pwc-archive/papers-with-abstracts huggingface dataset:
Does higher capital maintenance drive up banks cost of equity? Evidence from Bangladesh
Authors: Md Shah Naoaj, Mir Md Moyazzem Hosen
Date: 2023-01-30 00:00:00

This paper assesses whether the higher capital maintenance drives up banks cost of equity. We investigate the hypothesis using fixed effect panel estimation with the data from a sample of 28 publicly listed commercial banks over the 2013 to 2019 periods. We find a significant negative relationship between banks capital and cost of equity. Empirically our baseline estimates entail that a 10 percent increase in capital would reduce the cost of equity by 4.39 percent.
OnRMap: An Online Radio Mapping Approach for Large Intelligent Surfaces
Authors: Herman L. Santos, Victor Croisfelt, Cristian J. Vaca-Rubio, Taufik Abrao, Petar Popovski
Date: 2023-02-06 00:00:00

We introduce OnRMap, an online radio mapping (RMap) approach for the sensing and localization of active users (AUs), devices that are transmitting radio signals, and passive elements (PEs), elements that are in the environment and are illuminated by the AUs' radio signals. OnRMap processes the signals received by a large intelligent surface and produces a radio map (RM) of the environment based on signal processing techniques. The method then senses and locate the different elements without the need for offline scanning phases, which is important for environments with frequently changing spatial layouts. Empirical results demonstrate that OnRMap presents a higher localization accuracy than an offline method, but the price paid for being an online method is a moderate reduction in the detection rate.
A Machine Learning Approach to Long-Term Drought Prediction using Normalized Difference Indices Computed on a Spatiotemporal Dataset
Authors: Veronica Wairimu Muriga, Benjamin Rich, Francesco Mauro, Alessandro Sebastianelli, Silvia Liberata Ullo
Date: 2023-02-05 00:00:00
Tasks: Prediction

Climate change and increases in drought conditions affect the lives of many and are closely tied to global agricultural output and livestock production. This research presents a novel approach utilizing machine learning frameworks for drought prediction around water basins. Our method focuses on the next-frame prediction of the Normalized Difference Drought Index (NDDI) by leveraging the recently developed SEN2DWATER database. We propose and compare two prediction methods for estimating NDDI values over a specific land area. Our work makes possible proactive measures that can ensure adequate water access for drought-affected communities and sustainable agriculture practices by implementing a proof-of-concept of short and long-term drought prediction of changes in water resources.
Deep Joint Source-Channel Coding for Wireless Image Transmission with Semantic Importance
Authors: Qizheng Sun, Caili Guo, Yang Yang, Jiujiu Chen, Rui Tang, Chuanhong Liu
Date: 2023-02-05 00:00:00

The sixth-generation mobile communication system proposes the vision of smart interconnection of everything, which requires accomplishing communication tasks while ensuring the performance of intelligent tasks. A joint source-channel coding method based on semantic importance is proposed, which aims at preserving semantic information during wireless image transmission and thereby boosting the performance of intelligent tasks for images at the receiver. Specifically, we first propose semantic importance weight calculation method, which is based on the gradient of intelligent task's perception results with respect to the features. Then, we design the semantic loss function in the way of using semantic weights to weight the features. Finally, we train the deep joint source-channel coding network using the semantic loss function. Experiment results demonstrate that the proposed method achieves up to 57.7% and 9.1% improvement in terms of intelligent task's performance compared with the source-channel separation coding method and the deep sourcechannel joint coding method without considering semantics at the same compression rate and signal-to-noise ratio, respectively.
Multi-Task Learning for Screen Content Image Coding
Authors: Rashid Zamanshoar Heris, Ivan V. Bajić
Date: 2023-02-03 00:00:00
Tasks: Multi-Task Learning, Segmentation

With the rise of remote work and collaboration, compression of screen content images (SCI) is becoming increasingly important. While there are efficient codecs for natural images, as well as codecs for purely-synthetic images, those SCIs that contain both synthetic and natural content pose a particular challenge. In this paper, we propose a learning-based image coding model developed for such SCIs. By training an encoder to provide a latent representation suitable for two tasks -- input reconstruction and synthetic/natural region segmentation -- we create an effective SCI image codec whose strong performance is verified through experiments. Once trained, the second task (segmentation) need not be used; the codec still benefits from the segmentation-friendly latent representation.
Band-of-Interest-based Channel Impulse Response Fusion for Breathing Rate Estimation with UWB
Authors: Sitian Li, Alexios Balatsoukas-Stimming, Andreas Burg
Date: 2023-02-06 00:00:00

The channel impulse response (CIR) obtained from the channel estimation step of various wireless systems is a widely used source of information in wireless sensing. Breathing rate is one of the important vital signs that can be retrieved from the CIR. Recently, there have been various works that extract the breathing rate from one carefully selected CIR delay bin that contains the breathing information. However, it has also been shown that the accuracy of this estimation is very sensitive to the measurement scenario, e.g., if there is any obstacle between the transceivers and the target, the position of the target, and the orientation of the target, since only one CIR delay bin does not contain a sufficient periodic component to retrieve the breathing rate. We focus on such scenarios and propose a CIR delay bin fusion method to merge several CIR bins to achieve a more accurate and reliable breathing rate estimate. We take measurements and showcase the advantages of the proposed method across scenarios.
Fatigue monitoring and maneuver identification for vehicle fleets using a virtual sensing approach
Authors: Leonhard Heindel, Peter Hantschke, Markus Kästner
Date: 2023-02-06 00:00:00
Tasks: regression

Extensive monitoring comes at a prohibitive cost, limiting Predictive Maintenance strategies for vehicle fleets. This paper presents a measurement-based virtual sensing technique where local strain gauges are only required for few reference vehicles, while the remaining fleet relies exclusively on accelerometers. The scattering transform is used to perform feature extraction, while principal component analysis provides a reduced, low dimensional data representation. This enables direct fatigue damage regression, parameterized from unlabeled usage data. Identification measurements allow for a physical interpretation of the reduced representation. The approach is demonstrated using experimental data from a sensor equipped eBike, which is made publicly available.
Statistical reconstruction of pulse shapes from pulse streams
Authors: Marek W. Rupniewski
Date: 2023-02-06 00:00:00

A short sample sequence of a finite-length pulse signal allows for its reconstruction only if the signal has a sparse representation in some basis. The recurrence of the pulse allows for a statistical approach to its reconstruction. We propose a novel method for this task. It is based on the distribution of short sample sequences treated as points which lie along a curve in a low-dimensional Euclidean space. We prove that the probability distribution of the points along this curve determines the underlying pulse signal uniquely. Based on this discovery, we propose an algorithm for pulse estimation from a finite number of short sequences of pulse-stream samples.
Joint Scattering Environment Sensing and Channel Estimation Based on Non-stationary Markov Random Field
Authors: Wenkang Xu, Yongbo Xiao, An Liu, Ming Lei, MinJian Zhao
Date: 2023-02-06 00:00:00
Tasks: Bayesian Inference, Integrated sensing and communication, Position

This paper considers an integrated sensing and communication system, where some radar targets also serve as communication scatterers. A location domain channel modeling method is proposed based on the position of targets and scatterers in the scattering environment, and the resulting radar and communication channels exhibit a two-dimensional (2-D) joint burst sparsity. We propose a joint scattering environment sensing and channel estimation scheme to enhance the target/scatterer localization and channel estimation performance simultaneously, where a spatially non-stationary Markov random field (MRF) model is proposed to capture the 2-D joint burst sparsity. An expectation maximization (EM) based method is designed to solve the joint estimation problem, where the E-step obtains the Bayesian estimation of the radar and communication channels and the M-step automatically learns the dynamic position grid and prior parameters in the MRF. However, the existing sparse Bayesian inference methods used in the E-step involve a high-complexity matrix inverse per iteration. Moreover, due to the complicated non-stationary MRF prior, the complexity of M-step is exponentially large. To address these difficulties, we propose an inverse-free variational Bayesian inference algorithm for the E-step and a low-complexity method based on pseudo-likelihood approximation for the M-step. In the simulations, the proposed scheme can achieve a better performance than the state-of-the-art method while reducing the computational overhead significantly.
Accelerated Dynamic Magnetic Resonance Imaging from Spatial-Subspace Reconstructions (SPARS)
Authors: Alexander J. Mertens, Hai-Ling Margaret Cheng
Date: 2023-02-06 00:00:00
Tasks: Image Reconstruction

Dynamic contrast-enhanced (DCE) magnetic resonance imaging (MRI) ideally requires a high spatial and high temporal resolution, but hardware limitations prevent acquisitions from simultaneously achieving both. Existing image reconstruction techniques can artificially create spatial resolution at a given temporal resolution by estimating data that is not acquired, but, ultimately, spatial details are sacrificed at very high acceleration rates. The purpose of this paper is to introduce the concept of spatial subspace reconstructions (SPARS) and demonstrate its ability to reconstruct high spatial resolution dynamic images from as few as one acquired radial spoke per dynamic frame. Briefly, a low-temporal-high-spatial resolution organization of the acquired raw data is used to estimate a spatial subspace in which the high-temporal-high-spatial ground truth data resides. This subspace is then used to estimate entire images from single k-space spokes. In both simulated and human in-vivo data, the proposed SPARS reconstruction method outperformed standard GRASP and GRASP-Pro reconstruction, providing a shorter reconstruction time and yielding higher accuracy from both a spatial and temporal perspective.
Fast Direct Localization for Millimeter Wave MIMO Systems via Deep ADMM Unfolding
Authors: Wenzhe Fan, Shengheng Liu, ChunGuo Li, Yongming Huang
Date: 2023-02-06 00:00:00

Massive arrays deployed in millimeter-wave systems enable high angular resolution performance, which in turn facilitates sub-meter localization services. Albeit suboptimal, up to now the most popular localization approach has been based on a so-called two-step procedure, where triangulation is applied upon aggregation of the angle-of-arrival (AoA) measurements from the collaborative base stations. This is mainly due to the prohibitive computational cost of the existing direct localization approaches in large-scale systems. To address this issue, we propose a deep unfolding based fast direct localization solver. First, the direct localization is formulated as a joint $l_1$-$l_{2,1}$ norm sparse recovery problem, which is then solved by using alternating direction method of multipliers (ADMM). Next, we develop a deep ADMM unfolding network (DAUN) to learn the ADMM parameter settings from the training data and a position refinement algorithm is proposed for DAUN. Finally, simulation results showcase the superiority of the proposed DAUN over the baseline solvers in terms of better localization accuracy, faster convergence and significantly lower computational complexity.
A Secure Dual-Function Radar Communication System via Time-Modulated Arrays
Authors: Zhaoyi Xu, Athina Petropulu
Date: 2023-02-05 00:00:00

Dual-function radar-communication (DFRC) systems offer high spectral, hardware and power efficiency, as such are prime candidates for 6G wireless systems. DFRC systems use the same waveform for simultaneously probing the surroundings and communicating with other equipment. By exposing the communication information to potential targets, DFRC systems are vulnerable to eavesdropping. In this work, we propose to mitigate the problem by leveraging directional modulation (DM) enabled by a time-modulated array (TMA) that transmits OFDM waveforms. DM can scramble the signal in all directions except the directions of the legitimate user. However, the signal reflected by the targets is also scrambled, thus complicating the extraction of target parameters. We propose a novel, low-complexity target estimation method that estimates the target parameters based on the scrambled received symbols. We also propose a novel method to refine the obtained target estimates at the cost of increased complexity. With the proposed refinement algorithm, the proposed DFRC system can securely communicate with users while having high-precision sensing functionality.
On the Downlink Coverage Performance of RIS-Assisted THz Networks
Authors: Waqas Aman, Nour Kouzayha, Muhammad Mahboob Ur Rahman, Tareq Y. Al-Naffouri
Date: 2023-02-05 00:00:00

This letter provides a stochastic geometry (SG)-based coverage probability (CP) analysis of an indoor terahertz (THz) downlink assisted by a single reconfigurable intelligent surface (RIS) panel. Specifically, multiple access points (AP) deployed on the ceiling of a hall (each equipped with multiple antennas) need to serve multiple user equipment (UE) nodes. Due to presence of blockages, a typical UE may either get served via a direct link, the RIS, or both links (the composite link). The locations of the APs and blockages are modelled as a Poisson point process (PPP) and SG framework is utilized to compute the CP, at a reference UE for all the three scenarios. Monte-Carlo simulation results validate our theoretical analysis.
Phase Retrieval for Rydberg Quantum Arrays
Authors: Peter Vouras, Kumar Vijay Mishra, Alexandra Artusio-Glimpse
Date: 2023-02-05 00:00:00
Tasks: Retrieval

Rydberg-aided atomic electrometry using alkali-metal atoms is gaining increased research interest for detecting external electric fields. However, the inability of Rydberg probes to detect phase is a serious impediment to their realistic deployment. In this paper, we derive a novel phase retrieval algorithm for use in a phased array or synthetic aperture applications where only measurements of electric field intensity are possible at each spatial sample. These array configurations exist if a Rydberg atom probe is used in place of an antenna. We employ three-stage alternating projections to solve the resulting optimization problem. Our numerical experiments demonstrate the effectiveness of the proposed algorithm in terms of beamformed array output.
Localization as a key enabler of 6G wireless systems: A comprehensive survey and an outlook
Authors: Stylianos E. Trevlakis, Alexandros-Apostolos A. Boulogeorgos, Dimitrios Pliatsios, Konstantinos Ntontin, Panagiotis Sarigiannidis, Symeon Chatzinotas, Marco Di Renzo
Date: 2023-02-04 00:00:00

When fully implemented, sixth generation (6G) wireless systems will constitute intelligent wireless networks that enable not only ubiquitous communication but also high-accuracy localization services. They will be the driving force behind this transformation by introducing a new set of characteristics and service capabilities in which location will coexist with communication while sharing available resources. To that purpose, this survey investigates the envisioned applications and use cases of localization in future 6G wireless systems, while analyzing the impact of the major technology enablers. Afterwards, system models for millimeter wave, terahertz and visible light positioning that take into account both line-of-sight (LOS) and non-LOS channels are presented, while localization key performance indicators are revisited alongside mathematical definitions. Moreover, a detailed review of the state of the art conventional and learning-based localization techniques is conducted. Furthermore, the localization problem is formulated, the wireless system design is considered and the optimization of both is investigated. Finally, insights that arise from the presented analysis are summarized and used to highlight the most important future directions for localization in 6G wireless systems.
Radar Clutter Covariance Estimation: A Nonlinear Spectral Shrinkage Approach
Authors: Shashwat Jain, Vikram Krishnamurthy, Muralidhar Rangaswamy, Bosung Kang, Sandeep Gogineni
Date: 2023-02-04 00:00:00

In this paper, we exploit the spiked covariance structure of the clutter plus noise covariance matrix for radar signal processing. Using state-of-the-art techniques high dimensional statistics, we propose a nonlinear shrinkage-based rotation invariant spiked covariance matrix estimator. We state the convergence of the estimated spiked eigenvalues. We use a dataset generated from the high-fidelity, site-specific physics-based radar simulation software RFView to compare the proposed algorithm against the existing Rank Constrained Maximum Likelihood (RCML)-Expected Likelihood (EL) covariance estimation algorithm. We demonstrate that the computation time for the estimation by the proposed algorithm is less than the RCML-EL algorithm with identical Signal to Clutter plus Noise (SCNR) performance. We show that the proposed algorithm and the RCML-EL-based algorithm share the same optimization problem in high dimensions. We use Low-Rank Adaptive Normalized Matched Filter (LR-ANMF) detector to compute the detection probabilities for different false alarm probabilities over a range of target SNR. We present preliminary results which demonstrate the robustness of the detector against contaminating clutter discretes using the Challenge Dataset from RFView. Finally, we empirically show that the minimum variance distortionless beamformer (MVDR) error variance for the proposed algorithm is identical to the error variance resulting from the true covariance matrix.
Hierarchical Model with Allee Effect, Immigration, and Holling Type II Functional Response
Authors: Eddy Kwessi
Date: 2023-02-03 00:00:00

In this paper, we discuss a hierarchical model, based on a Ricker competition model. The species considered are competing for resources and may be subject to an Allee effect due to mate limitation, anti-predator vigilance or aggression, cooperative predation or resource defense, or social thermoregulation. The species may be classified into a more dominant species and less dominant or ``wimpy" species or just as a predator and prey. The model under consideration also has components taking into account immigration in both species and a more structured Holling type II functional response. Local and global stability analyses are discussed and simulations are provided. We also consider demographic stochasticity on the species due to environmental fluctuations in the form of Wiener processes and we show that there are conditions under which a global solution exists, stationary distributions exists, and strong persistence in mean of the species is possible. We also use Wasserstein distance to show empirically that stochasticity can acts as bifurcation parameter.
Monitoring oligomerization dynamics of individual human neurotensin receptors 1 in living cells and in SMALP nanodiscs
Authors: Lukas Spantzel, Iván Pérez, Thomas Heitkamp, Anika Westphal, Stefanie Reuter, Ralf Mrowka, Michael Börsch
Date: 2023-02-05 00:00:00

The human neurotensin receptor 1 (NTSR1) is a G protein-coupled receptor. The receptor is activated by a small peptide ligand neurotensin. NTSR1 can be expressed in HEK cells by stable transfection. Previously we used the fluorescent protein markers mRuby3 or mNeonGreen fused to NTSR1 for EMCCD-based structured illumination microscopy (SIM) in living HEK cells. Ligand binding induced conformational changes in NTSR1 which triggered the intracellular signaling processes. Recent single-molecule studies revealed a dynamic monomer/dimer equilibrium of this receptor in artificial lipid bilayers. Here we report on the oligomerization state of human NTSR1 from living cells by trapping them into lipid nanodiscs. Briefly, SMALPs (styrene-maleic acid copolymer lipid nanoparticles) were produced directly from the plasma membranes of living HEK293T FlpIn cells. SMALPs with a diameter of 15 nm were soluble and stable. NTSR1 in SMALPs were analyzed by single-molecule intensity measurements one membrane patch at a time using a custom-built confocal anti-Brownian electrokinetic trap (ABEL trap) microscope. We found oligomerization changes before and after stimulation of the receptor with its ligand neurotensin.
Persistent Dirac for molecular representation
Authors: JunJie Wee, Ginestra Bianconi, Kelin Xia
Date: 2023-02-05 00:00:00
Tasks: Drug Design, molecular representation, Topological Data Analysis

Molecular representations are of fundamental importance for the modeling and analysis of molecular systems. Representation models and in general approaches based on topological data analysis (TDA) have demonstrated great success in various steps of drug design and materials discovery. Here we develop a mathematically rigorous computational framework for molecular representation based on the persistent Dirac operator. The properties of the spectrum of the discrete weighted and unweighted Dirac matrices are systemically discussed and used to demonstrate the geometric and topological properties of both non-homology and homology eigenvectors of real molecular structures. This allows us to asses the influence of weighting schemes on the information encoded in the Dirac eigenspectrum. A series of physical persistent attributes, which characterize the spectrum of the Dirac matrices across a filtration, are proposed and used as efficient molecular fingerprints. Finally, our persistent Dirac-based model is used for clustering molecular configurations from nine types of organic-inorganic halide perovskites. We found that our model can cluster the structures very well, demonstrating the representation and featurization power of the current approach.