Estimation of spatially-varying parameters for computationally expensive forward models governed by partial differential equations is addressed. A novel multiscale Bayesian inference approach is introduced based on deep probabilistic generative models. Such generative models provide a flexible representation by inferring on each scale a low-dimensional latent encoding while allowing hierarchical parameter generation from coarse- to fine-scales. Combining the multiscale generative model with Markov Chain Monte Carlo (MCMC), inference across scales is achieved enabling us to efficiently obtain posterior parameter samples at various scales. The estimation of coarse-scale parameters using a low-dimensional latent embedding captures global and notable parameter features using an inexpensive but inaccurate solver. MCMC sampling of the fine-scale parameters is enabled by utilizing the posterior information in the immediate coarser-scale. In this way, the global features are identified in the coarse-scale with inference of low-dimensional variables and inexpensive forward computation, and the local features are refined and corrected in the fine-scale. The developed method is demonstrated with two types of permeability estimation for flow in heterogeneous media. One is a Gaussian random field (GRF) with uncertain length scales, and the other is channelized permeability with the two regions defined by different GRFs. The obtained results indicate that the method allows high-dimensional parameter estimation while exhibiting stability, efficiency and accuracy.

Veracity is an essential key in research and development of innovative products. Live Emotion analysis and verification nullify deceit made to complainers on live chat, corroborate messages of both ends in messaging apps and promote an honest conversation between users. The main concept behind this emotion artificial intelligent verifier is to license or decline message accountability by comparing variegated emotions of chat app users recognized through facial expressions and text prediction. In this paper, a proposed emotion intelligent live detector acts as an honest arbiter who distributes facial emotions into labels namely, Happiness, Sadness, Surprise, and Hate. Further, it separately predicts a label of messages through text classification. Finally, it compares both labels and declares the message as a fraud or a bonafide. For emotion detection, we deployed Convolutional Neural Network (CNN) using a miniXception model and for text prediction, we selected Support Vector Machine (SVM) natural language processing probability classifier due to receiving the best accuracy on training dataset after applying Support Vector Machine (SVM), Random Forest Classifier, Naive Bayes Classifier, and Logistic regression.

Time series data is a collection of chronological observations which is generated by several domains such as medical and financial fields. Over the years, different tasks such as classification, forecasting, and clustering have been proposed to analyze this type of data. Time series data has been also used to study the effect of interventions over time. Moreover, in many fields of science, learning the causal structure of dynamic systems and time series data is considered an interesting task which plays an important role in scientific discoveries. Estimating the effect of an intervention and identifying the causal relations from the data can be performed via causal inference. Existing surveys on time series discuss traditional tasks such as classification and forecasting or explain the details of the approaches proposed to solve a specific task. In this paper, we focus on two causal inference tasks, i.e., treatment effect estimation and causal discovery for time series data, and provide a comprehensive review of the approaches in each task. Furthermore, we curate a list of commonly used evaluation metrics and datasets for each task and provide in-depth insight. These metrics and datasets can serve as benchmarks for research in the field.

This graduate textbook on machine learning tells a story of how patterns in data support predictions and consequential actions. Starting with the foundations of decision making, we cover representation, optimization, and generalization as the constituents of supervised learning. A chapter on datasets as benchmarks examines their histories and scientific bases. Self-contained introductions to causality, the practice of causal inference, sequential decision making, and reinforcement learning equip the reader with concepts and tools to reason about actions and their consequences. Throughout, the text discusses historical context and societal impact. We invite readers from all backgrounds; some experience with probability, calculus, and linear algebra suffices.

Disruption management during the airline scheduling process can be compartmentalized into proactive and reactive processes depending upon the time of schedule execution. The state of the art for decision-making in airline disruption management involves a heuristic human-centric approach that does not categorically study uncertainty in proactive and reactive processes for managing airline schedule disruptions. Hence, this paper introduces an uncertainty transfer function model (UTFM) framework that characterizes uncertainty for proactive airline disruption management before schedule execution, reactive airline disruption management during schedule execution, and proactive airline disruption management after schedule execution to enable the construction of quantitative tools that can allow an intelligent agent to rationalize complex interactions and procedures for robust airline disruption management. Specifically, we use historical scheduling and operations data from a major U.S. airline to facilitate the development and assessment of the UTFM, defined by hidden Markov models (a special class of probabilistic graphical models) that can efficiently perform pattern learning and inference on portions of large data sets.

Multi-agent influence diagrams (MAIDs) are a popular form of Previous work on MAIDs has focussed on Nash equilibria as graphical model that, for certain classes of games, have been shown the core solution concept [20]. Whilst this is arguably the most important to offer key complexity and explainability advantages over traditional solution concept in non-cooperative game theory, if there extensive form game (EFG) representations. In this paper, we are many Nash equilibria we often wish to remove some of those extend previous work on MAIDs by introducing the concept of a that are less'rational'. Many refinements to the Nash equilibrium MAID subgame, as well as subgame perfect and trembling hand have been proposed [17], with two of the most important being perfect equilibrium refinements. We then prove several equivalence subgame perfect Nash equilibria [26] and trembling hand perfect results between MAIDs and EFGs. Finally, we describe an open equilibria [27]. The first rules out'non-credible' threats and the second source implementation for reasoning about MAIDs and computing requires that each player is still playing a best-response when their equilibria.

The increased adoption of Artificial Intelligence (AI) presents an opportunity to solve many socio-economic and environmental challenges; however, this cannot happen without securing AI-enabled technologies. In recent years, most AI models are vulnerable to advanced and sophisticated hacking techniques. This challenge has motivated concerted research efforts into adversarial AI, with the aim of developing robust machine and deep learning models that are resilient to different types of adversarial scenarios. In this paper, we present a holistic cyber security review that demonstrates adversarial attacks against AI applications, including aspects such as adversarial knowledge and capabilities, as well as existing methods for generating adversarial examples and existing cyber defence models. We explain mathematical AI models, especially new variants of reinforcement and federated learning, to demonstrate how attack vectors would exploit vulnerabilities of AI models. We also propose a systematic framework for demonstrating attack techniques against AI applications and reviewed several cyber defences that would protect AI applications against those attacks. We also highlight the importance of understanding the adversarial goals and their capabilities, especially the recent attacks against industry applications, to develop adaptive defences that assess to secure AI applications. Finally, we describe the main challenges and future research directions in the domain of security and privacy of AI technologies.

Sampling is a popular method for approximate inference when exact inference is impractical. Generally, sampling algorithms do not exploit context-specific independence (CSI) properties of probability distributions. We introduce context-specific likelihood weighting (CS-LW), a new sampling methodology, which besides exploiting the classical conditional independence properties, also exploits CSI properties. Unlike the standard likelihood weighting, CS-LW is based on partial assignments of random variables and requires fewer samples for convergence due to the sampling variance reduction. Furthermore, the speed of generating samples increases. Our novel notion of contextual assignments theoretically justifies CS-LW. We empirically show that CS-LW is competitive with state-of-the-art algorithms for approximate inference in the presence of a significant amount of CSIs.

The recent outbreak of the COVID-19 shocked humanity leading to the death of millions of people worldwide. To stave off the spread of the virus, the authorities in the US, employed different strategies including the mask mandate (MM) order issued by the states' governors. Although most of the previous studies pointed in the direction that MM can be effective in hindering the spread of viral infections, the effectiveness of MM in reducing the degree of exposure to the virus and, consequently, death rates remains indeterminate. Indeed, the extent to which the degree of exposure to COVID-19 takes part in the lethality of the virus remains unclear. In the current work, we defined a parameter called the average death ratio as the monthly average of the ratio of the number of daily deaths to the total number of daily cases. We utilized survey data provided by New York Times to quantify people's abidance to the MM order. Additionally, we implicitly addressed the extent to which people abide by the MM order that may depend on some parameters like population, income, and political inclination. Using different machine learning classification algorithms we investigated how the decrease or increase in death ratio for the counties in the US West Coast correlates with the input parameters. Our results showed a promising score as high as 0.94 with algorithms like XGBoost, Random Forest, and Naive Bayes. To verify the model, the best performing algorithms were then utilized to analyze other states (Arizona, New Jersey, New York and Texas) as test cases. The findings show an acceptable trend, further confirming usability of the chosen features for prediction of similar cases.

In the multi-armed bandit problem, a decision maker needs to sequentially decide between acting to reveal data about a system and acting to generate profit. The central idea of the multi-armed bandit is that the agent has K'options' or equivalently, a bandit with K arms, and must choose which arm to play at each time. Playing an arm results in a reward generated from a fixed but unknown distribution which must be inferred'on-the-fly'. In the classic multi-armed bandit problem, the reward of each arm is assumed to be independent of the others (Gittins and Jones [9], Agrawal [1], Lattimore and Szepesvári [13]) and it is the only observation obtained by the decision maker at each step. In practice, we often observe signals in addition to the rewards and there is often correlation between the distributions of outcomes for different choices. For example, in a dynamic pricing problem (Dubé and Misra [6], Misra et al. [14]), an agent wants to fix the price of a single product, from a finite set of prices {c