Learning Continuous Network Emerging Dynamics from Scarce Observations via Data-Adaptive Stochastic Processes

Learning network dynamics from the empirical structure and spatio-temporal observation data is crucial to revealing the interaction mechanisms of complex networks in a wide range of domains. However, most existing methods only aim at learning network dynamic behaviors generated by a specific ordinary differential equation instance, resulting in ineffectiveness for new ones, and generally require dense observations. The observed data, especially from network emerging dynamics, are usually difficult to obtain, which brings trouble to model learning. Therefore, how to learn accurate network dynamics with sparse, irregularly-sampled, partial, and noisy observations remains a fundamental challenge. We introduce Neural ODE Processes for Network Dynamics (NDP4ND), a new class of stochastic processes governed by stochastic data-adaptive network dynamics, to overcome the challenge and learn continuous network dynamics from scarce observations. Intensive experiments conducted on various network dynamics in ecological population evolution, phototaxis movement, brain activity, epidemic spreading, and real-world empirical systems, demonstrate that the proposed method has excellent data adaptability and computational efficiency, and can adapt to unseen network emerging dynamics, producing accurate interpolation and extrapolation with reducing the ratio of required observation data to only about 6\% and improving the learning speed for new dynamics by three orders of magnitude.Comment: preprin

System Dynamics Modeling-based Study of Contingent Sourcing under Supply Disruptions

AbstractIn this paper, using the methodology of system dynamics modeling, we separately build two models for a supply chain under two circumstances of supply disruptions, without backup supplier, and with a contingent supplier. The retailer's total profits are also compared under these two circumstances of supply disruptions to help the decision-makers better understanding the backup purchasing strategy. The supply chain studied only involves one retailer and two independent suppliers that are referred to as major supplier and backup supplier. The paper contributes to the literature by providing a better understanding of the impacts of supply disruptions on the system performance and by shedding insights into the value of a backup supply

Voters' Impacts on Creators' Popularity Disparity and Network Size in Two-sided Decentralized User-Generated Content Market

The development of decentralized technologies greatly facilitates the growth of user-generated content (UGC) markets. However, existing literature debates whether the decentralized UGC platform model can be economically sustainable. This study investigates the differential impacts of four voter groups, categorized by their social engagement and financial investment, on the two critical issues pertaining to decentralized UGC markets (i.e., creator popularity disparity and content contribution). We empirically tested our hypotheses using data from a leading decentralized UGC platform. The results indicate a consumer engagement tradeoff between promoting fair growth opportunities in the interest of the creators and extending the creator network in the interest of the platform. Our findings shed light on how creator popularity disparity may arise through votes from the four voter groups and their differential network externalities exerted on the creator network

Response of the East Asian climate system to water and heat changes of global frozen soil using NCAR CAM model

El siguiente trabajo tiene como objetivo analizar un corpus de obras poéticas publicadas en Chile durante la primera década del siglo XXI, a partir de las distintas simbolizaciones que ellas plantean en torno a dos figuras culturales que resultan claves para entender el imaginario de la letra poética actual en nuestro país. Nos referimos a la casa y el niño, ambas como metáforas de una habitabilidad fracasada e, incluso, imposible, que se produce como efecto de la economía neoliberal, consolidada en el campo cultural chileno tras el retorno a la democracia.The following article aims to analyze a corpus of poetic productions published in Chile during the first decade of the 21st century, starting from the different symbolizations they propose around two cultural figures, that are key to understand the imaginary of nowadays poetics. Those are, the home and the child, both as symbolizations of an unsuccessful habitability, even impossible, due to the effects that the neoliberal economy has created in the Chilean cultural space, after the return of democracy.El següent treball té com a objectiu analitzar un corpus d'obres poètiques publicades a Xile durant la primera dècada del segle XXI, a partir de les diferents simbolitzacions que aquestes plantegen entorn de dues figures culturals que resulten clau per entendre l'imaginari de la lletra poètica actual al nostre país. Ens referim a la casa i el nen, ambdues com a metàfores d'una habitabilitat fracassada i, fins i tot, impossible, que es produeix com a efecte de l'economia neoliberal, consolidada en el camp cultural xilè després del retorn a la democràcia

Nonlinear dielectric geometric-phase metasurface with simultaneous structure and lattice symmetry design

In this work, we utilize thin dielectric meta-atoms placed on a silver substrate to efficiently enhance and manipulate the third harmonic generation. We theoretically and experimentally reveal that when the structural symmetry of the meta-atom is incompatible with the lattice symmetry of an array, some generalized nonlinear geometric phases appear, which offers new possibilities for harmonic generation control beyond the accessible symmetries governed by the selection rule. The underlying mechanism is attributed to the modified rotation of the effective principal axis of a dense meta-atom array, where the strong coupling among the units gives rise to a generalized linear geometric phase modulation on the pump light. Therefore, nonlinear geometric phases carried by the third-harmonic emissions are the natural result of the wave-mixing process among the modes excited at the fundamental frequency. This mechanism further points out a new strategy to predict the nonlinear geometric phases delivered by the nanostructures according to their linear responses. Our design is simple and efficient, and offers alternatives for the nonlinear meta-devices that are capable of flexible photon generation and manipulation

Dominant patterns of winter Arctic surface wind variability

Dominant statistical patterns of winter Arctic surface wind (WASW) variability and their impacts on Arctic sea ice motion are investigated using the complex vector empirical orthogonal function (CVEOF) method. The results indicate that the leading CVEOF of Arctic surface wind variability, which accounts for 33% of the covariance, is characterized by two different and alternating spatial patterns (WASWP1 and WASWP2). Both WASWP1 and WASWP2 show strong interannual and decadal variations, superposed on their declining trends over past decades. Atmospheric circulation anomalies associated with WASWP1 and WASWP2 exhibit, respectively, equivalent barotropic and some baroclinic characteristics, differing from the Arctic dipole anomaly and the seesaw structure anomaly between the Barents Sea and the Beaufort Sea. On decadal time scales, the decline trend of WASWP2 can be attributed to persistent warming of sea surface temperature in the Greenland—Barents—Kara seas from autumn to winter, reflecting the effect of the Arctic warming. The second CVEOF, which accounts for 18% of the covariance, also contains two different spatial patterns (WASWP3 and WASWP4). Their time evolutions are significantly correlated with the North Atlantic Oscillation (NAO) index and the central Arctic Pattern, respectively, measured by the leading EOF of winter sea level pressure (SLP) north of 70°N. Thus, winter anomalous surface wind pattern associated with the NAO is not the most important surface wind pattern. WASWP3 and WASWP4 primarily reflect natural variability of winter surface wind and neither exhibits an apparent trend that differs from WASWP1 or WASWP2. These dominant surface wind patterns strongly influence Arctic sea ice motion and sea ice exchange between the western and eastern Arctic. Furthermore, the Fram Strait sea ice volume flux is only significantly correlated with WASWP3. The results demonstrate that surface and geostrophic winds are not interchangeable in terms of describing wind field variability over the Arctic Ocean. The results have important implications for understanding and investigating Arctic sea ice variations: Dominant patterns of Arctic surface wind variability, rather than simply whether there are the Arctic dipole anomaly and the Arctic Oscillation (or NAO), effectively affect the spatial distribution of Arctic sea ice anomalies

MADiff: Offline Multi-agent Learning with Diffusion Models

Diffusion model (DM), as a powerful generative model, recently achieved huge success in various scenarios including offline reinforcement learning, where the policy learns to conduct planning by generating trajectory in the online evaluation. However, despite the effectiveness shown for single-agent learning, it remains unclear how DMs can operate in multi-agent problems, where agents can hardly complete teamwork without good coordination by independently modeling each agent's trajectories. In this paper, we propose MADiff, a novel generative multi-agent learning framework to tackle this problem. MADiff is realized with an attention-based diffusion model to model the complex coordination among behaviors of multiple diffusion agents. To the best of our knowledge, MADiff is the first diffusion-based multi-agent offline RL framework, which behaves as both a decentralized policy and a centralized controller, which includes opponent modeling and can be used for multi-agent trajectory prediction. MADiff takes advantage of the powerful generative ability of diffusion while well-suited in modeling complex multi-agent interactions. Our experiments show the superior performance of MADiff compared to baseline algorithms in a range of multi-agent learning tasks.Comment: 17 pages, 7 figures, 4 table

A Novel Cross-layer Communication Protocol for Vehicular Sensor Networks

Communication protocols in Vehicular Sensor Networks (VSNs) in urban areas play an important role in intelligent transport systems applications. Many cross layer communication protocols studies are originated from topology-based algorithms, which is not suitable for the frequently-changing computational scenario. In addition, the influence factors that have been considered for VSNs routing are not enough. With these aspects in mind, this paper proposes a multi-factor cross layer position-based routing (MCLPR) protocol for VSNs to improve reliability and efficiency in message delivery. Considering the complex intersection environment, the algorithm for vehicles selection at intersections (called AVSI) is further proposed, in which comprehensive factors are taken into account including the position and direction of vehicle, the vehicle density, the signal-to-noise-plus-interference ratio (SNIR), as well as the frame error rate (FER) in MAC layer. Meanwhile, the dynamic HELLO STREAM broadcasting system with the various vehicle speeds is proposed to increase the decisions accuracy. Experimental results in Network Simulator 3 (NS-3) show the advantage of MCLPR protocol over traditional state-of the-art algorithms in terms of packet delivery ratio (PDR), overhead and the mean end-to-end delay