Multiple target detection using Bayesian learning

n this paper, we study multiple target detection using Bayesian learning. The main aim of the paper is to present a computationally efficient way to compute the belief map update exactly and efficiently using results from the theory of symmetric polynomials. In order to illustrate the idea, we consider a simple search scenario with multiple search agents and an unknown but fixed number of stationary targets in a given region that is divided into cells. To estimate the number of targets, a belief map for number of targets is also propagated. The belief map is updated using Bayes' theorem and an optimal reassignment of vehicles based on the values of the current belief map is adopted. Exact computation of the belief map update is combinatorial in nature and often an approximation is needed. We show that the Bayesian update can be exactly computed in an efficient manner using Newton's identities and the detection history in each cell

Resisting Borders, Resisting Control Examining the multiplicity of identities in A Map of Home and The Girl in The Tangerine Scarf

In this project I examine identities as they are expressed through the use of language in the novels A Map of Home by Randa Jarrar and The Girl in the Tangerine Scarf by Mohja Kahf. Both novels are coming-of-age narratives of two Arab and Muslim-American female protagonists that depict their exploration of identity as they undergo experiences of war, migration, displacement, and racism in their respective contexts. I explore the protagonists’ negotiation of identity in the face of familial and societal pressure to conform to clearly demarcated categorizations of identity, arguing that the protagonists recognize clear borders between identities as a form of control. The use of language in both texts that combines multiple languages, dialects, and cultural and religious registers thus acts as a form of resistance to a fixed identity, to physical and figurative borders and to control, as the protagonists construct through their language a “third space,” a space in which “home” and “identity” can be multiple and hybrid and where such hybridity opens up possibilities of negotiating and recreating identities beyond fixed borders

Not Minding the Gap: Intercultural Shakespeare in Britain

The article takes issue with the perceived space/gap between the multiple identities of mixed-heritage groups, as most of these people often pick and choose elements from all of their identities and amalgamate them into a cross-cultural whole. In recent years, such mixed-heritage groups in the U.K. have increasingly found cultural expression in Shakespeare. Focusing specifically on a number of recent Shakespearean productions, by what I term Brasian (my preferred term for British-Asians as it suggests a more fused identity) theatre companies, the article demonstrates how these productions employ hybrid aesthetic styles, stories, and theatre forms to present a layered Braisian identity. It argues that these productions not only provide a nuanced understanding of the intercultural map of Britain but are also a rich breeding ground for innovative Shakespeare productions in the U.K

Deep Learning Face Attributes in the Wild

Predicting face attributes in the wild is challenging due to complex face variations. We propose a novel deep learning framework for attribute prediction in the wild. It cascades two CNNs, LNet and ANet, which are fine-tuned jointly with attribute tags, but pre-trained differently. LNet is pre-trained by massive general object categories for face localization, while ANet is pre-trained by massive face identities for attribute prediction. This framework not only outperforms the state-of-the-art with a large margin, but also reveals valuable facts on learning face representation. (1) It shows how the performances of face localization (LNet) and attribute prediction (ANet) can be improved by different pre-training strategies. (2) It reveals that although the filters of LNet are fine-tuned only with image-level attribute tags, their response maps over entire images have strong indication of face locations. This fact enables training LNet for face localization with only image-level annotations, but without face bounding boxes or landmarks, which are required by all attribute recognition works. (3) It also demonstrates that the high-level hidden neurons of ANet automatically discover semantic concepts after pre-training with massive face identities, and such concepts are significantly enriched after fine-tuning with attribute tags. Each attribute can be well explained with a sparse linear combination of these concepts.Comment: To appear in International Conference on Computer Vision (ICCV) 201