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Machine Learning is the study of methods for programming computers to learn. Computers are applied to a wide range of tasks, and for most of these it is relatively easy for programmers to design and implement the necessary software. However, there are many tasks for which this is difficult or impossible. These can be divided into four general categories. First, there are problems for which there exist no human experts. For example, in modern automated manufacturing facilities, there is a need to predict machine failures before they occur by analyzing sensor readings. Because the machines are new, there are no human experts who can be interviewed by a programmer to provide the knowledge necessary to build a computer system. A machine learning system can study recorded data and subsequent machine failures and learn prediction rules. Second, there are problems where human experts exist, but where they are unable to explain their expertise. This is the case in many perceptual tasks, such as speech recognition, hand-writing recognition, and natural language understanding. Virtually all humans exhibit expert-level abilities on these tasks, but none of them can describe the detailed steps that they follow as they perform them. Fortunately, humans can provide machines with examples of the inputs and correct outputs for these tasks, so machine learning algorithms can learn to map the inputs to the outputs. Third, there are problems where phenomena are changing rapidly. In finance, for example, people would like to predict the future behavior of the stock market, of consumer purchases, or of exchange rates. These behaviors change frequently, so that even if a programmer could construct a good predictive computer program, it would need to be rewritten frequently. A learning program can relieve the programmer of this burden by constantly modifying and tuning a set of learned prediction rules. Fourth, there are applications that need to be customized for each computer user separately. Consider, for example, a program to filter unwanted electronic mail messages. Different users will need different filters. It is unreasonable to expect each user to program his or her own rules, and it is infeasible to provide every user with a software engineer to keep the rules up-to-date. A machine learning system can learn which mail messages the user rejects and maintain the filtering rules automatically. Machine learning addresses many of the same research questions as the fields of statistics, data mining, and psychology, but with differences of emphasis. Statistics focuses on understanding the phenomena that have generated the data, often with the goal of testing different hypotheses about those phenomena. Data mining seeks to find patterns in the data that are understandable by people. Psychological studies of human learning aspire to understand the mechanisms underlying the various learning behaviors exhibited by people (concept learning, skill acquisition, strategy change, etc.).

Machine learning

[신간의 별자리x] 우리/미술, 그리고 ‘슬픔의 박물관’

The lion's share of bacteria in various environments cannot be cloned in the laboratory and thus cannot be sequenced using existing technologies. A major goal of single-cell genomics is to complement gene-centric metagenomic data with whole-genome assemblies of uncultivated organisms. Assembly of single-cell data is challenging because of highly non-uniform read coverage as well as elevated levels of sequencing errors and chimeric reads. We describe SPAdes, a new assembler for both single-cell and standard (multicell) assembly, and demonstrate that it improves on the recently released E+V-SC assembler (specialized for single-cell data) and on popular assemblers Velvet and SoapDeNovo (for multicell data). SPAdes generates single-cell assemblies, providing information about genomes of uncultivatable bacteria that vastly exceeds what may be obtained via traditional metagenomics studies. SPAdes is available online ( http://bioinf.spbau.ru/spades ). It is distributed as open source software.

SPAdes, a new genome assembly algorithm and its applications to single-cell sequencing ( 7th Annual SFAF Meeting, 2012)

Data Mining - Concepts and Techniques.

Filter pruning is one of the most popular approaches for compressing convolutional neural networks (CNNs). The most critical task in pruning is to evaluate the importance of each convolutional filter, such that the less important filters can be removed while the overall model performance is minimally affected. In each layer, some filters may be linearly dependent on each other, which means that they have replaceable information. Redundant information can be removed without significantly affecting information richness and model performance. In this paper, we propose a novel low-rank guided pruning scheme to obtain skeleton neural networks by alternatively training and pruning CNNs. In each step, training is performed with nuclear-norm regularization to low-rank the filters in each layer, followed by filter pruning to maintain the information richness via the maximally linearly independent subsystem. A novel “smaller-norm-and-linearly-dependent-less-important" pruning criterion is proposed to compress the model. The training and pruning processes can be repeated until the model is fully trained. To investigate the performance, we applied the proposed joint training and pruning scheme to train the CNNs for image classification. We considered three benchmark datasets: MNIST, CIFAR-10 and ILSVRC-2012. The proposed method successfully achieved a higher pruning rate and better classification performance compared to state-of-the-art compression methods. 

Skeleton Neural Networks via Low-rank Guided Filter Pruning

Causal-Trivial Attention Graph Neural Network for Fault Diagnosis of Complex Industrial Processes

In a real social network, each user has attributes for self-description called user attributes which are semantically hierarchical. With these attributes, we can implement personalized services such as user classification and targeted recommendations. Most traditional approaches mainly focus on the flat inference problem without considering the semantic hierarchy of user attributes which will cause serious inconsistency in multilevel tasks. To address these issues, in this paper, we propose a cross-level model called IWM. It is based on the theory of maximum entropy which collects attribute information by mining the global graph structure. Meanwhile, we propose a correction method based on the predefined hierarchy to realize the mutual correction between different layers of attributes. Finally, we conduct extensive verification experiments on the DBLP data set and it has been proved that compared with other algorithms, our method has a superior effect.

A Multilevel Inference Mechanism for User Attributes over Social Networks

For video recognition task, a global representation summarizing the whole
contents of the video snippets plays an important role for the final
performance. However, existing video architectures usually generate it by using
a simple, global average pooling (GAP) method, which has limited ability to
capture complex dynamics of videos. For image recognition task, there exist
evidences showing that covariance pooling has stronger representation ability
than GAP. Unfortunately, such plain covariance pooling used in image
recognition is an orderless representative, which cannot model spatio-temporal
structure inherent in videos. Therefore, this paper proposes a
Temporal-attentive Covariance Pooling(TCP), inserted at the end of deep
architectures, to produce powerful video representations. Specifically, our TCP
first develops a temporal attention module to adaptively calibrate
spatio-temporal features for the succeeding covariance pooling, approximatively
producing attentive covariance representations. Then, a temporal covariance
pooling performs temporal pooling of the attentive covariance representations
to characterize both intra-frame correlations and inter-frame
cross-correlations of the calibrated features. As such, the proposed TCP can
capture complex temporal dynamics. Finally, a fast matrix power normalization
is introduced to exploit geometry of covariance representations. Note that our
TCP is model-agnostic and can be flexibly integrated into any video
architectures, resulting in TCPNet for effective video recognition. The
extensive experiments on six benchmarks using various video architectures show
our TCPNet is clearly superior to its counterparts, while having strong
generalization
ability.$\href{https://github.com/ZilinGao/Temporal-attentive-Covariance-Pooling-Networks-for-Video-Recognition}{\textit{The
source code is publicly available.}}$

Temporal-attentive Covariance Pooling Networks for Video Recognition.

As a member of purinoceptors, the P2Y6 receptor (P2Y6R) plays a crucial role in modulating immune signals and has been considered as a potential therapeutic target for inflammatory diseases. On the basis of the speculated probable conformation and binding determinants of P2Y6R, a hierarchical strategy that combines virtual screening, bioassays, and chemical optimization was presented. A potent P2Y6R antagonist (compound 50) was identified to possess excellent antagonistic activity (IC50 = 5.914 nM) and high selectivity. In addition, binding assays and chemical pull-down experiments confirmed that compound 50 was nicely bound to P2Y6R. Notably, compound 50 could effectively ameliorate DSS-induced ulcerative colitis in mice through inhibiting the activation of NLRP3 inflammasome in colon tissues. Moreover, treatment with compound 50 reduced LPS-induced pulmonary edema and infiltration of inflammatory cells in mice. These findings suggest that compound 50 could serve as a specific P2Y6R antagonist for treating inflammatory diseases and deserve further optimization studies.

Qinghua Hu

Papers

Skeleton Neural Networks via Low-rank Guided Filter Pruning

Causal-Trivial Attention Graph Neural Network for Fault Diagnosis of Complex Industrial Processes

A Multilevel Inference Mechanism for User Attributes over Social Networks

Temporal-attentive Covariance Pooling Networks for Video Recognition.

Discovery of Selective P2Y6R Antagonists with High Affinity and In Vivo Efficacy for Inflammatory Disease Therapy.