Active Learning with Statistical Models

For many types of machine learning algorithms, one can compute the statistically `optimal' way to select training data. In this paper, we review how optimal data selection techniques have been used with feedforward neural networks. We then show how the same principles may be used to select data for two alternative, statistically-based learning architectures: mixtures of Gaussians and locally weighted regression. While the techniques for neural networks are computationally expensive and approximate, the techniques for mixtures of Gaussians and locally weighted regression are both efficient and accurate. Empirically, we observe that the optimality criterion sharply decreases the number of training examples the learner needs in order to achieve good performance.Comment: See http://www.jair.org/ for any accompanying file

Latent Topic Text Representation Learning on Statistical Manifolds

The explosive growth of text data requires effective methods to represent and classify these texts. Many text learning methods have been proposed, like statistics-based methods, semantic similarity methods, and deep learning methods. The statistics-based methods focus on comparing the substructure of text, which ignores the semantic similarity between different words. Semantic similarity methods learn a text representation by training word embedding and representing text as the average vector of all words. However, these methods cannot capture the topic diversity of words and texts clearly. Recently, deep learning methods such as CNNs and RNNs have been studied. However, the vanishing gradient problem and time complexity for parameter selection limit their applications. In this paper, we propose a novel and efficient text learning framework, named Latent Topic Text Representation Learning. Our method aims to provide an effective text representation and text measurement with latent topics. With the assumption that words on the same topic follow a Gaussian distribution, texts are represented as a mixture of topics, i.e., a Gaussian mixture model. Our framework is able to effectively measure text distance to perform text categorization tasks by leveraging statistical manifolds. Experimental results on text representation and classification, and topic coherence demonstrate the effectiveness of the proposed method

Spontaneous vs. posed facial behavior: automatic analysis of brow actions

Past research on automatic facial expression analysis has focused mostly on the recognition of prototypic expressions of discrete emotions rather than on the analysis of dynamic changes over time, although the importance of temporal dynamics of facial expressions for interpretation of the observed facial behavior has been acknowledged for over 20 years. For instance, it has been shown that the temporal dynamics of spontaneous and volitional smiles are fundamentally different from each other. In this work, we argue that the same holds for the temporal dynamics of brow actions and show that velocity, duration, and order of occurrence of brow actions are highly relevant parameters for distinguishing posed from spontaneous brow actions. The proposed system for discrimination between volitional and spontaneous brow actions is based on automatic detection of Action Units (AUs) and their temporal segments (onset, apex, offset) produced by movements of the eyebrows. For each temporal segment of an activated AU, we compute a number of mid-level feature parameters including the maximal intensity, duration, and order of occurrence. We use Gentle Boost to select the most important of these parameters. The selected parameters are used further to train Relevance Vector Machines to determine per temporal segment of an activated AU whether the action was displayed spontaneously or volitionally. Finally, a probabilistic decision function determines the class (spontaneous or posed) for the entire brow action. When tested on 189 samples taken from three different sets of spontaneous and volitional facial data, we attain a 90.7 % correct recognition rate. Categories and Subject Descriptors I.2.10 [Vision and Scene Understanding]: motion, modeling and recovery of physical attribute

The FANCD2-FANCI complex is recruited to DNA interstrand crosslinks before monoubiquitination of FANCD2

The Fanconi Anemia (FA) pathway is important for the repair of DNA interstrand crosslinks (ICL). The FANCD2-FANCI complex is central to the pathway, and localizes to ICLs dependent on its monoubiquitination. It has remained elusive whether the complex is recruited before or after the critical monoubiquitination. Here we report the first structural insight into the human FANCD2-FANCI complex by obtaining the cryo- EM structure. The complex contains an inner cavity, large enough to accommodate a double stranded DNA helix, as well as a protruding Tower domain. Disease-causing mutations in the Tower domain is observed in several FA patients. Our work reveals that recruitment of the complex to a stalled replication fork serves as the trigger for the activating monoubiquitination event. Taken together, our results uncover the mechanism of how the FANCD2-FANCI complex activates the FA pathway, and explains the underlying molecular defect in FA patients with mutations in the Tower domain

Distribution and turnover of Langerhans cells during delayed immune responses in human skin

The changes in distribution and turnover of T6+ Langerhans cells (LC) in the skin during delayed immune responses to tuberculin, and in the lesions of tuberculoid leprosy and cutaneous Leishmaniasis were investigated. In each situation, there was a dermal accumulation of monocytes and T cells and epidermal thickening with keratinocyte Ia expression. In the tuberculin response a dramatic change in the distribution of LC was observed. By 41 h, T6+ LC were displaced to the upper zone of the thickening epidermis followed by an almost complete loss of LC from the epidermis by approximately 72 h. After 7 d, T6+ cells started to reappear in the epidermis, which regained almost normal numbers of T6+ LC by 14 d. After antigen administration and initiation of the delayed immune response, enhanced numbers of T6+ cells appeared in association with the mononuclear cell infiltrate of the upper dermal lesions. Their numbers peaked by 72 h, were reduced at 7 d, and again enhanced by 14 d, when the epidermis was being repopulated. Similar numbers of T6+ cells were found in the chronic lesions of tuberculoid leprosy and cutaneous Leishmaniasis but not lepromatous leprosy. The cells of the dermis were identified as typical LC by the presence of Birbeck granules and surface T6 antigen at the electron microscope level. These cells were closely associated with lymphocytes. We have quantified the number of LC, evaluated their directional flux into the epidermis and dermis, determined nearest neighbors, and made predictions as to their fate