Model structures on modules over Ding-Chen rings

An $n$-FC ring is a left and right coherent ring whose left and right self FP-injective dimension is $n$. The work of Ding and Chen in \cite{ding and chen 93} and \cite{ding and chen 96} shows that these rings possess properties which generalize those of $n$-Gorenstein rings. In this paper we call a (left and right) coherent ring with finite (left and right) self FP-injective dimension a Ding-Chen ring. In case the ring is Noetherian these are exactly the Gorenstein rings. We look at classes of modules we call Ding projective, Ding injective and Ding flat which are meant as analogs to Enochs' Gorenstein projective, Gorenstein injective and Gorenstein flat modules. We develop basic properties of these modules. We then show that each of the standard model structures on Mod-$R$, when $R$ is a Gorenstein ring, generalizes to the Ding-Chen case. We show that when $R$ is a commutative Ding-Chen ring and $G$ is a finite group, the group ring $R[G]$ is a Ding-Chen ring.Comment: 12 page

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

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

Non-parametric synthesis of laminar volumetric texture

International audienceThe goal of this paper is to evaluate several extensions of Wei and Levoy's algorithm for the synthesis of laminar volumetric textures constrained only by a single 2D sample. Hence, we shall also review in a unified form the improved algorithm proposed by Kopf et al. and the particular histogram matching approach of Chen and Wang. Developing a genuine quantitative study we are able to compare the performances of these algorithms that we have applied to the synthesis of volumetric structures of dense carbons. The 2D samples are lattice fringe images obtained by high resolution transmission electronic microscopy (HRTEM)

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