41 research outputs found
Impact of Investor's Varying Risk Aversion on the Dynamics of Asset Price Fluctuations
While the investors' responses to price changes and their price forecasts are
well accepted major factors contributing to large price fluctuations in
financial markets, our study shows that investors' heterogeneous and dynamic
risk aversion (DRA) preferences may play a more critical role in the dynamics
of asset price fluctuations. We propose and study a model of an artificial
stock market consisting of heterogeneous agents with DRA, and we find that DRA
is the main driving force for excess price fluctuations and the associated
volatility clustering. We employ a popular power utility function,
with agent specific and
time-dependent risk aversion index, , and we derive an approximate
formula for the demand function and aggregate price setting equation. The
dynamics of each agent's risk aversion index, (i=1,2,...,N), is
modeled by a bounded random walk with a constant variance . We show
numerically that our model reproduces most of the ``stylized'' facts observed
in the real data, suggesting that dynamic risk aversion is a key mechanism for
the emergence of these stylized facts.Comment: 17 pages, 7 figure
Cultural heritage in Asia series. Vol. 1, Tulou and the Hakka people
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Creating a New Generation of Software Development Environments, Compilers, and Algorithms for High-Performance Computing, Networks, and Data Management
Correlated Stochastic Knapsack with a Submodular Objective
We study the correlated stochastic knapsack problem of a submodular target function, with optional additional constraints. We utilize the multilinear extension of submodular function, and bundle it with an adaptation of the relaxed linear constraints from Ma [Mathematics of Operations Research, Volume 43(3), 2018] on correlated stochastic knapsack problem. The relaxation is then solved by the stochastic continuous greedy algorithm, and rounded by a novel method to fit the contention resolution scheme (Feldman et al. [FOCS 2011]). We obtain a pseudo-polynomial time (1 - 1/?e)/2 ? 0.1967 approximation algorithm with or without those additional constraints, eliminating the need of a key assumption and improving on the (1 - 1/?e)/2 ? 0.1106 approximation by Fukunaga et al. [AAAI 2019]
An Insight into Current IoT Security Methods
This paper examines the security methods in the Internet-of-Things. The security methods are carefully studied and categorized into six layers according to the Internet-ofThings framework namely Event Producer and Consumer, Event Queuing System, Transformation and Analysis, Storage, Presentation and Action, and Users and Systems. It can be observed that most security methods emphasizes on Event Producer and Consumer layer whereas the least focused layer is Users and Systems layer. This study aims to present a comprehensive overview to researchers working in the domain of the Internet-of-Things security
The Intelligent Transportation System Using the Infrared Sensors Based on the ZigBee Protocol and Eclipse
There are more and more cars. The traffic jam cost people a lot of time on the road. This system got the number of the cars in waiting on every crossroad by the infrared sensors in the CC2530. Based on the Zigbee protocol, the routers embedded in the infrared sensors sent the information of cars in waiting to the coordinator wirelessly. Then the coordinator sent the information to the website which is developed on the Eclipse platform through the serial port. People can check the designated crossroad whether there is the traffic jam by smart telephone or the web browser. This system can release the newest information. It can be widely applied in the transportation system
Architectural Vision for Quantum Computing in the Edge-Cloud Continuum
Quantum processing units (QPUs) are currently exclusively available from
cloud vendors. However, with recent advancements, hosting QPUs is soon possible
everywhere. Existing work has yet to draw from research in edge computing to
explore systems exploiting mobile QPUs, or how hybrid applications can benefit
from distributed heterogeneous resources. Hence, this work presents an
architecture for Quantum Computing in the edge-cloud continuum. We discuss the
necessity, challenges, and solution approaches for extending existing work on
classical edge computing to integrate QPUs. We describe how warm-starting
allows defining workflows that exploit the hierarchical resources spread across
the continuum. Then, we introduce a distributed inference engine with hybrid
classical-quantum neural networks (QNNs) to aid system designers in
accommodating applications with complex requirements that incur the highest
degree of heterogeneity. We propose solutions focusing on classical layer
partitioning and quantum circuit cutting to demonstrate the potential of
utilizing classical and quantum computation across the continuum. To evaluate
the importance and feasibility of our vision, we provide a proof of concept
that exemplifies how extending a classical partition method to integrate
quantum circuits can improve the solution quality. Specifically, we implement a
split neural network with optional hybrid QNN predictors. Our results show that
extending classical methods with QNNs is viable and promising for future work.Comment: 16 pages, 5 figures, Vision Pape