Search

The half-life of $^{39}$Ar is measured using the DEAP-3600 detector located 2 km underground at SNOLAB. In 2016-2020, DEAP-3600 used a target mass of (3269 $\pm$ 24) kg of liquid argon distilled from the atmosphere in a direct-detection dark matter search. Such an argon mass also enables direct measurements of argon isotope properties. The decay of $^{39}$Ar in DEAP-3600 is the dominant source of triggers by two orders of magnitude, ensuring high statistics and making DEAP-3600 well-suited for measuring this isotope's half-life. Use of the pulse-shape discrimination technique in DEAP-3600 allows for powerful discrimination between nuclear recoils and electron recoils, resulting in the selection of a clean sample of $^{39}$Ar decays. Observing over a period of 3.4 years, the $^{39}$Ar half-life is measured to be $(302 \pm 8_{\rm stat} \pm 6_{\rm sys})$ years. This new direct measurement suggests that the half-life of $^{39}$Ar may be significantly longer than the accepted value, with potential implications for measurements using this isotope's half-life as input., Comment: 11 pages, 8 figures

The first direct measurement of gravitational waves by the LIGO and Virgo collaborations has opened up new avenues to explore our Universe. This white paper outlines the challenges and gains expected in gravitational-wave searches at frequencies above the LIGO/Virgo band. The scarcity of possible astrophysical sources in most of this frequency range provides a unique opportunity to discover physics beyond the Standard Model operating both in the early and late Universe, and we highlight some of the most promising of these sources. We review several detector concepts that have been proposed to take up this challenge, and compare their expected sensitivity with the signal strength predicted in various models. This report is the summary of a series of workshops on the topic of high-frequency gravitational wave detection, held in 2019 (ICTP, Trieste, Italy), 2021 (online) and 2023 (CERN, Geneva, Switzerland)., Comment: 138 pages, 13 figures. Update and extension of Living Rev.Rel. 24 (2021) 1, 4 on high frequency gravitational waves (arxiv: 2011.12414). Comments welcome

We present a comparison of observed polycyclic aromatic hydrocarbon (PAH) feature ratios in 19 nearby galaxies with a grid of theoretical expectations for near- and mid-infrared dust emission. The PAH feature ratios are drawn from Cycle 1 JWST observations and are measured for 7224 stellar clusters and 29176 stellar associations for which we have robust ages and mass estimates from HST five-band photometry. Though there are galaxy-to-galaxy variations, the observed PAH feature ratios largely agree with the theoretical models, particularly those that are skewed toward more ionized and larger PAH size distributions. For each galaxy we also extract PAH feature ratios for 200 pc-wide circular regions in the diffuse interstellar medium, which serve as a non-cluster/association control sample. Compared to what we find for stellar clusters and associations, the 3.3um/7.7um and 3.3um/11.3um ratios from the diffuse interstellar medium are $\sim 0.10-0.15$ dex smaller. When the observed PAH feature ratios are compared to the radiation field hardness as probed by the [OIII]/H$\beta$ ratio, we find anti-correlations for nearly all galaxies in the sample. These results together suggest that the PAH feature ratios are driven by the shape intensity of the radiation field, and that the smallest PAHs -- observed via JWST F335M imaging -- are increasingly 'processed' or destroyed in regions with the most intense and hard radiation fields., Comment: Accepted for publication in AJ

In a recent series of papers, supermassive black holes were used to discern pathways in galaxy evolution. By considering the black holes' coupling with their host galaxy's bulge/spheroid, the progression of mass within each component has shed light on the chronological sequence of galaxy speciation. Offsets between the galaxy-morphology-dependent M_bh-M_*,sph scaling relations trace a pattern of 'punctuated equilibrium' arising from merger-driven transitions between galaxy types, such as from spirals to dust-rich lenticulars and further to `ellicular' and elliptical galaxies. This study delves deeper into the distinction between the ellicular galaxies - characterised by their intermediate-scale discs - and elliptical galaxies. Along the way, it is shown how some anti-truncated large-scale discs in lenticular galaxies can arise from the coexistence of a steep intermediate-scale disc and a relatively shallow large-scale disc. This observation undermines application of the popular exponential-disc plus S\'ersic-bulge model for lenticular galaxies and suggests some past bulge mass measurements have been overestimated. Furthermore, it is discussed how merger-driven disc-heating and blending likely leads to the spheroidalisation of discs and the conglomeration of multiple discs leads to the (high n) Sersicification of light profiles. The ellicular and elliptical galaxy distribution in the $M_{\rm bh}$-$M_{\rm\star,sph}$ diagram is explored relative to major-merger-built lenticular galaxies and brightest cluster galaxies. The (super-)quadratic $M_{\rm bh}$-$M_{\rm\star}$ relations, presented herein, for merger-built systems should aid studies of massive black hole collisions and the gravitational wave background. Finally, connections to dwarf compact elliptical and ultra-compact dwarf galaxies, with their 100-1000 times higher $M_{\rm bh}/M_{\rm\star,sph}$ ratios, are presented.

As Large Language Models (LLMs) are integrated into electronic health record (EHR) workflows, validated instruments are essential to evaluate their performance before implementation. Existing instruments for provider documentation quality are often unsuitable for the complexities of LLM-generated text and lack validation on real-world data. The Provider Documentation Summarization Quality Instrument (PDSQI-9) was developed to evaluate LLM-generated clinical summaries. Multi-document summaries were generated from real-world EHR data across multiple specialties using several LLMs (GPT-4o, Mixtral 8x7b, and Llama 3-8b). Validation included Pearson correlation for substantive validity, factor analysis and Cronbach's alpha for structural validity, inter-rater reliability (ICC and Krippendorff's alpha) for generalizability, a semi-Delphi process for content validity, and comparisons of high-versus low-quality summaries for discriminant validity. Seven physician raters evaluated 779 summaries and answered 8,329 questions, achieving over 80% power for inter-rater reliability. The PDSQI-9 demonstrated strong internal consistency (Cronbach's alpha = 0.879; 95% CI: 0.867-0.891) and high inter-rater reliability (ICC = 0.867; 95% CI: 0.867-0.868), supporting structural validity and generalizability. Factor analysis identified a 4-factor model explaining 58% of the variance, representing organization, clarity, accuracy, and utility. Substantive validity was supported by correlations between note length and scores for Succinct (rho = -0.200, p = 0.029) and Organized ($\rho = -0.190$, $p = 0.037$). Discriminant validity distinguished high- from low-quality summaries ($p < 0.001$). The PDSQI-9 demonstrates robust construct validity, supporting its use in clinical practice to evaluate LLM-generated summaries and facilitate safer integration of LLMs into healthcare workflows.

At the Kavli Institute for Theoretical Physics, participants of the rapid response workshop on the gravitational wave background explored discrepancies between experimental results and theoretical models for a background originating from supermassive black hole binary mergers. Underestimated theoretical and/or experimental uncertainties are likely to be the explanation. Another key focus was the wide variety of search methods for supermassive black hole binaries, with the conclusion that the most compelling detections would involve systems exhibiting both electromagnetic and gravitational wave signatures, Comment: Nature Astronomy Meeting Report for KITP Rapid Response Workshop

The ratio of airway tree lumen to lung size (ALR), assessed at full inspiration on high resolution full-lung computed tomography (CT), is a major risk factor for chronic obstructive pulmonary disease (COPD). There is growing interest to infer ALR from cardiac CT images, which are widely available in epidemiological cohorts, to investigate the relationship of ALR to severe COVID-19 and post-acute sequelae of SARS-CoV-2 infection (PASC). Previously, cardiac scans included approximately 2/3 of the total lung volume with 5-6x greater slice thickness than high-resolution (HR) full-lung (FL) CT. In this study, we present a novel attention-based Multi-view Swin Transformer to infer FL ALR values from segmented cardiac CT scans. For the supervised training we exploit paired full-lung and cardiac CTs acquired in the Multi-Ethnic Study of Atherosclerosis (MESA). Our network significantly outperforms a proxy direct ALR inference on segmented cardiac CT scans and achieves accuracy and reproducibility comparable with a scan-rescan reproducibility of the FL ALR ground-truth., Comment: Accepted to appear in Proceedings of International Symposium on Biomedical Imaging (ISBI), 2025

Safety is the main concern in the aviation industry, where even minor operational issues can lead to serious consequences. This study addresses the need for comprehensive aviation accident analysis by leveraging natural language processing (NLP) and advanced AI models to classify the phase of flight from unstructured aviation accident analysis narratives. The research aims to determine whether the phase of flight can be inferred from narratives of post-accident events using NLP techniques. The classification performance of various deep learning models was evaluated. For single RNN-based models, LSTM achieved an accuracy of 63%, precision 60%, and recall 61%. BiLSTM recorded an accuracy of 64%, precision 63%, and a recall of 64%. GRU exhibited balanced performance with an accuracy and recall of 60% and a precision of 63%. Joint RNN-based models further enhanced predictive capabilities. GRU-LSTM, LSTM-BiLSTM, and GRU-BiLSTM demonstrated accuracy rates of 62%, 67%, and 60%, respectively, showcasing the benefits of combining these architectures. To provide a comprehensive overview of model performance, single and combined models were compared in terms of the various metrics. These results underscore the models' capacity to classify the phase of flight from raw text narratives, equipping aviation industry stakeholders with valuable insights for proactive decision-making. Therefore, this research signifies a substantial advancement in the application of NLP and deep learning models to enhance aviation safety., Comment: Aviation Safety, Deep learning algorithms, Flight phase, NLP, ASN, and Classification

Aviation safety is paramount, demanding precise analysis of safety occurrences during different flight phases. This study employs Natural Language Processing (NLP) and Deep Learning models, including LSTM, CNN, Bidirectional LSTM (BLSTM), and simple Recurrent Neural Networks (sRNN), to classify flight phases in safety reports from the Australian Transport Safety Bureau (ATSB). The models exhibited high accuracy, precision, recall, and F1 scores, with LSTM achieving the highest performance of 87%, 88%, 87%, and 88%, respectively. This performance highlights their effectiveness in automating safety occurrence analysis. The integration of NLP and Deep Learning technologies promises transformative enhancements in aviation safety analysis, enabling targeted safety measures and streamlined report handling., Comment: NLP, Aviation Safety, ATSB, Deep learning, Flight phase. arXiv admin note: substantial text overlap with arXiv:2501.01694

Aviation safety is a global concern, requiring detailed investigations into incidents to understand contributing factors comprehensively. This study uses the National Transportation Safety Board (NTSB) dataset. It applies advanced natural language processing (NLP) techniques, including Latent Dirichlet Allocation (LDA), Non-Negative Matrix Factorization (NMF), Latent Semantic Analysis (LSA), Probabilistic Latent Semantic Analysis (pLSA), and K-means clustering. The main objectives are identifying latent themes, exploring semantic relationships, assessing probabilistic connections, and cluster incidents based on shared characteristics. This research contributes to aviation safety by providing insights into incident narratives and demonstrating the versatility of NLP and topic modelling techniques in extracting valuable information from complex datasets. The results, including topics identified from various techniques, provide an understanding of recurring themes. Comparative analysis reveals that LDA performed best with a coherence value of 0.597, pLSA of 0.583, LSA of 0.542, and NMF of 0.437. K-means clustering further reveals commonalities and unique insights into incident narratives. In conclusion, this study uncovers latent patterns and thematic structures within incident narratives, offering a comparative analysis of multiple-topic modelling techniques. Future research avenues include exploring temporal patterns, incorporating additional datasets, and developing predictive models for early identification of safety issues. This research lays the groundwork for enhancing the understanding and improvement of aviation safety by utilising the wealth of information embedded in incident narratives.

Deep operator network (DeepONet) has shown great promise as a surrogate model for systems governed by partial differential equations (PDEs), learning mappings between infinite-dimensional function spaces with high accuracy. However, achieving low generalization errors often requires highly overparameterized networks, posing significant challenges for large-scale, complex systems. To address these challenges, latent DeepONet was proposed, introducing a two-step approach: first, a reduced-order model is used to learn a low-dimensional latent space, followed by operator learning on this latent space. While effective, this method is inherently data-driven, relying on large datasets and making it difficult to incorporate governing physics into the framework. Additionally, the decoupled nature of these steps prevents end-to-end optimization and the ability to handle data scarcity. This work introduces PI-Latent-NO, a physics-informed latent operator learning framework that overcomes these limitations. Our architecture employs two coupled DeepONets in an end-to-end training scheme: the first, termed Latent-DeepONet, identifies and learns the low-dimensional latent space, while the second, Reconstruction-DeepONet, maps the latent representations back to the original physical space. By integrating governing physics directly into the training process, our approach requires significantly fewer data samples while achieving high accuracy. Furthermore, the framework is computationally and memory efficient, exhibiting nearly constant scaling behavior on a single GPU and demonstrating the potential for further efficiency gains with distributed training. We validate the proposed method on high-dimensional parametric PDEs, demonstrating its effectiveness as a proof of concept and its potential scalability for large-scale systems.

The air transport system recognizes the criticality of safety, as even minor anomalies can have severe consequences. Reporting accidents and incidents play a vital role in identifying their causes and proposing safety recommendations. However, the narratives describing pre-accident events are presented in unstructured text that is not easily understood by computer systems. Classifying and categorizing safety occurrences based on these narratives can support informed decision-making by aviation industry stakeholders. In this study, researchers applied natural language processing (NLP) and artificial intelligence (AI) models to process text narratives to classify the flight phases of safety occurrences. The classification performance of two deep learning models, ResNet and sRNN was evaluated, using an initial dataset of 27,000 safety occurrence reports from the NTSB. The results demonstrated good performance, with both models achieving an accuracy exceeding 68%, well above the random guess rate of 14% for a seven-class classification problem. The models also exhibited high precision, recall, and F1 scores. The sRNN model greatly outperformed the simplified ResNet model architecture used in this study. These findings indicate that NLP and deep learning models can infer the flight phase from raw text narratives, enabling effective analysis of safety occurrences., Comment: NLP, Aviation reports, Text analysis, Deep learning algorithms, Flight phase classification

Safety is a critical aspect of the air transport system given even slight operational anomalies can result in serious consequences. To reduce the chances of aviation safety occurrences, accidents and incidents are reported to establish the root cause, propose safety recommendations etc. However, analysis narratives of the pre-accident events are presented using human-understandable, raw, unstructured, text that a computer system cannot understand. The ability to classify and categorise safety occurrences from their textual narratives would help aviation industry stakeholders make informed safety-critical decisions. To classify and categorise safety occurrences, we applied natural language processing (NLP) and AI (Artificial Intelligence) models to process text narratives. The study aimed to answer the question. How well can the damage level caused to the aircraft in a safety occurrence be inferred from the text narrative using natural language processing. The classification performance of various deep learning models including LSTM, BLSTM, GRU, sRNN, and combinations of these models including LSTM and GRU, BLSTM+GRU, sRNN and LSTM, sRNN and BLSTM, sRNN and GRU, sRNN and BLSTM and GRU, and sRNN and LSTM and GRU was evaluated on a set of 27,000 safety occurrence reports from the NTSB. The results of this study indicate that all models investigated performed competitively well recording an accuracy of over 87.9% which is well above the random guess of 25% for a four-class classification problem. Also, the models recorded high precision, recall, and F1 scores above 80%, 88%, and 85%, respectively. sRNN slightly outperformed other single models in terms of recall (90%) and accuracy (90%) while LSTM reported slightly better performance in terms of precision (87%).

We simulate the Lipkin-Meshkov-Glick (LMG) model using the Variational-Quantum-Eigensolver (VQE) algorithm on a neutral atom quantum computer. We test the ground-state energy of spin systems with up to 15 spins. Two different encoding schemes are used: an individual spin encoding where each spin is represented by one qubit, and an efficient Gray code encoding scheme which only requires a number of qubits that scales with the logarithm of the number of spins. This more efficient encoding, together with zero noise extrapolation techniques, is shown to improve the fidelity of the simulated energies with respect to exact solutions., Comment: v2: added additional references

Explainable AI (XAI) in medical histopathology is essential for enhancing the interpretability and clinical trustworthiness of deep learning models in cancer diagnosis. However, the black-box nature of these models often limits their clinical adoption. We introduce GRAPHITE (Graph-based Interpretable Tissue Examination), a post-hoc explainable framework designed for breast cancer tissue microarray (TMA) analysis. GRAPHITE employs a multiscale approach, extracting patches at various magnification levels, constructing an hierarchical graph, and utilising graph attention networks (GAT) with scalewise attention (SAN) to capture scale-dependent features. We trained the model on 140 tumour TMA cores and four benign whole slide images from which 140 benign samples were created, and tested it on 53 pathologist-annotated TMA samples. GRAPHITE outperformed traditional XAI methods, achieving a mean average precision (mAP) of 0.56, an area under the receiver operating characteristic curve (AUROC) of 0.94, and a threshold robustness (ThR) of 0.70, indicating that the model maintains high performance across a wide range of thresholds. In clinical utility, GRAPHITE achieved the highest area under the decision curve (AUDC) of 4.17e+5, indicating reliable decision support across thresholds. These results highlight GRAPHITE's potential as a clinically valuable tool in computational pathology, providing interpretable visualisations that align with the pathologists' diagnostic reasoning and support precision medicine., Comment: 24 Pages, 9 Figures, 1 Tables

We present multi-wavelength analysis of ZTF23abelseb (AT 2023sva), an optically discovered fast-fading ($\Delta m_r = 2.2$ mag in $\Delta t = 0.74 $ days), luminous ($M_r \sim -30.0$ mag) and red ($g-r = 0.50$ mag) transient at $z = 2.28$ with accompanying luminous radio emission. AT 2023sva does not possess a $\gamma$-ray burst (GRB) counterpart to an isotropic equivalent energy limit of $E_{\rm{\gamma, \, iso}} < 1.6 \times 10^{52}$ erg, determined through searching $\gamma$-ray satellite archives between the last non-detection and first detection, making it the sixth example of an optically-discovered afterglow with a redshift measurement and no detected GRB counterpart. We analyze AT 2023sva's optical, radio, and X-ray observations to characterize the source. From radio analyses, we find the clear presence of strong interstellar scintillation (ISS) 72 days after the initial explosion, allowing us to place constraints on the source's angular size and bulk Lorentz factor. When comparing the source sizes derived from ISS of orphan events to those of the classical GRB population, we find orphan events have statistically smaller source sizes. We also utilize Bayesian techniques to model the multi-wavelength afterglow. Within this framework, we find evidence that AT 2023sva possesses a shallow power-law structured jet viewed slightly off-axis ($\theta_{\rm{v}} = 0.07 \pm 0.02$) just outside of the jet's core opening angle ($\theta_{\rm{c}} = 0.06 \pm 0.02$). We determine this is likely the reason for the lack of a detected GRB counterpart, but also investigate other scenarios. AT 2023sva's evidence for possessing a structured jet stresses the importance of broadening orphan afterglow search strategies to a diverse range of GRB jet angular energy profiles, to maximize the return of future optical surveys., Comment: 22 pages, 14 Figures, Submitted to MNRAS

Cosmic strings are powerful witnesses to cosmic events including any period of early matter domination. If such a period of matter domination was catalysed by metastable, long-lived particles, then there will be complementary signals to ascertain the nature of dark sector in experiments detecting primordial features in the gravitational wave (GW) power spectrum and laboratory searches for long-lived particles. We give explicit examples of global and local U(1) gauge extended dark sectors to demonstrate such a complementarity as the union of the two experiments reveals more information about the dark sector than either experiment. Demanding that Higgs-portal long-lived scalar be looked for, in various experiments such as DUNE, FASER, FASER-II, MATHUSLA, SHiP, we identify the parameter space which leads to complementary observables for GW detectors such as LISA and ET., Comment: 23 pages + references, 6 figures, comments are welcome

This study explores using Natural Language Processing in aviation safety, focusing on machine learning algorithms to enhance safety measures. There are currently May 2024, 34 Scopus results from the keyword search natural language processing and aviation safety. Analyzing these studies allows us to uncover trends in the methodologies, findings and implications of NLP in aviation. Both qualitative and quantitative tools have been used to investigate the current state of literature on NLP for aviation safety. The qualitative analysis summarises the research motivations, objectives, and outcomes, showing how NLP can be utilized to help identify critical safety issues and improve aviation safety. This study also identifies research gaps and suggests areas for future exploration, providing practical recommendations for the aviation industry. We discuss challenges in implementing NLP in aviation safety, such as the need for large, annotated datasets, and the difficulty in interpreting complex models. We propose solutions like active learning for data annotation and explainable AI for model interpretation. Case studies demonstrate the successful application of NLP in improving aviation safety, highlighting its potential to make aviation safer and more efficient.

Given the paramount importance of safety in the aviation industry, even minor operational anomalies can have significant consequences. Comprehensive documentation of incidents and accidents serves to identify root causes and propose safety measures. However, the unstructured nature of incident event narratives poses a challenge for computer systems to interpret. Our study aimed to leverage Natural Language Processing (NLP) and deep learning models to analyze these narratives and classify the aircraft damage level incurred during safety occurrences. Through the implementation of LSTM, BLSTM, GRU, and sRNN deep learning models, our research yielded promising results, with all models showcasing competitive performance, achieving an accuracy of over 88% significantly surpassing the 25% random guess threshold for a four-class classification problem. Notably, the sRNN model emerged as the top performer in terms of recall and accuracy, boasting a remarkable 89%. These findings underscore the potential of NLP and deep learning models in extracting actionable insights from unstructured text narratives, particularly in evaluating the extent of aircraft damage within the realm of aviation safety occurrences.

This study explores the application of topic modelling techniques Latent Dirichlet Allocation (LDA), Nonnegative Matrix Factorization (NMF), and Probabilistic Latent Semantic Analysis (PLSA) on the Socrata dataset spanning from 1908 to 2009. Categorized by operator type (military, commercial, and private), the analysis identified key themes such as pilot error, mechanical failure, weather conditions, and training deficiencies. The study highlights the unique strengths of each method: LDA ability to uncover overlapping themes, NMF production of distinct and interpretable topics, and PLSA nuanced probabilistic insights despite interpretative complexity. Statistical analysis revealed that PLSA achieved a coherence score of 0.32 and a perplexity value of -4.6, NMF scored 0.34 and 37.1, while LDA achieved the highest coherence of 0.36 but recorded the highest perplexity at 38.2. These findings demonstrate the value of topic modelling in extracting actionable insights from unstructured aviation safety narratives, aiding in the identification of risk factors and areas for improvement across sectors. Future directions include integrating additional contextual variables, leveraging neural topic models, and enhancing aviation safety protocols. This research provides a foundation for advanced text-mining applications in aviation safety management.

Continuous gravitational waves (CWs) emission from neutron stars carries information about their internal structure and equation of state, and it can provide tests of General Relativity. We present a search for CWs from a set of 45 known pulsars in the first part of the fourth LIGO--Virgo--KAGRA observing run, known as O4a. We conducted a targeted search for each pulsar using three independent analysis methods considering the single-harmonic and the dual-harmonic emission models. We find no evidence of a CW signal in O4a data for both models and set upper limits on the signal amplitude and on the ellipticity, which quantifies the asymmetry in the neutron star mass distribution. For the single-harmonic emission model, 29 targets have the upper limit on the amplitude below the theoretical spin-down limit. The lowest upper limit on the amplitude is $6.4\!\times\!10^{-27}$ for the young energetic pulsar J0537-6910, while the lowest constraint on the ellipticity is $8.8\!\times\!10^{-9}$ for the bright nearby millisecond pulsar J0437-4715. Additionally, for a subset of 16 targets we performed a narrowband search that is more robust regarding the emission model, with no evidence of a signal. We also found no evidence of non-standard polarizations as predicted by the Brans-Dicke theory., Comment: main paper: 12 pages, 6 figures, 4 tables

White dwarfs (WDs) are the most abundant compact objects, and recent surveys have suggested that over a third of WDs in accreting binaries host a strong (B $\gtrsim$ 1 MG) magnetic field. However, the origin and evolution of WD magnetism remain under debate. Two WD pulsars, AR Sco and J191213.72-441045.1 (J1912), have been found, which are non-accreting binaries hosting rapidly spinning (1.97-min and 5.30-min, respectively) magnetic WDs. The WD in AR Sco is slowing down on a $P/\dot{P}\approx 5.6\times 10^6$ yr timescale. It is believed they will eventually become polars, accreting systems in which a magnetic WD (B $\approx 10-240$ MG) accretes from a Roche lobe-filling donor spinning in sync with the orbit ($\gtrsim 78$ min). Here, we present multiwavelength data and analysis of Gaia22ayj, which outbursted in March 2022. We find that Gaia22ayj is a magnetic accreting WD that is rapidly spinning down ($P/\dot{P} = 6.1^{+0.3}_{-0.2}\times 10^6$ yr) like WD pulsars, but shows clear evidence of accretion, like polars. Strong linear polarization (40%) is detected in Gaia22ayj; such high levels have only been seen in the WD pulsar AR Sco and demonstrate the WD is magnetic. High speed photometry reveals a 9.36-min period accompanying a high amplitude ($\sim 2$ mag) modulation. We associate this with a WD spin or spin-orbit beat period, not an orbital period as was previously suggested. Fast (60-s) optical spectroscopy reveals a broad ``hump'', reminiscent of cyclotron emission in polars, between 4000-8000 Angstrom. We find an X-ray luminosity of $L_X = 2.7_{-0.8}^{+6.2}\times10^{32} \textrm{ erg s}^{-1}$ in the 0.3-8 keV energy range, while two VLA radio campaigns resulted in a non-detection with a $F_r < 15.8\mu\textrm{Jy}$ 3$ \sigma$ upper limit. The shared properties of both WD pulsars and polars suggest that Gaia22ayj is a missing link between the two classes of magnetic WD binaries., Comment: Submitted to PASP; comments welcome

Improvements in aviation safety analysis call for innovative techniques to extract valuable insights from the abundance of textual data available in accident reports. This paper explores the application of four prominent topic modelling techniques, namely Probabilistic Latent Semantic Analysis (pLSA), Latent Semantic Analysis (LSA), Latent Dirichlet Allocation (LDA), and Non-negative Matrix Factorization (NMF), to dissect aviation incident narratives using the Australian Transport Safety Bureau (ATSB) dataset. The study examines each technique's ability to unveil latent thematic structures within the data, providing safety professionals with a systematic approach to gain actionable insights. Through a comparative analysis, this research not only showcases the potential of these methods in aviation safety but also elucidates their distinct advantages and limitations., Comment: conference paper

Ensuring safety in the aviation industry is critical, even minor anomalies can lead to severe consequences. This study evaluates the performance of four different models for DP (deep learning), including: Bidirectional Long Short-Term Memory (BLSTM), Convolutional Neural Networks (CNN), Long Short-Term Memory (LSTM), and Simple Recurrent Neural Networks (sRNN), on a multi-class classification task involving Commercial, Military, and Private categories using the Socrata aviation dataset of 4,864 records. The models were assessed using a classification report, confusion matrix analysis, accuracy metrics, validation loss and accuracy curves. Among the models, BLSTM achieved the highest overall accuracy of 72%, demonstrating superior performance in stability and balanced classification, while LSTM followed closely with 71%, excelling in recall for the Commercial class. CNN and sRNN exhibited lower accuracies of 67% and 69%, with significant misclassifications in the Private class. While the results highlight the strengths of BLSTM and LSTM in handling sequential dependencies and complex classification tasks, all models faced challenges with class imbalance, particularly in predicting the Military and Private categories. Addressing these limitations through data augmentation, advanced feature engineering, and ensemble learning techniques could enhance classification accuracy and robustness. This study underscores the importance of selecting appropriate architectures for domain specific tasks, Comment: conference paper; aviation safety, NLP, DL, operational record classification, Socrata

Designing structured visuals such as presentation slides is essential for communicative needs, necessitating both content creation and visual planning skills. In this work, we tackle the challenge of automated slide generation, where models produce slide presentations from natural language (NL) instructions. We first introduce the SlidesBench benchmark, the first benchmark for slide generation with 7k training and 585 testing examples derived from 310 slide decks across 10 domains. SlidesBench supports evaluations that are (i)reference-based to measure similarity to a target slide, and (ii)reference-free to measure the design quality of generated slides alone. We benchmark end-to-end image generation and program generation methods with a variety of models, and find that programmatic methods produce higher-quality slides in user-interactable formats. Built on the success of program generation, we create AutoPresent, an 8B Llama-based model trained on 7k pairs of instructions paired with code for slide generation, and achieve results comparable to the closed-source model GPT-4o. We further explore iterative design refinement where the model is tasked to self-refine its own output, and we found that this process improves the slide's quality. We hope that our work will provide a basis for future work on generating structured visuals.

We study the torus-equivariant cohomology of weighted flag varieties, and prove a positivity property in the equivariant cohomology and Chow groups of weighted flag varieties, analogous to the non-weighted positivity proved in [Graham 2001]. Our result strengthens and generalizes the positivity proved for weighted Grassmannians by [Abe-Matsumura 2015]. The positivity property is expressed in terms of weighted roots, which are used to describe weights of torus equivariant curves in weighted flag varieties. This provides a geometric interpretation of the parameters used in [Abe-Matsumura 2015]. We approach weighted flag varieties from a uniform Lie-theoretic point of view, providing a more general definition than has appeared previously, and prove other general results about weighted flag varieties in this setting, including a Borel presentation of the equivariant cohomology. In addition, we generalize some results obtained for weighted Grassmannians or more generally type $A$ ([Abe-Matsumura 2015], [Azam-Nazir-Qureshi 2020]); in particular, we obtain a weighted Chevalley formula, descriptions of restrictions to fixed points, the GKM description of the cohomology, and a weighted Chevalley formula.

In this paper we develop a systematic calculus for the Duistermaat index, a symplectic invariant defined for triples of Lagrangian subspaces. Introduced nearly half a century ago, this index has lately been the subject of renewed attention, due to its central role in eigenvalue interlacing problems on quantum graphs (and more abstractly for self-adjoint extensions of symmetric operators). Here we give an axiomatic characterization of the index that leads to elementary proofs of its fundamental properties. We also relate the index to other quantities often appearing in symplectic geometry, such as the H\"ormander--Kashiwara--Wall index and the Maslov index. Among other things, this leads to a curious formula for the Morse index of a difference of Hermitian matrices., Comment: 15 pages

We present SWE-Gym, the first environment for training real-world software engineering (SWE) agents. SWE-Gym contains 2,438 real-world Python task instances, each comprising a codebase with an executable runtime environment, unit tests, and a task specified in natural language. We use SWE-Gym to train language model based SWE agents , achieving up to 19% absolute gains in resolve rate on the popular SWE-Bench Verified and Lite test sets. We also experiment with inference-time scaling through verifiers trained on agent trajectories sampled from SWE-Gym. When combined with our fine-tuned SWE agents, we achieve 32.0% and 26.0% on SWE-Bench Verified and Lite, respectively, reflecting a new state-of-the-art for open-weight SWE agents. To facilitate further research, we publicly release SWE-Gym, models, and agent trajectories., Comment: Code at https://github.com/SWE-Gym/SWE-Gym

The impact of cell segregation and margination in blood disorders on microcirculatory hemodynamics within bifurcated vessels are physiologically significant, yet poorly understood. This study presents a comprehensive computational investigation of red blood cell (RBC) suspension dynamics, with a focus on a model of sickle cell disease (SCD) as an example of a disorder associated with subpopulations of aberrant RBCs. The findings reveal how cell margination influences cellular partitioning and distributions as well as vessel wall shear stress (WSS) at vascular bifurcations. Normal RBCs, which migrate toward the channel center, exhibit the Zweifach-Fung effect, preferentially entering high-flow-rate branches. In contrast, sickle cells, which marginate near the vessel wall, demonstrate an anti-Zweifach-Fung effect, favoring lower-flow-rate branches due to their position within the cell-free layer (CFL). The upstream segregation of cells remains downstream through the bifurcation, where sickle cells accumulate along the outer branch walls. This accumulation of sickle cells increases the frequency of high WSS events via direct physical interactions, particularly on the outer side of high-velocity branches, potentially contributing to the vascular damage and endothelial disruption observed in many disorders that affect RBCs. In geometrically asymmetric bifurcations, cells preferentially enter branches with larger radii, underscoring the influence of geometric complexity on microcirculatory blood flow. These findings provide insights into microvascular hemodynamics in SCD and other blood disorders.

We study the effect of the two independent parity-even cubic interactions $I_1 = {R^{\alpha \beta}}_{\mu \nu} {R^{\mu \nu}}_{\rho \sigma} {R^{\rho \sigma}}_{\alpha \beta}$ and $ G_3 = I_1 -2 {R^{\mu \nu \alpha}}_\beta {R^{\beta \gamma}}_{\nu \sigma} {R^\sigma}_{\mu \gamma \alpha}$ on the spectrum of gravitational waves emitted in the quasi-circular inspiral phase of the merger of two spinning objects. Focusing on the aligned spin configuration, we extract the corrections to Newton's potential at linear order in the perturbations, using the four-point amplitude of the massive spinning objects evaluated in the Post-Minkowskian expansion. We then derive the modifications to the quadrupole moments at leading order in the cubic perturbations, using a five-point amplitude with emission of a soft graviton. These modified moments, along with the corresponding potentials, are then employed to calculate the power emitted by gravitational waves during the inspiral phase. Using these results, we determine the changes to the waveforms, up to linear order in spin, in the Stationary Phase Approximation. Finally, we comment on the relation between cubic and tidal perturbations., Comment: 22 pages

Optical wireless communication (OWC) is a promising technology anticipated to play a key role in the next-generation network of networks. To this end, this paper details the potential of OWC, as a complementary technology to traditional radio frequency communications, in enhancing networking capabilities beyond conventional terrestrial networks. Several usage scenarios and the current state of development are presented. Furthermore, a summary of existing challenges and opportunities are provided. Emerging technologies aimed at further enhancing future OWC capabilities are introduced. Additionally, value-added OWC-based technologies that leverage the unique properties of light are discussed, including applications such as positioning and gesture recognition. The paper concludes with the reflection that OWC provides unique functionalities that can play a crucial role in building convergent and resilient future network of networks., Comment: This work has been submitted to the IEEE for possible publication. 15pp, 16 figures, and one table

Today's generative AI systems are tuned to present information by default rather than engage users in service of learning as a human tutor would. To address the wide range of potential education use cases for these systems, we reframe the challenge of injecting pedagogical behavior as one of \textit{pedagogical instruction following}, where training and evaluation examples include system-level instructions describing the specific pedagogy attributes present or desired in subsequent model turns. This framing avoids committing our models to any particular definition of pedagogy, and instead allows teachers or developers to specify desired model behavior. It also clears a path to improving Gemini models for learning -- by enabling the addition of our pedagogical data to post-training mixtures -- alongside their rapidly expanding set of capabilities. Both represent important changes from our initial tech report. We show how training with pedagogical instruction following produces a LearnLM model (available on Google AI Studio) that is preferred substantially by expert raters across a diverse set of learning scenarios, with average preference strengths of 31\% over GPT-4o, 11\% over Claude 3.5, and 13\% over the Gemini 1.5 Pro model LearnLM was based on.

The NAHU$^2$ project is a Franco-Mexican collaboration aimed at building the $\pi$-YALLI corpus adapted to machine learning, which will subsequently be used to develop computer resources for the Nahuatl language. Nahuatl is a language with few computational resources, even though it is a living language spoken by around 2 million people. We have decided to build $\pi$-YALLI, a corpus that will enable to carry out research on Nahuatl in order to develop Language Models (LM), whether dynamic or not, which will make it possible to in turn enable the development of Natural Language Processing (NLP) tools such as: a) a grapheme unifier, b) a word segmenter, c) a POS grammatical analyser, d) a content-based Automatic Text Summarization; and possibly, e) a translator translator (probabilistic or learning-based)., Comment: 9 pages, in French language, 2 figures

This paper presents a comprehensive study of a compact two-element meandered dipole array aimed at achieving super-realized gain. An optimization was performed using a Genetic Algorithm (GA) implemented in MATLAB, targeting the maximization of the realized gain in the end-fire direction. Three primary excitation schemes were evaluated: the conventional optimization to achieve superdirectivity (signal magnitude and phase angle), phase-only optimization, and uniform excitation (characterized by equal amplitude and no phase shift). The results show that by carefully optimizing both the signal magnitudes and phase angles, the array could achieve a substantial improvement in realized gain. Phase-only optimization provided a competitive realized gain with only minor reductions compared to conventional optimization, suggesting that optimizing the signal phase alone can be an effective strategy in practical implementations., Comment: A conference paper submitted to EuCAP 2025

A new dark matter candidate is proposed that arises as the lightest baryon from a confining $SU(N)$ gauge theory which equilibrates with the Standard Model only through electroweak interactions. Surprisingly, this candidate can be as light as a few GeV. The lower bound arises from the intersection of two competing requirements: i) the equilibration sector of the model must be sufficiently heavy, at least several TeV, to avoid bounds from colliders, and ii) the lightest dark meson (that may be the dark $\eta'$, $\sigma$, or the lightest glueball) has suppressed interactions with the SM, and must decay before BBN. The low energy dark sector consists of one flavor that is electrically neutral and an almost electroweak singlet. The dark matter candidate is the lightest baryon consisting of $N$ of these light flavors leading to a highly suppressed elastic scattering rate with the SM. The equilibration sector consists of vector-like dark quarks that transform under the electroweak group, ensuring that the dark sector can reach thermal equilibrium with the SM in the early Universe. The lightest dark meson lifetimes vary between $10^{-3} \lesssim c \tau \lesssim 10^7$~meters, providing an outstanding target for LHC production and experimental detection. We delineate the interplay between the lifetime of the light mesons, the suppressed direct detection cross section of the lightest baryon, and the scale of equilibration sector that can be probed at the LHC., Comment: 20 pages, 8 figures

We present the discovery of 14 new (and recovery of 4 known) low accretion rate magnetic white dwarfs in post-common envelope binaries that emit strong cyclotron emission using the Zwicky Transient Facility (ZTF) light curves, doubling the known sample size. In addition, we discovered a candidate magnetic period bouncer and recovered three known ones. We confirmed the presence of cyclotron emission using low-resolution spectra in 19 objects. Using the ZTF light curves, follow-up spectra, and the spectral energy distribution, we measured the orbital period, magnetic field strength, and white dwarf temperature of each system. Although the phase-folded light curves have diverse shapes and show a much larger variability amplitude, we show that their intrinsic properties (e.g. period distribution, magnetic field strength) are similar to those of previously known systems. The diversity in light curve shapes can be explained by differences in the optical depth of the accretion spot and geometric differences, the inclination angle and the magnetic spot latitude. The evolutionary states of the longer period binaries are somewhat uncertain but are vary; we found systems consistent with being pre-polars, detached polars, or low-state polars. In addition, we discovered two new low-state polars that likely have brown dwarf companions and could be magnetic period bouncers.

This summary of the second Terrestrial Very-Long-Baseline Atom Interferometry (TVLBAI) Workshop provides a comprehensive overview of our meeting held in London in April 2024, building on the initial discussions during the inaugural workshop held at CERN in March 2023. Like the summary of the first workshop, this document records a critical milestone for the international atom interferometry community. It documents our concerted efforts to evaluate progress, address emerging challenges, and refine strategic directions for future large-scale atom interferometry projects. Our commitment to collaboration is manifested by the integration of diverse expertise and the coordination of international resources, all aimed at advancing the frontiers of atom interferometry physics and technology, as set out in a Memorandum of Understanding signed by over 50 institutions., Comment: Summary of the second Terrestrial Very-Long-Baseline Atom Interferometry Workshop held at Imperial College London: https://indico.cern.ch/event/1369392/

Time-delay interferometry (TDI) suppresses laser frequency noise by forming linear combinations of time-shifted interferometric measurements. The time-shift operation is implemented by interpolating discretely sampled data. To enable in-band laser noise reduction by eight to nine orders of magnitude, interpolation has to be performed with high accuracy. Optimizing the design of those interpolation methods is the focus of this work. Previous research that studied constant time-shifts suggested Lagrange interpolation as the interpolation method for TDI. Its transfer function performs well at low frequency but requires a high number of coefficients. Furthermore, when applied in TDI we observed prominent time-domain features when a time-varying shift scanned over a pure integer sample shift. To limit this effect we identify an additional requirement for the interpolation kernel: when considering time-varying shifts the interpolation kernel must be sufficiently smooth to avoid unwanted time-domain transitions that produce glitch-like features in power spectral density estimates. The Lagrange interpolation kernel exhibits a discontinuous first derivative by construction, which is insufficient for the application to LISA or other space-based GW observatories. As a solution we propose a novel design method for interpolation kernels that respect a predefined requirement on in-band interpolation residuals and that possess continuous derivatives up to a prescribed order. Using this method we show that an interpolation kernel with 22 coefficients is sufficient to respect LISA's picometre-requirement and to allow for a continuous first derivative which suppresses the magnitude of the time-domain transition adequately. The reduction from 42 (Lagrange interpolation) to 22 coefficients enables us to save computational cost and increases robustness against artefacts in the data., Comment: 27 pages, 10 figures

Labourie raised the question of determining the possible asymptotics for the growth rate of compact $k$-surfaces, counted according to energy, in negatively curved $3$-manifolds, indicating the possibility of a theory of thermodynamical formalism for this class of surfaces. Motivated by this question and by analogous results for the geodesic flow, we prove a number of results concerning the asymptotic behavior of high energy $k$-surfaces, especially in relation to the curvature of the ambient space. First, we determine a rigid upper bound for the growth rate of quasi-Fuchsian $k$-surfaces, counted according to energy, and with asymptotically round limit set, subject to a lower bound on the sectional curvature of the ambient space. We also study the marked energy spectrum for $k$-surfaces, proving a number of domination and rigidity theorems in this context. Finally, we show that the marked area and energy spectra for $k$-surfaces in $3$-dimensional manifolds of negative curvature are asymptotic if and only if the sectional curvature is constant., Comment: 27 pages

Gaining a deeper understanding of the interplay between charge density wave (CDW) order and superconductivity in transition metal dichalcogenides (TMDs), particularly within the (4H/2H)-NbX$_{2}$ (X=Se,S) family, remains an open and intriguing challenge. A systematic microscopic study across various compounds in this family is therefore required to unravel this complex interplay. Here, we report on muon spin rotation and magnetotransport experiments investigating the effects of hydrostatic pressure on the superconducting transition temperature ($T_{\rm c}$), the temperature-dependent magnetic penetration depth ($\lambda_\mathrm{eff}$), and the charge density wave order (CDW) in two layered chalcogenide superconductors: 4H-NbSe$_{2}$, which exhibits CDW order, and 2H-NbS$_{2}$, which lacks such order. Our observations reveal a substantial 75$\%$ enhancement of the superfluid density ($n_{s}/m^{*}$) in 4H-NbSe$_{2}$ upon maximum applied pressure of 2 GPa, surpassing that of 2H-NbSe$_{2}$. Despite the absence of CDW order, a sizeable 20$\%$ growth in superfluid density is also observed for 2H-NbS$_{2}$ under an applied pressure of 1.8 GPa. Notably, the evaluated superconducting gaps in all these TMDs remain largely unaffected by changes in applied pressure, irrespective of pressure-induced partial suppression of CDW order in (4H/2H)-NbSe$_{2}$ or its general absence in 2H-NbS$_{2}$. These results underscore the complex nature of pressure-induced behaviors in these TMDs, challenging a simplistic view of competition solely between CDW order and superconductivity. Remarkably, the relationship between $n_{s}/m^{*}$ and $T_{\rm c}$ exhibits an unconventional correlation, indicating a noteworthy similarity with the behavior observed in cuprate, kagome, and iron-based superconductors.

In this study, we explore the characteristics of `low-mass' ($\log(M_{\star}/M_{\odot}) \leq 8$) and `intermediate-mass' ($8 \lt \log(M_{\star}/M_{\odot}) \leq 10$) host galaxies of Type Ia supernovae (SNe Ia) from the second data release (DR2) of the Zwicky Transient Facility survey and investigate their correlations with different sub-types of SNe Ia. We use the photospheric velocities measured from the Si II $\lambda$6355 feature, SALT2 light-curve stretch ($x_1$) and host-galaxy properties of SNe Ia to re-investigate the existing relationship between host galaxy mass and Si II $\lambda$6355 velocities. We also investigate sub-type preferences for host populations and show that while the more energetic and brighter 91T-like SNe Ia tends to populate the younger host populations, 91bg-like SNe Ia populate in the older populations. Our findings suggest High Velocity SNe Ia (HV SNe Ia) not only comes from the older populations but they also come from young populations as well. Therefore, while our findings can partially provide support for HV SNe Ia relating to single degenerate progenitor models, they indicate that HV SNe Ia other than being a different population, might be a continued distribution with different explosion mechanisms. We lastly investigate the specific rate of SNe Ia in the volume-limited SN Ia sample of DR2 and compare with other surveys., Comment: 13 pages, 8 figures, accepted for publication in Astronomy & Astrophysics

Dark matter could be a baryonic composite of strongly-coupled constituents transforming under SU(2)$_L$. We classify the SU(2)$_L$ representations of baryons in a class of simple confining dark sectors and find that the lightest state can be a pure singlet or a singlet that mixes with other neutral components of SU(2)$_L$ representations, which strongly suppresses the dark matter candidate's interactions with the Standard Model. We focus on models with a confining $\text{SU}(N_c)$ and heavy dark quarks constituting vector-like $N_f$-plet of SU(2)$_L$. For benchmark $N_c$ and $N_f$, we calculate baryon mass spectra, incorporating electroweak gauge boson exchange in the non-relativistic quark model, and demonstrate that above TeV mass scales, dark matter is dominantly a singlet state. The combination of this singlet nature with the recently discovered $\mathcal{H}$-parity results in an inert state analogous to noble gases, hence we coin the term Noble Dark Matter. Our results can be understood in the non-relativistic effective theory that treats the dark baryons as elementary states, where we find singlets accompanying triplets, 5-plets, or more exotic representations. This generalization of WIMP-like theories is more difficult to find or rule out than dark matter models that include only a single SU(2)$_L$ multiplet (such as a Wino), motivating new searches in colliders and a re-analysis of direct and indirect detection prospects in astrophysical observations., Comment: 8 (double-column) + 20 (single-column) pages, 2 figures, 1+3 tables

We interact with computers on an everyday basis, be it in everyday life or work, and many aspects of work can be done entirely with access to a computer and the Internet. At the same time, thanks to improvements in large language models (LLMs), there has also been a rapid development in AI agents that interact with and affect change in their surrounding environments. But how performant are AI agents at helping to accelerate or even autonomously perform work-related tasks? The answer to this question has important implications for both industry looking to adopt AI into their workflows, and for economic policy to understand the effects that adoption of AI may have on the labor market. To measure the progress of these LLM agents' performance on performing real-world professional tasks, in this paper, we introduce TheAgentCompany, an extensible benchmark for evaluating AI agents that interact with the world in similar ways to those of a digital worker: by browsing the Web, writing code, running programs, and communicating with other coworkers. We build a self-contained environment with internal web sites and data that mimics a small software company environment, and create a variety of tasks that may be performed by workers in such a company. We test baseline agents powered by both closed API-based and open-weights language models (LMs), and find that with the most competitive agent, 24% of the tasks can be completed autonomously. This paints a nuanced picture on task automation with LM agents -- in a setting simulating a real workplace, a good portion of simpler tasks could be solved autonomously, but more difficult long-horizon tasks are still beyond the reach of current systems., Comment: Preprint

Beyond conventional paradigms of translating speech and text, recently, there has been interest in automated transcreation of images to facilitate localization of visual content across different cultures. Attempts to define this as a formal Machine Learning (ML) problem have been impeded by the lack of automatic evaluation mechanisms, with previous work relying solely on human evaluation. In this paper, we seek to close this gap by proposing a suite of automatic evaluation metrics inspired by machine translation (MT) metrics, categorized into: a) Object-based, b) Embedding-based, and c) VLM-based. Drawing on theories from translation studies and real-world transcreation practices, we identify three critical dimensions of image transcreation: cultural relevance, semantic equivalence and visual similarity, and design our metrics to evaluate systems along these axes. Our results show that proprietary VLMs best identify cultural relevance and semantic equivalence, while vision-encoder representations are adept at measuring visual similarity. Meta-evaluation across 7 countries shows our metrics agree strongly with human ratings, with average segment-level correlations ranging from 0.55-0.87. Finally, through a discussion of the merits and demerits of each metric, we offer a robust framework for automated image transcreation evaluation, grounded in both theoretical foundations and practical application. Our code can be found here: https://github.com/simran-khanuja/automatic-eval-transcreation

Photosystem II (PSII) can achieve near-unity quantum efficiency of light harvesting in ideal conditions and can dissipate excess light energy as heat to prevent formation of reactive oxygen species under light stress. Understanding how this pigment-protein complex accomplishes these opposing goals is a topic of great interest that has so far been explored primarily through the lens of the system energetics. Despite PSII's known flat energy landscape, a thorough consideration of the entropic effects on energy transfer in PSII is lacking. In this work, we aim to discern the free energetic design principles underlying the PSII energy transfer network. To accomplish this goal, we employ a structure-based rate matrix and compute the free energy terms in time following a specific initial excitation to discern how entropy and enthalpy drive ensemble system dynamics. We find that the interplay between the entropy and enthalpy components differs among each protein subunit, which allows each subunit to fulfill a unique role in the energy transfer network. This individuality ensures PSII can accomplish efficient energy trapping in the RC, effective NPQ in the periphery, and robust energy trapping in the other-monomer RC if the same-monomer RC is closed. We also show that entropy, in particular, is a dynamically tunable feature of the PSII free energy landscape accomplished through regulation of LHCII binding. These findings help rationalize natural photosynthesis and provide design principles for novel, more efficient solar energy harvesting technologies., Comment: 9 pages, 5 figures

This paper addresses key challenges in object-centric representation learning of video. While existing approaches struggle with complex scenes, we propose a novel weakly-supervised framework that emphasises geometric understanding and leverages pre-trained vision models to enhance object discovery. Our method introduces an efficient slot decoder specifically designed for object-centric learning, enabling effective representation of multi-object scenes without requiring explicit depth information. Results on synthetic video benchmarks with increasing complexity in terms of objects and their movement, object occlusion and camera motion demonstrate that our approach achieves comparable performance to supervised methods while maintaining computational efficiency. This advances the field towards more practical applications in complex real-world scenarios., Comment: 6 pages, 4 Figures, 2 Tables

DNA barcodes are crucial in biodiversity analysis for building automatic identification systems that recognize known species and discover unseen species. Unlike human genome modeling, barcode-based invertebrate identification poses challenges in the vast diversity of species and taxonomic complexity. Among Transformer-based foundation models, BarcodeBERT excelled in species-level identification of invertebrates, highlighting the effectiveness of self-supervised pretraining on barcode-specific datasets. Recently, structured state space models (SSMs) have emerged, with a time complexity that scales sub-quadratically with the context length. SSMs provide an efficient parameterization of sequence modeling relative to attention-based architectures. Given the success of Mamba and Mamba-2 in natural language, we designed BarcodeMamba, a performant and efficient foundation model for DNA barcodes in biodiversity analysis. We conducted a comprehensive ablation study on the impacts of self-supervised training and tokenization methods, and compared both versions of Mamba layers in terms of expressiveness and their capacity to identify "unseen" species held back from training. Our study shows that BarcodeMamba has better performance than BarcodeBERT even when using only 8.3% as many parameters, and improves accuracy to 99.2% on species-level accuracy in linear probing without fine-tuning for "seen" species. In our scaling study, BarcodeMamba with 63.6% of BarcodeBERT's parameters achieved 70.2% genus-level accuracy in 1-nearest neighbor (1-NN) probing for unseen species. The code repository to reproduce our experiments is available at https://github.com/bioscan-ml/BarcodeMamba., Comment: 9 pages, 2 figures, accepted at Foundation Models for Science: Progress, Opportunities, and Challenges Workshop (NeurIPS 2024)

Direct observation of proton emission with very small emission energy is often unfeasible due to the long partial half-lives associated with tunneling through the Coulomb barrier. Therefore proton emitters with very small Q-values may require masses of both parent and daughter nuclei to establish them as proton unbound. Nuclear mass models have been used to predict the proton drip-line of the thulium (Tm) isotopic chain ($Z=69$), but up until now the proton separation energy has not been experimentally tested. Mass measurements were therefore performed using a Multiple Reflection Time-Of-Flight Mass Spectrometer (MR-TOF-MS) at TRIUMF's TITAN facility to definitively map the limit of proton-bound Tm. The masses of neutron-deficient, $^{149}$Tm and $^{150}$Tm, combined with measurements of $^{149m,g}$Er (which were found to deviate from literature by $\sim$150 keV), provide the first experimental confirmation that $^{149}$Tm is the first proton-unbound nuclide in the Tm chain. Our measurements also enable the strength of the $N=82$ neutron shell gap to be determined at the Tm proton drip-line, providing evidence supporting its continued existence.

We investigate electron heating by magnetic-field-aligned electric fields ($E_\parallel$) during anti-parallel magnetic reconnection in the Earth's magnetotail. Using a statistical sample of 140 reconnection outflows, we infer the acceleration potential associated with $E_\parallel$ from the shape of the electron velocity distribution functions. We show that heating by $E_\parallel$ in the reconnection outflow can reach up to ten times the inflow electron temperature. We demonstrate that the magnitude of the acceleration potential scales with the inflow Alfv\'en and electron thermal speeds to maintain quasi-neutrality in the reconnection region. Our results suggest that $E_\parallel$ plays a major role in the ion-to-electron energy partition associated with magnetic reconnection.

The extremely narrow natural linewidths of rovibrational energy levels in the molecular hydrogen ion $\textrm{H}_2^{\,+}$, and the prospect of synthesising its antimatter counterpart $\overline{\textrm{H}}_2^{\,-}$, make it a promising candidate for high-precision tests of fundamental symmetries such as Lorentz and CPT invariance. In this paper, we present a detailed analysis of the rovibrational spectrum of the (anti-)hydrogen molecular ion in a low-energy effective theory incorporating Lorentz and CPT violation. The focus is on the spin-independent couplings in this theory, for which the best current bounds come from measurements of the 1S-2S transition in atomic hydrogen and antihydrogen. We show that in addition to the improvement in these bounds from the increased precision of the transition frequencies, potentially reaching 1 part in $10^{17}$, rovibrational transitions have an enhanced sensitivity to Lorentz and CPT violation of $O(m_p/m_e)$ in the proton (hadron) sector compared to atomic transitions., Comment: 55 pages, 9 figures