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arXiv 2024年

作者： Liao, Shaolin School of Electronics and Information Technology School of Microelectronics Sun Yat-sen University Guangdong Guangzhou510006 China Department of Electrical and Computer Engineering Illinois Institute of Technology ChicagoIL60616 United States Elmore Family School of Electrical and Computer Engineering Purdue University West LafayetteIN47907 United States

A general framework of Numerical Singular Integrals (NSI) method based on the Integration By Parts (IBP) has been developed for integrals involving singular and nearly singular integrands, or NSI-IBP. Through a general integration by parts formula and by choosing some analytically integrable function to approximate the original integrand, various well-known integration by parts methods can be derived. Rigorous mathematical derivations have been performed to transform the original singular or nearly singular integrals into non-singular integrals that can be computed efficiently, along with the boundary values added. What’s more important, the NSI-IBP method works well even when the exact form of the singular integrand is not known. Criteria on how to choose the appropriate function with a known analytical integral that closely approximates the original integrand have been outlined and explained. Numerical recipe has been presented to apply the proposed NSI-IBP. Numerical experiments have been carried out on various singular integrals such as the power-law decaying integrand, the logarithmic function, and their hybrid products. It can be shown that various relative accuracy up to 10−15 can be achieved, even the exact singular function is not known. Finally, the nearly singular integrals involving the scalar Green’s function have been evaluated for both electrostatics applications and Computational Electromagnetics (CEM) *** Codes 45E99 © 2024, CC BY.

关键词： Choquet integral

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Misspelling correction with pre-trained contextual language model

arXiv

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arXiv 2021年

作者： Hu, Yifei Jing, Xiaonan Ko, Youlim Rayz, Julia Taylor Computer and Information Technology Purdue University United States

Spelling irregularities, known now as spelling mistakes, have been found for several centuries. As humans, we are able to understand most of the misspelled words based on their location in the sentence, perceived pronunciation, and context. Unlike humans, computer systems do not possess the convenient auto complete functionality of which human brains are capable. While many programs provide spelling correction functionality, many systems do not take context into account. Moreover, Artificial Intelligence systems function in the way they are trained on. With many current Natural Language Processing (NLP) systems trained on grammatically correct text data, many are vulnerable against adversarial examples, yet correctly spelled text processing is crucial for learning. In this paper, we investigate how spelling errors can be corrected in context, with a pre-trained language model BERT. We present two experiments, based on BERT and the edit distance algorithm, for ranking and selecting candidate corrections. The results of our experiments demonstrated that when combined properly, contextual word embeddings of BERT and edit distance are capable of effectively correcting spelling errors. © 2021, CC BY-NC-ND.

关键词： Errors

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Silicon Nitride Integrated Photonics with Intrinsic Quantum Emitters

Silicon Nitride Integrated Photonics with Intrinsic Quantum ...

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Optica Quantum 2.0 Conference and Exhibition, Quantum 2.0

作者： Senichev, Alexander Martin, Zachariah O. Peana, Samuel Yesilyurt, Omer Matthiessen, Owen M. Sychev, Demid Lawrie, Benjamin Lagutchev, Alexei S. Boltasseva, Alexandra Shalaev, Vladimir M. Elmore Family School of Electrical and Computer Engineering Birck Nanotechnology Center Purdue Quantum Science and Engineering Institute Purdue University West LafayetteIN47907 United States Quantum Science Center Department of Energy a National Quantum Information Science Research Center of the U.S. Oak RidgeTN37931 United States Materials Science and Technology Division Oak Ridge National Laboratory Oak RidgeTN37831 United States

We create intrinsic quantum emitters in silicon nitride, study their structure and temperature-dependent optical properties, and demonstrate monolithic integration with photonic waveguides to evaluate the potential of... 详细信息

ISBN: (纸本)9781957171272

关键词： Integrated photonics Optical properties Photonic integrated circuits Quantum information Quantum light sources Silicon nitride

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Research on multimodal modal fusion application in pulmonary embolism diagnoses

Research on multimodal modal fusion application in pulmonary...

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5th International Conference on Signal Processing and Machine Learning (CONF SPML 2025)

作者： Haonan Deng Xinyu Zhang Yiyi Lu Zhongxu Ye Institute of Information and Software Engineering University of Electronic Science and Technology of China Chengdu 610054 People's Republic of China Department of Computing Science University of Glasgow Glasgow G11 6QT UK Duke Kunshan University Suzhou 215316 People's Republic of China Department of Computer and Information Technology Purdue University West Lafayette 47907 USA

ISBN: (数字)9781837243143

Pulmonary embolism is a life-threatening illness caused by blockages in the pulmonary arteries, usually due to blood clots. This condition requires accurate diagnosis on time in order to prevent critical complications like cardiac arrest or chronic pulmonary hypertension. As a result, this study explores the capability of data fusion in a multimodal scenario to improve diagnostic accuracy in PE through the integration of Electronic Medical Records with imaging data from CT Pulmonary Angiography. Deep learning models such as PENet, ResNet3D_50, ResNext3D_101, and DenseNet were used for the analysis of imaging data. The EMR data were analyzed using logistic regression and other machine learning methods. A Late Fusion approach that combined the outputs from the EMR and imaging models, using averages, resulted in a diagnostic accuracy with an AUC of 0.94. This is a perfect example where combined clinical and imaging features addressed the shortcomings of one-modality approaches. Advanced fusion techniques, such as MISA, were also tried, but simpler strategies often performed better than them when the amount of data was limited. Overall, the results point out the promise of multimodal fusion in advancing PE diagnostics and also take note of the selection of an appropriate strategy for clinical application.

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PENet: Object Detection Using Points Estimation in High Definition Aerial Images 19

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

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19th IEEE International Conference on Machine Learning and Applications, ICMLA 2020

作者： Tang, Ziyang Liu, Xiang Yang, Baijian Purdue University Computer and Information Technology West LafayetteIN United States

ISBN: (纸本)9781728184708

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. © 2020 IEEE.

关键词： Object detection

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Student and AI responses to physics problems examined through the lenses of sensemaking and mechanistic reasoning

引用

computers and Education: Artificial Intelligence 2024年 7卷

作者： Sirnoorkar, Amogh Zollman, Dean Laverty, James T. Magana, Alejandra J. Rebello, N. Sanjay Bryan, Lynn A. Purdue University 100 N University St West LafayetteIN47907 United States Department of Physics Kansas State University Cardwell Hall 1228 N Martin Luther King Junior Drive ManhattanKS66506 United States Computer and Information Technology Purdue University 401 Grant Street West LafayetteIN47907 United States Department of Physics and Astronomy Purdue University 525 Northwestern Avenue West LafayetteIN47907 United States Department of Curriculum and Instruction Purdue University 100 N University St West LafayetteIN47907 United States

Several reports in education have called for transforming physics learning environments by promoting sensemaking of real-world scenarios in light of curricular ideas. Recent advancements in Generative-Artificial Intelligence have garnered increasing traction in educators' community by virtue of its potential to transform STEM learning. In this exploratory study, we adopt a mixed-methods approach in comparatively examining student- and AI-generated responses to two different formats of a physics problem through the theoretical lenses of sensemaking and mechanistic reasoning. The student data is derived from think-aloud interviews of introductory students and the AI data comes from ChatGPT's (versions 3.5 and 4o) solutions collected using Zero shot approach. The results highlight AI responses to evidence most features of the two processes through well-structured solutions and student responses to effectively leverage representations in their solutions through iterative refinement of arguments. In other words, while AI responses reflect how physics is talked about, the student responses reflect how physics is practiced. Implications of these results in light of development and deployment of AI systems in physics pedagogy are discussed. © 2024 The Authors

关键词： Contrastive Learning

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Ghost-in-Wave: How Speaker-Irrelative Features Interfere DeepFake Voice Detectors

Ghost-in-Wave: How Speaker-Irrelative Features Interfere Dee...

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IEEE International Conference on Multimedia and Expo (ICME)

作者： Xuan Hai Xin Liu Zhaorun Chen Yuan Tan Song Li Weina Niu Gang Liu Rui Zhou Qingguo Zhou School of Information Science and Engineering Lanzhou University Lanzhou China Elmore Family School of Electrical and Computer Engineering Purdue University IN USA The State Key Laboratory of Blockchain and Data Security Zhejiang University Hangzhou China School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu China

ISBN: (数字)9798350390155

ISBN: (纸本)9798350390162

Recent speech synthesis technology can generate high-quality speech indistinguishable from human speech, thus introducing various security and privacy risks. Numerous recent studies have focused on fake voice detection to address these risks, with many claiming to achieve ideal performance. However, is this really the case? A recent research work introduced Speaker-Irrelative-Features (SiFs), unrelated to the information in speech files but capable of influencing fake detectors. This means that existing detectors may rely on SiFs to a certain extent to distinguish real and fake speech. In this paper, we introduce an evaluation framework to evaluate the influence of SiFs in existing fake voice detectors in depth. We evaluate three SiFs which include background noise, the mute parts before and after voice, and the sampling rate on ASVspoof2019 and FoR. Our results confirm the substantial influence of SiFs on fake voice detection performance, and we delve into the analysis of the underlying mechanisms.

关键词： Privacy Deepfakes Detectors Feature extraction Background noise Speech synthesis Security

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Challenge Summary: U-MedSAM Uncertainty-aware MedSAM for Medical Image Segmentation

arXiv

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arXiv 2024年

作者： Wang, Xin Liu, Xiaoyu Huang, Peng Huang, Pu Hu, Shu Zhu, Hongtu United States School of Physics and Electronics Shandong Normal University China School of Computing and Artificial Intelligence Southwest Jiaotong University China Department of Computer and Information Technology Purdue University United States University of North Carolina Chapel Hill United States

Medical Image Foundation Models have proven to be powerful tools for mask prediction across various datasets. However, accurately assessing the uncertainty of their predictions remains a significant challenge. To address this, we propose a new model, U-MedSAM, which integrates the MedSAM model with an uncertainty-aware loss function and the Sharpness-Aware Minimization (SharpMin) optimizer. The uncertainty-aware loss function automatically combines region-based, distribution-based, and pixel-based loss designs to enhance segmentation accuracy and robustness. SharpMin improves generalization by finding flat minima in the loss landscape, thereby reducing overfitting. Our method was evaluated in the CVPR24 MedSAM on Laptop challenge, where U-MedSAM demonstrated promising performance. © 2024, CC BY-NC-SA.

关键词： Medical imaging

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Evaluating LoRaWAN performance in intentional and unintentional DoS attacks by legacy 900MHz network devices

Evaluating LoRaWAN performance in intentional and unintentio...

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International Conference on Advanced Communication Technologies and Networking (CommNet)

作者： Seokhyeon Bang Junyoung Jang Minju Ro Yuseon Choi Doyong Kwon Kangyeon Lee Anthony Smith School of Computer Science and Engineering Chung-Ang University Seoul Korea Department of Software Engineering Chonnam National University Gwangju Korea School of Computer Science and Engineering Kyungpook National University Daegu Korea Department of Computer and Information Technology Purdue University Indiana United States

The deployment of LoRaWAN on the Internet of Things (IoT) has increased since its advent and LoRaWAN now predominates the IoT market over other Low Powered Wide Area Networks (LPWAN). However, since LoRaWAN uses Chirp Spread Spectrum (CSS), it is susceptible to wideband jamming attacks. In this paper, we demonstrate with experiments and concrete numerical results that jamming US915 LoRaWAN frequency is possible by the usual data transmission and reception process of 900MHz Canopy, one of the legacy 900MHz network device. Intentional attack is possible in the same manner. The experiments emulate the real-world environment operated in medical and agriculture industries, in outdoor and indoor conditions, respectively. In addition, this paper introduces and utilizes the novel metric, Jamming Effect (JE), that indicates the network performance of wireless networks that spread the data on air.

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Towards the automatic coding of medical transcripts to improve patient-centered communication

arXiv

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arXiv 2021年

作者： Park, Gilchan Rayz, Julia Taylor Shields, Cleveland G. Computer and Information Technology Purdue University Human Development & Family Studies Purdue University

This paper aims to provide an approach for automatic coding of physician-patient communication transcripts to improve patient-centered communication (PCC). PCC is a central part of high-quality health care. To improve PCC, dialogues between physicians and patients have been recorded and tagged with predefined codes. Trained human coders have manually coded the transcripts. Since it entails huge labor costs and poses possible human errors, automatic coding methods should be considered for efficiency and effectiveness. We adopted three machine learning algorithms (Naïve Bayes, Random Forest, and Support Vector Machine) to categorize lines in transcripts into corresponding codes. The result showed that there is evidence to distinguish the codes, and this is considered to be sufficient for training of human annotators. © 2021, CC BY.

关键词： Wages

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