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Jun Zhang

Publications and source records attributed to Jun Zhang.

At least 739 records · Page 41Linked to original sources

Holographic entanglement entropy of the double Wick rotated BTZ black hole

In this paper, we analyze the holographic covariant entanglement entropy in the double Wick rotated version of a rotating BTZ black hole (3 dimensional Kerr-AdS solution), where the periodicity of Euclidean time and spatial direction are changed. The dual field theory has negative energy in the Lorentzian signature. The holographic entanglement entropy agrees with its CFT counterpart, which is obtained by a conformal transformation of the correlation functions of twisted operators.

hep-th↗

Miniaturizing Color-Sensitive Photodetectors via Hybrid Nanoantennas towards Sub-micron Dimensions

Digital camera sensors utilize color filters on photodiodes to achieve color selectivity. As color filters and photosensitive silicon layers are separate elements, these sensors suffer from optical cross-talk, which sets limits to the minimum pixel size. In this paper, we report hybrid silicon-aluminum nanostructures in the extreme limit of zero distance between color filters and sensors. This design could essentially achieve sub micron pixel dimensions and minimize the optical cross-talk originated from tilt illuminations. The designed hybrid silicon-aluminum nanostructure has dual functionalities. Crucially, it supports a hybrid Mie-plasmon resonance of magnetic dipole to achieve the color-selective light absorption, generating electron hole pairs. Simultaneously, the silicon-aluminum interface forms a Schottky barrier for charge separation and photodetection. This design could potentially replace the traditional dye based filters for camera sensors at ultra-high pixel densities with advanced functionalities in sensing polarization and directionality, as well as UV selectivity via interband plasmons of silicon.

physics.optics↗

Shear Measurement with Poorly Resolved Images

Weak lensing studies typically require excellent seeing conditions for the purpose of maximizing the number density of well-resolved galaxy images. It is interesting to ask to what extent the seeing size limits the usefulness of the astronomical images in weak lensing. In this work, we study this issue with the data of the DECam Legacy Survey (DECaLS), which is a part of the target selection program for the Dark Energy Spectroscopic Instrument (DESI). Using the Fourier Quad shear measurement pipeline, we demonstrate that images with relatively poor seeing conditions (around 1.5 arcsec) can still yield accurate shear estimators. We do not find any correlation between systematic shear error and the image resolution.

astro-ph.CO↗

Tolerance For the Pixelation Effect in Shear Measurement

Images taken by space telescopes typically have a superb spatial resolution, but a relatively poor sampling rate due to the finite CCD pixel size. Beyond the Nyquist limit, it becomes uncertain how much the pixelation effect may affect the accuracy of galaxy shape measurement. It is timely to study this issue given that a number of space-based large-scale weak lensing surveys are planned. Using the Fourier_Quad method, we quantify the shear recovery error as a function of the sampling factor Q, i.e., the ratio between the FWHM of the point-spread-function (PSF) and the pixel size of the CCD, for different PSFs and galaxies of different sizes and noise levels. We show that sub-percent-level accuracy in shear recovery is achievable with single-exposure images for $Q\lesssim 2$. The conclusion holds for galaxies much smaller than the PSF, and those with a significant level of noise.

astro-ph.CO↗

PyramidCLIP: Hierarchical Feature Alignment for Vision-language Model Pretraining

Large-scale vision-language pre-training has achieved promising results on downstream tasks. Existing methods highly rely on the assumption that the image-text pairs crawled from the Internet are in perfect one-to-one correspondence. However, in real scenarios, this assumption can be difficult to hold: the text description, obtained by crawling the affiliated metadata of the image, often suffers from the semantic mismatch and the mutual compatibility. To address these issues, we introduce PyramidCLIP, which constructs an input pyramid with different semantic levels for each modality, and aligns visual elements and linguistic elements in the form of hierarchy via peer-level semantics alignment and cross-level relation alignment. Furthermore, we soften the loss of negative samples (unpaired samples) so as to weaken the strict constraint during the pre-training stage, thus mitigating the risk of forcing the model to distinguish compatible negative pairs. Experiments on five downstream tasks demonstrate the effectiveness of the proposed PyramidCLIP. In particular, with the same amount of 15 million pre-training image-text pairs, PyramidCLIP exceeds CLIP on ImageNet zero-shot classification top-1 accuracy by 10.6%/13.2%/10.0% with ResNet50/ViT-B32/ViT-B16 based image encoder respectively. When scaling to larger datasets, PyramidCLIP achieves the state-of-the-art results on several downstream tasks. In particular, the results of PyramidCLIP-ResNet50 trained on 143M image-text pairs surpass that of CLIP using 400M data on ImageNet zero-shot classification task, significantly improving the data efficiency of CLIP.

cs.CV↗

Evolution as a Service: A Privacy-Preserving Genetic Algorithm for Combinatorial Optimization

Evolutionary algorithms (EAs), such as the genetic algorithm (GA), offer an elegant way to handle combinatorial optimization problems (COPs). However, limited by expertise and resources, most users do not have enough capability to implement EAs to solve COPs. An intuitive and promising solution is to outsource evolutionary operations to a cloud server, whilst it suffers from privacy concerns. To this end, this paper proposes a novel computing paradigm, evolution as a service (EaaS), where a cloud server renders evolutionary computation services for users without sacrificing users' privacy. Inspired by the idea of EaaS, this paper designs PEGA, a novel privacy-preserving GA for COPs. Specifically, PEGA enables users outsourcing COPs to the cloud server holding a competitive GA and approximating the optimal solution in a privacy-preserving manner. PEGA features the following characteristics. First, any user without expertise and enough resources can solve her COPs. Second, PEGA does not leak contents of optimization problems, i.e., users' privacy. Third, PEGA has the same capability as the conventional GA to approximate the optimal solution. We implements PEGA falling in a twin-server architecture and evaluates it in the traveling salesman problem (TSP, a widely known COP). Particularly, we utilize encryption cryptography to protect users' privacy and carefully design a suit of secure computing protocols to support evolutionary operators of GA on encrypted data. Privacy analysis demonstrates that PEGA does not disclose the contents of the COP to the cloud server. Experimental evaluation results on four TSP datasets show that PEGA is as effective as the conventional GA in approximating the optimal solution.

cs.NE↗

Schwarzschild quasi-normal modes of non-minimally coupled vector fields

We study perturbations of massive and massless vector fields on a Schwarzschild black-hole background, including a non-minimal coupling between the vector field and the curvature. The coupling is given by the Horndeski vector-tensor operator, which we show to be unique, also when the field is massive, provided that the vector has a vanishing background value. We determine the quasi-normal mode spectrum of the vector field, focusing on the fundamental mode of monopolar and dipolar perturbations of both even and odd parity, as a function of the mass of the field and the coupling constant controlling the non-minimal interaction. In the massless case, we also provide results for the first two overtones, showing in particular that the isospectrality between even and odd modes is broken by the non-minimal gravitational coupling. We also consider solutions to the mode equations corresponding to quasi-bound states and static configurations. Our results for quasi-bound states provide strong evidence for the stability of the spectrum, indicating the impossibility of a vectorization mechanism within our set-up. For static solutions, we analytically and numerically derive results for the electromagnetic susceptibilities (the spin-1 analogs of the tidal Love numbers), which we show to be non-zero in the presence of the non-minimal coupling.

gr-qc↗

Massive Star-Forming Galaxies Have Converted Most of Their Halo Gas into Stars

In the local Universe, the efficiency for converting baryonic gas into stars is very low. In dark matter halos where galaxies form and evolve, the average efficiency varies with galaxy stellar mass and has a maximum of about twenty percent for Milky-Way-like galaxies. The low efficiency at higher mass is believed to be produced by some quenching processes, such as the feedback from active galactic nuclei. We perform an analysis of weak lensing and satellite kinematics for SDSS central galaxies. Our results reveal that the efficiency is much higher, more than sixty percent, for a large population of massive star-forming galaxies around $10^{11}M_{\odot}$. This suggests that these galaxies acquired most of the gas in their halos and converted it into stars without being affected significantly by quenching processes. This population of galaxies is not reproduced in current galaxy formation models, indicating that our understanding of galaxy formation is incomplete. The implications of our results on circumgalactic media, star formation quenching and disc galaxy rotation curves are discussed. We also examine systematic uncertainties in halo-mass and stellar-mass measurements that might influence our results.

astro-ph.GA↗

Photoelastic Stress Response of Complex 3D-Printed Particle Shapes

While stress visualization within 3-dimensional particles would greatly advance our understanding of the behaviors of complex particles, traditional photoelastic methods suffer from a lack of available technology for producing suitable complex particles. Recently, 3D-printing has created new possibilities for enhancing the scope of stress analysis within physically representative granules. Here, we investigate and evaluate opportunities offered by 3D-printing a single particle with a complex external shape with photoelastic properties. We report the results of X-ray computed tomography and 3D-printing, combined with traditional photoelastic analysis, to visualize strain for particles ranging from simple 2D discs to complex 3D printed coffee beans, including with internal voids. We find that the relative orientation of the print layers and the loading force affects the optical response of the discs, but without a significant difference in their mechanical properties. Furthermore, we present semi-quantitative measurements of stresses within 3D-printed complex particles. The paper outlines the potential limitations and areas of future interest for stress visualization of 3-dimensional particles.

cond-mat.soft↗

Error Rate Analysis for Grant-free Massive Random Access with Short-Packet Transmission

Grant-free massive random access (RA) is a promising protocol to support the massive machine-type communications (mMTC) scenario in 5G and beyond networks. In this paper, we focus on the error rate analysis in grant-free massive RA, which is critical for practical deployment but has not been well studied. We consider a two-phase frame structure, with a pilot transmission phase for activity detection and channel estimation, followed by a data transmission phase with coded data symbols. Considering the characteristics of short-packet transmission, we analyze the block error rate (BLER) in the finite blocklength regime to characterize the data transmission performance. The analysis involves characterizing the activity detection and channel estimation errors as well as applying the random matrix theory (RMT) to analyze the distribution of the post-processing signal-to-noise ratio (SNR). As a case study, the derived BLER expression is further simplified to optimize the pilot length. Simulation results verify our analysis and demonstrate its effectiveness in pilot length optimization.

cs.IT↗

First measurement of the characteristic depletion radius of dark matter haloes from weak lensing

We use weak lensing observations to make the first measurement of the characteristic depletion radius, one of the three radii that characterize the region where matter is being depleted by growing haloes. The lenses are taken from the halo catalog produced by the extended halo-based group/cluster finder applied to DESI Legacy Imaging Surveys DR9, while the sources are extracted from the DECaLS DR8 imaging data with the Fourier_Quad pipeline. We study halo masses $12 < \log ( M_{\rm grp} ~[{\rm M_{\odot}}/h] ) \leq 15.3$ within redshifts $0.2 \leq z \leq 0.3$. The virial and splashback radii are also measured and used to test the original findings on the depletion region. When binning haloes by mass, we find consistency between most of our measurements and predictions from the CosmicGrowth simulation, with exceptions to the lowest mass bins. The characteristic depletion radius is found to be roughly $2.5$ times the virial radius and $1.7 - 3$ times the splashback radius, in line with an approximately universal outer density profile, and the average enclosed density within the characteristic depletion radius is found to be roughly $29$ times the mean matter density of the Universe in our sample. When binning haloes by both mass and a proxy for halo concentration, we do not detect a significant variation of the depletion radius with concentration, on which the simulation prediction is also sensitive to the choice of concentration proxy. We also confirm that the measured splashback radius varies with concentration differently from simulation predictions.

astro-ph.CO↗

Can the graviton have a large mass near black holes?

The mass of the graviton, if nonzero, is usually considered to be very small, e.g. of the Hubble scale, from several observational constraints. In this paper, we propose a gravity model where the graviton mass is very small in the usual weak gravity environments, below all the current graviton mass bounds, but becomes much larger in the strong gravity regime such as a black hole's vicinity. For black holes in this model, significant deviations from general relativity emerge very close to the black hole horizon and alter the black hole quasi-normal modes, which can be extracted from the ringdown waveform of black hole binary mergers. Also, the enhancement of the graviton mass near the horizon can result in echoes in the late time ringdown, which can be verified in the upcoming gravitational wave observations of higher sensitivity.

gr-qc↗

An Extended Halo-based Group/Cluster finder: application to the DESI legacy imaging surveys DR8

We extend the halo-based group finder developed by \citet[][]{Yang2005a} to use data {\it simultaneously} with either photometric or spectroscopic redshifts. A mock galaxy redshift survey constructed from a high-resolution N-body simulation is used to evaluate the performance of this extended group finder. For galaxies with magnitude ${\rm z\le 21}$ and redshift $0<z\le 1.0$ in the DESI legacy imaging surveys (the Legacy Surveys), our group finder successfully identifies more than 60\% of the members in about $90\%$ of halos with mass $\ga 10^{12.5}\msunh$. Detected groups with mass $\ga 10^{12.0}\msunh$ have a purity (the fraction of true groups) greater than 90\%. The halo mass assigned to each group has an uncertainty of about 0.2 dex at the high mass end $\ga 10^{13.5}\msunh$ and 0.40 dex at the low mass end. Groups with more than 10 members have a redshift accuracy of $\sim 0.008$. We apply this group finder to the Legacy Surveys DR8 and find 5.2 Million groups with at least 3 members. About 387,000 of these groups have at least 10 members. The resulting catalog containing 3D coordinates, richness, halo masses, and total group luminosities, is made publicly available.

astro-ph.GA↗

Attention Distraction: Watermark Removal Through Continual Learning with Selective Forgetting

Fine-tuning attacks are effective in removing the embedded watermarks in deep learning models. However, when the source data is unavailable, it is challenging to just erase the watermark without jeopardizing the model performance. In this context, we introduce Attention Distraction (AD), a novel source data-free watermark removal attack, to make the model selectively forget the embedded watermarks by customizing continual learning. In particular, AD first anchors the model's attention on the main task using some unlabeled data. Then, through continual learning, a small number of \textit{lures} (randomly selected natural images) that are assigned a new label distract the model's attention away from the watermarks. Experimental results from different datasets and networks corroborate that AD can thoroughly remove the watermark with a small resource budget without compromising the model's performance on the main task, which outperforms the state-of-the-art works.

cs.CV↗

A class of twisted generalized Reed-Solomon codes

Let $\mathbb{F}_q$ be a finite field of size $q$ and $\mathbb{F}_q^*$ the set of non-zero elements of $\mathbb{F}_q$. In this paper, we study a class of twisted generalized Reed-Solomon code $C_\ell(D, k, η, \vec{v})\subset \mathbb{F}_q^n$ generated by the following matrix \[ \left(\begin{array}{cccc} v_{1} & v_{2} & \cdots & v_{n} \\ v_{1} α_{1} & v_{2} α_{2} & \cdots & v_{n} α_{n} \\ \vdots & \vdots & \ddots & \vdots \\ v_{1} α_{1}^{\ell-1} & v_{2} α_{2}^{\ell-1} & \cdots & v_{n} α_{n}^{\ell-1} \\ v_{1} α_{1}^{\ell+1} & v_{2} α_{2}^{\ell+1} & \cdots & v_{n} α_{n}^{\ell+1} \\ \vdots & \vdots & \ddots & \vdots \\ v_{1} α_{1}^{k-1} & v_{2} α_{2}^{k-1} & \cdots & v_{n} α_{n}^{k-1} \\ v_{1}\left(α_{1}^{\ell}+ηα_{1}^{q-{2}}\right) & v_{2}\left(α_{2}^{\ell}+ ηα_{2}^{q-2}\right) &\cdots & v_{n}\left(α_{n}^{\ell}+ηα_{n}^{q-2}\right) \end{array}\right) \] where $0\leq \ell\leq k-1,$ the evaluation set $D=\{α_{1},α_{2},\cdots, α_{n}\}\subseteq \mathbb{F}_q^*$, scaling vector $\vec{v}=(v_1,v_2,\cdots,v_n)\in (\mathbb{F}_q^*)^n$ and $η\in\mathbb{F}_q^*$. The minimum distance and dual code of $C_\ell(D, k, η, \vec{v})$ will be determined. For the special case $\ell=k-1,$ a sufficient and necessary condition for $C_{k-1}(D, k, η, \vec{v})$ to be self-dual will be given. We will also show that the code is MDS or near-MDS. Moreover, a complete classification when the code is near-MDS or MDS will be presented.

cs.IT↗

Who is next: rising star prediction via diffusion of user interest in social networks

Finding items with potential to increase sales is of great importance in online market. In this paper, we propose to study this novel and practical problem: rising star prediction. We call these potential items Rising Star, which implies their ability to rise from low-turnover items to best-sellers in the future. Rising stars can be used to help with unfair recommendation in e-commerce platform, balance supply and demand to benefit the retailers and allocate marketing resources rationally. Although the study of rising star can bring great benefits, it also poses challenges to us. The sales trend of rising star fluctuates sharply in the short-term and exhibits more contingency caused by some external events (e.g., COVID-19 caused increasing purchase of the face mask) than other items, which cannot be solved by existing sales prediction methods. To address above challenges, in this paper, we observe that the presence of rising stars is closely correlated with the early diffusion of user interest in social networks, which is validated in the case of Taocode (an intermediary that diffuses user interest in Taobao). Thus, we propose a novel framework, RiseNet, to incorporate the user interest diffusion process with the item dynamic features to effectively predict rising stars. Specifically, we adopt a coupled mechanism to capture the dynamic interplay between items and user interest, and a special designed GNN based framework to quantify user interest. Our experimental results on large-scale real-world datasets provided by Taobao demonstrate the effectiveness of our proposed framework.

cs.SI↗

Self-Supervised Light Field Depth Estimation Using Epipolar Plane Images

Exploiting light field data makes it possible to obtain dense and accurate depth map. However, synthetic scenes with limited disparity range cannot contain the diversity of real scenes. By training in synthetic data, current learning-based methods do not perform well in real scenes. In this paper, we propose a self-supervised learning framework for light field depth estimation. Different from the existing end-to-end training methods using disparity label per pixel, our approach implements network training by estimating EPI disparity shift after refocusing, which extends the disparity range of epipolar lines. To reduce the sensitivity of EPI to noise, we propose a new input mode called EPI-Stack, which stacks EPIs in the view dimension. This method is less sensitive to noise scenes than traditional input mode and improves the efficiency of estimation. Compared with other state-of-the-art methods, the proposed method can also obtain higher quality results in real-world scenarios, especially in the complex occlusion and depth discontinuity.

cs.CV↗

Symplectic non-convexity of toric domains

We investigate the convexity up to symplectomorphism (called symplectic convexity) of star-shaped toric domains in $\mathbb R^4$. In particular, based on the criterion from Chaidez-Edtmair via Ruelle invariant and systolic ratio of the boundary of star-shaped toric domains, we provide elementary operations on domains that can kill the symplectic convexity. These operations only result in $C^0$-small perturbations in terms of domains' volume. Moreover, one of the operations is a systematic way to produce examples of dynamically convex but not symplectically convex toric domains. Finally, we are able to provide concrete bounds for the constants that appear in Chaidez-Edtmair's criterion.

math.SG↗