Faculty recital: Hung-Kuan Chen, March 18, 1990

This is the concert program of the Faculty Recital: Hung-Kuan Chen performance on Sunday, March 18, 1990 at 8:00 p.m., at the Tsai Performance Center, 685 Commonwealth Avenue, Boston, Massachusetts. Works performed were Nocturne, Op. 31 by Lowell Liebermann, Sonata in B-flat major, Op. 106 "Hammerklavier" by Ludwig van Beethoven, Nocturne in E-flat Major, Op. 55 No. 2 and Nocturne in C minor, Op. 48 No. 1 by Frédéric Chopin, and Variations on a Theme by Paganini, Op. 35 by Johannes Brahms. Digitization for Boston University Concert Programs was supported by the Boston University Humanities Library Endowed Fund

Faculty recital: Hung-Kuan Chen, piano, October 29, 1986

This is the concert program of the Faculty Recital: Hung-Kuan Chen, piano performance on Wednesday, October 29, 1986 at 8:00 p.m., at the Concert Hall, 855 Commonwealth Avenue. Works performed were Sonata Op. 111 in C minor by Ludwig van Beethoven, "Gaspard de la Nuit" by Maurice Ravel, and Sonata in G minor by Franz Liszt. Digitization for Boston University Concert Programs was supported by the Boston University Humanities Library Endowed Fund

Faculty recital: Hung-Kuan Chen, piano, February 4, 1987

This is the concert program of the Faculty Recital: Hung-Kuan Chen, piano performance on Wednesday, February 4, 1987 at 8:00 p.m., at the Concert Hall, 855 Commonwealth Avenue. Works performed were the following by Ludwig van Beethoven: Sonata Op. 10, No. 1 in C minor, Sonata Op. 27, No. 1 in E-flat major Quasi una Fantasia, Sonata Op. 54, in F major, and Sonata Op. 106 in B-flat major Hammerklaviersonata. Digitization for Boston University Concert Programs was supported by the Boston University Humanities Library Endowed Fund

Peramalan Harga Ethereum Menggunakan Chen Fuzzy Time Series dengan Fuzzy C-Means Clustering (FCM)

Peramalan biasanya digunakan untuk meramalkan sesuatu yang akan terjadi di masa depan menggunakan data historis yang sudah ada. Penelitian ini bertujuan untuk meramalkan harga Ethereum menggunakan data historis pada bulan November 2021, terdapat tiga jenis data yang dipakai, yaitu open, high, dan low. Pada penelitian ini digunakan Chen fuzzy time series dengan fuzzy c-means clustering (FCM) untuk mencari intervalnya dan single exponential smoothing (SES) sebagai pembandingnya. Perhitungan menggunakan metode Chen fuzzy time series dengan fuzzy c-means clustering (FCM) dan single exponential smoothing (SES) didapatkan ramalan harga Ethereum pada tanggal 1 Desember 2021 untuk nilai open, high, dan low. Selanjutnya dihitung nilai average forecasting error rates (AFER) dari metode Chen fuzzy time series dengan fuzzy c-means clustering (FCM) dan metode single exponential smoothing (SES). Nilai AFER dari metode Chen fuzzy time series dengan fuzzy c-means clustering (FCM) untuk nilai open, high, dan low adalah {2,763%, 2,044%, 1,787%} sedangkan nilai AFER dari metode single exponential smoothing (SES) untuk nilai open, high, dan low adalah {3,063%, 2,336%, 2,870%}. Ini berarti bahwa peramalan dari Chen fuzzy time series dengan fuzzy c-means clustering lebih baik daripada single exponential smoothing dalam meramalkan harga Ethereum pada bulan November 2021

Quantifying Facial Age by Posterior of Age Comparisons

We introduce a novel approach for annotating large quantity of in-the-wild facial images with high-quality posterior age distribution as labels. Each posterior provides a probability distribution of estimated ages for a face. Our approach is motivated by observations that it is easier to distinguish who is the older of two people than to determine the person's actual age. Given a reference database with samples of known ages and a dataset to label, we can transfer reliable annotations from the former to the latter via human-in-the-loop comparisons. We show an effective way to transform such comparisons to posterior via fully-connected and SoftMax layers, so as to permit end-to-end training in a deep network. Thanks to the efficient and effective annotation approach, we collect a new large-scale facial age dataset, dubbed `MegaAge', which consists of 41,941 images. Data can be downloaded from our project page mmlab.ie.cuhk.edu.hk/projects/MegaAge and github.com/zyx2012/Age_estimation_BMVC2017. With the dataset, we train a network that jointly performs ordinal hyperplane classification and posterior distribution learning. Our approach achieves state-of-the-art results on popular benchmarks such as MORPH2, Adience, and the newly proposed MegaAge.Comment: To appear on BMVC 2017 (oral) revised versio

CC Ann Chen Interview

Artist Bio: C. C. Ann Chen is an artist and educator based in Chicago, IL. She was born in Taiwan, and grew up in suburban Maryland. Chen holds a BA in Architectural History from the University of Maryland, and MFA from the School of the Art Institute of Chicago. Chen’s work stems from architecture and landscape, and explores perceptual translations and misinterpretations of place, time, and memory. Projects range from direct observation to site-specific ideas, following an intuitive, experiment-based approach in her studio practice. She has been awarded artist residencies by Marble House Project, the Ragdale Foundation, and will be joining the Clipperton Project to sail around the Faroe Islands in 2016. Chen has exhibited in numerous Chicago venues, including slow gallery, The Bike Room, Glass Curtain Gallery at Columbia College, Heaven Gallery, and Research House for Asian Art, Beverly Art Center, Zhou B. Art Center, Hyde Park Art Center, and The Franklin. In 2012, Chen was a recipient of the Midwest Voices and Visions Award and exhibition at the Museum of Contemporary Art Detroit. Chen is Adjunct Assistant Professor in the Liberal Arts Department at the School of the Art Institute of Chicago. (bio from: www.ccannchen.com