arXiv subjects

Jun Zhang

Publications and source records attributed to Jun Zhang.

At least 901 records · Page 50Linked to original sources

arXiv2020

Robust Ego and Object 6-DoF Motion Estimation and Tracking

The problem of tracking self-motion as well as motion of objects in the scene using information from a camera is known as multi-body visual odometry and is a challenging task. This paper proposes a robust solution to achieve accurate estimation and consistent track-ability for dynamic multi-body visual odometry. A compact and effective framework is proposed leveraging recent advances in semantic instance-level segmentation and accurate optical flow estimation. A novel formulation, jointly optimizing SE(3) motion and optical flow is introduced that improves the quality of the tracked points and the motion estimation accuracy. The proposed approach is evaluated on the virtual KITTI Dataset and tested on the real KITTI Dataset, demonstrating its applicability to autonomous driving applications. For the benefit of the community, we make the source code public.

cs.RO↗

arXiv2020

Black Hole Gravitational Waves in the Effective Field Theory of Gravity

We investigate the propagation of gravitational waves on a black hole background within the low energy effective field theory of gravity, where effects from heavy fields are captured by higher dimensional curvature operators. Depending on the spin of the particles integrated out, the speed of gravitational waves at low energy can be either superluminal or subluminal as compared to the causal structure observed by other species. Interestingly however, gravitational waves are always exactly luminal at the black hole horizon, implying that the horizon is identically defined for all species. We further compute the corrections on quasinormal frequencies caused by the higher dimensional curvature operators and highlight the corrections arising from the low energy effective field.

hep-th↗

arXiv2020

Rational points on complete symmetric hypersurfaces over finite fields

For any affine hypersurface defined by a complete symmetric polynomial in $k\geq 3$ variables of degree $m$ over the finite field $\mathbb{F}_{q}$ of $q$ elements, a special case of our theorem says that this hypersurface has at least $6q^{k-3}$ rational points over $\mathbb{F}_{q}$ if $1\leq m \leq q-3$ and $q$ is odd. A key ingredient in our proof is Segre's classical theorem on ovals in finite projective planes.

math.NT↗

arXiv2020

Complete Dictionary Learning via $\ell_p$-norm Maximization

Dictionary learning is a classic representation learning method that has been widely applied in signal processing and data analytics. In this paper, we investigate a family of $\ell_p$-norm ($p>2,p \in \mathbb{N}$) maximization approaches for the complete dictionary learning problem from theoretical and algorithmic aspects. Specifically, we prove that the global maximizers of these formulations are very close to the true dictionary with high probability, even when Gaussian noise is present. Based on the generalized power method (GPM), an efficient algorithm is then developed for the $\ell_p$-based formulations. We further show the efficacy of the developed algorithm: for the population GPM algorithm over the sphere constraint, it first quickly enters the neighborhood of a global maximizer, and then converges linearly in this region. Extensive experiments will demonstrate that the $\ell_p$-based approaches enjoy a higher computational efficiency and better robustness than conventional approaches and $p=3$ performs the best.

cs.LG↗

arXiv2020

InGaAs/InP single-photon detectors with 60% detection efficiency at 1550 nm

InGaAs/InP single-photon detectors (SPDs) are widely used for near-infrared photon counting in practical applications. Photon detection efficiency (PDE) is one of the most important parameters for SPD characterization, and therefore increasing PDE consistently plays a central role in both industrial development and academic research. Here we present the implementation of high-frequency gating InGaAs/InP SPD with a PDE as high as 60% at 1550 nm. On one hand, we optimize the structure design and device fabrication of InGaAs/InP single-photon avalanche diode with an additional dielectric-metal reflection layer to relatively increase the absorption efficiency of incident photons by ~ 20%. On the other hand, we develop a monolithic readout circuit of weak avalanche extraction to minimize the parasitic capacitance for the suppression of the afterpulsing effect. With 1.25 GHz sine wave gating and optimized gate amplitude and operation temperature, the SPD is characterized to reach a PDE of 60% with a dark count rate (DCR) of 340 kcps. For practical use, given 3 kcps DCR as a reference the PDE reaches ~ 40% PDE with an afterpulse probability of 5.5%, which can significantly improve the performance for the near-infrared SPD based applications.

physics.app-ph↗

arXiv2020

Measuring the integrated Sachs-Wolfe effect from the low-density regions of the universe

The integrated Sachs-Wolfe (ISW) effect is caused by the decay of cosmological gravitational potential, and is therefore a unique probe of dark energy. However, its robust detection is still problematic. Various tensions between different data sets, different large scale structure (LSS) tracers, and between data and the $Λ$CDM theory prediction, exist. We propose a novel method of ISW measurement by cross correlating CMB and the LSS traced by "low-density-position" (LDP, \citet{2019ApJ...874....7D}). It isolates the ISW effect generated by low-density regions of the universe, but insensitive to selection effects associated with voids. We apply it to the DR8 galaxy catalogue of the DESI Legacy imaging surveys, and obtain the LDPs at $z\leq 0.6$ over $\sim$ 20000 $deg^2$ sky coverage. We then cross correlate with the Planck temperature map, and detect the ISW effect at $3.2σ$. We further compare the measurement with numerical simulations of the concordance $Λ$CDM cosmology, and find the ISW amplitude parameter $A_{ISW}=1.14\pm0.38$ when we adopt a LDP definition radius $R_s=3^{'}$, fully consistent with the prediction of the standard $Λ$CDM cosmology ($A_{ISW}=1$). This agreement with $Λ$CDM cosmology holds for all the galaxy samples and $R_s$ that we have investigated. Furthermore, the S/N is comparable to that of galaxy ISW measurement. These results demonstrate the LDP method as a competitive alternative to existing ISW measurement methods, and provide independent checks to existing tensions.

astro-ph.CO↗

arXiv2020

Coronal Mini-jets in an Activated Solar Tornado-like Prominence

High-resolution observations from the $Interface~Region~Imaging~Spectrometer$ ($IRIS$) reveal the existence of a particular type of small solar jets, which arose singly or in clusters from a tornado-like prominence suspended in the corona. In this study, we perform a detailed statistical analysis of 43 selected mini-jets in the tornado event. Our results show that the mini-jets typically have: (1) a projected length of 1.0-6.0 Mm, (2) a width of 0.2-1.0 Mm, (3) a lifetime of 10-50 s, (4) a velocity of 100-350 km s$^{-1}$, and (5) an acceleration of 3-20 km s$^{-2}$. Based on spectral diagnostics and EM-Loci analysis, these jets seem to be multi-thermal small-scale plasma ejections with an estimated average electron density of $\sim$2.4 $\times$ 10$^{10}$ cm$^{-3}$ and an approximate mean temperature of $\sim$2.6 $\times$ 10$^{5}$ K. Their mean kinetic energy density, thermal energy density and dissipated magnetic field strength are roughly estimated to be $\sim$9 erg cm$^{-3}$, 3 erg cm$^{-3}$, and 16 G, respectively. The accelerations of the mini-jets, the UV and EUV brightenings at the footpoints of some mini-jets, and the activation of the host prominence suggest that the tornado mini-jets are probably created by fine-scale external or internal magnetic reconnections (a) between the prominence field and the enveloping or background field or (b) between twisted or braided flux tubes within the prominence. The observations provide insight into the geometry of such reconnection events in the corona and have implications for the structure of the prominence magnetic field and the instability that is responsible for the eruption of prominences and coronal mass ejections.

astro-ph.SR↗

arXiv2020

GATCluster: Self-Supervised Gaussian-Attention Network for Image Clustering

We propose a self-supervised Gaussian ATtention network for image Clustering (GATCluster). Rather than extracting intermediate features first and then performing the traditional clustering algorithm, GATCluster directly outputs semantic cluster labels without further post-processing. Theoretically, we give a Label Feature Theorem to guarantee the learned features are one-hot encoded vectors, and the trivial solutions are avoided. To train the GATCluster in a completely unsupervised manner, we design four self-learning tasks with the constraints of transformation invariance, separability maximization, entropy analysis, and attention mapping. Specifically, the transformation invariance and separability maximization tasks learn the relationships between sample pairs. The entropy analysis task aims to avoid trivial solutions. To capture the object-oriented semantics, we design a self-supervised attention mechanism that includes a parameterized attention module and a soft-attention loss. All the guiding signals for clustering are self-generated during the training process. Moreover, we develop a two-step learning algorithm that is memory-efficient for clustering large-size images. Extensive experiments demonstrate the superiority of our proposed method in comparison with the state-of-the-art image clustering benchmarks. Our code has been made publicly available at https://github.com/niuchuangnn/GATCluster.

cs.CV↗

arXiv2020

BottleNet++: An End-to-End Approach for Feature Compression in Device-Edge Co-Inference Systems

The emergence of various intelligent mobile applications demands the deployment of powerful deep learning models at resource-constrained mobile devices. The device-edge co-inference framework provides a promising solution by splitting a neural network at a mobile device and an edge computing server. In order to balance the on-device computation and the communication overhead, the splitting point needs to be carefully picked, while the intermediate feature needs to be compressed before transmission. Existing studies decoupled the design of model splitting, feature compression, and communication, which may lead to excessive resource consumption of the mobile device. In this paper, we introduce an end-to-end architecture, named BottleNet++, that consists of an encoder, a non-trainable channel layer, and a decoder for more efficient feature compression and transmission. The encoder and decoder essentially implement joint source-channel coding via convolutional neural networks (CNNs), while explicitly considering the effect of channel noise. By exploiting the strong sparsity and the fault-tolerant property of the intermediate feature in a deep neural network (DNN), BottleNet++ achieves a much higher compression ratio than existing methods. Furthermore, by providing the channel condition to the encoder as an input, our method enjoys a strong generalization ability in different channel conditions. Compared with merely transmitting intermediate data without feature compression, BottleNet++ achieves up to 64x bandwidth reduction over the additive white Gaussian noise channel and up to 256x bit compression ratio in the binary erasure channel, with less than 2% reduction in accuracy. With a higher compression ratio, BottleNet++ enables splitting a DNN at earlier layers, which leads to up to 3x reduction in on-device computation compared with other compression methods.

cs.LG↗

arXiv2020

A Non-uniform Time-stepping Convex Splitting Scheme for the Time-fractional Cahn-Hilliard Equation

In this paper, a non-uniform time-stepping convex-splitting numerical algorithm for solving the widely used time-fractional Cahn-Hilliard equation is introduced. The proposed numerical scheme employs the $L1^+$ formula for discretizing the time-fractional derivative and a second-order convex-splitting technique to deal with the non-linear term semi-implicitly. Then the pseudospectral method is utilized for spatial discretization. As a result, the fully discrete scheme has several advantages: second-order accurate in time, spectrally accurate in space, uniquely solvable, mass preserving, and unconditionally energy stable. Rigorous proofs are given, along with several numerical results to verify the theoretical results, and to show the accuracy and effectiveness of the proposed scheme. Also, some interesting phase separation dynamics of the time-fractional Cahn-Hilliard equation has been investigated.

math.NA↗

arXiv2020

Accelerating Physics-Informed Neural Network Training with Prior Dictionaries

Physics-Informed Neural Networks (PINNs) can be regarded as general-purpose PDE solvers, but it might be slow to train PINNs on particular problems, and there is no theoretical guarantee of corresponding error bounds. In this manuscript, we propose a variant called Prior Dictionary based Physics-Informed Neural Networks (PD-PINNs). Equipped with task-dependent dictionaries, PD-PINNs enjoy enhanced representation power on the tasks, which helps to capture features provided by dictionaries so that the proposed neural networks can achieve faster convergence in the process of training. In various numerical simulations, compared with existing PINN methods, combining prior dictionaries can significantly enhance convergence speed. In terms of theory, we obtain the error bounds applicable to PINNs and PD-PINNs for solving elliptic partial differential equations of second order. It is proved that under certain mild conditions, the prediction error made by neural networks can be bounded by expected loss of PDEs and boundary conditions.

cs.LG↗

arXiv2020

A Note on Self-Dual Generalized Reed-Solomon Codes

A linear code is called an MDS self-dual code if it is both an MDS code and a self-dual code with respect to the Euclidean inner product. The parameters of such codes are completely determined by the code length. In this paper, we consider new constructions of MDS self-dual codes via generalized Reed-Solomon (GRS) codes and their extended codes. The critical idea of our constructions is to choose suitable evaluation points such that the corresponding (extended) GRS codes are self-dual. The evaluation set of our constructions is consists of a subgroup of finite fields and its cosets in a bigger subgroup. Four new families of MDS self-dual codes are obtained and they have better parameters than previous results in certain region. Moreover, by the Mobius action over finite fields, we give a systematic way to construct self-dual GRS codes with different evaluation points provided any known self-dual GRS codes. Specially, we prove that all the self-dual extended GRS codes over $\mathbb{F}_{q}$ with length $n< q+1$ can be constructed from GRS codes with the same parameters.

cs.IT↗

arXiv2020

Imaging and spectral study on the null point of a fan-spine structure during a solar flare

Using the multi-instrument observations, we make the first simultaneous imaging and spectral study on the null point of a fan-spine magnetic topology during a solar flare. When magnetic reconnection occurs at the null point, the fan-spine configuration brightens in the (extreme-)ultraviolet channels. In the H$α$ images, the fan-spine structure is partly filled and outlined by the bi-directional material flows ejected from the reconnection site. The extrapolated coronal magnetic field confirms the existence of the fan-spine topology. Before and after the flare peak, the total velocity of the outflows is estimated to be about 60 km s$^{-1}$. During the flare, the Si IV line profile at the reconnection region is enhanced both in the blue-wing and red-wing. At the flare peak time, the total velocity of the outflows is found to be 144 km s$^{-1}$. Superposed on the Si IV profile, there are several deep absorption lines with the blueshift of several tens of km s$^{-1}$. The reason is inferred to be that the bright reconnection region observed in Si IV channel is located under the cooler material appearing as dark features in the H$α$ line. The blueshifted absorption lines indicate the movement of the cooler material toward the observer. The depth of the absorption lines also depends on the amount of cooler material. These results imply that this kind of spectral profiles can be used as a tool to diagnose the properties of cooler material above reconnection site.

astro-ph.SR↗

arXiv2020

Fairness and efficiency for probabilistic allocations with participation constraints

We propose a notion of fairness for allocation problems in which different agents may have different reservation utilities, stemming from different outside options, or property rights. Fairness is usually understood as the absence of envy, but this can be incompatible with reservation utilities. It is possible that Alice's envy of Bob's assignment cannot be remedied without violating Bob's participation constraint. Instead, we seek to rule out {\em justified envy}, defined as envy for which a remedy would not violate any agent's participation constraint. We show that fairness, meaning the absence of justified envy, can be achieved together with efficiency and individual rationality. We introduce a competitive equilibrium approach with price-dependent incomes obtaining the desired properties.

econ.TH↗

arXiv2020

Compressive Sensing Based Massive Access for IoT Relying on Media Modulation Aided Machine Type Communications

A fundamental challenge of the large-scale Internet-of-Things lies in how to support massive machine-type communications (mMTC). This letter proposes a media modulation based mMTC solution for increasing the throughput, where a massive multi-input multi-output based base station (BS) is used for enhancing the detection performance. For such a mMTC scenario, the reliable active device detection and data decoding pose a serious challenge. By leveraging the sparsity of the uplink access signals of mMTC received at the BS, a compressive sensing based massive access solution is proposed for tackling this challenge. Specifically, we propose a block sparsity adaptive matching pursuit algorithm for detecting the active devices, whereby the block-sparsity of the uplink access signals exhibited across the successive time slots and the structured sparsity of media modulated symbols are exploited for enhancing the detection performance. Moreover, a successive interference cancellation based structured subspace pursuit algorithm is conceived for data demodulation of the active devices, whereby the structured sparsity of media modulation based symbols found in each time slot is exploited for improving the detection performance. Finally, our simulation results verify the superiority of the proposed scheme over state-of-the-art solutions.

cs.IT↗

arXiv2020

DIHARD II is Still Hard: Experimental Results and Discussions from the DKU-LENOVO Team

In this paper, we present the submitted system for the second DIHARD Speech Diarization Challenge from the DKULENOVO team. Our diarization system includes multiple modules, namely voice activity detection (VAD), segmentation, speaker embedding extraction, similarity scoring, clustering, resegmentation and overlap detection. For each module, we explore different techniques to enhance performance. Our final submission employs the ResNet-LSTM based VAD, the Deep ResNet based speaker embedding, the LSTM based similarity scoring and spectral clustering. Variational Bayes (VB) diarization is applied in the resegmentation stage and overlap detection also brings slight improvement. Our proposed system achieves 18.84% DER in Track1 and 27.90% DER in Track2. Although our systems have reduced the DERs by 27.5% and 31.7% relatively against the official baselines, we believe that the diarization task is still very difficult.

eess.AS↗

arXiv2020

Scene Text Recognition via Transformer

Scene text recognition with arbitrary shape is very challenging due to large variations in text shapes, fonts, colors, backgrounds, etc. Most state-of-the-art algorithms rectify the input image into the normalized image, then treat the recognition as a sequence prediction task. The bottleneck of such methods is the rectification, which will cause errors due to distortion perspective. In this paper, we find that the rectification is completely unnecessary. What all we need is the spatial attention. We therefore propose a simple but extremely effective scene text recognition method based on transformer [50]. Different from previous transformer based models [56,34], which just use the decoder of the transformer to decode the convolutional attention, the proposed method use a convolutional feature maps as word embedding input into transformer. In such a way, our method is able to make full use of the powerful attention mechanism of the transformer. Extensive experimental results show that the proposed method significantly outperforms state-of-the-art methods by a very large margin on both regular and irregular text datasets. On one of the most challenging CUTE dataset whose state-of-the-art prediction accuracy is 89.6%, our method achieves 99.3%, which is a pretty surprising result. We will release our source code and believe that our method will be a new benchmark of scene text recognition with arbitrary shapes.

cs.CV↗

arXiv2020

A New Quasi One-Dimensional Compound Ba3TiTe5 and Superconductivity Induced by Pressure

We report systematical studies of a new quasi-one-dimensional (1D) compound Ba3TiTe5 and the high-pressure induced superconductivity therein. Ba3TiTe5 was synthesized at high pressure and high temperature. It crystallizes into a hexagonal structure (P63/mcm), which consists of infinite face-sharing octahedral TiTe6 chains and Te chains along the c axis, exhibiting a strong 1D characteristic structure. The first-principles calculations demonstrate that Ba3TiTe5 is a well-defined 1D conductor and thus, it can be considered a starting point to explore the exotic physics induced by pressure via enhancing the interchain hopping to move the 1D conductor to a high dimensional metal. For Ba3TiTe5, high-pressure techniques were employed to study the emerging physics dependent on interchain hopping, such as the Umklapp scattering effect, spin/charge density wave (SDW/CDW), superconductivity and non-Fermi Liquid behavior. Finally, a complete phase diagram was plotted. The superconductivity emerges from 8.8 GPa, near which the Umklapp gap is mostly suppressed. Tc is enhanced and reaches the maximum ~6 K at about 36.7 GPa, where the spin/charge density wave (SDW/CDW) is completely suppressed, and a non-Fermi Liquid behavior appears. Our results suggest that the appearance of superconductivity is associated with the fluctuation due to the suppression of Umklapp gap and the enhancement of Tc is related with the fluctuation of the SDW/CDW.

cond-mat.supr-con↗