pwc-archive/papers-with-abstracts huggingface dataset:
Joint Optimization of Communication Enhancement and Location Privacy Protection in RIS-Assisted Underwater Communication System
Authors: Ziqi Chen, Jun Du, Chunxiao Jiang, Zhu Han
Date: 2024-11-30 00:00:00

As the demand for underwater communication continues to grow, underwater acoustic RIS (UARIS), as an emerging paradigm in underwater acoustic communication (UAC), can significantly improve the communication rate of underwater acoustic systems. However, in open underwater environments, the location of the source node is highly susceptible to being obtained by eavesdropping nodes through correlation analysis, leading to the issue of location privacy in underwater communication systems, which has been overlooked by many studies. To enhance underwater communication and protect location privacy, we propose a novel UARIS architecture integrated with an artificial noise (AN) module. This architecture not only improves communication quality but also introduces noise to interfere with the eavesdroppers' attempts to locate the source node. We derive the Cram\'er-Rao Lower Bound (CRLB) for the localization method deployed by the eavesdroppers and, based on this, model the UARIS-assisted communication scenario as a multi-objective optimization problem. This problem optimizes transmission beamforming, reflective precoding, and noise factors to maximize communication performance and location privacy protection. To efficiently solve this non-convex optimization problem, we develop an iterative algorithm based on fractional programming. Simulation results validate that the proposed system significantly enhances data transmission rates while effectively maintaining the location privacy of the source node in UAC systems.
Differentiable High-Order Markov Models for Spectrum Prediction
Authors: Vincent Corlay, Tatsuya Nakazato, Kanako Yamaguchi, Akinori Nakajima
Date: 2024-11-30 00:00:00
Tasks: Prediction

The advent of deep learning and recurrent neural networks revolutionized the field of time-series processing. Therefore, recent research on spectrum prediction has focused on the use of these tools. However, spectrum prediction, which involves forecasting wireless spectrum availability, is an older field where many "classical" tools were considered around the 2010s, such as Markov models. This work revisits high-order Markov models for spectrum prediction in dynamic wireless environments. We introduce a framework to address mismatches between sensing length and model order as well as state-space complexity arising with large order. Furthermore, we extend this Markov framework by enabling fine-tuning of the probability transition matrix through gradient-based supervised learning, offering a hybrid approach that bridges probabilistic modeling and modern machine learning. Simulations on real-world Wi-Fi traffic demonstrate the competitive performance of high-order Markov models compared to deep learning methods, particularly in scenarios with constrained datasets containing outliers.
Joint Coverage and Electromagnetic Field Exposure Analysis in Downlink and Uplink for RIS-assisted Networks
Authors: Lin Chen, Ahmed Elzanaty, Mustafa A Kishk, Ying-Jun Angela Zhang
Date: 2024-11-30 00:00:00

Reconfigurable intelligent surfaces (RISs) have shown the potential to improve signal-to-interference-plus-noise ratio (SINR) related coverage, especially at high-frequency communications. However, assessing electromagnetic filed exposure (EMFE) and establishing EMFE regulations in RIS-assisted large-scale networks are still open issues. This paper proposes a framework to characterize SINR and EMFE in such networks for downlink and uplink scenarios. Particularly, we carefully consider the association rule with the presence of RISs, accurate antenna pattern at base stations (BSs), fading model, and power control mechanism at mobile devices in the system model. Under the proposed framework, we derive the marginal and joint distributions of SINR and EMFE in downlink and uplink, respectively. The first moment of EMFE is also provided. Additionally, we design the compliance distance (CD) between a BS/RIS and a user to comply with the EMFE regulations. To facilitate efficient identification, we further provide approximate closed-form expressions for CDs. From numerical results of the marginal distributions, we find that in the downlink scenario, deploying RISs may not always be beneficial, as the improved SINR comes at the cost of increased EMFE. However, in the uplink scenario, RIS deployment is promising to enhance coverage while still maintaining EMFE compliance. By simultaneously evaluating coverage and compliance metrics through joint distributions, we demonstrate the feasibility of RISs in improving uplink and downlink performance. Insights from this framework can contribute to establishing EMFE guidelines and achieving a balance between coverage and compliance when deploying RISs.
Efficient Off-Grid Bayesian Parameter Estimation for Kronecker-Structured Signals
Authors: Yanbin He, Geethu Joseph
Date: 2024-11-30 00:00:00
Tasks: Denoising, parameter estimation

This work studies the problem of jointly estimating unknown parameters from Kronecker-structured multidimensional signals, which arises in applications like intelligent reflecting surface (IRS)-aided channel estimation. Exploiting the Kronecker structure, we decompose the estimation problem into smaller, independent subproblems across each dimension. Each subproblem is posed as a sparse recovery problem using basis expansion and solved using a novel off-grid sparse Bayesian learning (SBL)-based algorithm. Additionally, we derive probabilistic error bounds for the decomposition, quantify its denoising effect, and provide convergence analysis for off-grid SBL. Our simulations show that applying the algorithm to IRS-aided channel estimation improves accuracy and runtime compared to state-of-the-art methods through the low-complexity and denoising benefits of the decomposition step and the high-resolution estimation capabilities of off-grid SBL.
Spectral Efficiency of Low Earth Orbit Satellite Constellations
Authors: Cuneyd Ozturk, Dongning Guo, Randall A. Berry, Michael L. Honig
Date: 2024-11-29 00:00:00

This paper investigates the maximum downlink spectral efficiency of low earth orbit (LEO) constellations. Spectral efficiency, in this context, refers to the sum rate of the entire network per unit spectrum per unit area on the earth's surface. For practicality all links employ single-user codebooks and treat interference as noise. To estimate the maximum achievable spectral efficiency, we propose and analyze a regular configuration, which deploys satellites and ground terminals in hexagonal lattices. Additionally, for wideband networks with arbitrary satellite configurations, we introduce a subband allocation algorithm aimed at maximizing the overall spectral efficiency. Simulation results indicate that the regular configuration is more efficient than random configurations. As the number of randomly placed satellites increases within an area, the subband allocation algorithm achieves a spectral efficiency that approaches the spectral efficiency achieved by the regular configuration. Further improvements are demonstrated by reconfiguring associations so that nearby transmitters avoid pointing to the same area.
Frequency-selective beamforming and single-shot beam training with dynamic metasurface antennas
Authors: Nitish Vikas Deshpande, Joseph Carlson, Miguel R. Castellanos, Robert W. Heath Jr
Date: 2024-11-29 00:00:00

Dynamic metasurface antennas (DMAs) beamform through low-powered components that enable reconfiguration of each radiating element. Previous research on a single-user multiple-input-single-output (MISO) system with a dynamic metasurface antenna at the transmitter has focused on maximizing the beamforming gain at a fixed operating frequency. The DMA, however, has a frequency-selective response that leads to magnitude degradation for frequencies away from the resonant frequency of each element. This causes reduction in beamforming gain if the DMA only operates at a fixed frequency. We exploit the frequency reconfigurability of the DMA to dynamically optimize both the operating frequency and the element configuration, maximizing the beamforming gain. We leverage this approach to develop a single-shot beam training procedure using a DMA sub-array architecture that estimates the receiver's angular direction with a single OFDM pilot signal. We evaluate the beamforming gain performance of the DMA array using the receiver's angular direction estimate obtained from beam training. Our results show that it is sufficient to use a limited number of resonant frequency states to do both beam training and beamforming instead of using an infinite resolution DMA beamformer.
Virtual Sensing to Enable Real-Time Monitoring of Inaccessible Locations \& Unmeasurable Parameters
Authors: Kazuma Kobayashi, Farid Ahmed, Syed Bahauddin Alam
Date: 2024-11-28 00:00:00

Real-time monitoring of critical parameters is essential for energy systems' safe and efficient operation. However, traditional sensors often fail and degrade in harsh environments where physical sensors cannot be placed (inaccessible locations). In addition, there are important parameters that cannot be directly measured by sensors. We need machine learning (ML)-based real-time monitoring in those remote locations to ensure system operations. However, traditional ML models struggle to process continuous sensor profile data to fit model requirements, leading to the loss of spatial relationships. Another challenge for real-time monitoring is ``dataset shift" and the need for frequent retraining under varying conditions, where extensive retraining prohibits real-time inference. To resolve these challenges, this study addressed the limitations of real-time monitoring methods by enabling monitoring in locations where physical sensors are impractical to deploy. Our proposed approach, utilizing Multi-Input Operator Network virtual sensors, leverages deep learning to seamlessly integrate diverse data sources and accurately predict key parameters in real-time without the need for additional physical sensors. The approach's effectiveness is demonstrated through thermal-hydraulic monitoring in a nuclear reactor subchannel, achieving remarkable accuracy.
Approximate Fiber Product: A Preliminary Algebraic-Geometric Perspective on Multimodal Embedding Alignment
Authors: Dongfang Zhao
Date: 2024-11-30 00:00:00
Tasks: Image-text Retrieval, Representation Learning, Text Retrieval

Multimodal tasks, such as image-text retrieval and generation, require embedding data from diverse modalities into a shared representation space. Aligning embeddings from heterogeneous sources while preserving shared and modality-specific information is a fundamental challenge. This paper provides an initial attempt to integrate algebraic geometry into multimodal representation learning, offering a foundational perspective for further exploration. We model image and text data as polynomials over discrete rings, \( \mathbb{Z}_{256}[x] \) and \( \mathbb{Z}_{|V|}[x] \), respectively, enabling the use of algebraic tools like fiber products to analyze alignment properties. To accommodate real-world variability, we extend the classical fiber product to an approximate fiber product with a tolerance parameter \( \epsilon \), balancing precision and noise tolerance. We study its dependence on \( \epsilon \), revealing asymptotic behavior, robustness to perturbations, and sensitivity to embedding dimensionality. Additionally, we propose a decomposition of the shared embedding space into orthogonal subspaces, \( Z = Z_s \oplus Z_I \oplus Z_T \), where \( Z_s \) captures shared semantics, and \( Z_I \), \( Z_T \) encode modality-specific features. This decomposition is geometrically interpreted via manifolds and fiber bundles, offering insights into embedding structure and optimization. This framework establishes a principled foundation for analyzing multimodal alignment, uncovering connections between robustness, dimensionality allocation, and algebraic structure. It lays the groundwork for further research on embedding spaces in multimodal learning using algebraic geometry.
The Dimension of the Disguised Toric Locus of a Reaction Network
Authors: Gheorghe Craciun, Abhishek Deshpande, Jiaxin Jin
Date: 2024-12-03 00:00:00

Under mass-action kinetics, complex-balanced systems emerge from biochemical reaction networks and exhibit stable and predictable dynamics. For a reaction network $G$, the associated dynamical system is called $\textit{disguised toric}$ if it can yield a complex-balanced realization on a possibly different network $G_1$. This concept extends the robust properties of toric systems to those that are not inherently toric. In this work, we study the $\textit{disguised toric locus}$ of a reaction network - i.e., the set of positive rate constants that make the corresponding mass-action system disguised toric. Our primary focus is to compute the exact dimension of this locus. We subsequently apply our results to Thomas-type and circadian clock models.
Multi-objective Deep Learning: Taxonomy and Survey of the State of the Art
Authors: Sebastian Peitz, Sedjro Salomon Hotegni
Date: 2024-12-02 00:00:00
Tasks: Deep Learning, Multi-Task Learning

Simultaneously considering multiple objectives in machine learning has been a popular approach for several decades, with various benefits for multi-task learning, the consideration of secondary goals such as sparsity, or multicriteria hyperparameter tuning. However - as multi-objective optimization is significantly more costly than single-objective optimization - the recent focus on deep learning architectures poses considerable additional challenges due to the very large number of parameters, strong nonlinearities and stochasticity. This survey covers recent advancements in the area of multi-objective deep learning. We introduce a taxonomy of existing methods - based on the type of training algorithm as well as the decision maker's needs - before listing recent advancements, and also successful applications. All three main learning paradigms supervised learning, unsupervised learning and reinforcement learning are covered, and we also address the recently very popular area of generative modeling.
Generalized EXTRA stochastic gradient Langevin dynamics
Authors: Mert Gurbuzbalaban, Mohammad Rafiqul Islam, Xiaoyu Wang, Lingjiong Zhu
Date: 2024-12-02 00:00:00

Langevin algorithms are popular Markov Chain Monte Carlo methods for Bayesian learning, particularly when the aim is to sample from the posterior distribution of a parametric model, given the input data and the prior distribution over the model parameters. Their stochastic versions such as stochastic gradient Langevin dynamics (SGLD) allow iterative learning based on randomly sampled mini-batches of large datasets and are scalable to large datasets. However, when data is decentralized across a network of agents subject to communication and privacy constraints, standard SGLD algorithms cannot be applied. Instead, we employ decentralized SGLD (DE-SGLD) algorithms, where Bayesian learning is performed collaboratively by a network of agents without sharing individual data. Nonetheless, existing DE-SGLD algorithms induce a bias at every agent that can negatively impact performance; this bias persists even when using full batches and is attributable to network effects. Motivated by the EXTRA algorithm and its generalizations for decentralized optimization, we propose the generalized EXTRA stochastic gradient Langevin dynamics, which eliminates this bias in the full-batch setting. Moreover, we show that, in the mini-batch setting, our algorithm provides performance bounds that significantly improve upon those of standard DE-SGLD algorithms in the literature. Our numerical results also demonstrate the efficiency of the proposed approach.
A model of strategic sustainable investment
Authors: Tiziano De Angelis, Caio César Graciani Rodrigues, Peter Tankov
Date: 2024-12-01 00:00:00
Tasks: model

We study a problem of optimal irreversible investment and emission reduction formulated as a nonzero-sum dynamic game between an investor with environmental preferences and a firm. The game is set in continuous time on an infinite-time horizon. The firm generates profits with a stochastic dynamics and may spend part of its revenues towards emission reduction (e.g., renovating the infrastructure). The firm's objective is to maximize the discounted expectation of a function of its profits. The investor participates in the profits, may decide to invest to support the firm's production capacity and uses a profit function which accounts for both financial and environmental factors. Nash equilibria of the game are obtained via a system of variational inequalities. We formulate a general verification theorem for this system in a diffusive setup and construct an explicit solution in the zero-noise limit. Our explicit results and numerical approximations show that both the investor's and the firm's optimal actions are triggered by moving boundaries that increase with the total amount of emission abatement.
Electricity Price Prediction Using Multi-Kernel Gaussian Process Regression Combined with Kernel-Based Support Vector Regression
Authors: Abhinav Das, Stephan Schlüter, Lorenz Schneider
Date: 2024-11-28 00:00:00
Tasks: GPR, Prediction, regression

This paper presents a new hybrid model for predicting German electricity prices. The algorithm is based on combining Gaussian Process Regression (GPR) and Support Vector Regression (SVR). While GPR is a competent model for learning the stochastic pattern within the data and interpolation, its performance for out-of-sample data is not very promising. By choosing a suitable data-dependent covariance function, we can enhance the performance of GPR for the tested German hourly power prices. However, since the out-of-sample prediction depends on the training data, the prediction is vulnerable to noise and outliers. To overcome this issue, a separate prediction is made using SVR, which applies margin-based optimization, having an advantage in dealing with non-linear processes and outliers, since only certain necessary points (support vectors) in the training data are responsible for regression. Both individual predictions are later combined using the performance-based weight assignment method. A test on historic German power prices shows that this approach outperforms its chosen benchmarks such as the autoregressive exogenous model, the naive approach, as well as the long short-term memory approach of prediction.
Mapping, modeling, and reprogramming cell-fate decision making systems
Authors: Lucy Ham, Taylor E. Woodford, Megan A. Coomer, Michael P. H. Stumpf
Date: 2024-12-01 00:00:00
Tasks: Decision Making

Many cellular processes involve information processing and decision making. We can probe these processes at increasing molecular detail. The analysis of heterogeneous data remains a challenge that requires new ways of thinking about cells in quantitative, predictive, and mechanistic ways. We discuss the role of mathematical models in the context of cell-fate decision making systems across the tree of life. Complex multi-cellular organisms have been a particular focus, but single celled organisms also have to sense and respond to their environment. We center our discussion around the idea of design principles which we can learn from observations and modeling, and exploit in order to (re)-design or guide cellular behavior.
The influence of chromosomal inversions on genetic variation and clinal patterns in genomic data of Drosophila melanogaster
Authors: Martin Kapun
Date: 2024-12-02 00:00:00

Chromosomal inversions are structural mutations resulting in the reversal of the gene order along the corresponding genomic region. Due to their influence on recombination patterns, they can have a major influence on genetic variation and the evolutionary process. Accordingly, inversions can act as supergenes that keep together co-adapted gene complexes that form the genetic basis of many complex phenotypes in diverse organisms. In this book chapter, I will present an analysis pipeline to investigate the influence of two common cosmopolitan inversion, In(2L)t and In(3R)Payne, on genome-wide genetic variation and differentiation in world-wide populations of the vinegar fly Drosophila melanogaster. We will use single-individual and pooled resequencing data in combination with population genomics analysis tools to explore the impact of these two inversions on genetic variation, population structure, and clinal variation in natural populations.
Microbial Mat Metagenomes from Waikite Valley, Aotearoa New Zealand
Authors: Beatrice Tauer, Elizabeth Trembath-Reichert, L. M. Ward
Date: 2024-12-02 00:00:00

The rise of complex multicellular ecosystems Neoproterozoic time was preceded by a microbial Proterozoic biosphere, where productivity may have been largely restricted to microbial mats made up of bacteria including oxygenic photosynthetic Cyanobacteria, anoxygenic phototrophs, and heterotrophs. In modern environments, analogous microbial mats can be found in restricted environments such as carbonate tidal flats and terrestrial hot springs. Here, we report metagenomic sequence data from an analog in the hot springs of Waikite Valley, Aotearoa New Zealand, where carbon-rich, slightly-alkaline geothermal waters support diverse phototrophic microbial mats. The Waikite Valley hot spring in the Taupo Volcanic Zone of Aotearoa New Zealand was sampled in duplicate at 8 points along a temperature gradient transect of the outflow, from ~62 C (near the source) to ~37 C (~100 meters downstream). ~686 Gb of shotgun metagenomic sequence data was generated by Illumina Novaseq. Each sample was assembled using SPAdes, followed by binning of metagenome-assembled genomes (MAGs) by MetaBAT. These data are useful for the genomic analysis of novel phototrophic bacteria, as well as for ecological comparisons between thermophilic communities with varying temperatures but otherwise similar conditions.
Harnessing the Potential of Spatial Statistics for Spatial Omics Data with pasta
Authors: Martin Emons, Samuel Gunz, Helena L. Crowell, Izaskun Mallona, Reinhard Furrer, Mark D. Robinson
Date: 2024-12-02 00:00:00

Spatial omics assays allow for the molecular characterisation of cells in their spatial context. Notably, the two main technological streams, imaging-based and high-throughput sequencing-based, can give rise to very different data modalities. The characteristics of the two data types are well known in adjacent fields such as spatial statistics as point patterns and lattice data, and there is a wide range of tools available. This paper discusses the application of spatial statistics to spatially-resolved omics data and in particular, discusses various advantages, challenges, and nuances. This work is accompanied by a vignette, pasta, that showcases the usefulness of spatial statistics in biology using several R packages.