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Design, Automation and Test in Europe Conference and Exhibition

作者： Yi Guo Qilin Zhou Xiu Chen Heming Sun Graduate School of Information Science and Engineering Yunnan University Kunming China Yunnan Key Laboratory of Intelligent Systems and Computing Yunnan University Kunming China Faculty of Engineering Yokohama National University Kanagawa Japan

ISBN: (数字)9783981926385

ISBN: (纸本)9798350348606

Approximate multiplier saves energy and improves hardware performance for error-tolerant computation-intensive applications. This work proposes hardware-efficient FPGA-based approximate multipliers with look-up table (LUT) sharing and carry switching. Sharing two LUTs with the same inputs enables to fully utilize the available LUT resources. To mitigate the accuracy loss incurred from this approach, the truncated carry is partially reserved by switching it to the adjacent calculation. In addition, we create a library of 8×8 approximate multipliers to provide various multiplication choices. The proposed design can provide enhancements of up to 38.75% in power, 17.29% in latency, and 28.17% in area compared to the Xilinx exact multiplier. Our proposed designs are open-source at https://***/YnuGuoLab/DATE_FPGA_Approx_Mul and assist in further reproducing and development.

关键词： Switches Libraries Hardware Table lookup Field programmable gate arrays

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computing Augustin Information via Hybrid Geodesically Convex Optimization

Computing Augustin Information via Hybrid Geodesically Conve...

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IEEE International Symposium on Information Theory

作者： Guan-Ren Wang Chung-En Tsai Hao-Chung Cheng Yen-Huan Li Graduate Institute of Networking and Multimedia National Taiwan University Department of Computer Science and Information Engineering National Taiwan University Department of Electrical Engineering and Graduate Institute of Communication Enaineering National Taiwan University Department of Mathematics National Taiwan University Hon Hai (Foxconn) Quantum Computing Centre Center for Quantum Science and Engineering National Taiwan University

ISBN: (数字)9798350382846

ISBN: (纸本)9798350382853

We propose a Riemannian gradient descent with the Poincaré metric to compute the $\mathbf{order}-\alpha$ Augustin information, a widely used quantity for characterizing exponential error behaviors in information theory. We prove that the algorithm converges to the optimum at a rate of $\mathcal{O}(1/T)$ . As far as we know, this is the first algorithm with a non-asymptotic optimization error guarantee for all positive orders. Numerical experimental results demonstrate the empirical efficiency of the algorithm. Our result is based on a novel hybrid analysis of Riemannian gradient descent for functions that are geodesically convex in a Riemannian metric and geodesically smooth in another.

关键词： Measurement Quantum computing Finance Linear programming Convex functions Optimization Ellipsoids

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Utilizing Risk Number and Program Slicing to Improve Human-Machine Pair Inspection

Utilizing Risk Number and Program Slicing to Improve Human-M...

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IEEE International Conference on engineering of Complex Computer Systems

作者： Yujun Dai Shaoying Liu Guangquan Xu Ai Liu Graduate School of Advanced Science and Engineering Hiroshima University Hiroshima Japan School of Cybersecurity College of Intelligence and Computing Tianjin University Tianjin China

Human-Machine Pair Inspection (HMPI) is a novel code inspection technology proposed in our previous work, which is the style that machine will intelligently guide the programmer to carry out inspections of the program code during programming. For large-scale software projects, the efficiency of HMPI needs to be improved due to the inaccurate measurement of the code structure and the excessive inspection scope. In this paper, to alleviate the above deficiencies, we propose the Risk Number, a code evaluation metric generated based on historical error data. The Risk Number is calculated by a statistical tool called regression analysis, which more accurately indicates the relationship between the nested structure of the code and the likelihood of containing bugs than Cognitive Complexity. Additionally, HMPI is supported by utilizing Risk Number to point out high-risk code and program slicing techniques to extract statements that have dependencies on the code to generate checklists, thereby reducing the scope of inspection. We describe a case study to evaluate the performance of this method by comparing its inspection time and number of detected errors with our previous work. The result shows that the method is likely to guide the programmer to inspect the faulty code earlier and be more efficient in detecting defects than HMPI based on Cognitive Complexity.

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Leveraging Zero-Level Distillation to Generate High-Fidelity Magic States

Leveraging Zero-Level Distillation to Generate High-Fidelity...

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Quantum computing and engineering (QCE), IEEE International Conference on

作者： Yutaka Hirano Tomohiro Itogawa Keisuke Fujii Graduate School of Engineering Science Osaka University Osaka Japan School of Engineering Science Osaka University Osaka Japan Center for Quantum Information and Quantum Biology Osaka University Toyonaka Japan RIKEN Center for Quantum Computing (RQC) Saitama Japan

ISBN: (数字)9798331541378

ISBN: (纸本)9798331541385

Magic state distillation plays an important role in universal fault-tolerant quantum computing, and its overhead is one of the major obstacles to realizing fault-tolerant quantum computers. Hence, many studies have been conducted to reduce this overhead. Among these, Litinski has provided a concrete assessment of resource-efficient distillation protocol implementations on the rotated surface code. On the other hand, recently, Itogawa et al. have proposed zero-level distillation, a distillation protocol offering very small spatial and temporal overhead to generate relatively low-fidelity magic states. While zero-level distillation offers preferable spatial and temporal overhead, it cannot directly generate high-fidelity magic states since it only reduces the logical error rate of the magic state quadratically. In this study, we evaluate the spatial and temporal overhead of two-level distillation implementations generating relatively high-fidelity magic states, including ones incorporating zero-level dis-tillation. To this end, we introduce (0+ 1)-level distillation, a two-level distillation protocol which combines zero-level distillation and the 15-to-1 distillation protocol. We refine the second-level 15-to-l implementation in it to capitalize on the small footprint of zero-level distillation. Under conditions of a physical error probability of Pphys = 10 -4 (10 -3 ) and targeting an error rate for the magic state within [5 × 10 -17 ,10 -11 ] ([5 × 10 -11 ,10 -8 ]), (0+1)-level distillation reduces the spatiotemporal overhead by more than 63% (61%) compared to the (15-to-l)x(15-to-l) protocol and more than 43% (44%) compared to the (15-to-l)x(20-to-4) protocol, offering a substantial efficiency gain over the traditional protocols.

关键词： Fault tolerance Protocols Quantum computing Costs Error analysis Error probability Fault tolerant systems Refining Numerical simulation Spatiotemporal phenomena

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The Combination of Recognition Technology and Artificial Intelligence for Questioning and Clarification Mechanisms to Facilitate Meaningful EFL Writing in Authentic Contexts 6th

The Combination of Recognition Technology and Artificial Int...

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Proceedings of the 6th International Conference on Innovative Technologies and Learning, ICITL 2023

作者： Hwang, Wu-Yuin Nurtantyana, Rio Lai, Yu-Fu Chiang, I-Chin Nonie Ghenia, George Tsai, Ming-Hsiu Michelle Graduate Institute of Network Learning Technology National Central University Taoyuan City Taiwan School of Computing Telkom University Bandung Indonesia Department of Humanities National Open University New Taipei City Taiwan Department of Computer Science Brunel University London United Kingdom Department of Foreign Languages and Literature Feng Chia University Taichung City Taiwan Department of Computer Science and Information Engineering College of Science and Engineering National Dong Hwa University Hualien County Taiwan

ISBN: (纸本)9783031401121

Most studies of English as a Foreign Language (EFL) writing usually used grammar checking to help EFL learners to check writing errors. However, it is not enough since EFL learners have to learn how to create more meaningful content, particularly using their surroundings in authentic contexts. Therefore, we develop one app, Ubiquitous English (UEnglish), with recognition technology, particularly Image-to-Text Recognition (ITR) texts to provide the vocabulary and description from authentic pictures, and generative-AI that can generate meaningful questions and clarifications to trigger EFL learners to write more. In addition, EFL learners need to answer the question from generative-AI before they receive the clarification. Hence, we proposed a Smart Questioning-Answering-Clarification (QAC) mechanism to help EFL writing. A total of 35 participants were assigned into two groups, experimental groups (EG) with 19 learners and control groups (CG) with 16 learners with/without Smart QAC mechanism support, respectively. In this study, the quasi-experiment was conducted over five weeks and we used quantitative analysis methods. The results revealed that the EG with ITR-texts and Smart QAC had a significant difference with CG in the learning behaviors and post-test. Furthermore, EG could write more meaningful words in the assignments. Therefore, the Smart QAC mechanism could facilitate EFL learners to enhance their EFL writing in authentic contexts. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Character recognition

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Local Periodicity-Based Beat Tracking for Expressive Classical Piano Music

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IEEE/ACM Transactions on Audio Speech and Language Processing 2023年 31卷 2824-2835页

作者： Chiu, Ching-Yu Muller, Meinard Davies, Matthew E. P. Su, Alvin Wen-Yu Yang, Yi-Hsuan National Cheng Kung University Academia Sinica Graduate Program of Multimedia Systems and Intelligent Computing Tainan701401 Taiwan International Audio Laboratories Erlangen Erlangen91058 Germany University of Coimbra Department of Informatics Engineering Centre for Informatics and Systems Coimbra3004-531 Portugal National Cheng Kung University Department of Computer Science and Information Engineering Tainan701401 Taiwan Taiwan AI Labs Yating Music Team Taipei103622 Taiwan Academia Sinica Research Center for IT Innovation Taipei11529 Taiwan

To model the periodicity of beats, state-of-the-art beat tracking systems use 'post-processing trackers' (PPTs) that rely on several empirically determined global assumptions for tempo transition, which work well for music with a steady tempo. For expressive classical music, however, these assumptions can be too rigid. With two large datasets of Western classical piano music, namely the Aligned Scores and Performances (ASAP) dataset and a dataset of Chopin's Mazurkas (Maz-5), we report on experiments showing the failure of existing PPTs to cope with local tempo changes, thus calling for new methods. In this paper, we propose a new local periodicity-based PPT, called predominant local pulse-based dynamic programming (PLPDP) tracking, that allows for more flexible tempo transitions. Specifically, the new PPT incorporates a method called 'predominant local pulses' (PLP) in combination with a dynamic programming (DP) component to jointly consider the locally detected periodicity and beat activation strength at each time instant. Accordingly, PLPDP accounts for the local periodicity, rather than relying on a global tempo assumption. Compared to existing PPTs, PLPDP particularly enhances the recall values at the cost of a lower precision, resulting in an overall improvement of F1-score for beat tracking in ASAP (from 0.473 to 0.493) and Maz-5 (from 0.595 to 0.838). © 2014 IEEE.

关键词： Hidden Markov models

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Palantir: Towards Efficient Super Resolution for Ultra-high-definition Live Streaming 25

Palantir: Towards Efficient Super Resolution for Ultra-high-...

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16th ACM Multimedia Systems Conference, MMSys 2025

作者： Jin, Xinqi Zhu, Zhui Sun, Xikai Dang, Fan Liu, Jiangchuan Xu, Jingao Liu, Kebin Chen, Xinlei Liu, Yunhao School of Software & BNRist Tsinghua University Beijing China Department of Automation & BNRist Tsinghua University Beijing China School of Software Engineering Beijing Jiaotong University Beijing China School of Computing Science Simon Fraser University Burnaby Canada Computer Science Department Carnegie Mellon University Pittsburgh United States Global Innovation Exchange Tsinghua University Beijing China Shenzhen International Graduate School Tsinghua University Shenzhen China

ISBN: (纸本)9798400714672

Neural enhancement through super-resolution (SR) deep neural networks (DNNs) opens up new possibilities for ultra-high-definition (UHD) live streaming. Yet, the heavy SR DNN inference overhead leads to severe deployment challenges. To reduce the overhead, existing systems propose to apply DNN-based SR only on carefully selected anchor frames while upscaling non-anchor frames via the lightweight reusing-based SR approach. However, frame-level scheduling is coarse-grained and fails to deliver optimal efficiency. In this work, we propose Palantír, the first neural-enhanced UHD live streaming system with fine-grained patch-level *** the core of Palantír is its SR video quality estimation strategy which guides the low-delay selection of the most beneficial anchor patches. Although existing systems propose estimation strategies for anchor frame selection, these strategies heavily rely on empirical insights that cannot be transferred to our context, making fine-grained scheduling a challenging problem that requires a fundamentally new solution. Facing the challenge, we follow the first-principles approach and derive a directed acyclic graph (DAG) model to address the problem. The model can also be generalized to various settings due to its first-principles nature. Compared to the state-of-the-art real-time frame-level scheduling strategy for live streaming, Palantír reduces the anchor size by 80.1% at most and 38.4% on average without compromising the quality gain. Furthermore, Palantír incurs a scheduling latency accounting for only 0.6-3.9% of the end-to-end latency requirement for UHD live streaming. © 2025 Copyright is held by the owner/author(s). Publication rights licensed to ACM.

关键词： Deep neural networks

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Task groupings regularization: data-free meta-learning with heterogeneous pre-trained models 24

Task groupings regularization: data-free meta-learning with ...

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Proceedings of the 41st International Conference on Machine Learning

作者： Yongxian Wei Zixuan Hu Li Shen Zhenyi Wang Yu Li Chun Yuan Dacheng Tao Tsinghua Shenzhen International Graduate School Tsinghua University China Tsinghua Shenzhen International Graduate School Tsinghua University China and College of Computing & Data Science Nanyang Technological University Singapore School of Cyber Science and Technology Sun Yatsen University China University of Maryland College Park Department of Computer Science and Engineering The Chinese University of Hong Kong China College of Computing & Data Science Nanyang Technological University Singapore

Data-Free Meta-Learning (DFML) aims to derive knowledge from a collection of pre-trained models without accessing their original data, enabling the rapid adaptation to new unseen tasks. Current methods often overlook the heterogeneity among pre-trained models, which leads to performance degradation due to task conflicts. In this paper, we empirically and theoretically identify and analyze the model heterogeneity in DFML. We find that model heterogeneity introduces a heterogeneity-homogeneity trade-off, where homogeneous models reduce task conflicts but also increase the overfitting risk. Balancing this trade-off is crucial for learning shared representations across tasks. Based on our findings, we propose Task Groupings Regularization, a novel approach that benefits from model heterogeneity by grouping and aligning conflicting tasks. Specifically, we embed pre-trained models into a task space to compute dissimilarity, and group heterogeneous models together based on this measure. Then, we introduce implicit gradient regularization within each group to mitigate potential conflicts. By encouraging a gradient direction suitable for all tasks, the meta-model captures shared representations that generalize across tasks. Comprehensive experiments showcase the superiority of our approach in multiple benchmarks, effectively tackling the model heterogeneity in challenging multidomain and multi-architecture scenarios.

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computing Augustin Information via Hybrid Geodesically Convex Optimization

arXiv

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arXiv 2024年

作者： Wang, Guan-Ren Tsai, Chung-En Cheng, Hao-Chung Li, Yen-Huan Graduate Institute of Networking and Multimedia National Taiwan University Taiwan Department of Computer Science and Information Engineering National Taiwan University Taiwan Department of Electrical Engineering Graduate Institute of Communication Engineering National Taiwan University Taiwan Department of Mathematics National Taiwan University Taiwan Quantum Computing Centre Taiwan Center for Quantum Science and Engineering National Taiwan University Taiwan

We propose a Riemannian gradient descent with the Poincaré metric to compute the order-α Augustin information, a widely used quantity for characterizing exponential error behaviors in information theory. We prove that the algorithm converges to the optimum at a rate of O(1/T). As far as we know, this is the first algorithm with a non-asymptotic optimization error guarantee for all positive orders. Numerical experimental results demonstrate the empirical efficiency of the algorithm. Our result is based on a novel hybrid analysis of Riemannian gradient descent for functions that are geodesically convex in a Riemannian metric and geodesically smooth in another. Copyright © 2024, The Authors. All rights reserved.

关键词： Information theory

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ChatGPT4PCG 2 Competition: Prompt engineering for science Birds Level Generation

arXiv

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arXiv 2024年

作者： Taveekitworachai, Pittawat Abdullah, Febri Dewantoro, Mury F. Xia, Yi Suntichaikul, Pratch Thawonmas, Ruck Togelius, Julian Renz, Jochen Graduate School of Information Science and Engineering Ritsumeikan University Kusatsu Japan College of Information Science and Engineering Ritsumeikan University Kusatsu Japan NYU Tandon School of Engineering New York University New York United States School of Computing The Australian National University Canberra Australia

This paper presents the second ChatGPT4PCG competition at the 2024 IEEE Conference on Games. In this edition of the competition, we follow the first edition, but make several improvements and changes. We introduce a new evaluation metric along with allowing a more flexible format for participants’ submissions and making several improvements to the evaluation pipeline. Continuing from the first edition, we aim to foster and explore the realm of prompt engineering (PE) for procedural content generation (PCG). While the first competition saw success, it was hindered by various limitations;we aim to mitigate these limitations in this edition. We introduce diversity as a new metric to discourage submissions aimed at producing repetitive structures. Furthermore, we allow submission of a Python program instead of a prompt text file for greater flexibility in implementing advanced PE approaches, which may require control flow, including conditions and iterations. We also make several improvements to the evaluation pipeline with a better classifier for similarity evaluation and better-performing function signatures. We thoroughly evaluate the effectiveness of the new metric and the improved classifier. Additionally, we perform an ablation study to select a function signature to instruct ChatGPT for level generation. Finally, we provide implementation examples of various PE techniques in Python and evaluate their preliminary performance. We hope this competition serves as a resource and platform for learning about PE and PCG in general1 © 2024, CC BY.

关键词： Birds

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