pwc-archive/papers-with-abstracts huggingface dataset:
Energy Efficiency Comparison of RIS Architectures in MISO Broadcast Channels
Authors: Mohammad Soleymani, Ignacio Santamaria, Eduard Jorswieck, Marco Di Renzo, Jesús Gutiérrez
Date: 2024-08-08 00:00:00

In this paper, we develop energy-efficient schemes for multi-user multiple-input single-output (MISO) broadcast channels (BCs), assisted by reconfigurable intelligent surfaces (RISs). To this end, we consider three architectures of RIS: locally passive diagonal (LP-D), globally passive diagonal (GP-D), and globally passive beyond diagonal (GP-BD). In a globally passive RIS, the power of the output signal of the RIS is not greater than its input power, but some RIS elements can amplify the signal. In a locally passive RIS, every element cannot amplify the incident signal. We show that these RIS architectures can substantially improve energy efficiency (EE) if the static power of the RIS elements is not too high. Moreover, GP-BD RIS, which has a higher complexity and static power than LP-D RIS and GP-D RIS, provides better spectral efficiency, but its EE performance highly depends on the static power consumption and may be worse than its diagonal counterparts.
Convolution Type of Metaplectic Cohen's Distribution Time-Frequency Analysis Theory, Method and Technology
Authors: Manjun Cui, Zhichao Zhang, Jie Han, Yunjie Chen, Chunzheng Cao
Date: 2024-08-08 00:00:00
Tasks: Denoising

The conventional Cohen's distribution can't meet the requirement of additive noises jamming signals high-performance denoising under the condition of low signal-to-noise ratio, it is necessary to integrate the metaplectic transform for non-stationary signal fractional domain time-frequency analysis. In this paper, we blend time-frequency operators and coordinate operator fractionizations to formulate the definition of the metaplectic Wigner distribution, based on which we integrate the generalized metaplectic convolution to address the unified representation issue of the convolution type of metaplectic Cohen's distribution (CMCD), whose special cases and essential properties are also derived. We blend Wiener filter principle and fractional domain filter mechanism of the metaplectic transform to design the least-squares adaptive filter method in the metaplectic Wigner distribution domain, giving birth to the least-squares adaptive filter-based CMCD whose kernel function can be adjusted with the input signal automatically to achieve the minimum mean-square error (MSE) denoising in Wigner distribution domain. We discuss the optimal symplectic matrices selection strategy of the proposed adaptive CMCD through the minimum MSE minimization modeling and solving. Some examples are also carried out to demonstrate that the proposed filtering method outperforms some state-of-the-arts including Wiener filter and fixed kernel functions-based or adaptive Cohen's distribution in noise suppression.
Adaptive Cohen's Class Time-Frequency Distribution
Authors: Manjun Cui, Zhichao Zhang, Jie Han, Yunjie Chen, Chunzheng Cao
Date: 2024-08-08 00:00:00
Tasks: Denoising

The fixed kernel function-based Cohen's class time-frequency distributions (CCTFDs) allow flexibility in denoising for some specific polluted signals. Due to the limitation of fixed kernel functions, however, from the view point of filtering they fail to automatically adjust the response according to the change of signal to adapt to different signal characteristics. In this letter, we integrate Wiener filter principle and the time-frequency filtering mechanism of CCTFD to design the least-squares adaptive filter method in the Wigner-Ville distribution (WVD) domain, giving birth to the least-squares adaptive filter-based CCTFD whose kernel function can be adjusted with the input signal automatically to achieve the minimum mean-square error denoising in the WVD domain. Some examples are also carried out to demonstrate that the proposed adaptive CCTFD outperforms some state-of-the-arts in noise suppression.
Resonant Beam Enabled DoA Estimation in Passive Positioning System
Authors: Yixuan Guo, Qingwei Jiang, Mengyuan Xu, Wen Fang, Qingwen Liu, Gang Yan, Qunhui Yang, Hai Lu
Date: 2024-08-08 00:00:00

The rapid advancement of the next generation of communications and internet of things (IoT) technologies has made the provision of location-based services for diverse devices an increasingly pressing necessity. Localizing devices with/without intelligent computing abilities, including both active and passive devices is essential, especially in indoor scenarios. For traditional RF positioning systems, aligning transmission signals and dealing with signal interference in complex environments are inevitable challenges. Therefore, this paper proposed a new passive positioning system, the RF-band resonant beam positioning system (RF-RBPS), which achieves energy concentration and beam alignment by amplifying echoes between the base station (BS) and the passive target (PT), without the need for complex channel estimation and time-consuming beamforming and provides high-precision direction of arrival (DoA) estimation for battery-free targets using the resonant mechanism. The direction information of the PT is estimated using the multiple signal classification (MUSIC) algorithm at the end of BS. The feasibility of the proposed system is validated through theoretical analysis and simulations. Results indicate that the proposed RF-RBPS surpasses RF-band active positioning system (RF-APS) in precision, achieving millimeter-level precision at 2m within an elevation angle of 35$^\circ$, and an error of less than 3cm at 2.5m within an elevation angle of 35$^\circ$.
Adapting to Reality: Over-the-Air Validation of AI-Based Receivers Trained with Simulated Channels
Authors: Riku Luostari, Dani Korpi, Mikko Honkala, Janne M. J. Huttunen
Date: 2024-08-08 00:00:00

Recent research shows that integrating artificial intelligence (AI) into wireless communication systems can significantly improve spectral efficiency. However, most AI-based receiver studies rely on simulated radio channel data for both training and validation, raising concerns about real-world generalization, which is vital for ensuring reliable field performance. In this study, we train DeepRx, a convolutional neural network (CNN)-based OFDM receiver, under various simulated channel scenarios and validate its performance over-the-air (OTA) using software-defined radio (SDR) technology in a small cell-type setup. To enhance receiver training, we investigate a randomized 3GPP TS38.901 channel model to diversify the training data, thereby improving performance over conventional receivers and matching or exceeding the performance of receivers trained on narrowly targeted channel models. These results demonstrate DeepRx's robust generalization capability and suggest that narrowly scoped, individual TS38.901 models can compromise both training and validation, underscoring the need for tailored channel models, careful training strategies, and OTA testing in learned receiver development.
Trigonometric Moments of a Generalized von Mises Distribution in 2-D Range-Only Tracking
Authors: Nikhil Sharma, Shovan Bhaumik, Ratnasingham Tharmarasa, Thia Kirubarajan
Date: 2024-08-07 00:00:00

A 2D range-only tracking scenario is non-trivial due to two main reasons. First, when the states to be estimated are in Cartesian coordinates, the uncertainty region is multi-modal. The second reason is that the probability density function of azimuth conditioned on range takes the form of a generalized von Mises distribution, which is hard to tackle. Even in the case of implementing a uni-modal Kalman filter, one needs expectations of trigonometric functions of conditional bearing density, which are not available in the current literature. We prove that the trigonometric moments (circular moments) of the azimuth density conditioned on range can be computed as an infinite series, which can be sufficiently approximated by relatively few terms in summation. The solution can also be generalized to any order of the moments. This important result can provide an accurate depiction of the conditional azimuth density in 2D range-only tracking geometries. We also present a simple optimization problem that results in deterministic samples of conditional azimuth density from the knowledge of its circular moments leading to an accurate filtering solution. The results are shown in a two-dimensional simulation, where the range-only sensor platform maneuvers to make the system observable. The results prove that the method is feasible in such applications.
A complete characterization of pairs of binary phylogenetic trees with identical $A_k$-alignments
Authors: Mirko Wilde, Mareike Fischer
Date: 2024-08-13 00:00:00
Tasks: 2k, 4k

Phylogenetic trees play a key role in the reconstruction of evolutionary relationships. Typically, they are derived from aligned sequence data (like DNA, RNA, or proteins) by using optimization criteria like, e.g., maximum parsimony (MP). It is believed that the latter is able to reconstruct the \enquote{true} tree, i.e., the tree that generated the data, whenever the number of substitutions required to explain the data with that tree is relatively small compared to the size of the tree (measured in the number $n$ of leaves of the tree, which represent the species under investigation). However, reconstructing the correct tree from any alignment first and foremost requires the given alignment to perform differently on the \enquote{correct} tree than on others. A special type of alignments, namely so-called $A_k$-alignments, has gained considerable interest in recent literature. These alignments consist of all binary characters (\enquote{sites}) which require precisely $k$ substitutions on a given tree. It has been found that whenever $k$ is small enough (in comparison to $n$), $A_k$-alignments uniquely characterize the trees that generated them. However, recent literature has left a significant gap between $n\leq 2k+2$ -- namely the cases in which no such characterization is possible -- and $n\geq 4k$ -- namely the cases in which this characterization works. It is the main aim of the present manuscript to close this gap, i.e., to present a full characterization of all pairs of trees that share the same $A_k$-alignment. In particular, we show that indeed every binary phylogenetic tree with $n$ leaves is uniquely defined by its $A_k$-alignments if $n\geq 2k+3$. By closing said gap, we also ensure that our result is optimal.
Adaptive Multilevel Stochastic Approximation of the Value-at-Risk
Authors: Stéphane Crépey, Noufel Frikha, Azar Louzi, Jonathan Spence
Date: 2024-08-12 00:00:00

Cr\'epey, Frikha, and Louzi (2023) introduced a multilevel stochastic approximation scheme to compute the value-at-risk of a financial loss that is only simulatable by Monte Carlo. The optimal complexity of the scheme is in $O({\varepsilon}^{-5/2})$, ${\varepsilon} > 0$ being a prescribed accuracy, which is suboptimal when compared to the canonical multilevel Monte Carlo performance. This suboptimality stems from the discontinuity of the Heaviside function involved in the biased stochastic gradient that is recursively evaluated to derive the value-at-risk. To mitigate this issue, this paper proposes and analyzes a multilevel stochastic approximation algorithm that adaptively selects the number of inner samples at each level, and proves that its optimal complexity is in $O({\varepsilon}^{-2}|\ln {\varepsilon}|^{5/2})$. Our theoretical analysis is exemplified through numerical experiments.
Stochastic Calculus for Option Pricing with Convex Duality, Logistic Model, and Numerical Examination
Authors: Zheng Cao
Date: 2024-08-11 00:00:00

This thesis explores the historical progression and theoretical constructs of financial mathematics, with an in-depth exploration of Stochastic Calculus as showcased in the Binomial Asset Pricing Model and the Continuous-Time Models. A comprehensive survey of stochastic calculus principles applied to option pricing is offered, highlighting insights from Peter Carr and Lorenzo Torricelli's ``Convex Duality in Continuous Option Pricing Models". This manuscript adopts techniques such as Monte-Carlo Simulation and machine learning algorithms to examine the propositions of Carr and Torricelli, drawing comparisons between the Logistic and Bachelier models. Additionally, it suggests directions for potential future research on option pricing methods.
A Probabilistic Approach for Queue Length Estimation Using License Plate Recognition Data: Considering Overtaking in Multi-lane Scenarios
Authors: Lyuzhou Luo, Hao Wu, Jiahao Liu, Keshuang Tang, Chaopeng Tan
Date: 2024-07-24 00:00:00
Tasks: License Plate Recognition

Multi-section license plate recognition (LPR) data provides input-output information and sampled travel times of the investigated link, serving as an ideal data source for lane-based queue length estimation in recent studies. However, most of these studies assumed the strict FIFO rule or a specific arrival process, thus ignoring the potential impact of overtaking and the variation of traffic flows, especially in multi-lane scenarios. To address this issue, we propose a probabilistic approach to derive the stochastic queue length by constructing a conditional probability model of no-delay arrival time (NAT), i.e., the arrival time of vehicles without experiencing any delay, based on multi-section LPR data. First, the NAT conditions for all vehicles are established based on upstream and downstream vehicle departure times and sequences. To reduce the computational dimensionality and complexity, a DP-based algorithm is developed for vehicle group partitioning based on potential interactions between vehicles. Then, the conditional probability of NATs of each vehicle group is derived and an MCMC sampling method is employed for calculation. Subsequently, the stochastic queue profile and maximum queue length for each cycle can be derived based on the NATs of vehicles. Eventually, to leverage the LPR data sufficiently, we extend our approach to multi-lane scenarios, where the problem can be converted to a weighted general exact coverage problem and solved by a backtracking algorithm with heuristics. Empirical and simulation experiments have shown that the proposed approach outperforms the state-of-the-art method, demonstrating significant improvements in accuracy and robustness across various traffic conditions, including different V/C ratios, matching rates, and FIFO violation rates. In addition, the performance of the proposed approach can be further improved by utilizing multi-lane LPR data.
Acto-myosin clusters as active units shaping living matter
Authors: Karsten Kruse, Rémi Berthoz, Luca Barberi, Anne-Cécile Reymann, Daniel Riveline
Date: 2024-08-09 00:00:00

Stress generation by the actin cytoskeleton shapes cells and tissues. Despite impressive progress in live imaging and quantitative physical descriptions of cytoskeletal network dynamics, the connection between processes at molecular scales and cell-scale spatio-temporal patterns is still unclear. Here we review studies reporting acto-myosin clusters of micrometer size and with lifetimes of several minutes in a large number of organisms ranging from fission yeast to humans. Such structures have also been found in reconstituted systems in vitro and in theoretical analysis of cytoskeletal dynamics. We propose that tracking these clusters can serve as a simple readout for characterising living matter. Spatio-temporal patterns of clusters could serve as determinants of morphogenetic processes that play similar roles in diverse organisms.
Optimal Strategy for Stabilizing Protein Folding Intermediates
Authors: Mengshou Wang, Liangrong Pengb, Baoguo Jia, Liu Hong
Date: 2024-07-28 00:00:00
Tasks: Protein Folding

To manipulate the protein population at certain functional state through chemical stabilizers is crucial for protein-related studies. It not only plays a key role in protein structure analysis and protein folding kinetics, but also affects protein functionality to a large extent and thus has wide applications in medicine, food industry, etc. However, due to concerns about side effects or financial costs of stabilizers, identifying optimal strategies for enhancing protein stability with a minimal amount of stabilizers is of great importance. Here we prove that either for the fixed terminal time (including both finite and infinite cases) or the free one, the optimal control strategy for stabilizing the folding intermediates with a linear strategy for stabilizer addition belongs to the class of Bang-Bang controls. The corresponding optimal switching time is derived analytically, whose phase diagram with respect to several key parameters is explored in detail. The Bang-Bang control will be broken when nonlinear strategies for stabilizer addition are adopted. Our current study on optimal strategies for protein stabilizers not only offers deep insights into the general picture of protein folding kinetics, but also provides valuable theoretical guidance on treatments for protein-related diseases in medicine.
Vertex-Edge Weighted Molecular Graphs: A study on topological indices and their relevance to physicochemical properties of drugs in use cancer treatment
Authors: Sezer Sorgun, Kahraman Birgin
Date: 2024-07-28 00:00:00
Tasks: Drug Design

Quantitative Structure-Property Relationship (QSPR) analysis plays a crucial role in predicting physicochemical properties and biological activities of pharmaceutical compounds, aiding in drug design and optimization. This study focuses on leveraging QSPR within the framework of vertex and edge weighted (VEW) molecular graphs, exploring their significance in drug research. By examining 48 drugs in used in the treatment of various cancers and their physicochemical properties, previous studies serve as a foundation for our research. Introducing a novel methodology for computing vertex and edge weights, exemplified by the drug Busulfan, we highlight the importance of considering atomic properties and inter-bond dynamics. Statistical analysis, employing linear regression models, reveals enhanced correlations between topological indices and physicochemical properties of drugs. Comparison with previous studies on unweighted molecular graphs highlights the enhancements achieved with our approach.
Counterions in RNA structure: Structural bioinformatics analysis to identify the role of Mg2+ ions in base pair formation
Authors: Swati Adhikari, Dhananjay Bhattacharyya, Parthajit Roy
Date: 2024-07-25 00:00:00

Contribution of metal ions on nucleic acids structures and functions is undeniable. Among the available metal ions like Na+, K+, Ca2+, Mg2+ etc., the role that play the Mg2+ ion is very significant related to the stability of the structures of RNA and this is quite well studied. But it is not possible to grasp the entire functionality of Mg2+ ion in the structure of RNA. So, to have a better understanding of the Mg-RNA complexes, in the present study, we have investigated 1541 non-redundant crystal structures of RNA and generated reports for various statistics related to these Mg-RNA complexes by computing base pairs and Mg2+ binding statistics. In this study, it has also been reported whether the presence of Mg2+ ions can alter the stability of base pairs or not by computing and comparing the base pairs stability. We noted that the Mg2+ ions do not affect the canonical base pair G:C W:WC while majority of the non-canonical base pair G:G W:HC, which is important also in DNA telomere structures, has Magnesium ion binding to O6 or N7 atoms of one of the Guanines.
Insights, opportunities and challenges provided by large cell atlases
Authors: Martin Hemberg, Federico Marini, Shila Ghazanfar, Ahmad Al Ajami, Najla Abassi, Benedict Anchang, Bérénice A. Benayoun, Yue Cao, Ken Chen, Yesid Cuesta-Astroz, Zach DeBruine, Calliope A. Dendrou, Iwijn De Vlaminck, Katharina Imkeller, Ilya Korsunsky, Alex R. Lederer, Pieter Meysman, Clint Miller, Kerry Mullan, Uwe Ohler, Nikolaos Patikas, Jonas Schuck, Jacqueline HY Siu, Timothy J. Triche Jr., Alex Tsankov, Sander W. van der Laan, Masanao Yajima, Jean Yang, Fabio Zanini, Ivana Jelic
Date: 2024-08-13 00:00:00

The field of single-cell biology is growing rapidly and is generating large amounts of data from a variety of species, disease conditions, tissues, and organs. Coordinated efforts such as CZI CELLxGENE, HuBMAP, Broad Institute Single Cell Portal, and DISCO, allow researchers to access large volumes of curated datasets. Although the majority of the data is from scRNAseq experiments, a wide range of other modalities are represented as well. These resources have created an opportunity to build and expand the computational biology ecosystem to develop tools necessary for data reuse, and for extracting novel biological insights. Here, we highlight achievements made so far, areas where further development is needed, and specific challenges that need to be overcome.
The evolution of systems biology and systems medicine: From mechanistic models to uncertainty quantification
Authors: Lingxia Qiao, Ali Khalilimeybodi, Nathaniel J Linden-Santangeli, Padmini Rangamani
Date: 2024-08-10 00:00:00
Tasks: Uncertainty Quantification

Understanding the mechanisms of interactions within cells, tissues, and organisms is crucial to driving developments across biology and medicine. Mathematical modeling is an essential tool for simulating biological systems and revealing biochemical regulatory mechanisms. Building on experiments, mechanistic models are widely used to describe small-scale intracellular networks and uncover biochemical mechanisms in healthy and diseased states. The rapid development of high-throughput sequencing techniques and computational tools has recently enabled models that span multiple scales, often integrating signaling, gene regulatory, and metabolic networks. These multiscale models enable comprehensive investigations of cellular networks and thus reveal previously unknown disease mechanisms and pharmacological interventions. Here, we review systems biology models from classical mechanistic models to larger, multiscale models that integrate multiple layers of cellular networks. We introduce several examples of models of hypertrophic cardiomyopathy, exercise, and cancer cell proliferation. Additionally, we discuss methods that increase the certainty and accuracy of model predictions. Integrating multiscale models has become a powerful tool for understanding disease and inspiring drug discoveries by incorporating omics data within the cell and across tissues and organisms.
Multiple Notch ligands in the synchronization of the segmentation clock
Authors: Marcos Wappner, Koichiro Uriu, Andrew C. Oates, Luis G. Morelli
Date: 2024-08-07 00:00:00
Tasks: Rhythm

Notch signaling is a ubiquitous and versatile intercellular signaling system that drives collective behaviors and pattern formation in biological tissues. During embryonic development, Notch is involved in generation of collective biochemical oscillations that form the vertebrate body segments, and its failure results in embryonic defects. Notch ligands of the Delta family are key components of this collective rhythm, but it is unclear how different Delta ligands with distinct properties contribute to relaying information among cells. Motivated by the zebrafish segmentation clock, in this work we propose a theory describing interactions between biochemical oscillators, where Notch receptor is bound by both oscillatory and nonoscillatory Delta ligands. Based on previous in vitro binding studies, we first consider Notch activation by Delta dimers. This hypothesis is consistent with experimental observations in conditions of perturbed Notch signaling. Then we test an alternative hypothesis where Delta monomers directly bind and activate Notch, and show that this second model can also describe the experimental observations. We show that these two hypotheses assign different roles for a non-oscillatory ligand, as a binding partner or as a baseline signal. Finally, we discuss experiments to distinguish between the two scenarios. Broadly, this work highlights how a multiplicity of ligands may be harnessed by a signaling system to generate versatile responses.