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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Yongxiang Feng Weihua He Kaichao You Bing Liu Ziyang Zhang Yaoyuan Wang Minglei Li Yihang Lou Jiawei Li Guoqi Li Jianxing Liao Advanced Computing and Storage Lab Huawei Technologies Co. Ltd. School of Electronics Engineering and Computer Science Peking University China Language & Speech Innovation Lab Huawei Technologies Co. Ltd. GoTen AI Lab Department of Intelligent Vision Huawei Technologies Co. Ltd. China Department of Production Automation Development Huawei Technologies Co. Ltd. China Institute of Automation Chinese Academy of Sciences China

Deploying deep learning models to new environments is very challenging. Domain adaptation (DA) is a promising paradigm to solve the problem by collecting and adapting to unlabeled data in new environments. Though research efforts have led to steady performance improvement over the past decade, DA algorithms are still hard to deploy, as training on unlabeled new data makes tuning difficult and not feasible for inference-only devices. To make DA practical, in this paper we study a new problem named Test-time Training-Free Domain Adaptation (TTDA), where trained models must adapt to a single input (mimicking the test-time scenario) without training. By exploiting spatial activation that was previously overlooked and simply averaged out, we propose a simple method based on Feature Statistics Transformation (FST) on-the-fly for each test example. The proposed algorithm is tested in the TTDA setting on two standard DA benchmarks. Surprisingly, it surpasses or performs on par with state-of-the-art DA methods, even though they require additional training. We envision that this training-free paradigm has the potential to bring DA to embedded devices and would be of interest to audience of community.

关键词： Training Performance evaluation Adaptation models Transfer learning Signal processing algorithms Statistical distributions Classification algorithms

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RCGA-Net: An Improved Multi-hybrid Attention Mechanism Network in Biomedical Image Segmentation

RCGA-Net: An Improved Multi-hybrid Attention Mechanism Netwo...

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2021 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2021

作者： Xiao, Feng Shen, Cong Chen, Yu Yang, Tian Chen, Shengyong Liao, Zhijun Tang, Jijun Tianjin University of Technology School of Computer Science and Engineering Tianjin China Engineering Research Center of Learning-Based Intelligent System Ministry of Education Tianjin China School of Basic Medical Sciences Fujian Medical University Fujian Fuzhou China College of Intelligence and Computing Tianjin University Tianjin China Key Laboratory of Systems Bioengineering Ministry of Education Tianjin China Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Guangdong Shenzhen China Department of Computer Science and Engineering University of South Carolina ColumbiaSC United States

ISBN: (纸本)9781665401265

Drawing support from an effective Medical Image Segmentation (MIS) is conducive to a substantial diagnostic basis for the physicians to identify the focus lesion in the patient body and give the subsequent clinical assessment of the patient status. Although various works have tried the challenging quantitative analysis problem, it is still difficult to conduct precise automatic segmentation, especially the soft tissue organs. In this decade, with the increased amount of available datasets, deep learning-based networks have achieved remarkable performance in image processing. Inspired by the state-of-the-art deep learning works, in this paper, we propose an end-to-end multi-layer network named RCGA-Net. It consists of an encoder-decoder backbone that integrates a coordinate attention mechanism based on space and channel and a global context extraction module to highlight more valuable information. To evaluate the performance of RCGA-Net, we apply it to different kinds of clinical and experimental MIS tasks to testify its generalization ability. Extensive experiments represent that our schema has taken the outperform or compatible results among the comparison methods group. Specifically, the numeric result of RCGA-Net on the pulmonary dataset has achieved a 99.12% optimum F1-score. © 2021 IEEE.

关键词： Diagnosis

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Applications of artificial intelligence for chemical analysis and monitoring of pharmaceutical and personal care products in water and wastewater: A review

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Chemosphere 2024年 368卷 143692页

作者： Kavianpour, Babak Piadeh, Farzad Gheibi, Mohammad Ardakanian, Atiyeh Behzadian, Kourosh Campos, Luiza C. School of Computing and Engineering University of West London St Mary's Rd LondonW5 5RF United Kingdom Centre for Engineering Research School of Physics Engineering and Computer Science University of Hertfordshire HatfieldAL10 9AB United Kingdom Institute for Nanomaterials Advanced Technologies and Innovation Technical University of Liberec Liberec46117 Czech Republic Centre for Urban Sustainability and Resilience Department of Civil Environmental and Geomatic Engineering University College London LondonWC1E6BT United Kingdom

Specifying and interpreting the occurrence of emerging pollutants is essential for assessing treatment processes and plants, conducting wastewater-based epidemiology, and advancing environmental toxicology research. In recent years, artificial intelligence (AI) has been increasingly applied to enhance chemical analysis and monitoring of contaminants in environmental water and wastewater. However, their specific roles targeting pharmaceuticals and personal care products (PPCPs) have not been reviewed sufficiently. This review aims to narrow the gap by highlighting, scoping, and discussing the incorporation of AI during the detection and quantification of PPCPs when utilising chemical analysis equipment and interpreting their monitoring data for the first time. In the chemical analysis of PPCPs, AI-assisted prediction of chromatographic retention times and collision cross-sections (CCS) in suspect and non-target screenings using high-resolution mass spectrometry (HRMS) enhances detection confidence, reduces analysis time, and lowers costs. AI also aids in interpreting spectroscopic analysis results. However, this approach still cannot be applied in all matrices, as it offers lower sensitivity than liquid chromatography coupled with tandem or HRMS. For the interpretation of monitoring of PPCPs, unsupervised AI methods have recently presented the capacity to survey regional or national community health and socioeconomic factors. Nevertheless, as a challenge, long-term monitoring data sources are not given in the literature, and more comparative AI studies are needed for both chemical analysis and monitoring. Finally, AI assistance anticipates more frequent applications of CCS prediction to enhance detection confidence and the use of AI methods in data processing for wastewater-based epidemiology and community health surveillance. © 2024 The Authors

关键词： Liquid chromatography

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Contextualizing Security and Privacy of Software-Defined Vehicles: State of the Art and Industry Perspectives

arXiv

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arXiv 2024年

作者： de Vincenzi, Marco Pesé, Mert D. Bodei, Chiara Matteucci, Ilaria Brooks, Richard R. Hasan, Monowar Saracino, Andrea Hamad, Mohammad Steinhorst, Sebastian School of Computing Clemson University 215 McAdams Hall ClemsonSC United States Department of Computer Science Università di Pisa Largo Bruno Pontecorvo 3 Pisa Italy Institute for Informatics and Telematics CNR CNR Via Giuseppe Moruzzi 1 Pisa Italy Department of Electrical and Computer Engineering Clemson University 313C Riggs Hall ClemsonSC United States School of EECS Washington State University 355 NE Spokane St PullmanWA United States TeCIP Institute Sant’Anna School of Advanced Studies Piazza Martiri della Libertà 33 Pisa Italy Technical University of Munich Arcisstr. 21 München Germany

The reliance on software in vehicles has led to the concept of Software-Defined Vehicles (SDVs), reshaping the automotive industry. We present an overview of security and privacy in SDVs, including a literature review and insights gathered from a questionnaire conducted with industry experts in the automotive supply chain. The findings underscore the need to address challenges in mixed criticality architecture, integrate intrusion detection systems tailored to SDV, and adopt privacy-preserving frameworks. The lessons learned emphasize the importance of a multilayered defense strategy, integrating in-vehicle and cloud-based security solutions, and the alignment with ISO/SAE 21434 and UNECE regulations. © 2024, CC BY.

关键词： Differential privacy

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BabyNet＋＋: Fetal birth weight prediction using biometry multimodal data acquired less than 24 hours before delivery

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computers in Biology and Medicine 2023年 167卷 107602-107602页

作者： Plotka, Szymon Grzeszczyk, Michal K. Brawura-Biskupski-Samaha, Robert Gutaj, Pawel Lipa, Michal Trzciński, Tomasz Išgum, Ivana Sánchez, Clara I. Sitek, Arkadiusz Sano Centre for Computational Medicine Cracow Poland Informatics Institute University of Amsterdam Amsterdam Netherlands Department of Biomedical Engineering and Physics Amsterdam University Medical Center Amsterdam Netherlands Second Department of Obstetrics and Gynecology The Medical Centre of Postgraduate Education Warsaw Poland Department of Reproduction Poznan University of Medical Sciences Poznan Poznan Poland First Department of Obstetrics and Gynecology Medical University of Warsaw Warsaw Poland Institute of Computer Science Warsaw University of Technology Warsaw Poland Department of Radiology and Nuclear Medicine Amsterdam University Medical Center location University of Amsterdam Amsterdam Netherlands Center for Advanced Medical Computing and Simulation Massachusetts General Hospital Harvard Medical School BostonMA United States

Accurate prediction of fetal weight at birth is essential for effective perinatal care, particularly in the context of antenatal management, which involves determining the timing and mode of delivery. The current standard of care involves performing a prenatal ultrasound 24 hours prior to delivery. However, this task presents challenges as it requires acquiring high-quality images, which becomes difficult during advanced pregnancy due to the lack of amniotic fluid. In this paper, we present a novel method that automatically predicts fetal birth weight by using fetal ultrasound video scans and clinical data. Our proposed method is based on a Transformer-based approach that combines a Residual Transformer Module with a Dynamic Affine Feature Map Transform. This method leverages tabular clinical data to evaluate 2D+t spatio-temporal features in fetal ultrasound video scans. Development and evaluation were carried out on a clinical set comprising 582 2D fetal ultrasound videos and clinical records of pregnancies from 194 patients performed less than 24 hours before delivery. Our results show that our method outperforms several state-of-the-art automatic methods and estimates fetal birth weight with an accuracy comparable to human experts. Hence, automatic measurements obtained by our method can reduce the risk of errors inherent in manual measurements. Observer studies suggest that our approach may be used as an aid for less experienced clinicians to predict fetal birth weight before delivery, optimizing perinatal care regardless of the available expertise. © 2023 The Author(s)

关键词： Forecasting

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Deep Reinforcement Learning Guided Graph Neural Networks for Brain Network Analysis

arXiv

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arXiv 2022年

作者： Zhao, Xusheng Wu, Jia Peng, Hao Beheshti, Amin Monaghan, Jessica J.M. McAlpine, David Hernandez-Perez, Heivet Dras, Mark Dai, Qiong Li, Yangyang Yu, Philip S. He, Lifang Institute of Information Engineering Chinese Academy of Sciences Beijing China School of Cyber Security University of Chinese Academy of Sciences Beijing China School of Computing Macquarie University Sydney Australia Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang University Beijing China National Acoustic Laboratories Sydney Australia Department of Linguistics The Australian Hearing Hub Macquarie University Sydney Australia CAEIT Beijing China Department of Computer Science University of Illinois ChicagoIL United States Computer Science & Engineering Lehigh University PA United States

Modern neuroimaging techniques enable us to construct human brains as brain networks or connectomes. Capturing brain networks' structural information and hierarchical patterns is essential for understanding brain functions and disease states. Recently, the promising network representation learning capability of graph neural networks (GNNs) has prompted related methods for brain network analysis to be proposed. Specifically, these methods apply feature aggregation and global pooling to convert brain network instances into vector representations encoding brain structure induction for downstream brain network analysis tasks. However, existing GNN-based methods often neglect that brain networks of different subjects may require various aggregation iterations and use GNN with a fixed number of layers to learn all brain networks. Therefore, how to fully release the potential of GNNs to promote brain network analysis is still non-trivial. In our work, a novel brain network representation framework, BN-GNN, is proposed to solve this difficulty, which searches for the optimal GNN architecture for each brain network. Concretely, BN-GNN employs deep reinforcement learning (DRL) to automatically predict the optimal number of feature propagations (reflected in the number of GNN layers) required for a given brain network. Furthermore, BN-GNN improves the upper bound of traditional GNNs' performance in eight brain network disease analysis tasks. © 2022, CC BY.

关键词： Reinforcement learning

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An assessment of measuring local levels of homelessness through proxy social media signals

arXiv

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arXiv 2023年

作者： Bird, Yoshi Meke Grobe, Sarah E. Arnold, Michael V. Rogers, Sean P. Fudolig, Mikaela I. Zimmerman, Julia Witte Danforth, Christopher M. Dodds, Peter Sheridan Computational Story Lab Vermont Complex Systems Center MassMutual Center of Excellence for Complex Systems and Data Science Vermont Advanced Computing Center University of Vermont BurlingtonVT05405 United States University of Vermont BurlingtonVT05405 United States Department of Mathematics and Statistics University of Vermont BurlingtonVT05405 United States Department of Computer Science University of Vermont BurlingtonVT05405 United States Santa Fe Institute 1399 Hyde Park Rd Santa FeNM87501 United States

Although nearly 600,000 people experience homelessness in the United States every year, efforts to address this public health crisis are limited by the underperformance of standard methods to estimate localized and nationwide homelessness. Recent studies suggest social media activity can function as a proxy for measures of state-level public health, detectable through straightforward applications of natural language processing. We present results of our efforts to apply this approach to estimate homelessness at the state level throughout the US during the period 2010-2019 and 2022 using a dataset of roughly 1 million geotagged tweets containing the substring "homeless." Correlations between homelessness-related tweet counts and ranked per capita homelessness volume, but not general-population densities, suggest a relationship between the likelihood of Twitter users to personally encounter or observe homelessness in their everyday lives and their likelihood to communicate about it online. An increase to the log-odds of the word "homeless" appearing in an English-language tweet, as well as an acceleration in the increase in average tweet sentiment, suggest that tweets about homelessness are also affected by trends at the nation-scale. Additionally, changes to the lexical content of tweets over time suggest that reversals to the polarity of national or state-level trends may be detectable through an increase in political or service-sector language over the semantics of charity or direct appeals. Although tweet sentiment does not correlate to changes in homelessness volume, an analysis of user account type undertaken to explain nationwide sentiment dynamics revealed changes to Twitter-use patterns by accounts authored by individuals versus entities that may provide an additional signal to confirm changes to homelessness density in a given jurisdiction. While a computational approach to social media analysis may provide a low-cost, real-time dataset rich with information

关键词： Public health

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Scalable differential privacy with certified robustness in adversarial learning 37

Scalable differential privacy with certified robustness in a...

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37th International Conference on Machine Learning, ICML 2020

作者： Phan, NhatHai Thai, My T. Hu, Han Jin, Ruoming Sun, Tong Dou, Dejing Ying Wu College of Computing New Jersey Institute of Technology NewarkNJ United States Department of Computer and Information Sciences and Engineering University of Florida GainesvilleFL United States Computer Science Department Kent State University KentOH United States Adobe Research San JoseCA United States Computer and Information Science Department University of Oregon EugeneOR United States Baidu Research Beijing China

ISBN: (纸本)9781713821120

In this paper, we aim to develop a scalable algorithm to preserve differential privacy (DP) in adversarial learning for deep neural networks (DNNs), with certified robustness to adversarial examples. By leveraging the sequential composition theory in DP, we randomize both input and latent spaces to strengthen our certified robustness bounds. To address the trade-off among model utility, privacy loss, and robustness, we design an original adversarial objective function, based on the post-processing property in DP, to tighten the sensitivity of our model. A new stochastic batch training is proposed to apply our mechanism on large DNNs and datasets, by bypassing the vanilla iterative batch-by-batch training in DP DNNs. An end-to-end theoretical analysis and evaluations show that our mechanism notably improves the robustness and scalability of DP DNNs. © 2020 37th International Conference on Machine Learning, ICML 2020. All rights reserved.

关键词： Deep neural networks

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High-Rate Four Photon Subtraction from Squeezed Vacuum: Preparing Cat State for Optical Quantum Computation

arXiv

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arXiv 2025年

作者： Endo, Mamoru Nomura, Takefumi Sonoyama, Tatsuki Takahashi, Kazuma Takasu, Sachiko Fukuda, Daiji Kashiwazaki, Takahiro Inoue, Asuka Umeki, Takeshi Nehra, Rajveer Marek, Petr Filip, Radim Takase, Kan Asavanant, Warit Furusawa, Akira Department of Applied Physics School of Engineering The University of Tokyo 7-3-1 Hongo Bunkyo Tokyo113-8656 Japan Optical Quantum Computing Research Team RIKEN Center for Quantum Computing 2-1 Hirosawa Saitama Wako351-0198 Japan National Institute of Advanced Industrial Science and Technology 1-1-1 Umezono Ibaraki Tsukuba305-8563 Japan AIST-UTokyo Advanced Operando-Measurement Technology Open Innovation Laboratory 1-1-1 Umezono Ibaraki Tsukuba305-8563 Japan NTT Device Technology Labs NTT Corporation 3-1 Morinosato Wakamiya Kanagawa Atsugi243-0198 Japan Department of Electrical and Computer Engineering University of Massachusetts Amherst 100 Natural Resources Rd AmherstMA01003 United States Department of Physics University of Massachusetts Amherst 100 Natural Resources Rd AmherstMA01003 United States College of Information and Computer Science University of Massachusetts Amherst 100 Natural Resources Rd AmherstMA01003 United States Department of Optics Palacky University 17. listopadu 1192/12 Olomouc77146 Czech Republic

Generating logical qubits, essential for error detection and correction in quantum computation, remains a critical challenge in continuous-variable (CV) optical quantum information processing. The Gottesman-Kitaev-Preskill (GKP) code is a leading candidate for logical qubits, and its generation requires large-amplitude coherent state superpositions—Schrödinger cat states. However, experimentally producing these resource states has been hindered in the optical domain by technical challenges. The photon subtraction method, a standard approach for generating cat states using a squeezed vacuum and a photon number-resolving detector, has proven difficult to scale to multi-photon operations. While the amplitude of the generated cat states increases with the number of subtracted photons, limitations in the generation rate have restricted the maximum photon subtraction to n = 3 for over a decade. In this work, we demonstrate high-rate photon subtraction of up to four photons from a squeezed vacuum with picosecond wavepackets generated by a broadband optical parametric amplifier. Using a Ti-Au superconducting-transition-edge sensor, we achieve high-speed, high-resolution photon number discrimination. The resulting states exhibit Wigner function negativity without loss correction, and their quantum coherence is verified through off-diagonal density matrix elements in CV representation. These results overcome long-standing limitations in multi-photon operations, providing a critical foundation for generating quantum resources essential for fault-tolerant quantum computing and advancing ultrafast optical quantum processors. Copyright © 2025, The Authors. All rights reserved.

关键词： Parametric amplifiers

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Global-and-Local Collaborative Learning for Co-Salient Object Detection

arXiv

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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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