GNN s deteriorating rapidly as the number of sensors and actuators grows.

A large Russian aerial strike on Ukraine has again left half of Kyiv's residential buildings without heating or power as temperatures across the country continue to hover around -10C. Drones, ballistic and cruise missiles targeted several locations in Ukraine, including Kyiv, Dnipro in the centre and Odesa in the south. Air raid alerts in the capital lasted for most of the night. On Tuesday, sirens rang out again as Russian drones and cruise missiles approached the capital. President Volodymyr Zelensky said a significant number of targets had been intercepted.

CC BY and have been used extensively by the research communities. Fine-tuning Table A2 summarizes the hyperparameters used for ASR fine-tuning. By default, the one pre-trained with modality dropout is used. Table B4 shows how fine-tuning modality dropout configurations affect ASR performance. Next, we study the impact of hyperparameters when fine-tuning on unimodal data.

This research discusses the critical need for early detection and treatment for early prediction of Freezing of Gaits (FOG) utilizing a wearable sensor technology powered with LoRa communication. The system consisted of an Esp-32 microcontroller, in which the trained model is utilized utilizing the Micromlgen Python library. The research investigates accurate FOG classification based on pertinent clinical data by utilizing machine learning (ML) algorithms like Catboost, XGBoost, and Extra Tree classifiers. The XGBoost could classify with approximately 97% accuracy, along with 96% for the catboost and 90% for the Extra Trees Classifier model. The SHAP analysis interpretability shows that GYR SI degree is the most affecting factor in the prediction of the diseases. These results show the possibility of monitoring and identifying the affected person with tracking location on GPS and providing aid as an assistive technology for aiding the affected. The developed sensor-based technology has great potential for real-world problem solving in the field of healthcare and biomedical technology enhancements.

Integration of diverse data will be a pivotal step towards improving scientific explorations in many disciplines. This work establishes a vision-language model (VLM) that encodes videos with text input in order to classify various behaviors of a mouse existing in and engaging with their environment. Importantly, this model produces a behavioral vector over time for each subject and for each session the subject undergoes. The output is a valuable dataset that few programs are able to produce with as high accuracy and with minimal user input. Specifically, we use the open-source Qwen2.5-VL model and enhance its performance through prompts, in-context learning (ICL) with labeled examples, and frame-level preprocessing. We found that each of these methods contributes to improved classification, and that combining them results in strong F1 scores across all behaviors, including rare classes like freezing and fleeing, without any model fine-tuning. Overall, this model will support interdisciplinary researchers studying mouse behavior by enabling them to integrate diverse behavioral features, measured across multiple time points and environments, into a comprehensive dataset that can address complex research questions.

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Freezing-of-Gait (FoG) affects over 50% of mid-to-late stage Parkinson's disease (PD) patients, significantly impairing patients' mobility independence and reducing quality of life. FoG is characterized by sudden episodes where walking cannot start or is interrupted, occurring exclusively during standing or walking, and never while sitting or lying down. Current FoG detection systems require extensive patient-specific training data and lack generalization, limiting clinical deployment. To address these issues, we introduce FM-FoG, a real-time foundation model-based wearable system achieving FoG detection in unseen patients without patient-specific training. Our approach combines self-supervised pretraining on diverse Inertial Measurement Unit (IMU) datasets with sensor context integration. Since FoG occurs only during ambulatory activities, a lightweight CNN-LSTM activity classifier selectively activates the foundation model only during walking or standing, avoiding unnecessary computation. Evaluated on the VCU FoG-IMU dataset with 23 PD patients, FM-FoG achieves a 98.5% F1-score when tested on previously unseen patients, substantially outperforming competitive baseline methods. Deployed on a Google Pixel 8a smartphone, the system extends battery life by up to 72% while maintaining sub-20ms intervention latency. The results indicate that our FM-FoG can enable practical, energy-efficient healthcare applications that generalize across patients without individual training requirements.

To reduce the computational and memory overhead of Large Language Models, various approaches have been proposed. These include a) Mixture of Experts (MoEs), where token routing affects compute balance; b) gradual pruning of model parameters; c) dynamically freezing layers; d) dynamic sparse attention mechanisms; e) early exit of tokens as they pass through model layers; and f) Mixture of Depths (MoDs), where tokens bypass certain blocks. While these approaches are effective in reducing overall computation, they often introduce significant workload imbalance across workers. In many cases, this imbalance is severe enough to render the techniques impractical for large-scale distributed training, limiting their applicability to toy models due to poor efficiency. We propose an autonomous dynamic load balancing solution, DynMo, which provably achieves maximum reduction in workload imbalance and adaptively equalizes compute loads across workers in pipeline-parallel training. In addition, DynMo dynamically consolidates computation onto fewer workers without sacrificing training throughput, allowing idle workers to be released back to the job manager. DynMo supports both single-node multi-GPU systems and multi-node GPU clusters, and can be used in practical deployment. Compared to static distributed training solutions such as Megatron-LM and DeepSpeed, DynMo accelerates the end-to-end training of dynamic GPT models by up to 1.23x for MoEs, 3.18x for parameter pruning, 2.23x for layer freezing, 4.02x for sparse attention, 4.52x for early exit, and 1.17x for MoDs.

The You Only Look Once (YOLO) architecture is crucial for real-time object detection. However, deploying it in resource-constrained environments such as unmanned aerial vehicles (UAVs) requires efficient transfer learning. Although layer freezing is a common technique, the specific impact of various freezing configurations on contemporary YOLOv8 and YOLOv10 architectures remains unexplored, particularly with regard to the interplay between freezing depth, dataset characteristics, and training dynamics. This research addresses this gap by presenting a detailed analysis of layer-freezing strategies. We systematically investigate multiple freezing configurations across YOLOv8 and YOLOv10 variants using four challenging datasets that represent critical infrastructure monitoring. Our methodology integrates a gradient behavior analysis (L2 norm) and visual explanations (Grad-CAM) to provide deeper insights into training dynamics under different freezing strategies. Our results reveal that there is no universal optimal freezing strategy but, rather, one that depends on the properties of the data. For example, freezing the backbone is effective for preserving general-purpose features, while a shallower freeze is better suited to handling extreme class imbalance. These configurations reduce graphics processing unit (GPU) memory consumption by up to 28% compared to full fine-tuning and, in some cases, achieve mean average precision (mAP@50) scores that surpass those of full fine-tuning. Gradient analysis corroborates these findings, showing distinct convergence patterns for moderately frozen models. Ultimately, this work provides empirical findings and practical guidelines for selecting freezing strategies. It offers a practical, evidence-based approach to balanced transfer learning for object detection in scenarios with limited resources.