title: Toward Large-Scale Continuous EDA: A Random Matrix Theory Perspective

authors: Kabán, Ata

subjects: Science & Technology

abstract: Estimations of distribution algorithms (EDAs) are a major branch of evolutionary algorithms (EA) with some unique advantages in principle. They are able to take advantage of correlation structure to drive the search more efficiently, and they are able to provide insights about the structure of the search space. However, model building in high dimensions is extremely challenging, and as a result existing EDAs may become less attractive in large-scale problems because of the associated large computational requirements. 
Large-scale continuous global optimisation is key to many modern-day real-world problems. Scaling up EAs to large-scale problems has become one of the biggest challenges of the field. 
This paper pins down some fundamental roots of the problem and makes a start at developing a new and generic framework to yield effective and efficient EDA-type algorithms for large-scale continuous global optimisation problems. Our concept is to introduce an ensemble of random projections to low dimensions of the set of fittest search points as a basis for developing a new and generic divide-and-conquer methodology. Our ideas are rooted in the theory of random projections developed in theoretical computer science, and in developing and analysing our framework we exploit some recent results in nonasymptotic random matrix theory.
MATLAB code is available from http://www.cs.bham.ac.uk/∼axk/rpm.zip

text: Toward Large-Scale Continuous EDA: A Random Matrix Theory Perspective Estimations of distribution algorithms (EDAs) are a major branch of evolutionary algorithms (EA) with some unique advantages in principle. They are able to take advantage of correlation structure to drive the search more efficiently, and they are able to provide insights about the structure of the search space. However, model building in high dimensions is extremely challenging, and as a result existing EDAs may become less attractive in large-scale problems because of the associated large computational requirements. 
Large-scale continuous global optimisation is key to many modern-day real-world problems. Scaling up EAs to large-scale problems has become one of the biggest challenges of the field. 
This paper pins down some fundamental roots of the problem and makes a start at developing a new and generic framework to yield effective and efficient EDA-type algorithms for large-scale continuous global optimisation problems. Our concept is to introduce an ensemble of random projections to low dimensions of the set of fittest search points as a basis for developing a new and generic divide-and-conquer methodology. Our ideas are rooted in the theory of random projections developed in theoretical computer science, and in developing and analysing our framework we exploit some recent results in nonasymptotic random matrix theory.
MATLAB code is available from http://www.cs.bham.ac.uk/∼axk/rpm.zip

year: 2016

title: Learning to extract geographic information from internet router hostnames

authors: Luckie, Matthew John

subjects: computer science

abstract: Geolocating Internet routers is a long-standing and notoriously difficult challenge, and current solutions lack the accuracy and adaptability to yield reliable results. We revisit this problem, designing a solution capable of accurately and comprehensively extracting geographic information that network operators embed into router interface hostnames. We train our system using dictionaries that map geographic codes to known locations, and constrain inferences with delay measurements conducted from a distributed set of vantage points. While most operators use known geographic codes, some devise their own mnemonic codes for locations, which our system also extracts and interprets. We evaluate our system on Internet-wide topology datasets, automatically learning regular expressions (regexes) for 1023 different domain suffixes with IPv4 routers, and 241 different domain suffixes with IPv6 routers. We received ground truth from operators of 13 domain suffixes, all of whom confirmed the correctness of our learned regexes, and that our system correctly interpreted 78.6% of the custom geographic codes. For these 13 suffixes, our solution more accurately extracts and interprets geographic information than the previous state-of-the-art, correctly geolocating 94.0% of router hostnames with a geohint compared to DRoP (56.6%) and HLOC (73.1%). This work advances the ability of researchers and network operators to characterize the location of critical Internet infrastructure, a foundational building block of network performance, security, and resilience analysis. We release the source code of our system and our inferred regexes.

text: Learning to extract geographic information from internet router hostnames Geolocating Internet routers is a long-standing and notoriously difficult challenge, and current solutions lack the accuracy and adaptability to yield reliable results. We revisit this problem, designing a solution capable of accurately and comprehensively extracting geographic information that network operators embed into router interface hostnames. We train our system using dictionaries that map geographic codes to known locations, and constrain inferences with delay measurements conducted from a distributed set of vantage points. While most operators use known geographic codes, some devise their own mnemonic codes for locations, which our system also extracts and interprets. We evaluate our system on Internet-wide topology datasets, automatically learning regular expressions (regexes) for 1023 different domain suffixes with IPv4 routers, and 241 different domain suffixes with IPv6 routers. We received ground truth from operators of 13 domain suffixes, all of whom confirmed the correctness of our learned regexes, and that our system correctly interpreted 78.6% of the custom geographic codes. For these 13 suffixes, our solution more accurately extracts and interprets geographic information than the previous state-of-the-art, correctly geolocating 94.0% of router hostnames with a geohint compared to DRoP (56.6%) and HLOC (73.1%). This work advances the ability of researchers and network operators to characterize the location of critical Internet infrastructure, a foundational building block of network performance, security, and resilience analysis. We release the source code of our system and our inferred regexes.

year: 2021

title: Waitangi Soda Springs alum dosing – bioavailability of aluminium 2013

authors: Ling, Nicholas

abstract: Continuous alum dosing of the Waitangi Soda Springs at Lake Rotoehu commenced in 2010 to reduce inflows of dissolved reactive phosphorus to Lake Rotoehu. Analyses of bioaccumulation in the tissues of kōura and goldfish from Lake Rotoehu were undertaken on animals collected in September 2013 to determine the bioavailability and bioaccumulation of aluminium. Comparative analyses were undertaken on kōura and goldfish collected from Lakes Rotorua and Rotoiti. Lake Rotoehu kōura and goldfish showed slightly elevated aluminium concentrations in the gills indicating enhanced exposure to aluminium but little evidence for significant bioaccumulation in tissues.

text: Waitangi Soda Springs alum dosing – bioavailability of aluminium 2013 Continuous alum dosing of the Waitangi Soda Springs at Lake Rotoehu commenced in 2010 to reduce inflows of dissolved reactive phosphorus to Lake Rotoehu. Analyses of bioaccumulation in the tissues of kōura and goldfish from Lake Rotoehu were undertaken on animals collected in September 2013 to determine the bioavailability and bioaccumulation of aluminium. Comparative analyses were undertaken on kōura and goldfish collected from Lakes Rotorua and Rotoiti. Lake Rotoehu kōura and goldfish showed slightly elevated aluminium concentrations in the gills indicating enhanced exposure to aluminium but little evidence for significant bioaccumulation in tissues.

year: 2015