arXiv subjects

Xin Yang

Publications and source records attributed to Xin Yang.

At least 415 records · Page 23Linked to original sources

arXiv2018

On the Bias of Reed-Muller Codes over Odd Prime Fields

We study the bias of random bounded-degree polynomials over odd prime fields and show that, with probability exponentially close to 1, such polynomials have exponentially small bias. This also yields an exponential tail bound on the weight distribution of Reed-Muller codes over odd prime fields. These results generalize bounds of Ben-Eliezer, Hod, and Lovett who proved similar results over $\mathbb{F}_2$. A key to our bounds is the proof of a new precise extremal property for the rank of sub-matrices of the generator matrices of Reed-Muller codes over odd prime fields. This extremal property is a substantial extension of an extremal property shown by Keevash and Sudakov for the case of $\mathbb{F}_2$. Our exponential tail bounds on the bias can be used to derive exponential lower bounds on the time for space-bounded learning of bounded-degree polynomials from their evaluations over odd prime fields.

cs.DM↗

arXiv2018

Spatial social networks identified from urban group travel

While the individual travel implicates the trace of individual mobility decision, group travels signify the possible social relationship behind these traces. Different from online social network, spatial interaction between individuals is a critical yet unknown dimension to understand the collective behaviors in a city. In this paper, based on over 127 million trips in Beijing metro network, we develop a method to distinguish the group travel of friends from the encounter travel of familiar strangers. We find travels of friends are among the most predictable groups. These identified friendships are interwoven and form a friendship network, with structural properties different from encounter network. The topological role of individuals in this network is found strongly correlated with her travel predictability. The overall time savings of about 34190 minutes after redistribution of inefficient group traveler with flexible travel purposes shows the potential of designing specific traffic fares for group travel. Our identification and understanding of group travel may help to develop and organize new traffic mode in the future smart transportation.

physics.soc-ph↗

arXiv2018

Active Object Reconstruction Using a Guided View Planner

Inspired by the recent advance of image-based object reconstruction using deep learning, we present an active reconstruction model using a guided view planner. We aim to reconstruct a 3D model using images observed from a planned sequence of informative and discriminative views. But where are such informative and discriminative views around an object? To address this we propose a unified model for view planning and object reconstruction, which is utilized to learn a guided information acquisition model and to aggregate information from a sequence of images for reconstruction. Experiments show that our model (1) increases our reconstruction accuracy with an increasing number of views (2) and generally predicts a more informative sequence of views for object reconstruction compared to other alternative methods.

cs.CV↗

arXiv2018

Neural Compatibility Modeling with Attentive Knowledge Distillation

Recently, the booming fashion sector and its huge potential benefits have attracted tremendous attention from many research communities. In particular, increasing research efforts have been dedicated to the complementary clothing matching as matching clothes to make a suitable outfit has become a daily headache for many people, especially those who do not have the sense of aesthetics. Thanks to the remarkable success of neural networks in various applications such as image classification and speech recognition, the researchers are enabled to adopt the data-driven learning methods to analyze fashion items. Nevertheless, existing studies overlook the rich valuable knowledge (rules) accumulated in fashion domain, especially the rules regarding clothing matching. Towards this end, in this work, we shed light on complementary clothing matching by integrating the advanced deep neural networks and the rich fashion domain knowledge. Considering that the rules can be fuzzy and different rules may have different confidence levels to different samples, we present a neural compatibility modeling scheme with attentive knowledge distillation based on the teacher-student network scheme. Extensive experiments on the real-world dataset show the superiority of our model over several state-of-the-art baselines. Based upon the comparisons, we observe certain fashion insights that add value to the fashion matching study. As a byproduct, we released the codes, and involved parameters to benefit other researchers.

cs.CV↗

arXiv2018

Image Correction via Deep Reciprocating HDR Transformation

Image correction aims to adjust an input image into a visually pleasing one. Existing approaches are proposed mainly from the perspective of image pixel manipulation. They are not effective to recover the details in the under/over exposed regions. In this paper, we revisit the image formation procedure and notice that the missing details in these regions exist in the corresponding high dynamic range (HDR) data. These details are well perceived by the human eyes but diminished in the low dynamic range (LDR) domain because of the tone mapping process. Therefore, we formulate the image correction task as an HDR transformation process and propose a novel approach called Deep Reciprocating HDR Transformation (DRHT). Given an input LDR image, we first reconstruct the missing details in the HDR domain. We then perform tone mapping on the predicted HDR data to generate the output LDR image with the recovered details. To this end, we propose a united framework consisting of two CNNs for HDR reconstruction and tone mapping. They are integrated end-to-end for joint training and prediction. Experiments on the standard benchmarks demonstrate that the proposed method performs favorably against state-of-the-art image correction methods.

cs.CV↗

arXiv2018

Improvements on lower bounds for the blow-up time under local nonlinear Neumann conditions

This paper studies the heat equation $u_t=Δu$ in a bounded domain $Ω\subset\mathbb{R}^{n}(n\geq 2)$ with positive initial data and a local nonlinear Neumann boundary condition: the normal derivative $\partial u/\partial n=u^{q}$ on partial boundary $Γ_1\subseteq \partialΩ$ for some $q>1$, while $\partial u/\partial n=0$ on the other part. We investigate the lower bound of the blow-up time $T^{*}$ of $u$ in several aspects. First, $T^{*}$ is proved to be at least of order $(q-1)^{-1}$ as $q\rightarrow 1^{+}$. Since the existing upper bound is of order $(q-1)^{-1}$, this result is sharp. Secondly, if $Ω$ is convex and $|Γ_{1}|$ denotes the surface area of $Γ_{1}$, then $T^{*}$ is shown to be at least of order $|Γ_{1}|^{-\frac{1}{n-1}}$ for $n\geq 3$ and $|Γ_{1}|^{-1}\big/\ln\big(|Γ_{1}|^{-1}\big)$ for $n=2$ as $|Γ_{1}|\rightarrow 0$, while the previous result is $|Γ_{1}|^{-α}$ for any $α<\frac{1}{n-1}$. Finally, we generalize the results for convex domains to the domains with only local convexity near $Γ_{1}$.

math.AP↗

arXiv2018

Lower bounds for the blow-up time of the heat equation in convex domains with local nonlinear boundary conditions

This paper studies the lower bound for the blow-up time $T^{*}$ of the heat equation $u_t=Δu$ in a bounded convex domain $Ω$ in $\mathbb{R}^{N}(N\geq 2)$ with positive initial data $u_{0}$ and a local nonlinear Neumann boundary condition: the normal derivative $\partial u/\partial n=u^{q}$ on partial boundary $Γ_1\subseteq\partialΩ$ for some $q>1$, while $\partial u/\partial n=0$ on the other part. For any $α<\frac{1}{N-1}$, we obtain a lower bound for $T^{*}$ which is of order $|Γ_{1}|^{-α}$ as $|Γ_{1}|\rightarrow 0^{+}$, where $|Γ_{1}|$ represents the surface area of $Γ_{1}$. As $|Γ_{1}|\rightarrow 0^{+}$, this result significantly improves the previous lower bound $\ln\big(|Γ_1|^{-1}\big)$ and is almost optimal in dimension $N=2$, since the existing upper bound is of order $|Γ_{1}|^{-1}$ as $|Γ_{1}|\rightarrow 0^{+}$. In addition, the optimal asymptotic order of the lower bound for $T^{*}$ on $q$ (as $q\rightarrow 1^{+}$) and on $M_{0}$ (as $M_{0}\rightarrow 0^{+}$) are obtained, where $M_{0}$ denotes the maximum of $u_{0}$.

math.AP↗

arXiv2018

Influential User Subscription on Time-Decaying Social Streams

Influence maximization which asks for $k$-size seed set from a social network such that maximizing the influence over all other users (called influence spread) has widely attracted attention due to its significant applications in viral marketing and rumor control. In real world scenarios, people are interested in the most influential users in particular topics, and want to subscribe the topics-of-interests over social networks. In this paper, we formulate the problem of influential users subscription on time-decaying social stream, which asks for maintaining the $k$-size influential users sets for each topic-aware subscription queries. We first analyze the widely adopted sliding window model and propose a newly time-decaying influence model to overcome the shortages when calculating the influence over social stream. Developed from sieve based streaming algorithm, we propose an efficient algorithm to support the calculation of time-decaying influence over dynamically updating social networks. Using information among subscriptions, we then construct the Prefix Tree Structure to allow us minimizing the times of calculating influence of each update and easily maintained. Pruning techniques are also applied to the Prefix Tree to optimize the performance of social stream update. Our approach ensures a $\frac{1}{2}-ε$ approximation ratio. Experimental results show that our approach significantly outperforms the baseline approaches in efficiency and result quality.

cs.SI↗

arXiv2017

REDBEE: A Visual-Inertial Drone System for Real-Time Moving Object Detection

Aerial surveillance and monitoring demand both real-time and robust motion detection from a moving camera. Most existing techniques for drones involve sending a video data streams back to a ground station with a high-end desktop computer or server. These methods share one major drawback: data transmission is subjected to considerable delay and possible corruption. Onboard computation can not only overcome the data corruption problem but also increase the range of motion. Unfortunately, due to limited weight-bearing capacity, equipping drones with computing hardware of high processing capability is not feasible. Therefore, developing a motion detection system with real-time performance and high accuracy for drones with limited computing power is highly desirable. In this paper, we propose a visual-inertial drone system for real-time motion detection, namely REDBEE, that helps overcome challenges in shooting scenes with strong parallax and dynamic background. REDBEE, which can run on the state-of-the-art commercial low-power application processor (e.g. Snapdragon Flight board used for our prototype drone), achieves real-time performance with high detection accuracy. The REDBEE system overcomes obstacles in shooting scenes with strong parallax through an inertial-aided dual-plane homography estimation; it solves the issues in shooting scenes with dynamic background by distinguishing the moving targets through a probabilistic model based on spatial, temporal, and entropy consistency. The experiments are presented which demonstrate that our system obtains greater accuracy when detecting moving targets in outdoor environments than the state-of-the-art real-time onboard detection systems.

cs.RO↗

arXiv2017

Optimal double resonant condition in metallic core-shell nanocavity for third harmonic generation

As the rapid development of nonlinear optics, the enhancement of optical third harmonic generation becomes a pop research realm in the fields of physics, chemistry, biology, materials science, information science and other fields. In this letter, theoretical analysis on double resonance situation in metallic core-shell nanostructure is performed in order to optimize the efficiency of third harmonic generation. As plenty of cavity modes ranging from visible area to near infrared can be excited efficiently in core-shell nanospheres, kinds of double resonant conditions can be formed by matching two different multipolar cavity modes. Numerical simulations show that the third harmonic generation (THG) intensity in the far field can be enhanced remarkably when the THG signal couples well with the high order cavity mode. More importantly, THG efficiency is optimum from the cavity on double resonant conditions coupling two modes with the same order. The THG intensity differs up to 3 magnitudes on this condition. Subsequent theoretical analysis indicates that changing third-order nonlinear susceptibility of the metal shell while keeping that of the core fixed has almost no effect on THG efficiency. This finding about optimal double resonance has an appreciable effect on optimizing THG in spherical cavity structures.

physics.optics↗

arXiv2017

Time-Space Tradeoffs for Learning from Small Test Spaces: Learning Low Degree Polynomial Functions

We develop an extension of recently developed methods for obtaining time-space tradeoff lower bounds for problems of learning from random test samples to handle the situation where the space of tests is signficantly smaller than the space of inputs, a class of learning problems that is not handled by prior work. This extension is based on a measure of how matrices amplify the 2-norms of probability distributions that is more refined than the 2-norms of these matrices. As applications that follow from our new technique, we show that any algorithm that learns $m$-variate homogeneous polynomial functions of degree at most $d$ over $\mathbb{F}_2$ from evaluations on randomly chosen inputs either requires space $Ω(mn)$ or $2^{Ω(m)}$ time where $n=m^{Θ(d)}$ is the dimension of the space of such functions. These bounds are asymptotically optimal since they match the tradeoffs achieved by natural learning algorithms for the problems.

cs.LG↗

arXiv2017

Automatic 3D Cardiovascular MR Segmentation with Densely-Connected Volumetric ConvNets

Automatic and accurate whole-heart and great vessel segmentation from 3D cardiac magnetic resonance (MR) images plays an important role in the computer-assisted diagnosis and treatment of cardiovascular disease. However, this task is very challenging due to ambiguous cardiac borders and large anatomical variations among different subjects. In this paper, we propose a novel densely-connected volumetric convolutional neural network, referred as DenseVoxNet, to automatically segment the cardiac and vascular structures from 3D cardiac MR images. The DenseVoxNet adopts the 3D fully convolutional architecture for effective volume-to-volume prediction. From the learning perspective, our DenseVoxNet has three compelling advantages. First, it preserves the maximum information flow between layers by a densely-connected mechanism and hence eases the network training. Second, it avoids learning redundant feature maps by encouraging feature reuse and hence requires fewer parameters to achieve high performance, which is essential for medical applications with limited training data. Third, we add auxiliary side paths to strengthen the gradient propagation and stabilize the learning process. We demonstrate the effectiveness of DenseVoxNet by comparing it with the state-of-the-art approaches from HVSMR 2016 challenge in conjunction with MICCAI, and our network achieves the best dice coefficient. We also show that our network can achieve better performance than other 3D ConvNets but with fewer parameters.

cs.CV↗

arXiv2016

Fine-grained Recurrent Neural Networks for Automatic Prostate Segmentation in Ultrasound Images

Boundary incompleteness raises great challenges to automatic prostate segmentation in ultrasound images. Shape prior can provide strong guidance in estimating the missing boundary, but traditional shape models often suffer from hand-crafted descriptors and local information loss in the fitting procedure. In this paper, we attempt to address those issues with a novel framework. The proposed framework can seamlessly integrate feature extraction and shape prior exploring, and estimate the complete boundary with a sequential manner. Our framework is composed of three key modules. Firstly, we serialize the static 2D prostate ultrasound images into dynamic sequences and then predict prostate shapes by sequentially exploring shape priors. Intuitively, we propose to learn the shape prior with the biologically plausible Recurrent Neural Networks (RNNs). This module is corroborated to be effective in dealing with the boundary incompleteness. Secondly, to alleviate the bias caused by different serialization manners, we propose a multi-view fusion strategy to merge shape predictions obtained from different perspectives. Thirdly, we further implant the RNN core into a multiscale Auto-Context scheme to successively refine the details of the shape prediction map. With extensive validation on challenging prostate ultrasound images, our framework bridges severe boundary incompleteness and achieves the best performance in prostate boundary delineation when compared with several advanced methods. Additionally, our approach is general and can be extended to other medical image segmentation tasks, where boundary incompleteness is one of the main challenges.

cs.CV↗

arXiv2016

Number Balancing is as hard as Minkowski's Theorem and Shortest Vector

The number balancing (NBP) problem is the following: given real numbers $a_1,\ldots,a_n \in [0,1]$, find two disjoint subsets $I_1,I_2 \subseteq [n]$ so that the difference $|\sum_{i \in I_1}a_i - \sum_{i \in I_2}a_i|$ of their sums is minimized. An application of the pigeonhole principle shows that there is always a solution where the difference is at most $O(\frac{\sqrt{n}}{2^n})$. Finding the minimum, however, is NP-hard. In polynomial time,the differencing algorithm by Karmarkar and Karp from 1982 can produce a solution with difference at most $n^{-Θ(\log n)}$, but no further improvement has been made since then. In this paper, we show a relationship between NBP and Minkowski's Theorem. First we show that an approximate oracle for Minkowski's Theorem gives an approximate NBP oracle. Perhaps more surprisingly, we show that an approximate NBP oracle gives an approximate Minkowski oracle. In particular, we prove that any polynomial time algorithm that guarantees a solution of difference at most $2^{\sqrt{n}} / 2^{n}$ would give a polynomial approximation for Minkowski as well as a polynomial factor approximation algorithm for the Shortest Vector Problem.

cs.DM↗

arXiv2016

Concept based Attention

Attention endows animals an ability to concentrate on the most relevant information among a deluge of distractors at any given time, either through volitionally 'top-down' biasing, or driven by automatically 'bottom-up' saliency of stimuli, in favour of advantageous competition in neural modulations for information processing. Nevertheless, instead of being limited to perceive simple features, human and other advanced animals adaptively learn the world into categories and abstract concepts from experiences, imparting the world meanings. This thesis suggests that the high-level cognitive ability of human is more likely driven by attention basing on abstract perceptions, which is defined as concept based attention (CbA).

cs.AI↗

arXiv2016

A Geographic Multi-Copy Routing Scheme for DTNs With Heterogeneous Mobility

Previous geographic routing schemes in Delay/Disruption Tolerant Networks (DTNs) only consider the homogeneous scenario where nodal mobility is identical. Motivated by this gap, we turn to design a DTN based geographic routing scheme in heterogeneous scenario. Systematically, our target is achieved via two steps: 1) We first propose "The-Best-Geographic-Relay (TBGR)" routing scheme to relay messages via a limited number of copies, under the homogeneous scenario. We further overcome the local maximum problem of TBGR given a sparse network density, different from those efforts in dense networks like clustered Wireless Sensor Networks (WSNs). 2) We next extend TBGR for heterogeneous scenario, and propose "The-Best-Heterogeneity-Geographic-Relay (TBHGR)" routing scheme considering individual nodal visiting preference (referred to non-identical nodal mobility). Extensive results under a realistic heterogeneous scenario show the advantage of TBHGR over literature works in terms of reliable message delivery, while with low routing overhead.

cs.DC↗

arXiv2016

Blow-up problems for the heat equation with a local nonlinear Neumann boundary condition

This paper estimates the blow-up time for the heat equation $u_t=Δu$ with a local nonlinear Neumann boundary condition: The normal derivative $\partial u/\partial n=u^{q}$ on $Γ_{1}$, one piece of the boundary, while on the rest part of the boundary, $\partial u/\partial n=0$. The motivation of the study is the partial damage to the insulation on the surface of space shuttles caused by high speed flying subjects. We prove the solution blows up in finite time and estimate both upper and lower bounds of the blow-up time in terms of the area of $Γ_1$. In many other work, they need the convexity of the domain $Ω$ and only consider the problem with $Γ_1=\partialΩ$. In this paper, we remove the convexity condition and only require $\partialΩ$ to be $C^{2}$. In addition, we deal with the local nonlinearity, namely $Γ_1$ can be just part of $\partialΩ$.

math.AP↗

arXiv2016

Single-laser 32.5 Tbit/s Nyquist WDM transmission

We demonstrate 32.5 Tbit/s 16QAM Nyquist WDM transmission over a total length of 227 km of SMF-28 without optical dispersion compensation. A number of 325 optical carriers are derived from a single laser and encoded with dual-polarization 16QAM data using sinc-shaped Nyquist pulses. As we use no guard bands, the carriers have a spacing of 12.5 GHz equal to the Nyquist bandwidth of the data. We achieve a high net spectral efficiency of 6.4 bit/s/Hz using a software-defined transmitter which generates the electrical modulator drive signals in real-time.

cs.NI↗