Deep multiband surface photometry on star forming galaxies: II. A volume limited sample of 21 emission lines galaxies

We present deep surface photometry of a volume--limited sample of 21 UM emission line galaxies in broadband optical UBVRI and near infra-red (NIR) HKs filters. The sample comprises 19 blue compact galaxies (BCGs) and two spirals. For some targets the exposure times are the deepest to date. For the BCG UM462 we observe a previously undetected second disk component beyond a surface brightness level of mu_B=26 mag arcsec^{-2}. This is a true low surface brightness component with central surface brightness mu_0=24.1 mag arcsec^{-2} and scale length h_r=1.5 kpc. All BCGs are dwarfs, with M_B>=-18, and very compact, with an average scale length of h_r~1 kpc. We separate the burst and host populations for each galaxy and compare them to stellar evolutionary models with and without nebular emission contribution. We also measure the A_{180} asymmetry in all filters and detect a shift from optical to NIR in the average asymmetry of the sample. This shift seems to be correlated with the morphological class of the BCGs. Using the color-asymmetry relation, we identify five BCGs in the sample as mergers, which is confirmed by their morphological class. Though clearly separated from normal galaxies in the concentration-asymmetry parameter space, we find that it is not possible to distinguish luminous starbursting BCGs from the merely star forming low luminosity BCGs.Comment: 48 pages, 39 figures, submitte

Development of Computational Techniques for Regulatory DNA Motif Identification Based on Big Biological Data

Accurate regulatory DNA motif (or motif) identification plays a fundamental role in the elucidation of transcriptional regulatory mechanisms in a cell and can strongly support the regulatory network construction for both prokaryotic and eukaryotic organisms. Next-generation sequencing techniques generate a huge amount of biological data for motif identification. Specifically, Chromatin Immunoprecipitation followed by high throughput DNA sequencing (ChIP-seq) enables researchers to identify motifs on a genome scale. Recently, technological improvements have allowed for DNA structural information to be obtained in a high-throughput manner, which can provide four DNA shape features. The DNA shape has been found as a complementary factor to genomic sequences in terms of transcription factor (TF)-DNA binding specificity prediction based on traditional machine learning models. Recent studies have demonstrated that deep learning (DL), especially the convolutional neural network (CNN), enables identification of motifs from DNA sequence directly. Although numerous algorithms and tools have been proposed and developed in this field, (1) the lack of intuitive and integrative web servers impedes the progress of making effective use of emerging algorithms and tools; (2) DNA shape has not been integrated with DL; and (3) existing DL models still suffer high false positive and false negative issues in motif identification. This thesis focuses on developing an integrated web server for motif identification based on DNA sequences either from users or built-in databases. This web server allows further motif-related analysis and Cytoscape-like network interpretation and visualization. We then proposed a DL framework for both sequence and shape motif identification from ChIP-seq data using a binomial distribution strategy. This framework can accept as input the different combinations of DNA sequence and DNA shape. Finally, we developed a gated convolutional neural network (GCNN) for capturing motif dependencies among long DNA sequences. Results show that our developed web server enables providing comprehensive motif analysis functionalities compared with existing web servers. The DL framework can identify motifs using an optimized threshold and disclose the strong predictive power of DNA shape in TF-DNA binding specificity. The identified sequence and shape motifs can contribute to TF-DNA binding mechanism interpretation. Additionally, GCNN can improve TF-DNA binding specificity prediction than CNN on most of the datasets

Cultural investigation on typography in branding in the United States and in Brazil.

This cultural thesis is set to investigate the use of typography in branding, and how the same is influenced by cultural aspects, specifically in the United States and Brazil. The contrasting experience I have had as a student of graphic design in these two countries led me to discover the influence culture has in dictating the typography design use in branding. Typography, branding and cultural influences have been significantly researched in the past, but historically as three separate subjects, without focusing on the importance of their association and how they influence one another cross-culturally. Since the impact of graphic design and the power of typography is important to branding, global brands need to adapt and be relatable to multiple cultures. The aim of this thesis is to fill this existing gap between these three matters and show the importance of the connection between typography, branding, and culture, specifically across Brazil and the United States

Investigating changing work and economic cultures through the lens of youth employment : a case study from a psychosocial perspective in Italy

Changes in the forms and cultural meanings of work have gone deep during the last decades, with the transient nature of work becoming the norm rather than the exception. This is impacting particularly on youth employment, as Italy’s case epitomizes. Based on interview and focus group data, our study provides a multidimensional model to read and map the multiple tensions young people experience, at an emotional level, on entering today’s corporations. Our findings show, on the one hand, that young professionals’ expectation of work as a place of social learning and exchange clashes with the corporate focus on assimilating young people into target-oriented environments. On the other hand, both in younger and older workers, we found the experience of labour relationships that struggle to direct themselves towards a creative purpose and a developmental prospect, while tending to collapse emotionally inwards, in a fight for security

Vehicle make and model recognition for intelligent transportation monitoring and surveillance.

Vehicle Make and Model Recognition (VMMR) has evolved into a significant subject of study due to its importance in numerous Intelligent Transportation Systems (ITS), such as autonomous navigation, traffic analysis, traffic surveillance and security systems. A highly accurate and real-time VMMR system significantly reduces the overhead cost of resources otherwise required. The VMMR problem is a multi-class classification task with a peculiar set of issues and challenges like multiplicity, inter- and intra-make ambiguity among various vehicles makes and models, which need to be solved in an efficient and reliable manner to achieve a highly robust VMMR system. In this dissertation, facing the growing importance of make and model recognition of vehicles, we present a VMMR system that provides very high accuracy rates and is robust to several challenges. We demonstrate that the VMMR problem can be addressed by locating discriminative parts where the most significant appearance variations occur in each category, and learning expressive appearance descriptors. Given these insights, we consider two data driven frameworks: a Multiple-Instance Learning-based (MIL) system using hand-crafted features and an extended application of deep neural networks using MIL. Our approach requires only image level class labels, and the discriminative parts of each target class are selected in a fully unsupervised manner without any use of part annotations or segmentation masks, which may be costly to obtain. This advantage makes our system more intelligent, scalable, and applicable to other fine-grained recognition tasks. We constructed a dataset with 291,752 images representing 9,170 different vehicles to validate and evaluate our approach. Experimental results demonstrate that the localization of parts and distinguishing their discriminative powers for categorization improve the performance of fine-grained categorization. Extensive experiments conducted using our approaches yield superior results for images that were occluded, under low illumination, partial camera views, or even non-frontal views, available in our real-world VMMR dataset. The approaches presented herewith provide a highly accurate VMMR system for rea-ltime applications in realistic environments.\\ We also validate our system with a significant application of VMMR to ITS that involves automated vehicular surveillance. We show that our application can provide law inforcement agencies with efficient tools to search for a specific vehicle type, make, or model, and to track the path of a given vehicle using the position of multiple cameras

Cross-Country Ethical Dilemmas in Business: A Descriptive Framework

As businesses span the globe, multinational and translational companies conduct their business operations in foreign settings, especially in developing countries and in countries in transition from Communist regimes. This poses new challenges to expatriate managers and to home-based staff in charge of foreign affiliates. They are called on to determine the right versus the wrong, the good versus the bad over international business transactions, negotiations, advertisement and supply chain management taking place in foreign settings. As most of the time, businessmen lack a certain degree of cultural awareness and knowledge, managing ethical diversity over cross-country business transactions ends up to be a major challenge for business people. This paper’s aim is to provide an introductory sketch on the cross-country issues facing international business, through detailed description of their level of disclosure (Political, Corporate, Internal) diverse areas and connected situations. The pros and cons of the traditional paradigms used by business people in dealing with such circumstances (Universalism and Relativism) will be weighed. In addition examples of “irresponsible business practices” resulting from cultural misunderstandings, ignorance and lack of contextualization on the behalf of business people will be provided.Business ethics, Cross-country ethical dilemmas, Corporate Social responsibility, Diversity

A novel shape descriptor based on salient keypoints detection for binary image matching and retrieval

We introduce a shape descriptor that extracts keypoints from binary images and automatically detects the salient ones among them. The proposed descriptor operates as follows: First, the contours of the image are detected and an image transformation is used to generate background information. Next, pixels of the transformed image that have specific characteristics in their local areas are used to extract keypoints. Afterwards, the most salient keypoints are automatically detected by filtering out redundant and sensitive ones. Finally, a feature vector is calculated for each keypoint by using the distribution of contour points in its local area. The proposed descriptor is evaluated using public datasets of silhouette images, handwritten math expressions, hand-drawn diagram sketches, and noisy scanned logos. Experimental results show that the proposed descriptor compares strongly against state of the art methods, and that it is reliable when applied on challenging images such as fluctuated handwriting and noisy scanned images. Furthermore, we integrate our descripto