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The COVID-19 pandemic has presented numerous challenges to students across the United States, particularly those who are parents enrolled in community colleges. California's community college system serves a diverse student population, including a significant number of non-traditional, working adults who are also parents. These students have faced unprecedented challenges due to the pandemic, including the difficulties of balancing childcare responsibilities with academic and professional obligations. This paper summarizes the preliminary findings of a study that intends to contribute to the crucial conversation around childcare needs among community college students. The focus of this study was understanding the experiences of BIPOC (Black, Indigenous, People of Color) mothers with young children and the impact of COVID-19 on their educational experiences in community colleges across the state of California.

We study a flaring activity of the HSP Mrk421 that was characterized from radio to very-high-energy (VHE; E $>0.1$TeV) gamma rays with MAGIC, Fermi-LAT, Swift, XMM-Newton and several optical and radio telescopes. These observations included, for the first time for a gamma-ray flare of a blazar, simultaneous X-ray polarization measurements with IXPE. We find substantial variability in both X-rays and VHE gamma rays throughout the campaign, with the highest VHE flux above 0.2 TeV occurring during the IXPE observing window, and exceeding twice the flux of the Crab Nebula. However, the VHE and X-ray spectra are on average softer, and the correlation between these two bands weaker that those reported in previous flares of Mrk421. IXPE reveals an X-ray polarization degree significantly higher than that at radio and optical frequencies. The X-ray polarization angle varies by $\sim$100$^\circ$ on timescales of days, and the polarization degree changes by more than a factor 4. The highest X-ray polarization degree reaches 26%, around which a X-ray counter-clockwise hysteresis loop is measured with XMM-Newton. It suggests that the X-ray emission comes from particles close to the high-energy cutoff, hence possibly probing an extreme case of the Turbulent Extreme Multi-Zone model. We model the broadband emission with a simplified stratified jet model throughout the flare. The polarization measurements imply an electron distribution in the X-ray emitting region with a very high minimum Lorentz factor, which is expected in electron-ion plasma, as well as a variation of the emitting region size up to a factor of three during the flaring activity. We find no correlation between the fluxes and the evolution of the model parameters, which indicates a stochastic nature of the underlying physical mechanism. Such behaviour would be expected in a highly turbulent electron-ion plasma crossing a shock front., Comment: Submitted to Astronomy and Astrophysics. Corresponding authors: Axel Arbet-Engels, Lea Heckmann, David Paneque

Imaging atmospheric Cherenkov telescopes (IACTs) are used to observe very high-energy photons from the ground. Gamma rays are indirectly detected through the Cherenkov light emitted by the air showers they induce. The new generation of experiments, in particular the Cherenkov Telescope Array Observatory (CTAO), sets ambitious goals for discoveries of new gamma-ray sources and precise measurements of the already discovered ones. To achieve these goals, both hardware and data analysis must employ cutting-edge techniques. This also applies to the LST-1, the first IACT built for the CTAO, which is currently taking data on the Canary island of La Palma. This paper introduces a new event reconstruction technique for IACT data, aiming to improve the image reconstruction quality and the discrimination between the signal and the background from misidentified hadrons and electrons. The technique models the development of the extensive air shower signal, recorded as a waveform per pixel, seen by CTAO telescopes' cameras. Model parameters are subsequently passed to random forest regressors and classifiers to extract information on the primary particle. The new reconstruction was applied to simulated data and to data from observations of the Crab Nebula performed by the LST-1. The event reconstruction method presented here shows promising performance improvements. The angular and energy resolution, and the sensitivity, are improved by 10 to 20% over most of the energy range. At low energy, improvements reach up to 22%, 47%, and 50%, respectively. A future extension of the method to stereoscopic analysis for telescope arrays will be the next important step., Comment: Accepted in A&A

Since the advent of mobile robots, obstacle detection has been a topic of great interest. It has also been a subject of study in neuroscience, where flying insects and bats could be considered two of the most interesting cases in terms of vision-based and sound-based mechanisms for obstacle detection, respectively. Currently, many studies focus on vision-based obstacle detection, but not many can be found regarding sound-based obstacle detection. This work focuses on the latter approach, which also makes use of a Spiking Neural Network to exploit the advantages of these architectures and achieve an approach closer to biology. The complete system was tested through a series of experiments that confirm the validity of the spiking architecture for obstacle detection. It is empirically demonstrated that, when the distance between the robot and the obstacle decreases, the output firing rate of the system increases in response as expected, and vice versa. Therefore, there is a direct relation between the two. Furthermore, there is a distance threshold between detectable and undetectable objects which is also empirically measured in this work. An in-depth study on how this system works at low level based on the Inter-Spike Interval concept was performed, which may be useful in the future development of applications based on spiking filters., Comment: 22 pages, 8 figures

Early childhood educators play an essential role in providing child care for families and learning and development supports for young children, yet they have long faced challenges due to low wages. Recognizing this, the District of Columbia (DC) introduced the Early Childhood Pay Equity Fund in 2022. This first-of-its-kind initiative aims to bridge the pay gap between early educators and teachers at public schools, addressing historical inequities and improving recruitment and retention efforts. This report provides an in-depth exploration of early educators' experiences with the Pay Equity Fund during its initial year of implementation. Drawing on data from surveys conducted in May 2023 and follow-up focus groups, the report offers insights into the transition from direct payments to an opt-in payment structure by employers in FY 2024. It covers educators' introduction to the Fund, their application and payment experiences, financial impacts, and perceived benefits for child care programs and the broader early childhood education field.

In a general optimized measurement scheme for discriminating between nonorthogonal quantum states, the error rate is minimized under the constraint of a fixed rate of inconclusive outcomes (FRIO). This so-called optimal FRIO measurement encompasses the standard and well known minimum-error and optimal unambiguous (or maximum-confidence) discrimination strategies as particular cases. Here, we experimentally demonstrate the optimal FRIO discrimination between $N=2,3,5,$ and $7$ equally likely symmetric states of a qubit encoded in photonic path modes. Our implementation consists of applying a probabilistic quantum map which increases the distinguishability between the inputs in a controlled way, followed by a minimum-error measurement on the successfully transformed outputs. The results obtained corroborate this two-step approach and, in our experimental scheme, it can be straightforwardly extended to higher dimensions. The optimized measurement demonstrated here will be useful for quantum communication scenarios where the error rate and the inconclusive rate must be kept below the levels provided by the respective standard strategies., Comment: 12 pages, 6 figures. Published version

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

Gaining insights from realistic dynamical models of biochemical systems can be challenging given their large number of state variables. Model reduction techniques can mitigate this by decreasing complexity by mapping the model onto a lower-dimensional state space. Exact constrained lumping identifies reductions as linear combinations of the original state variables in systems of nonlinear ordinary differential equations, preserving specific user-defined output variables without error. However, exact reductions can be too stringent in practice, as model parameters are often uncertain or imprecise -- a particularly relevant problem for biochemical systems. We propose approximate constrained lumping. It allows for a relaxation of exactness within a given tolerance parameter $\varepsilon$, while still working in polynomial time. We prove that the accuracy, i.e., the difference between the output variables in the original and reduced model, is in the order of $\varepsilon$. Furthermore, we provide a heuristic algorithm to find the smallest $\varepsilon$ for a given maximum allowable size of the lumped system. Our method is applied to several models from the literature, resulting in coarser aggregations than exact lumping while still capturing the dynamics of the original system accurately.

We report measurements of the absolute branching fractions $\mathcal{B}(B_s^0 \to D_s^{\pm} X)$, $\mathcal{B}(B_s^0 \to D^0/\bar{D}^0 X)$, and $\mathcal{B}(B_s^0 \to D^{\pm} X)$, where the latter is measured for the first time. The results are based on a 121.4\,fb$^{-1}$ data sample collected at the $\Upsilon(10860)$ resonance by the Belle detector at the KEKB asymmetric-energy $e^+ e^-$ collider. We reconstruct one $B_s^0$ meson in $e^+e^- \to \Upsilon(10860) \to B_s^{*} \bar{B}_s^{*}$ events and measure yields of $D_s^+$, $D^0$, and $D^+$ mesons in the rest of the event. We obtain $\mathcal{B}(B_s^0 \to D_s^{\pm} X) = (68.6 \pm 7.2 \pm 4.0)\%$, $\mathcal{B}(B_s^0 \to D^0/\bar{D}^0 X) = (21.5 \pm 6.1 \pm 1.8)\%$, and $\mathcal{B}(B_s^0 \to D^{\pm} X) = (12.6 \pm 4.6 \pm 1.3)\%$, where the first uncertainty is statistical and the second is systematic. Averaging with previous Belle measurements gives $\mathcal{B}(B_s^0 \to D_s^{\pm} X) = (63.4 \pm 4.5 \pm 2.2)\%$ and $\mathcal{B}(B_s^0 \to D^0/\bar{D}^0 X) = (23.9 \pm 4.1 \pm 1.8)\%$. For the $B_s^0$ production fraction at the $\Upsilon(10860)$, we find $f_s = (21.4^{+1.5}_{-1.7})\%$., Comment: 23 pages, 9 figures, submitted to JHEP

We introduce a systematic method to derive the effective action for domain walls directly from the scalar field theory that gives rise to their solitonic solutions. The effective action for the Goldstone mode, which characterizes the soliton's position, is shown to consist of the Nambu-Goto action supplemented by higher-order curvature invariants associated to its worldvolume metric. Our approach constrains the corrections to a finite set of Galileon terms, specifying both their functional forms and the procedure to compute their coefficients. We do a collection of tests across various models in $2+1$ and $3+1$ dimensions that confirm the validity of this framework. Additionally, the method is extended to include bound scalar fields living on the worldsheet, along with their couplings to the Goldstone mode. These interactions reveal a universal non-minimal coupling of these scalar fields to the Ricci scalar on the worldsheet. A significant consequence of this coupling is the emergence of a parametric instability, driven by interactions between the bound states and the Goldstone mode., Comment: 46 pages, 10 figures

This paper presents and examines computationally convenient goodness-of-fit tests for the family of generalized Poisson distributions, which encompasses notable distributions such as the Compound Poisson and the Katz distributions. The tests are consistent against fixed alternatives and their null distribution can be consistently approximated by a parametric bootstrap. The goodness of the bootstrap estimator and the power for finite sample sizes are numerically assessed through an extensive simulation experiment, including comparisons with other tests. In many cases, the novel tests either outperform or match the performance of existing ones. Real data applications are considered for illustrative purposes.

We assessed the validity of one of the most frequently used methods to estimate cancer incidence, on the basis of cancer mortality data and the incidence-to-mortality ratio IMR, the IMR method. Using the previous 15 year cancer mortality time series, we derived the expected yearly number of cancer cases in the period 2004 to 2013 for six cancer sites for each sex. Generalized linear mixed models, including a polynomial function for the year of death and smoothing splines for age, were adjusted. Models were fitted under a Bayesian framework based on Markov chain Monte Carlo methods. The IMR method was applied to five scenarios reflecting different assumptions regarding the behavior of the IMR. We compared incident cases estimated with the IMR method to observed cases diagnosed in 2004 to 2013 in Granada. A goodness-of-fit GOF indicator was formulated to determine the best estimation scenario. The relative differences between the observed and predicted numbers of cancer cases were less than 10 percent for most cancer sites. The constant assumption for the IMR trend provided the best GOF for colon, rectal, lung, bladder, and stomach cancers in men and colon, rectum, breast, and corpus uteri in women.

Recent advances in machine learning have highlighted Federated Learning (FL) as a promising approach that enables multiple distributed users (so-called clients) to collectively train ML models without sharing their private data. While this privacy-preserving method shows potential, it struggles when data across clients is not independent and identically distributed (non-IID) data. The latter remains an unsolved challenge that can result in poorer model performance and slower training times. Despite the significance of non-IID data in FL, there is a lack of consensus among researchers about its classification and quantification. This systematic review aims to fill that gap by providing a detailed taxonomy for non-IID data, partition protocols, and metrics to quantify data heterogeneity. Additionally, we describe popular solutions to address non-IID data and standardized frameworks employed in FL with heterogeneous data. Based on our state-of-the-art review, we present key lessons learned and suggest promising future research directions.

Resistive memories are outstanding electron devices that have displayed a large potential in a plethora of applications such as nonvolatile data storage, neuromorphic computing, hardware cryptography, etc. Their fabrication control and performance have been notably improved in the last few years to cope with the requirements of massive industrial production. However, the most important hurdle to progress in their development is the so-called cycle-to-cycle variability, which is inherently rooted in the resistive switching mechanism behind the operational principle of these devices. In order to achieve the whole picture, variability must be assessed from different viewpoints going from the experimental characterization to the adequation of modeling and simulation techniques. Herein, special emphasis is put on the modeling part because the accurate representation of the phenomenon is critical for circuit designers. In this respect, a number of approaches are used to the date: stochastic, behavioral, mesoscopic..., each of them covering particular aspects of the electron and ion transport mechanisms occurring within the switching material. These subjects are dealt with in this review, with the aim of presenting the most recent advancements in the treatment of variability in resistive memories.

To cope with environments with high levels of radiation, non-silicon semiconductors such as silicon carbide detectors are being proposed for instrumentation. 4H-SiC diodes for radiation detection have been fabricated in the IMB-CNM Clean Room, for which different strategies to define the electrical contact of the implants had been implemented, in an attempt to optimise the technology for, e.g., medical applications or low energy radiation detection, as the material choice can affect the sensitivity of the device. Among these technologies, it is included an epitaxially-grown graphene layer as part of the electrical contact. In this paper, a selection of four configurations of the IMB-CNM SiC diodes are characterised in terms of radiation detector response. Photodiode performance under 20 keV X-rays irradiation in the XALOC beam line at ALBA Synchrotron is presented. Over-responses in the range of 12-19% linked to the interaction of the radiation with the metallic layers are observed. A good uniformity response as well as a good linearity at 0~V bias is reported, even in the under-depleted devices. This work exemplifies the good performance of SiC detectors fabricated at IMB-CNM specifically for low-energy X ray characterization at high X-ray intensities., Comment: 12 pages, 5 figures. accepted to JINST

Operational data in next-generation networks offers a valuable resource for Mobile Network Operators to autonomously manage their systems and predict potential network issues. Machine Learning and Digital Twin can be applied to gain important insights for intelligent decision-making. This paper proposes a framework for Radio Frequency planning and failure detection using Digital Twin reducing the level of manual intervention. In this study, we propose a methodology for analyzing Radio Frequency issues as external interference employing clustering techniques in operational networks, and later incorporating this in the planning process. Simulation results demonstrate that the architecture proposed can improve planning operations through a data-aided anomaly detection strategy.

We present measurements of $B \to K{}^{*}(892)\gamma$ decays using $365\,{\rm fb}^{-1}$ of data collected from 2019 to 2022 by the Belle~II experiment at the SuperKEKB asymmetric-energy $e^+e^-$ collider. The data sample contains $(387 \pm 6) \times 10^6$ $B\overline{B}$ events. We measure branching fractions ($\mathcal{B}$) and $C\!P$ asymmetries ($\mathcal{A}_{C\!P}$) for both $B^{0}\to K{}^{*0}\gamma$ and $B^{+}\to K{}^{*+}\gamma$ decays. The difference in $C\!P$ asymmetries ($\Delta \mathcal{A}_{C\!P}$) and the isospin asymmetry ($\Delta_{0+}$) between these neutral and charged channels are also measured. We obtain the following branching fractions and $C\!P$ asymmetries: $\mathcal{B} (B^{0} \to K{}^{*0}\gamma) = (4.14 \pm 0.10 \pm 0.11 ) \times 10^{-5}$, $\mathcal{B} (B^{+} \to K{}^{*+}\gamma) = (4.02 \pm 0.13 \pm 0.13 )\times 10^{-5}$, $\mathcal{A}_{C\!P} (B^{0} \to K{}^{*0}\gamma) = (-3.3 \pm 2.3 \pm 0.4 )\%$, and $\mathcal{A}_{C\!P} (B^{+} \to K{}^{*+}\gamma) = (-0.7 \pm 2.9 \pm 0.6 )\%$. The measured difference in $C\!P$ asymmetries is $\Delta \mathcal{A}_{C\!P} = (+2.6 \pm 3.8 \pm 0.7 )\%$, and the measured isospin asymmetry is $\Delta_{0+} = (+5.0 \pm 2.0 \pm 1.5 )\%$. The first uncertainties listed are statistical and the second are systematic. These results are consistent with world-average values and theory predictions.

The orbital period of a cataclysmic variable stands as a crucial parameter for investigating the structure and physics of these binary systems, as well as understanding their evolution. We use photometric Gaia data for dwarf novae (DNe) in the quiescent state which are available for a number of years to determine new orbital periods and improve/modify previously suggested values. Two approaches are implemented for selecting high-inclination targets, either eclipsing or with ellipsoidal variations. We determine new orbital periods for 75 DNe and improve ephemerides for 27 more (three of which change significantly), contributing 9.4% of the known DNe periods of 0.05-2.0 days, and doubling the number of known periods exceeding 0.44 days. Their phase-folded light curves are presented and arranged by orbital period, illustrating the transition from short-period systems, dominated by radiation from the accretion disc and the hot spot, to longer-period DNe, where the Roche-lobe-filling secondary star is the primary visual flux source. This transition -- which occurs around the well-known period gap (around 2-3 hours) -- is expected, as DNe with larger orbital periods typically harbour more massive donors, which contribute to the visible flux. However, this transition is not abrupt. Within the same range of periods, we observe systems dominated by ellipsoidal variations, where the companion star is clearly visible, as well as others dominated by the disc and hot spot. The presence of some DNe with ellipsoidal variations near the lower edge of the period gap is striking, as the companions in these systems are expected to be cool low-mass M-dwarfs not visible in the light curve. This could indicate that we are observing systems where the donor star was originally much more massive and underwent significant nuclear evolution before mass-transfer began, as has been suggested previously for QZ Ser., Comment: Accepted for publication in A&A, 15 pages, 3 figures

Robots in human-centered environments require accurate scene understanding to perform high-level tasks effectively. This understanding can be achieved through instance-aware semantic mapping, which involves reconstructing elements at the level of individual instances. Neural networks, the de facto solution for scene understanding, still face limitations such as overconfident incorrect predictions with out-of-distribution objects or generating inaccurate masks.Placing excessive reliance on these predictions makes the reconstruction susceptible to errors, reducing the robustness of the resulting maps and hampering robot operation. In this work, we propose Voxeland, a probabilistic framework for incrementally building instance-aware semantic maps. Inspired by the Theory of Evidence, Voxeland treats neural network predictions as subjective opinions regarding map instances at both geometric and semantic levels. These opinions are aggregated over time to form evidences, which are formalized through a probabilistic model. This enables us to quantify uncertainty in the reconstruction process, facilitating the identification of map areas requiring improvement (e.g. reobservation or reclassification). As one strategy to exploit this, we incorporate a Large Vision-Language Model (LVLM) to perform semantic level disambiguation for instances with high uncertainty. Results from the standard benchmarking on the publicly available SceneNN dataset demonstrate that Voxeland outperforms state-of-the-art methods, highlighting the benefits of incorporating and leveraging both instance- and semantic-level uncertainties to enhance reconstruction robustness. This is further validated through qualitative experiments conducted on the real-world ScanNet dataset.

In this study, we investigate the flow around a canonical blunt body, specifically a D-shaped body of width $D$, in a closed water channel. Our goal is to explore near-wake flow modifications when a series of rigid and flexible plates ($l=1.8D$) divided into filaments ($h=0.2D$) are added. We focus on assessing the interaction between the flexible filaments and the wake dynamics, with the aim of reducing the recirculation bubble and decreasing the velocity deficit in the wake. To achieve this, we conduct a comparative study varying the stiffness and position of the filaments at different flow velocities. The study combines Particle Image Velocimetry (PIV) measurements in the wake behind the body with recordings of the deformation of the flexible filaments. Our observations show that the flexible filaments can passively reconfigure in a two-dimensional fashion, with a mean tip deflection angle that increases with the incoming flow velocity. Deflection angles up to approximately $\sim 9^\circ$ and vibration tip amplitude of around $\sim 4^\circ$ are achieved for flow velocities $U^{*}\simeq f_{n}D/u_{\infty}\geq 1.77$, where $f_n$ is the natural frequency of the flexible filaments. This reconfiguration results in a reduction of the recirculation bubble and a decrease in the velocity deficit in the wake compared to the reference and rigid cases. In addition, curved filaments with a prescribed rigid deformation exhibit very similar behavior to that of flexible filaments, indicating that the vibration of flexible filaments does not significantly disturb the wake. The obtained results highlight the interest of testing flexible appendages in the wake of blunt bodies for designing effective flow control devices., Comment: 13 pages, 9 figures

We compute the evolution of linear perturbations on top of a background solution of a general nonlinear electromagnetic theory. This evolution can be described in terms of two effective metrics, and we analyse under what conditions they are conformally related, so that they can be regarded as analogue models of non-trivial gravitational fields in the eikonal approximation. This is the case of Born-Infeld theory. For the background created by a static point electric charge in the Born-Infeld theory, the effective metric describes a wormhole geometry for light rays. Depending on the impact parameter, incoming light rays are either scattered to infinity or approach the wormhole slowing down their pace until they hit the charge at vanishing speed. The same effective wormhole geometry is obtained for a magnetic monopole and a dyon and we relate it to the duality invariance of Born-Infeld electromagnetism. Finally, we analyse the scalar Dirac-Born-Infeld theory and show that the effective wormhole geometry is not generated by a particle with scalar charge., Comment: 21 pages, 5 figures

Massive, intensely star-forming galaxies at high redshift require a supply of molecular gas from their gas reservoirs, replenished by infall from the surrounding circumgalactic medium, to sustain their immense star-formation rates. However, our knowledge of the extent and morphology of their cold-gas reservoirs is still in its infancy. We present the results of stacking 80 hours of JVLA observations of CO(1--0) emission -- which traces the cold molecular gas -- in 19 $z=2.0-4.5$ dusty, star-forming galaxies from the AS2VLA survey. The visibility-plane stack reveals extended emission with a half-light radius of $3.8\pm0.5$~kpc, 2--3$\times$ more extended than the dust-obscured star formation and $1.4\pm0.2\times$ more extended than the stellar emission. Similarly, stacking the [CI](1--0) observations for a subsample of our galaxies yields sizes consistent with CO(1--0). The CO(1--0) size is comparable to the [CII] halos detected around high-redshift star-forming galaxies.The bulk (up to 80\%) of molecular gas resides outside the star-forming region; only a small part of their molecular gas reservoir directly contributes to their current star formation. Photon-dissociation region modelling indicates that the extended CO(1--0) emission arises from clumpy, dense clouds rather than smooth, diffuse gas., Comment: Submitted to A&A. 9 pages, 5 figures

Fiducial markers are a computer vision tool used for object pose estimation and detection. These markers are highly useful in fields such as industry, medicine and logistics. However, optimal lighting conditions are not always available,and other factors such as blur or sensor noise can affect image quality. Classical computer vision techniques that precisely locate and decode fiducial markers often fail under difficult illumination conditions (e.g. extreme variations of lighting within the same frame). Hence, we propose DeepArUco++, a deep learning-based framework that leverages the robustness of Convolutional Neural Networks to perform marker detection and decoding in challenging lighting conditions. The framework is based on a pipeline using different Neural Network models at each step, namely marker detection, corner refinement and marker decoding. Additionally, we propose a simple method for generating synthetic data for training the different models that compose the proposed pipeline, and we present a second, real-life dataset of ArUco markers in challenging lighting conditions used to evaluate our system. The developed method outperforms other state-of-the-art methods in such tasks and remains competitive even when testing on the datasets used to develop those methods. Code available in GitHub: https://github.com/AVAuco/deeparuco/

The growing demand for accurate and equitable AI models in digital dermatology faces a significant challenge: the lack of diverse, high-quality labeled data. In this work, we investigate the potential of domain-specific foundation models for dermatology in addressing this challenge. We utilize self-supervised learning (SSL) techniques to pre-train models on a dataset of over 240,000 dermatological images from public and private collections. Our study considers several SSL methods and compares the resulting foundation models against domain-agnostic models like those pre-trained on ImageNet and state-of-the-art models such as MONET across 12 downstream tasks. Unlike previous research, we emphasize the development of smaller models that are more suitable for resource-limited clinical settings, facilitating easier adaptation to a broad range of use cases. Results show that models pre-trained in this work not only outperform general-purpose models but also approach the performance of models 50 times larger on clinically relevant diagnostic tasks. To promote further research in this direction, we publicly release both the training code and the foundation models, which can benefit clinicians in dermatological applications., Comment: Findings paper presented at Machine Learning for Health (ML4H) symposium 2024, December 15-16, 2024, Vancouver, Canada, 11 pages

Tabular data plays a pivotal role in various fields, making it a popular format for data manipulation and exchange, particularly on the web. The interpretation, extraction, and processing of tabular information are invaluable for knowledge-intensive applications. Notably, significant efforts have been invested in annotating tabular data with ontologies and entities from background knowledge graphs, a process known as Semantic Table Interpretation (STI). STI automation aids in building knowledge graphs, enriching data, and enhancing web-based question answering. This survey aims to provide a comprehensive overview of the STI landscape. It starts by categorizing approaches using a taxonomy of 31 attributes, allowing for comparisons and evaluations. It also examines available tools, assessing them based on 12 criteria. Furthermore, the survey offers an in-depth analysis of the Gold Standards used for evaluating STI approaches. Finally, it provides practical guidance to help end-users choose the most suitable approach for their specific tasks while also discussing unresolved issues and suggesting potential future research directions.

Africa faces a huge shortage of dermatologists, with less than one per million people. This is in stark contrast to the high demand for dermatologic care, with 80% of the paediatric population suffering from largely untreated skin conditions. The integration of AI into healthcare sparks significant hope for treatment accessibility, especially through the development of AI-supported teledermatology. Current AI models are predominantly trained on white-skinned patients and do not generalize well enough to pigmented patients. The PASSION project aims to address this issue by collecting images of skin diseases in Sub-Saharan countries with the aim of open-sourcing this data. This dataset is the first of its kind, consisting of 1,653 patients for a total of 4,901 images. The images are representative of telemedicine settings and encompass the most common paediatric conditions: eczema, fungals, scabies, and impetigo. We also provide a baseline machine learning model trained on the dataset and a detailed performance analysis for the subpopulations represented in the dataset. The project website can be found at https://passionderm.github.io/., Comment: MICCAI 2024

Probing and manipulating the spatiotemporal dynamics of hot carriers in nanoscale metals is crucial to a plethora of applications ranging from nonlinear nanophotonics to single molecule photochemistry. The direct investigation of these highly non-equilibrium carriers requires the experimental capability of high energy resolution (~ meV) broadband femtosecond spectroscopy. When considering the ultimate limits of atomic scale structures, this capability has remained out of reach until date. Using a two color femtosecond pump-probe spectroscopy, we present here the real-time tracking of hot carrier dynamics in a well-defined plasmonic picocavity, formed in the tunnel junction of a scanning tunneling microscope (STM). The excitation of hot carriers in the picocavity enables ultrafast all optical control over the broadband (~ eV) anti Stokes electronic resonance Raman scattering (ERRS) and the four-wave mixing (FWM) signals generated at the atomic length scale. By mapping the ERRS and FWM signals from a single graphene nanoribbon (GNR), we demonstrate that both signals are more efficiently generated along the edges of the GNR: a manifestation of atomic-scale nonlinear optical microscopy. This demonstration paves the way to the development of novel ultrafast nonlinear picophotonic platforms, affording unique opportunities in a variety of contexts, from the direct investigation of non equilibrium light matter interactions in complex quantum materials, to the development of robust strategies for hot carriers harvesting in single molecules and the next generation of active metasurfaces with deep-sub-wavelength meta-atoms.

Magnetic cycles of solar-like stars influence their internal physics. Thus, the frequency, amplitude, excitation rate, and damping of the acoustic oscillation modes (p-modes) vary with the cycle over time. We need to understand the impact of magnetic activity on p-modes in order to characterise precisely stars that will be observed by the ESA PLATO mission, to be launched late 2026 with the objective to find Earth-like planets around solar-type stars. In this work, we investigate the variation of mode excitation in the Sun during Cycles 23, 24 and the beginning of Cycle 25. To do so, we analyse data obtained since 1996 by two instruments onboard the SoHO satellite: the GOLF spectrometer and the VIRGO sunphotometer. We use a method enabling us to reach a better temporal resolution than classical methods. Combining the variations of energy for several modes l=[0-2] in three frequency bands (i.e. [1800, 2450], [2450, 3110], [3110, 3790] {\mu}Hz), our preliminary results show that more energy is associated to several modes during cycle minima, suggesting that there could be a second source of excitation other than turbulent convection that would excite several modes at a time during solar minima., Comment: Proceedings of the French Society for Astronomy and Astrophysics (SF2A) conference 2024, 3 pages, 2 figures

This poster paper describes the ongoing research project for the creation of a use-case-driven Knowledge Graph resource tailored to the needs of teaching education in Knowledge Graphs (KGs). We gather resources related to KG courses from lectures offered by the Semantic Web community, with the help of the COST Action Distributed Knowledge Graphs and the interest group on KGs at The Alan Turing Institute. Our goal is to create a resource-focused KG with multiple interconnected semantic layers that interlink topics, courses, and materials with each lecturer. Our approach formulates a domain KG in teaching and relates it with multiple Personal KGs created for the lecturers.

This demo presents SeizNet, an innovative system for predicting epileptic seizures benefiting from a multi-modal sensor network and utilizing Deep Learning (DL) techniques. Epilepsy affects approximately 65 million people worldwide, many of whom experience drug-resistant seizures. SeizNet aims at providing highly accurate alerts, allowing individuals to take preventive measures without being disturbed by false alarms. SeizNet uses a combination of data collected through either invasive (intracranial electroencephalogram (iEEG)) or non-invasive (electroencephalogram (EEG) and electrocardiogram (ECG)) sensors, and processed by advanced DL algorithms that are optimized for real-time inference at the edge, ensuring privacy and minimizing data transmission. SeizNet achieves > 97% accuracy in seizure prediction while keeping the size and energy restrictions of an implantable device., Comment: 1 page, 1 figure, Proceedings of the IEEE 20th International Conference on Body Sensor Networks (BSN), October 2024

OT 081 is a well-known, luminous blazar that is remarkably variable in many energy bands. We present the first broadband study of the source which includes very-high-energy (VHE, $E>$100\,GeV) $\gamma$-ray data taken by the MAGIC and H.E.S.S. imaging Cherenkov telescopes. The discovery of VHE $\gamma$-ray emission happened during a high state of $\gamma$-ray activity in July 2016, observed by many instruments from radio to VHE $\gamma$-rays. We identify four states of activity of the source, one of which includes VHE $\gamma$-ray emission. Variability in the VHE domain is found on daily timescales. The intrinsic VHE spectrum can be described by a power-law with index $3.27\pm0.44_{\rm stat}\pm0.15_{\rm sys}$ (MAGIC) and $3.39\pm0.58_{\rm stat}\pm0.64_{\rm sys}$ (H.E.S.S.) in the energy range of 55--300\,GeV and 120--500\,GeV, respectively. The broadband emission cannot be sucessfully reproduced by a simple one-zone synchrotron self-Compton model. Instead, an additional external Compton component is required. We test a lepto-hadronic model that reproduces the dataset well and a proton-synchrotron dominated model that requires an extreme proton luminosity. Emission models that are able to successfully represent the data place the emitting region well outside of the Broad Line Region (BLR) to a location at which the radiative environment is dominated by the infrared thermal radiation field of the dusty torus. In the scenario described by this flaring activity, the source appears to be an FSRQ, in contrast with past categorizations. This suggests that the source can be considered to be a transitional blazar, intermediate between BL~Lac and FSRQ objects., Comment: Accepted on MNRAS Corresponding authors: M. Manganaro, J. Becerra Gonz\'alez, M. Seglar-Arroyo, D. A. Sanchez

We explore the reliability and robustness in measuring the age, metallicity and mass of a sample of old Milky Way globular clusters (GCs) from their integrated light, setting the stage for using GCs as cosmic clocks at high redshift. We analyse 77 GCs from the WAGGS project, first by measuring Lick indices and spectroscopic breaks with PyLick, then performing full-spectral-fitting (FSF) with BAGPIPES. The analysis of Lick indices offers an estimate of the GCs' age and [Z/H], generally aligning with literature values, but highlights a subset of old GCs for which we estimate younger ages. This discrepancy is attributed to the presence of blue horizontal branches (HB), which are not accounted for in the stellar population models. With FSF we measure the GCs' ages, [Z/H], and masses, also testing the cosmological prior's impact on ages. Compared to isochrone fitting estimates, ages are best recovered when the cosmological prior is removed, with a 20% increase in GCs' ages compatible with literature values (within $\pm$1.5 Gyr). The derived [Z/H] and mass agree with the reference values, regardless of HB morphology or fit setting, with average discrepancies across the entire sample of $\Delta$[Z/H]=-0.02$\pm$0.24 dex and $\Delta log(M/M_{\odot})=0.04\pm 0.28$ dex. Ages are best recovered for metal-rich GCs ([Z/H]$\geq$-0.4) showing a red HB (HBR>0), with 70% of the results compatible with literature values. Using a Gaussian Mixture Model, we identify a tail of 24 old GCs with age=13.4$\pm$1.1 Gyr. Being a natural lower limit to the age of the Universe, we use this value to constrain $H_0$, obtaining $H_0 = 70.5^{+7.7}_{-6.3}$ km/s/Mpc (stat+syst) when a flat $\Lambda$CDM with $\Omega_m =0.30 \pm 0.02$ is assumed. Validating the study of GCs based on integrated light lays the foundation to extend this type of study to high-z, where lensed GCs have begun to appear, thanks to JWST. (abridged), Comment: 16 pages, 9 figures. Submitted to A&A

Reconfigurable intelligent surfaces (RISs) offer enhanced control over propagation through phase and amplitude manipulation but face practical challenges like cost and power usage, especially at high frequencies. This is specifically a major problem at high frequencies (Ka- and V-band) where the high cost of semiconductor components (i.e., diodes, varactors, MEMSs) can make RISs prohibitively costly. In recent years, it is shown that liquid crystals (LCs) are low-cost and low-energy alternative which can address the aforementioned challenges but at the cost of lower response time. In LiquiRIS, we enable leveraging LC-based RIS in mobile networks. Specifically, we devise techniques that minimize the beam switching time of LC-based RIS by tapping into the physical properties of LCs and the underlying mathematical principles of beamforming. We achieve this by modeling and optimizing the beamforming vector to account for the rotation characteristics of LC molecules to reduce their transition time from one state to another. In addition to prototyping the proposed system, we show via extensive experimental analysis that LiquiRIS substantially reduces the response time (up to 70.80%) of liquid crystal surface (LCS).

Over the past decade, several millimeter interferometer programs have mapped the nearby star-forming galaxy M51 at a spatial resolution of ${\le}170$ pc. This study combines observations from three major programs: the PdBI Arcsecond Whirlpool Survey (PAWS), the SMA M51 large program (SMA-PAWS), and the Surveying the Whirlpool at Arcseconds with NOEMA (SWAN). The dataset includes the (1-0) and (2-1) rotational transitions of $^{12}$CO, $^{13}$CO, and C$^{18}$O isotopologues. The observations cover the $r{<}\rm 3\,kpc$ region including center and part of the disk, thereby ensuring strong detections of the weaker $^{13}$CO and C$^{18}$O lines. All observations are convolved in this analysis to an angular resolution of 4$''$, corresponding to a physical scale of ${\sim}$170 pc. We investigate empirical line ratio relations and quantitatively evaluate molecular gas conditions such as temperature, density, and the CO-to-H$_2$ conversion factor ($\alpha_{\rm CO}$). We employ two approaches to study the molecular gas conditions: (i) assuming local thermal equilibrium (LTE) to analytically determine the CO column density and $\alpha_{\rm CO}$, and (ii) using non-LTE modeling with RADEX to fit physical conditions to observed CO isotopologue intensities. We find that the $\alpha_{\rm CO}$ values {in the center and along the inner spiral arm} are $\sim$0.5 dex (LTE) and ${\sim}$0.1 dex (non-LTE) below the Milky Way inner disk value. The average non-LTE $\alpha_{\rm CO}$ is $2.4{\pm}0.5$ M$_\odot$ pc$^{-2}$ (K km s$^{-1}$)$^{-1}$. While both methods show dispersion due to underlying assumptions, the scatter is larger for LTE-derived values. This study underscores the necessity for robust CO line modeling to accurately constrain the molecular ISM's physical and chemical conditions in nearby galaxies., Comment: accepted for publication in AJ; 31 pages, 16 figures, 7 tables

The new generation of upcoming deep photometric and spectroscopic surveys will allow us to measure the astrophysical properties of faint galaxies in massive clusters. This would demand to produce simulations of galaxy clusters with better mass resolution than the ones available today if we want to make comparisons between the upcoming observations and predictions of cosmological models. But producing full-physics hydrodynamical simulations of the most massive clusters is not an easy task. This would involve billions of computational elements to reliably resolve low mass galaxies similar to those measured in observations. On the other hand, dark matter only simulations of cluster size halos can be done with much larger mass resolution but at the cost of having to apply a model that populate galaxies within each of the subhalos in these simulations. In this paper we present the results of a new set of dark matter only simulations with different mass resolutions within the THE THREE HUNDRED project. We have generated catalogs of galaxies with stellar and luminosity properties by applying the SAGE Semi-Analytical Model of galaxy formation. To obtain the catalogs consistent with the results from hydrodynamical simulations, the internal physical parameters of SAGE were calibrated with the Particle Swarm Optimization method using a subset of full-physics runs with the same mass resolution than the dark matter only ones., Comment: To appear in Proc. of the mm Universe 2023 conference, Grenoble (France), June 2023, published by F. Mayet et al. (Eds), EPJ Web of conferences, EDP Sciences

Extreme Multi-label Classification (XMC) methods predict relevant labels for a given query in an extremely large label space. Recent works in XMC address this problem using deep encoders that project text descriptions to an embedding space suitable for recovering the closest labels. However, learning deep models can be computationally expensive in large output spaces, resulting in a trade-off between high performing brute-force approaches and efficient solutions. In this paper, we propose PRIME, a XMC method that employs a novel prototypical contrastive learning technique to reconcile efficiency and performance surpassing brute-force approaches. We frame XMC as a data-to-prototype prediction task where label prototypes aggregate information from related queries. More precisely, we use a shallow transformer encoder that we coin as Label Prototype Network, which enriches label representations by aggregating text-based embeddings, label centroids and learnable free vectors. We jointly train a deep encoder and the Label Prototype Network using an adaptive triplet loss objective that better adapts to the high granularity and ambiguity of extreme label spaces. PRIME achieves state-of-the-art results in several public benchmarks of different sizes and domains, while keeping the model efficient.

Free-space optical communication (FSOC) systems offer high-bandwidth and secure communication with minimal capital costs. Adaptive optics (AO) are typically added to these systems to decrease atmospheric channel losses; however, the performance of traditional AO wavefront sensors degrades in long-range, deep turbulence conditions. Alternative wavefront sensors using phase diversity can successfully reconstruct wavefronts in deep turbulence, but current implementations require bulky setups with high latency. In this work, we employ a nanostructured birefringent metasurface optic that enables low-latency phase diversity wavefront sensing in a compact form factor. We prove the effectiveness of this approach in mid-to-high turbulence (Rytov numbers from 0.2 to 0.6) through simulation and experimental demonstration. In both cases an average 16-fold increase in signal from the corrected beam is obtained. Our approach opens a pathway for compact, robust wavefront sensing that enhances range and accuracy of FSOC systems.

The increasing quality of cosmological data has revealed some tensions that could be signalling the necessity of incorporating new physics into our cosmological model. One particularly intriguing possibility is the existence of elastic interactions between dark matter and dark energy. Not only do these interactions provide a natural mechanism to relief cosmological tensions, but there is also compelling observational evidence for them, to the extent that a detection could even be claimed. We review the potential of these scenarios in relation to the cosmological tensions and discuss distinctive signatures that can be probed with future data, thus providing a smoking gun for these interactions., Comment: 11 pages, 6 Figures

Context. The formation of stars has been subject to extensive studies in the past decades from molecular cloud to protoplanetary disk scales. It is still not fully understood how the surrounding material in a protostellar system, that often shows asymmetric structures with complex kinematic properties, feeds the central protostar(s) and their disk(s). Aims. We study the spatial morphology and kinematic properties of the molecular gas surrounding the IRS3A and IRS3B protostellar systems in the L1448N region located in the Perseus molecular cloud. Methods. We present 1 mm NOEMA observations of the PRODIGE large program and analyze the kinematic properties of molecular lines. Given the complexity of the spectral profiles, the lines are fitted with up to three Gaussian velocity components. The clustering algorithm DBSCAN is used to disentangle the velocity components into the underlying physical structure. Results. We discover an extended gas bridge (~3000 au) surrounding both the IRS3A and IRS3B systems in six molecular line tracers (C18O, SO, DCN, H2CO, HC3N, and CH3OH). This gas bridge is oriented along the northeast-southwest direction and shows clear velocity gradients on the order of 100 km/s/pc towards the IRS3A system. We find that the observed velocity profile is consistent with analytical streamline models of gravitational infall towards IRS3A. The high-velocity C18O (2-1) emission towards IRS3A indicates a protostellar mass of ~1.2 Msun. Conclusions. While high angular resolution continuum data often show IRS3A and IRS3B in isolation, molecular gas observations reveal that these systems are still embedded within a large-scale mass reservoir with a complex spatial morphology as well as velocity profiles. The kinematic properties of the extended gas bridge are consistent with gravitational infall toward the IRS3A protostar., Comment: 16 pages, 10 figures; accepted for publication in A&A

We studied the effects of point defects and Hg impurities in the electronic properties of bismuth iodide (Bi$_4$I$_4$). Our transport measurements after annealing at different temperatures show that the resistivity of Bi$_4$I$_4$ depends on its thermal history, suggesting that the formation of native defects and impurities can shape the temperature dependence of electrical resistivity. Our density functional theory calculations indicate that the bismuth and iodine antisites, and bismuth vacancies are the dominant native point defects. We find that bismuth antisites introduce resonant states in the band-edges, while iodine antisites and bismuth vacancies lead to a $n$-type and $p$-type doping of Bi$_4$I$_4$, respectively. The Hg impurities are likely to be found at Bi substitutional sites, giving rise to the $p$-type doping of Bi$_4$I$_4$. Overall, our findings indicate that the presence of native point defects and impurities can significantly modify the electronic properties, and, thus, impact the resistivity profile of Bi$_4$I$_4$ due to modifications in the amount and type of carriers, and the associated defect(impurity) scattering. Our results suggest possible routes for pursuing fine-tuning of the electronic properties of quasi-one-dimensional quantum materials.

Cosmic voids, distinguished by their low-density environment, provide a unique opportunity to explore the interplay between the cosmic environment and the processes of galaxy formation and evolution. Data on the molecular gas has been scarce so far. In this paper, we continue previous research done in the CO-CAVITY pilot project to study the molecular gas content and properties in void galaxies to search for possible differences compared to galaxies that inhabit denser structures. We observed at the IRAM 30 m telescope the CO(1-0) and CO(2-1) emission of 106 void galaxies selected from the CAVITY survey. Together with data from the literature, we obtained a sample of 200 void galaxies with CO data. We conducted a comprehensive comparison of the specific star formation rate (sSFR = SFR/M$_*$), the molecular gas fraction (MH$_2$/M$_*$), and the star formation efficiency (SFE = SFR/MH$_2$) between the void galaxies and a comparison sample of galaxies in filaments and walls, selected from the xCOLD GASS survey. We found no statistically significant difference between void galaxies and the comparison sample in the molecular gas fraction as a function of stellar mass for galaxies on the star-forming main sequence (SFMS). However, for void galaxies, the SFE was found to be constant across all stellar mass bins, while there is a decreasing trend with M$_*$ for the comparison sample. Finally, we found some indications for a smaller dynamical range in the molecular gas fraction as a function of distance to the SFMS in void galaxies. Overall, our analysis finds that the molecular gas properties of void galaxies are not very different from denser environments. The physical origin of the most significant difference that we found - a constant SFE as a function of stellar mass in void galaxies - is unclear and requires further investigation and higher-resolution data., Comment: 26 pages, 9 figures. Accepted for publication in A&A

Large pre-trained models exhibit impressive zero-shot performance across diverse tasks, but fine-tuning often leads to catastrophic forgetting, where improvements on a target domain degrade generalization on other tasks. To address this challenge, we introduce LiNeS, Layer-increasing Network Scaling, a post-training editing technique designed to preserve pre-trained generalization while enhancing fine-tuned task performance. LiNeS scales parameter updates linearly based on their layer depth within the network, maintaining shallow layers close to their pre-trained values to preserve general features while allowing deeper layers to retain task-specific representations. We further extend this approach to multi-task model merging scenarios, where layer-wise scaling of merged parameters reduces negative task interference. LiNeS demonstrates significant improvements in both single-task and multi-task settings across various benchmarks in vision and natural language processing. It mitigates forgetting, enhances out-of-distribution generalization, integrates seamlessly with existing multi-task model merging baselines improving their performance across benchmarks and model sizes, and can boost generalization when merging LLM policies aligned with different rewards via RLHF. Importantly, our method is simple to implement and complementary to many existing techniques., Comment: The first two authors contributed equally to this work; Project website: \url{https://lines-merging.github.io/}

Akinetic swept-sources are essential for high-speed optical coherence tomography (OCT) imaging. Time-stretched supercontinuum (TSSC) lasers have proven to be efficient for multi-MHz swept-sources. However, lack of low-noise broadband lasers and of large dispersion devices in the water low-absorption band at 1060 nm have limited the biomedical applications of TSSC lasers. In this letter, an approach to tune the wavelength around 1050 nm over 90 nm with low-noise at 10 MHz is presented. This is based on all-normal dispersion (ANDi) supercontinuum dynamics, and employs a long chirped fiber Bragg grating (CFBG) to time-stretch a broadband pulse with a duty cycle of 93 %. Retinal images are demonstrated, with a sensitivity of 84 dB - approaching the shot noise limit. We believe this high-speed low-noise swept-source will greatly promote the development of OCT techniques for biomedical applications.

LLMs are frequently used tools for conversational generation. Without additional information LLMs can generate lower quality responses due to lacking relevant content and hallucinations, as well as the perception of poor emotional capability, and an inability to maintain a consistent character. Knowledge graphs are commonly used forms of external knowledge and may provide solutions to these challenges. This paper introduces three proposals, utilizing knowledge graphs to enhance LLM generation. Firstly, dynamic knowledge graph embeddings and recommendation could allow for the integration of new information and the selection of relevant knowledge for response generation. Secondly, storing entities with emotional values as additional features may provide knowledge that is better emotionally aligned with the user input. Thirdly, integrating character information through narrative bubbles would maintain character consistency, as well as introducing a structure that would readily incorporate new information., Comment: 7 pages with citations, 1 figure, accepted to the ISWC 2024 Special Session

We propose a self-contained, resilient and fully distributed solution for locating the maximum of an unknown 3D scalar field using a swarm of robots that travel at constant speeds. Unlike conventional reactive methods relying on gradient information, our methodology enables the swarm to determine an ascending direction so that it approaches the source with arbitrary precision. Our source-seeking solution consists of three algorithms. The first two algorithms run sequentially and distributively at a high frequency providing barycentric coordinates and the ascending direction respectively to the individual robots. The third algorithm is the individual control law for a robot to track the estimated ascending direction. We show that the two algorithms with higher frequency have an exponential convergence to their eventual values since they are based on the standard consensus protocol for first-order dynamical systems; their high frequency depends on how fast the robots travel through the scalar field. The robots are not constrained to any particular geometric formation, and we study both discrete and continuous distributions of robots within swarm shapes. The shape analysis reveals the resiliency of our approach as expected in robot swarms, i.e., by amassing robots we ensure the source-seeking functionality in the event of missing or misplaced individuals or even if the robot network splits into two or more disconnected subnetworks. In addition, we also enhance the robustness of the algorithm by presenting conditions for \emph{optimal} swarm shapes, in the sense that the ascending directions can be closely parallel to the field's gradient. We exploit such an analysis so that the swarm can adapt to unknown environments by morphing its shape and maneuvering while still following an ascending direction., Comment: 15 pages, submitted version to T-RO. This version does not contain the field experiments. arXiv admin note: text overlap with arXiv:2309.02937

We introduce a toluene-filled fiber platform for two-photon absorption measurements. By confining both the light and molecular sample inside the 5 $\mu$m hollow core of the fiber, we increase the distance over which the nonlinear light-matter interaction occurs. With only a 7.3 nL excitation volume, we measure classical two-photon absorption (C2PA) at an average laser power as low as 1.75 nW, which is a 45-fold improvement over a conventional free-space technique. We use this platform to attempt to measure entangled two-photon absorption (E2PA), a process with a limited operating regime due to a crossover in dominating processes from E2PA to C2PA as photon flux is increased. Recently, several teams of researchers have reported that E2PA cross sections are much smaller than previously claimed. As a result, the process dominates at photon fluxes so low that it is extremely difficult or impossible to measure using conventional free-space techniques. In this report, we implement the first E2PA measurement using a waveguide. We see no evidence of E2PA, and we set an upper bound on the cross section consistent with these recent reports.

This paper analyses the centralized fusion linear estimation problem in multi-sensor systems with multiple packet dropouts and correlated noises. Packet dropouts are modeled by independent Bernoulli distributed random variables. This problem is addressed in the tessarine domain under conditions of T1 and T2-properness, which entails a reduction in the dimension of the problem and, consequently, computational savings. The methodology proposed enables us to provide an optimal (in the least-mean-squares sense) linear fusion filtering algorithm for estimating the tessarine state with a lower computational cost than the conventional one devised in the real field. Simulation results illustrate the performance and advantages of the solution proposed in different settings.

Segmentation and classification of large numbers of instances, such as cell nuclei, are crucial tasks in digital pathology for accurate diagnosis. However, the availability of high-quality datasets for deep learning methods is often limited due to the complexity of the annotation process. In this work, we investigate the impact of noisy annotations on the training and performance of a state-of-the-art CNN model for the combined task of detecting, segmenting and classifying nuclei in histopathology images. In this context, we investigate the conditions for determining an appropriate number of training epochs to prevent overfitting to annotation noise during training. Our results indicate that the utilisation of a small, correctly annotated validation set is instrumental in avoiding overfitting and maintaining model performance to a large extent. Additionally, our findings underscore the beneficial role of pre-training.

This paper presents the IETF Insights project, an automated system that streamlines the generation of comprehensive reports on the activities of the Internet Engineering Task Force (IETF) Working Groups. The system collects, consolidates, and analyzes data from various IETF sources, including meeting minutes, participant lists, drafts and agendas. The core components of the system include data preprocessing code and a report generation module that produces high-quality documents in LaTeX or Markdown. By integrating large Language Models (LLMs) for summaries based on the data as ground truth, the IETF Insights project enhances the accessibility and utility of IETF records, providing a valuable overview of the IETF's activities and contributions to the community., Comment: 5 pages plus Appendix