Learning from Structured Data with High Dimensional Structured Input and Output Domain

Structured data is accumulated rapidly in many applications, e.g. Bioinformatics, Cheminformatics, social network analysis, natural language processing and text mining. Designing and analyzing algorithms for handling these large collections of structured data has received significant interests in data mining and machine learning communities, both in the input and output domain. However, it is nontrivial to adopt traditional machine learning algorithms, e.g. SVM, linear regression to structured data. For one thing, the structural information in the input domain and output domain is ignored if applying the normal algorithms to structured data. For another, the major challenge in learning from many high-dimensional structured data is that input/output domain can contain tens of thousands even larger number of features and labels. With the high dimensional structured input space and/or structured output space, learning a low dimensional and consistent structured predictive function is important for both robustness and interpretability of the model. In this dissertation, we will present a few machine learning models that learn from the data with structured input features and structured output tasks. For learning from the data with structured input features, I have developed structured sparse boosting for graph classification, structured joint sparse PCA for anomaly detection and localization. Besides learning from structured input, I also investigated the interplay between structured input and output under the context of multi-task learning. In particular, I designed a multi-task learning algorithms that performs structured feature selection & task relationship Inference. We will demonstrate the applications of these structured models on subgraph based graph classification, networked data stream anomaly detection/localization, multiple cancer type prediction, neuron activity prediction and social behavior prediction. Finally, through my intern work at IBM T.J. Watson Research, I will demonstrate how to leverage structural information from mobile data (e.g. call detail record and GPS data) to derive important places from people's daily life for transit optimization and urban planning

Mathematical Modeling and Intelligent Algorithm for Multirobot Path Planning

10.1155/2017/1465158Mathematical Problems in Engineering2017146515

GLSVM: Integrating Structured Feature Selection and Large Margin Classification

Abstract—High dimensional data challenges current feature selection methods. For many real world problems we often have prior knowledge about the relationship of features. For example in microarray data analysis, genes from the same biological pathways are expected to have similar relationship to the outcome that we target to predict. Recent regularization methods on Support Vector Machine (SVM) have achieved great success to perform feature selection and model selection simultaneously for high dimensional data, but neglect such re-lationship among features. To build interpretable SVM models, the structure information of features should be incorporated. In this paper, we propose an algorithm GLSVM that automatically perform model selection and feature selection in SVMs. To incorporate the prior knowledge of feature relationship, we extend standard 2 norm SVM and use a penalty function that employs a L2 norm regularization term including the normalized Laplacian of the graph and L1 penalty. We have demonstrated the effectiveness of our methods and compare them to the state-of-the-art using two real-world benchmarks. I

Tree-based Text-Vision BERT for Video Search in Baidu Video Advertising

The advancement of the communication technology and the popularity of the smart phones foster the booming of video ads. Baidu, as one of the leading search engine companies in the world, receives billions of search queries per day. How to pair the video ads with the user search is the core task of Baidu video advertising. Due to the modality gap, the query-to-video retrieval is much more challenging than traditional query-to-document retrieval and image-to-image search. Traditionally, the query-to-video retrieval is tackled by the query-to-title retrieval, which is not reliable when the quality of tiles are not high. With the rapid progress achieved in computer vision and natural language processing in recent years, content-based search methods becomes promising for the query-to-video retrieval. Benefited from pretraining on large-scale datasets, some visionBERT methods based on cross-modal attention have achieved excellent performance in many vision-language tasks not only in academia but also in industry. Nevertheless, the expensive computation cost of cross-modal attention makes it impractical for large-scale search in industrial applications. In this work, we present a tree-based combo-attention network (TCAN) which has been recently launched in Baidu's dynamic video advertising platform. It provides a practical solution to deploy the heavy cross-modal attention for the large-scale query-to-video search. After launching tree-based combo-attention network, click-through rate gets improved by 2.29\% and conversion rate get improved by 2.63\%.Comment: This revision is based on a manuscript submitted in October 2020, to ICDE 2021. We thank the Program Committee for their valuable comment

Activating Transcription Factor 3 Deficiency Promotes Cardiac Hypertrophy, Dysfunction, and Fibrosis Induced by Pressure Overload

Activating transcription factor 3 (ATF3), which is encoded by an adaptive-response gene induced by various stimuli, plays an important role in the cardiovascular system. However, the effect of ATF3 on cardiac hypertrophy induced by a pathological stimulus has not been determined. Here, we investigated the effects of ATF3 deficiency on cardiac hypertrophy using in vitro and in vivo models. Aortic banding (AB) was performed to induce cardiac hypertrophy in mice. Cardiac hypertrophy was estimated by echocardiographic and hemodynamic measurements and by pathological and molecular analysis. ATF3 deficiency promoted cardiac hypertrophy, dysfunction and fibrosis after 4 weeks of AB compared to the wild type (WT) mice. Furthermore, enhanced activation of the MEK-ERK1/2 and JNK pathways was found in ATF3-knockout (KO) mice compared to WT mice. In vitro studies performed in cultured neonatal mouse cardiomyocytes confirmed that ATF3 deficiency promotes cardiomyocyte hypertrophy induced by angiotensin II, which was associated with the amplification of MEK-ERK1/2 and JNK signaling. Our results suggested that ATF3 plays a crucial role in the development of cardiac hypertrophy via negative regulation of the MEK-ERK1/2 and JNK pathways

Cellular repressor of E1A-stimulated genes attenuates cardiac hypertrophy and fibrosis

Cellular repressor of E1A-stimulated genes (CREG) is a secreted glycoprotein of 220 amino acids. It has been proposed that CREG acts as a ligand that enhances differentiation and/or reduces cell proliferation. CREG has been shown previously to attenuate cardiac hypertrophy in vitro. However, such a role has not been determined in vivo. In the present study, we tested the hypothesis that overexpression of CREG in the murine heart would protect against cardiac hypertrophy and fibrosis in vivo. The effects of constitutive human CREG expression on cardiac hypertrophy were investigated using both in vitro and in vivo models. Cardiac hypertrophy was produced by aortic banding and infusion of angiotensin II in CREG transgenic mice and control animals. The extent of cardiac hypertrophy was quantitated by two-dimensional and M-mode echocardiography as well as by molecular and pathological analyses of heart samples. Constitutive over-expression of human CREG in the murine heart attenuated the hypertrophic response, markedly reduced inflammation. Cardiac function was also preserved in hearts with increased CREG levels in response to hypertrophic stimuli. These beneficial effects were associated with attenuation of the mitogen-activated protein kinase (MAPK)-extracellular signal-regulated kinase 1 (MEK-ERK1)/2-dependent signalling cascade. In addition, CREG expression blocked fibrosis and collagen synthesis through blocking MEK-ERK1/2-dependent Smad 2/3 activation in vitro and in vivo. Therefore, the expression of CREG improves cardiac functions and inhibits cardiac hypertrophy, inflammation and fibrosis through blocking MEK-ERK1/2-dependent signalling

Evidences for pressure-induced two-phase superconductivity and mixed structures of NiTe₂ and NiTe in type-II Dirac semimetal NiTe_(2-x) (x = 0.38 ± 0.09) single crystals

Bulk NiTe₂ is a type-II Dirac semimetal with non-trivial Berry phases associated with the Dirac fermions. Theory suggests that monolayer NiTe₂ is a two-gap superconductor, whereas experimental investigation of bulk NiTe_(1.98) for pressures (P) up to 71.2 GPa do not reveal any superconductivity. Here we report experimental evidences for pressure-induced two-phase superconductivity as well as mixed structures of NiTe₂ and NiTe in Te-deficient NiTe_(2-x) (x = 0.38±0.09) single crystals. Hole-dominant multi-band superconductivity with the P3M1 hexagonal-symmetry structure of NiTe₂ appears at P ≥ 0.5 GPa, whereas electron-dominant single-band superconductivity with the P2/m monoclinic-symmetry structure of NiTe emerges at 14.5 GPa < P < 18.4 GPa. The coexistence of hexagonal and monoclinic structures and two-phase superconductivity is accompanied by a zero Hall coefficient up to ∼ 40 GPa, and the second superconducting phase prevails above 40 GPa, reaching a maximum T_c = 7.8 K and persisting up to 52.8 GPa. Our findings suggest the critical role of Te-vacancies in the occurrence of superconductivity and potentially nontrivial topological properties in NiTe_(2-x)