New developmental stages for common marmosets (Callithrix jacchus) using mass and age variables obtained by K-means algorithm and self-organizing maps (SOM)

LEÃO, Adriano de Castro; DÓRIA NETO, Adrião Duarte; SOUSA, Maria Bernardete Cordeiro de. New developmental stages for common marmosets (Callithrix jacchus) using mass and age variables obtained by K-means algorithm and self-organizing maps (SOM). Computers in Biology and Medicine, v. 39, p. 853-859, 2009

Methods, Models & Systems for Dealing with Some Business, Management & Engineering Problems

This keynote presentation will report some of our research work and experience on the development and applications of relevant methods, models, systems and simulation techniques in support of different types and various levels of decision making for business, management and engineering. In particular, the following topics will be covered. Modelling, multi-agent-based simulation and analysis of the allocation management of carbon dioxide emission permits in China (Nanfeng Liu & Shuliang Li Agent-based simulation of the dynamic evolution of enterprise carbon assets (Yin Zeng & Shuliang Li) A framework & system for extracting and representing project knowledge contexts using topic models and dynamic knowledge maps: a big data perspective (Jin Xu, Zheng Li, Shuliang Li & Yanyan Zhang) Open innovation: intelligent model, social media & complex adaptive system simulation (Shuliang Li & Jim Zheng Li) A framework, model and software prototype for modelling and simulation for deshopping behaviour and how companies respond (Shawkat Rahman & Shuliang Li) Integrating multiple agents, simulation, knowledge bases and fuzzy logic for international marketing decision making (Shuliang Li & Jim Zheng Li) A Web-based hybrid intelligent system for combined conventional, digital, mobile, social media and mobile marketing strategy formulation (Shuliang Li & Jim Zheng Li) A hybrid intelligent model for Web & social media dynamics, and evolutionary and adaptive branding (Shuliang Li) A hybrid paradigm for modelling, simulation and analysis of brand virality in social media (Shuliang Li & Jim Zheng Li) Network configuration management: attack paradigms and architectures for computer network survivability (Tero Karvinen & Shuliang Li)

LDA-TM : A two-step approach to twitter topic data clustering

The Twitter System is the biggest social network in the world, and everyday millions of tweets are posted and talked about, expressing various views and opinions. A large variety of research activities have been conducted to study how the opinions can be clustered and analyzed, so that some tendencies can be uncovered. Due to the inherent weaknesses of the tweets - very short texts and very informal styles of writing - it is rather hard to make an investigation of tweet data analysis giving results with good performance and accuracy. In this paper, we intend to attack the problem from another aspect - using a two-layer structure to analyze the twitter data: LDA with topic map modelling. The experimental results demonstrate that this approach shows a progress in twitter data analysis. However, more experiments with this method are expected in order to ensure that the accurate analytic results can be maintained.

Investigating the AGN-starburst connection in a nearby Seyfert galaxy with ALMA and multiwavelength data

The astrophysical context in which this thesis project lies concerns the comprehension of the mutual interaction between the accretion onto a Super Massive Black Hole (SMBH) and the Star Formation (SF), that take place in the host galaxy. This is one of the key topic of the modern extragalactic astrophysical research. Indeed, it is widely accepted that to understand the physics of a galaxy, the contribution of a possible central AGN must be taken into account. The aim of this thesis is the study of the physical processes of the nearby Seyfert galaxy NGC 34. This source was selected because of the wide collection of multiwavelength data available in the literature. In addition, recently, it has been observed with the Atacama Large Submillimeter/Millimeter Array (ALMA) in Band 9. This project is divided in two main parts: first of all, we reduced and analyzed the ALMA data, obtaining the continuum and CO(6-5) maps; then, we looked for a coherent explaination of NGC 34 physical characteristics. In particular, we focused on the ISM physics, in order to understand its properties in terms of density, chemical composition and dominant radiation field (SF or accretion). This work has been done through the analysis of the spectral distribution of several CO transitions as a function of the transition number (CO SLED), obtained joining the CO(6-5) line with other transitions available in the literature. More precisely, the observed CO SLED has been compared with ISM models, including Photo-Dissociation Regions (PDRs) and X-ray-Dominated Regions (XDRs). These models have been obtained through the state-of-the-art photoionization code CLOUDY. Along with the observed CO SLED, we have taken into account other physical properties of NGC 34, such as the Star Formation Rate (SFR), the gas mass and the X-ray luminosity.

From local scenarios to national maps: a participatory framework for envisioning the future of Tanzania

Tackling societal and environmental challenges requires new approaches that connect top-down global oversight with bottom-up subnational knowledge. We present a novel framework for participatory development of spatially explicit scenarios at national scale that model socioeconomic and environmental dynamics by reconciling local stakeholder perspectives and national spatial data. We illustrate results generated by this approach and evaluate its potential to contribute to a greater understanding of the relationship between development pathways and sustainability. Using the lens of land use and land cover changes, and engaging 240 stakeholders representing subnational (seven forest management zones) and the national level, we applied the framework to assess alternative development strategies in the Tanzania mainland to the year 2025, under either a business as usual or a green development scenario. In the business as usual scenario, no productivity gain is expected, cultivated land expands by ~ 2% per year (up to 88,808 km²), with large impacts on woodlands and wetlands. Despite legal protection, encroachment of natural forest occurs along reserve borders. Additional wood demand leads to degradation, i.e., loss of tree cover and biomass, up to 80,426 km² of wooded land. The alternative green economy scenario envisages decreasing degradation and deforestation with increasing productivity (+10%) and implementation of payment for ecosystem service schemes. In this scenario, cropland expands by 44,132 km² and the additional degradation is limited to 35,778 km². This scenario development framework captures perspectives and knowledge across a diverse range of stakeholders and regions. Although further effort is required to extend its applicability, improve users’ equity, and reduce costs the resulting spatial outputs can be used to inform national level planning and policy implementation associated with sustainable development, especially the REDD+ climate mitigation strategy.

Exploring social representations of adapting to climate change using topic modeling and Bayesian networks

When something unfamiliar emerges or when something familiar does something unexpected people need to make sense of what is emerging or going on in order to act. Social representations theory suggests how individuals and society make sense of the unfamiliar and hence how the resultant social representations (SRs) cognitively, emotionally, and actively orient people and enable communication. SRs are social constructions that emerge through individual and collective engagement with media and with everyday conversations among people. Recent developments in text analysis techniques, and in particular topic modeling, provide a potentially powerful analytical method to examine the structure and content of SRs using large samples of narrative or text. In this paper I describe the methods and results of applying topic modeling to 660 micronarratives collected from Australian academics / researchers, government employees, and members of the public in 2010-2011. The narrative fragments focused on adaptation to climate change (CC) and hence provide an example of Australian society making sense of an emerging and conflict ridden phenomena. The results of the topic modeling reflect elements of SRs of adaptation to CC that are consistent with findings in the literature as well as being reasonably robust predictors of classes of action in response to CC. Bayesian Network (BN) modeling was used to identify relationships among the topics (SR elements) and in particular to identify relationships among topics, sentiment, and action. Finally the resulting model and topic modeling results are used to highlight differences in the salience of SR elements among social groups. The approach of linking topic modeling and BN modeling offers a new and encouraging approach to analysis for ongoing research on SRs.

HaptOSM – Creating tactile maps for the blind and visually impaired

This paper describes the latest accomplishments on the current research that is based on the master’s thesis “Ein System zur Erstellung taktiler Karten für blinde und sehbehinderte Menschen” (German for “A system creating tactile maps for blind and visually impaired people”) (Hänßgen, 2012). The system consists of two parts. The first part is new software especially designed and developed for creating tactile maps addressing the needs of blind and visually impaired people on tactile information. The second is an embossing device based on a modified CNC (computer numerical control) router. By using OpenStreetMap-data, the developed system is capable of embossing tactile maps into Braille paper and writing film.

Recognition of Activities of Daily Living from Topic Model

Research in ubiquitous and pervasive technologies have made it possible to recognise activities of daily living through non-intrusive sensors. The data captured from these sensors are required to be classified using various machine learning or knowledge driven techniques to infer and recognise activities. The process of discovering the activities and activity-object patterns from the sensors tagged to objects as they are used is critical to recognising the activities. In this paper, we propose a topic model process of discovering activities and activity-object patterns from the interactions of low level state-change sensors. We also develop a recognition and segmentation algorithm to recognise activities and recognise activity boundaries. Experimental results we present validates our framework and shows it is comparable to existing approaches.

New developmental stages for common marmosets (Callithrix jacchus) using mass and age variables obtained by K-means algorithm and self-organizing maps (SOM)

LEÃO, Adriano de Castro; DÓRIA NETO, Adrião Duarte; SOUSA, Maria Bernardete Cordeiro de. New developmental stages for common marmosets (Callithrix jacchus) using mass and age variables obtained by K-means algorithm and self-organizing maps (SOM). Computers in Biology and Medicine, v. 39, p. 853-859, 2009

ANOSOV MAPS, POLYCYCLIC GROUPS AND HOMOLOGY

Many manifolds that do not admit Anosov diffeomorphisms are constructed. For example: the Cartesian product of the Klein bottle and a torus.