pwc-archive/papers-with-abstracts huggingface dataset:
Trust-informed Decision-Making Through An Uncertainty-Aware Stacked Neural Networks Framework: Case Study in COVID-19 Classification
Authors: Hassan Gharoun, Mohammad Sadegh Khorshidi, Fang Chen, Amir H. Gandomi
Date: 2024-09-19 00:00:00
Tasks: Decision Making, Diagnostic, Uncertainty Quantification

This study presents an uncertainty-aware stacked neural networks model for the reliable classification of COVID-19 from radiological images. The model addresses the critical gap in uncertainty-aware modeling by focusing on accurately identifying confidently correct predictions while alerting users to confidently incorrect and uncertain predictions, which can promote trust in automated systems. The architecture integrates uncertainty quantification methods, including Monte Carlo dropout and ensemble techniques, to enhance predictive reliability by assessing the certainty of diagnostic predictions. Within a two-tier model framework, the tier one model generates initial predictions and associated uncertainties, which the second tier model uses to produce a trust indicator alongside the diagnostic outcome. This dual-output model not only predicts COVID-19 cases but also provides a trust flag, indicating the reliability of each diagnosis and aiming to minimize the need for retesting and expert verification. The effectiveness of this approach is demonstrated through extensive experiments on the COVIDx CXR-4 dataset, showing a novel approach in identifying and handling confidently incorrect cases and uncertain cases, thus enhancing the trustworthiness of automated diagnostics in clinical settings.
Image-to-Image Translation Based on Deep Generative Modeling for Radiotherapy Synthetic Dataset Creation
Authors: Olga Glazunova, Cecile J. A. Wolfs, Frank Verhaegen
Date: 2024-09-17 00:00:00
Tasks: Image-to-Image Translation, Translation, Unsupervised Image-To-Image Translation

Objective: Radiotherapy uses precise doses of radiation to treat cancer, requiring accurate verification, e.g. using the Electronic Portal Imaging Device (EPID), to guide treatment. To develop an effective artificial intelligence (AI) model for error detection and treatment verification, a large and well-annotated dataset of EPID images is needed, however, acquiring such high quality real data is difficult. While synthetic EPID data could be a viable alternative, it is critical to ensure that this data is as realistic as possible to effectively train an accurate and reliable AI model. The measurement uncertainty that is not modeled in EPID predictions but is present on real measured EPID images can hinder downstream tasks such as error detection and classification. Our research aims to improve synthetic EPID data through image-to-image (I2I) translation based on deep generative modeling. Approach: A dataset of 989 predicted EPID images and corresponding measured EPID images was used. We evaluate both paired and unpaired generative modeling approaches for this task. For the former, we introduce a novel modification of Variational Autoencoder (VAE) to I2I, a method that, to the best of our knowledge, has not been previously explored for this task. For the latter, we use UNsupervised Image-to-Image Translation Networks (UNIT). Results: Our results show that both models achieved some degree of I2I translation, with our novel modification of the VAE model outperforming the UNIT model in improving key metrics (mean absolute error: 4.1 cGy vs 6.4 cGy; relative dose difference in-field: 2.5% vs 5.5%; absolute dose difference in-field: 5.3 cGy vs 10.8 cGy). Significance: This enhanced synthetic data is expected to improve downstream tasks such as training neural networks for automated error detection and error classification in radiotherapy.
Analysis of Convolutional Neural Network-based Image Classifications: A Multi-Featured Application for Rice Leaf Disease Prediction and Recommendations for Farmers
Authors: Biplov Paneru, Bishwash Paneru, Krishna Bikram Shah
Date: 2024-09-17 00:00:00
Tasks: Disease Prediction, Transfer Learning

This study presents a novel method for improving rice disease classification using 8 different convolutional neural network (CNN) algorithms, which will further the field of precision agriculture. Tkinter-based application that offers farmers a feature-rich interface. With the help of this cutting-edge application, farmers will be able to make timely and well-informed decisions by enabling real-time disease prediction and providing personalized recommendations. Together with the user-friendly Tkinter interface, the smooth integration of cutting-edge CNN transfer learning algorithms-based technology that include ResNet-50, InceptionV3, VGG16, and MobileNetv2 with the UCI dataset represents a major advancement toward modernizing agricultural practices and guaranteeing sustainable crop management. Remarkable outcomes include 75% accuracy for ResNet-50, 90% accuracy for DenseNet121, 84% accuracy for VGG16, 95.83% accuracy for MobileNetV2, 91.61% accuracy for DenseNet169, and 86% accuracy for InceptionV3. These results give a concise summary of the models' capabilities, assisting researchers in choosing appropriate strategies for precise and successful rice crop disease identification. A severe overfitting has been seen on VGG19 with 70% accuracy and Nasnet with 80.02% accuracy. On Renset101, only an accuracy of 54% could be achieved, along with only 33% on efficientNetB0. A MobileNetV2-trained model was successfully deployed on a TKinter GUI application to make predictions using image or real-time video capture.
Point Data for Site-Specific Mid-band Radio Propagation Channel Statistics in the Indoor Hotspot (InH) Environment for 3GPP and Next Generation Alliance (NGA) Channel Modeling
Authors: Theodore S. Rappaport, Dipankar Shakya, Mingjun Ying
Date: 2024-09-30 00:00:00

Extensive work has been carried out in the past year by various organizations in an effort to determine standardized statistical channel impulse response (CIR) parameters for the newly-released FR3 mid-band spectrum (7.25 GHz -- 24.25 GHz). In this work, we show that the wireless community currently lacks a unified method for presenting key parameters required for transparency and utilization by several constituencies when presenting propagation data for use by standard bodies or third parties to create statistical CIR models. This paper aims to solve the existing problem by offering a standard method to provide key propagation parameters in a point-data format that supports both statistical and site-specific channel characterization. As shown here, the point-data format enables multiple contributors to create channel model standards or pool measurement data to create larger datasets for exploring ray-tracing (e.g. site-specific) channel modeling or training in AI/ML propagation work, and to ensure the most accurate model using a larger dataset that is continually expanded through measurement contributions. The point-data approach includes site-specific point-by-point propagation data while readily supporting the creation of commonly-used cumulative distribution function (CDF) plot. The indoor hotspot (InH) datasets collected in Spring 2024 at 6.75 GHz and 16.95 GHZ by NYU WIRELESS are provided for the first time in point-data form, to augment statistical models previously presented solely as CDFs, in order to demonstrate how a standardized approach to measurement data could allow others to utilize the site-specific locations and key channel parameters observed at each location, to better understand, vet, and build upon statistical or site-specific CIRs from the contributions of many different data sources.
Optimized Resource Allocation for Cloud-Native 6G Networks: Zero-Touch ML Models in Microservices-based VNF Deployments
Authors: Swarna Bindu Chetty, Avishek Nag, Ahmed Al-Tahmeesschi, Qiao Wang, Berk Canberk, Johann Marquez-Barja, Hamed Ahmadi
Date: 2024-10-09 00:00:00
Tasks: Management, Q-Learning, Scheduling

6G, the next generation of mobile networks, is set to offer even higher data rates, ultra-reliability, and lower latency than 5G. New 6G services will increase the load and dynamism of the network. Network Function Virtualization (NFV) aids with this increased load and dynamism by eliminating hardware dependency. It aims to boost the flexibility and scalability of network deployment services by separating network functions from their specific proprietary forms so that they can run as virtual network functions (VNFs) on commodity hardware. It is essential to design an NFV orchestration and management framework to support these services. However, deploying bulky monolithic VNFs on the network is difficult, especially when underlying resources are scarce, resulting in ineffective resource management. To address this, microservices-based NFV approaches are proposed. In this approach, monolithic VNFs are decomposed into micro VNFs, increasing the likelihood of their successful placement and resulting in more efficient resource management. This article discusses the proposed framework for resource allocation for microservices-based services to provide end-to-end Quality of Service (QoS) using the Double Deep Q Learning (DDQL) approach. Furthermore, to enhance this resource allocation approach, we discussed and addressed two crucial sub-problems: the need for a dynamic priority technique and the presence of the low-priority starvation problem. Using the Deep Deterministic Policy Gradient (DDPG) model, an Adaptive Scheduling model is developed that effectively mitigates the starvation problem. Additionally, the impact of incorporating traffic load considerations into deployment and scheduling is thoroughly investigated.
Balanced Space- and Time-based Duty-cycle Scheduling for Light-based IoT
Authors: Khojiakbar Botirov, Hazem Sallouha, Sofie Pollin, Marcos Katz
Date: 2024-10-09 00:00:00
Tasks: Scheduling

In this work, we propose a Multiple Access Control (MAC) protocol for Light-based IoT (LIoT) networks, where the gateway node orchestrates and schedules batteryless nodes duty-cycles based on their location and sleep time. The LIoT concept represents a sustainable solution for massive indoor IoT applications, offering an alternative communication medium through Visible Light Communication (VLC). While most existing scheduling algorithms for intermittent batteryless IoT aim to maximize data collection and enhance dataset size, our solution is tailored for environmental sensing applications, such as temperature, humidity, and air quality monitoring, optimizing measurement distribution and minimizing blind spots to achieve comprehensive and uniform environmental sensing. We propose a Balanced Space and Time-based Time Division Multiple Access scheduling (BST-TDMA) algorithm, which addresses environmental sensing challenges by balancing spatial and temporal factors to improve the environmental sensing efficiency of batteryless LIoT nodes. Our measurement-based results show that BST-TDMA was able to efficiently schedule duty-cycles with given intervals.
RIS-Assisted D2D Communication in the Presence of Interference: Outage Performance Analysis and DNN-Based Prediction
Authors: Hamid Amiriara, Farid Ashtiani, Mahtab Mirmohseni, Masoumeh Nasiri-Kenari, Behrouz Maham
Date: 2024-10-09 00:00:00
Tasks: Diversity, Form

This paper analyses the performance of reconfigurable intelligent surface (RIS)-assisted device-to-device (D2D) communication systems, focusing on addressing co-channel interference, a prevalent issue due to the frequency reuse of sidelink in the underlay in-band D2D communications. In contrast to previous studies that either neglect interference or consider it only at the user, our research investigates a performance analysis in terms of outage probability (OP) for RIS-assisted D2D communication systems considering the presence of interference at both the user and the RIS. More specifically, we introduce a novel integral-form expression for an exact analysis of OP. Additionally, we present a new accurate approximation expression for OP, using the gamma distributions to approximate the fading of both desired and interference links, thereby yielding a closed-form expression. Nevertheless, both derived expressions, i.e., the exact integral-form and the approximate closed-form, contain special functions, such as Meijer's G-function and the parabolic cylinder function, which complicate real-time OP analysis. To circumvent this, we employ a deep neural network (DNN) for real-time OP prediction, trained with data generated by the exact expression. Moreover, we present a tight upper bound that quantifies the impact of interference on achievable diversity order and coding gain. We validate the derived expressions through Monte Carlo simulations. Our analysis reveals that while interference does not affect the system's diversity order, it significantly degrades the performance by reducing the coding gain. The results further demonstrate that increasing the number of RIS's reflecting elements is an effective strategy to mitigate the adverse effects of the interference on the system performance.
Mitigation of Polarization-Induced Fading in Optical Vector Network Analyzer for the Characterization of km-scale Space-Division Multiplexing Fibers
Authors: Besma Kalla, Martina Cappelletti, Menno van den Hout, Vincent van Vliet, Simon Rommel, Luca Palmieri, Thomas Bradley, Chigo Okonkwo
Date: 2024-10-09 00:00:00

We propose an optimized optical vector network analyzer with automatic polarization control to stabilize the reference arm polarization throughout the sweep range. We demonstrate this technique, successfully removing the polarization-induced fading and measurement distortions in insertion loss by characterizing a 10 km multi-core fiber.
Towards optimal algorithms for the recovery of low-dimensional models with linear rates
Authors: Yann Traonmilin, Jean François Aujol, Antoine Guennec
Date: 2024-10-09 00:00:00

We consider the problem of recovering elements of a low-dimensional model from linear measurements. From signal and image processing to inverse problems in data science, this question has been at the center of many applications. Lately, with the success of models and methods relying on deep neural networks, there has been a multiplication of different algorithms and recovery results. Comparing the performance of recovery algorithms becomes a complex task without a unifying framework. In this article, as a first step for the study of general algorithms for low-dimensional recovery, we study a class of generalized projected gradient descent algorithms that can recover a given low-dimensional model with linear rates. The obtained rates decouple the impact of the quality of the measurements with respect to the model from the geometry of the properties of the chosen generalized projection: we can directly measure performance through a restricted Lipschitz constant of the projection with respect to the low dimensional model. By optimizing this constant, we define an optimal generalized projected gradient descent. Our general approach provides an optimality result in the case of sparse recovery. Moreover, our framework allows for a common interpretation of linear rates of recovery in the context of both sparse models and models induced by some ``plug-and-play'' imaging methods that rely on deep neural networks. These rates of recovery are observed in experiments on synthetic and real data.
Assessing the Circadian Rhythm of Cats Living in a Group using Accelerometers
Authors: Alix Enault, Alia Chebly, Sarah Jeannin, Thierry Legou, Leslie Moinet, Thierry Bedossa
Date: 2024-10-09 00:00:00
Tasks: Rhythm

This study explores the biological rhythms of domestic cats. Twelve cats from the AVA shelter in Cuy-Saint-Fiacre, France, participated in the experimental study, wearing collars equipped with IMU sensors for about three weeks. Recorded data were analyzed to measure the cats activity and to gain further insights into their biological rhythms. We first determined the time budget of the cats by categorizing behavior into inactivity and activity. Next, we analyzed the day/night activity repartition and the hourly distribution of activity. Results showed an average of 14.5% of global activity and a higher activity during the day in comparison with the night. Moreover, a bimodal activity pattern with increased activity at the time of the caretaker's interventions at feeding time was found.
Generative Artificial Intelligence (GAI) for Mobile Communications: A Diffusion Model Perspective
Authors: Xiaoxia Xu, Xidong Mu, Yuanwei Liu, Hong Xing, Yue Liu, Arumugam Nallanathan
Date: 2024-10-08 00:00:00
Tasks: Deep Reinforcement Learning, Management

This article targets at unlocking the potentials of a class of prominent generative artificial intelligence (GAI) method, namely diffusion model (DM), for mobile communications. First, a DM-driven communication architecture is proposed, which introduces two key paradigms, i.e., conditional DM and DM-driven deep reinforcement learning (DRL), for wireless data generation and communication management, respectively. Then, we discuss the key advantages of DM-driven communication paradigms. To elaborate further, we explore DM-driven channel generation mechanisms for channel estimation, extrapolation, and feedback in multiple-input multiple-output (MIMO) systems. We showcase the numerical performance of conditional DM using the accurate DeepMIMO channel datasets, revealing its superiority in generating high-fidelity channels and mitigating unforeseen distribution shifts in sophisticated scenes. Furthermore, several DM-driven communication management designs are conceived, which is promising to deal with imperfect channels and task-oriented communications. To inspire future research developments, we highlight the potential applications and open research challenges of DM-driven communications. Code is available at https://github.com/xiaoxiaxusummer/GAI_COMM/
A Physical Layer Security Framework for IRS-Assisted Integrated Sensing and Semantic Communication Systems
Authors: Hamid Amiriara, Mahtab Mirmohseni, Ahmed Elzanaty, Yi Ma, Rahim Tafazolli
Date: 2024-10-08 00:00:00
Tasks: Semantic Communication

In this paper, we propose a physical layer security (PLS) framework for an intelligent reflecting surface (IRS)-assisted integrated sensing and semantic communication (ISASC) system, where a multi-antenna dual-functional semantic base station (BS) serves multiple semantic communication users (SCUs) and monitors a potentially malicious sensing target (MST) in the presence of an eavesdropper (EVE). Both MST and EVE attempt to wiretap information from the signals transmitted to the SCUs. The deployment of the IRS not only enhances PLS by directing a strong beam towards the SCUs, but also improves the localization information for the target without disclosing information about the SCUs. To further strengthen PLS, we employ joint artificial noise (AN) and dedicated sensing signal (DSS), in addition to wiretap coding. To evaluate sensing accuracy, we derive the Cramer-Rao bound (CRB) for estimating the direction of arrival (DoA), and to assess the PLS level of the ISASC system, we determine a closed-form expression for the semantic secrecy rate (SSR). To achieve an optimal trade-off between these two competing objectives, we formulate a multi-objective optimization problem (MOOP) for the joint design of the BS's beamforming (BF) vectors and the IRS's phase shift vector. To tackle this MOOP problem, the $\epsilon$-constraint method is employed, followed by an alternating optimization (AO)-based algorithm that leverages the classical successive convex approximation (SCA) and semidefinite relaxation (SDR) techniques. Simulation results demonstrate that the proposed scheme outperforms the baseline schemes, achieving a superior trade-off between SSR and CRB. Specifically, our proposed approach improves the sensing accuracy by 5 dB compared to the commonly adopted maximal ratio transmission (MRT) approach.
Optimizing Energy Efficiency with RSMA: Balancing Low and High QoS Requirements
Authors: Srivardhan Sivadevuni, Kevin Weinberger, Aydin Sezgin
Date: 2024-10-08 00:00:00

Future wireless systems are expected to deliver significantly higher quality-of-service (QoS) albeit with fewer energy resources for diverse, already existing and also novel wireless applications. The optimal resource allocation for a system in this regard could be investigated by reducing the overall power available at the expense of reduced QoS for the inefficient users. In other words, we maximize the system energy efficiency by achieving power saving through a minimal back-off in terms of QoS. In this paper, we investigate the energy efficiency vs. delivered QoS trade-off for the rate-splitting multiple access (RSMA) assisted downlink system. We first determine the user grouping with a normalised channel similarity metric so as to allow a large number of users with non-zero achievable private message rates. Through the private message removal (PMR) of these users, we aim to investigate the QoS vs. energy efficiency trade-off. Numerical results indicate a peak of ~$10\%$ increase in the network energy efficiency for the proposed normalised channel similarity metric based user grouping with scheduled PMR.
Corrections to "Computer Vision Aided mmWave Beam Alignment in V2X Communications"
Authors: Weihua Xu, Feifei Gao, Xiaoming Tao, Jianhua Zhang, Ahmed Alkhateeb
Date: 2024-10-08 00:00:00

In this document, we revise the results of [1] based on more reasonable assumptions regarding data shuffling and parameter setup of deep neural networks (DNNs). Thus, the simulation results can now more reasonably demonstrate the performance of both the proposed and compared beam alignment methods. We revise the simulation steps and make moderate modifications to the design of the vehicle distribution feature (VDF) for the proposed vision based beam alignment when the MS location is available (VBALA). Specifically, we replace the 2D grids of the VDF with 3D grids and utilize the vehicle locations to expand the dimensions of the VDF. Then, we revise the simulation results of Fig. 11, Fig. 12, Fig. 13, Fig. 14, and Fig. 15 in [1] to reaffirm the validity of the conclusions.