Spatial matter density mapping of the STAGES Abell A901/2 supercluster field with 3D lensing

We present weak lensing data from the Hubble Space Telescope(HST)/Space Telescope A901/902 Galaxy Evolution Survey (STAGES) survey to study the three-dimensional spatial distribution of matter and galaxies in the Abell 901/902 supercluster complex. Our method improves over the existing 3D lensing mapping techniques by calibrating and removing redshift bias and accounting for the effects of the radial elongation of 3D structures. We also include the first detailed noise analysis of a 3D lensing map, showing that even with deep HST-quality data, only the most massive structures, for example M200≳ 1015M⊙h-1 at z∼ 0.8, can be resolved in 3D with any reasonable redshift accuracy (Δz≈ 0.15). We compare the lensing map to the stellar mass distribution and find luminous counterparts for all mass peaks detected with a peak significance >3σ. We see structures in and behind the z= 0.165 foreground supercluster, finding structure directly behind the A901b cluster at z∼ 0.6 and also behind the south-west (SW) group at z∼ 0.7. This 3D structure viewed in projection has no significant impact on recent mass estimates of A901b or the SW group components SWa and SWb. © 2011 The Authors Monthly Notices of the Royal Astronomical Society © 2011 RAS

Natural language and the genetic code: from the semiotic analogy to biolinguistics

[Abstract] With the discovery of the DNA structure (Watson and Crick, 1953), the idea of DNA as a linguistic code arose (Monod, 1970). Many researchers have considered DNA as a language, pointing out the semiotic parallelism between genetic code and natural language. This idea had been discussed, almost dismissed and somehow accepted. This paper does not claim that the genetic code is a linguistic structure, but it highlights several important semiotic analogies between DNA and verbal language. Genetic code and natural language share a number of units, structures and operations. The syntactic and semantic parallelisms between those codes should lead to a methodological exchange between biology, linguistics and semiotics. During the 20th century, biology has become a pilot science, so that many disciplines have formulated their theories under models taken from biology. Computer science has become almost a bioinspired field thanks to the great development of natural computing and DNA computing. Biology and semiotics are two different sciences challenged by the same common goal of deciphering the codes of the nature. Linguistics could become another «bio-inspired» science by taking advantage of the structural and «semantic» similarities between the genetic code and natural language. Biological methods coming from computer science can be very useful in the field of linguistics, since they provide flexible and intuitive tools for describing natural languages. In this way, we obtain a theoretical framework where biology, linguistics and computer science exchange methods and interact, thanks to the semiotic parallelism between the genetic code a natural language. The influence of the semiotics of the genetic code in linguistics is parallel to the need of achieving an implementable formal description of natural language. In this paper we present an overview of different bio-inspired methods — from theoretical computer science — that during the last years have been successfully applied to several linguistics issues, from syntax to pragmatics

Computation of Khun-Tucker triples in optimum design problems in the presence of parametric singularities

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/76242/1/AIAA-1994-4416-414.pd

On Degeneracy Issues in Multi-parametric Programming and Critical Region Exploration based Distributed Optimization in Smart Grid Operations

Improving renewable energy resource utilization efficiency is crucial to reducing carbon emissions, and multi-parametric programming has provided a systematic perspective in conducting analysis and optimization toward this goal in smart grid operations. This paper focuses on two aspects of interest related to multi-parametric linear/quadratic programming (mpLP/QP). First, we study degeneracy issues of mpLP/QP. A novel approach to deal with degeneracies is proposed to find all critical regions containing the given parameter. Our method leverages properties of the multi-parametric linear complementary problem, vertex searching technique, and complementary basis enumeration. Second, an improved critical region exploration (CRE) method to solve distributed LP/QP is proposed under a general mpLP/QP-based formulation. The improved CRE incorporates the proposed approach to handle degeneracies. A cutting plane update and an adaptive stepsize scheme are also integrated to accelerate convergence under different problem settings. The computational efficiency is verified on multi-area tie-line scheduling problems with various testing benchmarks and initial states

Optimization of integrated water and multiregenerator membrane systems

A thesis submitted in partial fulfilment of the requirements for the degree of Doctor of Philosophy (Chemical Engineering), September 2017Water and energy are key resources in the process industry. The water-energy nexus considers the interdependence of water and energy resources and their effect on the environment. The increasing awareness of environmental regulations has heightened the need for process integration techniques that are environmentally benign and economically feasible. Process integration techniques within water network synthesis require a holistic approach for the sustainable use of water through reuse and recycle and regeneration reuse and recycle. Conventional methods for water minimisation through water network synthesis often use the “black-box” approach to represent the performance of the regenerators. The degree of contaminant removal and cost of regeneration are represented by linear functions. This, therefore, leads to suboptimal operating conditions and inaccurate cost representation of the regeneration units. This work proposes a robust water network superstructure optimisation approach for the synthesis of a multi-regenerator network for the simultaneous minimisation of water and energy. Two types of membrane regenerators are considered for this work, namely, electrodialysis and reverse osmosis. Detailed models of the regeneration units are embedded into the water network superstructure optimisation model to simultaneously minimise water, energy, operating and capital costs. The presence of continuous and integer variables, as well as nonlinear constraints renders the problem a mixed integer nonlinear program (MINLP). The developed model is applied to two illustrative examples involving a single contaminant and multiple contaminants and one industrial case study of a power utility plant involving a single contaminant to demonstrate its applicability. The application of the model to the single contaminant illustrative example lead to a 43.7% freshwater reduction, 50.9% decrease in wastewater generation and 46% savings in total water network cost. The multi-contaminant illustrative example showed 11.6% freshwater savings, 15.3% wastewater reduction, 57.3% savings in regeneration and energy cost compared to the water network superstructure with “black-box” regeneration model. The industrial case study showed a savings of up to 18.7% freshwater consumption, 82.4% wastewater reduction and up to 17% savings on total water network cost.XL201

Mixed-integer Nonlinear Optimization: a hatchery for modern mathematics

The second MFO Oberwolfach Workshop on Mixed-Integer Nonlinear Programming (MINLP) took place between 2nd and 8th June 2019. MINLP refers to one of the hardest Mathematical Programming (MP) problem classes, involving both nonlinear functions as well as continuous and integer decision variables. MP is a formal language for describing optimization problems, and is traditionally part of Operations Research (OR), which is itself at the intersection of mathematics, computer science, engineering and econometrics. The scientific program has covered the three announced areas (hierarchies of approximation, mixed-integer nonlinear optimal control, and dealing with uncertainties) with a variety of tutorials, talks, short research announcements, and a special "open problems'' session

Neural approaches to sequence labeling for information extraction

Een belangrijk aspect binnen artificiële intelligentie (AI) is het interpreteren van menselijke taal uitgedrukt in tekstuele (geschreven) vorm: natural Language processing (NLP) is belangrijk gezien tekstuele informatie nuttig is voor veel toepassingen. Toch is het verstaan ervan (zogenaamde natural Language understanding, (NLU) een uitdaging, gezien de ongestructureerde vorm van tekst, waarvan de betekenis vaak dubbelzinnig en contextafhankelijk is. In dit proefschrift introduceren we oplossingen voor tekortkomingen van gerelateerd werk bij het behandelen van fundamentele taken in natuurlijke taalverwerking, zoals named entity recognition (i.e. het identificeren van de entiteiten die in een zin voorkomen) en relatie-extractie (het identificeren van relaties tussen entiteiten). Vertrekkend van een specifiek probleem (met name het identificeren van de structuur van een huis aan de hand van een tekstueel zoekertje), bouwen we stapsgewijs een complete (geautomatiseerde) oplossing voor de bovengenoemde taken, op basis van neutrale netwerkarchitecturen. Onze oplossingen zijn algemeen toepasbaar op verschillende toepassingsdomeinen en talen. We beschouwen daarnaast ook de taak van het identificeren van relevante gebeurtenissen tijdens een evenement (bv. een doelpunt tijdens een voetbalwedstrijd), in informatiestromen op Twitter. Meer bepaald formuleren we dit probleem als het labelen van woord sequenties (vergelijkbaar met named entity recognition), waarbij we de chronologische relatie tussen opeenvolgende tweets benutten