检索结果-内蒙古大学图书馆

38th Conference on Neural Information Processing Systems, NeurIPS 2024

作者： Kang, Bong Gyun Lee, Dongjun Kim, HyunGi Chung, DoHyun Yoon, Sungroh Interdisciplinary Program in Artificial Intelligence Seoul National University Korea Republic of Department of Electrical and Computer Engineering Seoul National University Korea Republic of Department of Future Automotive Mobility Seoul National University Korea Republic of

Sequence modeling faces challenges in capturing long-range dependencies across diverse tasks. Recent linear and transformer-based forecasters have shown superior performance in time series forecasting. However, they are constrained by their inherent inability to effectively address long-range dependencies in time series data, primarily due to using fixed-size inputs for prediction. Furthermore, they typically sacrifice essential temporal correlation among consecutive training samples by shuffling them into mini-batches. To overcome these limitations, we introduce a fast and effective Spectral Attention mechanism, which preserves temporal correlations among samples and facilitates the handling of long-range information while maintaining the base model structure. Spectral Attention preserves long-period trends through a low-pass filter and facilitates gradient to flow between samples. Spectral Attention can be seamlessly integrated into most sequence models, allowing models with fixed-sized look-back windows to capture long-range dependencies over thousands of steps. Through extensive experiments on 11 real-world time series datasets using 7 recent forecasting models, we consistently demonstrate the efficacy of our Spectral Attention mechanism, achieving state-of-the-art results. © 2024 Neural information processing systems foundation. All rights reserved.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Text2PointCloud: Text-Driven Stylization for Sparse PointCloud

Text2PointCloud: Text-Driven Stylization for Sparse PointClo...

引用

44th Annual Conference on European Association for computer Graphics, EUROGRAPHICS 2023

作者： Hwang, Inwoo Kim, Hyeonwoo Lim, Donggeun Park, Inbum Kim, Young Min Seoul National University Department of Electrical and Computer Engineering Korea Republic of Seoul National University Interdisciplinary Program in Artificial Intelligence and INMC Korea Republic of

ISBN: (纸本)9783038682097

We present Text2PointCloud, a method to process sparse, noisy point cloud input and generate high-quality stylized output. Given point cloud data, our iterative pipeline stylizes and deforms points guided by a text description and gradually densifies the point cloud. As our framework utilizes the existing resources of image and text embedding, it does not require dedicated 3D datasets with high-quality textures, which are produced by skillful artists or high-resolution colored 3D models. Also, since we represent 3D shapes as a point cloud, we can visualize fine-grained geometric variations with a complex topology such as flowers or fire. To the best of our knowledge, it is the first approach for directly stylizing the uncolored, sparse point cloud input without converting it into a mesh or implicit representation, which might fail to express the original information in the measurements, especially when the object exhibits complex topology. © 2023 Authors.

关键词： Textures

来源：评论

学校读者我要写书评

暂无评论

Textual training for the hassle-free removal of unwanted visual data: case studies on OOD and hateful image detection 24

Textual training for the hassle-free removal of unwanted vis...

引用

Proceedings of the 38th International Conference on Neural Information Processing Systems

作者： Saehyung Lee Jisoo Mok Sangha Park Yongho Shin Dahuin Jung Sungroh Yoon Department of Electrical and Computer Engineering Seoul National University Qualcomm Korea YH Seoul South Korea School of Computer Science and Engineering Soongsil University Department of Electrical and Computer Engineering and Interdisciplinary Program in Artificial Intelligence Seoul National University

ISBN: (纸本)9798331314385

In our study, we explore methods for detecting unwanted content lurking in visual datasets. We provide a theoretical analysis demonstrating that a model capable of successfully partitioning visual data can be obtained using only textual data. Based on the analysis, we propose Hassle-Free Textual Training (HFTT), a streamlined method capable of acquiring detectors for unwanted visual content, using only synthetic textual data in conjunction with pre-trained vision-language models. HFTT features an innovative objective function that significantly reduces the necessity for human involvement in data annotation. Furthermore, HFTT employs a clever textual data synthesis method, effectively emulating the integration of unknown visual data distribution into the training process at no extra cost. The unique characteristics of HFTT extend its utility beyond traditional out-of-distribution detection, making it applicable to tasks that address more abstract concepts. We complement our analyses with experiments in out-of-distribution detection and hateful image detection. Our codes are available at https://***/Saehyung-Lee/HFTT

关键词：

来源：评论

学校读者我要写书评

暂无评论

A Graph Neural Network Multi-Task Learning-Based Approach for Detection and Localization of Cyberattacks in Smart Grids 48

A Graph Neural Network Multi-Task Learning-Based Approach fo...

引用

48th IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2023

作者： Takiddin, Abdulrahman Atat, Rachad Ismail, Muhammad Davis, Katherine Serpedin, Erchin Texas A and M University Department of Electrical and Computer Engineering College StationTX United States Texas A and M University at Qatar Electrical and Computer Engineering Program Doha Qatar Tennessee Technological University Department of Computer Science CookevilleTN United States

ISBN: (纸本)9781728163277

False data injection attacks (FDIAs) on smart power grids' measurement data present a threat to system stability. When malicious entities launch cyberattacks to manipulate the measurement data, different grid components will be affected, which leads to failures. For effective attack mitigation, two tasks are required: determining the status of the system (normal operation/under attack) and localizing the attacked bus/power substation. Existing mitigation techniques carry out these tasks separately and offer limited detection performance. In this paper, we propose a multi-Task learning-based approach that performs both tasks simultaneously using a graph neural network (GNN) with stacked convolutional Chebyshev graph layers. Our results show that the proposed model presents superior system status identification and attack localization abilities with detection rates of 98.5-100% and 99-100%, respectively, presenting improvements of 5-30% compared to benchmarks. © 2023 IEEE.

关键词： Smart power grids

来源：评论

学校读者我要写书评

暂无评论

Photonic Crystals with Split Ring Unit Cells for Subwavelength Light Confinement

Photonic Crystals with Split Ring Unit Cells for Subwaveleng...

引用

CLEO: science and Innovations, S and I 2022

作者： Arnold, Kellen P. Halimi, Sami I. Allen, Joshua A. Hu, Shuren Weiss, Sharon M. Interdisciplinary Materials Science Program Vanderbilt University NashvilleTN37235 United States Department of Electrical Engineering and Computer Science Vanderbilt University NashvilleTN37235 United States

We report a split ring photonic crystal that demonstrates an order of magnitude larger peak energy density compared to traditional photonic crystals. The split ring offers highly focused optical energy in an accessibl...

ISBN: (纸本)9781557528209

关键词：

来源：评论

学校读者我要写书评

暂无评论

Unsupervised homography estimation on multimodal image pair via alternating optimization 24

Unsupervised homography estimation on multimodal image pair ...

引用

Proceedings of the 38th International Conference on Neural Information Processing Systems

作者： Sanghyeob Song Jaihyun Lew Hyemi Jang Sungroh Yoon Interdisciplinary Program in Artificial Intelligence Seoul National University and Samsung Electro-Mechanics Interdisciplinary Program in Artificial Intelligence Seoul National University Department of Electrical and Computer Engineering Seoul National University Interdisciplinary Program in Artificial Intelligence Seoul National University and Department of Electrical and Computer Engineering Seoul National University

ISBN: (纸本)9798331314385

Estimating the homography between two images is crucial for mid- or high-level vision tasks, such as image stitching and fusion. However, using supervised learning methods is often challenging or costly due to the difficulty of collecting ground-truth data. In response, unsupervised learning approaches have emerged. Most early methods, though, assume that the given image pairs are from the same camera or have minor lighting differences. Consequently, while these methods perform effectively under such conditions, they generally fail when input image pairs come from different domains, referred to as multimodal image *** address these limitations, we propose AltO, an unsupervised learning framework for estimating homography in multimodal image pairs. Our method employs a two-phase alternating optimization framework, similar to Expectation-Maximization (EM), where one phase reduces the geometry gap and the other addresses the modality gap. To handle these gaps, we use Barlow Twins loss for the modality gap and propose an extended version, Geometry Barlow Twins, for the geometry gap. As a result, we demonstrate that our method, AltO, can be trained on multimodal datasets without any ground-truth data. It not only outperforms other unsupervised methods but is also compatible with various architectures of homography estimators. The source code can be found at: https://***/songsang7/AltO

关键词：

来源：评论

学校读者我要写书评

暂无评论

Generating barcodes for nanopore sequencing data with PRO

引用

Fundamental Research 2024年第4期4卷 785-794页

作者： Ting Yu Zitong Ren Xin Gao Guojun Li Renmin Han Research Center for Mathematics and Interdisciplinary Sciences Frontiers Science Center for Nonlinear Expectations(Ministry of Education)Shandong UniversityShandong 266000China Computer Electrical and Mathematical Sciences and Engineering Division&Computational Bioscience Research CenterKing Abdullah University of Science and TechnologyThuwal 23955Saudi Arabia

DNA barcodes,short and unique DNA sequences,play a crucial role in sample identification when processing many samples simultaneously,which helps reduce experimental ***,the low quality of long-read sequencing makes it difficult to identify barcodes accurately,which poses significant challenges for the design of barcodes for large numbers of samples in a single sequencing ***,we present a comprehensive study of the generation of barcodes and develop a tool,PRO,that can be used for selecting optimal barcode sets and *** formulate the barcode design problem as a combinatorial problem and prove that finding the optimal largest barcode set in a given DNA sequence space in which all sequences have the same length is theoretically *** practical applications,we developed the novel method PRO by introducing the probability divergence between two DNA sequences to expand the capacity of barcode kits while ensuring demultiplexing ***,the maximum size of the barcode kits designed by PRO is 2,292,which keeps the length of barcodes the same as that of the official ones used by Oxford Nanopore Technologies(ONT).We validated the performance of PRO on a simulated nanopore dataset with high error *** demultiplexing accuracy of PRO reached 98.29%for a barcode kit of size 2,922,4.31%higher than that of Guppy,the official demultiplexing *** the size of the barcode kit generated by PRO is the same as the official size provided by ONT,both tools show superior and comparable demultiplexing accuracy.

关键词： Third-generation sequencing Nanopore sequencing DNA barcode Farthest point sampling algorithm High throughput

来源：评论

学校读者我要写书评

暂无评论

Computation-Efficient Model Predictive Control of a Multilevel Current Source Converter 49

Computation-Efficient Model Predictive Control of a Multilev...

引用

49th Annual Conference of the IEEE Industrial Electronics Society, IECON 2023

作者： Chen, Muyu Yuan, Zhige Ghias, Amer M. Y. M. Nicholas, Vun Chan Hua School of Electrical and Electronic Engineering Collab. Initiative Interdisciplinary Graduate Programme Nanyang Technological University Singapore School of Electrical and Electronic Engineering Nanyang Technological University Singapore School of Computer Science and Engineering Nanyang Technological University Singapore

ISBN: (纸本)9798350331820

This paper presents a modified finite control set model predictive control (FCS-MPC) that can significantly reduce computation time and maintain good quality of output voltage. The switching states of a three-phase five-level current source converter (5L-CSC) are sorted according to the corresponding output converter current level. The influences of every switching state on the converter inductor current are analyzed. Then the optimal switching state can be anchored based on the converter current level that minimizes the cost function and converter inductor current conditions. The proposed method eliminates the need for the discrete-time model establishment for the converter. It avoids the ergodic search through all switching states, easing the controller's computation burden. Besides, this algorithm achieves multiple control goals without assigning different weighting factors to various control objectives. The simulated output voltage total harmonics distortion (THD), the inductor current ripple, and the execution time of the modified model predictive control and traditional model predictive control are compared to validate the effectiveness of the proposed algorithm. © 2023 IEEE.

关键词： Model predictive control

来源：评论

学校读者我要写书评

暂无评论

Density Based Spatial Clustering of Applications with Noise and Sentence Bert Embedding for Indonesian Utterance Clustering

Density Based Spatial Clustering of Applications with Noise ...

引用

2023 International Conference on computer science, Information Technology and engineering, ICCoSITE 2023

作者： Hasani, Muhammad Fikri Heryadi, Yaya Arifin, Yulyani Lukas Suparta, Wayan Bina Nusantara University BINUS Graduate Program Computer Science School of Computer Science Computer Science Department Jakarta11480 Indonesia Bina Nusantara University BINUS Graduate Program Computer Science Computer Science Department Jakarta11480 Indonesia Jakarta12930 Indonesia Institut Teknologi Nasional Yogyakarta Faculty of Industrial Technology Department of Electrical Engineering Yogyakarta55281 Indonesia

ISBN: (纸本)9798350320954

Task oriented chatbots are a sub-topic related to chatbots, where chatbots will perform certain tasks with specific goals. One part of creating a task-oriented chatbot is doing intent classification. Intent classification is a task of text classification. As in general text classification, the required dataset requires a label to carry out the classification process. To speed up and help the utterance analysis process, there is already a method, namely clustering, and Density-based clustering is a part of clustering that can determine cluster patterns based on arbitrary data, with DBScan as one of its algorithms. This research used 10000 client utterance data of awhatsapp based e-commerce conversation. SentenceBert also used as a state of art sentence embedding. This research yield silhouette score of 0.327 as the best result from eps of 0.1 and MinPts of 95. However, based on the cluster result, sentences labelled as noise can be further clustered. Text Preprocessing, text augmentation and sentence embedding techniques can be explored to increase the cluster performance. © 2023 IEEE.

关键词： Embeddings

来源：评论

学校读者我要写书评

暂无评论

Modified Isolated SEPIC Converter for EV Charging and High Voltage Applications

Modified Isolated SEPIC Converter for EV Charging and High V...

引用

2023 International Conference on Power, Instrumentation, Energy and Control, PIECON 2023

作者： Ahmad, Javed Lin, Chang-Hua Khan, Mohammad Suhail Liu, Hwa-Dong Fahad, Mohammad National Taiwan University of Science and Technology College of Electrical Engineering and Computer Science Department of Electrical Engineering Taipei Taiwan National Taiwan Normal University Undergraduate Program of Vehicle and Energy Engineering Taipei Taiwan

ISBN: (纸本)9798350399769

This research article presents a modified isolated SEPIC converter suitable for high-power applications. It uses a transformer instead of coupled inductor for isolation between the input power source and the output load. The steady-state analysis is explained in detail. Thermal loss analysis is performed by using PLECS software. Further, a hardware prototype is developed to verify the performance aspects of the proposed converter. © 2023 IEEE.

关键词： High frequency transformers

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：