Efficient Construction of Spanners in dd-Dimensions

In this paper we consider the problem of efficiently constructing $k$-vertex fault-tolerant geometric $t$-spanners in \dspace (for $k \ge 0$ and $t >1$). Vertex fault-tolerant spanners were introduced by Levcopoulus et. al in 1998. For $k=0$, we present an $O(n \log n)$ method using the algebraic computation tree model to find a $t$-spanner with degree bound O(1) and weight O(\weight(MST)). This resolves an open problem. For $k \ge 1$, we present an efficient method that, given $n$ points in \dspace, constructs $k$-vertex fault-tolerant $t$-spanners with the maximum degree bound O(k) and weight bound O(k^2 \weight(MST)) in time $O(n \log n)$. Our method achieves the best possible bounds on degree, total edge length, and the time complexity, and solves the open problem of efficient construction of (fault-tolerant) $t$-spanners in \dspace in time $O(n \log n)$.Comment: 29 pages, 4 figure

Kato's inequality and Liouville theorems on locally finite graphs

In this paper we study the Kato' inequality on locally finite graph. We also study the application of Kato inequality to Ginzburg-Landau equations on such graphs. Interesting properties of Schrodinger equation and a Liouville type theorem are also derived.Comment: 8 page

Do More Dropouts in Pool5 Feature Maps for Better Object Detection

Deep Convolutional Neural Networks (CNNs) have gained great success in image classification and object detection. In these fields, the outputs of all layers of CNNs are usually considered as a high dimensional feature vector extracted from an input image and the correspondence between finer level feature vectors and concepts that the input image contains is all-important. However, fewer studies focus on this deserving issue. On considering the correspondence, we propose a novel approach which generates an edited version for each original CNN feature vector by applying the maximum entropy principle to abandon particular vectors. These selected vectors correspond to the unfriendly concepts in each image category. The classifier trained from merged feature sets can significantly improve model generalization of individual categories when training data is limited. The experimental results for classification-based object detection on canonical datasets including VOC 2007 (60.1%), 2010 (56.4%) and 2012 (56.3%) show obvious improvement in mean average precision (mAP) with simple linear support vector machines.Comment: 9 pages, 7 figure

Metal-insulator transition in VO2_{2}: a Peierls-Mott-Hubbard mechanism

The electronic structure of VO$_2$ is studied in the frameworks of local density approximation (LDA) and LDA+$U$ to give a quantitative description of the metal-insulator (MI) transition in this system. It is found that, both structural distortion and the local Coulomb interaction, play important roles in the transition. An optical gap, comparable to the experimental value has been obtained in the monoclinic structure by using the LDA+$U$ method. Based on our results, we believe that both, the Peierls and the Mott-Hubbard mechanism, are essential for a description of the MI transition in this system.Comment: 11 pages, 6 figure

Learning Autonomous Exploration and Mapping with Semantic Vision

We address the problem of autonomous exploration and mapping for a mobile robot using visual inputs. Exploration and mapping is a well-known and key problem in robotics, the goal of which is to enable a robot to explore a new environment autonomously and create a map for future usage. Different to classical methods, we propose a learning-based approach this work based on semantic interpretation of visual scenes. Our method is based on a deep network consisting of three modules: semantic segmentation network, mapping using camera geometry and exploration action network. All modules are differentiable, so the whole pipeline is trained end-to-end based on actor-critic framework. Our network makes action decision step by step and generates the free space map simultaneously. To our best knowledge, this is the first algorithm that formulate exploration and mapping into learning framework. We validate our approach in simulated real world environments and demonstrate performance gains over competitive baseline approaches.Comment: Accepted at IVSP 201

CODA: Counting Objects via Scale-aware Adversarial Density Adaption

Recent advances in crowd counting have achieved promising results with increasingly complex convolutional neural network designs. However, due to the unpredictable domain shift, generalizing trained model to unseen scenarios is often suboptimal. Inspired by the observation that density maps of different scenarios share similar local structures, we propose a novel adversarial learning approach in this paper, i.e., CODA (\emph{Counting Objects via scale-aware adversarial Density Adaption}). To deal with different object scales and density distributions, we perform adversarial training with pyramid patches of multi-scales from both source- and target-domain. Along with a ranking constraint across levels of the pyramid input, consistent object counts can be produced for different scales. Extensive experiments demonstrate that our network produces much better results on unseen datasets compared with existing counting adaption models. Notably, the performance of our CODA is comparable with the state-of-the-art fully-supervised models that are trained on the target dataset. Further analysis indicates that our density adaption framework can effortlessly extend to scenarios with different objects. \emph{The code is available at https://github.com/Willy0919/CODA.}Comment: Accepted to ICME201

Symmetry Analysis of ZnSe(100) Surface in Air By Second Harmonic Generation

Polarized and azimuthal dependencies of optical second harmonics generation (SHG) at the surface of noncentrosymmetric semiconductor crystals have been measured on polished surfaces of ZnSe(100), using a fundamental wavelength of 1.06$\mu m$. The SHG intensity patterns were analyzed for all four combination of p- and s-polarized incidence and output, considering both the bulk and surface optical nonlinearities in the electric dipole approximation. We found that the measurement using $S_{in}-S_{out}$ is particularly useful in determining the symmetry of the oxdized layer interface, which would lower the effective symmetry of the surface from $C_{4v}$ to $C_{2v}.$ We also have shown that the [011] and [0$\bar{1}$1] directions can be distinguished through the analysis of p-incident and p-output confugration.Comment: 21 pages, 5 figure

Action Recognition with Joint Attention on Multi-Level Deep Features

We propose a novel deep supervised neural network for the task of action recognition in videos, which implicitly takes advantage of visual tracking and shares the robustness of both deep Convolutional Neural Network (CNN) and Recurrent Neural Network (RNN). In our method, a multi-branch model is proposed to suppress noise from background jitters. Specifically, we firstly extract multi-level deep features from deep CNNs and feed them into 3d-convolutional network. After that we feed those feature cubes into our novel joint LSTM module to predict labels and to generate attention regularization. We evaluate our model on two challenging datasets: UCF101 and HMDB51. The results show that our model achieves the state-of-art by only using convolutional features.Comment: 13 pages, submitted to BMV

Sliding-Window Optimization on an Ambiguity-Clearness Graph for Multi-object Tracking

Multi-object tracking remains challenging due to frequent occurrence of occlusions and outliers. In order to handle this problem, we propose an Approximation-Shrink Scheme for sequential optimization. This scheme is realized by introducing an Ambiguity-Clearness Graph to avoid conflicts and maintain sequence independent, as well as a sliding window optimization framework to constrain the size of state space and guarantee convergence. Based on this window-wise framework, the states of targets are clustered in a self-organizing manner. Moreover, we show that the traditional online and batch tracking methods can be embraced by the window-wise framework. Experiments indicate that with only a small window, the optimization performance can be much better than online methods and approach to batch methods

Learning to Point and Count

This paper proposes the problem of point-and-count as a test case to break the what-and-where deadlock. Different from the traditional detection problem, the goal is to discover key salient points as a way to localize and count the number of objects simultaneously. We propose two alternatives, one that counts first and then point, and another that works the other way around. Fundamentally, they pivot around whether we solve "what" or "where" first. We evaluate their performance on dataset that contains multiple instances of the same class, demonstrating the potentials and their synergies. The experiences derive a few important insights that explains why this is a much harder problem than classification, including strong data bias and the inability to deal with object scales robustly in state-of-art convolutional neural networks