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21 Nov

Machine learned reconstruction of tsunami dynamics from sparse observations

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arXiv:2411.12948v1 Announce Type: new Abstract: We investigate the use of the Senseiver, a transformer neural network designed for sparse sensing applications, to estimate full-field surface height measurements of tsunami waves from sparse observations. The model is trained on a large ensemble of simulated data generated via a shallow water equations solver, which we show to be a faithful reproduction for the underlying dynamics by comparison to historical events. We train the model on a dataset consisting of 8 tsunami simulations whose epicenters correspond to historical USGS earthquake records, and where the model inputs are restricted to measurements obtained at actively…

21 Nov

Medical Video Generation for Disease Progression Simulation

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arXiv:2411.11943v1 Announce Type: new Abstract: Modeling disease progression is crucial for improving the quality and efficacy of clinical diagnosis and prognosis, but it is often hindered by a lack of longitudinal medical image monitoring for individual patients. To address this challenge, we propose the first Medical Video Generation (MVG) framework that enables controlled manipulation of disease-related image and video features, allowing precise, realistic, and personalized simulations of disease progression. Our approach begins by leveraging large language models (LLMs) to recaption prompt for disease trajectory. Next, a controllable multi-round diffusion model simulates the disease progression state for each patient, creating realistic…

21 Nov

WaterPark: A Robustness Assessment of Language Model Watermarking

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arXiv:2411.13425v1 Announce Type: cross Abstract: To mitigate the misuse of large language models (LLMs), such as disinformation, automated phishing, and academic cheating, there is a pressing need for the capability of identifying LLM-generated texts. Watermarking emerges as one promising solution: it plants statistical signals into LLMs' generative processes and subsequently verifies whether LLMs produce given texts. Various watermarking methods (``watermarkers'') have been proposed; yet, due to the lack of unified evaluation platforms, many critical questions remain under-explored: i) What are the strengths/limitations of various watermarkers, especially their attack robustness? ii) How do various design choices impact their robustness? iii) How…

21 Nov

UCSD’s New SCIDS School: All About the Apps

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San Diego Supercomputer Center/Image courtesy SDSC) Some research universities excel in exploring intellectual possibilities and postulating abstract theories. Those terms aren’t likely to be associated with the University of California, San Diego’s new School of Computing, Information and Data Sciences (SCIDS), which is all about taking the power of data and computer science and applying it to solve problems in the real world. SCIDS was officially formed last month by the union of the San Diego Supercomputer Center (SDSC) and the Halıcıoğlu Data Science Institute (HDSI). As the fourth school on the rapidly expanding UC campus, SCIDS is generating quite…

21 Nov

Introducing an exclusively Databricks-hosted Assistant

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We’re excited to announce that the Databricks Assistant, now fully hosted and managed within Databricks, is available in public preview! This version ensures the Assistant relies exclusively on Databricks-hosted models, leveraging the same secure infrastructure that powers Databricks Model Serving. With this enhancement, all customers can enjoy the full benefits of our AI-powered assistant whilst ensuring that their data processing remains within their Databricks account.Databricks AssistantWith Databricks Assistant, we set out to build the best AI-powered productivity tool for enterprise data. The adoption since the preview has been overwhelming. Assistant is one of the fastest-growing Databricks features ever, with over…

21 Nov

AI-generated Image Detection: Passive or Watermark?

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arXiv:2411.13553v1 Announce Type: cross Abstract: While text-to-image models offer numerous benefits, they also pose significant societal risks. Detecting AI-generated images is crucial for mitigating these risks. Detection methods can be broadly categorized into passive and watermark-based approaches: passive detectors rely on artifacts present in AI-generated images, whereas watermark-based detectors proactively embed watermarks into such images. A key question is which type of detector performs better in terms of effectiveness, robustness, and efficiency. However, the current literature lacks a comprehensive understanding of this issue. In this work, we aim to bridge that gap by developing ImageDetectBench, the first comprehensive benchmark to…

21 Nov

Improving Visual Place Recognition Based Robot Navigation By Verifying Localization Estimates

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[Submitted on 11 Jul 2024 (v1), last revised 19 Nov 2024 (this version, v2)] View a PDF of the paper titled Improving Visual Place Recognition Based Robot Navigation By Verifying Localization Estimates, by Owen Claxton and 6 other authors View PDF HTML (experimental) Abstract:Visual Place Recognition (VPR) systems often have imperfect performance, affecting the `integrity' of position estimates and subsequent robot navigation decisions. Previously, SVM classifiers have been used to monitor VPR integrity. This research introduces a novel Multi-Layer Perceptron (MLP) integrity monitor which demonstrates improved performance and generalizability, removing per-environment training and reducing manual tuning requirements. We test our…

21 Nov

Patience Is The Key to Large Language Model Reasoning

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arXiv:2411.13082v1 Announce Type: new Abstract: Recent advancements in the field of large language models, particularly through the Chain of Thought (CoT) approach, have demonstrated significant improvements in solving complex problems. However, existing models either tend to sacrifice detailed reasoning for brevity due to user preferences, or require extensive and expensive training data to learn complicated reasoning ability, limiting their potential in solving complex tasks. To bridge this gap, following the concept of scaling test-time, we propose a simple method by encouraging models to adopt a more patient reasoning style without the need of introducing new knowledge or skills. To employ…

21 Nov

LEDRO: LLM-Enhanced Design Space Reduction and Optimization for Analog Circuits

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[Submitted on 19 Nov 2024] View a PDF of the paper titled LEDRO: LLM-Enhanced Design Space Reduction and Optimization for Analog Circuits, by Dimple Vijay Kochar and 3 other authors View PDF HTML (experimental) Abstract:Traditional approaches for designing analog circuits are time-consuming and require significant human expertise. Existing automation efforts using methods like Bayesian Optimization (BO) and Reinforcement Learning (RL) are sub-optimal and costly to generalize across different topologies and technology nodes. In our work, we introduce a novel approach, LEDRO, utilizing Large Language Models (LLMs) in conjunction with optimization techniques to iteratively refine the design space for analog circuit…

21 Nov

TimeFormer: Capturing Temporal Relationships of Deformable 3D Gaussians for Robust Reconstruction

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arXiv:2411.11941v1 Announce Type: new Abstract: Dynamic scene reconstruction is a long-term challenge in 3D vision. Recent methods extend 3D Gaussian Splatting to dynamic scenes via additional deformation fields and apply explicit constraints like motion flow to guide the deformation. However, they learn motion changes from individual timestamps independently, making it challenging to reconstruct complex scenes, particularly when dealing with violent movement, extreme-shaped geometries, or reflective surfaces. To address the above issue, we design a plug-and-play module called TimeFormer to enable existing deformable 3D Gaussians reconstruction methods with the ability to implicitly model motion patterns from a learning perspective. Specifically, TimeFormer…