pwc-archive/papers-with-abstracts huggingface dataset:
AE-DENet: Enhancement for Deep Learning-based Channel Estimation in OFDM Systems
Authors: Ephrem Fola, Yang Luo, Chunbo Luo
Date: 2024-11-10 00:00:00

Deep learning (DL)-based methods have demonstrated remarkable achievements in addressing orthogonal frequency division multiplexing (OFDM) channel estimation challenges. However, existing DL-based methods mainly rely on separate real and imaginary inputs while ignoring the inherent correlation between the two streams, such as amplitude and phase information that are fundamental in communication signal processing. This paper proposes AE-DENet, a novel autoencoder(AE)-based data enhancement network to improve the performance of existing DL-based channel estimation methods. AE-DENet focuses on enriching the classic least square (LS) estimation input commonly used in DL-based methods by employing a learning-based data enhancement method, which extracts interaction features from the real and imaginary components and fuses them with the original real/imaginary streams to generate an enhanced input for better channel inference. Experimental findings in terms of the mean square error (MSE) results demonstrate that the proposed method enhances the performance of all state-of-the-art DL-based channel estimators with negligible added complexity. Furthermore, the proposed approach is shown to be robust to channel variations and high user mobility.
SuperResolution Radar Gesture Recognitio
Authors: Netanel Blumenfeld, Inna Stainvas, Igal Bilik
Date: 2024-11-10 00:00:00
Tasks: Gesture Recognition, Super-Resolution

"This work has been submitted to the IEEE for possible publication. Copyright may be transferred without notice, after which this version may no longer be accessible." Driver's interaction with a vehicle via automatic gesture recognition is expected to enhance driving safety by decreasing driver's distraction. Optical and infrared-based gesture recognition systems are limited by occlusions, poor lighting, and varying thermal conditions and, therefore, have limited performance in practical in-cabin applications. Radars are insensitive to lighting or thermal conditions and, therefore, are more suitable for in-cabin applications. However, the spatial resolution of conventional radars is insufficient for accurate gesture recognition. The main objective of this research is to derive an accurate gesture recognition approach using low-resolution radars with deep learning-based super-resolution processing. The main idea is to reconstruct high-resolution information from the radar's low-resolution measurements. The major challenge is the derivation of the real-time processing approach. The proposed approach combines conventional signal processing and deep learning methods. The radar echoes are arranged in 3D data cubes and processed using a super-resolution model to enhance range and Doppler resolution. The FFT is used to generate the range-Doppler maps, which enter the deep neural network for efficient gesture recognition. The preliminary results demonstrated the proposed approach's efficiency in achieving high gesture recognition performance using conventional low-resolution radars.
Low Dynamic Range for RIS-aided Bistatic Integrated Sensing and Communication
Authors: Ahmad Bazzi, Marwa Chafii
Date: 2024-11-09 00:00:00
Tasks: Benchmarking, Integrated sensing and communication, ISAC

The following paper presents a reconfigurable intelligent surface (RIS)-aided integrated sensing and communication (ISAC) system model scenario, where a base station communicates with a user, and a bi-static sensing unit, i.e. the passive radar (PR), senses targets using downlink signals. Given that the RIS aids with communication and sensing tasks, this paper introduces new interfering paths that can overwhelm the PR with unnecessarily high power, namely the path interference (PI), \textit{which is itself a combination of two interfering paths, the direct path interference (DPI) and the reflected path interference (RPI)}. For this, we formulate an optimization framework that allows the system to carry on with its ISAC tasks, through analog space-time beamforming at the sensing unit, in collaboration with RIS phase shift and statistical transmit covariance matrix optimization, while minimizing the PI power. As the proposed optimization problem is non-convex, we tailor a block-cyclic coordinate descent (BCCD) method to decouple the non-convex sub-problem from the convex one. A Riemannian conjugate gradient method is devised to generate the RIS and PR space-time beamforming phase shifts per BCCD iteration, while the convex sub-problem is solved via off-the-shelf solvers. Simulation results demonstrate the effectiveness of the proposed solver when compared with benchmarking ones.
Altitude-Dependent Cellular Spectrum Occupancy: from Measurements to Stochastic Geometry Models
Authors: Sung Joon Maeng, Ismail Guvenc
Date: 2024-11-09 00:00:00

The growing demand for aerial connectivity with unmanned aerial vehicles (UAVs) across diverse settings, ranging from urban to rural scenarios, requires developing a better understanding of spectrum occupancy at aerial corridors. In particular, understanding the altitude-dependent behavior of spectrum occupancy in cellular networks, which could be used in the future for enabling beyond visual line of sight (BVLOS) UAV connectivity, is critical. While there are existing models for characterizing altitude-dependent interference in the literature, they are not validated with data and need to be compared with real-world measurements. To address these gaps, in this paper, we conduct cellular spectrum occupancy measurements at various sub-6 GHz bands for altitudes up to 300 meters, in both urban and rural environments. To model the spectrum occupancy measurements, we introduce two different approaches: a theoretical model utilizing stochastic geometry with altitude-dependent factors (SOSGAD), and a ray-tracing model tailored to site-specific line of sight (LOS) and non-LOS scenarios. We analyze the asymptotic behavior of the SOSGAD model as the UAV altitude increases. Through comparative analysis, we assess the effectiveness of the SOSGAD and ray-tracing models for characterizing actual spectrum occupancy as a function of altitude. Our results show that the proposed SOSGAD model can be tuned to closely characterize the real-world spectrum occupancy behavior as the UAV altitude increases.
Quasi-Newton OMP Approach for Super-Resolution Channel Estimation and Extrapolation
Authors: Yi Zeng, Mingguang Han, Xiaoguang Li, Tiejun Li
Date: 2024-11-09 00:00:00
Tasks: Super-Resolution

Channel estimation and extrapolation are fundamental issues in MIMO communication systems. In this paper, we proposed the quasi-Newton orthogonal matching pursuit (QNOMP) approach to overcome these issues with high efficiency while maintaining accuracy. The algorithm consists of two stages on the super-resolution recovery: we first performed a cheap on-grid OMP estimation of channel parameters in the sparsity domain (e.g., delay or angle), then an off-grid optimization to achieve the super-resolution. In the off-grid stage, we employed the BFGS quasi-Newton method to jointly estimate the parameters through a multipath model, which improved the speed and accuracy significantly. Furthermore, we derived the optimal extrapolated solution in the linear minimum mean squared estimator criterion, revealed its connection with Slepian basis, and presented a practical algorithm to realize the extrapolation based on the QNOMP results. Special treatment utilizing the block sparsity nature of the considered channels was also proposed. Numerical experiments on the simulated models and CDL-C channels demonstrated the high performance and low computational complexity of QNOMP.
Filter-Banks for Ultra-Wideband for Communications, Sensing, and Localization
Authors: Brian Nelson, Hussein Moradi, Behrouz Farhang-Boroujeny
Date: 2024-11-08 00:00:00

Recently, filter-bank multicarrier spread spectrum (FBMC-SS) has been proposed as a candidate waveform for ultra-wideband (UWB) communications, sensing, and localization. It has been noted that FBMC-SS is a perfect match to this application, leading to a trivial method of matching to the required spectral mask at different regions of the world. FBMC-SS also allows easy rejection of high-power interfering signals that may appear over different parts of the UWB spectral band. Moreover, passing the received signal through a matched filter provides precise information for sensing and localization. In this paper, we concentrate on the use of staggered multitone spread spectrum (SMT-SS) for UWB communications. SMT makes use of offset quadrature amplitude modulation (OQAM) to transmit data symbols over overlapping subcarrier bands. This form of FBMC-SS is well-suited to UWB communications because it has good spectral efficiency and a flat power spectral density (PSD), resulting in good utilization of the UWB spectral mask.
Maximum likelihood estimation of log-affine models using detailed-balanced reaction networks
Authors: Oskar Henriksson, Carlos Améndola, Jose Israel Rodriguez, Polly Y. Yu
Date: 2024-11-12 00:00:00

A fundamental question in the field of molecular computation is what computational tasks a biochemical system can carry out. In this work, we focus on the problem of finding the maximum likelihood estimate (MLE) for log-affine models. We revisit a construction due to Gopalkrishnan of a mass-action system with the MLE as its unique positive steady state, which is based on choosing a basis for the kernel of the design matrix of the model. We extend this construction to allow for any finite spanning set of the kernel, and explore how the choice of spanning set influences the dynamics of the resulting network, including the existence of boundary steady states, the deficiency of the network, and the rate of convergence. In particular, we prove that using a Markov basis as the spanning set guarantees global stability of the MLE steady state.
A vector Allee effect in mosquito dynamics
Authors: J. Banasiak, Bime M. Ghakanyuy, Gideon A. Ngwa
Date: 2024-11-10 00:00:00

We consider a recently introduced model of mosquito dynamics that includes mating and progression through breeding, questing and egg-laying stages of mosquitoes using human and other vertebrate sources for blood meals. By exploiting a multiscale character of the model and recent results on their uniform-in-time asymptotics, we derive a simplified monotone model with the same long-term dynamics. Using the theory of monotone dynamical systems, we show that for a range of parameters, the latter displays Allee-type dynamics; that is, it has one extinction and two positive equilibria ordered with respect to the positive cone $\mathbb R_+^7$, with the extinction and the larger equilibrium being attractive and the middle one unstable. Using asymptotic analysis, we show that the original system also displays this pattern.
Provably Faster Algorithms for Bilevel Optimization via Without-Replacement Sampling
Authors: Junyi Li, Heng Huang
Date: 2024-11-07 00:00:00
Tasks: Bilevel Optimization

Bilevel Optimization has experienced significant advancements recently with the introduction of new efficient algorithms. Mirroring the success in single-level optimization, stochastic gradient-based algorithms are widely used in bilevel optimization. However, a common limitation in these algorithms is the presumption of independent sampling, which can lead to increased computational costs due to the complicated hyper-gradient formulation of bilevel problems. To address this challenge, we study the example-selection strategy for bilevel optimization in this work. More specifically, we introduce a without-replacement sampling based algorithm which achieves a faster convergence rate compared to its counterparts that rely on independent sampling. Beyond the standard bilevel optimization formulation, we extend our discussion to conditional bilevel optimization and also two special cases: minimax and compositional optimization. Finally, we validate our algorithms over both synthetic and real-world applications. Numerical results clearly showcase the superiority of our algorithms.
Assessing and Enhancing Graph Neural Networks for Combinatorial Optimization: Novel Approaches and Application in Maximum Independent Set Problems
Authors: Chenchuhui Hu
Date: 2024-11-06 00:00:00
Tasks: Combinatorial Optimization

Combinatorial optimization (CO) problems are challenging as the computation time grows exponentially with the input. Graph Neural Networks (GNNs) show promise for researchers in solving CO problems. This study investigates the effectiveness of GNNs in solving the maximum independent set (MIS) problem, inspired by the intriguing findings of Schuetz et al., and aimed to enhance this solver. Despite the promise shown by GNNs, some researchers observed discrepancies when reproducing the findings, particularly compared to the greedy algorithm, for instance. We reproduced Schuetz' Quadratic Unconstrained Binary Optimization (QUBO) unsupervised approach and explored the possibility of combining it with a supervised learning approach for solving MIS problems. While the QUBO unsupervised approach did not guarantee maximal or optimal solutions, it provided a solid first guess for post-processing techniques like greedy decoding or tree-based methods. Moreover, our findings indicated that the supervised approach could further refine the QUBO unsupervised solver, as the learned model assigned meaningful probabilities for each node as initial node features, which could then be improved with the QUBO unsupervised approach. Thus, GNNs offer a valuable method for solving CO problems by integrating learned graph structures rather than relying solely on traditional heuristic functions. This research highlights the potential of GNNs to boost solver performance by leveraging ground truth during training and using optimization functions to learn structural graph information, marking a pioneering step towards improving prediction accuracy in a non-autoregressive manner.
A Unified View of Allostery
Authors: Eric Rouviere, Olivier Rivoire, Rama Ranganathan
Date: 2024-11-10 00:00:00

Allostery is a fundamental property of proteins that represents the functional coupling between distantly located sites. In different manifestations, this property underlies signal transduction, gene expression, and regulation -- elementary reactions in networks comprising cellular information and metabolic processing systems. In this work, we present a reduced theoretical model for allostery that encompasses the many diverse mechanisms described in past work. What emerges is a basic classification of different forms of allostery into two groups: those that couple distant sites through soft normal modes within a single state, and those that possess multiple states, with effective interactions between sites emerging only upon transitioning between these states. This work serves to unify the extensive past theoretical work on the phenomenology and machinery of allostery in proteins.
Polymerization and replication of primordial RNA explained by clay-water interface dynamics
Authors: Carla Alejandre, Adrián Aguirre-Tamaral, Carlos Briones, Jacobo Aguirre
Date: 2024-10-25 00:00:00

RNA is the only known biopolymer that combines genotype and phenotype in a single molecular entity. This has suggested that the flow of genetic information DNA-RNA-proteins already operating in LUCA could have been preceded by a primordial era when RNA was the genetic and catalytic macromolecule. However, understanding how RNA could have polymerized and subsequently replicated in early Earth remains challenging. We present a theoretical and computational framework to model the non-enzymatic polymerization of ribonucleotides and the template-dependent replication of primordial RNA molecules, at the interfaces between the aqueous solution and a clay mineral supplied by its interlayers and channels. Our results demonstrate that efficient polymerization and accurate replication of single-stranded RNA polymers, sufficiently long to fold and acquire basic functions ($>15$ nt), were possible at clay-water interfaces in early Earth, provided the physico-chemical environment exhibited an oscillatory pattern of large amplitude and a period compatible with spring tide dynamics. Interestingly, the theoretical analysis presents rigorous evidence that RNA replication efficiency increases in oscillating environments compared to constant ones. Moreover, the versatility of our framework enables comparisons between different genetic alphabets, showing that a four-letter alphabet -- particularly when allowing non-canonical base pairs, as in current RNA -- represents an optimal balance of replication speed and sequence diversity in the pathway to life.
A lipidated peptide derived from the C-terminal tail of the vasopressin 2 receptor shows promise as a new $β$-arrestin inhibitor
Authors: Rebecca L. Brouillette, Christine E. Mona, Michael Desgagné, Malihe Hassanzedeh, Émile Breault, Frédérique Lussier, Karine Belleville, Jean-Michel Longpré, Michel Grandbois, Pierre-Luc Boudreault, Élie Besserer-Offroy, Philippe Sarret
Date: 2024-11-02 00:00:00

$\beta$-arrestins play pivotal roles in seven transmembrane receptor (7TMR) signalling and trafficking. To study their functional role in the regulation of specific receptor systems, current research relies mainly on genetic tools, as few pharmacological options are available. To address this issue, we designed and synthesised a novel lipidated phosphomimetic peptide inhibitor targeting $\beta$-arrestins, called ARIP, which was developed based on the C-terminal tail (A343-S371) of the vasopressin V2 receptor. As the V2R sequence has been shown to bind $\beta$-arrestins with high affinity and stability, we added an N-terminal palmitate residue to allow membrane tethering and subsequent cell entry. Here, using BRET2-based biosensors, we demonstrated the ability of ARIP to inhibit agonist-induced $\beta$-arrestin recruitment on a series of 7TMRs belonging to class A (low stable associations with arrestins) or class B (high stability), with efficiencies that dependent on receptor type. In addition, we showed that ARIP was unable to recruit $\beta$-arrestins to the cell membrane by itself, and that it did not interfere with canonical G protein signalling. Molecular modelling studies also revealed that ARIP binds $\beta$-arrestins in the same way as V2Rpp, the phosphorylated peptide derived from the V2R C-terminal domain, and that replacing the p-Ser and p-Thr residues of V2Rpp with Glu residues does not alter the inhibitory activity of ARIP on $\beta$-arrestin recruitment. Importantly, ARIP exerted an opioid-sparing effect in vivo, as intrathecal injection of ARIP potentiated the analgesic effect of morphine in the tail-flick nociceptive model, a behavioural response consistent with $\beta$-arrestin genetic inhibition. ARIP therefore represents a promising pharmacological tool for investigating the fine-tuning roles of $\beta$-arrestins in 7TMR-driven pathophysiological processes.
Expansion microscopy reveals neural circuit organization in genetic animal models
Authors: Shakila Behzadi, Jacquelin Ho, Zainab Tanvir, Gal Haspel, Limor Freifeld, Kristen E. Severi
Date: 2024-11-11 00:00:00
Tasks: Super-Resolution

Expansion Microscopy is a super-resolution technique in which physically enlarging samples in an isotropic manner increases inter-molecular distances such that nano-scale structures can be resolved using light microscopy. This is particularly useful in neuroscience as many important structures are smaller than the diffraction limit. Since its invention in 2015, a variety of Expansion Microscopy protocols have been generated and applied to advance knowledge in many prominent organisms in neuroscience, including zebrafish, mice, Drosophila, and C. elegans. Here we review the last decade of Expansion Microscopy-enabled advances with a focus on neuroscience.
Evaluating tDCS Intervention Effectiveness via Functional Connectivity Network on Resting-State EEG Data in Major Depressive Disorder
Authors: Vishwani Singh, Rohit Verma, Shaurya Shriyam, Tapan K. Gandhi
Date: 2024-11-10 00:00:00
Tasks: Binarization, EEG, Functional Connectivity

Transcranial direct current stimulation (tDCS) has emerged as a promising non-invasive therapeutic intervention for major depressive disorder (MDD), yet its effects on neural mechanisms remain incompletely understood. This study investigates the impact of tDCS in individuals with MDD using resting-state EEG data and network neuroscience to analyze functional connectivity. We examined power spectral density (PSD) changes and functional connectivity (FC) patterns across theta, alpha, and beta bands before and after tDCS intervention. A notable aspect of this research involves the modification of the binarizing threshold algorithm to assess functional connectivity networks, facilitating a meaningful comparison at the group level. Our analysis using optimal threshold binarization techniques revealed significant modifications in network topology, particularly evident in the beta band, indicative of reduced randomization or enhanced small-worldness after tDCS. Furthermore, the hubness analysis identified specific brain regions, notably the dorsolateral prefrontal cortex (DLPFC) regions across all frequency bands, exhibiting increased functional connectivity, suggesting their involvement in the antidepressant effects of tDCS. Notably, tDCS intervention transformed the dispersed high connectivity into localized connectivity and increased left-sided asymmetry across all frequency bands. Overall, this study provides valuable insights into the effects of tDCS on neural mechanisms in MDD, offering a potential direction for further research and therapeutic development in the field of neuromodulation for mental health disorders.
From Electrode to Global Brain: Integrating Multi- and Cross-Scale Brain Connections and Interactions Under Cross-Subject and Within-Subject Scenarios
Authors: Chen Zhige, Qin Chengxuan
Date: 2024-11-07 00:00:00
Tasks: Domain Adaptation, Motor Imagery, STS

The individual variabilities of electroencephalogram signals pose great challenges to cross-subject motor imagery (MI) classification, especially for the data-scarce single-source to single-target (STS) scenario. The multi-scale spatial data distribution differences can not be fully eliminated in MI experiments for the topological structure and connection are the inherent properties of the human brain. Overall, no literature investigates the multi-scale spatial data distribution problem in STS cross-subject MI classification task, neither intra-subject nor inter-subject scenarios. In this paper, a novel multi-scale spatial domain adaptation network (MSSDAN) consists of both multi-scale spatial feature extractor (MSSFE) and deep domain adaptation method called multi-scale spatial domain adaptation (MSSDA) is proposed and verified, our goal is to integrate the principles of multi-scale brain topological structures in order to solve the multi-scale spatial data distribution difference problem.