pwc-archive/papers-with-abstracts huggingface dataset:
Cautious Dual-Self Expected Utility and Weak Uncertainty Aversion
Authors: Kensei Nakamura, Shohei Yanagita
Date: 2025-01-23 00:00:00

Uncertainty aversion introduced by Gilboa and Schmeidler (1989) has played a central role in decision theory, but at the same time, many incompatible behaviors have been observed in the real world. In this paper, we consider an axiom that postulates only a minimal degree of uncertainty aversion, and examine its implications in the preferences with the basic structure, called the invariant biseparable preferences. We provide three representation theorems for these preferences. Our main result shows that a decision maker with such a preference evaluates each act by considering two "dual" scenarios and then adopting the worse one as its evaluation in a cautious manner. The other two representations share a structure similar to the main result, which clarifies the key implication of weak uncertainty aversion. Furthermore, we offer another foundation for the main representation in the objective/subjective rationality model and characterizations of extensions of the main representation.
Efficient Segmentation of Search Markets
Authors: Teddy Mekonnen
Date: 2025-01-20 00:00:00
Tasks: Segmentation

I consider a two-sided frictional search market where buyers search and match to vertically differentiated sellers. The market is segmented into submarkets based on seller types, with segmentation serving as a public signal that directs buyers' search. I characterize the socially efficient and equilibrium allocations of buyers across submarkets for any fixed segmentation, and identify a Hosios condition under which the equilibrium allocation is efficient. I further examine the design of surplus-maximizing segmentations, demonstrating the role of search externalities in determining whether the constrained-efficient segmentation fully reveals seller types or pools types into at most a binary partition. These results clarify the conditions under which the provision of public information is welfare enhancing in search markets with externalities.
Learning-based A Posteriori Speech Presence Probability Estimation and Applications
Authors: Shuai Tao, Jesper Rindom Jensen, Yang Xiang, Himavanth Reddy, Qingzheng Zhang, Mads Græsbøll Christensen
Date: 2025-01-23 00:00:00
Tasks: Speech Enhancement, speech-recognition, Speech Recognition

The a posteriori speech presence probability (SPP) is the fundamental component of noise power spectral density (PSD) estimation, which can contribute to speech enhancement and speech recognition systems. Most existing SPP estimators can estimate SPP accurately from the background noise. Nevertheless, numerous challenges persist, including the difficulty of accurately estimating SPP from non-stationary noise with statistics-based methods and the high latency associated with deep learning-based approaches. This paper presents an improved SPP estimation approach based on deep learning to achieve higher SPP estimation accuracy, especially in non-stationary noise conditions. To promote the information extraction performance of the DNN, the global information of the observed signal and the local information of the decoupled frequency bins from the observed signal are connected as hybrid global-local information. The global information is extracted by one encoder. Then, one decoder and two fully connected layers are used to estimate SPP from the information of residual connection. To evaluate the performance of our proposed SPP estimator, the noise PSD estimation and speech enhancement tasks are performed. In contrast to existing minimum mean-square error (MMSE)-based noise PSD estimation approaches, the noise PSD is estimated by the sub-optimal MMSE based on the current frame SPP estimate without smoothing. Directed by the noise PSD estimate, a standard speech enhancement framework, the log spectral amplitude estimator, is employed to extract clean speech from the observed signal. From the experimental results, we can confirm that our proposed SPP estimator can achieve high noise PSD estimation accuracy and speech enhancement performance while requiring low model complexity.
Hybridization of Attention UNet with Repeated Atrous Spatial Pyramid Pooling for Improved Brain Tumour Segmentation
Authors: Satyaki Roy Chowdhury, Golrokh Mirzaei
Date: 2025-01-22 00:00:00
Tasks: Segmentation, Semantic Segmentation, Tumor Segmentation

Brain tumors are highly heterogeneous in terms of their spatial and scaling characteristics, making tumor segmentation in medical images a difficult task that might result in wrong diagnosis and therapy. Automation of a task like tumor segmentation is expected to enhance objectivity, repeatability and at the same time reducing turn around time. Conventional convolutional neural networks (CNNs) exhibit sub-par performance as a result of their inability to accurately represent the range of tumor sizes and forms. Developing on that, UNets have been a commonly used solution for semantic segmentation, and it uses a downsampling-upsampling approach to segment tumors. This paper proposes a novel architecture that integrates Attention-UNet with repeated Atrous Spatial Pyramid Pooling (ASPP). ASPP effectively captures multi-scale contextual information through parallel atrous convolutions with varying dilation rates. This allows for efficient expansion of the receptive field while maintaining fine details. The attention provides the necessary context by incorporating local characteristics with their corresponding global dependencies. This integration significantly enhances semantic segmentation performance. Our approach demonstrates significant improvements over UNet, Attention UNet and Attention UNet with Spatial Pyramid Pooling allowing to set a new benchmark for tumor segmentation tasks.
Linea alba 3D morphometric variability by CT scan exploration
Authors: P. Gueroult, V. Joppin, K. Chaumoitre, M. Di Bisceglie, C. Masson, T. Bege
Date: 2025-01-20 00:00:00
Tasks: 3D Reconstruction, Anatomy

Purpose: The width of the Linea alba, which is often gauged by inter-rectus distance, is a key risk factor for incisional hernia and recurrence. Previous studies provided limited descriptions with no consideration for width, location variability, or curvature. We aimed to offer a comprehensive 3D anatomical analysis of the Linea alba, emphasizing its variations across diverse demographics. Methods: Using open source software, 2D sagittal plane and 3D reconstructions were performed on 117 patients' CT scans. Linea alba length, curvature assessed by the sagitta (the longest perpendicular segment between xipho-pubic line and the Linea alba), and continuous width along the height were measured. Results: The Linea alba had a rhombus shape, with a maximum width at the umbilicus of 4.4$\pm$1.9 cm and a larger width above the umbilicus than below. Its length was 37.5$\pm$3.6 cm, which increased with body mass index (BMI) (p$\leq$0.001), and was shorter in women (p$\leq$0.001). The sagitta was 2.6$\pm$2.2 cm, three times higher in the obese group (p$\leq$0.001), majorated with age (p=0.009), but was independent of gender (p=0.212). Linea alba width increased with both age and BMI (p$\leq$0.001, p=0.002), being notably wider in women halfway between the umbilicus and pubis (p=0.007). Conclusion: This study provides an exhaustive 3D description of Linea alba's anatomical variability, presenting new considerations for curvature. This method provides a patient-specific anatomy description of the Linea alba. Further studies are needed to determine whether 3D reconstruction correlates with pathologies, such as hernias and diastasis recti.
Delay-Doppler Multiplexing With Global Filtering
Authors: Jialiang Zhu, Mohsen Bayat, Arman Farhang
Date: 2025-01-23 00:00:00

This paper proposes a novel modulation technique called globally filtered orthogonal time frequency space (GF-OTFS) which integrates single-carrier frequency division multiple access (SC-FDMA)-based delay-Doppler representation with universal filtered multi-carrier (UFMC) modulation. Our proposed technique first arranges the frequency-Doppler bins of an orthogonal time frequency space (OTFS) frame in adjacency using SC-FDMA and then applies universal filtering to the neighboring signals to mitigate inter-Doppler interference (IDI). By employing this approach, GF-OTFS achieves superior spectral containment and effectively mitigates interference caused by Doppler shifts in dynamic, time-varying channels. This paper also presents a detailed mathematical formulation of the proposed modulation technique. Furthermore, a comprehensive performance evaluation is conducted, comparing our GF-OTFS approach to state-of-the-art techniques, including Doppler-resilient UFMC (DR-UFMC) and receiver windowed OTFS (RW-OTFS). Key performance metrics, such as bit error rate (BER) and out-of-band (OOB) emissions, as well as the Doppler spread reduction are analyzed to assess the effectiveness of each approach. The results indicate that our proposed technique achieves comparable BER performance while significantly improving spectral containment.
Maximum-Entropy-Rate Selection of Features for Classifying Changes in Knee and Ankle Dynamics During Running
Authors: Garry A. Einicke, Haider A. Sabti, David V. Thiel, Marta Fernandez
Date: 2025-01-23 00:00:00

This paper investigates deteriorations in knee and ankle dynamics during running. Changes in lower limb accelerations are analyzed by a wearable musculo-skeletal monitoring system. The system employs a machine learning technique to classify joint stiffness. A maximum-entropyrate method is developed to select the most relevant features. Experimental results demonstrate that distance travelled and energy expended can be estimated from observed changes in knee and ankle motions during 5 km runs.
Parameterized Hardware Architecture for Frame Synchronization at all Noise Levels
Authors: Dimitris Nikolaidis
Date: 2025-01-23 00:00:00
Tasks: All

Frame synchronization is the act of discerning the first bit of a valid data frame inside an incoming transmission. This is particularly important in high-noise environments where the communication channel significantly alters transmitted signals. Sync word frame synchronization is a subcategory of synchronization methods where sync words are detected through digital correlation. Despite its simplicity, this method has been overlooked in literature in favor of more sophisticated and mathematically more optimal solutions. In this article we employ binary sync-word correlation-based synchronization to achieve near perfect frame synchronization at any noise level. The proposed architecture leverages XNOR gates, adder and comparator tree structures to detect sync words that are placed in front of the frames through digital correlation. The tree structures are circuit elements that mimic binary trees in form and provide the summation (adder tree) or the maximum/minimum (comparator tree) of a set of binary numbers as output. Due to their minimalistic nature, synchronization can be implemented practically for very large sync word sizes (>500 bit) with multigigabit bit rates (>20 Gbps) and very high accuracy (10e-5 synchronization error when the bit error rate on the bitstream is close to 0.3) on commercial FPGAs. The architecture also delivers the payload of the frames to its output as an extra function.
A Dual-Polarization Feature Fusion Network for Radar Automatic Target Recognition Based On HRRP Sequence
Authors: Yangbo Zhou, Sen Liu, Hong-Wei Gao, Hai Lin, Guohua Wei, Xiaoqing Wang, Xiao-Min Pan
Date: 2025-01-23 00:00:00

Recent advances in radar automatic target recognition (RATR) techniques utilizing deep neural networks have demonstrated remarkable performance, largely due to their robust generalization capabilities. To address the challenge for applications with polarimetric HRRP sequences, a dual-polarization feature fusion network (DPFFN) is proposed along with a novel two-stage feature fusion strategy. Moreover, a specific fusion loss function is developed, which enables the adaptive generation of comprehensive multi-modal representations from polarimetric HRRP sequences. Experimental results demonstrate that the proposed network significantly improves performance in radar target recognition tasks, thus validating its effectiveness. The PyTorch implementation of our proposed DPFFN is available at https://github.com/xmpan/DPFFN.
Interference Prediction Using Gaussian Process Regression and Management Framework for Critical Services in Local 6G Networks
Authors: Syed Luqman Shah, Nurul Huda Mahmood, Matti Latva-aho
Date: 2025-01-23 00:00:00
Tasks: GPR, Management

Interference prediction and resource allocation are critical challenges in mission-critical applications where stringent latency and reliability constraints must be met. This paper proposes a novel Gaussian process regression (GPR)-based framework for predictive interference management and resource allocation in future 6G networks. Firstly, the received interference power is modeled as a Gaussian process, enabling both the prediction of future interference values and their corresponding estimation of uncertainty bounds. Differently from conventional machine learning methods that extract patterns from a given set of data without any prior belief, a Gaussian process assigns probability distributions to different functions that possibly represent the data set which can be further updates using Bayes' rule as more data points are observed. For instance, unlike deep neural networks, the GPR model requires only a few sample points to update its prior beliefs in real-time. Furthermore, we propose a proactive resource allocation scheme that dynamically adjusts resources according to predicted interference. The performance of the proposed approach is evaluated against two benchmarks prediction schemes, a moving average-based estimator and the ideal genie-aided estimator. The GPR-based method outperforms the moving average-based estimator and achieves near-optimal performance, closely matching the genie-aided benchmark.
Integrated 6G TN and NTN Localization: Challenges, Opportunities, and Advancements
Authors: Sharief Saleh, Pinjun Zheng, Xing Liu, Hui Chen, Musa Furkan Keskin, Basuki Priyanto, Martin Beale, Yasaman Ettefagh, Gonzalo Seco-Granados, Tareq Y. Al-Naffouri, Henk Wymeersch
Date: 2025-01-23 00:00:00

The rapid evolution of cellular networks has introduced groundbreaking technologies, including large and distributed antenna arrays and reconfigurable intelligent surfaces in terrestrial networks (TNs), as well as aerial and space-based nodes in non-terrestrial networks (NTNs). These advancements enable applications beyond traditional communication, such as high-precision localization and sensing. While integrating TN and NTN enablers will lead to unparalleled opportunities for seamless global localization, such integration attempts are expected to face several challenges. To understand these opportunities and challenges, we first examine the distinctive characteristics of the key 6G enablers, evaluating their roles in localization from both technical and practical perspectives. Next, to identify developments driving TN-NTN localization, we review the latest standardization and industrial innovation progress. Finally, we discuss the opportunities and challenges of TN-NTN integration, illustrating its potential through two numerical case studies.
Deep Multi-modal Neural Receiver for 6G Vehicular Communication
Authors: Osama Saleem, Mohammed Alfaqawi, Pierre Merdrignac, Abdelaziz Bensrhair, Soheyb Ribouh
Date: 2025-01-23 00:00:00

Deep Learning (DL) based neural receiver models are used to jointly optimize PHY of baseline receiver for cellular vehicle to everything (C-V2X) system in next generation (6G) communication, however, there has been no exploration of how varying training parameters affect the model's efficiency. Additionally, a comprehensive evaluation of its performance on multi-modal data remains largely unexplored. To address this, we propose a neural receiver designed to optimize Bit Error Rate (BER) for vehicle to network (V2N) uplink scenario in 6G network. We train multiple neural receivers by changing its trainable parameters and use the best fit model as proposition for large scale deployment. Our proposed neural receiver gets signal in frequency domain at the base station (BS) as input and generates optimal log likelihood ratio (LLR) at the output. It estimates the channel based on the received signal, equalizes and demodulates the higher order modulated signal. Later, to evaluate multi-modality of the proposed model, we test it across diverse V2X data flows (e.g., image, video, gps, lidar cloud points and radar detection signal). Results from simulation clearly indicates that our proposed multi-modal neural receiver outperforms state-of-the-art receiver architectures by achieving high performance at low Signal to Noise Ratio (SNR).
Joint Beamforming and Position Optimization for Fluid RIS-aided ISAC Systems
Authors: Junjie Ye, Peichang Zhang, Xiao-Peng Li, Lei Huang, Yuanwei Liu
Date: 2025-01-23 00:00:00
Tasks: ISAC, Position

A fluid reconfigurable intelligent surface (fRIS)-aided integrated sensing and communications (ISAC) system is proposed to enhance multi-target sensing and multi-user communication. Unlike the conventional RIS, the fRIS incorporates movable elements whose positions can be flexibly adjusted to provide extra spatial degrees of freedom. In this system, a joint optimization problem is formulated to minimize sensing beampattern mismatch and communication symbol estimation error by optimizing the symbol estimator, transmit beamformer, fRIS phase shifts, and element positions. To solve this problem, an algorithm based on alternating minimization is devised, where subproblems are solved leveraging augmented Lagrangian method, quadratic programming, semidefinite-relaxation, and majorization-minimization techniques. A key challenge exists that the fRIS element positions affect both the incident and reflective channels, leading to the high-order composite functions regarding the positions. As a remedy, it is proved that the high-order terms can be transformed to linear and linear-difference forms using the characteristics of fRIS and structural channels, which facilitates the position optimization. Numerical results validate the effectiveness of the proposed scheme as compared to the conventional RIS-aided ISAC systems and other benchmarks.
Hardware-in-the-Loop Evaluation of Goodness of Fit (GoF) Testing for Dynamic Spectrum Sharing
Authors: Mir Lodro, Simon Armour, Mark A. Beach
Date: 2025-01-22 00:00:00

In contrast to parametric spectrum sensing, non-parametric spectrum sensing can effectively detect the primary user's presence or absence without prior information about the primary user. Particularly, non-parametric spectrum sensing can be useful in dynamic spectrum sharing. The secondary user must detect incumbents and peer secondary users in dynamic spectrum sharing. The secondary user can use the licensed spectrum if the primary user is not detected using its band. The primary user detection problem is the goodness-of-fit testing problem. In this work, we performed a hardware-in-the-loop evaluation of goodness-of-fit tests such as Cramer-von-Mises (CM), Anderson-Darling (AD) and Kolmogorov-Smirnov (KS) tests. We used a wideband radio transceiver RFSoC 4x2 from AMD and an F8 radio channel emulator to perform GoF tests.
Introducing Resilience to IRS-Assisted Secure Wireless Systems
Authors: Yifei Wu, Mehmet Emin Arslan, Niels Neumann, Wolfgang Gerstacker, Robert Schober
Date: 2025-01-22 00:00:00

Intelligent reflecting surfaces (IRSs) are envisioned as a transformative method for enhancing the physical layer security of future communication networks. However, current IRS-assisted secure wireless system designs incur high time cost for the joint optimization of beamforming vectors and IRS phase shifts and are susceptible to unforeseen disruption. Therefore, introducing resilience in IRS-assisted secure systems is essential. In this paper, we first quantify the resilience performance as the combination of the absorption and adaptation performance. In particular, the absorption performance measures the system's robustness when facing failure while the adaptation performance reflects the system's ability to recover from failure. Then, we propose a two-timescale transmission protocol aiming to enhance system resilience while limiting maximum information leakage to eavesdroppers. Specifically, in the initialization phase, the base station (BS) beamforming vectors and IRS phase shifts are optimized iteratively using an alternating optimization (AO) approach to improve the absorption performance of the system, i.e., the worst-case achievable rate, given a predefined maximum tolerable leakage rate, by accounting for the impact of long-term channel variations. When the system detects an outage, a fast system recovery mechanism is activated to restore operation by adjusting only the BS beamforming vectors, which enhances the adaptation performance of the system. Our numerical results reveal that the proposed algorithm significantly improves system resilience, including system robustness and recovery performance. Moreover, the proposed design guarantees outage-free transmission over a long time interval for moderate-size IRSs.
Sum Rate Enhancement using Machine Learning for Semi-Self Sensing Hybrid RIS-Enabled ISAC in THz Bands
Authors: Sara Farrag Mobarak, Tingnan Bao, Melike Erol-Kantarci
Date: 2025-01-21 00:00:00
Tasks: Deep Reinforcement Learning, ISAC

This paper proposes a novel semi-self sensing hybrid reconfigurable intelligent surface (SS-HRIS) in terahertz (THz) bands, where the RIS is equipped with reflecting elements divided between passive and active elements in addition to sensing elements. SS-HRIS along with integrated sensing and communications (ISAC) can help to mitigate the multipath attenuation that is abundant in THz bands. In our proposed scheme, sensors are configured at the SS-HRIS to receive the radar echo signal from a target. A joint base station (BS) beamforming and HRIS precoding matrix optimization problem is proposed to maximize the sum rate of communication users while maintaining satisfactory sensing performance measured by the Cramer-Rao bound (CRB) for estimating the direction of angles of arrival (AoA) of the echo signal and thermal noise at the target. The CRB expression is first derived and the sum rate maximization problem is formulated subject to communication and sensing performance constraints. To solve the complex non-convex optimization problem, deep deterministic policy gradient (DDPG)-based deep reinforcement learning (DRL) algorithm is proposed, where the reward function, the action space and the state space are modeled. Simulation results show that the proposed DDPG-based DRL algorithm converges well and achieves better performance than several baselines, such as the soft actor-critic (SAC), proximal policy optimization (PPO), greedy algorithm and random BS beamforming and HRIS precoding matrix schemes. Moreover, it demonstrates that adopting HRIS significantly enhances the achievable sum rate compared to passive RIS and random BS beamforming and HRIS precoding matrix schemes.