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Peng Gao

Publications and source records attributed to Peng Gao.

At least 433 records · Page 24Linked to original sources
arXiv2019

Learning Cascaded Siamese Networks for High Performance Visual Tracking

Visual tracking is one of the most challenging computer vision problems. In order to achieve high performance visual tracking in various negative scenarios, a novel cascaded Siamese network is proposed and developed based on two different deep learning networks: a matching subnetwork and a classification subnetwork. The matching subnetwork is a fully convolutional Siamese network. According to the similarity score between the exemplar image and the candidate image, it aims to search possible object positions and crop scaled candidate patches. The classification subnetwork is designed to further evaluate the cropped candidate patches and determine the optimal tracking results based on the classification score. The matching subnetwork is trained offline and fixed online, while the classification subnetwork performs stochastic gradient descent online to learn more target-specific information. To improve the tracking performance further, an effective classification subnetwork update method based on both similarity and classification scores is utilized for updating the classification subnetwork. Extensive experimental results demonstrate that our proposed approach achieves state-of-the-art performance in recent benchmarks.

cs.CV↗
arXiv2019

Scaling-up atomically thin coplanar semiconductor-metal circuitry via phase engineered chemical assembly

Two-dimensional (2D) layered semiconductors, with their ultimate atomic thickness, have shown promise to scale down transistors for modern integrated circuitry. However, the electrical contacts that connect these materials with external bulky metals are usually unsatisfactory, which limits the transistor performance. Recently, contacting 2D semiconductors using coplanar 2D conductors has shown promise in reducing the problematic high resistance contacts. However, many of these methods are not ideal for scaled production. Here, we report on the large-scale, spatially controlled chemical assembly of the integrated 2H-MoTe2 field-effect transistors (FETs) with coplanar metallic 1T' MoTe2 contacts via phase engineered approaches. We demonstrate that the heterophase FETs exhibit ohmic contact behavior with low contact resistance, resulting from the coplanar seamless contact between 2H and 1T' MoTe2 confirmed by transmission electron microscopy characterizations. The average mobility of the heterophase FETs was measured to be as high as 23 cm2 V-1 s-1 (comparable with those of exfoliated single crystals), due to the large 2H MoTe2 single-crystalline domain (486μm). By developing a patterned growth method, we realize the 1T' MoTe2 gated heterophase FET array whose components of channel, gate, and contacts are all 2D materials. Finally, we transfer the heterophase device array onto a flexible substrate and demonstrate the near-infrared photoresponse with high photoresponsivity (~1.02 A/W). Our study provides a basis for the large-scale application of phase-engineered coplanar MoTe2 semiconductors-meter structure in advanced electronics and optoelectronics.

cond-mat.mtrl-sci↗
arXiv2019

Solution Processed Large-scale Multiferroic Complex Oxide Epitaxy with Magnetically Switched Polarization

Complex oxides with tunable structures have many fascinating properties, though high-quality complex oxide epitaxy with precisely controlled composition is still out of reach. Here we have successfully developed solution-based single crystalline epitaxy for multiferroic (1-x)BiTi(1-y)/2FeyMg(1-y)/2O3-(x)CaTiO3 (BTFM-CTO) solid solution in large area, confirming its ferroelectricity at atomic-scale with a spontaneous polarization of 79~89uC/cm2. Careful compositional tuning leads to a bulk magnetization of ~0.07uB/Fe at room temperature, enabling magnetically induced polarization switching exhibiting a large magnetoelectric coefficient of 2.7-3.0X10-7s/m. This work demonstrates the great potential of solution processing in large-scale complex oxide epitaxy and establishes novel room-temperature magnetoelectric coupling in epitaxial BTFM-CTO film, making it possible to explore a much wider space of composition, phase, and structure that can be easily scaled up for industrial applications.

cond-mat.mtrl-sci↗
arXiv2019

One level density of low-lying zeros of quadratic and quartic Hecke $L$-functions

In this paper, we prove some one level density results for the low-lying zeros of famliies of quadratic and quartic Hecke $L$-functions of the Gaussian field. As corollaries, we deduce that, respectively, at least $94.27 \%$ and $5\%$ of the members of the quadratic family and the quartic family do not vanish at the central point.

math.NT↗
arXiv2019

Primes $p \equiv 1 \bmod{d}$ and $a^{(p-1)/d} \equiv 1 \bmod{p}$}

Suppose that $d \in \{ 2, 3, 4, 6 \}$ and $a \in \mathbb{Z}$ with $a\neq -1$ and $a$ is not square. Let $P_{(a,d)}$ be the number of primes $p$ not exceeding $x$ such that $p \equiv 1 \pmod{d}$ and $a^{(p-1)/d} \equiv 1 \pmod{p}$. In this paper, we study the mean value of $P_{(a,d)}$.

math.NT↗
arXiv2019

A Query System for Efficiently Investigating Complex Attack Behaviors for Enterprise Security

The need for countering Advanced Persistent Threat (APT) attacks has led to the solutions that ubiquitously monitor system activities in each enterprise host, and perform timely attack investigation over the monitoring data for uncovering the attack sequence. However, existing general-purpose query systems lack explicit language constructs for expressing key properties of major attack behaviors, and their semantics-agnostic design often produces inefficient execution plans for queries. To address these limitations, we build AIQL, a novel query system that is designed with novel types of domain-specific optimizations to enable efficient attack investigation. AIQL provides (1) domain-specific data model and storage for storing the massive system monitoring data, (2) a domain-specific query language, Attack Investigation Query Language (AIQL) that integrates critical primitives for expressing major attack behaviors, and (3) an optimized query engine based on the characteristics of the data and the semantics of the query to efficiently schedule the execution. We have deployed AIQL in NEC Labs America comprising 150 hosts. In our demo, we aim to show the complete usage scenario of AIQL by (1) performing an APT attack in a controlled environment, and (2) using AIQL to investigate such attack by querying the collected system monitoring data that contains the attack traces. The audience will have the option to perform the APT attack themselves under our guidance, and interact with the system and investigate the attack via issuing queries and checking the query results through our web UI.

cs.CR↗
arXiv2018

Mean values of divisors of forms $n^2+Nm^2$

Let $N$ be any fixed positive integer and define \begin{align*} S_N(x)=\sum_{m, n \leq x}d(n^2+Nm^2), \end{align*} where $d(n)$ is the divisor function. We evaluate asymptotically $S_N(x)$ for several $N$, extending earlier works of Gafurov and Yu on the case $N=1$.

math.NT↗
arXiv2018

Towards the growth of single-crystal boron nitride monolayer on Cu

Atom-layered hexagonal boron nitride (hBN), with excellent stability, flat surface and large bandgap, has been reported to be the best 2D insulator to open up the great possibilities for exciting potential applications in electronics, optoelectronics and photovoltaics. The ability to grow high-quality large single crystals of hBN is at the heart for those applications, but the size of single-crystal 2D BN is less than a millimetre till now. Here, we report the first epitaxial growth of a 10*10 cm2 single-crystal hBN monolayer on a low symmetry Cu(110) "vicinal surface". The growth kinetics, unidirectional alignment and seamless stitching of hBN domains are unambiguously illustrated using centimetre- to the atomic-scale characterization techniques. The findings in this work are expected to significantly boost the massive applications of 2D materials-based devices, and also pave the way for the epitaxial growth of broad non-centrosymmetric 2D materials.

cond-mat.mtrl-sci↗
arXiv2018

Atomic Structure of Polar Surface in SrTiO3

Rearrangement of atoms due to broken translational symmetries at the surface of SrTiO$_3$ is scarcely debatable in the present day scenario. Actual concern demands to unveil the true structure and precise mechanism responsible for atomic reconstructions at a unit cell level. Here, we reveal the atomic structure of SrTiO$_3$ surface by using annular bright field imaging in spherical aberration corrected scanning transmission electron microscope. We measure structural parameters such as lattice constant, bond length, atomic displacement and subsequently we find a polarization for the top five unit cells is ~10-20 uCcm$^{-2}$. Both Sr-O and Ti-O planes reconstruct thus contribute to net dipole moment that is towards surface. Elongation of the out of plane lattice for skin layers is also witnessed. The electron energy loss spectroscopy experiment illustrates the presence of oxygen vacancies at the surface which seems to be largely responsible for the stability of surface polarization. Our study provides a sound foundation into mechanistic understanding for structure and properties of surfaces for complex oxides. These outcomes can be considered as a pragmatic boost particularly for examining other insulator-like materials as well as in bridging gap for hitherto fewer experimental investigation along with various theoretical studies.

cond-mat.mtrl-sci↗
arXiv2018

Probing lattice vibration at surface and interface of SiO$_2$/Si with nanometer resolution

Recent advances in monochromatic aberration corrected electron microscopy make it possible to detect the lattice vibration with both high-energy resolution and high spatial resolution. Here, we use sub-10 meV electron energy loss spectroscopy to investigate the local vibrational properties at surface and interface of an amorphous SiO$_2$ (a-SiO$_2$) thin film on Si substrate. We find that each optical mode splits into three sub-modes, i.e., surface mode, bulk mode and interface mode, which can be measured from different locations. The pure surface modes can be measured in the vacuum near the surface, and the pure interface modes are expected to be obtained either at the interface location or in the Si, while inside the SiO$_2$ the measured signal is a mixture of bulk, surface, and interface modes. The bulk mode has the largest vibration energy and surface mode has the lowest. The energy of surface mode is thickness dependent, showing a blue-shift as z-thickness (parallel to fast electron beam) of SiO$_2$ film increases, while the bulk and interface modes have constant vibration energy. The intensity of bulk mode linearly increases with thickness being increased, and it drops steeply to zero near the surface and interface (within a few nanometers). The surface modes decay slowly in the vacuum following a Bessel function. The mechanism of the observed spatially dependent vibration behavior is discussed and quantitatively compared with dielectric response theory analysis. Our nanometer scale measurements of vibrations properties provide useful information about the bonding conditions at the surface and interface and thus may help to design better silicon-based electronic devices via surface and interface treatments

cond-mat.mtrl-sci↗
arXiv2018

FPGA-based Acceleration System for Visual Tracking

Visual tracking is one of the most important application areas of computer vision. At present, most algorithms are mainly implemented on PCs, and it is difficult to ensure real-time performance when applied in the real scenario. In order to improve the tracking speed and reduce the overall power consumption of visual tracking, this paper proposes a real-time visual tracking algorithm based on DSST(Discriminative Scale Space Tracking) approach. We implement a hardware system on Xilinx XC7K325T FPGA platform based on our proposed visual tracking algorithm. Our hardware system can run at more than 153 frames per second. In order to reduce the resource occupation, our system adopts the batch processing method in the feature extraction module. In the filter processing module, the FFT IP core is time-division multiplexed. Therefore, our hardware system utilizes LUTs and storage blocks of 33% and 40%, respectively. Test results show that the proposed visual tracking hardware system has excellent performance.

cs.CV↗
arXiv2018

Efficient Multi-level Correlating for Visual Tracking

Correlation filter (CF) based tracking algorithms have demonstrated favorable performance recently. Nevertheless, the top performance trackers always employ complicated optimization methods which constraint their real-time applications. How to accelerate the tracking speed while retaining the tracking accuracy is a significant issue. In this paper, we propose a multi-level CF-based tracking approach named MLCFT which further explores the potential capacity of CF with two-stage detection: primal detection and oriented re-detection. The cascaded detection scheme is simple but competent to prevent model drift and accelerate the speed. An effective fusion method based on relative entropy is introduced to combine the complementary features extracted from deep and shallow layers of convolutional neural networks (CNN). Moreover, a novel online model update strategy is utilized in our tracker, which enhances the tracking performance further. Experimental results demonstrate that our proposed approach outperforms the most state-of-the-art trackers while tracking at speed of exceeded 16 frames per second on challenging benchmarks.

cs.CV↗
arXiv2018

High Performance Visual Tracking with Circular and Structural Operators

In this paper, a novel circular and structural operator tracker (CSOT) is proposed for high performance visual tracking, it not only possesses the powerful discriminative capability of SOSVM but also efficiently inherits the superior computational efficiency of DCF. Based on the proposed circular and structural operators, a set of primal confidence score maps can be obtained by circular correlating feature maps with their corresponding structural correlation filters. Furthermore, an implicit interpolation is applied to convert the multi-resolution feature maps to the continuous domain and make all primal confidence score maps have the same spatial resolution. Then, we exploit an efficient ensemble post-processor based on relative entropy, which can coalesce primal confidence score maps and create an optimal confidence score map for more accurate localization. The target is localized on the peak of the optimal confidence score map. Besides, we introduce a collaborative optimization strategy to update circular and structural operators by iteratively training structural correlation filters, which significantly reduces computational complexity and improves robustness. Experimental results demonstrate that our approach achieves state-of-the-art performance in mean AUC scores of 71.5% and 69.4% on the OTB-2013 and OTB-2015 benchmarks respectively, and obtains a third-best expected average overlap (EAO) score of 29.8% on the VOT-2017 benchmark.

cs.CV↗
arXiv2018

V$_5$S$_8$: a Kondo lattice based on intercalation of van der Waals layered transition metal dichalcogenide

Since the discovery of graphene, a tremendous amount of two dimensional (2D) materials have surfaced. Their electronic properties can usually be well understood without considering correlations between electrons. On the other hand, strong electronic correlations are known to give rise to a variety of exotic properties and new quantum phases, for instance, high temperature superconductivity, heavy fermions and quantum spin liquids. The study of these phenomena has been one of the main focuses of condensed matter physics. There is a strong incentive to introduce electronic correlations into 2D materials. Via intercalating a van der Waals layered compound VS$_2$, we show an emergence of a Kondo lattice, an extensively studied strongly correlated system, by magnetic, specific heat, electrical and thermoelectric transport studies. In particular, an exceptionally large Sommerfeld coefficient, 440 mJ$\cdot$K$^{-2}\cdot$mol$^{-1}$, indicates a strong electron correlation. The obtained Kadowaki-Woods ratio, $2.7\times 10^{-6}$ $μΩ\cdot$cm$\cdot$mol$^2\cdot$K$^2\cdot$mJ$^{-2}$, also supports the strong electron-electron interaction. The temperature dependence of the resistivity and thermopower corroborate the Kondo lattice picture. The intercalated compound is one of a few rare examples of $d$-electron Kondo lattices. We further show that the Kondo physics persists in ultra-thin films. This work thus demonstrates a route to generate strong correlations in 2D materials.

cond-mat.mtrl-sci↗
arXiv2018

Question-Guided Hybrid Convolution for Visual Question Answering

In this paper, we propose a novel Question-Guided Hybrid Convolution (QGHC) network for Visual Question Answering (VQA). Most state-of-the-art VQA methods fuse the high-level textual and visual features from the neural network and abandon the visual spatial information when learning multi-modal features.To address these problems, question-guided kernels generated from the input question are designed to convolute with visual features for capturing the textual and visual relationship in the early stage. The question-guided convolution can tightly couple the textual and visual information but also introduce more parameters when learning kernels. We apply the group convolution, which consists of question-independent kernels and question-dependent kernels, to reduce the parameter size and alleviate over-fitting. The hybrid convolution can generate discriminative multi-modal features with fewer parameters. The proposed approach is also complementary to existing bilinear pooling fusion and attention based VQA methods. By integrating with them, our method could further boost the performance. Extensive experiments on public VQA datasets validate the effectiveness of QGHC.

cs.CV↗
arXiv2018

Large Margin Structured Convolution Operator for Thermal Infrared Object Tracking

Compared with visible object tracking, thermal infrared (TIR) object tracking can track an arbitrary target in total darkness since it cannot be influenced by illumination variations. However, there are many unwanted attributes that constrain the potentials of TIR tracking, such as the absence of visual color patterns and low resolutions. Recently, structured output support vector machine (SOSVM) and discriminative correlation filter (DCF) have been successfully applied to visible object tracking, respectively. Motivated by these, in this paper, we propose a large margin structured convolution operator (LMSCO) to achieve efficient TIR object tracking. To improve the tracking performance, we employ the spatial regularization and implicit interpolation to obtain continuous deep feature maps, including deep appearance features and deep motion features, of the TIR targets. Finally, a collaborative optimization strategy is exploited to significantly update the operators. Our approach not only inherits the advantage of the strong discriminative capability of SOSVM but also achieves accurate and robust tracking with higher-dimensional features and more dense samples. To the best of our knowledge, we are the first to incorporate the advantages of DCF and SOSVM for TIR object tracking. Comprehensive evaluations on two thermal infrared tracking benchmarks, i.e. VOT-TIR2015 and VOT-TIR2016, clearly demonstrate that our LMSCO tracker achieves impressive results and outperforms most state-of-the-art trackers in terms of accuracy and robustness with sufficient frame rate.

cs.CV↗