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arXiv 2024年

作者： Kim, Gyunghun Li, Jensen Piao, Xianji Park, Namkyoo Yu, Sunkyu Intelligent Wave Systems Laboratory Department of Electrical and Computer Engineering Seoul National University Seoul08826 Korea Republic of Department of Engineering University of Exeter EX4 4QF United Kingdom Wave Engineering Laboratory School of Electrical and Computer Engineering University of Seoul Seoul02504 Korea Republic of Photonic Systems Laboratory Department of Electrical and Computer Engineering Seoul National University Seoul08826 Korea Republic of

Non-Abelian physics, originating from noncommutative sequences of operations, unveils novel topological degrees of freedom for advancing band theory and quantum computation. In photonics, significant efforts have been devoted to developing reconfigurable non-Abelian platforms, serving both as classical testbeds for non-Abelian quantum phenomena and as programmable systems that harness topological complexities. Here we establish topological spinor lattices for non-Abelian programmable photonics. We design a building block for reconfigurable unitary coupling between pseudospin resonances, achieving a universal set of rotation gates through coupling along the unit cell boundary. The lattice assembly of our building blocks enables the emulation of the extended quantum Hall family across various eigenspinor bases. Particularly, we reveal the emergence of a non-Abelian interface even when the bulks are Abelian, which allows the topologically trivial engineering of topologically protected edge states. We also define the braid group for pseudospin observables, demonstrating non-Abelian braiding operations and the Yang–Baxter relations. Our results pave the way for realizing a reconfigurable testbed for a wide class of Abelian and non-Abelian topological phenomena and braiding operations. © 2024, CC BY.

关键词： Topology

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Directional emission of a readout resonator for qubit measurement

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Physical Review Applied 2024年第3期22卷 034035页

作者： Alec Yen Yufeng Ye Kaidong Peng Jennifer Wang Gregory Cunningham Michael Gingras Bethany M. Niedzielski Hannah Stickler Kyle Serniak Mollie E. Schwartz Kevin P. O’Brien Department of Electrical Engineering and Computer Science Research Laboratory of Electronics Harvard John A. Paulson School of Engineering and Applied Sciences Lexington Massachusetts 02421 USA

We propose and demonstrate transmission-based dispersive readout of a superconducting qubit using an all-pass resonator, which preferentially emits readout photons toward the output. This is in contrast to typical readout schemes, which intentionally mismatch the feedline at one end so that the readout signal preferentially decays toward the output. We show that this intentional mismatch creates scaling challenges, including larger spread of effective resonator linewidths due to nonideal impedance environments and added infrastructure for impedance matching. A future architecture using multiplexed all-pass readout resonators would avoid the need for intentional mismatch and potentially improve the scaling prospects of quantum computers. As a proof-of-concept demonstration of “all-pass readout,” we design and fabricate an all-pass readout resonator that demonstrates insertion loss below 1.17 dB at the readout frequency and a maximum insertion loss of 1.53 dB across its full bandwidth for the lowest three states of a transmon qubit. We demonstrate qubit readout with an average single-shot fidelity of 98.1% in 600 ns; to assess the effect of larger dispersive shift, we implement a shelving protocol and achieve a fidelity of 99.0% in 300 ns.

关键词： Circuit quantum electrodynamics Optoelectronics Quantum error correction Quantum information with solid state qubits Quantum measurements Superconducting quantum optics Superconducting devices Microwave techniques

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A Spatial-temporal Model for Tourism Demand Forecasting 23

A Spatial-temporal Model for Tourism Demand Forecasting

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23rd IEEE International Conference on High Performance Computing and Communications, 7th IEEE International Conference on Data Science and systems, 19th IEEE International Conference on Smart City and 7th IEEE International Conference on Dependability in Sensor, Cloud and Big Data systems and Applications, HPCC-DSS-SmartCity-DependSys 2021

作者： Dong, Yunxuan Zhou, Binggui Yang, Guanghua Hou, Fen Ma, Shaodan School of Intelligent Systems Science and Engineering Jinan University Zhuhai519070 China University of Macau Department of Electrical and Computer Engineering 999078 China

ISBN: (纸本)9781665494571

Accurate forecasting of tourism demand is important to the development of tourism. However, the difficulties in recognizing complex spatial and temporal features make it challenging to accurately forecast tourism demand. In addition, existing methods are not practical and flexible enough since they usually established multiple models for different scenic spots. In this paper, we propose a novel method for tourism demand forecasting based on the fully connected long short-term neural network, which enables simultaneous identification of spatial and temporal features for better forecasting accuracy. To enhance the practicality and flexibility of our method, we propose to establish one general model for multiple scenic spots. Experimental results demonstrate that the proposed method outperforms other models in the daily tourism demand forecasting for the Wanshan Archipelago, an emerging tourism spot in Zhuhai, China. © 2021 IEEE.

关键词： Smart cities Neural networks Demand forecasting Transportation Predictive models

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engineering CsPbI2Br Perovskite Thin Film Morphology with Ionic Liquid Additives for Enhanced Performance Solar Cells 52

Engineering CsPbI2Br Perovskite Thin Film Morphology with Io...

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52nd IEEE Photovoltaic Specialist Conference, PVSC 2024

作者： Zhumadil, Gulzhan Pavlenko, Vladimir Nigmetova, Gaukhar Yelzhanova, Zhuldyz Balanay, Mannix P. Ng, Annie School of Engineering and Digital Sciences Nazarbayev University Department of Electrical and Computer Engineering Astana010000 Kazakhstan Nazarbayev University National Laboratory of Astana Astana010000 Kazakhstan Nazarbayev University Department of Chemistry Sciences and Humanities Astana010000 Kazakhstan

ISBN: (纸本)9781665464260

Ionic liquids (ILs) have emerged as a promising class of additive for modulating perovskite crystallization behavior and thin film morphology. However, the influence of incorporating ILs directly within the cesium lead iodide bromide (CsPbI2Br) perovskite precursor system is less explored. This work aims to investigate the impact of using various IL additives in the bulk of the CsPbI2Br perovskite material and how it affects thin film formation. We study the morphology of CsPbI2Br when incorporated with five different ILs based on the common 1-ethyl-3-methylimidazolium [EMIM]+cation, but varying the anion: bis(pentafluoroethylsulfonyl)imide ([EMIM]+[PFSI]-), bis(trifluoromethylsulfonyl)imide ([EMIM]+[TFSI]-), diethyl phosphate ([EMIM]+[DEP]-), dimethyl phosphate ([EMIM]+[DMP]-), and hexafluorophosphate ([EMIM]+[PF6]-). Through characterization of the perovskite films, we found that the morphology of CsPbI2Br is largely different depending on the choice of IL used. These morphological changes provide insights into how different IL-perovskite interactions affect crystallization and thus thin film quality. The experimental results help in understanding of IL-mediated crystallization, facilitating the development of additive engineering for high-performance perovskite solar cells (PSCs). © 2024 IEEE.

关键词： Perovskite

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Mamba State Space Model (SSM) Method for the Automatic Segmentation of Organs at Risk from Head and Neck CT Images

Mamba State Space Model (SSM) Method for the Automatic Segme...

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electrical, computer, Communications and Mechatronics engineering (ICECCME), International Conference on

作者： Theodoros P. Vagenas Konstantinos Georgas George K. Matsopoulos Biomedical Engineering Laboratory School of Electrical and Computer Engineering National Technical University of Athens Athens Greece

ISBN: (数字)9798350391183

ISBN: (纸本)9798350391190

Radiotherapy (RT) is one of the main treatment modalities using high-energy radiation to eliminate cancer cells in Head and Neck cancer (HNC) for patients worldwide. Advances such as intensity-modulated radiotherapy (IMRT) improve the precision of targeting tumors and prevent damaging healthy tissues. Segmentation of Organs At Risk (OARs) on Computed Tomography (CT) images is crucial for RT treatment planning in order to avoid damaging healthy structures during the sessions. However, manual delineation of OARs is challenging as their size varies and both small and large organs are present in images with low contrast against the background. In this regard, manual delineation cannot be performed in a timely manner and suffers from potential interobserver variability among clinicians, leading to delays in clinical workflow. In this study, we propose an automatic Deep Learning segmentation framework utilizing effectively the recently introduced Mamba State Space Models (SSM) to localize and delineate multiple OARs with high accuracy from the 3D HNC CT images. Due to the Mamba-based encoder, the model can learn the long-range dependencies of the largest organs by efficiently handling large sequences while also segmenting the smaller ones. The proposed model was evaluated on two public datasets, demonstrating superior performance with an average Dice Similarity Coefficient (DSC) of 75.45% and a Hausdorff distance of 8.22 mm against established deep learning models, such as UNet-based and Transformer-based, in segmenting 9 OARs, indicating promising results to support the clinical workflow.

关键词： Deep learning Image segmentation Solid modeling Head Three-dimensional displays Computed tomography Manuals Transformers Neck Radiation therapy

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An AI Architecture with the Capability to Classify and Explain Hardware Trojans

An AI Architecture with the Capability to Classify and Expla...

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IEEE National Conference on Aerospace and Electronics (NAECON)

作者： Paul Whitten Francis Wolff Chris Papachristou Electrical Computer and Systems Engineering Case School of Engineering Case Western Reserve University Cleveland OH USA

ISBN: (数字)9798350367621

ISBN: (纸本)9798350367638

Hardware trojan detection methods, based on ma-chine learning (ML) techniques, mainly identify suspected circuits but lack the ability to explain how the decision was arrived at. An explainable methodology and architecture is introduced based on the existing hardware trojan detection features. Results are provided for explaining digital hardware trojans within a netlist using trust-hub trojan benchmarks.

关键词： Training Support vector machines Measurement Accuracy Costs Aerospace electronics Feature extraction

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Characteristics, Classification and Challenges in Searching for CEMP Stars

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Research in Astronomy and Astrophysics 2025年第5期25卷 184-193页

作者： Lefeng He Xiangru Li Xiaoming Kong A-Li Luo Haifeng Yang School of Computer Science South China Normal University School of Mechanical Electrical&Information EngineeringShandong University Shandong Key Laboratory of Intelligent Electronic Packaging Testing and Application Shandong University Key Lab of Optical Astronomy National Astronomical ObservatoriesChinese Academy of Sciences University of Chinese Academy of Sciences School of Computer Science and Technology Taiyuan University of Science and Technology

The study of carbon-enhanced metal-poor (CEMP) stars is of great significance for understanding the chemical evolution of the early universe and stellar *** stars are characterized by carbon overabundance and are classified into several subclasses based on the abundance patterns of neutron-capture elements,including CEMP-s,CEMP-no,CEMP-r,and CEMP-r/*** subclasses provide important insights into the formation of thefirst stars,early stellar nucleosynthesis,and supernova ***,one of the major challenges in CEMP star research is the relatively small sample size of identified stars,which limits statistical analyses and hinders a comprehensive understanding of their ***,a series of large-scale spectroscopic survey projects have been launched and developed in recent years,providing unprecedented opportunities and technical challenges for the search and study of CEMP *** this end,this paper draws on the progress and future prospects of existing methods in constructing large CEMP data sets and offers an in-depth discussion from a technical standpoint,focusing on the strengths and *** addition,we review recent advancements in the identification of CEMP stars,emphasizing the growing role of machine learning in processing and analyzing the increasingly large data sets generated by modern astronomical *** to traditional spectral analysis methods,machine learning offers greater efficiency in handling complex data,automatic extraction of stellar parameters,and improved prediction *** these advancements,the research faces persistent challenges,including the scarcity of labeled samples,limitations imposed by low-resolution spectra,and the lack of interpretability in machine learning *** address these issues,the paper proposes potential solutions and future research directions aimed at advancing the study of CEMP stars and enhancing our understanding of their role in the chemical evolutio

关键词： methods: data analysis methods: observational stars: abundances stars: carbon surveys

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Self-Supervised Reflective Learning Through Self-Distillation and Online Clustering for Speaker Representation Learning

IEEE Transactions on Audio, Speech and Language Processing

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IEEE Transactions on Audio, Speech and Language Processing 2025年 33卷 1535-1550页

作者： Danwei Cai Zexin Cai Ze Li Ming Li Department of Electrical and Computer Engineering Duke University Durham NC USA Suzhou Municipal Key Laboratory of Multimodal Intelligent Systems Digital Innovation Research Center Duke Kunshan University Kunshan China School of Computer Science Wuhan University Wuhan China

Speaker representation learning is crucial for voice recognition systems, with recent advances in self-supervised approaches reducing dependency on labeled data. Current two-stage iterative frameworks, while effective, suffer from significant computational overhead due to repeated rounds of clustering and training. They also struggle with noisy pseudo labels that can impair model learning. This paper introduces self-supervised reflective learning (SSRL), an improved framework that addresses these limitations by enabling continuous refinement of pseudo labels during training. Through a teacher-student architecture and online clustering mechanism, SSRL eliminates the need for iterative training rounds. To handle label noise, we incorporate noisy label modeling and pseudo label queues that maintain temporal consistency. Experiments on VoxCeleb show SSRL's superiority over current two-stage iterative approaches, surpassing the performance of a 5-round method in just a single training round. Ablation studies validate the contributions of key components like noisy label modeling and pseudo label queues. Moreover, consistent improvements in pseudo labeling and the convergence of cluster counts demonstrate SSRL's effectiveness in deciphering unlabeled data. This work marks an important advancement in efficient and accurate self-supervised speaker representation learning through the novel reflective learning paradigm.

关键词： Training Representation learning Iterative methods Noise Adaptation models Noise measurement Predictive models Speech processing Robustness Convergence

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A Microwave Sensor for Detection of Heavy Metals in Traditional Chinese Medicine 12

A Microwave Sensor for Detection of Heavy Metals in Traditio...

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12th IEEE Asia-Pacific Conference on Antennas and Propagation, APCAP 2024

作者： Wu, Jing Li, Hua-Feng Xu, Ming-Feng He, Jin-Peng Zhang, Zhong-Qi Chen, Min Chen, Rui-Sen Huang, Guan-Long Pang, Zi-Yu Tam, Kam-Weng Foshan University School of Electronic and Information Engineering Guangdong Foshan528225 China Foshan University Guangdong Provincial Key Laboratory of Industrial Intelligent Inspection Technology Foshan528225 China University of Macau Department of Electrical and Computer Engineering China

ISBN: (纸本)9798350351019

This paper presents a microwave sensor for the detection of heavy metals in traditional Chinese medicine decoctions. The sensor is composed of interdigital capacitors (IDC), a split-ring resonators (SRR) coupled with a microstrip line on the substrate, and a test substance placed above the sensor. By altering the dielectric constant of the liquid, the sensor demonstrates a frequency shift, enabling the detection of heavy metal content. This design offers a high-sensitivity, non-invasive detection method suitable for real-time monitoring of heavy metals in traditional Chinese medicine decoctions. © 2024 IEEE.

关键词： Microwave sensors

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Multi-domain feature fusion for target color decoding from multichannel neural recordings of pigeons

Multi-domain feature fusion for target color decoding from m...

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2023 International Conference on Image, Signal Processing, and Pattern Recognition, ISPP 2023

作者： Li, Hongxun Liu, Ruiqing Niu, Xiaoke Wang, Zhizhong Henan Key Laboratory of Brain Science and Brain-Computer Interface Technology School of Electrical Engineering Zhengzhou University Zhengzhou450001 China Department of Mechanical and Electrical Engineering Henan Light Industry Vocational College Zhengzhou450000 China

ISBN: (纸本)9781510666351

The entopallium is a high-level neural structure of the avian visual system that is related to the processing of color- and shape-related information during target recognition. The mechanism underlying it remains unclear and has drawn a considerable amount of research interest. Decoding has invariably been used in relevant studies to verify whether the extracted response-related features contain visual information. However, local field potentials (LFPs) often contain rich features. In this study, We combine the features of different domains to decode color information, including those in the time and frequency domains as well as features related to non-linear and functional network connectivity. To extract valuable response-related features, three types of feature selection methods are compared. The LFP data from the entopallium of a pigeon were recorded during periods of stimulus presentation in which two colors were randomly presented in each trial. The selected feature set was finally used to decode the colors by using several general classifiers. The results showed that the fusion of multi-domain features helped improve decoding performance, and the optimal feature set was a fusion of multiple domains of features. These results improve our understanding of the avian neural mechanisms involved in encoding color-related information. © 2023 SPIE.

关键词： Color

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