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A Scalable Framework for Automation of Quantum network Experiments 5

A Scalable Framework for Automation of Quantum Network Exper...

引用

2024 International conference on Quantum Computing and Engineering

作者： Taherkhani, Amin Teramoto, Kentaro Todd, Andrew Van Meter, Rodney Nagayama, Shota Keio Univ Grad Sch Media & Governance Shonan Fujisawa Campus Fujisawa Kanagawa Japan Mercari Inc Mercari R4D Tokyo Japan Keio Univ Fac Environm & Informat Studies Shonan Fujisawa Campus Fujisawa Kanagawa Japan

ISBN: (纸本)9798331541378

As quantum computing continues to progress, there is an increasing need for standardized quantum network technology [I], pi to connect quantum computers together. Experimental testbeds are necessary in order to develop quantum network software and hardware. Building a scalable testbed for data center-level quantum networks requires overcoming numerous engineering and technical challenges in quantum communications, quantum processing and quantum storage. In this work, we describe a software framework for use with an all-photonic quantum networking testbed. This framework is designed for rapid development and capable of automated execution of quantum experiments. We introduce PnPQ, an open-source, unified driver library for hardware commonly used in quantum optical experiments. On top of this low-level hardware control library, we present software modules for controlling and coordinating quantum network nodes using remote procedure calls (RPC). Finally, we describe high-level software for performing and analyzing quantum photonic experiments using these software modules. As a whole, these software components comprise the first version of a framework that will enable us to implement and test quantum network architectures. This framework is intended to help us move forward from building simple experiments to complex quantum systems.

关键词： Quantum network testbed Quantum optical experiment Hardware control Quantum system automation

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Detection of Asphalt Pavement Cracks with YOLO architectures from Unmanned Aerial Vehicle Images 32

Detection of Asphalt Pavement Cracks with YOLO Architectures...

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32nd ieee Signal Processing and Communications Applications conference (SIU)

作者： Odubek, Ebrar Atik, Muhammed Enes Istanbul Tech Univ Geomat Muhendisligi Istanbul Turkiye

ISBN: (纸本)9798350388978;9798350388961

Monitoring road condition has been a strategic area of research in maintaining an extensive transportation infrastructure network. Although damages on road surfaces initially appear as slight cracks, the depth and danger of these damages may increase over time and changing weather conditions. Cracks on the road surface are one of the main factors affecting the performance of the road. Automatic detection of road cracks is an important task in road maintenance. However, automatic crack detection is a challenging application area due to the inhomogeneity of the density of cracks and the complexity of the background (e.g. low contrast with the surrounding coating and possible shadows of similar intensity). Recently, deep learning-based object detection and segmentation methods have begun to be used effectively in detecting cracks on road surfaces. In this study, a comparative analysis was carried out for the detection of cracks on road surfaces using the current versions of You Only Look Once (YOLO), a popular single-step object detection algorithm. The open source dataset UAPD, consisting of unmanned aerial vehicle (UAV) images, was used in the analysis. In the application carried out to detect different types of cracks with YOLOv5x, 0.639 mean average precision (mAP) and 0.759 sensitivity metrics were obtained. Using the YOLOv5x algorithm, the highest accuracy was achieved compared to other algorithms.

关键词： deep learning asphalt pavement crack YOLO object detection unmanned aerial vehicle

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SOMALib : Library of Exact and Approximate Activation Functions for Hardware-efficient Neural network Accelerators 40

SOMALib : Library of Exact and Approximate Activation Functi...

引用

ieee 40th International conference on Computer Design (ICCD)

作者： Prashanth, H. C. Rao, Madhav Int Inst Informat Technol Bangalore Bangalore Karnataka India

ISBN: (数字)9781665461863

ISBN: (纸本)9781665461863

Approximate computing along with quantized low-precision computing has gained significant interest in today's neural network (NN) implementation. This paper proposes a library of VLSI implementations of different activation functions, aimed towards designing hardware-efficient NN accelerators. Cartesian genetic programming (CGP), an evolutionary algorithm was employed to generate gate-level designs of approximate and exact representations of activation functions. We open-source the hardware library of 9444 circuits containing a majority of the activation functions employed in NN architectures, including Sigmoid, Hyperbolic-Tangent, Gaussian, ReLU, GeLU, Softplus, and Binary-Step. The library also presents the error characteristics and hardware metrics of the designs which will aid in the usage of the library in future research. Additionally a hardware comparison of the proposed circuits against existing implementations including piecewise-linear (PWL), memory-based, hls4ml, DNNweaver implementations to realize activation functions on FPGA and ASIC flow is presented. The CGP evolved hardware library shows minimal silicon space requirement, least power consumption when investigated for ASIC flow, and the least LUT utilization's in FPGA flow. Besides, SOMALib designs are purely combinatorial, allowing various synthesis stage optimizations towards the target Power-Performance-Area budget, which is not possible in standard memory block implementations.

关键词： Activation functions Hardware design Approximate Computing Neural network Accelerators

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Machine Learning architectures for price formation models with common noise 62

Machine Learning architectures for price formation models wi...

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62nd ieee conference on Decision and Control (CDC)

作者： Gomes, Diogo Gutierrez, Julian Lauriere, Mathieu King Abdullah Univ Sci & Technol KAUST CEMSE Div Thuwal 239556900 Saudi Arabia New York Univ Shanghai NYU Shanghai Shanghai 200122 Peoples R China

ISBN: (纸本)9798350301243

We propose a machine learning method to solve a mean-field game price formation model with common noise. This involves determining the price of a commodity traded among rational agents subject to a market clearing condition imposed by random supply, which presents additional challenges compared to the deterministic counterpart. Our approach uses a dual recurrent neural network encoding noise dependence and a particle approximation of the mean-field model with a single loss function optimized by adversarial training. We provide a posteriori estimates for convergence and illustrate our method through numerical experiments.

关键词： Mean Field Games Neural networks Price formation

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Optimal Transmission Grid Assets Maneuvering for Improving Voltage Profile in RES Scenarios 7

Optimal Transmission Grid Assets Maneuvering for Improving V...

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7th ieee International Humanitarian Technologies conference, IHTC 2024

作者： Cometa, Roberto Velini, Angelo Nasab, Mohammad Rajabi Bruno, Sergio Scala, Massimo La DEI - Politecnico di Bari Bari Italy

ISBN: (纸本)9798350354645

The deep penetration of renewable energy sources (RES) in power systems is increasingly influencing electricity markets and the physical operations of transmission networks. Besides the environmental and climate benefits, RES volatility and aleatory behaviour, combined with the consequent difficulties in control, might lead to voltage issues, which could be particularly critical when the grid lacks reactive power support. In this context, transmission system operators are called upon to make decisions about the use of available grid assets, such as compensating shunt devices and tap-changing transformers. In addition, transmission line switching can be adopted to provide further reactive power control resource. However, it is often difficult to determine which network assets should be enabled within the operation time requirements. Therefore, in this paper, an Optimal Reactive Power Flow tool for operators is proposed, including both shunt devices and transmission line switching, as well as the adjustment of transformers' tap settings. The optimization problem is solved by resorting to a genetic algorithm to account for network model nonlinearities. In order to improve inclusiveness and replicability, the whole tool was developed exploiting open-source libraries only. Numerical tests are carried out on a modified ieee 118-Bus Test System. © 2024 ieee.

关键词： Integer linear programming

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network Amplification with Efficient MACs Allocation

Network Amplification with Efficient MACs Allocation

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ieee/CVF conference on Computer Vision and Pattern Recognition (CVPR)

作者： Liu, Chuanjian Han, Kai Xiao, An Nie, Ying Zhang, Wei Wang, Yunhe Huawei Noahs Ark Lab Montreal PQ Canada

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

Recent studies on deep convolutional neural networks present a simple paradigm of architecture design, i.e., models with more MACs typically achieve better accuracies, such as EfficientNet and RegNet. These works try to enlarge the network architecture with one unified rule by sampling and statistical methods. However, the rule is not prospective to the design of large networks because it is obtained from the experience of researchers on small network architectures. In this paper, we propose to enlarge the capacity of CNN models by fine-grained MACs allocation for the width, depth and resolution on the stage level. In particular, starting from a base small model, we gradually add extra channels, layers or resolution by using a dynamic programming manner. With step-by-step modifying the computations on different stages, the enlarged network will be equipped with optimal allocation and utilization of MACs. On EfficientNet, our method consistently outperforms the performance of the original scaling method. In particular, the proposed method is used to enlarge models sourced by GhostNet, we achieve state-of-the-art 80.9% and 84.3% ImageNet top-1 accuracies under the setting of 600M and 4.4B MACs, respectively.

关键词： Statistical analysis Computational modeling Heuristic algorithms Neural networks network architecture Pattern recognition Dynamic programming

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Fat-B+Tree: Fast B+tree Indexing with In-network Memory

Fat-B+Tree: Fast B+tree Indexing with In-Network Memory

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2024 ieee International Performance, Computing, and Communications conference, IPCCC 2024

作者： Zhao, Yikai Li, Yuanpeng Xu, Zicang Yang, Tong Yang, Kaicheng Chen, Li Yao, Xin Zhang, Gong Peking University China Zhongguancun Laboratory China Huawei Theory Lab China

ISBN: (纸本)9798350367942

In-memory database in the data center plays an indispensable role in many fields. B+tree is the most recognized index in in-memory database, but its indexing latency has become the bottleneck that prevents the database from achieving higher performance. The existing work reduces latency by caching B+tree nodes using slow DRAM on computing clients, or directly caching data using fast SRAM on programmable switch. However, no existing work can meet all three key requirements: (1) Efficiency: providing enough fast memory to accelerate indexing. (2) Compatibility: compatible with multiple architectures and query types. (3) Adaptability: adapting to various workloads and database scales. Inspired by structrual similarity between the network topology of modern data centers and the B+tree structure, we propose Fat-B+Tree, which embeds the B+tree into the in-network fast memory provided by the hierarchical connected programmable switches, thus meeting all design requirements. We have fully implemented the Fat-B+Tree prototype, and the experimental results show that compared with the baseline system, it reduces query latency by up to 76% and improves throughput by up to 3.93 times. The source codes of Fat-B+Tree are open-sourced at GitHub. © 2024 ieee.

关键词： Database systems

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Development and Testing of an Artificial Neural network Model to Analyse Data, Image Data

Development and Testing of an Artificial Neural Network Mode...

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2024 International Russian Smart Industry conference, SmartIndustryCon 2024

作者： Eremina, Irina I. Saltanaeva, Elena A. Kutsenko, Svetlana M. Kazan State Power Engineering University Department of Information Technologies and Intelligent Systems Kazan Russia

ISBN: (纸本)9798350395044

The relevance of the study is related to the increased needs of medical personnel in qualitative solution of image data analysis tasks, in particular lung scans during the period of controlling the consequences of coronavirus infection. An artificial neural network model was developed and tested to improve the quality of research results, reduce time, and optimise the work of medical staff. A convolutional neural network was chosen as a tool to solve this problem. The tools and software products most commonly used to build and design artificial neural networks were analysed. Python programming language and PyQt5 framework were used as tools for conducting software product development and testing. Three architectures were involved in the experiment, and the number of layers, epochs, average epoch training time and network accuracy were taken into account to evaluate their efficiency. It was found that the optimal architecture is the one consisting of 9 layers, 10 epochs, giving an accuracy of 72.39%. The effectiveness of the regularisation methods for modifying the convolutional neural network on the publicly available CIFAR-10 dataset was evaluated. Among the six modification methods considered, the modification using a combination of Weight Initialisation and Batch Normalisation methods was preferred. Next, Data Augmentation method was applied to the base models and modifications to reduce overfitting. However, using Data Augmentation in conjunction with other regularisers resulted in worse results. The interface of convolutional neural network was developed. The result of the study was a convolutional neural network with 90% accuracy. © 2024 ieee.

关键词： Image recognition

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Efficient computation of Lipschitz constants for MPC with symmetries 62

Efficient computation of Lipschitz constants for MPC with sy...

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62nd ieee conference on Decision and Control (CDC)

作者： Teichrib, Dieter Darup, Moritz Schulze TU Dortmund Univ Control & Cyberphys Syst Grp Dept Mech Engn Dortmund Germany

ISBN: (纸本)9798350301243

Lipschitz constants for linear MPC are useful for certifying inherent robustness against unmodeled disturbances or robustness for neural network-based approximations of the control law. In both cases, knowing the minimum Lipschitz constant leads to less conservative certifications. Computing this minimum Lipschitz constant is trivial given the explicit MPC. However, the computation of the explicit MPC may be intractable for complex systems. The paper discusses a method for efficiently computing the minimum Lipschitz constant without using the explicit control law. The proposed method simplifies a recently presented mixed-integer linear program (MILP) that computes the minimum Lipschitz constant. The simplification is obtained by exploiting saturation and symmetries of the control law and irrelevant constraints of the optimal control problem.

关键词： Model Predictive Control Optimization Mixed-Integer programming Computational methods

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Symbolic Hyperdimensional Vectors with Sparse Graph Convolutional Neural networks

Symbolic Hyperdimensional Vectors with Sparse Graph Convolut...

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ieee International conference on Fuzzy Systems (FUZZ-ieee) / ieee World Congress on Computational Intelligence (ieee WCCI) / International Joint conference on Neural networks (IJCNN) / ieee Congress on Evolutionary Computation (ieee CEC)

作者： Cornell, Filip Karlgren, Jussi Animesh Girdzijauskas, Sarunas KTH Royal Inst Technol Sch Elect Engn & Comp Sci Stockholm Sweden Gavagai Stockholm Sweden Indian Inst Technol Kharagpur Ctr Excellence Artificial Intelligence Kharagpur W Bengal India

ISBN: (数字)9781728186719

ISBN: (纸本)9781728186719

In this paper, we propose a novel way of representing graphs for processing in Graph Neural networks. We reduce the dimensionality of the input data by using Random Indexing, a Vector Symbolic Architectural framework;we implement a new trainable neural layer, also inspired by Vector Symbolic architectures;we leverage the sparseness of the incoming data in a Sparse Neural network framework. Our experiments on a number of publicly available datasets and standard benchmarks demonstrate that we can reduce the number of parameters by up to two orders of magnitude. We show how this parsimonious approach not only delivers competitive results but even improves performance for node classification and link prediction. We find that this holds in particular for cases where the graph lacks node features.

关键词： vector symbolic architectures graph neural networks random indexing

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