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Master Plan 2020 Menurut PUPR

Berikut ini adalah skema master plan pengendalian banjir Jakarta menurut Kementerian PUPR (Kementerian Pekerjaan Umum dan Perumahan Rakyat)

Gambar di bawah ini adalah presentasi menteri PUPR setelah kasus banjir Januari 2020

Sumber dokumen aslinya dari “Skematik Pengendalian Banjir di DKI Jakarta” , gambar ini juga dirujuk di dokumen Kajian Pengelolaan Banjir DKI dan Sekitarnya Bagi Pembangunan Infrastruktur Berkelanjutan

Pada skema tersebut terdapat angka-angka debit sungai di Jakarta dan sekitarnya. Tidak disebutkan kemampuan drainage ke laut, serta pengaruh pasang surut.

Master Plan 1973

Penjelasan:

1. Aliran air dari hulu DKI dialihkan ke Banjir Kanal langsung ke laut
2. Aliran di wilayah selatan DKI dengan kontur tanah yang cukup tinggi mengalir secara gravitasi
3. Di bagian utara yang rendah aliran air dikelola dengan sistem polder (tanggul, waduk dan pompa)
4. Bagian hulu / selatan perlu dibangun / dilestarikan situ-situ, waduk dan penghijauan untuk menahan aliran air ke Jakarta

Hasil dari posting pak Muslim Muin: https://www.facebook.com/muslin.muin/posts/10218250932121467

Referensi

• Kajian Pengelolaan Banjir DKI dan Sekitarnya Bagi Pembangunan Infrastruktur Berkelanjutan, Center for Infrastructure and Built Environment (CIBE) Institut Teknologi Bandung. https://www.itb.ac.id/files/focus/Laporan_Kajian_Banjir_(030320).pdf
• Pengembangan Terpadu Pesisir Ibukota Negara (PTPIN)
• Skematik Pengendalian Banjir di DKI Jakarta
• Penanganan Banjir DKI dan Jabodetabek (2010)

Referensi

• Worldwide chip shortage expected to last into next year, and that’s good news for semiconductor stocks
• Revenue of Top 10 Foundries Expected to Increase by 20% YoY in 1Q21 in Light of Fully Loaded Capacities, Says TrendForce

Ebook PDF: https://mml-book.github.io/book/mml-book.pdf

Jupyter Notebook: https://github.com/mml-book/mml-book.github.io

Table of Contents

Part I: Mathematical Foundations

1. Introduction and Motivation
2. Linear Algebra
3. Analytic Geometry
4. Matrix Decompositions
5. Vector Calculus
6. Probability and Distribution
7. Continuous Optimization

Part II: Central Machine Learning Problems

8. When Models Meet Data
9. Linear Regression
10. Dimensionality Reduction with Principal Component Analysis
11. Density Estimation with Gaussian Mixture Models
12. Classification with Support Vector Machines

Article from blog.intent.to

• Part 1 : Introduction
• Part 2: CPU
• Part 3: GPU
• Part 4: ASIC
• Part 5: FPGA
• Part 6: Mobile AI
• Part 7: Neuromorphic computing
• Part 8: Quantum computing

Probabilistic Machine Learning: an Introduction by Kevin P. Murphy

URL: https://probml.github.io/pml-book/book1.html

Web server Apache akan menjadi lambat / hang jika diakses oleh terlalu banyak client, sedangkan memori sudah habis. Akan terjadi permintaan memory swap yang besar sehingga semuanya akan semakin lambat. Pada akhirnya koneksi akan terputus. Untuk membatasi jumlah client, dapat digunakan setting “MaxRequestWorkers”.

Berikut ini salah satu contoh setting untuk server yang memorynya sedikit.

<IfModule mpm_worker_module>
	ServerLimit         5
	StartServers         2
	MaxRequestWorkers  20
	MinSpareThreads     5
	MaxSpareThreads     5
	ThreadsPerChild     5
</IfModule>

URL: https://zindi.africa/competitions/ai4d-african-language-dataset-challenge

In recent times, pre-trained language models have led to significant improvement in various Natural Language Processing (NLP) tasks and transfer learning is rapidly changing the field. Transfer Learning is the process of training a model on a large-scale dataset and then using that pre-trained model to conduct learning for another downstream task (i.e. a target task like name entity recognition).

Among leading architectures for pre-training models for transfer learning in NLP, pre-trained models in African languages are barely represented mainly due to a lack of data. (However, there are some examples, for example this multilingual BERT that includes likes like Swahili and Yoruba.) While these architectures are freely available for use, most are data-hungry. The GPT-2 model, for instance, used millions, possibly billions of text to train. (ref)

This gap exists due to a lack of availability of data for African languages on the Internet. The languages selected for BERT pre-training “were chosen because they are the top languages with the largest Wikipedias”. (ref) Similarly, the 157 pre-trained language models made available by fastText were trained on Wikipedia and Common Crawl. (ref)

Therefore, this challenge’s objective is the creation, curation and collation of good quality African language datasets for a specific NLP task. This task-specific NLP dataset will serve as the downstream task we can evaluate future language models on.

This challenge is sponsored by GIZ and is hosted in partnership with the Artificial Intelligence for Development Africa(AI4D-Africa) Network.

URL: https://www.aicrowd.com/challenges/cyd-campus-aircraft-localization-competition

This competition is about finding the best methods to localize aircraft based on crowdsourced air traffic control communication data. The data is collected by the OpenSky Network, a large-scale ADS-B sensor network for research and organised by the Swiss Cyber-Defence Campus of armasuisse Science and Technology.