Deep Learning for Semantic Part Segmentation with High-Level Guidance

In this work we address the task of segmenting an object into its parts, or semantic part segmentation. We start by adapting a state-of-the-art semantic segmentation system to this task, and show that a combination of a fully-convolutional Deep CNN system coupled with Dense CRF labelling provides excellent results for a broad range of object categories. Still, this approach remains agnostic to high-level constraints between object parts. We introduce such prior information by means of the Restricted Boltzmann Machine, adapted to our task and train our model in an discriminative fashion, as a hidden CRF, demonstrating that prior information can yield additional improvements. We also investigate the performance of our approach ``in the wild'', without information concerning the objects' bounding boxes, using an object detector to guide a multi-scale segmentation scheme. We evaluate the performance of our approach on the Penn-Fudan and LFW datasets for the tasks of pedestrian parsing and face labelling respectively. We show superior performance with respect to competitive methods that have been extensively engineered on these benchmarks, as well as realistic qualitative results on part segmentation, even for occluded or deformable objects. We also provide quantitative and extensive qualitative results on three classes from the PASCAL Parts dataset. Finally, we show that our multi-scale segmentation scheme can boost accuracy, recovering segmentations for finer parts.Comment: 11 pages (including references), 3 figures, 2 table

Political budget cycles and reelection prospects in Greece's municipalities

This paper considers the presence of political budget cycles in Greece's municipalities. We construct a new dataset from primary sources and we find strong evidence of pre-electoral manipulation through increased expenditures and excessive borrowing. We use a dynamic panel data approach producing evidence of opportunistic behavior in local government finances. Our results are robust in the face of a series of controls including mayors running for reelection, their political alignment with the central government, and prolonged terms. Moreover, the results are robust to the exclusion of small sized municipalities and to the restriction of the time range of our investigation to the post-Maastricht period. We also consider whether opportunistic policies influence incumbents' reelection prospects finding that increased expenditures and election year opportunistic excesses are electorally rewarding. Our findings provide a characterization of opportunistic public finance management in Greek municipalities where electorally motivated budgetary decisions appear impervious to the various municipal reform attempts

Accumulation-based computing using phasechange memories with FET access devices

Copyright © 2015 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.Phase-change materials and devices have received much attention as a potential route to the realization of various types of unconventional computing paradigms. In this letter, we present non-von Neumann arithmetic processing that exploits the accumulative property of phase-change memory (PCM) cells. Using PCM cells with integrated FET access devices, we perform a detailed study of accumulation-based computation. We also demonstrate efficient factorization using PCM cells, a technique that could pave the way for massively parallelized computations.Engineering and Physical Sciences Research Council (EPSRC