Malignant Pleural Mesothelioma (MPM) or malignant mesothelioma (MM) is an atypical, aggressive tumor that matures into cancer in the pleura, a stratum of tissue bordering the lungs. Diagnosis of MPM is difficult and it accounts for about seventy-five percent of all mesothelioma diagnosed yearly in the United States of America. Being a fatal disease, early identification of MPM is crucial for patient survival. Our study implements logistic regression and develops association rules to identify early stage symptoms of MM. We retrieved medical reports generated by Dicle University and implemented logistic regression to measure the model accuracy. We conducted (a) logistic correlation, (b) Omnibus test and (c) Hosmer and Lemeshow test for model evaluation. Moreover, we also developed association rules by confidence, rule support, lift, condition support and deployability. Categorical logistic regression increases the training accuracy from 72.30% to 81.40% with a testing accuracy of 63.46%. The study also shows the top 5 symptoms that is mostly likely indicates the presence in MM. This study concludes that using predictive modeling can enhance primary presentation and diagnosis of MM.

Language is dynamic, constantly evolving and adapting with respect to time, domain or topic. The adaptability of language is an active research area, where researchers discover social, cultural and domain-specific changes in language using distributional tools such as word embeddings. In this paper, we introduce the global anchor method for detecting corpus-level language shifts. We show both theoretically and empirically that the global anchor method is equivalent to the alignment method, a widely-used method for comparing word embeddings, in terms of detecting corpus-level language shifts. Despite their equivalence in terms of detection abilities, we demonstrate that the global anchor method is superior in terms of applicability as it can compare embeddings of different dimensionalities. Furthermore, the global anchor method has implementation and parallelization advantages. We show that the global anchor method reveals fine structures in the evolution of language and domain adaptation. When combined with the graph Laplacian technique, the global anchor method recovers the evolution trajectory and domain clustering of disparate text corpora.

The model proposed in the "Learn to Pay Attention" paper introduced a novel way to generate a trainable attention module for convolutional neural networks. The paper demonstrated the attention modulein VGG-based and ResNet-based architectures, provided several options for implementation (includingthree options for layer depths at which attention modules are to be implemented, two options for calculating the compatibility between the global and local features in generating the attention maps, and two options for what method will be used to produce output probabilities from global-level feature vectors), described the dataset preprocessing and model training routines, and reported the results of the consequent models in several tasks. We have successfully implemented allpossible configurations of both VGG-based and ResNet-based attention models, and have replicated the paper's reported results in image classification and fine-grain recognition task using the (VGG-att2)-concat-pc configuration on the CIFAR-10 dataset and (VGG-att3)-concatpc configurationon CIFAR-100 and the SVHN dataset.

To address Task 5 in the Detection and Classification of Acoustic Scenes and Events (DCASE) 2018 challenge, in this paper, we propose an ensemble learning system. The proposed system consists of three different models, based on convolutional neural network and long short memory recurrent neural network. With extracted features such as spectrogram and mel-frequency cepstrum coefficients from different channels, the proposed system can classify different domestic activities effectively. Experimental results obtained from the provided development dataset show that good performance with F1-score of 92.19% can be achieved. Compared with the baseline system, our proposed system significantly improves the performance of F1-score by 7.69%.

Air quality forecasting has been regarded as the key problem of air pollution early warning and control management. In this paper, we propose a novel deep learning model for air quality (mainly PM2.5) forecasting, which learns the spatial-temporal correlation features and interdependence of multivariate air quality related time series data by hybrid deep learning architecture. Due to the nonlinear and dynamic characteristics of multivariate air quality time series data, the base modules of our model include one-dimensional Convolutional Neural Networks (CNN) and Bi-directional Long Short-term Memory networks (Bi-LSTM). The former is to extract the local trend features and the latter is to learn long temporal dependencies. Then we design a jointly hybrid deep learning framework which based on one-dimensional CNN and Bi-LSTM for shared representation features learning of multivariate air quality related time series data. The experiment results show that our model is capable of dealing with PM2.5 air pollution forecasting with satisfied accuracy.

The networked systems are ubiquitous in many fields, including social-tech science [1, 2], bioinformatics [3-6], epidemic dynamics [7-9] and power grid [10, 11]. However, as is often the case, it is not able to observe the topology of a network, while data generated by this network are available. Therefore, in interdisciplinary science, one of the most important but challenging problems is to reconstruct the complex network from the observed data or time series [12]. This problem has been widely investigated in the past three decades, where the classical method is the delay-coordinate embedding method proposed by Takens [13], which, nevertheless, is only suitable for small-scale networks. Nowadays, with the advent of big data era [14], it is of great urgency solve this issue for large-scale complex networks. Suppose that a complex network consists of N nodes, in practice we are often given the time series of the states for the N nodes. Generally speaking, the core idea of many data-driven network reconstruction investigations is to first calculate the correlation between two nodes. Then, a threshold can be set mutually or automatically to make the network binary.

Current generation of memory-augmented neural networks has limited scalability as they cannot efficiently process data that are too large to fit in the external memory storage. One example of this is lifelong learning scenario where the model receives unlimited length of data stream as an input which contains vast majority of uninformative entries. We tackle this problem by proposing a memory network fit for long-term lifelong learning scenario, which we refer to as Long-term Episodic Memory Networks (LEMN), that features a RNN-based retention agent that learns to replace less important memory entries based on the retention probability generated on each entry that is learned to identify data instances of generic importance relative to other memory entries, as well as its historical importance. Such learning of retention agent allows our long-term episodic memory network to retain memory entries of generic importance for a given task. We validate our model on a path-finding task as well as synthetic and real question answering tasks, on which our model achieves significant improvements over the memory augmented networks with rule-based memory scheduling as well as an RL-based baseline that does not consider relative or historical importance of the memory.

The traditional mode of recording faults in heavy factory equipment has been via hand marked inspection sheets, wherein a machine engineer manually marks the faulty machine regions on a paper outline of the machine. Over the years, millions of such inspection sheets have been recorded and the data within these sheets has remained inaccessible. However, with industries going digital and waking up to the potential value of fault data for machine health monitoring, there is an increased impetus towards digitization of these hand marked inspection records. To target this digitization, we propose a novel visual pipeline combining state of the art deep learning models, with domain knowledge and low level vision techniques, followed by inference of visual relationships. Our framework is robust to the presence of both static and non-static background in the document, variability in the machine template diagrams, unstructured shape of graphical objects to be identified and variability in the strokes of handwritten text. The proposed pipeline incorporates a capsule and spatial transformer network based classifier for accurate text reading, and a customized CTPN network for text detection in addition to hybrid techniques for arrow detection and dialogue cloud removal. We have tested our approach on a real world dataset of 50 inspection sheets for large containers and boilers. The results are visually appealing and the pipeline achieved an accuracy of 87.1% for text detection and 94.6% for text reading.

While a new visa system approved by the Diet, set to take effect in April, has been welcomed by Japan's labor-short nursing care businesses, questions remain about how to provide sufficient Japanese-language education for foreign workers. Despite steps taken by the government in recent years to relax rules on foreign workers in the sector, language skills have been a major barrier. Vu Thi Thu Trang from Vietnam is among those who could obtain working status after the government eased the requirement for foreign nationals to work as caregivers. The 31-year-old certified caregiver, who came to Japan in 2014, said she first found out about nursing care work while studying at a Japanese language school. She then entered a caregiver vocational school and finished a two-year curriculum earlier this year.

YANGON – Myanmar's first-ever training course for Japanese-language teachers is opening as part of Prime Minister Shinzo Abe's plan to invite more Asian youths to work in Japan. The initial phase of the training program starts this month at the Yangon University of Foreign Languages for students majoring in Japanese and for teachers from private Japanese-language schools, the Japan Foundation said. The foundation, a government-backed institution that carries out international cultural exchange programs, picked Myanmar as the third country in which to offer such training courses, after India and Vietnam, following Abe's speech at an international conference in Tokyo in 2017 where he said Japan would choose three locations in Asia to nurture Japanese-language teachers. Noriyuki Matsukawa, executive director of the Japan Foundation Center for Japanese Language Testing, said the yearlong program aims to support Myanmar's human resources through Japanese-language learning, recruit a new kind of teacher and improve current teachers' skills. "Myanmar has high demand for Japanese-language proficiency," he said, adding that the number of people in Myanmar taking the Japanese-Language Proficiency Test nearly tripled from 13,099 in 2016 to 37,786 in 2018.