pwc-archive/papers-with-abstracts huggingface dataset:
A Low-Complexity Range Estimation with Adjusted Affine Frequency Division Multiplexing Waveform
Authors: Jiajun Zhu, Yanqun Tang, Xizhang Wei, Haoran Yin, Jinming Du, Zhengpeng Wang, Yuqinng Liu
Date: 2023-12-18 00:00:00
Tasks: Integrated sensing and communication, ISAC

Affine frequency division multiplexing (AFDM) is a recently proposed communication waveform for time-varying channel scenarios. As a chirp-based multicarrier modulation technique it can not only satisfy the needs of multiple scenarios in future mobile communication networks but also achieve good performance in radar sensing by adjusting the built-in parameters, making it a promising air interface waveform in integrated sensing and communication (ISAC) applications. In this paper, we investigate an AFDM-based radar system and analyze the radar ambiguity function of AFDM with different built-in parameters, based on which we find an AFDM waveform with the specific parameter c2 owns the near-optimal time-domain ambiguity function. Then a low-complexity algorithm based on matched filtering for high-resolution target range estimation is proposed for this specific AFDM waveform. Through simulation and analysis, the specific AFDM waveform has near-optimal range estimation performance with the proposed low-complexity algorithm while having the same bit error rate (BER) performance as orthogonal time frequency space (OTFS) using simple linear minimum mean square error (LMMSE) equalizer.
Holographic Imaging with XL-MIMO and RIS: Illumination and Reflection Design
Authors: Giulia Torcolacci, Anna Guerra, Haiyang Zhang, Francesco Guidi, Qianyu Yang, Yonina C. Eldar, Davide Dardari
Date: 2023-12-18 00:00:00

This paper addresses a near-field imaging problem utilizing extremely large-scale multiple-input multiple-output (XL-MIMO) antennas and reconfigurable intelligent surfaces (RISs) already in place for wireless communications. To this end, we consider a system with a fixed transmitting antenna array illuminating a region of interest (ROI) and a fixed receiving antenna array inferring the ROI's scattering coefficients. Leveraging XL-MIMO and high frequencies, the ROI is situated in the radiative near-field region of both antenna arrays, thus enhancing the degrees of freedom (DoF) (i.e., the channel matrix rank) of the illuminating and sensing channels available for imaging, here referred to as holographic imaging. To further boost the imaging performance, we optimize the illuminating waveform by solving a min-max optimization problem having the upper bound of the mean squared error (MSE) of the image estimate as the objective function. Additionally, we address the challenge of non-line-of-sight (NLOS) scenarios by considering the presence of a RIS and deriving its optimal reflection coefficients. Numerical results investigate the interplay between illumination optimization, geometric configuration (monostatic and bistatic), the DoF of the illuminating and sensing channels, image estimation accuracy, and image complexity.
3-D Distributed Localization with Mixed Local Relative Measurements
Authors: Xu Fang, Xiaolei Li, Lihua Xie
Date: 2023-12-18 00:00:00
Tasks: Position

This paper studies 3-D distributed network localization using mixed types of local relative measurements. Each node holds a local coordinate frame without a common orientation and can only measure one type of information (relative position, distance, relative bearing, angle, or ratio-of-distance measurements) about its neighboring nodes in its local coordinate frame. A novel rigidity-theory-based distributed localization is developed to overcome the challenge due to the absence of a global coordinate frame. The main idea is to construct displacement constraints for the positions of the nodes by using mixed local relative measurements. Then, a linear distributed localization algorithm is proposed for each free node to estimate its position by solving the displacement constraints. The algebraic condition and graph condition are obtained to guarantee the global convergence of the proposed distributed localization algorithm.
Power-Efficient Sampling
Authors: Satish Mulleti, Timur Zirtiloglu, Arman Tan, Rabia Tugce Yazicigil, Yonina C. Eldar
Date: 2023-12-18 00:00:00
Tasks: Quantization

Analog-to-digital converters (ADCs) facilitate the conversion of analog signals into a digital format. While the specific designs and settings of ADCs can vary depending on their applications, it is crucial in many modern applications to minimize their power consumption. The significance of low-power ADCs is particularly evident in fields like mobile and handheld devices reliant on battery operation. Key parameters of the ADCs that dictate the ADC's power are its sampling rate, dynamic range, and number of quantization bits. Typically, these parameters are required to be higher than a threshold value but can be reduced by using the structure of the signal and by leveraging preprocessing and the system application needs. In this review, we discuss four approaches relevant to a variety of applications.
Data-based Model Identification of the Hypothalamus-Pituitary-Thyroid Complex
Authors: Clara Horvath, Andreas Körner, Corinna Modiz
Date: 2023-12-18 00:00:00

The thyroid gland, in conjunction with the pituitary and the hypothalamus, forms a regulated system due to their mutual influence through released hormones. The equilibrium point of this system, commonly referred to as the "set point", is individually determined. This means that determining the correct amount of medication to be administered to patients with hypothyroidism requires several treatment appointments creating an extended treatment process. Because the dynamics of the system have not yet been fully explored, mathematical models are needed to simulate the mutual influence of the respective hormones as well as their course over time. These models enable a deeper understanding of the functionality in the context of data measurements. Therefore, two existing time-dependent mathematical models are used and further analyzed to replicate this overall influence of disparate systems. Both are based on a system of two differential equations modelling the interacting hormones. The parameters of the two models are identified according to different calibration approaches by means of patient data collected in a retrospective study in collaboration with the Medical University of Vienna. The hormonal course in the time domain as well as equilibrium curves including the set-point are then simulated and analyzed with respect to the normalized mean squared error. These calibrated systems allow a more profound insight into the functionality of the formed complex.
CrossBind: Collaborative Cross-Modal Identification of Protein Nucleic-Acid-Binding Residues
Authors: Linglin Jing, Sheng Xu, Yifan Wang, Yuzhe Zhou, Tao Shen, Zhigang Ji, Hui Fang, Zhen Li, Siqi Sun
Date: 2023-12-19 00:00:00
Tasks: Contrastive Learning, Drug Design, Language Modelling, Protein Language Model

Accurate identification of protein nucleic-acid-binding residues poses a significant challenge with important implications for various biological processes and drug design. Many typical computational methods for protein analysis rely on a single model that could ignore either the semantic context of the protein or the global 3D geometric information. Consequently, these approaches may result in incomplete or inaccurate protein analysis. To address the above issue, in this paper, we present CrossBind, a novel collaborative cross-modal approach for identifying binding residues by exploiting both protein geometric structure and its sequence prior knowledge extracted from a large-scale protein language model. Specifically, our multi-modal approach leverages a contrastive learning technique and atom-wise attention to capture the positional relationships between atoms and residues, thereby incorporating fine-grained local geometric knowledge, for better binding residue prediction. Extensive experimental results demonstrate that our approach outperforms the next best state-of-the-art methods, GraphSite and GraphBind, on DNA and RNA datasets by 10.8/17.3% in terms of the harmonic mean of precision and recall (F1-Score) and 11.9/24.8% in Matthews correlation coefficient (MCC), respectively. We release the code at https://github.com/BEAM-Labs/CrossBind.
Scaling Monte-Carlo-Based Inference on Antibody and TCR Repertoires
Authors: Josiah Couch, Rohit Arora, Jasper Braun, Joesph Kaplinsky, Elliot Hill, Anthony Li, Brett Altschul, Ramy Arnaout
Date: 2023-12-19 00:00:00

Previously, it has been shown that maximum-entropy models of immune-repertoire sequence can be used to determine a person's vaccination status. However, this approach has the drawback of requiring a computationally intensive method to compute each model's partition function ($Z$), the normalization constant required for calculating the probability that the model will generate a given sequence. Specifically, the method required generating approximately $10^{10}$ sequences via Monte-Carlo simulations for each model. This is impractical for large numbers of models. Here we propose an alternative method that requires estimating $Z$ this way for only a few models: it then uses these expensive estimates to estimate $Z$ more efficiently for the remaining models. We demonstrate that this new method enables the generation of accurate estimates for 27 models using only three expensive estimates, thereby reducing the computational cost by an order of magnitude. Importantly, this gain in efficiency is achieved with only minimal impact on classification accuracy. Thus, this new method enables larger-scale investigations in computational immunology and represents a useful contribution to energy-based modeling more generally.
Characterization of Phosphorylated Tau-Microtubule complex with Molecular Dynamics (MD) simulation
Authors: Seongwon Kim, Parisa Mollaei, Amir Barati Farimani, Anne Skaja Robinson
Date: 2023-12-18 00:00:00
Tasks: Dimensionality Reduction

Alzheimer's Disease (AD), a neurodegenerative disorder, is reported as one of the most severe health and socioeconomic problems in current public health. Tau proteins are assumed to be a crucial driving factor of AD that detach from microtubules (MT) and accumulate as neurotoxic aggregates in the brains of AD patients. Extensive experimental and computational research has observed that phosphorylation at specific tau residues enhances aggregation, but the exact mechanisms underlying this phenomenon remain unclear. In this study, we employed molecular dynamics (MD) simulations on pseudo-phosphorylated tau-MT complex (residue 199 ~ 312), incorporating structural data from recent cryo-electron microscopy studies. Simulation results have revealed altered tau conformations after applying pseudo-phosphorylation. Additionally, root-mean-square deviation (RMSD) analyses and dimensionality reduction of dihedral angles revealed key residues responsible for these conformational shifts
Modeling the extracellular matrix in cell migration and morphogenesis: A guide for the curious biologist
Authors: Rebecca M. Crossley, Samuel Johnson, Erika Tsingos, Zoe Bell, Massimiliano Berardi, Margherita Botticelli, Quirine J. S. Braat, John Metzcar, Marco Ruscone, Yuan Yin, Robyn Shuttleworth
Date: 2023-12-19 00:00:00

The extracellular matrix (ECM) is a highly complex structure through which biochemical and mechanical signals are transmitted. In processes of cell migration, the ECM also acts as a scaffold, providing structural support to cells as well as points of potential attachment. Although the ECM is a well-studied structure, its role in many biological processes remains difficult to investigate comprehensively due to its complexity and structural variation within an organism. In tandem with experiments, mathematical models are helpful in refining and testing hypotheses, generating predictions, and exploring conditions outside the scope of experiments. Such models can be combined and calibrated with in vivo and in vitro data to identify critical cell-ECM interactions that drive developmental and homeostatic processes, or the progression of diseases. In this review, we focus on mathematical and computational models of the ECM in processes such as cell migration including cancer metastasis, and in tissue structure and morphogenesis. By highlighting the predictive power of these models, we aim to help bridge the gap between experimental and computational approaches to studying the ECM and to provide guidance on selecting an appropriate model framework to complement corresponding experimental studies.
HDL and plaque regression in a multiphase model of early atherosclerosis
Authors: Ishraq U. Ahmed, Mary R. Myerscough
Date: 2023-12-19 00:00:00
Tasks: regression

Atherosclerotic plaques are accumulations of cholesterol-engorged macrophages in the artery wall. Plaque growth is initiated and sustained by the deposition of low density lipoproteins (LDL) in the artery wall. High density lipoproteins (HDL) counterbalance the effects of LDL by accepting cholesterol from macrophages and removing it from the plaque. In this paper, we develop a free boundary multiphase model to investigate the effects of LDL and HDL on early plaque development. We examine how the rates of LDL and HDL deposition affect cholesterol accumulation in macrophages, and how this impacts cell death rates and emigration. We identify a region of LDL-HDL parameter space where plaque growth stabilises for low LDL and high HDL influxes, due to macrophage emigration and HDL clearance that counterbalances the influx of new cells and cholesterol. We explore how the efferocytic uptake of dead cells and the recruitment of new macrophages affect plaque development for a range of LDL and HDL levels. Finally, we consider how changes in the LDL-HDL profile can change the course of plaque development. We show that changes towards lower LDL and higher HDL can slow plaque growth and even induce regression. We find that these changes have less effect on larger, more established plaques, and that temporary changes will only slow plaque growth in the short term.
MEG Evidence That Modality-Independent Conceptual Representations Encode Visual but Not Lexical Representations
Authors: Julien Dirani, Liina Pylkkänen
Date: 2023-12-18 00:00:00

The semantic knowledge stored in our brains can be accessed from different stimulus modalities. For example, a picture of a cat and the word "cat" both engage similar conceptual representations. While existing research has found evidence for modality-independent representations, their content remains unknown. Modality-independent representations could be abstract, or they might be perceptual or even lexical in nature. We used a novel approach combining word/picture cross-condition decoding with neural network classifiers that learned latent modality-independent representations from MEG data. We then compared these representations to models representing semantic, sensory, and lexical features. Results show that modality-independent representations are not strictly amodal; rather, they also contain visual representations. There was no evidence that lexical properties contributed to the representation of modality-independent concepts. These findings support the notion that perceptual processes play a fundamental role in encoding modality-independent conceptual representations. Conversely, lexical representations did not appear to partake in modality-independent semantic knowledge.
Enhancing Understanding of Driving Attributes through Quantitative Assessment of Driver Cognition
Authors: Pallabjyoti Kakoti, Mukesh Kumar Kamti, Rauf Iqbal, Eeshankur Saikia
Date: 2023-11-03 00:00:00
Tasks: EEG

This paper presents a novel approach for analysing EEG data from drivers in a simulated driving test. We focused on the Hurst exponent, Shannon entropy, and fractal dimension as markers of the nonlinear dynamics of the brain. The results show significant trends: Shannon Entropy and Fractal Dimension exhibit variations during driving condition transitions, whereas the Hurst exponent reflects memory retention portraying learning patterns. These findings suggest that the tools of Non-linear Dynamical (NLD) Theory as indicators of cognitive state and driving memory changes for assessing driver performance and advancing the understanding of non-linear dynamics of human cognition in the context of driving and beyond. Our study reveals the potential of NLD tools to elucidate brain state and system variances, enabling their integration into current Deep Learning and Machine Learning models. This integration can extend beyond driving applications and be harnessed for cognitive learning, thereby improving overall productivity and accuracy levels.
Hierarchy of codon usage frequencies from codon-anticodon interaction in the crystal basis model
Authors: Antonino Sciarrino, Paul Sorba
Date: 2023-12-18 00:00:00

Analyzing the codon usage frequencies of a specimen of 20 plants, for which the codon-anticodon pattern is known, we have remarked that the hierarchy of the usage frequencies present an almost "universal" behavior. Searching to explain this behavior, we assume that the codon usage probability results from the sum of two contributions: the first dominant term is an almost "universal" one and it depends on the codon-anticodon interaction; the second term is a local one, i.e. depends on the biological species. The codon-anticodon interaction is written as a spin-spin plus a z-spin term in the formalism of the crystal basis model. From general considerations, in particular from the choice of the signs and some constraints on the parameters defining the interaction, we are able to explain most of the observed data.
A generalized Price equation for fuzzy set-mappings
Authors: Matthias Borgstede
Date: 2023-12-20 00:00:00

The Price equation provides a formal account of selection building on a right-total mapping between two classes of individuals, that is usually interpreted as a parent-offspring relation. This paper presents a new formulation of the Price equation in terms of fuzzy set-mappings to account for structures where the targets of selection may vary in the degree to which they belong to the classes of 'parents' and 'offspring,' and in the degree to which these two classes of individuals are related. The fuzzy set formulation widens the scope of the Price equation such that it equally applies to natural selection, cultural selection, operant selection, and even selection in physical systems.
Explaining excitable population dynamics in bark beetles: From life history to large, episodic outbreaks
Authors: Evan C. Johnson, Antonia Musso, José F. Negron, Mark A. Lewis
Date: 2023-12-19 00:00:00

Bark beetles are significant forest pests, with some species capable of causing widespread tree mortality. Among these, the mountain pine beetle (MPB) stands out for its exceptionally destructive outbreak in the 2000s. We use MPB as a case study to explore the concept of =excitable dynamics, where ephemeral perturbations produce large excursions from equilibrium. Our empirically-calibrated model of reveals five features of MPB biology: stand density-dependent dispersal, an Allee effect, time-scale separation between beetle and tree life cycles, tree size-dependent fecundity, and a large-tree preference. The first three features explain MPB's characteristic boom-bust dynamics, while the latter two explain outbreak magnitude. Other bark beetles lack one or more of these traits, partially explaining their generally lower impact. However, predicting bark beetle impact requires consideration of both life history and landscape factors: total damage increases linearly with host-tree biomass, but this relationship holds only for irruptive (i.e., excitable) beetle species. We distill our findings into a minimal mechanistic model that captures the essence of irruptive bark beetle dynamics. This model firmly establishes MPB as one of the first empirical examples of excitable dynamics in ecology.
Efficient coupling of within- and between-host infectious disease dynamics
Authors: Cameron A. Smith, Ben Ashby
Date: 2023-12-18 00:00:00

Mathematical models of infectious disease transmission typically neglect within-host dynamics. Yet within-host dynamics - including pathogen replication, host immune responses, and interactions with microbiota - are crucial not only for determining the progression of disease at the individual level, but also for driving within-host evolution and onwards transmission, and therefore shape dynamics at the population level. Various approaches have been proposed to model both within- and between-host dynamics, but these typically require considerable simplifying assumptions to couple processes at contrasting scales (e.g., the within-host dynamics quickly reach a steady state) or are computationally intensive. Here we propose a novel, readily adaptable and broadly applicable method for modelling both within- and between-host processes which can fully couple dynamics across scales and is both realistic and computationally efficient. By individually tracking the deterministic within-host dynamics of infected individuals, and stochastically coupling these to continuous host state variables at the population-level, we take advantage of fast numerical methods at both scales while still capturing individual transient within-host dynamics and stochasticity in transmission between hosts. Our approach closely agrees with full stochastic individual-based simulations and is especially useful when the within-host dynamics do not rapidly reach a steady state or over longer timescales to track pathogen evolution. By applying our method to different pathogen growth scenarios we show how common simplifying assumptions fundamentally change epidemiological and evolutionary dynamics.
On the Impact of School Closures on COVID-19 Transmission in Germany using an agent-based Simulation
Authors: Lucas Heger, Kerem Akdogan, Matthias Schott
Date: 2023-12-18 00:00:00

The effect of school closures on the spread of COVID-19 has been discussed among experts and the general public since those measures have been taken only a few months after the start of the pandemic in 2020. Within this study, the JuneGermany framework, is used to quantify the impact of school closures in the German state Rhineland Palatinate using an agent-based simulation approach. It was found that the simulations predicts a reduction of the number of infections, hospitalizations as well as death by a factor of 2.5 compared to scenarios, where no school closures are enforced, during the second wave between October 2020 and February 2021
Bio-Image Informatics Index BIII: A unique database of image analysis tools and workflows for and by the bioimaging community
Authors: Chong Zhang, Alban Gaignard, Matus Kalas, Florian Levet, Felipe Delestro, Joakim Lindblad, Natasa Sladoje, Laure Plantard, Alain Latour, Robert Haase, Gabriel Martins, Paula Sampaio, Leandro Scholz, NEUBIAS taggers, Sébastien Tosi, Kota Miura, Julien Colombelli, Perrine Paul-Gilloteaux
Date: 2023-12-18 00:00:00
Tasks: Benchmarking

Bio image analysis has recently become one keystone of biological research but biologists tend to get lost in a plethora of available software and the way to adjust available tools to their own image analysis problem. We present BIII, BioImage Informatic Index (www.biii.eu), the result of the first large community effort to bridge the communities of algorithm and software developers, bioimage analysts and biologists, under the form of a web-based knowledge database crowdsourced by these communities. Software tools (> 1300), image databases for benchmarking (>20) and training materials (>70) for bio image analysis are referenced and curated following standards constructed by the community and then reaching a broader audience. Software tools are organized as full protocol of analysis (workflow), specific brick (component) to construct a workflow, or software platform or library (collection). They are described using Edam Bio Imaging, which is iteratively defined using this website. All entries are exposed following FAIR principles and accessible for other usage.