Mitochondrial dysfunction is implicated in a wide array of human diseases ranging from neurodegenerative disorders to cardiovascular defects. The coordinated localization and import proteins into mitochondria are essential processes that ensure mitochondrial homeostasis. most driven by N-terminal targeting sequences (MTS’s), which interact with machinery removed the processing peptidase (MPP). recent discovery internal MTS’s—those distributed throughout protein act as regulators or secondary...

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, supports the simultaneous 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 hybrid paradigm to refine estimator, provide informative...

Intelligent robots frequently need to explore the objects in their working environments. Modern sensors have enabled learn object properties via perception of multiple modalities. However, exploration real world poses a challenging trade-off between information gains and action costs. Mixed observability Markov decision process (MOMDP) is framework for planning under uncertainty, while accounting both fully partially observable components state. Robot has face such mixed observability. This...

Abstract Multiplexing samples from distinct individuals prior to sequencing is a promising step toward achieving population-scale single-cell RNA by reducing the restrictive costs of technology. Individual genetic demultiplexing tools resolve donor-of-origin identity pooled cells using natural variation but present diminished accuracy on highly multiplexed experiments, impeding analytic potential dataset. In response, we introduce Ensemblex: an accuracy-weighted, ensemble framework that...

We present a technique for clustering categorical data by generating many dissimilarity matrices and averaging over them. begin demonstrating our on low dimensional comparing it to several other techniques that have been proposed. Then we give conditions under which method should yield good results in general. Our extends high of equal lengths ensembling choices explanatory variables. In this context compare with two methods. Finally, extend vectors unequal length using alignment equalize...

Some robots can interact with humans using natural language, and identify service requests through human-robot dialog. However, few are able to improve their language capabilities from this experience. In paper, we develop a dialog agent for that is interpret user commands semantic parser, while asking clarification questions probabilistic manager. This augment its knowledge base by learning experiences, e.g., adding new entities ways of referring existing entities. We have extensively...

We explore the use of bootstrap for testing independence two categorical variables. develop a theoretical justification bootstrapping contingency table and provide more accurate inference small sample sizes. also study effect equalized marginals on tests independence. The properties proposed existing are examined using Monte Carlo simulations. It is shown that Fisher exact test Chi-squared with continuity correction very conservative cannot be recommended to

Classical planning systems have shown great advances in utilizing rule-based human knowledge to compute accurate plans for service robots, but they face challenges due the strong assumptions of perfect perception and action executions. To tackle these challenges, one solution is connect symbolic states actions generated by classical planners robot's sensory observations, thus closing perception-action loop. This research proposes a visually-grounded framework, named TPVQA, which leverages...

Abstract In this article, we propose a new test for examining the equality of coefficient variation between two different populations. The proposed is based on nonparametric bootstrap method. It appears to yield several appreciable advantages over current tests. quick and easy implementation can be considered as test. examined by Monte Carlo simulations, also evaluated using various numerical studies. Keywords: Bootstrap methodCoefficient variationMonte simulationMathematics Subject...

The coefficient of variation is frequently used in the comparison and precision results with different scales. This work examines without any assumptions about underlying distribution. A family tests based on bootstrap method proposed, its properties are illustrated using Monte Carlo simulations. proposed applied to chemical experiments iid non‐ observations. Copyright © 2011 John Wiley & Sons, Ltd.

In this paper, we propose an analytical method for performing tractable approximate Gaussian inference (TAGI) in Bayesian neural networks. The enables the of posterior mean vector and diagonal covariance matrix weights biases. proposed has a computational complexity $\mathcal{O}(n)$ with respect to number parameters $n$, tests performed on regression classification benchmarks confirm that, same network architecture, it matches performance existing methods relying gradient backpropagation.

Support Vector Machine (SVM) is known in classification and regression modeling. It has been receiving attention the application of nonlinear functions. The aim to motivate use SVM ...

Reinforcement learning (RL) enables an agent to learn from trial-and-error experiences toward achieving long-term goals; automated planning aims compute plans for accomplishing tasks using action knowledge. Despite their shared goal of completing complex tasks, the development RL and has been largely isolated due different computational modalities. Focusing on improving agents' efficiency, we develop Guided Dyna-Q (GDQ) enable agents reason with knowledge avoid exploring less-relevant...

Here, we propose a clustering technique for general problems including those that have nonconvex clusters. For given desired number of clusters K, use three stages to find The first stage uses hybrid produce series clusterings various sizes (randomly selected). key step in this is K-means using Kℓ where Kℓ≫K and then join these small by single linkage clustering. second stabilizes the result one reclustering via “membership matrix” under Hamming distance generate dendrogram. third cut...

Applications such as structural health monitoring (SHM) often rely on the analysis of time-series using methods state-space models (SSM). In this paper, we propose an analytical method called Gaussian multiplicative approximation (GMA) that is applicable to are encountered in practical SHM applications. The enables inference mean vector and covariance matrix for product two hidden states transition and/or observation linear estimation theory online model parameters states. potential...