pwc-archive/papers-with-abstracts huggingface dataset:
An open problem: Why are motif-avoidant attractors so rare in asynchronous Boolean networks?
Authors: Samuel Pastva, Kyu Hyong Park, Ondrej Huvar, Jordan C Rozum, Reka Albert
Date: 2024-10-04 00:00:00

Asynchronous Boolean networks are a type of discrete dynamical system in which each variable can take one of two states, and a single variable state is updated in each time step according to pre-selected rules. Boolean networks are popular in systems biology due to their ability to model long-term biological phenotypes within a qualitative, predictive framework. Boolean networks model phenotypes as attractors, which are closely linked to minimal trap spaces (inescapable hypercubes in the system's state space). In biological applications, attractors and minimal trap spaces are typically in one-to-one correspondence. However, this correspondence is not guaranteed: motif-avoidant attractors (MAAs) that lie outside minimal trap spaces are possible. MAAs are rare and (despite recent efforts) poorly understood. Here we summarize the current state of knowledge regarding MAAs and present several novel observations regarding their response to node deletion reductions and linear extensions of edges. We conduct large-scale computational studies on an ensemble of 14,000 models derived from published Boolean models of biological systems, and more than 100 million Random Boolean Networks. Our findings quantify the rarity of MAAs (in particular, we found no MAAs in the biological models), but highlight the role of network reduction in introducing MAAs into the dynamics. We also show that MAAs are fragile to linear extensions: in sparse networks, even a single linear node can disrupt virtually all MAAs. Motivated by this observation, we improve the upper bound on the number of delays needed to disrupt a motif-avoidant attractor.
Traveling vegetation-herbivore waves can sustain ecosystems threatened by droughts and population growth
Authors: Joydeep Singha, Hannes Uecker, Ehud Meron
Date: 2024-10-05 00:00:00

Dryland vegetation can survive water stress by forming spatial patterns but is often subjected to herbivory as an additional stress that puts it at risk of desertification. Understanding the mutual relationships between vegetation patterning and herbivory is crucial for securing food production in drylands, which constitute the majority of rangelands worldwide. Here, we introduce a novel vegetation-herbivore model that captures pattern-forming feedbacks associated with water and herbivory stress and a behavioral aspect of herbivores representing an exploitation strategy.Applying numerical continuation methods, we analyze the bifurcation structure of uniform and patterned vegetation-herbivore solutions, and use direct numerical simulations to study various forms of collective herbivore dynamics. We find that herbivory stress can induce traveling vegetation-herbivore waves and uncover the ecological mechanism that drives their formation. In the traveling-wave state, the herbivore distribution is asymmetric with higher density on one side of each vegetation patch. At low precipitation values their distribution is localized, while at high precipitation the herbivores are spread over the entire landscape. Importantly, their asymmetric distribution results in uneven herbivory stress, strong on one side of each vegetation patch and weak on the opposing side - weaker than the stress exerted in spatially uniform herbivore distribution. Consequently, the formation of traveling waves results in increased sustainability to herbivory stress. We conclude that vegetation-herbivore traveling waves may play an essential role in sustaining herbivore populations under conditions of combined water and herbivory stress, thereby contributing to food security in endangered regions threatened by droughts and population growth.
Global dynamical structures from infinitesimal data
Authors: Benjamin McInroe, Robert J. Full, Daniel E. Koditschek, Yuliy Baryshnikov
Date: 2024-10-03 00:00:00

Discovering mechanisms underlying the behaviors of complex, high dimensional, and nonlinear dynamical systems is a central goal of the natural and synthetic sciences. Breakthroughs in machine learning in concert with increasing capacities for computation and data collection have enabled the use of trajectory measurements for learning predictive models. However, rigorous approaches for interpreting mechanisms from such models remain elusive, and asymptotic prediction accuracy suffers if the model does not capture important state space structures (e.g., attracting invariant sets). These limitations are especially pressing for system-level behaviors such as whole-body locomotion, where discontinuous, transient, and multiscale phenomena are common and prior models are rare. To take the next step towards a theory and practice for dynamical inference of complex multiscale systems in biology and beyond, we introduce VERT, a framework for learning the attracting sets that characterize global system behavior without recourse to learning a global model. Our approach is based on an infinitesimal-local-global (ILG) framework for estimating the proximity of any sampled state to the attracting set, if one exists, with formal accuracy guarantees. We demonstrate our approach on phenomenological and physical oscillators with hierarchical and impulsive dynamics, finding sensitivity to both global and intermediate attractors composed in sequence and parallel. Application of VERT to human running kinematics data reveals insight into control modules that stabilize task-level dynamics, supporting a longstanding neuromechanical control hypothesis. The VERT framework thus enables rigorous inference of underlying dynamical structure even for systems where learning a global dynamics model is impractical or impossible.
Data-Driven Discovery of Conservation Laws from Trajectories via Neural Deflation
Authors: Shaoxuan Chen, Panayotis G. Kevrekidis, Hong-Kun Zhang, Wei Zhu
Date: 2024-10-07 00:00:00

In an earlier work by a subset of the present authors, the method of the so-called neural deflation was introduced towards identifying a complete set of functionally independent conservation laws of a nonlinear dynamical system. Here, we extend by a significant step this proposal. Instead of using the explicit knowledge of the underlying equations of motion, we develop the method directly from system trajectories. This is crucial towards enhancing the practical implementation of the method in scenarios where solely data reflecting discrete snapshots of the system are available. We showcase the results of the method and the number of associated conservation laws obtained in a diverse range of examples including 1D and 2D harmonic oscillators, the Toda lattice, the Fermi-Pasta-Ulam-Tsingou lattice and the Calogero-Moser system.
Continuous Ensemble Weather Forecasting with Diffusion models
Authors: Martin Andrae, Tomas Landelius, Joel Oskarsson, Fredrik Lindsten
Date: 2024-10-07 00:00:00
Tasks: Weather Forecasting

Weather forecasting has seen a shift in methods from numerical simulations to data-driven systems. While initial research in the area focused on deterministic forecasting, recent works have used diffusion models to produce skillful ensemble forecasts. These models are trained on a single forecasting step and rolled out autoregressively. However, they are computationally expensive and accumulate errors for high temporal resolution due to the many rollout steps. We address these limitations with Continuous Ensemble Forecasting, a novel and flexible method for sampling ensemble forecasts in diffusion models. The method can generate temporally consistent ensemble trajectories completely in parallel, with no autoregressive steps. Continuous Ensemble Forecasting can also be combined with autoregressive rollouts to yield forecasts at an arbitrary fine temporal resolution without sacrificing accuracy. We demonstrate that the method achieves competitive results for global weather forecasting with good probabilistic properties.
El Nino Southern Oscillation and Atlantic Multidecadal Oscillation Impact on Hurricanes North Atlantic Basin
Authors: Suchit Basineni
Date: 2024-10-05 00:00:00

Tropical cyclones (TCs), including hurricanes and typhoons, cause significant property damage and result in fatalities, making it crucial to understand the factors driving extreme TCs. The El Nino Southern Oscillation (ENSO) influences TC formation through tropospheric vorticity, wind shear, and atmospheric circulations. Apart from atmospheric changes, oceans influence activity through sea surface temperatures (SSTs) and deep ocean heat content. These Atlantic SSTs determine the Atlantic Multidecadal Oscillation (AMO), which indicates SST variability in the Atlantic. This research focuses on ENSO, AMO, and SSTs impact on the strength and frequency of TCs in the North Atlantic Basin. AMO and SST anomalies are increasing at an alarming rate, but it remains unclear how their dynamics will influence future TC behavior. I used observational cyclone track data from 1950 to 2023, the Oceanic Nino Index (ONI), and NOAAs Extended Reconstructed SST V5 (ERSST). I found that Increasing SSTs over the past decade indicate stronger TCs, while warm phase AMO periods correspond with higher TC frequency. Meanwhile, a greater frequency of landfalling TCs can be attributed to La Nina or ENSO-neutral, with El Nino decreasing the frequency of landfalling TCs. Such relationships suggest that as the seasonal predictability of ENSO and SSTs improve, seasonal TC forecasts may improve.
The multiscale self-similarity of the weighted human brain connectome
Authors: Laia Barjuan, Muhua Zheng, M. Ángeles Serrano
Date: 2024-10-09 00:00:00

Anatomical connectivity between different regions in the brain can be mapped to a network representation, the connectome, where the intensities of the links, the weights, influence its structural resilience and the functional processes it sustains. Yet, many features associated with the weights in the human brain connectome are not fully understood, particularly their multiscale organization. In this paper, we elucidate the architecture of weights, including weak ties, in multiscale hierarchical human brain connectomes reconstructed from empirical data. Our findings reveal multiscale self-similarity in the weighted statistical properties, including the ordering of weak ties, that remain consistent across the analyzed length scales of every individual and the group representatives. This phenomenon is effectively captured by a renormalization of the weighted structure applied to hyperbolic embeddings of the connectomes, based on a unique weighted geometric model that integrates links of all weights across all length scales. This eliminates the need for separate generative weighted connectivity rules for each scale or to replicate weak and strong ties at specific scales in brain connectomes. The observed symmetry represents a distinct signature of criticality in the weighted connectivity of human brain connectomes, aligning with the fractality observed in their topology, and raises important questions for future research, like the existence of a resolution threshold where the observed symmetry breaks, or whether it is preserved in cases of neurodegenerative disease or psychiatric disorder.
Dynamics of Adaptive Continuous Attractor Neural Networks
Authors: YuJun Li, Tianhao Chu, Si Wu
Date: 2024-10-09 00:00:00

Attractor neural networks consider that neural information is stored as stationary states of a dynamical system formed by a large number of interconnected neurons. The attractor property empowers a neural system to encode information robustly, but it also incurs the difficulty of rapid update of network states, which can impair information update and search in the brain. To overcome this difficulty, a solution is to include adaptation in the attractor network dynamics, whereby the adaptation serves as a slow negative feedback mechanism to destabilize which are otherwise permanently stable states. In such a way, the neural system can, on one hand, represent information reliably using attractor states, and on the other hand, perform computations wherever rapid state updating is involved. Previous studies have shown that continuous attractor neural networks with adaptation (A-CANNs) exhibits rich dynamical behaviors accounting for various brain functions. In this paper, we present a comprehensive view of the rich diverse dynamics of A-CANNs. Moreover, we provide a unified mathematical framework to understand these different dynamical behaviors, and briefly discuss about their biological implications.
Node-reconfiguring multilayer networks of human brain function
Authors: Tarmo Nurmi, Pietro De Luca, Maria Hakonen, Mikko Kivelä, Onerva Korhonen
Date: 2024-10-08 00:00:00
Tasks: Functional Connectivity

Functional brain network properties are heavily influenced by how the the network nodes are defined. A common approach uses Regions of Interest (ROIs), i.e., predetermined collections of functional magnetic resonance imaging (fMRI) measurement voxels, as nodes. Their definition is always a compromise, as static ROIs cannot capture the dynamics and temporal reconfigurations of the brain areas. Consequently, the ROIs do not align with the functionally homogeneous regions, which can explain the low functional homogeneity values observed for the ROIs. This is in violation of the underlying homogeneity assumption in functional brain network analysis pipelines, which can cause serious problems such as spurious network structure. We introduce the node-reconfiguring multilayer network model, where nodes represent ROIs with boundaries optimized for high functional homogeneity in each time window. In this representation, network layers correspond to time windows, intralayer links depict functional connectivity between ROIs, and interlayer links quantify the overlap between ROIs on different layers. The ROI optimization approach increases functional homogeneity notably, yielding an over 10-fold increase in the fraction of ROIs with high homogeneity compared to static ROIs from the Brainnetome atlas. The optimized ROIs reorganize non-trivially at short time scales of consecutive time windows and across several windows. The amount of reorganization across time windows is connected to intralayer hubness: ROIs with intermediate levels of reorganization have stronger intralayer links than extremely stable or unstable ROIs. Our results demonstrate that reconfiguring parcellations yield more accurate network models of brain function. This supports the ongoing paradigm shift towards the chronnectome that sees the brain as a set of sources with continuously reconfiguring spatial and connectivity profiles.
Electronics-free passive ultrasonic communication link for deep-tissue sensor implants
Authors: Umut Can Yener, Alp Timucin Toymus, Kivanc Esat, Mehdi Alem, Levent Beker
Date: 2024-09-20 00:00:00

Wireless communication is critical for the non-invasive in-situ monitoring of vital signs in deep tissues. Wireless ultrasonic links demonstrated so far solved the shortcomings of electromagnetic wave-based communication methods. However, they either require highly customized and complex designs for the implant electronics or rely on physical changes in implanted metamaterials. Here, we report a wireless, passive, frequency-based, and electronics-free ultrasonic communication method for deep tissue sensor implants. The device consists of a piezoelectric crystal used as the ultrasonic antenna, considerably simplifying the implant design, and can be integrated with any capacitive implantable sensor. We demonstrate the applicability of the passive communication method using a commercial pressure sensor at a depth of 5 cm with decent precision and accuracy.
Comparative Analysis of Deep Learning Architectures for Breast Region Segmentation with a Novel Breast Boundary Proposal
Authors: Sam Narimani, Solveig Roth Hoff, Kathinka Dæhli Kurz, Kjell-Inge Gjesdal, Jurgen Geisler, Endre Grovik
Date: 2024-10-03 00:00:00
Tasks: Boundary Detection, Segmentation

Purpose: Segmentation of the breast region in dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) is essential for the automatic measurement of breast density and the quantitative analysis of imaging findings. This study aims to compare various deep learning methods to enhance whole breast segmentation and reduce computational costs as well as environmental effect for future research. Methods: We collected fifty-nine DCE-MRI scans from Stavanger University Hospital and, after preprocessing, analyzed fifty-eight scans. The preprocessing steps involved standardizing imaging protocols and resampling slices to ensure consistent volume across all patients. Using our novel approach, we defined new breast boundaries and generated corresponding segmentation masks. We evaluated seven deep learning models for segmentation namely UNet, UNet++, DenseNet, FCNResNet50, FCNResNet101, DeepLabv3ResNet50, and DeepLabv3ResNet101. To ensure robust model validation, we employed 10-fold cross-validation, dividing the dataset into ten subsets, training on nine, and validating on the remaining one, rotating this process to use all subsets for validation. Results: The models demonstrated significant potential across multiple metrics. UNet++ achieved the highest performance in Dice score, while UNet excelled in validation and generalizability. FCNResNet50, notable for its lower carbon footprint and reasonable inference time, emerged as a robust model following UNet++. In boundary detection, both UNet and UNet++ outperformed other models, with DeepLabv3ResNet also delivering competitive results.
Topological Components in a Community Currency Network
Authors: Teodoro Criscione
Date: 2024-09-20 00:00:00
Tasks: Humanitarian

Transaction data from digital payment systems can be used to study economic processes in such detail that was not possible previously. Here, data from the Sarafu token network, a Community Inclusion Currency in Kenya, is analysed. During the COVID-19 emergency, Sarafu was distributed as part of a humanitarian aid project. In this work, the transactions are analysed using network science. A topological categorisation is defined to identify cyclic and acyclic components. Furthermore, temporal aspects of the circulation that takes place within these components are considered. The significant presence of different types of strongly connected components compared to randomised null models shows the importance of cycles in this economic network. Especially, indicating their key role in currency recirculation. In some acyclic components, the most significant triad suggests the presence of a group of users collecting currency from accounts that are active only once, hinting at a possible misuse of the system. In some other acyclic components, small isolated groups of users were active only once, suggesting the presence of users only interested in trying the system out. The methods used in this paper can answer specific questions related to user activities, currency design, and assessment of monetary interventions. The methodology provides a general quantitative tool to analyse the behaviour of users in a currency network.
Fractional and fractal extensions of epidemiological models
Authors: Enrique C. Gabrick, Ervin K. Lenzi, Antonio M. Batista
Date: 2024-09-08 00:00:00

One way to study the spread of disease is through mathematical models. The most successful models compartmentalize the host population according to their infectious stage, e.g., susceptible (S), infected (I), exposed (E), and recovered (R). The composition of these compartments leads to the SI, SIS, SIR, and SEIR models. In this Chapter, we present and compare three formulations of SI, SIS, SIR, and SEIR models in the framework of standard (integer operators), fractional (Caputo sense), and fractal derivatives (Hausdorff sense). As an application of the SI model, we study the evolution of AIDS cases in Bangladesh from 2001 to 2021. For this case, our simulations suggest that fractal formulation describes the data well. For the SIS model, we consider syphilis data from Brazil from 2006 to 2017. In this case, the three frameworks describe the data with good accuracy. We used data from Influenza A to adjust the SIR model in previous approaches and observed that the fractional formulation was better. The last application considers the COVID-19 data from India in the range 2020-04-10 to 2020-12-31 to adjust the parameters of the SEIR model. The standard formulation fits the data better than the other approaches. As a common result, all models exhibit steady solutions in the different formulations. The time to reach a steady solution is correlated to the considered approach. The standard and fractal formulations reach the steady state earlier when compared with the fractional formulation.
Quantum-Inspired Portfolio Optimization In The QUBO Framework
Authors: Ying-Chang Lu, Chao-Ming Fu, Lien-Po Yu, Yen-Jui Chang, Ching-Ray Chang
Date: 2024-10-08 00:00:00
Tasks: Computational Efficiency, Management, Portfolio Optimization

A quantum-inspired optimization approach is proposed to study the portfolio optimization aimed at selecting an optimal mix of assets based on the risk-return trade-off to achieve the desired goal in investment. By integrating conventional approaches with quantum-inspired methods for penalty coefficient estimation, this approach enables faster and accurate solutions to portfolio optimization which is validated through experiments using a real-world dataset of quarterly financial data spanning over ten-year period. In addition, the proposed preprocessing method of two-stage search further enhances the effectiveness of our approach, showing the ability to improve computational efficiency while maintaining solution accuracy through appropriate setting of parameters. This research contributes to the growing body of literature on quantum-inspired techniques in finance, demonstrating its potential as a useful tool for asset allocation and portfolio management.
Green bubbles: a four-stage paradigm for detection and propagation
Authors: Gian Luca Vriz, Luigi Grossi
Date: 2024-10-09 00:00:00
Tasks: Time Series

Climate change has emerged as a significant global concern, attracting increasing attention worldwide. While green bubbles may be examined through a social bubble hypothesis, it is essential not to neglect a Climate Minsky moment triggered by sudden asset price changes. The significant increase in green investments highlights the urgent need for a comprehensive understanding of these market dynamics. Therefore, the current paper introduces a novel paradigm for studying such phenomena. Focusing on the renewable energy sector, Statistical Process Control (SPC) methodologies are employed to identify green bubbles within time series data. Furthermore, search volume indexes and social factors are incorporated into established econometric models to reveal potential implications for the financial system. Inspired by Joseph Schumpeter's perspectives on business cycles, this study recognizes green bubbles as a necessary evil for facilitating a successful transition towards a more sustainable future.