2,102 research outputs found
Keyword-aware Optimal Route Search
Identifying a preferable route is an important problem that finds
applications in map services. When a user plans a trip within a city, the user
may want to find "a most popular route such that it passes by shopping mall,
restaurant, and pub, and the travel time to and from his hotel is within 4
hours." However, none of the algorithms in the existing work on route planning
can be used to answer such queries. Motivated by this, we define the problem of
keyword-aware optimal route query, denoted by KOR, which is to find an optimal
route such that it covers a set of user-specified keywords, a specified budget
constraint is satisfied, and an objective score of the route is optimal. The
problem of answering KOR queries is NP-hard. We devise an approximation
algorithm OSScaling with provable approximation bounds. Based on this
algorithm, another more efficient approximation algorithm BucketBound is
proposed. We also design a greedy approximation algorithm. Results of empirical
studies show that all the proposed algorithms are capable of answering KOR
queries efficiently, while the BucketBound and Greedy algorithms run faster.
The empirical studies also offer insight into the accuracy of the proposed
algorithms.Comment: VLDB201
Stacking sequence determines Raman intensities of observed interlayer shear modes in 2D layered materials - A general bond polarizability model
2D layered materials have recently attracted tremendous interest due to their
fascinating properties and potential applications. The interlayer interactions
are much weaker than the intralayer bonds, allowing the as-synthesized
materials to exhibit different stacking sequences (e.g. ABAB, ABCABC), leading
to different physical properties. Here, we show that regardless of the space
group of the 2D material, the Raman frequencies of the interlayer shear modes
observed under the typical configuration blue shift for AB stacked materials,
and red shift for ABC stacked materials, as the number of layers increases. Our
predictions are made using an intuitive bond polarizability model which shows
that stacking sequence plays a key role in determining which interlayer shear
modes lead to the largest change in polarizability (Raman intensity); the modes
with the largest Raman intensity determining the frequency trends. We present
direct evidence for these conclusions by studying the Raman modes in few layer
graphene, MoS2, MoSe2, WSe2 and Bi2Se3, using both first principles
calculations and Raman spectroscopy. This study sheds light on the influence of
stacking sequence on the Raman intensities of intrinsic interlayer modes in 2D
layered materials in general, and leads to a practical way of identifying the
stacking sequence in these materials.Comment: 30 pages, 8 figure
Rotation-invariant features for multi-oriented text detection in natural images.
Texts in natural scenes carry rich semantic information, which can be used to assist a wide range of applications, such as object recognition, image/video retrieval, mapping/navigation, and human computer interaction. However, most existing systems are designed to detect and recognize horizontal (or near-horizontal) texts. Due to the increasing popularity of mobile-computing devices and applications, detecting texts of varying orientations from natural images under less controlled conditions has become an important but challenging task. In this paper, we propose a new algorithm to detect texts of varying orientations. Our algorithm is based on a two-level classification scheme and two sets of features specially designed for capturing the intrinsic characteristics of texts. To better evaluate the proposed method and compare it with the competing algorithms, we generate a comprehensive dataset with various types of texts in diverse real-world scenes. We also propose a new evaluation protocol, which is more suitable for benchmarking algorithms for detecting texts in varying orientations. Experiments on benchmark datasets demonstrate that our system compares favorably with the state-of-the-art algorithms when handling horizontal texts and achieves significantly enhanced performance on variant texts in complex natural scenes
Adaptive Domain Generalization via Online Disagreement Minimization
Deep neural networks suffer from significant performance deterioration when
there exists distribution shift between deployment and training. Domain
Generalization (DG) aims to safely transfer a model to unseen target domains by
only relying on a set of source domains. Although various DG approaches have
been proposed, a recent study named DomainBed, reveals that most of them do not
beat the simple Empirical Risk Minimization (ERM). To this end, we propose a
general framework that is orthogonal to existing DG algorithms and could
improve their performance consistently. Unlike previous DG works that stake on
a static source model to be hopefully a universal one, our proposed AdaODM
adaptively modifies the source model at test time for different target domains.
Specifically, we create multiple domain-specific classifiers upon a shared
domain-generic feature extractor. The feature extractor and classifiers are
trained in an adversarial way, where the feature extractor embeds the input
samples into a domain-invariant space, and the multiple classifiers capture the
distinct decision boundaries that each of them relates to a specific source
domain. During testing, distribution differences between target and source
domains could be effectively measured by leveraging prediction disagreement
among source classifiers. By fine-tuning source models to minimize the
disagreement at test time, target domain features are well aligned to the
invariant feature space. We verify AdaODM on two popular DG methods, namely ERM
and CORAL, and four DG benchmarks, namely VLCS, PACS, OfficeHome, and
TerraIncognita. The results show AdaODM stably improves the generalization
capacity on unseen domains and achieves state-of-the-art performance.Comment: 11 pages, 4 figure
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