BEGIN:VCALENDAR
VERSION:2.0
PRODID:-//Biomedical Mathematics Group - ECPv6.15.20//NONSGML v1.0//EN
CALSCALE:GREGORIAN
METHOD:PUBLISH
X-ORIGINAL-URL:https://www.ibs.re.kr/bimag
X-WR-CALDESC:Events for Biomedical Mathematics Group
REFRESH-INTERVAL;VALUE=DURATION:PT1H
X-Robots-Tag:noindex
X-PUBLISHED-TTL:PT1H
BEGIN:VTIMEZONE
TZID:Asia/Seoul
BEGIN:STANDARD
TZOFFSETFROM:+0900
TZOFFSETTO:+0900
TZNAME:KST
DTSTART:20200101T000000
END:STANDARD
END:VTIMEZONE
BEGIN:VEVENT
DTSTART;TZID=Asia/Seoul:20211210T150000
DTEND;TZID=Asia/Seoul:20211210T170000
DTSTAMP:20260509T233125
CREATED:20211209T210000Z
LAST-MODIFIED:20211209T112916Z
UID:5122-1639148400-1639155600@www.ibs.re.kr
SUMMARY:The Graph convolutional Networks (GCN) with Persistent Homology and its applications 3/4
DESCRIPTION:Neural Networks with the Persistent Diagrams and Graph Classification. We introduce the first paper connecting persistent diagrams to the Neural Networks by Carrier et al\,” A neural Network Layer for Persistent Diagrams and New Graph Topological Signatures\, 2019\, arXiv. We are going to analyse the End-to-End algorithm and learning processes and applications.\nCode; tensorflow at https:// github.com/MathieuCarriere/perslay
URL:https://www.ibs.re.kr/bimag/event/2021-12-10/
LOCATION:B378 Seminar room\, IBS\, 55 Expo-ro Yuseong-gu\, Daejeon\, 34126\, Korea\, Republic of
CATEGORIES:Biomedical Mathematics Seminar
ORGANIZER;CN="Jae Kyoung Kim":MAILTO:jaekkim@kaist.ac.kr
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Seoul:20211215T143000
DTEND;TZID=Asia/Seoul:20211215T160000
DTSTAMP:20260509T233125
CREATED:20211214T190000Z
LAST-MODIFIED:20211214T070933Z
UID:5299-1639578600-1639584000@www.ibs.re.kr
SUMMARY:Stiff-PINN: Physics-Informed Neural Network for Stiff Chemical Kinetics
DESCRIPTION:We will discuss about “Stiff-PINN: Physics-Informed Neural Network for Stiff Chemical Kinetics”\, Ji et al.\, The Journal of Physical Chemistry A\, 2020 \nThe recently developed physics-informed neural network (PINN) has achieved success in many science and engineering disciplines by encoding physics laws into the loss functions of the neural network such that the network not only conforms to the measurements and initial and boundary conditions but also satisfies the governing equations. This work first investigates the performance of the PINN in solving stiff chemical kinetic problems with governing equations of stiff ordinary differential equations (ODEs). The results elucidate the challenges of utilizing the PINN in stiff ODE systems. Consequently\, we employ quasi-steady-state assumption (QSSA) to reduce the stiffness of the ODE systems\, and the PINN then can be successfully applied to the converted non-/mild-stiff systems. Therefore\, the results suggest that stiffness could be the major reason for the failure of the regular PINN in the studied stiff chemical kinetic systems. The developed stiff-PINN approach that utilizes QSSA to enable the PINN to solve stiff chemical kinetics shall open the possibility of applying the PINN to various reaction-diffusion systems involving stiff dynamics.
URL:https://www.ibs.re.kr/bimag/event/2021-12-15/
LOCATION:B378 Seminar room\, IBS\, 55 Expo-ro Yuseong-gu\, Daejeon\, 34126\, Korea\, Republic of
CATEGORIES:Journal Club
ORGANIZER;CN="Jae Kyoung Kim":MAILTO:jaekkim@kaist.ac.kr
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Seoul:20211223T163000
DTEND;TZID=Asia/Seoul:20211223T173000
DTSTAMP:20260509T233125
CREATED:20211222T220000Z
LAST-MODIFIED:20211220T121513Z
UID:5357-1640277000-1640280600@www.ibs.re.kr
SUMMARY:Methods for characterizing circadian physiology from wearables
DESCRIPTION:Abstract \nNon-invasive data collection in real-world settings with wearables provides a new opportunity for characterizing daily physiology. However\, accurate and efficient characterization remains an open problem because the complex autoregressive noise of the data makes it challenging to use a simple and efficient method for inference of clock proxies\, least squares method. In this talk\, we will introduce a simple approximation that alters the noise structure and thus enables one to use the least squares method. We will show its usefulness for real-time personalized fever detection in cancer patients.
URL:https://www.ibs.re.kr/bimag/event/2021-12-23-2/
LOCATION:B378 Seminar room\, IBS\, 55 Expo-ro Yuseong-gu\, Daejeon\, 34126\, Korea\, Republic of
CATEGORIES:Biomedical Mathematics Seminar
ORGANIZER;CN="Jae Kyoung Kim":MAILTO:jaekkim@kaist.ac.kr
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Seoul:20211224T130000
DTEND;TZID=Asia/Seoul:20211224T140000
DTSTAMP:20260509T233125
CREATED:20211223T190000Z
LAST-MODIFIED:20211221T043551Z
UID:5302-1640350800-1640354400@www.ibs.re.kr
SUMMARY:Information Integration and Energy Expenditure in Gene Regulation
DESCRIPTION:We will discuss about “Information Integration and Energy Expenditure in Gene Regulation”\, Estrada et al.\, Cell\, 2016 \nAbstract: The quantitative concepts used to reason about gene regulation largely derive from bacterial studies. We show that this bacterial paradigm cannot explain the sharp expression of a canonical developmental gene in response to a regulating transcription factor (TF). In the absence of energy expenditure\, with regulatory DNA at thermodynamic equilibrium\, information integration across multiple TF binding sites can generate the required sharpness\, but with strong constraints on the resultant “higher-order cooperativities.” Even with such integration\, there is a “Hopfield barrier” to sharpness; for n TF binding sites\, this barrier is represented by the Hill function with the Hill coefficient n. If\, however\, energy is expended to maintain regulatory DNA away from thermodynamic equilibrium\, as in kinetic proofreading\, this barrier can be breached and greater sharpness achieved. Our approach is grounded in fundamental physics\, leads to testable experimental predictions\, and suggests how a quantitative paradigm for eukaryotic gene regulation can be formulated.
URL:https://www.ibs.re.kr/bimag/event/2021-12-24/
LOCATION:B378 Seminar room\, IBS\, 55 Expo-ro Yuseong-gu\, Daejeon\, 34126\, Korea\, Republic of
CATEGORIES:Journal Club
ORGANIZER;CN="Jae Kyoung Kim":MAILTO:jaekkim@kaist.ac.kr
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Seoul:20211229T150000
DTEND;TZID=Asia/Seoul:20211229T160000
DTSTAMP:20260509T233125
CREATED:20211228T210000Z
LAST-MODIFIED:20211227T001218Z
UID:5385-1640790000-1640793600@www.ibs.re.kr
SUMMARY:디지털 표현형의 진단 및 치료적 적용
DESCRIPTION:디지털 표현형의 진단 및 치료적 적용 조철현(세종충남대학교병원) 디지털 표현형(digital phenotype)은 각 개개인이 일상생활에서 사용하는 다양한 디지털 기기를 통해서 실시간으로 얻어지는 다양한 형태의 데이터를 뜻하는 것으로\, 디지털 기기의 사용이 보편화되면서 의료적 적용에 대한 가능성이 한층 높아지고 있다. 디지털 표현형은 이전에는 측정(measure)하기 힘들었던 영역에 대한 측정을 가능케 함으로써\, 의학적 평가나 진단적인 측면에서 임상적 함의를 갖는다고 볼 수 있겠다. 실제 의료현장에서 충분히 접근하고 파악하지 못했던 임상적인 의미를 도출해 내거나 새로운 발견을 할 수 있는 근거로 활용할 수도 있겠다. 임상적 상태의 변화나 치료 효과\, 예후 평가를 위한 기준으로 활용할 수도 있겠다. 또한\, 디지털치료제의 개발과 적용에 있어서 디지털 표현형을 고려하고 반영하는 것은 매우 중요한 부분이 될 것이다. 디지털치료제(Digital Therapeutics)는 사람을 대상으로 치료\, 예방\, 예후 개선 등을 목적으로 인지\, 행동\, 생활습관 등의 변화를 이끌어내기 위한 소프트웨어 형태로서 디지털 시대의 새로운 치료적 옵션으로 주목받고 있다. 특히\, 개인별\, 맞춤형 치료적 접근을 위해서는 디지털 표현형에 대한 이해를 높이고 잘 활용하는 것이 필수적이다. 본 발표에서는 디지털 표현형의 정의와 특성\, 임상적으로 어떤 함의를 가지고 있는 지에 대해 논의하고자 한다. 아울러\, 디지털 표현형의 활용 가능성\, 실제적 적용\, 디지털치료제에의 적용을 위한 방향성에 대해 발표하고자 한다.
URL:https://www.ibs.re.kr/bimag/event/2021-12-29/
LOCATION:B378 Seminar room\, IBS\, 55 Expo-ro Yuseong-gu\, Daejeon\, 34126\, Korea\, Republic of
CATEGORIES:Biomedical Mathematics Seminar
ORGANIZER;CN="Jae Kyoung Kim":MAILTO:jaekkim@kaist.ac.kr
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Seoul:20211231T130000
DTEND;TZID=Asia/Seoul:20211231T140000
DTSTAMP:20260509T233125
CREATED:20211230T190000Z
LAST-MODIFIED:20211227T004211Z
UID:5306-1640955600-1640959200@www.ibs.re.kr
SUMMARY:The Generalized Multiset Sampler
DESCRIPTION:We will discuss about “The Generalized Multiset Sampler”\, Kim and MacEachern\, The Journal of Computation and Graphical Statistics\, 2021 \nAbstract: The multiset sampler\, an MCMC algorithm recently proposed by Leman and coauthors\, is an easy-to-implement algorithm which is especially well-suited to drawing samples from a multimodal distribution. We generalize the algorithm by redefining the multiset sampler with an explicit link between target distribution and sampling distribution. The generalized formulation replaces the multiset with a K-tuple\, which allows us to use the algorithm on unbounded parameter spaces\, improves estimation\, and sets up further extensions to adaptive MCMC techniques. Theoretical properties of the algorithm are provided and guidance is given on its implementation. Examples\, both simulated and real\, confirm that the generalized multiset sampler provides a simple\, general and effective approach to sampling from multimodal distributions. Supplementary materials for this article are available online.
URL:https://www.ibs.re.kr/bimag/event/2021-12-31/
LOCATION:B378 Seminar room\, IBS\, 55 Expo-ro Yuseong-gu\, Daejeon\, 34126\, Korea\, Republic of
CATEGORIES:Journal Club
ORGANIZER;CN="Jae Kyoung Kim":MAILTO:jaekkim@kaist.ac.kr
END:VEVENT
END:VCALENDAR