pwc-archive/papers-with-abstracts huggingface dataset:
Antenna Array Design for Mono-Static ISAC
Authors: Alexander Felix, Silvio Mandelli, Marcus Henninger, Stephan ten Brink
Date: 2024-02-19 00:00:00
Tasks: ISAC

Mono-static sensing operations in ISAC perform joint beamforming between transmitter and receiver. However, in contrast to pure radar systems, ISAC requires to fulfill communications tasks and to retain the corresponding design constraints for at least one half-duplex antenna array. This shifts the available degrees of freedom to the design of the second half-duplex array, that completes the mono-static sensing setup of the 6G ISAC system. Consequently, although it is still possible to achieve the gains foreseen by the radar sparse array literature, it is necessary to adapt these considerations to the new ISAC paradigm. In this work, we propose a model to evaluate the angular capabilities of a mono-static setup, constrained to the shape of the communications array and its topology requirements in wireless networks. Accordingly, we enhance the joint angular capabilities by utilizing a sparse element topology of the sensing array with the same number of elements. Our analysis is validated by simulation experiments, confirming the value of our model in providing system designers with a tool to drastically improve the trade-off between angular capabilities for sensing and the cost of the deployed hardware.
CRAP Part II: Clutter Removal with Continuous Acquisitions Under Phase Noise
Authors: Marcus Henninger, Silvio Mandelli, Artjom Grudnitsky, Stephan ten Brink
Date: 2024-02-19 00:00:00
Tasks: Integrated sensing and communication, ISAC

The mitigation of clutter is an important research branch in Integrated Sensing and Communication (ISAC), one of the emerging technologies of future cellular networks. In this work, we extend our previously introduced method Clutter Removal with Acquisitions Under Phase Noise (CRAP) by means to track clutter over time. This is necessary in scenarios that require high reliability but can change dynamically, like safety applications in factory floors. To that end, exponential smoothing is leveraged to process new measurements and previous clutter information in a unique matrix using the singular value decomposition, allowing adaptation to changing environments in an efficient way.We further propose a singular value threshold based on the Marchenko-Pastur distribution to select the meaningful clutter components. Results from both simulations and measurements show that continuously updating the clutter components with new acquisitions according to our proposed algorithm Smoothed CRAP (SCRAP) enables coping with dynamic clutter environments and facilitates the detection of sensing targets.
Automatic Radio Map Adaptation for Robust Localization with Dynamic Adversarial Learning
Authors: Lingyan Zhang, Junlin Huang, Tingting Zhang, Qinyu Zhang
Date: 2024-02-19 00:00:00

Wireless fingerprint-based localization has become one of the most promising technologies for ubiquitous location-aware computing and intelligent location-based services. However, due to RF vulnerability to environmental dynamics over time, continuous radio map updates are time-consuming and infeasible, resulting in severe accuracy degradation. To address this issue, we propose a novel approach of robust localization with dynamic adversarial learning, known as DadLoc which realizes automatic radio map adaptation by incorporating multiple robust factors underlying RF fingerprints to learn the evolving feature representation with the complicated environmental dynamics. DadLoc performs a finer-grained distribution adaptation with the developed dynamic adversarial adaptation network and quantifies the contributions of both global and local distribution adaptation in a dynamics-adaptive manner. Furthermore, we adopt the strategy of prediction uncertainty suppression to conduct source-supervised training, target-unsupervised training, and source-target dynamic adversarial adaptation which can trade off the environment adaptability and the location discriminability of the learned deep representation for safe and effective feature transfer across different environments. With extensive experimental results, the satisfactory accuracy over other comparative schemes demonstrates that the proposed DanLoc can facilitate fingerprint-based localization for wide deployments.
Hybrid Online-Offline Learning for Task Offloading in Mobile Edge Computing Systems
Authors: Muhammad Sohaib, Sang-Woon Jeon, Wei Yu
Date: 2024-02-19 00:00:00
Tasks: Edge-computing

We consider a multi-user multi-server mobile edge computing (MEC) system, in which users arrive on a network randomly over time and generate computation tasks, which will be computed either locally on their own computing devices or be offloaded to one of the MEC servers. Under such a dynamic network environment, we propose a novel task offloading policy based on hybrid online-offline learning, which can efficiently reduce the overall computation delay and energy consumption only with information available at nearest MEC servers from each user. We provide a practical signaling and learning framework that can train deep neural networks for both online and offline learning and can adjust its offloading policy based on the queuing status of each MEC server and network dynamics. Numerical results demonstrate that the proposed scheme significantly reduces the average computation delay for a broad class of network environments compared to the conventional offloading methods. It is further shown that the proposed hybrid online-offline learning framework can be extended to a general cost function reflecting both delay and energy-dependent metrics.
Disturbance Ratio for Optimal Multi-Event Classification in Power Distribution Networks
Authors: M. D. Borrás, J. C. Bravo, J. C. Montaño
Date: 2024-02-18 00:00:00

This paper presents an effective approach to identify power quality events based on IEEE Std 1159-2009 caused by intermittent power sources like those of renewable energy. An efficient characterization of these disturbances is granted by the use of two useful wavelet based indices. For this purpose, a wavelet-based Global Disturbance Ratio index (GDR), defined through its instantaneous precursor (Instantaneous Transient Disturbance index ITD(t)), is used in power distribution networks (PDN) under steady-state and/or transient conditions. An intelligent disturbance classification is done using a Support Vector Machine (SVM) with a minimum input vector based on the GDR index. The effectiveness of the proposed technique is validated using a real-time experimental system with single events and multi-events signals.
Reliable long timescale decision-directed channel estimation for OFDM system
Authors: Xun Wang, Xin Xie, Cunqing Hua, Jianan Hong, Pengwenlong Gu
Date: 2024-02-18 00:00:00

Decision-directed channel estimation (DDCE) is one kind of blind channel estimation method that tracks the channel blindly by an iterative algorithm without relying on the pilots, which can increase the utilization of wireless resource. However, one major problem of DDCE is the performance degradation caused by error accumulation during the tracking process. In this paper, we propose an reliable DDCE (RDDCE) scheme for an OFDM-based communication system in the time-varying deep fading environment. By combining the conventional DDCE and discrete Fourier transform (DFT) channel estimation method, the proposed RDDCE scheme selects the reliable estimated channels on the subcarriers which are less affected by deep fading, and then estimates the channel based on the selected subcarriers by an extended DFT channel estimation where the indices of selected subcarriers are not distributed evenly. Simulation results show that RRDCE can alleviate the performance degradation effectively, track the channel with high accuracy on a long time scale, and has good performance under time-varying and noisy channel conditions.
Thin-film Lithium Niobate on Insulator Surface Acoustic Wave Devices for 6G Centimeter Bands
Authors: Tzu-Hsuan Hsu, Joshua Campbell, Jack Kramer, Sinwoo Cho, Zhi-Qiang Lee, Ming-Huang Li, Ruochen Lu
Date: 2024-02-18 00:00:00

In this work, we investigate the frequency scaling of shear-horizontal (S.H.) surface acoustic wave (SAW) resonators based on a lithium niobate on insulator (LNOI) substrate into the centimeter bands for 6G wireless systems. Prototyped resonators with wavelengths ranging between 240 nm and 400 nm were fabricated, and the experimental results exhibit a successful frequency scaling between 9.05 and 13.37 GHz. However, a noticeable performance degradation can be observed as the resonance frequency (fs) scales. Such an effect is expected to be caused by non-ideal helec/{\lambda} for smaller {\lambda} devices. The optimized LNOI SH-SAW with a {\lambda} of 400 nm exhibits a fs of 9.05 GHz, a keff2 of 15%, Qmax of 213 and a FoM of 32, which indicates a successful implementation for device targeting centimeter bands.
A Self-Healing Magnetic-Array-Type Current Sensor with Data-Driven Identification of Abnormal Magnetic Measurement Units
Authors: Xiaohu Liu, Kang Ma, Jian Liu, Wei Zhao, Lisha Peng, Songling Huang, Shisong Li
Date: 2024-02-18 00:00:00

Magnetic-array-type current sensors have garnered increasing popularity owing to their notable advantages, including broadband functionality, a large dynamic range, cost-effectiveness, and compact dimensions. However, the susceptibility of the measurement error of one or more magnetic measurement units (MMUs) within the current sensor to drift significantly from the nominal value due to environmental factors poses a potential threat to the measurement accuracy of the current sensor. In light of the need to ensure sustained measurement accuracy over the long term, this paper proposes an innovative self-healing approach rooted in cyber-physics correlation. This approach aims to identify MMUs exhibiting abnormal measurement errors, allowing for the exclusive utilization of the remaining unaffected MMUs in the current measurement process. To achieve this, principal component analysis (PCA) is employed to discern the primary component, arising from fluctuations of the measured current, from the residual component, attributed to the drift in measurement error. This analysis is conducted by scrutinizing the measured data obtained from the MMUs. Subsequently, the squared prediction error (SPE) statistic (also called $Q$ statistic) is deployed to individually identify any MMU displaying abnormal behavior. The experimental results demonstrate the successful online identification of abnormal MMUs without the need for a standard magnetic field sensor. By eliminating the contributions from the identified abnormal MMUs, the accuracy of the current measurement is effectively preserved.
Experimental Study of Spatial Statistics for Ultra-Reliable Communications
Authors: Tobias Kallehauge, Anders E. Kalør, Fengchun Zhang, Petar Popovski
Date: 2024-02-17 00:00:00
Tasks: Spatial Interpolation

This paper presents an experimental validation for prediction of rare fading events using channel distribution information (CDI) maps that predict channel statistics from measurements acquired at surrounding locations using spatial interpolation. Using experimental channel measurements from 127 locations, we demonstrate the use case of providing statistical guarantees for rate selection in ultra-reliable low-latency communication (URLLC) using CDI maps. By using only the user location and the estimated map, we are able to meet the desired outage probability with a probability between 93.6-95.6% targeting 95%. On the other hand, a model-based baseline scheme that assumes Rayleigh fading meets the target outage requirement with a probability of 77.2%. The results demonstrate the practical relevance of CDI maps for resource allocation in URLLC.
Extinction, persistence and growing in a degenerate logistic model with impulses
Authors: Willian Cintra, Zhigui Lin, Carlos Alberto Santos, Phyu Phyu Win
Date: 2024-02-19 00:00:00

This paper deals with an impulsive degenerate logistic model, where pulses are introduced for modeling interventions or disturbances, and degenerate logistic term may describe refugees or protections zones for the species. Firstly, the principal eigenvalue depending on impulse rate, which is regarded as a threshold value, is introduced and characterized. Secondly, the asymptotic behavior of the population is fully investigated and sufficient conditions for the species to be extinct, persist or grow unlimitedly are given. Our results extend those of well-understood logistic and Malthusian models. Finally, numerical simulations emphanzise our theoretical results highlighting that medium impulse rate is more favorable for species to persist, small rate results in extinction and large rate leads the species to an unlimited growth.
Identifying circular orders for blobs in phylogenetic networks
Authors: John A. Rhodes, Hector Banos, Jingcheng Xu, Cécile Ané
Date: 2024-02-18 00:00:00

Interest in the inference of evolutionary networks relating species or populations has grown with the increasing recognition of the importance of hybridization, gene flow and admixture, and the availability of large-scale genomic data. However, what network features may be validly inferred from various data types under different models remains poorly understood. Previous work has largely focused on level-1 networks, in which reticulation events are well separated, and on a general network's tree of blobs, the tree obtained by contracting every blob to a node. An open question is the identifiability of the topology of a blob of unknown level. We consider the identifiability of the circular order in which subnetworks attach to a blob, first proving that this order is well-defined for outer-labeled planar blobs. For this class of blobs, we show that the circular order information from 4-taxon subnetworks identifies the full circular order of the blob. Similarly, the circular order from 3-taxon rooted subnetworks identifies the full circular order of a rooted blob. We then show that subnetwork circular information is identifiable from certain data types and evolutionary models. This provides a general positive result for high-level networks, on the identifiability of the ordering in which taxon blocks attach to blobs in outer-labeled planar networks. Finally, we give examples of blobs with different internal structures which cannot be distinguished under many models and data types.
On some dynamical features of the complete Moran model for neutral evolution in the presence of mutations
Authors: Giuseppe Gaeta
Date: 2024-02-20 00:00:00

We present a version of the classical Moran model, in which mutations are taken into account; the possibility of mutations was introduced by Moran in his seminal paper, but it is more often overlooked in discussing the Moran model. For this model, fixation is prevented by mutation, and we have an ergodic Markov process; the equilibrium distribution for such a process was determined by Moran. The problems we consider in this paper are those of first hitting either one of the ``pure'' (uniform population) states, depending on the initial state; and that of first hitting times. The presence of mutations leads to a nonlinear dependence of the hitting probabilities on the initial state, and to a larger mean hitting time compared to the mutation-free process (in which case hitting corresponds to fixation of one of the alleles).
CowScape: Quantitative reconstruction of the conformational landscape of biological macromolecules from cryo-EM data
Authors: Felix Lambrecht, Andreas Kröpelin, Mario Lüttich, Michael Habeck, David Haselbach, Holger Stark
Date: 2024-02-18 00:00:00
Tasks: Density Estimation, image-classification, Image Classification

Cryo-EM data processing typically focuses on the structure of the main conformational state under investigation and discards images that belong to other states. This approach can reach atomic resolution, but ignores vast amounts of valuable information about the underlying conformational ensemble and its dynamics. CowScape analyzes an entire cryo-EM dataset and thereby obtains a quantitative description of structural variability of macromolecular complexes that represents the biochemically relevant conformational space. By combining extensive image classification with principal component analysis (PCA) of the classified 3D volumes and kernel density estimation, CowScape can be used as a quantitative tool to analyze this variability. PCA projects all 3D structures along the major modes spanning a low-dimensional space that captures a large portion of structural variability. The number of particle images in a given state can be used to calculate an energy landscape based on kernel density estimation and Boltzmann inversion. By revealing allosteric interactions in macromolecular complexes, CowScape allows us to distinguish and interpret dynamic changes in macromolecular complexes during function and regulation.
On the importance of assessing topological convergence in Bayesian phylogenetic inference
Authors: Marius Brusselmans, Luiz Max Carvalho, Samuel L. Hong, Jiansi Gao, Frederick A. Matsen IV, Andrew Rambaut, Philippe Lemey, Marc A. Suchard, Gytis Dudas, Guy Baele
Date: 2024-02-18 00:00:00
Tasks: Diagnostic

Modern phylogenetics research is often performed within a Bayesian framework, using sampling algorithms such as Markov chain Monte Carlo (MCMC) to approximate the posterior distribution. These algorithms require careful evaluation of the quality of the generated samples. Within the field of phylogenetics, one frequently adopted diagnostic approach is to evaluate the effective sample size (ESS) and to investigate trace graphs of the sampled parameters. A major limitation of these approaches is that they are developed for continuous parameters and therefore incompatible with a crucial parameter in these inferences: the tree topology. Several recent advancements have aimed at extending these diagnostics to topological space. In this reflection paper, we present two case studies - one on Ebola virus and one on HIV - illustrating how these topological diagnostics can contain information not found in standard diagnostics, and how decisions regarding which of these diagnostics to compute can impact inferences regarding MCMC convergence and mixing. Our results show the importance of running multiple replicate analyses and of carefully assessing topological convergence using the output of these replicate analyses. To this end, we illustrate different ways of assessing and visualizing the topological convergence of these replicates. Given the major importance of detecting convergence and mixing issues in Bayesian phylogenetic analyses, the lack of a unified approach to this problem warrants further action, especially now that additional tools are becoming available to researchers.
Predicting Breast Cancer Phenotypes from Single-cell RNA-seq Data Using CloudPred
Authors: Hossein Moghimianavval, Baharan Meghdadi, Tasmine Clement, Man I Wu
Date: 2024-02-17 00:00:00

Numerous tools have been recently developed to predict disease phenotypes using single-cell RNA sequencing (RNA-seq) data. CloudPred is an end-to-end differentiable learning algorithm coupled with a biologically informed mixture model, originally tested on lupus data. This study extends CloudPred's applications to breast cancer disease phenotype prediction to test its robustness and applicability on untested and unrelated biological data. When applying a breast cancer single-cell RNA seq dataset, CloudPred achieved an area under the ROC curve (AUC) of 1 in predicting cancer status and performed better than a linear and Deepset models.
Toward a Team of AI-made Scientists for Scientific Discovery from Gene Expression Data
Authors: Haoyang Liu, Yijiang Li, Jinglin Jian, Yuxuan Cheng, Jianrong Lu, Shuyi Guo, Jinglei Zhu, Mianchen Zhang, Miantong Zhang, Haohan Wang
Date: 2024-02-15 00:00:00
Tasks: Language Modeling, Language Modelling, Large Language Model, scientific discovery

Machine learning has emerged as a powerful tool for scientific discovery, enabling researchers to extract meaningful insights from complex datasets. For instance, it has facilitated the identification of disease-predictive genes from gene expression data, significantly advancing healthcare. However, the traditional process for analyzing such datasets demands substantial human effort and expertise for the data selection, processing, and analysis. To address this challenge, we introduce a novel framework, a Team of AI-made Scientists (TAIS), designed to streamline the scientific discovery pipeline. TAIS comprises simulated roles, including a project manager, data engineer, and domain expert, each represented by a Large Language Model (LLM). These roles collaborate to replicate the tasks typically performed by data scientists, with a specific focus on identifying disease-predictive genes. Furthermore, we have curated a benchmark dataset to assess TAIS's effectiveness in gene identification, demonstrating our system's potential to significantly enhance the efficiency and scope of scientific exploration. Our findings represent a solid step towards automating scientific discovery through large language models.
Effective Kinetics of Chemical Reaction Networks
Authors: Tomoharu Suda
Date: 2024-02-19 00:00:00

Chemical reaction network theory is a powerful framework to describe and analyze chemical systems. While much about the concentration profile in an equilibrium state can be determined in terms of the graph structure, the overall reaction's time evolution depends on the network's kinetic rate function. In this article, we consider the problem of the effective kinetics of a chemical reaction network regarded as a conversion system from the feeding species to products. We define the notion of effective kinetics as a partial solution of a system of non-autonomous ordinary differential equations determined from a chemical reaction network. Examples of actual calculations include the Michaelis-Menten mechanism, for which it is confirmed that our notion of effective kinetics yields the classical formula. Further, we introduce the notion of straight-line solutions of non-autonomous ordinary differential equations to formalize the situation where a well-defined reaction rate exists and consider its relation with the quasi-stationary state approximation used in microkinetics. Our considerations here give a unified framework to formulate the reaction rate of chemical reaction networks.