Dampak Fenomena El Nino dan La Nina terhadap Keseimbangan Air Lahan Pertanian dan Periode Tumbuh Tersedia di Daerah Waeapo Pulau Buru

Penelitian bertujuan untuk menentukan tahun-tahun kejadian El Nino dan La Nina, menganalisis dampak kejadian El Nino dan La Nina terhadap neraca air lahan, dan menetapkan periode tumbuh tersedia di daerah Waeapo pada berbagai kondisi curah hujan. Analisis data iklim dilakukan dengan tahapan sebagai berikut: a) pembangkitan data curah hujan; b) analisis curah hujan ekstrim El Nino dan La Nina; c) perhitungan neraca air lahan menggunakan metode Thornthwaite-Mather; dan d) penentuan periode tumbuh tersedia pada berbagai kondisi curah hujan. Hasil penelitian menunjukkan bahwa dalam periode 30 tahun terakhir di Daerah Waeapo sudah terjadi fenomena curah hujan ekstrem kering (El Nino) sebanyak delapan kali, dengan intensitas rata-rata tiga tahun sekali. Dan fenomena curah hujan ekstrem basah (La Nina) terjadi sebanyak enam kali dengan intensitas rata-rata lima tahun sekali. Berdasarkan perhitungan neraca air lahan, pada kondisi curah hujan El Nino terjadi defisit air tanah tahunan sebesar 403 mm atau 172% dari kondisi normalnya, sebaliknya pada kondisi curah hujan La Nina terjadi surplus air tanah tahunan sebesar 775 mm atau 222% dari kondisi normalnya. Ketika terjadi fenomena El Nino periode tumbuh yang tersedia hanya lima bulan (Januari s.d Mei), dan ketika terjadi fenomena La-Nina periode tumbuh berlangsung sepanjang tahun (12 bulan). Kata kunci: El Nino, La Nina, neraca air lahan, periode tumbuh tersedia, Pulau Bur

Parents' Weekend Concert, October 19, 2002

This is the concert program of the Boston University Men's Chorus, Boston University Women's Chorale, Boston University Repertory Chorus, and Boston University Chamber Chorus performance on Saturday, October 19, 2002 at 8:00 p.m., at the Concert Hall, 855 Commonwealth Avenue. Works performed were "Ain'a that Good News," Traditionl, arranged by William L. Dawson, "Sometimes I Feel Like a Motherless Child," Traditional. arranged by Nina Gilbert, No.1 "Nanie" and No.2 "Triolett" from "Drei Lieder fur Frauenstimme," Op. 114 by Robert Schumann, No. 2 "Lied" from "Drei Gedichte," Op. 29 by R. Schumann, Choral Hymns from the "Rig Veda," Set 3 by Gustav Holst, "Kyrie," K. 89 (arr. J. Harris) by Wolfgang Amadeus Mozart, Mass in G Major, D. Digitization for Boston University Concert Programs was supported by the Boston University Humanities Library Endowed Fund

Efficient Average-Case Population Recovery in the Presence of Insertions and Deletions

A number of recent works have considered the trace reconstruction problem, in which an unknown source string x in {0,1}^n is transmitted through a probabilistic channel which may randomly delete coordinates or insert random bits, resulting in a trace of x. The goal is to reconstruct the original string x from independent traces of x. While the asymptotically best algorithms known for worst-case strings use exp(O(n^{1/3})) traces [De et al., 2017; Fedor Nazarov and Yuval Peres, 2017], several highly efficient algorithms are known [Yuval Peres and Alex Zhai, 2017; Nina Holden et al., 2018] for the average-case version of the problem, in which the source string x is chosen uniformly at random from {0,1}^n. In this paper we consider a generalization of the above-described average-case trace reconstruction problem, which we call average-case population recovery in the presence of insertions and deletions. In this problem, rather than a single unknown source string there is an unknown distribution over s unknown source strings x^1,...,x^s in {0,1}^n, and each sample given to the algorithm is independently generated by drawing some x^i from this distribution and returning an independent trace of x^i. Building on the results of [Yuval Peres and Alex Zhai, 2017] and [Nina Holden et al., 2018], we give an efficient algorithm for the average-case population recovery problem in the presence of insertions and deletions. For any support size 1 <= s <= exp(Theta(n^{1/3})), for a 1-o(1) fraction of all s-element support sets {x^1,...,x^s} subset {0,1}^n, for every distribution D supported on {x^1,...,x^s}, our algorithm can efficiently recover D up to total variation distance at most epsilon with high probability, given access to independent traces of independent draws from D. The running time of our algorithm is poly(n,s,1/epsilon) and its sample complexity is poly (s,1/epsilon,exp(log^{1/3} n)). This polynomial dependence on the support size s is in sharp contrast with the worst-case version of the problem (when x^1,...,x^s may be any strings in {0,1}^n), in which the sample complexity of the most efficient known algorithm [Frank Ban et al., 2019] is doubly exponential in s

Swedish Red Cross Schutzpass Signed by Vlademar Langlet

Navy booklet titled, Skyddsbrev Otalomlevél Schutzbrief with a red cross on cover. Information Provided by Michael D. Bulmash: Vlademar Langlet and his wife Nina Borovko-Langlet are credited with saving many Jews in Budapest by providing documents saying that the person carrying them were awaiting Swedish nationality. The Langlets were recognized by Yad Vashem as righteous among nations.https://digital.kenyon.edu/bulmash/1642/thumbnail.jp

Herald of Holiness Volume 03, Number 44 (1915)

01 Refusing and Choosing 04 Editor\u27s Survey Open Parliament 05 The Reproach of Sanctification By John Matthews, D. D. 06 Joseph: His Trials and Triumphs By Gilbert E. Martin 07 The Bible - E. D. Hinchman 07 A Message to the Sinner Written by Nina Deeter Mother and Little Ones 08 Edith\u27s Sacrifice By Minnie W. Parr 08 The New Year and the Children 08 The Great Opportunity 08 “Happy New Year” The Work and the Workers 09 Announcements 09 District News 10 General Church News 11 CASH REPORT GENERAL MISSIONARY BOARD PENTECOSTAL CHURCH OF THE NAZARENE 12 Peniel University 16 Superintendents\u27 Directoryhttps://digitalcommons.olivet.edu/cotn_hoh/2968/thumbnail.jp

Symbol detection in online handwritten graphics using Faster R-CNN

Symbol detection techniques in online handwritten graphics (e.g. diagrams and mathematical expressions) consist of methods specifically designed for a single graphic type. In this work, we evaluate the Faster R-CNN object detection algorithm as a general method for detection of symbols in handwritten graphics. We evaluate different configurations of the Faster R-CNN method, and point out issues relative to the handwritten nature of the data. Considering the online recognition context, we evaluate efficiency and accuracy trade-offs of using Deep Neural Networks of different complexities as feature extractors. We evaluate the method on publicly available flowchart and mathematical expression (CROHME-2016) datasets. Results show that Faster R-CNN can be effectively used on both datasets, enabling the possibility of developing general methods for symbol detection, and furthermore, general graphic understanding methods that could be built on top of the algorithm.Comment: Submitted to DAS-201

Herald of Holiness Volume 79 Number 03 (1990)

Cover Photo Credit: Pam Greenfield Inside Front Cover Photo Credit: Paul S. Myers FEATURE ARTICLES 4 A Rabbit’s-Foot Jesus, Victor Schreffler 10 Tributes to Samuel Young 15 Entire Sanctification Makes a Difference, Richard S. Taylor 16 Family and Sexual Violence: What the Church Needs to Know and Do, Carmen Renee Berry 22 Devotions for Lent, Wesley Tracy 26 Evangelist Preaches with Venom, Nina E. Beegle PERSONAL EXPERIENCE FEATURES 46 A Green Beret Wins His Battle, Nina E. Beegle CONTINUING COLUMNS 8 Rhythms of the Spirit, Morris A. Weigelt 9 General Superintendent’s Viewpoint, Jerald D. Johnson 12 Into the Word, Reuben Welch 14 When You Pray, E. Dee Freeborn 41 In a Woman’s Voice, Rebecca Laird 45 Observer at Large, W. E. McCumber DEPARTMENTS 1 Late News, Mark Graham and Tom Felder 6 Editor’s Choice, Wesley Tracy 28 News 35 Etcetera 40 Question Box 40 Because You Gave Sons of Cape Verde, Gloria Henickhttps://digitalcommons.olivet.edu/cotn_hoh/1098/thumbnail.jp