pwc-archive/papers-with-abstracts huggingface dataset:
FD-Net: An Unsupervised Deep Forward-Distortion Model for Susceptibility Artifact Correction in EPI
Authors: Abdallah Zaid Alkilani, Tolga Çukur, Emine Ulku Saritas
Date: 2023-03-18 00:00:00
Tasks: Computational Efficiency

Recent learning-based correction approaches in EPI estimate a displacement field, unwarp the reversed-PE image pair with the estimated field, and average the unwarped pair to yield a corrected image. Unsupervised learning in these unwarping-based methods is commonly attained via a similarity constraint between the unwarped images in reversed-PE directions, neglecting consistency to the acquired EPI images. This work introduces an unsupervised deep-learning method for fast and effective correction of susceptibility artifacts in reversed phase-encode (PE) image pairs acquired with EPI. FD-Net predicts both the susceptibility-induced displacement field and the underlying anatomically-correct image. Unlike previous methods, FD-Net enforces the forward-distortions of the correct image in both PE directions to be consistent with the acquired reversed-PE image pair. FD-Net further leverages a multiresolution architecture to maintain high local and global performance. FD-Net performs competitively with a gold-standard reference method (TOPUP) in image quality, while enabling a leap in computational efficiency. Furthermore, FD-Net outperforms recent unwarping-based methods for unsupervised correction in terms of both image and field quality. The unsupervised FD-Net method introduces a deep forward-distortion approach to enable fast, high-fidelity correction of susceptibility artifacts in EPI by maintaining consistency to measured data. Therefore, it holds great promise for improving the anatomical accuracy of EPI imaging.
Covariance properties under natural image transformations for the generalized Gaussian derivative model for visual receptive fields
Authors: Tony Lindeberg
Date: 2023-03-17 00:00:00

This paper presents a theory for how geometric image transformations can be handled by a first layer of linear receptive fields, in terms of true covariance properties, which, in turn, enable geometric invariance properties at higher levels in the visual hierarchy. Specifically, we develop this theory for a generalized Gaussian derivative model for visual receptive fields, which is derived in an axiomatic manner from first principles, that reflect symmetry properties of the environment, complemented by structural assumptions to guarantee internally consistent treatment of image structures over multiple spatio-temporal scales. It is shown how the studied generalized Gaussian derivative model for visual receptive fields obeys true covariance properties under spatial scaling transformations, spatial affine transformations, Galilean transformations and temporal scaling transformations, implying that a vision system, based on image and video measurements in terms of the receptive fields according to this model, can to first order of approximation handle the image and video deformations between multiple views of objects delimited by smooth surfaces, as well as between multiple views of spatio-temporal events, under varying relative motions between the objects and events in the world and the observer. We conclude by describing implications of the presented theory for biological vision, regarding connections between the variabilities of the shapes of biological visual receptive fields and the variabilities of spatial and spatio-temporal image structures under natural image transformations.
One-Bit Massive MIMO Precoding for Frequency-Selective Fading Channels
Authors: Ly V. Nguyen, Lu Liu, Nguyen Linh-Trung, A. Lee Swindlehurst
Date: 2023-03-20 00:00:00

One-bit digital-to-analog converters (DACs) are a practical and promising solution for reducing cost and power consumption in massive multiple-input multiple-output (MIMO) systems. However, the one-bit precoding problem is NP-hard and even more challenging in frequency-selective fading channels compared to the flat-fading scenario. While block-wise processing (BWP) can effectively address the inter-symbol-interference (ISI) in frequency-selective fading channels, its computational complexity and processing delay can be too high for practical implementation. An alternative solution to alleviate the processing complexity and delay issues is symbol-wise processing (SWP) which sequentially designs the transmit signals. However, existing SWP work leaves unwanted interference for later signal designs. In this paper, we propose an SWP approach which can efficiently address the ISI even at the symbol rate. The idea is to design the transmit signal to not only be beneficial for its time slot, but also to provide constructive interference for subsequent symbols. We develop two active ISI processing methods that significantly outperform a conventional approach, one of which that even outperforms the BWP approach at low SNR.
Making Sense of Meaning: A Survey on Metrics for Semantic and Goal-Oriented Communication
Authors: Tilahun M. Getu, Georges Kaddoum, Mehdi Bennis
Date: 2023-03-20 00:00:00
Tasks: Semantic Communication

Semantic communication (SemCom) aims to convey the meaning behind a transmitted message by transmitting only semantically-relevant information. This semantic-centric design helps to minimize power usage, bandwidth consumption, and transmission delay. SemCom and goal-oriented SemCom (or effectiveness-level SemCom) are therefore promising enablers of 6G and developing rapidly. Despite the surge in their swift development, the design, analysis, optimization, and realization of robust and intelligent SemCom as well as goal-oriented SemCom are fraught with many fundamental challenges. One of the challenges is that the lack of unified/universal metrics of SemCom and goal-oriented SemCom can stifle research progress on their respective algorithmic, theoretical, and implementation frontiers. Consequently, this survey paper documents the existing metrics -- scattered in many references -- of wireless SemCom, optical SemCom, quantum SemCom, and goal-oriented wireless SemCom. By doing so, this paper aims to inspire the design, analysis, and optimization of a wide variety of SemCom and goal-oriented SemCom systems. This article also stimulates the development of unified/universal performance assessment metrics of SemCom and goal-oriented SemCom, as the existing metrics are purely statistical and hardly applicable to reasoning-type tasks that constitute the heart of 6G and beyond.
MF-JMoDL-Net: A Deep Network for Azimuth Undersampling Pattern Design and Ambiguity Suppression for Sparse SAR Imaging
Authors: Yuwei Wu, Zhe Zhang, Xiaolan Qiu, Yao Zhao, Weidong Yu
Date: 2023-03-20 00:00:00

repetition frequency (PRF). Given the system complexity and resource constraints, it is often difficult to achieve high imaging performance and low ambiguity without compromising the swath. In this paper, we propose a joint optimization framework for sparse strip SAR imaging algorithms and azimuth undersampling patterns based on a deep convolutional neural network, combined with matched filter (MF) approximate measurement operators and inverse MF operators, referred to as MF-JMoDL-Net, for sparse SAR imaging methods. Compared with conventional sparse SAR imaging, MF-JMoDL-Net enables us to alleviate the limitations imposed by PRF. In the proposed scheme, joint and continuous optimization of azimuth undersampling patterns and convolutional neural network parameters are implemented to suppress azimuth ambiguity and enhance sparse SAR imaging quality. Experiments and comparisons under various conditions demonstrate the effectiveness and superiority of the proposed framework in imaging results.
Multi-Channel Attentive Feature Fusion for Radio Frequency Fingerprinting
Authors: Yuan Zeng, Yi Gong, Jiawei Liu, Shangao Lin, Zidong Han, Ruoxiao Cao, Kaibin Huang, Khaled Ben Letaief
Date: 2023-03-19 00:00:00

Radio frequency fingerprinting (RFF) is a promising device authentication technique for securing the Internet of things. It exploits the intrinsic and unique hardware impairments of the transmitters for RF device identification. In real-world communication systems, hardware impairments across transmitters are subtle, which are difficult to model explicitly. Recently, due to the superior performance of deep learning (DL)-based classification models on real-world datasets, DL networks have been explored for RFF. Most existing DL-based RFF models use a single representation of radio signals as the input. Multi-channel input model can leverage information from different representations of radio signals and improve the identification accuracy of the RF fingerprint. In this work, we propose a novel multi-channel attentive feature fusion (McAFF) method for RFF. It utilizes multi-channel neural features extracted from multiple representations of radio signals, including IQ samples, carrier frequency offset, fast Fourier transform coefficients and short-time Fourier transform coefficients, for better RF fingerprint identification. The features extracted from different channels are fused adaptively using a shared attention module, where the weights of neural features from multiple channels are learned during training the McAFF model. In addition, we design a signal identification module using a convolution-based ResNeXt block to map the fused features to device identities. To evaluate the identification performance of the proposed method, we construct a WiFi dataset, named WFDI, using commercial WiFi end-devices as the transmitters and a Universal Software Radio Peripheral (USRP) as the receiver. ...
Two-tier User Association and Resource Allocation Design for Integrated Satellite-Terrestrial Networks
Authors: Hung Nguyen-Kha, Vu Nguyen Ha, Eva Lagunas, Symeon Chatzinotas, Joel Grotz
Date: 2023-03-19 00:00:00

This paper presents a study of an integrated satellite-terrestrial network, where Low-Earth-Orbit (LEO) satellites are used to provide the backhaul link between base stations (BSs) and the core network. The mobility of LEO satellites raises the challenge of determining the optimal association between LEO satellites, BSs, and users (UEs). The goal is to satisfy the UE demand while ensuring load balance and optimizing the capacity of the serving link between the BS and the LEO satellite. To tackle this complex optimization problem, which involves mixed-integer non-convex programming, we propose an iterative algorithm that leverages approximation and relaxation methods. The proposed solution aims to find the optimal two-tier satellite-BS-UE association, sub-channel assignment, power and bandwidth allocation in the shortest possible time, fulfilling the requirements of the integrated satellite-terrestrial network.
Dynamic EM Ray Tracing for Large Urban Scenes with Multiple Receivers
Authors: Ruichen Wang, Dinesh Manocha
Date: 2023-03-19 00:00:00
Tasks: Blocking

Radio applications are increasingly being used in urban environments for cellular radio systems and safety applications that use vehicle-vehicle, and vehicle-to-infrastructure. We present a novel ray tracing-based radio propagation algorithm that can handle large urban scenes with hundreds or thousands of dynamic objects and receivers. Our approach is based on the use of coherence-based techniques that exploit spatial and temporal coherence for efficient wireless propagation and radio network planning. Our formulation also utilizes channel coherence which is used to determine the effectiveness of the propagation model within a certain time in dynamically generated paths; and spatial consistency which is used to estimate the similarity and accuracy of changes in a dynamic environment with varying propagation models and blocking obstacles. We highlight the performance of our simulator in large urban traffic scenes with an area of 2*2 km^2 and more than 10,000 users and devices. We evaluate the accuracy by comparing the results with discrete model simulations performed using WinProp. In practice, our approach scales linearly with the area of the urban environment and the number of dynamic obstacles or receivers.
Signal Processing on Product Spaces
Authors: T. Mitchell Roddenberry, Vincent P. Grande, Florian Frantzen, Michael T. Schaub, Santiago Segarra
Date: 2023-03-18 00:00:00

We establish a framework for signal processing on product spaces of simplicial and cellular complexes. For simplicity, we focus on the product of two complexes representing time and space, although our results generalize naturally to products of simplicial complexes of arbitrary dimension. Our framework leverages the structure of the eigenmodes of the Hodge Laplacian of the product space to jointly filter along time and space. To this end, we provide a decomposition theorem of the Hodge Laplacian of the product space, which highlights how the product structure induces a decomposition of each eigenmode into a spatial and temporal component. Finally, we apply our method to real world data, specifically for interpolating trajectories of buoys in the ocean from a limited set of observed trajectories.
Diffusion LMS for Distributed Estimation over Wireless Networks with Inter-Node Interference Perturbation
Authors: Mohammadjavad Mirzazadeh Moallem, Mehdi Korki
Date: 2023-03-18 00:00:00

In this paper, we investigate the diffusion least mean square (DLMS) algorithm over fading channel, where in addition to channel noise and path-loss the inter-node-interference (INI) among neighboring nodes of a host node is also taken into account. We also analyze the mean-square convergence behavior of DLMS algorithm, under such condition. In addition, based on an upper bound of the derived network MSD, an optimization problem is defined to find an optimal combination strategy. Furthermore, the adaptive version of the proposed combination strategy is presented. Simulation results corroborate the theoretical findings and indicate the superiority of the proposed combination methods over some previously reported algorithms.
Optical IRSs: Power Scaling Law, Optimal Deployment, and Comparison with Relays
Authors: Hedieh Ajam, Marzieh Najafi, Vahid Jamali, Robert Schober
Date: 2023-03-17 00:00:00

The line-of-sight (LOS) requirement of free-space optical (FSO) systems can be relaxed by employing optical relays or optical intelligent reflecting surfaces (IRSs). In this paper, we show that the power reflected from FSO IRSs and collected at the receiver (Rx) lens may scale quadratically or linearly with the IRS size or may saturate at a constant value. We analyze the power scaling law for optical IRSs and unveil its dependence on the wavelength, transmitter (Tx)-to-IRS and IRS-to-Rx distances, beam waist, and Rx lens size. We also consider the impact of linear, quadratic, and focusing phase shift profiles across the IRS on the power collected at the Rx lens for different IRS sizes. Our results reveal that surprisingly the powers received for the different phase shift profiles are identical, unless the IRS operates in the saturation regime. Moreover, IRSs employing the focusing (linear) phase shift profile require the largest (smallest) size to reach the saturation regime. We also compare optical IRSs in different power scaling regimes with optical relays in terms of the outage probability, diversity and coding gains, and optimal placement. Our results show that, at the expense of a higher hardware complexity, relay-assisted FSO links yield a better outage performance at high signal-to-noise-ratios (SNRs), but optical IRSs can achieve a higher performance at low SNRs. Moreover, while it is optimal to place relays equidistant from Tx and Rx, the optimal location of optical IRSs depends on the phase shift profile and the power scaling regime they operate in.
Mode Vector Modulation: Extending Stokes Vector Modulation to Higher Dimensions
Authors: Jaroslaw Kwapisz, Ioannis Roudas, Eric Fink
Date: 2023-03-17 00:00:00

The use of multidimensional modulations can decrease the energy consumption of optical links. In this paper, we propose and study Mode Vector Modulation (MVM), a generalized polarization modulation scheme for transmission over multimode/multicore optical fibers or free space. Similar to Polarization Shift Keying (PolSK) and Stokes Vector Modulation (SVM), MVM can be used in conjunction with direct detection and, therefore, is suitable for next-generation, short-haul optical interconnects. This paper focuses on the MVM transceiver architecture, the back-to-back performance of optically-preamplified MVM direct detection (DD) receivers, the optimized geometric shaping of the MVM constellation, and the related bit-to-symbol mapping. We show that MVM DD outperforms conventional single-mode, direct-detection-compliant, digital modulation formats by several dBs in terms of receiver sensitivity and the SNR gain increases with the number of spatial degrees of freedom (SDOFs) N.
LiFi Technology Overview: taxonomy, and future directions
Authors: Victor Monzon Baeza, Rafael Arellano Garcia
Date: 2023-03-16 00:00:00

The looming electromagnetic spectrum crisis -- due to the fact of the explosive growth in the increasing user data demand -- has encouraged the emergence of new wireless technologies. This paper surveys the state-of-the-art leading and rapid developments in the current Light Fidelity (LiFi) technology. First, an overview is shown to help readers understand the potential of this technology. A comprehensive comparison with legacy wireless technologies is analyzed. We devise a taxonomy based on the main characteristics such as modulation techniques, applications, scenarios, network topologies, and architecture requirements, which enables a foundation for the research community. We highlight the powerful opportunities, the ongoing works, and results, identifying the challenges for future research.
Closed-loop neurostimulation in real-time for the treatment of pathological brain rhythms
Authors: Thomas Wahl, Michel Duprez, Axel Hutt
Date: 2023-03-20 00:00:00

Mental disorders may exhibit pathological brain rhythms and neurostimulation promises to alleviate of patients' symptoms by modifying these rhythms. Today, most neurostimulation schemes are open-loop, i.e. administer experimental stimulation protocols independent of the patients brain activity which may yield a sub-optimal treatment. We propose a closed-loop feedback control scheme estimating an optimal stimulation based on observed brain activity. The optimal stimulation is chosen according to a user-defined target frequency distribution, which permits frequency tuning of the brain activity in real-time. The mathematical description details the major control elements and applications to biologically realistic simulated brain activity illustrate the scheme's possible power in medical practice. Clinical relevance - The proposed neurostimulation control theme promises to permit the medical personnel to tune a patient's brain activity in real-time.
Estimating the Repertoire Size in Birds using Unsupervised Clustering techniques
Authors: Joachim Poutaraud
Date: 2023-03-19 00:00:00
Tasks: Clustering

Birds produce multiple types of vocalizations that, together, constitute a vocal repertoire. For some species, the repertoire size is of importance because it informs us about their brain capacity, territory size or social behaviour. Estimating the repertoire size is challenging because it requires large amounts of data which can be difficult to obtain and analyse. From birds vocalizations recordings, songs are extracted and segmented as sequences of syllables before being clustered. Segmenting songs in such a way can be done either by simple enumeration, where one counts unique vocalization types until there are no new types detected, or by specific algorithms permitting reproducible studies. In this paper, we present a specific automatic method to compute a syllable distance measure that allows an unsupervised classification of bird song syllables. The results obtained from the segmenting of the bird songs are evaluated using the Silhouette metric score.
STGIC: a graph and image convolution-based method for spatial transcriptomic clustering
Authors: Chen Zhang, Junhui Gao, Lingxin Kong, Guangshuo cao, Xiangyu Guo, Wei Liu
Date: 2023-03-19 00:00:00
Tasks: Clustering, Contrastive Learning, Graph Attention

Spatial transcriptomic (ST) clustering employs spatial and transcription information to group spots spatially coherent and transcriptionally similar together into the same spatial domain. Graph convolution network (GCN) and graph attention network (GAT), fed with spatial coordinates derived adjacency and transcription profile derived feature matrix are often used to solve the problem. Our proposed method STGIC (spatial transcriptomic clustering with graph and image convolution) utilizes an adaptive graph convolution (AGC) to get high quality pseudo-labels and then resorts to dilated convolution framework (DCF) for virtual image converted from gene expression information and spatial coordinates of spots. The dilation rates and kernel sizes are set appropriately and updating of weight values in the kernels is made to be subject to the spatial distance from the position of corresponding elements to kernel centers so that feature extraction of each spot is better guided by spatial distance to neighbor spots. Self-supervision realized by KL-divergence, spatial continuity loss and cross entropy calculated among spots with high confidence pseudo-labels make up the training objective of DCF. STGIC attains state-of-the-art (SOTA) clustering performance on the benchmark dataset of human dorsolateral prefrontal cortex (DLPFC). Besides, it's capable of depicting fine structures of other tissues from other species as well as guiding the identification of marker genes. Also, STGIC is expandable to Stereo-seq data with high spatial resolution.
Financial Structure, Firm Size and Financial Growth of Non-Financial Firms Listed at the Nairobi Securities Exchange
Authors: David Haritone Shikumo, Oluoch Oluoch, Joshua Matanda Wepukhulu
Date: 2023-03-20 00:00:00

A significant number of the non-financial firms listed at the Nairobi Securities Exchange have been experiencing declining financial performance and financial growth, which deter investors from investing in such firms. Hence, the study aimed at establishing the effect of financial structure on the financial growth of non-financial firms listed at the Nairobi Securities Exchange.
Model-free Hedging of Impermanent Loss in Geometric Mean Market Makers
Authors: Masaaki Fukasawa, Basile Maire, Marcus Wunsch
Date: 2023-03-20 00:00:00

We consider Geometric Mean Market Makers -- a special type of Decentralized Exchange -- with two types of users: liquidity takers and arbitrageurs. Liquidity takers trade at prices that can create arbitrage opportunities, while arbitrageurs align the exchange's price with the external market price. We show that in Geometric Mean Market Makers charging proportional transaction fees, Impermanent Loss can be super-hedged by a model-free rebalancing strategy. Moreover, we demonstrate that in such a DEX, the exchange rate is of finite variation, so that loss-versus-rebalancing (the shortfall of providing liquidity versus the corresponding constant-weights portfolio) vanishes.