326 research outputs found
Iteratively Optimized Patch Label Inference Network for Automatic Pavement Disease Detection
We present a novel deep learning framework named the Iteratively Optimized
Patch Label Inference Network (IOPLIN) for automatically detecting various
pavement diseases that are not solely limited to specific ones, such as cracks
and potholes. IOPLIN can be iteratively trained with only the image label via
the Expectation-Maximization Inspired Patch Label Distillation (EMIPLD)
strategy, and accomplish this task well by inferring the labels of patches from
the pavement images. IOPLIN enjoys many desirable properties over the
state-of-the-art single branch CNN models such as GoogLeNet and EfficientNet.
It is able to handle images in different resolutions, and sufficiently utilize
image information particularly for the high-resolution ones, since IOPLIN
extracts the visual features from unrevised image patches instead of the
resized entire image. Moreover, it can roughly localize the pavement distress
without using any prior localization information in the training phase. In
order to better evaluate the effectiveness of our method in practice, we
construct a large-scale Bituminous Pavement Disease Detection dataset named
CQU-BPDD consisting of 60,059 high-resolution pavement images, which are
acquired from different areas at different times. Extensive results on this
dataset demonstrate the superiority of IOPLIN over the state-of-the-art image
classification approaches in automatic pavement disease detection. The source
codes of IOPLIN are released on \url{https://github.com/DearCaat/ioplin}.Comment: Revision on IEEE Trans on IT
Deformable Kernel Expansion Model for Efficient Arbitrary-shaped Scene Text Detection
Scene text detection is a challenging computer vision task due to the high
variation in text shapes and ratios. In this work, we propose a scene text
detector named Deformable Kernel Expansion (DKE), which incorporates the merits
of both segmentation and contour-based detectors. DKE employs a segmentation
module to segment the shrunken text region as the text kernel, then expands the
text kernel contour to obtain text boundary by regressing the vertex-wise
offsets. Generating the text kernel by segmentation enables DKE to inherit the
arbitrary-shaped text region modeling capability of segmentation-based
detectors. Regressing the kernel contour with some sampled vertices enables DKE
to avoid the complicated pixel-level post-processing and better learn contour
deformation as the contour-based detectors. Moreover, we propose an Optimal
Bipartite Graph Matching Loss (OBGML) that measures the matching error between
the predicted contour and the ground truth, which efficiently minimizes the
global contour matching distance. Extensive experiments on CTW1500, Total-Text,
MSRA-TD500, and ICDAR2015 demonstrate that DKE achieves a good tradeoff between
accuracy and efficiency in scene text detection
Text Assisted Insight Ranking Using Context-Aware Memory Network
Extracting valuable facts or informative summaries from multi-dimensional
tables, i.e. insight mining, is an important task in data analysis and business
intelligence. However, ranking the importance of insights remains a challenging
and unexplored task. The main challenge is that explicitly scoring an insight
or giving it a rank requires a thorough understanding of the tables and costs a
lot of manual efforts, which leads to the lack of available training data for
the insight ranking problem. In this paper, we propose an insight ranking model
that consists of two parts: A neural ranking model explores the data
characteristics, such as the header semantics and the data statistical
features, and a memory network model introduces table structure and context
information into the ranking process. We also build a dataset with text
assistance. Experimental results show that our approach largely improves the
ranking precision as reported in multi evaluation metrics.Comment: Accepted to AAAI 201
Visualizing topological edge states of single and double bilayer Bi supported on multibilayer Bi(111) films
Freestanding single-bilayer Bi(111) is a two-dimensional topological
insulator with edge states propagating along its perimeter. Given the
interlayer coupling experimentally, the topological nature of Bi(111) thin
films and the impact of the supporting substrate on the topmost Bi bilayer are
still under debate. Here, combined with scanning tunneling microscopy and
first-principles calculations, we systematically study the electronic
properties of Bi(111) thin films grown on a NbSe2 substrate. Two types of
non-magnetic edge structures, i.e., a conventional zigzag edge and a 2x1
reconstructed edge, coexist alternately at the boundaries of single bilayer
islands, the topological edge states of which exhibit remarkably different
energy and spatial distributions. Prominent edge states are persistently
visualized at the edges of both single and double bilayer Bi islands,
regardless of the underlying thickness of Bi(111) thin films. We provide an
explanation for the topological origin of the observed edge states that is
verified with first-principles calculations. Our paper clarifies the
long-standing controversy regarding the topology of Bi(111) thin films and
reveals the tunability of topological edge states via edge modifications.Comment: 36 pages, 10 figure
Soundly Handling Linearity
We propose a novel approach to soundly combining linear types with effect
handlers. Linear type systems statically ensure that resources such as file
handles are used exactly once. Effect handlers provide a modular programming
abstraction for implementing features ranging from exceptions to concurrency.
Whereas linear type systems bake in the assumption that continuations are
invoked exactly once, effect handlers allow continuations to be discarded or
invoked more than once. This mismatch leads to soundness bugs in existing
systems such as the programming language Links, which combines linearity (for
session types) with effect handlers. We introduce control flow linearity as a
means to ensure that continuations are used in accordance with the linearity of
any resources they capture, ruling out such soundness bugs.
We formalise control flow linearity in a System F-style core calculus Feffpop
equipped with linear types, effect types, and effect handlers. We define a
linearity-aware semantics to formally prove that Feffpop preserves the
integrity of linear values in the sense that no linear value is discarded or
duplicated. In order to show that control flow linearity can be made practical,
we adapt Links based on the design of Feffpop, in doing so fixing a
long-standing soundness bug.
Finally, to better expose the potential of control flow linearity, we define
an ML-style core calculus Qeffpop, based on qualified types, which requires no
programmer provided annotations, and instead relies entirely on type inference
to infer control flow linearity. Both linearity and effects are captured by
qualified types. Qeffpop overcomes a number of practical limitations of
Feffpop, supporting abstraction over linearity, linearity dependencies between
type variables, and a much more fine-grained notion of control flow linearity.Comment: 51 pages, accepted for POPL 202
Human-like Energy Management Based on Deep Reinforcement Learning and Historical Driving Experiences
Development of hybrid electric vehicles depends on an advanced and efficient
energy management strategy (EMS). With online and real-time requirements in
mind, this article presents a human-like energy management framework for hybrid
electric vehicles according to deep reinforcement learning methods and
collected historical driving data. The hybrid powertrain studied has a
series-parallel topology, and its control-oriented modeling is founded first.
Then, the distinctive deep reinforcement learning (DRL) algorithm, named deep
deterministic policy gradient (DDPG), is introduced. To enhance the derived
power split controls in the DRL framework, the global optimal control
trajectories obtained from dynamic programming (DP) are regarded as expert
knowledge to train the DDPG model. This operation guarantees the optimality of
the proposed control architecture. Moreover, the collected historical driving
data based on experienced drivers are employed to replace the DP-based
controls, and thus construct the human-like EMSs. Finally, different categories
of experiments are executed to estimate the optimality and adaptability of the
proposed human-like EMS. Improvements in fuel economy and convergence rate
indicate the effectiveness of the constructed control structure.Comment: 8 pages, 10 figure
A Calculus for Scoped Effects & Handlers
Algebraic effects & handlers have become a standard approach for side-effects
in functional programming. Their modular composition with other effects and
clean separation of syntax and semantics make them attractive to a wide
audience. However, not all effects can be classified as algebraic; some need a
more sophisticated handling. In particular, effects that have or create a
delimited scope need special care, as their continuation consists of two
parts-in and out of the scope-and their modular composition introduces
additional complexity. These effects are called scoped and have gained
attention by their growing applicability and adoption in popular libraries.
While calculi have been designed with algebraic effects & handlers built in to
facilitate their use, a calculus that supports scoped effects & handlers in a
similar manner does not yet exist. This work fills this gap: we present
, a calculus with native support for both algebraic and
scoped effects & handlers. It addresses the need for polymorphic handlers and
explicit clauses for forwarding unknown scoped operations to other handlers.
Our calculus is based on Eff, an existing calculus for algebraic effects,
extended with Koka-style row polymorphism, and consists of a formal grammar,
operational semantics, a (type-safe) type-and-effect system and type inference.
We demonstrate on a range of examples
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