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arXiv 2022年

作者： Zhang, Chaohe Chu, Xu Ma, Liantao Zhu, Yinghao Wang, Yasha Wang, Jiangtao Zhao, Junfeng Key Lab of High Confidence Software Technologies Ministry of Education School of Computer Science Peking University Beijing China Department of Computer Science and Technology Tsinghua University Key Lab of High Confidence Software Technologies Ministry of Education Beijing China Key Lab of High Confidence Software Technologies Ministry of Education National Engineering Research Center of Software Engineering Peking University Beijing China Center for Intelligent Healthcare Coventry University Coventry United Kingdom

Multimodal electronic health record (EHR) data are widely used in clinical applications. Conventional methods usually assume that each sample (patient) is associated with the unified observed modalities, and all modalities are available for each sample. However, missing modality caused by various clinical and social reasons is a common issue in real-world clinical scenarios. Existing methods mostly rely on solving a generative model that learns a mapping from the latent space to the original input space, which is an unstable ill-posed inverse problem. To relieve the underdetermined system, we propose a model solving a direct problem, dubbed learning with Missing Modalities in Multimodal healthcare data (M3Care). M3Care is an end-to-end model compensating the missing information of the patients with missing modalities to perform clinical analysis. Instead of generating raw missing data, M3Care imputes the task-related information of the missing modalities in the latent space by the auxiliary information from each patient's similar neighbors, measured by a task-guided modality-adaptive similarity metric, and thence conducts the clinical tasks. The task-guided modality-adaptive similarity metric utilizes the uncensored modalities of the patient and the other patients who also have the same uncensored modalities to find similar patients. Experiments on real-world datasets show that M3Care outperforms the state-of-the-art baselines. Moreover, the findings discovered by M3Care are consistent with experts and medical knowledge, demonstrating the capability and the potential of providing useful insights and explanations. Copyright © 2022, The Authors. All rights reserved.

关键词： Inverse problems

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IMPROVED ANALYSIS OF SPARSE LINEAR REGRESSION IN LOCAL DIFFERENTIAL PRIVACY MODEL

arXiv

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arXiv 2023年

作者： Zhu, Liyang Ding, Meng Aggarwal, Vaneet Xu, Jinhui Wang, Di Saudi Arabia Department of Computer Science and Engineering The State University of New York Buffalo United States School of IE ECE Purdue University United States Computational Bioscience Research Center Japan SDAIA-KAUST Center of Excellence in Data Science and Artificial Intelligence Saudi Arabia

In this paper, we revisit the problem of sparse linear regression in the local differential privacy (LDP) model. Existing research in the non-interactive and sequentially local models has focused on obtaining the lower bounds for the case where the underlying parameter is 1-sparse, and extending such bounds to the more general k-sparse case has proven to be challenging. Moreover, it is unclear whether efficient non-interactive LDP (NLDP) algorithms exist. To address these issues, we first consider the problem in the ∊ non-interactive LDP model and provide a lower bound of (formula presented) on the 2-norm estimation error for sub-Gaussian data, where n is the sample size and d is the dimension of the space. We propose an innovative NLDP algorithm, the very first of its kind for the problem. As a remarkable outcome, this algorithm also yields a novel and_ highly efficient estimator as a valuable by-product. Our algorithm achieves an upper bound of (formula presented) for the estimation error when the data is sub-Gaussian, which can be further improved by a factor of O(√d) if the server has additional public but_ unlabeled data. For the sequentially interactive LDP model, we show a similar lower bound of (formula presented). As for the upper bound, we rectify a previous method and show that it is possible to achieve a bound of (formula presented). Our findings reveal fundamental differences between the non-private case, central DP model, and local DP model in the sparse linear regression problem. © 2023, CC BY.

关键词： Linear regression

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E3-UAV: An Edge-based Energy-Efficient Object Detection System for Unmanned Aerial Vehicles

arXiv

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arXiv 2023年

作者： Suo, Jiashun Zhang, Xingzhou Shi, Weisong Zhou, Wei Engineering Research Center of Cyberspace The School of Software Yunnan University Kunming650091 China Research Center of Distributed Systems Institute of Computing Technology Chinese Academy of Sciences Beijing100190 China Department of Computer and Information Sciences University of Delaware NewarkDE19716 United States

Motivated by the advances in deep learning techniques, the application of Unmanned Aerial Vehicle (UAV)-based object detection has proliferated across a range of fields, including vehicle counting, fire detection, and city monitoring. While most existing research studies only a subset of the challenges inherent to UAV-based object detection, there are few studies that balance various aspects to design a practical system for energy consumption reduction. In response, we present the E3-UAV, an edge-based energyefficient object detection system for UAVs. The system is designed to dynamically support various UAV devices, edge devices, and detection algorithms, with the aim of minimizing energy consumption by deciding the most energy-efficient flight parameters (including flight altitude, flight speed, detection algorithm, and sampling rate) required to fulfill the detection requirements of the task. We first present an effective evaluation metric for actual tasks and construct a transparent energy consumption model based on hundreds of actual flight data to formalize the relationship between energy consumption and flight parameters. Then we present a lightweight energy-efficient priority decision algorithm based on a large quantity of actual flight data to assist the system in deciding flight parameters. Finally, we evaluate the performance of the system, and our experimental results demonstrate that it can significantly decrease energy consumption in real-world scenarios. Additionally, we provide four insights that can assist researchers and engineers in their efforts to study UAV-based object detection further. Copyright © 2023, The Authors. All rights reserved.

关键词： Energy utilization

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Multi-version tensor completion for time-delayed spatio-temporal data

arXiv

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arXiv 2021年

作者： Qian, Cheng Kargas, Nikos Xiao, Cao Glass, Lucas Sidiropoulos, Nicholas Sun, Jimeng Analytics Center of Excellence IQVIA Department of Electrical and Computer Engineering University of Minnesota Twin Cities Department of Electrical and Computer Engineering University of Virginia Department of Computer Science University of Illinois Urbana-Champaign

Real-world spatio-temporal data is often incomplete or inaccurate due to various data loading delays. For example, a location-disease-time tensor of case counts can have multiple delayed updates of recent temporal slices for some locations or diseases. Recovering such missing or noisy (under-reported) elements of the input tensor can be viewed as a generalized tensor completion problem. Existing tensor completion methods usually assume that i) missing elements are randomly distributed and ii) noise for each tensor element is i.i.d. zero-mean. Both assumptions can be violated for spatio-temporal tensor data. We often observe multiple versions of the input tensor with different under-reporting noise levels. The amount of noise can be time- or location-dependent as more updates are progressively introduced to the tensor. We model such dynamic data as a multi-version tensor with an extra tensor mode capturing the data updates. We propose a low-rank tensor model to predict the updates over time. We demonstrate that our method can accurately predict the ground-truth values of many real-world tensors. We obtain up to 27.2% lower root mean-squared-error compared to the best baseline method. Finally, we extend our method to track the tensor data over time, leading to significant computational savings. © 2021, CC BY.

关键词： Tensors

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NER- RoBERTa: Fine-Tuning RoBERTa for Named Entity Recognition (NER) within low-resource languages

arXiv

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arXiv 2024年

作者： Abdullah, Abdulhady Abas Abdulla, Srwa Hasan Toufiq, Dalia Mohammad Maghdid, Halgurd S. Rashid, Tarik A. Farho, Pakshan F. Sabr, Shadan Sh. Taher, Akar H. Hamad, Darya S. Veisi, Hadi Asaad, Aras T. Artificial Intelligence and Innovation Centre University of Kurdistan Hewler Erbil Iraq Computer Science and Engineering Department University of Kurdistan Hewler KR Erbil Iraq Department of Horticulture College of Agricultural Engineering Sciences University of Sulaimani- Kurdistan Region Iraq Branch of Basic Medical Science College of medicine University of Sulaimani- Kurdistan Region Iraq Department of Engineering Research Center Deanship of R&D Centers Koya University Kurdistan Region Iraq Department of Kurdish Language Faculty of Education Koya University Kurdistan Region Iraq Department of Kurdish Faculty of Education Koya University Kurdistan Region Iraq Department of Software Engineering Faculty of Engineering Koya University Kurdistan Region Iraq School of Intelligent Systems College of Interdisciplinary Science and Technologies University of Tehran Iran

Nowadays natural Language Processing (NLP) is an important tool for most people’s daily life routines, ranging from understating speech, translation, name entity recognition (ENR), and text categorizing, to the generative text models such as ChatGPT. Due to existing big data and consequently big corpora for the most usable languages including English, Spanish, Turkish, Persia, and many more, these applications have been utilized accurately. However, the Kurdish language still needs more corpora and big datasets to be included in the NLP applications. This is because the Kurdish language has a rich linguistic structure, varied dialects, and a poor dataset, which poses unique challenges for Kurdish NLP (KNLP) application development. While several types of research have been conducted in KNLP for various applications, the Kurdish ENR (KNER) remained a challenge for most of the KNLP applications including text analysis and text classification. In this work, we address this limitation by proposing a methodology for fine-tuning the pre-trained RoBERTa model for KNER1. To this end, first, a Kurdish corpus is created, followed by designing a modified model architecture, and the training procedures are implemented. To evaluate the trained model, a set of experiments is conducted to show the performance of the KNER model using different tokenization methods and trained models. The experimental results showed that fine-tuned RoBERTa with sentence-piece tokenization method substantially improves KNER performance by 12.8% (F1-score improvement) in comparison to traditional models and consequently establishes a new benchmark for KNLP. © 2024, CC BY.

关键词： Natural language processing systems

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Low-energy spin excitations in field-induced phases of the spin-ladder antiferromagnet BiCu2PO6

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Physical Review B 2025年第2期111卷 024423-024423页

作者： Patrick Pilch Kirill Amelin Gary Schmiedinghoff Anneke Reinold Changqing Zhu Kirill Yu. Povarov Sergei Zvyagin Hans Engelkamp Yin-Ping Lan Department of Physics TU Dortmund University 44227 Dortmund Germany National Institute of Chemical Physics and Biophysics 12618 Tallinn Estonia Institute of Software Technology German Aerospace Center (DLR) 51147 Cologne Germany Dresden High Magnetic Field Laboratory (HLD-EMFL) and Würzburg-Dresden Cluster of Excellence ct.qmat Helmholtz-Zentrum Dresden-Rossendorf (HZDR) 01328 Dresden Germany High Field Magnet Laboratory (HFML-EMFL) Radboud University Toernooiveld 7 6525 ED Nijmegen The Netherlands Department of Materials & Mineral Resources Engineering Institute of Mineral Resources Engineering National Taipei University of Technology Taipei 10608 Taiwan

We report on terahertz spectroscopic measurements of quantum spin dynamics on single crystals of a spin-1/2 frustrated spin-ladder antiferromagnet BiCu2PO6 as a function of temperature, polarization, and applied external magnetic fields. Spin triplon excitations are observed at zero field and split in applied magnetic fields. For magnetic fields applied along the crystallographic a axis, a quantum phase transition at Bc1=21.4 T is featured by a low-energy excitation mode emerging above Bc1, which indicates a gap reopening. For fields along the b axis and the c axis, different field dependencies are observed for the spin triplon excitations, whereas no low-lying modes could be resolved at field-induced phase transitions. We perform a theoretical analysis of the magnetic field dependence of the spin triplon modes by using continuous unitary transformations to determine an effective low-energy Hamiltonian. Through an exhaustive parameter search we find numerically optimized parameters to very well describe the experimentally observed modes, which corroborate the importance of significant magnetic anisotropy in the system.

关键词： Single crystals

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Nonlinear Discrete-time System Identification without Persistence of Excitation: Finite-time Concurrent Learning Methods

arXiv

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arXiv 2021年

作者： Tatari, Farzaneh Panayiotou, Christos Polycarpou, Marios The KIOS Research Innovation Center of Excellence The Department of Electrical and Computer Engineering University of Cyprus Nicosia Cyprus

This paper deals with the problem of finite-time learning for unknown discrete-time nonlinear systems' dynamics, without the requirement of the persistence of excitation. Two finite-time concurrent learning methods are presented to approximate the uncertainties of the discrete-time nonlinear systems in an online fashion by employing current data along with recorded experienced data satisfying an easy-to-check rank condition on the richness of the recorded data which is less restrictive in comparison with persistence of excitation condition. For the proposed finite-time concurrent learning methods, rigorous proofs guarantee the finite-time convergence of the estimated parameters to their optimal values based on the discrete-time Lyapunov analysis. Compared with the existing work in the literature, simulation results illustrate that the proposed methods can timely and precisely approximate the uncertainties. Copyright © 2021, The Authors. All rights reserved.

关键词： Digital control systems

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Atomic Defect-Aware Physical Design of Silicon Dangling Bond Logic on the H-Si(100)-2×1 Surface

arXiv

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arXiv 2023年

作者： Walter, Marcel Croshaw, Jeremiah Ng, Samuel Sze Hang Walus, Konrad Wolkow, Robert Wille, Robert Chair for Design Automation Technical University of Munich Germany Department of Physics University of Alberta Edmonton Canada Quantum Silicon Inc. Edmonton Canada Department of Electrical and Computer Engineering University of British Columbia Vancouver Canada Software Competence Center Hagenberg GmbH Austria

Although fabrication capabilities of Silicon Dangling Bonds have rapidly advanced from manual labor-driven laboratory work to automated manufacturing in just recent years, sub-nanometer substrate defects still pose a hindrance to production due to the need for atomic precision. In essence, unpassivated or missing surface atoms, contaminants, and structural deformations disturb the fabricated logic or prevent its realization altogether. Moreover, design automation techniques in this domain have not yet adopted any defect-aware behavior to circumvent the present obstacles. In this paper, we derive a surface defect model for design automation from experimentally verified defect types that we apply to identify sensitivities in an established gate library in an effort to generate more robust designs. Furthermore, we present an automatic placement and routing algorithm that considers scanning tunneling microscope data obtained from physical experiments to lay out dot-accurate circuitry that is resilient against the presence of atomic surface defects. This culminates in a holistic evaluation on surface data of varying defect rates that enables us to quantify the severity of such defects. We project that fabrication capabilities must achieve defect rates of around 0.1 %, if charged defects can be completely eliminated, or Copyright © 2023, The Authors. All rights reserved.

关键词： Substrates

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Bilingual Lexicon Induction for Low-Resource Languages using Graph Matching via Optimal Transport

arXiv

引用

arXiv 2022年

作者： Marchisio, Kelly Saad-Eldin, Ali Duh, Kevin Priebe, Carey Koehn, Philipp Department of Computer Science United States Department of Applied Mathematics and Statistics United States Department of Biomedical Engineering United States Human Language Technology Center of Excellence Johns Hopkins University United States

Bilingual lexicons form a critical component of various natural language processing applications, including unsupervised and semisupervised machine translation and crosslingual information retrieval. We improve bilingual lexicon induction performance across 40 language pairs with a graph-matching method based on optimal transport. The method is especially strong with low amounts of supervision. Copyright © 2022, The Authors. All rights reserved.

关键词： Natural language processing systems

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Unlocking High Performance, Ultra-Low Power Van der Waals Transistors: Towards Back-End-of-Line In-Sensor Machine Vision Applications

arXiv

引用

arXiv 2023年

作者： Alolaiyan, Olaiyan Albwardi, Shahad Alsaggaf, Sarah Tabbakh, Thamer DelRio, Frank W. Amer, Moh R. Center of Excellence for Green Nanotechnologies Microelectronics and Semiconductor Institute King Abdulaziz City for Science and Technology Riyadh11442 Saudi Arabia Sandia National Laboratories Material Physical and Chemical Sciences Center AlbuquerqueNM87123 United States Department of Electrical and Computer Engineering University of Southern California Los AngelesCA90089 United States Department of Electrical and Computer Engineering University of California Los Angeles Los AngelesCA90095 United States

Recent reports on machine learning (ML) and machine vision (MV) devices have demonstrated the potentials of 2D materials and devices. Yet, scalable 2D devices are being challenged by contact resistance and Fermi Level Pinning (FLP), power consumption, and low-cost CMOS compatible lithography processes. To enable CMOS+2D, it is essential to find a proper lithography strategy that can fulfill these requirements. Here, we explore modified van der Waals (vdW) deposition lithography and demonstrate a relatively new class of van-der-WaalsField-Effect-Transistors (vdW-FETs) based on 2D materials. This lithography strategy enables us to unlock high performance devices evident by high current on-off ratio (Ion/Ioff), high turn-on current density (Ion), and weak Fermi Level Pinning (FLP). We utilize this approach to demonstrate a gate-tunable near-ideal diode using MoS2/WSe2 heterojunction with an ideality factor of ~1.65 and current rectification of 102. We finally demonstrate a highly sensitive, scalable, and ultra-low power phototransistor using MoS2/WSe2 vdW-FET for Back-End-of-Line (BEOL) integration. Our phototransistor exhibits the highest gate-tunable photoresponsivity achieved to date for white light detection with ultra-low power dissipation, enabling ultra-sensitive, ultra-fast, and efficient optoelectronic applications such as in-sensor neuromorphic machine vision. Our approach shows the great potential of modified vdW deposition lithography for back-end-of-line CMOS+2D applications. © 2023, CC BY.

关键词： Van der Waals forces

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