2,899 research outputs found
Securing UAV Communications Via Trajectory Optimization
Unmanned aerial vehicle (UAV) communications has drawn significant interest
recently due to many advantages such as low cost, high mobility, and on-demand
deployment. This paper addresses the issue of physical-layer security in a UAV
communication system, where a UAV sends confidential information to a
legitimate receiver in the presence of a potential eavesdropper which are both
on the ground. We aim to maximize the secrecy rate of the system by jointly
optimizing the UAV's trajectory and transmit power over a finite horizon. In
contrast to the existing literature on wireless security with static nodes, we
exploit the mobility of the UAV in this paper to enhance the secrecy rate via a
new trajectory design. Although the formulated problem is non-convex and
challenging to solve, we propose an iterative algorithm to solve the problem
efficiently, based on the block coordinate descent and successive convex
optimization methods. Specifically, the UAV's transmit power and trajectory are
each optimized with the other fixed in an alternating manner until convergence.
Numerical results show that the proposed algorithm significantly improves the
secrecy rate of the UAV communication system, as compared to benchmark schemes
without transmit power control or trajectory optimization.Comment: Accepted by IEEE GLOBECOM 201
Task Driven Generative Modeling for Unsupervised Domain Adaptation: Application to X-ray Image Segmentation
Automatic parsing of anatomical objects in X-ray images is critical to many
clinical applications in particular towards image-guided invention and workflow
automation. Existing deep network models require a large amount of labeled
data. However, obtaining accurate pixel-wise labeling in X-ray images relies
heavily on skilled clinicians due to the large overlaps of anatomy and the
complex texture patterns. On the other hand, organs in 3D CT scans preserve
clearer structures as well as sharper boundaries and thus can be easily
delineated. In this paper, we propose a novel model framework for learning
automatic X-ray image parsing from labeled CT scans. Specifically, a Dense
Image-to-Image network (DI2I) for multi-organ segmentation is first trained on
X-ray like Digitally Reconstructed Radiographs (DRRs) rendered from 3D CT
volumes. Then we introduce a Task Driven Generative Adversarial Network
(TD-GAN) architecture to achieve simultaneous style transfer and parsing for
unseen real X-ray images. TD-GAN consists of a modified cycle-GAN substructure
for pixel-to-pixel translation between DRRs and X-ray images and an added
module leveraging the pre-trained DI2I to enforce segmentation consistency. The
TD-GAN framework is general and can be easily adapted to other learning tasks.
In the numerical experiments, we validate the proposed model on 815 DRRs and
153 topograms. While the vanilla DI2I without any adaptation fails completely
on segmenting the topograms, the proposed model does not require any topogram
labels and is able to provide a promising average dice of 85% which achieves
the same level accuracy of supervised training (88%)
The Dynamic Impact of Web Search Volume on Product Sales ā An Empirical Study Based on Box Office Revenues
In order to explore how Web search volume dynamically influences product sales during the whole product life cycle, this paper collects Web search volume and sales data of movies and does an empirical analysis using econometric models. The empirical results show that Web search volume before the launch of a new product has a positive impact on the product sales in the initial period of introduction stage. During the whole product life cycle, Web search volume has a positive and significant impact on product sales, but the impact declines gradually across the life cycle. The impact of Web search volume on sales is larger in the early stage of the product life cycle than in the late stage of the product life cycle
A New Framework for Online Testing of Heterogeneous Treatment Effect
We propose a new framework for online testing of heterogeneous treatment
effects. The proposed test, named sequential score test (SST), is able to
control type I error under continuous monitoring and detect multi-dimensional
heterogeneous treatment effects. We provide an online p-value calculation for
SST, making it convenient for continuous monitoring, and extend our tests to
online multiple testing settings by controlling the false discovery rate. We
examine the empirical performance of the proposed tests and compare them with a
state-of-art online test, named mSPRT using simulations and a real data. The
results show that our proposed test controls type I error at any time, has
higher detection power and allows quick inference on online A/B testing.Comment: 8 pages, no figures. To be published on AAAI 2020 proceeding
Sustainable Design of Urban Rooftop Food-Energy-Land Nexus
Funding Information: Authors in particular M.G. would like to acknowledge the UK Engineering and Physical Sciences Research Council ( EPSRC ) for providing financial support for research under project āResilient and Sustainable Bio-renewable Systems Engineering Modelā [ EP/N034740/1 ]. A.H. would like to acknowledge financial support from Natural Environment Research Council (NERC) ADVENT project [ 1806209 ].Peer reviewedPublisher PD
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