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Wyoming faces significant challenges with weather conditions on its interstate highways, particularly due to its location in the Rocky Mountains region. The state’s three major interstates regularly endure severe weather, making travel difficult and hazardous.  Severe weather heightens crash risks, leading to delays, disrupting traffic flow, and increasing road maintenance costs. These issues are exacerbated by the impacts of climate change, making the situation increasingly difficult. The weather is not just a local issue. Wyoming’s interstates serve as key national truck routes: two run coast to coast, while the third connects the state to New Mexico and Colorado from north…

arXiv:2408.12006v1 Announce Type: new Abstract: Last-mile carriers increasingly incorporate electric vehicles (EVs) into their delivery fleet to achieve sustainability goals. This goal presents many challenges across multiple planning spaces including but not limited to how to plan EV routes. In this paper, we address the problem of predicting energy consumption of EVs for Last-Mile delivery routes using deep learning. We demonstrate the need to move away from thinking about range and we propose using energy as the basic unit of analysis. We share a range of deep learning solutions, beginning with a Feed Forward Neural Network (NN) and Recurrent Neural…

[Submitted on 20 Aug 2024] View a PDF of the paper titled Revisiting Min-Max Optimization Problem in Adversarial Training, by Sina Hajer Ahmadi and 1 other authors View PDF HTML (experimental) Abstract:The rise of computer vision applications in the real world puts the security of the deep neural networks at risk. Recent works demonstrate that convolutional neural networks are susceptible to adversarial examples - where the input images look similar to the natural images but are classified incorrectly by the model. To provide a rebuttal to this problem, we propose a new method to build robust deep neural networks against…

arXiv:2408.11852v1 Announce Type: new Abstract: We investigate the use of in-context learning and prompt engineering to estimate the contributions of training data in the outputs of instruction-tuned large language models (LLMs). We propose two novel approaches: (1) a similarity-based approach that measures the difference between LLM outputs with and without provided context, and (2) a mixture distribution model approach that frames the problem of identifying contribution scores as a matrix factorization task. Our empirical comparison demonstrates that the mixture model approach is more robust to retrieval noise in in-context learning, providing a more reliable estimation of data contributions. Source link…

Artificial Intelligence (AI) has transformed numerous industries, and travel is no exception. From personalized recommendations to smart itinerary planning, AI has made holiday planning more convenient, efficient, and tailored to individual preferences. In this article, we explore how AI is revolutionizing the way we plan holidays, and why it's becoming an essential tool for travelers. Personalized Travel Recommendations One of the most significant impacts of AI in travel planning is the ability to offer personalized recommendations. AI algorithms analyze user data, such as past travel experiences, preferences, and online behavior, to suggest destinations, activities, and accommodations that match individual tastes.…

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arXiv:2408.11210v1 Announce Type: new Abstract: Since the release of Segment Anything 2 (SAM2), the medical imaging community has been actively evaluating its performance for 3D medical image segmentation. However, different studies have employed varying evaluation pipelines, resulting in conflicting outcomes that obscure a clear understanding of SAM2's capabilities and potential applications. We shortly review existing benchmarks and point out that the SAM2 paper clearly outlines a zero-shot evaluation pipeline, which simulates user clicks iteratively for up to eight iterations. We reproduced this interactive annotation simulation on 3D CT datasets and provided the results and code~url{https://github.com/Project-MONAI/VISTA}. Our findings reveal that directly…

arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them. Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs. Source link lol

arXiv:2408.11990v1 Announce Type: new Abstract: Advancing the capabilities of earthquake nowcasting, the real-time forecasting of seismic activities remains a crucial and enduring objective aimed at reducing casualties. This multifaceted challenge has recently gained attention within the deep learning domain, facilitated by the availability of extensive, long-term earthquake datasets. Despite significant advancements, existing literature on earthquake nowcasting lacks comprehensive evaluations of pre-trained foundation models and modern deep learning architectures. These architectures, such as transformers or graph neural networks, uniquely focus on different aspects of data, including spatial relationships, temporal patterns, and multi-scale dependencies. This paper addresses the mentioned gap by analyzing…

[Submitted on 20 Aug 2024] View a PDF of the paper titled PooDLe: Pooled and dense self-supervised learning from naturalistic videos, by Alex N. Wang and 4 other authors View PDF HTML (experimental) Abstract:Self-supervised learning has driven significant progress in learning from single-subject, iconic images. However, there are still unanswered questions about the use of minimally-curated, naturalistic video data, which contain dense scenes with many independent objects, imbalanced class distributions, and varying object sizes. In this paper, we propose a novel approach that combines an invariance-based SSL objective on pooled representations with a dense SSL objective that enforces equivariance to…