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Historically Black Colleges and Universities (HBCUs) have been a catalyst for transformation in K-12 through initiatives, including diversifying teaching pipelines, starting new schools, and establishing programs designed to meet the aspirations of students far away from quality opportunities. HBCUs and their alumni have played powerful roles in K-12 public education, including charter schools. Alumni are leading outstanding charter learning institutions with exceptional student outcomes, and some HBCUs have partnered with charter schools in effective ways including integrating charter schools on their campuses. This arrangement provides students with a unique experience in which they are introduced to the promise and prestige of higher education earlier in their educational journey. Utilizing that expertise and record of achievement is especially important now. America's public schools lag behind those of our international competitors, both in terms of student attainment and educational equity. The lasting influences of a global pandemic, particularly for lower-income students and students of color, have magnified the challenges of our outdated K-12 system -- one that was not working well for many students even before COVID-19. This report examines the potential for HBCUs to make high-stakes decisions about who is able to start new public schools, what outcomes those schools should meet, and what to do when adults fail students.

We present the first emission spectrum of the hot Jupiter WASP-17 b using one eclipse observation from the JWST Near Infrared Imager and Slitless Spectrograph (NIRISS) Single Object Slitless Spectroscopy (SOSS) mode. Covering a wavelength range of 0.6 to 2.8 microns, our retrieval analysis reveals a strong detection of H2O in WASP-17b dayside atmosphere (6.4sigma). Our retrievals consistently favor a super-solar dayside H2O abundance and a non-inverted temperature-pressure profile over a large pressure range. Additionally, our examination of the brightness temperature reveals excess emission below 1 microns, suggesting the possibility of a high internal temperature (600 to 700 K) and/or contributions from reflected light. We highlight that JWST emission spectroscopy retrieval results can be sensitive to whether negative eclipse depths are allowed at optical wavelengths during light curve fitting. Our findings deepen our understanding of WASP-17b atmospheric composition while also highlighting the sensitivity of our results to pressure-temperature profile parameterizations. This work is part of a series of studies by our JWST Telescope Scientist Team (JWST-TST), in which we will use Guaranteed Time Observations to perform Deep Reconnaissance of Exoplanet Atmospheres through Multi-instrument Spectroscopy (DREAMS)., Comment: Accepted for Publication in the Astronomical Journal (AJ) October 4, 2024

We present the first spectroscopic characterisation of the dayside atmosphere of WASP-17b in the mid-infrared using a single JWST MIRI/LRS eclipse observation. From forward-model fits to the 5-12 $\mu$m emission spectrum, we tightly constrain the heat redistribution factor of WASP-17b to be 0.92$\pm$0.02 at the pressures probed by this data, indicative of inefficient global heat redistribution. We also marginally detect a supersolar abundance of water, consistent with previous findings for WASP-17b, but note our weak constraints on this parameter. These results reflect the thermodynamically rich but chemically poor information content of MIRI/LRS emission data for high-temperature hot Jupiters. Using the eclipse mapping method, which utilises the signals that the spatial emission profile of an exoplanet imprints on the eclipse light curve during ingress/egress due to its partial occultation by the host star, we also construct the first eclipse map of WASP-17b, allowing us to diagnose its multidimensional atmospheric dynamics for the first time. We find a day-night temperature contrast of order 1000 K at the pressures probed by this data, consistent with our derived heat redistribution factor, along with an eastward longitudinal hotspot offset of $18.7^{+11.1\deg}_{-3.8}$, indicative of the presence of an equatorial jet induced by day-night thermal forcing being the dominant redistributor of heat from the substellar point. These dynamics are consistent with general circulation model predictions for WASP-17b. This work is part of a series of studies by the JWST Telescope Scientist Team (JWST-TST), in which we use Guaranteed Time Observations to perform Deep Reconnaissance of Exoplanet Atmospheres through Multi-instrument Spectroscopy (DREAMS)., Comment: 28 pages, 8 figures. Published in The Astronomical Journal

We present a transmission spectrum of the misaligned hot Jupiter WASP-15b from 2.8--5.2 microns observed with JWST's NIRSpec/G395H grating. Our high signal to noise data, which has negligible red noise, reveals significant absorption by H$_2$O ($4.2\sigma$) and CO$_2$ ($8.9\sigma$). From independent data reduction and atmospheric retrieval approaches, we infer that WASP-15b's atmospheric metallicity is super-solar ($\gtrsim 15\times$ solar) and its C/O is consistent with solar, that together imply planetesimal accretion. Our GCM simulations for WASP-15b suggest that the C/O we measure at the limb is likely representative of the entire photosphere due to the mostly uniform spatial distribution of H$_2$O, CO$_2$ and CO. We additionally see evidence for absorption by SO$_2$ and absorption at 4.9$\mu$m, for which the current leading candidate is OCS, albeit with several caveats. If confirmed, this would be the first detection of OCS in an exoplanet atmosphere and point towards complex photochemistry of sulphur-bearing species in the upper atmosphere. These are the first observations from the BOWIE-ALIGN survey which is using JWST's NIRSpec/G395H instrument to compare the atmospheric compositions of aligned/low-obliquity and misaligned/high-obliquity hot Jupiters around F stars above the Kraft break. The goal of our survey is to determine whether the atmospheric composition differs across two populations of planets that have likely undergone different migration histories (disc versus disc-free) as evidenced by their obliquities (aligned versus misaligned)., Comment: 24 pages, 23 figures, 6 tables. Submitted to MNRAS

Particle accelerators with superconducting magnets operating at cryogenic temperatures use a beam screen (BS) liner that extracts heat generated by the circulating bunched charge particle beam before it can reach the magnets. The BS surface, commonly made of high-conductivity copper, provides a low impedance for beam stability reasons, low secondary electron yield (SEY)to mitigate the electron-cloud (EC) effect, and low electron-stimulated desorption yield (ESD) to limit the dynamic pressure rise due to EC. Rare-earth barium copper oxide (REBCO) high-temperature superconductors (HTs) recently reached technical maturity, are produced as coated conductor tapes (REBCO-CCs), and will be considered for application in future colliders to decrease the BS impedance and enable operation at around 50 K, consequently relaxing the cryogenic requirements. Aside from HTS properties, industry-grade REBCO-CCs also need qualification for EC and dynamic vacuum compatibility under accelerator-like conditions. Hence, we report the SEY and ESD measured at cryogenic temperatures of 12 K under low-energy electron irradiation of 0 - 1.4 keV. We also verify the sample compositions and morphologies using XPS, SEM, and EDS methods. The energy and dose dependencies of ESD are comparable to those of technical-grade metals and one sample reached SEY$_{MAX}=$1.2 after electron conditioning.

Triggered by the realization that AI emulators can rival the performance of traditional numerical weather prediction models running on HPC systems, there is now an increasing number of large AI models that address use cases such as forecasting, downscaling, or nowcasting. While the parallel developments in the AI literature focus on foundation models -- models that can be effectively tuned to address multiple, different use cases -- the developments on the weather and climate side largely focus on single-use cases with particular emphasis on mid-range forecasting. We close this gap by introducing Prithvi WxC, a 2.3 billion parameter foundation model developed using 160 variables from the Modern-Era Retrospective Analysis for Research and Applications, Version 2 (MERRA-2). Prithvi WxC employs an encoder-decoder-based architecture, incorporating concepts from various recent transformer models to effectively capture both regional and global dependencies in the input data. The model has been designed to accommodate large token counts to model weather phenomena in different topologies at fine resolutions. Furthermore, it is trained with a mixed objective that combines the paradigms of masked reconstruction with forecasting. We test the model on a set of challenging downstream tasks namely: Autoregressive rollout forecasting, Downscaling, Gravity wave flux parameterization, and Extreme events estimation. The pretrained model with 2.3 billion parameters, along with the associated fine-tuning workflows, has been publicly released as an open-source contribution via Hugging Face.

Teleoperation of humanoid robots has long been a challenging domain, necessitating advances in both hardware and software to achieve seamless and intuitive control. This paper presents an integrated solution based on several elements: calibration-free motion capture and retargeting, low-latency fast whole-body kinematics streaming toolbox and high-bandwidth cycloidal actuators. Our motion retargeting approach stands out for its simplicity, requiring only 7 IMUs to generate full-body references for the robot. The kinematics streaming toolbox, ensures real-time, responsive control of the robot's movements, significantly reducing latency and enhancing operational efficiency. Additionally, the use of cycloidal actuators makes it possible to withstand high speeds and impacts with the environment. Together, these approaches contribute to a teleoperation framework that offers unprecedented performance. Experimental results on the humanoid robot Nadia demonstrate the effectiveness of the integrated system.

The integration of new modalities into frontier AI systems offers exciting capabilities, but also increases the possibility such systems can be adversarially manipulated in undesirable ways. In this work, we focus on a popular class of vision-language models (VLMs) that generate text outputs conditioned on visual and textual inputs. We conducted a large-scale empirical study to assess the transferability of gradient-based universal image "jailbreaks" using a diverse set of over 40 open-parameter VLMs, including 18 new VLMs that we publicly release. Overall, we find that transferable gradient-based image jailbreaks are extremely difficult to obtain. When an image jailbreak is optimized against a single VLM or against an ensemble of VLMs, the jailbreak successfully jailbreaks the attacked VLM(s), but exhibits little-to-no transfer to any other VLMs; transfer is not affected by whether the attacked and target VLMs possess matching vision backbones or language models, whether the language model underwent instruction-following and/or safety-alignment training, or many other factors. Only two settings display partially successful transfer: between identically-pretrained and identically-initialized VLMs with slightly different VLM training data, and between different training checkpoints of a single VLM. Leveraging these results, we then demonstrate that transfer can be significantly improved against a specific target VLM by attacking larger ensembles of "highly-similar" VLMs. These results stand in stark contrast to existing evidence of universal and transferable text jailbreaks against language models and transferable adversarial attacks against image classifiers, suggesting that VLMs may be more robust to gradient-based transfer attacks.

The contribution focuses on the problem of exploration within the task of knowledge transfer. Knowledge transfer refers to the useful application of the knowledge gained while learning the source task in the target task. The intended benefit of knowledge transfer is to speed up the learning process of the target task. The article aims to compare several exploration methods used within a deep transfer learning algorithm, particularly Deep Target Transfer $Q$-learning. The methods used are $\epsilon$-greedy, Boltzmann, and upper confidence bound exploration. The aforementioned transfer learning algorithms and exploration methods were tested on the virtual drone problem. The results have shown that the upper confidence bound algorithm performs the best out of these options. Its sustainability to other applications is to be checked., Comment: 19 pages

We develop a theory based on quasi-geometric (QG) approach to transform a small number of qubits into a larger number of error-correcting qubits by considering four different cases. More precisely, we use the 2-dimensional quasi-orthogonal complete complementary codes (2D-QOCCCSs) and quasi-cyclic asymmetric quantum error-correcting codes (AQECCs) via quasigroup and group theory properties. We integrate the Pauli $X$-gate to detect and correct errors, as well as the Hadamard $H$-gate to superpose the initial and final qubits in the quantum circuit diagram. We compare the numerical results to analyze the success, consistency, and performance of the corrected errors through bar graphs for 2D-QOCCCs and AQECCs according to their characteristics. The difficulty in generating additional sets of results and counts for AQECCs arises because mapping a smaller initial number of qubits to a larger final number is necessary to correct more errors. For AQECCs, the number of errors that can be corrected must be equal to or less than the initial number of qubits. High error correction performance is observed when mapping 1-qubit state to 29-qubits to correct 5 errors using 2D-QOCCCs. Similarly, transforming 1-qubit to 13-qubits using AQECCs also shows high performance, successfully correcting 2 errors. The results show that our theory has the advantage of providing a basis for refining and optimizing these codes in future quantum computing applications due to its high performance in error correction., Comment: 24 pages, 20 figures, 10 tables

A primary objective of exoplanet atmosphere characterisation is to learn about planet formation and evolution, however, this is challenged by degeneracies. To determine whether differences in atmospheric composition can be reliably traced to differences in evolution, we are undertaking a transmission spectroscopy survey with JWST to compare the compositions of a sample of hot Jupiters that have different orbital alignments around F stars above the Kraft break. Under the assumption that aligned planets migrate through the inner disc, while misaligned planets migrate after disc dispersal, the act of migrating through the inner disc should cause a measurable difference in the C/O between aligned and misaligned planets. We expect the amplitude and sign of this difference to depend on the amount of planetesimal accretion and whether silicates accreted from the inner disc release their oxygen. Here, we identify all known exoplanets that are suitable for testing this hypothesis, describe our JWST survey, and use noise simulations and atmospheric retrievals to estimate our survey's sensitivity. With the selected sample of four aligned and four misaligned hot Jupiters, we will be sensitive to the predicted differences in C/O between aligned and misaligned hot Jupiters for a wide range of model scenarios., Comment: 14 pages, 8 figures, accepted to RASTI on 8th October 2024

The count of mitotic figures (MFs) observed in hematoxylin and eosin (H&E)-stained slides is an important prognostic marker as it is a measure for tumor cell proliferation. However, the identification of MFs has a known low inter-rater agreement. Deep learning algorithms can standardize this task, but they require large amounts of annotated data for training and validation. Furthermore, label noise introduced during the annotation process may impede the algorithm's performance. Unlike H&E, the mitosis-specific antibody phospho-histone H3 (PHH3) specifically highlights MFs. Counting MFs on slides stained against PHH3 leads to higher agreement among raters and has therefore recently been used as a ground truth for the annotation of MFs in H&E. However, as PHH3 facilitates the recognition of cells indistinguishable from H&E stain alone, the use of this ground truth could potentially introduce noise into the H&E-related dataset, impacting model performance. This study analyzes the impact of PHH3-assisted MF annotation on inter-rater reliability and object level agreement through an extensive multi-rater experiment. We found that the annotators' object-level agreement increased when using PHH3-assisted labeling. Subsequently, MF detectors were evaluated on the resulting datasets to investigate the influence of PHH3-assisted labeling on the models' performance. Additionally, a novel dual-stain MF detector was developed to investigate the interpretation-shift of PHH3-assisted labels used in H&E, which clearly outperformed single-stain detectors. However, the PHH3-assisted labels did not have a positive effect on solely H&E-based models. The high performance of our dual-input detector reveals an information mismatch between the H&E and PHH3-stained images as the cause of this effect., Comment: 10 pages, 5 figures, 1 Table

Mass spectrometry is a widely used method to study molecules and processes in medicine, life sciences, chemistry, catalysis, and industrial product quality control, among many other applications. One of the main features of some mass spectrometry techniques is the extensive level of characterization (especially when coupled with chromatography and ion mobility methods, or a part of tandem mass spectrometry experiment) and a large amount of generated data per measurement. Terabyte scales can be easily reached with mass spectrometry studies. Consequently, mass spectrometry has faced the challenge of a high level of data disappearance. Researchers often neglect and then altogether lose access to the rich information mass spectrometry experiments could provide. With the development of machine learning methods, the opportunity arises to unlock the potential of these data, enabling previously inaccessible discoveries. The present perspective highlights reevaluation of mass spectrometry data analysis in the new generation of methods and describes significant challenges in the field, particularly related to problems involving the use of electrospray ionization. We argue that further applications of machine learning raise new requirements for instrumentation (increasing throughput and information density, decreasing pricing, and making more automation-friendly software), and once met, the field may experience significant transformation., Comment: 20 pages, 4 figures

Generalized Kauer moves are local moves of multiple edges in a Brauer graph that yield derived equivalences between Brauer graph algebras of multiplicity identically 1. Moreover, these derived equivalences are given by a tilting mutation. The goal of this paper is to generalize this result first for Brauer graph algebras with arbitrary multiplicity and second for a generalization of Brauer graph algebras called skew Brauer graph algebras. In these contexts, we prove that the generalized Kauer moves induce derived equivalences via tilting mutations. We also show that skew Brauer graph algebras of multiplicity identically 1 can be seen as the trivial extension of skew gentle algebras., Comment: 39 pages

We expect quantum field theories for matter to acquire intricate corrections due to their coupling to quantum fluctuations of the gravitational field. This can be precisely worked out in 3d quantum gravity: after integrating out quantum gravity, matter fields are effectively described as noncommutative quantum field theories, with quantum-deformed Lorentz symmetries. An open question remains: Are such theories unitary or not? On the one hand, since these are effective field theories obtained after integrating out high energy degrees of freedom, we may expect the loss of unitarity. On the other hand, as rigorously defined field theories built with Lorentz symmetries and standing on their own, we naturally expect the conservation of unitarity. In an effort to settle this issue, we explicitly check unitarity for a scalar field at one-loop level in both Euclidean and Lorentzian space-time signatures. We find that unitarity requires adding an extra-term to the propagator of the noncommutative theory, corresponding to a massless mode and given by a representation with vanishing Plancherel measure, thus usually ignored in spinfoam path integrals for quantum gravity. This indicates that the inclusion of matter in spinfoam models, and more generally in quantum gravity, might be more subtle than previously thought., Comment: 29 pages

As interest in implementing artificial intelligence (AI) in medical systems grows, discussion continues on how to evaluate the fairness of these systems, or the disparities they may perpetuate. Socioeconomic status (SES) is commonly included in machine learning models to control for health inequities, with the underlying assumption that increased SES is associated with better health. In this work, we considered a large cohort of patients from the Mount Sinai Health System in New York City to investigate the effect of patient SES, race, and sex on schizophrenia (SCZ) diagnosis rates via a logistic regression model. Within an intersectional framework, patient SES, race, and sex were found to have significant interactions. Our findings showed that increased SES is associated with a higher probability of obtaining a SCZ diagnosis in Black Americans ($\beta=4.1\times10^{-8}$, $SE=4.5\times10^{-9}$, $p < 0.001$). Whereas high SES acts as a protective factor for SCZ diagnosis in White Americans ($\beta=-4.1\times10^{-8}$, $SE=6.7\times10^{-9}$, $p < 0.001$). Further investigation is needed to reliably explain and quantify health disparities. Nevertheless, we advocate that building fair AI tools for the health care space requires recognizing the intersectionality of sociodemographic factors., Comment: NeurIPS: Queer in AI Workshop [QueerIPS], New Orleans, December 2023

In psychiatry, negative patient descriptions and stigmatizing language can contribute to healthcare disparities in two ways: (1) read by patients they can harm their trust and engagement with the medical center; (2) read by future providers they may negatively influence the future perspective of a patient. By leveraging large language models, this work aims to identify the sentiment expressed in psychiatric clinical notes based on the reader's point of view. Extracting sentences from the Mount Sinai Health System's large and diverse clinical notes, we used prompts and in-context learning to adapt three large language models (GPT-3.5, Llama 2, Mistral) to classify the sentiment conveyed by the sentences according to the provider or non-provider point of view. Results showed that GPT-3.5 aligns best to provider point of view, whereas Mistral aligns best to non-provider point of view., Comment: Oral presentation at NAACL 2024 Queer in AI Workshop

Deep neural networks (DNNs) have heavily relied on traditional computational units like CPUs and GPUs. However, this conventional approach brings significant computational burdens, latency issues, and high power consumption, limiting their effectiveness. This has sparked the need for lightweight networks like ExtremeC3Net. On the other hand, there have been notable advancements in optical computational units, particularly with metamaterials, offering the exciting prospect of energy-efficient neural networks operating at the speed of light. Yet, the digital design of metamaterial neural networks (MNNs) faces challenges such as precision, noise, and bandwidth, limiting their application to intuitive tasks and low-resolution images. In this paper, we propose a large kernel lightweight segmentation model, ExtremeMETA. Based on the ExtremeC3Net, the ExtremeMETA maximizes the ability of the first convolution layer by exploring a larger convolution kernel and multiple processing paths. With the proposed large kernel convolution model, we extend the optic neural network application boundary to the segmentation task. To further lighten the computation burden of the digital processing part, a set of model compression methods is applied to improve model efficiency in the inference stage. The experimental results on three publicly available datasets demonstrate that the optimized efficient design improved segmentation performance from 92.45 to 95.97 on mIoU while reducing computational FLOPs from 461.07 MMacs to 166.03 MMacs. The proposed the large kernel lightweight model ExtremeMETA showcases the hybrid design's ability on complex tasks.

Atomic carbon in its ground electronic state, C(3P), is expected to be present at high abundances during the evolution of dense molecular clouds. Consequently, its reactions with other interstellar species could have a strong influence on the chemical composition of these regions. Here, we report the results of an investigation of the reaction between C(3P) and dimethylether, CH3OCH3, which was recently detected in dark cloud TMC-1. Experiments were performed to study the kinetics of this reaction using a continuous supersonic flow reactor employing pulsed laser photolysis and pulsed laser induced fluorescence for atomic radical generation and detection respectively. Rate constants for this process were measured between 50 K and 296 K, while additional measurements of the product atomic hydrogen yields were also performed over the 75-296 K range. To better understand the experimental results, statistical rate theory was used to calculate rate constants over the same temperature range and to provide insight on the major product channels. These simulations, based on quantum chemical calculations of the ground triplet state of the C3H6O molecule, allowed us to obtain the most important features of the underlying potential energy surface. The measured rate constant increases as the temperature falls, reaching a value of k_(C+CH_3 OCH_3 )= 7.5 x 10-11 cm3 s-1 at 50 K, while the low measured H-atom yields support the theoretical prediction that the major reaction products are CH3 + CH3 + CO. The effects of this reaction on the abundances of interstellar CH3OCH3 and related species were tested using a gas-grain dense cloud model, employing an expression for the rate constant, k(T) = alpha(T/300)^beta, with alpha = 1.27 x 10-11 and beta = -1.01. These simulations predict that the C(3P) + CH3OCH3 reaction decreases gas-phase CH3OCH3 abundances by more than an order of magnitude at early times., Comment: accepted for publication in ACS Earth and Space Chemistry

With the advent of multispectral imagery and AI, there have been numerous works on automatic plant segmentation for purposes such as counting, picking, health monitoring, localized pesticide delivery, etc. In this paper, we tackle the related problem of automatic and selective plant-clearing in a sustainable forestry context, where an autonomous machine has to detect and avoid specific plants while clearing any weeds which may compete with the species being cultivated. Such an autonomous system requires a high level of robustness to weather conditions, plant variability, terrain and weeds while remaining cheap and easy to maintain. We notably discuss the lack of robustness of spectral imagery, investigate the impact of the reference database's size and discuss issues specific to AI systems operating in uncontrolled environments.

The bounded derived category of a finite dimensional algebra of finite global dimension is equivalent the stable category of $\mathbb{Z}$-graded modules over its trivial extension \cite{Happel}. In particular, given two derived equivalent finite dimensional algebras $\Lambda_{1}$ and $\Lambda_{2}$ of finite global dimension, their trivial extensions are stable equivalent. The converse is not true in general. The goal of this paper is to study cases where derived equivalences between $\Lambda_{1}$ and $\Lambda_{2}$ arise from an equivalence of categories involving their trivial extension. Thanks to a graded version of Happel's theorem, we show that one can construct a $\mathbb{Z}$-grading on the $\Lambda_{i}$ so that such an equivalence involving their trivial extension yield a derived equivalence between the category of $\mathbb{Z}$-graded module over $\Lambda_{i}$. We describe explicitly the tilting object associated to this derived equivalence (in the non-graded and in the graded case) for triangular matrix algebras. Finally, we apply these results to the particular case of gentle algebras. In this context, we study how one can obtain a derived equivalence between $\Lambda_{1}$ and $\Lambda_{2}$ (in the non-graded and the graded case) from graded generalized Kauer moves., Comment: 34 pages

Common methods for aligning large language models (LLMs) with desired behaviour heavily rely on human-labelled data. However, as models grow increasingly sophisticated, they will surpass human expertise, and the role of human evaluation will evolve into non-experts overseeing experts. In anticipation of this, we ask: can weaker models assess the correctness of stronger models? We investigate this question in an analogous setting, where stronger models (experts) possess the necessary information to answer questions and weaker models (non-experts) lack this information. The method we evaluate is debate, where two LLM experts each argue for a different answer, and a non-expert selects the answer. We find that debate consistently helps both non-expert models and humans answer questions, achieving 76% and 88% accuracy respectively (naive baselines obtain 48% and 60%). Furthermore, optimising expert debaters for persuasiveness in an unsupervised manner improves non-expert ability to identify the truth in debates. Our results provide encouraging empirical evidence for the viability of aligning models with debate in the absence of ground truth., Comment: For code please check: https://github.com/ucl-dark/llm_debate

We construct a gauge theory model on the 4-dimensional $\rho$-Minkowski space-time, a particular deformation of the Minkowski space-time recently considered. The corresponding star product results from a combination of Weyl quantization map and properties of the convolution algebra of the special Euclidean group. We use noncommutative differential calculi based on twisted derivations together with a twisted notion of noncommutative connection. The twisted derivations pertain to the Hopf algebra of $\rho$-deformed translations, a Hopf subalgebra of the $\rho$-deformed Poincar\'e algebra which can be viewed as defining the quantum symmetries of the $\rho$-Minkowski space-time. The gauge theory model is left invariant under the action of the $\rho$-deformed Poincar\'e algebra. The kinetic part of the action is found to coincide with the one of the usual (commutative) electrodynamics., Comment: Minor changes. Version to be published in JHEP

The Hunting Outbursting Young Stars (HOYS) project performs long-term, optical, multi-filter, high cadence monitoring of 25 nearby young clusters and star forming regions. Utilising Gaia DR3 data we have identified about 17000 potential young stellar members in 45 coherent astrometric groups in these fields. Twenty one of them are clear young groups or clusters of stars within one kiloparsec and they contain 9143 Gaia selected potential members. The cluster distances, proper motions and membership numbers are determined. We analyse long term (about 7yr) V, R, and I-band light curves from HOYS for 1687 of the potential cluster members. One quarter of the stars are variable in all three optical filters, and two thirds of these have light curves that are symmetric around the mean. Light curves affected by obscuration from circumstellar materials are more common than those affected by accretion bursts, by a factor of 2-4. The variability fraction in the clusters ranges from 10 to almost 100 percent, and correlates positively with the fraction of stars with detectable inner disks, indicating that a lot of variability is driven by the disk. About one in six variables shows detectable periodicity, mostly caused by magnetic spots. Two thirds of the periodic variables with disk excess emission are slow rotators, and amongst the stars without disk excess two thirds are fast rotators - in agreement with rotation being slowed down by the presence of a disk., Comment: accepted for publication in MNRAS, 1 table, 9 figures

Interpreting deep learning time series models is crucial in understanding the model's behavior and learning patterns from raw data for real-time decision-making. However, the complexity inherent in transformer-based time series models poses challenges in explaining the impact of individual features on predictions. In this study, we leverage recent local interpretation methods to interpret state-of-the-art time series models. To use real-world datasets, we collected three years of daily case data for 3,142 US counties. Firstly, we compare six transformer-based models and choose the best prediction model for COVID-19 infection. Using 13 input features from the last two weeks, we can predict the cases for the next two weeks. Secondly, we present an innovative way to evaluate the prediction sensitivity to 8 population age groups over highly dynamic multivariate infection data. Thirdly, we compare our proposed perturbation-based interpretation method with related work, including a total of eight local interpretation methods. Finally, we apply our framework to traffic and electricity datasets, demonstrating that our approach is generic and can be applied to other time-series domains.

Non-significant randomized control trials can hide subgroups of good responders to experimental drugs, thus hindering subsequent development. Identifying such heterogeneous treatment effects is key for precision medicine and many post-hoc analysis methods have been developed for that purpose. While several benchmarks have been carried out to identify the strengths and weaknesses of these methods, notably for binary and continuous endpoints, similar systematic empirical evaluation of subgroup analysis for time-to-event endpoints are lacking. This work aims to fill this gap by evaluating several subgroup analysis algorithms in the context of time-to-event outcomes, by means of three different research questions: Is there heterogeneity? What are the biomarkers responsible for such heterogeneity? Who are the good responders to treatment? In this context, we propose a new synthetic and semi-synthetic data generation process that allows one to explore a wide range of heterogeneity scenarios with precise control on the level of heterogeneity. We provide an open source Python package, available on Github, containing our generation process and our comprehensive benchmark framework. We hope this package will be useful to the research community for future investigations of heterogeneity of treatment effects and subgroup analysis methods benchmarking., Comment: 9 pages, 8 figures, 2 tables. Code available at https://github.com/owkin/hte . Comments are welcome!