4,135 research outputs found
Multi-agent quality of experience control
In the framework of the Future Internet, the aim of the Quality of Experience (QoE) Control functionalities is to track the personalized desired QoE level of the applications. The paper proposes to perform such a task by dynamically selecting the most appropriate Classes of Service (among the ones supported by the network), this selection being driven by a novel heuristic Multi-Agent Reinforcement Learning (MARL) algorithm. The paper shows that such an approach offers the opportunity to cope with some practical implementation problems: in particular, it allows to face the so-called “curse of dimensionality” of MARL algorithms, thus achieving satisfactory performance results even in the presence of several hundreds of Agents
A control theoretic approach for security of cyber-physical systems
In this dissertation, several novel defense methodologies for cyber-physical systems have been proposed. First, a special type of cyber-physical system, the RFID system, is considered for which a lightweight mutual authentication and ownership management protocol is proposed in order to protect the data confidentiality and integrity. Then considering the fact that the protection of the data confidentiality and integrity is insufficient to guarantee the security in cyber-physical systems, we turn to the development of a general framework for developing security schemes for cyber-physical systems wherein the cyber system states affect the physical system and vice versa. After that, we apply this general framework by selecting the traffic flow as the cyber system state and a novel attack detection scheme that is capable of capturing the abnormality in the traffic flow in those communication links due to a class of attacks has been proposed. On the other hand, an attack detection scheme that is capable of detecting both sensor and actuator attacks is proposed for the physical system in the presence of network induced delays and packet losses. Next, an attack detection scheme is proposed when the network parameters are unknown by using an optimal Q-learning approach. Finally, this attack detection and accommodation scheme has been further extended to the case where the network is modeled as a nonlinear system with unknown system dynamics --Abstract, page iv
Physics-Informed Machine Learning for Data Anomaly Detection, Classification, Localization, and Mitigation: A Review, Challenges, and Path Forward
Advancements in digital automation for smart grids have led to the
installation of measurement devices like phasor measurement units (PMUs),
micro-PMUs (-PMUs), and smart meters. However, a large amount of data
collected by these devices brings several challenges as control room operators
need to use this data with models to make confident decisions for reliable and
resilient operation of the cyber-power systems. Machine-learning (ML) based
tools can provide a reliable interpretation of the deluge of data obtained from
the field. For the decision-makers to ensure reliable network operation under
all operating conditions, these tools need to identify solutions that are
feasible and satisfy the system constraints, while being efficient,
trustworthy, and interpretable. This resulted in the increasing popularity of
physics-informed machine learning (PIML) approaches, as these methods overcome
challenges that model-based or data-driven ML methods face in silos. This work
aims at the following: a) review existing strategies and techniques for
incorporating underlying physical principles of the power grid into different
types of ML approaches (supervised/semi-supervised learning, unsupervised
learning, and reinforcement learning (RL)); b) explore the existing works on
PIML methods for anomaly detection, classification, localization, and
mitigation in power transmission and distribution systems, c) discuss
improvements in existing methods through consideration of potential challenges
while also addressing the limitations to make them suitable for real-world
applications
Critical Market Crashes
This review is a partial synthesis of the book ``Why stock market crash''
(Princeton University Press, January 2003), which presents a general theory of
financial crashes and of stock market instabilities that his co-workers and the
author have developed over the past seven years. The study of the frequency
distribution of drawdowns, or runs of successive losses shows that large
financial crashes are ``outliers'': they form a class of their own as can be
seen from their statistical signatures. If large financial crashes are
``outliers'', they are special and thus require a special explanation, a
specific model, a theory of their own. In addition, their special properties
may perhaps be used for their prediction. The main mechanisms leading to
positive feedbacks, i.e., self-reinforcement, such as imitative behavior and
herding between investors are reviewed with many references provided to the
relevant literature outside the confine of Physics. Positive feedbacks provide
the fuel for the development of speculative bubbles, preparing the instability
for a major crash. We demonstrate several detailed mathematical models of
speculative bubbles and crashes. The most important message is the discovery of
robust and universal signatures of the approach to crashes. These precursory
patterns have been documented for essentially all crashes on developed as well
as emergent stock markets, on currency markets, on company stocks, and so on.
The concept of an ``anti-bubble'' is also summarized, with two forward
predictions on the Japanese stock market starting in 1999 and on the USA stock
market still running. We conclude by presenting our view of the organization of
financial markets.Comment: Latex 89 pages and 38 figures, in press in Physics Report
Coherent Price Systems and Uncertainty-Neutral Valuation
We consider fundamental questions of arbitrage pricing arising when the
uncertainty model is given by a set of possible mutually singular probability
measures. With a single probability model, essential equivalence between the
absence of arbitrage and the existence of an equivalent martingale measure is a
folk theorem, see Harrison and Kreps (1979). We establish a microeconomic
foundation of sublinear price systems and present an extension result. In this
context we introduce a prior dependent notion of marketed spaces and viable
price systems. We associate this extension with a canonically altered concept
of equivalent symmetric martingale measure sets, in a dynamic trading framework
under absence of prior depending arbitrage. We prove the existence of such sets
when volatility uncertainty is modeled by a stochastic differential equation,
driven by Peng's G-Brownian motions
Measuring time preferences
We review research that measures time preferences—i.e., preferences over intertemporal tradeoffs. We distinguish between studies using financial flows, which we call “money earlier or later” (MEL) decisions and studies that use time-dated consumption/effort. Under different structural models, we show how to translate what MEL experiments directly measure (required rates of return for financial flows) into a discount function over utils. We summarize empirical regularities found in MEL studies and the predictive power of those studies. We explain why MEL choices are driven in part by some factors that are distinct from underlying time preferences.National Institutes of Health (NIA R01AG021650 and P01AG005842) and the Pershing Square Fund for Research in the Foundations of Human Behavior
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