pwc-archive/papers-with-abstracts huggingface dataset:
Controllable Neural Architectures for Multi-Task Control
Authors: Umberto Casti, Giacomo Baggio, Sandro Zampieri, Fabio Pasqualetti
Date: 2025-01-31 00:00:00
Tasks: Multi-Task Learning

This paper studies a multi-task control problem where multiple linear systems are to be regulated by a single non-linear controller. In particular, motivated by recent advances in multi-task learning and the design of brain-inspired architectures, we consider a neural controller with (smooth) ReLU activation function. The parameters of the controller are a connectivity matrix and a bias vector: although both parameters can be designed, the connectivity matrix is constant while the bias vector can be varied and is used to adapt the controller across different control tasks. The bias vector determines the equilibrium of the neural controller and, consequently, of its linearized dynamics. Our multi-task control strategy consists of designing the connectivity matrix and a set of bias vectors in a way that the linearized dynamics of the neural controller for the different bias vectors provide a good approximation of a set of desired controllers. We show that, by properly choosing the bias vector, the linearized dynamics of the neural controller can replicate the dynamics of any single, linear controller. Further, we design gradient-based algorithms to train the parameters of the neural controller, and we provide upper and lower bounds for the performance of our neural controller. Finally, we validate our results using different numerical examples.
Scalable Distributed Reproduction Numbers of Network Epidemics with Differential Privacy
Authors: Bo Chen, Baike She, Calvin Hawkins, Philip E. Paré, Matthew T. Hale
Date: 2025-01-31 00:00:00

Reproduction numbers are widely used for the estimation and prediction of epidemic spreading processes over networks. However, conventional reproduction numbers of an overall network do not indicate where an epidemic is spreading. Therefore, we propose a novel notion of local distributed reproduction numbers to capture the spreading behaviors of each node in a network. We first show how to compute them and then use them to derive new conditions under which an outbreak can occur. These conditions are then used to derive new conditions for the existence, uniqueness, and stability of equilibrium states of the underlying epidemic model. Building upon these local distributed reproduction numbers, we define cluster distributed reproduction numbers to model the spread between clusters composed of nodes. Furthermore, we demonstrate that the local distributed reproduction numbers can be aggregated into cluster distributed reproduction numbers at different scales. However, both local and cluster distributed reproduction numbers can reveal the frequency of interactions between nodes in a network, which raises privacy concerns. Thus, we next develop a privacy framework that implements a differential privacy mechanism to provably protect the frequency of interactions between nodes when computing distributed reproduction numbers. Numerical experiments show that, even under differential privacy, the distributed reproduction numbers provide accurate estimates of the epidemic spread while also providing more insights than conventional reproduction numbers.
Non-Asymptotic Analysis of Subspace Identification for Stochastic Systems Using Multiple Trajectories
Authors: Shuai Sun
Date: 2025-01-31 00:00:00

This paper is concerned with the analysis of identification errors for $n$-dimensional discrete-time Linear Time-Invariant (LTI) systems with $m$ outputs and no external inputs, using Subspace Identification Methods (SIM) with finite sample data. We provide non-asymptotic high-probability upper bounds for matrices $A,C$, the Kalman filter gain $K$, and the closed loop matrix $A-KC $, based on multiple sample trajectories, and further give the first non-asymptotic high-probability upper bounds for the system poles, which cover both (marginally) stable systems and unstable systems. We show that, with high probability, the non-asymptotic estimation errors of these matrices decay at a rate of at least $ \mathcal{O}(\sqrt{1/N}) $, while the estimation error of the system poles decays at a rate of at least $ \mathcal{O}(N^{-\frac{1}{2n}}) $, where $ N $ represents the number of sample trajectories. Furthermore, we prove that SIMs become ill-conditioned when the ratio $n/m$ is large, regardless of the system parameters. Numerical experiments are conducted to validate the non-asymptotic results and the ill-conditionedness of SIM.
Tracking Error Based Fault Tolerant Scheme for Marine Vehicles with Thruster Redundancy
Authors: Ji-Hong Li, Hyungjoo Kang, Min-Gyu Kim, Mun-Jik Lee, Han-Sol Jin, Gun Rae Cho
Date: 2025-01-31 00:00:00
Tasks: Fault Detection, parameter estimation

This paper proposes an active model-based fault and failure tolerant control scheme for a class of marine vehicles with thruster redundancy. Unlike widely used state and parameter estimation methods, where the estimation errors are utilized to generate residual, in this paper we directly apply the trajectory tracking error terms to construct residual and detect thruster fault and failure in the steady state of the tracking system. As for identification or diagnosis, this paper proposes a novel scheme through a detailed examination of the tracking error trends and the combinations of thruster configurations. Since this fault detection and identification operates within the same closed-loop of the tracking control system, control reconfiguration can be easily achieved by adjusting the weight parameter of the isolated thruster to minimize tracking errors or residual. Numerical studies with the real world vehicle model is also carried out to verify the effectiveness of the proposed method.
A General-Purpose Neuromorphic Sensor based on Spiketrum Algorithm: Hardware Details and Real-life Applications
Authors: MHD Anas Alsakkal, Runze Wang, Piotr Dudek, Jayawan Wijekoon
Date: 2025-01-30 00:00:00
Tasks: ECG Classification

Spiking Neural Networks (SNNs) offer a biologically inspired computational paradigm, enabling energy-efficient data processing through spike-based information transmission. Despite notable advancements in hardware for SNNs, spike encoding has largely remained software-dependent, limiting efficiency. This paper addresses the need for adaptable and resource-efficient spike encoding hardware by presenting an area-optimized hardware implementation of the Spiketrum algorithm, which encodes time-varying analogue signals into spatiotemporal spike patterns. Unlike earlier performance-optimized designs, which prioritize speed, our approach focuses on reducing hardware footprint, achieving a 52% reduction in Block RAMs (BRAMs), 31% fewer Digital Signal Processing (DSP) slices, and a 6% decrease in Look-Up Tables (LUTs). The proposed implementation has been verified on an FPGA and successfully integrated into an IC using TSMC180 technology. Experimental results demonstrate the system's effectiveness in real-world applications, including sound and ECG classification. This work highlights the trade-offs between performance and resource efficiency, offering a flexible, scalable solution for neuromorphic systems in power-sensitive applications like cochlear implants and neural devices.
Optimizing Bidding Curves for Renewable Energy in Two-Settlement Electricity Markets
Authors: Dongwei Zhao, Stefanos Delikaraogloub, Vladimir Dvorkin Alberto J. Lamadrid L., Audun Botterud
Date: 2025-01-30 00:00:00
Tasks: Bilevel Optimization

Coordination of day-ahead and real-time electricity markets is imperative for cost-effective electricity supply and also to provide efficient incentives for the energy transition. Although stochastic market designs feature the least-cost coordination, they are incompatible with current deterministic markets. This paper proposes a new approach for compatible coordination in two-settlement markets based on benchmark bidding curves for variable renewable energy. These curves are optimized based on a bilevel optimization problem, anticipating per-scenario responses of deterministic market-clearing problems and ultimately minimizing the expected cost across day-ahead and real-time markets. Although the general bilevel model is challenging to solve, we theoretically prove that a single-segment bidding curve with a zero bidding price is sufficient to achieve system optimality if the marginal cost of variable renewable energy is zero, thus addressing the computational challenge. In practice, variable renewable energy producers can be allowed to bid multi-segment curves with non-zero prices. We test the bilevel framework for both single- and multiple-segment bidding curves under the assumption of fixed bidding prices. We leverage duality theory and McCormick envelopes to derive the linear programming approximation of the bilevel problem, which scales to practical systems such as a 1576-bus NYISO system. We benchmark the proposed coordination and find absolute dominance over the baseline solution, which assumes that renewables agnostically bid their expected forecasts. We also demonstrate that our proposed scheme provides a good approximation of the least-cost, yet unattainable in practice, stochastic market outcome.
Financial instability transition under heterogeneous investments and portfolio diversification
Authors: Preben Forer, Barak Budnick, Pierpaolo Vivo, Sabrina Aufiero, Silvia Bartolucci, Fabio Caccioli
Date: 2025-01-31 00:00:00

We analyze the stability of financial investment networks, where financial institutions hold overlapping portfolios of assets. We consider the effect of portfolio diversification and heterogeneous investments using a random matrix dynamical model driven by portfolio rebalancing. While heterogeneity generally correlates with heightened volatility, increasing diversification may have a stabilizing or destabilizing effect depending on the connectivity level of the network. The stability/instability transition is dictated by the largest eigenvalue of the random matrix governing the time evolution of the endogenous components of the returns, for which different approximation schemes are proposed and tested against numerical diagonalization.
Three-dimensional chiral active Ornstein-Uhlenbeck model for helical motion of microorganisms
Authors: Leon Lettermann, Falko Ziebert, Mirko Singer, Friedrich Frischknecht, Ulrich S. Schwarz
Date: 2025-01-31 00:00:00

Active movement is essential for the survival of microorganisms like bacteria, algae and unicellular parasites. In three dimensions, both swimming and gliding microorganisms often exhibit helical trajectories. Here we introduce a general stochastic dynamics model for these cases, namely a chiral active particle with an Ornstein-Uhlenbeck process for torque, which implies a finite correlation time for internal noise. We present an analytical solution which is in very good agreement with computer simulations. We then show that for this type of internal noise, chirality and rotation increase the persistence of motion and results in helical trajectories that have a larger long-time mean squared displacement than straight trajectories at the same propulsion speed. We finally provide an experimental example by analyzing imaging data for malaria parasites that glide through hydrogels on helical trajectories.
Fixed-Population Causal Inference for Models of Equilibrium
Authors: Konrad Menzel
Date: 2025-01-31 00:00:00
Tasks: Causal Inference

In contrast to problems of interference in (exogenous) treatments, models of interference in unit-specific (endogenous) outcomes do not usually produce a reduced-form representation where outcomes depend on other units' treatment status only at a short network distance, or only through a known exposure mapping. This remains true if the structural mechanism depends on outcomes of peers only at a short network distance, or through a known exposure mapping. In this paper, we first define causal estimands that are identified and estimable from a single experiment on the network under minimal assumptions on the structure of interference, and which represent average partial causal responses which generally vary with other global features of the realized assignment. Under a fixed-population, design-based approach, we show unbiasedness and consistency for inverse-probability weighting (IPW) estimators for those causal parameters from a randomized experiment on a single network. We also analyze more closely the case of marginal interventions in a model of equilibrium with smooth response functions where we can recover LATE-type weighted averages of derivatives of those response functions. Under additional structural assumptions, these ``agnostic" causal estimands can be combined to recover model parameters, but also retain their less restrictive causal interpretation.
Untestability of Average Slutsky Symmetry
Authors: Haruki Kono
Date: 2025-01-31 00:00:00

Slutsky symmetry and negative semidefiniteness are necessary and sufficient conditions for the rationality of demand functions. While the empirical implications of Slutsky negative semidefiniteness in repeated cross-sectional demand data are well understood, the empirical content of Slutsky symmetry remains largely unexplored. This paper takes an important first step toward addressing this gap. We demonstrate that the average Slutsky matrix is not identified and that its identified set always contains a symmetric matrix. A key implication of our findings is that the symmetry of the average Slutsky matrix is untestable, and consequently, individual Slutsky symmetry cannot be tested using the average Slutsky matrix.
Model-Adaptive Approach to Dynamic Discrete Choice Models with Large State Spaces
Authors: Ertian Chen
Date: 2025-01-30 00:00:00
Tasks: Computational Efficiency, counterfactual, Discrete Choice Models

Estimation and counterfactual experiments in dynamic discrete choice models with large state spaces pose computational difficulties. This paper develops a novel model-adaptive approach to solve the linear system of fixed point equations of the policy valuation operator. We propose a model-adaptive sieve space, constructed by iteratively augmenting the space with the residual from the previous iteration. We show both theoretically and numerically that model-adaptive sieves dramatically improve performance. In particular, the approximation error decays at a superlinear rate in the sieve dimension, unlike a linear rate achieved using conventional methods. Our method works for both conditional choice probability estimators and full-solution estimators with policy iteration. We apply the method to analyze consumer demand for laundry detergent using Kantar's Worldpanel Take Home data. On average, our method is 51.5% faster than conventional methods in solving the dynamic programming problem, making the Bayesian MCMC estimator computationally feasible.
Unveiling Plant-Product Productivity via First-Order Conditions: Robust Replication of Orr (2022)
Authors: Joonkyo Hong, Davide Luparello
Date: 2025-01-31 00:00:00

In this study, we evaluate the reproducibility and replicability of Scott Orr's (Journal of Political Economy 2022, 130(11): 2771-2828) innovative approach for identifying within-plant productivity differences across product lines. Orr's methodology allows the estimation of plant-product level productivity, contingent upon a well-behaved pre-estimated demand system, which requires carefully chosen instrumental variables (IVs) for output prices. Using Orr's STATA replication package, we successfully replicate all primary estimates with the ASI Indian plant-level panel data from 2000 to 2007. Additionally, applying Orr's replication codes to a sample from 2011 to 2020 reveals that the suggested IVs do not perform as expected.
ReviewCoin: Paying for Real Work
Authors: Chris Welty
Date: 2025-01-30 00:00:00

The peer-review process is broken and the problem is getting worse, especially in AI: large conferences like NeurIPS increasingly struggle to adequately review huge numbers of paper submissions. I propose a scalable solution that, foremost, recognizes reviewing as important, necessary, \emph{work} and rewards it with crypto-coins owned and managed by the conferences themselves. The idea is at its core quite simple: paper submissions require work (reviews, meta-reviews, etc.) to be done, and therefore the submitter must pay for that work. Each reviewer submits their review to be approved by some designated conference officer (e.g. PC chair, Area Chair, etc.), and upon approval is paid a single coin for a single review. If three reviews are required, the cost of submission should be three coins + a tax that covers payments to all the volunteers who organize the conference. After some one-time startup costs to fairly distribute coins, the process should be relatively stable with new coins minted only when a conference grows.
Multispectral 3D mapping on a Roman sculpture to study ancient polychromy
Authors: Francesca Uccheddu, Umair Shafqat Malik, Emanuela Massa, Anna Pelagotti, Maria Emilia Masci, Gabriele Guidi
Date: 2025-01-30 00:00:00

Research into the polychromy of Greek and Roman sculptures has surged to explore the hypothesis that ancient sculptures were originally not pristine white but adorned with colors. Multispectral and multimodal imaging techniques have been crucial in studying painted surfaces, revealing polychromies even in traces. In fact, imaging techniques, such as reflectance and fluorescence, can identify different materials and map inhomogeneities, guiding further investigations such as Raman, XRays Fluorescence, and Fourier Transform InfraRed Spectroscopy (FTIR) to investigate residual colors. However, this approach may underestimate the original polychromies' extent over the complex articulation of a sculptured surface. This study proposes a methodology to analyze the original appearance of ancient sculptures using reality-based 3D models with textures not limited to those visible to the naked eye. We employ Visible Reflected Imaging (VIS) and Ultraviolet-induced Fluorescence Imaging (UVF). From the UVF and VIS datasets, the underlying 3D model is built by means of photogrammetry. Through raw data processing, images taken with different illuminating sources are successfully aligned and processed, creating a single 3D model with multiple textures mapped onto the same bi-dimensional space. The pixel-to-pixel correspondence of different textures allows for the implementation of a classification algorithm that can directly map its outcome onto the 3D model surface. This enables conservators to deepen their understanding of artifact preservation, observe mate-rial distribution in detail, and correlate this with 3D geometrical data. In this study, we experiment with this approach on an ancient Roman sculpture of Artemis, conserved at the Archeological and Art Museum of Maremma (MAAM) in Grosseto, Italy.
PSO-Net: Development of an automated psoriasis assessment system using attention-based interpretable deep neural networks
Authors: Sharif A. Kamran, Molly V. Lucas, Brendon Lutnick, Chaitanya Parmar, Basudha Pal, Asha Patel Shah, David Apfel, Steven Fakharzadeh, Lloyd Miller, Stephen Yip, Kristopher Standish, Gabriela Oana Cula
Date: 2025-01-30 00:00:00

Psoriasis is a chronic skin condition that requires long-term treatment and monitoring. Although, the Psoriasis Area and Severity Index (PASI) is utilized as a standard measurement to assess psoriasis severity in clinical trials, it has many drawbacks such as (1) patient burden for in-person clinic visits for assessment of psoriasis, (2) time required for investigator scoring and (3) variability of inter- and intra-rater scoring. To address these drawbacks, we propose a novel and interpretable deep learning architecture called PSO-Net, which maps digital images from different anatomical regions to derive attention-based scores. Regional scores are further combined to estimate an absolute PASI score. Moreover, we devise a novel regression activation map for interpretability through ranking attention scores. Using this approach, we achieved inter-class correlation scores of 82.2% [95% CI: 77- 87%] and 87.8% [95% CI: 84-91%] with two different clinician raters, respectively.
Distillation-Driven Diffusion Model for Multi-Scale MRI Super-Resolution: Make 1.5T MRI Great Again
Authors: Zhe Wang, Yuhua Ru, Fabian Bauer, Aladine Chetouani, Fang Chen, Liping Zhang, Didier Hans, Rachid Jennane, Mohamed Jarraya, Yung Hsin Chen
Date: 2025-01-30 00:00:00
Tasks: Super-Resolution

Magnetic Resonance Imaging (MRI) offers critical insights into microstructural details, however, the spatial resolution of standard 1.5T imaging systems is often limited. In contrast, 7T MRI provides significantly enhanced spatial resolution, enabling finer visualization of anatomical structures. Though this, the high cost and limited availability of 7T MRI hinder its widespread use in clinical settings. To address this challenge, a novel Super-Resolution (SR) model is proposed to generate 7T-like MRI from standard 1.5T MRI scans. Our approach leverages a diffusion-based architecture, incorporating gradient nonlinearity correction and bias field correction data from 7T imaging as guidance. Moreover, to improve deployability, a progressive distillation strategy is introduced. Specifically, the student model refines the 7T SR task with steps, leveraging feature maps from the inference phase of the teacher model as guidance, aiming to allow the student model to achieve progressively 7T SR performance with a smaller, deployable model size. Experimental results demonstrate that our baseline teacher model achieves state-of-the-art SR performance. The student model, while lightweight, sacrifices minimal performance. Furthermore, the student model is capable of accepting MRI inputs at varying resolutions without the need for retraining, significantly further enhancing deployment flexibility. The clinical relevance of our proposed method is validated using clinical data from Massachusetts General Hospital. Our code is available at https://github.com/ZWang78/SR.
Review and Recommendations for using Artificial Intelligence in Intracoronary Optical Coherence Tomography Analysis
Authors: Xu Chen, Yuan Huang, Benn Jessney, Jason Sangha, Sophie Gu, Carola-Bibiane Schönlieb, Martin Bennett, Michael Roberts
Date: 2025-01-24 00:00:00
Tasks: Diagnostic

Artificial intelligence (AI) methodologies hold great promise for the rapid and accurate diagnosis of coronary artery disease (CAD) from intravascular optical coherent tomography (IVOCT) images. Numerous papers have been published describing AI-based models for different diagnostic tasks, yet it remains unclear which models have potential clinical utility and have been properly validated. This systematic review considered published literature between January 2015 and February 2023 describing AI-based diagnosis of CAD using IVOCT. Our search identified 5,576 studies, with 513 included after initial screening and 35 studies included in the final systematic review after quality screening. Our findings indicate that most of the identified models are not currently suitable for clinical use, primarily due to methodological flaws and underlying biases. To address these issues, we provide recommendations to improve model quality and research practices to enhance the development of clinically useful AI products.