments. By offering customizable features for data distribution, privacy parameters, and communication strategies, FLEX empowers researchers to innovate and develop novel FL techniques. The framework also includes libraries for specific FL implementations including: (1) anomalies, (2) blockchain, (3) adversarial attacks and defences, (4) natural language processing and (5) decision trees, enhancing its versatility and applicability in various domains. Overall, FLEX represents a significant advancement in FL research, facilitating the development of robust and efficient FL applications., Comment: Submitted to Information Fusion

High-speed interconnects, such as NVLink, are integral to modern multi-GPU systems, acting as a vital link between CPUs and GPUs. This study highlights the vulnerability of multi-GPU systems to covert and side channel attacks due to congestion on interconnects. An adversary can infer private information about a victim's activities by monitoring NVLink congestion without needing special permissions. Leveraging this insight, we develop a covert channel attack across two GPUs with a bandwidth of 45.5 kbps and a low error rate, and introduce a side channel attack enabling attackers to fingerprint applications through the shared NVLink interconnect., Comment: Accepted to SEED 2024

We present JWST NIRCam imaging of B14-65666 ("Big Three Dragons"), a bright Lyman-break galaxy system ($M_\text{UV}=-22.5$ mag) at $z=7.15$. The high angular resolution of NIRCam reveals the complex morphology of two galaxy components: galaxy E has a compact core (E-core), surrounded by diffuse, extended, rest-frame optical emission, which is likely to be tidal tails; and galaxy W has a clumpy and elongated morphology with a blue UV slope ($\beta_\text{UV}=-2.2\pm0.1$). The flux excess, F356W$-$F444W, peaks at the E-core ($1.05^{+0.08}_{-0.09}$ mag), tracing the presence of strong [OIII] 4960,5008 \r{A} emission. ALMA archival data show that the bluer galaxy W is brighter in dust continua than the redder galaxy E, while the tails are bright in [OIII] 88 $\mathrm{\mu m}$. The UV/optical and sub-mm SED fitting confirms that B14-65666 is a major merger in a starburst phase as derived from the stellar mass ratio (3:1 to 2:1) and the star-formation rate, $\simeq1$ dex higher than the star-formation main sequence at the same redshift. The galaxy E is a dusty ($A_\text{V}=1.2\pm0.1$ mag) starburst with a possible high dust temperature ($\ge63$-$68$ K). The galaxy W would have a low dust temperature ($\le27$-$33$ K) or patchy stellar-and-dust geometry, as suggested from the infrared excess (IRX) and $\beta_\text{UV}$ diagram. The high optical-to-FIR [OIII] line ratio of the E-core shows its lower gas-phase metallicity ($\simeq0.2$ Z$_{\odot}$) than the galaxy W. These results agree with a scenario where major mergers disturb morphology and induce nuclear dusty starbursts triggered by less-enriched inflows. B14-65666 shows a picture of complex stellar buildup processes during major mergers in the epoch of reionization., Comment: 18 pages, 6 figures, 4 tables. Submitted to ApJ

The Mid-Infrared Instrument (MIRI) aboard the James Webb Space Telescope (JWST) provides the observatory with a huge advance in mid-infrared imaging and spectroscopy covering the wavelength range of 5 to 28 microns. This paper describes the performance and characteristics of the MIRI imager as understood during observatory commissioning activities, and through its first year of science operations. We discuss the measurements and results of the imager's point spread function, flux calibration, background, distortion and flat fields as well as results pertaining to best observing practices for MIRI imaging, and discuss known imaging artefacts that may be seen during or after data processing. Overall, we show that the MIRI imager has met or exceeded all its pre-flight requirements, and we expect it to make a significant contribution to mid-infrared science for the astronomy community for years to come.

The International Linear Collider (ILC) will allow the precise study of $e^{-}e^{+}\rightarrow q\bar{q}$ interactions at different center-of-mass energies from the $Z$-pole to 1 TeV. In this paper, we discuss the experimental prospects for measuring differential observables in $e^{-}e^{+}\rightarrow b\bar{b}$ and $e^{-}e^{+}\rightarrow c\bar{c}$ at the ILC baseline energies, 250 and 500 GeV. The study is based on full simulation and reconstruction of the International Large Detector (ILD) concept. Two gauge-Higgs unification models predicting new high-mass resonances beyond the Standard Model are discussed. These models predict sizable deviations of the forward-backward observables at the ILC running above the $Z$ mass and with longitudinally polarized electron and positron beams. The ability of the ILC to probe these models via high-precision measurements of the forward-backward asymmetry is discussed. Alternative scenarios at other energies and beam polarization schemes are also discussed, extrapolating the estimated uncertainties from the two baseline scenarios.

The detection of gravitational waves from a binary neutron star merger by Advanced LIGO and Advanced Virgo (GW170817), along with the discovery of the electromagnetic counterparts of this gravitational wave event, ushered in a new era of multimessenger astronomy, providing the first direct evidence that BNS mergers are progenitors of short gamma-ray bursts (GRBs). Such events may also produce very-high-energy (VHE, > 100GeV) photons which have yet to be detected in coincidence with a gravitational wave signal. The Cherenkov Telescope Array (CTA) is a next-generation VHE observatory which aims to be indispensable in this search, with an unparalleled sensitivity and ability to slew anywhere on the sky within a few tens of seconds. New observing modes and follow-up strategies are being developed for CTA to rapidly cover localization areas of gravitational wave events that are typically larger than the CTA field of view. This work will evaluate and provide estimations on the expected number of of gravitational wave events that will be observable with CTA, considering both on- and off-axis emission. In addition, we will present and discuss the prospects of potential follow-up strategies with CTA., Comment: Presented at the 38th International Cosmic Ray Conference (ICRC 2023), 2023 (arXiv:2309.08219)

Galaxy clusters are expected to be dark matter (DM) reservoirs and storage rooms for the cosmic-ray protons (CRp) that accumulate along the cluster's formation history. Accordingly, they are excellent targets to search for signals of DM annihilation and decay at gamma-ray energies and are predicted to be sources of large-scale gamma-ray emission due to hadronic interactions in the intracluster medium. We estimate the sensitivity of the Cherenkov Telescope Array (CTA) to detect diffuse gamma-ray emission from the Perseus galaxy cluster. We perform a detailed spatial and spectral modelling of the expected signal for the DM and the CRp components. For each, we compute the expected CTA sensitivity. The observing strategy of Perseus is also discussed. In the absence of a diffuse signal (non-detection), CTA should constrain the CRp to thermal energy ratio within the radius $R_{500}$ down to about $X_{500}<3\times 10^{-3}$, for a spatial CRp distribution that follows the thermal gas and a CRp spectral index $\alpha_{\rm CRp}=2.3$. Under the optimistic assumption of a pure hadronic origin of the Perseus radio mini-halo and depending on the assumed magnetic field profile, CTA should measure $\alpha_{\rm CRp}$ down to about $\Delta\alpha_{\rm CRp}\simeq 0.1$ and the CRp spatial distribution with 10% precision. Regarding DM, CTA should improve the current ground-based gamma-ray DM limits from clusters observations on the velocity-averaged annihilation cross-section by a factor of up to $\sim 5$, depending on the modelling of DM halo substructure. In the case of decay of DM particles, CTA will explore a new region of the parameter space, reaching models with $\tau_{\chi}>10^{27}$s for DM masses above 1 TeV. These constraints will provide unprecedented sensitivity to the physics of both CRp acceleration and transport at cluster scale and to TeV DM particle models, especially in the decay scenario., Comment: 93 pages (including author list, appendix and references), 143 figures. Submitted to JCAP

Despite the importance of the dusty star-forming galaxies (DSFGs) at $z$>2 for understanding the galaxy evolution in the early Universe, their stellar distributions traced by the near-IR emission were spatially unresolved until the arrival of the JWST. In this work we present, for the first time, a spatially-resolved morphological analysis of the rest-frame near-IR (~1.1-3.5$\mu$m) emission in DSFGs traced with the JWST/MIRI. In particular, we study the mature stellar component for the three DSFGs and a Lyman-break galaxy (LBG) present in an overdensity at $z$=4.05. Moreover, we use MIRI images along with UV to (sub)-mm ancillary photometric data to model their SEDs and extract their main physical properties. The sub-arcsec resolution MIRI images have revealed that the stellar component present a wide range of morphologies, from disc-like to compact and clump-dominated structures. These near-IR structures contrast with their UV emission, which is usually diffuse and off-centered. The SED fitting analysis shows that GN20 dominates the total SFR with a value ~2500 $M_\odot$yr$^{-1}$ while GN20.2b has the highest stellar mass in the sample ($M_*$~2$\times$10$^{11}$ $M_\odot$). The two DSFGs classified as LTGs (GN20 and GN20.2a) show high specific SFR (sSFR>30 Gyr$^{-1}$) placing them above the star-forming main sequence (SFMS) at z~4 by >0.5 dex while the ETG (i.e.,GN20.2b) is compatible with the high-mass end of the main sequence. When comparing with other DSFGs in overdensities at $z$~2-7 we observe that our objects present similar SFRs, depletion times and projected separations. Nevertheless, the effective radii computed for GN20 and GN20.2a are up to two times larger than those of isolated galaxies observed in CEERS and ALMA-HUDF at similar redshifts. We interpret this difference in size as an effect of rapid growth induced by the dense environment., Comment: 13 pages, 6 figures, 3 tables. Accepted for publication in A&A

We report the discovery of Cerberus, an extremely red object detected with the MIRI Deep Imaging Survey (MIDIS) observations in the F1000W filter of the Hubble Ultra Deep Field. The object is detected at $S/N\sim6$, with $\mathrm{F1000W}\sim27$ mag, and undetected in the NIRCam data gathered by the JWST Advanced Deep Extragalactic Survey, JADES, fainter than the 30.0-30.5 mag $5\sigma$ detection limits in individual bands, as well as in the MIDIS F560W ultra-deep data ($\sim$29 mag, $5\sigma$). Analyzing the spectral energy distribution built with low-$S/N$ ($<5$) measurements in individual optical-to-mid-infrared filters and higher $S/N$ ($\gtrsim5$) in stacked NIRCam data, we discuss the possible nature of this red NIRCam-dark source using a battery of codes. We discard the possibility of Cerberus being a Solar System body based on the $<0.016$" proper motion in the 1-year apart JADES and MIDIS observations. A sub-stellar Galactic nature is deemed unlikely, given that the Cerberus' relatively flat NIRCam-to-NIRCam and very red NIRCam-to-MIRI flux ratios are not consistent with any brown dwarf model. The extragalactic nature of Cerberus offers 3 possibilities: (1) A $z\sim0.4$ galaxy with strong emission from polycyclic aromatic hydrocarbons; the very low inferred stellar mass, $\mathrm{M}_\star=10^{5-6}$ M$_\odot$, makes this possibility highly improbable. (2) A dusty galaxy at $z\sim4$ with an inferred stellar mass $\mathrm{M}_\star\sim10^{8}$ M$_\odot$. (3) A galaxy with observational properties similar to those of the reddest little red dots discovered around $z\sim7$, but Cerberus lying at $z\sim15$, with the rest-frame optical dominated by emission from a dusty torus or a dusty starburst., Comment: Accepted for publication in ApJL, results unchanged, manuscript shortened to publish as a letter

The use of hyperspectral imaging for medical applications is becoming more common in recent years. One of the main obstacles that researchers find when developing hyperspectral algorithms for medical applications is the lack of specific, publicly available, and hyperspectral medical data. The work described in this paper was developed within the framework of the European project HELICoiD (HypErspectraL Imaging Cancer Detection), which had as a main goal the application of hyperspectral imaging to the delineation of brain tumors in real-time during neurosurgical operations. In this paper, the methodology followed to generate the first hyperspectral database of in-vivo human brain tissues is presented. Data was acquired employing a customized hyperspectral acquisition system capable of capturing information in the Visual and Near InfraRed (VNIR) range from 400 to 1000 nm. Repeatability was assessed for the cases where two images of the same scene were captured consecutively. The analysis reveals that the system works more efficiently in the spectral range between 450 and 900 nm. A total of 36 hyperspectral images from 22 different patients were obtained. From these data, more than 300 000 spectral signatures were labeled employing a semi-automatic methodology based on the spectral angle mapper algorithm. Four different classes were defined: normal tissue, tumor tissue, blood vessel, and background elements. All the hyperspectral data has been made available in a public repository., Comment: 19 pages, 12 figures

In this paper, by applying the multiplier method we obtain a complete classification of low-order local conservation laws for a generalized seventh-order KdV equation depending on seven arbitrary nonzero parameters. We apply the Lie method in order to classify all point symmetries admitted by the equation in terms of the arbitrary parameters. We find that there are no special cases of the parameters for which the equation admits extra symmetries, other than those that can be found by inspection (scaling symmetry and space and time translation symmetries). We consider the reduced ordinary differential equations and we determined all integrating factors of the reduced equation from the combined $x$- and $t$- translation symmetries. Finally, we observe that all integrating factors arise by reduction of the low-order multipliers of the generalized seventh-order KdV equation., Comment: 12 pages

Lateral flow immunoassays (LFIA) are widely used worldwide for the detection of different analytes because they combine multiple advantages such as low production cost, simplicity, and portability, which allows biomarkers detection without requiring infrastructure or highly trained personnel. Here we propose to provide solutions to the manufacturing process of LFIA at laboratory-scale, particularly to the controlled and active dispensing of the reagents in the form the Test Lines (TL) and the Control Lines (CL). To accomplish this task, we adapted a 3D printer to also control Syringe Pumps (SP), since the proposed adaptation of a 3D printer is easy, free and many laboratories already have it in their infrastructure. In turn, the standard function of the 3D printer can be easily restored by disconnecting the SPs and reconnecting the extruder. Additionally, the unified control of the 3D printer enables dual, active, regulable and simultaneous dispensing, four features that are typically found only in certain high-cost commercial equipment. With the proposed setup, the challenge of dispensing simultaneously at least 2 lines (CL and TL) with SPs controlled by a 3D printer was addressed, including regulation in the width of dispensed lines within experimental limits. Also, the construction of a LFIA for the detection of leptospirosis is shown as a practical example of automatized reagent dispensing., Comment: 19 pages, 4 figures

After over a century of internal combustion engines ruling the transport sector, electric vehicles appear to be on the verge of gaining traction due to a slew of advantages, including lower operating costs and lower CO2 emissions. By using the Vehicle-to-Grid (or Grid-to-Vehicle if Electric vehicles (EVs) are utilized as load) approach, EVs can operate as both a load and a source. Primary frequency regulation and congestion management are two essential characteristics of this technology that are added to an industrial microgrid. Industrial Microgrids are made up of different energy sources such as wind farms and PV farms, storage systems, and loads. EVs have gained a lot of interest as a technique for frequency management because of their ability to regulate quickly. Grid reliability depends on this quick reaction. Different contingency, state of charge of the electric vehicles, and a varying number of EVs in an EV fleet are considered in this work, and a proposed control scheme for frequency management is presented. This control scheme enables bidirectional power flow, allowing for primary frequency regulation during the various scenarios that an industrial microgrid may encounter over the course of a 24-h period. The presented controller will provide dependable frequency regulation support to the industrial microgrid during contingencies, as will be demonstrated by simulation results, achieving a more reliable system. However, simulation results will show that by increasing a number of the EVs in a fleet for the Vehicle-to-Grid approach, an industrial microgrid\'s frequency can be enhanced even further., Comment: Published in energies (MDPI) 2022

Artificial intelligence (AI) has seen remarkable advancements across various domains, including natural language processing, computer vision, autonomous vehicles, and biology. However, the rapid expansion of AI technologies has escalated the demand for more powerful computing resources. As digital computing approaches fundamental limits, neuromorphic photonics emerges as a promising platform to complement existing digital systems. In neuromorphic photonic computing, photonic devices are controlled using analog signals. This necessitates the use of digital-to-analog converters (DAC) and analog-to-digital converters (ADC) for interfacing with these devices during inference and training. However, data movement between memory and these converters in conventional von Neumann computing architectures consumes energy. To address this, analog memory co-located with photonic computing devices is proposed. This approach aims to reduce the reliance on DACs and ADCs and minimize data movement to enhance compute efficiency. This paper demonstrates a monolithically integrated neuromorphic photonic circuit with co-located capacitive analog memory and compares various analog memory technologies for neuromorphic photonic computing using the MNIST dataset as a benchmark., Comment: 23 pages, 10 figures

An adaptive control approach for a three-phase grid-interfaced solar photovoltaic system based on the new Neuro-Fuzzy Inference System with Rain Optimization Algorithm (ANROA) methodology is proposed and discussed in this manuscript. This method incorporates an Adaptive Neuro-fuzzy Inference System (ANFIS) with a Rain Optimization Algorithm (ROA). The ANFIS controller has excellent maximum tracking capability because it includes features of both neural and fuzzy techniques. The ROA technique is in charge of controlling the voltage source converter switching. Avoiding power quality problems including voltage fluctuations, harmonics, and flickers as well as unbalanced loads and reactive power usage is the major goal. Besides, the proposed method performs at zero voltage regulation and unity power factor modes. The suggested control approach has been modeled and simulated, and its performance has been assessed using existing alternative methods. A statistical analysis of proposed and existing techniques has been also presented and discussed. The results of the simulations demonstrate that, when compared to alternative approaches, the suggested strategy may properly and effectively identify the best global solutions. Furthermore, the system's robustness has been studied by using MATLAB/SIMULINK environment and experimentally by Field Programmable Gate Arrays Controller (FPGA)-based Hardware-in-Loop (HLL)., Comment: The paper was published in Energy Reports journal (ELSEVIER). Cite as: Prasad, D., Kumar, N., Sharma, R., Malik, H., M\'arquez, F. P. G., & Pinar-P\'erez, J. M. (2023). A novel ANROA based control approach for grid-tied multi-functional solar energy conversion system. Energy Reports, 9, 2044-2057

We calculate the neutrino-electron elastic scattering cross section, extending the results previously obtained in arXiv:1702.05721v2, in the presence of generic new interactions that take into account all the effects caused by finite neutrino masses. We address the potential significance of a heavy neutrino sector during precision measurements, particularly for tau neutrinos scattering with masses in the MeV range, for which the existing upper bounds on $|U_{\tau 4}|^2$ would result in conceivably measurable contributions. Finally, we comment on the possibility to distinguish between Dirac and Majorana neutrinos, including the analysis of the new emerging parameters and its application to illustrative model-dependent scenarios., Comment: 24 pages, 1 figure. Minor changes. Comments on neutrino flavor and mass bases were implemented and references added. Matches JHEP version

Wind power generated by wind has non-schedule nature due to stochastic nature of meteorological variable. Hence energy business and control of wind power generation requires prediction of wind speed (WS) from few seconds to different time steps in advance. To deal with prediction shortcomings, various WS prediction methods have been used. Predictive data mining offers variety of methods for WS predictions where artificial neural network (ANN) is one of the reliable and accurate methods. It is observed from the result of this study that ANN gives better accuracy in comparison conventional model. The accuracy of WS prediction models is found to be dependent on input parameters and architecture type algorithms utilized. So the selection of most relevant input parameters is important research area in WS predicton field. The objective of the paper is twofold: first extensive review of ANN for wind power and WS prediction is carried out. Discussion and analysis of feature selection using Relief Algorithm (RA) in WS prediction are considered for different Indian sites. RA identify atmospheric pressure, solar radiation and relative humidity are relevant input variables. Based on relevant input variables Cascade ANN model is developed and prediction accuracy is evaluated. It is found that root mean square error (RMSE) for comparison between predicted and measured WS for training and testing wind speed are found to be 1.44 m/s and 1.49 m/s respectively. The developed cascade ANN model can be used to predict wind speed for sites where there are not WS measuring instruments are installed in India., Comment: Malik, H., Yadav, A. K., M\'arquez, F. P. G., & Pinar-P\'erez, J. M. (2022). Novel application of Relief Algorithm in cascaded artificial neural network to predict wind speed for wind power resource assessment in India. Energy Strategy Reviews, 41, 100864

We study radial oscillations of hybrid neutron stars composed of hadronic external layers followed by a quark matter core. We employ a density-dependent relativistic mean-field model including hyperons and ${\Delta}$ baryons to describe hadronic matter, and a density-dependent quark model for quark matter. We obtain the ten lowest eigenfrequencies and the corresponding oscillation functions of N, N+${\Delta}$, N+H, and N+H+${\Delta}$ equations-of-state with a phase transition to the quark matter at 1.4 and 1.8 ${M_{\odot}}$, focusing on the effects of a slow phase transition at the hadron-quark interface. We observe that the maximum mass is reached before the fundamental mode's frequency vanishes for slow phase transitions, suggesting that some stellar configurations with higher central densities than the maximum mass remain stable even when they undergo small radial perturbations. Future gravitational wave detectors and multi-messenger astronomy, complemented by robust microscopic models enabling exploration of various neutron star compositions, including hyperon content, are anticipated to impose precise limitations on the equation of state of baryonic matter under high-density conditions., Comment: 31 pages, 9 figures, 5 tables: updated version accepted for publication in JCAP

Contrastive Language-Image Pretraining (CLIP) stands out as a prominent method for image representation learning. Various neural architectures, spanning Transformer-based models like Vision Transformers (ViTs) to Convolutional Networks (ConvNets) like ResNets, are trained with CLIP and serve as universal backbones across diverse vision tasks. Despite utilizing the same data and training objectives, the effectiveness of representations learned by these architectures raises a critical question. Our investigation explores the differences in CLIP performance among these backbone architectures, revealing significant disparities in their classifications. Notably, normalizing these representations results in substantial performance variations. Our findings showcase a remarkable possible synergy between backbone predictions that could reach an improvement of over 20% through informed selection of the appropriate backbone. Moreover, we propose a simple, yet effective approach to combine predictions from multiple backbones, leading to a notable performance boost of up to 6.34\%. We will release the code for reproducing the results.

When applied to binary solutions, thermal gradients lead to the generation of concentration-gradients and thus to inhomogeneous systems. While being known for more than 150 years, the molecular origins for this phenomenon are still debated, and there is no consensus on the underlying physical models or theories that could explain the amplitude of the concentration gradient in response to a given temperature gradients. Notably, there have been some attempts to relate this non-equilibrium, steady-state manifestation, to equilibrium properties of these solutions, for example, to the temperature dependence of the self-diffusion coefficient or to the solvation free-energies of each of their components. Here, we use molecular dynamics simulations on dilute solutions containing molecular size solutes, both in a thermophoretic setting as well as under equilibrium conditions, to test the validity of such models. We show that these approaches are inadequate and lead to completely uncorrelated estimates as compared to those based on the out-of-equilibrium measurements. Crucially, they fail to explain the strong mass-dependence (to which thermodynamics or single particle diffusion are insensitive) observed in the simulations and measured in the experiments. However, our results suggest an interesting correlation between the amplitude of the short-time molecular motion and that of the concentration-gradient that would deserve future investigations.

Geometric flux-based Volume-of-Fluid (VOF) methods are widely considered consistent in handling two-phase flows with high density ratios. However, although the conservation of mass and momentum is consistent for two-phase incompressible single-field Navier-Stokes equations without phase-change, discretization may easily introduce small inconsistencies that result in very large errors or catastrophic failure. We apply the consistency conditions derived for the unstructured Level Set / Front Tracking method to flux-based geometric VOF methods, and implement our discretization into the plicRDF-isoAdvector geometrical VOF method. We find that computing the mass flux by scaling the geometrically computed fluxed phase-specific volume destroys the equivalence between the scaled volume fraction equation and the mass conservation equation, depending on the choice for the temporal and convective term discretization schemes. We propose two solutions. First, based on the analysis of discretization errors, we suggest a consistent combination of the temporal discretization scheme and the interpolation scheme for the momentum convection term. Second, similar to our previous work on the unstructured Level Set / Front Tracking method, we solve an auxiliary mass conservation equation with a geometrical calculation of the face-centered density. We prove the equivalence between these two approaches mathematically and verify and validate their numerical stability for density ratios in the range $[1,10^6]$ and viscosity ratios in the range $[10^2,10^5]$.

A comprehensive numerical investigation has been conducted on the angular distribution and spectrum of radiation emitted by 855 MeV electron and positron beams while traversing a 'quasi-mosaic' bent silicon (111) crystal. This interaction of charged particles with a bent crystal gives rise to various phenomena such as channeling, dechanneling, volume reflection, and volume capture. The crystal's geometry, emittance of the collimated particle beams, as well as their alignment with respect to the crystal, have been taken into account as they are essential for an accurate quantitative description of the processes. The simulations have been performed using a specialized relativistic molecular dynamics module implemented in the MBN Explorer package. The angular distribution of the particles after traversing the crystal has been calculated for beams of different emittances as well as for different anticlastic curvatures of the bent crystals. For the electron beam, the angular distributions of the deflected particles and the spectrum of radiation obtained in the simulations are compared with the experimental data collected at the Mainz Microtron facility. For the positron beam such calculations have been performed for the first time. We predict significant differences in the angular distributions and the radiation spectra for positrons versus electrons., Comment: 17 pages, 9 figures. Submitted to: J. Phys. B: At. Mol. Opt. Phys