Geometric Mining: Scaling Geometric Hashing to Large Datasets

It is known that relative feature location is important in representing objects, but assumptions that make learning tractable often simplify how structure is encoded e.g. spatial pooling or star models. For example, techniques such as spatial pyramid matching (SPM), in-conjunction with machine learning techniques perform well [13]. However, there are limitations to such spatial encoding schemes which discard important information about the layout of features. In contrast, we propose to use the object itself to choose the basis of the features in an object centric approach. In doing so we return to the early work of geometric hashing [18] but demonstrate how such approaches can be scaled-up to modern day object detection challenges in terms of both the number of examples and their variability. We apply a two stage process, initially filtering background features to localise the objects and then hashing the remaining pairwise features in an affine invariant model. During learning, we identify class-wise key feature predictors. We validate our detection and classification of objects on the PASCAL VOC'07 and' 11 [6] and CarDb [21] datasets and compare with state of the art detectors and classifiers. Importantly we demonstrate how structure in features can be efficiently identified and how its inclusion can increase performance. This feature centric learning technique allows us to localise objects even without object annotation during training and the resultant segmentation provides accurate state of the art object localization, without the need for annotations

The Regulation of Conflict Resources: Diamonds in Sierra Leone. Paper for the Transformation of War Economies Seminar, University of Plymouth 16-19 June 2005.

yesThe last few years have seen the emergence of a series of regulatory initiatives that have been developed, partly in response to the twin agendas of human security and strong states, but which represent a specific reaction to the political economies deemed to underpin contemporary civil conflicts ¿ most notably the way in which local and global markets in everything from diamonds to drugs have been exploited to fund often vicious civil conflicts, particularly in environments characterised by endemic corruption. This new body of local and global regulation, what might loosely be characterised as new laws and new codes to address the political economies of the new wars, include: UN embargoes on diamonds and timber being used to fund conflicts, the development of regimes such as the Kimberley certification system, and initiatives to ensure the transparent and effective use of natural resource revenues. Generally represented as a progressive response to the political economies that drive contemporary civil conflicts, these new initiatives have produced a set of formal and informal regulatory frameworks that are, in fact, profoundly asymmetric in their scope and application. Indeed, one of the defining features of these initiatives is not so much the impartial application of regulations to firms and corrupt elites but either their selective application or, alternatively, their selective relegation in favour of an emphasis on far weaker norms and voluntary codes. The aim of this paper then, is first, to examine the operation of the new codes and regulations in general and to demonstrate the problems in their implementation. Second, the paper will then go onto examine one specific innovation ¿ the Kimberley Certification Scheme designed to prevent the trade in conflict diamonds in order to demonstrate the asymmetries that exist in current regulatory mechanisms designed to introduce ethical markets. It will do this in particular by focussing on the impact of certification for the diamond sector in Sierra Leone. A key argument in this section will be that whilst this new regime for conflict diamonds aims to transform behaviour through transparency and policing, and whilst it appears to have had some success, it has not in fact transformed the conditions that gave rise to the illicit diamond trade in Sierra Leone prior to conflict. Along with the problems inherent in broader development policy on Sierra Leone this raises serious questions. In particular, whilst there may be little short-term risk of conflict, the planned departure of UNAMSIL, continued regional instability, persistent corruption and the failure to fundamentally transform the nature of the diamond market in Sierra Leone, all raise question marks regarding the nature (and indeed sustainability) of the peace that is being created

Interpretability and Explainability: A Machine Learning Zoo Mini-tour

In this review, we examine the problem of designing interpretable and explainable machine learning models. Interpretability and explainability lie at the core of many machine learning and statistical applications in medicine, economics, law, and natural sciences. Although interpretability and explainability have escaped a clear universal definition, many techniques motivated by these properties have been developed over the recent 30 years with the focus currently shifting towards deep learning methods. In this review, we emphasise the divide between interpretability and explainability and illustrate these two different research directions with concrete examples of the state-of-the-art. The review is intended for a general machine learning audience with interest in exploring the problems of interpretation and explanation beyond logistic regression or random forest variable importance. This work is not an exhaustive literature survey, but rather a primer focusing selectively on certain lines of research which the authors found interesting or informative

A Survey on Label-efficient Deep Image Segmentation: Bridging the Gap between Weak Supervision and Dense Prediction

The rapid development of deep learning has made a great progress in image segmentation, one of the fundamental tasks of computer vision. However, the current segmentation algorithms mostly rely on the availability of pixel-level annotations, which are often expensive, tedious, and laborious. To alleviate this burden, the past years have witnessed an increasing attention in building label-efficient, deep-learning-based image segmentation algorithms. This paper offers a comprehensive review on label-efficient image segmentation methods. To this end, we first develop a taxonomy to organize these methods according to the supervision provided by different types of weak labels (including no supervision, inexact supervision, incomplete supervision and inaccurate supervision) and supplemented by the types of segmentation problems (including semantic segmentation, instance segmentation and panoptic segmentation). Next, we summarize the existing label-efficient image segmentation methods from a unified perspective that discusses an important question: how to bridge the gap between weak supervision and dense prediction -- the current methods are mostly based on heuristic priors, such as cross-pixel similarity, cross-label constraint, cross-view consistency, and cross-image relation. Finally, we share our opinions about the future research directions for label-efficient deep image segmentation.Comment: Accepted to IEEE TPAM

Physics of three dimensional bosonic topological insulators: Surface Deconfined Criticality and Quantized Magnetoelectric Effect

We discuss physical properties of `integer' topological phases of bosons in D=3+1 dimensions, protected by internal symmetries like time reversal and/or charge conservation. These phases invoke interactions in a fundamental way but do not possess topological order and are bosonic analogs of free fermion topological insulators and superconductors. While a formal cohomology based classification of such states was recently discovered, their physical properties remain mysterious. Here we develop a field theoretic description of several of these states and show that they possess unusual surface states, which if gapped, must either break the underlying symmetry, or develop topological order. In the latter case, symmetries are implemented in a way that is forbidden in a strictly two dimensional theory. While this is the usual fate of the surface states, exotic gapless states can also be realized. For example, tuning parameters can naturally lead to a deconfined quantum critical point or, in other situations, a fully symmetric vortex metal phase. We discuss cases where the topological phases are characterized by quantized magnetoelectric response \theta, which, somewhat surprisingly, is an odd multiple of 2\pi. Two different surface theories are shown to capture these phenomena - the first is a nonlinear sigma model with a topological term. The second invokes vortices on the surface that transform under a projective representation of the symmetry group. A bulk field theory consistent with these properties is identified, which is a multicomponent `BF' theory supplemented, crucially, with a topological term. A possible topological phase characterized by the thermal analog of the magnetoelectric effect is also discussed.Comment: 25 pages+ 3 pages Appendices, 3 figures. Introduction rewritten for clarity, minor technical changes and additional details of surface topological order adde

Fine-Grained Linguistic Soft Constraints on Statistical Natural Language Processing Models

This dissertation focuses on effective combination of data-driven natural language processing (NLP) approaches with linguistic knowledge sources that are based on manual text annotation or word grouping according to semantic commonalities. I gainfully apply fine-grained linguistic soft constraints -- of syntactic or semantic nature -- on statistical NLP models, evaluated in end-to-end state-of-the-art statistical machine translation (SMT) systems. The introduction of semantic soft constraints involves intrinsic evaluation on word-pair similarity ranking tasks, extension from words to phrases, application in a novel distributional paraphrase generation technique, and an introduction of a generalized framework of which these soft semantic and syntactic constraints can be viewed as instances, and in which they can be potentially combined. Fine granularity is key in the successful combination of these soft constraints, in many cases. I show how to softly constrain SMT models by adding fine-grained weighted features, each preferring translation of only a specific syntactic constituent. Previous attempts using coarse-grained features yielded negative results. I also show how to softly constrain corpus-based semantic models of words (“distributional profiles”) to effectively create word-sense-aware models, by using semantic word grouping information found in a manually compiled thesaurus. Previous attempts, using hard constraints and resulting in aggregated, coarse-grained models, yielded lower gains. A novel paraphrase generation technique incorporating these soft semantic constraints is then also evaluated in a SMT system. This paraphrasing technique is based on the Distributional Hypothesis. The main advantage of this novel technique over current “pivoting” techniques for paraphrasing is the independence from parallel texts, which are a limited resource. The evaluation is done by augmenting translation models with paraphrase-based translation rules, where fine-grained scoring of paraphrase-based rules yields significantly higher gains. The model augmentation includes a novel semantic reinforcement component: In many cases there are alternative paths of generating a paraphrase-based translation rule. Each of these paths reinforces a dedicated score for the “goodness” of the new translation rule. This augmented score is then used as a soft constraint, in a weighted log-linear feature, letting the translation model learn how much to “trust” the paraphrase-based translation rules. The work reported here is the first to use distributional semantic similarity measures to improve performance of an end-to-end phrase-based SMT system. The unified framework for statistical NLP models with soft linguistic constraints enables, in principle, the combination of both semantic and syntactic constraints -- and potentially other constraints, too -- in a single SMT model

Deep Neural Network Compression with Filter Pruning

The rapid development of convolutional neural networks (CNNs) in computer vision tasks has inspired researchers to apply their potential to embedded or mobile devices. However, it typically requires a large amount of computation and memory footprint, limiting their deployment in those resource-limited systems. Therefore, how to compress complex networks while maintaining competitive performance has become the focus of attention in recent years. On the subject of network compression, filter pruning methods that achieve structured compact model by finding and removing redundant filters, have attracted widespread attention. Inspired by previous dedicated works, this thesis focuses on the way to obtain the compact model while maximizing the retention of the original model performance. In particular, aiming at the limitations of choosing filters on the existing popular pruning methods, several novel filter pruning strategies are proposed to find and remove redundant filters more accurately to reduce the performance loss of the model caused by pruning. For instance, the filter pruning method with an attention mechanism (Chapter 3), data-dependent filter pruning guided by LSTM (Chapter 4), and filter pruning with uniqueness mechanism in the frequency domain (Chapter 5). This thesis first addresses the filter pruning issue from a global perspective. To this end, we propose a new scheme, termed Pruning Filter with an Attention Mechanism (PFAM). That is, by establishing the dependency/relationship between filters at each layer, we explore the long-term dependence between filters via attention module in order to choose the tobe-pruned filters. Unlike prior approaches that identify the to-be-pruned filters simply based on their intrinsic properties, the less correlated filters are first pruned after the pruning step in the current training epoch and then reconstructed and updated during the subsequent training epoch. Thus, the compressed network model can be achieved without the requirement for a pre-trained model since input data can be manipulated with the maximum information maintained when the original training strategy is executed. Next, it is noticed that most existing pruning algorithms seek to prune the filter layer by layer. Specifically, they guide filter pruning at each layer by setting a global pruning rate, which indicates that each convolutional layer is treated equally without regard to its depth and width. In this situation, we argue that the convolutional layers in the network also have varying degrees of significance. Besides, we propose that selecting the appropriate layers for pruning is more reasonable since it can result in more complexity reduction with less performance loss by keeping and removing more filters in those critical and nonsignificant layers, respectively. In order to do this, long short-term memory (LSTM) is employed to learn the hierarchical properties of a network and to generalize a global network pruning scheme. On top of that, we present a data-dependent soft pruning strategy named Squeeze-Excitation-Pruning (SEP), which does not physically prune any filters but removes specific kernels involved in calculating forward and backward propagations based on the pruning scheme. Doing so can further decrease the model’s performance decline while achieving a deep model compression. Lastly, we transfer the concept of relationship from the filter level to the feature map level because the feature maps can reflect the comprehensive information of both input data and filters. Hence, we propose Filter Pruning with Uniqueness Mechanism in the Frequency Domain (FPUM) to serve as a guideline for the filter pruning strategy by generating the correlation between feature maps. Specifically, we first transfer features to the frequency domain by Discrete Cosine Transform (DCT). Then, for each feature map, we compute a uniqueness score, which measures its probability of being replaced by others. Doing so allows us to prune the filters corresponding to the low-uniqueness maps without significant performance degradation. In addition, our strategy is more resistant to noise than spatial methods, further enhancing the network’s compactness while maintaining performance, as the critical pruning clues are more concentrated following DCT

Leptonic Decays of Neutral B Mesons in the Three-Spurion Two-Higgs-Doublet Model

Leptonische Zerfälle $B_{q=d,s} \to l^+ l^-$ neutraler $B$-Mesonen sind im Standardmodell der Teilchenphysik stark unterdrückt und stellen daher einen idealen Prüfstand für Modelle neuer Physik dar. In dieser Arbeit wird ein das sogenannte Typ-II-Modell erweiterndes Zwei-Higgs-Dublett-Modell vorgestellt, welches im up-artigen Quark-Sektor flavourändernde Yukawa-Kopplungen der schweren neutralen Higgs-Bosonen beinhaltet. Dieses Modell wird als Drei-Spurion-Modell ohne diskrete $\mathbb{Z}_2$-Symmetrie bezeichnet. Die zur Beschreibung mittels einer effektiven Feldtheorie benötigten Wilson-Koeffizienten für $B_q \to l^+ l^-$ werden in dieser Arbeit zur nächstführenden Zwei-Schleifen-Ordnung in QCD berechnet. Besonderes Augenmerk wird auf die Renormierung des vorgestellten Modells im Kontext dieser Zerfällt gelegt, insbesondere auf die konzeptionellen Unterschiede zu den besser bekannten $\mathbb{Z}_2$-symmetrischen Modellen. Weiter werden relevante Einschränkungen aus der Flavour-Physik und von Beschleuniger-Experimenten präsentiert und daraus Informationen über den erlaubten Parameterbereich der am stärksten eingehenden Kopplungen abgeleitet. Insbesondere wird in dieser Dissertation gezeigt, dass selbst sehr kleine Werte mancher dieser Kopplungen beträchtlichen Einfluss auf Observablen wie das Zerfallsverhältnis in $B_s \to \mu^+ \mu^-$ haben können

Tevatron-for-LHC Report: Preparations for Discoveries

This is the "TeV4LHC" report of the "Physics Landscapes" Working Group, focused on facilitating the start-up of physics explorations at the LHC by using the experience gained at the Tevatron. We present experimental and theoretical results that can be employed to probe various scenarios for physics beyond the Standard Model.Comment: 222 pp., additional contribution added, typos/layout correcte