pwc-archive/papers-with-abstracts huggingface dataset:
YNetr: Dual-Encoder architecture on Plain Scan Liver Tumors (PSLT)
Authors: Wen Sheng, Zhong Zheng, Jiajun Liu, Han Lu, Hanyuan Zhang, Zhengyong Jiang, Zhihong Zhang, Daoping Zhu
Date: 2024-03-30 00:00:00
Tasks: Segmentation

Background: Liver tumors are abnormal growths in the liver that can be either benign or malignant, with liver cancer being a significant health concern worldwide. However, there is no dataset for plain scan segmentation of liver tumors, nor any related algorithms. To fill this gap, we propose Plain Scan Liver Tumors(PSLT) and YNetr. Methods: A collection of 40 liver tumor plain scan segmentation datasets was assembled and annotated. Concurrently, we utilized Dice coefficient as the metric for assessing the segmentation outcomes produced by YNetr, having advantage of capturing different frequency information. Results: The YNetr model achieved a Dice coefficient of 62.63% on the PSLT dataset, surpassing the other publicly available model by an accuracy margin of 1.22%. Comparative evaluations were conducted against a range of models including UNet 3+, XNet, UNetr, Swin UNetr, Trans-BTS, COTr, nnUNetv2 (2D), nnUNetv2 (3D fullres), MedNext (2D) and MedNext(3D fullres). Conclusions: We not only proposed a dataset named PSLT(Plain Scan Liver Tumors), but also explored a structure called YNetr that utilizes wavelet transform to extract different frequency information, which having the SOTA in PSLT by experiments.
Exploiting Self-Supervised Constraints in Image Super-Resolution
Authors: Gang Wu, Junjun Jiang, Kui Jiang, Xianming Liu
Date: 2024-03-30 00:00:00
Tasks: Image Super-Resolution, Self-Supervised Learning, Super-Resolution

Recent advances in self-supervised learning, predominantly studied in high-level visual tasks, have been explored in low-level image processing. This paper introduces a novel self-supervised constraint for single image super-resolution, termed SSC-SR. SSC-SR uniquely addresses the divergence in image complexity by employing a dual asymmetric paradigm and a target model updated via exponential moving average to enhance stability. The proposed SSC-SR framework works as a plug-and-play paradigm and can be easily applied to existing SR models. Empirical evaluations reveal that our SSC-SR framework delivers substantial enhancements on a variety of benchmark datasets, achieving an average increase of 0.1 dB over EDSR and 0.06 dB over SwinIR. In addition, extensive ablation studies corroborate the effectiveness of each constituent in our SSC-SR framework. Codes are available at https://github.com/Aitical/SSCSR.
YOLOOC: YOLO-based Open-Class Incremental Object Detection with Novel Class Discovery
Authors: Qian Wan, Xiang Xiang, Qinhao Zhou
Date: 2024-03-30 00:00:00
Tasks: Class-Incremental Object Detection, Novel Class Discovery, object-detection, Object Detection, Open World Object Detection

Because of its use in practice, open-world object detection (OWOD) has gotten a lot of attention recently. The challenge is how can a model detect novel classes and then incrementally learn them without forgetting previously known classes. Previous approaches hinge on strongly-supervised or weakly-supervised novel-class data for novel-class detection, which may not apply to real applications. We construct a new benchmark that novel classes are only encountered at the inference stage. And we propose a new OWOD detector YOLOOC, based on the YOLO architecture yet for the Open-Class setup. We introduce label smoothing to prevent the detector from over-confidently mapping novel classes to known classes and to discover novel classes. Extensive experiments conducted on our more realistic setup demonstrate the effectiveness of our method for discovering novel classes in our new benchmark.
GreenSaliency: A Lightweight and Efficient Image Saliency Detection Method
Authors: Zhanxuan Mei, Yun-Cheng Wang, C. -C. Jay Kuo
Date: 2024-03-30 00:00:00
Tasks: Saliency Detection, Saliency Prediction

Image saliency detection is crucial in understanding human gaze patterns from visual stimuli. The escalating demand for research in image saliency detection is driven by the growing necessity to incorporate such techniques into various computer vision tasks and to understand human visual systems. Many existing image saliency detection methods rely on deep neural networks (DNNs) to achieve good performance. However, the high computational complexity associated with these approaches impedes their integration with other modules or deployment on resource-constrained platforms, such as mobile devices. To address this need, we propose a novel image saliency detection method named GreenSaliency, which has a small model size, minimal carbon footprint, and low computational complexity. GreenSaliency can be a competitive alternative to the existing deep-learning-based (DL-based) image saliency detection methods with limited computation resources. GreenSaliency comprises two primary steps: 1) multi-layer hybrid feature extraction and 2) multi-path saliency prediction. Experimental results demonstrate that GreenSaliency achieves comparable performance to the state-of-the-art DL-based methods while possessing a considerably smaller model size and significantly reduced computational complexity.
Learned Scanpaths Aid Blind Panoramic Video Quality Assessment
Authors: Kanglong Fan, Wen Wen, Mu Li, Yifan Peng, Kede Ma
Date: 2024-03-30 00:00:00
Proceeding: CVPR 2024 1
Tasks: Video Quality Assessment

Panoramic videos have the advantage of providing an immersive and interactive viewing experience. Nevertheless, their spherical nature gives rise to various and uncertain user viewing behaviors, which poses significant challenges for panoramic video quality assessment (PVQA). In this work, we propose an end-to-end optimized, blind PVQA method with explicit modeling of user viewing patterns through visual scanpaths. Our method consists of two modules: a scanpath generator and a quality assessor. The scanpath generator is initially trained to predict future scanpaths by minimizing their expected code length and then jointly optimized with the quality assessor for quality prediction. Our blind PVQA method enables direct quality assessment of panoramic images by treating them as videos composed of identical frames. Experiments on three public panoramic image and video quality datasets, encompassing both synthetic and authentic distortions, validate the superiority of our blind PVQA model over existing methods.
An Interpretable Cross-Attentive Multi-modal MRI Fusion Framework for Schizophrenia Diagnosis
Authors: Ziyu Zhou, Anton Orlichenko, Gang Qu, Zening Fu, Vince D Calhoun, Zhengming Ding, Yu-Ping Wang
Date: 2024-03-29 00:00:00

Both functional and structural magnetic resonance imaging (fMRI and sMRI) are widely used for the diagnosis of mental disorder. However, combining complementary information from these two modalities is challenging due to their heterogeneity. Many existing methods fall short of capturing the interaction between these modalities, frequently defaulting to a simple combination of latent features. In this paper, we propose a novel Cross-Attentive Multi-modal Fusion framework (CAMF), which aims to capture both intra-modal and inter-modal relationships between fMRI and sMRI, enhancing multi-modal data representation. Specifically, our CAMF framework employs self-attention modules to identify interactions within each modality while cross-attention modules identify interactions between modalities. Subsequently, our approach optimizes the integration of latent features from both modalities. This approach significantly improves classification accuracy, as demonstrated by our evaluations on two extensive multi-modal brain imaging datasets, where CAMF consistently outperforms existing methods. Furthermore, the gradient-guided Score-CAM is applied to interpret critical functional networks and brain regions involved in schizophrenia. The bio-markers identified by CAMF align with established research, potentially offering new insights into the diagnosis and pathological endophenotypes of schizophrenia.
FetalDiffusion: Pose-Controllable 3D Fetal MRI Synthesis with Conditional Diffusion Model
Authors: Molin Zhang, Polina Golland, Patricia Ellen Grant, Elfar Adalsteinsson
Date: 2024-03-29 00:00:00
Tasks: GPU, Pose Estimation

The quality of fetal MRI is significantly affected by unpredictable and substantial fetal motion, leading to the introduction of artifacts even when fast acquisition sequences are employed. The development of 3D real-time fetal pose estimation approaches on volumetric EPI fetal MRI opens up a promising avenue for fetal motion monitoring and prediction. Challenges arise in fetal pose estimation due to limited number of real scanned fetal MR training images, hindering model generalization when the acquired fetal MRI lacks adequate pose. In this study, we introduce FetalDiffusion, a novel approach utilizing a conditional diffusion model to generate 3D synthetic fetal MRI with controllable pose. Additionally, an auxiliary pose-level loss is adopted to enhance model performance. Our work demonstrates the success of this proposed model by producing high-quality synthetic fetal MRI images with accurate and recognizable fetal poses, comparing favorably with in-vivo real fetal MRI. Furthermore, we show that the integration of synthetic fetal MR images enhances the fetal pose estimation model's performance, particularly when the number of available real scanned data is limited resulting in 15.4% increase in PCK and 50.2% reduced in mean error. All experiments are done on a single 32GB V100 GPU. Our method holds promise for improving real-time tracking models, thereby addressing fetal motion issues more effectively.
Boosting Cardiac Color Doppler Frame Rates with Deep Learning
Authors: Julia Puig, Denis Friboulet, Hang Jung Ling, François Varray, Jonathan Porée, Jean Provost, Damien Garcia, Fabien Millioz
Date: 2024-03-28 00:00:00
Tasks: Data Augmentation, Deep Learning

Color Doppler echocardiography enables visualization of blood flow within the heart. However, the limited frame rate impedes the quantitative assessment of blood velocity throughout the cardiac cycle, thereby compromising a comprehensive analysis of ventricular filling. Concurrently, deep learning is demonstrating promising outcomes in post-processing of echocardiographic data for various applications. This work explores the use of deep learning models for intracardiac Doppler velocity estimation from a reduced number of filtered I/Q signals. We used a supervised learning approach by simulating patient-based cardiac color Doppler acquisitions and proposed data augmentation strategies to enlarge the training dataset. We implemented architectures based on convolutional neural networks. In particular, we focused on comparing the U-Net model and the recent ConvNeXt models, alongside assessing real-valued versus complex-valued representations. We found that both models outperformed the state-of-the-art autocorrelator method, effectively mitigating aliasing and noise. We did not observe significant differences between the use of real and complex data. Finally, we validated the models on in vitro and in vivo experiments. All models produced quantitatively comparable results to the baseline and were more robust to noise. ConvNeXt emerged as the sole model to achieve high-quality results on in vivo aliased samples. These results demonstrate the interest of supervised deep learning methods for Doppler velocity estimation from a reduced number of acquisitions.
Robust Trajectory and Resource Optimization for Communication-assisted UAV SAR Sensing
Authors: Mohamed-Amine Lahmeri, Walid R. Ghanem, Christina Bonfert, Robert Schober
Date: 2024-04-01 00:00:00
Tasks: Trajectory Planning

In this paper, we investigate joint 3-dimensional (3D) trajectory planning and resource allocation for rotary-wing unmanned aerial vehicle (UAV) synthetic aperture radar (SAR) sensing. To support emerging real-time SAR applications and enable live mission control, we incorporate real-time communication with a ground station (GS). The UAV's main mission is the mapping of large areas of interest (AoIs) using an onboard SAR system and transferring the unprocessed raw radar data to the ground in real time. We propose a robust trajectory and resource allocation design that takes into account random UAV trajectory deviations. To this end, we model the UAV trajectory deviations and study their effect on the radar coverage. Then, we formulate a robust non-convex mixed-integer non-linear program (MINLP) such that the UAV 3D trajectory and resources are jointly optimized for maximization of the radar ground coverage. A low-complexity sub-optimal solution for the formulated problem is presented. Furthermore, to assess the performance of the sub-optimal algorithm, we derive an upper bound on the optimal solution based on monotonic optimization theory. Simulation results show that the proposed sub-optimal algorithm achieves close-to-optimal performance and not only outperforms several benchmark schemes but is also robust with respect to UAV trajectory deviations.
Automatic Modulation Classification using a Waveform Signature
Authors: William H. Clark IV, Joseph M. Ernst, Robert W. McGwier
Date: 2024-04-01 00:00:00
Tasks: Classification

Cognitive Radios (CRs) build upon Software Defined Radios (SDRs) to allow for autonomous reconfiguration of communication architectures. In recent years, CRs have been identified as an enabler for Dynamic Spectrum Access (DSA) applications in which secondary users opportunistically share licensed spectrum. A major challenge for DSA is accurately characterizing the spectral environment, which requires blind signal classification. Existing work in this area has focused on simplistic channel models; however, more challenging fading channels (e.g., frequency selective fading channels) cause existing methods to be computationally complex or insufficient. This paper develops a novel blind modulation classification algorithm, which uses a set of higher order statistics to overcome these challenges. The set of statistics forms a signature, which can either be used directly for classification or can be processed using big data analytical techniques, such as principle component analysis (PCA), to learn the environment. The algorithm is tested in simulation on both flat fading and selective fading channel models. Results of this blind classification algorithm are shown to improve upon those which use single value higher order statistical methods.
GI-Free Pilot-Aided Channel Estimation for Affine Frequency Division Multiplexing Systems
Authors: Yu Zhou, Haoran Yin, Nanhao Zhou, Yanqun Tang, Xiaoying Zhang, Weijie Yuan
Date: 2024-04-01 00:00:00
Tasks: Diversity

The recently developed affine frequency division multiplexing (AFDM) can achieve full diversity in doubly selective channels, providing a comprehensive sparse representation of the delay-Doppler domain channel. Thus, accurate channel estimation is feasible by using just one pilot symbol. However, traditional AFDM channel estimation schemes necessitate the use of guard intervals (GI) to mitigate data-pilot interference, leading to spectral efficiency degradation. In this paper, we propose a GI-free pilot-aided channel estimation algorithm for AFDM systems, which improves spectral efficiency significantly. To mitigate the interference between the pilot and data symbols caused by the absence of GI, we perform joint interference cancellation, channel estimation, and signal detection iterately. Simulation results show that the bit error rate (BER) performance of the proposed method can approach the ideal case with perfect channel estimation.
SONIC: Synergizing VisiON Foundation Models for Stress RecogNItion from ECG signals
Authors: Orchid Chetia Phukan, Ankita Das, Arun Balaji Buduru, Rajesh Sharma
Date: 2024-03-31 00:00:00

Stress recognition through physiological signals such as Electrocardiogram (ECG) signals has garnered significant attention. Traditionally, research in this field predominantly focused on utilizing handcrafted features or raw signals as inputs for learning algorithms. However, there is now a burgeoning interest within the community in leveraging large-scale vision foundation models (VFMs) like ResNet50, VGG19, and others. These VFMs are increasingly preferred due to their ability to capture complex features, enhancing the accuracy and effectiveness of stress recognition systems. However, no particular focus has been given on combining these VFMs. The combination of VFMs offers promising benefits by harnessing their collective knowledge to extract richer representations for improved stress recognition. So, to mitigate this research gap, we focus on combining different VFMs for stress recognition from ECG and propose SONIC, a novel framework that combines VFMs through their logits and training a fully connected network on the combined logits. Through extensive experimentation, SONIC showed the top performance against individual VFMs performance on the WESAD benchmark. With SONIC, we report state-of-the-art (SOTA) performance in WESAD with 99.36% and 99.24% (stress vs non-stress) and 97.66% and 97.10% (amusement vs stress vs baseline) in accuracy and F1 respectively.
Supplementary File: Cooperative Gradient Coding for Semi-Decentralized Federated Learning
Authors: Shudi Weng, Chengxi Li, Ming Xiao, Mikael Skoglund
Date: 2024-03-31 00:00:00
Tasks: Federated Learning, valid

Stragglers' effects are known to degrade FL performance. In this paper, we investigate federated learning (FL) over wireless networks in the presence of communication stragglers, where the power-constrained clients collaboratively train a global model by iteratively optimizing a local objective function with their local datasets and transmitting local model updates to the central parameter server (PS) through fading channels. To tackle communication stragglers without dataset sharing or prior information about the network at PS, we propose cooperative gradient coding (CoGC) for semi-decentralized FL to enable the exact global model recovery at PS. Furthermore, we conduct a thorough theoretical analysis of the proposed approach. Namely, an outage analysis of the proposed approach is provided, followed by a convergence analysis based on the failure probability of the global model recovery at PS. Nevertheless, simulation results reveal the superiority of the proposed approach in the presence of stragglers under imbalanced data distribution.
Complex Neural Network based Joint AoA and AoD Estimation for Bistatic ISAC
Authors: Salmane Naoumi, Ahmad Bazzi, Roberto Bomfin, Marwa Chafii
Date: 2024-03-31 00:00:00
Tasks: Computational Efficiency, Integrated sensing and communication, ISAC

Integrated sensing and communication (ISAC) in wireless systems has emerged as a promising paradigm, offering the potential for improved performance, efficient resource utilization, and mutually beneficial interactions between radar sensing and wireless communications, thereby shaping the future of wireless technologies. In this work, we present two novel methods to address the joint angle of arrival and angle of departure estimation problem for bistatic ISAC systems. Our proposed methods consist of a deep learning (DL) solution leveraging complex neural networks, in addition to a parameterized algorithm. By exploiting the estimated channel matrix and incorporating a preprocessing step consisting of a coarse timing estimation, we are able to notably reduce the input size and improve the computational efficiency. In our findings, we emphasize the remarkable potential of our DL-based approach, which demonstrates comparable performance to the parameterized method that explicitly exploits the multiple-input multiple-output (MIMO) model, while exhibiting significantly lower computational complexity.
From Learning to Analytics: Improving Model Efficacy with Goal-Directed Client Selection
Authors: Jingwen Tong, Zhenzhen Chen, Liqun Fu, Jun Zhang, Zhu Han
Date: 2024-03-30 00:00:00
Tasks: Federated Learning

Federated learning (FL) is an appealing paradigm for learning a global model among distributed clients while preserving data privacy. Driven by the demand for high-quality user experiences, evaluating the well-trained global model after the FL process is crucial. In this paper, we propose a closed-loop model analytics framework that allows for effective evaluation of the trained global model using clients' local data. To address the challenges posed by system and data heterogeneities in the FL process, we study a goal-directed client selection problem based on the model analytics framework by selecting a subset of clients for the model training. This problem is formulated as a stochastic multi-armed bandit (SMAB) problem. We first put forth a quick initial upper confidence bound (Quick-Init UCB) algorithm to solve this SMAB problem under the federated analytics (FA) framework. Then, we further propose a belief propagation-based UCB (BP-UCB) algorithm under the democratized analytics (DA) framework. Moreover, we derive two regret upper bounds for the proposed algorithms, which increase logarithmically over the time horizon. The numerical results demonstrate that the proposed algorithms achieve nearly optimal performance, with a gap of less than 1.44% and 3.12% under the FA and DA frameworks, respectively.
Portfolio management using graph centralities: Review and comparison
Authors: Bahar Arslan, Vanni Noferini, Spyridon Vrontos
Date: 2024-03-29 00:00:00
Tasks: Management, Portfolio Optimization

We investigate an application of network centrality measures to portfolio optimization, by generalizing the method in [Pozzi, Di Matteo and Aste, \emph{Spread of risks across financial markets: better to invest in the peripheries}, Scientific Reports 3:1665, 2013], that however had significant limitations with respect to the state of the art in network theory. In this paper, we systematically compare many possible variants of the originally proposed method on S\&P 500 stocks. We use daily data from twenty-seven years as training set and their following year as test set. We thus select the best network-based methods according to different viewpoints including for instance the highest Sharpe Ratio and the highest expected return. We give emphasis in new centrality measures and we also conduct a thorough analysis, which reveals significantly stronger results compared to those with more traditional methods. According to our analysis, this graph-theoretical approach to investment can be used successfully by investors with different investment profiles leading to high risk-adjusted returns.
An age-distributed immuno-epidemiological model with information-based vaccination decision
Authors: Samiran Ghosh, Malay Banerjee, Vitaly Volpert
Date: 2024-03-30 00:00:00

A new age-distributed immuno-epidemiological model with information-based vaccine uptake suggested in this work represents a system of integro-differential equations for the numbers of susceptible individuals, infected individuals, vaccinated individuals and recovered individuals. This model describes the influence of vaccination decision on epidemic progression in different age groups. We prove the existence and uniqueness of a positive solution using the fixed point theory. In a particular case of age-independent model, we determine the final size of epidemic, that is, the limiting number of susceptible individuals at asymptotically large time. Numerical simulations show that the information-based vaccine acceptance can significantly influence the epidemic progression. Though the initial stage of epidemic progression is the same for all memory kernels, as the epidemic progresses and more information about the disease becomes available, further epidemic progression strongly depends on the memory effect. Short-range memory kernel appears to be more effective in restraining the epidemic outbreaks because it allows for more responsive and adaptive vaccination decisions based on the most recent information about the disease.