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Arxiv
4h
135
Image Credit: Arxiv
Automated Classification of Volcanic Earthquakes Using Transformer Encoders: Insights into Data Quality and Model Interpretability
- Researchers developed a deep learning model using a transformer encoder to classify volcanic earthquakes objectively and efficiently, reducing reliance on subjective human judgment.
- The model achieved high F1 scores for volcano tectonic, low-frequency earthquakes, and noise classification, outperforming a traditional CNN-based method.
- Attention weight visualizations revealed the model focuses on key waveform features similar to human experts, but inconsistencies in training data influenced classification accuracy.
- Experiments emphasized the importance of balancing data quality and diversity, with proximity to the crater impacting model performance and interpretability, aiding in better understanding seismic activity at Mount Asama.
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Arxiv
4h
20
Image Credit: Arxiv
VLAD: A VLM-Augmented Autonomous Driving Framework with Hierarchical Planning and Interpretable Decision Process
- Researchers have proposed VLAD, a vision-language autonomous driving model that integrates a fine-tuned Visual Language Model (VLM) with VAD, a state-of-the-art end-to-end system.
- VLAD utilizes a specialized fine-tuning approach using custom question-answer datasets to enhance spatial reasoning capabilities.
- The system generates high-level navigational commands for vehicle operation and provides interpretable natural language explanations of driving decisions to increase transparency and trustworthiness.
- Evaluation on the nuScenes dataset shows that VLAD reduces average collision rates by 31.82% compared to baseline methodologies, setting a new benchmark for VLM-augmented autonomous driving systems.
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Arxiv
4h
67
Image Credit: Arxiv
DiffusionLight-Turbo: Accelerated Light Probes for Free via Single-Pass Chrome Ball Inpainting
- Researchers have developed a technique called DiffusionLight-Turbo for estimating lighting from a single low-dynamic-range image by reframing it as a chrome ball inpainting problem.
- DiffusionLight-Turbo leverages a pre-trained diffusion model and iterative inpainting to generate high-quality lighting estimates, including high-dynamic-range light probes.
- The original DiffusionLight method was time-intensive, taking about 30 minutes per estimation, while DiffusionLight-Turbo reduces this to approximately 30 seconds with minimal quality loss.
- Experimental results demonstrate that DiffusionLight-Turbo produces convincing light estimates across various scenarios and exhibits superior generalization to real-world settings.
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Arxiv
4h
271
Image Credit: Arxiv
SWinMamba: Serpentine Window State Space Model for Vascular Segmentation
- Vascular segmentation in medical images is crucial for disease diagnosis and surgical navigation.
- A new model called Serpentine Window Mamba (SWinMamba) has been proposed to improve the accuracy of vascular segmentation.
- SWinMamba utilizes serpentine window sequences to capture features and improve global visual context modeling for vascular structures.
- Extensive experiments showed that SWinMamba outperformed existing methods in achieving complete and connected vessel segmentation.
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Arxiv
4h
244
Image Credit: Arxiv
Skywork-Reward-V2: Scaling Preference Data Curation via Human-AI Synergy
- Researchers have identified limitations in current reward models in reinforcement learning from human feedback.
- To address these limitations, they introduced a large-scale preference dataset named SynPref-40M.
- A two-stage pipeline involving human annotations and AI scalability was designed to curate the data.
- Their Skywork-Reward-V2 suite of eight reward models shows state-of-the-art performance across various benchmarks.
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Arxiv
4h
3
Image Credit: Arxiv
Coherent Online Road Topology Estimation and Reasoning with Standard-Definition Maps
- Researchers are working on addressing the limitations of autonomous cars relying on high-definition maps by predicting these elements from onboard sensors and reasoning about their relationships with traffic elements.
- A new approach has been proposed to construct high-definition maps online more coherently utilizing standard-definition maps and advanced network architecture.
- The proposed method focuses on predicting lane segments, corresponding topology, and road boundaries, using prior map information represented by commonly available standard-definition maps.
- Experimental evaluation shows that this approach surpasses previous methods significantly, emphasizing the advantages of this modeling scheme for road topology estimation.
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Arxiv
4h
33
Image Credit: Arxiv
Evaluating LLM Agent Collusion in Double Auctions
- Large language models (LLMs) are being examined for collusion behavior as sellers in simulated continuous double auction markets.
- Study analyzes factors influencing collusive tendencies, including communication abilities, choice of model, and environmental pressures.
- Direct seller communication found to increase collusive behavior, with variations across different models.
- Research emphasizes economic and ethical implications of deploying LLM agents in market scenarios.
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Arxiv
4h
47
Image Credit: Arxiv
Gradient Short-Circuit: Efficient Out-of-Distribution Detection via Feature Intervention
- Out-of-Distribution (OOD) detection is crucial for deploying deep models safely in open-world environments.
- A salient gradient phenomenon has been observed during inference on a model trained only with In-Distribution (ID) data.
- Based on this observation, a technique has been proposed to short-circuit feature coordinates exploited by spurious gradients in OOD samples while preserving ID classification.
- Experiments on OOD benchmarks demonstrate significant improvements with this approach, which is lightweight and integrates seamlessly into the standard inference pipeline.
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Arxiv
4h
261
Image Credit: Arxiv
EdgeLoRA: An Efficient Multi-Tenant LLM Serving System on Edge Devices
- Large Language Models (LLMs) are versatile and can be fine-tuned with parameter-efficient adapters like Low-Rank Adaptation (LoRA) for efficient adaptation to downstream tasks.
- Deploying fine-tuned LLMs on multi-tenant edge devices can reduce latency, enhance privacy, and provide personalized responses but poses challenges in efficient serving due to adapter complexity and memory overhead.
- A new system called EdgeLoRA addresses these challenges by introducing adaptive adapter selection, heterogeneous memory management, and batch LoRA inference, resulting in significant improvements in latency and throughput over existing methods.
- EdgeLoRA shows up to a 4 times increase in throughput and the ability to handle multiple adapters simultaneously, indicating its potential to enhance LLM edge deployment in multi-tenant environments.
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Arxiv
4h
230
Image Credit: Arxiv
Symbolic identification of tensor equations in multidimensional physical fields
- A new data-driven framework, called Symbolic Identification of Tensor Equations (SITE), has been proposed for identifying tensor equations.
- SITE represents tensor equations using a host-plasmid structure inspired by multidimensional gene expression programming (M-GEP).
- SITE introduces innovations like a dimensional homogeneity check and a tensor linear regression technique to enhance efficiency in identifying tensor relationships.
- Validation of SITE using benchmark scenarios demonstrates its ability to recover target equations accurately from data and its potential for data-driven discovery of tensor equations.
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Arxiv
4h
322
Image Credit: Arxiv
Optimizing Methane Detection On Board Satellites: Speed, Accuracy, and Low-Power Solutions for Resource-Constrained Hardware
- Methane detection via hyperspectral satellite imagery can aid in mitigating climate change by early leak detection.
- Onboard methane detection on satellites is proposed to overcome slow downlink rates cost-effectively.
- Efficient, low-power algorithms like Mag1c-SAS and CEM have shown promise for accurate and fast methane detection on resource-limited hardware.
- The research paves the way for improved onboard methane detection with minimal hardware requirements, enhancing timely data delivery.
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Arxiv
4h
105
Image Credit: Arxiv
How to Securely Shuffle? A survey about Secure Shufflers for privacy-preserving computations
- Secure shuffling is a crucial component for private data aggregation and has gained renewed interest in the field of differential privacy.
- A survey has identified, categorized, and compared 26 secure protocols that enable the necessary shuffling functionality.
- Existing works often overlook practical vulnerabilities and performance trade-offs of secure shufflers, leaving a key question on what constitutes a good secure shuffler.
- The survey aims to provide practical guidelines for selecting appropriate protocols and outlines future directions for research in privacy-preserving technologies relying on secure shufflers.
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Arxiv
4h
128
Image Credit: Arxiv
Meteoroid stream identification with HDBSCAN unsupervised clustering algorithm
- Accurate identification of meteoroid streams is crucial for understanding their origins and evolution, especially for missions like ESA's LUMIO that rely on meteor shower observations.
- This study assesses the performance of the HDBSCAN unsupervised clustering algorithm in identifying meteoroid streams and compares it with the traditional CAMS look-up table method.
- Using three different feature vectors, HDBSCAN successfully identifies meteoroid streams, with 39 streams confirmed using the GEO vector and 30 using the ORBIT vector.
- HDBSCAN, while requiring careful parameter selection, outperformed the CAMS method in statistical coherence, showing potential as an effective alternative for meteoroid stream identification.
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Arxiv
4h
156
Image Credit: Arxiv
Mamba Guided Boundary Prior Matters: A New Perspective for Generalized Polyp Segmentation
- Polyp segmentation in colonoscopy images is essential for early detection of colorectal cancer, but it remains challenging due to variations in polyp characteristics and indistinct boundaries.
- Existing CNN and transformer-based methods struggle with weak or blurry boundary segmentation and lack generalizability for real-time clinical use.
- A new approach called SAM-MaGuP has been introduced to address these limitations, incorporating a boundary distillation module and a Mamba adapter within the Segment Anything Model (SAM).
- SAM-MaGuP has shown superior segmentation accuracy over current methods by leveraging a Mamba-guided boundary prior and a 1D-2D Mamba block, setting a new standard in polyp segmentation.
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Arxiv
4h
112
Image Credit: Arxiv
Consistency of Learned Sparse Grid Quadrature Rules using NeuralODEs
- This paper discusses the consistency of sparse grid quadrature for integrating high dimensional distributions.
- A transport map is learned to normalize the distribution to a noise distribution on the unit cube using neural ordinary differential equations.
- The generative map is integrated numerically with the quantity of interest using Clenshaw-Curtis sparse grid quadrature.
- The paper proves that all error terms can be controlled in the sense of PAC learning, ensuring that the numerical integral approximates the theoretical value with small error as the data set size grows and the network capacity increases.
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