pwc-archive/papers-with-abstracts huggingface dataset:
An Algorithm to Speed up the Spatial Power Profile Calculation in Backward Raman Amplified Systems
Authors: Jad Sarkis, Yanchao Jiang, Pierluigi Poggiolini
Date: 2024-11-19 00:00:00

As data transmission demands grow, long-haul optical transmission links face increasing pressure to increase their throughput. Expanding usable bandwidth through Ultra-Wide Band (UWB) systems has become the primary strategy for increasing transmission capacity. However, UWB systems present challenges, such as the reliance on backward Raman amplification and the complications posed by inter-channel stimulated Raman scattering (ISRS), which causes uneven signal propagation across bands. To address these issues, accurate and efficient physical models are required for real-time optimization, which rely on the knowledge of the power profile. This paper develops a novel, more efficient method for computing the power profile of signals and pumps, utilizing the integral form of the equations with matrix-based approximations. The algorithm achieves up to a thirty-fold average speed increase over conventional approaches while maintaining an error margin under 0.05 dBm. These results represent a significant step forward towards reducing optimization times and enabling more extensive studies in ultra-wide band long haul optical transmission, further facilitating research and commercialization of UWB systems, in an effort to address the growing demand for higher throughput.
Power Efficient Cooperative Communication within IIoT Subnetworks: Relay or RIS?
Authors: Hamid Reza Hashempour, Gilberto Berardinelli, Ramoni Adeogun, Eduard A. Jorswieck
Date: 2024-11-19 00:00:00

The forthcoming sixth-generation (6G) industrial Internet-of-Things (IIoT) subnetworks are expected to support ultra-fast control communication cycles for numerous IoT devices. However, meeting the stringent requirements for low latency and high reliability poses significant challenges, particularly due to signal fading and physical obstructions. In this paper, we propose novel time division multiple access (TDMA) and frequency division multiple access (FDMA) communication protocols for cooperative transmission in IIoT subnetworks. These protocols leverage secondary access points (sAPs) as Decode-and-Forward (DF) and Amplify-and-Forward (AF) relays, enabling shorter cycle times while minimizing overall transmit power. A classification mechanism determines whether the highest-gain link for each IoT device is a single-hop or two-hop connection, and selects the corresponding sAP. We then formulate the problem of minimizing transmit power for DF/AF relaying while adhering to the delay and maximum power constraints. In the FDMA case, an additional constraint is introduced for bandwidth allocation to IoT devices during the first and second phases of cooperative transmission. To tackle the nonconvex problem, we employ the sequential parametric convex approximation (SPCA) method. We extend our analysis to a system model with reconfigurable intelligent surfaces (RISs), enabling transmission through direct and RIS-assisted channels, and optimizing for a multi-RIS scenario for comparative analysis. Simulation results show that our cooperative communication approach reduces the emitted power by up to 7.5 dB while maintaining an outage probability and a resource overflow rate below $10^{-6}$. While the RIS-based solution achieves greater power savings, the relay-based protocol outperforms RIS in terms of outage probability.
High-Throughput Blind Co-Channel Interference Cancellation for Edge Devices Using Depthwise Separable Convolutions, Quantization, and Pruning
Authors: Mostafa Naseri, Eli de Poorter, Ingrid Moerman, H. Vincent Poor, Adnan Shahid
Date: 2024-11-19 00:00:00
Tasks: blind source separation, Computational Efficiency, Quantization

Co-channel interference cancellation (CCI) is the process used to reduce interference from other signals using the same frequency channel, thereby enhancing the performance of wireless communication systems. An improvement to this approach is blind CCI, which reduces interference without relying on prior knowledge of the interfering signal characteristics. Recent work suggested using machine learning (ML) models for this purpose, but high-throughput ML solutions are still lacking, especially for edge devices with limited resources. This work explores the adaptation of U-Net Convolutional Neural Network models for high-throughput blind source separation. Our approach is established on architectural modifications, notably through quantization and the incorporation of depthwise separable convolution, to achieve a balance between computational efficiency and performance. Our results demonstrate that the proposed models achieve superior MSE scores when removing unknown interference sources from the signals while maintaining significantly lower computational complexity compared to baseline models. One of our proposed models is deeper and fully convolutional, while the other is shallower with a convolutional structure incorporating an LSTM. Depthwise separable convolution and quantization further reduce the memory footprint and computational demands, albeit with some performance trade-offs. Specifically, applying depthwise separable convolutions to the model with the LSTM results in only a 0.72% degradation in MSE score while reducing MACs by 58.66%. For the fully convolutional model, we observe a 0.63% improvement in MSE score with even 61.10% fewer MACs. Overall, our findings underscore the feasibility of using optimized machine-learning models for interference cancellation in devices with limited resources.
Improved Estimation Accuracy in OFDM-based Joint Communication and Sensing through Kalman Tracking and Interpolation
Authors: Charlotte Muth, Leon Schmidt, Shrinivas Chimmalgi, Laurent Schmalen
Date: 2024-11-19 00:00:00
Tasks: Object Tracking

We investigate a monostatic orthogonal frequency-division multiplexing (OFDM)-based joint communication and sensing (JCAS) system for object tracking. Our setup consists of a transmitter and receiver equipped with an antenna array for fully digital beamforming. The native resolution of range and velocity in all radar-like sensing, including OFDM radar sensing, is limited by the observation time and bandwidth. In this work, we improve the parameter estimates (estimates of range) through interpolation methods and tracking algorithms. We verify our method by comparing the root mean squared error (RMSE) of the estimated range, velocity and angle and by comparing the mean Euclidean distance between the estimated and true position. We demonstrate how both a Kalman filter for tracking, and interpolation methods using zero-padding and the chirp Z-transform (CZT) improve the estimation error. We discuss the computational complexity of the different methods. We propose the KalmanCZT approach that combines tracking via Kalman filtering and interpolation via the CZT, resulting in a solution with flexible resolution that significantly improves the range RMSE.
Bi-LSTM neural network for EEG-based error detection in musicians' performance
Authors: Isaac Ariza, Lorenzo J. Tardon, Ana M. Barbancho, Irene De-Torres, Isabel Barbancho
Date: 2024-11-19 00:00:00
Tasks: Activity Recognition, EEG

Electroencephalography (EEG) is a tool that allows us to analyze brain activity with high temporal resolution. These measures, combined with deep learning and digital signal processing, are widely used in neurological disorder detection and emotion and mental activity recognition. In this paper, a new method for mental activity recognition is presented; instantaneous frequency, spectral entropy and Mel-frequency cepstral coefficients (MFCC) are used to classify EEG signals using bidirectional LSTM neural networks. It is shown that this method can be used for intra-subject or inter-subject analysis and has been applied to error detection in musician performance reaching compelling accuracy.
Temperature-Aware Phase-shift Design of LC-RIS for Secure Communication
Authors: Mohamadreza Delbari, Bowu Wang, Nairy Moghadas Gholian, Arash Asadi, Vahid Jamali
Date: 2024-11-19 00:00:00

Liquid crystal (LC) technology enables low-power and cost-effective solutions for implementing the reconfigurable intelligent surface (RIS). However, the phase-shift response of LC-RISs is temperature-dependent, which, if unaddressed, can degrade the performance. This issue is particularly critical in applications such as secure communications, where variations in phase-shift response may lead to significant information leakage. In this paper, we consider secure communication through an LC-RIS and developed a temperature-aware algorithm adapting the RIS phase shifts to thermal conditions. Our simulation results demonstrate that the proposed algorithm significantly improves the secure data rate compared to scenarios where temperature variations are not accounted for.
Dual-Functional FMCW Waveform for Terahertz Space Debris Detection and Inter-Satellite Communications
Authors: Zhepu Yin, Weijun Gao, Chong Han
Date: 2024-11-19 00:00:00
Tasks: parameter estimation

Terahertz (THz) band communication, ranging from 0.1 THz to 10 THz, is envisioned as a key enabling technology for next-generation networks and future applications such as inter-satellite communications and environmental sensing. The surging number of space debris in Low Earth Orbit poses a big threat to orbital infrastructure and the development of the space economy. In particular, despite the ability to detect and track large-scale space debris, millions of space debris with a radius within the range of 0.1-10 cm and velocity exceeding 1 km/s remains hard to detect with conventional ground-based radars and optical telescopes. In this study, a dual-functional frequency modulated continuous waveform (FMCW) operating in the THz band is adopted for space debris sensing and inter-satellite communications. Specifically, the radar cross section of space debris with various sizes in the THz band is analyzed to demonstrate the feasibility of THz space debris detection. A joint space debris detection and inter-satellite communications based on the FMCW waveform is derived. Then, the parameter estimation and demodulation algorithms are illustrated. Extensive simulations demonstrate that the proposed method can realize high-accuracy parameter estimation of hypervelocity space debris while achieving high reliability for inter-satellite communications.
Modeling and Analysis of Terahertz Wave Propagation in Charged Dust Using Extended Mie Scattering Theory
Authors: Weijun Gao, Chong Han
Date: 2024-11-19 00:00:00
Tasks: Friction

Terahertz (THz) band (0.1-10 THz) possesses multi-gigahertz continuous bandwidth resources, making it a promising frequency band for high-speed wireless communications and environment sensing. The interaction between the THz wave and the external environment has been studied for various scenarios. However, it has recently been revealed that the friction forces in dust storms as well as the irradiation of sunlight and solar wind lead to the electrification of dust particles on Earth and the Moon. The THz wave propagation in these charged dust has not been fully investigated, which is essential for THz aerial communications in dust storms and lunar communications. In this paper, a channel model for THz wave propagation in charged dust is developed for wireless communications. Specifically, an extended Mie scattering model for charged dust is first introduced, which captures the electrodynamic feature of the interaction between THz wave and charged particles. Then, the diameter and density distributions of dust particles are modeled, based on which the propagation loss of THz wave in charged dust is modeled and elaborated. Finally, numerical results on the additional loss caused by these charged dust with different sizes in the THz band are evaluated and compared. Extensive results demonstrate that as the number of dust charges increases, the extinction cross section of smaller-sized particles significantly increases, and the overall attenuation led by charged dust increases by at most 50% at 0.3 THz.
Robust Deep Joint Source-Channel Coding Enabled Distributed Image Transmission with Imperfect Channel State Information
Authors: Biao Dong, Bin Cao, Guan Gui, Qinyu Zhang
Date: 2024-11-19 00:00:00
Tasks: Decoder

This work is concerned with robust distributed multi-view image transmission over a severe fading channel with imperfect channel state information (CSI), wherein the sources are slightly correlated. Since the signals are further distorted at the decoder, traditional distributed deep joint source-channel coding (DJSCC) suffers considerable performance degradation. To tackle this problem, we leverage the complementarity and consistency characteristics among the distributed, yet correlated sources, and propose an enhanced robust DJSCC, namely RDJSCC. In RDJSCC, we design a novel cross-view information extraction (CVIE) mechanism to capture more nuanced cross-view patterns and dependencies. In addition, a complementarity-consistency fusion (CCF) mechanism is utilized to fuse the complementarity and consistency from multi-view information in a symmetric and compact manner. Theoretical analysis and simulation results show that our proposed RDJSCC can effectively leverage the advantages of correlated sources even under severe fading conditions, leading to an improved reconstruction performance. The open source code of this work is available at:https://dongbiao26.github.io/rdjscc/.
Bounds on the Treewidth of Level-k Rooted Phylogenetic Networks
Authors: Alexey Markin, Sriram Vijendran, Oliver Eulenstein
Date: 2024-11-20 00:00:00
Tasks: Tree Decomposition

Phylogenetic networks are directed acyclic graphs that depict the genomic evolution of related taxa. Reticulation nodes in such networks (nodes with more than one parent) represent reticulate evolutionary events, such as recombination, reassortment, hybridization, or horizontal gene transfer. Typically, the complexity of a phylogenetic network is expressed in terms of its level, i.e., the maximum number of edges that are required to be removed from each biconnected component of the phylogenetic network to turn it into a tree. Here, we study the relationship between the level of a phylogenetic network and another popular graph complexity parameter - treewidth. We show a $\frac{k+3}{2}$ upper bound on the treewidth of level-$k$ phylogenetic networks and an improved $(1/3 + \delta) k$ upper bound for large $k$. These bounds imply that many computational problems on phylogenetic networks, such as the small parsimony problem or some variants of phylogenetic diversity maximization, are polynomial-time solvable on level-$k$ networks with constant $k$. Our first bound is applicable to any $k$, and it allows us to construct an explicit tree decomposition of width $\frac{k+3}{2}$ that can be used to analyze phylogenetic networks generated by tools like SNAQ that guarantee bounded network level. Finally, we show a $k/13$ lower bound on the maximum treewidth among level-$k$ phylogenetic networks for large enough $k$ based on expander graphs.
Optimisation of neoadjuvant pembrolizumab therapy for locally advanced MSI-H/dMMR colorectal cancer using data-driven delay integro-differential equations
Authors: Georgio Hawi, Peter S. Kim, Peter P. Lee
Date: 2024-11-18 00:00:00

Colorectal cancer (CRC) poses a major public health challenge due to its increasing prevalence, particularly among younger populations. Microsatellite instability-high (MSI-H) CRC and deficient mismatch repair (dMMR) CRC constitute 15% of all CRC and exhibit remarkable responsiveness to immunotherapy, especially with PD-1 inhibitors. Despite this, there is a significant need to optimise immunotherapeutic regimens to maximise clinical efficacy and patient quality of life whilst minimising monetary costs. To address this, we employ a novel framework driven by delay integro-differential equations to model the interactions among cancer cells, immune cells, and immune checkpoints. Several of these components are being modelled deterministically for the first time in cancer, paving the way for a deeper understanding of the complex underlying immune dynamics. We consider two compartments: the tumour site and the tumour-draining lymph node, incorporating phenomena such as dendritic cell (DC) migration, T cell proliferation, and CD8+ T cell exhaustion and reinvigoration. Parameter values and initial conditions are derived from experimental data, integrating various pharmacokinetic, bioanalytical, and radiographic studies, along with deconvolution of bulk RNA-sequencing data from the TCGA COADREAD and GSE26571 datasets. We finally optimised neoadjuvant treatment with pembrolizumab, a widely used PD-1 inhibitor, to balance efficacy, efficiency, and toxicity in locally advanced MSI-H/dMMR CRC patients. We mechanistically analysed factors influencing treatment success and improved upon currently FDA-approved therapeutic regimens for metastatic MSI-H/dMMR CRC, demonstrating that a single medium-to-high dose of pembrolizumab may be sufficient for effective tumour eradication while being efficient, safe and practical.
The impact of recovery rate heterogeneity in achieving herd immunity
Authors: Gabriel Turinici
Date: 2024-11-20 00:00:00
Tasks: Epidemiology

Herd immunity is a critical concept in epidemiology, describing a threshold at which a sufficient proportion of a population is immune, either through infection or vaccination, thereby preventing sustained transmission of a pathogen. In the classic Susceptible-Infectious-Recovered (SIR) model, which has been widely used to study infectious disease dynamics, the achievement of herd immunity depends on key parameters, including the transmission rate ($\beta$) and the recovery rate ($\gamma$), where $\gamma$ represents the inverse of the mean infectious period. While the transmission rate has received substantial attention, recent studies have underscored the significant role of $\gamma$ in determining the timing and sustainability of herd immunity. Additionally, it is becoming increasingly evident that assuming $\gamma$ as a constant parameter might oversimplify the dynamics, as variations in recovery times can reflect diverse biological, social, and healthcare-related factors. In this paper, we investigate how heterogeneity in the recovery rate affects herd immunity. We show empirically that the mean of the recovery rate is not a reliable metric for determining the achievement of herd immunity. Furthermore, we provide a theoretical result demonstrating that it is instead the mean recovery time, which is the mean of the inverse $1/\gamma$ of the recovery rate that is critical in deciding whether herd immunity is achievable within the SIR framework. A similar result is proved for the SEIR model. These insights have significant implications for public health interventions and theoretical modeling of epidemic dynamics.
Conditional gene genealogies given the population pedigree for a diploid Moran model with selfing
Authors: Maximillian Newman, John Wakeley, Wai-Tong Louis Fan
Date: 2024-11-20 00:00:00

We introduce a stochastic model of a population with overlapping generations and arbitrary levels of self-fertilization versus outcrossing. We study how the global graph of reproductive relationships, or population pedigree, influences the genealogical relationships of a sample of two gene copies at a genetic locus. Specifically, we consider a diploid Moran model with constant population size $N$ over time, in which a proportion of offspring are produced by selfing. We show that the conditional distribution of the pairwise coalescence time at a single locus given the random pedigree converges to a limit law as $N$ tends to infinity. The distribution of coalescence times obtained in this way predicts variation among unlinked loci in a sample of individuals. Traditional coalescent analyses implicitly average over pedigrees and generally make different predictions. We describe three different behaviors in the limit depending on the relative strengths, from large to small, of selfing versus outcrossing: partial selfing, limited outcrossing, and negligible outcrossing. In the case of partial selfing, coalescence times are related to the Kingman coalescent, similar to what is found in traditional analyses. In the case of limited outcrossing, the retained pedigree information forms a random graph, with coalescence times given by the meeting times of random walks on this graph. In the case of negligible outcrossing, which represents complete or nearly complete selfing, coalescence times are determined entirely by the fixed times to common ancestry of diploid individuals in the pedigree.
Mirror Descent Algorithms for Risk Budgeting Portfolios
Authors: Martin Arnaiz Iglesias, Adil Rengim Cetingoz, Noufel Frikha
Date: 2024-11-19 00:00:00

This paper introduces and examines numerical approximation schemes for computing risk budgeting portfolios associated to positive homogeneous and sub-additive risk measures. We employ Mirror Descent algorithms to determine the optimal risk budgeting weights in both deterministic and stochastic settings, establishing convergence along with an explicit non-asymptotic quantitative rate for the averaged algorithm. A comprehensive numerical analysis follows, illustrating our theoretical findings across various risk measures -- including standard deviation, Expected Shortfall, deviation measures, and Variantiles -- and comparing the performance with that of the standard stochastic gradient descent method recently proposed in the literature.
Energy-based generative models for monoclonal antibodies
Authors: Paul Pereira, Hervé Minoux, Aleksandra M. Walczak, Thierry Mora
Date: 2024-11-20 00:00:00
Tasks: Drug Discovery

Since the approval of the first antibody drug in 1986, a total of 162 antibodies have been approved for a wide range of therapeutic areas, including cancer, autoimmune, infectious, or cardiovascular diseases. Despite advances in biotechnology that accelerated the development of antibody drugs, the drug discovery process for this modality remains lengthy and costly, requiring multiple rounds of optimizations before a drug candidate can progress to preclinical and clinical trials. This multi-optimization problem involves increasing the affinity of the antibody to the target antigen while refining additional biophysical properties that are essential to drug development such as solubility, thermostability or aggregation propensity. Additionally, antibodies that resemble natural human antibodies are particularly desirable, as they are likely to offer improved profiles in terms of safety, efficacy, and reduced immunogenicity, further supporting their therapeutic potential. In this article, we explore the use of energy-based generative models to optimize a candidate monoclonal antibody. We identify tradeoffs when optimizing for multiple properties, concentrating on solubility, humanness and affinity and use the generative model we develop to generate candidate antibodies that lie on an optimal Pareto front that satisfies these constraints.