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Deterministic Distributed Sparse and Ultra-Sparse Spanners and Connectivity Certificates 22

Deterministic Distributed Sparse and Ultra-Sparse Spanners a...

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34th acm symposium on Parallelism in Algorithms and Architectures (SPAA)

作者： Bezdrighin, Marcel Elkin, Michael Ghaffari, Mohsen Grunau, Christoph Haeupler, Bernhard Ilchi, Saeed Rozhon, Vaclav Swiss Fed Inst Technol Zurich Switzerland Ben Gurion Univ Negev Beer Sheva Israel Carnegie Mellon Univ Pittsburgh PA USA

ISBN: (纸本)9781450391467

this paper presents efficient distributed algorithms for a number of fundamental problems in the area of graph sparsification: circle We provide the first deterministic distributed algorithm that computes an ultra-sparse spanner in polylog(n) rounds in weighted graphs. Concretely, our algorithm outputs a spanning subgraph with only n +o (n) edges in which the pairwise distances are stretched by a factor of at most O ( log n center dot 2(O (log*n))). circle We provide a polylog(n)-round deterministic distributed algorithm that computes a spanner with stretch (2k - 1) and O (nk +n(1+1/k) logk) edges in unweighted graphs and with O(n(1+1/k)k) edges in weighted graphs. circle We present the first polylog(n) round randomized distributed algorithm that computes a sparse connectivity certificate. For an n-node graph G, a certificate for connectivity kappa is a spanning subgraph H that is kappa-edge-connected if and only if G is kappa-edge-connected, and this subgraph.. is called sparse if it has O(nk) edges. Our algorithm achieves a sparsity of (1 +o (1))nk edges, which is within a 2(1 +o(1)) factor of the best possible.

关键词： Distributed computing Spanners Ultra-Sparse Spanners LowDiameter Clustering Connectivity Certificates

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C2|Q>: Unifying Classical and Quantum Programming 25

C2|Q>: Unifying Classical and Quantum Programming

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proceedings of the 40th acm/sigapp symposium on applied computing

作者： Boshuai Ye University of Oulu Oulu Finland

ISBN: (纸本)9798400706295

this paper presents C2|Q>, a quantum software framework designed to bridge the gap between classical and quantum programming, specifically aimed at traditional developers who lack quantum computing expertise but want to leverage the potential benefits of quantum computers. C2|Q> transforms classical code written in high-level languages into quantum representations, enabling execution on Noisy Intermediate-Scale Quantum (NISQ) computers or simulators, and provides a user-friendly interface. the framework focuses on problems where quantum computing offers advantages, particularly nondeterministic polynomial-time complete (NP-complete) problems, which are considered classically *** key components of C2|Q> include a parser that identifies problem categories, extracts inputs, and converts classical inputs into quantum formats; a reducer that reformulates NP problems into 3-satisfiability problems (3SAT) when necessary; and an algorithm generator that constructs corresponding Hamiltonians and recommends feasible quantum algorithms. Additionally, the C2|Q> backend includes modules to optimize and compile quantum circuits, request and select suitable quantum hardware, and interpret computational results. the proposed framework empowers researchers and practitioners without prior quantum experience to harness the potential of hybrid quantum computation.

关键词： quantum computing

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Advancing Quantum Software Engineering: A Vision of Hybrid Full-Stack Iterative Model 25

Advancing Quantum Software Engineering: A Vision of Hybrid F...

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proceedings of the 40th acm/sigapp symposium on applied computing

作者： Arif Khan Davide Taibi Cécile M. Perrault Muhammad Azeem Akbar University of Oulu Oulu Finland Alice & Bob Paris France Paris France LUT University Lapparante Finland

ISBN: (纸本)9798400706295

this paper introduces a vision for Quantum Software Development lifecycle, proposing a hybrid full-stack iterative model that integrates quantum and classical computing. Addressing the current challenges in Quantum computing (QC) such as the need for integrating diverse programming languages and managing the complexities of quantum-classical systems, this model is rooted in the principles of DevOps and continuous software engineering. It presents a comprehensive lifecycle for quantum software development, encompassing quantum-agnostic coding, testing, deployment, cloud computing services, orchestration, translation, execution, and interpretation phases. Each phase is designed to accommodate the unique demands of QC, enabling traditional software developers to engage with QC environments without needing in-depth QC expertise. the paper presents a detailed implementation roadmap, utilizing a range of existing tools and frameworks, thereby making quantum software development more accessible and efficient. the proposed model not only addresses current challenges in quantum software development but also makes a substantial contribution to the field of Quantum Software Engineering (QSE). By proposing a structured and accessible model, it sets the stage for further advancements and research in QSE, enhancing its practicality and relevance in a wide range of applications.

关键词： quantum computing

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Exploiting Omega Network and Inexact Accumulative Parallel Counter to Enhance Energy Efficiency in Stochastic computing 25

Exploiting Omega Network and Inexact Accumulative Parallel C...

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proceedings of the 40th acm/sigapp symposium on applied computing

作者： Donghui Lee Yongtae Kim Kyungpook National University Daegu Republic of Korea

ISBN: (纸本)9798400706295

Stochastic computing (SC) has garnered a great interest due to its energy efficiency and robustness against external noise, yet a long latency on stochastic computations and considerable overheads caused by conversions between binary numbers and stochastic numbers persist as notable challenges. this paper introduces a novel parallel random number generator (RNG) and accumulative parallel counters (APCs) to address both challenges. In particular, we propose a new parallel RNG design based on Omega network to bolster the randomness of generated numbers, thereby enhancing accuracy and reducing latency. Additionally, we introduce a novel APC design technique leveraging approximate 4-2 compressors to improve hardware efficiency while preserving the accuracy of SC computations. When implemented using a 65-nm CMOS technology, our proposed SC architecture outperforms other SC alternatives in terms of both hardware efficiency and computation accuracy. Specifically, our APC designs exhibit substantial enhancements of up to 30.1×, 26.6×, 5.9×, and 151× in area, power, delay, and energy, respectively, compared to traditional APCs. Also, we validate the efficacy of the proposed SC design through an image processing application, demonstrating superior processing quality alongside significantly enhanced hardware efficiency.

关键词： stochastic computing

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Energy-Aware Vision Model Partitioning for Edge AI 25

Energy-Aware Vision Model Partitioning for Edge AI

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proceedings of the 40th acm/sigapp symposium on applied computing

作者： Dewant Katare Mengying Zhou Yang Chen Marijn Janssen Aaron Yi Ding Delft University of Technology Delft Netherlands Fudan University Shanghai China

ISBN: (纸本)9798400706295

Deploying scalable Vision Transformer applications on mobile and edge devices is constrained by limited memory and computational resources. Existing model development and deployment strategies include distributed computing and inference methods such as federated learning, split computing, collaborative inference and edge-cloud offloading mechanisms. While these strategies have deployment advantages, they fail to optimize memory usage and processing efficiency, resulting in increased energy consumption. this paper optimizes energy consumption by introducing adaptive model partitioning mechanisms and dynamic scaling methods for ViTs such as EfficientViT and TinyViT, adjusting model complexity based on the available computational resources and operating conditions. We implement energy-efficient strategies that minimize inter-layer communication for distributed machine learning across edge devices, thereby reducing energy consumption from data flow and computation. Our evaluations on a series of benchmark models show improvements, including up to a 32.6% reduction in latency and 16.6% energy savings, while maintaining mean average precision sacrifices within 2.5 to 4.5% of baseline models. these results show that our proposal is a practical approach for improving edge AI sustainability and efficiency.

关键词： energy-aware computing

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LonelyTracker: An IoT Solution with Multimodal Data and Split computing for Secure Loneliness Detection 25

LonelyTracker: An IoT Solution with Multimodal Data and Spli...

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proceedings of the 40th acm/sigapp symposium on applied computing

作者： Tarannum Ara Bivas Mitra Computer Science & Engineering Indian Institute of Technology Kharagpur Kharagpur West Bengal India

ISBN: (纸本)9798400706295

Loneliness is a critical public health issue, linked to increased morbidity and cognitive decline. While previous studies have explored loneliness detection using smartphones and wearables, they often rely on phone calls, SMS, and app usage, which may not fully capture loneliness's complexity. Others focus on limited physiological signals, but both approaches raise privacy concerns. In this paper, we introduce LonelyTracker, a privacy-aware device designed to detect loneliness through continuous monitoring of physiological signals and daily activity patterns. Our system integrates wearable data, such as heart rate variability (HRV), electrodermal activity (EDA), and mobility data from IMU sensors, to develop a comprehensive loneliness detection model. We use a deep neural network (DNN) with a split computing framework to ensure data privacy, processing sensor data locally and sending anonymized feature representations to the cloud for model training. Our 10-week study with 20 participants shows that combining multimodal data improves accuracy, achieving results between 81.1% and 96.5%. Furthermore, LonelyTracker outperforms smartphone-based solutions in energy efficiency and real-time performance, demonstrating its potential for improved mental health monitoring.

关键词： loneliness detection

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Language Flavors in the Lusophone World: A BERT-Based Social Media Study of Portuguese in Brazil, Portugal, and Mozambique 25

Language Flavors in the Lusophone World: A BERT-Based Social...

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proceedings of the 40th acm/sigapp symposium on applied computing

作者： Annie Amorim Gabriel Assis Laura Ribeiro Daniel de Oliveira Aline Paes Institute of Computing Universidade Federal Fluminense Niterói RJ Brazil

ISBN: (纸本)9798400706295

Social networks have become invaluable data sources, enabling many studies about human behavior, language, and social prediction. Users commonly propose new expressions to make communication faster, more fluid, and more original. Moreover, although they follow a specific idiom, the employed language is also mixed-coded, making even the same idiom go beyond the usual variations. this way, texts in Portuguese from different regions that already have linguistic, syntactic, lexical, and orthographic differences go to a new level when written in social media posts. this paper proposes relying on Portuguese language models to analyze the variations of Portuguese in social media posts in Brazil, Portugal, and Mozambique. Our methodology involves pre-trained BERT-based models tuned for the task of text classification and the analysis of the attribution value of the models in the tokens. Moreover, we propose inspecting the ability of the models to predict masked terms specific to regional variants. the analysis reveals that models trained on one variation of Portuguese can perform well on more than one variation. However, there are still challenges in distinguishing between the Portuguese and Mozambican variants.

关键词： NLP

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WMN-CDA: Contrastive Domain Adaptation for Wireless Mesh Network Configuration 25

WMN-CDA: Contrastive Domain Adaptation for Wireless Mesh Net...

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proceedings of the 40th acm/sigapp symposium on applied computing

作者： Aitian Ma Mo Sha Knight Foundation School of Computing and Information Sciences Florida International University Miami FL USA

ISBN: (纸本)9798400706295

Wireless Mesh Networks (WMNs) have become essential for a wide range of applications, such as industrial automation, environmental monitoring, and smart cities. Network operators encounter significant challenges when selecting WMN parameters to ensure good network performance under various ambient operating conditions. Current domain adaptation methods designed for WMN configurations transfer the network configuration knowledge learned from simulations (source domain) to the physical deployment (target domain) at the domain level and fail to consider the simulation-to-reality gap variance under different network configurations, which causes misalignment and overfitting issues. To address such issues, we introduce the WMN Contrastive Domain Adaptation (WMN-CDA) framework, which leverages contrastive learning to transfer the network configuration knowledge learned from simulations to the physical deployment in a discriminative way at a granular scale. WMN-CDA employs the Network Configuration and Simulation-to-Reality contrastive losses to align feature representations and provide good network configuration predictions for physical deployments. We have implemented WMN-CDA and evaluated it with the data collected from a physical testbed with 50 devices and four wireless simulators. Experimental results show significant improvements over the baseline.

关键词： cyber-physical systems

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Unsupervised Training Framework for 3D Point Cloud Object Detection Model 25

Unsupervised Training Framework for 3D Point Cloud Object De...

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proceedings of the 40th acm/sigapp symposium on applied computing

作者： Min-Jyun Wu Hsiang-Jui Lin Shih-Hao Hung Chi-Sheng Shih Dept. of Computer Science and Information Engineering National Taiwan University Taipei Taiwan High Performance and Scientific Computing (HPSC) Center National Taiwan University Taipei Taiwan Graduate Institute of Networking and Multimedia National Taiwan University Taipei Taiwan Center of Artificial Intelligence and Advanced Robotics National Taiwan University Taipei Taiwan

ISBN: (纸本)9798400706295

Point cloud data frames are critical, if not indispensable, for precise robot navigation and localization, but training the object detection models for them remains challenging. Many models require labeled 3D objects to train the model. However, the sparse and occluded 3D point cloud data make it difficult, if not impossible, to automate the labeling process. this work proposes a training framework to generate 3D labels on point clouds to tackle the aforementioned challenges. the proposed method takes advantage of the consecutive presence of the same object on different frames to automate the labeling process. the experimental results show that the unsupervised framework trains a robust model for 3D object detection. On the roadside data, the model archives 90.27% AP for scooters and 91.33% AP for cars. On nuScenes dataset, the framework demonstrates the detection precision doubles on IoU 0.25 and IoU 0.5 when the recalls remain similar, compared to the model trained by the MODEST framework.

关键词： computer systems organization

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Advanced Knowledge Transfer: Refined Feature Distillation for Zero-Shot Quantization in Edge computing 25

Advanced Knowledge Transfer: Refined Feature Distillation fo...

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proceedings of the 40th acm/sigapp symposium on applied computing

作者： Inpyo Hong Youngwan Jo Hyojeong Lee Sunghyun Ahn Sanghyun Park Department of Computer Science Yonsei University Seoul Republic of Korea Department of Artificial Intelligence Yonsei University Seoul Republic of Korea

ISBN: (纸本)9798400706295

We introduce AKT (Advanced Knowledge Transfer), a novel method to enhance the training ability of low-bit quantized (Q) models in the field of zero-shot quantization (ZSQ). Existing research in ZSQ has focused on generating high-quality data from full-precision (FP) models. However, these approaches struggle with reduced learning ability in low-bit quantization due to its limited information capacity. To overcome this limitation, we propose an effective training strategy compared to data generation. Particularly, we analyzed that refining feature maps in the feature distillation process is an effective way to transfer knowledge to the Q model. Based on this analysis, AKT efficiently transfers core information from the FP model to the Q model. AKT is the first approach to utilize both spatial and channel attention information in feature distillation in ZSQ. Our method addresses the fundamental gradient exploding problem in low-bit Q models. Experiments on CIFAR-10 and CIFAR-100 datasets demonstrated the effectiveness of the AKT. Our method led to significant performance enhancement in existing generative models. Notably, AKT achieved significant accuracy improvements in low-bit Q models, achieving state-of-the-art in the 3-bit and 5-bit scenarios on CIFAR-10. the code is available at https://***/Inpyo-Hong/AKT-Advanced-knowledge-Transfer.

关键词： AI for edge computing

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