11,298 research outputs found
Compositional coding capsule network with k-means routing for text classification
Text classification is a challenging problem which aims to identify the
category of texts. Recently, Capsule Networks (CapsNets) are proposed for image
classification. It has been shown that CapsNets have several advantages over
Convolutional Neural Networks (CNNs), while, their validity in the domain of
text has less been explored. An effective method named deep compositional code
learning has been proposed lately. This method can save many parameters about
word embeddings without any significant sacrifices in performance. In this
paper, we introduce the Compositional Coding (CC) mechanism between capsules,
and we propose a new routing algorithm, which is based on k-means clustering
theory. Experiments conducted on eight challenging text classification datasets
show the proposed method achieves competitive accuracy compared to the
state-of-the-art approach with significantly fewer parameters
Evaluating Generalization Ability of Convolutional Neural Networks and Capsule Networks for Image Classification via Top-2 Classification
Image classification is a challenging problem which aims to identify the
category of object in the image. In recent years, deep Convolutional Neural
Networks (CNNs) have been applied to handle this task, and impressive
improvement has been achieved. However, some research showed the output of CNNs
can be easily altered by adding relatively small perturbations to the input
image, such as modifying few pixels. Recently, Capsule Networks (CapsNets) are
proposed, which can help eliminating this limitation. Experiments on MNIST
dataset revealed that capsules can better characterize the features of object
than CNNs. But it's hard to find a suitable quantitative method to compare the
generalization ability of CNNs and CapsNets. In this paper, we propose a new
image classification task called Top-2 classification to evaluate the
generalization ability of CNNs and CapsNets. The models are trained on single
label image samples same as the traditional image classification task. But in
the test stage, we randomly concatenate two test image samples which contain
different labels, and then use the trained models to predict the top-2 labels
on the unseen newly-created two label image samples. This task can provide us
precise quantitative results to compare the generalization ability of CNNs and
CapsNets. Back to the CapsNet, because it uses Full Connectivity (FC) mechanism
among all capsules, it requires many parameters. To reduce the number of
parameters, we introduce the Parameter-Sharing (PS) mechanism between capsules.
Experiments on five widely used benchmark image datasets demonstrate the method
significantly reduces the number of parameters, without losing the
effectiveness of extracting features. Further, on the Top-2 classification
task, the proposed PS CapsNets obtain impressive higher accuracy compared to
the traditional CNNs and FC CapsNets by a large margin.Comment: This paper is under consideration at Computer Vision and Image
Understandin
Revisiting the OLI Paradigm: The Institutions, the State, and China's OFDI
We propose a modified theoretical framework based on John Dunning’s classical OLI paradigm in the international business literature to analyze Chinese firms’ fast-growing and aggressive outward foreign direct investment (OFDI). In particular, from an institutional perspective, we suggest a “state-stewardship” view to incorporate state institutions into the OLI paradigm. This paper supplements our earlier work (Ren, Liang, and Zheng, 2011) on identifying the formal institutional determinants of Chinese firms’ OFDI motivations and strategies, by further looking at the impact of direct and indirect policies, and the OFDI state-controlled financial intermediaries. Under our modified OLI framework we also examine the potential concerns on China’s state-backed OFDI and its implication on long-term sustainability.outward foreign direct investment, institutions, state-stewardship view, OLI paradigm
Model-based compositional verification approaches and tools development for cyber-physical systems
The model-based design for embedded real-time systems utilizes the veriable reusable components and proper architectures, to deal with the verification scalability problem caused by state-explosion. In this thesis, we address verification approaches for both low-level individual component correctness and high-level system correctness, which are equally important under this scheme. Three prototype tools are developed, implementing our approaches and algorithms accordingly.
For the component-level design-time verification, we developed a symbolic verifier, LhaVrf, for the reachability verification of concurrent linear hybrid systems (LHA). It is unique in translating a hybrid automaton into a transition system that preserves the discrete transition structure, possesses no continuous dynamics, and preserves reachability of discrete states. Afterward, model-checking is interleaved in the counterexample fragment based specification relaxation framework. We next present a simulation-based bounded-horizon reachability analysis approach for the reachability verification of systems modeled by hybrid automata (HA) on a run-time basis. This framework applies a dynamic, on-the-fly, repartition-based error propagation control method with the mild requirement of Lipschitz continuity on the continuous dynamics. The novel features allow state-triggered discrete jumps and provide eventually constant over-approximation error bound for incremental stable dynamics. The above approaches are implemented in our prototype verifier called HS3V. Once the component properties are established, the next thing is to establish the system-level properties through compositional verication. We present our work on the role and integration of quantier elimination (QE) for property composition and verication. In our approach, we derive in a single step, the strongest system property from the given component properties for both time-independent and time-dependent scenarios. The system initial condition can also be composed, which, alongside the strongest system property, are used to verify a postulated system property through induction. The above approaches are implemented in our prototype tool called ReLIC
The extremal genus embedding of graphs
Let Wn be a wheel graph with n spokes. How does the genus change if adding a
degree-3 vertex v, which is not in V (Wn), to the graph Wn? In this paper,
through the joint-tree model we obtain that the genus of Wn+v equals 0 if the
three neighbors of v are in the same face boundary of P(Wn); otherwise,
{\deg}(Wn + v) = 1, where P(Wn) is the unique planar embedding of Wn. In
addition, via the independent set, we provide a lower bound on the maximum
genus of graphs, which may be better than both the result of D. Li & Y. Liu and
the result of Z. Ouyang etc: in Europ. J. Combinatorics. Furthermore, we obtain
a relation between the independence number and the maximum genus of graphs, and
provide an algorithm to obtain the lower bound on the number of the distinct
maximum genus embedding of the complete graph Km, which, in some sense,
improves the result of Y. Caro and S. Stahl respectively
The Top Quark Production Asymmetries and
A large forward-backward asymmetry is seen in both the top quark rapidity
distribution and in the rapidity distribution of charged leptons
from top quarks produced at the Tevatron. We study the kinematic
and dynamic aspects of the relationship of the two observables arising from the
spin correlation between the charged lepton and the top quark with different
polarization states. We emphasize the value of both measurements, and we
conclude that a new physics model which produces more right-handed than
left-handed top quarks is favored by the present data.Comment: accepted for publication in Physical Review Letter
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