pwc-archive/papers-with-abstracts huggingface dataset:
Model-driven deep neural network for enhanced direction finding with commodity 5G gNodeB
Authors: Shengheng Liu, Zihuan Mao, Xingkang Li, Mengguan Pan, Peng Liu, Yongming Huang, Xiaohu You
Date: 2024-12-14 00:00:00

Pervasive and high-accuracy positioning has become increasingly important as a fundamental enabler for intelligent connected devices in mobile networks. Nevertheless, current wireless networks heavily rely on pure model-driven techniques to achieve positioning functionality, often succumbing to performance deterioration due to hardware impairments in practical scenarios. Here we reformulate the direction finding or angle-of-arrival (AoA) estimation problem as an image recovery task of the spatial spectrum and propose a new model-driven deep neural network (MoD-DNN) framework. The proposed MoD-DNN scheme comprises three modules: a multi-task autoencoder-based beamformer, a coarray spectrum generation module, and a model-driven deep learning-based spatial spectrum reconstruction module. Our technique enables automatic calibration of angular-dependent phase error thereby enhancing the resilience of direction-finding precision against realistic system non-idealities. We validate the proposed scheme both using numerical simulations and field tests. The results show that the proposed MoD-DNN framework enables effective spectrum calibration and accurate AoA estimation. To the best of our knowledge, this study marks the first successful demonstration of hybrid data-and-model-driven direction finding utilizing readily available commodity 5G gNodeB.
Spatial-Division ISAC: A Practical Waveform Design Strategy via Null-Space Superimposition
Authors: Byunghyun Lee, Hwanjin Kim, David J. Love, James V. Krogmeier
Date: 2024-12-13 00:00:00
Tasks: ISAC, Radar waveform design

Integrated sensing and communications (ISAC) is a key enabler of new applications, such as precision agriculture, extended reality (XR), and digital twins, for 6G wireless systems. However, the implementation of ISAC technology is very challenging due to practical constraints such as high complexity. In this paper, we introduce a novel ISAC waveform design strategy, called the spatial-division ISAC (SD-ISAC) waveform, which simplifies the ISAC waveform design problem by decoupling it into separate communication and radar waveform design tasks. Specifically, the proposed strategy leverages the null-space of the communication channel to superimpose sensing signals onto communication signals without interference. This approach offers multiple benefits, including reduced complexity and the reuse of existing communication and radar waveforms. We then address the problem of optimizing the spatial and temporal properties of the proposed waveform. We develop a low-complexity beampattern matching algorithm, leveraging a majorization-minimization (MM) technique. Furthermore, we develop a range sidelobe suppression algorithm based on manifold optimization. We provide comprehensive discussions on the practical advantages and potential challenges of the proposed method, including null-space feedback. We evaluate the performance of the proposed waveform design algorithm through extensive simulations. Simulation results show that the proposed method can provide similar or even superior performance to existing ISAC algorithms while reducing computation time significantly.
A model-based approach for transforming InSAR-derived vertical land motion from a local to a global reference frame
Authors: Mahmoud Reshadati, Manoochehr Shirzaei
Date: 2024-12-13 00:00:00

Vertical land motion (VLM) observations obtained from Interferometric Synthetic Aperture Radar (InSAR) have transformed our understanding of crustal deformation processes over the past 3 decades. However, these observations are often related to a local reference frame, posing challenges for studies that require large-scale observations within a global reference frame, such as assessments of relative sea level rise and associated hazards. Here, we present a novel approach that enables transforming InSAR-derived VLM at any location worldwide to a global (e.g., International Terrestrial Reference Frame) reference frame without a direct need for GNSS (Global Navigation Satellite System) measurements. To this end, we employ a coarse resolution model of global VLM obtained by interpolating rates of all available GNSS stations over the global land areas. Our rationale is that the high-resolution InSAR-derived VLM data do not capture the long-wavelength signals present in the global VLM model. Therefore, we employ a set of 2D polynomial models to evaluate the difference between InSAR-derived VLM and the global model and then add it back to the InSAR-derived VLM. We examined the validity of our rationale using normalized power spectrum analysis and tested the effect of polynomial order on the accuracy of transformed VLM and the overall success of our approach using two datasets from Los Angeles and New York City. This approach improves the usability of InSAR-derived VLM in geophysical applications, including monitoring regional land subsidence.
Data-Driven Quantification of Battery Degradation Modes via Critical Features from Charging
Authors: Yuanhao Cheng, Hanyu Bai, Yichen Liang, Xiaofan Cui, Weiren Jiang, Ziyou Song
Date: 2024-12-13 00:00:00

Battery degradation modes influence the aging behavior of Li-ion batteries, leading to accelerated capacity loss and potential safety issues. Quantifying these aging mechanisms poses challenges for both online and offline diagnostics in charging station applications. Data-driven algorithms have emerged as effective tools for addressing state-of-health issues by learning hard-to-model electrochemical properties from data. This paper presents a data-driven method for quantifying battery degradation modes. Ninety-one statistical features are extracted from the incremental capacity curve derived from 1/3C charging data. These features are then screened based on dispersion, contribution, and correlation. Subsequently, machine learning models, including four baseline algorithms and a feedforward neural network, are used to estimate the degradation modes. Experimental validation indicates that the feedforward neural network outperforms the others, achieving a root mean square error of around 10\% across all three degradation modes (i.e., loss of lithium inventory, loss of active material on the positive electrode, and loss of active material on the negative electrode). The findings in this paper demonstrate the potential of machine learning for diagnosing battery degradation modes in charging station scenarios.
Importance Sampling With Stochastic Particle Flow and Diffusion Optimization
Authors: Wenyu Zhang, Mohammad J. Khojasteh, Nikolay A. Atanasov, Florian Meyer
Date: 2024-12-13 00:00:00
Tasks: Numerical Integration

Particle flow (PFL) is an effective method for overcoming particle degeneracy, the main limitation of particle filtering. In PFL, particles are migrated towards regions of high likelihood based on the solution of a partial differential equation. Recently proposed stochastic PFL introduces a diffusion term in the ordinary differential equation (ODE) that describes particle motion. This diffusion term reduces the stiffness of the ODE and makes it possible to perform PFL with a lower number of numerical integration steps compared to traditional deterministic PFL. In this work, we introduce a general approach to perform importance sampling (IS) based on stochastic PFL. Our method makes it possible to evaluate a "flow-induced" proposal probability density function (PDF) after the parameters of a Gaussian mixture model (GMM) have been migrated by stochastic PFL. Compared to conventional stochastic PFL, the resulting processing step is asymptotically optimal. Within our method, it is possible to optimize the diffusion matrix that describes the diffusion term of the ODE to improve the accuracy-computational complexity tradeoff. Our simulation results in a highly nonlinear 3-D source localization scenario showcase a reduced stiffness of the ODE and an improved estimating accuracy compared to state-of-the-art deterministic and stochastic PFL.
Implicit Neural Compression of Point Clouds
Authors: Hongning Ruan, Yulin Shao, Qianqian Yang, Liang Zhao, Zhaoyang Zhang, Dusit Niyato
Date: 2024-12-11 00:00:00
Tasks: Attribute

Point clouds have gained prominence in numerous applications due to their ability to accurately depict 3D objects and scenes. However, compressing unstructured, high-precision point cloud data effectively remains a significant challenge. In this paper, we propose NeRC$^{\textbf{3}}$, a novel point cloud compression framework leveraging implicit neural representations to handle both geometry and attributes. Our approach employs two coordinate-based neural networks to implicitly represent a voxelized point cloud: the first determines the occupancy status of a voxel, while the second predicts the attributes of occupied voxels. By feeding voxel coordinates into these networks, the receiver can efficiently reconstructs the original point cloud's geometry and attributes. The neural network parameters are quantized and compressed alongside auxiliary information required for reconstruction. Additionally, we extend our method to dynamic point cloud compression with techniques to reduce temporal redundancy, including a 4D spatial-temporal representation termed 4D-NeRC$^{\textbf{3}}$. Experimental results validate the effectiveness of our approach: for static point clouds, NeRC$^{\textbf{3}}$ outperforms octree-based methods in the latest G-PCC standard. For dynamic point clouds, 4D-NeRC$^{\textbf{3}}$ demonstrates superior geometry compression compared to state-of-the-art G-PCC and V-PCC standards and achieves competitive results for joint geometry and attribute compression.
Quasispecies dynamics with time lags and periodic fluctuations in replication
Authors: Edward A. Turner, Francisco Crespo, Josep Sardanyés, Nolbert Morales
Date: 2024-12-13 00:00:00

Quasispecies theory provides the conceptual and theoretical bases for describing the dynamics of biological information of replicators subject to large mutation rates. This theory, initially conceived within the framework of prebiotic evolution, is also being used to investigate the evolutionary dynamics of RNA viruses and heterogeneous cancer cells populations. In this sense, efforts to approximate the initial quasispecies theory to more realistic scenarios have been made in recent decades. Despite this, how time lags in RNA synthesis and periodic fluctuations impact quasispecies dynamics remains poorly studied. In this article, we combine the theory of delayed ordinary differential equations and topological Leray-Schauder degree to investigate the classical quasispecies model in the single-peak fitness landscape considering time lags and periodic fluctuations in replication. First, we prove that the dynamics with time lags under the constant population constraint remains in the simplex in both forward and backward times. With backward mutation and periodic fluctuations, we prove the existence of periodic orbits regardless of time lags. Nevertheless, without backward mutation, neither periodic fluctuation nor the introduction of time lags leads to periodic orbits. However, in the case of periodic fluctuations, solutions converge exponentially to a periodic oscillation around the equilibria associated with a constant replication rate. We check the validity of the error catastrophe hypothesis assuming no backward mutation; we determine that the error threshold remains sound for the case of time of periodic fitness and time lags with constant fitness. Finally, our results show that the error threshold is not found with backward mutations.
Applications of Knot Theory for the Improvement of the AlphaFold Protein Database
Authors: Pranshu Jahagirdar
Date: 2024-12-15 00:00:00
Tasks: Protein Structure Prediction

AlphaFold, a groundbreaking protein prediction model, has revolutionized protein structure prediction, populating the AlphaFold Protein Database (AFDB) with millions of predicted structures. However, AlphaFold's accuracy in predicting proteins with intricate topologies, such as knots, remains a concern. This study investigates AlphaFold's performance in predicting knotted proteins and explores potential solutions to enhance the AFDB's reliability. Forty-five experimentally verified knotted protein structures from the KnotProt database were compared to their AlphaFold-generated counterparts. Knot analysis was performed using PyKnot, a PyMOL plugin, employing both Gauss codes and Alexander-Briggs knot notations. Results showed 95.6% accuracy in predicting the general shape of knots using Alexander-Briggs notation. However, Gauss code analysis revealed a 55.6% discrepancy, indicating AlphaFold's limitations in accurately representing the intricate orientation and directionality of knots. This Applications of Knot Theory for the improvement of the AlphaFold Protein Database suggests potential inaccuracies in a significant portion of the AFDB's knotted protein structures. The study underscores the need for improved knot representation in AlphaFold and proposes potential solutions, including transitioning to a single-module design or removing incorrectly predicted structures from the AFDB. These findings highlight the importance of continuous refinement for AI-based protein structure prediction tools to ensure the accuracy and reliability of protein databases for research and drug development.
Hierarchical cell identities emerge from animal gene regulatory mechanisms
Authors: Anton Grishechkin, Abhirup Mukherjee, Omer Karin
Date: 2024-12-15 00:00:00

The hierarchical organisation of cell identity is a fundamental feature of animal development with rich and well-characterized experimental phenomenology, yet the mechanisms driving its emergence remain unknown. The regulation of cell identity genes relies on a distinct mechanism involving higher-order interactions of transcription factors on distant regulatory regions called enhancers. These interactions are mediated by epigenetic regulators that are broadly shared between enhancers. Through the development of a new and predictive mathematical theory on the effects of epigenetic regulator activity on gene network dynamics, we demonstrate that hierarchical identities are essential emergent properties of animal-specific gene regulatory mechanisms. Hierarchical identities arise from the interplay between enhancer competition for epigenetic readers and cooperation through activation of shared transcriptional programs. We show that epigenetic regulatory mechanisms provide the network with self-similar properties that enable multilineage priming and signal-dependent control of progenitor states. The stabilisation of progenitor states is predicted to be controlled by the balance in activities between epigenetic writers and erasers. Our model quantitatively predicts lineage relationships, reconstructs all known blood progenitor states from terminal states, and explains mechanisms of cell identity dysregulation in cancer and the general differentiation effects of histone deacetylase inhibition. We identify non-specific modulation of enhancer competition as a central regulatory axis, with implications for developmental biology, cancer, and differentiation therapy.
VEPerform: a web resource for evaluating the performance of variant effect predictors
Authors: Cindy Zhang, Frederick P. Roth
Date: 2024-12-13 00:00:00

Computational variant effect predictors (VEPs) are providing increasingly strong evidence to classify the pathogenicity of missense variants. Precision vs. recall analysis is useful in evaluating VEP performance, especially when adjusted for imbalanced test sets. Here, we describe VEPerform, a web-based tool for evaluating the performance of VEPs at the gene level using balanced precision vs. recall curve (BPRC) analysis.
A theoretical investigation provides an integrated understanding of the complex regulatory network controlling Arabidopsis root hair patterning
Authors: Hayley Mills, George Janes, Anthony Bishopp, Natasha Savage
Date: 2024-12-15 00:00:00

A complex regulatory network controlling Arabidopsis root hair patterning has been defined using data collected over decades. The network, embedded in the root epidermis, contains positive and negative feedback loops, cell-cell signalling within the epidermal tissue, and receives positional signalling from underlying tissue. While there are extensive data regarding individual components and their interactions within the network, the sufficiency of the regulatory network to produce robust epidermal patterning is not clear, nor is it clear how individual components and interactions work together to ensure correct epidermal patterning. Mathematical modelling was used to address the questions of sufficiency, and to gain an integrated understanding of collective behaviour emerging from individual components and interactions within the regulatory network. Theoretical experiments were performed on a model of the root hair patterning regulatory network. It was found that our current understanding of the epidermal patterning network was insufficient to reproduce experimental data. The model was used to hypothesise the existence of an additional negative feedback loop which, when added to the currently understood regulatory network, enabled the model to reproduce wild type and mutant root hair patterning. Results of theoretical experiments on the modified regulatory network were used to; define an essential relationship between diffusive movement of two cell-cell signalling proteins; show that directed movement dysregulation of another cell-cell signalling protein causes root hair patterning to appear as though it no longer receives positional cues; and show that cooperativity or oligomerisation within the regulatory network's positive feedback loop are essential. This work presents an integrated, exploration of the regulatory network controlling epidermal root hair patterning in Arabidopsis.
Nap-induced modulations of tinnitus -a cross-sectional database analysis
Authors: Robin Guillard, Martin Schecklmann, Jorge Simoes, Berthold Langguth, Alain Londero, Marco Congedo, Sarah Michiels, Markku Vesala, Hazel Goedhart, Thomas Wetter, Franziska C Weber
Date: 2024-12-13 00:00:00

The influence of naps on tinnitus was systematically assessed by exploring the frequency, clinical and demographic characteristics of this phenomenon. 9,724 data from two different tinnitus databases (Tinnitus Hub: $n = 6115$; Tinnitus Research Initiative (TRI): $n = 3627$) were included. After separate analysis of the databases, these results were then compared with each other. In the Tinnitus Hub survey database, a total of 31.1% reported an influence on tinnitus by taking a nap (26.9% in the TRI database), with much more frequent worsening after a nap than improvement (23.0% a little or a lot worse; TRI: 17.7% worse; 8.1% a little or a lot better; TRI: 9.2% better). The influence of napping on tinnitus was associated in both databases with other clinical features, such as the dependence of tinnitus on night quality, stress and somatosensory maneuvers. The present study confirms the clinical observation that more tinnitus sufferers report worsening after a nap than tinnitus sufferers reporting an improvement. It was consistently shown that tinnitus sufferers reporting nap-induced modulation of tinnitus also report more frequently an influence of night sleep on their tinnitus. Further clinical and polysomnographic research is warranted to better understand the interaction between sleep and tinnitus.
Optimizing growth performance of Abelmoschus esculentus (L.) via synergistic effects of biogenic Cu/Ni/Co oxide nanoparticles in conjunction with rice straw and pressmud based vermicompost
Authors: Shalu Yadav, Ankit Singh, Praveen Kumar Srivastava, Abhay Kumar Choubey
Date: 2024-12-16 00:00:00

This study is a continuation of previous work, which highlights the nutrient enhancement by using rice straw (RS) and pressmud (PM) on vermicomposting. Herein, we demonstrate the significant impact of Moringa oleifera derived Cu/Ni/Co oxide nanoparticles (TmONs) in conjunction with these vermicompost on the growth performance of Abelmoschus esculentus. Vermicompost produced under various combinations (T0, cow dung (CD) only; T1, 1CD:1RS; T2, 1CD:1PM, and T3, 1CD:1RS:1PM) were further enriched by blending with biogenic nanoparticles. This strategic combination enhances the nutritional composition of the vermicompost, contributing to its overall effectiveness in promoting plant growth and health. Various analytical techniques, including FTIR, XRD, XPS, FESEM-EDX, TEM, and ICP-OES, were employed for comprehensive characterization. The synthesized TmONs with sizes ranging from 13 to 54 nm exhibited distinct CuO, NiO, and CoO phases. The vermicompost blended TmONs demonstrated significant improvements (P < 0.05) in seed germination (167%), coefficient velocity (67%), and vigour index (95%), while reducing the mean germination time by 41% for A. esculentus compared to the control group. The plant culture group nT3 (T3 + TmONs) showed the best growth performance. Furthermore, trace element concentrations in both soil and plant leaves were found to be below the maximum permissible limits set by WHO (1996). This investigation extends the understanding of the role played by these nanoparticles in fostering optimal conditions for plant growth and development as these micronutrients are essential components for several plant enzymes.
Decoding Drug Discovery: Exploring A-to-Z In silico Methods for Beginners
Authors: Hezha O. Rasul, Dlzar D. Ghafour, Bakhtyar K. Aziz, Bryar A. Hassan, Tarik A. Rashid, Arif Kivrak
Date: 2024-12-15 00:00:00
Tasks: Drug Design, Drug Discovery

The drug development process is a critical challenge in the pharmaceutical industry due to its time-consuming nature and the need to discover new drug potentials to address various ailments. The initial step in drug development, drug target identification, often consumes considerable time. While valid, traditional methods such as in vivo and in vitro approaches are limited in their ability to analyze vast amounts of data efficiently, leading to wasteful outcomes. To expedite and streamline drug development, an increasing reliance on computer-aided drug design (CADD) approaches has merged. These sophisticated in silico methods offer a promising avenue for efficiently identifying viable drug candidates, thus providing pharmaceutical firms with significant opportunities to uncover new prospective drug targets. The main goal of this work is to review in silico methods used in the drug development process with a focus on identifying therapeutic targets linked to specific diseases at the genetic or protein level. This article thoroughly discusses A-to-Z in silico techniques, which are essential for identifying the targets of bioactive compounds and their potential therapeutic effects. This review intends to improve drug discovery processes by illuminating the state of these cutting-edge approaches, thereby maximizing the effectiveness and duration of clinical trials for novel drug target investigation.