Sharing vs. eminent domain

Controversy over the public taking of land through eminent domain intensified after the Supreme Court backed a 2000 New London taking. In contrast, an approach practiced abroad can help all stakeholders share in the benefits of economic development projects.Eminent domain

Divide and Fuse: A Re-ranking Approach for Person Re-identification

As re-ranking is a necessary procedure to boost person re-identification (re-ID) performance on large-scale datasets, the diversity of feature becomes crucial to person reID for its importance both on designing pedestrian descriptions and re-ranking based on feature fusion. However, in many circumstances, only one type of pedestrian feature is available. In this paper, we propose a "Divide and use" re-ranking framework for person re-ID. It exploits the diversity from different parts of a high-dimensional feature vector for fusion-based re-ranking, while no other features are accessible. Specifically, given an image, the extracted feature is divided into sub-features. Then the contextual information of each sub-feature is iteratively encoded into a new feature. Finally, the new features from the same image are fused into one vector for re-ranking. Experimental results on two person re-ID benchmarks demonstrate the effectiveness of the proposed framework. Especially, our method outperforms the state-of-the-art on the Market-1501 dataset.Comment: Accepted by BMVC201

Sketch-a-Net that Beats Humans

We propose a multi-scale multi-channel deep neural network framework that, for the first time, yields sketch recognition performance surpassing that of humans. Our superior performance is a result of explicitly embedding the unique characteristics of sketches in our model: (i) a network architecture designed for sketch rather than natural photo statistics, (ii) a multi-channel generalisation that encodes sequential ordering in the sketching process, and (iii) a multi-scale network ensemble with joint Bayesian fusion that accounts for the different levels of abstraction exhibited in free-hand sketches. We show that state-of-the-art deep networks specifically engineered for photos of natural objects fail to perform well on sketch recognition, regardless whether they are trained using photo or sketch. Our network on the other hand not only delivers the best performance on the largest human sketch dataset to date, but also is small in size making efficient training possible using just CPUs.Comment: Accepted to BMVC 2015 (oral

Audrey Yu, Oboe Performance

Fantasia No. 7 / G.P. Telemann; Sonata for Oboe and Piano / F. Poulenc; Oboe Concerto in C Major, RV 447 / A. Vivaldi; Pas de deux / A. Y

The Peter Humphrey/Yu Yingzeng Case and Business Intelligence in China

The case of Peter Humphrey and Yu Yingzeng, convicted in China on August 2014 on charges of unlawful acquisition of citizens’ personal information, raises important issues about Chinese law. A narrow but important issue is how Chinese law draws the line between lawful and unlawful acquisition of information, a practice routinely carried out by businesses and individuals. This article examines the trial transcript and judgment in the Humphrey/Yu case and finds that it sheds regrettably little light on what remains a murky question. A broader issue is whether the Chinese legal system can be counted on to operate in a fair and impartial manner. This article presents the results of a study of all reported cases in Shanghai (ninety-two cases) involving the same provision of the Criminal Law that was the basis of the Humphrey/Yu conviction. It finds that the Humphrey/Yu sentences are outliers relative to other cases with comparable facts. In particular, Humphrey’s sentence of 30 months’ imprisonment was by far the heaviest sentence ever meted out by Shanghai courts, even though the circumstances seem conspicuously less serious than those of many other cases where lesser sentences were imposed

A Deep Primal-Dual Network for Guided Depth Super-Resolution

In this paper we present a novel method to increase the spatial resolution of depth images. We combine a deep fully convolutional network with a non-local variational method in a deep primal-dual network. The joint network computes a noise-free, high-resolution estimate from a noisy, low-resolution input depth map. Additionally, a high-resolution intensity image is used to guide the reconstruction in the network. By unrolling the optimization steps of a first-order primal-dual algorithm and formulating it as a network, we can train our joint method end-to-end. This not only enables us to learn the weights of the fully convolutional network, but also to optimize all parameters of the variational method and its optimization procedure. The training of such a deep network requires a large dataset for supervision. Therefore, we generate high-quality depth maps and corresponding color images with a physically based renderer. In an exhaustive evaluation we show that our method outperforms the state-of-the-art on multiple benchmarks.Comment: BMVC 201

Solving Visual Madlibs with Multiple Cues

This paper focuses on answering fill-in-the-blank style multiple choice questions from the Visual Madlibs dataset. Previous approaches to Visual Question Answering (VQA) have mainly used generic image features from networks trained on the ImageNet dataset, despite the wide scope of questions. In contrast, our approach employs features derived from networks trained for specialized tasks of scene classification, person activity prediction, and person and object attribute prediction. We also present a method for selecting sub-regions of an image that are relevant for evaluating the appropriateness of a putative answer. Visual features are computed both from the whole image and from local regions, while sentences are mapped to a common space using a simple normalized canonical correlation analysis (CCA) model. Our results show a significant improvement over the previous state of the art, and indicate that answering different question types benefits from examining a variety of image cues and carefully choosing informative image sub-regions

Quantitative KK-theory for SQpSQ_p-algebras

Quantitative (or controlled) $K$-theory for $C^*$-algebras was introduced by Guoliang Yu in his work on the Novikov conjecture for groups with finite asymptotic dimension, and was later expanded into a general theory, with further applications, by Yu together with Hervé Oyono-Oyono. Motivated by investigations of the $L_p$ Baum-Connes conjecture, we will describe an analogous framework of quantitative $K$-theory that applies to algebras of bounded linear operators on subquotients of $L_p$ spaces