pwc-archive/papers-with-abstracts huggingface dataset:
Optimizing Reconfigurable Intelligent Surfaces for Improved Space-based Communication Amidst Phase Shift Errors
Authors: Muhammad I Khalil
Date: 2023-08-10 00:00:00

Reconfigurable Intelligent Surfaces (RISs) have emerged as a promising technology for enhancing satellite communication systems by manipulating the phase of electromagnetic waves. This study addresses optimising phase shift values (\phi_{R}) in RIS networks under both ideal and non-ideal conditions. For ideal scenarios, we introduce a novel approach that simplifies the traditional optimisation methods for determining the optimal value. Leveraging trigonometric identities and the law of cosines, we create a more tractable formulation for the received power that allows for efficient optimisation of \phi_{R}. However, practical applications often grapple with non-ideal conditions. These conditions can introduce phase errors, significantly affecting the received signal and overall system performance. To accommodate these complexities, our optimisation framework extends to include phase errors, which are modelled as a uniform distribution. To solve this optimisation problem, we propose a stochastic framework that harnesses the Monte Carlo method to consider all plausible phase error values. Furthermore, we employ the Broyden Fletcher Goldfarb Shanno (BFGS) algorithm, an iterative method known for its efficacy. This algorithm systematically updates \phi_{R} values, incorporating the gradient of the objective function and Hessian matrix approximations. The algorithm also monitors convergence to balance computational complexity and accuracy. The results of the theoretical analysis are illustrated with several examples. As herein demonstrated, the proposed solution offers profound insights into the impacts of phase errors on RIS system performance. It also unveils innovative optimisation strategies for real-world satellite communication scenarios under diverse conditions.
Analysis and Compensation of Carrier Frequency Offset Impairments in Unique Word OFDM
Authors: Christian Hofbauer, Werner Haselmayr, Hans-Peter Bernhard, Mario Huemer
Date: 2023-08-10 00:00:00

Unique Word-orthogonal frequency division multiplexing (UW-OFDM) is known to provide various performance benefits over conventional cyclic prefix (CP) based OFDM. Most important, UW-OFDM features excellent spectral sidelobe suppression properties and an outstanding bit error ratio (BER) performance. Carrier frequency offset (CFO) induced impairments denote a challenging task for OFDM systems of any kind. In this work we investigate the CFO effects on UW-OFDM and compare it to conventional multi-carrier and single-carrier systems. Different CFO compensation approaches with different computational complexity are considered throughout this work and assessed against each other. A mean squared error analysis carried out after data estimation reveals a significant higher robustness of UW-OFDM over CP-OFDM against CFO effects. Additionally, the conducted BER simulations generally support this conclusion for various scenarios, ranging from uncoded to coded transmission in a frequency selective environment.
Deep generative models for unsupervised delamination detection using guided waves
Authors: Mahindra Rautela, Amin Maghareh, Shirley Dyke, S. Gopalakrishnan
Date: 2023-08-10 00:00:00
Tasks: Anomaly Detection, Structural Health Monitoring

With the rising demands for robust structural health monitoring procedures for aerospace structures, the scope of intelligent algorithms and learning techniques is expanding. Supervised algorithms have shown promising results in the field provided a large, balanced, and labeled amount of data for training. For some applications like aerospace, the data collection process is cumbersome, time-taking, and costly. Also, generating possible damage scenarios in a laboratory setup is challenging because of the complexity of the damage initiation and failure mechanism. Besides this, the uncertainties of the real-time operation restrict the online prediction accuracy with supervised learning. In this paper, deep generative models are proposed for unsupervised delamination prediction as an anomaly detection problem. In this one-class-based model, the deep learning network is trained to learn the distribution of baseline signals. In the testing phase, damage signals and unseen baseline signals are fed into the trained network to predict the state of the structure, i.e., healthy or unhealthy (delamination). It is seen that the proposed method can successfully predict the delamination with high accuracy.
Robust Interference Mitigation techniques for Direct Position Estimation
Authors: Haoqing Li, Shuo Tang, Peng Wu, Pau Closas
Date: 2023-08-09 00:00:00
Tasks: Position, Sensitivity

Global Navigation Satellite System (GNSS) is pervasive in navigation and positioning applications, where precise position and time referencing estimations are required. Conventional methods for GNSS positioning involve a two-step process, where intermediate measurements such as Doppler shift and time delay of received GNSS signals are computed and then used to solve for the receiver's position. Alternatively, Direct Position Estimation (DPE) was proposed to infer the position directly from the sampled signal without intermediate variables, yielding to superior levels of sensitivity and operation under challenging environments. However, the positioning resilience of DPE method is still under the threat of various interferences. Robust Interference Mitigation (RIM) processing has been studied and proved to be efficient against various interference in conventional two-step positioning (2SP) methods, and therefore worthy to be explored regarding its potential to enhance DPE. This article extends DPE methodology by incorporating RIM strategies that address the increasing need to protect GNSS receivers against intentional or unintentional interferences, such as jamming signals, which can deny GNSS-based positioning. RIM, which leverages robust statistics, was shown to provide competitive results in two-step approaches and is here employed in a high-sensitivity DPE framework with successful results. The article also provides a quantification of the loss of efficiency of using RIM when no interference is present and validates the proposed methodology on relevant interference cases, while the approach can be used to mitigate other common interference signals.
Striking The Right Balance: Three-Dimensional Ocean Sound Speed Field Reconstruction Using Tensor Neural Networks
Authors: Siyuan Li, Lei Cheng, Ting Zhang, Hangfang Zhao, Jianlong Li
Date: 2023-08-09 00:00:00

Accurately reconstructing a three-dimensional ocean sound speed field (3D SSF) is essential for various ocean acoustic applications, but the sparsity and uncertainty of sound speed samples across a vast ocean region make it a challenging task. To tackle this challenge, a large body of reconstruction methods has been developed, including spline interpolation, matrix/tensor-based completion, and deep neural networks-based reconstruction. However, a principled analysis of their effectiveness in 3D SSF reconstruction is still lacking. This paper performs a thorough analysis of the reconstruction error and highlights the need for a balanced representation model that integrates both expressiveness and conciseness. To meet this requirement, a 3D SSF-tailored tensor deep neural network is proposed, which utilizes tensor computations and deep neural network architectures to achieve remarkable 3D SSF reconstruction. The proposed model not only includes the previous tensor-based SSF representation model as a special case, but also has a natural ability to reject noise. The numerical results using the South China Sea 3D SSF data demonstrate that the proposed method outperforms state-of-the-art methods. The code is available at https://github.com/OceanSTARLab/Tensor-Neural-Network.
Deep Plug-and-Play Prior for Multitask Channel Reconstruction in Massive MIMO Systems
Authors: Weixiao Wan, Wei Chen, Shiyue Wang, Geoffrey Ye Li, Bo Ai
Date: 2023-08-09 00:00:00
Tasks: Multi-Task Learning

Scalability is a major concern in implementing deep learning (DL) based methods in wireless communication systems. Given various channel reconstruction tasks, applying one DL model for one specific task is costly in both model training and model storage. In this paper, we propose a novel unsupervised deep plug-and-play prior method for three channel reconstruction tasks in the downlink of massive multiple-input multiple-output (MIMO) systems, including channel estimation, antenna extrapolation and channel state information (CSI) feedback. The proposed method corresponding to these three channel reconstruction tasks employs a common DL model, which greatly reduces the overhead of model training and storage. Unlike general multi-task learning, the DL model of the proposed method does not require further fine-tuning for specific channel reconstruction tasks. Extensive experiments are conducted on the DeepMIMO dataset to demonstrate the convergence, performance, and storage overhead of the proposed method for the three channel reconstruction tasks.
Enhancing Secret Key Generation in Block Fading Channels using Reconfigurable Intelligent Surfaces
Authors: Hibatallah Alwazani, Anas Chaaban
Date: 2023-08-09 00:00:00

Physical layer security (PLS) is superior to classical cryptography techniques due to its notion of perfect secrecy and independence to an eavesdropper's computational power. One form of PLS arises when Alice and Bob (the legitimate users) exchange signals to extract a common key from the random common channels. The drawback of extracting keys from wireless channels is the ample dependence on the dynamicity and fluctuations of the radio channel. However, some radio channels are constant such as line of sight (LoS) and can be estimated by Eve (an illegitimate user), or can be quite static in behaviour due to the presence low-mobility users thus restricting the amount of randomness. This in turn lowers the secret key rate (SKR) defined as the number of bits of key generated per channel use. In this work, we aim to address this challenge by using a reconfigurable intelligent surface (RIS) to produce random phases at certain carefully curated intervals such that it disrupts the channel in low entropy environments. We propose an RIS assisted key generation method, study its performance, and compare with benchmarks to observe the benefit of using an RIS while considering various important metrics such as key mismatch rate and average secret key throughput. Simulations are made to validate our theoretical findings showing an improvement in performance when an RIS is deployed.
Reconfigurable Intelligent Surfaces Assisted Communication Under Different CSI Assumptions
Authors: Bayan Al-Nahhas, Qurrat-UI-Ain Nadeem, Aryan Kaushik, Anas Chaaban
Date: 2023-08-08 00:00:00

This work studies the net sum-rate performance of a distributed reconfigurable intelligent surfaces (RISs)-assisted multi-user multiple-input-single-output (MISO) downlink communication system under imperfect instantaneous-channel state information (I-CSI) to implement precoding at the base station (BS) and statistical-CSI (S-CSI) to design the RISs phase-shifts. Two channel estimation (CE) protocols are considered for I-CSI acquisition: (i) a full CE protocol that estimates all direct and RISs-assisted channels over multiple training sub-phases, and (ii) a low-overhead direct estimation (DE) protocol that estimates the end-to-end channel in a single sub-phase. We derive the asymptotic equivalents of signal-to-interference-plus-noise ratio (SINR) and ergodic net sum-rate under both protocols for given RISs phase-shifts, which are then optimized based on S-CSI. Simulation results reveal that the low-complexity DE protocol yields better net sum-rate than the full CE protocol when used to obtain CSI for precoding. A benchmark full I-CSI based RISs design is also outlined and shown to yield higher SINR but lower net sum-rate than the S-CSI based RISs design.
Comparative Analysis of Epileptic Seizure Prediction: Exploring Diverse Pre-Processing Techniques and Machine Learning Models
Authors: Md. Simul Hasan Talukder, Rejwan Bin Sulaiman
Date: 2023-08-06 00:00:00
Tasks: EEG, Electroencephalogram (EEG), Prediction, Seizure prediction

Epilepsy is a prevalent neurological disorder characterized by recurrent and unpredictable seizures, necessitating accurate prediction for effective management and patient care. Application of machine learning (ML) on electroencephalogram (EEG) recordings, along with its ability to provide valuable insights into brain activity during seizures, is able to make accurate and robust seizure prediction an indispensable component in relevant studies. In this research, we present a comprehensive comparative analysis of five machine learning models - Random Forest (RF), Decision Tree (DT), Extra Trees (ET), Logistic Regression (LR), and Gradient Boosting (GB) - for the prediction of epileptic seizures using EEG data. The dataset underwent meticulous preprocessing, including cleaning, normalization, outlier handling, and oversampling, ensuring data quality and facilitating accurate model training. These preprocessing techniques played a crucial role in enhancing the models' performance. The results of our analysis demonstrate the performance of each model in terms of accuracy. The LR classifier achieved an accuracy of 56.95%, while GB and DT both attained 97.17% accuracy. RT achieved a higher accuracy of 98.99%, while the ET model exhibited the best performance with an accuracy of 99.29%. Our findings reveal that the ET model outperformed not only the other models in the comparative analysis but also surpassed the state-of-the-art results from previous research. The superior performance of the ET model makes it a compelling choice for accurate and robust epileptic seizure prediction using EEG data.
Modelling non-local cell-cell adhesion: a multiscale approach
Authors: Anna Zhigun, Mabel Lizzy Rajendran
Date: 2023-08-10 00:00:00

Cell-cell adhesion plays a vital role in the development and maintenance of multicellular organisms. One of its functions is regulation of cell migration, such as occurs, e.g. during embryogenesis or in cancer. In this work, we develop a versatile multiscale approach to modelling a moving self-adhesive cell population that combines a careful microscopic description of a deterministic adhesion-driven motion component with an efficient mesoscopic representation of a stochastic velocity-jump process. This approach gives rise to mesoscopic models in the form of kinetic transport equations featuring multiple non-localities. Subsequent parabolic and hyperbolic scalings produce general classes of equations with non-local adhesion and myopic diffusion, a special case being the classical macroscopic model proposed in [4]. Our simulations show how the combination of the two motion effects can unfold. Cell-cell adhesion relies on the subcellular cell adhesion molecule binding. Our approach lends itself conveniently to capturing this microscopic effect. On the macroscale, this results in an additional non-linear integral equation of a novel type that is coupled to the cell density equation.
Topological classification of tumour-immune interactions and dynamics
Authors: Jingjie Yang, Heidi Fang, Jagdeep Dhesi, Iris H. R. Yoon, Joshua A. Bull, Helen M. Byrne, Heather A. Harrington, Gillian Grindstaff
Date: 2023-08-10 00:00:00
Tasks: Classification

The complex and dynamic crosstalk between tumour and immune cells results in tumours that can exhibit distinct qualitative behaviours - elimination, equilibrium, and escape - and intricate spatial patterns, yet share similar cell configurations in the early stages. We offer a topological approach to analyse time series of spatial data of cell locations (including tumour cells and macrophages) in order to predict malignant behaviour. We propose four topological vectorisations specialised to such cell data: persistence images of Vietoris-Rips and radial filtrations at static time points, and persistence images for zigzag filtrations and persistence vineyards varying in time. To demonstrate the approach, synthetic data are generated from an agent-based model with varying parameters. We compare the performance of topological summaries in predicting - with logistic regression at various time steps - whether tumour niches surrounding blood vessels are present at the end of the simulation, as a proxy for metastasis (i.e., tumour escape). We find that both static and time-dependent methods accurately identify perivascular niche formation, significantly earlier than simpler markers such as the number of tumour cells and the macrophage phenotype ratio. We find additionally that dimension 0 persistence applied to macrophage data, representing multi-scale clusters of the spatial arrangement of macrophages, performs best at this classification task at early time steps, prior to full tumour development, and performs even better when time-dependent data are included; in contrast, topological measures capturing the shape of the tumour, such as tortuosity and punctures in the cell arrangement, perform best at intermediate and later stages. The logistic regression coefficients reveal detailed shape differences between the classes.
AbDiffuser: Full-Atom Generation of in vitro Functioning Antibodies
Authors: Karolis Martinkus, Jan Ludwiczak, Kyunghyun Cho, Wei-Ching Liang, Julien Lafrance-Vanasse, Isidro Hotzel, Arvind Rajpal, Yan Wu, Richard Bonneau, Vladimir Gligorijevic, Andreas Loukas
Date: 2023-07-28 00:00:00
Proceeding: NeurIPS 2023 11
Tasks: Denoising

We introduce AbDiffuser, an equivariant and physics-informed diffusion model for the joint generation of antibody 3D structures and sequences. AbDiffuser is built on top of a new representation of protein structure, relies on a novel architecture for aligned proteins, and utilizes strong diffusion priors to improve the denoising process. Our approach improves protein diffusion by taking advantage of domain knowledge and physics-based constraints; handles sequence-length changes; and reduces memory complexity by an order of magnitude, enabling backbone and side chain generation. We validate AbDiffuser in silico and in vitro. Numerical experiments showcase the ability of AbDiffuser to generate antibodies that closely track the sequence and structural properties of a reference set. Laboratory experiments confirm that all 16 HER2 antibodies discovered were expressed at high levels and that 57.1% of the selected designs were tight binders.
Functional analysis of a gene locus in response to non-canonical combinations of transcription factors
Authors: Netta Haroush, Michal Levo, Eric Wieschaus, Thomas Gregor
Date: 2023-08-10 00:00:00

Transcription factor combinations determine gene locus activity and thereby cell identity. However, the precise link between concentrations of such activating transcription factors and target-gene activity is ambiguous. Here we investigate this link for the gap gene dependent activation of the even-skipped (eve) locus in the Drosophila embryo. We simultaneously measure the spatiotemporal gap gene concentrations in hemizygous and homozygous gap mutants, and link these to eve activity. Although changes in expression extend well beyond the genetically manipulated gene, nearly all expression alternations approximate the canonical combinations of activating levels in wild-type, sometimes necessitating pattern shifts. Expression levels that diverge from the wild-type repertoire still drive locus activation. Specific stripes in the homozygous mutants show partial penetrance, justifying their renown variable phenotypes. However, all eve stripes appear at highly reproducible positions, even though a broader span of gap gene expression levels activates eve. Our results suggest a correction capacity of the gap gene network and set constraints on the activity of multi-enhancer gene loci.
Desiderata for normative models of synaptic plasticity
Authors: Colin Bredenberg, Cristina Savin
Date: 2023-08-09 00:00:00

Normative models of synaptic plasticity use a combination of mathematics and computational simulations to arrive at predictions of behavioral and network-level adaptive phenomena. In recent years, there has been an explosion of theoretical work on these models, but experimental confirmation is relatively limited. In this review, we organize work on normative plasticity models in terms of a set of desiderata which, when satisfied, are designed to guarantee that a model has a clear link between plasticity and adaptive behavior, consistency with known biological evidence about neural plasticity, and specific testable predictions. We then discuss how new models have begun to improve on these criteria and suggest avenues for further development. As prototypes, we provide detailed analyses of two specific models -- REINFORCE and the Wake-Sleep algorithm. We provide a conceptual guide to help develop neural learning theories that are precise, powerful, and experimentally testable.
Predicting Pathogenicity Of nsSNPs Associated With Rb1 -- An In Silico Approach
Authors: Anum Munir
Date: 2023-07-16 00:00:00

Single nucleotide polymorphisms (SNPs) are variations at specific locations in DNA. Sequence responsible for marking genes associated with diseases or tracking inherited diseases within The family. These variations in the Rb1 gene can cause Retinoblastoma and cancer in the retina Of one eye or both, Osteosarcoma, Melanoma, Leukemias, Lungs, and Breast cancer. First of all,The SNP database hosted by NCBI was used to extract some principal data. The association of Rb1 to Other genes were analyzed by GeneMANIA. Ten different computational tools, i.eSIFT,Polyphen-2, I-Mutant 3.0,PROVEAN, SNAP2, PHD-SNP, PMut, SNPs&GO were used for the screening of damaging SNP for the estimation of conserved regions of amino acids Consurf Server was used for the evaluation of the structural stability of both native and mutant proteins, Project Hope was used to examine the structural effects of mutant protein.GeneMANIA predicted that RB1 Gene was expected to have a strong association with 20 other genes i.e. CCND1 and RBP2 etc. As per data retrieved from dbSNP hosted by NCBI,the Rb1 gene probed in this study carried a total of 36,358 SNPs. 345 were found in 3'UTR, 65 in 5'UTR, and 34,543 were found in the intron region. 844 were coding SNPs, and out of 844, 199 were synonymous And 450 were non-synonymous, including 425 missense, five nonsense, and 20 frameshift mutations. And remaining all are other types of SNPs. We took 425 missense SNPs for our investigation. A total of 17 mutations i.e. D332G, R445Q, E492V, P515T, W516G, V531G, E533K, E539K, M558R,W563G, L657Q, A658T, R661Q, D697H, D697E, P796L and R798W were predicted to have Damaging effects on structure and function of Rb1 protein..