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41st International Conference on Machine Learning, ICML 2024

作者： Yue, Sheng Hua, Xingyuan Ren, Ju Lin, Sen Zhang, Junshan Zhang, Yaoxue Department of Computer Science and Technology Tsinghua University Beijing China Zhongguancun Laboratory Beijing China Department of Computer Science University of Houston Texas United States Department of Electrical and Computer Engineering University of California Davis United States

In this paper, we study offline-to-online Imitation Learning (IL) that pretrains an imitation policy from static demonstration data, followed by fast finetuning with minimal environmental interaction. We find the naïve combination of existing offline IL and online IL methods tends to behave poorly in this context, because the initial discriminator (often used in online IL) operates randomly and discordantly against the policy initialization, leading to misguided policy optimization and unlearning of pretraining knowledge. To overcome this challenge, we propose a principled offline-to-online IL method, named OLLIE, that simultaneously learns a near-expert policy initialization along with an aligned discriminator initialization, which can be seamlessly integrated into online IL, achieving smooth and fast finetuning. Empirically, OLLIE consistently and significantly outperforms the baseline methods in 20 challenging tasks, from continuous control to vision-based domains, in terms of performance, demonstration efficiency, and convergence speed. This work may serve as a foundation for further exploration of pretraining and finetuning in the context of IL. Copyright 2024 by the author(s)

关键词： Contrastive Learning

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Search for Extraterrestrial Intelligence as One-Shot Hypothesis Testing 57

Search for Extraterrestrial Intelligence as One-Shot Hypothe...

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57th Annual Conference on Information sciences and Systems, CISS 2023

作者： George, Ian Chen, Xinan Varshney, Lav R. Coordinated Science Laboratory Department of Electrical and Computer Engineering UrbanaIL United States

ISBN: (纸本)9781665451819

Both the search for extraterrestrial intelligence (SETI) and messaging extraterrestrial intelligence (METI) struggle with a strong indeterminacy in what data to look for and when to do so. This has led to attempts at finding both fundamental mathematical limits for SETI as well as benchmarks regarding specific signals. Due to the natural correspondence, previous information-theoretic work has been formulated in terms of communication between extraterrestrial and human civilizations. In this work, we instead formalize SETI as a detection problem, specifically (quantum) one-shot asymmetric hypothesis testing. This framework holds for all detection scenarios-in particular, it is relevant for detection of any technosignature, including quantum mechanical signals. To the best of our knowledge, this is the first work to consider the applicability of SETI for quantum signals. Using this formalism, we are able to unify the analysis of fundamental limits and benchmarking specific signals. To show a distinction between METI and SETI, we show that significantly weaker signals may be useful in detection in comparison to communication. Furthermore, the framework is computationally efficient, so it can be implemented by practicing astrobiologists. © 2023 IEEE.

关键词： Quantum optics

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Optimizing Different Methods of Fabricating 3D Printed UV Blocking Sheets for Solar Cells 1

Optimizing Different Methods of Fabricating 3D Printed UV Bl...

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1st Renewable Energy Technologies and Modern Communications Systems: Future and Challenges Conference, RETMCS 2024

作者： Alhindi, Amjad Fawzi Elatab, Nazek Baawad, Lujain Khalid Bahabry, Rabab R. Allam, Fahad Laboratory Electrical and Computer Engineering Computer Electrical Mathematical Science and Engineering Thuwal Saudi Arabia College of Science Department of Physical Sciences Jeddah21589 Saudi Arabia Materials Science and Engineering Department King Fahd University of Petroleum and Minerals Dhahran31261 Saudi Arabia

ISBN: (纸本)9798331519445

This study investigates the fabrication of innovative UV-blocking sheets that effectively transmit visible light while simultaneously obstructing harmful ultraviolet (UV) radiation, utilizing Cerium Oxide (CeO2) and Zinc Oxide (ZnO) nanoparticles as UV-blocking agents. Three distinct fabrication methods were explored: The Powder Method, where nanoparticles were incorporated directly into polydimethylsiloxane (PDMS);the Ball milling Mixture method, which involved mixing nanoparticles through ball milling;and the Solvent-Based Mixture method, where nanoparticles were dissolved, mixed, and dried;before incorporation into PDMS. Samples were produced using a 3D printing technique, which is Direct Ink Writing (DIW) and characterized for their optical properties via UV-Vis spectroscopy. The results indicated CeO2 demonstrated superior UV-blocking capabilities (a maximum blockage of ~99%) compared to ZnO, while maintaining high transmittance for visible light (a maximum of ~90%). Notably, the solvent-based method exhibited better results than the ball milling method;however, the powder method had the best results. Therefore, this investigation offers a promising solution for solar panel protection that aligns with Saudi Arabia's Vision 2030 energy objectives. The relentless exposure of devices, particularly solar cells, to harmful UV radiation poses a formidable challenge and leads to annual degradation rates of between −0.8% and −4.9%, besides other factors. These findings underscore the critical importance of developing effective UV-blocking solutions. ©2024 IEEE.

关键词： Polydimethylsiloxane

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Spectral period doubling and encoding of dissipative optical solitons via gain control

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PhotoniX 2024年第1期5卷 290-305页

作者： Kangwen Yang Yi Zhou Yuqing Ling Kevin K.Tsia Heping Zeng Kenneth K.Y.Wong Shanghai Key Laboratory of Modern Optical System and Engineering Research Center of Optical Instrument and SystemMinistry of EducationSchool of Optical Electrical and Computer EngineeringUniversity of Shanghai for Science and TechnologyShanghai 200093China Department of Electrical and Electronic Engineering The University of Hong KongHong KongChina Advanced Biomedical Instrumentation Centre Hong Kong Science ParkHong KongChina State Key Laboratory of Precision Spectroscopy East China Normal UniversityShanghai 200062China

Period-doubling bifurcation,as an intermediate state between order and chaos,is ubiquitous in all disciplines of nonlinear ***,previous experimental observations of period doubling in ultrafast fiber lasers are mainly restricted to self-sustained steady state,controllable manipulation and dynamic switching between period doubling and other intriguing dynamical states are still largely ***,we propose to expand the vision of dissipative soliton periodic doubling,which we illustrate experimentally by reporting original spontaneous,collisional,and controllable spectral period doubling in a polarization-maintaining ultrafast fiber ***,the spontaneous period doubling can be observed in both single-and *** mechanism of the switchable state and periodic doubling was revealed by numerical ***,state transformation of individual solitons can be resolved during the collision of triple solitons involving stationary,oscillating,and period ***,controllable deterministic switching between period doubling and other dynamical states,as well as exemplifying the application of period-doubling-based digital encoding,is achieved under programmable pump *** results open a new window for unveiling complex Hopf bifurcation in dissipative systems and bring useful insights into nonlinear science and applications.

关键词： Period doubling Mode locking Ultrafast fiber lasers

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Secure speaker identification in open and closed environments modeled with symmetric comb filters

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Multimedia Tools and Applications 2025年第18期84卷 19147-19189页

作者： Shafik, Amira Monir, Mohamad El-Shafai, Walid Khalaf, Ashraf A. M. Nassar, M.M. El-Fishawy, Adel S. El-Din, M. A. Zein Dessouky, Moawad I. El-Rabaie, El-Sayed M. Abd El-Samie, Fathi E. Department Electronics and Electrical Communications Engineering Faculty of Electronic Engineering Menoufia University Menouf32952 Egypt Security Engineering Laboratory Department of Computer Science Prince Sultan University Riyadh11586 Saudi Arabia Electrical Engineering Department Faculty of Engineering Minia University Minia61519 Egypt Department of Information Technology College of Computer and Information Sciences Princess Nourah bint Abdulrahman University P.O. Box 84428 Riyadh11671 Saudi Arabia

Speech is a fundamental means of human interaction. Speaker Identification (SI) plays a crucial role in various applications, such as authentication systems, forensic investigation, and personal voice assistance. However, achieving robust and secure SI in both open and closed environments remains challenging. To address this issue, researchers have explored new techniques that enable computers to better understand and interact with humans. Smart systems leverage Artificial Neural Networks (ANNs) to mimic the human brain in identifying speakers. However, speech signals often suffer from interference, leading to signal degradation. The performance of a Speaker Identification System (SIS) is influenced by various environmental factors, such as noise and reverberation in open and closed environments, respectively. This research paper is concerned with the investigation of SI using Mel-Frequency Cepstral Coefficients (MFCCs) and polynomial coefficients, with an ANN serving as the classifier. To tackle the challenges posed by environmental interference, we propose a novel approach that depends on symmetric comb filters for modeling. In closed environments, we study the effect of reverberation on speech signals, as it occurs due to multiple reflections. To address this issue, we model the reverberation effect with comb filters. We explore different domains, including time, Discrete Wavelet Transform (DWT), Discrete Cosine Transform (DCT), and Discrete Sine Transform (DST) domains for feature extraction to determine the best combination for SI in case of reverberation environments. Simulation results reveal that DWT outperforms other transforms, leading to a recognition rate of 93.75% at a Signal-to-Noise Ratio (SNR) of 15 dB. Additionally, we investigate the concept of cancelable SI to ensure user privacy, while maintaining high recognition rates. Our simulation results show a recognition rate of 97.5% at 0 dB using features extracted from speech signals and their DCTs. Fo

关键词： Speech enhancement

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Non-planar dielectrics derived thermal and electrostatic field inhomogeneity for boosted weather-adaptive energy harvesting

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National science Review 2023年第9期10卷 248-257页

作者： Yi Zhou Tianpeng Ding Yin Cheng Yi Huang Wu Wang Jianmin Yang Lin Xie Ghim Wei Ho Jiaqing He Shenzhen Key Laboratory of Thermoelectric Materials and Department of Physics Southern University of Science and Technology Department of Electrical and Computer Engineering National University of Singapore School of Electronic Science and Engineering State Key Laboratory of Electronic Thin Film and Integrated Devices University of Electronic Science and Technology of China Department of Materials Science and Engineering National University of Singapore Institute of Materials Research and Engineering Agency for ScienceTechnology and Research (A*STAR) GuangdongHong Kong-Macao Joint Laboratory for Photonic-Thermal-Electrical Energy Materials and Devices Southern University of Science and Technology

Weather-adaptive energy harvesting of omnipresent waste heat and rain droplets,though promising in the field of environmental energy sustainability,is still far from practice due to its low electrical output owing to diele ctric structure irrationality and *** we present atypical upcycling of ambient heat and raindrop energy via an all-in-one non-planar energy harvester,simultaneously increasing solar pyroelectricity and droplet-based triboelectricity by two-fold,in contrast to conventional *** delivered non-planar dielectric with high transmittance confines the solar irradiance onto a focal hotspot,offering transverse thermal field propagation towards boosted inhomogeneous polarization with a generated power density of 6.1 mW m-2at 0.2 ***,the enlarged lateral surface area of curved architecture promotes droplet spreading/separation,thus travelling the electrostatic field towards increased *** enhanced pyroelectric and triboelectric outputs,upgraded with advanced manufacturing,demonstrate applicability in adaptive sustainable energy harvesting on sunny,cloudy,night,and rainy *** findings highlight a facile yet efficient strategy,not only for weather-adaptive environmental energy recovery but also in providing key insights for spatial thermal/electrostatic field manipulation in thermo electrics and ferroelectrics.

关键词： non-planar dielectrics heat manipulation pyroelectrics weather-adaptive energy harvesting

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Modeling and Clustering of Parabolic Granular Data

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2024年第7期5卷 3728-3742页

作者： Tang, Yiming Gao, Jianwei Pedrycz, Witold Hu, Xianghui Xi, Lei Ren, Fuji Hu, Min Hefei University of Technology Anhui Province Key Laboratory of Affective Computing and Advanced Intelligent Machine School of Computer and Information Hefei230601 China University of Alberta Department of Electrical and Computer Engineering EdmontonABT6R 2V4 Canada Hefei University of Technology School of Computer and Information Hefei230601 China University of Alberta Department of Electrical and Computer Engineering EdmontonABT6G 2R3 Canada Polish Academy of Sciences Systems Research Institute Warsaw00-901 Poland Istinye University Research Center of Performance and Productivity Analysis Istanbul34010 Turkey Southeast University School of Computer Science and Engineering Nanjing211189 China University of Electronic Science and Technology of China School of Computer Science and Engineering Sichuan Chengdu611731 China

At present, there exist some problems in granular clustering methods, such as lack of nonlinear membership description and global optimization of granular data boundaries. To address these issues, in this study, revolving around the parabolic granular data, we propose an overall architecture for parabolic granular modeling and clustering. To begin with, novel coverage and specificity functions are established, and then a parabolic granular data structure is proposed. The fuzzy c-means (FCM) algorithm is used to obtain the numeric prototypes, and then particle swarm optimization (PSO) is introduced to construct the parabolic granular data from the global perspective under the guidance of principle of justifiable granularity (PJG). Combining the advantages of FCM and PSO, we propose the parabolic granular modeling and optimization (PGMO) method. Moreover, we put forward attribute weights and sample weights as well as a distance measure induced by the Gaussian kernel similarity, and then come up with the algorithm of weighted kernel fuzzy clustering for parabolic granularity (WKFC-PG). In addition, the assessment mechanism of parabolic granular clustering is discussed. In summary, we set up an overall architecture including parabolic granular modeling, clustering, and assessment. Finally, comparative experiments on artificial, UCI, and high-dimensional datasets validate that our overall architecture delivers a good improvement over previous strategies. The parameter analysis and time complexity are also given for WKFC-PG. In contrast with related granular clustering algorithms, it is observed that WKFC-PG performs better than other granular clustering algorithms and has superior stability in handling outliers, especially on high-dimensional datasets. © 2020 IEEE.

关键词： Fuzzy clustering

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The synergistic interplay of artificial intelligence and digital twin in environmentally planning sustainable smart cities:A comprehensive systematic review

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Environmental science and Ecotechnology 2024年第4期20卷 79-104页

作者： Simon Elias Bibri Jeffrey Huang Senthil Kumar Jagatheesaperumal John Krogstie Swiss Federal Institute of Technology Lausanne(EPFL) Institute of Computer and Communication Sciences(IINFCOM)School of ArchitectureCivil and Environmental Engineering(ENAC)Media and Design Laboratory(LDM)1015LausanneSwitzerland Department of Electronics&Communication Engineering Mepco Schlenk Engineering CollegeSivakasi626005TamilnaduIndia Department of Computer Science Norwegian University of Science and Technology(NTNU)TrondheimNorway

The dynamic landscape of sustainable smart cities is witnessing a significant transformation due to the integration of emerging computational technologies and innovative *** advancements are reshaping data-driven planning strategies,practices,and approaches,thereby facilitating the achievement of environmental sustainability *** transformative wave signals a fundamental shift d marked by the synergistic operation of artificial intelligence(AI),artificial intelligence of things(AIoT),and urban digital twin(UDT)*** previous research has largely explored urban AI,urban AIoT,and UDT in isolation,a significant knowledge gap exists regarding their synergistic interplay,collaborative integration,and collective impact on data-driven environmental planning in the dynamic context of sustainable smart *** address this gap,this study conducts a comprehensive systematic review to uncover the intricate interactions among these interconnected technologies,models,and domains while elucidating the nuanced dynamics and untapped synergies in the complex ecosystem of sustainable smart *** to this study are four guiding research questions:*** theoretical and practical foundations underpin the convergence of AI,AIoT,UDT,data-driven planning,and environmental sustainability in sustainable smart cities,and how can these components be synthesized into a novel comprehensive framework?*** does integrating AI and AIoT reshape the landscape of datadriven planning to improve the environmental performance of sustainable smart cities?*** can AI and AIoT augment the capabilities of UDT to enhance data-driven environmental planning processes in sustainable smart cities?*** challenges and barriers arise in integrating and implementing AI,AIoT,and UDT in data-driven environmental urban planning,and what strategies can be devised to surmount or mitigate them?Methodologically,this study involves a rigorous analysis and synthesis of studies publis

关键词： Sustainable smart cities Artificial intelligence Artificial intelligence of things Urban digital twin Data-driven urban planning Environmental planning Environmental sustainability

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Adaptive Fine-Grained Feature Mining and RoI Feature Interaction Network for Small Object Detection in Aerial Images

Adaptive Fine-Grained Feature Mining and RoI Feature Interac...

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2025 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Xie, Fangmin Xiong, Zhan Wang, Zhuoyue Yang, Kun Xiao, Guoqiang College of Computer and Information Science Southwest University Chongqing China Institute of Advanced Computer Science Leiden University Netherlands Department of Electrical Engineering and Computer Sciences University of California Berkeley Berkeley United States

ISBN: (纸本)9798350368741

Object detection in drone view images remains challenging due to small-scale objects distributed non-uniformly and exhibiting weak semantic features. Additionally, external factors such as lighting conditions, viewing angles, and background clutter further complicate the detection of small objects. To address these issues, we propose a small object detector called Adaptive Fine-grained Feature Mining Network (AFMNet) based on an anchor-free framework. Firstly, an Adaptive Fine-grained Feature Mining (AFFM) module is designed to generate Regions of Interest (RoI) on high-resolution feature maps and mine fine-grained information that benefits the representation of small objects. Secondly, an Inter-RoI Feature Interaction (IRFI) block is developed to efficiently integrate complementary cues-namely, deep layer semantic information and shallow layer spatial details-and enhance RoI features. Experiments on the VisDrone dataset demonstrate that our method achieves state-of-the-art performance. The source code of our AFMNet is available at https://***/xiefangmin/AFMNet. © 2025 IEEE.

关键词： Drone-View Image Feature Fusion Fine-grained Features Region of Interest Small object detection

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A large-area bionic skin for high-temperature energy harvesting applications

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Nano Research 2023年第7期16卷 10245-10255页

作者： Zhaojun Liu Bian Tian Yao Li Jiaming Lei Zhongkai Zhang Jiangjiang Liu Qijing Lin Chengkuo Lee Zhuangde Jiang State Key Laboratory for Mechanical Manufacturing Systems Engineering School of Mechanical EngineeringXi’an Jiaotong UniversityXi’an 710049China Department of Electrical&Computer Engineering National University of SingaporeSingapore 117576Singapore Shandong Laboratory of Yantai Advanced Materials and Green Manufacturing Yantai 265503China

For the large amount of waste heat wasted in daily life and industrial production,we propose a new type of flexible thermoelectric generators(F-TEGs)which can be used as a large area bionic skin to achieve energy harvesting of thermal *** reference to biological structures such as pinecone,succulent,and feathers,we have designed and fabricated a biomimetic flexible TEG that can be applied in a wide temperature range which has the highest temperature energy harvesting capability *** laminated free structure of the bionic F-TEG dramatically increases the efficiency and density of energy *** F-TEGs(single TEG only 101.2 mg in weight),without an additional heat sink,demonstrates the highest output voltage density of 286.1 mV/cm^(2)and the maximum power density is 66.5 mW/m^(2) at a temperature difference of nearly 1000℃.The flexible characteristics of F-TEGs make it possible to collect the diffused thermal energy by flexible attachment to the outer walls of high-temperature pipes and vessels of different diameters and *** work shows a new design and application concept for flexible thermal energy collectors,which fills the gap of flexible energy harvesting in high-temperature environment.

关键词： bionic structure high-temperature flexible thermoelectric generators(TEGs)

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