pwc-archive/papers-with-abstracts huggingface dataset:
Model-Based Closed-Loop Control Algorithm for Stochastic Partial Differential Equation Control
Authors: Peiyan Hu, Haodong Feng, Yue Wang, ZhiMing Ma
Date: 2025-05-08 00:00:00
Tasks: Data Augmentation

Neural operators have demonstrated promise in modeling and controlling systems governed by Partial Differential Equations (PDEs). Beyond PDEs, Stochastic Partial Differential Equations (SPDEs) play a critical role in modeling systems influenced by randomness, with applications in finance, physics, and beyond. However, controlling SPDE-governed systems remains a significant challenge. On the one hand, the regularity of the system's state (which can be intuitively understood as smoothness) deteriorates, making modeling and generalization more challenging. On the other hand, this stochasticity also renders control more unstable and thus less accurate. To address this gap, we propose the Model-Based Closed-Loop Control Algorithm (MB-CC), the first model-based closed-loop control method for SPDEs. MB-CC introduces two key innovations to enhance control robustness and efficiency: a Regularity Feature (RF) block and a closed-loop strategy with an operator-encoded policy network. The RF block, inspired by the regularity structure theory of SPDEs, addresses noise-induced irregularities by transforming the network's input, including the system state and noise-perturbed external forces, into a refined feature space for improved forward prediction. Compared to previous works using regularity features, we introduce a new parameterization, data augmentation, and extend the RF block as a plug-and-play component. Additionally, to achieve closed-loop control, we introduce an operator-encoded policy network to map the current state to optimal control, which integrates physical priors and swiftly makes decisions based on states returned by the environment. We conduct a systematic evaluation of MB-CC on two notable SPDEs, showcasing its effectiveness and efficiency. The ablation studies show its ability to handle stochasticity more effectively.
A critical note on the sideband peak count-index technique: failure for nonlinear damage characterization of impacted CFRP plates
Authors: Bernd Köhler, Frank Schubert
Date: 2025-05-08 00:00:00

It is widely accepted, that nonlinear elastodynamic methods are superior to linear methods in detecting early stages of material deterioration. A number of recently developed methods are reported to be particularly sensitive to nonlinearities and thus appropriate to indicate early damage. We applied systematically one of the methods, the sideband peak count index (SPC-I), to a series of increasingly damaged carbon fiber reinforced plastic (CFRP) plates. Our data leads to different conclusions. The SPC-I values are influenced by (usually undocumented) variations in the index calculation procedure, which is not acceptable for a robust method. Moreover, the behavior of the index when the ultrasound amplitude is varied contradicts material nonlinearity as a direct and significant contributor to the index value. To clarify the apparent contradiction of our results with the previously published statements, it is recommended that (a) our data are re-evaluated by independent researchers and (b) the experiments already published are repeated or (if sufficient data is available) also re-evaluated.
Spatially Structured Flocking in a Proliferating Population of Self-Propelled Organisms without Explicit Alignment Interactions
Authors: Seiya Takamura, Nen Saito
Date: 2025-05-10 00:00:00

While it is well established that self-propelled particles with alignment interactions can exhibit orientational order, the impact of self-replication and annihilation, which are key characteristics in cellular systems, on spatiotemporal order remains poorly understood. To explore the interplay between self-propulsion and self-replication, we introduce the active Brownian bug (ABB) model, in which self-propelled agents undergo stochastic, density-dependent replication and constant-rate death. Despite the absence of alignment interactions, the system exhibits flocking behavior characterized by high orientational order, while maintaining ordered hexagonal arrays. This emergent order arises from stochastic birth and death processes, offering a novel mechanism for flocking in proliferating cellular populations.
How spatial patterns can lead to less resilient ecosystems
Authors: David Pinto-Ramos, Ricardo Martinez-Garcia
Date: 2025-05-13 00:00:00

Several theoretical models predict that spatial patterning increases ecosystem resilience. However, these predictions rely on strong simplifying assumptions, such as isotropic and infinite ecosystems, and we lack empirical evidence directly linking spatial patterning to enhanced resilience. We introduce a unifying framework, encompassing existing models for vegetation pattern formation in water-stressed ecosystems, that relaxes these assumptions. This framework incorporates finite vegetated areas surrounded by desert and anisotropic environmental conditions that induce non-reciprocal plant interactions. Under these more realistic conditions, we identify a novel desertification mechanism, known as convective instability in physics but largely overlooked in ecology. These instabilities form when non-reciprocal interactions destabilize the vegetation-desert interface and can trigger desertification fronts even under stress levels where isotropic models predict stability. Importantly, ecosystems with periodic vegetation patterns are more vulnerable to convective instabilities than those with homogeneous vegetation, suggesting that spatial patterning may reduce, rather than enhance, resilience. These findings challenge the view of self-organized patterns as indicators of resilience and provide a new framework to investigate how spatial dynamics determine the stability and resilience of ecological systems across scales.
Antagonistic coinfection in rock-paper-scissors models during concurrent epidemics
Authors: J. Menezes, R. Menezes, S. Batista, E. Rangel
Date: 2025-05-09 00:00:00

We investigate the dynamics of dual disease epidemics within the spatial rock-paper-scissors model. In this framework, individuals from all species are equally susceptible to infection by two distinct pathogens transmitted via person-to-person contact. We assume antagonistic mortality, where the simultaneous occurrence of coinfection reduces the probability of host mortality due to complications arising from either coexisting disease. Specifically, we explore two scenarios: global antagonism, where the presence of one pathogen inhibits the progression of the other in coinfected hosts, and uneven antagonism, where only one pathogen affects the development of the other. Using stochastic simulations, we show that the characteristic length scale of the spatial patterns emerging from random initial conditions diminishes as antagonism becomes more significant. We find that antagonism enhances species population growth and reduces the average probability of healthy organisms becoming infected. Additionally, introducing individuals' mobility restrictions significantly decreases both organisms' infection risk and selection pressures. Our results demonstrate that combining mobility restrictions with antagonistic coinfection can increase organisms' life expectancy by up to $54\%$. Our findings show that integrating antagonistic coinfection and mobility restriction strategies into ecological models may provide insights into designing interventions for managing concurrent epidemics in complex systems.
AI-driven multi-source data fusion for algal bloom severity classification in small inland water bodies: Leveraging Sentinel-2, DEM, and NOAA climate data
Authors: Ioannis Nasios
Date: 2025-05-02 00:00:00

Harmful algal blooms are a growing threat to inland water quality and public health worldwide, creating an urgent need for efficient, accurate, and cost-effective detection methods. This research introduces a high-performing methodology that integrates multiple open-source remote sensing data with advanced artificial intelligence models. Key data sources include Copernicus Sentinel-2 optical imagery, the Copernicus Digital Elevation Model (DEM), and NOAA's High-Resolution Rapid Refresh (HRRR) climate data, all efficiently retrieved using platforms like Google Earth Engine (GEE) and Microsoft Planetary Computer (MPC). The NIR and two SWIR bands from Sentinel-2, the altitude from the elevation model, the temperature and wind from NOAA as well as the longitude and latitude were the most important features. The approach combines two types of machine learning models, tree-based models and a neural network, into an ensemble for classifying algal bloom severity. While the tree models performed strongly on their own, incorporating a neural network added robustness and demonstrated how deep learning models can effectively use diverse remote sensing inputs. The method leverages high-resolution satellite imagery and AI-driven analysis to monitor algal blooms dynamically, and although initially developed for a NASA competition in the U.S., it shows potential for global application. The complete code is available for further adaptation and practical implementation, illustrating the convergence of remote sensing data and AI to address critical environmental challenges (https://github.com/IoannisNasios/HarmfulAlgalBloomDetection).
Full simulation on the dynamics of auditory synaptic fusion: Strong clustering of calcium channel might be the origin of the coherent release in the auditory hair cells
Authors: Jaeyun Yoo, Kang-Hun Ahn
Date: 2025-05-12 00:00:00
Tasks: speech-recognition, Speech Recognition

The precise timing of synaptic transmission in auditory hair cells is important to hearing and speech recognition. Neurotransmitter release is an underlying step in translating sound. Thus, understanding nature of the synaptic fusion is key to understand the hearing mechanism. Extraordinary large excitatory postsynaptic currents (EPSCs) have been observed in the auditory hair cell synapse, and its origin has been controversial. It is not known yet whether the size and shape of the EPSCs are results of a big vesicle or many small vesicles. We report our numerical simulation of the vesicular fusion process from calcium channel process to the generation of EPSC currents. Our numerical experiments indicate that the origin of the large EPSC with its mysterious form is close to the scenario of the multivesicular release. The large EPSCs might be triggered by strong calcium channeling of the calcium channel clusters.
CVTree for 16S rRNA: Constructing Taxonomy-Compatible All-Species Living Tree Effectively and Efficiently
Authors: Yi-Fei Lu, Xiao-Yang Zhi, Guang-Hong Zuo
Date: 2025-05-12 00:00:00
Tasks: All, Multiple Sequence Alignment

The Composition Vector Tree (CVTree) method, developed under the leadership of Professor Hao Bailin, is an alignment-free algorithm for constructing phylogenetic trees. Although initially designed for studying prokaryotic evolution based on whole-genome, it has demonstrated broad applicability across diverse biological systems and gene sequences. In this study, we employed two methods, InterList and Hao, of CVTree to investigate the phylogeny and taxonomy of prokaryote based on the 16S rRNA sequences from All-Species Living Tree Project. We have established a comprehensive phylogenetic tree that incorporates the majority of species documented in human scientific knowledge and compared it with the taxonomy of prokaryotes. And the performance of CVTree were also compared with multiple sequence alignment-based approaches. Our results revealed that CVTree methods achieve computational speeds 1-3 orders of magnitude faster than conventional alignment methods while maintaining high consistency with established taxonomic relationships, even outperforming some multiple sequence alignment methods. These findings confirm CVTree's effectiveness and efficiency not only for whole-genome evolutionary studies but also for phylogenetic and taxonomic investigations based on genes.
The Dynamics of Inducible Genetic Circuits
Authors: Zitao Yang, Rebecca J. Rousseau, Sara D. Mahdavi, Hernan G. Garcia, Rob Phillips
Date: 2025-05-11 00:00:00

Genes are connected in complex networks of interactions where often the product of one gene is a transcription factor that alters the expression of another. Many of these networks are based on a few fundamental motifs leading to switches and oscillators of various kinds. And yet, there is more to the story than which transcription factors control these various circuits. These transcription factors are often themselves under the control of effector molecules that bind them and alter their level of activity. Traditionally, much beautiful work has shown how to think about the stability of the different states achieved by these fundamental regulatory architectures by examining how parameters such as transcription rates, degradation rates and dissociation constants tune the circuit, giving rise to behavior such as bistability. However, such studies explore dynamics without asking how these quantities are altered in real time in living cells as opposed to at the fingertips of the synthetic biologist's pipette or on the computational biologist's computer screen. In this paper, we make a departure from the conventional dynamical systems view of these regulatory motifs by using statistical mechanical models to focus on endogenous signaling knobs such as effector concentrations rather than on the convenient but more experimentally remote knobs such as dissociation constants, transcription rates and degradation rates that are often considered. We also contrast the traditional use of Hill functions to describe transcription factor binding with more detailed thermodynamic models. This approach provides insights into how biological parameters are tuned to control the stability of regulatory motifs in living cells, sometimes revealing quite a different picture than is found by using Hill functions and tuning circuit parameters by hand.
From Brain to Motion: Harnessing Higher-Derivative Mechanics for Neural Control
Authors: O. White, F. Buisseret, F. Dierick, N. Boulanger
Date: 2025-05-12 00:00:00

Optimal Feedback Control (OFC) provides a theoretical framework for goal-directed movements, where the nervous system adjusts actions based on sensory feedback. In OFC, the central nervous system (CNS) not only reacts to stimuli but proactively predicts and adjusts motor commands, minimizing errors and (often energetic) costs through internal models. OFC theory assumes that there exists a cost function that is optimized throughout one's movement. It is natural to assume that mechanical quantities should be involved in cost functions. This does not imply that the mechanical principles that govern human voluntary movements are necessarily Newtonian. Indeed, the undisputed efficiency of Newtonian mechanics to model and predict the motion of non-living systems does not guarantee its relevance to model human behavior. We propose that integrating principles from Lagrangian and Hamiltonian higher-derivative mechanics, i.e. dynamical models that go beyond Newtonian mechanics, provides a more natural framework to study the constraints hidden in human voluntary movement within OFC theory.
A general physics-constrained method for the modelling of equation's closure terms with sparse data
Authors: Tian Chen, Shengping Liu, Li Liu, Heng Yong
Date: 2025-04-30 00:00:00

Accurate modeling of closure terms is a critical challenge in engineering and scientific research, particularly when data is sparse (scarse or incomplete), making widely applicable models difficult to develop. This study proposes a novel approach for constructing closure models in such challenging scenarios. We introduce a Series-Parallel Multi-Network Architecture that integrates Physics-Informed Neural Networks (PINNs) to incorporate physical constraints and heterogeneous data from multiple initial and boundary conditions, while employing dedicated subnetworks to independently model unknown closure terms, enhancing generalizability across diverse problems. These closure models are integrated into an accurate Partial Differential Equation (PDE) solver, enabling robust solutions to complex predictive simulations in engineering applications.
Attonsecond Streaking Phase Retrieval Via Deep Learning Methods
Authors: Yuzhou Zhu, Zheng Zhang, Ruyi Zhang, Liang Zhou
Date: 2025-05-06 00:00:00
Tasks: Deep Learning, Graph Attention, Inductive Bias, Retrieval, Sensitivity

Attosecond streaking phase retrieval is essential for resolving electron dynamics on sub-femtosecond time scales yet traditional algorithms rely on iterative minimization and central momentum approximations that degrade accuracy for broadband pulses. In this work phase retrieval is reformulated as a supervised computer-vision problem and four neural architectures are systematically compared. A convolutional network demonstrates strong sensitivity to local streak edges but lacks global context; a vision transformer captures long-range delay-energy correlations at the expense of local inductive bias; a hybrid CNN-ViT model unites local feature extraction and full-graph attention; and a capsule network further enforces spatial pose agreement through dynamic routing. A theoretical analysis introduces local, global and positional sensitivity measures and derives surrogate error bounds that predict the strict ordering $CNN<ViT<Hybrid<Capsule$. Controlled experiments on synthetic streaking spectrograms confirm this hierarchy, with the capsule network achieving the highest retrieval fidelity. Looking forward, embedding the strong-field integral into physics-informed neural networks and exploring photonic hardware implementations promise pathways toward real-time attosecond pulse characterization under demanding experimental conditions.
On Selection of Cross-Section Averages in Non-stationary Environments
Authors: Jan Ditzen, Ovidijus Stauskas
Date: 2025-05-13 00:00:00

Information criteria (IC) have been widely used in factor models to estimate an unknown number of latent factors. It has recently been shown that IC perform well in Common Correlated Effects (CCE) and related setups in selecting a set of cross-section averages (CAs) sufficient for the factor space under stationary factors. As CAs can proxy non-stationary factors, it is tempting to claim such generality of IC, too. We show formally and in simulations that IC have a severe underselection issue even under very mild forms of factor non-stationarity, which goes against the sentiment in the literature.
Team Networks with Partially Observed Links
Authors: Yang Xu
Date: 2025-05-13 00:00:00

This paper studies a linear production model in team networks with missing links. In the model, heterogeneous workers, represented as nodes, produce jointly and repeatedly within teams, represented as links. Links are omitted when their associated outcome variables fall below a threshold, resulting in partial observability of the network. To address this, I propose a Generalized Method of Moments estimator under normally distributed errors and develop a distribution-free test for detecting link truncation. Applied to academic publication data, the estimator reveals and corrects a substantial downward bias in the estimated scaling factor that aggregates individual fixed effects into team-specific fixed effects. This finding suggests that the collaboration premium may be systematically underestimated when missing links are not properly accounted for.
Exploring Monetary Policy Shocks with Large-Scale Bayesian VARs
Authors: Dimitris Korobilis
Date: 2025-05-10 00:00:00

I introduce a high-dimensional Bayesian vector autoregressive (BVAR) framework designed to estimate the effects of conventional monetary policy shocks. The model captures structural shocks as latent factors, enabling computationally efficient estimation in high-dimensional settings through a straightforward Gibbs sampler. By incorporating time variation in the effects of monetary policy while maintaining tractability, the methodology offers a flexible and scalable approach to empirical macroeconomic analysis using BVARs, well-suited to handle data irregularities observed in recent times. Applied to the U.S. economy, I identify monetary shocks using a combination of high-frequency surprises and sign restrictions, yielding results that are robust across a wide range of specification choices. The findings indicate that the Federal Reserve's influence on disaggregated consumer prices fluctuated significantly during the 2022-24 high-inflation period, shedding new light on the evolving dynamics of monetary policy transmission.
Beyond the Mean: Limit Theory and Tests for Infinite-Mean Autoregressive Conditional Durations
Authors: Giuseppe Cavaliere, Thomas Mikosch, Anders Rahbek, Frederik Vilandt
Date: 2025-05-09 00:00:00

Integrated autoregressive conditional duration (ACD) models serve as natural counterparts to the well-known integrated GARCH models used for financial returns. However, despite their resemblance, asymptotic theory for ACD is challenging and also not complete, in particular for integrated ACD. Central challenges arise from the facts that (i) integrated ACD processes imply durations with infinite expectation, and (ii) even in the non-integrated case, conventional asymptotic approaches break down due to the randomness in the number of durations within a fixed observation period. Addressing these challenges, we provide here unified asymptotic theory for the (quasi-) maximum likelihood estimator for ACD models; a unified theory which includes integrated ACD models. Based on the new results, we also provide a novel framework for hypothesis testing in duration models, enabling inference on a key empirical question: whether durations possess a finite or infinite expectation. We apply our results to high-frequency cryptocurrency ETF trading data. Motivated by parameter estimates near the integrated ACD boundary, we assess whether durations between trades in these markets have finite expectation, an assumption often made implicitly in the literature on point process models. Our empirical findings indicate infinite-mean durations for all the five cryptocurrencies examined, with the integrated ACD hypothesis rejected -- against alternatives with tail index less than one -- for four out of the five cryptocurrencies considered.
Estimation and Inference in Boundary Discontinuity Designs
Authors: Matias D. Cattaneo, Rocio Titiunik, Ruiqi Rae Yu
Date: 2025-05-08 00:00:00
Tasks: regression

Boundary Discontinuity Designs are used to learn about treatment effects along a continuous boundary that splits units into control and treatment groups according to a bivariate score variable. These research designs are also called Multi-Score Regression Discontinuity Designs, a leading special case being Geographic Regression Discontinuity Designs. We study the statistical properties of commonly used local polynomial treatment effects estimators along the continuous treatment assignment boundary. We consider two distinct approaches: one based explicitly on the bivariate score variable for each unit, and the other based on their univariate distance to the boundary. For each approach, we present pointwise and uniform estimation and inference methods for the treatment effect function over the assignment boundary. Notably, we show that methods based on univariate distance to the boundary exhibit an irreducible large misspecification bias when the assignment boundary has kinks or other irregularities, making the distance-based approach unsuitable for empirical work in those settings. In contrast, methods based on the bivariate score variable do not suffer from that drawback. We illustrate our methods with an empirical application. Companion general-purpose software is provided.