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arXiv 2024年

作者： Huang, Zixin Lami, Ludovico Wilde, Mark M. School of Mathematical and Physical Sciences Macquarie University NSW2109 Australia Centre for Quantum Software and Information Faculty of Engineering and Information Technology University of Technology Sydney SydneyNSW Australia QuSoft Science Park 123 Amsterdam1098 XG Netherlands Korteweg–de Vries Institute for Mathematics University of Amsterdam Science Park 105-107 Amsterdam1098 XG Netherlands Institute for Theoretical Physics University of Amsterdam Science Park 904 Amsterdam1098 XH Netherlands School of Electrical and Computer Engineering Cornell University IthacaNY14850 United States

Dephasing is a prominent noise mechanism that afflicts quantum information carriers, and it is one of the main challenges towards realizing useful quantum computation, communication, and sensing. Here we consider discrimination and estimation of bosonic dephasing channels, when using the most general adaptive strategies allowed by quantum mechanics. We reduce these difficult quantum problems to simple classical ones based on the probability densities defining the bosonic dephasing channels. By doing so, we rigorously establish the optimal performance of various distinguishability and estimation tasks and construct explicit strategies to achieve this performance. To the best of our knowledge, this is the first example of a non-Gaussian bosonic channel for which there are exact solutions for these tasks. Copyright © 2024, The Authors. All rights reserved.

关键词： Quantum computers

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Parallel Design Patterns vs Parallel Object Compositions. Two Proposals for Parallelization of the Divide & Conquer Technique 33

Parallel Design Patterns vs Parallel Object Compositions. Tw...

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33rd European Modeling and Simulation Symposium, EMSS 2021

作者： Rossainz-López, Mario Pineda-Torres, Ivo Sánchez-Rinza, Bárbara Capel-Tuñón, Manuel Faculty of Computer Science Autonomous University of Puebla Av. San Claudio and 14 Sur Street Puebla San ManuelC.P. 72570 Mexico Software Engineering Department College of Informatics and Telecommunications ETSIIT University of Granada Daniel Saucedo Aranda s/n Granada18071 Spain

The present work shows the parallelization of the algorithmic design technique Divide & Conquer in two different ways: As a Parallel Design Pattern (PDP) through Active Objects and as Composition High-Level Parallel (HLPC). The overall purpose is to provide the user and novice programmer with two approaches within the object-oriented programming environment, particularly within the programming of Parallel Objects (PO), so that they can develop their programs according to a sequential programming style, automatically obtaining, easy and without much effort, the parallel counterpart of your code with the help of a specific programming environment like the one proposed. It is common for parallel applications to follow predetermined patterns in communication between processes. That is why this proposal proposes two different methods that solve problems with the same parallel control structure. Both methods use Structured Parallel Programming and Parallel Objects. The proposal is specialized in the algorithmic technique of divide & conquer to solve ordering, search, and optimization problems. The default pattern used to communicate problem solving processes is the tree structure. The proposed methods are novel because they offer the programmer the communication pattern between tree-like processes that is already defined in its structure. The programmer is only concerned with implementing the sequential algorithms that solve the problem under the divide & conquer paradigm. Both approaches are effective because they show good speedup analysis, and their usefulness, programmability, and performance are demonstrated. © 2021 The Authors.

关键词： Parallel programming

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Innovative Poverty Estimation through Machine Learning Approaches

Innovative Poverty Estimation through Machine Learning Appro...

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International Conference on Emerging Technologies, ICET

作者： Ahmad Mashhood Zain ul Abideen Usama Arshad Raja Hashim Ali Ammad Aslam Khan Bahram Khan Faculty of Computer Science & Engineering GIK Institute of Engg. Sciences & Technology Topi Pakistan BCMaterials Basque Center for Materials Applications & Nanostructure Leioa Basque Country Spain Dept. of Technology & Software Engineering University of Europe for Applied Sciences Berlin Germany Dept. of Biological Sciences M.A. Road Satellite Town Virtual University of Pakistan Rawalpindi Pakistan

This paper delves into the application and capabilities of machine learning methodologies in forecasting poverty scenarios, underlining the importance of varied data sources, along with the interpretability and explainability of models to refine the precision and transparency of poverty prediction mechanisms. It primarily utilizes an iteration of the LightGBM algorithm to infer poverty stages premised on household variables. This investigation showcases the potential of machine learning methodologies in poverty estimation, elucidating the relevance of additional, diverse data resources, and the critical role of model interpretability in enhancing model accuracy and clarity. The LightGBM version employed in our study demonstrated high efficiency on the validation set, affirming its effectiveness in projecting poverty conditions based on household features. In analyzing SHAP values, we unearthed crucial insights into the contributing elements to poverty. These findings can aid in shaping policies aimed at poverty alleviation and elevating social welfare. Our work provides the basis for future research into poverty prediction and affirms the potential of machine learning methodologies in tackling intricate social challenges.

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CB-HVTNet: A channel-boosted hybrid vision transformer network for lymphocyte assessment in histopathological images

arXiv

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arXiv 2023年

作者： Ali, Momina Liaqat Rauf, Zunaira Khan, Asifullah Sohail, Anabia Ullah, Rafi Gwak, Jeonghwan Pattern Recognition Lab Department of Computer & Information Sciences Pakistan Institute of Engineering & Applied Sciences Nilore Islamabad45650 Pakistan Pakistan Institute of Engineering & Applied Sciences Nilore Islamabad45650 Pakistan Center for Mathematical Sciences Pakistan Institute of Engineering & Applied Sciences Nilore Islamabad45650 Pakistan Department of Computer Science Faculty of Computing and Artificial Intelligence Air University IslamabadE-9 Pakistan Perak31750 Malaysia Department of Software Korea National University of Transportation Chungju27469 Korea Republic of

Transformers, due to their ability to learn long-range dependencies, have overcome the shortcomings of convolutional neural networks (CNNs) for global perspective learning. However, their multi-head attention module only captures global-level feature representations, which is insufficient for medical images. To address this issue, we propose a Channel Boosted Hybrid Vision Transformer (CB-HVT) that uses transfer learning to generate boosted channels and employs both transformers and CNNs to analyse lymphocytes in histopathological images. The proposed CB-HVT comprises five modules to effectively identify lymphocytes. Its (i) channel generation module, uses the idea of channel boosting through transfer learning to extract diverse channels from different auxiliary learners. These boosted channels are first concatenated and ranked using an attention mechanism in the (ii) channel exploitation module. A fusion block is then utilized in the (iii) channel merging module for a gradual and systematic merging of the diverse boosted channels to improve the network’s learning representations. The CB-HVT also employs a proposal network in its (iv) region aware module followed by its (v) detection and segmentation head to accurately identify and distinguish objects, even in the regions with crowded presence and artifacts. We evaluated the proposed CB-HVT on two publicly available datasets for lymphocyte assessment in histopathological images. The results demonstrate that CB-HVT has a good generalization on unseen data therefore, it can serve as a valuable tool for pathologists. Copyright © 2023, The Authors. All rights reserved.

关键词： Lymphocytes

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A fast humanoid robot arm for boxing based on servo motors

A fast humanoid robot arm for boxing based on servo motors

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High Performance Big Data and Intelligent Systems (HPBD&IS)

作者： Jianchu Lin Zhouzhi Gu Ali Mokhtarzadeh Amir Xiaobin Chen Kahadka Ashim Kun Shi Faculty of Computer and Software Engineering Huaiyin Institute of Technology Huai’an China Gandaki College of Engineering and Science Pokhara Nepal Beijing Liaowang Shenzhou Technology LCO Beijing China

ISBN: (纸本)9781665413282

The sport of boxing has been gradually accepted by the public as a kind of fitness excise. A humanoid robot arm is necessary for this sport since it can reduce the tedious training work of coaching and increase the fun of this exercise. A 4-degree of freedom (DOF) robotic arm was designed by analyzing the basic gestures of boxing, and the servo motor was employed to support the arm’s fast movement. The results show that the robotic arm can fulfill the basic gestures of jab, hook, and swing correctly and the arm joints with servo motor are faster than with stepper motor.

关键词： Training Humanoid robots Arms Manipulators Servomotors Sports

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Ivomfuse: An Image Fusion Method Based on Infrared-to-Visible Object Mapping

SSRN

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SSRN 2022年

作者： Zhang, Xiangbo Huang, Lei Liu, Gang Ren, Qin Bavirisetti, Durga Prasad School of Automation Engineering Shanghai University of Electric Power Shanghai201306 China Library Shanghai University of Electric Power Shanghai201306 China School of Software and Microelectronics Peking University Beijing102600 China Department of Computer Science Faculty of Information Technology and Electrical Engineering Norwegian University of Science and Technology Trondheim7034 Norway

Due to the significant difference in the structure of infrared and visible images, most of the existing algorithms use globally consistent strategy, which may weaken the contrast of the target region. To solve this problem, we propose an infrared and visible image fusion method based on infrared-to-visible object mapping (IVOMFuse) First of all, we use the probability induced intuitionistic fuzzy C-means algorithm (PIIFCM) to process the infrared image, and extract the target region of the infrared image according to the image structure features. However, because of the poor contrast of the target of the visible image, it can not be segmented directly. For this reason, we map the infrared target region to the visible image, and the target region with the same coordinates of the two images can be obtained very quickly. Secondly, due to the significant differences in image structure, we set a hybrid fusion strategies for different regions. We use the fusion strategy based on the Expectation-Maximization (EM) algorithm to iterate and optimize the probability model for the target region. The fourth-order partial differential equation (FPDE) is used for the background region to decompose the source image into high-frequency texture and low-frequency approximation images respectively. Then, the principal component analysis (PCA) and the average fusion strategy are used to obtain the fused image. Finally, we evaluated our algorithm with thirteen other state-of-the-art image fusion methods on three commonly used datasets, namely TNO, CVC14, and RoadScene. Among the ten evaluation indexes, our algorithm achieved the best six times. © 2022, The Authors. All rights reserved.

关键词： Mapping

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Multi-Modal Sentiment Analysis Based on Multiple Kernel Learning with Margin Dimension Constraint

SSRN

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SSRN 2022年

作者： Liu, Jun Xie, Mingyue Lin, Mingwei Nour, Majid Alenezi, Fayadh AAlthubiti, Sara Polat, Kemal School of Software Engineering Chongqing University of Posts and Telecommunications Chongqing 400065 China College of Computer and Cyber Security Fujian Normal University Fujian Fuzhou350117 China Department of Electrical and Computer Engineering Faculty of Engineering King Abdulaziz University Jeddah21589 Saudi Arabia Department of Electrical Engineering College of Engineering Jouf University Saudi Arabia Department of Computer Science College of Computer and Information Sciences Majmaah University Saudi Arabia Department of Electrical and Electronics Engineering Bolu Abant Izzet Baysal University Bolu Turkey

Most of the sentiment analysis studies focus on the sentimental classification of pictures in the video, ignoring the spatio-temporal information of the sequence of picture frames as well as text and audio information. The multiple kernel learning is a new hotspot in the field of nuclear machine learning, capable of handling multiple modalities. For multiple kernel learning, it is easy to ignore the basic features that are not discriminative, and cannot make full use of the base features of different modes. This paper puts forward a novel multi-modal fusion model for sentiment analysis, in which a multiple kernel learning algorithm based on convolution margin dimension constraint is proposed for feature fusion. Moreover, the 3D convolutional neural network is used to extract the features of visual information, and the multiple kernel learning algorithm based on margin dimension constraint is used to fuse visual, text and audio sentiment features. Experiments conducted on the MOUD and IEMOCAP sentiment databases show that the proposed model outperforms existing models in the field of multi-modal sentiment analysis research. © 2022, The Authors. All rights reserved.

关键词： Sentiment analysis

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A Smartphone Enabled Approach to Manage COVID-19 Lockdown and Economic Crisis

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SN computer science 2020年第5期1卷 1-9页

作者： Maghdid, Halgurd S. Ghafoor, Kayhan Zrar Department of Software Engineering Faculty of Engineering Koya University Kurdistan Region-F.R. Koysinjaq 4400 Iraq Department of Software Engineering Salahaddin University-Erbil Erbil 4500 Iraq School of Mathematics and Computer Science University of Wolverhampton Wulfruna Street Wolverhampton WV1 1LY United Kingdom

The emergence of novel COVID-19 causes an over-load in health system and high mortality rate. The key priority is to contain the epidemic and prevent the infection rate. In this context, many countries are now in some degree of lockdown to ensure extreme social distancing of entire population and hence slowing down the epidemic spread. Furthermore, authorities use case quarantine strategy and manual second/third contact-tracing to contain the COVID-19 disease. However, manual contact-tracing is time-consuming and labor-intensive task which tremendously over-load public health systems. In this paper, we developed a smartphone-based approach to automatically and widely trace the contacts for confirmed COVID-19 cases. Particularly, contact-tracing approach creates a list of individuals in the vicinity and notifying contacts or officials of confirmed COVID-19 cases. This approach is not only providing awareness to individuals they are in the proximity to the infected area, but also tracks the incidental contacts that the COVID-19 carrier might not recall. Thereafter, we developed a dashboard to provide a plan for policymakers on how lockdown/mass quarantine can be safely lifted, and hence tackling the economic crisis. The dashboard used to predict the level of lockdown area based on collected positions and distance measurements of the registered users in the vicinity. The prediction model uses k-means algorithm as an unsupervised machine learning technique for lockdown management. © 2020, Springer Nature Singapore Pte Ltd.

关键词： Contact-tracing COVID-19 GPS positioning Smartphone

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Emulating Brain-like Rapid Learning in Neuromorphic Edge Computing

arXiv

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arXiv 2024年

作者： Stewart, Kenneth Neumeier, Michael Shrestha, Sumit Bam Orchard, Garrick Neftci, Emre Department of Computer Science UC Irvine IrvineCA United States Department of Neuromorphic Computing Fortiss-Research Institute of the Free State of Bavaria Munich Germany Intel Labs Intel Corporation Santa ClaraCA United States Peter Grünberg Institute - Neuromorphic Software Ecosystems Forschungszentrum Jülich Germany Faculty of Electrical Engineering and Information Technology RWTH Aachen Germany

Achieving personalized intelligence at the edge with real-time learning capabilities holds enormous promise in enhancing our daily experiences and helping decision making, planning, and sensing. However, efficient and reliable edge learning remains difficult with current technology due to the lack of personalized data, insufficient hardware capabilities, and inherent challenges posed by online learning. Over time and across multiple developmental stages, the brain has evolved to efficiently incorporate new knowledge by gradually building on previous knowledge. In this work, we emulate the multiple stages of learning with digital neuromorphic technology that simulates the neural and synaptic processes of the brain using two stages of learning. First, a meta-training stage trains the hyperparameters of synaptic plasticity for one-shot learning using a differentiable simulation of the neuromorphic hardware. This meta-training process refines a hardware local three-factor synaptic plasticity rule and its associated hyperparameters to align with the trained task domain. In a subsequent deployment stage, these optimized hyperparameters enable fast, data-efficient, and accurate learning of new classes. We demonstrate our approach using event-driven vision sensor data and the Intel Loihi neuromorphic processor with its plasticity dynamics, achieving real-time one-shot learning of new classes that is vastly improved over transfer learning. Our methodology can be deployed with arbitrary plasticity models and can be applied to situations demanding quick learning and adaptation at the edge, such as navigating unfamiliar environments or learning unexpected categories of data through user engagement. © 2024, CC BY.

关键词： Zero-shot learning

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Quantum Parameterized Complexity

arXiv

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arXiv 2022年

作者： Bremner, Michael J. Ji, Zhengfeng Mann, Ryan L. Mathieson, Luke Morales, Mauro E.S. Shaw, Alexis T.E. Centre for Quantum Computation and Communication Technology Centre for Quantum Software and Information School of Computer Science Faculty of Engineering & Information Technology University of Technology SydneyNSW2007 Australia Department of Computer Science and Technology Tsinghua University Beijing China School of Mathematics University of Bristol BristolBS8 1UG United Kingdom

Parameterized complexity theory was developed in the 1990s to enrich the complexity-theoretic analysis of problems that depend on a range of parameters. In this paper we establish a quantum equivalent of classical parameterized complexity theory, motivated by the need for new tools for the classifications of the complexity of real-world problems. We introduce the quantum analogues of a range of parameterized complexity classes and examine the relationship between these classes, their classical counterparts, and well-studied problems. This framework exposes a rich classification of the complexity of parameterized versions of QMA-hard problems, demonstrating, for example, a clear separation between the Quantum Circuit Satisfiability problem and the Local Hamiltonian problem. Copyright © 2022, The Authors. All rights reserved.

关键词： Computational complexity

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