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arXiv 2022年

作者： Cong, Runmin Yang, Ning Li, Chongyi Fu, Huazhu Zhao, Yao Huang, Qingming Kwong, Sam The Institute of Information Science Beijing Jiaotong University Beijing100044 China The Beijing Key Laboratory of Advanced Information Science and Network Technology Beijing100044 China The Department of Computer Science City University of Hong Kong Hong Kong The School of Computer Science and Engineering Nanyang Technological University Singapore A*STAR Singapore The School of Computer Science and Technology University of Chinese Academy of Sciences Beijing101408 China University of Chinese Academy of Sciences Beijing101408 China The Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences Beijing100190 China Peng Cheng Laboratory Shenzhen518055 China The City University of Hong Kong Shenzhen Research Institute Shenzhen51800 China

The goal of co-salient object detection (CoSOD) is to discover salient objects that commonly appear in a query group containing two or more relevant images. Therefore, how to effectively extract inter-image correspondence is crucial for the CoSOD task. In this paper, we propose a global-and-local collaborative learning architecture, which includes a global correspondence modeling (GCM) and a local correspondence modeling (LCM) to capture comprehensive inter-image corresponding relationship among different images from the global and local perspectives. Firstly, we treat different images as different time slices and use 3D convolution to integrate all intra features intuitively, which can more fully extract the global group semantics. Secondly, we design a pairwise correlation transformation (PCT) to explore similarity correspondence between pairwise images and combine the multiple local pairwise correspondences to generate the local inter-image relationship. Thirdly, the inter-image relationships of the GCM and LCM are integrated through a global-and-local correspondence aggregation (GLA) module to explore more comprehensive inter-image collaboration cues. Finally, the intra- and inter-features are adaptively integrated by an intra-and-inter weighting fusion (AEWF) module to learn co-saliency features and predict the co-saliency map. The proposed GLNet is evaluated on three prevailing CoSOD benchmark datasets, demonstrating that our model trained on a small dataset (about 3k images) still outperforms eleven state-of-the-art competitors trained on some large datasets (about 8k̴200k images). © 2022, CC BY-NC-SA.

关键词： Convolution

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Capabilities of ARIANNA: Neutrino Pointing Resolution and Implications for Future Ultra-high Energy Neutrino Astronomy 37

Capabilities of ARIANNA: Neutrino Pointing Resolution and Im...

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37th International Cosmic Ray Conference, ICRC 2021

作者： Barwick, Steven W. Gaswint, Geoffrey Anker, Astrid Baldi, Pierre Barwick, Steven W. Beise, Jakob Bernhoff, Hans Besson, Dave Z. Bingefors, Nils Cataldo, Maddalena Chen, Pisin Fernández, Daniel García Gaswint, Geoffrey Glaser, Christian Hallgren, Allan Hallmann, Steffen Hanson, Jordan C. Klein, Spencer R. Kleinfelder, Stuart A. Lahmann, Robert Liu, Jiayi Magnuson, Mitchell McAleer, Stephen Meyers, Zach Nam, Jiwoo Nelles, Anna Novikov, Alexander Paul, Manuel P. Persichilli, Christopher Plaisier, Ilse Pyras, Lilly Rice-Smith, Ryan Tatar, Joulien Wang, Shih-Hao Welling, Christoph Zhao, Leshan University of California Department of Physics and Astronomy IrvineCA92617 United States Department of Physics and Astronomy University of California IrvineCA92697 United States Department of Information and Computer Science University of California IrvineCA92697 United States Uppsala University Department of Physics and Astronomy UppsalaSE-752 37 Sweden Uppsala University Department of Engineering Sciences Division of Electricity UppsalaSE-752 37 Sweden Department of Physics and Astronomy University of Kansas LawrenceKS66045 United States Moscow115409 Russia ECAP Friedrich-Alexander Universität Erlangen-Nürnberg Erlangen91058 Germany Department of Physics Leung Center for Cosmology and Particle Astrophysics National Taiwan University Taipei10617 Taiwan DESY Zeuthen15738 Germany Whittier College Department of Physics WhittierCA90602 United States Lawrence Berkeley National Laboratory BerkeleyCA94720 United States Department of Electrical Engineering and Computer Science University of California IrvineCA92697 United States Research Cyberinfrastructure Center University of California IrvineCA92697 United States

A radio-frequency polarization measurement by the ARIANNA surface station was performed using a residual hole from the South Pole Ice Core (SPICEcore) Project. Radio pulses were emitted from a transmitter located down to 1.7 km below the snow surface. After deconvolving the raw signals for the detector response and attenuation from propagation through the ice, the signal pulses show no significant distortion and agree with a reference measurement of the emitter made in an anechoic chamber. The direction to transmitted radio pulse was measured with an angular resolution of 0.37 degree [statistical error]. For polarization, the statistical error of the polarization vector is depth dependent and below 1 degree. In addition, a slowly-varying systematic variation is observed as a function of depth with an rms error of 2.7 degrees. Neither the direction or polarization measurement show a significant offset as a function of depth relative to expectation. We also report on the results of a simulation study of the ARIANNA neutrino direction and energy resolution. The software tool NuRadioMC was used to reconstruct the polarization and viewing angle to determine the neutrino direction. Multiple models of Askaryan radiation and detector sites along with a range of neutrino energies were evaluated. The neutrino space angle resolution was determined to be below 3 degrees, which is comparable to the systematic polarization uncertainty. Therefore it is expected that the polarization resolution, which is the dominant contribution to the neutrino space angle resolution, will be improved in future studies by determining and eliminating systematic effects. © Copyright owned by the author(s).

关键词： Polarization

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AI-optimized electrochemical aptasensors for stable, reproducible detection of neurodegenerative diseases, cancer, and coronavirus

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Heliyon 2025年第1期11卷 e41338页

作者： Tayfour Ahmed, Amira Elsir Dhahi, Th.S. Attia, Tahani A. Elhassan Ali, Fawzia Awad Elobaid, Mohamed Elshaikh Adam, Tijjani Gopinath, Subash C.B. Department of Information System College of Science & Arts King Khalid University Asser Mohyel Saudi Arabia Health and Medical Technicals College Southern Technical University Basrah Iraq Department of Computer Engineering College of Computer Science and Engineering University of Ha'il Saudi Arabia DEEE Faculty of Engineering University of Khartoum Sudan Imam Abdulrahman Bin Faisal University Applied College Computer Department Saudi Arabia Faculty of Electronic Engineering & Technology Universiti Malaysia Perlis Perlis Arau 02600 Malaysia Institute of Nano Electronic Engineering Universiti Malaysia Perlis Perlis Kangar 01000 Malaysia Micro System Technology Centre of Excellence (CoE) Universiti Malaysia Perlis (UniMAP) Perlis Malaysia Center for Global Health Research Saveetha Medical College & Hospital Saveetha Institute of Medical and Technical Sciences (SIMATS) Thandalam Tamil Nadu Chennai 602 105 India Faculty of Chemical Engineering & Technology Universiti Malaysia Perlis (UniMAP) Perlis Arau 02600 Malaysia

AI-optimized electrochemical aptasensors are transforming diagnostic testing by offering high sensitivity, selectivity, and rapid response times. Leveraging data-driven AI techniques, these sensors provide a non-invasive, cost-effective alternative to traditional methods, with applications in detecting molecular biomarkers for neurodegenerative diseases, cancer, and coronavirus. The performance metrics outlined in the comparative table illustrate the significant advancements enabled by AI integration. Sensitivity increases from 60 to 75 % in ordinary aptasensors to 85–95 %, while specificity improves from 70-80 % to 90–98 %. This enhanced performance allows for ultra-low detection limits, such as 10 fM for carcinoembryonic antigen (CEA) and 20 fM for mucin-1 (MUC1) using Electrochemical Impedance Spectroscopy (EIS), and 1 pM for prostate-specific antigen (PSA) with Differential Pulse Voltammetry (DPV). Similarly, Square Wave Voltammetry (SWV) and potentiometric sensors have detected alpha-fetoprotein (AFP) at 5 fM and epithelial cell adhesion molecule (EpCAM) at 100 fM, respectively. AI integration also enhances reproducibility, reduces false positives and negatives (from 15-20 % to 5–10 %), and significantly decreases response times (from 10-15 s to 2–3 s). These advancements improve data processing speeds (from 10 to 20 min per sample to 2–5 min) and calibration accuracy (<2 % margin of error compared to 5–10 %), while expanding application scope to multi-target biomarker detection. This review highlights how these advancements position AI-optimized electrochemical aptasensors as powerful tools for personalized treatment, point-of-care testing, and continuous health monitoring. Despite a higher cost ($500-$1,500/unit), their enhanced portability and diagnostic performance promise to revolutionize healthcare, environmental monitoring, and food safety, ultimately improving public health outcomes. © 2024

关键词： AI Aptasensors Cancer Coronavirus Electrochemical Integrated biosensor Molecular cells Neurodegenerative

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A Single-Ion information Engine for Charging Quantum Battery

arXiv

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arXiv 2024年

作者： Zhang, Jialiang Wang, Pengfei Chen, Wentao Cai, Zhengyang Qiao, Mu Li, Riling Huang, Yingye Tian, Haonan Tu, Henchao Cui, Kaifeng Yan, Leilei Zhang, Junhua Zhang, Jingning Yung, Manhong Kim, Kihwan State Key Laboratory of Low Dimensional Quantum Physics Department of Physics Tsinghua University Beijing100084 China Beijing Academy of Quantum Information Sciences Beijing100193 China State Key Laboratory of Computer Science Institute of Software Chinese Academy of Sciences Beijing100086 China School of Physics Zhengzhou University Zhengzhou450001 China Shenzhen Institute for Quantum Science and Engineering Southern University of Science and Technology Shenzhen518055 China International Quantum Academy Shenzhen518048 China Hefei National Laboratory Hefei230088 China Frontier Science Center for Quantum Information Beijing100084 China

information engines produce mechanical work through measurement and adaptive control. For information engines, the principal challenge lies in how to store the generated work for subsequent utilization. Here, we report an experimental demonstration where quantized mechanical motion serves as a quantum battery and gets charged in repeated cycles by a single trapped-ion in-formation engine. This is enabled by a key technological advancement in rapid state discrimination, allowing us to suppress measurement-induced disturbances. Consequently, we were able to obtain a charging efficiency over 50% of the theoretical limit at the optimal temperature. The experimental results substantiate that this approach can render trapped ions a promising platform for microscopic information engines with potential applications in the future upon scaling up. Copyright © 2024, The Authors. All rights reserved.

关键词： Trapped ions

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Convex Estimation of Sparse-Smooth Power Spectral Densities from Mixtures of Realizations with Application to Weather Radar

arXiv

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arXiv 2023年

作者： Kuroda, Hiroki Kitahara, Daichi Yoshikawa, Eiichi Kikuchi, Hiroshi Ushio, Tomoo Department of Information and Management Systems Engineering Nagaoka University of Technology Niigata940-2188 Japan Division of Electrical Electronic and Infocommunications Engineering Osaka University Osaka565-0871 Japan Aeronautical Technology Directorate Japan Aerospace Exploration Agency Tokyo181-0015 Japan Department of Electrical and Computer Engineering Colorado State University Fort Collins80521 CO United States Center for Space Science and Radio Engineering The University of Electro-Communications Tokyo182-8585 Japan

In this paper, we propose a convex optimization-based estimation of sparse and smooth power spectral densities (PSDs) of complex-valued random processes from mixtures of realizations. While the PSDs are related to the magnitude of the frequency components of the realizations, it has been a major challenge to exploit the smoothness of the PSDs, because penalizing the difference of the magnitude of the frequency components results in a nonconvex optimization problem that is difficult to solve. To address this challenge, we design the proposed model that jointly estimates the complex-valued frequency components and the nonnegative PSDs, which are respectively regularized to be sparse and sparse-smooth. By penalizing the difference of the nonnegative variable that estimates the PSDs, the proposed model can enhance the smoothness of the PSDs via convex optimization. Numerical experiments on the phased array weather radar, an advanced weather radar system, demonstrate that the proposed model achieves superior estimation accuracy compared to existing sparse estimation models, regardless of whether they are combined with a smoothing technique as a post-processing step or not. © 2023, CC BY.

关键词： Convex optimization

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science Case and Detector Concept for the ARIANNA High Energy Neutrino Telescope at Moore’s Bay, Antarctica 37

Science Case and Detector Concept for the ARIANNA High Energ...

引用

37th International Cosmic Ray Conference, ICRC 2021

作者： Barwick, Steven W. Anker, Astrid Baldi, Pierre Beise, Jakob Bernhoff, Hans Besson, Dave Z. Bingefors, Nils Cataldo, Maddalena Chen, Pisin Fernández, Daniel García Gaswint, Geoffrey Glaser, Christian Hallgren, Allan Hallmann, Steffen Hanson, Jordan C. Klein, Spencer R. Kleinfelder, Stuart A. Lahmann, Robert Liu, Jiayi Magnuson, Mitchell McAleer, Stephen Meyers, Zach Nam, Jiwoo Nelles, Anna Novikov, Alexander Paul, Manuel P. Persichilli, Christopher Plaisier, Ilse Pyras, Lilly Rice-Smith, Ryan Tatar, Joulien Wang, Shih-Hao Welling, Christoph Zhao, Leshan University of California Department of Physics and Astronomy IrvineCA92617 United States Department of Physics and Astronomy University of California IrvineCA92697 United States Department of Information and Computer Science University of California IrvineCA92697 United States Uppsala University Department of Physics and Astronomy UppsalaSE-752 37 Sweden Uppsala University Department of Engineering Sciences Division of Electricity UppsalaSE-752 37 Sweden Department of Physics and Astronomy University of Kansas LawrenceKS66045 United States Moscow115409 Russia ECAP Friedrich-Alexander Universität Erlangen-Nürnberg Erlangen91058 Germany Department of Physics Leung Center for Cosmology and Particle Astrophysics National Taiwan University Taipei10617 Taiwan DESY Zeuthen15738 Germany Whittier College Department of Physics WhittierCA90602 United States Lawrence Berkeley National Laboratory BerkeleyCA94720 United States Department of Electrical Engineering and Computer Science University of California IrvineCA92697 United States Research Cyberinfrastructure Center University of California IrvineCA92697 United States

The ARIANNA neutrino detector, located at sea-level on the Ross Ice Shelf, Antarctica, consists of 200 autonomous and independent detector stations separated by 1 kilometer in a uniform triangular mesh, and serves to inform the planning of the future projects. The primary science mission of ARIANNA is to search for sources of neutrinos with energies greater than 1017 eV, complementing the reach of IceCube and other neutrino telescopes that focus on lower energies. An ARIANNA observation of a neutrino source would provide strong insight into the enigmatic sources of cosmic rays. ARIANNA observes the radio emission from high energy neutrino interactions in the Antarctic ice. Among radio based concepts under current investigation, ARIANNA would uniquely survey the vast majority of the southern sky at any instant in time, and an important region of the northern sky, by virtue of its location on the surface of the Ross Ice Shelf at Moore’s Bay. The broad sky coverage is specific to the Moore’s Bay site, providing capabilities to observe explosive sources from unknown directions. The ARIANNA architecture is designed to measure the angular direction to within 4 degrees for every neutrino candidate, which too plays an important role in the pursuit of multi-messenger observations of astrophysical sources. The sea level location reduces the impact of a potentially serious background associated with the cores of cosmic ray air showers striking the ice surface, generating radio pulses which are similar to those expected from neutrino events. Reflecting layers at the bottom or within the ice sheet (which are known to exist in thick ice sheets) might create a troublesome rate of background events, provided the reflection coefficients are large enough, that arrive at the detector from the same directions as neutrinos. © Copyright owned by the author(s).

关键词： Sea level

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Jamming Modulation: An Active Anti-Jamming Scheme

arXiv

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arXiv 2022年

作者： Ma, Jianhui Li, Qiang Liu, Zilong Du, Linsong Chen, Hongyang Ansari, Nirwan Chengdu611731 China The School of Computer Science and Electronics Engineering University of Essex ColchesterCO4 3SQ United Kingdom The School of Information Science and Technology Southwest Jiaotong University Chengdu611756 China The Research Center for Graph Computing Zhejiang Lab Hangzhou311121 China The Department of Electrical and Computer Engineering New Jersey Institute of Technology NewarkNJ07102 United States

Providing quality communications under adversarial electronic attacks, e.g., broadband jamming attacks, is a challenging task. Unlike state-of-the-art approaches which treat jamming signals as destructive interference, this paper presents a novel active anti-jamming (AAJ) scheme for a jammed channel to enhance the communication quality between a transmitter node (TN) and receiver node (RN), where the TN actively exploits the jamming signal as a carrier to send messages. Specifically, the TN is equipped with a programmable-gain amplifier, which is capable of re-modulating the jamming signals for jamming modulation. Considering four typical jamming types, we derive both the bit error rates (BER) and the corresponding optimal detection thresholds of the AAJ scheme. The asymptotic performances of the AAJ scheme are discussed under the high jamming-to-noise ratio (JNR) and sampling rate cases. Our analysis shows that there exists a BER floor for sufficiently large JNR. Simulation results indicate that the proposed AAJ scheme allows the TN to communicate with the RN reliably even under extremely strong and/or broadband jamming. Additionally, we investigate the channel capacity of the proposed AAJ scheme, and show that the channel capacity of the AAJ scheme outperforms that of the direct transmission when the JNR is relatively high. Copyright © 2022, The Authors. All rights reserved.

关键词： Jamming

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Quantum Computing Universal Thermalization Dynamics in a (2+1)D Lattice Gauge Theory

arXiv

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arXiv 2024年

作者： Mueller, Niklas Wang, Tianyi Katz, Or Davoudi, Zohreh Cetina, Marko Department of Physics University of Washington SeattleWA98195 United States Department of Physics Duke University DurhamNC27708 United States Duke Quantum Center Duke University DurhamNC27701 United States The NSF Institute for Robust Quantum Simulation University of Maryland College ParkMD20742 United States Department of Electrical and Computer Engineering Duke University DurhamNC27708 United States School of Applied and Engineering Physics Cornell University IthacaNY14853 United States Department of Physics Maryland Center for Fundamental Physics University of Maryland College ParkMD20742 United States Joint Center for Quantum Information and Computer Science NIST University of Maryland College ParkMD20742 United States University of Maryland College ParkMD20742 United States

Simulating non-equilibrium phenomena in strongly-interacting quantum many-body systems, including thermalization, is a promising application of near-term and future quantum computation. By performing experiments on a digital quantum computer consisting of fully-connected optically-controlled trapped ions, we study the role of entanglement in the thermalization dynamics of a Z2 lattice gauge theory in 2+1 spacetime dimensions. Using randomized-measurement protocols, we efficiently learn a classical approximation of non-equilibrium states that yields the gap-ratio distribution and the spectral form factor of the entanglement Hamiltonian. These observables exhibit universal early-time signals for quantum chaos, a prerequisite for thermalization. Our work, therefore, establishes quantum computers as robust tools for studying universal features of thermalization in complex many-body systems, including in gauge theories. Copyright © 2024, The Authors. All rights reserved.

关键词： Quantum computers

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LARGE LANGUAGE MULTIMODAL MODELS FOR 5-YEAR CHRONIC DISEASE COHORT PREDICTION USING EHR DATA

arXiv

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arXiv 2024年

作者： Ding, Jun-En Thao, Phan Nguyen Minh Peng, Wen-Chih Wang, Jian-Zhe Chug, Chun-Cheng Hsieh, Min-Chen Tseng, Yun-Chien Chen, Ling Luo, Dongsheng Wu, Chenwei Wang, Chi-Te Chen, Pei-Fu Liu, Feng Hung, Fang-Ming School of Systems and Enterprises Stevens Institute of Technology Hoboken United States Department of Computer Science National Yang Ming Chiao Tung University Hsinchu City Taiwan Institute of Hospital and Health Care Administration National Yang Ming Chiao Tung University Taipei City Taiwan School of Computing and Information Science Florida International University United States Center of Artificial Intelligence Far Eastern Memorial Hospital New Taipei City Taiwan Surgical Trauma Intensive Care Unit Far Eastern Memorial Hospital New Taipei City Taiwan Electrical Engineering and Computer Science University of Michigan MI United States

Chronic diseases such as diabetes are the leading causes of morbidity and mortality worldwide. Numerous research studies have been attempted with various deep learning models in diagnosis. However, most previous studies had certain limitations, including using publicly available datasets (e.g. MIMIC), and imbalanced data. In this study, we collected five-year electronic health records (EHRs) from the Taiwan hospital database, including 1,420,596 clinical notes, 387,392 laboratory test results, and more than 1,505 laboratory test items, focusing on research pre-training large language models. We proposed a novel Large Language Multimodal Models (LLMMs) framework incorporating multimodal data from clinical notes and laboratory test results for the prediction of chronic disease risk. Our method combined a text embedding encoder and multi-head attention layer to learn laboratory test values, utilizing a deep neural network (DNN) module to merge blood features with chronic disease semantics into a latent space. In our experiments, we observe that clinicalBERT and BiomedBERT, when combined with attention fusion, can achieve an accuracy of 73% in multiclass chronic diseases and diabetes prediction. By transforming laboratory test values into textual descriptions and employing the Flan T-5 model, we achieved a 76% Area Under the ROC Curve (AUROC), demonstrating the effectiveness of leveraging numerical text data for training and inference in language models. This approach significantly improves the accuracy of early-stage diabetes prediction. © 2024, CC BY.

关键词： Electronic health record

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MatChat: A Large Language Model and Application Service Platform for Materials science

arXiv

引用

arXiv 2023年

作者： Chen, Ziyi Xie, Fankai Wan, Meng Yuan, Yang Liu, Miao Wang, Zongguo Meng, Sheng Wang, Yangang Computer Network Information Center Chinese Academy of Sciences Beijing100083 China University of Chinese Academy of Sciences Beijing100049 China Institute of Physics Chinese Academy of Sciences Beijing100190 China School of Physical Sciences University of Chinese Academy of Sciences Beijing100190 China Songshan Lake Materials Laboratory Guangdong Dongguan523808 China Center of Materials Science and Optoelectronics Engineering University of Chinese Academy of Sciences Beijing100049 China

The prediction of chemical synthesis pathways plays a pivotal role in materials science research. Challenges, such as the complexity of synthesis pathways and the lack of comprehensive datasets, currently hinder our ability to predict these chemical processes accurately. However, recent advancements in generative artificial intelligence (GAI), including automated text generation and question-answering systems, coupled with fine-tuning techniques, have facilitated the deployment of large-scale AI models tailored to specific domains. In this study, we harness the power of the LLaMA2-7B model and enhance it through a learning process that incorporates 13,878 pieces of structured material knowledge data. This specialized AI model, named MatChat, focuses on predicting inorganic material synthesis pathways. MatChat exhibits remarkable proficiency in generating and reasoning with knowledge in materials science. Although MatChat requires further refinement to meet the diverse material design needs, this research undeniably highlights its impressive reasoning capabilities and innovative potential in the field of materials science. MatChat is now accessible online and open for use, with both the model and its application framework available as open source. This study establishes a robust foundation for collaborative innovation in the integration of generative AI in materials science. Copyright © 2023, The Authors. All rights reserved.

关键词： Forecasting

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