pwc-archive/papers-with-abstracts huggingface dataset:
NeuroRIS: Neuromorphic-Inspired Metasurfaces
Authors: Christos G. Tsinos, Alexandros-Apostolos A. Boulogeorgos, Theodoros A. Tsiftsis
Date: 2023-12-22 00:00:00

Reconfigurable intelligent surfaces (RISs) operate similarly to electromagnetic (EM) mirrors and remarkably go beyond Snell law to generate an applicable EM environment allowing for flexible adaptation and fostering sustainability in terms of economic deployment and energy efficiency. However, the conventional RIS is controlled through high-latency field programmable gate array or micro-controller circuits usually implementing artificial neural networks (ANNs) for tuning the RIS phase array that have also very high energy requirements. Most importantly, conventional RIS are unable to function under realistic scenarios i.e, high-mobility/low-end user equipment (UE). In this paper, we benefit from the advanced computing power of neuromorphic processors and design a new type of RIS named \emph{NeuroRIS}, to supporting high mobility UEs through real time adaptation to the ever-changing wireless channel conditions. To this end, the neuromorphic processing unit tunes all the RIS meta-elements in the orders of $\rm{ns}$ for particular switching circuits e.g., varactors while exhibiting significantly low energy requirements since it is based on event-driven processing through spiking neural networks for accurate and efficient phase-shift vector design. Numerical results show that the NeuroRIS achieves very close rate performance to a conventional RIS-based on ANNs, while requiring significantly reduced energy consumption with the latter.
Dynamic Programming-based Approximate Optimal Control for Model-Based Reinforcement Learning
Authors: Prakash Mallick, Zhiyong Chen
Date: 2023-12-22 00:00:00
Tasks: Model-based Reinforcement Learning, reinforcement-learning

This article proposes an improved trajectory optimization approach for stochastic optimal control of dynamical systems affected by measurement noise by combining optimal control with maximum likelihood techniques to improve the reduction of the cumulative cost-to-go. A modified optimization objective function that incorporates dynamic programming-based controller design is presented to handle the noise in the system and sensors. Empirical results demonstrate the effectiveness of the approach in reducing stochasticity and allowing for an intermediate step to switch optimization that can allow an efficient balance of exploration and exploitation mechanism for complex tasks by constraining policy parameters to parameters obtained as a result of this improved optimization. This research study also includes theoretical work on the uniqueness of control parameter estimates and also leverages a structure of the likelihood function which has an established theoretical guarantees. Furthermore, a theoretical result is also explored that bridge the gap between the proposed optimization objective function and existing information theory (relative entropy) and optimal control dualities.
Coriolis Factorizations and their Connections to Riemannian Geometry
Authors: Patrick M. Wensing, Jean-Jacques E. Slotine
Date: 2023-12-22 00:00:00

Many energy-based control strategies for mechanical systems require the choice of a Coriolis factorization satisfying a skew-symmetry property. This paper (a) explores if and when a control designer has flexibility in this choice, (b) develops a canonical choice related to the Christoffel symbols, and (c) describes how to efficiently perform control computations with it for constrained mechanical systems. We link the choice of a Coriolis factorization to the notion of an affine connection on the configuration manifold and show how properties of the connection relate with the associated factorization. In particular, the factorization based on the Christoffel symbols is linked with a torsion-free property that can limit the twisting of system trajectories during passivity-based control. We then develop a way to induce Coriolis factorizations for constrained mechanisms from unconstrained ones, which provides a pathway to use the theory for efficient control computations with high-dimensional systems such as humanoids and quadruped robots with open- and closed-chain mechanisms. A collection of algorithms is provided (and made available open source) to support the recursive computation of passivity-based control laws, adaptation laws, and regressor matrices in future applications.
GreenScan: Towards large-scale terrestrial monitoring the health of urban trees using mobile sensing
Authors: Akshit Gupta, Simone Mora, Fan Zhang, Martine Rutten, R. Venkatesha Prasad, Carlo Ratti
Date: 2023-12-22 00:00:00

Healthy urban greenery is a fundamental asset to mitigate climate change phenomena such as extreme heat and air pollution. However, urban trees are often affected by abiotic and biotic stressors that hamper their functionality, and whenever not timely managed, even their survival. While the current greenery inspection techniques can help in taking effective measures, they often require a high amount of human labor, making frequent assessments infeasible at city-wide scales. In this paper, we present GreenScan, a ground-based sensing system designed to provide health assessments of urban trees at high spatio-temporal resolutions, with low costs. The system utilises thermal and multi-spectral imaging sensors fused using a custom computer vision model in order to estimate two tree health indexes. The evaluation of the system was performed through data collection experiments in Cambridge, USA. Overall, this work illustrates a novel approach for autonomous mobile ground-based tree health monitoring on city-wide scales at high temporal resolutions with low-costs.
Fast data-driven iterative learning control for linear system with output disturbance
Authors: Jia Wang, Leander Hemelhof, Ivan Markovsky, Panagiotis Patrinos
Date: 2023-12-21 00:00:00

This paper studies data-driven iterative learning control (ILC) for linear time-invariant (LTI) systems with unknown dynamics, output disturbances and input box-constraints. Our main contributions are: 1) using a non-parametric data-driven representation of the system dynamics, for dealing with the unknown system dynamics in the context of ILC, 2) design of a fast ILC method for dealing with output disturbances, model uncertainty and input constraints. A complete design method is given in this paper, which consists of the data-driven representation, controller formulation, acceleration strategy and convergence analysis. A batch of numerical experiments and a case study on a high-precision robotic motion system are given in the end to show the effectiveness of the proposed method.
Nonlinear Model Predictive Control of a Conductance-Based Neuron Model via Data-Driven Forecasting
Authors: Christof Fehrman, C. Daniel Meliza
Date: 2023-12-21 00:00:00
Tasks: model, Model Predictive Control

Objective. Precise control of neural systems is essential to experimental investigations of how the brain controls behavior and holds the potential for therapeutic manipulations to correct aberrant network states. Model predictive control, which employs a dynamical model of the system to find optimal control inputs, has promise for dealing with the nonlinear dynamics, high levels of exogenous noise, and limited information about unmeasured states and parameters that are common in a wide range of neural systems. However, the challenge still remains of selecting the right model, constraining its parameters, and synchronizing to the neural system. Approach. As a proof of principle, we used recent advances in data-driven forecasting to construct a nonlinear machine-learning model of a Hodgkin-Huxley type neuron when only the membrane voltage is observable and there are an unknown number of intrinsic currents. Main Results. We show that this approach is able to learn the dynamics of different neuron types and can be used with MPC to force the neuron to engage in arbitrary, researcher-defined spiking behaviors. Significance. To the best of our knowledge, this is the first application of nonlinear MPC of a conductance-based model where there is only realistically limited information about unobservable states and parameters.
Isolated pulsar population synthesis with simulation-based inference
Authors: Vanessa Graber, Michele Ronchi, Celsa Pardo-Araujo, Nanda Rea
Date: 2023-12-22 00:00:00

We combine pulsar population synthesis with simulation-based inference (SBI) to constrain the magnetorotational properties of isolated Galactic radio pulsars. We first develop a framework to model neutron star birth properties and their dynamical and magnetorotational evolution. We specifically sample initial magnetic field strengths, $B$, and spin periods, $P$, from lognormal distributions and capture the late-time magnetic field decay with a power law. Each lognormal is described by a mean, $\mu_{\log B}, \mu_{\log P}$, and standard deviation, $\sigma_{\log B}, \sigma_{\log P}$, while the power law is characterized by the index, $a_{\rm late}$. We subsequently model the stars' radio emission and observational biases to mimic detections with three radio surveys, and we produce a large database of synthetic $P$--$\dot{P}$ diagrams by varying our five magnetorotational input parameters. We then follow an SBI approach that focuses on neural posterior estimation and train deep neural networks to infer the parameters' posterior distributions. After successfully validating these individual neural density estimators on simulated data, we use an ensemble of networks to infer the posterior distributions for the observed pulsar population. We obtain $\mu_{\log B} = 13.10^{+0.08}_{-0.10}$, $\sigma_{\log B} = 0.45^{+0.05}_{-0.05}$ and $\mu_{\log P} = -1.00^{+0.26}_{-0.21}$, $\sigma_{\log P} = 0.38^{+0.33}_{-0.18}$ for the lognormal distributions and $a_{\rm late} = -1.80^{+0.65}_{-0.61}$ for the power law at the $95\%$ credible interval. We contrast our results with previous studies and highlight uncertainties of the inferred $a_{\rm late}$ value. Our approach represents a crucial step toward robust statistical inference for complex population synthesis frameworks and forms the basis for future multiwavelength analyses of Galactic pulsars.
Communication protocol for a satellite-swarm interferometer
Authors: Oliver Nagy, Manish Pandey, Georgios Exarchakos, Mark Bentum, Remco van der Hofstad
Date: 2023-12-25 00:00:00
Tasks: Astronomy

Orbiting low frequency antennas for radio astronomy (OLFAR) that capture cosmic signals in the frequency range below 30MHz could provide valuable insights on our Universe. These wireless swarms of satellites form a connectivity graph that allows data exchange between most pairs of satellites. Since this swarm acts as an interferometer, the aim is to compute the cross-correlations between most pairs of satellites. We propose a k-nearest-neighbour communication protocol, and investigate the minimum neighbourhood size of each satellite that ensures connectivity of at least 95% of the swarm. We describe the proportion of cross-correlations that can be computed in our method given an energy budget per satellite. Despite the method's apparent simplicity, it allows us to gain insight into the requirements for such satellite swarms. In particular, we give specific advice on the energy requirements to have sufficient coverage of the relevant baselines.
Eco-evolutionary dynamics of cooperative antimicrobial resistance in a population of fluctuating volume and size
Authors: Lluís Hernández-Navarro, Matthew Asker, Mauro Mobilia
Date: 2023-12-22 00:00:00

Antimicrobial resistance to drugs (AMR), a global threat to human and animal health, is often regarded as resulting from cooperative behaviour. Moreover, microbes generally evolve in volatile environments that, together with demographic fluctuations (birth and death events), drastically alter population size and strain survival. Motivated by the need to better understand the evolution of AMR, we study a population of time-varying size consisting of two competing strains, one drug-resistant and one drug-sensitive, subject to demographic and environmental variability. This is modelled by a binary carrying capacity randomly switching between mild and harsh environmental conditions, and driving the fluctuating volume (total amount of nutrients and antimicrobials at fixed concentration), and thus the size of the community (number of resistant and sensitive cells). We assume that AMR is a shared public good when the concentration of resistant cells exceeds a fixed concentration cooperation threshold, above which the sensitive strain has a growth advantage, whereas resistant cells dominate below it. Using computational means, and devising an analytical treatment (built on suitable quenched and annealed averaging procedures), we fully characterise the influence of fluctuations on the eco-evolutionary dynamics of AMR, and notably obtain specific strain fixation and long-lasting coexistence probabilities as a function of the environmental variation rate and cooperation threshold. We find that microbial strains tend to coexistence, but demographic fluctuations eventually lead to the extinction of resistant or sensitive cells for small or large values of the concentration cooperation threshold, respectively. This also holds for dynamic environments, whose specific properties determine the extinction timescale.
A discrete-to-continuum model for the human cornea with application to keratoconus
Authors: J. Köry, P. S. Stewart, N. A. Hill, X. Y. Luo, A. Pandolfi
Date: 2023-12-23 00:00:00

We introduce a discrete mathematical model for the mechanical behaviour of a planar slice of human corneal tissue, in equilibrium under the action of physiological intraocular pressure (IOP). The model considers a regular (two-dimensional) network of structural elements mimicking a discrete number of parallel collagen lamellae connected by proteoglycan-based chemical bonds (crosslinks). Since the thickness of each collagen lamella is small compared to the overall corneal thickness, we upscale the discrete force balance into a continuum system of partial differential equations and deduce the corresponding macroscopic stress tensor and strain energy function for the micro-structured corneal tissue. We demonstrate that, for physiological values of the IOP, the predictions of the discrete model converge to those of the continuum model. We use the continuum model to simulate the progression of the degenerative disease known as keratoconus, characterized by a localized bulging of the corneal shell. We assign a spatial distribution of damage (i. e., reduction of the stiffness) to the mechanical properties of the structural elements and predict the resulting macroscopic shape of the cornea, showing that a large reduction in the element stiffness results in substantial corneal thinning and a significant increase in the curvature of both the anterior and posterior surfaces.
Brain Status Transferring Generative Adversarial Network for Decoding Individualized Atrophy in Alzheimer’s Disease
Authors: Xingyu Gao; Hongrui Liu; Feng Shi; Dinggang Shen; Manhua Liu
Date: 2023-08-22 00:00:00
Proceeding: journal 2023 8
Tasks: Generative Adversarial Network

Deep learning has been widely investigated in brain image computational analysis for diagnosing brain diseases such as Alzheimer's disease (AD). Most of the existing methods built end-to-end models to learn discriminative features by group-wise analysis. However, these methods cannot detect pathological changes in each subject, which is essential for the individualized interpretation of disease variances and precision medicine. In this article, we propose a brain status transferring generative adversarial network (BrainStatTrans-GAN) to generate corresponding healthy images of patients, which are further used to decode individualized brain atrophy. The BrainStatTrans-GAN consists of generator, discriminator, and status discriminator. First, a normative GAN is built to generate healthy brain images from normal controls. However, it cannot generate healthy images from diseased ones due to the lack of paired healthy and diseased images. To address this problem, a status discriminator with adversarial learning is designed in the training process to produce healthy brain images for patients. Then, the residual between the generated and input images can be computed to quantify pathological brain changes. Finally, a residual-based multi-level fusion network (RMFN) is built for more accurate disease diagnosis. Compared to the existing methods, our method can model individualized brain atrophy for facilitating disease diagnosis and interpretation. Experimental results on T1-weighted magnetic resonance imaging (MRI) data of 1,739 subjects from three datasets demonstrate the effectiveness of our method.
Explosive epidemic transitions induced by quarantine fatigue
Authors: L. D. Valdez
Date: 2023-12-22 00:00:00

Quarantine measures are one of the first lines of defense against the spread of infectious diseases. However, maintaining these measures over extended periods can be challenging due to a phenomenon known as quarantine fatigue. In this paper, we investigate the impact of quarantine fatigue on the spread of infectious diseases by using an epidemic model on random networks with cliques. In our model, susceptible individuals can be quarantined up to $n$ times, after which they stop complying with quarantine orders due to fatigue. Our results show that quarantine fatigue may induce a regime in which increasing the probability of detecting and isolating infected individuals (along with their close contacts) could subsequently increase the expected number of cases at the end of an outbreak. Moreover, we observe that quarantine fatigue can trigger an abrupt phase transition at the critical reproduction number $R_0=1$. Finally, we explore a scenario where a non-negligible number of individuals are infected at the beginning of an epidemic, and our results show that, depending on the value of $n$, an abrupt transition between a controlled epidemic and a large epidemic event can occur for $R_0<1$.
Unsupervised Random Quantum Networks for PDEs
Authors: Josh Dees, Antoine Jacquier, Sylvain Laizet
Date: 2023-12-21 00:00:00

Classical Physics-informed neural networks (PINNs) approximate solutions to PDEs with the help of deep neural networks trained to satisfy the differential operator and the relevant boundary conditions. We revisit this idea in the quantum computing realm, using parameterised random quantum circuits as trial solutions. We further adapt recent PINN-based techniques to our quantum setting, in particular Gaussian smoothing. Our analysis concentrates on the Poisson, the Heat and the Hamilton-Jacobi-Bellman equations, which are ubiquitous in most areas of science. On the theoretical side, we develop a complexity analysis of this approach, and show numerically that random quantum networks can outperform more traditional quantum networks as well as random classical networks.
Direct Multi-Step Forecast based Comparison of Nested Models via an Encompassing Test
Authors: Jean-Yves Pitarakis
Date: 2023-12-26 00:00:00

We introduce a novel approach for comparing out-of-sample multi-step forecasts obtained from a pair of nested models that is based on the forecast encompassing principle. Our proposed approach relies on an alternative way of testing the population moment restriction implied by the forecast encompassing principle and that links the forecast errors from the two competing models in a particular way. Its key advantage is that it is able to bypass the variance degeneracy problem afflicting model based forecast comparisons across nested models. It results in a test statistic whose limiting distribution is standard normal and which is particularly simple to construct and can accommodate both single period and longer-horizon prediction comparisons. Inferences are also shown to be robust to different predictor types, including stationary, highly-persistent and purely deterministic processes. Finally, we illustrate the use of our proposed approach through an empirical application that explores the role of global inflation in enhancing individual country specific inflation forecasts.
Negative Control Falsification Tests for Instrumental Variable Designs
Authors: Oren Danieli, Daniel Nevo, Itai Walk, Bar Weinstein, Dan Zeltzer
Date: 2023-12-25 00:00:00
Tasks: valid

The validity of instrumental variable (IV) designs is typically tested using two types of falsification tests. We characterize these tests as conditional independence tests between negative control variables -- proxies for unobserved variables posing a threat to the identification -- and the IV or the outcome. We describe the conditions that variables must satisfy in order to serve as negative controls. We show that these falsification tests examine not only independence and the exclusion restriction, but also functional form assumptions. Our analysis reveals that conventional applications of these tests may flag problems even in valid IV designs. We offer implementation guidance to address these issues.
Variable Selection in High Dimensional Linear Regressions with Parameter Instability
Authors: Alexander Chudik, M. Hashem Pesaran, Mahrad Sharifvaghefi
Date: 2023-12-24 00:00:00
Tasks: Variable Selection

This paper considers the problem of variable selection allowing for parameter instability. It distinguishes between signal and pseudo-signal variables that are correlated with the target variable, and noise variables that are not, and investigate the asymptotic properties of the One Covariate at a Time Multiple Testing (OCMT) method proposed by Chudik et al. (2018) under parameter insatiability. It is established that OCMT continues to asymptotically select an approximating model that includes all the signals and none of the noise variables. Properties of post selection regressions are also investigated, and in-sample fit of the selected regression is shown to have the oracle property. The theoretical results support the use of unweighted observations at the selection stage of OCMT, whilst applying down-weighting of observations only at the forecasting stage. Monte Carlo and empirical applications show that OCMT without down-weighting at the selection stage yields smaller mean squared forecast errors compared to Lasso, Adaptive Lasso, and boosting.
Exploring Distributions of House Prices and House Price Indices
Authors: Jiong Liu, Hamed Farahani, R. A. Serota
Date: 2023-12-21 00:00:00

We use house prices (HP) and house price indices (HPI) as a proxy to income distribution. Specifically, we analyze sale prices in the 1970-2010 window of over 116,000 single-family homes in Hamilton County, Ohio, including Cincinnati metro area of about 2.2 million people. We also analyze HPI, published by Federal Housing Finance Agency (FHFA), for nearly 18,000 US ZIP codes that cover a period of over 40 years starting in 1980's. If HP can be viewed as a first derivative of income, HPI can be viewed as its second derivative. We use generalized beta (GB) family of functions to fit distributions of HP and HPI since GB naturally arises from the models of economic exchange described by stochastic differential equations. Our main finding is that HP and multi-year HPI exhibit a negative Dragon King (nDK) behavior, wherein power-law distribution tail gives way to an abrupt decay to a finite upper limit value, which is similar to our recent findings for realized volatility of S\&P500 index in the US stock market. This type of tail behavior is best fitted by a modified GB (mGB) distribution. Tails of single-year HPI appear to show more consistency with power-law behavior, which is better described by a GB Prime (GB2) distribution. We supplement full distribution fits by mGB and GB2 with direct linear fits (LF) of the tails. Our numerical procedure relies on evaluation of confidence intervals (CI) of the fits, as well as of p-values that give the likelihood that data come from the fitted distributions.