Keynote： 

Abstract：

Keynote： 

Harnessing Artificial Intelligence

Abstract：

With the rise of Artificial intelligence, numerous governments, academic institutions and enterprises have launched various initiatives to capitalize on the opportunities that arise from advances in Artificial Intelligence. In this talk, I will give an overview of the research activities and research goals two academic institutes, the Vector Institute for AI and the Waterloo AI Institute, as well as one industrial institute, the Borealis AI Institute funded by the Royal Bank of Canada.

Keynote： 

venue：

Time：

2018年12月4日 下午14:00~16:00

Speaker：

Dr. Rafael Vescovi, Narayanam “Bobby” Kasthuri

University of Chicago, Argonne National Laboratories

Abstract：

Keynote：  

Venue：

NCHC , Hsinchu

Time：

2018/9/21 10:00~12:00 am

Speaker：

prof.Sheu, Wen-Hann

Professor,NTU

Abstract：

Keynote： 

Venue：

NCHC, Hsinchu 

Time：

2018/8/7 10:00~12:00 am

Speaker：

Mr.Chie-Jen Ko

Biodiversity Research Center, Academia Sinica

Abstract：

Keynote： 

SDN/NFV-Based Security On Demand System

Venue：

NCHC, Hsinchu 

Time：

2018/7/31/  10:00~12:00 am

Speaker：

Dr. Li-Der Chou

NCU , Information Management Center director

Abstract：

Keynote： 

Venue：

Taichung

Time：

2018/7/11/ 10:00~12:00am

Speaker：

prof. Keisuke Nakayama

Kobe University

Abstract：

Keynote： 

Data Applications in the Political Field

2018/6/13   10:30~12:30 am

Dr. I Ping Hsieh

SwayStrategy Development Director

Abstract：

From the data point of view, observe and analyze human behavior, finance, sports, politics, etc.

For the era of information explosion, Studying political behavior from data  for example, electoral voting, topical offensive and defensive, cyber community, street sports,

This presentation describes how data applications in the field of politics

Keynote： 

The shape of Data: A Short Introduction to Topological Data Analysis

2018/5/31 10:00~12:00 am

Mr. Frédéric Chazal

Inria Research Center Sacla

Keynote： 

2018/5/28 10:00~12:00a.m.

Ms. Mei-Ling Hsu

Ditmanson Medical Foundation Chia - Yi Christian Hospital

Facing the ageing future, The integration of medical care and conservation will become a global trend.

When medical services gradually emerge from the wall of the hospital, Cloud and operations will be necessary information to support services , Integration, Big Data and Artificial Intelligence will bring important breakthroughs to human medical care and care.

But how to integrate data from different sources?

How to exchange information between service teams?

It is a problem that must be faced when integrating services and big data construction.

This presentation will share with you how to move toward the generation of medical care.

In this webinar we will describe the research goals and computational capabilities of Lifemapper software. The Lifemapper platform takes as input georeferenced point data of known species occurrences based on biological museum specimens and observations of species in the wild.  Lifemapper generates predicted species distribution models or suitable habitat models for plants and animals based on correlations between distributions documented by specimens, and environmental data layers such as climate, substrates, and vegetation cover.  In addition the Project computes multi-species distribution models for regions or continents into data structures known as presence-absence matrices which are then used to compute various biodiversity indices and statistics. These derived products are also useful for analyzing the relative contribution of historic biogeography and phylogeny on patterns of species diversity found in any particular area, region, or island.

Keynote： 

Data scientist as police

NCHC, Hsinchu 

2018/4/13 10:00~12:00a.m.

Mr. 柯維然

Hsinchu City Government

1. See traffic safety from the data
2. Police data analysis
3. Apply deep learning to traffic forecasting
4. Intelligentization of the monitoring system and the move towards artificial intelligence

Keynote：

New Developments in German and European HPC

2018/3/28/ 10:00~12:00 a.m

Dr. Michael M. Resch

HPC Center Stuttgart (HLRS), Germany

2018/3/20日  2:00~3:00p.m.

Prof. S. Felix Wu

 Professor of Computer Science and Associate Dean of Academic Personnel and Research, College of Engineering, at UC Davis.

2018/1/25 02:00~05:00 p.m

講者：

Dr. Satoshi Matsuoka

Professor, Tokyo Institute of Technology and Fellow, Advanced Institute for Science and Technology (AIST), Japan

Director, Joint AIST-Tokyo Tech. Open Innovation Lab on Real World Big Data Computing

With rapid rise and increase of Big Data and AI as a new breed of high-performance workloads on supercomputers, we need to accommodate them at scale, traditional simulation-based HPC and BD/AI will converge. Our TSUBAME3 supercomputer at Tokyo Institute of Technology became online in Aug. 2017, and became the greenest supercomputer in the world on the Green 500 ranking at 14.11 GFlops/W; the other aspect of TSUBAME3, is to embody various Data or "BYTES-oriented" features to allow for HPC to BD/AI convergence at scale, including significant scalable horizontal bandwidth as well as support for deep memory hierarchy and capacity, along with high flops in low precision arithmetic for deep learning. Furthermore, TSUBAM3's technologies will be commoditized to construct one of the world’s largest BD/AI focused and "open-source" cloud infrastructure called ABCI (AI-Based Bridging Cloud Infrastructure), hosted by AIST-AIRC (AI Research Center), the largest public funded AI research center in Japan. The performance of the machine is slated to be several hundred AI-Petaflops for machine learning; the true nature of the machine however, is its BYTES-oriented, optimization acceleration in the memory hiearchy, I/O, the interconnect etc, for high-performance BD/AI. ABCI will be online Spring 2018 and its archiecture, software, as well as the datacenter infrastructure design itself will be made open to drive rapid adoptions and improvements by the community, unlike the concealed cloud infrastructures of today. Finally, transcending from FLOPS-centric mindset to being BYTES-oriented will be one of the key solutions to the upcoming "end-of-Moore's law" in the mind 2020s, upon which FLOPS increase will cease and BYTES-oriented advances will be the new source of performance increases over time in general for any compputing.

Keynote： 

2017/11/22  10:00~11:00 a.m

Mr. Handol Kim

D-wave System Inc.

Kanokvate Tungpimolrut was born in Bangkok, Thailand, in 1968. He received the B.Eng. degree in electrical and electronics engineering from King Mongkut’s Institue of Technology Ladkraband, in 1989, and the M. Eng. as well as D. Eng. degree in electrical and electronics engineering from Tokyo Institute of Technology, in 1992 and 1995, respectively.

Following receipt of the D.Eng. degree, he was a researcher with Fuji Elctric Co., Ltd. Since 1996, he has been a researcher with National Electronics and Computer Technology Center, Thailand. His research interests are the induction motor drive system and switched reluctance motor interests are the switched reluctance motor and drive system as well as motor drive applications.

 Rafael Vescovi

At low resolution scales (e.g. mm voxels), magnetic resonance imaging (MRI) techniques allow for in vivo or ex vivo mapping of neuronal tracts through a combination of diffusion-weighted imaging techniques and post-imaging computational tractography. While powerful, they cannot achieve the nanometer-scale resolution required to identify neuronal connections. Moreover, they have never been thoroughly validated against ground truth high-resolution data. At the highest resolution scale (e.g. nm voxels), recent efforts at automated electron microcopy (EM) provide synapse-level resolution (3 nm) but are currently limited to brain volumes of the size of a grain of sand and face serious computational challenges scaling to even 2 pixels of MRI data at 1 mm resolution (i.e. almost 2 petabytes of EM data). This unresolved mismatch between these imaging modalities (and others) partitions our understanding of the brain into ‘silos’ divided by resolution scales with little to no cross validation. The strengths of each modality are not leveraged for a more comprehensive understanding of the brain. This problem is only exacerbated since brains potentially operate at multiple scales in parallel (from communication via individual connections between neurons to communication between brain regions). Multi-resolution multi-modal brain maps are critically necessary for a more complete understanding of the brain.
We propose to use synchrotron-based micro-CT (uCT) to fill this gap, bridging the resolution divide between MRI and EM by providing intermediate resolution (e.g. micron voxels) over entire brains and with sample preparation conditions compatible with MRI and automated EM on the same brains. We propose to use uCT as a ‘Rosetta Stone’ enabling mapping of complete neuronal paths, allowing, for the first time, validation of dtMRI, and identifying areas of interest in both coarser resolution modalities for subsequent nanometer reconstructions with automated serial EM.  

Keynote： 

Personalized Transfer Learning

In several application domains, data instances are produced by a population of individuals that exhibit a variety of different characteristics. For instance, in activity recognition, different individuals might walk or run with different gait patterns. Similarly, in sleep studies, different individuals might exhibit different patterns for the same sleep stages. In telecommunication networks, software applications might generate packet flows between servers according to different patterns. In such scenarios, it is tempting to treat the population as a homogeneous source of data and to learn a single average model for the entire population. However, this average model will perform poorly in recognition tasks for individuals that differ significantly from the average. Hence, there is a need for transfer learning techniques that take into account the variations between individuals within a population. In this talk, I will describe online algorithms to transfer knowledge on the fly from specific individuals within a population to a new individual in order to bootstrap the learning process in sequential tasks such as activity recognition, sleep stage identification and packet flow prediction in telecommunication networks.

Aquatic Ecotoxicology and Ecosystem Metabolism under Changing Environment:
Insights from High-Frequency Sensor Data and Mechanistic Modeling

Dr. Tsai  study demonstrates that terrestrial loads of CDOM serve as a

Abstract：

We recently initiated a project to develop a cloud-based machine learning system based on the FCHA. Flow in Cloud is a pool of multiple kinds of processing engines such as FPGA and GPU connected by a circuit switch network. A special operating system, Flow OS, will combine, connect, and provide such engines to users based on job requirements. There are many technology developed behind the scene. One of them is the container technology. This talk will elaborate this effort for future AI platform.

Abstract：

Dr. Kasthuri developed an automated approach to large-volume serial electron microscopy ("connectomics"). The Kasthuri lab continues to innovate new approaches to electron microscopic based connectomics reconstructions including making samples more amenable to automatic segmentation and combining proteomic and genomic approaches with electron microscopy. We are also now exploring the use of high-energy X-rays from synchrotron sources for mapping brains in their entirety. The Kasthuri lab is applying these techniques to developing, adult, and aged brains in service of answering the question: How do brains grow up and age?

Abstract：

In this webinar we will describe the research goals and computational capabilities of Lifemapper software. The Lifemapper platform takes as input georeferenced point data of known species occurrences based on biological museum specimens and observations of species in the wild.  Lifemapper generates predicted species distribution models or suitable habitat models for plants and animals based on correlations between distributions documented by specimens, and environmental data layers such as climate, substrates, and vegetation cover.  In addition the Project computes multi-species distribution models for regions or continents into data structures known as presence-absence matrices which are then used to compute various biodiversity indices and statistics. These derived products are also useful for analyzing the relative contribution of historic biogeography and phylogeny on patterns of species diversity found in any particular area, region, or island.

Abstract：

The rise of big data science has created new demands for modern computer systems. While floating performance has driven computer architecture and system design for the past few decades, there is renewed interest in the speed at which data can be ingested and processed. Early exemplars such as Gordon, the NSF funded system at the San Diego Supercomputing Centre, shifted the focus from pure floating point performance to memory and IO rates. At the University of Queensland we have continued this trend with the design of FlashLite, a parallel cluster equiped with large amounts of main memory, Flash disk, and a distributed shared memory system (ScaleMP’s vSMP). This allows applications to place data “close” to the processor, enhancing processing speeds. Further, we have built a geographically distributed multi-tier hierarchical data fabric called MeDiCI, which provides an abstraction very large data stores cross the metropolitan area. MeDiCI leverages industry solutions such as IBM’s Spectrum Scale and SGI’s DMF platforms. Caching underpins both FlashLite and MeDiCI. In this talk I will describe the design decisions and illustrate some early application studies that benefit from the approach.

Abstract：

European and German HPC are closely connected. Over the last years Germany has provided the backbone of the European PRACE infrastructure that supplies researchers all over Europe with access to world class supercomputer. In the talk we discuss how the German concept of three centers has helped to stay competitive even with the US and how European politics is targeting to become a major player in HPC worldwide.

Abstract：

Nature-inspired metaheuristic algorithms, like the particle swarm optimization and many others, enjoy fast convergence towards optimal solution via a series of inter-particle communication. Such methods are common for the optimization problem in engineering, but few in statistics problem. It is especially difficult to implement in some fields of statistics as the search spaces are mostly discrete, while most metaheuristic methods require continuous search domains. This talk introduces a new method called the Swarm Intelligence Based (SIB) method for optimization in experimental design problems within both discrete and continuous spaces. In specific, the supersaturated designs (SSD) , Latin hypercube designs (LHD) and minimum energy designs (MED) are optimized. The SSD optimization is served as a demonstration of the standard framework of the SIB method. The LHD optimization shows how the framework is extended to multiple objective problems. The MED optimization shows how the SIB method is used in continuous domain and how efficient if the initial particles are preselected. Ones can modify their own algorithms from the standard SIB method to tackle their own problems, like community detection in social network analysis, change-point analysis in functional analysis, feature selection in classification problem, etc.

Abstract：

Modeling is an important research field in computer graphics. Based on spatial grammars, we developed a HTML5 visualization interface for modeling of Chinese traditional architectures. Spatial grammars are implemented using Object-Oriented design patterns. With the help of spatial grammars, modeling of wooden structures no longer needs to be done manually. Instead, our system can generate traditional architectures automatically.

Abstract:

In this talk, we present a participatory urban sensing project for PM2.5 monitoring. The key feature of this project is its open architecture, which is based on the principles of open hardware, open source software, and open data. By working closely with government authorities, industry partners, and maker communities, we have constructed an effective ecosystem 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 project is highly scalable and sustainable with the potential to facilitate the Internet of Things, smart cities and citizen science in the future.