[Links of the Day] 27/09/2018: data-centric internet master thesis, Critic of deep learning, Fairer machine learning

• Scalable mobility support in future internet architectures : MIT Master Thesis by Xavier K. Mwangi where he argues for a move away from host centric (IP) toward a data-centric approach where the naming and routing scheme revolves around object name resolution architecture. The argument behind this is to eliminate the tight coupling between location and data and allow a more fluid interaction, especially in an ever increasing mobile world. 
• Deep Learning: A Critical Appraisal : Critical analysis of the recent deep learning revival and argumentation by the authors that deep learning will be soon replaced by other technique as we want to progress toward artificial intelligence.
• Delayed impact of fair machine learning : A paper that tries to answer the central question of fairness of machine learning algorithm. I.e. how to ensure fair treatment across demographic groups in a population when we let a machine learning system decide who gets an opportunity (e.g. is offered a loan) and who doesn’t.

[Links of the Day] 29/06/2017 : BeeGFS distributed FS, Virtual memory in Big memory systems, PdfX

• An Introduction to BeeGFS : Fraunhofer distributed parallel file system for HPC system. Mainly a concurrent of Lustre I would say. This had potential and they are making a foray into the business side of storage. Let's see how well they fare. [website]
• Preserving the Virtual Memory Abstraction : the author work aims at maintaining the virtual memory abstraction throughout a set of various hardware implementation. [thesis]
• PDFx : really cool tool that allows you to extract all the reference and metadata and download them!!

[Links of the Day] 05/04/2017 : Large network resilience, Distributed Systems, Machine Learning & Bayesian reasoning

• Understanding resilience in large networks : Thesis looking into the analysis of robustness in large interconnected networks. It looks at how disturbance propagates through nodes in networks and the characteristics that help these network to handle such shock and recuperate from it. 
• Notes on Theory of Distributed Systems :  Yale distributed system course note. 
• Bayesian Reasoning and Machine Learning : Book that does an excellent job at teaching machine learning that doesn't assume an extended background in undergraduate calculus and linear algebra of the reader.

[Links of the day] 31/10/2016 : AWS open guide, uncertainty in deep learning, Hacking Google interview

• Amazon Web Services Open Guide : This is THE practical guide for anybody that use AWS. Really well constructed and easy to use guide for the majority of AWS services out there. 
• Uncertainty in Deep Learning : Thesis looking into the probabilistic aspect of Bayesian network and how not everything is black and white in deep learning land. It seems that we need to  see an emergence of probabilistic programmation + deep learning hybrid in order to handle the new world of uncertainty that is opening up with the progress of AI research. [PhD Thesis]
• Google Interview University : program to help you beat the google technical interview process. While this is excellent as a refresh course in basic computer science I would also recommend MIT course : hacking a google interview. When interview by google i almost got a carbon copy of the question in the MIT course.. Sadly the google interview process is, lets say, "abysmal" for senior people. They want you to go through this marathon of interview without providing you an idea of what you are applying or other informations.. 

Links of the day 02 - 10 - 2014

Today's links 02/10/2014: Binary Analysis Platform, scheduling , Thesis

• BAP : Binary Analysis Platform - from Carnegie Mellon University. Translate binary to assembly to higher level language while retaining the explicit behavior of assembly code
• Parallel scheduling of task trees with limited memory : scheduling process in a resource constrained environment. 
• The Thesis bible : for those brave enough to do a PhD ...