pwc-archive/papers-with-abstracts huggingface dataset:
Decentralized Localization of Distributed Antenna Array Elements Using an Evolutionary Algorithm
Authors: Matthew J. Dula, Naim Shandi, Jeffrey A. Nanzer
Date: 2024-11-16 00:00:00

Distributed phased arrays have recently garnered interest in applications such as satellite communications and high-resolution remote sensing. High-performance coherent distributed operations such as distributed beamforming are dependent on the ability to synchronize the spatio-electrical states of the elements in the array to the order of the operational wavelength, so that coherent signal summation can be achieved at any arbitrary target destination. In this paper, we address the fundamental challenge of precise distributed array element localization to enable coherent operation, even in complex environments where the array may not be capable of directly estimating all nodal link distances. We employ a two-way time transfer technique to synchronize the nodes of the array and perform internode ranging. We implement the classical multidimensional scaling algorithm to recover a decentralized array geometry from a set of range estimates. We also establish the incomplete set of range estimates as a multivariable non-convex optimization problem, and define the differential evolution algorithm which searches the solution space to complete the set of ranges. We experimentally demonstrate wireless localization using a spectrally-sparse pulsed two-tone waveform with 40 MHz tone separation in a laboratory environment, achieving a mean localization error vector magnitude of 0.82 mm in an environment with an average link SNR of 34 dB, theoretically supporting distributed beamforming operation up to 24.3 GHz.
Body-Resonance Human Body Communication
Authors: Samyadip Sarkar, Qi Huang, Sarthak Antal, Mayukh Nath, Shreyas Sen
Date: 2024-11-16 00:00:00
Tasks: Distributed Computing

Seamless interaction between Humans and AI-empowered battery-operated miniaturized electronic devices, exponentially transforming the wearable technology industry while forming an anthropomorphic artificial nervous system for distributed computing around the human body, demands high-speed low-power connectivity. If interconnected via radio frequency (RF) based wireless communication techniques, that being radiative, incur substantial absorption losses from the body during non-line-of-sight scenarios and consume higher power (more than 10s of mW). Although as a promising alternative with its non-radiative nature that resulted in 100X improvement in energy efficiency (sub-10 pJ/bit) and better signal confinement, Electro-Quasistatic Human Body Communication (EQS HBC) incurs moderate path loss (60-70 dB), limited data rate (less than 20 Mbps), making it less suitable for applications demanding fast connectivity like HD audio-video streaming, AR-VR-based products, distributed computing with wearable AI devices. Hence, to meet the requirement of energy-efficient connectivity at 100s of Mbps between wearables, we propose Body-Resonance (BR) HBC, which operates in the near-intermediate field and utilizes the transmission-line-like behavior of the body channel to offer 30X improvement in channel capacity. Our work sheds new light on the wireless communication system for wearables with potential to increase the channel gain by 20 dB with a 10X improvement in bandwidth compared to the EQS HBC for communication over on-body channels (whole-body coverage area). Experimentally demonstrating BR HBC, we presented low-loss (40-50 dB) and wide-band (hundreds of MHz) body channels that are 10X less leaky than radiative wireless communication, hence, can revolutionize the design of wireless communication system for several applications with wearables from healthcare, defense, to consumer electronics.
Performance Analysis and Power Allocation for Massive MIMO ISAC Systems
Authors: Nhan Thanh Nguyen, Van-Dinh Nguyen, Hieu V. Nguyen, Hien Quoc Ngo, A. Lee Swindlehurst, Markku Juntti
Date: 2024-11-16 00:00:00
Tasks: ISAC

Integrated sensing and communications (ISAC) is envisioned as a key feature in future wireless communications networks. Its integration with massive multiple-input-multiple-output (MIMO) techniques promises to leverage substantial spatial beamforming gains for both functionalities. In this work, we consider a massive MIMO-ISAC system employing a uniform planar array with zero-forcing and maximum-ratio downlink transmission schemes combined with monostatic radar-type sensing. Our focus lies on deriving closed-form expressions for the achievable communications rate and the Cram\'er--Rao lower bound (CRLB), which serve as performance metrics for communications and sensing operations, respectively. The expressions enable us to investigate important operational characteristics of massive MIMO-ISAC, including the mutual effects of communications and sensing as well as the advantages stemming from using a very large antenna array for each functionality. Furthermore, we devise a power allocation strategy based on successive convex approximation to maximize the communications rate while guaranteeing the CRLB constraints and transmit power budget. Extensive numerical results are presented to validate our theoretical analyses and demonstrate the efficiency of the proposed power allocation approach.
Brain-to-Text Decoding with Context-Aware Neural Representations and Large Language Models
Authors: Jingyuan Li, Trung Le, Chaofei Fan, Mingfei Chen, Eli Shlizerman
Date: 2024-11-16 00:00:00

Decoding attempted speech from neural activity offers a promising avenue for restoring communication abilities in individuals with speech impairments. Previous studies have focused on mapping neural activity to text using phonemes as the intermediate target. While successful, decoding neural activity directly to phonemes ignores the context dependent nature of the neural activity-to-phoneme mapping in the brain, leading to suboptimal decoding performance. In this work, we propose the use of diphone - an acoustic representation that captures the transitions between two phonemes - as the context-aware modeling target. We integrate diphones into existing phoneme decoding frameworks through a novel divide-and-conquer strategy in which we model the phoneme distribution by marginalizing over the diphone distribution. Our approach effectively leverages the enhanced context-aware representation of diphones while preserving the manageable class size of phonemes, a key factor in simplifying the subsequent phoneme-to-text conversion task. We demonstrate the effectiveness of our approach on the Brain-to-Text 2024 benchmark, where it achieves state-of-the-art Phoneme Error Rate (PER) of 15.34% compared to 16.62% PER of monophone-based decoding. When coupled with finetuned Large Language Models (LLMs), our method yields a Word Error Rate (WER) of 5.77%, significantly outperforming the 8.93% WER of the leading method in the benchmark.
The role of inducible defence in ecological models: Effects of nonlocal intraspecific competitions
Authors: Sangeeta Saha, Swadesh Pal, Roderick Melnik
Date: 2024-11-15 00:00:00

Phenotypic plasticity is a key factor in driving the evolution of species in the predator-prey interaction. The natural environment is replete with phenotypic plasticity, which is the source of inducible defences against predators, including concealment, cave-dwelling, mimicry, evasion, and revenge. In this work, a predator-prey model is proposed where the prey species shows inducible defence against their predators. The dynamics produce a wide range of non-trivial and impactful results, including the stabilizing effect of the defence mechanism. The model is also analyzed in the presence of spatio-temporal diffusion in a bounded domain. It is found in the numerical simulation that the Turing domain shrinks with the increase of defence level. The work is extended further by introducing a nonlocal term in the intra-specific competition of the prey species. The Turing instability condition has been studied for the local model around the coexisting steady state, followed by the Turing and non-Turing patterns in the presence of the nonlocal interaction term. The work reveals how an increase in inducible defence reduces the Turing domain in the local interaction model but expands it when the range of nonlocal interactions is extended, suggesting a higher likelihood of species colonization.
SCOP: A Sequence-Structure Contrast-Aware Framework for Protein Function Prediction
Authors: Runze Ma, Chengxin He, Huiru Zheng, Xinye Wang, Haiying Wang, Yidan Zhang, Lei Duan
Date: 2024-11-18 00:00:00
Tasks: Drug Discovery, Protein Function Prediction

Improving the ability to predict protein function can potentially facilitate research in the fields of drug discovery and precision medicine. Technically, the properties of proteins are directly or indirectly reflected in their sequence and structure information, especially as the protein function is largely determined by its spatial properties. Existing approaches mostly focus on protein sequences or topological structures, while rarely exploiting the spatial properties and ignoring the relevance between sequence and structure information. Moreover, obtaining annotated data to improve protein function prediction is often time-consuming and costly. To this end, this work proposes a novel contrast-aware pre-training framework, called SCOP, for protein function prediction. We first design a simple yet effective encoder to integrate the protein topological and spatial features under the structure view. Then a convolutional neural network is utilized to learn the protein features under the sequence view. Finally, we pretrain SCOP by leveraging two types of auxiliary supervision to explore the relevance between these two views and thus extract informative representations to better predict protein function. Experimental results on four benchmark datasets and one self-built dataset demonstrate that SCOP provides more specific results, while using less pre-training data.
Character-level Tokenizations as Powerful Inductive Biases for RNA Foundational Models
Authors: Adrián Morales-Pastor, Raquel Vázquez-Reza, Miłosz Wieczór, Clàudia Valverde, Manel Gil-Sorribes, Bertran Miquel-Oliver, Álvaro Ciudad, Alexis Molina
Date: 2024-11-05 00:00:00
Tasks: Drug Discovery

RNA is a vital biomolecule with numerous roles and functions within cells, and interest in targeting it for therapeutic purposes has grown significantly in recent years. However, fully understanding and predicting RNA behavior, particularly for applications in drug discovery, remains a challenge due to the complexity of RNA structures and interactions. While foundational models in biology have demonstrated success in modeling several biomolecules, especially proteins, achieving similar breakthroughs for RNA has proven more difficult. Current RNA models have yet to match the performance observed in the protein domain, leaving an important gap in computational biology. In this work, we present ChaRNABERT, a suite of sample and parameter-efficient RNA foundational models, that through a learnable tokenization process, are able to reach state-of-the-art performance on several tasks in established benchmarks. We extend its testing in relevant downstream tasks such as RNA-protein and aptamer-protein interaction prediction. Weights and inference code for ChaRNABERT-8M will be provided for academic research use. The other models will be available upon request.
A Mechanical Model for the Failure of Reconstructive Breast Implant Surgery Due to Capsular Contracture
Authors: Yuqi Xiao, Leah Edelstein-Keshet, Alain Goriely, Kathryn V Issac
Date: 2024-11-15 00:00:00

Capsular contracture is a pathological response to implant-based reconstructive breast surgery, where the ``capsule'' (tissue surrounding an implant) painfully thickens, contracts and deforms. It is known to affect breast-cancer survivors at higher rates than healthy women opting for cosmetic cosmetic breast augmentation with implants. We model the early stages of capsular contracture based on stress-dependent recruitment of contractile and mechanosensitive cells to the implant site. We derive a one-dimensional continuum spatial model for the spatio-temporal evolution of cells and collagen densities away from the implant surface. Various mechanistic assumptions are investigated for linear versus saturating mechanical cell responses and cell traction forces. Our results point to specific risk factors for capsular contracture, and indicate how physiological parameters, as well as initial states (such as inflammation after surgery) contribute to patient susceptibility.
Leveraging genomic deep learning models for non-coding variant effect prediction
Authors: Pooja Kathail, Ayesha Bajwa, Nilah M. Ioannidis
Date: 2024-11-17 00:00:00

The majority of genetic variants identified in genome-wide association studies of complex traits are non-coding, and characterizing their function remains an important challenge in human genetics. Genomic deep learning models have emerged as a promising approach to enable in silico prediction of variant effects. These include supervised sequence-to-activity models, which predict genome-wide chromatin states or gene expression levels directly from DNA sequence, and self-supervised genomic language models. Here, we review progress in leveraging these models for non-coding variant effect prediction. We describe practical considerations for making such predictions and categorize the types of ground truth data that have been used to evaluate deep learning-based variant effect predictions, providing insight into the settings in which current models are most useful. We also discuss downstream applications of such models to understanding disease-relevant non-coding variants. Our review highlights key considerations for practitioners and opportunities for future improvements in model development and evaluation.
In silico discovery of representational relationships across visual cortex
Authors: Alessandro T. Gifford, Maya A. Jastrzębowska, Johannes J. D. Singer, Radoslaw M. Cichy
Date: 2024-11-16 00:00:00

Now published in Nature Human Behavior doi: https://doi.org/10.1038/s41562-025-02252-z Human vision is mediated by a complex interconnected network of cortical brain areas that jointly represent visual information. While these areas are increasingly understood in isolation, their representational relationships remain elusive. Here we developed relational neural control (RNC), and used it to investigate the representational relationships for univariate and multivariate fMRI responses of areas across visual cortex. Through RNC we generated and explored in silico fMRI responses for large amounts of images, discovering controlling images that align or disentangle responses across areas, thus indicating their shared or unique representational content. This revealed a typical network-level configuration of representational relationships in which shared or unique representational content varied based on cortical distance, categorical selectivity, and position within the visual hierarchy. Closing the empirical cycle, we validated the in silico discoveries on in vivo fMRI responses from independent subjects. Together, this reveals how visual areas jointly represent the world as an interconnected network.
Pragmatic information of aesthetic appraisal
Authors: Peter beim Graben
Date: 2024-11-15 00:00:00

A phenomenological model for aesthetic appraisal is proposed in terms of pragmatic information for a dynamic update semantics over belief states on an aesthetic appreciator. The model qualitatively correlates with aesthetic pleasure ratings in an experimental study on cadential effects in Western tonal music. Finally, related computational and neurodynamical accounts are discussed.
Neural decoding from stereotactic EEG: accounting for electrode variability across subjects
Authors: Georgios Mentzelopoulos, Evangelos Chatzipantazis, Ashwin G. Ramayya, Michelle J. Hedlund, Vivek P. Buch, Kostas Daniilidis, Konrad P. Kording, Flavia Vitale
Date: 2024-11-01 00:00:00
Tasks: Data Integration, EEG

Deep learning based neural decoding from stereotactic electroencephalography (sEEG) would likely benefit from scaling up both dataset and model size. To achieve this, combining data across multiple subjects is crucial. However, in sEEG cohorts, each subject has a variable number of electrodes placed at distinct locations in their brain, solely based on clinical needs. Such heterogeneity in electrode number/placement poses a significant challenge for data integration, since there is no clear correspondence of the neural activity recorded at distinct sites between individuals. Here we introduce seegnificant: a training framework and architecture that can be used to decode behavior across subjects using sEEG data. We tokenize the neural activity within electrodes using convolutions and extract long-term temporal dependencies between tokens using self-attention in the time dimension. The 3D location of each electrode is then mixed with the tokens, followed by another self-attention in the electrode dimension to extract effective spatiotemporal neural representations. Subject-specific heads are then used for downstream decoding tasks. Using this approach, we construct a multi-subject model trained on the combined data from 21 subjects performing a behavioral task. We demonstrate that our model is able to decode the trial-wise response time of the subjects during the behavioral task solely from neural data. We also show that the neural representations learned by pretraining our model across individuals can be transferred in a few-shot manner to new subjects. This work introduces a scalable approach towards sEEG data integration for multi-subject model training, paving the way for cross-subject generalization for sEEG decoding.
Coexistence vs collapse in transposon populations
Authors: Aria Yom, Nathan E. Lewis
Date: 2024-11-17 00:00:00

Transposons are small, self-replicating DNA sequences found in every branch of life. Often, one transposon will parasitize another, forming a tiny intracellular ecosystem. In some species these ecosystems thrive, while in others they go extinct, yet little is known about when or why this occurs. Here, we present a stochastic model for these ecosystems and discover a transition from stable coexistence to population collapse when the propensity for a transposon to replicate comes to exceed that of its parasites. Our model also predicts that replication rates should be low in equilibrium, which appears to be true of many transposons in nature.
Random walk models in the life sciences: including births, deaths and local interactions
Authors: Michael J. Plank, Matthew J. Simpson, Ruth E. Baker
Date: 2024-11-17 00:00:00
Tasks: Epidemiology

Random walks and related spatial stochastic models have been used in a range of application areas including animal and plant ecology, infectious disease epidemiology, developmental biology, wound healing, and oncology. Classical random walk models assume that all individuals in a population behave independently, ignoring local physical and biological interactions. This assumption simplifies the mathematical description of the population considerably, enabling continuum-limit descriptions to be derived and used in model analysis and fitting. However, interactions between individuals can have a crucial impact on population-level behaviour. In recent decades, research has increasingly been directed towards models that include interactions, including physical crowding effects and local biological processes such as adhesion, competition, dispersal, predation and adaptive directional bias. In this article, we review the progress that has been made with models of interacting individuals. We aim to provide an overview that is accessible to researchers in application areas, as well as to specialist modellers. We focus particularly on derivation of asymptotically exact or approximate continuum-limit descriptions and simplified deterministic models of mean-field behaviour and resulting spatial patterns. We provide worked examples and illustrative results of selected models. We conclude with a discussion of current areas of focus and future challenges.
Artificial Intelligence for Infectious Disease Prediction and Prevention: A Comprehensive Review
Authors: Selestine Melchane, Youssef Elmir, Farid Kacimi, Larbi Boubchir
Date: 2024-11-14 00:00:00
Tasks: Disease Prediction

Artificial Intelligence (AI) and infectious diseases prediction have recently experienced a common development and advancement. Machine learning (ML) apparition, along with deep learning (DL) emergence, extended many approaches against diseases apparition and their spread. And despite their outstanding results in predicting infectious diseases, conflicts appeared regarding the types of data used and how they can be studied, analyzed, and exploited using various emerging methods. This has led to some ongoing discussions in the field. This research aims not only to provide an overview of what has been accomplished, but also to highlight the difficulties related to the types of data used, and the learning methods applied for each research objective. It categorizes these contributions into three areas: predictions using Public Health Data to prevent the spread of a transmissible disease within a region; predictions using Patients' Medical Data to detect whether a person is infected by a transmissible disease; and predictions using both Public and patient medical data to estimate the extent of disease spread in a population. The paper also critically assesses the potential of AI and outlines its limitations in infectious disease management.
Random feature baselines provide distributional performance and feature selection benchmarks for clinical and 'omic machine learning
Authors: Randall J. Ellis, Audrey Airaud, Chirag J. Patel
Date: 2024-11-15 00:00:00
Tasks: All, feature selection

Identifying predictive features from high-dimensional datasets is a major task in biomedical research. However, it is difficult to determine the robustness of selected features. Here, we investigate the performance of randomly chosen features, what we term "random feature baselines" (RFBs), in the context of disease risk prediction from blood plasma proteomics data in the UK Biobank. We examine two published case studies predicting diagnosis of (1) dementia and (2) hip fracture. RFBs perform similarly to published proteins of interest (using the same number, randomly chosen). We then measure the performance of RFBs for all 607 disease outcomes in the UK Biobank, with various numbers of randomly chosen features, as well as all proteins in the dataset. 114/607 outcomes showed a higher mean AUROC when choosing 5 random features than using all proteins, and the absolute difference in mean AUC was 0.075. 163 outcomes showed a higher mean AUROC when choosing 1000 random features than using all proteins, and the absolute difference in mean AUC was 0.03. Incorporating RFBs should become part of ML practice when feature selection or target discovery is a goal.