pwc-archive/papers-with-abstracts huggingface dataset:
Swap-Net: A Memory-Efficient 2.5D Network for Sparse-View 3D Cone Beam CT Reconstruction
Authors: Xiaojian Xu, Marc Klasky, Michael T. McCann, Jason Hu, Jeffrey A. Fessler
Date: 2024-09-29 00:00:00
Tasks: CT Reconstruction, Image Reconstruction

Reconstructing 3D cone beam computed tomography (CBCT) images from a limited set of projections is an important inverse problem in many imaging applications from medicine to inertial confinement fusion (ICF). The performance of traditional methods such as filtered back projection (FBP) and model-based regularization is sub-optimal when the number of available projections is limited. In the past decade, deep learning (DL) has gained great popularity for solving CT inverse problems. A typical DL-based method for CBCT image reconstruction is to learn an end-to-end mapping by training a 2D or 3D network. However, 2D networks fail to fully use global information. While 3D networks are desirable, they become impractical as image sizes increase because of the high memory cost. This paper proposes Swap-Net, a memory-efficient 2.5D network for sparse-view 3D CBCT image reconstruction. Swap-Net uses a sequence of novel axes-swapping operations to produce 3D volume reconstruction in an end-to-end fashion without using full 3D convolutions. Simulation results show that Swap-Net consistently outperforms baseline methods both quantitatively and qualitatively in terms of reducing artifacts and preserving details of complex hydrodynamic simulations of relevance to the ICF community.
Adaptive Data Transport Mechanism for UAV Surveillance Missions in Lossy Environments
Authors: Niloufar Mehrabi, Sayed Pedram Haeri Boroujeni, Jenna Hofseth, Abolfazl Razi, Long Cheng, Manveen Kaur, James Martin, Rahul Amin
Date: 2024-09-30 00:00:00
Tasks: Deep Reinforcement Learning, Moving Object Detection, object-detection, Object Detection, Scheduling

Unmanned Aerial Vehicles (UAVs) play an increasingly critical role in Intelligence, Surveillance, and Reconnaissance (ISR) missions such as border patrolling and criminal detection, thanks to their ability to access remote areas and transmit real-time imagery to processing servers. However, UAVs are highly constrained by payload size, power limits, and communication bandwidth, necessitating the development of highly selective and efficient data transmission strategies. This has driven the development of various compression and optimal transmission technologies for UAVs. Nevertheless, most methods strive to preserve maximal information in transferred video frames, missing the fact that only certain parts of images/video frames might offer meaningful contributions to the ultimate mission objectives in the ISR scenarios involving moving object detection and tracking (OD/OT). This paper adopts a different perspective, and offers an alternative AI-driven scheduling policy that prioritizes selecting regions of the image that significantly contributes to the mission objective. The key idea is tiling the image into small patches and developing a deep reinforcement learning (DRL) framework that assigns higher transmission probabilities to patches that present higher overlaps with the detected object of interest, while penalizing sharp transitions over consecutive frames to promote smooth scheduling shifts. Although we used Yolov-8 object detection and UDP transmission protocols as a benchmark testing scenario the idea is general and applicable to different transmission protocols and OD/OT methods. To further boost the system's performance and avoid OD errors for cluttered image patches, we integrate it with interframe interpolations.
Advancements in Ship Detection: Comparative Analysis of Optical and Hyperspectral Sensors
Authors: Alyazia Al Shamsi, Alavikunhu Panthakkan, Saeed Al Mansoori, Hussain Al Ahmad
Date: 2024-10-11 00:00:00
Tasks: Management

In marine surveillance, applications span military and civilian domains, including ship detection, marine traffic control, and disaster management. Optical and hyperspectral satellites are key for this purpose. This paper focuses on ship detection and classification techniques, particularly comparing optical and hyperspectral remote sensing approaches. It presents a comprehensive analysis of these technologies, covering feature extraction, methodologies, and their suitability for different missions. The study highlights the importance of selecting the right sensor aligned with mission objectives and conditions, aiming to improve detection accuracy through integrated strategies. The paper examines the strengths and limitations of both technologies in various maritime applications, enhancing understanding of their usability in different operational scenarios.
Translation Canvas: An Explainable Interface to Pinpoint and Analyze Translation Systems
Authors: Chinmay Dandekar, Wenda Xu, Xi Xu, Siqi Ouyang, Lei LI
Date: 2024-10-07 00:00:00
Tasks: Benchmarking, Machine Translation, Translation

With the rapid advancement of machine translation research, evaluation toolkits have become essential for benchmarking system progress. Tools like COMET and SacreBLEU offer single quality score assessments that are effective for pairwise system comparisons. However, these tools provide limited insights for fine-grained system-level comparisons and the analysis of instance-level defects. To address these limitations, we introduce Translation Canvas, an explainable interface designed to pinpoint and analyze translation systems' performance: 1) Translation Canvas assists machine translation researchers in comprehending system-level model performance by identifying common errors (their frequency and severity) and analyzing relationships between different systems based on various evaluation metrics. 2) It supports fine-grained analysis by highlighting error spans with explanations and selectively displaying systems' predictions. According to human evaluation, Translation Canvas demonstrates superior performance over COMET and SacreBLEU packages under enjoyability and understandability criteria.
Volatility Forecasting in Global Financial Markets Using TimeMixer
Authors: Alex Li
Date: 2024-09-27 00:00:00
Tasks: Management, Time Series, Time Series Forecasting

Predicting volatility in financial markets, including stocks, index ETFs, foreign exchange, and cryptocurrencies, remains a challenging task due to the inherent complexity and non-linear dynamics of these time series. In this study, I apply TimeMixer, a state-of-the-art time series forecasting model, to predict the volatility of global financial assets. TimeMixer utilizes a multiscale-mixing approach that effectively captures both short-term and long-term temporal patterns by analyzing data across different scales. My empirical results reveal that while TimeMixer performs exceptionally well in short-term volatility forecasting, its accuracy diminishes for longer-term predictions, particularly in highly volatile markets. These findings highlight TimeMixer's strength in capturing short-term volatility, making it highly suitable for practical applications in financial risk management, where precise short-term forecasts are critical. However, the model's limitations in long-term forecasting point to potential areas for further refinement.
CVAE-SM: A Conditional Variational Autoencoder with Style Modulation for Efficient Uncertainty Quantification
Authors: Amin Ullah, Taiqing Yan, Li Fuxin
Date: 2024-05-13 00:00:00
Proceeding: IEEE International Conference on Robotics and Automation (ICRA) 2024 5
Tasks: Image Segmentation, Management, Segmentation, Semantic Segmentation, Uncertainty Quantification, Underwater Acoustic Classification

Deep learning has brought transformative advancements to object segmentation, especially in marine robotics contexts such as waste management and subaquatic infrastructure oversight. However, a central challenge persists: calibrating the prediction confidence of the model to ensure robust and reliable outcomes, especially within the demanding underwater environment. Existing solutions for estimating uncertainty are often computationally intensive and have largely centered around Bayesian neural networks or ensemble methods. In this paper, we present a Conditional Variational Autoencoder-based framework (CVAE-SM), which is capable of generating diverse latent codes for improved uncertainty quantification in image segmentation. Our method, enhanced by a style modulator, merges content features, and latent codes more effectively, leading to refined prediction of uncertainty levels. We further introduce a dataset of perturbed underwater images to benchmark uncertainty quantification in this domain. The proposed model not only surpasses peers in segmentation metrics but also matches ensemble models in uncertainty predictions, all while being 2.5 times faster.
Heterogeneous Contrastive Learning for Foundation Models and Beyond
Authors: Lecheng Zheng, Baoyu Jing, Zihao Li, Hanghang Tong, Jingrui He
Date: 2024-03-30 00:00:00
Tasks: Contrastive Learning, Self-Supervised Learning, Survey

In the era of big data and Artificial Intelligence, an emerging paradigm is to utilize contrastive self-supervised learning to model large-scale heterogeneous data. Many existing foundation models benefit from the generalization capability of contrastive self-supervised learning by learning compact and high-quality representations without relying on any label information. Amidst the explosive advancements in foundation models across multiple domains, including natural language processing and computer vision, a thorough survey on heterogeneous contrastive learning for the foundation model is urgently needed. In response, this survey critically evaluates the current landscape of heterogeneous contrastive learning for foundation models, highlighting the open challenges and future trends of contrastive learning. In particular, we first present how the recent advanced contrastive learning-based methods deal with view heterogeneity and how contrastive learning is applied to train and fine-tune the multi-view foundation models. Then, we move to contrastive learning methods for task heterogeneity, including pretraining tasks and downstream tasks, and show how different tasks are combined with contrastive learning loss for different purposes. Finally, we conclude this survey by discussing the open challenges and shedding light on the future directions of contrastive learning.
TrajFM: A Vehicle Trajectory Foundation Model for Region and Task Transferability
Authors: Yan Lin, Tonglong Wei, Zeyu Zhou, Haomin Wen, Jilin Hu, Shengnan Guo, Youfang Lin, Huaiyu Wan
Date: 2024-08-09 00:00:00
Tasks: Computational Efficiency

Vehicle trajectories provide valuable movement information that supports various downstream tasks and powers real-world applications. A desirable trajectory learning model should transfer between different regions and tasks without retraining, thus improving computational efficiency and effectiveness with limited training data. However, a model's ability to transfer across regions is limited by the unique spatial features and POI arrangements of each region, which are closely linked to vehicle movement patterns and difficult to generalize. Additionally, achieving task transferability is challenging due to the differing generation schemes required for various tasks. Existing efforts towards transferability primarily involve learning embedding vectors for trajectories, which perform poorly in region transfer and still require retraining of prediction modules for task transfer. To address these challenges, we propose TrajFM, a vehicle trajectory foundation model that excels in both region and task transferability. For region transferability, we introduce STRFormer as the main learnable model within TrajFM. It integrates spatial, temporal, and POI modalities of trajectories to effectively manage variations in POI arrangements across regions and includes a learnable spatio-temporal Rotary position embedding module for handling spatial features. For task transferability, we propose a trajectory masking and recovery scheme. This scheme unifies the generation processes of various tasks into the masking and recovery of modalities and sub-trajectories, allowing TrajFM to be pre-trained once and transferred to different tasks without retraining. Experiments on two real-world vehicle trajectory datasets under various settings demonstrate the effectiveness of TrajFM. Code is available at https://anonymous.4open.science/r/TrajFM-30E4.
Automatic Integration for Spatiotemporal Neural Point Processes
Authors: ZiHao Zhou, Rose Yu
Date: 2023-10-09 00:00:00
Proceeding: NeurIPS 2023 11
Tasks: Point Processes

Learning continuous-time point processes is essential to many discrete event forecasting tasks. However, integration poses a major challenge, particularly for spatiotemporal point processes (STPPs), as it involves calculating the likelihood through triple integrals over space and time. Existing methods for integrating STPP either assume a parametric form of the intensity function, which lacks flexibility; or approximating the intensity with Monte Carlo sampling, which introduces numerical errors. Recent work by Omi et al. [2019] proposes a dual network approach for efficient integration of flexible intensity function. However, their method only focuses on the 1D temporal point process. In this paper, we introduce a novel paradigm: AutoSTPP (Automatic Integration for Spatiotemporal Neural Point Processes) that extends the dual network approach to 3D STPP. While previous work provides a foundation, its direct extension overly restricts the intensity function and leads to computational challenges. In response, we introduce a decomposable parametrization for the integral network using ProdNet. This approach, leveraging the product of simplified univariate graphs, effectively sidesteps the computational complexities inherent in multivariate computational graphs. We prove the consistency of AutoSTPP and validate it on synthetic data and benchmark real-world datasets. AutoSTPP shows a significant advantage in recovering complex intensity functions from irregular spatiotemporal events, particularly when the intensity is sharply localized. Our code is open-source at https://github.com/Rose-STL-Lab/AutoSTPP.
Testing the order of fractional integration in the presence of smooth trends, with an application to UK Great Ratios
Authors: Mustafa R. Kılınç, Michael Massmann, Maximilian Ambros
Date: 2024-10-14 00:00:00

This note proposes semi-parametric tests for investigating whether a stochastic process is fractionally integrated of order $\delta$, where $|\delta| < 1/2$, when smooth trends are present in the model. We combine the semi-parametric approach by Iacone, Nielsen & Taylor (2022) to model the short range dependence with the use of Chebyshev polynomials by Cuestas & Gil-Alana to describe smooth trends. Our proposed statistics have standard limiting null distributions and match the asymptotic local power of infeasible tests based on unobserved errors. We also establish the conditions under which an information criterion can consistently estimate the order of the Chebyshev polynomial. The finite sample performance is evaluated using simulations, and an empirical application is given for the UK Great Ratios.
Fake it till you predict it: data augmentation strategies to detect initiation and termination of oncology treatment
Authors: Valentin Pohyer, Elizabeth Fabre, Stéphane Oudard, Laure Fournier, Bastien Rance
Date: 2024-10-14 00:00:00
Tasks: Data Augmentation

At the hospital, the dispersion of information regarding anti-cancer treatment makes it difficult to extract. We proposed a solution capable of identifying dates, drugs and their temporal relationship within free-text oncology reports with very few manual annotations. We used pattern recognition for dates, dictionaries for drugs and transformer language models for the relationship, combined with a data augmentation strategy. Our models achieved good prediction F1-scores, reaching 0.872. The performance of models with data augmentation outperforms those of models without. By inferring such models, we can now identify and structure thousands of previously unavailable treatment events to better apprehend solutions and patient response.
Enhanced Federated Anomaly Detection Through Autoencoders Using Summary Statistics-Based Thresholding
Authors: Sofiane Laridi, Gregory Palmer, Kam-Ming Mark Tam
Date: 2024-10-11 00:00:00
Tasks: Anomaly Detection, Federated Learning, Fraud Detection

In Federated Learning (FL), anomaly detection (AD) is a challenging task due to the decentralized nature of data and the presence of non-IID data distributions. This study introduces a novel federated threshold calculation method that leverages summary statistics from both normal and anomalous data to improve the accuracy and robustness of anomaly detection using autoencoders (AE) in a federated setting. Our approach aggregates local summary statistics across clients to compute a global threshold that optimally separates anomalies from normal data while ensuring privacy preservation. We conducted extensive experiments using publicly available datasets, including Credit Card Fraud Detection, Shuttle, and Covertype, under various data distribution scenarios. The results demonstrate that our method consistently outperforms existing federated and local threshold calculation techniques, particularly in handling non-IID data distributions. This study also explores the impact of different data distribution scenarios and the number of clients on the performance of federated anomaly detection. Our findings highlight the potential of using summary statistics for threshold calculation in improving the scalability and accuracy of federated anomaly detection systems.
FSOS-AMC: Few-Shot Open-Set Learning for Automatic Modulation Classification
Authors: Hao Zhang, Fuhui Zhou, Qihui Wu, Chau Yuen
Date: 2024-10-14 00:00:00
Tasks: Classification, Open Set Learning

Automatic modulation classification (AMC) is essential for the advancement and efficiency of future wireless communication networks. Deep learning (DL)-based AMC frameworks have garnered extensive attention for their impressive classification performance. However, existing DL-based AMC frameworks rely on two assumptions, large-scale training data and the same class pool between the training and testing data, which are not suitable for \emph{few-shot and open-set} scenarios. To address this issue, a novel few-shot open-set automatic modulation classification (FSOS-AMC) framework is proposed by exploiting a multi-scale attention network, meta-prototype training, and a modular open-set classifier. The multi-scale attention network is used to extract the features from the input signal, the meta-prototype training is adopted to train the feature extractor and the modular open-set classifier can be utilized to classify the testing data into one of the known modulations or potential unknown modulations. Extensive simulation results demonstrate that the proposed FSOS-AMC framework can achieve higher classification accuracy than the state-of-the-art methods for known modulations and unknown modulations in terms of accuracy and area under the receiver operating characteristic curve (AUROC). Moreover, the performance of the proposed FSOS-AMC framework under low signal-to-noise ratio (SNR) conditions is much better than the compared schemes.
Context-Aware Adapter Tuning for Few-Shot Relation Learning in Knowledge Graphs
Authors: Ran Liu, Zhongzhou Liu, XiaoLi Li, Yuan Fang
Date: 2024-10-11 00:00:00
Tasks: Knowledge Graphs, Meta-Learning, Relation

Knowledge graphs (KGs) are instrumental in various real-world applications, yet they often suffer from incompleteness due to missing relations. To predict instances for novel relations with limited training examples, few-shot relation learning approaches have emerged, utilizing techniques such as meta-learning. However, the assumption is that novel relations in meta-testing and base relations in meta-training are independently and identically distributed, which may not hold in practice. To address the limitation, we propose RelAdapter, a context-aware adapter for few-shot relation learning in KGs designed to enhance the adaptation process in meta-learning. First, RelAdapter is equipped with a lightweight adapter module that facilitates relation-specific, tunable adaptation of meta-knowledge in a parameter-efficient manner. Second, RelAdapter is enriched with contextual information about the target relation, enabling enhanced adaptation to each distinct relation. Extensive experiments on three benchmark KGs validate the superiority of RelAdapter over state-of-the-art methods.
LISAC: Learned Coded Waveform Design for ISAC with OFDM
Authors: Chenghong Bian, Yumeng Zhang, Deniz Gunduz
Date: 2024-10-14 00:00:00
Tasks: Decoder, Integrated sensing and communication, ISAC

We propose a novel deep learning based method to design a coded waveform for integrated sensing and communication (ISAC) system based on orthogonal frequency-division multiplexing (OFDM). Our ultimate goal is to design a coded waveform, which is capable of providing satisfactory sensing performance of the target while maintaining high communication quality measured in terms of the bit error rate (BER). The proposed LISAC provides an improved waveform design with the assistance of deep neural networks for the encoding and decoding of the information bits. In particular, the transmitter, parameterized by a recurrent neural network (RNN), encodes the input bit sequence into the transmitted waveform for both sensing and communications. The receiver employs a RNN-based decoder to decode the information bits while the transmitter senses the target via maximum likelihood detection. We optimize the system considering both the communication and sensing performance. Simulation results show that the proposed LISAC waveform achieves a better trade-off curve compared to existing alternatives.
Synchronization for Multiuser Uplink OTFS
Authors: Mohsen Bayat, Sanoopkumar P. S., Arman Farhang
Date: 2024-10-14 00:00:00

In this paper, we propose time and frequency synchronization techniques for the uplink of multiuser orthogonal time frequency space (MU-OTFS) in high-mobility scenarios. We introduce a spectrally efficient and practical pilot pattern where each user utilizes a pilot with a cyclic prefix (PCP) within a shared pilot region on the delay-Doppler plane. At the receiver, a bank of filters is deployed to separate the users' signals and accurately estimate their timing offsets (TOs) and carrier frequency offsets (CFOs). Our technique employs a threshold-based approach that provides precise TO estimates. Our proposed CFO estimation technique reduces the multi-dimensional maximum likelihood (ML) search problem into multiple one-dimensional search problems. Furthermore, we apply the Chebyshev polynomials of the first kind basis expansion model (CPF-BEM) to effectively handle the time-variations of the channel in obtaining the CFO estimates for all the users. Finally, we numerically investigate the error performance of our proposed synchronization technique in high mobility scenarios for the MU-OTFS uplink. Our simulation results confirm the efficacy of the proposed technique in estimating the TOs and CFOs which also leads to an improved channel estimation performance.