Learning Deep Latent Spaces for Multi-Label Classification

Multi-label classification is a practical yet challenging task in machine learning related fields, since it requires the prediction of more than one label category for each input instance. We propose a novel deep neural networks (DNN) based model, Canonical Correlated AutoEncoder (C2AE), for solving this task. Aiming at better relating feature and label domain data for improved classification, we uniquely perform joint feature and label embedding by deriving a deep latent space, followed by the introduction of label-correlation sensitive loss function for recovering the predicted label outputs. Our C2AE is achieved by integrating the DNN architectures of canonical correlation analysis and autoencoder, which allows end-to-end learning and prediction with the ability to exploit label dependency. Moreover, our C2AE can be easily extended to address the learning problem with missing labels. Our experiments on multiple datasets with different scales confirm the effectiveness and robustness of our proposed method, which is shown to perform favorably against state-of-the-art methods for multi-label classification.Comment: published in AAAI-201

Interactions Between Reinforcement Corrosion and Chloride Ion Diffusion in Mortar

This study explored the diffusion of the chloride ions influenced by the reinforcement corrosion in the mortar. It is believed that, during the corroding process, a small current is generated at the surface of the reinforcement. Such current is supposed to influence the diffusion of the chloride ions, but the relationship between both was not well studied in the literature. In this study, the corroded reinforcements were prepared by applied currents. Reinforced mortar specimens with w/c of 0.6 were then prepared and cured by either salt or fresh water. Results showed that the chloride ion distribution was likely associated with the reinforcement corrosion. During the early hydration, the chloride ions were attracted by the reinforcement corrosion in the specimens prepared with fresh water and cured in salt water. The concentration of the chloride ions near the surface of the reinforcement was increased with the increases of the charging time during the preparation for the corroded reinforcement. On the contrary, the chloride ions were likely bound in those specimens prepared with salt water and cured by saturated lime water. The concentration of the chloride ions near the surface of the reinforcement was higher than those near the outer surface. However, such influencing effects were not clear in the long term, possibly due to the hydration. The results of this study show that the reinforcement corrosion have influences on the diffusion of the chloride ions and such effect should be considered during the refinement of the traditional chloride ion diffusion models

Phylo-mLogo: an interactive and hierarchical multiple-logo visualization tool for alignment of many sequences

BACKGROUND: When aligning several hundreds or thousands of sequences, such as epidemic virus sequences or homologous/orthologous sequences of some big gene families, to reconstruct the epidemiological history or their phylogenies, how to analyze and visualize the alignment results of many sequences has become a new challenge for computational biologists. Although there are several tools available for visualization of very long sequence alignments, few of them are applicable to the alignments of many sequences. RESULTS: A multiple-logo alignment visualization tool, called Phylo-mLogo, is presented in this paper. Phylo-mLogo calculates the variabilities and homogeneities of alignment sequences by base frequencies or entropies. Different from the traditional representations of sequence logos, Phylo-mLogo not only displays the global logo patterns of the whole alignment of multiple sequences, but also demonstrates their local homologous logos for each clade hierarchically. In addition, Phylo-mLogo also allows the user to focus only on the analysis of some important, structurally or functionally constrained sites in the alignment selected by the user or by built-in automatic calculation. CONCLUSION: With Phylo-mLogo, the user can symbolically and hierarchically visualize hundreds of aligned sequences simultaneously and easily check the changes of their amino acid sites when analyzing many homologous/orthologous or influenza virus sequences. More information of Phylo-mLogo can be found at URL

The Impacts Of Presentation Modes And Product Involvements On “Line” Short Message Service (SMS) Advertising Effectiveness

In today’s ubiquitous commerce (UC) era, short message service (SMS) advertisement has played an important role in the world of marketing. Convenience and economical reasons influence SMS usage frequency along with social involvement to influence attitudes towards SMS advertising. SMS advertising creates numerous opportunities for the marketers in promoting their products effectively. Adopting the competition for attention theory as the theoretical framework, we developed hypotheses to investigate the influences of presentation mode and involvement on SMS advertising performance (recall of advertising information). An experiment was conducted to examine the effects of three types of information presentation modes (text-only, image-text, and emoji-text) in the contexts of two product types (high- versus low-involvement products) in the “LINE” SMS environment. Specifically, in this current study, we allocate participants to six experimental environments (text-only for high-involvement products, text-only for low-involvement products, image-text for high-involvement products, image-text for low-involvement products, emoji-text for high-involvement products and emoji-text for low-involvement products) randomly to collected empirical data to examine the proposed hypotheses. The research findings are expected to provide instrumental guidelines for the practitioners to better achieve the goals of ads in the “LINE” SMS environment. Also, the empirical results may provide insights into the research of advertising interface design of SMS and integrating efforts from cognitive science and vision research to understand users’ involvement of SMS advertising processes

Seismic analysis of the condensate storage tank in a nuclear power plant

Following the nuclear power plant accident in Fukushima Japan, seismic capacity evaluation has become a crucial issue in combination building safety. Condensate storage tanks are designed to supplies water to the condensate transfer pumps, the control rod drive hydraulic system pumps, and the condenser makeup. A separate connection to the condensate storage tank is used to supply water for the high pressure coolant injection system, reactor core isolation cooling system, and core spray system pumps. A condensate storage tank is defined as a seismic class I structure, playing the important role of providing flow to the operational system and the required static head for the suction of the condensate transfer pumps and the normal supply pump. According to the latest nuclear safety requirements, soil structure interaction must be considered in all seismic analyses. This study aims to rebuild the computer model of condensate storage tanks in Taiwan using the SAP 2000 program in conjunction with the lumped mass stick model and to evaluate the soil structure interaction by employing the SASSI 2000 program. The differences between the results with the soil structure interaction and spring model are compared via natural frequency and response spectrum curves. This computer model enables engineers to rapidly evaluate the safety margin of condensate storage tank following the occurrence of earthquakes or tsunamis

A preliminary study on the dynamic friction behavior of a one-third scale-down vertical cylindrical cask

In Taiwan, the capacities of spent fuel pools for temporary storage in nuclear power plant will reach depletion soon, and the site of final disposal facility is still to be decided. Therefore, the installation of dry-type interim storage facilities is urgent. The dry storage systems in Taiwan utilize a freestanding cask and design to non-anchored to the foundation pad. It is necessary to establish the simulation techniques for the non-anchored structure, such as the dry storage cask, for the reasonable assessment of its seismic behavior when the earthquake hit. This study is cast a 1/3 scale-down pedestal specimen of the INER-dry storage cask system, which were conducted to acquire the actual friction coefficient at the cask/pad interface as well as the effect of normal stress and sliding rate on it. Based on the results of cyclic loading testing, the cyclic frequency almost had no influence on the friction coefficient but the friction coefficient increased with the normal stress increased. Apparent rocking of the cask was induced at a higher friction coefficient, while sliding dominated the cask motion at a lower one. In addition, the cast motions were almost purely sliding and the range of the friction coefficient was between 0.60 and 0.73 under various compositions of dry storage cask system

Seismic test and numerical verification of the scaled-down reinforced concrete containment vessel

According to the ASME-359 code, a scaled-down structure of Reinforced Concrete Containment Vessel (RCCV) of Advanced Boiling Water Reactor (ABWR) building is constructed for the seismic test on the shaking table. Several acceleration time history satisfing design response spectrum with different magnitudes are used in the test. Besides, the numerical finite element model of RCCV is built by SAP2000 for calculating the dynamic responses numerically

Distributed Training Large-Scale Deep Architectures

Scale of data and scale of computation infrastructures together enable the current deep learning renaissance. However, training large-scale deep architectures demands both algorithmic improvement and careful system configuration. In this paper, we focus on employing the system approach to speed up large-scale training. Via lessons learned from our routine benchmarking effort, we first identify bottlenecks and overheads that hinter data parallelism. We then devise guidelines that help practitioners to configure an effective system and fine-tune parameters to achieve desired speedup. Specifically, we develop a procedure for setting minibatch size and choosing computation algorithms. We also derive lemmas for determining the quantity of key components such as the number of GPUs and parameter servers. Experiments and examples show that these guidelines help effectively speed up large-scale deep learning training

Comparison between simulation and experimental result of the scale down vertical concrete cask under the historical earthquake hit

In recent years, the final disposal facility for the long-term storage of high radioactive waste is an important issue and an urgent task for nuclear Industry. This study is focus on the understanding the procedure of dry storage system in a nuclear power plant. The dry storage system is known as transportable storage canister (TSC) packing by using spent fuel placed in vertical concrete cask (VCC) or VCC with add-on-shell (AOS) canister and then those conveyed VCC or AOS will be put on the concrete pad. A numerical simulation for evaluation the behavior of VCC and AOC under the earthquake hits is used the finite element model by LS-DYNA and the real seismic behavior is obtained using shaking table test. Test results indicate that the simulation by numerical model can slightly simulate the dynamic records by shaking table test. This is because that the behavior of shaking table test is the rocking motion but the numerical model is the sliding motion. The surface roughness between the cask and concrete pad is an important variable and it needs more numerical simulation to solve the interface and friction coefficient between the cask and concrete pad