pwc-archive/papers-with-abstracts huggingface dataset:
Artificial Immunofluorescence in a Flash: Rapid Synthetic Imaging from Brightfield Through Residual Diffusion
Authors: Xiaodan Xing, Chunling Tang, Siofra Murdoch, Giorgos Papanastasiou, Yunzhe Guo, Xianglu Xiao, Jan Cross-Zamirski, Carola-Bibiane Schönlieb, Kristina Xiao Liang, Zhangming Niu, Evandro Fei Fang, Yinhai Wang, Guang Yang
Date: 2024-07-25 00:00:00
Tasks: Cell Segmentation, Image Generation, SSIM

Immunofluorescent (IF) imaging is crucial for visualizing biomarker expressions, cell morphology and assessing the effects of drug treatments on sub-cellular components. IF imaging needs extra staining process and often requiring cell fixation, therefore it may also introduce artefects and alter endogenouous cell morphology. Some IF stains are expensive or not readily available hence hindering experiments. Recent diffusion models, which synthesise high-fidelity IF images from easy-to-acquire brightfield (BF) images, offer a promising solution but are hindered by training instability and slow inference times due to the noise diffusion process. This paper presents a novel method for the conditional synthesis of IF images directly from BF images along with cell segmentation masks. Our approach employs a Residual Diffusion process that enhances stability and significantly reduces inference time. We performed a critical evaluation against other image-to-image synthesis models, including UNets, GANs, and advanced diffusion models. Our model demonstrates significant improvements in image quality (p<0.05 in MSE, PSNR, and SSIM), inference speed (26 times faster than competing diffusion models), and accurate segmentation results for both nuclei and cell bodies (0.77 and 0.63 mean IOU for nuclei and cell true positives, respectively). This paper is a substantial advancement in the field, providing robust and efficient tools for cell image analysis.
Cuboid-Net: A Multi-Branch Convolutional Neural Network for Joint Space-Time Video Super Resolution
Authors: Congrui Fu, Hui Yuan, Hongji Xu, Hao Zhang, Liquan Shen
Date: 2024-07-24 00:00:00
Tasks: Space-time Video Super-resolution, Super-Resolution, Video Super-Resolution

The demand for high-resolution videos has been consistently rising across various domains, propelled by continuous advancements in science, technology, and societal. Nonetheless, challenges arising from limitations in imaging equipment capabilities, imaging conditions, as well as economic and temporal factors often result in obtaining low-resolution images in particular situations. Space-time video super-resolution aims to enhance the spatial and temporal resolutions of low-resolution and low-frame-rate videos. The currently available space-time video super-resolution methods often fail to fully exploit the abundant information existing within the spatio-temporal domain. To address this problem, we tackle the issue by conceptualizing the input low-resolution video as a cuboid structure. Drawing on this perspective, we introduce an innovative methodology called "Cuboid-Net," which incorporates a multi-branch convolutional neural network. Cuboid-Net is designed to collectively enhance the spatial and temporal resolutions of videos, enabling the extraction of rich and meaningful information across both spatial and temporal dimensions. Specifically, we take the input video as a cuboid to generate different directional slices as input for different branches of the network. The proposed network contains four modules, i.e., a multi-branch-based hybrid feature extraction (MBFE) module, a multi-branch-based reconstruction (MBR) module, a first stage quality enhancement (QE) module, and a second stage cross frame quality enhancement (CFQE) module for interpolated frames only. Experimental results demonstrate that the proposed method is not only effective for spatial and temporal super-resolution of video but also for spatial and angular super-resolution of light field.
3DAttGAN: A 3D Attention-based Generative Adversarial Network for Joint Space-Time Video Super-Resolution
Authors: Congrui Fu, Hui Yuan, Liquan Shen, Raouf Hamzaoui, Hao Zhang
Date: 2024-07-24 00:00:00
Tasks: Generative Adversarial Network, Space-time Video Super-resolution, Super-Resolution, Video Super-Resolution

In many applications, including surveillance, entertainment, and restoration, there is a need to increase both the spatial resolution and the frame rate of a video sequence. The aim is to improve visual quality, refine details, and create a more realistic viewing experience. Existing space-time video super-resolution methods do not effectively use spatio-temporal information. To address this limitation, we propose a generative adversarial network for joint space-time video super-resolution. The generative network consists of three operations: shallow feature extraction, deep feature extraction, and reconstruction. It uses three-dimensional (3D) convolutions to process temporal and spatial information simultaneously and includes a novel 3D attention mechanism to extract the most important channel and spatial information. The discriminative network uses a two-branch structure to handle details and motion information, making the generated results more accurate. Experimental results on the Vid4, Vimeo-90K, and REDS datasets demonstrate the effectiveness of the proposed method. The source code is publicly available at https://github.com/FCongRui/3DAttGan.git.
Semantics Guided Disentangled GAN for Chest X-ray Image Rib Segmentation
Authors: Lili Huang, Dexin Ma, Xiaowei Zhao, Chenglong Li, Haifeng Zhao, Jin Tang, Chuanfu Li
Date: 2024-07-22 00:00:00
Tasks: Generative Adversarial Network, Segmentation

The label annotations for chest X-ray image rib segmentation are time consuming and laborious, and the labeling quality heavily relies on medical knowledge of annotators. To reduce the dependency on annotated data, existing works often utilize generative adversarial network (GAN) to generate training data. However, GAN-based methods overlook the nuanced information specific to individual organs, which degrades the generation quality of chest X-ray image. Hence, we propose a novel Semantics guided Disentangled GAN (SD-GAN), which can generate the high-quality training data by fully utilizing the semantic information of different organs, for chest X-ray image rib segmentation. In particular, we use three ResNet50 branches to disentangle features of different organs, then use a decoder to combine features and generate corresponding images. To ensure that the generated images correspond to the input organ labels in semantics tags, we employ a semantics guidance module to perform semantic guidance on the generated images. To evaluate the efficacy of SD-GAN in generating high-quality samples, we introduce modified TransUNet(MTUNet), a specialized segmentation network designed for multi-scale contextual information extracting and multi-branch decoding, effectively tackling the challenge of organ overlap. We also propose a new chest X-ray image dataset (CXRS). It includes 1250 samples from various medical institutions. Lungs, clavicles, and 24 ribs are simultaneously annotated on each chest X-ray image. The visualization and quantitative results demonstrate the efficacy of SD-GAN in generating high-quality chest X-ray image-mask pairs. Using generated data, our trained MTUNet overcomes the limitations of the data scale and outperforms other segmentation networks.
CIC: Circular Image Compression
Authors: Honggui Li, Sinan Chen, Nahid Md Lokman Hossain, Maria Trocan, Dimitri Galayko, Mohamad Sawan
Date: 2024-07-18 00:00:00
Tasks: Image Compression

Learned image compression (LIC) is currently the cutting-edge method. However, the inherent difference between testing and training images of LIC results in performance degradation to some extent. Especially for out-of-sample, out-of-distribution, or out-of-domain testing images, the performance of LIC dramatically degraded. Classical LIC is a serial image compression (SIC) approach that utilizes an open-loop architecture with serial encoding and decoding units. Nevertheless, according to the theory of automatic control, a closed-loop architecture holds the potential to improve the dynamic and static performance of LIC. Therefore, a circular image compression (CIC) approach with closed-loop encoding and decoding elements is proposed to minimize the gap between testing and training images and upgrade the capability of LIC. The proposed CIC establishes a nonlinear loop equation and proves that steady-state error between reconstructed and original images is close to zero by Taylor series expansion. The proposed CIC method possesses the property of Post-Training and plug-and-play which can be built on any existing advanced SIC methods. Experimental results on five public image compression datasets demonstrate that the proposed CIC outperforms five competing state-of-the-art open-source SIC algorithms in reconstruction capacity. Experimental results further show that the proposed method is suitable for out-of-sample testing images with dark backgrounds, sharp edges, high contrast, grid shapes, or complex patterns.
Simultaneous Near-Field THz Communications and Sensing with Full Duplex Metasurface Transceivers
Authors: Ioannis Gavras, George C. Alexandropoulos
Date: 2024-07-25 00:00:00

In this paper, a Full Duplex (FD) eXtremely Large (XL) Multiple-Input Multiple-Output (MIMO) node equipped with reconfigurable metasurface antennas at its transmission and reception sides is considered, which is optimized for simultaneous multi-user communications and sensing in the near-field regime at THz frequencies. We first present a novel Position Error Bound (PEB) analysis for the spatial parameters of multiple targets in the vicinity of the FD node, via the received backscattered data signals, and devise an optimization framework for its metasurface-based precoder and combiner. Then, we formulate and solve an optimization problem aiming at the downlink sum-rate maximization, while simultaneously ensuring a minimum PEB requirement for targets' localization. Our simulation results for a sub-THz system setup validate the joint near-field communications and sensing capability of the proposed FD XL MIMO scheme with metasurfaces antennas, showcasing the interplay of its various design parameters.
Multipath Identification and Mitigation with FDA-MIMO Radar
Authors: Yizhen Jia, Jie Cheng, Wen-Qin Wang, Hui Chen
Date: 2024-07-25 00:00:00
Tasks: Diversity

In smart city development, the automatic detection of structures and vehicles within urban or suburban areas via array radar (airborne or vehicle platforms) becomes crucial. However, the inescapable multipath effect adversely affects the radar's capability to detect and track targets. Frequency Diversity Array (FDA)-MIMO radar offers innovative solutions in mitigating multipath due to its frequency flexibility and waveform diversity traits amongst array elements. Hence, utilizing FDA-MIMO radar, this research proposes a multipath discrimination and suppression strategy to augment target detection and suppress false alarms. The primary advancement is the transformation of conventional multipath suppression into a multipath recognition issue, thereby enabling multipath components from single-frame echo data to be separated without prior knowledge. By offsetting the distance steering vectors of different objects to be detected, the accurate spectral information corresponding to the current distance unit can be extracted during spatial spectrum estimation. The direct and multipath components are differentiated depending on whether the transmitting and receiving angles match. Additionally, to mitigate high-order multipath, the echo intensity of multipath components is reduced via joint optimization of array transmit weighting and frequency increment. The numerical results show that the proposed algorithm can identify multipath at different distances in both single-target and multi-target scenarios, which is superior to the general MIMO radar.
Access Point Selection for Bistatic Backscatter Communication in Cell-Free MIMO
Authors: Ahmet Kaplan, Diana P. M. Osorio, Erik G. Larsson
Date: 2024-07-25 00:00:00

Backscatter communication (BC) has emerged as a key technology to satisfy the increasing need for low-cost and green Internet-of-Things (IoT) connectivity, especially in large-scale deployments. Unlike the monostatic BC (MoBC), the bistatic BC (BiBC) has the possibility to decrease the round-trip path loss by having the carrier emitter (CE) and the reader in different locations. Therefore, this work investigates the BiBC in the context of cell-free multiple-input multiple-output (MIMO) networks by exploring the optimal selection of CE and reader among all access points, leveraging prior knowledge about the area where the backscatter device (BD) is located. First, a maximum a posteriori probability (MAP) detector to decode the BD information bits is derived. Then, the exact probability of error for this detector is obtained. In addition, an algorithm to select the best CE-reader pair for serving the specified area is proposed. Finally, simulation results show that the error performance of the BC is improved by the proposed algorithm compared to the benchmark scenario.
Energy Efficient Aerial RIS: Phase Shift Optimization and Trajectory Design
Authors: Hajar El Hammouti, Adnane Saoud, Asma Ennahkami, El Houcine Bergou
Date: 2024-07-25 00:00:00
Tasks: Model Predictive Control

Reconfigurable Intelligent Surface (RIS) technology has gained significant attention due to its ability to enhance the performance of wireless communication systems. The main advantage of RIS is that it can be strategically placed in the environment to control wireless signals, enabling improvements in coverage, capacity, and energy efficiency. In this paper, we investigate a scenario in which a drone, equipped with a RIS, travels from an initial point to a target destination. In this scenario, the aerial RIS (ARIS) is deployed to establish a direct link between the base station and obstructed users. Our objective is to maximize the energy efficiency of the ARIS while taking into account its dynamic model including its velocity and acceleration along with the phase shift of the RIS. To this end, we formulate the energy efficiency problem under the constraints of the dynamic model of the drone. The studied problem is challenging to solve. To address this, we proceed as follows. First, we introduce an efficient solution that involves decoupling the phase shift optimization and the trajectory design. Specifically, the closed-form expression of the phase-shift is obtained using a convex approximation, which is subsequently integrated into the trajectory design problem. We then employ tools inspired by economic model predictive control (EMPC) to solve the resulting trajectory optimization. Our simulation results show a significant improvement in energy efficiency against the scenario where the dynamic model of the UAV is ignored.
Explain EEG-based End-to-end Deep Learning Models in the Frequency Domain
Authors: Hanqi Wang, Kun Yang, Jingyu Zhang, Tao Chen, Liang Song
Date: 2024-07-25 00:00:00
Tasks: Deep Learning, EEG, Eeg Decoding

The recent rise of EEG-based end-to-end deep learning models presents a significant challenge in elucidating how these models process raw EEG signals and generate predictions in the frequency domain. This challenge limits the transparency and credibility of EEG-based end-to-end models, hindering their application in security-sensitive areas. To address this issue, we propose a mask perturbation method to explain the behavior of end-to-end models in the frequency domain. Considering the characteristics of EEG data, we introduce a target alignment loss to mitigate the out-of-distribution problem associated with perturbation operations. Additionally, we develop a perturbation generator to define perturbation generation in the frequency domain. Our explanation method is validated through experiments on multiple representative end-to-end deep learning models in the EEG decoding field, using an established EEG benchmark dataset. The results demonstrate the effectiveness and superiority of our method, and highlight its potential to advance research in EEG-based end-to-end models.
The Analytic Stockwell Transform and its Zeros
Authors: Ali Moukadem, Barbara Pascal, Jean-Baptiste Courbot, Nicolas Juillet
Date: 2024-07-24 00:00:00

A recent original line of research in time--frequency analysis has shifted the interest in energy maxima toward zeros. Initially motivated by the intriguing uniform spread of the zeros of the spectrogram of white noise, it has led to fruitful theoretical developments combining probability theory, complex analysis and signal processing. In this vein, the present work proposes a characterization of the zeros of the Stockwell Transform of white noise, which consists in an hybrid time--frequency multiresolution representation. First of all, an analytic version of the Stockwell Transform is designed. Then, analyticity is leveraged to establish a connection with the hyperbolic Gaussian Analytic Function, whose zero set is invariant under the isometries of the Poincar\'e disk. Finally, the theoretical spatial statistics of the zeros of the hyperbolic Gaussian Analytic Function and the empirical statistics of the zeros the Analytic Stockwell Transform of white noise are compared through intensive Monte Carlo simulations, supporting the established connection. A publicly available documented Python toolbox accompanies this work.
Speed-enhanced Subdomain Adaptation Regression for Long-term Stable Neural Decoding in Brain-computer Interfaces
Authors: Jiyu Wei, Dazhong Rong, Xinyun Zhu, Qinming He, Yueming Wang
Date: 2024-07-25 00:00:00
Tasks: Contrastive Learning, Domain Adaptation, regression, Subdomain adaptation

Brain-computer interfaces (BCIs) offer a means to convert neural signals into control signals, providing a potential restoration of movement for people with paralysis. Despite their promise, BCIs face a significant challenge in maintaining decoding accuracy over time due to neural nonstationarities. However, the decoding accuracy of BCI drops severely across days due to the neural data drift. While current recalibration techniques address this issue to a degree, they often fail to leverage the limited labeled data, to consider the signal correlation between two days, or to perform conditional alignment in regression tasks. This paper introduces a novel approach to enhance recalibration performance. We begin with preliminary experiments that reveal the temporal patterns of neural signal changes and identify three critical elements for effective recalibration: global alignment, conditional speed alignment, and feature-label consistency. Building on these insights, we propose the Speed-enhanced Subdomain Adaptation Regression (SSAR) framework, integrating semi-supervised learning with domain adaptation techniques in regression neural decoding. SSAR employs Speed-enhanced Subdomain Alignment (SeSA) for global and speed conditional alignment of similarly labeled data, with Contrastive Consistency Constraint (CCC) to enhance the alignment of SeSA by reinforcing feature-label consistency through contrastive learning. Our comprehensive set of experiments, both qualitative and quantitative, substantiate the superior recalibration performance and robustness of SSAR.
Distributed Memory Approximate Message Passing
Authors: Jun Lu, Lei Liu, Shunqi Huang, Ning Wei, Xiaoming Chen
Date: 2024-07-25 00:00:00
Tasks: Distributed Computing

Approximate message passing (AMP) algorithms are iterative methods for signal recovery in noisy linear systems. In some scenarios, AMP algorithms need to operate within a distributed network. To address this challenge, the distributed extensions of AMP (D-AMP, FD-AMP) and orthogonal/vector AMP (D-OAMP/D-VAMP) were proposed, but they still inherit the limitations of centralized algorithms. In this letter, we propose distributed memory AMP (D-MAMP) to overcome the IID matrix limitation of D-AMP/FD-AMP, as well as the high complexity and heavy communication cost of D-OAMP/D-VAMP. We introduce a matrix-by-vector variant of MAMP tailored for distributed computing. Leveraging this variant, D-MAMP enables each node to execute computations utilizing locally available observation vectors and transform matrices. Meanwhile, global summations of locally updated results are conducted through message interaction among nodes. For acyclic graphs, D-MAMP converges to the same mean square error performance as the centralized MAMP.
Harnessing DRL for URLLC in Open RAN: A Trade-off Exploration
Authors: Rana Muhammad Sohaib, Syed Tariq Shah, Oluwakayode Onireti, Muhammad Ali Imran
Date: 2024-07-24 00:00:00
Tasks: Deep Reinforcement Learning, Management, Navigate

The advent of Ultra-Reliable Low Latency Communication (URLLC) alongside the emergence of Open RAN (ORAN) architectures presents unprecedented challenges and opportunities in Radio Resource Management (RRM) for next-generation communication systems. This paper presents a comprehensive trade-off analysis of Deep Reinforcement Learning (DRL) approaches designed to enhance URLLC performance within ORAN's flexible and dynamic framework. By investigating various DRL strategies for optimising RRM parameters, we explore the intricate balance between reliability, latency, and the newfound adaptability afforded by ORAN principles. Through extensive simulation results, our study compares the efficacy of different DRL models in achieving URLLC objectives in an ORAN context, highlighting the potential of DRL to navigate the complexities introduced by ORAN. The proposed study provides valuable insights into the practical implementation of DRL-based RRM solutions in ORAN-enabled wireless networks. It sheds light on the benefits and challenges of integrating DRL and ORAN for URLLC enhancements. Our findings contribute to the ongoing discourse on advancements in URLLC and ORAN, offering a roadmap for future research to pursue efficient, reliable, and flexible communication systems.
Joint Transmit and Jamming Power Optimization for Secrecy in Energy Harvesting Networks: A Reinforcement Learning Approach
Authors: Shalini Tripathi, Chinmoy Kundu, Animesh Yadav, Ankur Bansal, Holger Claussen, Lester Ho
Date: 2024-07-24 00:00:00

In this paper, we address the problem of joint allocation of transmit and jamming power at the source and destination, respectively, to enhance the long-term cumulative secrecy performance of an energy-harvesting wireless communication system until it stops functioning in the presence of an eavesdropper. The source and destination have energy-harvesting devices with limited battery capacities. The destination also has a full-duplex transceiver to transmit jamming signals for secrecy. We frame the problem as an infinite-horizon Markov decision process (MDP) problem and propose a reinforcement learning-based optimal joint power allocation (OJPA) algorithm that employs a policy iteration (PI) algorithm. Since the optimal algorithm is computationally expensive, we develop a low-complexity sub-optimal joint power allocation (SJPA) algorithm, namely, reduced state joint power allocation (RSJPA). Two other SJPA algorithms, the greedy algorithm (GA) and the naive algorithm (NA), are implemented as benchmarks. In addition, the OJPA algorithm outperforms the individual power allocation (IPA) algorithms termed individual transmit power allocation (ITPA) and individual jamming power allocation (IJPA), where the transmit and jamming powers, respectively, are optimized individually. The results show that the OJPA algorithm is also more energy efficient. Simulation results show that the OJPA algorithm significantly improves the secrecy performance compared to all SJPA algorithms. The proposed RSJPA algorithm achieves nearly optimal performance with significantly less computational complexity marking it the balanced choice between the complexity and the performance. We find that the computational time for the RSJPA algorithm is around 75 percent less than the OJPA algorithm.