pwc-archive/papers-with-abstracts huggingface dataset:
Fusing Physics-Driven Strategies and Cross-Modal Adversarial Learning: Toward Multi-Domain Applications
Authors: Hana Satou, Alan Mitkiy
Date: 2024-11-30 00:00:00
Tasks: Cross-Modal Retrieval

The convergence of cross-modal adversarial learning and physics-driven methods represents a cutting-edge direction for tackling challenges in complex multi-modal tasks and scientific computing. This review focuses on systematically analyzing how these two approaches can be synergistically integrated to enhance performance and robustness across diverse application domains. By addressing key obstacles such as modality discrepancies, limited data availability, and insufficient model robustness, this paper highlights the role of physics-based optimization frameworks in facilitating efficient and interpretable adversarial perturbation generation. The review also explores significant advancements in cross-modal adversarial learning, including applications in tasks such as image cross-modal retrieval (e.g., infrared and RGB matching), scientific computing (e.g., solving partial differential equations), and optimization under physical consistency constraints in vision systems. By examining theoretical foundations and experimental outcomes, this study demonstrates the potential of combining these approaches to handle complex scenarios and improve the security of multi-modal systems. Finally, we outline future directions, proposing a novel framework that unifies physical principles with adversarial optimization, providing a pathway for researchers to develop robust and adaptable cross-modal learning methods with both theoretical and practical significance.
Volumetric Reconstruction of Prostatectomy Specimens from Histology
Authors: Tom Bisson, Isil Dogan O, Iris Piwonski, Tim-Rasmus Kiehl, Georg Lukas Baumgärtner, Rita Carvalho, Peter Hufnagl, Tobias Penzkofer, Norman Zerbe, Sefer Elezkurtaj
Date: 2024-11-29 00:00:00
Tasks: 3D Reconstruction, Diagnostic

Surgical treatment for prostate cancer often involves organ removal, i.e., prostatectomy. Pathology reports on these specimens convey treatment-relevant information. Beyond these reports, the diagnostic process generates extensive and complex information that is difficult to represent in reports, although it is of significant interest to the other medical specialties involved. 3D tissue reconstruction would allow for better spatial visualization, as well as combinations with other imaging modalities. Existing approaches in this area have proven labor-intensive and challenging to integrate into clinical workflows. 3D-SLIVER provides a simplified solution, implemented as an open-source 3DSlicer extension. We outline three specific real-world scenarios to illustrate its potential to improve transparency in diagnostic workflows and contribute to multi-modal research endeavors. Implementing the 3D reconstruction process involved four sub-modules of 3D-SLIVER: digitization of slicing protocol, virtual slicing of arbitrary 3D models based on that protocol, registration of slides with virtual slices using the Coherent Point Drift algorithm, and 3D reconstruction of registered information using convex hulls, Gaussian splatter and linear extrusion. Three use cases to employ 3D-SLIVER are presented: a low-effort approach to pathology workflow integration and two research-related use cases illustrating how to perform retrospective evaluations of PI-RADS predictions and statistically model 3D distributions of morphological patterns. 3D-SLIVER allows for improved interdisciplinary communication among specialties. It is designed for simplicity in application, allowing for flexible integration into various workflows and use cases. Here we focused on the clinical care of prostate cancer patients, but future possibilities are extensive with other neoplasms and in education and research.
Curriculum Fine-tuning of Vision Foundation Model for Medical Image Classification Under Label Noise
Authors: Yeonguk Yu, Minhwan Ko, Sungho Shin, KangMin Kim, Kyoobin Lee
Date: 2024-11-29 00:00:00
Tasks: image-classification, Image Classification, Medical Image Classification

Deep neural networks have demonstrated remarkable performance in various vision tasks, but their success heavily depends on the quality of the training data. Noisy labels are a critical issue in medical datasets and can significantly degrade model performance. Previous clean sample selection methods have not utilized the well pre-trained features of vision foundation models (VFMs) and assumed that training begins from scratch. In this paper, we propose CUFIT, a curriculum fine-tuning paradigm of VFMs for medical image classification under label noise. Our method is motivated by the fact that linear probing of VFMs is relatively unaffected by noisy samples, as it does not update the feature extractor of the VFM, thus robustly classifying the training samples. Subsequently, curriculum fine-tuning of two adapters is conducted, starting with clean sample selection from the linear probing phase. Our experimental results demonstrate that CUFIT outperforms previous methods across various medical image benchmarks. Specifically, our method surpasses previous baselines by 5.0%, 2.1%, 4.6%, and 5.8% at a 40% noise rate on the HAM10000, APTOS-2019, BloodMnist, and OrgancMnist datasets, respectively. Furthermore, we provide extensive analyses to demonstrate the impact of our method on noisy label detection. For instance, our method shows higher label precision and recall compared to previous approaches. Our work highlights the potential of leveraging VFMs in medical image classification under challenging conditions of noisy labels.
Auto-Encoded Supervision for Perceptual Image Super-Resolution
Authors: MinKyu Lee, Sangeek Hyun, Woojin Jun, Jae-Pil Heo
Date: 2024-11-28 00:00:00
Proceeding: CVPR 2025 1
Tasks: Image Super-Resolution, Super-Resolution

This work tackles the fidelity objective in the perceptual super-resolution~(SR). Specifically, we address the shortcomings of pixel-level $L_\text{p}$ loss ($\mathcal{L}_\text{pix}$) in the GAN-based SR framework. Since $L_\text{pix}$ is known to have a trade-off relationship against perceptual quality, prior methods often multiply a small scale factor or utilize low-pass filters. However, this work shows that these circumventions fail to address the fundamental factor that induces blurring. Accordingly, we focus on two points: 1) precisely discriminating the subcomponent of $L_\text{pix}$ that contributes to blurring, and 2) only guiding based on the factor that is free from this trade-off relationship. We show that they can be achieved in a surprisingly simple manner, with an Auto-Encoder (AE) pretrained with $L_\text{pix}$. Accordingly, we propose the Auto-Encoded Supervision for Optimal Penalization loss ($L_\text{AESOP}$), a novel loss function that measures distance in the AE space, instead of the raw pixel space. Note that the AE space indicates the space after the decoder, not the bottleneck. By simply substituting $L_\text{pix}$ with $L_\text{AESOP}$, we can provide effective reconstruction guidance without compromising perceptual quality. Designed for simplicity, our method enables easy integration into existing SR frameworks. Experimental results verify that AESOP can lead to favorable results in the perceptual SR task.
Real-time volumetric free-hand ultrasound imaging for large-sized organs: A study of imaging the whole spine
Authors: Caozhe Li, Enxiang Shen, Haoyang Wang, Yuxin Wang, Jie Yuan, Li Gong, Di Zhao, Weijing Zhang, Zhibin Jin
Date: 2024-11-25 00:00:00
Tasks: Anatomy

Three-dimensional (3D) ultrasound imaging can overcome the limitations of conventional two dimensional (2D) ultrasound imaging in structural observation and measurement. However, conducting volumetric ultrasound imaging for large-sized organs still faces difficulties including long acquisition time, inevitable patient movement, and 3D feature recognition. In this study, we proposed a real-time volumetric free-hand ultrasound imaging system optimized for the above issues and applied it to the clinical diagnosis of scoliosis. This study employed an incremental imaging method coupled with algorithmic acceleration to enable real-time processing and visualization of the large amounts of data generated when scanning large-sized organs. Furthermore, to deal with the difficulty of image feature recognition, we proposed two tissue segmentation algorithms to reconstruct and visualize the spinal anatomy in 3D space by approximating the depth at which the bone structures are located and segmenting the ultrasound images at different depths. We validated the adaptability of our system by deploying it to multiple models of ultra-sound equipment and conducting experiments using different types of ultrasound probes. We also conducted experiments on 6 scoliosis patients and 10 normal volunteers to evaluate the performance of our proposed method. Ultrasound imaging of a volunteer spine from shoulder to crotch (more than 500 mm) was performed in 2 minutes, and the 3D imaging results displayed in real-time were compared with the corresponding X-ray images with a correlation coefficient of 0.96 in spinal curvature. Our proposed volumetric ultrasound imaging system might hold the potential to be clinically applied to other large-sized organs.
Uncertainty-Aware Regularization for Image-to-Image Translation
Authors: Anuja Vats, Ivar Farup, Marius Pedersen, Kiran Raja
Date: 2024-11-24 00:00:00
Tasks: Image-to-Image Translation, Translation

The importance of quantifying uncertainty in deep networks has become paramount for reliable real-world applications. In this paper, we propose a method to improve uncertainty estimation in medical Image-to-Image (I2I) translation. Our model integrates aleatoric uncertainty and employs Uncertainty-Aware Regularization (UAR) inspired by simple priors to refine uncertainty estimates and enhance reconstruction quality. We show that by leveraging simple priors on parameters, our approach captures more robust uncertainty maps, effectively refining them to indicate precisely where the network encounters difficulties, while being less affected by noise. Our experiments demonstrate that UAR not only improves translation performance, but also provides better uncertainty estimations, particularly in the presence of noise and artifacts. We validate our approach using two medical imaging datasets, showcasing its effectiveness in maintaining high confidence in familiar regions while accurately identifying areas of uncertainty in novel/ambiguous scenarios.
User Grouping and Resource Allocation in Multiuser MIMO Systems under SWIPT
Authors: Javier Rubio, Antonio Pascual-Iserte
Date: 2024-12-03 00:00:00
Tasks: Scheduling

This paper considers a broadcast multiple-input multiple-output (MIMO) network with multiple users and simultaneous wireless information and power transfer (SWIPT). In this scenario, it is assumed that some users are able to harvest power from radio frequency (RF) signals to recharge batteries through wireless power transfer from the transmitter, while others are served simultaneously with data transmission. The criterion driving the optimization and design of the system is based on the weighted sum rate for the users being served with data. At the same time, constraints stating minimum per-user harvested powers are included in the optimization problem. This paper derives the structure of the optimal transmit covariance matrices in the case where both types of users are present simultaneously in the network, particularizing the results to the cases where either only harvesting nodes or only information users are to be served. The tradeoff between the achieved weighted sum rate and the powers harvested by the user terminals is analyzed and evaluated using the rate-power (R-P) region. Finally, we propose a two-stage user grouping mechanism that decides which users should be scheduled to receive information and which users should be configured to harvest energy from the RF signals in each particular scheduling period, this being one of the main contributions of this paper.
A Tractable Closed-Form Approximation of the Ergodic Rate in Poisson Cellular Networks
Authors: Alexis I. Aravanis, Thanh Tu Lam, Olga Muñoz, Antonio Pascual-Iserte, Marco Di Renzo
Date: 2024-12-03 00:00:00
Tasks: Form

The employment of stochastic geometry for the analysis and design of ultra dense networks (UDNs) has provided significant insights into network densification. In addition to the characterization of the network performance and behavior, these tools can also be exploited toward solving complex optimization problems that could maximize the capacity benefits arising in UDNs. However, this is preconditioned on the existence of tractable closed form expressions for the considered figures of merit. In this course, the present paper introduces an accurate approximation for the moment generating function (MGF) of the aggregate other-cell interference created by base stations whose positions follow a Poisson point process of given spatial density. Given the pivotal role of the MGF of the aggregate interference in stochastic geometry and the tractability of the derived MGF, the latter can be employed to substantially simplify ensuing stochastic geometry analyses. Subsequently, the present paper employs the introduced MGF to provide closed form expressions for the downlink ergodic capacity for the interference limited case, and validates the accuracy of these expressions by the use of extensive Monte Carlo simulations. The derived expressions depend on the density of users and base stations, setting out a densification road map for network operators and designers of significant value.
Robust Precoding for Multi-User Visible Light Communications with Quantized Channel Information
Authors: Olga Muñoz, Antonio Pascual-Iserte, Guillermo San Arranz
Date: 2024-12-03 00:00:00
Tasks: Quantization

In this paper, we address the design of multi-user multiple-input single-output (MU-MISO) precoders for indoor visible light communication (VLC) systems. The goal is to minimize the transmitted optical power per light emitting diode (LED) under imperfect channel state information (CSI) at the transmitter side. Robust precoders for imperfect CSI available in the literature include noisy and outdated channel estimation cases. However, to the best of our knowledge, no work has considered adding robustness against channel quantization. In this paper, we fill this gap by addressing the case of imperfect CSI due to the quantization of VLC channels. We model the quantization errors in the CSI through polyhedric uncertainty regions. For polyhedric uncertainty regions and positive real channels, as is the case of VLC channels, we show that the robust precoder against channel quantization errors that minimizes the transmitted optical power while guaranteeing a target signal to noise plus interference ratio (SNIR) per user is the solution of a second order cone programming (SOCP) problem. Finally, we evaluate its performance under different quantization levels through numerical simulations.
A New Statistical Model for Waveguide Invariant-Based Range Estimation in Shallow Water
Authors: Junsu Jang, Florian Meyer
Date: 2024-12-03 00:00:00

Navigation and source localization in the undersea environment are challenged by the absence of a ubiquitous positioning system. Passive acoustic ranging offers a valuable means of obtaining location information underwater. We present a range estimation method based on waveguide invariant (WI) theory, using ship noise recorded by a hydrophone as an acoustic source. The WI is a scalar parameter that describes the interference patterns in spectrograms caused by the interaction of acoustic wave modes propagating in a waveguide, such as shallow water. WI theory enables ranging using a single receiver without detailed knowledge of the environment. In this paper, underwater acoustic signals radiated by a moving large ship, which include broadband and tonal components, are employed for WI-based ranging in a range-independent shallow water environment. In particular, we develop a likelihood function for WI-based range estimation by introducing a statistical model for high signal-to-noise ratio scenarios. Here, the broadband component is assumed to dominate the background noise. The effectiveness of the proposed range estimation method is demonstrated using real acoustic measurements of a moving container ship recorded during the Seabed Characterization Experiment 2017 (SBCEX17).
Radar-based Measurement of the Body Movements of Multiple Students in Classroom Environments
Authors: Yu Oshima, Tianyi Wang, Masaya Kato, Haruto Kobayashi, Itsuki Iwata, Yuji Tanaka, Shuqiong Wu, Manabu Wakuta, Masako Myowa, Tomoko Nishimura, Atsushi Senju, Takuya Sakamoto
Date: 2024-12-03 00:00:00

We demonstrate the feasibility of the radar-based measurement of body movements in scenarios involving multiple students using a pair of 79-GHz millimeter-wave radar systems with array antennas. We quantify the body motion using the Doppler frequency calculated from radar echoes. The measurement accuracy is evaluated for two experimental scenarios, namely university students in an office and elementary school students in a classroom. The body movements measured using the two radar systems are compared to evaluate the repeatability and angle dependency of the measurement. Moreover, in the first scenario, we compare the radar-estimated body movement with subjective evaluation scores provided by two evaluators. In the first scenario, the coefficient of correlation between the radar-estimated body movement and the subjective evaluation score is 0.73 on average, with a maximum value of 0.97; in the second scenario, the average correlation coefficient of body movements measured using two radar systems is as high as 0.78. These results indicate that the proposed approach can be used to monitor the body movements of multiple students in realistic scenarios.
A new flower pollination algorithm for equalization in synchronous DS/CDMA multiuser communication systems
Authors: Luis M. San-José-Revuelta, Pablo Casaseca-de-la-Higuera
Date: 2024-12-02 00:00:00
Tasks: Diversity

This work proposes a modified version of an emerging nature-inspired technique, named Flower Pollination Algorithm (FPA), for equalizing digital multiuser channels. This equalization involves two different tasks: 1) estimation of the channel impulse response, and 2) estimation of the users' transmitted symbols. The new algorithm is developed and applied in a Direct-Sequence / Code-Division Multiple-Access (DS/CDMA) multiuser communications system. Important issues such as robustness, convergence speed and population diversity control have been in deep investigated. A method based on the entropy of the flowers' fitness is proposed for in-service monitoring and adjusting population diversity. Numerical simulations analyze the performance, showing comparisons with well-known conventional multiuser detectors such as Matched Filter (MF), Minimum Mean Square Error Estimator (MMSEE) or several Bayesian schemes, as well as with other nature-inspired strategies. Numerical analysis shows that the proposed algorithm enables transmission at higher symbol rates under stronger fading and interference conditions, constituting an attractive alternative to previous algorithms, both conventional and nature-inspired, whose performance is frequently sensible to near-far effects and multiple-access interference problems. These results have been validated by running hypothesis tests to confirm statistical significance.
Joint Phase Time Array: Opportunities, Challenges and System Design Considerations
Authors: Young-Han Nam, Ahmad AlAmmouri, Jianhua Mo, Jianzhong Chalrie Zhang
Date: 2024-12-02 00:00:00

This paper presents a novel approach to designing millimeter-wave (mmWave) cellular communication systems, based on joint phase time array (JPTA) radio frequency (RF) frontend architecture. JPTA architecture comprises time-delay components appended to conventional phase shifters, which offer extra degrees of freedom to be exploited for designing frequency-selective analog beams. Hence, a mmWave device equipped with JPTA can receive and transmit signals in multiple directions in a single time slot per RF chain, one direction per frequency subband, which alleviates the traditional constraint of one analog beam per transceiver chain per time slot. The utilization of subband-specific analog beams offers a new opportunity in designing mmWave systems, allowing for enhanced cell capacity and reduced pilot overhead. To understand the practical feasibility of JPTA, a few challenges and system design considerations are discussed in relation to the performance and complexity of the JPTA systems. For example, frequency-selective beam gain losses are present for the subband analog beams, e.g., up to 1 dB losses for 2 subband cases, even with the state-of-the-art JPTA delay and phase optimization methods. Despite these side effects, system-level analysis reveals that the JPTA system is capable of improving cell capacity: 5-percentile cell throughput by up to 65 per cent. To the best of the author's knowledge, this paper is the first paper explaining the system-level benefits and system-design challenges of JPTA, with an analysis of the performance tradeoff based on an intuitive metric of beam gain losses.
Detection of Performance Interference Among Network Slices in 5G/6G Systems
Authors: Van Sy Mai, Richard La, Tao Zhang
Date: 2024-12-02 00:00:00

Recent studies showed that network slices (NSs), which are logical networks supported by shared physical networks, can experience service interference due to sharing of physical and virtual resources. Thus, from the perspective of providing end-to-end (E2E) service quality assurance in 5G/6G systems, it is crucial to discover possible service interference among the NSs in a timely manner and isolate the potential issues before they can lead to violations of service quality agreements. We study the problem of detecting service interference among NSs in 5G/6G systems, only using E2E key performance indicator measurements, and propose a new algorithm. Our numerical studies demonstrate that, even when the service interference among NSs is weak to moderate, provided that a reasonable number of measurements are available, the proposed algorithm can correctly identify most of shared resources that can cause service interference among the NSs that utilize the shared resources.
Topological Signal Processing and Learning: Recent Advances and Future Challenges
Authors: Elvin Isufi, Geert Leus, Baltasar Beferull-Lozano, Sergio Barbarossa, Paolo Di Lorenzo
Date: 2024-12-02 00:00:00
Tasks: Semantic Communication

Developing methods to process irregularly structured data is crucial in applications like gene-regulatory, brain, power, and socioeconomic networks. Graphs have been the go-to algebraic tool for modeling the structure via nodes and edges capturing their interactions, leading to the establishment of the fields of graph signal processing (GSP) and graph machine learning (GML). Key graph-aware methods include Fourier transform, filtering, sampling, as well as topology identification and spatiotemporal processing. Although versatile, graphs can model only pairwise dependencies in the data. To this end, topological structures such as simplicial and cell complexes have emerged as algebraic representations for more intricate structure modeling in data-driven systems, fueling the rapid development of novel topological-based processing and learning methods. This paper first presents the core principles of topological signal processing through the Hodge theory, a framework instrumental in propelling the field forward thanks to principled connections with GSP-GML. It then outlines advances in topological signal representation, filtering, and sampling, as well as inferring topological structures from data, processing spatiotemporal topological signals, and connections with topological machine learning. The impact of topological signal processing and learning is finally highlighted in applications dealing with flow data over networks, geometric processing, statistical ranking, biology, and semantic communication.