pwc-archive/papers-with-abstracts huggingface dataset:
Bayesian reconstruction of sparse raster-scanned mid-infrared optoacoustic signals enables fast, label-free chemical microscopy
Authors: Constantin Berger, Myeongseop Kim, Lukas Scheel-Platz, Vasilis Ntziachristos, Dominik Jüstel, Miguel A. Pleitez
Date: 2024-11-07 00:00:00
Tasks: Decision Making, Diagnostic, Image Reconstruction, SSIM, Translation

Hyperspectral optoacoustic microscopy (OAM) enables obtaining images with label-free biomolecular contrast, offering excellent perspectives as a diagnostic tool to assess freshly excised and unprocessed tissues. However, time-consuming raster-scanning image formation currently limits the translation potential of OAM into the clinical setting-for instance, in intraoperative histopathological assessments-where micrographs of excised tissue need to be taken within a few minutes for fast clinical decision-making. Here, we present a non-data-driven computational framework tailored to enable fast OAM by sparse data acquisition and model-based image reconstruction, termed Bayesian raster-computed optoacoustic microscopy (BayROM). Unlike conventional machine learning, BayROM doesn't require training datasets, but instead, it employs 1) optomechanical system properties to define a forward model and 2) prior knowledge of the imaged samples to facilitate reconstructing images based on the sparsely acquired data. We show that BayROM enables acquiring micrographs ten times faster and with structural similarity (SSIM) indices greater than 0.93 compared to conventional raster scanning microscopy, thus facilitating the clinical translation of OAM for fast, label-free intraoperative histopathology.
Compressive Spectrum Sensing with 1-bit ADCs
Authors: Jian Yang, Zihang Song, Han Zhang, Yue Gao
Date: 2024-11-07 00:00:00
Tasks: compressed sensing, Quantization

Efficient wideband spectrum sensing (WSS) is essential for managing spectrum scarcity in wireless communications. However, existing compressed sensing (CS)-based WSS methods require high sampling rates and power consumption, particularly with high-precision analog-to-digital converters (ADCs). Although 1-bit CS with low-precision ADCs can mitigate these demands, most approaches still depend on multi-user cooperation and prior sparsity information, which are often unavailable in WSS scenarios. This paper introduces a non-cooperative WSS method using multicoset sampling with 1-bit ADCs to achieve sub-Nyquist sampling without requiring sparsity knowledge. We analyze the impact of 1-bit quantization on multiband signals, then apply eigenvalue decomposition to isolate the signal subspace from noise, enabling spectrum support estimation without signal reconstruction. This approach provides a power-efficient solution for WSS that eliminates the need for cooperation and prior information.
URLLC Networks enabled by STAR-RIS, Rate Splitting, and Multiple Antennas
Authors: Eduard Jorswieck, Mohammad Soleymani, Ignacio Santamaria, Jesús Gutiérrez
Date: 2024-11-07 00:00:00

The challenges in dense ultra-reliable low-latency communication networks to deliver the required service to multiple devices are addressed by three main technologies: multiple antennas at the base station (MISO), rate splitting multiple access (RSMA) with private and common message encoding, and simultaneously transmitting and reflecting reconfigurable intelligent surfaces (STAR-RIS). Careful resource allocation, encompassing beamforming and RIS optimization, is required to exploit the synergy between the three. We propose an alternating optimization-based algorithm, relying on minorization-maximization. Numerical results show that the achievable second-order max-min rates of the proposed scheme outperform the baselines significantly. MISO, RSMA, and STAR-RIS all contribute to enabling ultra-reliable low-latency communication (URLLC).
Generative Semantic Communications with Foundation Models: Perception-Error Analysis and Semantic-Aware Power Allocation
Authors: Chunmei Xu, Mahdi Boloursaz Mashhadi, Yi Ma, Rahim Tafazolli, Jiangzhou Wang
Date: 2024-11-07 00:00:00
Tasks: Decoder, Semantic Communication

Generative foundation models can revolutionize the design of semantic communication (SemCom) systems allowing high fidelity exchange of semantic information at ultra low rates. In this work, a generative SemCom framework with pretrained foundation models is proposed, where both uncoded forward-with-error and coded discard-with-error schemes are developed for the semantic decoder. To characterize the impact of transmission reliability on the perceptual quality of the regenerated signal, their mathematical relationship is analyzed from a rate-distortion-perception perspective, which is proved to be non-decreasing. The semantic values are defined to measure the semantic information of multimodal semantic features accordingly. We also investigate semantic-aware power allocation problems aiming at power consumption minimization for ultra low rate and high fidelity SemComs. To solve these problems, two semantic-aware power allocation methods are proposed by leveraging the non-decreasing property of the perception-error relationship. Numerically, perception-error functions and semantic values of semantic data streams under both schemes for image tasks are obtained based on the Kodak dataset. Simulation results show that our proposed semanticaware method significantly outperforms conventional approaches, particularly in the channel-coded case (up to 90% power saving).
Low Complexity Joint Chromatic Dispersion and Time/Frequency Offset Estimation Based on Fractional Fourier Transform
Authors: Guozhi Xu, Zekun Niu, Lyu Li, Weisheng Hu, Lilin Yi
Date: 2024-11-07 00:00:00

We propose and experimentally validate a joint estimation method for chromatic dispersion and time-frequency offset based on the fractional Fourier transform, which reduces computational complexity by more than 50% while keeping estimation accuracy.
Model Predictive Control Enabled UAV Trajectory Optimization and Secure Resource Allocation
Authors: Zhendong Li, Chang Su, Zhou Su, Haixia Peng, Yuntao Wang, Wen Chen, Qingqing Wu
Date: 2024-11-07 00:00:00
Tasks: Model Predictive Control

In this paper, we investigate a secure communication architecture based on unmanned aerial vehicle (UAV), which enhances the security performance of the communication system through UAV trajectory optimization. We formulate a control problem of minimizing the UAV flight path and power consumption while maximizing secure communication rate over infinite horizon by jointly optimizing UAV trajectory, transmit beamforming vector, and artificial noise (AN) vector. Given the non-uniqueness of optimization objective and significant coupling of the optimization variables, the problem is a non-convex optimization problem which is difficult to solve directly. To address this complex issue, an alternating-iteration technique is employed to decouple the optimization variables. Specifically, the problem is divided into three subproblems, i.e., UAV trajectory, transmit beamforming vector, and AN vector, which are solved alternately. Additionally, considering the susceptibility of UAV trajectory to disturbances, the model predictive control (MPC) approach is applied to obtain UAV trajectory and enhance the system robustness. Numerical results demonstrate the superiority of the proposed optimization algorithm in maintaining accurate UAV trajectory and high secure communication rate compared with other benchmark schemes.
Joint Discrete Antenna Positioning and Beamforming Optimization in Movable Antenna Enabled Full-Duplex ISAC Networks
Authors: Zhendong Li, Jianle Ba, Zhou Su, Haixia Peng, Yuntao Wang, Wen Chen, Qingqing Wu
Date: 2024-11-07 00:00:00
Tasks: Integrated sensing and communication, ISAC

In this paper, we propose a full-duplex integrated sensing and communication (ISAC) system enabled by a movable antenna (MA). By leveraging the characteristic of MA that can increase the spatial diversity gain, the performance of the system can be enhanced. We formulate a problem of minimizing the total transmit power consumption via jointly optimizing the discrete position of MA elements, beamforming vectors, sensing signal covariance matrix and user transmit power. Given the significant coupling of optimization variables, the formulated problem presents a non-convex optimization challenge that poses difficulties for direct resolution. To address this challenging issue, the discrete binary particle swarm optimization (BPSO) algorithm framework is employed to solve the formulated problem. Specifically, the discrete positions of MA elements are first obtained by iteratively solving the fitness function. The difference-of-convex (DC) programming and successive convex approximation (SCA) are used to handle non-convex and rank-1 terms in the fitness function. Once the BPSO iteration is complete, the discrete positions of MA elements can be determined, and we can obtain the solutions for beamforming vectors, sensing signal covariance matrix and user transmit power. Numerical results demonstrate the superiority of the proposed system in reducing the total transmit power consumption compared with fixed antenna arrays.
Radio-Based Passive Target Tracking by a Mobile Receiver with Unknown Transmitter Position
Authors: Ke Xu, Rui Zhang, He, Chen
Date: 2024-11-07 00:00:00
Tasks: Position

In this paper, we propose a radio-based passive target tracking algorithm using multipath measurements, including the angle of arrival and relative distance. We focus on a scenario in which a mobile receiver continuously receives radio signals from a transmitter located at an unknown position. The receiver utilizes multipath measurements extracted from the received signal to jointly localize the transmitter and the scatterers over time, with scatterers comprising a moving target and stationary objects that can reflect signals within the environment. We develop a comprehensive probabilistic model for the target tracking problem, incorporating the localization of the transmitter and scatterers, the identification of false alarms and missed detections in the measurements, and the association between scatterers and measurements. We employ a belief propagation approach to compute the posterior distributions of the positions of the scatterers and the transmitter. Additionally, we introduce a particle implementation for the belief propagation method. Simulation results demonstrate that our proposed algorithm outperforms existing benchmark methods in terms of target tracking accuracy.
Holographic-Pattern Based Multi-User Beam Training in RHS-Aided Hybrid Near-Field and Far-Field Communications
Authors: Shupei Zhang, Boya Di, Aryan Kaushik, Yonina C. Eldar
Date: 2024-11-07 00:00:00

Reconfigurable holographic surfaces (RHSs) have been suggested as an energy-efficient solution for extremely large-scale arrays. By controlling the amplitude of RHS elements, high-gain directional holographic patterns can be achieved. However, the complexity of acquiring real-time channel state information (CSI) for beamforming is exceedingly high, particularly in large-scale RHS-assisted communications, where users may distribute in the near-field region of RHS. This paper proposes a one-shot multi-user beam training scheme in large-scale RHS-assisted systems applicable to both near and far fields. The proposed beam training scheme comprises two phases: angle search and distance search, both conducted simultaneously for all users. For the angle search, an RHS angular codebook is designed based on holographic principles so that each codeword covers multiple angles in both near-field and far-field regions, enabling simultaneous angular search for all users. For the distance search, we construct the distance-adaptive codewords covering all candidate angles of users in a real-time way by leveraging the additivity of holographic patterns, which is different from the traditional phase array case. Simulation results demonstrate that the proposed scheme achieves higher system throughput compared to traditional beam training schemes. The beam training accuracy approaches the upper bound of exhaustive search at a significantly reduced overhead.
Non-Reciprocal Beyond Diagonal RIS: Multiport Network Models and Performance Benefits in Full-Duplex Systems
Authors: Hongyu Li, Bruno Clerckx
Date: 2024-11-07 00:00:00

Beyond diagonal reconfigurable intelligent surfaces (BD-RIS) is a new advance in RIS techniques that introduces reconfigurable inter-element connections to generate scattering matrices not limited to being diagonal. BD-RIS has been recently proposed and proven to have benefits in enhancing channel gain and enlarging coverage in wireless communications. Uniquely, BD-RIS enables reciprocal and non-reciprocal architectures characterized by symmetric and non-symmetric scattering matrices. However, the performance benefits and new use cases enabled by non-reciprocal BD-RIS for wireless systems remain unexplored. This work takes a first step toward closing this knowledge gap and studies the non-reciprocal BD-RIS in full-duplex systems and its performance benefits over reciprocal counterparts. We start by deriving a general RIS aided full-duplex system model using a multiport circuit theory, followed by a simplified channel model based on physically consistent assumptions. With the considered channel model, we investigate the effect of BD-RIS non-reciprocity and identify the theoretical conditions for reciprocal and non-reciprocal BD-RISs to simultaneously achieve the maximum received power of the signal of interest in the uplink and the downlink. Simulation results validate the theories and highlight the significant benefits offered by non-reciprocal BD-RIS in full-duplex systems. The significant gains are achieved because of the non-reciprocity principle which implies that if a wave hits the non-reciprocal BD-RIS from one direction, the surface behaves differently than if it hits from the opposite direction. This enables an uplink user and a downlink user at different locations to optimally communicate with the same full-duplex base station via a non-reciprocal BD-RIS, which would not be possible with reciprocal surfaces.
Efficient Position Determination of Highly Directional RF Emitters via Iterated Beampattern Analysis
Authors: Fraser Williams, Akila Pemasiri, Dhammika Jayalath, Terry Martin, Clinton Fookes
Date: 2024-11-07 00:00:00
Tasks: Position

The localization of RF emitters has attracted significant attention particularly within the domain of electronic warfare. Most localization methods found in open literature are based on omnidirectional emitters. Directional emitters significantly modulate received signal strength (RSS) resulting in degraded performance for localization techniques not modeling this behavior. This paper introduces a direct position determination (DPD) approach utilizing RSS information and adaptive beamforming to localize emitters at very low signal-to-noise-ratio (SNR). The technique is then applied to directional emitters taking the imposed RSS modulation into account using a beampattern library. This results in significantly improved localization region confidence as compared to omnidirectional assumption approaches.
ShEPhERD: Diffusing shape, electrostatics, and pharmacophores for bioisosteric drug design
Authors: Keir Adams, Kento Abeywardane, Jenna Fromer, Connor W. Coley
Date: 2024-10-22 00:00:00
Tasks: Drug Design

Engineering molecules to exhibit precise 3D intermolecular interactions with their environment forms the basis of chemical design. In ligand-based drug design, bioisosteric analogues of known bioactive hits are often identified by virtually screening chemical libraries with shape, electrostatic, and pharmacophore similarity scoring functions. We instead hypothesize that a generative model which learns the joint distribution over 3D molecular structures and their interaction profiles may facilitate 3D interaction-aware chemical design. We specifically design ShEPhERD, an SE(3)-equivariant diffusion model which jointly diffuses/denoises 3D molecular graphs and representations of their shapes, electrostatic potential surfaces, and (directional) pharmacophores to/from Gaussian noise. Inspired by traditional ligand discovery, we compose 3D similarity scoring functions to assess ShEPhERD's ability to conditionally generate novel molecules with desired interaction profiles. We demonstrate ShEPhERD's potential for impact via exemplary drug design tasks including natural product ligand hopping, protein-blind bioactive hit diversification, and bioisosteric fragment merging.
Perspective on recent developments and challenges in regulatory and systems genomics
Authors: Julia Zeiltinger, Sushmita Roy, Ferhat Ay, Anthony Mathelier, Alejandra Medina-Rivera, Shaun Mahony, Saurabh Sinha, Jason Ernst
Date: 2024-11-07 00:00:00
Tasks: Benchmarking

Predicting how genetic variation affects phenotypic outcomes at the organismal, cellular, and molecular levels requires deciphering the cis-regulatory code, the sequence rules by which non-coding regions regulate genes. In this perspective, we discuss recent computational progress and challenges towards solving this fundamental problem. We describe how cis-regulatory elements are mapped and how their sequence rules can be learned and interpreted with sequence-to-function neural networks, with the goal of identifying genetic variants in human disease. We also discuss how studies of the 3D chromatin organization could help identifying long-range regulatory effects and how current methods for mapping gene regulatory networks could better describe biological processes. We point out current gaps in knowledge along with technical limitations and benchmarking challenges of computational methods. Finally, we discuss newly emerging technologies, such as spatial transcriptomics, and outline strategies for creating a more general model of the cis-regulatory code that is more broadly applicable across cell types and individuals.
Upcycling Human Excrement: The Gut Microbiome to Soil Microbiome Axis
Authors: Jeff Meilander, Chloe Herman, Andrew Manley, Georgia Augustine, Dawn Birdsell, Evan Bolyen, Kimberly R. Celona, Hayden Coffey, Jill Cocking, Teddy Donoghue, Alexis Draves, Daryn Erickson, Marissa Foley, Liz Gehret, Johannah Hagen, Crystal Hepp, Parker Ingram, David John, Katarina Kadar, Paul Keim, Victoria Lloyd, Christina Osterink, Victoria Queeney, Diego Ramirez, Antonio Romero, Megan C. Ruby, Jason W. Sahl, Sydni Soloway, Nathan E. Stone, Shannon Trottier, Kaleb Van Orden, Alexis Painter, Sam Wallace, Larissa Wilcox, Colin V. Wood, Jaiden Yancey, J. Gregory Caporaso
Date: 2024-11-05 00:00:00
Tasks: Management, Time Series, Time Series Analysis

Human excrement composting (HEC) is a sustainable strategy for human excrement (HE) management that recycles nutrients and mitigates health risks while reducing reliance on freshwater, fossil fuels, and fertilizers. We present a comprehensive microbial time series analysis of HEC and show that the initial gut-like microbiome of HEC systems transitions to a microbiome similar to soil and traditional compost in fifteen biological replicates tracked weekly for one year.
More variable circadian rhythms in epilepsy captured by long-term heart rate recordings from wearable sensors
Authors: Billy C. Smith, Christopher Thornton, Rachel E. Stirling, Guillermo M. Besne, Sarah J. Gascoigne, Nathan Evans, Peter N. Taylor, Karoline Leiberg, Philippa J. Karoly, Yujiang Wang
Date: 2024-11-07 00:00:00
Tasks: Rhythm

Objective: The circadian rhythm synchronizes physiological and behavioural patterns with the 24-hour light-dark cycle. Disruption to the circadian rhythm is linked to various health conditions, though optimal methods to describe these disruptions remain unclear. An emerging approach is to examine the intra-individual variability in measurable properties of the circadian rhythm over extended periods. Epileptic seizures are modulated by circadian rhythms, but the relevance of circadian rhythm disruption in epilepsy remains unexplored. Our study investigates intra-individual circadian variability in epilepsy and its relationship with seizures. Methods: We retrospectively analyzed over 70,000 hours of wearable smartwatch data (Fitbit) from 143 people with epilepsy (PWE) and 31 healthy controls. Circadian oscillations in heart rate time series were extracted, daily estimates of circadian period, acrophase, and amplitude properties were produced, and estimates of the intra-individual variability of these properties over an entire recording were calculated. Results: PWE exhibited greater intra-individual variability in period (76 min vs. 57 min, d=0.66, p<0.001) and acrophase (64 min vs. 48 min, d=0.49, p=0.004) compared to controls, but not in amplitude (2 bpm, d=-0.15, p=0.49). Variability in circadian properties showed no correlation with seizure frequency, nor any differences between weeks with and without seizures. Significance: For the first time, we show that heart rate circadian rhythms are more variable in PWE, detectable via consumer wearable devices. However, no association with seizure frequency or occurrence was found, suggesting that this variability might be underpinned by the epilepsy aetiology rather than being a seizure-driven effect.
Neuroradiological features of traumatic encephalopathy syndrome using MRI and FDG-PET imaging: a case series in Australia
Authors: Rowena Mobbs, Fatima Nasrallah, Xuan Vinh To, John Magnussen, Jennifer Batchelor, Edward Hsiao, Mark Walterfang
Date: 2024-11-07 00:00:00
Tasks: Diagnostic

Objectives: This study examined whether currently existing clinical structural magnetic resonance imaging (MRI) and fluorodeoxyglucose positron emission tomography (18FDG-PET) capabilities and board-certified radiologists' reports and interpretations can assist with traumatic encephalopathy syndrome (TES) diagnosis. Design: retrospective case series. Setting: this study assessed six patients with TES criteria recruited from the Sydney area, Australia Main outcomes: patients' clinical history and clinical presentation along TES diagnostic criteria, board-certified radiologist reports of structural MRI and 18FDG-PET. Results: one patient was classified as possible CTE, and the others were classified as probable CTE with significant RHI exposure history and a spectrum of cognitive deficits and other neuropsychiatric disturbances consistent with TES diagnostic criteria. Most common radiological features included atrophy of posterior superior parietal region and Evans Index > 0.25. FDG-PET's pattern of common regions of hypometabolism and differing hypometabolism across participants suggested that FDG-PET and structural MRI have the potential for stratifying different TES stages. Conclusions: this study highlights the potential of a combined clinical and radiological approach using solely current capabilities to improve TES diagnosis and suggests a larger study. Such expanded investigation is crucial for advancing the ante-mortem diagnosis and management of CTE.
An Approach for Molecular and Biological Characterizations of Virulent Influenza A Viruses
Authors: Meitner Cadena, Alejandro Yerovi
Date: 2024-11-07 00:00:00
Tasks: Position

We propose an approach based on a combination of physical, chemical, and mathematical methods to identify and characterize virulent influenza A viruses (IAVs) through the analysis of the hemagglutinin protein. These methods include the isoelectric point, extreme value theory, and tree-like classification. The characterization process involves molecular and biological aspects. This procedure was applied to an IAV sample that included strains related to known influenza pandemics. The results provided clear position and amino acid pairs that identify these virulent viruses. These results show that our approach is promising to contribute new methodologies to identify and characterize virulent IAVs.