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Mapping Quantum Circuits to 2-Dimensional Quantum architectures

Mapping Quantum Circuits to 2-Dimensional Quantum Architectu...

2022 Informatik in den Naturwissenschaften, INFORMATIK 2022 - 2022 computer Science in the Natural Sciences, INFORMATIK 2022

作者： Datta, Kamalika Kole, Abhoy Sengupta, Indranil Drechsler, Rolf Cyber Physical System Bremen Germany JIS University Department of Computer Science and Engineering Kolkata India Indian Institute of Technology Kharagpur Department of Computer Science and Engineering India University of Bremen Group of Computer Architecture Bremen Germany

ISBN: (纸本)9783885797203

We have been witnessing a rapid growth in quantum computing research over the years, with the emergence of demonstrable quantum computers of moderate size. The major issues that are faced to run a quantum algorithm reliably on these systems are: (i) lower qubit coherence period, (ii) noisy primitive gate operations, (iii) limited number of available physical qubits, and (iv) support of restricted set of 2-qubit operations. Overcoming these issues mandates physical resources that exceeds the capabilities of these noisy intermediate scale quantum (NISQ) systems. On the other hand, computation using bare qubits get further disturbed due to the inclusion of additional gates to mitigate the nearest neighbor constraints. In the present work, the 2-dimensional square, heavy-hex and fully hexagonal qubit coupling lattices are considered for mapping quantum circuits. The benefits are assessed in terms of minimal additional gates needed to satisfy the nearest neighbor (NN) constraint and the compilation complexity of mapping circuits on these architectures. From the experiments by mapping benchmark circuits on 16-qubit square and 65-qubit heavy-hex architectures from IBM as well as on a 64-qubit fully hexagonal architecture, it is observed that none of the square or heavy-hex lattice architecture provides uniform compilation advantage compared to the fully hexagonal architecture. It is expected that beyond the NISQ era, strongly connected lattices like hexagonal will become practically feasible. © 2022 Gesellschaft fur Informatik (GI). All rights reserved.

关键词： Qubits

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When Do Neuromorphic Sensors Outperform cameras? Learning from Dynamic Features

When Do Neuromorphic Sensors Outperform cameras? Learning fr...

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Annual Conference on Information Sciences and Systems (CISS)

作者： Daniel Deniz Eduardo Ros Cornelia Fermller Francisco Barranco Dep. of Computer Architecture and Technology University of Granada Spain Perception and Robotics Group University of Maryland USA

Visual event sensors only output data when changes in the scene happen at very high frequency. This allows for smartly compressing the scene and thus, enabling real-time operation. Despite these advantages, works in the literature have struggled to show a niche for these event-driven approaches compared to conventional sensors, especially when focusing on accuracy performance. In this work, we show a case that fully exploits event sensor advantages: for manipulation action recognition, learning events achieves superior accuracy and time performance. The recognition of manipulation actions requires extracting and learning features from the hand pose and trajectory and the interaction with the object. As shown in our work, approaches based on event sensors are the best fit for extracting these dynamic features contrarily to conventional approaches based on full frames, which mostly extract spatial features and need to reconstruct the dynamics from sequences of frames. Finally, we show how using a tracker to extract the features to be learned only around the hand, we obtain an approach that is scene- and almost object-agnostic and achieves good time performance with a very limited impact in accuracy.

关键词： Visualization Neuromorphics Neural networks Human-robot interaction Sensor phenomena and characterization Feature extraction Real-time systems

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A New Methodology for Software-Simulation of Membrane Systems Using a Multi-Thread Programming Model

SSRN

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SSRN 2024年

作者： Cascado-Caballero, Daniel Diaz-Del-Rio, Fernando Cagigas-Muñiz, Daniel Orellana-Martín, David Pérez-Hurtado, Ignacio Department of Computer Architecture and Technology Universidad de Sevilla Seville Spain Research Group on Natural Computing Department of Computer Science and Artificial Intelligence Universidad de Sevilla Seville Spain

The evolution of simulation and implementation of P systems has been intense since the theoretical model of computation was created. In the field of software simulation of P systems, the proposals made so far have taken advantage mainly of the parallelism of GPUs, but not of the parallelism of existing multi-core processors. This paper proposes a methodology for simulating P systems using a multi-threaded methodological approach in a multi-core processor. This proposal has been implemented and tested using a simulator programmed in C#, and its correct operation has been tested for confluent and non-confluent systems. The experimental results confirm that the simulator scales well with the number of hardware threads of a mul-tiprocessor. The results obtained suggest that the methodology is valid and that it is worth testing it with more complex systems to find the limits of the methodology. © 2024, The Authors. All rights reserved.

关键词： Program processors

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EVENT-BASED VISION FOR EARLY PREDICTION OF MANIPULATION ACTIONS

arXiv

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arXiv 2023年

作者： Deniz, Daniel Fermüller, Cornelia Ros, Eduardo Rodriguez-Alvarez, Manuel Barranco, Francisco Computer Architecture and Technology CITIC University of Granada Granada Spain Perception and Robotics Group University of Maryland Maryland United States

Neuromorphic visual sensors are artificial retinas that output sequences of asynchronous events when brightness changes occur in the scene. These sensors offer many advantages including very high temporal resolution, no motion blur and smart data compression ideal for real-time processing. In this study, we introduce an event-based dataset on fine-grained manipulation actions and perform an experimental study on the use of transformers for action prediction with events. There is enormous interest in the fields of cognitive robotics and human-robot interaction on understanding and predicting human actions as early as possible. Early prediction allows anticipating complex stages for planning, enabling effective and real-time interaction. Our Transformer network uses events to predict manipulation actions as they occur, using online inference. The model succeeds at predicting actions early on, building up confidence over time and achieving state-of-the-art classification. Moreover, the attention-based transformer architecture allows us to study the role of the spatio-temporal patterns selected by the model. Our experiments show that the Transformer network captures action dynamic features outperforming video-based approaches and succeeding with scenarios where the differences between actions lie in very subtle cues. Finally, we release the new event dataset, which is the first in the literature for manipulation action recognition. Code will be available at https://***/DaniDeniz/EventVisionTransformer. © 2023, CC BY.

关键词： Human robot interaction

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STEAM for all: New Computational Thinking Curricula in Spanish Formal Secondary Education 2021

STEAM for all: New Computational Thinking Curricula in Spani...

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10th International Conference on Digital and Interactive Arts

作者： Rocio Garcia-Robles Santiago Fernández-Cabaleiro Department of Computer Architecture and Technology ASTERISM Research Group University of Seville Spain

ISBN: (纸本)9781450384209

In this article, the authors introduce the new educational curricula for Computing and Robotics in Andalusia [1], that will be offered as an optional subject for more than three hundred thousand students during 2021/2022 academic year and so *** curriculum offers young students, aged 12-15 years old, with the opportunity to gain a better understanding on how our digital world works, as well as, developing a set of skills known as computational thinking. This term refers to a way of understanding and solving problems, in any discipline, with the help of computers.ç

关键词： Computational thinking Educational programming Robotics STEAM-STEAM-STEAM+A

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TG-LLaVA: Text Guided LLaVA via Learnable Latent Embeddings 39

TG-LLaVA: Text Guided LLaVA via Learnable Latent Embeddings

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39th Annual AAAI Conference on Artificial Intelligence, AAAI 2025

作者： Yan, Dawei Li, Pengcheng Li, Yang Chen, Hao Chen, Qingguo Luo, Weihua Dong, Wei Yan, Qingsen Zhang, Haokui Shen, Chunhua School of Cybersecurity Northwestern Polytechnical University China AI Business Alibaba Group China College of Computer Science and Technology Zhejiang University China College of Information and Control Engineering Xi’an University of Architecture and Technology China School of Computer Science Northwestern Polytechnical University China

ISBN: (纸本)157735897X

Currently, inspired by the success of vision-language models (VLMs), an increasing number of researchers are focusing on improving VLMs and have achieved promising results. However, most existing methods concentrate on optimizing the connector and enhancing the language model component, while neglecting improvements to the vision encoder itself. In contrast, we propose Text Guided LLaVA (TG-LLaVA) in this paper, which optimizes VLMs by guiding the vision encoder with text, offering a new and orthogonal optimization direction. Specifically, inspired by the purpose-driven logic inherent in human behavior, we use learnable latent embeddings as a bridge to analyze textual instruction and add the analysis results to the vision encoder as guidance, refining it. Subsequently, another set of latent embeddings extracts additional detailed text-guided information from high-resolution local patches as auxiliary information. Finally, with the guidance of text, the vision encoder can extract text-related features, similar to how humans focus on the most relevant parts of an image when considering a question. This results in generating better answers. Experiments on various datasets validate the effectiveness of the proposed method. Remarkably, without the need for additional training data, our proposed method can bring more benefits to the baseline (LLaVA-1.5) compared with other concurrent methods. Furthermore, the proposed method consistently brings improvement in different settings. Copyright © 2025, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

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Redundant and Loosely Coupled LiDAR-Wi-Fi Integration for Robust Global Localization in Autonomous Mobile Robotics

arXiv

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arXiv 2023年

作者： Stathoulopoulos, Nikolaos Pagliari, Emanuele Davoli, Luca Nikolakopoulos, George The Robotics and AI Group Department of Computer Electrical and Space Engineering Luleå University of Technology Luleå97187 Sweden Lab Department of Engineering and Architecture University of Parma Parma43124 Italy

This paper presents a framework addressing the challenge of global localization in autonomous mobile robotics by integrating LiDAR-based descriptors and Wi-Fi fingerprinting in a pre-mapped environment. This is motivated by the increasing demand for reliable localization in complex scenarios, such as urban areas or underground mines, requiring robust systems able to overcome limitations faced by traditional Global Navigation Satellite System (GNSS)-based localization methods. By leveraging the complementary strengths of LiDAR and Wi-Fi sensors used to generate predictions and evaluate the confidence of each prediction as an indicator of potential degradation, we propose a redundancy-based approach that enhances the system’s overall robustness and accuracy. The proposed framework allows independent operation of the LiDAR and Wi-Fi sensors, ensuring system redundancy. By combining the predictions while considering their confidence levels, we achieve enhanced and consistent performance in localization tasks. Copyright © 2023, The Authors. All rights reserved.

关键词： Forecasting

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Redundant and Loosely Coupled LiDAR-Wi-Fi Integration for Robust Global Localization in Autonomous Mobile Robotics

Redundant and Loosely Coupled LiDAR-Wi-Fi Integration for Ro...

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International Conference on Advanced Robotics (ICAR)

作者： Nikolaos Stathoulopoulos Emanuele Pagliari Luca Davoli George Nikolakopoulos Department of Computer Electrical and Space Engineering Robotics and AI Group Luleå University of Technology Luleå Sweden Department of Engineering and Architecture Internet of Things (IoT) Lab University of Parma Parma Italy

This paper presents a framework addressing the challenge of global localization in autonomous mobile robotics by integrating LiDAR-based descriptors and Wi-Fi finger-printing in a pre-mapped environment. This is motivated by the increasing demand for reliable localization in complex scenarios, such as urban areas or underground mines, requiring robust systems able to overcome limitations faced by traditional Global Navigation Satellite System (GNSS)-based localization methods. By leveraging the complementary strengths of LiDAR and Wi-Fi sensors used to generate predictions and evaluate the confidence of each prediction as an indicator of potential degradation, we propose a redundancy-based approach that enhances the system's overall robustness and accuracy. The proposed framework allows independent operation of the LiDAR and Wi-Fi sensors, ensuring system redundancy. By combining the predictions while considering their confidence levels, we achieve enhanced and consistent performance in localization tasks.

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How to Evaluate Deep Neural Network Processors: TOPS/W (Alone) Considered Harmful

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IEEE Solid-State Circuits Magazine 2020年第3期12卷 28-41页

作者： Sze, Vivienne Chen, Yu-Hsin Yang, Tien-Ju Emer, Joel S. Electrical Engineering and Computer Science Massachusetts Institute of Technology MA United States Facebook United States Architecture Research Group Nvidia United States

A significant amount of specialized hardware has been developed for processing deep neural networks (DNNs) in both academia and industry. This article aims to highlight the key concepts required to evaluate and compare these DNN processors. We discuss existing challenges, such as the flexibility and scalability needed to support a wide range of neural networks, as well as design considerations for both the DNN processors and the DNN models themselves. We also describe specific metrics that can be used to evaluate and compare existing solutions beyond the commonly used tera-operations per second per watt (TOPS/W). This article is based on the tutorial 'How to Understand and Evaluate Deep Learning Processors' that was given at the 2020 International Solid-State Circuits Conference, as well as excerpts from the book, Efficient Processing of Deep Neural Networks [36]. © 2009-2012 IEEE.

关键词： Deep neural networks

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Employment Data Acquiring Method Based on WeChat Scan Code

Employment Data Acquiring Method Based on WeChat Scan Code

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International Conference on Systems and Informatics (ICSAI)

作者： Bianping Su Yunsong Wang Yantao He Longqing Zhang Jiayu He Jieming Lin School of Computer Guangdong University of Science & Technology Dongguan Guangdong China Xi'an International Inland Port Investment and Development Group Co. Ltd. Xi'an Chan-Ba International Port Management Committee Xi'an China School of Science Xi'an University of Architecture and Technology Xi'an Chin

ISBN: (数字)9798331510138

ISBN: (纸本)9798331510145

Based on the current employment situation and methods for obtaining unemployment and employment data in China, a new method for obtaining unemployment and employment data was constructed using the Naive Bayes classification principle in machine learning by WeChat scan data of travelers. This method divides the unemployment or employment status of individuals using prior and posterior probabilities. The evaluation index values of the experimental results on the dataset demonstrate the effectiveness of the method, and the accuracy of determining the employment status of travelers through mobile phone calls has increased by 22.9%. A strategy of data preprocessing combined with this method was proposed for large-scale data and preliminary discussions were conducted. The new method of obtaining unemployment and employment data has opened up new avenues for traditional statistical data acquisition, providing strong intelligent support for the government to dynamically grasp the employment situation in real time.

关键词： Social networking (online) Posterior probability Employment Machine learning Message services Real-time systems Mobile handsets Naive Bayes methods Unemployment Informatics

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