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Research on liquid crystal display technology based on regional dynamic dimming algorithm

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Research on liquid crystal display technology based on regio...

IEEE International Conference on Data science in Cyberspace (DSC)

作者： Zhitao Yu Peng Sun Mingle Zhou Qianlong Liu Shilong Zhao Hisense Video Technology Co. Ltd Qing Dao China Hisense Visual Technology Co. Ltd Qing Dao China Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center (National Supercomputer Center in Jinan) Qilu University of Technology (Shandong Academy of Sciences) Jinan China Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan China

ISBN: (数字)9798350391367

ISBN: (纸本)9798350391374

In vehicle liquid crystal display (LCD) technology has attracted much attention for its wide range of applications in automotive infotainment systems. However, conventional LCD technologies have limitations in terms of energy consumption and display quality, especially in the context of the increasing demand for high contrast and low energy consumption. To address these challenges, this paper carries out a study of LCD technology based on an area dynamic dimming algorithm. The technology significantly improves the contrast ratio of LCDs and effectively reduces energy consumption by dividing the backlight module into multiple independently adjustable regions and dynamically adjusting the backlight brightness of each region according to the image content. The results show that the region dynamic dimming algorithm proposed in this paper can effectively adjust the brightness of the backlight according to the image content, achieving higher display contrast and lower energy consumption.

关键词： Energy consumption Heuristic algorithms Brightness Light emitting diodes Liquid crystal displays Robustness Entropy Energy efficiency Vehicle dynamics Optimization

On authenticated skyline query processing over road networks

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On authenticated skyline query processing over road networks

作者： Zhu, Xiaoyu Wu, Jie Chang, Wei Zakirul Alam Bhuiyan, Md Raymond Choo, Kim-Kwang Qi, Fang Liu, Qin Wang, Guojun School of Computer Science and Engineering Central South University Changsha China Center for Networked Computing Temple University PhiladelphiaPA United States Department of Computer and Information Sciences Saint Joseph's University PhiladelphiaPA United States Department of Computer Science Fordham University New York CityNY United States Department of Information Systems and Cyber Security University of Texas at San Antonio San AntonioTX United States School of Computer Science and Electronic Engineering Hunan University Changsha China School of Computer Science Guangzhou University Guangzhou China

In recent times, many location-based service providers (LBSPs) choose to outsource data query services to third-party cloud service providers (CSPs). This allows users to easily search for points of interests (POIs), such as restaurants and parking lots in their vicinity, using their mobile devices and in-vehicle infotainment units. Skyline query is one potential technique to be deployed for road networks. However, the untrusted CSPs may forge or omit query results, intentionally or not. Therefore, in this article, we posit that by observing the unique properties of skyline query results in road networks, we can bind each POI with four nearby POIs with special properties using signature chain technology. Our proposed approach not only provides users with skyline query result authentication ability over the road network, but also have low communication overhead. Specifically, the overhead analysis and experimental results show that our proposed approach decreases the communication overhead. © 2020 John Wiley & Sons, Ltd.

关键词： Location based services

A Privacy-Preserving Approach for Multi-Source Domain Adaptive Object Detection

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A Privacy-Preserving Approach for Multi-Source Domain Adapti...

IEEE International Conference on Image Processing

作者： Peggy Joy Lu Chia-Yung Jui Jen-Hui Chuang Department of Computer Science National Yang Ming Chiao Tung University Hsinchu Taiwan National Center for High-Performance Computing Taichung Taiwan

A new research topic, multi-source domain adaptive object detection (MSDAOD) under privacy-preserving constraint is explored in this paper, where the clients can only access their own source data while the server can only access unlabeled target data. Accordingly, a novel MSDAOD framework is proposed wherein the clients employ a source-only Probabilistic Faster R-CNN (PFRCNN) to generate models with localization uncertainty, while a Multi-teacher Pseudo-label Ensemble Network (MPEN) is developed on the server side. In MPEN, FedMA-based algorithm aggregates the above models to a domain-invariant backbone while a novel pseudo-label ensemble (PLE) scheme is employed to reduce false positives arising from domain specific parts, and enhance the overall system performance using target domain information. Experiments demonstrate that our method outperforms other state-of-the-art MSDAOD and privacy-preserving methods by 10%~16% in average precision (AP).

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3D Human Mesh Estimation from Virtual Markers

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arXiv 2023年

作者： Ma, Xiaoxuan Su, Jiajun Wang, Chunyu Zhu, Wentao Wang, Yizhou School of Computer Science Center on Frontiers of Computing Studies Peking University China Inst. for Artificial Intelligence Peking University China Microsoft Research Asia China Nat’l Eng. Research Center of Visual Technology China

Inspired by the success of volumetric 3D pose estimation, some recent human mesh estimators propose to estimate 3D skeletons as intermediate representations, from which, the dense 3D meshes are regressed by exploiting the mesh topology. However, body shape information is lost in extracting skeletons, leading to mediocre performance. The advanced motion capture systems solve the problem by placing dense physical markers on the body surface, which allows to extract realistic meshes from their non-rigid motions. However, they cannot be applied to wild images without markers. In this work, we present an intermediate representation, named virtual markers, which learns 64 landmark keypoints on the body surface based on the large-scale mocap data in a generative style, mimicking the effects of physical markers. The virtual markers can be accurately detected from wild images and can reconstruct the intact meshes with realistic shapes by simple interpolation. Our approach outperforms the state-of-the-art methods on three datasets. In particular, it surpasses the existing methods by a notable margin on the SURREAL dataset, which has diverse body shapes. Code is available at https://***/ShirleyMaxx/VirtualMarker. Copyright © 2023, The Authors. All rights reserved.

关键词： Mesh generation

Optimizing CDN Architectures: Multi-Metric Algorithmic Breakthroughs for Edge and Distributed Performance

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Optimizing CDN Architectures: Multi-Metric Algorithmic Break...

2025 International Conference on computing, Networking and Communications, ICNC 2025

作者： Absur, Md Nurul Saha, Sourya Nova, Sifat Nawrin Fahim Ahmad Nasif, Kazi Ul Nasib, Md Rahat City University of New York Department of Computer Science New York United States Chalmers University of Technology Department of Computer Science Gothenburg Sweden Kennesaw State University College of Computing and Software Engineering Georgia United States Samsung Austin Research Center AustinTX United States

ISBN: (纸本)9798331520960

A Content Delivery Network (CDN) is a powerful system of distributed caching servers that aims to accelerate content delivery, like high-definition video, IoT applications, and ultra-low-latency services, efficiently and with fast velocity. This has become of paramount importance in the post-pandemic era. Challenges arise when exponential content volume growth and scalability across different geographic locations are required. This paper investigates data-driven evaluations of CDN algorithms in dynamic server selection for latency reduction, bandwidth throttling for efficient resource management, real-time Round Trip Time analysis for adaptive routing, and programmatic network delay simulation to emulate various conditions. Key performance metrics, such as round-trip time (RTT) and CPU usage, are carefully analyzed to evaluate scalability and algorithmic efficiency through two experimental setups: a constrained edge-like local system and a scalable FABRIC testbed. The statistical validation of RTT trends, alongside CPU utilization, is presented in the results. The optimization process reveals significant trade-offs between scalability and resource consumption, providing actionable insights for effectively deploying and enhancing CDN algorithms in edge and distributed computing environments. © 2025 IEEE.

关键词： Cluster computing

Q-GEAR: Improving quantum simulation framework

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arXiv 2025年

作者： Guo, Ziqing Pan, Ziwen Balewski, Jan National Energy Research Scientific Computing Center Lawrence Berkeley National Laboratory BerkeleyCA United States Department of Computer Science Texas Tech University LubbockTX United States

Fast execution of complex quantum circuit simulations are crucial for verification of theoretical algorithms paving the way for their successful execution on the quantum hardware. However, the main stream CPU-based platforms for circuit simulation are well-established but slower. Despite this, adoption of GPU platforms remains limited because different hardware architectures require specialized quantum simulation frameworks, each with distinct implementations and optimization strategies. Therefore, we introduce Q-Gear, a software framework that transforms Qiskit quantum circuits into Cuda-Q kernels. By leveraging Cuda-Q seamless execution on GPUs, Q-Gear accelerates both CPU and GPU based simulations by respectively two orders of magnitude and ten times with minimal coding effort. Furthermore, Q-Gear leverages Cuda-Q configuration to interconnect GPUs memory allowing the execution of much larger circuits, beyond the memory limit set by a single GPU or CPU node. Additionally, we created and deployed a Podman container and a Shifter image at Perlmutter (NERSC/LBNL), both derived from NVIDIA public image. These public NERSC containers were optimized for the Slurm job scheduler allowing for close to 100% GPU utilization. We present various benchmarks of the Q-Gear to prove the efficiency of our computation paradigm. © 2025, CC BY.

关键词： Program processors

Learning Open-vocabulary Semantic Segmentation Models From Natural Language Supervision

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arXiv 2023年

作者： Xu, Jilan Hou, Junlin Zhang, Yuejie Feng, Rui Wang, Yi Qiao, Yu Xie, Weidi School of Computer Science Shanghai Key Lab of Intelligent Information Processing Shanghai Collaborative Innovation Center of Intelligent Visual Computing Fudan University China Shanghai AI Laboratory China Shanghai Jiaotong University China

In this paper, we consider the problem of open-vocabulary semantic segmentation (OVS), which aims to segment objects of arbitrary classes instead of pre-defined, closed-set categories. The main contributions are as follows: First, we propose a transformer-based model for OVS, termed as OVSegmentor, which only exploits web-crawled image-text pairs for pre-training without using any mask annotations. OVSegmentor assembles the image pixels into a set of learnable group tokens via a slot-attention based binding module, and aligns the group tokens to the corresponding caption embedding. Second, we propose two proxy tasks for training, namely masked entity completion and cross-image mask consistency. The former aims to infer all masked entities in the caption given the group tokens, that enables the model to learn fine-grained alignment between visual groups and text entities. The latter enforces consistent mask predictions between images that contain shared entities, which encourages the model to learn visual invariance. Third, we construct CC4M dataset for pre-training by filtering CC12M with frequently appeared entities, which significantly improves training efficiency. Fourth, we perform zero-shot transfer on three benchmark datasets, PASCAL VOC 2012, PASCAL Context, and COCO Object. Our model achieves superior segmentation results over the state-of-the-art method by using only 3% data (4M vs 134M) for pre-training. Code and pre-trained models will be released for future research. © 2023, CC BY.

关键词： Semantics

Questionable Fairness in Federated Learning

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Questionable Fairness in Federated Learning

Conference on Data science and Machine Learning Applications (CDMA)

作者： Raed Alharbi Truc Nguyen Nooshin Yousefzadeh Ahmed Aljohani College of Computing and Informatics Saudi Electronic University Riyadh Saudi Arabia Computatioual Science Center National Renewable Energy Laboratory Golden CO USA Computer and Information Science and Engineering Department University of Florida Gainesville FL USA Computer Science and Engineering University of North Texas Denton USA

ISBN: (数字)9798331539696

ISBN: (纸本)9798331539702

Federated learning (FL) has emerged as a powerful framework for training deep learning models across numerous distributed clients, where a central server distributes and aggregates model updates without accessing clients' raw data, ensuring privacy. However, this decentralized approach introduces significant challenges regarding fairness. Existing research shows that non-i.i.d. data distributions among clients can lead to biased global models, particularly affecting decisions based on sensitive attributes. This paper aims to investigate these fairness issues rigorously by analyzing how disparities in client data distribution impact model fairness. We conduct experiments using various models and datasets to evaluate and mitigate biases. We also propose novel data partitioning strategies to reflect real-world client data distributions accurately. Our finding shows that FL is highly vulnerable to the fairness problem due to the Non-IID property. The experiment's code can be found here: https://***/raed19/Questionable-Fairness-in-Federated-Learning.

关键词： Training Deep learning Data privacy Federated learning Prevention and mitigation Distributed databases Data science Data models Servers Multiaccess communication

On the complexity of sampling from shallow Brownian circuits

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arXiv 2024年

作者： Bentsen, Gregory Fefferman, Bill Ghosh, Soumik Gullans, Michael J. Liu, Yinchen Department of Physics The College of William & Mary United States Department of Computer Science The University of Chicago United States Joint Center for Quantum Information and Computer Science University of Maryland and NIST United States Department of Combinatorics and Optimization University of Waterloo Canada Institute for Quantum Computing University of Waterloo Canada

While many statistical properties of deep random quantum circuits can be deduced, often rigorously and other times heuristically, by an approximation to global Haar-random unitaries, the statistics of constant-depth random quantum circuits are generally less well-understood due to a lack of amenable tools and techniques. We circumvent this barrier by considering a related constant-time Brownian circuit model which shares many similarities with constant-depth random quantum circuits but crucially allows for direct calculations of higher order moments of its output distribution. Using mean-field (large-n) techniques, we fully characterize the output distributions of Brownian circuits at shallow depths and show that they follow a Porter-Thomas distribution, just like in the case of deep circuits, but with a truncated Hilbert space. The access to higher order moments allows for studying the expected and typical Linear Cross-entropy (XEB) benchmark scores achieved by an ideal quantum computer versus the state-of-the-art classical spoofers for shallow Brownian circuits. We discover that for these circuits, while the quantum computer typically scores within a constant factor of the expected value, the classical spoofer suffers from an exponentially larger variance. Numerical evidence suggests that the same phenomenon also occurs in constant-depth discrete random quantum circuits, like those defined over the all-to-all architecture. We conjecture that the same phenomenon is also true for random brickwork circuits in high enough spatial dimension. Copyright © 2024, The Authors. All rights reserved.

关键词： Quantum computers