193 research outputs found
Cesiribacter roseus sp. nov., a pink-pigmented bacterium isolated from desert sand
No full textMing Liu, Huan Qi, Xuesong Luo, Jun Dai, Fang Peng and Chengxiang Fang (2012): Cesiribacter roseus sp. nov., a pink-pigmented bacterium isolated from desert sand. International Journal of Systematic and Evolutionary Microbiology 62: 96-99, DOI: 10.1099/ijs.0.028423-
Fig. 1 in Cesiribacter roseus sp. nov., a pink-pigmented bacterium isolated from desert sand
No full textFig. 1. Neighbour-joining tree based on 16S rRNA gene sequences showing the phylogenetic relationship between strain 311T and related taxa. Bootstrap values (expressed as percentages of 1000 replications).70 % are shown at nodes. Escherichia coli ATCC 11775T was used as an outgroup (not shown). Bar, 0.02 changes per nucleotide position.Published as part of Ming Liu, Huan Qi, Xuesong Luo, Jun Dai, Fang Peng and Chengxiang Fang, 2012, Cesiribacter roseus sp. nov., a pink-pigmented bacterium isolated from desert sand, pp. 96-99 in International Journal of Systematic and Evolutionary Microbiology 62 on page 97, DOI: 10.1099/ijs.0.028423-0, http://zenodo.org/record/26942
Fig. 2 in Cesiribacter roseus sp. nov., a pink-pigmented bacterium isolated from desert sand
No full textFig. 2. Transmission electron micrographs of cells of strain 311T grown on R2A agar at 30 ̊C for 60 h (a) and on 0.1× TSA at 30 ̊C for 60 h (b). Bars, 700 nm (a) and 1 µm (b).Published as part of Ming Liu, Huan Qi, Xuesong Luo, Jun Dai, Fang Peng and Chengxiang Fang, 2012, Cesiribacter roseus sp. nov., a pink-pigmented bacterium isolated from desert sand, pp. 96-99 in International Journal of Systematic and Evolutionary Microbiology 62 on page 97, DOI: 10.1099/ijs.0.028423-0, http://zenodo.org/record/26942
Quantifying the functional and evolutionary relationships among sequences, transcription factor binding and gene expression
No full textA central challenge in regulatory genomics today is to understand the precise relationship between regulatory sequences, transcription factor (TF) binding and gene expression. Many studies have discussed how TFs recognize their DNA binding sites. However, it is not well understood how the various factors that influence TF-DNA binding alter the cascade of gene expression. Moreover, mutations in regulatory sequences are a key driving force of evolution and diseases. A number of studies have examined the sequence motif turnover and divergence in TF binding across species. However, there is currently a lack of clarity on what these changes mean to enhancer function. In this thesis, we used computational and statistical methods to quantitatively and systematically examine the relationships among regulatory sequences, TF binding, and gene expression, from both functional and evolutionary perspectives.
At the functional level, we extended thermodynamics-based statistical models of the genetic sequence-to-function relationship to accurately predict gene expression. We incorporated chromatin accessibility and structural biological data into the models, described in Chapter 2 and 3. In doing so, we aimed to better identify transcription factor binding sites likely to influence gene expression, and thus, enhance the models’ capacity to predict gene expression. We demonstrated these improvements to gene expression modeling in Drosophila melanogaster by integrating DNaseI hypersensitivity assays and DNA shape. At the evolutionary level, we focused on regulatory variations between two distant Drosophila species to access inherent properties of enhancers, as described in Chapter 4. We used statistical and computational approaches to quantitatively examine the extent to which sequence and accessibility variations can predict TF occupancy divergence and enhancer activity change. We also found combinatorial TF binding can buffer variations at individual TF level to avoid drastic gene expression changes.Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2019-02-05 without embargo termsThe student, Pei-Chen Peng, accepted the attached license on 2018-09-07 at 15:59.The student, Pei-Chen Peng, submitted this Dissertation for approval on 2018-09-07 at 16:05.This Dissertation was approved for publication on 2018-09-10 at 11:17.DSpace SAF Submission Ingestion Package generated from Vireo submission #13000 on 2019-02-05 at 11:07:54Made available in DSpace on 2019-02-06T19:32:40Z (GMT). No. of bitstreams: 2
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The urban change in modern Shanghai descript by urban maps, 1840s-1930s
No full textThe urban change in modern Shanghai is a complex process which fused a lot of reasons in many ways. It is also the most important window to understand the urban modernization in china coastal city. This paper described the process of urban modernization in modern Shanghai according the analysis of the map of modern shanghai urban renewal. It separated to three parts. The first part, from river to sea, new and old existed together. Following the port opening, the old Chengxiang area and the new settlements area dramatic existed together. The second part, from street to the road. The new city views in settlements influenced the old town. The administrative department initiated the original change in Chengxiang area concentrate on building roads. And the influence from old land shape to the settlement’s road plan was different in British and French settlement. The third part, port areas and the city. With the city development, the bund function changed. And the relationship of Shiliupu dock and Chengxiang area was intimate. The general city structur
Leveraging knowledge networks for precision medicine
No full textAkin to the exponential growth of genomic sequencing data, high-throughput techniques in proteomics and biotechnology have been creating ever-expanding repositories of proteomic, pharmacological, and interactomic data. Other molecular data, including expression profiles, genomic mutations and cell conditions, have also been massively generated and they are further refining our understanding of disease mechanisms. In addition, patient data, gathered by electronic medical record systems and social medias, complement biological data and pave the way for personalized treatment strategies. Therefore, efficiently and effectively integrating and mining these invaluable data hold the great promising of making precision medicine a reality.
However, integrating and mining these large-scale, heterogeneous, and noisy dataset pose several fundamental computational challenges and have therefore become a bottleneck to clinical decision making and medical knowledge discovery. This thesis is a systematic study of mining these biological and healthcare data for precision medicine. I take a network perspective and integrate these datasets into a large knowledge network where nodes are biological concepts and links are biological relationships. I then propose a novel computational framework to mine these knowledge networks. To demonstrate the effectiveness of mining knowledge networks, I will introduce how this framework can be used to understand molecular functions, accelerate drug discovery, and support clinical decision making. To understand molecular functions, I will show how a knowledge network can substantially improve gene function prediction performance and further annotate novel gene sets by mining scientific literature-based knowledge network. To accelerate drug discovery, I will use the knowledge network to predict drug targets and identify drug associated pathways. To support clinical decision making, I will discuss our efforts in integrating genomics data with clinical data to cluster patients, predict patient survival and visualize patient records. Finally, I will conclude this thesis by summarizing how mining knowledge networks advance precision medicine and discussing the promising future work of this thesis.Submission published under a 24 month embargo labeled 'U of I Access', the embargo will last until 2020-05-01The student, Sheng Wang, accepted the attached license on 2018-04-18 at 12:25.The student, Sheng Wang, submitted this Dissertation for approval on 2018-04-18 at 12:26.This Dissertation was approved for publication on 2018-04-18 at 15:37.DSpace SAF Submission Ingestion Package generated from Vireo submission #12351 on 2018-08-31 at 17:20:28Made available in DSpace on 2018-09-04T20:36:44Z (GMT). No. of bitstreams: 3
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Previous issue date: 2018-04-18Embargo set by: Seth Robbins for item 107273
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Low-rank estimation and embedding learning: theory and applications
No full textIn many real-world applications of data mining, datasets can be represented using matrices, where rows of the matrix correspond to objects (or data instances) and columns to features (or attributes). Often the datasets are in high-dimensional feature space. For example, in the vector space model of text data, the feature dimension is the vocabulary size. If representing a social network using an adjacency matrix, the feature dimension corresponds to the number of objects in the network. Many other datasets also fall into this category, such as genetic datasets, images, and medical datasets. Even though the feature dimension is enormous, a common observation is that the high-dimensional datasets may (approximately) lie in a subspace of smaller dimensionality, due to dependency or correlation among features. This thesis studies the problem of automatically identifying the low-dimensional space that high-dimensional datasets (approximately) lie in based on dimension reduction models: one is low-rank estimation models and the other is embedding learning models. For data matrices, low-rank estimation is to recover an underlying data matrix, subject to the constraint the matrix is of reduced rank. Such analysis is also generalized to the high-dimensional higher-order tensor data. Meanwhile, embedding learning models are to directly project the observation data into a low-dimensional vector space.
In the first part, the theoretical analysis of low-rank estimation models is established in the regime of high-dimensional statistics. For matrices, the low-rank structure corresponds to the sparsity of the singular values; while for tensors, the low-rank model can be defined as the low-rankness of the unfolding matrices of the tensor. To achieve low-rank solutions, two categories of regularization are imposed. Firstly, the problem of robust tensor decomposition with gross corruption is considered. To recover the underlying true tensor and corruption of large magnitude, structure assumptions of low-rankness and sparsity are imposed on the tensor and corruption, respectively. The Schatten-1 norm is applied as convex regularization for the low-rank structure. Secondly, the problem of matrix estimation is considered with a nonconvex penalty. Compared with convex regularization, nonconvex penalty takes advantage of the large singular values, which leads to faster statistical convergence rate and oracle property under a mild condition on the magnitude of the singular values. For both problems, efficient optimization algorithms are proposed, and extensive numerical experiments are conducted to corroborate the efficacy of the proposed algorithms and the theoretical analysis.
In the second part, embedding learning models for real-world applications are presented. The high-dimensional data is projected into a low-dimensional vector space via preserving the proximity among objects. Each object is represented by a low-dimensional vector, called embedding or distributed representation. In the first application, the heterogeneity of the objects is considered. Based on the observation that several interactions among the strongly-typed objects happen simultaneously as an event, the embeddings of objects in each event are learned as a whole. In other words, the model preserves the proximity among all the participating objects in each event. Experimental results provide evidence that the learned embeddings are more effective while being robust to data sparsity and noises for various classification tasks. In the second application, the task of expert finding is studied, which is to rank candidates with appropriate expertise based on a given query. To capture the subtle semantic information regarding specific queries with narrow semantic meanings, locally-trained embedding learning with concept hierarchy as guidance is proposed for query expansion. The locally-trained embeddings preserve the proximity among terms constrained on a sub-corpus. Compared with global embedding trained on the whole dataset, locally-trained embedding has stronger representation power. Experimental results show that the proposed embedding learning method achieves high precision regarding the task of expert finding.
To summarize, this thesis provides important results of low-rank estimation and embedding learning models for high-dimensional data analysis and real-world applications.Submission published under a 24 month embargo labeled 'Closed Access', the embargo will last until 2019-08-01The student, Huan Gui, accepted the attached license on 2017-07-12 at 00:00.The student, Huan Gui, submitted this Dissertation for approval on 2017-07-12 at 00:08.This Dissertation was approved for publication on 2017-07-13 at 15:11.DSpace SAF Submission Ingestion Package generated from Vireo submission #11417 on 2017-09-29 at 11:19:04Made available in DSpace on 2017-09-29T16:39:46Z (GMT). No. of bitstreams: 3
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Text cube: construction, summarization and mining
No full textA large portion of real world data is either text or structured (\eg, relational) data. Such data objects are often linked together (\eg, structured product information linking with their descriptions and customer reviews.). To systematically analyze large numbers of such textual documents, it is often desirable to manage the text data with the associated structured data in a multi-dimensional space (hence \emph{text cube}).
This thesis studies the multi-dimensional representation of large textual data. Since Jim Gray introduced the concept of ``data cube'', data cube, associated with online analytical processing (OLAP), has become a driving engine in data warehouse industry. By modeling a large textual corpus as a ``cube'', \ie multi-dimensional and hierarchical structure, we bridge the power of traditional OLAP and Information Retrieval / Natural Language Processing techniques. In particular, this thesis focuses on two lines of work, one is to construct a multi-dimensional text cube from raw text data with limited user guidance; the other is to develop effective summarization and mining techniques tailored for multi-dimensional queries on text cubes.
In the first part of the thesis, the problem of \emph{dimension-based structure creation} is studied. We propose an end-to-end framework for extracting multi-dimensional structure from a corpus, taking the input of a corpus of specific domain and limited seeds to generate a high-quality dimension values as output. We introduce the novel concept of Semantic Pattern Graph to leverage web signals to understand the underlying semantics of lexical patterns, improve pattern evaluation using mined semantics, and yield more accurate and complete structure. Experiments show the effectiveness of our approach.
In the second part, with all the dimensions discovered, we study the problem of \emph{cell-based document allocation}. That is, linking the created dimensions with text data and construct a multi-dimensional text cube. To allocate documents into correct multi-dimensional subsets, \ie a cell. Traditional approaches, in this particular task, may require substantial labeling from user. Instead, we propose a model that requires no additional training data besides the given (label) name of each cube dimension as weak supervision. With such weak supervision, we develop a \emph{dimension-aware joint embedding} framework that learns joint representations for terms, documents, and labels.
In the joint embedding process, our method iteratively learns dimension-aware document representations by selectively focusing on discriminative keywords for different dimensions. Furthermore, it alleviates label sparsity by leveraging label representations to enrich the labeled term set. Numerical experiments corroborate the effectiveness of our solution.
In the third part, we introduce the concept of \emph{Context-Aware Semantic Online Analytical Processing} (\ie \emph{CASeOLAP}) in text cubes, and use \emph{top- representative phrases} to represent the semantics of the document subset in a text cube cell. By ranking phrases with a newly proposed ranking measure according to three criteria: integrity, popularity and distinctiveness. We identify phrases that can successfully digest the main content of a subset of documents of interest and contrast with other neighboring subsets. Our experiments in a large news dataset demonstrate the effectiveness of the newly proposed ranking measure in finding representative phrases and the efficiency in both query processing time and storage cost. The approach is also applied to clinical biomarker analysis and protest news analysis with success.
In the last part, the system of \emph{EventCube} is proposed to support end-to-end pipeline of text cube in an informative, interactive, and user-friendly manner. The system serves as a general platform for construction, search, summarization, OLAP (online analytical processing) and data mining on integrated text and structured data. The system is a growing testbed for various text cube based research and has been successfully applied to NASA for aviation safety report analysis and Army Research Lab for Counter-Terrorism Report analysis.
To summarize, this thesis provides important results of construction and consumption of multi-dimensional text cubes and shows its power in tackling real-world text analysis tasks.Submission published under a 24 month embargo labeled 'U of I Access', the embargo will last until 2019-12-01The student, Fangbo Tao, accepted the attached license on 2017-12-06 at 11:45.The student, Fangbo Tao, submitted this Dissertation for approval on 2017-12-06 at 11:53.This Dissertation was approved for publication on 2017-12-06 at 13:21.DSpace SAF Submission Ingestion Package generated from Vireo submission #11883 on 2018-03-13 at 09:57:28Made available in DSpace on 2018-03-13T15:25:31Z (GMT). No. of bitstreams: 2
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Data quality in the deep learning era: Active semi-supervised learning and text normalization for natural language understanding
No full textDeep Learning, a growing sub-field of machine learning, has been applied with tremendous success in a variety of domains, opening opportunities for achieving human level performance in many applications. However, Deep Learning methods depend on large quantities of data with millions of annotated instances. And while well-formed academic datasets have helped advance supervised learning research, in the real-word we are daily deluged by massive amounts of unstructured data, that remain unusable for current supervised learning approaches, as only a small portion is either labeled, cleaned or structured.
In order for a machine learning model to be effective, volume is not the only data dimension that is necessary. Quality is equally important and has proven to be a critical factor for the success of industrial applications of machine learning. According to IBM, poor data quality can cost more than 3 trillion US dollars per year for the US market alone. Inspired by the need for advanced methods that can efficiently address such bottlenecks, we develop machine learning techniques can be leveraged to improve upon data quality in both data-related dimensions: input and output space.
Having a set of labeled examples that can capture the task characteristics is one of the most important prerequisites for successfully applying machine learning. As such, we first focus on minimizing the annotation effort for any arbitrary user-defined task by exploring active learning methods. We show that the best performing active learning strategy depends on the task at-hand and we propose a combination of active learners, maximizing annotation performance early in the process. We demonstrate the viability of the approach on several relation extraction tasks.
Next, we observe that even though our method can be used to speed up the collection of labeled training data, the rest will remain unlabeled and thus unexploited. Semi-supervised learning methods proposed in the literature can utilize additional unlabeled data, however, are typically compared on computer vision datasets such as CIFAR10. Here, we perform a systematic exploration of several semi-supervised methods for three sequence labeling tasks and two classification tasks.
Additionally, most methods have assumptions that are less suitable to realistic scenarios. For example, proposed methods in the recent literature treat all unlabeled examples equally. Yet, in many cases we would like to sort out examples that might be less useful or confusing, particularly in noisy settings where examples with low training loss or high confidence are more likely to be clean examples. In addition, most methods assume that the unlabeled data can be classified into the same classes as the labeled data. This does not take into consideration the very possible scenario of out-of-class instances. For example, our classifier may be distinguishing cats from dogs, but the unlabeled examples may contain additional classes, such as shells, butterflies, etc. To this end, we design methods to mitigate these issues, with a re-weighting mechanism that can be incorporated to any consistency-based regularizer.
Both active and semi-supervised learning methods aim to reduce labeling efforts by either automatically expanding the training set or selecting the most informative examples for human annotation. However, bootstrapping approaches often result in negative effects on NLP tasks due to the addition of falsely labeled instances. We address the challenge of producing good quality proxy labels, by leveraging the continuously growing stream of human annotations. We introduce a calibration of semi-supervised active learning where the confidence of the classifier is weighted by an auxiliary neural model that remove incorrectly labeled instances and dynamically adjusts the number of proxy labels included in each iteration. Experimental results show that our strategy outperforms baselines that combine traditional active learning with self-training.
We have explored various ways on how to improve the output space of examples. But the input representation is also equally important. Particularly for social media, (the most abundant source of raw data nowadays) informal writing can cause several bottlenecks. For example, most Information Extraction (IE) tools rely on accurate understanding of text and struggle with the noisy and informal nature of social media due to high out-of-vocabulary (OOV) word rates. In this work, we design a social media text normalization hybrid word-character attention-based encoder-decoder model that can serve as a pre-processing step for any off-the-shelf NLP tool to adapt to social media noisy text. Our model surpasses baseline neural models designed for text normalization and achieves comparable performance with state-of-the-art related work.
Although we evaluate on NLP tasks, all methods developed are fairly general and can be applied to other supervised machine learning tasks in need of techniques that create meaningful data representations and simultaneously reduce the burden and cost of human annotations.Submission published under a 24 month embargo labeled 'U of I Access', the embargo will last until 2021-12-01The student, Ismini Lourentzou, accepted the attached license on 2019-12-04 at 21:09.The student, Ismini Lourentzou, submitted this Dissertation for approval on 2019-12-04 at 21:34.This Dissertation was approved for publication on 2019-12-05 at 09:21.DSpace SAF Submission Ingestion Package generated from Vireo submission #14706 on 2020-02-28 at 17:23:29Made available in DSpace on 2020-03-02T22:15:08Z (GMT). No. of bitstreams: 2
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Previous issue date: 2019-12-05Embargo set by: Seth Robbins for item 113917
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Exploiting sparsity for machine learning in big data
No full textThe rapid development of modern information technology has significantly facilitated the generation, collection, transmission and storage of all kinds of data. With the so-called “big data” generated in an unprecedented rate, we are facing significant challenges in learning knowledge from it. Traditional machine learning algorithms often suffer from the unmatched volume and complexity of such big data, however, sparsity has been recently studied to tackle this challenge. With reasonable assumptions and effective utilization of sparsity, we can learn models that are simpler, more efficient and robust to noise.
The goal of this dissertation is studying and exploiting sparsity to design learning algorithms to effectively and efficiently solve various challenging and significant real-world machine learning tasks. I will integrate and introduce my work from three different perspectives: sample complexity, computational complexity, and noise reduction. Intuitively, these three aspects correspond to models that require less data to learn, are more computationally efficient, and still perform well when the data is noisy. Specifically, this thesis is integrated from the three aspects as follows:
First, I focus on the sample complexity of machine learning algorithms for an important machine learning task, compressed sensing. I propose a novel algorithm based on nonconvex sparsity-inducing penalty, which is the first work that utilizes such penalty. I also prove that our algorithm improves the best previous sample complexity significantly by extensive theoretical derivation and numerical experiments.
Second, from the perspective of computational complexity, I study the expectation-maximization (EM) algorithms in high dimensional scenarios. In contrast to the conventional regime, the maximization step (M-step) in high dimensional scenario can be very computationally expensive or even not well defined. To address this challenge, I propose an efficient algorithm based on novel semi-stochastic gradient descent with variance reduction, which naturally incorporates the sparsity in model parameters, greatly economizes the computational cost at each iteration and enjoys faster convergence rates simultaneously. We believe the proposed unique semi-stochastic variance-reduced gradient is of general interest of nonconvex optimization of bivariate structure.
Third, I look into the noise reduction problem and target on an important text mining task, event detection.
To overcome the noise in the text data which hampers the detection of real events, I design an efficient algorithm based on sparsity-inducing fused lasso framework. Experiment results on various datasets show that our algorithm effectively smooths out noises and captures the real event, outperforming several state- of-the-art methods consistently in noisy setting.
To sum up, this thesis focuses on the critical issues of machine learning in big data from the perspective of sparsity in the data and model. Our proposed methods clearly show that utilizing sparsity is of great importance for various significant machine learning tasks.Submission published under a 24 month embargo labeled 'Closed Access', the embargo will last until 2019-05-01The student, Rongda Zhu, accepted the attached license on 2017-04-18 at 20:11.The student, Rongda Zhu, submitted this Dissertation for approval on 2017-04-18 at 20:18.This Dissertation was approved for publication on 2017-04-19 at 18:13.DSpace SAF Submission Ingestion Package generated from Vireo submission #10875 on 2017-08-10 at 14:31:43Made available in DSpace on 2017-08-10T19:52:11Z (GMT). No. of bitstreams: 2
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Previous issue date: 2017-04-19Embargo set by: Colleen Fallaw for item 102651
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