266,050 research outputs found
Align before Search: Aligning Ads Image to Text for Accurate Cross-Modal Sponsored Search
Cross-Modal sponsored search displays multi-modal advertisements (ads) when
consumers look for desired products by natural language queries in search
engines. Since multi-modal ads bring complementary details for query-ads
matching, the ability to align ads-specific information in both images and
texts is crucial for accurate and flexible sponsored search. Conventional
research mainly studies from the view of modeling the implicit correlations
between images and texts for query-ads matching, ignoring the alignment of
detailed product information and resulting in suboptimal search performance.In
this work, we propose a simple alignment network for explicitly mapping
fine-grained visual parts in ads images to the corresponding text, which
leverages the co-occurrence structure consistency between vision and language
spaces without requiring expensive labeled training data. Moreover, we propose
a novel model for cross-modal sponsored search that effectively conducts the
cross-modal alignment and query-ads matching in two separate processes. In this
way, the model matches the multi-modal input in the same language space,
resulting in a superior performance with merely half of the training data. Our
model outperforms the state-of-the-art models by 2.57% on a large commercial
dataset. Besides sponsored search, our alignment method is applicable for
general cross-modal search. We study a typical cross-modal retrieval task on
the MSCOCO dataset, which achieves consistent performance improvement and
proves the generalization ability of our method. Our code is available at
https://github.com/Pter61/AlignCMSS
PlaceRaider: Virtual Theft in Physical Spaces with Smartphones
As smartphones become more pervasive, they are increasingly targeted by
malware. At the same time, each new generation of smartphone features
increasingly powerful onboard sensor suites. A new strain of sensor malware has
been developing that leverages these sensors to steal information from the
physical environment (e.g., researchers have recently demonstrated how malware
can listen for spoken credit card numbers through the microphone, or feel
keystroke vibrations using the accelerometer). Yet the possibilities of what
malware can see through a camera have been understudied. This paper introduces
a novel visual malware called PlaceRaider, which allows remote attackers to
engage in remote reconnaissance and what we call virtual theft. Through
completely opportunistic use of the camera on the phone and other sensors,
PlaceRaider constructs rich, three dimensional models of indoor environments.
Remote burglars can thus download the physical space, study the environment
carefully, and steal virtual objects from the environment (such as financial
documents, information on computer monitors, and personally identifiable
information). Through two human subject studies we demonstrate the
effectiveness of using mobile devices as powerful surveillance and virtual
theft platforms, and we suggest several possible defenses against visual
malware
Nonlinear multigrid based on local spectral coarsening for heterogeneous diffusion problems
This work develops a nonlinear multigrid method for diffusion problems
discretized by cell-centered finite volume methods on general unstructured
grids. The multigrid hierarchy is constructed algebraically using aggregation
of degrees of freedom and spectral decomposition of reference linear operators
associated with the aggregates. For rapid convergence, it is important that the
resulting coarse spaces have good approximation properties. In our approach,
the approximation quality can be directly improved by including more spectral
degrees of freedom in the coarsening process. Further, by exploiting local
coarsening and a piecewise-constant approximation when evaluating the nonlinear
component, the coarse level problems are assembled and solved without ever
re-visiting the fine level, an essential element for multigrid algorithms to
achieve optimal scalability. Numerical examples comparing relative performance
of the proposed nonlinear multigrid solvers with standard single-level
approaches -- Picard's and Newton's methods -- are presented. Results show that
the proposed solver consistently outperforms the single-level methods, both in
efficiency and robustness
A reduced basis localized orthogonal decomposition
In this work we combine the framework of the Reduced Basis method (RB) with
the framework of the Localized Orthogonal Decomposition (LOD) in order to solve
parametrized elliptic multiscale problems. The idea of the LOD is to split a
high dimensional Finite Element space into a low dimensional space with
comparably good approximation properties and a remainder space with negligible
information. The low dimensional space is spanned by locally supported basis
functions associated with the node of a coarse mesh obtained by solving
decoupled local problems. However, for parameter dependent multiscale problems,
the local basis has to be computed repeatedly for each choice of the parameter.
To overcome this issue, we propose an RB approach to compute in an "offline"
stage LOD for suitable representative parameters. The online solution of the
multiscale problems can then be obtained in a coarse space (thanks to the LOD
decomposition) and for an arbitrary value of the parameters (thanks to a
suitable "interpolation" of the selected RB). The online RB-LOD has a basis
with local support and leads to sparse systems. Applications of the strategy to
both linear and nonlinear problems are given
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