Heterogeneous domain adaptation for multiple classes

In this paper, we present an efficient multi-class heterogeneous domain adaptation method, where data from source and target domains are represented by heterogeneous features of different dimensions. Specifically, we propose to reconstruct a sparse feature transformation matrix to map the weight vector of classifiers learned from the source domain to the target domain. We cast this learning task as a compressed sensing problem, where each binary classifier induced from multiple classes can be deemed as a measurement sensor. Based on the compressive sensing theory, the estimation error of the transformation matrix decreases with the increasing number of classifiers. Therefore, to guarantee reconstruction performance, we construct sufficiently many binary classifiers based on the error correcting output coding. Extensive experiments are conducted on both a toy dataset and three real-world datasets to verify the superiority of our proposed method over existing state-of-the-art HDA methods in terms of prediction accuracy

Multi-class Heterogeneous Domain Adaptation

© 2019 Joey Tianyi Zhou, Ivor W. Tsang, Sinno Jialin Pan, Mingkui Tan. A crucial issue in heterogeneous domain adaptation (HDA) is the ability to learn a feature mapping between different types of features across domains. Inspired by language translation, a word translated from one language corresponds to only a few words in another language, we present an efficient method named Sparse Heterogeneous Feature Representation (SHFR) in this paper for multi-class HDA to learn a sparse feature transformation between domains with multiple classes. Specifically, we formulate the problem of learning the feature transformation as a compressed sensing problem by building multiple binary classifiers in the target domain as various measurement sensors, which are decomposed from the target multi-class classification problem. We show that the estimation error of the learned transformation decreases with the increasing number of binary classifiers. In other words, for adaptation across heterogeneous domains to be successful, it is necessary to construct a sufficient number of incoherent binary classifiers from the original multi-class classification problem. To achieve this, we propose to apply the error correcting output correcting (ECOC) scheme to generate incoherent classifiers. To speed up the learning of the feature transformation across domains, we apply an efficient batch-mode algorithm to solve the resultant nonnegative sparse recovery problem. Theoretically, we present a generalization error bound of our proposed HDA method under a multi-class setting. Lastly, we conduct extensive experiments on both synthetic and real-world datasets to demonstrate the superiority of our proposed method over existing state-of-the-art HDA methods in terms of prediction accuracy and training efficiency

Neural systems for auditory perception of lexical tones

Previous neuroimaging research on cognitive processing of speech tone has generated dramatically different patterns of findings. Even at the basic perception level, brain mapping studies of lexical tones have yielded inconsistent results. Apart from the data inconsistency problem, experimental materials in past studies of tone perception carried little or minimal lexical semantics, an important dimension that should not be dispensed with because speech tones serve to distinguish lexical meanings. The present study sought to examine the neural correlates of the perception of speech tone using lexically meaningful experimental stimuli. A simple lexical tone perception task was devised in which native Mandarin speakers were asked to judge whether or not the two syllables of an auditorily presented Chinese bisyllabic word had the same tone. We selected bisyllabic words as experimental stimuli because Chinese monosyllables often convey little or very vague meanings due to rampant homophony. We found that the left inferior frontal gyrus, the right middle temporal gyrus and bilateral superior temporal gyri are responsible for basic perception of linguistic pitches. Our interpretation of the data sees the left superior temporal gyrus as engaged in primary acoustic analysis of the auditory stimuli, while the right middle superior temporal gyrus and the left inferior frontal region are involved in both tonal and semantic processing of the language stimuli.postprin

Riemannian pursuit for big matrix recovery

Copyright © (2014) by the International Machine Learning Society (IMLS) All rights reserved. Low rank matrix recovery is a fundamental task in many real-world applications. The perfor-mance of existing methods, however, deteriorates significantly when applied to ill-conditioned or large-scale matrices. In this paper, we therefore propose an efficient method, called Riemannian Pursuit (RP), that aims to address these two problems simultaneously. Our method consists of a sequence of fixed-rank optimization problems. Each subproblem, solved by a nonlinear Rieman-nian conjugate gradient method, aims to correct the solution in the most important subspace of increasing size. Theoretically, RP converges linearly under mild conditions and experimental results show that it substantially outperforms existing methods when applied to large-scale and ill-conditioned matrices

Proliferating tricholemmal tumour: clinicopathological aspects of a case.

We report the case of a 49-year-old man who presented with an enlarging mass over his occipital scalp. The clinical impression was either a squamous cell carcinoma or an unusual adnexal tumour. A wide excision was performed with skin grafting. Gross examination revealed a large exophytic tumour mass measuring 10 cm. Histopathological examination showed a circumscribed, well-differentiated squamoproliferative lesion with a lobulated architecture. Clear cell features, pilar-type keratinisation, microcalcifications and the presence of mucinous degeneration were noted. A diagnosis of proliferating tricholemmal tumour was made. This entity incorporates a spectrum of lesions, ranging from the mostly benign proliferating tricholemmal cyst to tumours having more atypical cellular and invasive features, the latter features correlating with an increased capacity for aggressive behaviour. Management-wise, such tumours require complete excision with follow-up. As the tumours are often large in size at presentation, reconstruction is required

Controlling Spatiotemporal Mechanics of Supramolecular Hydrogel Networks with Highly Branched Cucurbit[8]uril Polyrotaxanes

Attempts to rationally tune the macroscopic mechanical performance of supramolecular hydrogel networks through noncovalent molecular interactions have led to a wide variety of supramolecular materials with desirable functions. While the viscoelastic properties are dominated by temporal hierarchy (crosslinking kinetics), direct mechanistic studies on spatiotemporal control of supramolecular hydrogel networks, based on host-guest chemistry, have not yet been established. Here, supramolecular hydrogel networks assembled from highly branched cucurbit[8]uril-threaded polyrotaxanes (HBP-CB[8] ) and naphthyl-functionalized hydroxyethyl cellulose (HECNp) are reported, exploiting the CB[8] host-guest complexation. Mechanically locking CB[8] host molecules onto a highly branched hydrophilic polymer backbone, through selective binary complexation with viologen derivatives, dramatically increases the solubility of CB[8]. Additionally, the branched architecture enables tuning of material dynamics of the supramolecular hydrogel networks via both topological (spatial hierarchy) and kinetic (temporal hierarchy) control. Relationship between macroscopic properties (time- and temperature-dependent rheological properties, thermal stability, and reversibility), spatiotemporal hierarchy, and chain dynamics of the highly branched polyrotaxane hydrogel networks is investigated in detail. Such kind of tuning of material mechanics through spatiotemporal hierarchy improves our understanding of the challenging relationship between design of supramolecular polymeric materials and their complex viscoelasticity, and also highlights a facile strategy to engineer dynamic supramolecular materials.Ministry of Education of Malaysia and Universiti Teknologi MARA, Marie Curie Fellowship. Grant Number: 65836

A meta-analytic study of the neural systems for auditory processing of lexical tones

The neural systems of lexical tone processing have been studied for many years. However, previous findings have been mixed with regard to the hemispheric specialization for the perception of linguistic pitch patterns in native speakers of tonal language. In this study, we performed two activation likelihood estimation (ALE) meta-analyses, one on neuroimaging studies of auditory processing of lexical tones in tonal languages (17 studies), and the other on auditory processing of lexical information in non-tonal languages as a control analysis for comparison (15 studies). The lexical tone ALE analysis showed significant brain activations in bilateral inferior prefrontal regions, bilateral superior temporal regions and the right caudate, while the control ALE analysis showed significant cortical activity in the left inferior frontal gyrus and left temporo-parietal regions. However, we failed to obtain significant differences from the contrast analysis between two auditory conditions, which might be caused by the limited number of studies available for comparison. Although the current study lacks evidence to argue for a lexical tone specific activation pattern, our results provide clues and directions for future investigations on this topic, more sophisticated methods are needed to explore this question in more depth as well.published_or_final_versio