From patterned response dependency to structured covariate dependency: categorical-pattern-matching

Data generated from a system of interest typically consists of measurements from an ensemble of subjects across multiple response and covariate features, and is naturally represented by one response-matrix against one covariate-matrix. Likely each of these two matrices simultaneously embraces heterogeneous data types: continuous, discrete and categorical. Here a matrix is used as a practical platform to ideally keep hidden dependency among/between subjects and features intact on its lattice. Response and covariate dependency is individually computed and expressed through mutliscale blocks via a newly developed computing paradigm named Data Mechanics. We propose a categorical pattern matching approach to establish causal linkages in a form of information flows from patterned response dependency to structured covariate dependency. The strength of an information flow is evaluated by applying the combinatorial information theory. This unified platform for system knowledge discovery is illustrated through five data sets. In each illustrative case, an information flow is demonstrated as an organization of discovered knowledge loci via emergent visible and readable heterogeneity. This unified approach fundamentally resolves many long standing issues, including statistical modeling, multiple response, renormalization and feature selections, in data analysis, but without involving man-made structures and distribution assumptions. The results reported here enhance the idea that linking patterns of response dependency to structures of covariate dependency is the true philosophical foundation underlying data-driven computing and learning in sciences.Comment: 32 pages, 10 figures, 3 box picture

Correlation between Pineal Activation and Religious Meditation Observed by Functional Magnetic Resonance Imaging

The human brain possesses plenty of functions but little is known about its scientific relationship with mind and spirit. Conferences^1,2^ focused on the connection between science and religion were held very recently in which neuroscientists, Buddhist scholars and Dalai Lama discussed attention, mental imagery, emotion, mind, brain functions and meditation, suggesting religious meditation offers an effective means to investigate the mystery of mind and spirit. In the past decade, scientists struggled to obtain brain mappings for various meditation styles using different brain imaging techniques and stimulating results have been observed^3-17^. In this letter we report that, together with other brain regions, pineal body exhibit significant activation during meditation process, supporting the long lasting speculation that pineal plays an important role in the intrinsic awareness which might concern spirit or soul. Pineal is known as an endocrine organ which produces substrates including melatonin and has been ascribed numerous even mysterious functions but its activation during meditation has never been observed by brain imaging technique. In seventeenth century, based on anatomic observation, Descartes ventured to suggest that pineal serves as the principal seat of the soul^18-20^. Inspired by its geometric center in the brain, physiologists, psychologists, philosophers and religionists have been speculating for centuries about pineal's function relevant to spirit and soul. In this study, we chose Chinese Original Quiet Sitting, one style of meditation, to explore this long lasting speculation by functional magnetic resonance imaging technique. Our results demonstrate a correlation between pineal activation and religious meditation which might have profound implications in physiological understanding of the intrinsic awareness

Attacking Visual Language Grounding with Adversarial Examples: A Case Study on Neural Image Captioning

Visual language grounding is widely studied in modern neural image captioning systems, which typically adopts an encoder-decoder framework consisting of two principal components: a convolutional neural network (CNN) for image feature extraction and a recurrent neural network (RNN) for language caption generation. To study the robustness of language grounding to adversarial perturbations in machine vision and perception, we propose Show-and-Fool, a novel algorithm for crafting adversarial examples in neural image captioning. The proposed algorithm provides two evaluation approaches, which check whether neural image captioning systems can be mislead to output some randomly chosen captions or keywords. Our extensive experiments show that our algorithm can successfully craft visually-similar adversarial examples with randomly targeted captions or keywords, and the adversarial examples can be made highly transferable to other image captioning systems. Consequently, our approach leads to new robustness implications of neural image captioning and novel insights in visual language grounding.Comment: Accepted by 56th Annual Meeting of the Association for Computational Linguistics (ACL 2018). Hongge Chen and Huan Zhang contribute equally to this wor

Robust Decision Trees Against Adversarial Examples

Although adversarial examples and model robustness have been extensively studied in the context of linear models and neural networks, research on this issue in tree-based models and how to make tree-based models robust against adversarial examples is still limited. In this paper, we show that tree based models are also vulnerable to adversarial examples and develop a novel algorithm to learn robust trees. At its core, our method aims to optimize the performance under the worst-case perturbation of input features, which leads to a max-min saddle point problem. Incorporating this saddle point objective into the decision tree building procedure is non-trivial due to the discrete nature of trees --- a naive approach to finding the best split according to this saddle point objective will take exponential time. To make our approach practical and scalable, we propose efficient tree building algorithms by approximating the inner minimizer in this saddle point problem, and present efficient implementations for classical information gain based trees as well as state-of-the-art tree boosting models such as XGBoost. Experimental results on real world datasets demonstrate that the proposed algorithms can substantially improve the robustness of tree-based models against adversarial examples