OpenRaiser/Intern-Atlas huggingface dataset:
A Brief Survey of Text Mining: Classification, Clustering and Extraction Techniques
Venue: arXiv cs.CL 2017
Year: 2017

The amount of text that is generated every day is increasing dramatically. This tremendous volume of mostly unstructured text cannot be simply processed and perceived by computers. Therefore, efficient and effective techniques and algorithms are required to discover useful patterns. Text mining is the task of extracting meaningful information from text, which has gained significant attentions in recent years. In this paper, we describe several of the most fundamental text mining tasks and techniques including text pre-processing, classification and clustering. Additionally, we briefly explain text mining in biomedical and health care domains.
Enhanced Deep Residual Networks for Single Image Super-Resolution
Venue: arXiv cs.CV 2017
Year: 2017

Recent research on super-resolution has progressed with the development of deep convolutional neural networks (DCNN). In particular, residual learning techniques exhibit improved performance. In this paper, we develop an enhanced deep super-resolution network (EDSR) with performance exceeding those of current state-of-the-art SR methods. The significant performance improvement of our model is due to optimization by removing unnecessary modules in conventional residual networks. The performance is further improved by expanding the model size while we stabilize the training procedure. We also propose a new multi-scale deep super-resolution system (MDSR) and training method, which can reconstruct high-resolution images of different upscaling factors in a single model. The proposed methods show superior performance over the state-of-the-art methods on benchmark datasets and prove its excellence by winning the NTIRE2017 Super-Resolution Challenge.
A Reconfigurable Streaming Deep Convolutional Neural Network Accelerator for Internet of Things
Venue: arXiv cs.CV 2017
Year: 2017

Convolutional neural network (CNN) offers significant accuracy in image detection. To implement image detection using CNN in the internet of things (IoT) devices, a streaming hardware accelerator is proposed. The proposed accelerator optimizes the energy efficiency by avoiding unnecessary data movement. With unique filter decomposition technique, the accelerator can support arbitrary convolution window size. In addition, max pooling function can be computed in parallel with convolution by using separate pooling unit, thus achieving throughput improvement. A prototype accelerator was implemented in TSMC 65nm technology with a core size of 5mm2. The accelerator can support major CNNs and achieve 152GOPS peak throughput and 434GOPS/W energy efficiency at 350mW, making it a promising hardware accelerator for intelligent IoT devices.
Learning Visual Reasoning Without Strong Priors
Venue: arXiv cs.CV 2017
Year: 2017

Achieving artificial visual reasoning - the ability to answer image-related questions which require a multi-step, high-level process - is an important step towards artificial general intelligence. This multi-modal task requires learning a question-dependent, structured reasoning process over images from language. Standard deep learning approaches tend to exploit biases in the data rather than learn this underlying structure, while leading methods learn to visually reason successfully but are hand-crafted for reasoning. We show that a general-purpose, Conditional Batch Normalization approach achieves state-of-the-art results on the CLEVR Visual Reasoning benchmark with a 2.4% error rate. We outperform the next best end-to-end method (4.5%) and even methods that use extra supervision (3.1%). We probe our model to shed light on how it reasons, showing it has learned a question-dependent, multi-step process. Previous work has operated under the assumption that visual reasoning calls for a specialized architecture, but we show that a general architecture with proper conditioning can learn to visually reason effectively.
Learning Heuristic Search via Imitation
Venue: arXiv cs.RO 2017
Year: 2017

Robotic motion planning problems are typically solved by constructing a search tree of valid maneuvers from a start to a goal configuration. Limited onboard computation and real-time planning constraints impose a limit on how large this search tree can grow. Heuristics play a crucial role in such situations by guiding the search towards potentially good directions and consequently minimizing search effort. Moreover, it must infer such directions in an efficient manner using only the information uncovered by the search up until that time. However, state of the art methods do not address the problem of computing a heuristic that explicitly minimizes search effort. In this paper, we do so by training a heuristic policy that maps the partial information from the search to decide which node of the search tree to expand. Unfortunately, naively training such policies leads to slow convergence and poor local minima. We present SaIL, an efficient algorithm that trains heuristic policies by imitating "clairvoyant oracles" - oracles that have full information about the world and demonstrate decisions that minimize search effort. We leverage the fact that such oracles can be efficiently computed using dynamic programming and derive performance guarantees for the learnt heuristic. We validate the approach on a spectrum of environments which show that SaIL consistently outperforms state of the art algorithms. Our approach paves the way forward for learning heuristics that demonstrate an anytime nature - finding feasible solutions quickly and incrementally refining it over time.
Rapid focus map surveying for whole slide imaging with continues sample motion
Venue: arXiv cs.CV 2017
Year: 2017

Whole slide imaging (WSI) has recently been cleared for primary diagnosis in the US. A critical challenge of WSI is to perform accurate focusing in high speed. Traditional systems create a focus map prior to scanning. For each focus point on the map, sample needs to be static in the x-y plane and axial scanning is needed to maximize the contrast. Here we report a novel focus map surveying method for WSI. The reported method requires no axial scanning, no additional camera and lens, works for stained and transparent samples, and allows continuous sample motion in the surveying process. It can be used for both brightfield and fluorescence WSI. By using a 20X, 0.75 NA objective lens, we demonstrate a mean focusing error of ~0.08 microns in the static mode and ~0.17 microns in the continuous motion mode. The reported method may provide a turnkey solution for most existing WSI systems for its simplicity, robustness, accuracy, and high-speed. It may also standardize the imaging performance of WSI systems for digital pathology and find other applications in high-content microscopy such as DNA sequencing and time-lapse live-cell imaging.
A Separation-Based Design to Data-Driven Control for Large-Scale Partially Observed Systems
Venue: arXiv cs.SY 2017
Year: 2017

This paper studies the partially observed stochastic optimal control problem for systems with state dynamics governed by Partial Differential Equations (PDEs) that leads to an extremely large problem. First, an open-loop deterministic trajectory optimization problem is solved using a black box simulation model of the dynamical system. Next, a Linear Quadratic Gaussian (LQG) controller is designed for the nominal trajectory-dependent linearized system, which is identified using input-output experimental data consisting of the impulse responses of the optimized nominal system. A computational nonlinear heat example is used to illustrate the performance of the approach.
SaltiNet: Scan-path Prediction on 360 Degree Images using Saliency Volumes
Venue: arXiv cs.CV 2017
Year: 2017

We introduce SaltiNet, a deep neural network for scanpath prediction trained on 360-degree images. The model is based on a temporal-aware novel representation of saliency information named the saliency volume. The first part of the network consists of a model trained to generate saliency volumes, whose parameters are fit by back-propagation computed from a binary cross entropy (BCE) loss over downsampled versions of the saliency volumes. Sampling strategies over these volumes are used to generate scanpaths over the 360-degree images. Our experiments show the advantages of using saliency volumes, and how they can be used for related tasks. Our source code and trained models available at https://github.com/massens/saliency-360salient-2017.
Impulsive noise removal from color images with morphological filtering
Venue: arXiv cs.CV 2017
Year: 2017

This paper deals with impulse noise removal from color images. The proposed noise removal algorithm employs a novel approach with morphological filtering for color image denoising; that is, detection of corrupted pixels and removal of the detected noise by means of morphological filtering. With the help of computer simulation we show that the proposed algorithm can effectively remove impulse noise. The performance of the proposed algorithm is compared in terms of image restoration metrics and processing speed with that of common successful algorithms.
State complexity of catenation combined with boolean operations
Venue: arXiv cs.FL 2017
Year: 2017

We exhaustively investigate possible combinations of a boolean operation together with a catenation. In many cases we prove and improve some conjectures by Brzozowski. For each family of operation, we endeavour to provide a common witness with a small size alphabet.
Mapping spatial and temporal changes of global corporate research and development activities by conducting a bibliometric analysis
Venue: arXiv cs.DL 2017
Year: 2017

Corporate research and development (R&D) activities have long been highly concentrated in a handful of world cities. This is due to the fact that these cities (e.g., Tokyo, New York, London, and Paris) are home to the largest and most powerful transnational corporations and are globally important sites for innovative start-up firms that operate in the fastest growing industries. However, in tandem with the rapid technological changes of our age, corporate R&D activities have shifted towards newly emerging and now globally significant R&D centres, like San Jose, San Francisco, and Boston in the United States, and Beijing, Seoul, and Shenzhen in East Asia. In this paper, I will conduct a bibliometric analysis to define which cities are centres of corporate R&D activities, how different industries influence their performance, and what spatial tendencies characterise the period from 1980 to 2014. The bibliometric analysis is based upon an assumption that implies there is a close connection between the number of scientific articles published by a given firm and the volume of its R&D activity. Results show that firms headquartered in Tokyo, New York, London, and Paris published the largest combined number of scientific articles in the period from 1980 to 2014, but that the growth rate of the annual output of scientific articles was much greater in Boston, San Jose, Beijing, and Seoul, as well as some Taiwanese cities. Furthermore, it can also be seen that those cities that have the largest number of articles; i.e., that can be considered as the most significant sites of corporate R&D in which firms operate in fast-growing industries, are primarily in the pharmaceutical and information technology industries. For these reasons, some mid-sized cities that are home to globally significant pharmaceutical or information technology firms are also top corporate R&D hubs.
Adversarial training and dilated convolutions for brain MRI segmentation
Venue: arXiv cs.CV 2017
Year: 2017

Convolutional neural networks (CNNs) have been applied to various automatic image segmentation tasks in medical image analysis, including brain MRI segmentation. Generative adversarial networks have recently gained popularity because of their power in generating images that are difficult to distinguish from real images. In this study we use an adversarial training approach to improve CNN-based brain MRI segmentation. To this end, we include an additional loss function that motivates the network to generate segmentations that are difficult to distinguish from manual segmentations. During training, this loss function is optimised together with the conventional average per-voxel cross entropy loss. The results show improved segmentation performance using this adversarial training procedure for segmentation of two different sets of images and using two different network architectures, both visually and in terms of Dice coefficients.
A Cognitive Theory-based Opportunistic Resource-Pooling Scheme for Ad hoc Networks
Venue: arXiv cs.NI 2017
Year: 2017

Resource pooling in ad hoc networks deals with accumulating computing and network resources to implement network control schemes such as routing, congestion, traffic management, and so on. Pooling of resources can be accomplished using the distributed and dynamic nature of ad hoc networks to achieve collaboration between the devices. Ad hoc networks need a resource-pooling technique that offers quick response, adaptability, and reliability. In this context, we are proposing an opportunistic resource pooling scheme that uses a cognitive computing model to accumulate the resources with faster resource convergence rate, reliability, and lower latency. The proposed scheme is implemented using the behaviors observations beliefs cognitive model, in which the resource pooling decisions are made based on accumulated knowledge over various behaviors exhibited by nodes in ad hoc networks.
DeepTrend: A Deep Hierarchical Neural Network for Traffic Flow Prediction
Venue: arXiv cs.LG 2017
Year: 2017

In this paper, we consider the temporal pattern in traffic flow time series, and implement a deep learning model for traffic flow prediction. Detrending based methods decompose original flow series into trend and residual series, in which trend describes the fixed temporal pattern in traffic flow and residual series is used for prediction. Inspired by the detrending method, we propose DeepTrend, a deep hierarchical neural network used for traffic flow prediction which considers and extracts the time-variant trend. DeepTrend has two stacked layers: extraction layer and prediction layer. Extraction layer, a fully connected layer, is used to extract the time-variant trend in traffic flow by feeding the original flow series concatenated with corresponding simple average trend series. Prediction layer, an LSTM layer, is used to make flow prediction by feeding the obtained trend from the output of extraction layer and calculated residual series. To make the model more effective, DeepTrend needs first pre-trained layer-by-layer and then fine-tuned in the entire network. Experiments show that DeepTrend can noticeably boost the prediction performance compared with some traditional prediction models and LSTM with detrending based methods.
Document Retrieval for Large Scale Content Analysis using Contextualized Dictionaries
Venue: arXiv cs.IR 2017
Year: 2017

This paper presents a procedure to retrieve subsets of relevant documents from large text collections for Content Analysis, e.g. in social sciences. Document retrieval for this purpose needs to take account of the fact that analysts often cannot describe their research objective with a small set of key terms, especially when dealing with theoretical or rather abstract research interests. Instead, it is much easier to define a set of paradigmatic documents which reflect topics of interest as well as targeted manner of speech. Thus, in contrast to classic information retrieval tasks we employ manually compiled collections of reference documents to compose large queries of several hundred key terms, called dictionaries. We extract dictionaries via Topic Models and also use co-occurrence data from reference collections. Evaluations show that the procedure improves retrieval results for this purpose compared to alternative methods of key term extraction as well as neglecting co-occurrence data.
Leipzig Corpus Miner - A Text Mining Infrastructure for Qualitative Data Analysis
Venue: arXiv cs.CL 2017
Year: 2017

This paper presents the "Leipzig Corpus Miner", a technical infrastructure for supporting qualitative and quantitative content analysis. The infrastructure aims at the integration of 'close reading' procedures on individual documents with procedures of 'distant reading', e.g. lexical characteristics of large document collections. Therefore information retrieval systems, lexicometric statistics and machine learning procedures are combined in a coherent framework which enables qualitative data analysts to make use of state-of-the-art Natural Language Processing techniques on very large document collections. Applicability of the framework ranges from social sciences to media studies and market research. As an example we introduce the usage of the framework in a political science study on post-democracy and neoliberalism.
Modeling the dynamics of domain specific terminology in diachronic corpora
Venue: arXiv cs.CL 2017
Year: 2017

In terminology work, natural language processing, and digital humanities, several studies address the analysis of variations in context and meaning of terms in order to detect semantic change and the evolution of terms. We distinguish three different approaches to describe contextual variations: methods based on the analysis of patterns and linguistic clues, methods exploring the latent semantic space of single words, and methods for the analysis of topic membership. The paper presents the notion of context volatility as a new measure for detecting semantic change and applies it to key term extraction in a political science case study. The measure quantifies the dynamics of a term's contextual variation within a diachronic corpus to identify periods of time that are characterised by intense controversial debates or substantial semantic transformations.
Common Metrics for Analyzing, Developing and Managing Telecommunication Networks
Venue: arXiv cs.NI 2017
Year: 2017

The metrics play increasingly fundamental role in the design, development, deployment and operation of telecommunication systems. Despite their importance, the studies of metrics are usually limited to a narrow area or a well-defined objective. Our study aims to more broadly survey the metrics that are commonly used for analyzing, developing and managing telecommunication networks in order to facilitate understanding of the current metrics landscape. The metrics are simple abstractions of systems, and they directly influence how the systems are perceived by different stakeholders. However, defining and using metrics for telecommunication systems with ever increasing complexity is a complicated matter which has not been so far systematically and comprehensively considered in the literature. The common metrics sources are identified, and how the metrics are used and selected is discussed. The most commonly used metrics for telecommunication systems are categorized and presented as energy and power metrics, quality-of-service metrics, quality-of-experience metrics, security metrics, and reliability and resilience metrics. Finally, the research directions and recommendations how the metrics can evolve, and be defined and used more effectively are outlined.
Trump vs. Hillary: What went Viral during the 2016 US Presidential Election
Venue: arXiv cs.SI 2017
Year: 2017

In this paper, we present quantitative and qualitative analysis of the top retweeted tweets (viral tweets) pertaining to the US presidential elections from September 1, 2016 to Election Day on November 8, 2016. For everyday, we tagged the top 50 most retweeted tweets as supporting or attacking either candidate or as neutral/irrelevant. Then we analyzed the tweets in each class for: general trends and statistics; the most frequently used hashtags, terms, and locations; the most retweeted accounts and tweets; and the most shared news and links. In all we analyzed the 3,450 most viral tweets that grabbed the most attention during the US election and were retweeted in total 26.3 million times accounting over 40% of the total tweet volume pertaining to the US election in the aforementioned period. Our analysis of the tweets highlights some of the differences between the social media strategies of both candidates, the penetration of their messages, and the potential effect of attacks on both
Creatism: A deep-learning photographer capable of creating professional work
Venue: arXiv cs.CV 2017
Year: 2017

Machine-learning excels in many areas with well-defined goals. However, a clear goal is usually not available in art forms, such as photography. The success of a photograph is measured by its aesthetic value, a very subjective concept. This adds to the challenge for a machine learning approach. We introduce Creatism, a deep-learning system for artistic content creation. In our system, we break down aesthetics into multiple aspects, each can be learned individually from a shared dataset of professional examples. Each aspect corresponds to an image operation that can be optimized efficiently. A novel editing tool, dramatic mask, is introduced as one operation that improves dramatic lighting for a photo. Our training does not require a dataset with before/after image pairs, or any additional labels to indicate different aspects in aesthetics. Using our system, we mimic the workflow of a landscape photographer, from framing for the best composition to carrying out various post-processing operations. The environment for our virtual photographer is simulated by a collection of panorama images from Google Street View. We design a "Turing-test"-like experiment to objectively measure quality of its creations, where professional photographers rate a mixture of photographs from different sources blindly. Experiments show that a portion of our robot's creation can be confused with professional work.
Multitask Learning for Fine-Grained Twitter Sentiment Analysis
Venue: arXiv cs.IR 2017
Year: 2017

Traditional sentiment analysis approaches tackle problems like ternary (3-category) and fine-grained (5-category) classification by learning the tasks separately. We argue that such classification tasks are correlated and we propose a multitask approach based on a recurrent neural network that benefits by jointly learning them. Our study demonstrates the potential of multitask models on this type of problems and improves the state-of-the-art results in the fine-grained sentiment classification problem.