arXiv subjects

Jun Zhang

Publications and source records attributed to Jun Zhang.

At least 847 records · Page 47Linked to original sources

arXiv2021

Single-photon imaging over 200 km

Long-range active imaging has widespread applications in remote sensing and target recognition. Single-photon light detection and ranging (lidar) has been shown to have high sensitivity and temporal resolution. On the application front, however, the operating range of practical single-photon lidar systems is limited to about tens of kilometers over the Earth's atmosphere, mainly due to the weak echo signal mixed with high background noise. Here, we present a compact coaxial single-photon lidar system capable of realizing 3D imaging at up to 201.5 km. It is achieved by using high-efficiency optical devices for collection and detection, and what we believe is a new noise-suppression technique that is efficient for long-range applications. We show that photon-efficient computational algorithms enable accurate 3D imaging over hundreds of kilometers with as few as 0.44 signal photons per pixel. The results represent a significant step toward practical, low-power lidar over extra-long ranges.

eess.IV↗

arXiv2021

Towards a Bias-Free Selection Function in Shear Measurement

Sample selection is a necessary preparation for weak lensing measurement. It is well-known that selection itself may introduce bias in the measured shear signal. Using image simulation and the Fourier_Quad shear measurement pipeline, we quantify the selection bias in various commonly used selection function (signal-to-noise-ratio, magnitude, etc.). We proposed a new selection function defined in the power spectrum of the galaxy image. This new selection function has low selection bias, and it is particularly convenient for shear measurement pipelines based on Fourier transformation.

astro-ph.CO↗

arXiv2021

s-d coupling enhanced phonon anharmonicity in copper-based compounds

Materials with ultralow thermal conductivity are of great interest for efficient energy conversion and thermal barrier coating. Copper-based semiconductors such as copper chalcogenides and copper halides are known to possess extreme low thermal conductivity, whereas the fundamental origin of the low thermal conductivity observed in the copper-based materials remains elusive. Here, we reveal that s-d coupling induced giant phonon anharmonicity is the fundamental mechanism responsible for the ultralow thermal conductivity of copper compounds. The symmetry controlled strong coupling of high-lying occupied copper 3d orbital with the unoccupied 4s state under thermal vibration remarkably lowers the lattice potential barrier, which enhances anharmonic scattering between phonons. This understanding is confirmed by temperature-dependent Raman spectra measurements. Our study offers an insight at atomic level connecting electronic structures with phonon vibration modes, and thus sheds light on materials properties that rely on electron-phonon coupling, such as thermoelectricity and superconductivity.

cond-mat.mtrl-sci↗

arXiv2021

Supporting More Active Users for Massive Access via Data-assisted Activity Detection

Massive machine-type communication (mMTC) has been regarded as one of the most important use scenarios in the fifth generation (5G) and beyond wireless networks, which demands scalable access for a large number of devices. While grant-free random access has emerged as a promising mechanism for massive access, its potential has not been fully unleashed. Particularly, the two key tasks in massive access systems, namely, user activity detection and data detection, were handled separately in most existing studies, which ignored the common sparsity pattern in the received pilot and data signal. Moreover, error detection and correction in the payload data provide additional mechanisms for performance improvement. In this paper, we propose a data-assisted activity detection framework, which aims at supporting more active users by reducing the activity detection error, consisting of false alarm and missed detection errors. Specifically, after an initial activity detection step based on the pilot symbols, the false alarm users are filtered by applying energy detection for the data symbols; once data symbols of some active users have been successfully decoded, their effect in activity detection will be resolved via successive pilot interference cancellation, which reduces the missed detection error. Simulation results show that the proposed algorithm effectively increases the activity detection accuracy, and it is able to support $\sim 20\%$ more active users compared to a conventional method in some sample scenarios.

eess.SP↗

arXiv2021

Experimental Realization of Device-Independent Quantum Randomness Expansion

Randomness expansion where one generates a longer sequence of random numbers from a short one is viable in quantum mechanics but not allowed classically. Device-independent quantum randomness expansion provides a randomness resource of the highest security level. Here, we report the first experimental realization of device-independent quantum randomness expansion secure against quantum side information established through quantum probability estimation. We generate $5.47\times10^8$ quantum-proof random bits while consuming $4.39\times10^8$ bits of entropy, expanding our store of randomness by $1.08\times10^8$ bits at a latency of about $13.1$ h, with a total soundness error $4.6\times10^{-10}$. Device-independent quantum randomness expansion not only enriches our understanding of randomness but also sets a solid base to bring quantum-certifiable random bits into realistic applications.

quant-ph↗

arXiv2021

Symplectic structure perturbations and continuity of symplectic invariants

This paper studies how symplectic invariants created from Hamiltonian Floer theory change under the perturbations of symplectic structures, not necessarily in the same cohomology class. These symplectic invariants include spectral invariants, boundary depth, and (partial) symplectic quasi-states. This paper can split into two parts. In the first part, we prove some energy estimations that control the shifts of symplectic action functionals. These directly imply positive conclusions on the continuity of spectral invariants and boundary depth, in some important cases including any symplectic surface $Σ_{g \geq1}$ and any closed symplectic manifold $M$ with $\dim_{\mathcal K} H^2(M; \mathcal K) = 1$. This follows by applications on some rigidity of the subsets of a symplectic manifold in terms of heaviness and superheaviness, as well as on the continuity property of some symplectic capacities. In the second part, we generalize the construction in the first part to any closed symplectic manifold. In particular, to deal with the change of Novikov rings from symplectic structure perturbations, we construct a family of variant Floer chain complexes over a common Novikov-type ring. In this set-up, we define a new family of spectral invariants called $t$-spectral invariants, and prove that they are upper semicontinuous under the symplectic structure perturbations. This implies a quasi-isometric embedding from $(\mathbb R^{\infty}, |-|_{\infty})$ to $({\widetilde {\rm Ham}}(M, ω), |-|_{\rm Hofer})$ under some dynamical assumption, imitating a result from Usher.

math.SG↗

arXiv2021

The shape invariant of symplectic ellipsoids

The shape invariant of a symplectic manifold encodes the possible area classes of embedded Lagrangian tori. Potentially this is a powerful invariant, but for most manifolds the shape is unknown. We compute the shape for 4 dimensional symplectic ellipsoids where the ratio of the factors is an integer. The full shape invariant gives stronger embedding obstructions than results by considering only monotone tori.

math.SG↗

arXiv2021

p-cyclic persistent homology and Hofer distance

In this paper, we generalize the result from L. Polterovich and E. Shelukhin's paper stating that Hofer distance from time-dependent Hamiltonian diffeomorphism to the set of p-th power Hamiltonian diffeomorphism can be arbitrarily large to hold in the product structure $Σ_g \times M$ for any closed symplectic manifold $M$ when $p$ is sufficiently large and $g \geq 4$. This implies that, on this product, Hofer distance can be arbitrarily large between time-dependent Hamiltonian diffeomorphism and autonomous Hamiltonian diffeomorphism.The basic tool we use is barcode and singular value decomposition that are developed in previous joint work with M. Usher, from which we borrow many proofs and modify them so that it can be adapted to the situation that filtered chain complex equipped with a group action.

math.SG↗

arXiv2021

Crystal structure and properties of iron-based spin-chain compound Ba9Fe3Se15

We report the synthesis of a new quasi one-dimensional (1D) iron selenide. Ba9Fe3Se15 was synthesized at high temperature and high pressure of 5.5 GPa and systematically studied via structural, magnetic and transport measurements at ambient and at high-pressures. Ba9Fe3Se15 crystallizes in a monoclinic structure and consists of face-sharing FeSe6 octahedral chains along the c axis. At ambient pressure it exhibits an insulating behavior with a band gap ~460 meV and undergoes a ferrimagnet-like phase transition at 14 K. Under high pressure, a complete metallization occurs at ~29 GPa, which is accompanied by a spin state crossover from high spin (HS) state to low spin (LS) state. The LS appears for pressures P >36 GPa.

cond-mat.supr-con↗

arXiv2021

Topological Persistence in Geometry and Analysis

The theory of persistence modules is an emerging field of algebraic topology which originated in topological data analysis. In these notes we provide a concise introduction into this field and give an account on some of its interactions with geometry and analysis. In particular, we present applications of persistence to symplectic topology, including the geometry of symplectomorphism groups and embedding problems. Furthermore, we discuss topological function theory which provides a new insight on oscillation of functions. The material should be accessible to readers with a basic background in algebraic and differential topology.

math.AT↗

arXiv2021

Measuring bulk and surface acoustic modes in diamond by angle-resolved Brillouin spectroscopy

The acoustic modes of diamond not only are of profound significance for studying its thermal conductivity, mechanical properties and optical properties, but also play a determined role in the performance of high-frequency and high-power acoustic wave devices. Here we report the bulk acoustic waves (BAWs) and surface acoustic waves (SAWs) of single crystal diamond by using the angle-resolved Brillouin light scattering (BLS) spectroscopy. We identify two high-velocity surface skimming bulk waves, with sound velocities of 1.277x10^6 and 1.727x10^6 cm/s, respectively. Furthermore, we conduct the relationship among the refractive index, incident angle and the velocities of BAWs propagating along an arbitrary direction. Our results may provide a valuable reference for fundamental researches and devices engineering in the community of diamond-based acoustic study.

cond-mat.mtrl-sci↗

arXiv2021

Contextual Non-Local Alignment over Full-Scale Representation for Text-Based Person Search

Text-based person search aims at retrieving target person in an image gallery using a descriptive sentence of that person. It is very challenging since modal gap makes effectively extracting discriminative features more difficult. Moreover, the inter-class variance of both pedestrian images and descriptions is small. So comprehensive information is needed to align visual and textual clues across all scales. Most existing methods merely consider the local alignment between images and texts within a single scale (e.g. only global scale or only partial scale) then simply construct alignment at each scale separately. To address this problem, we propose a method that is able to adaptively align image and textual features across all scales, called NAFS (i.e.Non-local Alignment over Full-Scale representations). Firstly, a novel staircase network structure is proposed to extract full-scale image features with better locality. Secondly, a BERT with locality-constrained attention is proposed to obtain representations of descriptions at different scales. Then, instead of separately aligning features at each scale, a novel contextual non-local attention mechanism is applied to simultaneously discover latent alignments across all scales. The experimental results show that our method outperforms the state-of-the-art methods by 5.53% in terms of top-1 and 5.35% in terms of top-5 on text-based person search dataset. The code is available at https://github.com/TencentYoutuResearch/PersonReID-NAFS

cs.CV↗

arXiv2021

Strategy-proof allocation with outside option

Strategy-proof mechanisms are widely used in market design. In an abstract allocation framework where outside options are available to agents, we obtain two results for strategy-proof mechanisms. They provide a unified foundation for several existing results in distinct models and imply new results in some models. The first result proves that, for individually rational and strategy-proof mechanisms, pinning down every agent's probability of choosing his outside option is equivalent to pinning down a mechanism. The second result provides a sufficient condition for two strategy-proof mechanisms to be equivalent when the number of possible allocations is finite.

econ.TH↗

arXiv2020

Mitigating the Noise-Source Coupling Effect in Shear Measurement

Sub-percent level accuracy in shear measurement is required by the Stage-IV weak lensing surveys. One important challenge is about suppressing the shear bias on source images of low signal-to-noise ratios (SNR$\lesssim10$). Previously, it has been demonstrated that the shear estimators defined in the Fourier_Quad (FQ) method can achieve sub-percent accuracy at the very faint end (SNR$\lesssim5$) through ensemble averaging. Later, it is found that we can approach the minimum statistical error (the Cramer-Rao Bound) by symmetrizing the full PDF of the FQ shear estimators (the PDF_SYM approach), instead of taking ensemble averages. Recently, with a large amount of mock galaxy images, we are able to identify some small amount of shear biases in the PDF_SYM approach at the faint end. The multiplicative bias goes up to $1-2\times10^{-2}$ at SNR $\lesssim 10$, and the anisotropy of the point spread function (PSF) causes an additive bias that can reach a few times $10^{-4}$. We find that these biases originate from the noise-source coupling in the galaxy power spectrum. It turns out that this problem can be largely fixed by adding additional terms to the FQ shear estimators. The resulting multiplicative and additive biases can be significantly suppressed to the level of $10^{-3}$ and $10^{-5}$ respectively. These corrections substantially extend the available SNR range for accurate shear measurement with the PDF_SYM approach.

astro-ph.CO↗

arXiv2020

Blind Data Detection in Massive MIMO via $\ell_3$-norm Maximization over the Stiefel Manifold

Massive MIMO has been regarded as a key enabling technique for 5G and beyond networks. Nevertheless, its performance is limited by the large overhead needed to obtain the high-dimensional channel information. To reduce the huge training overhead associated with conventional pilot-aided designs, we propose a novel blind data detection method by leveraging the channel sparsity and data concentration properties. Specifically, we propose a novel $\ell_3$-norm-based formulation to recover the data without channel estimation. We prove that the global optimal solution to the proposed formulation can be made arbitrarily close to the transmitted data up to a phase-permutation ambiguity. We then propose an efficient parameter-free algorithm to solve the $\ell_3$-norm problem and resolve the phase permutation ambiguity. We also derive the convergence rate in terms of key system parameters such as the number of transmitters and receivers, the channel noise power, and the channel sparsity level. Numerical experiments will show that the proposed scheme has superior performance with low computational complexity.

eess.SP↗

arXiv2020

Avoiding the Geometric Boundary Effect in Shear Measurement

In image processing, source detections are inevitably affected by the presence of the geometric boundaries in the images, including the physical boundaries of the CCD, and the boundaries of masked regions due to column defects, bright diffraction spikes, etc.. These boundary conditions make the source detection process not statistically isotropic. It can lead to additive shear bias near the boundaries. We build a phenomenological model to understand the bias, and propose a simple method to effectively eliminate the influence of geometric boundaries on shear measurement. We demonstrate the accuracy and efficiency of this method using both simulations and the z-band imaging data from the third data release of the DECam Legacy Survey.

astro-ph.IM↗

arXiv2020

The Formation and Eruption of A Sigmoidal Filament Driven by Rotating Network Magnetic Fields

We present the formation and eruption of a sigmoidal filament driven by rotating network magnetic fields (RNFs) near the center of the solar disk, which was observed by the one-meter aperture New Vacuum Solar Telescope (NVST) at Fuxian Solar Observatory (FSO) on 2018 July 12. Counterclockwise RNFs twist two small-scale filaments at their northeastern foot-point region, giving a rotation of nearly 200 degree within about 140 minutes. The motion of the RNF has a tendency to accelerate at first and then decelerate obviously, as the average rotation speed increased from 10 to 150 ,and then slowed down to 50 . Coalescence then occurs between filaments F1 and F2. Meanwhile the fine structures in the southwestern region of the filament was involved in another interaction of coalescence. The subsequent EUV brightening due to plasma heating is observed in the two interaction regions. These interacting structures, including F1, F2 and the fine structures in the southwestern region, eventually evolve into a larger-scale sigmoidal filament twisted in the same direction as the RNFs gave. The twist of the sigmoidal filament has exceeded 4π and the filament erupted finally. The motion of the sigmoidal filament keeps uniform until a nearby jet collides, causing the filament to erupt faster. These results provide evidence that RNF plays an important role in the formation and eruption of the sigmoidal filament. The phenomena also suggests that the kink instability is the trigger mechanism for the filament eruption.

astro-ph.SR↗

arXiv2020

Exploiting Behavioral Consistence for Universal User Representation

User modeling is critical for developing personalized services in industry. A common way for user modeling is to learn user representations that can be distinguished by their interests or preferences. In this work, we focus on developing universal user representation model. The obtained universal representations are expected to contain rich information, and be applicable to various downstream applications without further modifications (e.g., user preference prediction and user profiling). Accordingly, we can be free from the heavy work of training task-specific models for every downstream task as in previous works. In specific, we propose Self-supervised User Modeling Network (SUMN) to encode behavior data into the universal representation. It includes two key components. The first one is a new learning objective, which guides the model to fully identify and preserve valuable user information under a self-supervised learning framework. The other one is a multi-hop aggregation layer, which benefits the model capacity in aggregating diverse behaviors. Extensive experiments on benchmark datasets show that our approach can outperform state-of-the-art unsupervised representation methods, and even compete with supervised ones.

cs.LG↗