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Ross Kulak - IEEE Xplore Author Profile

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Author details

Ross Kulak

Also published under: Ross A. Kulak

Publications

6

Citations

207

Publications by Year

20082021

Co-Authors:

Ankur AgarwalMeghna AgarwalZeshan AhmadSangamesh AnandwadeSachin Bhardwaj

Show All Co-Authors (59)

Ross Kulak

Also published under: Ross A. Kulak

Affiliation

Texas Instruments, Dallas, TX

Publication Topics

Advanced Driver Assistance Systems,
Angular Resolution,
Antenna Array,
Metal Layer,
Noise Figure,
1st Stage,
3rd Order,
45-nm CMOS,
Adjacent Antennas,
Antenna Gain,
Antenna Pattern,
Antenna Radiation Pattern

Biography

Ross A. Kulak (M'86) received the B.S. degree in electrical engineering in 1990 from the University of Alberta, Edmonton, Alberta, Canada. He subsequently pursued a number of graduate-level course in signal processing and digital communications at the University of Calgary, Calgary, Alberta, Canada.,He is currently working at Texas Instruments in radio characterization.(Based on document published on 8 December 2008).

Publications

6

Citations

207

Publications by Year

20082021

Co-Authors:

Ankur Agarwal
Meghna Agarwal
Zeshan Ahmad
Sangamesh Anandwade
Sachin Bhardwaj

Show All Co-Authors (59)

Author's Published Works

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Showing 1-12 of 12 results

Conferences (5)

Journals (5)

Early Access Articles (2)

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Xiaoran Yan(12)
Xiangjie Kong(3)
Feng Xia(2)
Yuchen Sun(2)
Guojiang Shen(2)
Research Institute of Artificial Intelligence, Zhejiang Lab, Hangzhou, China(3)
Zhejiang Lab, Hangzhou, China(2)
College of Computer Science and Technology, Zhejiang University of Technology, Hangzhou, China(2)
Zhejiang Lab, Research Center for Knowledge Engineering, Hangzhou, China(1)
Research Center for Astronomical Computing, Zhejiang Lab, Zhejiang, China(1)
2016 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM)(1)
2020 IEEE International Conference on Networking, Sensing and Control (ICNSC)(1)
2023 IEEE International Conference on Knowledge Graph (ICKG)(1)
2024 27th International Conference on Computer Supported Cooperative Work in Design (CSCWD)(1)
2024 IEEE International Conference on Multimedia and Expo (ICME)(1)
IEEE(12)
Media(2)
Nanjing, China(1)
Niagara Falls, ON, Canada(1)
San Francisco, CA, USA(1)
Shanghai, China(1)
Tianjin, China(1)
Random Walk(3)
Bayesian Model(2)
Biased Random Walk(2)
Data Privacy(2)
Diagonal Matrix(2)

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Oh-FedRec: One-Shot and Heterogeneous Vertical Federated Recommendation System

Xiangjie Kong;Xiaohua He;Xulin Ma;Xiaoran Yan;Lingyun Wang;Guojiang Shen;Zhi Liu

IEEE Transactions on Consumer Electronics

Year: 2025 | Early Access Article |

Federated learning has a wide range of applications in recommendation systems, but most federated recommendation systems can only achieve federated communication between users and servers. Only a few are vertical federated recommendation systems, achieving federated server communication. In addition, the current federated recommendation frameworks require that each participant have the same model....Show More

Oh-FedRec: One-Shot and Heterogeneous Vertical Federated Recommendation System

Xiangjie Kong;Xiaohua He;Xulin Ma;Xiaoran Yan;Lingyun Wang;Guojiang Shen;Zhi Liu

IEEE Transactions on Consumer Electronics

Year: 2025 | Early Access Article |

Enhancing Human Action Recognition with Fine-grained Body Movement Attention

Rui Zhang;Junxiao Xue;Feng Lin;Qing Zhang;Pavel Smirnov;Xiao Ma;Xiaoran Yan

2024 IEEE International Conference on Multimedia and Expo (ICME)

Year: 2024 | Conference Paper |

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In the field of vision-language models (VLMs), human action recognition models, while effective, always rely on large pre-trained models or high-resolution inputs, leading to computational challenges. To address this, we propose a novel VLM approach with fine-grained attention to body movements. Unlike methods relying on coarse video-text matching, we guide the model to infer actions from fine-gra...Show More

Enhancing Human Action Recognition with Fine-grained Body Movement Attention

Rui Zhang;Junxiao Xue;Feng Lin;Qing Zhang;Pavel Smirnov;Xiao Ma;Xiaoran Yan

2024 IEEE International Conference on Multimedia and Expo (ICME)

Year: 2024 | Conference Paper |

Point-Correlate Adversarial Transformer for Unsupervised Multivariate Time Series Anomaly Detection

Huan Li;Xiangjie Kong;Guojiang Shen;Xiaoran Yan;Yao Yang;Mario Collotta

2024 27th International Conference on Computer Supported Cooperative Work in Design (CSCWD)

Year: 2024 | Conference Paper |

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Multivariate time series anomaly detection plays a crucial role in industrial production. However, the inherent complexity and randomness of time series pose significant challenges. Furthermore, existing detection methods struggle to provide reliable explanations for outliers. To address these issues, this paper presents an unsupervised multivariate time series anomaly detection model named Point-...Show More

Point-Correlate Adversarial Transformer for Unsupervised Multivariate Time Series Anomaly Detection

Huan Li;Xiangjie Kong;Guojiang Shen;Xiaoran Yan;Yao Yang;Mario Collotta

2024 27th International Conference on Computer Supported Cooperative Work in Design (CSCWD)

Year: 2024 | Conference Paper |

Federated Graph Anomaly Detection via Contrastive Self-Supervised Learning

Xiangjie Kong;Wenyi Zhang;Hui Wang;Mingliang Hou;Xin Chen;Xiaoran Yan;Sajal K. Das

IEEE Transactions on Neural Networks and Learning Systems

Year: 2024 | Early Access Article |

Cited by: Papers (5)

Attribute graph anomaly detection aims to identify nodes that significantly deviate from the majority of normal nodes, and has received increasing attention due to the ubiquity and complexity of graph-structured data in various real-world scenarios. However, current mainstream anomaly detection methods are primarily designed for centralized settings, which may pose privacy leakage risks in certain...Show More

Federated Graph Anomaly Detection via Contrastive Self-Supervised Learning

Xiangjie Kong;Wenyi Zhang;Hui Wang;Mingliang Hou;Xin Chen;Xiaoran Yan;Sajal K. Das

IEEE Transactions on Neural Networks and Learning Systems

Year: 2024 | Early Access Article |

FVFL: A Flexible and Verifiable Privacy-Preserving Federated Learning Scheme

Gang Wang;Li Zhou;Qingming Li;Xiaoran Yan;Ximeng Liu;Yuncheng Wu

IEEE Internet of Things Journal

Year: 2024 | Volume: 11, Issue: 13 | Journal Article |

Cited by: Papers (2)

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With the development of deep learning, people are more and more concerned about the security of data. Federated learning can solve the problem of data island, but it also brings more serious data privacy problems. Furthermore, in the process of multisource data collaboration, the efficiency of the whole federated learning system is usually not high. In this article, we introduce a scheme named FVF...Show More

FVFL: A Flexible and Verifiable Privacy-Preserving Federated Learning Scheme

Gang Wang;Li Zhou;Qingming Li;Xiaoran Yan;Ximeng Liu;Yuncheng Wu

IEEE Internet of Things Journal

Year: 2024 | Volume: 11, Issue: 13 | Journal Article |

Z-Laplacian Matrix Factorization: Network Embedding With Interpretable Graph Signals

Liangtian Wan;Zhengqiang Fu;Yi Ling;Yuchen Sun;Xiaona Li;Lu Sun;Feng Xia;Xiaoran Yan;Charu C. Aggarwal

IEEE Transactions on Knowledge and Data Engineering

Year: 2024 | Volume: 36, Issue: 8 | Journal Article |

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Network embedding aims to represent nodes with low dimensional vectors while preserving structural information. It has been recently shown that many popular network embedding methods can be transformed into matrix factorization problems. In this paper, we propose the unifying framework “Z-NetMF,” which generalizes random walk samplers to Z-Laplacian graph filters, leading to embedding algorithms w...Show More

Z-Laplacian Matrix Factorization: Network Embedding With Interpretable Graph Signals

Liangtian Wan;Zhengqiang Fu;Yi Ling;Yuchen Sun;Xiaona Li;Lu Sun;Feng Xia;Xiaoran Yan;Charu C. Aggarwal

IEEE Transactions on Knowledge and Data Engineering

Year: 2024 | Volume: 36, Issue: 8 | Journal Article |

Investigations on Scientific Literature Meta Information Extraction Using Large Language Models

Menghao Guo;Fan Wu;Jinling Jiang;Xiaoran Yan;Guangyong Chen;Wenhui Li;Yunhong Zhao;Zeyi Sun

2023 IEEE International Conference on Knowledge Graph (ICKG)

Year: 2023 | Conference Paper |

Cited by: Papers (2)

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The meta information in scientific literature including article title, author, institutions, year, journal, etc., plays a critical role in providing useful information to research peers. Traditional meta information extraction methods usually rely on rules and templates. Recently, due to the booming of Large Language Models (LLMs), its application in scientific literature meta-information extracti...Show More

Investigations on Scientific Literature Meta Information Extraction Using Large Language Models

Menghao Guo;Fan Wu;Jinling Jiang;Xiaoran Yan;Guangyong Chen;Wenhui Li;Yunhong Zhao;Zeyi Sun

2023 IEEE International Conference on Knowledge Graph (ICKG)

Year: 2023 | Conference Paper |

DPVisCreator: Incorporating Pattern Constraints to Privacy-preserving Visualizations via Differential Privacy

Jiehui Zhou;Xumeng Wang;Jason K. Wong;Huanliang Wang;Zhongwei Wang;Xiaoyu Yang;Xiaoran Yan;Haozhe Feng;Huamin Qu;Haochao Ying;Wei Chen

IEEE Transactions on Visualization and Computer Graphics

Year: 2023 | Volume: 29, Issue: 1 | Journal Article |

Cited by: Papers (9)

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Data privacy is an essential issue in publishing data visualizations. However, it is challenging to represent multiple data patterns in privacy-preserving visualizations. The prior approaches target specific chart types or perform an anonymization model uniformly without considering the importance of data patterns in visualizations. In this paper, we propose a visual analytics approach that facili...Show More

DPVisCreator: Incorporating Pattern Constraints to Privacy-preserving Visualizations via Differential Privacy

Jiehui Zhou;Xumeng Wang;Jason K. Wong;Huanliang Wang;Zhongwei Wang;Xiaoyu Yang;Xiaoran Yan;Haozhe Feng;Huamin Qu;Haochao Ying;Wei Chen

IEEE Transactions on Visualization and Computer Graphics

Year: 2023 | Volume: 29, Issue: 1 | Journal Article |

Detecting Outlier Patterns With Query-Based Artificially Generated Searching Conditions

Shuo Yu;Feng Xia;Yuchen Sun;Tao Tang;Xiaoran Yan;Ivan Lee

IEEE Transactions on Computational Social Systems

Year: 2021 | Volume: 8, Issue: 1 | Journal Article |

Cited by: Papers (25)

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In the age of social computing, finding interesting network patterns or motifs is significant and critical for various areas, such as decision intelligence, intrusion detection, medical diagnosis, social network analysis, fake news identification, and national security. However, subgraph matching remains a computationally challenging problem, let alone identifying special motifs among them. This i...Show More

Detecting Outlier Patterns With Query-Based Artificially Generated Searching Conditions

Shuo Yu;Feng Xia;Yuchen Sun;Tao Tang;Xiaoran Yan;Ivan Lee

IEEE Transactions on Computational Social Systems

Year: 2021 | Volume: 8, Issue: 1 | Journal Article |

Efficient network navigation with partial information

Xiaoran Yan;Olaf Sporns;Andrea Avena-Koenigsberger

2020 IEEE International Conference on Networking, Sensing and Control (ICNSC)

Year: 2020 | Conference Paper |

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We propose a information theoretical framework to capture transition and information costs of network navigation models. Based on the minimum description length principle and the Markov decision process, we demonstrate that efficient global navigation can be achieved with only partial information. Additionally, we derived a scalable algorithm for optimal solutions under certain conditions. The pro...Show More

Efficient network navigation with partial information

Xiaoran Yan;Olaf Sporns;Andrea Avena-Koenigsberger

2020 IEEE International Conference on Networking, Sensing and Control (ICNSC)

Year: 2020 | Conference Paper |

Graph Filters and the Z-Laplacian

Xiaoran Yan;Brian M. Sadler;Robert J. Drost;Paul L. Yu;Kristina Lerman

IEEE Journal of Selected Topics in Signal Processing

Year: 2017 | Volume: 11, Issue: 6 | Journal Article |

Cited by: Papers (15)

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In network science, the interplay between dynamical processes and the underlying topologies of complex systems has led to a diverse family of models with different interpretations. In graph signal processing, this is manifested in the form of different graph shifts and their induced algebraic systems. In this paper, we propose the unifying Z-Laplacian framework, whose instances can act as graph sh...Show More

Graph Filters and the Z-Laplacian

Xiaoran Yan;Brian M. Sadler;Robert J. Drost;Paul L. Yu;Kristina Lerman

IEEE Journal of Selected Topics in Signal Processing

Year: 2017 | Volume: 11, Issue: 6 | Journal Article |

Bayesian model selection of stochastic block models

2016 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM)

Year: 2016 | Conference Paper |

Cited by: Papers (22)

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A central problem in analyzing networks is partitioning them into modules or communities. One of the best tools for this is the stochastic block model, which clusters vertices into blocks with statistically homogeneous pattern of links. Despite its flexibility and popularity, there has been a lack of principled statistical model selection criteria for the stochastic block model. Here we propose a ...Show More

Bayesian model selection of stochastic block models

2016 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM)

Year: 2016 | Conference Paper |

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