On variance estimation and a goodness-of-fit test using the bootstrap method

This thesis deals with the study of variance estimation using the bootstrap method, including the problem of choosing between nonparametric and parametric bootstrap methods. Paper I compares the two approaches, determines which method is preferable and analyses the accuracy of the approximations. The underlying concept of parametric bootstrap is based on the assumption of correct choice of parametric distribution. Paper II therefore considers goodness-of-fit tests and presents a new test based on the bootstrap method

Learning and Reasoning for Robot Sequential Decision Making under Uncertainty

Robots frequently face complex tasks that require more than one action, where sequential decision-making (SDM) capabilities become necessary. The key contribution of this work is a robot SDM framework, called LCORPP, that supports the simultaneous capabilities of supervised learning for passive state estimation, automated reasoning with declarative human knowledge, and planning under uncertainty toward achieving long-term goals. In particular, we use a hybrid reasoning paradigm to refine the state estimator, and provide informative priors for the probabilistic planner. In experiments, a mobile robot is tasked with estimating human intentions using their motion trajectories, declarative contextual knowledge, and human-robot interaction (dialog-based and motion-based). Results suggest that, in efficiency and accuracy, our framework performs better than its no-learning and no-reasoning counterparts in office environment.Comment: In proceedings of 34th AAAI conference on Artificial Intelligence, 202

EnsCat: clustering of categorical data via ensembling

Background: Clustering is a widely used collection of unsupervised learning techniques for identifying natural classes within a data set. It is often used in bioinformatics to infer population substructure. Genomic data are often categorical and high dimensional, e.g., long sequences of nucleotides. This makes inference challenging: The distance metric is often not well-defined on categorical data; running time for computations using high dimensional data can be considerable; and the Curse of Dimensionality often impedes the interpretation of the results. Up to the present, however, the literature and software addressing clustering for categorical data has not yet led to a standard approach. Results: We present software for an ensemble method that performs well in comparison with other methods regardless of the dimensionality of the data. In an ensemble method a variety of instantiations of a statistical object are found and then combined into a consensus value. It has been known for decades that ensembling generally outperforms the components that comprise it in many settings. Here, we apply this ensembling principle to clustering. We begin by generating many hierarchical clusterings with different clustering sizes. When the dimension of the data is high, we also randomly select subspaces also of variable size, to generate clusterings. Then, we combine these clusterings into a single membership matrix and use this to obtain a new, ensembled dissimilarity matrix using Hamming distance. Conclusions: Ensemble clustering, as implemented in R and called EnsCat, gives more clearly separated clusters than other clustering techniques for categorical data. The latest version with manual and examples is available at https://github.com/jlp2duke/EnsCat

Some properties of residual mapping and convexity in ∧-hyperlattices

The aime of this paper is the study of residual mappings and convexity in hyperlattices. To get this point, we study principal down set in hyperlattices and we give some conditions for a mapping between two hyperlattices to be equivalent with a residual maping. Also, we investigate convex subsets in ∧-hyperlattices

1-[((E)-{2-[(2-Nitro­benz­yl)(2-{[(E)-(2-oxidonaphthalen-1-yl)methyl­idene]aza­nium­yl}eth­yl)amino]­eth­yl}aza­niumyl­idene)meth­yl]naphthalen-2-olate monohydrate

The title Schiff base compound, C33H30N4O4·H2O, adopts an E configuration with respect to each C=N double bond. In the mol­ecule, there are naphthoxide anions and the protonated imino N atoms. Intra­molecular N—H⋯O hydrogen bonds lead to the formation of approximately planar (maximum deviation 0.029 Å for H atom) six-membered rings.. In the crystal, mol­ecules are linked by O—H⋯O and N—H⋯O hydrogen bonds as well as C—H⋯O contacts, leading to the formation of a three-dimensional network

Genetics and epidemiology of Middle East Respiratory Syndrome-Coronavirus (MERS-CoV)

Background: Middle East respiratory syndrome (MERS) is a viral respiratory illness caused by a coronavirus. After the primary onset of MERS in Saudi Arabia, in September 2015 cases began to increase. The number of laboratory-affirmed cases by MERS-CoV in the Middle East has been being increased recently. Method: In this current review article, by using the terms “MERS” and “coronavirus” we first searched for English language articles in the PubMed database, published in last five years. Then by a detailed review of related articles, we provided a comprehensive information about epidemiology, genetic, host and coronavirus treatment. Result: More importantly, evidences of human-to-human transmission in Europe and America indicate that the viral adaptations in humans may precede a large-scale epidemic. The genome of Coronaviruses is a linear positive-sense single stranded large RNA and they are enveloped viruses that have a helical symmetric nucleocapsid. Some new insights have been provided in previous few months in to the animal Coronavirus hosts, transmissibility, contagion of MERS Co-V and ideal laboratory diagnostic methods. Conclusion: It seems crucial to control this new human infection “MERS-CoV” by collaborating global and local health authorities and their continual support for further research on it

Guiding Robot Exploration in Reinforcement Learning via Automated Planning

Reinforcement learning (RL) enables an agent to learn from trial-and-error experiences toward achieving long-term goals; automated planning aims to compute plans for accomplishing tasks using action knowledge. Despite their shared goal of completing complex tasks, the development of RL and automated planning has been largely isolated due to their different computational modalities. Focusing on improving RL agents' learning efficiency, we develop Guided Dyna-Q (GDQ) to enable RL agents to reason with action knowledge to avoid exploring less-relevant states. The action knowledge is used for generating artificial experiences from an optimistic simulation. GDQ has been evaluated in simulation and using a mobile robot conducting navigation tasks in a multi-room office environment. Compared with competitive baselines, GDQ significantly reduces the effort in exploration while improving the quality of learned policies.Comment: Accepted in International Conference of Planning and Scheduling (ICAPS-21