61 research outputs found
Deep Decision Trees for Discriminative Dictionary Learning with Adversarial Multi-Agent Trajectories
With the explosion in the availability of spatio-temporal tracking data in
modern sports, there is an enormous opportunity to better analyse, learn and
predict important events in adversarial group environments. In this paper, we
propose a deep decision tree architecture for discriminative dictionary
learning from adversarial multi-agent trajectories. We first build up a
hierarchy for the tree structure by adding each layer and performing feature
weight based clustering in the forward pass. We then fine tune the player role
weights using back propagation. The hierarchical architecture ensures the
interpretability and the integrity of the group representation. The resulting
architecture is a decision tree, with leaf-nodes capturing a dictionary of
multi-agent group interactions. Due to the ample volume of data available, we
focus on soccer tracking data, although our approach can be used in any
adversarial multi-agent domain. We present applications of proposed method for
simulating soccer games as well as evaluating and quantifying team strategies.Comment: To appear in 4th International Workshop on Computer Vision in Sports
(CVsports) at CVPR 201
Tracking by Prediction: A Deep Generative Model for Mutli-Person localisation and Tracking
Current multi-person localisation and tracking systems have an over reliance
on the use of appearance models for target re-identification and almost no
approaches employ a complete deep learning solution for both objectives. We
present a novel, complete deep learning framework for multi-person localisation
and tracking. In this context we first introduce a light weight sequential
Generative Adversarial Network architecture for person localisation, which
overcomes issues related to occlusions and noisy detections, typically found in
a multi person environment. In the proposed tracking framework we build upon
recent advances in pedestrian trajectory prediction approaches and propose a
novel data association scheme based on predicted trajectories. This removes the
need for computationally expensive person re-identification systems based on
appearance features and generates human like trajectories with minimal
fragmentation. The proposed method is evaluated on multiple public benchmarks
including both static and dynamic cameras and is capable of generating
outstanding performance, especially among other recently proposed deep neural
network based approaches.Comment: To appear in IEEE Winter Conference on Applications of Computer
Vision (WACV), 201
A Quantized Interband Topological Index in Two-Dimensional Systems
We introduce a novel gauge-invariant, quantized interband index in
two-dimensional (2D) multiband systems. It provides a bulk topological
classification of a submanifold of parameter space (e.g., an electron valley in
a Brillouin zone), and therefore overcomes difficulties in characterizing
topology of submanifolds. We confirm its topological nature by numerically
demonstrating a one-to-one correspondence to the valley Chern number in models (e.g., gapped Dirac fermion model), and the first Chern number in
lattice models (e.g., Haldane model). Furthermore, we derive a band-resolved
topological charge and demonstrate that it can be used to investigate the
nature of edge states due to band inversion in valley systems like multilayer
graphene.Comment: 6 pages and 3 figures in main text, 2 pages in supplementary materia
Tree Memory Networks for Modelling Long-term Temporal Dependencies
In the domain of sequence modelling, Recurrent Neural Networks (RNN) have
been capable of achieving impressive results in a variety of application areas
including visual question answering, part-of-speech tagging and machine
translation. However this success in modelling short term dependencies has not
successfully transitioned to application areas such as trajectory prediction,
which require capturing both short term and long term relationships. In this
paper, we propose a Tree Memory Network (TMN) for modelling long term and short
term relationships in sequence-to-sequence mapping problems. The proposed
network architecture is composed of an input module, controller and a memory
module. In contrast to related literature, which models the memory as a
sequence of historical states, we model the memory as a recursive tree
structure. This structure more effectively captures temporal dependencies
across both short term and long term sequences using its hierarchical
structure. We demonstrate the effectiveness and flexibility of the proposed TMN
in two practical problems, aircraft trajectory modelling and pedestrian
trajectory modelling in a surveillance setting, and in both cases we outperform
the current state-of-the-art. Furthermore, we perform an in depth analysis on
the evolution of the memory module content over time and provide visual
evidence on how the proposed TMN is able to map both long term and short term
relationships efficiently via a hierarchical structure
Metabolic analysis of Developmental progression in Drosophila
The growth and development of all animals involves transitions between different physiological states. The key developmental transition of critical weight (CW) in the fruit fly Drosophila melanogaster dramatically changes the growing larva’s response to nutrient restriction (NR). Developmental progression is arrested by NR before CW whereas it proceeds without delay when NR occurs after CW. It is known that the time of onset of CW and other developmental transitions are regulated by the steroid hormone ecdysone but questions remain concerning the nature of the physiological changes at CW and how they might confer NR-resistant developmental progression. To begin to answer these questions, I have analysed how the larval metabolome changes when nutrition is altered either side of the CW transition. The larval metabolome was recorded via nuclear magnetic resonance (NMR) spectroscopy and fitting reference spectra to recorded peaks enabled identification of the metabolites. Absolute metabolite concentrations could then be back-calculated from these spectra using the volume determination with two standards (VDTS) technique (Ragan, et al. 2013), which was further adapted to measure metabolite concentrations from the volume released from homogenisation of solid whole larval and adult samples. Through use of these techniques, I found that progression past CW correlates with the ability of fed and NR larvae to sustain a substantial increase in the concentration of tyrosine. An interesting interplay between tyrosine and a possible storage form of the metabolite: o-phosophotyrosine (OPT), suggests a process regulating the conversion between the two that may indirectly affect the biosynthesis of ecdysone. Dietary and genetic manipulations have been undertaken to draw a molecular mechanism for how varying tyrosine levels affected by CW attainment can effect time to pupariation (larval maturation). These results highlight how the field of NMR-metabolomics can be used to direct subsequent experiments to address biological questions
A Robust Interpretable Deep Learning Classifier for Heart Anomaly Detection Without Segmentation
Traditionally, abnormal heart sound classification is framed as a three-stage
process. The first stage involves segmenting the phonocardiogram to detect
fundamental heart sounds; after which features are extracted and classification
is performed. Some researchers in the field argue the segmentation step is an
unwanted computational burden, whereas others embrace it as a prior step to
feature extraction. When comparing accuracies achieved by studies that have
segmented heart sounds before analysis with those who have overlooked that
step, the question of whether to segment heart sounds before feature extraction
is still open. In this study, we explicitly examine the importance of heart
sound segmentation as a prior step for heart sound classification, and then
seek to apply the obtained insights to propose a robust classifier for abnormal
heart sound detection. Furthermore, recognizing the pressing need for
explainable Artificial Intelligence (AI) models in the medical domain, we also
unveil hidden representations learned by the classifier using model
interpretation techniques. Experimental results demonstrate that the
segmentation plays an essential role in abnormal heart sound classification.
Our new classifier is also shown to be robust, stable and most importantly,
explainable, with an accuracy of almost 100% on the widely used PhysioNet
dataset
Learning Temporal Strategic Relationships using Generative Adversarial Imitation Learning
This paper presents a novel framework for automatic learning of complex
strategies in human decision making. The task that we are interested in is to
better facilitate long term planning for complex, multi-step events. We observe
temporal relationships at the subtask level of expert demonstrations, and
determine the different strategies employed in order to successfully complete a
task. To capture the relationship between the subtasks and the overall goal, we
utilise two external memory modules, one for capturing dependencies within a
single expert demonstration, such as the sequential relationship among
different sub tasks, and a global memory module for modelling task level
characteristics such as best practice employed by different humans based on
their domain expertise. Furthermore, we demonstrate how the hidden state
representation of the memory can be used as a reward signal to smooth the state
transitions, eradicating subtle changes. We evaluate the effectiveness of the
proposed model for an autonomous highway driving application, where we
demonstrate its capability to learn different expert policies and outperform
state-of-the-art methods. The scope in industrial applications extends to any
robotics and automation application which requires learning from complex
demonstrations containing series of subtasks.Comment: International Foundation for Autonomous Agents and Multiagent
Systems, 201
Physical Adversarial Attacks for Surveillance: A Survey
Modern automated surveillance techniques are heavily reliant on deep learning
methods. Despite the superior performance, these learning systems are
inherently vulnerable to adversarial attacks - maliciously crafted inputs that
are designed to mislead, or trick, models into making incorrect predictions. An
adversary can physically change their appearance by wearing adversarial
t-shirts, glasses, or hats or by specific behavior, to potentially avoid
various forms of detection, tracking and recognition of surveillance systems;
and obtain unauthorized access to secure properties and assets. This poses a
severe threat to the security and safety of modern surveillance systems. This
paper reviews recent attempts and findings in learning and designing physical
adversarial attacks for surveillance applications. In particular, we propose a
framework to analyze physical adversarial attacks and provide a comprehensive
survey of physical adversarial attacks on four key surveillance tasks:
detection, identification, tracking, and action recognition under this
framework. Furthermore, we review and analyze strategies to defend against the
physical adversarial attacks and the methods for evaluating the strengths of
the defense. The insights in this paper present an important step in building
resilience within surveillance systems to physical adversarial attacks
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