pwc-archive/papers-with-abstracts huggingface dataset:
Sensor Fusion and Resource Management in MIMO-OFDM Joint Sensing and Communication
Authors: Elia Favarelli, Elisabetta Matricardi, Lorenzo Pucci, Wen Xu, Enrico Paolini, Andrea Giorgetti
Date: 2023-12-12 00:00:00
Tasks: Management, Sensor Fusion

This study explores the promising potential of integrating sensing capabilities into multiple-input multiple-output (MIMO)-orthogonal frequency division multiplexing (OFDM)-based networks through innovative multi-sensor fusion techniques, tracking algorithms, and resource management. A novel data fusion technique is proposed within the MIMO-OFDM system, which promotes cooperative sensing among monostatic joint sensing and communication (JSC) base stations by sharing range-angle maps with a central fusion center. To manage data sharing and control network overhead introduced by cooperation, an excision filter is introduced at each base station. After data fusion, the framework employs a three-step clustering procedure combined with a tracking algorithm to effectively handle point-like and extended targets. Delving into the sensing/communication trade-off, resources such as transmit power, frequency, and time are varied, providing valuable insights into their impact on the overall system performance. Additionally, a sophisticated channel model is proposed, accounting for complex urban propagation scenarios and addressing multipath effects and multiple reflection points for extended targets like vehicles. Evaluation metrics, including optimal sub-pattern assignment (OSPA), downlink sum rate, and bit rate, offer a comprehensive assessment of the system's localization and communication capabilities, as well as network overhead.
Impact of Array Configuration on Head-Mounted Display Performance at mmWave Bands
Authors: Alexander Marinšek, Xuesong Cai, Lieven De Strycker, Fredrik Tufvesson, Liesbet Van der Perre
Date: 2023-12-12 00:00:00

Immersing a user in life-like extended reality (XR) scenery using a head-mounted display (HMD) with a constrained form factor and hardware complexity requires remote rendering on a nearby edge server or computer. Millimeter-wave (mmWave) communication technology can provide sufficient data rate for wireless XR content transmission. However, mmWave channels exhibit severe sparsity in the angular domain. This means that distributed antenna arrays are required to cover a larger angular area and to combat outage during HMD rotation. At the same time, one would prefer fewer antenna elements/arrays for a lower complexity system. Therefore, it is important to evaluate the trade-off between the number of antenna arrays and the achievable performance to find a proper practical solution. This work presents indoor 28 GHz mmWave channel measurement data, collected during HMD mobility, and studies the dominant eigenmode (DE) gain. DE gain is a significant factor in understanding system performance since mmWave channel sparsity and eigenmode imbalance often results in provisioning the majority of the available power to the DE. Moreover, it provides the upper performance bounds for widely-adopted analog beamformers. We propose 3 performance metrics - gain trade-off, gain volatility, and minimum service trade-off - for evaluating the performance of a multi-array HMD and apply the metrics to indoor 28 GHz channel measurement data. Evaluation results indicate, that 3 arrays provide stable temporal channel gain. Adding a 4th array further increases channel capacity, while any additional arrays do not significantly increase physical layer performance.
Reconfigurable Intelligent Surfaces in 6G Radio Localization: A Survey of Recent Developments, Opportunities, and Challenges
Authors: Anum Umer, Ivo Müürsepp, Muhammad Mahtab Alam, Henk Wymeersch
Date: 2023-12-12 00:00:00
Tasks: Survey

In this survey paper, we present an extensive review of the use of RIS in 6G radio localization, highlighting their pivotal role as a low-cost, energy-efficient technology that reshapes wireless communication and localization landscapes. Investigating the versatile capabilities of RIS, we explore their dynamic control over electromagnetic wave manipulation, including reflection, refraction, and transmission, which opens new horizons in diverse applications ranging from IOT connectivity to advanced mobile communication, and various innovative applications in Industry 4.0. Our comprehensive review provides an overview of RIS use in 6G radio localization, highlighting recent progress in RIS technology assisted localization. It focuses on key aspects, including network scenarios, transmission bands, deployment environments, and near-field operations. We discuss studies to examine the state-of-the-art RIS-assisted localization and optimization techniques and their performance evaluation matrices. In addition, we present a detailed taxonomy of RIS-assisted radio localization, emphasizing the rapid evolution and potential of RIS technology in non-line-of-sight scenarios as an alternative to traditional base stations. Based on the careful investigation of the reviewed studies, the survey also sheds light on future research directions, technical challenges, and limitations, offering a clear perspective on the integration and optimization of RIS in 6G networks for enhanced localization capabilities.
Improving DCE-MRI through unfolded low-rank + sparse optimisation
Authors: Ondřej Mokrý, Jiří Vitouš, Pavel Rajmic, Radovan Jiřík
Date: 2023-12-12 00:00:00

A method for perfusion imaging with DCE-MRI is developed based on two popular paradigms: the low-rank + sparse model for optimisation-based reconstruction, and the deep unfolding. A learnable algorithm derived from a proximal algorithm is designed with emphasis on simplicity and interpretability. The resulting deep network is trained and evaluated using a simulated measurement of a rat with a brain tumor, showing large performance gain over the classical low-rank + sparse baseline. Moreover, quantitative perfusion analysis is performed based on the reconstructed sequence, proving that even training based on a simple pixel-wise error can lead to significant improvement of the quality of the perfusion maps.
Permutation Entropy as a Conceptual Model to Analyse Brain Activity in Sleep
Authors: Alexander Edthofer, Iris Feldhammer, Thomas Fenzl, Andreas Körner, Matthias Kreuzer
Date: 2023-12-12 00:00:00

Sleep stage classification is a widely discussed topic, due to its importance in the diagnosis of sleep disorders, e.g. insomnia. Analysis of the brain activity during sleep is necessary to gain further insight into the processing that occurs in our brains. We want to use permutation entropy as a model for this analysis. Therefore, the signal processing in terms of electroencephalography is described. This results in a time discrete signal, that can be further processed by applying the method of permutation entropy, which is a modification of the Shannon entropy as a measure of information processing. The method is applied to 18 data sets, nine electroencephalography measurements of patients suffering from insomnia and nine of people without a sleep disorder. A strong correlation between the permutation entropy value and the sleep stages was found during the simulation runs. The results are analysed and presented using boxplot diagrams of the permutation entropy over the sleep stages. Furthermore, it is investigated that there is a steady decrease in the value when the patient is in a deeper sleep. This suggests that the method is a good parameter for sleep stage classification. Finally, we propose an extension of the conceptual model to other pathological conditions and also to the analysis of brain activity during surgery.
CSI-Based Cross-Domain Activity Recognition via Zero-Shot Prototypical Networks
Authors: Guillermo Diaz, Iker Sobron, Inaki Eizmendi, Iratxe landa, Manuel Velez
Date: 2023-12-12 00:00:00
Tasks: Activity Recognition, Cross-Domain Activity Recognition, Human Activity Recognition, Zero-Shot Learning

The cross-domain capability of wireless sensing is currently one of the major challenges on human activity recognition (HAR) based on the channel state information (CSI) of wireless signals. The difficulty of labeling samples from new domains has encouraged the use of few and zero shot strategies. In this context, prototype networks have attracted attention due to their reasonable cross-domain transferability. This paper presents a novel zero-shot prototype recurrent convolutional network that implements a zero-shot learning strategy for HAR via CSI. This method extracts the prototypes from an available source domain to classify unseen and unlabeled data from the target domain for the same or similar classes. The experiments have been developed using three datasets with real measurements, and the results include an inter-datasets evaluation. Overall, the results improve the state of the art and make it a promising solution for cross-domain HAR.
Ergodic Secrecy Rate Analysis for LEO Satellite Downlink Networks
Authors: Daeun Kim, Namyoon Lee
Date: 2023-12-12 00:00:00
Tasks: Point Processes

Satellite networks are recognized as an effective solution to ensure seamless connectivity worldwide, catering to a diverse range of applications. However, the broad coverage and broadcasting nature of satellite networks also expose them to security challenges. Despite these challenges, there is a lack of analytical understanding addressing the secrecy performance of these networks. This paper presents a secrecy rate analysis for downlink low Earth orbit (LEO) satellite networks by modeling the spatial distribution of satellites, users, and potential eavesdroppers as homogeneous Poisson point processes on concentric spheres. Specifically, we provide an analytical expression for the ergodic secrecy rate of a typical downlink user in terms of the satellite network parameters, fading parameters, and path-loss exponent. Simulation results show the exactness of the provided expressions and we find that optimal satellite altitude increases with eavesdropper density.
Sensing and Backscatter Communication Integration: Realizing Efficiency in Wireless Systems for IoT
Authors: Shayan Zargari, Diluka Galappaththige, Chintha Tellambura
Date: 2023-12-11 00:00:00
Tasks: Attribute

In an era driven by the Internet of Things (IoT) and rapid wireless communication advances, the synergy between sensing and backscatter communication (BackCom) has emerged as a frontier of research. This paper delves deep into the integration of sensing technologies with BackCom, a burgeoning field with significant implications for energy-efficient wireless systems. By tracing the historical developments and principles of BackCom, we establish the foundational understanding crucial for integrating advanced sensing methodologies. Our study adopts a mixed-method approach, combining quantitative analyses of system performances with qualitative assessments of various integration techniques. Furthermore, these integrated systems showcase enhanced adaptability in dynamic environments, a pivotal attribute for future IoT applications. These findings hold profound implications for industries focusing on smart technologies, as they underscore the potential for achieving both sustainable and efficient communication. Our research distinguishes itself by not only highlighting the benefits but also addressing the challenges faced in this integration, providing a comprehensive overview of the topic.
Inferring interaction networks from transcriptomic data: methods and applications
Authors: Vikram Singh
Date: 2023-12-12 00:00:00

Transcriptomic data is a treasure-trove in modern molecular biology, as it offers a comprehensive viewpoint into the intricate nuances of gene expression dynamics underlying biological systems. This genetic information must be utilised to infer biomolecular interaction networks that can provide insights into the complex regulatory mechanisms underpinning the dynamic cellular processes. Gene regulatory networks and protein-protein interaction networks are two major classes of such networks. This chapter thoroughly investigates the wide range of methodologies used for distilling insightful revelations from transcriptomic data that include association based methods (based on correlation among expression vectors), probabilistic models (using Bayesian and Gaussian models), and interologous methods. We reviewed different approaches for evaluating the significance of interactions based on the network topology and biological functions of the interacting molecules, and discuss various strategies for the identification of functional modules. The chapter concludes with highlighting network based techniques of prioritising key genes, outlining the centrality based, diffusion based and subgraph based methods. The chapter provides a meticulous framework for investigating transcriptomic data to uncover assembly of complex molecular networks for their adaptable analyses across a broad spectrum of biological domains.
Two types of pyramidal cells and their role in temporal processing
Authors: Anh Duong Vo, Elisabeth Abs, Pau Vilimelis Aceituno, Benjamin Friedrich Grewe, Katharina Anna Wilmes
Date: 2023-12-12 00:00:00

Recent work has provided new insights into the temporal specialization of Intratelencephalic (IT) and Pyramidal tract neurons (PT). However, functional and anatomical differences of IT and PT have not been connected yet. This perspective article contributes by highlighting empirical studies about the connectivity of IT and PT as well as their specialization in sensory and motor processing in diverse brain regions. We further review conceptual models that would align with the connectivity motif. We conclude that further research is needed to understand the role of the unidirectional connectivity from IT to PT in temporal processing and suggest concrete experiments.
Interactive effects of multiple stressors in coastal ecosystems
Authors: Shubham Krishna, Carsten Lemmen, Serra Örey, Jennifer Rehren, Julien Di Pane, Moritz Mathis, Miriam Püts, Sascha Hokamp, Himansu Pradhan, Matthias Hasenbein, Jürgen Scheffran, Kai Wirtz
Date: 2023-12-12 00:00:00

Coastal ecosystems are increasingly experiencing anthropogenic pressures such as climate heating, CO2 increase, metal and organic pollution, overfishing and resource extraction. Some resulting stressors are more direct like fisheries, others more indirect like ocean acidification, yet they jointly affect marine biota, communities and entire ecosystems. While single-stressor effects have been widely investigated, the interactive effects of multiple stressors on ecosystems are less researched. In this study, we review the literature on multiple stressors and their interactive effects in coastal environments across organisms. We classify the interactions into three categories: synergistic, additive, and antagonistic. We found phytoplankton and mollusks to be the most studied taxonomic groups. The stressor combinations of climate warming, ocean acidification, eutrophication, and metal pollution are the most critical for coastal ecosystems as they exacerbate adverse effects on physiological traits such as growth rate, basal respiration, and size. Phytoplankton appears to be most sensitive to interactions between metal and nutrient pollution. In nutrient-enriched environments, the presence of metals considerably affects the uptake of nutrients, and increases respiration costs and toxin production in phytoplankton. For mollusks, warming and low pH are the most lethal stressors. The combined effect of heat stress and ocean acidification leads to decreased growth rate, shell size, and acid-base regulation capacity in mollusks. However, for a holistic understanding of how coastal food webs will evolve with ongoing changes, we suggest more research on ecosystem-level responses. This can be achieved by combining in-situ observations from controlled environments (e.g. mesocosm experiments) with modelling approaches.
A picture guide to cancer progression and monotonic accumulation models: evolutionary assumptions, plausible interpretations, and alternative uses
Authors: Ramon Diaz-Uriarte, Iain G. Johnston
Date: 2023-12-11 00:00:00
Tasks: Virology

Cancer progression and monotonic accumulation models were developed to discover dependencies in the irreversible acquisition of binary traits from cross-sectional data. They have been used in computational oncology and virology but also in widely different problems such as malaria progression. These methods have been applied to predict future states of the system, identify routes of feature acquisition, and improve patient stratification, and they hold promise for evolutionary-based treatments. New methods continue to be developed. But these methods have shortcomings, which are yet to be systematically critiqued, regarding key evolutionary assumptions and interpretations. After an overview of the available methods, we focus on why inferences might not be about the processes we intend. Using fitness landscapes, we highlight difficulties that arise from bulk sequencing and reciprocal sign epistasis, from conflating lines of descent, path of the maximum, and mutational profiles, and from ambiguous use of the idea of exclusivity. We examine how the previous concerns change when bulk sequencing is explicitly considered, and underline opportunities for addressing dependencies due to frequency-dependent selection. This review identifies major standing issues, and should encourage the use of these methods in other areas with a better alignment between entities and model assumptions.
A review of mechanistic learning in mathematical oncology
Authors: John Metzcar, Catherine R. Jutzeler, Paul Macklin, Alvaro Köhn-Luque, Sarah C. Brüningk
Date: 2023-12-12 00:00:00

Mechanistic learning, the synergistic combination of knowledge-driven and data-driven modeling, is an emerging field. In particular, in mathematical oncology, the application of mathematical modeling to cancer biology and oncology, the use of mechanistic learning is growing. This review aims to capture the current state of the field and provide a perspective on how mechanistic learning may further progress in mathematical oncology. We highlight the synergistic potential of knowledge-driven mechanistic mathematical modeling and data-driven modeling, such as machine and deep learning. We point out similarities and differences regarding model complexity, data requirements, outputs generated, and interpretability of the algorithms and their results. Then, organizing combinations of knowledge- and data-driven modeling into four categories (sequential, parallel, intrinsic, and extrinsic mechanistic learning), we summarize a variety of approaches at the interface between purely data- and knowledge-driven models. Using examples predominantly from oncology, we discuss a range of techniques including physics-informed neural networks, surrogate model learning, and digital twins. We see that mechanistic learning, with its intentional leveraging of the strengths of both knowledge and data-driven modeling, can greatly impact the complex problems of oncology. Given the increasing ubiquity and impact of machine learning, it is critical to incorporate it into the study of mathematical oncology with mechanistic learning providing a path to that end. As the field of mechanistic learning advances, we aim for this review and proposed categorization framework to foster additional collaboration between the data- and knowledge-driven modeling fields. Further collaboration will help address difficult issues in oncology such as limited data availability, requirements of model transparency, and complex input data
An Association Test Based on Kernel-Based Neural Networks for Complex Genetic Association Analysis
Authors: Tingting Hou, Chang Jiang, Qing Lu
Date: 2023-12-06 00:00:00

The advent of artificial intelligence, especially the progress of deep neural networks, is expected to revolutionize genetic research and offer unprecedented potential to decode the complex relationships between genetic variants and disease phenotypes, which could mark a significant step toward improving our understanding of the disease etiology. While deep neural networks hold great promise for genetic association analysis, limited research has been focused on developing neural-network-based tests to dissect complex genotype-phenotype associations. This complexity arises from the opaque nature of neural networks and the absence of defined limiting distributions. We have previously developed a kernel-based neural network model (KNN) that synergizes the strengths of linear mixed models with conventional neural networks. KNN adopts a computationally efficient minimum norm quadratic unbiased estimator (MINQUE) algorithm and uses KNN structure to capture the complex relationship between large-scale sequencing data and a disease phenotype of interest. In the KNN framework, we introduce a MINQUE-based test to assess the joint association of genetic variants with the phenotype, which considers non-linear and non-additive effects and follows a mixture of chi-square distributions. We also construct two additional tests to evaluate and interpret linear and non-linear/non-additive genetic effects, including interaction effects. Our simulations show that our method consistently controls the type I error rate under various conditions and achieves greater power than a commonly used sequence kernel association test (SKAT), especially when involving non-linear and interaction effects. When applied to real data from the UK Biobank, our approach identified genes associated with hippocampal volume, which can be further replicated and evaluated for their role in the pathogenesis of Alzheimer's disease.
Optimal pair trading: consumption-investment problem
Authors: Yuri Kabanov, Aleksei Kozhevnikov
Date: 2023-12-11 00:00:00
Tasks: PAIR TRADING

We expose a simple solution of the consumption-investment problem pair trading. The proof is based on the remark that the HJB equation can be reduced to a linear parabolic equation solvable explicitly.
Intelligent Virtual Assistants with LLM-based Process Automation
Authors: Yanchu Guan, Dong Wang, Zhixuan Chu, Shiyu Wang, Feiyue Ni, Ruihua Song, Longfei Li, Jinjie Gu, Chenyi Zhuang
Date: 2023-12-04 00:00:00
Tasks: Language Modelling, Large Language Model

While intelligent virtual assistants like Siri, Alexa, and Google Assistant have become ubiquitous in modern life, they still face limitations in their ability to follow multi-step instructions and accomplish complex goals articulated in natural language. However, recent breakthroughs in large language models (LLMs) show promise for overcoming existing barriers by enhancing natural language processing and reasoning capabilities. Though promising, applying LLMs to create more advanced virtual assistants still faces challenges like ensuring robust performance and handling variability in real-world user commands. This paper proposes a novel LLM-based virtual assistant that can automatically perform multi-step operations within mobile apps based on high-level user requests. The system represents an advance in assistants by providing an end-to-end solution for parsing instructions, reasoning about goals, and executing actions. LLM-based Process Automation (LLMPA) has modules for decomposing instructions, generating descriptions, detecting interface elements, predicting next actions, and error checking. Experiments demonstrate the system completing complex mobile operation tasks in Alipay based on natural language instructions. This showcases how large language models can enable automated assistants to accomplish real-world tasks. The main contributions are the novel LLMPA architecture optimized for app process automation, the methodology for applying LLMs to mobile apps, and demonstrations of multi-step task completion in a real-world environment. Notably, this work represents the first real-world deployment and extensive evaluation of a large language model-based virtual assistant in a widely used mobile application with an enormous user base numbering in the hundreds of millions.