pwc-archive/papers-with-abstracts huggingface dataset:
Impact of Sensing Range on Real-Time Adaptive Control of Signalized Intersections Using Vehicle Trajectory Information
Authors: Andalib Shams, Christopher M. Day
Date: 2023-08-03 00:00:00

Advanced signal control algorithms are anticipated with the increasing availability of vehicle speed and position data from vehicle-to-infrastructure communication and from sensors. This study examines the impact of the sensing range, meaning the distance from the intersection that such data can be obtained, on the quality of the signal control. Two advanced signal control methods, a Self-Organizing Algorithm (SOA) and Phase Allocation Algorithm (PAA), were implemented in simulation and tested to understand the impact of sensing ranges. SOA is based on fully-actuated control with an added secondary extension for vehicle platoons along the arterial. PAA uses dynamic programming to optimize phase sequences and phase duration within a planning horizon. Three different traffic scenarios were developed: symmetric, asymmetric, and balanced. In general, both algorithms exhibited improvements in performance as the sensing range was increased. Under the symmetric volume scenario, SOA converged at 1000 ft and PAA converged at 1500 ft (with vehicles traveling at 45mph). Under the asymmetrical and balanced volume scenarios, both algorithms outperformed conventional methods. Both algorithms performed better than coordinated-actuated control if the sensing range is 660 ft or higher. For low sensing ranges, SOA experiences similar delay compared to fully-actuated control with advance detector and PAA experienced more delay than coordinated-actuated control in symmetric and asymmetric scenario but performed better for asymmetric scenario. The results suggest that for the SOA algorithm, the sensing range may constrain the maximum allowable secondary extension, hence as the sensing range increases, the vehicular delay would decrease for arterial movements and increase for non-arterial movements. For PAA, arterial and non-arterial delay decreases with increase in sensing range until it converges.
Data-Driven Nonlinear TDOA for Accurate Source Localization in Complex Signal Dynamics
Authors: Chinmay Sahu, Mahesh Banavar, Jie Sun
Date: 2023-08-03 00:00:00

The complex and dynamic propagation of oscillations and waves is often triggered by sources at unknown locations. Accurate source localization enables the elimination of the rotor core in atrial fibrillation (AFib) as an effective treatment for such severe cardiac disorder; it also finds potential use in locating the spreading source in natural disasters such as forest fires and tsunamis. However, existing approaches such as time of arrival (TOA) and time difference of arrival (TDOA) do not yield accurate localization results since they tacitly assume a constant signal propagation speed whereas realistic propagation is often non-static and heterogeneous. In this paper, we develop a nonlinear TDOA (NTDOA) approach which utilizes observational data from various positions to jointly learn the propagation speed at different angles and distances as well as the location of the source itself. Through examples of simulating the complex dynamics of electrical signals along the surface of the heart and satellite imagery from forest fires and tsunamis, we show that with a small handful of measurements, NTDOA, as a data-driven approach, can successfully locate the spreading source, leading also to better forecasting of the speed and direction of subsequent propagation.
Local and extensive fluctuations in sparsely-interacting ecological communities
Authors: Stav Marcus, Ari M Turner, Guy Bunin
Date: 2023-08-03 00:00:00

Ecological communities with many species can be classified into dynamical phases. In systems with all-to-all interactions, a phase where a fixed point is always reached and a dynamically-fluctuating phase have been found. The dynamics when interactions are sparse, with each species interacting with only several others, has remained largely unexplored. Here we show that a new type of phase appears in the phase diagram, where for the same control parameters different communities may reach either a fixed point or a state where the abundances of a finite subset of species fluctuate, and calculate the probability for each outcome. These fluctuating species are organized around short cycles in the interaction graph, and their abundances undergo large non-linear fluctuations. We characterize the approach from this phase to a phase with extensively many fluctuating species, and show that the probability of fluctuations grows continuously to one as the transition is approached, and that the number of fluctuating species diverges. This is qualitatively distinct from the transition to extensive fluctuations coming from a fixed point phase, which is marked by a loss of linear stability. The differences are traced back to the emergent binary character of the dynamics when far away from short cycles in the local fluctuations phase.
A new probabilistic analysis of the yard-sale model
Authors: Christoph Börgers, Claude Greengard
Date: 2023-08-03 00:00:00

In Chakraborti's yard-sale model of an economy, identical agents engage in trades that result in wealth exchanges, but conserve the combined wealth of all agents and each agent's expected wealth. In this model, wealth condensation, that is, convergence to a state in which one agent owns everything and the others own nothing, occurs almost surely. We give a proof of this fact that is much shorter than existing ones and extends to a modified model in which there is a wealth-acquired advantage, i.e., the wealthier of two trading partners is more likely to benefit from the trade.
Individual Shrinkage for Random Effects
Authors: Raffaella Giacomini, Sokbae Lee, Silvia Sarpietro
Date: 2023-08-03 00:00:00
Tasks: Time Series

This paper develops a novel approach to random effects estimation and individual-level forecasting in micropanels, targeting individual accuracy rather than aggregate performance. The conventional shrinkage methods used in the literature, such as the James-Stein estimator and Empirical Bayes, target aggregate performance and can lead to inaccurate decisions at the individual level. We propose a complementary class of shrinkage estimators with individual weights (IW) that leverage an individual's own past history, instead of the cross-sectional dimension. This approach overcomes the "tyranny of the majority" inherent in existing methods, while relying on weaker assumptions. We discuss the theoretical optimality of IW and recommend using feasible weights determined through a Minimax Regret analysis in practice.
Limit Theory under Network Dependence and Nonstationarity
Authors: Christis Katsouris
Date: 2023-08-02 00:00:00
Tasks: Econometrics, regression, Time Series, Time Series Regression, Unity

These lecture notes represent supplementary material for a short course on time series econometrics and network econometrics. We give emphasis on limit theory for time series regression models as well as the use of the local-to-unity parametrization when modeling time series nonstationarity. Moreover, we present various non-asymptotic theory results for moderate deviation principles when considering the eigenvalues of covariance matrices as well as asymptotics for unit root moderate deviations in nonstationary autoregressive processes. Although not all applications from the literature are covered we also discuss some open problems in the time series and network econometrics literature.
Trading and wealth evolution in the Proof of Stake protocol
Authors: Wenpin Tang
Date: 2023-08-03 00:00:00
Tasks: POS

With the increasing adoption of the Proof of Stake (PoS) blockchain, it is timely to study the economy created by such blockchain. In this chapter, we will survey recent progress on the trading and wealth evolution in a cryptocurrency where the new coins are issued according to the PoS protocol. We first consider the wealth evolution in the PoS protocol assuming no trading, and focus on the problem of decentralisation. Next we consider each miner's trading incentive and strategy through the lens of optimal control, where the miner needs to trade off PoS mining and trading. Finally, we study the collective behavior of the miners in a PoS trading environment by a mean field model. We use both stochastic and analytic tools in our study. A list of open problems are also presented.
The Banks Set and the Bipartisan Set May Be Disjoint
Authors: Felix Brandt, Florian Grundbacher
Date: 2023-08-03 00:00:00

Tournament solutions play an important role within social choice theory and the mathematical social sciences at large. We construct a tournament of order 36 for which the Banks set and the bipartisan set are disjoint. This implies that refinements of the Banks set, such as the minimal extending set and the tournament equilibrium set, can also be disjoint from the bipartisan set.
Repeated Bidding with Dynamic Value
Authors: Benjamin Heymann, Alexandre Gilotte, Rémi Chan-Renous
Date: 2023-08-03 00:00:00

We consider a repeated auction where the buyer's utility for an item depends on the time that elapsed since his last purchase. We present an algorithm to build the optimal bidding policy, and then, because optimal might be impractical, we discuss the cost for the buyer of limiting himself to shading policies.
Estimation of motion blur kernel parameters using regression convolutional neural networks
Authors: Luis G. Varela, Laura E. Boucheron, Steven Sandoval, David Voelz, Abu Bucker Siddik
Date: 2023-08-02 00:00:00
Tasks: Blind Image Deblurring, Deblurring, Image Deblurring, regression

Many deblurring and blur kernel estimation methods use a maximum a posteriori (MAP) approach or deep learning-based classification techniques to sharpen an image and/or predict the blur kernel. We propose a regression approach using convolutional neural networks (CNNs) to predict parameters of linear motion blur kernels, the length and orientation of the blur. We analyze the relationship between length and angle of linear motion blur that can be represented as digital filter kernels. A large dataset of blurred images is generated using a suite of blur kernels and used to train a regression CNN for prediction of length and angle of the motion blur. The coefficients of determination for estimation of length and angle are found to be greater than or equal to 0.89, even under the presence of significant additive Gaussian noise, up to a variance of 10\% (SNR of 10 dB). Using our estimated kernel in a non-blind image deblurring method, the sum of squared differences error ratio demonstrates higher cumulative histogram values than comparison methods, with most test images yielding an error ratio of less than or equal to 1.25.
Super-Resolution Estimation of UWB Channels including the Diffuse Component -- An SBL-Inspired Approach
Authors: Stefan Grebien, Erik Leitinger, Klaus Witrisal, Bernard H. Fleury
Date: 2023-08-03 00:00:00
Tasks: Super-Resolution

In this paper, we present an iterative algorithm that detects and estimates the specular components and estimates the diffuse component of single-input-multiple-output (SIMO) ultra-wide-band (UWB) multipath channels. Specifically, the algorithm super-resolves the specular components in the delay-angle-of-arrival domain and estimates the parameters of a parametric model of the delay-angle power spectrum characterizing the diffuse component. Channel noise is also estimated. In essence, the algorithm solves the problem of estimating spectral lines (the specular components) in colored noise (generated by the diffuse component and channel noise). Its design is inspired by the sparse Bayesian learning (SBL) framework. As a result the iteration process contains a threshold condition that determines whether a candidate specular component shall be retained or pruned. By relying to results from extreme-value analysis the threshold of this condition is suitably adapted to ensure a prescribed probability of detecting spurious specular components. Studies using synthetic and real channel measurement data demonstrate the virtues of the algorithm: it is able to still detect and accurately estimate specular components, even when their separation in delay and angle is down to half the Rayleigh resolution limit (RRL) of the equipment; it is robust in the sense that it tends to return no more specular components than the actual ones. Finally, the algorithm is shown to outperform a state-of-the-art super-resolution channel estimator.
Millimeter Wave V2V Beam Tracking using Radar: Algorithms and Real-World Demonstration
Authors: Hao Luo, Umut Demirhan, Ahmed Alkhateeb
Date: 2023-08-03 00:00:00

Utilizing radar sensing for assisting communication has attracted increasing interest thanks to its potential in dynamic environments. A particularly interesting problem for this approach appears in the vehicle-to-vehicle (V2V) millimeter wave and terahertz communication scenarios, where the narrow beams change with the movement of both vehicles. To address this problem, in this work, we develop a radar-aided beam-tracking framework, where a single initial beam and a set of radar measurements over a period of time are utilized to predict the future beams after this time duration. Within this framework, we develop two approaches with the combination of various degrees of radar signal processing and machine learning. To evaluate the feasibility of the solutions in a realistic scenario, we test their performance on a real-world V2V dataset. Our results indicated the importance of high angular resolution radar for this task and affirmed the potential of using radar for the V2V beam management problems.
Exploiting Structured Sparsity with Low Complexity Sparse Bayesian Learning for RIS-assisted MIMO Channel Estimation
Authors: W. Li, Z. Lin, Q. Guo, B. Vucetic
Date: 2023-08-02 00:00:00

As an emerging communication auxiliary technology, reconfigurable intelligent surface (RIS) is expected to play a significant role in the upcoming 6G networks. Due to its total reflection characteristics, it is challenging to implement conventional channel estimation algorithms. This work focuses on RIS-assisted MIMO communications. Although many algorithms have been proposed to address this issue, there are still ample opportunities for improvement in terms of estimation accuracy, complexity, and applicability. To fully exploit the structured sparsity of the multiple-input-multiple-output (MIMO) channels, we propose a new channel estimation algorithm called unitary approximate message passing sparse Bayesian learning with partial common support identification (UAMPSBL-PCI). Thanks to the mechanism of PCI and the use of UAMP, the proposed algorithm has a lower complexity while delivering enhanced performance relative to existing channel estimation algorithms. Extensive simulations demonstrate its excellent performance in various environments.
Dispersal-enhanced resilience in two-patch metapopulations: origin's instability type matters
Authors: Marc Jorba-Cuscó, Ruth I. Oliva-Zúniga, Josep Sardanyés, Daniel Pérez-Palau
Date: 2023-08-02 00:00:00

Many populations of animals or plants, exhibit a metapopulation structure with close, spatially-separated subpopulations. The field of metapopulation theory has made significant advancements since the influential Levins model. Various modeling approaches have provided valuable insights to theoretical Ecology. Despite extensive research on metapopulation models, there are still challenging questions that are difficult to answer from ecological metapopulational data or multi-patch models. Low-dimension mathematical models offer a promising avenue to address these questions, especially for global dynamics which have been scarcely investigated. In this study, we investigate a two-patch metapopulation model with logistic growth and diffusion between patches. By using analytical and numerical methods, we thoroughly analyze the impact of diffusion on the dynamics of the metapopulation. We identify the equilibrium points and assess their local and global stability. Furthermore, we analytically derive the optimal diffusion rate that leads to the highest metapopulation values. Our findings demonstrate that increased diffusion plays a crucial role in the preservation of both subpopulations and the full metapopulation, especially under the presence of stochastic perturbations. Specifically, at low diffusion values, the origin is a repeller, causing orbits starting around it to travel closely parallel to the axes. This configuration makes the metapopulation less resilient and thus more susceptible to local and global extinctions. However, as diffusion increases, the repeller transitions to a saddle point, and orbits starting near the origin rapidly converge to the unstable manifold of the saddle. This phenomenon reduces the likelihood of stochastic extinctions and the metapopulation becomes more resilient due to these changes in the vector field of the phase space.
Machine Learning-guided Lipid Nanoparticle Design for mRNA Delivery
Authors: Daisy Yi Ding, Yuhui Zhang, Yuan Jia, Jiuzhi Sun
Date: 2023-08-02 00:00:00

While RNA technologies hold immense therapeutic potential in a range of applications from vaccination to gene editing, the broad implementation of these technologies is hindered by the challenge of delivering these agents effectively. Lipid nanoparticles have emerged as one of the most widely used delivery agents, but their design optimization relies on laborious and costly experimental methods. We propose to in silico optimize LNP design with machine learning models. On a curated dataset of 622 LNPs from published studies, we demonstrate the effectiveness of our model in predicting the transfection efficiency of unseen LNPs, with the multilayer perceptron achieving a classification accuracy of 98% on the test set. Our work represents a pioneering effort in combining ML and LNP design, offering significant potential for improving screening efficiency by computationally prioritizing LNP candidates for experimental validation and accelerating the development of effective mRNA delivery systems.
Semi-supervised Cooperative Learning for Multiomics Data Fusion
Authors: Daisy Yi Ding, Xiaotao Shen, Michael Snyder, Robert Tibshirani
Date: 2023-08-02 00:00:00

Multiomics data fusion integrates diverse data modalities, ranging from transcriptomics to proteomics, to gain a comprehensive understanding of biological systems and enhance predictions on outcomes of interest related to disease phenotypes and treatment responses. Cooperative learning, a recently proposed method, unifies the commonly-used fusion approaches, including early and late fusion, and offers a systematic framework for leveraging the shared underlying relationships across omics to strengthen signals. However, the challenge of acquiring large-scale labeled data remains, and there are cases where multiomics data are available but in the absence of annotated labels. To harness the potential of unlabeled multiomcis data, we introduce semi-supervised cooperative learning. By utilizing an "agreement penalty", our method incorporates the additional unlabeled data in the learning process and achieves consistently superior predictive performance on simulated data and a real multiomics study of aging. It offers an effective solution to multiomics data fusion in settings with both labeled and unlabeled data and maximizes the utility of available data resources, with the potential of significantly improving predictive models for diagnostics and therapeutics in an increasingly multiomics world.
Identifiability in Functional Connectivity May Unintentionally Inflate Prediction Results
Authors: Anton Orlichenko, Gang Qu, Kuan-Jui Su, Anqi Liu, Hui Shen, Hong-Wen Deng, Yu-Ping Wang
Date: 2023-08-02 00:00:00
Tasks: Functional Connectivity

Functional magnetic resonance (fMRI) is an invaluable tool in studying cognitive processes in vivo. Many recent studies use functional connectivity (FC), partial correlation connectivity (PC), or fMRI-derived brain networks to predict phenotypes with results that sometimes cannot be replicated. At the same time, FC can be used to identify the same subject from different scans with great accuracy. In this paper, we show a method by which one can unknowingly inflate classification results from 61% accuracy to 86% accuracy by treating longitudinal or contemporaneous scans of the same subject as independent data points. Using the UK Biobank dataset, we find one can achieve the same level of variance explained with 50 training subjects by exploiting identifiability as with 10,000 training subjects without double-dipping. We replicate this effect in four different datasets: the UK Biobank (UKB), the Philadelphia Neurodevelopmental Cohort (PNC), the Bipolar and Schizophrenia Network for Intermediate Phenotypes (BSNIP), and an OpenNeuro Fibromyalgia dataset (Fibro). The unintentional improvement ranges between 7% and 25% in the four datasets. Additionally, we find that by using dynamic functional connectivity (dFC), one can apply this method even when one is limited to a single scan per subject. One major problem is that features such as ROIs or connectivities that are reported alongside inflated results may confuse future work. This article hopes to shed light on how even minor pipeline anomalies may lead to unexpectedly superb results.
A Mathematically Robust Model of Exotic Pine Invasions
Authors: Elliott Hughes, Miguel Moyers-Gonzalez, Rua Murray, Phillip L. Wilson
Date: 2023-08-02 00:00:00
Tasks: model

Invasive pine trees pose a threat to biodiversity in a variety of Southern Hemisphere countries, but understanding of the dynamics of invasions and the factors that retard or accelerate spread is limited. Here, we consider the past models of wilding pine spread and develop a new model of pine invasion. We show that many prior models feature parameter estimates which are not biologically supported and rely on a conjecture to obtain an asymptotic spread speed of invasive pine populations, the main output of these models. In contrast to prior approaches, we use partial differential equations to model an invasion. We show that invasions are almost static for a significant period of time before rapidly accelerating to spread at a constant rate, matching observed behaviour in at least some field sites. Our work suggests that prior methods for estimating invasion speeds may not accurately predict spread and are sensitive to assumptions about the distribution of parameters. However, we present alternative estimation methods and suggest directions for further research.
Using Single Molecule Imaging to Explore Intracellular Heterogeneity
Authors: James A. Galbraith, Catherine G. Galbraith
Date: 2023-08-02 00:00:00

Despite more than 100 years of study, it is unclear if the movement of proteins inside the cell is best described as a mosh pit or an exquisitely choreographed dance. Recent studies suggest the latter. Local interactions induce molecular condensates such as liquid-liquid phase separations (LLPSs) or non-liquid, functionally significant molecular aggregates, including synaptic densities, nucleoli, and Amyloid fibrils. Molecular condensates trigger intracellular signaling and drive processes ranging from gene expression to cell division. However, the descriptions of condensates tend to be qualitative and correlative. Here, we indicate how single-molecule imaging and analyses can be applied to quantify condensates. We discuss the pros and cons of different techniques for measuring differences between transient molecular behaviors inside and outside condensates. Finally, we offer suggestions for how imaging and analyses from different time and space regimes can be combined to identify molecular behaviors indicative of condensates within the dynamic high-density intracellular environment.