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Graphdiyne as a saturable absorber for 2-µm all-solid-state Q-switched laser

science China Materials 2021年第3期64卷 683-690页

作者： Yuqian Zu Jia Guo Qianqian Hao Feng Zhang Cong Wang Jie Liu Bing Wang Shandong Provincial Engineering and Technical Center of Light Manipulations and Shandong Provincial Key Laboratory of Optics and Photonic Device School of Physics and ElectronicsShandong Normal UniversityJinan250014China Collaborative Innovation Center of Light Manipulations and Applications Shandong Normal UniversityJinan250358China Institute of Data Science and Technology Shandong Normal UniversityJinan250014China Institute of Microscale Optoelectronics Collaborative Innovation Center for Optoelectronic Science&TechnologyKey Laboratory of Optoelectronic Devices and Systems of Ministry of Education and Guangdong ProvinceCollege of Physics and Optoelectronic EngineeringShenzhen Key Laboratory of Micro-Nano Photonic Information TechnologyGuangdong Laboratory of Artificial Intelligence and Digital Economy(SZ)Shenzhen UniversityShenzhen518060China

As a novel one-atom-thick carbon allotrope,graphdiyne(GDY)has attracted wide interest in two-dimensional materials owing to its unique feature with a planar layer comprising sp2-and sp-hybridized carbon atoms and a direct natural bandgap.A high-quality saturable absorber(SA)based on GDY was successfully fabricated,and the nonlinear saturable absorption properties were investigated.A compact diode-pumped solid-state passively Q-switched laser based on the as-prepared GDY-SA could be operated at 2µ*** Q-switched laser delivered a maximum average output power of 1.29 W at a central wavelength of 1908.41 nm with up to 54.8%slope efficiency.A repetition rate of 91.58 kHz and single pulse energy of 23.08µJ were *** work is the first application of GDY as an SA to generate all-solid-state pulsed *** results indicate that GDY is a promising SA for solid-state pulsed lasers and has further application potential for ultrafast photonic devices in the 2-µm region.

关键词： graphdiyne saturable absorber all-solid-state laser passively Q-switched two-dimensional materials

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Optimization-driven Deep Reinforcement Learning for Robust Beamforming in IRS-assisted Wireless Communications

arXiv

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arXiv 2020年

作者： Lin, Jiaye Zou, Yuze Dong, Xiaoru Gong, Shimin Hoang, Dinh Thai Niyato, Dusit School of Intelligent Systems Engineering Sun Yat-sen University China School of Electronic Information and Communications Huazhong University of Science and Technology China School of Electrical and Data Engineering University of Technology Sydney Australia School of Computer Science and Engineering Nanyang Technological University Singapore

Intelligent reflecting surface (IRS) is a promising technology to assist downlink information transmissions from a multi-antenna access point (AP) to a receiver. In this paper, we minimize the AP’s transmit power by a joint optimization of the AP’s active beamforming and the IRS’s passive beamforming. Due to uncertain channel conditions, we formulate a robust power minimization problem subject to the receiver’s signal-to-noise ratio (SNR) requirement and the IRS’s power budget constraint. We propose a deep reinforcement learning (DRL) approach that can adapt the beamforming strategies from past experiences. To improve the learning performance, we derive a convex approximation as a lower bound on the robust problem, which is integrated into the DRL framework and thus promoting a novel optimization-driven deep deterministic policy gradient (DDPG) approach. In particular, when the DDPG algorithm generates a part of the action (e.g., passive beamforming), we can use the model-based convex approximation to optimize the other part (e.g., active beamforming) of the action more efficiently. Our simulation results demonstrate that the optimization-driven DDPG algorithm can improve both the learning rate and reward performance significantly compared to the conventional model-free DDPG algorithm. Copyright © 2020, The Authors. All rights reserved.

关键词： Deep learning

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POMDP-based candy server: Lessons learned from a seven day demo 29

POMDP-based candy server: Lessons learned from a seven day d...

引用

29th International Conference on Automated Planning and Scheduling, ICAPS 2019

作者： Hoerger, Marcus Song, Joshua Kurniawati, Hanna Elfes, Alberto School of Information Technology and Electrical Engineering University of Queensland Australia Research School of Computer Science Australian National University Australia Robotics and Autonomous Systems Group Data61 CSIRO

ISBN: (纸本)9781577358077

An autonomous robot must decide a good strategy to achieve its long term goal, despite various types of uncertainty. The Partially Observable Markov Decision Processes (POMDPs) is a principled framework to address such a decision making problem. Despite the computational intractability of solving POMDPs, the past decade has seen substantial advancement in POMDP solvers. This paper presents our experience in enabling on-line POMDP solving to become the sole motion planner for a robot manipulation demo at IEEE SIMPAR and ICRA 2018. The demo scenario is a candy-serving robot: A 6-DOFs robot arm must pick-up a cup placed on a table by a user, use the cup to scoop candies from a box, and put the cup of candies back on the table. The average perception error is ∼3cm (≈ the radius of the cup), affecting the position of the cup and the surface level of the candies. This paper presents a strategy to alleviate the curse of history issue plaguing this scenario, the perception system and its integration with the planner, and lessons learned in enabling an online POMDP solver to become the sole motion planner of this entire task. The POMDP-based system were tested through a 7 days live demo at the two conferences. In this demo, 150 runs were attempted and 98% of them were successful. We also conducted further experiments to test the capability of our POMDP-based system when the environment is relatively cluttered by obstacles and when the user moves the cup while the robot tries to pick it up. In both cases, our POMDP-based system reaches a success rate of 90% and above. © 2019 Association for the Advancement of Artificial Intelligence. All rights reserved.

关键词： Decision making

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Correction to: development of a cloud-assisted classification technique for the preservation of secure data storage in smart cities

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Journal of Cloud Computing 2023年第1期12卷 1-1页

作者： Kumar, Ankit Khan, Surbhi Bhatia Pandey, Saroj Kumar Shankar, Achyut Maple, Carsten Mashat, Arwa Malibari, Areej A. Department of Computer Engineering & Applications GLA University Mathura India Department of Electrical and Computer Engineering Lebanese American University Byblos Lebanon Department of Data Science School of Science Engineering and Environment University of Salford Manchester Byblos UK WMG University of Warwick Coventry UK Secure Cyber Systems Research Group (SCSRG) WMG University of Warwick Coventry UK Faculty of Computing and Information Science King Abdulaziz University Rabigh Saudi Arabia Department of Industrial and Systems Engineering College of Engineering Princess Nourah Bint Abdulrahman University Riyadh Saudi Arabia

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Process pruner: A tool for sequence-based event log preprocessing

Process pruner: A tool for sequence-based event log preproce...

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ICPM Demo Track 2019, ICPM Demo Track 2019

作者： Baumgartner, David Haghofer, Andreas Limberger, Martin Helm, Emmanuel Department of Data Science and Engineering University of Applied Sciences Upper Austria Hagenberg4232 Austria Advanced Information Systems and Technology University of Applied Sciences Upper Austria Hagenberg4232 Austria

A major challenge in applying process mining on real event data is the presence of noisy or incomplete cases or unusual behaviors. Applying process mining on raw event data leads to wrong conclusions during the discovery of process models, concealing the typical behavior. In this paper, an alternative for filtering event data without the need for extensive preprocessing is presented. The method is based on generated footprint matrices of randomly pruned sub-logs and works in a semi-automated manner. By identifying the most similar matrices to validate the whole log, traces representing unusual behavior can be excluded or highlighted. The tool was implemented with Python 3, NumPy and Pandas and is publicly available on GitHub. We evaluated our tool using benchmark data-sets and compared it to human filtering and discovery results. © 2019 CEUR-WS. All rights reserved.

关键词： data mining

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Learning with Group Noise

arXiv

引用

arXiv 2021年

作者： Wang, Qizhou Yao, Jiangchao Gong, Chen Liu, Tongliang Gong, Mingming Yang, Hongxia Han, Bo Department of Computer Science Hong Kong Baptist University Hong Kong Key Laboratory of Intelligent Perception and Systems for High-Dimensional Information of MoE School of Computer Science and Engineering Nanjing University of Science and Technology China Data Analytics and Intelligence Lab Alibaba Group China Department of Computing Hong Kong Polytechnic University Hong Kong Trustworthy Machine Learning Lab School of Computer Science University of Sydney Australia School of Mathematics and Statistics University of Melbourne Australia

Machine learning in the context of noise is a challenging but practical setting to plenty of real-world applications. Most of the previous approaches in this area focus on the pairwise relation (casual or correlational relationship) with noise, such as learning with noisy labels. However, the group noise, which is parasitic on the coarse-grained accurate relation with the fine-grained uncertainty, is also universal and has not been well investigated. The challenge under this setting is how to discover true pairwise connections concealed by the group relation with its fine-grained noise. To overcome this issue, we propose a novel Max-Matching method for learning with group noise. Specifically, it utilizes a matching mechanism to evaluate the relation confidence of each object (cf. Figure 1) w.r.t. the target, meanwhile considering the Non-IID characteristics among objects in the group. Only the most confident object is considered to learn the model, so that the fine-grained noise is mostly dropped. The performance on a range of real-world datasets in the area of several learning paradigms demonstrates the effectiveness of Max-Matching. Copyright © 2021, The Authors. All rights reserved.

关键词： Machine learning

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Deep Reinforcement Learning for Robust Beamforming in IRS-assisted Wireless Communications

Deep Reinforcement Learning for Robust Beamforming in IRS-as...

引用

GLOBECOM 2020 - 2020 IEEE Global Communications Conference

作者： Jiaye Lin Yuze Zout Xiaoru Dong Shimin Gong Dinh Thai Hoang Dusit Niyato School of Intelligent Systems Engineering Sun Yat-sen University China School of Electronic Information and Communications Huazhong University of Science and Technology China China School of Electrical and Data Engineering University of Technology Sydney Australia School of Computer Science and Engineering Nanyang Technological University Singapore

ISBN: (数字)9781728182988

ISBN: (纸本)9781728182995

Intelligent reflecting surface (IRS) is a promising technology to assist downlink information transmissions from a multi-antenna access point (AP) to a receiver. In this paper, we minimize the AP's transmit power by a joint optimization of the AP's active beamforming and the IRS's passive beamforming. Due to uncertain channel conditions, we formulate a robust power minimization problem subject to the receiver's signal-to-noise ratio (SNR) requirement and the IRS's power budget constraint. We propose a deep reinforcement learning (DRL) approach that can adapt the beamforming strategies from past experiences. To improve the learning performance, we derive a convex approximation as a lower bound on the robust problem, which is integrated with the DRL framework and thus promoting a novel optimization-driven deep deterministic policy gradient (DDPG) approach. In particular, when the DDPG algorithm generates a part of the action (e.g., passive beamforming), we can use the model-based convex approximation to optimize the other part of the action (e.g., active beamforming) efficiently. Our simulation results demonstrate that the optimization-driven DDPG algorithm can improve both the learning rate and reward significantly compared to the conventional DDPG algorithm.

关键词： Array signal processing Receivers Channel estimation Wireless communication Scattering Reinforcement learning Approximation algorithms

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Multimodal Whole Slide Foundation Model for Pathology

arXiv

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arXiv 2024年

作者： Ding, Tong Wagner, Sophia J. Song, Andrew H. Chen, Richard J. Lu, Ming Y. Zhang, Andrew Vaidya, Anurag J. Jaume, Guillaume Shaban, Muhammad Kim, Ahrong Williamson, Drew F.K. Chen, Bowen Almagro-Perez, Cristina Doucet, Paul Sahai, Sharifa Chen, Chengkuan Komura, Daisuke Kawabe, Akihiro Ishikawa, Shumpei Gerber, Georg Peng, Tingying Le, Long Phi Mahmood, Faisal Department of Pathology Mass General Brigham Harvard Medical School BostonMA United States Data Science Program Dana-Farber Cancer Institute BostonMA United States Cancer Program Broad Institute of Harvard and MIT CambridgeMA United States John A. Paulson School of Engineering and Applied Sciences Harvard University CambridgeMA United States Helmholtz Munich German Research Center for Environment and Health Munich Germany School of Computation Information and Technology Technical University of Munich Munich Germany Department of Electrical Engineering and Computer Science Massachusetts Institute of Technology CambridgeMA United States Harvard-MIT Division of Health Sciences and Technology Massachusetts Institute of Technology CambridgeMA United States Department of Pathology Pusan National University Busan Korea Republic of Department of Pathology and Laboratory Medicine Emory University School of Medicine AtlantaGA United States Harvard Data Science Initiative Harvard University CambridgeMA United States Department of Systems Biology Harvard Medical School BostonMA United States Department of Preventive Medicine Graduate School of Medicine The University of Tokyo Tokyo Japan Division of Pathology National Cancer Center Exploratory Oncology Research & Clinical Trial Center Chiba Japan

The field of computational pathology has been transformed with recent advances in foundation models that encode histopathology region-of-interests (ROIs) into versatile and transferable feature representations via self-supervised learning (SSL). However, translating these advancements to address complex clinical challenges at the patient and slide level remains constrained by limited clinical data in disease-specific cohorts, especially for rare clinical conditions. We propose TITAN, a multimodal whole slide foundation model pretrained using 335,645 WSIs via visual self-supervised learning and vision-language alignment with corresponding pathology reports and 423,122 synthetic captions generated from a multimodal generative AI copilot for pathology. Without any finetuning or requiring clinical labels, TITAN can extract general-purpose slide representations and generate pathology reports that generalize to resource-limited clinical scenarios such as rare disease retrieval and cancer prognosis. We evaluate TITAN on diverse clinical tasks and find that TITAN outperforms both ROI and slide foundation models across machine learning settings such as linear probing, few-shot and zero-shot classification, rare cancer retrieval and cross-modal retrieval, and pathology report generation. The model is publicly accessible at https://***/mahmoodlab/TITAN © 2024, CC BY-NC-ND.

关键词： Self-supervised learning

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BeLDPC: Bit Errors Aware Adaptive Rate LDPC Codes for 3D TLC NAND Flash Memory

BeLDPC: Bit Errors Aware Adaptive Rate LDPC Codes for 3D TLC...

引用

Design, Automation and Test in Europe Conference and Exhibition

作者： Meng Zhang Fei Wu Qin Yu Weihua Liu Lanlan Cui Yahui Zhao Changsheng Xie Wuhan National Laboratory for Optoelectronics Key Laboratory of Information Storage System Engineering Research Center of data storage systems and Technology Ministry of Education of China School of Computer Science and Technology Huazhong University of Science and Technology Wuhan China

ISBN: (数字)9783981926347

ISBN: (纸本)9781728144689

Three-dimensional (3D) NAND flash memory has high capacity and cell storage density by using the multi-bit technology and vertical stack architecture, but degrading data reliability due to high raw bit error rates (RBER) caused by program/erase (P/E) cycles and retention periods. Low-density parity-check (LDPC) codes become more popular error-correcting technologies to improve data reliability due to strong error correction capability, but introducing more decoding iterations at higher RBER. To reduce decoding iterations, this paper proposes BeLDPC: bit errors aware adaptive rate LDPC codes for 3D triple-level cell (TLC) NAND flash memory. Firstly, bit error characteristics in 3D charge trap TLC NAND flash memory are studied on a real FPGA testing platform, including asymmetric bit flipping and temporal locality of bit errors. Then, based on these characteristics, a high-efficiency LDPC code is designed. Experimental results show BeLDPC can reduce decoding iterations under different P/E cycles and retention periods.

关键词： Iterative decoding Three-dimensional displays Error analysis Reliability Encoding Computer architecture

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Improving Automatic Speech Recognition Utilizing Audio-codecs for data Augmentation

Improving Automatic Speech Recognition Utilizing Audio-codec...

引用

IEEE Workshop on Multimedia Signal Processing

作者： Nirayo Hailu Ingo Siegert Andreas Nürnberger Faculty of Computer Science Data and Knowledge Engineering Group Otto-von-Guericke University Magdeburg Magdeburg Germany Faculty of Electrical Engineering and Information Technology Mobile Dialog Systems Otto-von-Guericke University Magdeburg Magdeburg Germany

ISBN: (数字)9781728193205

ISBN: (纸本)9781728193236

To train end-to-end automatic speech recognition models, it requires a large amount of labeled speech data. This goal is challenging for languages with fewer resources. In contrast to the commonly used feature level data augmentation, we propose to expand the training set by using different audio codecs at the data level. The augmentation method consists of using different audio codecs with changed bit rate, sampling rate, and bit depth. The change reassures variation in the input data without drastically affecting the audio quality. Besides, we can ensure that humans still perceive the audio, and any feature extraction is possible later. To demonstrate the general applicability of the proposed augmentation technique, we evaluated it in an end-to-end automatic speech recognition architecture in four languages. After applying the method, on the Amharic, Dutch, Slovenian, and Turkish datasets, we achieved a 1.57 average improvement in the character error rates (CER) without integrating language models. The result is comparable to the baseline result, showing CER improvement of 2.78, 1.25, 1.21, and 1.05 for each language. On the Amharic dataset, we reached a syllable error rate reduction of 6.12 compared to the baseline result.

关键词： Training Codecs Speech coding Error analysis Bit rate Feature extraction Automatic speech recognition

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