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arXiv 2024年

作者： Sundaresan, Avirath Parham, Jason Crall, Jonathan Warungu, Rosemary Muthami, Timothy Miliko, Jackson Mwangi, Margaret Holmberg, Jason Berger-Wolf, Tanya Rubenstein, Daniel Stewart, Charles Beery, Sara Computing and Mathematical Sciences California Institute of Technology PasadenaCA United States Kitware Clifton ParkNY United States Rensselaer Polytechnic Institute TroyNY United States Wild Me Conservation X Labs PortlandOR United States Laikipia Zebra Project Mpala Research Centre Laikipia Kenya Computer Science and Engineering Ohio State University ColumbusOH United States Ecology and Evolutionary Biology Princeton University PrincetonNJ United States Electrical Engineering and Computer Science Massachusetts Institute of Technology CambridgeMA United States

The Grevy's zebra, an endangered species native to Kenya and southern Ethiopia, has been the target of sustained conservation efforts in recent years. Accurately monitoring Grevy's zebra populations is essential for ecologists to evaluate ongoing conservation initiatives. Recently, in both 2016 and 2018, a full census of the Grevy's zebra population has been enabled by the Great Grevy's Rally (GGR), a citizen science event that combines teams of volunteers to capture data with computer vision algorithms that help experts estimate the number of individuals in the population. A complementary, scalable, cost-effective, and long-term Grevy's population monitoring approach involves deploying a network of camera traps, which we have done at the Mpala Research Centre in Laikipia County, Kenya. In both scenarios, a substantial majority of the images of zebras are not usable for individual identification, due to "in-the-wild" imaging conditions - occlusions from vegetation or other animals, oblique views, low image quality, and animals that appear in the far background and are thus too small to identify. Camera trap images, without an intelligent human photographer to select the framing and focus on the animals of interest, are of even poorer quality, with high rates of occlusion and high spatio-temporal similarity within image bursts. We employ an image filtering pipeline incorporating animal detection, species identification, viewpoint estimation, quality evaluation, and temporal subsampling to compensate for these factors and obtain individual crops from camera trap and GGR images of suitable quality for re-ID. We then employ the Local Clusterings and their Alternatives (LCA) algorithm, a hybrid computer vision & graph clustering method for animal re-ID, on the resulting high-quality crops. Our method processed images taken during GGR-16 and GGR-18 in Meru County, Kenya, into 4,142 highly-comparable annotations, requiring only 120 contrastive same-vs-different-individual

关键词： Livestock

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All Grains, One Scheme (AGOS): Learning Multi-grain Instance Representation for Aerial Scene Classification

arXiv

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arXiv 2022年

作者： Bi, Qi Zhou, Beichen Qin, Kun Ye, Qinghao Xia, Gui-Song School of Remote Sensing and Information Engineering Wuhan University Wuhan China University of California San Diego United States The National Engineering Research Center for Multimedia Software School of Computer Science Institute of Artificial Intelligence China The State Key Lab. LIESMARS Wuhan University Wuhan China

Aerial scene classification remains challenging as: 1) the size of key objects in determining the scene scheme varies greatly;2) many objects irrelevant to the scene scheme are often flooded in the image. Hence, how to effectively perceive the region of interests (RoIs) from a variety of sizes and build more discriminative representation from such complicated object distribution is vital to understand an aerial scene. In this paper, we propose a novel all grains, one scheme (AGOS) framework to tackle these challenges. To the best of our knowledge, it is the first work to extend the classic multiple instance learning into multi-grain formulation. Specially, it consists of a multi-grain perception module (MGP), a multi-branch multi-instance representation module (MBMIR) and a self-aligned semantic fusion (SSF) module. Firstly, our MGP preserves the differential dilated convolutional features from the backbone, which magnifies the discriminative information from multi-grains. Then, our MBMIR highlights the key instances in the multi-grain representation under the MIL formulation. Finally, our SSF allows our framework to learn the same scene scheme from multi-grain instance representations and fuses them, so that the entire framework is optimized as a whole. Notably, our AGOS is flexible and can be easily adapted to existing CNNs in a plug-and-play manner. Extensive experiments on UCM, AID and NWPU benchmarks demonstrate that our AGOS achieves a comparable performance against the state-of-the-art methods. Copyright © 2022, The Authors. All rights reserved.

关键词： Antennas

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An end-to-end deep learning solution for automated LiDAR tree detection in the urban environment

ISPRS Open Journal of Photogrammetry and Remote Sensing

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ISPRS Open Journal of Photogrammetry and Remote Sensing 2025年

作者： Julian R. Rice G. Andrew Fricker Jonathan Ventura Department of Computer Science & Software Engineering California Polytechnic State University 1 Grand Ave San Luis Obispo CA USA Pacific Northwest National Laboratory 902 Battelle Blvd Richland WA USA Department of Social Sciences California Polytechnic State University 1 Grand Ave San Luis Obispo CA USA

Cataloging and classifying trees in the urban environment is a crucial step in urban and environmental planning, however manual collection and maintenance of this data is expensive and time-consuming. Although algorithmic approaches that rely on remote sensing data have been developed for tree detection in forests, they generally struggle in the more varied urban environment. This work proposes a novel end-to-end deep learning method for the detection of trees in the urban environment from remote sensing data. Specifically, we develop and train a novel PointNet-based neural network architecture to predict tree locations directly from LiDAR data augmented with multi-spectral imagery. We compare this model to a number of high-performing baselines on a large and varied dataset in the Southern california region, and find that our method outperforms all baselines in terms of tree detection ability (75.5% F-score) and positional accuracy (2.28 meter root mean squared error), while being highly efficient. We then analyze and compare the sources of errors, and how these reveal the strengths and weaknesses of each approach. Our results highlight the importance of fusing spectral and structural information for remote sensing tasks in complex urban environments.

关键词： Tree detection Urban forestry Aerial imagery Deep learning LiDAR

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Measurements of Correlated Insulator Gaps in a Transition Metal Dichalcogenide Moiré Superlattice

arXiv

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arXiv 2023年

作者： Huang, Xiong Chen, Dongxue Lian, Zhen Wu, Qiran Rashetnia, Mina Blei, Mark Taniguchi, Takashi Watanabe, Kenji Tongay, Sefaattin Shi, Su-Fei Cui, Yong-Tao Department of Physics and Astronomy University of California RiversideCA92521 United States Department of Materials Science and Engineering University of California RiversideCA92521 United States Department of Chemical and Biological Engineering Rensselaer Polytechnic Institute TroyNY12180 United States School for Engineering of Matter Transport and Energy Arizona State University TempeAZ85287 United States Research Center for Materials Nanoarchitectonics National Institute for Materials Science 1-1 Namiki Tsukuba305-0044 Japan Research Center for Electronic and Optical Materials National Institute for Materials Science 1-1 Namiki Tsukuba305-0044 Japan Department of Electrical Computer & Systems Engineering Rensselaer Polytechnic Institute TroyNY12180 United States

Moiré superlattices of transitional metal dichalcogenides exhibit strong electron-electron interaction that has led to experimental observations of Mott insulators and generalized Wigner crystals. In this letter, we report direct measurements of the thermodynamic gaps of these correlated insulating states in a dual-gate WS2/WSe2 moiré bilayer. We employ the microwave impedance microscopy to probe the electronic features in both the graphene top gate and the moiré bilayer, from which we extract the doping dependence of the chemical potential of the moiré bilayer and the energy gaps for various correlated insulating states utilizing the Landau quantization of graphene. These gaps are relatively insensitive to the application of an external electric field to the WS2/WSe2 moiré bilayer. © 2023, CC BY.

关键词： Graphene

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Position: topological deep learning is the new frontier for relational learning 24

Position: topological deep learning is the new frontier for ...

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Proceedings of the 41st International Conference on Machine Learning

作者： Theodore Papamarkou Tolga Birdal Michael Bronstein Gunnar Carlsson Justin Curry Yue Gao Mustafa Hajij Roland Kwitt Pietro Liò Paolo Di Lorenzo Vasileios Maroulas Nina Miolane Farzana Nasrin Karthikeyan Natesan Ramamurthy Bastian Rieck Simone Scardapane Michael T. Schaub Petar Veličković Bei Wang Yusu Wang Guo-Wei Wei Ghada Zamzmi Department of Mathematics The University of Manchester Manchester UK Department of Computing Imperial College London London UK Department of Computer Science University of Oxford Oxford UK Department of Mathematics Stanford University Stanford and BlueLightAI Inc University at Albany New York School of Software Tsinghua University Beijing China University of San Francisco San Francisco Department of Artificial Intelligence and Human Interfaces University of Salzburg Austria Department of Computer Science and Technology University of Cambridge Cambridge UK Department of Information Engineering Electronics and Telecommunications Sapienza University of Rome Rome Italy Department of Mathematics University of Tennessee Knoxville Department of Electrical and Computer Engineering UC Santa Barbara Santa Barbara Department of Mathematics University of Hawai' at M-ānoa Hawai'i IBM Corporation New York Helmholtz Munich Munich Germany and Technical University of Munich Munich Germany Technical University of Munich Munich Germany Google DeepMind and Department of Computer Science and Technology University of Cambridge Cambridge UK School of Computing University of Utah Utah Computer Science and Engineering Department University of California San Diego San Diego Department of Mathematics Michigan State University East Lansing Michigan University of South Florida Florida

Topological deep learning (TDL) is a rapidly evolving field that uses topological features to understand and design deep learning models. This paper posits that TDL is the new frontier for relational learning. TDL may complement graph representation learning and geometric deep learning by incorporating topological concepts, and can thus provide a natural choice for various machine learning settings. To this end, this paper discusses open problems in TDL, ranging from practical benefits to theoretical foundations. For each problem, it outlines potential solutions and future research opportunities. At the same time, this paper serves as an invitation to the scientific community to actively participate in TDL research to unlock the potential of this emerging field.

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Quadrupolar Excitons and Hybridized Interlayer Mott Insulator in a Trilayer Moiré Superlattice

arXiv

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arXiv 2023年

作者： Lian, Zhen Chen, Dongxue Ma, Lei Meng, Yuze Su, Ying Yan, Li Huang, Xiong Wu, Qiran Chen, Xinyue Blei, Mark Taniguchi, Takashi Watanabe, Kenji Tongay, Sefaattin Zhang, Chuanwei Cui, Yong-Tao Shi, Su-Fei Department of Chemical and Biological Engineering Rensselaer Polytechnic Institute TroyNY12180 United States Department of Physics University of Texas at Dallas DallasTX75083 United States Department of Physics and Astronomy University of California RiversideCA92521 United States Department of Materials Science and Engineering University of California RiversideCA92521 United States School for Engineering of Matter Transport and Energy Arizona State University TempeAZ85287 United States International Center for Materials Nanoarchitectonics National Institute for Materials Science 1-1 Namiki Tsukuba305-0044 Japan Research Center for Functional Materials National Institute for Materials Science 1-1 Namiki Tsukuba305-0044 Japan Department of Electrical Computer & Systems Engineering Rensselaer Polytechnic Institute TroyNY12180 United States

Transition metal dichalcogenide (TMDC) moiré superlattices, owing to the moiré flatbands and strong correlation, can host periodic electron crystals and fascinating correlated physics. The TMDC heterojunctions in the type-II alignment also enable long-lived interlayer excitons that are promising for correlated bosonic states, while the interaction is dictated by the asymmetry of the heterojunction. Here we demonstrate a new excitonic state, quadrupolar exciton, in a symmetric WSe2-WS2-WSe2 trilayer moiré superlattice. The quadrupolar excitons exhibit a quadratic dependence on the electric field, distinctively different from the linear Stark shift of the dipolar excitons in heterobilayers. This quadrupolar exciton stems from the hybridization of WSe2 valence moiré flatbands. The same mechanism also gives rise to an interlayer Mott insulator state, in which the two WSe2 layers share one hole laterally confined in one moiré unit cell. In contrast, the hole occupation probability in each layer can be continuously tuned via an out-of-plane electric field, reaching 100% in the top or bottom WSe2 under a large electric field, accompanying the transition from quadrupolar excitons to dipolar excitons. Our work demonstrates a trilayer moiré system as a new exciting playground for realizing novel correlated states and engineering quantum phase transitions. © 2023, CC BY.

关键词： Excitons

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The Overlooked Elephant of Object Detection: Open Set

The Overlooked Elephant of Object Detection: Open Set

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IEEE Workshop on Applications of computer Vision (WACV)

作者： Akshay Raj Dhamija Manuel Günther Jonathan Ventura Terrance E. Boult Vision and Security Technology Lab University of Colorado Colorado Springs Department of Computer Science & Software Engineering California Polytechnic State University

ISBN: (数字)9781728165530

ISBN: (纸本)9781728165547

Even though object detection is a popular area of research that has found considerable applications in the real world, it has some fundamental aspects that have never been formally discussed and experimented. One of the core aspects of evaluating object detectors has been the ability to avoid false detections. While major datasets like PASCAL VOC or MSCOCO extensively test the detectors on their ability to avoid false positives, they do not differentiate between their closed-set and open-set performance. Despite systems being trained to reject everything other than the classes of interest, unknown objects from the open world end up being incorrectly detected as known objects, often with very high confidence. This paper is the first to formalize the problem of open-set object detection and propose the first open-set object detection protocol. Moreover, the paper provides a new evaluation metric to analyze the performance of some state-of-the-art detectors and discusses their performance differences.

关键词： Detectors Object detection Training Protocols Object recognition Proposals Training data

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Valley-polarized Exitonic Mott Insulator in WS2/WSe2 Moiré Superlattice

arXiv

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arXiv 2023年

作者： Lian, Zhen Meng, Yuze Ma, Lei Maity, Indrajit Yan, Li Wu, Qiran Huang, Xiong Chen, Dongxue Chen, Xiaotong Chen, Xinyue Blei, Mark Taniguchi, Takashi Watanabe, Kenji Tongay, Sefaattin Lischner, Johannes Cui, Yong-Tao Shi, Su-Fei Department of Chemical and Biological Engineering Rensselaer Polytechnic Institute TroyNY12180 United States Departments of Materials The Thomas Young Centre for Theory and Simulation of Materials Imperial College London United Kingdom Department of Physics and Astronomy University of California RiversideCA92521 United States Department of Materials Science and Engineering University of California RiversideCA92521 United States Materials Science and Engineering Program School for Engineering of Matter Transport and Energy Arizona State University TempeAZ85287 United States International Center for Materials Nanoarchitectonics National Institute for Materials Science 1-1 Namiki Tsukuba305-0044 Japan Research Center for Functional Materials National Institute for Materials Science 1-1 Namiki Tsukuba305-0044 Japan Department of Electrical Computer & Systems Engineering Rensselaer Polytechnic Institute TroyNY12180 United States

Strongly enhanced electron-electron interaction in semiconducting moiré superlattices formed by transition metal dichalcogenides (TMDCs) heterobilayers has led to a plethora of intriguing fermionic correlated states1–7. Meanwhile, interlayer excitons in a type-II aligned TMDC heterobilayer moiré superlattice, with electrons and holes separated in different layers8,9, inherit this enhanced interaction and strongly interact with each other, promising for realizing tunable correlated bosonic quasiparticles10,11 with valley degree of freedom. We employ photoluminescence spectroscopy to investigate the strong repulsion between interlayer excitons and correlated electrons in a WS2/WSe2 moiré superlattice and combine with theoretical calculations to reveal the spatial extent of interlayer excitons and the band hierarchy of correlated states. We further find that an excitonic Mott insulator state emerges when one interlayer exciton occupies one moiré cell, evidenced by emerging photoluminescence peaks under increased optical excitation power. Double occupancy of excitons in one unit cell requires overcoming the energy cost of exciton-exciton repulsion of about 30-40 meV, depending on the stacking configuration of the WS2/WSe2 heterobilayer. Further, the valley polarization of the excitonic Mott insulator state is enhanced by nearly one order of magnitude. Our study demonstrates the WS2/WSe2 moiré superlattice as a promising platform for engineering and exploring new correlated states of fermion, bosons, and a mixture of both12 © 2023, CC BY.

关键词： Excitons

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Integrated Optical Phased Arrays for LiDAR, Communications, Augmented Reality, and Beyond

Integrated Optical Phased Arrays for LiDAR, Communications, ...

引用

Applied Industrial Optics: Spectroscopy, Imaging and Metrology, AIO 2021

作者： Notaros, Jelena Notaros, Milica Raval, Manan Poulton, Christopher V. Byrd, Matthew J. Li, Nanxi Su, Zhan Magden, Emir Salih Timurdogan, Erman Dyer, Thomas Baiocco, Christopher Kim, Taehwan Bhargava, Pavan Stojanovic, Vladimir Watts, Michael R. Research Laboratory of Electronics Massachusetts Institute of Technology CambridgeMA02139 United States John A. Paulson School of Engineering and Applied Science Harvard University CambridgeMA02138 United States Colleges of Nanoscale Science and Engineering State University of New York Polytechnic Institute AlbanyNY12203 United States Department of Electrical Engineering and Computer Science University of California Berkeley BerkeleyCA94720 United States Currently with Analog Photonics One Marina Park Drive BostonMA02210 United States Department of Electrical and Electronics Engineering Koç University Sariyer Istanbul34450 Turkey

Recent integrated optical phased array architectures, results, and applications will be reviewed, including beam steering for LiDAR and communications, near-field optical manipulation, and holographic displays for aug... 详细信息

ISBN: (纸本)9781557528209

关键词： Augmented reality

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Towards Improved Network Security Requirements and Policy: Domain-Specific Completeness Analysis via Topic Modeling

Towards Improved Network Security Requirements and Policy: D...

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IEEE International Workshop on Artificial Intelligence for Requirements engineering (AIRE)

作者： Jane Huffman Hayes Jared Payne Emily Essex Kelsey Cole Joseph Alverson Alex Dekhtyar Dongfeng Fang Grant Bernosky University of Kentucky Lexington KY USA Computer Science University of Kentucky Lexington Kentucky USA Computer Science and Software Engineering California Polytechnic State University San Luis Obispo

ISBN: (数字)9781728183527

ISBN: (纸本)9781728183534

Network security policies contain requirements - including system and software features as well as expected and desired actions of human actors. In this paper, we present a framework for evaluation of textual network security policies as requirements documents to identify areas for improvement. Specifically, our framework concentrates on completeness. We use topic modeling coupled with expert evaluation to learn the complete list of important topics that should be addressed in a network security policy. Using these topics as a checklist, we evaluate (students) a collection of network security policies for completeness, i.e., the level of presence of these topics in the text. We developed three methods for topic recognition to identify missing or poorly addressed topics. We examine network security policies and report the results of our analysis: preliminary success of our approach.

关键词： Security Communication networks Loading Wireless networks Semantics Communication system security Text recognition

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