Current Research Results
"Non-overlapping Subsequence Matching of Stream Synopses," IEEE Tans. on Knowledge and Data Mining, To Appear.
Authors: Su-Chen Lin, Mi-Yen Yeh, and Ming-Syan Chen

In this paper, we propose SUbsequence Matching framework with cell MERgence (SUMMER) for online subsequence matching between histogram-based stream synopsis structures under the dynamic time warping distance. Given a query synopsis pattern, SUMMER continuously identifies all the matching subsequences for a stream as the bins are generated. To effectively reduce the computation time, we design a Weighted Dynamic Time Warping (WDTW) algorithm, which computes the warping distance directly between two histogram-based synopses. Furthermore, a Stack-based Overlapping Filter Algorithm (SOFA) is provided to remove the overlapping subsequences to avoid the redundant information. Finally, we design an optional refinement module to relax the subsequence range limit and improve the matching accuracy. Our experiments on real datasets show that the proposed method significantly speeds up the pattern matching without compromising the accuracy required when compared with other approaches.
Current Research Results
Authors: Hui-Yin Chang, Ching-Tai Chen, Chu-Ling Ko, Yi-Ju Chen, Yu-Ju Chen, Wen-Lian Hsu, Chiun-Gung Juo, Ting-Yi Sung

Top-down proteomics using liquid chromatogram coupled with mass spectrometry has been increasingly applied for analyzing intact proteins to study genetic variation, alternative splicing, and post-translational modifications (PTMs) of the proteins (proteoforms). However, only a few tools have been developed for charge state deconvolution, monoisotopic/average molecular weight determination and quantitation of proteoforms from LC-MS1 spectra. Though Decon2LS and MASH Suite Pro have been available to provide intra-spectrum charge state deconvolution and quantitation, manual processing is still required to quantify proteoforms across multiple MS1 spectra. An automated tool for inter-spectrum quantitation is a pressing need. Thus in this paper, we present a user-friendly tool, called iTop-Q (intelligent Top-down Proteomics Quantitation), that automatically performs large-scale proteoform quantitation based on inter-spectrum abundance in top-down proteomics. Instead of utilizing single spectrum for proteoform quantitation, iTop-Q constructs extracted ion chromatograms (XICs) of possible proteoform peaks across adjacent MS1 spectra to calculate abundances for accurate quantitation. Notably, iTop-Q is implemented with a newly proposed algorithm, called DYAMOND, using dynamic programming for charge state deconvolution. In addition, iTop-Q performs proteoform alignment to support quantitation analysis across replicates/samples. The performance evaluations on an in-house standard data set and a public large-scale yeast lysate data set show that iTop-Q achieves highly accurate quantitation, more consistent quantitation than using intra-spectrum quantitation. Furthermore, the DYAMOND algorithm is suitable for high charge state deconvolution and can distinguish shared peaks in co-eluting proteoforms. iTop-Q is publicly available for download at http://ms.iis.sinica.edu.tw/COmics/Software_iTop-Q.
Current Research Results
Authors: Cheng-Te Li, Yu-Jen Lin, and Mi-Yen Yeh

Social networking services allow users to adopt and spread information via diffusion actions, e.g., share, retweet, and reply. Real applications such as viral marketing and trending topic detection rely on information diffusion. Given past items with diffusion records on a social network, this paper aims at forecasting who will participate in the diffusion of a new item c (we use hashtags in the paper) with its k earliest adopters, without using content and profile information, i.e., finding which users will adopt c in the future. We define the Diffusion Participation Forecasting (DPF) problem, which is challenging since all users except for early adopters can be the candidates, comparing to existing studies that predict which one-layer followers will adopt a new hashtag given past diffusion observations with content and profile info. To solve the DFP problem, we propose an Adoption-based Participation Ranking (APR) model, which aims to rank the actual participants in reality at higher positions. The first is to estimate the adoption probability of a new hashtag for each user while the second is a random walk-based model that incorporates nodes with higher adoption probability values and early adopters to generate the forecasted participants. Experiments conducted on Twitter exhibit that our model can significantly outperform several competing methods in terms of Precision and Recall. Moreover, we demonstrate that an accurate DPF can be applied for effective targeted marketing using influence maximization and boosting the accuracy of popularity prediction in social media.
"PRUNE: Preserving Proximity and Global Ranking for Node Embedding," The 31st Annual Conference on Neural Information Processing Systems (NIPS-2017), December 2017.
Authors: Yi-An Lai, Chin-Chi Hsu, Chin-Chi Hsu, Mi-Yen Yeh, and Shou-De Lin

We investigate an unsupervised generative approach for network embedding. A multi-task Siamese neural network structure is formulated to connect embedding vectors and our objective to preserve the global node ranking and local proximity of nodes. We provide deeper analysis to connect the proposed proximity objective to link prediction and community detection in the network. We show our model can satisfy the following design properties: scalability, asymmetry, unity and simplicity. Experiment results not only verify the above design properties but also demonstrate the superior performance in learning-to-rank, classification, regression, and link prediction tasks.
Current Research Results
Authors: Wai-Kok Choong, T. Mamie Lih, Yu-Ju Chen, Ting-Yi Sung

To confirm the existence of missing proteins, we need to identify at least two unique peptides with length of 9–40 amino acids of a missing protein in bottom-up mass-spectrometry-based proteomic experiments. However, an identified unique peptide of the missing protein, even identified with high level of confidence, could possibly coincide with a peptide of a commonly observed protein due to isobaric substitutions, mass modifications, alternative splice isoforms, or single amino acid variants (SAAVs). Besides unique peptides of missing proteins, identified variant peptides (SAAV-containing peptides) could also alternatively map to peptides of other proteins due to the aforementioned issues. Therefore, we conducted a thorough comparative analysis on data sets in PeptideAtlas Tiered Human Integrated Search Proteome (THISP, 2017-03 release), including neXtProt (2017-01 release), to systematically investigate the possibility of unique peptides in missing proteins (PE2–4), unique peptides in dubious proteins, and variant peptides affected by isobaric substitutions, causing doubtful identification results. In this study, we considered 11 isobaric substitutions. From our analysis, we found <5% of the unique peptides of missing proteins and >6% of variant peptides became shared with peptides of PE1 proteins after isobaric substitutions.
"Aesthetic Critiques Generation for Photos," International Conference on Computer Vision, ICCV 2017, October 2017.
Authors: Kuang-Yu Chang, Kung-Hung Lu, and Chu-Song Chen

It is said that a picture is worth a thousand words. Thus, there are various ways to describe an image, especially in aesthetic quality analysis. Although aesthetic quality assessment has generated a great deal of interest in the last decade, most studies focus on providing a quality rating of good or bad for an image. In this work, we extend the task to produce captions related to photo aesthetics and/or photography skills. To the best of our knowledge, this is the first study that deals with aesthetics captioning instead of AQ scoring. In contrast to common image captioning tasks that depict the objects or their relations in a picture, our approach can select a particular aesthetics aspect and generate captions with respect to the aspect chosen. Meanwhile, the proposed aspect-fusion method further uses an attention mechanism to generate more abundant aesthetics captions. We also introduce a new dataset for aesthetics captioning called the Photo Critique Captioning Dataset (PCCD), which contains pair-wise image-comment data from professional photographers. The results of experiments on PCCD demonstrate that our approaches outperform existing methods for generating aesthetic-oriented captions for images.
Current Research Results
Authors: Hsin-Nan Lin and Wen-Lian Hsu

In recent years, the massively parallel cDNA sequencing (RNA-Seq) technologies have become a powerful tool to provide high resolution measurement of expression and high sensitivity in detecting low abundance transcripts. However, RNA-seq data requires a huge amount of computational efforts. The very fundamental and critical step is to align each sequence fragment against the reference genome. Various de novo spliced RNA aligners have been developed in recent years. Though these aligners can handle spliced alignment and detect splice junctions, some challenges still remain to be solved. With the advances in sequencing technologies and the ongoing collection of sequencing data in the ENCODE project, more efficient alignment algorithms are highly demanded. Most read mappers follow the conventional seed-and-extend strategy to deal with inexact matches for sequence alignment. However, the extension is much more time consuming than the seeding step.
We proposed a novel RNA-seq de novo mapping algorithm, call DART, which adopts a partitioning strategy to avoid the extension step. The experiment results on synthetic datasets and real NGS datasets showed that DART is a highly efficient aligner that yields the highest or comparable sensitivity and accuracy compared to most state-of-the-art aligners, and more importantly, it spends the least amount of time among the selected aligners.
Current Research Results
Authors: Ng, I.M. , Huang, J.H., Tsai, S.C., and Tsai, H.K.*


Alternative splicing (AS), a mechanism by which different forms of mature messenger RNAs (mRNAs) are generated from the same gene, widely occurs in the metazoan genomes. Knowledge about isoform variants and abundance is crucial for understanding the functional context in the molecular diversity of the species. With increasing transcriptome data of model and non-model species, a database for visualization and comparison of AS events with up-to-date information is needed for further research. IsoPlot is a publicly available database with visualization tools for exploration of AS events, including three major species of mosquitoes, Aedes aegyptiAnopheles gambiae, and Culex quinquefasciatus, and fruit fly Drosophila melanogaster, the model insect species. IsoPlot includes not only 88,663 annotated transcripts but also 17,037 newly predicted transcripts from massive transcriptome data at different developmental stages of mosquitoes. The web interface enables users to explore the patterns and abundance of isoforms in different experimental conditions as well as cross-species sequence comparison of orthologous transcripts. IsoPlot provides a platform for researchers to access comprehensive information about AS events in mosquitoes and fruit fly. Our database is available on the web via an interactive user interface with an intuitive graphical design, which is applicable for the comparison of complex isoforms within or between species.

Database URLhttp://isoplot.iis.sinica.edu.tw/

Current Research Results
Authors: Hsin-Nan Lin and Wen-Lian Hsu

Motivation: Next-generation sequencing (NGS) provides a great opportunity to investigate genome-wide variation at nucleotide resolution. Due to the huge amount of data, NGS applications require very fast and accurate alignment algorithms. Most existing algorithms for read mapping basically adopt seed-and-extend strategy, which is sequential in nature and takes much longer time on longer reads. Results: We develop a divide-and-conquer algorithm, called Kart, which can process long reads as fast as short reads by dividing a read into small fragments that can be aligned independently. Our experiment result indicates that the average size of fragments requiring the more time-consuming gapped alignment is around 20 bp regardless of the original read length. Furthermore, it can tolerate much higher error rates. The experiments show that Kart spends much less time on longer reads than other aligners and still produce reliable alignments even when the error rate is as high as 15%.
Current Research Results
Authors: Yi-Ting Wang, Szu-Hua Pan, Chia-Feng Tsai, Ting-Chun Kuo, Yuan-Ling Hsu, Hsin-Yung Yen, Wai-Kok Choong, Hsin-Yi Wu, Yen-Chen Liao, Tse-Ming Hong, Ting-Yi Sung, Pan-Chyr Yang, and Yu-Ju Chen

Although EGFR tyrosine kinase inhibitors (TKIs) have demonstrated good efficacy in non-small-cell lung cancer (NSCLC) patients harboring EGFR mutations, most patients develop intrinsic and acquired resistance. We quantitatively profiled the phosphoproteome and proteome of drug-sensitive and drug-resistant NSCLC cells under gefitinib treatment. The construction of a dose-dependent responsive kinase-substrate network of 1548 phosphoproteins and 3834 proteins revealed CK2-centric modules as the dominant core network for the potential gefitinib resistance-associated proteins. CK2 knockdown decreased cell survival in gefitinib-resistant NSCLCs. Using motif analysis to identify the CK2 core sub-network, we verified that elevated phosphorylation level of a CK2 substrate, HMGA1 was a critical node contributing to EGFR-TKI resistance in NSCLC cell. Both HMGA1 knockdown or mutation of the CK2 phosphorylation site, S102, of HMGA1 reinforced the efficacy of gefitinib in resistant NSCLC cells through reactivation of the downstream signaling of EGFR. Our results delineate the TKI resistance-associated kinase-substrate network, suggesting a potential therapeutic strategy for overcoming TKI-induced resistance in NSCLC.
Current Research Results
"ADF: an Anomaly Detection Framework for Large-scale PM2.5 Sensing Systems," IEEE Internet of Things Journal, To Appear.
Authors: Ling-Jyh Chen, Yao-Hua Ho, Hsin-Hung Hsieh, Shih-Ting Huang, Hu-Cheng Lee, and Sachit Mahajan

As the population density continues to grow in the urban settings, air quality is degrading and becoming a serious issue. Air pollution, especially fine particulate matter (PM2.5), has raised a series of concerns for public health. As a result, a number of large-scale, low cost PM2.5 monitoring systems have been deployed in several international smart city projects. One of the major challenges for such environmental sensing systems is ensuring the data quality. In this paper, we propose an Anomaly Detection Framework (ADF) for large-scale, real-world environmental sensing systems. The framework is comprised of four modules: 1) Time-Sliced Anomaly Detection (TSAD), which detects Spatial, Temporal, and Spatio-temporal anomalies in the real-time sensor measurement data stream; 2) Real-time Emission Detection (RED), which detects potential regional emission sources; 3) Device Ranking (DR), which provides a ranking for each sensing device; and 4) Malfunction Detection (MD), which identifies malfunctioning devices. Using real world measurement data from the AirBox project, we demonstrate that the proposed framework can effectively identify outliers in the raw measurement data as well as infer anomalous events that are perceivable by the general public and government authorities. Because of its simple design, ADF is highly extensible to other advanced applications, and it can be exploited to support various large-scale environmental sensing systems.
Current Research Results
Authors: Shu-Hwa Chen, Wen-Yu Kuo, Sheng-Yao Su, Wei-Chun Chung, Jen-Ming Ho, Henry Horng-Shing Lu, Chung-Yen Lin

A new emerged cancer treatment utilizes intrinsic immune surveillance mechanism that is silenced by those malicious cells. Hence, studies of tumor infiltrating lymphocyte populations (TILs) are key to the success of advanced treatments. In addition to laboratory methods such as immunohistochemistry and flow cytometry, in silico gene expression deconvolution methods are available for analyses of relative proportions of immune cell types.
Herein, we used microarray data from the public domain to profile gene expression pattern of twenty-two immune cell types. Initially, outliers were detected based on the consistency of gene profiling clustering results and the original cell phenotype notation. Subsequently, we filtered out genes that are expressed in non-hematopoietic normal tissues and cancer cells. For every pair of immune cell types, we ran t-tests for each gene, and defined differentially expressed genes (DEGs) from this comparison. Equal numbers of DEGs were then collected as candidate lists and numbers of conditions and minimal values for building signature matrixes were calculated. Finally, we used 𝛎-Support Vector Regression to construct a deconvolution model. The performance of our system was finally evaluated using blood biopsies from 20 adults, in which 9 immune cell types were identified using flow cytometry. The present computations performed better than current state-of-the-art deconvolution methods.
Finally, we implemented the proposed method into R and tested extensibility and usability on Windows, MacOS, and Linux operating systems. The method, MySort, is wrapped as the Galaxy platform pluggable tool and usage details are available at https://testtoolshed.g2.bx.psu.edu/view/moneycat/mysort/e3afe097e80a.
"Identifying Protein-protein Interactions in Biomedical Literature using Recurrent Neural Networks with Long Short-Term Memory," The 8th International Joint Conference on Natural Language Processing (IJCNLP 2017), November 2017.
Authors: Yu-Lun Hsieh, Yung-Chun Chang, Nai-Wen Chang and Wen-Lian Hsu

Accurate identification of protein-protein interaction (PPI) helps biomedical researchers to quickly capture crucial information in literatures. This work proposes a recurrent neural network (RNN) model to identify PPIs. Experiments on two largest public benchmark datasets, AIMed and BioInfer, demonstrate that RNN outperforms state-of-the-art methods with relative improvements of 10% and 18%, respectively. Cross-corpus evaluation also indicates that RNN is robust even when trained on data from different domains. These results suggest that RNN effectively captures semantic relationships among proteins without any feature engineering.
Current Research Results
Authors: Liu, W.H., Tsai, Z. T., and Tsai, H. K.*

The regulatory roles of long intergenic noncoding RNAs (lincRNAs) in humans have been revealed through the use of advanced sequencing technology. Recently, three possible scenarios of lincRNA origins have been proposed: de novo origination from intergenic regions, duplication from other long noncoding RNAs, and pseudogenization from protein-coding genes. The first two scenarios are largely studied and supported, yet few studies focused on the evolution from pseudogenized protein-coding sequence to lincRNA. Due to the non-mutually exclusive nature of these three scenarios and the need of systematic investigation of lincRNA origination, we conducted a comparative genomics study to investigate the evolution of human lincRNAs. Results
Combining with syntenic analysis and stringent Blastn e-value cutoff, we found that the majority of lincRNAs are aligned to intergenic regions of other species. Interestingly, 193 human lincRNAs could have protein-coding orthologs in at least two of nine vertebrates. Transposable elements in these conserved regions in human genome are much less than expectation. Moreover, 19% of these lincRNAs have overlaps with or are close to pseudogenes in the human genome. Conclusions
We suggest that a notable portion of lincRNAs could be derived from pseudogenized protein-coding genes. Furthermore, based on our computational analysis, we hypothesize that a subset of these lincRNAs could have potential to regulate their paralogs by functioning as competing endogenous RNAs. Our results provide evolutionary evidence of the relationship between human lincRNAs and protein-coding genes.
Current Research Results
Authors: Ming-Chin Chuang, Meng Chang Chen

Meng ChangChenAbstract:
In the vehicular network, the smart car provides variously network services via user equipment (UE). However, the services might be interrupted since the handover procedure of the UE is performed frequently. The existing handover decision schemes in cellular networks are mostly based on received signal strength (RSS) to make the handover decision. However, these schemes suffer from a ping-pong effect and, as a result, perform unnecessary handovers incurring extra signaling overhead and packet loss, which is even more exaggerated in the ultra-dense environment. The failure probability of the handover procedure will increase when the velocity of the car is fast. Therefore, in this paper we propose a navigation-assisted seamless handover (NASH) scheme for reducing unnecessary handovers and the ping-pong effect. NASH uses the massive Multiple Input Multiple Output (MIMO) technique to perform the bicasting scheme and then it utilizes coordination multi-point (CoMP) and carrier aggregation (CA) mechanisms in the handover procedure to avoid the packet loss problem and enhance throughput. Moreover, a dynamic time to trigger (TTT) mechanism is proposed according to the velocity of the car. The simulation results show that the proposed scheme has a better performance in terms of handover latency, packet loss, network throughput, handover failure probability, and the number of unnecessary handovers.
"Accurate audio-to-score alignment for expressive violin recordings," International Society of Music Information Retrieval Conference (ISMIR), October 2017.
Authors: Jia-Ling Syue, Li Su, Yi-Ju Lin, Yen-Kuang Lu, Yu-Lin Wang and Alvin W. Y. Su

An audio-to-score alignment system adaptive to various playing styles and techniques, and also with high accuracy for onset/offset annotation is the key step toward advanced research on automatic music expression analysis. Technical barriers include the processing of overlapped notes, repeated note sequences, and silence. Most of these characteristics vary with expressions. In this paper, the audio-to-score alignment problem of expressive violin performance is addressed. We propose a two-stage alignment system composed of the dynamic time warping (DTW) algorithm, simulation of overlapped sustain notes, background noise model, silence detection, and refinement process, to better capture the onset. More importantly, we utilize the non-negative matrix factorization (NMF) method for synthesis of the reference signal in order to deal with highly diverse timbre in real-world performance. A dataset of annotated expressive violin recordings in which each piece is played with various expressive musical terms is used. The optimal choice of basic parameters considered in conventional alignment systems, such as features, distance functions in DTW, synthesis methods for the reference signal, and energy ratios, is analyzed. Different settings on different expressions are compared and discussed. Results show that the proposed methods notably improve the conventional DTW-based alignment method.
Current Research Results
"Building/environment Data/information Enabled Location Specificity and Indoor Positioning," IEEE Internet of Things Journal, September 2017.
Authors: C. C. Li, J. Su, E. T. H. Chu and J. W. S. Liu

Jane Win ShihLiuAbstract:

The building/environment data and information system (BeDIS) described here is a part of infrastructure needed to support location-specific, active emergency preparedness and responses within large buildings. BeDIPS (Building/environment Data and information based Indoor Positioning System) is one of its components. BeDIPS can provide people in large buildings with sufficiently accurate location data. It is scalable, disaster resilient and is easy to configure, deploy and maintain. BeDIPS works without Internet and serves both smart phones and most legacy Bluetooth devices. The other component of BeDIS is BeDi mist, a virtual repository of data and information on the building, interior layouts and facilities. The mist uses micro data servers and smart gateways to deliver fine-scale, location-specific decision support data on a timely basis to hundreds and thousands of active devices and mobile applications.

Current Research Results
"A Position-Aware Language Modeling Framework for Extractive Broadcast News Speech Summarization," ACM Transactions on Asian and Low-Resource Language Information Processing, August 2017.
Authors: Shih-Hung Liu, Kuan-Yu Chen, Yu-Lun Hsieh, Berlin Chen, Hsin-Min Wang, Hsu-Chun Yen, and Wen-Lian Hsu

Extractive summarization, a process that automatically picks exemplary sentences from a text (or spoken) document with the goal of concisely conveying key information therein, has seen a surge of attention from scholars and practitioners recently. Using a language modeling (LM) approach for sentence selection has been proven effective for performing unsupervised extractive summarization. However, one of the major difficulties facing the LM approach is to model sentences and estimate their parameters more accurately for each text (or spoken) document. We extend this line of research and make the following contributions in this work. First, we propose a position-aware language modeling framework using various granularities of position-specific information to better estimate the sentence models involved in the summarization process. Second, we explore disparate ways to integrate the positional cues into relevance models through a pseudo-relevance feedback procedure. Third, we extensively evaluate various models originated from our proposed framework and several well-established unsupervised methods. Empirical evaluation conducted on a broadcast news summarization task further demonstrates performance merits of the proposed summarization methods.
Current Research Results
Authors: Ling-Jyh Chen, Yao-Hua Ho, Hu-Cheng Lee, Hsuan-Cho Wu, Hao-Min Liu, Hsin-Hung Hsieh, Yu-Te Huang, and Shih-Chun Candice Lung

As the population in cities continues to increase rapidly, air pollution become a serious issue from public health to social economy.  Among all pollutants, fine particulate matters (PM2.5) directly related to various serious health concerns, e.g., lung cancer, premature death, asthma, cardiovascular and respiratory diseases. To enhance the quality of urban living, sensors are deployed to create smart cities. In this paper, we present a participatory urban sensing framework for PM2.5 monitoring with more than 2,500 devices deployed in Taiwan and 29 other countries.  It is one of the largest deployment project for PM2.5 monitor in the world as we know until May 2017. The key feature of the framework is its open system architecture, which is based on the principles of open hardware, open source software, and open data. To facilitate the deployment of the framework, we investigate the accuracy issue of low-cost particle sensors with a comprehensive set of comparison evaluations to identify the most reliable sensor. By working closely with government authorities, industry partners, and maker communities, we can construct an effective eco-system for participatory urban sensing of PM2.5 particles. Based on our deployment achievements to date, we provide a number of data services to improve environmental awareness, trigger on-demand responses, and assist future government policymaking. The proposed framework is highly scalable and sustainable with the potential to facilitate the Internet of Things, smart cities and citizen science in the future.
Current Research Results
"Mixture of Gaussian Blur Kernel Representation for Blind Image Restoration," IEEE Transactions on Computational Imaging, To Appear.
Authors: Chia-Chen Lee , Wen-Liang Hwang

Blind image restoration is a non-convex problem involving the restoration of images using unknown blur kernels. The success of the restoration process depends on three factors: 1) the amount of prior information concerning the image and blur kernel; 2) the algorithm used to perform restoration; and 3) the initial guesses made by the algorithm. Prior information of an image can often be used to restore the sharpness of edges. By contrast, there is no consensus concerning the use of prior information in the restoration of images from blur kernels, due to the complex nature of image blurring processes. In this paper, we model a blur kernel as a linear combination of basic 2-D patterns. To illustrate this process, we constructed a dictionary comprising atoms of Gaussian functions derived from the Kronecker product of 1-D Gaussian sequences. Our results show that the proposed method is more robust than other state-of-the-art methods in a noisy environment, due to its increased signal-to-noise ratio (ISNR). This approach also proved more stable than the other methods, due to the steady increase in ISNR as the number of iterations is increased.
"Automatic Music Video Generation Based on Simultaneous Soundtrack Recommendation and Video Editing," ACM Multimedia Conference 2017, October 2017.
Authors: Jen-Chun Lin, Wen-Li Wei, James Yang, Hsin-Min Wang, and Hong-Yuan Mark Liao

An automated process that can suggest a soundtrack to a user-generated video (UGV) and make the UGV a music-compliant professional-like video is challenging but desirable. To this end, this paper presents an automatic music video (MV) generation system that conducts soundtrack recommendation and video editing simultaneously. Given a long UGV, it is first divided into a sequence of fixed-length short (e.g., 2 seconds) segments, and then a multi-task deep neural network (MDNN) is applied to predict the pseudo acoustic (music) features (or called the pseudo song) from the visual (video) features of each video segment. In this way, the distance between any pair of video and music segments of same length can be computed in the music feature space. Second, the sequence of pseudo acoustic (music) features of the UGV and the sequence of the acoustic (music) features of each music track in the music collection are temporarily aligned by the dynamic time warping (DTW) algorithm with a pseudo-song-based deep similarity matching (PDSM) metric. Third, for each music track, the video editing module selects and concatenates the segments of the UGV based on the target and concatenation costs given by a pseudo-song-based deep concatenation cost (PDCC) metric according to the DTW-aligned result to generate a music-compliant professional-like video. Finally, all the generated MVs are ranked, and the best MV is recommended to the user. The MDNN for pseudo song prediction and the PDSM and PDCC metrics are trained by an annotated official music video (OMV) corpus. The results of objective and subjective experiments demonstrate that the proposed system performs well and can generate appealing MVs with better viewing and listening experiences.
"Exploiting Asymmetric SIMD Register Configurations in ARM-to-x86 Dynamic Binary Translation," The 26th International Conference on Parallel Architectures and Compilation Techniques (PACT), September 2017.
Authors: Yu-Ping Liu, Ding-Yong Hong, Jan-Jan Wu, Sheng-Yu Fu, Wei-Chung Hsu

Processor manufacturers have adopted SIMD for decades because of its superior performance and power efficiency. The configurations of SIMD registers (i.e., the number and width) have evolved and diverged rapidly through various ISA extensions on different architectures. However, migrating legacy or proprietary applications optimized for one guest ISA to another host ISA that has fewer but longer SIMD registers through binary translation raises the issues of asymmetric SIMD register configurations. To date, these issues have been overlooked. As a result, only a small fraction of the potential performance gain is realized due to underutilization of the host’s SIMD parallelism and register capacity. In this paper, we present a novel dynamic binary translation technique called spill-aware SLP (saSLP), which combines short ARMv8 NEON instructions and registers in guest binary loops to fully utilize the x86 AVX host’s parallelism as well as minimize register spilling. Our experiment results show that saSLP improves the performance by 1.6X (2.3X) across a number of benchmarks, and reduces spilling by 97% (99%) for ARMv8 NEON to x86 AVX2 (AVX-512) translation.
"Virtual Persistent Cache: Remedy the Long Latency Behavior of Host-Aware Shingled Magnetic Recording Drives," ACM/IEEE International Conference on Computer-Aided Design (ICCAD), November 2017.
Authors: Ming-Chang Yang, Yuan-Hao Chang, Fenggang Wu, Tei-Wei Kuo, and David Hung-Chang Du

This paper presents a Virtual Persistent Cache design to remedy the long latency behavior of the Host-Aware Shingled Magnetic Recording (HA-SMR) drive. Our design keeps the cost-effective model of the existing HA-SMR drives, but at the same time asks the great help from the host system for adaptively providing some computing and management resources to improve the drive performance when needed. The technical contribution is to trick the HA-SMR drives by smartly reshaping the access patterns to HA-SMR drives, so as to avoid the occurrences of long latencies in most cases and thus to ultimately improve the drive performance and responsiveness. We conduct experiments on real Seagate 8TB HA-SMR drives to demonstrate the advantages of \\textit{Virtual Persistent Cache} over the real workloads from Microsoft Research Cambridge. The results show that the proposed design can avoid at least 94.93% of long latencies and improve the drive performance by at least 58.11%, under the evaluated workloads.
Current Research Results
"Three TF Co-expression Modules Regulate Pressure-Overload Cardiac Hypertrophy in Male Mice," Scientific Reports, August 2017.
Authors: Yao-Ming Chang, Li Ling, Ya-Ting Chang, Yu-Wang Chang, Wen-Hsiung Li, Arthur Chun-Chieh Shih*, and Chien-Chang Chen*

Arthur Chun-ChiehShihAbstract:
Pathological cardiac hypertrophy, a dynamic remodeling process, is a major risk factor for heart failure. Although a number of key regulators and related genes have been identified, how the transcription factors (TFs) dynamically regulate the associated genes and control the morphological and electrophysiological changes during the hypertrophic process are still largely unknown. In this study, we obtained the time-course transcriptomes at five time points in four weeks from male murine hearts subjected to transverse aorta banding surgery. From a series of computational analyses, we identified three major co-expression modules of TF genes that may regulate the gene expression changes during the development of cardiac hypertrophy in mice. After pressure overload, the TF genes in Module 1 were up-regulated before the occurrence of significant morphological changes and one week later were down-regulated gradually, while those in Modules 2 and 3 took over the regulation as the heart size increased. Our analyses revealed that the TF genes up-regulated at the early stages likely initiated the cascading regulation and most of the well-known cardiac miRNAs were up-regulated at later stages for suppression. In addition, the constructed time-dependent regulatory network reveals some TFs including Egr2 as new candidate key regulators of cardiovascularassociated (CV) genes.
Current Research Results
"On Space Utilization Enhancement of File Systems for Embedded Storage Systems," ACM Transactions on Embedded Computing Systems (TECS), April 2017.
Authors: Tseng-Yi Chen, Yuan-Hao Chang, Shuo-Han Chen, Nien-I Hsu, Hsin-Wen Wei, and Wei-Kuan Shih

In the past decade, mobile/embedded computing systems conventionally have limited computing power, RAM space, and storage capacity due to the consideration of their cost, energy consumption, and physical size. Recently, some of these systems, such as mobile phone and embedded consumer electronics, have more powerful computing capability, so they manage their data in small flash storage devices  (e.g., eMMC and SD cards) with a simple file system. However, the existing file systems usually have low space utilization for managing small files and the tail data of large files. In this work, we thus propose a dynamic tail packing scheme to enhance the space utilization of file systems over flash storage devices in embedded computing systems by dynamically aggregating/packing the tail data of (small) files together. To evaluate the benefits and overheads of the proposed scheme, we theoretically formulate analysis equations for obtaining the best settings in the dynamic tail packing scheme. Additionally, the proposed scheme was implemented in the file system of Linux operating systems to evaluate its capability. The results demonstrate that the proposed scheme could significantly improve the space utilization of existing file systems.
Current Research Results
"Supervised Learning of Semantics-Preserving Hash via Deep Convolutional Neural Networks," IEEE Transactions on Pattern Analysis and Machine Intelligence, 2017.
Authors: Huei-Fang Yang, Kevin Lin, and Chu-Song Chen

This paper presents a simple yet effective supervised deep hash approach that constructs binary hash codes from labeled data for large-scale image search. We assume that the semantic labels are governed by several latent attributes with each attribute on or off, and classification relies on these attributes. Based on this assumption, our approach, dubbed supervised semantics-preserving deep hashing (SSDH), constructs hash functions as a latent layer in a deep network and the binary codes are learned by minimizing an objective function defined over classification error and other desirable hash codes properties. With this design, SSDH has a nice characteristic that classification and retrieval are unified in a single learning model. Moreover, SSDH performs joint learning of image representations, hash codes, and classification in a point-wised manner, and thus is scalable to large-scale datasets. SSDH is simple and can be realized by a slight enhancement of an existing deep architecture for classification; yet it is effective and outperforms other hashing approaches on several benchmarks and large datasets. Compared with state-of-the-art approaches, SSDH achieves higher retrieval accuracy, while the classification performance is not sacrificed.