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arXiv 2023年

作者： Lu, Hsuan-Hao Alshowkan, Muneer Myilswamy, Karthik V. Weiner, Andrew M. Lukens, Joseph M. Peters, Nicholas A. Quantum Information Science Section Computational Sciences and Engineering Division Oak Ridge National Laboratory Oak RidgeTN37831 United States School of Electrical and Computer Engineering Purdue Quantum Science and Engineering Institute Purdue University West LafayetteIN47907 United States Quantum Information Science Group Oak Ridge National Laboratory Oak RidgeTN37831 United States Research Technology Office and Quantum Collaborative Arizona State University TempeAZ85287 United States

We generate ultrabroadband photon pairs entangled in both polarization and frequency bins through an all-waveguided Sagnac source covering the entire optical C- and L-bands (1530–1625 nm). We perform comprehensive characterization of high-fidelity states in multiple dense wavelength-division multiplexed channels, achieving full tomography of effective four-qubit systems. Additionally, leveraging the inherent high dimensionality of frequency encoding and our electro-optic measurement approach, we demonstrate the scalability of our system to higher dimensions, reconstructing states in a 36-dimensional Hilbert space consisting of two polarization qubits and two frequency-bin qutrits. Our findings hold potential significance for quantum networking, particularly dense coding and entanglement distillation in wavelength-multiplexed quantum networks. © 2023, CC BY.

关键词： Photons

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External human-machine interface on delivery robots: Expression of navigation intent of the robot

arXiv

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arXiv 2021年

作者： Kannan, Shyam Sundar Lee, Ahreum Min, Byung-Cheol SMART Lab Department of Computer and Information Technology Purdue University West LafayetteIN47907 United States The University of Eastern Finland Joensuu Finland

External Human-Machine Interfaces (eHMI) are widely used on robots and autonomous vehicles to convey the machine's intent to humans. Delivery robots are getting common, and they share the sidewalk along with the pedestrians. Current research has explored the design of eHMI and its effectiveness for social robots and autonomous vehicles, but the use of eHMIs on delivery robots still remains unexplored. There is a knowledge gap on the effective use of eHMIs on delivery robots for indicating the robot's navigational intent to the pedestrians. An online survey with 152 participants was conducted to investigate the comprehensibility of the display and light-based eHMIs that convey the delivery robot's navigational intent under common navigation scenarios. Results show that display is preferred over lights in conveying the intent. The preferred type of content to be displayed varies according to the scenarios. Additionally, light is preferred as an auxiliary eHMI to present redundant information. The findings of this study can contribute to the development of future designs of eHMI on delivery robots. © 2021, CC BY.

关键词： Autonomous vehicles

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Vision Uformer: Long-Range Monocular Absolute Depth Estimation

SSRN

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SSRN 2022年

作者： Polasek, Tomas Cadik, Martin Keller, Yosi Benes, Bedrich Faculty of Information Technology Brno University of Technology Bozetechova 2/1 Brno612 00 Czech Republic Faculty of Information Technology Brno University of Technology Bozetechova 1/2 Brno612 00 Czech Republic Faculty of Engineering Bar-Ilan University 5290002 Ramat Gan1102-1105 Israel Computer Graphics Purdue University 305 N University St. West LafayetteIN47907-2107 United States

We introduce Vision UFormer (ViUT), a novel deep neural long-range monocular depth estimator. The input is an RGB image, and the output is an image that stores the absolute distance of the object in the scene as its per-pixel values. ViUT consists of a Transformer encoder and a ResNet decoder combined with UNet style of skip connections. It is trained on 1M images across ten datasets in a staged regime that starts with easier-to-predict data such as indoor photographs and continues to more complex long-range outdoor scenes. We show that ViUT provides comparable results for normalized relative distances and short-range classical datasets such as NYUv2 and KITTI. We further show that it successfully estimates absolute long-range depth in meters. We validate ViUT on a wide variety of long-range scenes showing its high estimation capabilities with a relative improvement of up to 23%. Absolute depth estimation finds application in many areas, and we show its usability in image composition, range annotation, defocus, and scene reconstruction. Our models are available at ***/viut. © 2022, The Authors. All rights reserved.

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Vox-UDA: Voxel-wise Unsupervised Domain Adaptation for Cryo-Electron Subtomogram Segmentation with Denoised Pseudo-Labeling 39

Vox-UDA: Voxel-wise Unsupervised Domain Adaptation for Cryo-...

引用

39th Annual AAAI Conference on Artificial Intelligence, AAAI 2025

作者： Li, Haoran Li, Xingjian Shi, Jiahua Chen, Huaming Du, Bo Kihara, Daisuke Barthelemy, Johan Shen, Jun Xu, Min School of Computing and Information Technology University of Wollongong Australia ARC Training Centre for Innovative Composites for the Future of Sustainable Mining University of Wollongong Australia Ray and Stephanie Lane Computational Biology Department Carnegie Mellon University United States Centre for Nutrition and Food Sciences University of Queensland Australia School of Electrical and Computer Engineering University of Sydney Australia Department of Business Strategy and Innovation Griffith University Australia Department of Biological Sciences Purdue University United States NVIDIA United States

ISBN: (纸本)157735897X

Cryo-Electron Tomography (cryo-ET) is a 3D imaging technology that facilitates the study of macromolecular structures at near-atomic resolution. Recent volumetric segmentation approaches on cryo-ET images have drawn widespread interest in the biological sector. However, existing methods heavily rely on manually labeled data, which requires highly professional skills, thereby hindering the adoption of fully-supervised approaches for cryo-ET images. Some unsupervised domain adaptation (UDA) approaches have been designed to enhance the segmentation network performance using unlabeled data. However, applying these methods directly to cryo-ET image segmentation tasks remains challenging due to two main issues: 1) the source dataset, usually obtained through simulation, contains a fixed level of noise, while the target dataset, directly collected from raw-data from the real-world scenario, have unpredictable noise levels. 2) the source data used for training typically consists of known macromoleculars. In contrast, the target domain data are often unknown, causing the model to be biased towards those known macromolecules, leading to a domain shift problem. To address such challenges, in this work, we introduce a voxel-wise unsupervised domain adaptation approach, termed Vox-UDA, specifically for cryo-ET subtomogram segmentation. Vox-UDA incorporates a noise generation module to simulate target-like noises in the source dataset for cross-noise level adaptation. Additionally, we propose a denoised pseudo-labeling strategy based on the improved Bilateral Filter to alleviate the domain shift problem. More importantly, we construct the first UDA cryo-ET subtomogram segmentation benchmark on three experimental datasets. Extensive experimental results on multiple benchmarks and newly curated real-world datasets demonstrate the superiority of our proposed approach compared to state-of-the-art UDA methods. © 2025, Association for the Advancement of Artificial Intelligence (***

关键词： Image segmentation

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Two-Stage Inverse Design of Superchiral Dielectric Metasurfaces

Two-Stage Inverse Design of Superchiral Dielectric Metasurfa...

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International Review of Progress in Applied Computational Electromagnetics (ACES)

作者： Omer Yesilyurt Vahagn Mkhitaryan Jer-Shing Huang Ilya Shadrivov Alexander V. Kildishev Elmore Family School of Electrical and Computer Engineering Birck Nanotechnology Center and Purdue Quantum Science and Engineering Institute Purdue University West Lafayette IN USA Department of Energy (DOE) The Quantum Science Center (QSC) a National Quantum Information Science Research Center of the U.S. Oak Ridge TN USA Leibniz Institute of Photonic Technology Jena Germany ARC Centre of Excellence for Transformative Meta-Optical Systems (TMOS) Research School of Physics the Australian National University Canberra Australia

ISBN: (数字)9781733509671

ISBN: (纸本)9798350362978

Chirality is a fundamental property of asymmetry and is crucial in distinguishing molecular enantiomers, with profound implications across chemistry, biology, and materials science. Circular dichroism (CD) spectroscopy, which measures the differential absorption of left-handed (LCP) and right-handed (RCP) circularly polarized light, serves as a conventional method for identifying and separating chiral enantiomers. Nevertheless, due to the inherently weak chiral-optical interactions, the CD signals of numerous chiral molecules are minimal, typically spanning 10– 3 to 10– 6 within the ultraviolet spectrum. Superchiral metasurfaces, producing fields with enhanced optical chirality that surpasses the chirality of circularly polarized light, emerge as a solution to this challenge. Such metasurfaces, with enhanced interaction between light and chiral molecules, offer a significant increase in CD signal, underscoring the necessity for their advanced development for more accurate and sensitive analysis. This study presents a novel, two-stage inverse design methodology for superchiral metasurfaces with iterative optimization of the topology of a dielectric metasurface unit cell to enhance optical chiral density in the near field. In the first stage, a neural network-based optimizer works in tandem with a rigorous coupled wave analysis (RCWA) solver. In the second stage, adjoint gradients of the optical chirality density are derived from the first principles and calculated with a Finite Difference Time Domain (FDTD) scheme. Our results offer a new platform for high-sensitivity CD spectroscopy applications, allowing for fast, constrained optimization of the unit cells.

关键词： Spectroscopy Optical polarization Biomedical optical imaging Metasurfaces Optical network units Optical coupling Optical sensors

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Creating Single Color Centers in Nanodiamonds with Ion Implantation

Creating Single Color Centers in Nanodiamonds with Ion Impla...

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Conference on Lasers and Electro-Optics (CLEO)

作者： Xiaohui Xu Zachariah O. Martin Michael Titze Yongqiang Wang Demid Sychev Jacob Henshaw Alexei S. Lagutchev Han Htoon Ed Bielejec Simeon Bogdanov Vladimir M. Shalaev Alexandra Boltasseva School of Materials Engineering Birck Nanotechnology Center Purdue University West Lafayette IN USA Elmore Family School of Electrical and Computer Engineering Birck Nanotechnology Center Purdue University West Lafayette IN USA Sandia National Laboratories Albuquerque New Mexico USA Los Alamos National Laboratory Los Alamos New Mexico USA Department of Electrical and Computer Engineering Nick Holonyak Jr. Micro and Nanotechnology Laboratory Illinois Quantum Information Science and Technology Center University of Illinois at Urbana-Champaign Urbana Illinois USA

We report the first demonstration of single silicon vacancy center creation in 20 nm nanodiamonds using silicon ion implantation combined with thermal annealing. Room-temperature single photon emission with linewidth ... 详细信息

ISBN: (纸本)9781957171258

关键词： Defect center materials Emission Ion beams Ions Near infrared radiation Photons

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PENet: Object Detection Using Points Estimation in High Definition Aerial Images

PENet: Object Detection Using Points Estimation in High Defi...

引用

International Conference on Machine Learning and Applications (ICMLA)

作者： Ziyang Tang Xiang Liu Baijian Yang Computer and Information Technology Purdue University West Lafayette IN

ISBN: (数字)9781728184708

ISBN: (纸本)9781728184715

Aerial imagery has been increasingly adopted in mission-critical tasks, such as traffic surveillance, smart cities, and disaster assistance. However, identifying objects from aerial images faces the following challenges: 1) objects of interests are often too small and too dense relative to the images; 2) objects of interests are often in different relative sizes; and 3) the number of objects in each category is imbalanced. A novel network structure, Points Estimated Network (PENet), is proposed in this work to answer these challenges. PENet uses a Mask Resampling Module (MRM) to augment the imbalanced datasets, a coarse anchor-free detector (CPEN) to effectively predict the center points of the small object clusters, and a fine anchor-free detector FPEN to locate the precise positions of the small objects. An adaptive merge algorithm Non-maximum Merge (NMM) is implemented in CPEN to address the issue of detecting dense small objects, and a hierarchical loss is defined in FPEN to further improve the classification accuracy. Our extensive experiments on aerial datasets visDrone [1] and UAVDT [2] showed that PENet achieved higher precision results than existing state-of-the-art approaches. Our best model achieved 8.7% improvement on visDrone and 20.3% on UAVDT.

关键词： Smart cities Surveillance Detectors Traffic control Prediction algorithms Object recognition Task analysis

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Content-Aware Detection of Temporal Metadata Manipulation

arXiv

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arXiv 2021年

作者： Padilha, Rafael Salem, Tawfiq Workman, Scott Andaló, Fernanda A. Rocha, Anderson Jacobs, Nathan The Institute of Computing University of Campinas Brazil The Department of Computer and Information Technology Purdue University United States DZYNE Technologies United States The Department of Computer Science University of Kentucky United States

Most pictures shared online are accompanied by temporal metadata (i.e., the day and time they were taken), which makes it possible to associate an image content with real-world events. Maliciously manipulating this metadata can convey a distorted version of reality. In this work, we present the emerging problem of detecting timestamp manipulation. We propose an end-to-end approach to verify whether the purported time of capture of an outdoor image is consistent with its content and geographic location. We consider manipulations done in the hour and/or month of capture of a photograph. The central idea is the use of supervised consistency verification, in which we predict the probability that the image content, capture time, and geographical location are consistent. We also include a pair of auxiliary tasks, which can be used to explain the network decision. Our approach improves upon previous work on a large benchmark dataset, increasing the classification accuracy from 59.0% to 81.1%. We perform an ablation study that highlights the importance of various components of the method, showing what types of tampering are detectable using our approach. Finally, we demonstrate how the proposed method can be employed to estimate a possible time-of-capture in scenarios in which the timestamp is missing from the metadata. Copyright © 2021, The Authors. All rights reserved.

关键词： Metadata

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Analysis of iOS SQLite Schema Evolution for Updating Forensic Data Extraction Tools 8

Analysis of iOS SQLite Schema Evolution for Updating Forensi...

引用

8th International Symposium on Digital Forensics and Security, ISDFS 2020

作者： Shimmi, Samiha S. Dorai, Gokila Karabiyik, Umit Aggarwal, Sudhir Ahsanullah University of Science and Technology Department of Computer Science and Engineering Dhaka Bangladesh Augusta University School of Computer and Cyber Sciences AugustaGA United States Purdue University Department of Computer and Information Technology West LafayetteIN United States Florida State University Department of Computer Science TallahasseFL United States

ISBN: (纸本)9781728169392

Files in the backup of iOS devices can be a potential source of evidentiary data. Particularly, the iOS backup (obtained through a logical acquisition technique) is widely used by many forensic tools to sift through the data. A significant challenge faced by several forensic tool developers is the changes in the data organization of the iOS backup. This is due to the fact that the iOS operating system is frequently updated by Apple Inc. Many iOS application developers release periodical updates to iOS mobile applications. Both these reasons can cause significant changes in the way user data gets stored in the iOS backup files. Moreover, approximately once every couple years, there could be a major iOS release which can cause the reorganization of files and folders in the iOS backup. Directories in the iOS backup contain SQLite databases, plist files, XML files, text files, and media files. Android/iOS devices generally use SQLite databases since it is a lightweight database. Our focus in this paper is to analyze the SQLite schema evolution specific to iOS and assist forensic tool developers in keeping their tools compatible with the latest iOS version. Our recommendations for updating the forensic data extraction tools is based on the observation of schema changes found in successive iOS versions. © 2020 IEEE.

关键词： Database systems

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Speedster: A TEE-assisted state channel system

arXiv

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arXiv 2021年

作者： Liao, Jinghui Zhang, Fengwei Sun, Wenhai Shi, Weisong Department of Computer Science Wayne State University United States Department of Computer Science and Engineering Southern University of Science and Technology China Research Institute of Trustworthy Autonomous Systems Southern University of Science and Technology China Department of Computer and Information Technology Purdue University United States

State channel network is the most popular layer-2 solution to the issues of scalability, high transaction fees, and low transaction throughput of public Blockchain networks. However, the existing works have limitations that curb the wide adoption of the technology, such as the expensive creation and closure of channels, strict synchronization between the main chain and off-chain channels, frozen deposits, and inability to execute multi-party smart contracts. In this work, we present Speedster, an account-based state-channel system that aims to address the above issues. To this end, Speedster leverages the latest development of secure hardware to create dispute-free certified channels that can be operated efficiently off the Blockchain. Speedster is fully decentralized and provides better privacy protection. It supports fast native multiparty contract execution, which is missing in prior TEE-enabled channel networks. Compared to the Lightning Network, Speedster improves the throughput by about 10, 000× and generates 97% less on-chain data with a comparable network scale. Copyright © 2021, The Authors. All rights reserved.

关键词： Smart contract

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