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检索条件"任意字段=IEEE Symposium on Large Data Analysis and Visualization"

共 15522 条记录，以下是211-220 订阅

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Speech2rtMRI: Speech-Guided Diffusion Model for Real-time MRI Video of the Vocal Tract during Speech

Speech2rtMRI: Speech-Guided Diffusion Model for Real-time MR...

引用

International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Hong Nguyen Sean Foley Kevin Huang Xuan Shi Tiantian Feng Shrikanth Narayanan Signal Analysis and Interpretation Lab University of Southern California Los Angeles CA

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Understanding speech production both visually and kinematically can inform second language learning system designs, as well as the creation of speaking characters in video games and animations. In this work, we introduce a data-driven method to visually represent articulator motion in Magnetic Resonance Imaging (MRI) videos of the human vocal tract during speech based on arbitrary audio or speech input. We leverage large pre-trained speech models, which are embedded with prior knowledge, to generalize the visual domain to unseen data using an speech-to-video diffusion model. Our findings demonstrate that the visual generation significantly benefits from the pre-trained speech representations. We also observed that evaluating phonemes in isolation is challenging but becomes more straightforward when assessed within the context of spoken words. Limitations of the current results include the presence of unsmooth tongue motion and video distortion when the tongue contacts the palate. The source code is available for the public at: https://***/Hong7Cong/***

关键词： visualization Video games Tongue Speech coding Magnetic resonance imaging Production Streaming media Diffusion models Speech Speech processing

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What Matters When Building Vision Language Models for Product Image analysis?

What Matters When Building Vision Language Models for Produc...

引用

ieee Winter Applications and Computer Vision Workshops (WACVW)

作者： Ameni Trabelsi Maria Zontak Yiming Qian Brian Jackson Suleiman Khan Umit Batur Amazon Inc. Seattle USA

ISBN: (数字)9798331536626

ISBN: (纸本)9798331536633

This paper investigates multi-modal large language models (MLLMs) for predicting product features from images, comparing fine-tuned versus proprietary models. We introduce two domain-specific benchmarks: (1) Inductive Bias vs. Image Evidence (IBIE) Benchmark, which evaluates MLLMs' ability to distinguish between image-derived features and latent knowledge, and (2) Catalog-bench, which assesses feature prediction using Catalog terminology. Our fine-tuned model outperforms proprietary models like Gemini by 9.4% and 29.13% on these benchmarks respectively. We address the crucial aspect of computational efficiency, exploring cost effective deployment solutions under limited hardware resources. The significance of this work extends beyond ecommerce to physical retail, where efficient MLLMs are essential for real-time processing of visual data from store cameras and shelf sensors. These models enable automated inventory management, produce quality monitoring, and planogram compliance while operating within in-store computing constraints. This capability is particularly valuable for physical retail environments where immediate decisions about restocking and quality control are critical, while also enabling real-time assistance to customers seeking information about product details, ingredients, and nutritional content.

关键词： visualization Terminology Biological system modeling Computational modeling Quality control Benchmark testing Predictive models Real-time systems Sensors Electronic commerce

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Advancements and Future Directions in Retinal Image analysis Using Deep Learning

Advancements and Future Directions in Retinal Image Analysis...

引用

International symposium on Applied Machine Intelligence and Informatics (SAMI)

作者： Gerlinda Boglarka Kis László Szilágyi Levente Kovács Doctoral School of Applied Informatics and Applied Mathematics Obuda University Budapest Hungary University Research and Innovation Center Obuda University Budapest Hungary Faculty of Technical and Human Sciences Sapientia Hungarian University of Transylvania Targu Mures Romania Physiological Controls Research Center Obuda University Budapest Hungary

ISBN: (数字)9798350379365

ISBN: (纸本)9798350379372

The integration of deep learning into retinal image analysis has catalyzed significant advancements in ophthalmol-ogy, offering innovative and precise tools for the detection, diag-nosis, and monitoring of a wide range of retinal conditions. With the ability to automatically analyze retinal images, deep learning has revolutionized traditional diagnostic processes, allowing for faster, more accurate, and often earlier identification of ocular diseases. This article provides a comprehensive review of the state-of-the-art in retinal image analysis, focusing on key areas such as retinal layer segmentation, vessel segmentation, and the prediction of diabetic retinopathy. It examines the cutting-edge methodologies employed, the critical datasets that have facilitated advancements in this domain, and the pivotal breakthroughs that have shaped current practices. Furthermore, it discusses the challenges faced by current techniques, such as variability in imaging quality, the need for large annotated datasets, and model interoperability, offering insights into how these hurdles may be addressed. The article also delves into emerging research trends, such as the integration of multi-modal data, the application of federated learning to preserve patient privacy, and the development of more generalized models that can perform across various imaging modalities and patient demographics. In conclusion, it explores potential future research directions, highlighting areas where deep learning can continue to evolve and contribute to the early detection and effective management of retinal diseases, ultimately improving patient outcomes.

关键词： Deep learning Image segmentation Image analysis Reviews Neural networks Retina Real-time systems Ophthalmology Monitoring Diseases

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IrriGaia: IoT-Integrated Robotic System for Comparative analysis of Mist and Drip Irrigation

IrriGaia: IoT-Integrated Robotic System for Comparative Anal...

引用

Robotics and Technologies for Industrial Automation (ROBOTHIA), ieee International Conference on

作者： Stephanie Rose P. Miranda Angelo Regis M. Celestino Fritz Symon D. Blancaflor Glenda S. Guanzon Jopearlson Icy Ituriaga Adha Theza Naputol Computer Engineering Department College of Technology University of San Agustin Iloilo City Philippines Computer Engineering Department College of Technology Iloilo City Philippines

ISBN: (数字)9798350356755

ISBN: (纸本)9798350356762

This study evaluates the performance of IoT-based mist and drip irrigation systems for Thai basil (Ocimum basilicum var. thyrsiflora) cultivation in a controlled indoor environment. We developed a prototype system that integrates ESP32 microcontrollers, capacitive soil moisture sensors, water flow sensors, and energy meters to automate irrigation scheduling and data collection. IoT integration facilitated real-time monitoring, automated control, optimized the irrigation schedules and resource management and enabled the remote visualization in the cloud dashboard. We found that the mist irrigation system demonstrated 33% less water consumption than the drip irrigation system but consumed 55% more energy due to continuous operation. Conversely, the drip irrigation system exhibited superior energy efficiency, making it more suitable for large-scale operations. These findings highlight the potential of IoT-enabled robotic irrigation systems to enhance sustainability and resource efficiency in agriculture. Researchers' future works will explore scalability to diverse crops and environments, as well as the integration of renewable energy sources.

关键词： Irrigation Schedules Renewable energy sources Service robots Soil moisture Robot sensing systems Sensor systems Real-time systems Sustainable development Water resources

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Towards Semi Real-Time Environmental Monitoring Using Shadowgraphy

Towards Semi Real-Time Environmental Monitoring Using Shadow...

引用

International symposium on Underwater Technology

作者： Mehul N. Sangekar Dhugal J. Lindsay X-STAR Japan Agency for Marine-Earth Science and Technology (JAMSTEC) Yokosuka Japan

ISBN: (数字)9798331534189

ISBN: (纸本)9798331534196

Environmental monitoring for detecting habitat changes as well as for mining impact assessment has become a crucial topic in recent years. This requires large-scale spatial and temporal data collection for analysis. At JAMSTEC, we have been working on the development of shadowgraph-based imaging systems to address this issue. Shadowgraph-based imaging systems consume low amounts of power and are not affected by external lighting conditions, making them ideal for monitoring. Also, since shadowgraphs use collimated light, it is easy to extract particle sizes along with particle counts. We have developed multiple inhouse shadowgraph systems with different viewing areas and resolutions for different monitoring requirements. A data processing pipeline has been made which can analyze the shadowgraph videos post-cruise and analyze plankton distributions and abundances as well as provide other information such as particle distributions, size distributions and detect events such as plumes and mixing layers using pattern matching. Since the shadowgraph videos can contain a large variety of animals, we are also using human-in-the-loop machine learning for training target-specific models based on the target assessment. The system has been installed on various underwater platforms, such as a long-term seafloor lander as well as CTD-rosette systems. We are now developing an extension to the system that can provide near real-time feedback to the host platform based on the survey results. We aim to provide alerts when particular events and observations are detected, which can be customized based on the monitoring targets. In the future, we are planning to integrate these shadowgraphs with other underwater platforms such as AUVs and seafloor stations.

关键词： Sea floor Pipelines Imaging Machine learning data collection Real-time systems Environmental monitoring data mining Underwater technology Videos

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Remote visualization of large Scale Fast Dynamic Simulations in a HPC Context 4

Remote Visualization of Large Scale Fast Dynamic Simulations...

引用

ieee symposium on large data analysis and visualization

作者： Vivodtzev, Fabien Bertron, Isabelle CEA French Atom Energy Commiss Paris France

ISBN: (纸本)9781479952151

This paper details the conception of a remote and powerful visualization tool intended to be directly used by physicists in order to better understand the physical phenomena in the field of fast dynamics simulations. The goal is to explore large dataset composed of billions of cells distributed in thousands of domains on a supercomputer. The solution uses the visualization tool VisIt [1] adapted with specific developments based on the knowledge of both the simulation code and the supercomputer architecture used to run the simulations.

关键词： visualization systems and tools Fast dynamics

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AdaptiveCoPilot: Design and Testing of a NeuroAdaptive LLM Cockpit Guidance System in both Novice and Expert Pilots

AdaptiveCoPilot: Design and Testing of a NeuroAdaptive LLM C...

引用

ieee Annual International symposium Virtual Reality

作者： Shaoyue Wen Michael Middleton Songming Ping Nayan N. Chawla Guande Wu Bradley S. Feest Chihab Nadri Yunmei Liu David Kaber Maryam Zahabi Ryan P. McMahan Sonia Castelo Ryan McKendrick Jing Qian Cláudio T. Silva New York University Northrop Grumman Imperial College Virginia Tech University of Illinois Urbana-Champaign University of Louisville University of Florida Texas A&M University

ISBN: (数字)9798331536459

ISBN: (纸本)9798331536466

Pilots operating modern cockpits often face high cognitive demands due to complex interfaces and multitasking requirements, which can lead to overload and decreased performance. This study introduces AdaptiveCoPilot, a neuroadaptive guidance system that adapts visual, auditory, and textual cues in real time based on the pilot’s cognitive workload, measured via functional Near-Infrared Spectroscopy (fNIRS). A formative study with expert pilots (N=3) identified adaptive rules for modality switching and information load adjustments during preflight tasks. These insights informed the design of AdaptiveCoPilot, which integrates cognitive state assessments, behavioral data, and adaptive strategies within a context-aware large Language Model (LLM). The system was evaluated in a virtual reality (VR) simulated cockpit with licensed pilots (N=8), comparing its performance against baseline and random feedback conditions. The results indicate that the pilots using AdaptiveCoPilot exhibited higher rates of optimal cognitive load states on the facets of working memory and perception, along with reduced task completion times. Based on the formative study, experimental findings, qualitative interviews, we propose a set of strategies for future development of neuroadaptive pilot guidance systems and highlight the potential of neuroadaptive systems to enhance pilot performance and safety in aviation environments.

关键词： visualization Adaptive systems Three-dimensional displays Virtual reality User interfaces Cognitive load Real-time systems Time measurement Functional near-infrared spectroscopy Testing

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Improving Food Recognition with Retrieval-Augmented and Domain-Adaptive LVLMs

Improving Food Recognition with Retrieval-Augmented and Doma...

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Dehua Ma Zhenbo Xu Tianshun Xing Lu Yuan Jinghan Yang Huijia Wu Ming Lei Zhaofeng He Sch. of Integrated Circuits Beijing Univ. of Posts and Telecommun. Beijing China Sch. of Artificial Intelligence Beijing Univ. of Posts and Telecommun. Beijing China ShiFang Technology Inc. Hangzhou China Sch. of Science Beijing Univ. of Posts and Telecommun. Beijing China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Food recognition is pivotal in enhancing intelligent food recommendation systems and nutritional management, contributing to balanced diets and overall health. Although large Vision-Language Models (LVLMs) have demonstrated impressive performances across various domains, their performance on the food recognition task still lags behind traditional vision models. To bridge this gap, this paper proposes two methods to improve the food recognition capabilities of LVLMs: Retrieval-Augmented Recognition (RAR) and Domain-Adaptive Recognition (DAR). On the one hand, the training-free RAR utilizes a vision model to retrieve relevant image-category pairs from an image-category memory pre-built from the training set, thus incorporating the categorical information into the input of LVLMs to enhance food recognition performance. On the other hand, DAR employs a two-stage training process by first pre-training LVLMs on diverse food analysis tasks and then fine-tuning LVLMs using food recognition data. Extensive evaluations on two large-scale food recognition datasets demonstrate that both RAR and DAR improve the food recognition performance of LVLMs and, compred to RAR, DAR achieves a higher precision that outperforms traditional vision models.

关键词： Training Hands Bridges visualization Image recognition Speech recognition Signal processing Multitasking Speech processing Recommender systems

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Proceedings ieee 2001 symposium on Parallel and large-data visualization and Graphics (Cat. No.01EX520)

Proceedings IEEE 2001 Symposium on Parallel and Large-Data V...

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ieee symposium on Parallel and large-data visualization and Graphics (PVG)

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Chronus: Understanding and Securing the Cutting-Edge Industry Solutions to DRAM Read Disturbance

Chronus: Understanding and Securing the Cutting-Edge Industr...

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ieee symposium on High-Performance Computer Architecture

作者： Oğuzhan Canpolat A. Giray Yağlıkçı Geraldo F. Oliveira Ataberk Olgun Nisa Bostancı Ismail Emir Yuksel Haocong Luo Oğuz Ergin Onur Mutlu ETH Zürich TOBB University of Economics and Technology University of Sharjah

ISBN: (数字)9798331506476

ISBN: (纸本)9798331506483

Read disturbance in modern DRAM is an important robustness (security, safety, and reliability) problem, where repeatedly accessing (hammering) a row of DRAM cells (DRAM row) induces bitflips in other physically nearby DRAM rows. Shrinking technology node size exacerbates DRAM read disturbance over generations. To help mitigate read disturbance, the latest DDR5 specifications (as of April 2024) introduced a new RowHammer mitigation framework, called Per Row Activation Counting (PRAC). PRAC 1) enables the DRAM chip to accurately track row activations by allocating an activation counter per row and 2) provides the DRAM chip with the necessary time window to perform RowHammer-preventive refreshes by introducing a new back-off signal. Unfortunately, no prior work rigorously studies PRAC’s security guarantees and overheads. In this paper, we 1) present the first rigorous security, performance, energy, and cost analyses of PRAC and 2) propose Chronus, a new mechanism that addresses PRAC’s two major weaknesses. Our analysis shows that PRAC’s system performance overhead on benign applications is non-negligible for modern DRAM chips and prohibitively large for future DRAM chips that are more vulnerable to read disturbance. We identify two weaknesses of PRAC that cause these overheads. First, PRAC increases critical DRAM access latency parameters due to the additional time required to increment activation counters. Second, PRAC performs a constant number of preventive refreshes at a time, making it vulnerable to an adversarial access pattern, known as the wave attack, and consequently requiring it to be configured for significantly smaller activation thresholds. To address PRAC’s two weaknesses, we propose a new on-DRAM-die RowHammer mitigation mechanism, Chronus. Chronus 1) updates row activation counters concurrently while serving accesses by separating counters from the data and 2) prevents the wave attack by dynamically controlling the number of preventive refreshes perfor

关键词： Industries System performance Prevention and mitigation DRAM chips Robustness Safety Performance analysis Security Cost benefit analysis Proposals

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