D.3 Open source performance on mini test set . . . . . . . . . . . . . . . . . . . . . A.1 V ersion 2 We have fixed some bugs in the evaluation code, resulting in slight differences compared to the previous release. The issue was that 149 samples were not evaluated in the previous version, and these have now been included in the new update. A.2 V ersion 3 We have clarified certain statements and added experimental results to address the reviewer's questions. B.1 Limitations Despite these advancements, our dataset does exhibit certain limitations, largely stemming from inherited biases from the source datasets: Currently, we only address scenarios where both the question and the answer span a single time duration. Given a question, the annotated time span must be a single, continuous duration, which might be limiting for all scenes. The presence of noisy or inaccurate annotations in the source datasets, including captions and timestamps, poses a challenge. Despite our efforts, some of these errors could not be automatically filtered out. The extent of this issue is detailed in the qualitative visualization conducted by our human reviewers, as presented in supplementary. The average duration of ground truth events in our dataset is relatively long. This characteristic has the unintended consequence of hindering the models' ability to detect and analyze fine-grained actions within shorter video segments. These drawbacks highlight areas for potential improvement and indicate the necessity for ongoing refinement to ensure the creation of more accurate and unbiased video language models. B.2 Social Impact Though we provide an assessment of temporal reasoning and moment localization, the types and scene diversity are still limited. We inherit the video classes from the two source video datasets, which may not be sufficient for a comprehensive assessment of all kinds of temporal reasoning. This limitation could introduce a bias. For both curated data and video data, they do not contain any personally identifiable information. Besides, some of the video samples in the source datasets might be slightly uncomfortable depending on the viewer. For example, some videos discuss tattoos and piercings, and some of them present news about social events including demonstrations or war reports. However, we only release the data of curated question-answer and time span.

For Speech Detection, a MEG-oriented SpecAugment provided a first exploration of MEG-specific augmentation. For Phoneme Classification, we used inverse-square-root class weighting and a dynamic grouping loader to handle 100-sample averaged examples. In addition, a simple instance-level normalization proved critical to mitigate distribution shifts on the holdout split. Using the official Standard track splits and F1-macro for model selection, our best systems achieved 88.9% (Speech) and 65.8% (Phoneme) on the leaderboard, surpassing the competition baselines and ranking within the top-10 in both tasks.

Objective: Chagas disease is a parasitic infection that is endemic to South America, Central America, and, more recently, the U.S., primarily transmitted by insects. Chronic Chagas disease can cause cardiovascular diseases and digestive problems. Serological testing capacities for Chagas disease are limited, but Chagas cardiomyopathy often manifests in ECGs, providing an opportunity to prioritize patients for testing and treatment. Approach: The George B. Moody PhysioNet Challenge 2025 invites teams to develop algorithmic approaches for identifying Chagas disease from electrocardiograms (ECGs). Main results: This Challenge provides multiple innovations. First, we leveraged several datasets with labels from patient reports and serological testing, provided a large dataset with weak labels and smaller datasets with strong labels. Second, we augmented the data to support model robustness and generalizability to unseen data sources. Third, we applied an evaluation metric that captured the local serological testing capacity for Chagas disease to frame the machine learning problem as a triage task. Significance: Over 630 participants from 111 teams submitted over 1300 entries during the Challenge, representing diverse approaches from academia and industry worldwide.

Contrast-enhanced spectral mammography (CESM) is an imaging modality that provides two types of images, commonly known as low-energy (LE) and dual-energy subtracted (DES) images. In many domains, particularly in medicine, the emergence of image-to-image translation techniques has enabled the artificial generation of images using other images as input. Within CESM, applying such techniques to generate DES images from LE images could be highly beneficial, potentially reducing patient exposure to radiation associated with high-energy image acquisition. In this study, we investigated three models for the artificial generation of DES images (virtual DES): a pre-trained U-Net model, a U-Net trained end-to-end model, and a CycleGAN model. We also performed a series of experiments to assess the impact of using virtual DES images on the classification of CESM examinations into malignant and non-malignant categories. To our knowledge, this is the first study to evaluate the impact of virtual DES images on CESM lesion classification. The results demonstrate that the best performance was achieved with the pre-trained U-Net model, yielding an F1 score of 85.59% when using the virtual DES images, compared to 90.35% with the real DES images. This discrepancy likely results from the additional diagnostic information in real DES images, which contributes to a higher classification accuracy. Nevertheless, the potential for virtual DES image generation is considerable and future advancements may narrow this performance gap to a level where exclusive reliance on virtual DES images becomes clinically viable.

High-energy cosmic rays and gamma quanta colliding with the upper atmosphere produce cascades of secondary particles known as extensive air showers (EASs). These showers can be detected and recorded using a variety of telescopes such as imaging atmospheric Cherenkov telescopes (IACTs), arrays of wide-angle integrating air detectors or water detectors; some experiments such as TAIGA [1] and LHAASO [2] combine several telescope types. The data from these observations can be used to identify the primary particle type and estimate its parameters such as energy and direction. In this paper, we estimate the EAS direction which is of interest because it can identify the gamma radiation source and is important in estimating the energy of the primary particle. Highly accurate shower direction estimates can be obtained from the timing measurements of multiple detectors spread over a large area such as TAIGA HiSCORE [3], LHAASO, or HAWC [4]. We use simulated data from TAIGA HiSCORE which is a non-imaging array of wide field-of-view integrating air Cherenkov detector stations. We use artificial neural networks (ANNs) to obtain shower direction estimates. Convolutional neural networks seem like a natural choice for the problem since the HiSCORE stations are positioned on a grid. However, the previous work using this approach [5, 6] produced estimates that were significantly less accurate than previously developed methods, e.g.

The aviation industry is vital for global transportation but faces increasing pressure to reduce its environmental footprint, particularly CO2 emissions from ground operations such as taxiing. Single Engine Taxiing (SET) has emerged as a promising technique to enhance fuel efficiency and sustainability. However, evaluating SET's benefits is hindered by the limited availability of SET-specific data, typically accessible only to aircraft operators. In this paper, we present a novel deep learning approach to detect SET operations using ground trajectory data. Our method involves using proprietary Quick Access Recorder (QAR) data of A320 flights to label ground movements as SET or conventional taxiing during taxi-in operations, while using only trajectory features equivalent to those available in open-source surveillance systems such as Automatic Dependent Surveillance-Broadcast (ADS-B) or ground radar. This demonstrates that SET can be inferred from ground movement patterns, paving the way for future work with non-proprietary data sources. Our results highlight the potential of deep learning to improve SET detection and support more comprehensive environmental impact assessments.

We contrast high effectiveness of state of the art deep learning architectures designed for general audio classification tasks, refined for respiratory insufficiency (RI) detection and blood oxygen saturation (SpO$_2$) estimation and classification through automated audio analysis. Recently, multiple deep learning architectures have been proposed to detect RI in COVID patients through audio analysis, achieving accuracy above 95% and F1-score above 0.93. RI is a condition associated with low SpO$_2$ levels, commonly defined as the threshold SpO$_2$ <92%. While SpO$_2$ serves as a crucial determinant of RI, a medical doctor's diagnosis typically relies on multiple factors. These include respiratory frequency, heart rate, SpO$_2$ levels, among others. Here we study pretrained audio neural networks (CNN6, CNN10 and CNN14) and the Masked Autoencoder (Audio-MAE) for RI detection, where these models achieve near perfect accuracy, surpassing previous results. Yet, for the regression task of estimating SpO$_2$ levels, the models achieve root mean square error values exceeding the accepted clinical range of 3.5% for finger oximeters. Additionally, Pearson correlation coefficients fail to surpass 0.3. As deep learning models perform better in classification than regression, we transform SpO$_2$-regression into a SpO$_2$-threshold binary classification problem, with a threshold of 92%. However, this task still yields an F1-score below 0.65. Thus, audio analysis offers valuable insights into a patient's RI status, but does not provide accurate information about actual SpO$_2$ levels, indicating a separation of domains in which voice and speech biomarkers may and may not be useful in medical diagnostics under current technologies.

ML-based computer vision models are promising tools for supporting emergency management operations following natural disasters. Arial photographs taken from small manned and unmanned aircraft can be available soon after a disaster and provide valuable information from multiple perspectives for situational awareness and damage assessment applications. However, emergency managers often face challenges finding the most relevant photos among the tens of thousands that may be taken after an incident. While ML-based solutions could enable more effective use of aerial photographs, there is still a lack of training data for imagery of this type from multiple perspectives and for multiple hazard types. To address this, we present the LADI v2 (Low Altitude Disaster Imagery version 2) dataset, a curated set of about 10,000 disaster images captured in the United States by the Civil Air Patrol (CAP) in response to federally-declared emergencies (2015-2023) and annotated for multi-label classification by trained CAP volunteers. We also provide two pretrained baseline classifiers and compare their performance to state-of-the-art vision-language models in multi-label classification. The data and code are released publicly to support the development of computer vision models for emergency management research and applications.