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Asia-Pacific Network Operations and Management Symposium (APNOMS)

作者： Mahnsuk Yoon Jaeuk Kwon Hyunjoo Lee Kyucheol Cho Jihun Seo Jangkyu Yun Future Mobile Communication Research Center GERI(Gumi Electronics & Information Technology Research Institute) Gumi Korea School of Computer Secience and Engineering Software Education Center Kyungpook National University Daegu Korea Research Department of The Fourth Industrial Revolution & Transport Center for Connected & Automated Driving Korea Transport Institute Sejong-si South Korea Vehicle Research Team GITC(Gyeongbuk Institute of IT Convergence Industry Technology) Gumi Korea

This paper investigates the technology for selecting communication scheme based on the amount of traffic in LTE-M (Marine) and 5G communication environments. Recently, it has been possible to use LTE-M and 5G communications for preventing collisions between ships and obstacles in coastal areas, moving from a focus on terrestrial users. This paper proposes a scheme for efficiently handling both small-scale and large-volume traffic generated from terminals on ships. LTE-M can accommodate more terminals by utilizing the wide coverage of coastal areas, but it can be vulnerable to high-volume video processing for real-time obstacle detection. 5G provides a high data transmission rate and low latency, making it suitable for large data exchanges between ships or real-time services, but its short coverage makes it unsuitable for accommodating a wide area. Therefore, this paper proposes a scheme for dynamically selecting LTE-M or 5G communication schemes according to the amount of data generated by terminals on ships, to transmit data. In this paper, we propose an algorithm to decide which communication scheme, LTE-M or 5G, should be chosen based on the amount and type of traffic. The algorithm dynamically selects the optimal communication scheme considering real-time traffic information and network status. By implementing a simulation environment for coastal areas using the technique proposed in this paper, we compare the scheme where terminals installed on ships selectively use LTE-M and 5G communication schemes with the scheme where two communication schemes are chosen randomly for transmission. The simulation results confirm that the proposed technique can process more data compared to the existing schemes.

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Quantum Dynamics of Attractive and Repulsive Polarons in a Doped MoSe2 Monolayer

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Physical Review X 2023年第1期13卷 011029-011029页

作者： Di Huang Kevin Sampson Yue Ni Zhida Liu Danfu Liang Kenji Watanabe Takashi Taniguchi Hebin Li Eric Martin Jesper Levinsen Meera M. Parish Emanuel Tutuc Dmitry K. Efimkin Xiaoqin Li Department of Physics and Center for Complex Quantum Systems The University of Texas at Austin Austin Texas 78712 USA Department of Physics Florida International University Miami Florida 33199 USA Research Center for Functional Materials National Institute for Materials Science 1-1 Namiki Tsukuba 305-0044 Japan International Center for Materials Nanoarchitectonics National Institute for Materials Science 1-1 Namiki Tsukuba 305-0044 Japan MONSTR Sense Technologies LLC Ann Arbor Michigan 48104 USA School of Physics and Astronomy and ARC Centre of Excellence in Future Low-Energy Electronics Technologies Monash University Victoria 3800 Australia Microelectronics Research Center Department of Electrical and Computer Engineering The University of Texas at Austin Austin Texas 78712 USA

When mobile impurities are introduced and coupled to a Fermi sea, new quasiparticles known as Fermi polarons are formed. There are two interesting, yet drastically different regimes of the Fermi polaron problem: (i) the attractive polaron (AP) branch connected to pairing phenomena spanning the crossover from BCS superfluidity to the Bose-Einstein condensation of molecules and (ii) the repulsive branch (RP), which underlies the physics responsible for Stoner’s itinerant ferromagnetism. Here, we study Fermi polarons in two-dimensional systems, where many questions and debates regarding their nature persist. The model system we investigate is a doped MoSe2 monolayer. We find the observed AP-RP energy splitting and the quantum dynamics of attractive polarons agree with the predictions of polaron theory. As the doping density increases, the quantum dephasing of the attractive polarons remains constant, indicative of stable quasiparticles, while the repulsive polaron dephasing rate increases nearly quadratically. The dynamics of Fermi polarons are of critical importance for understanding the pairing and magnetic instabilities that lead to the formation of rich quantum phases found in a wide range of physical systems including nuclei, cold atomic gases, and solids.

关键词： Fermions Optical & microwave phenomena Polarons Quantum coherence & coherence measures Transition metal dichalcogenides Four-wave mixing

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Infrared Optical Anisotropy in Quasi-1D Hexagonal Chalcogenide BaTiSe3

arXiv

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arXiv 2024年

作者： Zhao, Boyang Mei, Hongyan Du, Zhengyu Singh, Shantanu Chang, Tieyan Li, Jiaheng Settineri, Nicholas S. Teat, Simon J. Chen, Yu-Sheng Cronin, Stephen B. Kats, Mikhail A. Ravichandran, Jayakanth Mork Family Department of Chemical Engineering and Materials Science University of Southern California Los AngelesCA90089 United States Department of Electrical and Computer Engineering University of Wisconsin–Madison MadisonWI53706 United States NSF’s ChemMatCARS The University of Chicago LemontIL60439 United States Beijing National Laboratory for Condensed Matter Physics Institute of Physics Chinese Academy of Sciences Beijing100190 China Advanced Light Source Lawrence Berkeley National Laboratory BerkeleyCA94720 United States Ming Hsieh Department of Electrical Engineering University of Southern California Los AngelesCA90089 United States Core Center of Excellence in Nano Imaging University of Southern California Los AngelesCA90089 United States

Polarimetric infrared detection bolsters IR thermography by leveraging the polarization of light. Optical anisotropy, i.e., birefringence and dichroism, can be leveraged to achieve polarimetric detection. Recently, giant optical anisotropy was discovered in quasi-1D narrow-bandgap hexagonal perovskite sulfides, A1+xTiS3, specifically BaTiS3[1,2] and Sr9/8TiS3[3,4]. In these materials, the critical role of atomic-scale structure modulations[4,5] in the unconventional electrical[5,6], optical[7,8], and thermal[7,9] properties raises the broader question of other materials that belong to this family. To address this issue, for the first time, we synthesized high-quality single crystals of a largely unexplored member of the A1+xTiX3 (X = S, Se) family, BaTiSe3. Single-crystal X-ray diffraction determined the room-temperature structure with the P31c space group, which is a superstructure of the earlier reported[10] P63/mmc structure. The crystal structure of BaTiSe3 features antiparallel c-axis displacements similar to BaTiS3,[2] but is of lower symmetry. Polarization-resolved Raman and Fourier transform infrared (FTIR) spectroscopy were used to characterize the optical anisotropy of BaTiSe3, whose refractive index along the ordinary (c) and extraordinary (|| c) optical axes was quantitatively determined by combining ellipsometry studies with FTIR. With a giant birefringence Δn~0.9, BaTiSe3 emerges as a new candidate for miniaturized birefringent optics for mid-wave infrared to long-wave infrared imaging. © 2024, CC BY-NC-ND.

关键词： X ray diffraction

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CHECKING CONTINUOUS STOCHASTIC LOGIC AGAINST QUANTUM CONTINUOUS-TIME MARKOV CHAINS

arXiv

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arXiv 2022年

作者： Mei, Jingyi Xu, Ming Guan, Ji Deng, Yuxin Yu, Nengkun Shanghai Key Lab of Trustworthy Computing East China Normal University China Shanghai Key Lab of Trustworthy Computing & MoE Engineering Research Center of Software/Hardware Co-design Technology and Application East China Normal University China State Key Lab of Computer Science Institute of Software Chinese Academy of Sciences China Department of Computer Science Stony Brook University United States

Verifying quantum systems has attracted a lot of interest in the last decades. In this paper, we study the quantitative model-checking of quantum continuous-time Markov chains (quantum CTMCs). The branching-time properties of quantum CTMCs are specified by continuous stochastic logic (CSL), which is famous for verifying real-time systems, including classical CTMCs. The core of checking the CSL formulas lies in tackling multiphase until formulas. We develop an algebraic method using proper projection, matrix exponentiation, and definite integration to symbolically calculate the probability measures of path formulas. Thus the decidability of CSL is established. To be efficient, numerical methods are incorporated to guarantee that the time complexity is polynomial in the encoding size of the input model and linear in the size of the input formula. A running example of Apollonian networks is further provided to demonstrate our method. © 2022, CC BY.

关键词： Stochastic systems

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AniGAN: Style-guided generative adversarial networks for unsupervised anime face generation

arXiv

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arXiv 2021年

作者： Li, Bing Zhu, Yuanlue Wang, Yitong Lin, Chia-Wen Ghanem, Bernard Shen, Linlin Visual Computing Center KAUST Thuwal Saudi Arabia ByteDance Shenzhen China Department of Electrical Engineering National Tsing Hua University Hsinchu Taiwan Computer Science and Software Engineering Shenzhen University Shenzhen China

In this paper, we propose a novel framework to translate a portrait photo-face into an anime appearance. Our aim is to synthesize anime-faces which are style-consistent with a given reference anime-face. However, unlike typical translation tasks, such anime-face translation is challenging due to complex variations of appearances among anime-faces. Existing methods often fail to transfer the styles of reference anime-faces, or introduce noticeable artifacts/distortions in the local shapes of their generated faces. We propose AniGAN, a novel GAN-based translator that synthesizes high-quality anime-faces. Specifically, a new generator architecture is proposed to simultaneously transfer color/texture styles and transform local facial shapes into anime-like counterparts based on the style of a reference anime-face, while preserving the global structure of the source photo-face. We propose a double-branch discriminator to learn both domain-specific distributions and domain-shared distributions, helping generate visually pleasing anime-faces and effectively mitigate artifacts. Extensive experiments on selfie2anime and a new face2anime dataset qualitatively and quantitatively demonstrate the superiority of our method over state-of-the-art methods. The new dataset is available at https://***/bing-liai/AniGAN. Copyright © 2021, The Authors. All rights reserved.

关键词： Generative adversarial networks

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Unified resilience model using deep learning for assessing power system performance

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Heliyon 2025年第4期11卷 e42802页

作者： Artemchuk, Volodymyr Garbuz, Iurii Jamil Alsayaydeh, Jamil Abedalrahim Shkarupylo, Vadym Oliinyk, Andrii Bin Yusof, Mohd Faizal Herawan, Safarudin Gazali Department of Mathematical and Econometric Modelling G.E. Pukhov Institute for Modelling in Energy Engineering of the NAS of Ukraine Kyiv Ukraine Department of Environmental Protection Technologies and Radiation Safety Center for Information-analytical and Technical Support of Nuclear Power Facilities Monitoring of the NAS of Ukraine Kyiv Ukraine Department of Іnformation Systems in Economics Kyiv National Economic University Named After Vadym Hetman Kyiv Ukraine Department of Computerized Control System National Aviation University Kyiv Ukraine Department of Engineering Technology Fakulti Teknologi Dan Kejuruteraan Elektronik Dan Komputer (FTKEK) Universiti Teknikal Malaysia Melaka (UTeM) Melaka 76100 Malaysia Department of Computer Systems Networks and Cybersecurity of the National University of Life and Environmental Sciences of Ukraine Kyiv Ukraine Department of Software Tools Faculty of Computer Science and Technology Zaporizhzhia Polytechnic National University Zaporizhzhia Ukraine Research Section Faculty of Resilience Rabdan Academy Abu Dhabi United Arab Emirates Industrial Engineering Department Faculty of Engineering Bina Nusantara University Jakarta 11480 Indonesia

Energy resilience in renewable energy sources dissemination components such as batteries and inverters is crucial for achieving high operational fidelity. Resilience factors play a vital role in determining the performance of power systems, regardless of their operating environment and interruptions. This article introduces a Unified Resilience Model (URM) using Deep Learning (DL) to enhance power system performance. The proposed model analyzes environmental factors impacting the resilience of batteries and energy storage devices. This deep learning approach trains performance-impacting factors using previously known low resilience drain data. The learning output is utilized to augment various strengthening factors, thereby improving resilience. Drain mitigation and performance improvements are combined for direct impact verification. This process validates the model's fidelity in enhancing power system performance, with a specific focus on the impact of weather factors. © 2025 The Authors

关键词： Deep learning Energy resilience Fidelity Weather impact

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A Van der Waals Moiré Bilayer Photonic Crystal Cavity

arXiv

引用

arXiv 2025年

作者： Spencer, Lesley Coste, Nathan Ni, Xueqi Park, Seungmin Schaeper, Otto C. Kim, Young Duck Taniguchi, Takashi Watanabe, Kenji Toth, Milos Zalogina, Anastasiia Tang, Haoning Aharonovich, Igor School of Mathematical and Physical Sciences University of Technology Sydney UltimoNSW2007 Australia ARC Centre of Excellence for Transformative Meta-Optical Systems University of Technology Sydney UltimoNSW2007 Australia Department of Physics National University of Singapore 119077 Singapore Department of Physics Kyung Hee University Seoul02447 Korea Republic of International Center for Materials Nanoarchitectonics National Institute for Materials Science 1-1 Namiki Tsukuba305-0044 Japan Research Center for Functional Materials National Institute for Materials Science 1-1 Namiki Tsukuba305-0044 Japan Department of Electrical Engineering and Computer Science University of California at Berkeley BerkeleyCA94720 United States School of Engineering and Applied Sciences Harvard University CambridgeMA02138 United States

Enhancing light-matter interactions with photonic structures is critical in classical and quantum nanophotonics. Recently, Moiré twisted bilayer optical materials have been proposed as a promising means towards a tunable and controllable platform for nanophotonic devices, with proof of principle realisations in the near infrared spectral range. However, the realisation of Moiré photonic crystal (PhC) cavities has been challenging, due to a lack of advanced nanofabrication techniques and availability of standalone transparent membranes. Here, we leverage the properties of the van der Waals material hexagonal Boron Nitride to realize Moiré bilayer PhC cavities. We design and fabricate a range of devices with controllable twist angles, with flatband modes in the visible spectral range (~ 450 nm). Optical characterization confirms the presence of spatially periodic cavity modes originating from the engineered dispersion relation (flatband). Our findings present a major step towards harnessing a two-dimensional van der Waals material for the next-generation of on chip, twisted nanophotonic systems. Copyright © 2025, The Authors. All rights reserved.

关键词： Van der Waals forces

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Blix-Ray: Chest P with Vision-Language Models and Reinforcement Learning

SSRN

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SSRN 2025年

作者： Akan, Taymaz Orujpour, Mohanna Salehpour, Pedram Montalvo, Sergio Alvarado Bhuiyan, Mohammad Alfrad Nobel Department of Medicine Louisiana State University Health Sciences Center Shreveport United States Department of Software Engineering Faculty of Engineering Topkapı University Istanbul Turkey Department of Computer Engineering University of Tabriz Tabriz Iran Department of Radiology and Nuclear Medicine Louisiana State University Health Sciences Center Shreveport United States Department of Neurology Center for Brain Health LSU Health Sciences Center Shreveport United States

The radiology report is essential for directing the course of treatment for a patient. However, writing radiology reports is a huge burden for radiologists. Manual reporting faces challenges due to intra-labeler and inter-labeler variability, causing inconsistent patient treatment and diagnostic results, and complicating matters due to alertness and changing judgment. Automated radiology report generation models expedite clinical practice, reduce workload, and improve consistency by minimizing intra-labeler and inter-labeler variability. Emerging vision-language models show the challenges of monitoring AI by understanding visual and textual concepts and their semantics. Current vision-language models and learning strategies require a web-scale dataset of image and text pairs, which is rarely possible in medical applications due to data shortages in the medical domain. To this end, we propose BLIX-Ray, a visual language model for chest X-ray image report generation that benefits from the transfer learning approach. BLIX-Ray fine-tunes the pre-trained BLIP to capture both visual and textual features and extract rich and multimodal representations that fulfill medical imaging needs. Additionally, reinforcement learning (RL) is used to further optimize the generated reports, allowing the model to perform better by directly optimizing language metrics. We conduct extensive experiments on two public datasets, namely IU-Xray and CheXpert Plus. The results demonstrate that our model outperforms state-of-the-art methods over semantic evaluation metrics. © 2025, The Authors. All rights reserved.

关键词： Arthroplasty

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An assume-guarantee approach to sampled-data quantized glucose control 59

An assume-guarantee approach to sampled-data quantized gluco...

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59th IEEE Conference on Decision and Control, CDC 2020

作者： Loreto, Ilaria Di Borri, Alessandro Di Benedetto, Maria Domenica Center of Excellence for Research Dews University of l'Aquila Department of Information Engineering Computer Science and Mathematics Italy CNR-IASI Biomathematics Laboratory National Research Council of Italy Rome Italy

ISBN: (纸本)9781728174471

Contract-based design is a compositional approach that enables to satisfy specifications for complex systems by partitioning responsibilities among the constituent subsystems. A natural application of assume-guarantee reasoning arises in the context of biological systems, which are composed of different modules, each responsible for particular actions. This work presents an application of the assume-guarantee contracts theory to the glucose regulation system, a topic of major importance in diabetes treatment, in the context of the so-called Artificial Pancreas. The glucose control problem is defined and solved in a decentralized way by defining suitable contracts to be satisfied by the glucose and insulin subsystems, while the framework naturally takes into account sampling and quantization arising from the digital environment. The results show that the approach is feasible and promising for further investigation. © 2020 IEEE.

关键词： Glucose

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Rethinking Client Drift in Federated Learning: A Logit Perspective

arXiv

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arXiv 2023年

作者： Yan, Yunlu Feng, Chun-Mei Ye, Mang Zuo, Wangmeng Li, Ping Goh, Rick Siow Mong Zhu, Lei Chen, C.L. Philip Guangzhou Nansha511400 China The Institute of High Performance Computing A*STAR Singapore138632 Singapore The Hubei Luojia Laboratory National Engineering Research Center for Multimedia Software School of Computer Science Wuhan University Wuhan430072 China The School of Computer Science and Technology Harbin Institute of Technology Harbin130407 China The Hong Kong University of Science and Technology Hong Kong The Department of Computing the School of Design The Hong Kong Polytechnic University Hong Kong The School of Computer Science and Engineering South China University of Technology Guangzhou510006 China The Pazhou Lab Guangzhou510335 China

Federated Learning (FL) enables multiple clients to collaboratively learn in a distributed way, allowing for privacy protection. However, the real-world non-IID data will lead to client drift which degrades the performance of FL. Interestingly, we find that the difference in logits between the local and global models increases as the model is continuously updated, thus seriously deteriorating FL performance. This is mainly due to catastrophic forgetting caused by data heterogeneity between clients. To alleviate this problem, we propose a new algorithm, named FedCSD, a Class prototype Similarity Distillation in a federated framework to align the local and global models. FedCSD does not simply transfer global knowledge to local clients, as an undertrained global model cannot provide reliable knowledge, i.e., class similarity information, and its wrong soft labels will mislead the optimization of local models. Concretely, FedCSD introduces a class prototype similarity distillation to align the local logits with the refined global logits that are weighted by the similarity between local logits and the global prototype. To enhance the quality of global logits, FedCSD adopts an adaptive mask to filter out the terrible soft labels of the global models, thereby preventing them to mislead local optimization. Extensive experiments demonstrate the superiority of our method over the state-of-the-art federated learning approaches in various heterogeneous settings. The source code will be released. Copyright © 2023, The Authors. All rights reserved.

关键词： Distillation

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