arXiv subjects

Zihao Li

Publications and source records attributed to Zihao Li.

At least 235 records · Page 13Linked to original sources

arXiv2023

Exploiting Inductive Bias in Transformer for Point Cloud Classification and Segmentation

Discovering inter-point connection for efficient high-dimensional feature extraction from point coordinate is a key challenge in processing point cloud. Most existing methods focus on designing efficient local feature extractors while ignoring global connection, or vice versa. In this paper, we design a new Inductive Bias-aided Transformer (IBT) method to learn 3D inter-point relations, which considers both local and global attentions. Specifically, considering local spatial coherence, local feature learning is performed through Relative Position Encoding and Attentive Feature Pooling. We incorporate the learned locality into the Transformer module. The local feature affects value component in Transformer to modulate the relationship between channels of each point, which can enhance self-attention mechanism with locality based channel interaction. We demonstrate its superiority experimentally on classification and segmentation tasks. The code is available at: https://github.com/jiamang/IBT

cs.CV↗

arXiv2023

A SPectroscopic survey of biased halos In the Reionization Era (ASPIRE): A First Look at the Rest-frame Optical Spectra of $z > 6.5$ Quasars Using JWST

Studies of rest-frame optical emission in quasars at $z>6$ have historically been limited by the wavelengths accessible by ground-based telescopes. The James Webb Space Telescope (JWST) now offers the opportunity to probe this emission deep into the reionization epoch. We report the observations of eight quasars at $z>6.5$ using the JWST/NIRCam Wide Field Slitless Spectroscopy, as a part of the ''A SPectroscopic survey of biased halos In the Reionization Era (ASPIRE)" program. Our JWST spectra cover the quasars' emission between rest frame $\sim$ 4100 and 5100 Å. The profiles of these quasars' broad H$β$ emission lines span a FWHM from 3000 to 6000 $\rm{km~s^{-1}}$. The H$β$-based virial black hole (BH) masses, ranging from 0.6 to 2.1 billion solar masses, are generally consistent with their MgII-based BH masses. The new measurements based on the more reliable H$β$ tracer thus confirm the existence of billion solar-mass BHs in the reionization epoch. In the observed [OIII] $λλ$4960,5008 doublets of these luminous quasars, broad components are more common than narrow core components ($\le~1200~\rm{km~s^{-1}}$), and only one quasar shows stronger narrow components than broad. Two quasars exhibit significantly broad and blueshifted [OIII] emission, thought to trace galactic-scale outflows, with median velocities of $-610~\rm{km~s^{-1}}$ and $-1430~\rm{km~s^{-1}}$ relative to the [CII] $158\,μ$m line. All eight quasars show strong optical FeII emission, and follow the Eigenvector 1 relations defined by low-redshift quasars. The entire ASPIRE program will eventually cover 25 quasars and provide a statistical sample for the studies of the BHs and quasar spectral properties.

astro-ph.GA↗

arXiv2023

A SPectroscopic survey of biased halos In the Reionization Era (ASPIRE): JWST Reveals a Filamentary Structure around a z=6.61 Quasar

We present the first results from the JWST ASPIRE program (A SPectroscopic survey of biased halos In the Reionization Era). This program represents an imaging and spectroscopic survey of 25 reionization-era quasars and their environments by utilizing the unprecedented capabilities of NIRCam Wide Field Slitless Spectroscopy (WFSS) mode. ASPIRE will deliver the largest ($\sim280~{\rm arcmin}^2$) galaxy redshift survey at 3-4 $μ$m among JWST Cycle-1 programs and provide extensive legacy values for studying the formation of the earliest supermassive black holes (SMBHs), the assembly of galaxies, early metal enrichment, and cosmic reionization. In this first ASPIRE paper, we report the discovery of a filamentary structure traced by the luminous quasar J0305-3150 and ten [OIII] emitters at $z=6.6$. This structure has a 3D galaxy overdensity of $δ_{\rm gal}=12.6$ over 637 cMpc$^3$, one of the most overdense structures known in the early universe, and could eventually evolve into a massive galaxy cluster. Together with existing VLT/MUSE and ALMA observations of this field, our JWST observations reveal that J0305-3150 traces a complex environment where both UV-bright and dusty galaxies are present, and indicate that the early evolution of galaxies around the quasar is not simultaneous. In addition, we discovered 31 [OIII] emitters in this field at other redshifts, $5.3<z<6.7$, with half of them situated at $z\sim5.4$ and $z\sim6.2$. This indicates that star-forming galaxies, such as [OIII] emitters, are generally clustered at high redshifts. These discoveries demonstrate the unparalleled redshift survey capabilities of NIRCam WFSS and the potential of the full ASPIRE survey dataset.

astro-ph.GA↗

arXiv2023

Testing Dark Energy Models with Gamma-Ray Bursts Calibrated from the Observational $H(z)$ Data through a Gaussian Process

We use a cosmology-independent method to calibrate gamma-ray burst (GRB) from the observational Hubble data (OHD) with the cosmic chronometers method. By using Gaussian Process to reconstruct OHD, we calibrate the Amati relation ($E_{\rm p}$--$E_{\rm iso}$) to construct a GRB Hubble diagram with the A118 data set, and constrain Dark Energy models in a flat space with the Markov Chain Monte Carlo numerical method. With the cosmology-independent GRBs at $1.4<z\leq8.2$ in the A118 data set and the Pantheon sample of type Ia supernovae (SNe Ia) at $0.01<z\leq2.3$, we obtained $Ω_{\rm m}$ = $0.379^{+0.033}_{-0.024}$, $h$ = $0.701^{+0.0035}_{-0.0035}$, $w$ = $-1.25^{+0.14}_{-0.12}$, $w_a$ = $-0.84^{+0.81}_{-0.38}$ for the flat Chevallier-Polarski-Linder model at the 1$σ$ confidence level. We find no significant evidence supporting deviations from the standard $Λ$CDM model.

astro-ph.CO↗

arXiv2023

Dynamic Local Feature Aggregation for Learning on Point Clouds

Existing point cloud learning methods aggregate features from neighbouring points relying on constructing graph in the spatial domain, which results in feature update for each point based on spatially-fixed neighbours throughout layers. In this paper, we propose a dynamic feature aggregation (DFA) method that can transfer information by constructing local graphs in the feature domain without spatial constraints. By finding k-nearest neighbors in the feature domain, we perform relative position encoding and semantic feature encoding to explore latent position and feature similarity information, respectively, so that rich local features can be learned. At the same time, we also learn low-dimensional global features from the original point cloud for enhancing feature representation. Between DFA layers, we dynamically update the constructed local graph structure, so that we can learn richer information, which greatly improves adaptability and efficiency. We demonstrate the superiority of our method by conducting extensive experiments on point cloud classification and segmentation tasks. Implementation code is available: https://github.com/jiamang/DFA.

cs.CV↗

arXiv2022

Calibrating Gamma-Ray Bursts by Using a Gaussian Process with Type Ia Supernovae

In this paper, we calibrate the Amati relation (the $E_{\rm p}$-${E}_{\rm iso}$ correlation) of gamma-ray bursts (GRBs) in a cosmology-independent way. By using Gaussian process to reconstruct the smoothed luminosity distance from the Pantheon type Ia supernovae (SNe Ia) sample, we utilize the reconstructed results to calibrate the $E_{\rm p}$-${E}_{\rm iso}$ correlation with the Markov Chain Monte Carlo method and construct a Hubble diagram with the A220 GRB data, in which there are A118 GRB data with the higher qualities appropriate for cosmological purposes. With 98 GRBs at $1.4<z\leq8.2$ in the A118 sample and the observed Hubble data, we obtain $Ω_{\rm m}$=$0.346^{+0.048}_{-0.069}$, $h$=$0.677^{+0.029}_{-0.029}$ for the flat $Λ$CDM model, and $Ω_{\rm m}$=$0.314^{+0.072}_{-0.055}$, $h$=$0.705^{+0.055}_{-0.069}$, $w$=$-1.23^{+0.33}_{-0.64}$ for the flat $w$CDM model, which are consistent with those from fitting the coefficients of the Amati relation and the cosmological parameters simultaneously.

astro-ph.CO↗

arXiv2022

Metal-Enriched Neutral Gas Reservoir around a Strongly-lensed, Low-mass Galaxy at $z=4$ Identified by JWST/NIRISS and VLT/MUSE

Direct observations of low-mass, low-metallicity galaxies at $z\gtrsim4$ provide an indispensable opportunity for detailed inspection of the ionization radiation, gas flow, and metal enrichment in sources similar to those that reionized the Universe. Combining the James Webb Space Telescope (JWST), VLT/MUSE, and ALMA, we present detailed observations of a strongly lensed, low-mass ($\approx 10^{7.6}$ ${\rm M}_\odot$) galaxy at $z=3.98$ (also see Vanzella et al. 2022). We identify strong narrow nebular emission, including CIV $λ\lambda1548,1550$, HeII $\lambda1640$, OIII] $λ\lambda1661,1666$, [NeIII] $\lambda3868$, [OII] $\lambda3727$, and Balmer series of Hydrogen from this galaxy, indicating a metal-poor HII region ($\lesssim 0.12\ {\rm Z}_\odot$) powered by massive stars. Further, we detect a metal-enriched damped Ly$α$ system (DLA) associated with the galaxy with the HI column density of $N_{\rm{HI}}\approx 10^{21.8}$ cm$^{-2}$. The metallicity of the associated DLA may reach the super solar metallicity (${\gtrsim Z}_\odot$). Moreover, thanks to JWST and gravitational lensing, we present the resolved UV slope ($β$) map at the spatial resolution of $\approx 100$ pc at $z=4$, with steep UV slopes reaching $β\approx -2.5$ around three star-forming clumps. Combining with low-redshift analogs, our observations suggest that low-mass, low-metallicity galaxies, which dominate reionization, could be surrounded by a high covering fraction of the metal-enriched, neutral-gaseous clouds. This implies that the metal enrichment of low-mass galaxies is highly efficient, and further support that in low-mass galaxies, only a small fraction of ionizing radiation can escape through the interstellar or circumgalactic channels with low column-density neutral gas.

astro-ph.GA↗

arXiv2022

The Identification of a Dusty Multiarm Spiral Galaxy at $z=3.06$ with JWST and ALMA

Spiral arms serve crucial purposes in star formation and galaxy evolution. In this paper, we report the identification of A2744-DSG-$z3$, a dusty, multiarm spiral galaxy at $z=3.059$ using the James Webb Space Telescope (JWST) NIRISS imaging and grism spectroscopy. A2744-DSG-$z3$ was discovered as a gravitationally lensed sub-millimeter galaxy with ALMA. This is the most distant stellar spiral structure seen thus far, consistent with cosmological simulations which suggest $z\approx3$ as the epoch when spirals emerge. Thanks to the gravitational lensing and excellent spatial resolution of JWST, the spiral arms are resolved with a spatial resolution of $\approx290$\,pc. Based on SED fitting, the spiral galaxy has a de-lensed star formation rate of $85\pm30 \ M_{\odot}$ yr$^{-1}$, and a stellar mass of $\approx10^{10.6}\ M_{\odot}$, indicating that A2744-DSG-$z3$ is a main-sequence galaxy. After fitting the spiral arms, we find a stellar effective radius ($R_{e, \rm{star}}$) of $5.0\pm1.5$ kpc. Combing with ALMA measurements, we find that the effective radii ratio between dust and stars is $\approx0.4$, similar to {those} of massive SFGs at $z\sim2$, indicating a compact dusty core in A2744-DSG-$z3$. Moreover, this galaxy appears to be living in a group environment: including A2744-DSG-$z3$, at least three galaxies at $z=3.05 - 3.06$ {are} spectroscopically confirmed by JWST/NIRISS and ALMA, residing within a lensing-corrected projected scale of $\approx 70$ kpc. This, along with the asymmetric brightness profile, further suggests that the spiral arms may be triggered by minor merger events at $z\gtrsim3$.

astro-ph.GA↗

arXiv2022

Fair Division with Prioritized Agents

We consider the fair division problem of indivisible items. It is well-known that an envy-free allocation may not exist, and a relaxed version of envy-freeness, envy-freeness up to one item (EF1), has been widely considered. In an EF1 allocation, an agent may envy others' allocated shares, but only up to one item. In many applications, we may wish to specify a subset of prioritized agents where strict envy-freeness needs to be guaranteed from these agents to the remaining agents, while ensuring the whole allocation is still EF1. Prioritized agents may be those agents who are envious in a previous EF1 allocation, those agents who belong to underrepresented groups, etc. Motivated by this, we propose a new fairness notion named envy-freeness with prioritized agents "EFPrior", and study the existence and the algorithmic aspects for the problem of computing an EFPrior allocation. With additive valuations, the simple round-robin algorithm is able to compute an EFPrior allocation. In this paper, we mainly focus on general valuations. In particular, we present a polynomial-time algorithm that outputs an EFPrior allocation with most of the items allocated. When all the items need to be allocated, we also present polynomial-time algorithms for some well-motivated special cases.

cs.GT↗

arXiv2022

Generative Modeling in Structural-Hankel Domain for Color Image Inpainting

In recent years, some researchers focused on using a single image to obtain a large number of samples through multi-scale features. This study intends to a brand-new idea that requires only ten or even fewer samples to construct the low-rank structural-Hankel matrices-assisted score-based generative model (SHGM) for color image inpainting task. During the prior learning process, a certain amount of internal-middle patches are firstly extracted from several images and then the structural-Hankel matrices are constructed from these patches. To better apply the score-based generative model to learn the internal statistical distribution within patches, the large-scale Hankel matrices are finally folded into the higher dimensional tensors for prior learning. During the iterative inpainting process, SHGM views the inpainting problem as a conditional generation procedure in low-rank environment. As a result, the intermediate restored image is acquired by alternatively performing the stochastic differential equation solver, alternating direction method of multipliers, and data consistency steps. Experimental results demonstrated the remarkable performance and diversity of SHGM.

cs.CV↗

arXiv2022

Latent Space Diffusion Models of Cryo-EM Structures

Cryo-electron microscopy (cryo-EM) is unique among tools in structural biology in its ability to image large, dynamic protein complexes. Key to this ability is image processing algorithms for heterogeneous cryo-EM reconstruction, including recent deep learning-based approaches. The state-of-the-art method cryoDRGN uses a Variational Autoencoder (VAE) framework to learn a continuous distribution of protein structures from single particle cryo-EM imaging data. While cryoDRGN can model complex structural motions, the Gaussian prior distribution of the VAE fails to match the aggregate approximate posterior, which prevents generative sampling of structures especially for multi-modal distributions (e.g. compositional heterogeneity). Here, we train a diffusion model as an expressive, learnable prior in the cryoDRGN framework. Our approach learns a high-quality generative model over molecular conformations directly from cryo-EM imaging data. We show the ability to sample from the model on two synthetic and two real datasets, where samples accurately follow the data distribution unlike samples from the VAE prior distribution. We also demonstrate how the diffusion model prior can be leveraged for fast latent space traversal and interpolation between states of interest. By learning an accurate model of the data distribution, our method unlocks tools in generative modeling, sampling, and distribution analysis for heterogeneous cryo-EM ensembles.

q-bio.QM↗

arXiv2022

Significance improvement by randomized test in random sampling without replacement

This paper studies one-sided hypothesis testing under random sampling without replacement. That is, when $n+1$ binary random variables $X_1,\ldots, X_{n+1}$ are subject to a permutation invariant distribution and $n$ binary random variables $X_1,\ldots, X_{n}$ are observed, we have proposed randomized tests with a randomization parameter for the upper confidence limit of the expectation of the $(n+1)$th random variable $X_{n+1}$ under a given significance level $δ>0$. Our proposed randomized test significantly improves over deterministic test unlike random sampling with replacement.

math.ST↗

arXiv2022

Nearly tight universal bounds for the binomial tail probabilities

We derive simple but nearly tight upper and lower bounds for the binomial lower tail probability (with straightforward generalization to the upper tail probability) that apply to the whole parameter regime. These bounds are easy to compute and are tight within a constant factor of $89/44$. Moreover, they are asymptotically tight in the regimes of large deviation and moderate deviation. By virtue of a surprising connection with Ramanujan's equation, we also provide strong evidences suggesting that the lower bound is tight within a factor of $1.26434$. It may even be regarded as the natural lower bound, given its simplicity and appealing properties. Our bounds significantly outperform the familiar Chernoff bound and reverse Chernoff bounds known in the literature and may find applications in various research areas.

math.PR↗

arXiv2022

Fair and Efficient Multi-Resource Allocation for Cloud Computing

We study the problem of allocating multiple types of resources to agents with Leontief preferences. The classic Dominant Resource Fairness (DRF) mechanism satisfies several desired fairness and incentive properties, but is known to have poor performance in terms of social welfare approximation ratio. In this work, we propose a new approximation ratio measure, called \emph{\fratio}, which is defined as the worst-case ratio between the optimal social welfare (resp. utilization) among all \emph{fair} allocations and that by the mechanism, allowing us to break the lower bound barrier under the classic approximation ratio. We then generalize DRF and present several new mechanisms with two and multiple types of resources that satisfy the same set of properties as DRF but with better social welfare and utilization guarantees under the new benchmark. We also demonstrate the effectiveness of these mechanisms through experiments on both synthetic and real-world datasets.

cs.GT↗

arXiv2022

Revealing Spatial-temporal Taxi Demand Patterns after Vaccination in COVID-19 Pandemic

The COVID-19 pandemic has had an unprecedented impact on our daily lives. With the increase in vaccination rate, normalcy gradually returns, so is the taxi demand. However, the changes in the spatial-temporal taxi demand pattern and factors impacting the recovery of this demand after COVID-19 vaccination started remain unclear. With the multisource time-series data from Chicago, including pandemic severity, vaccination progress and taxi trip volume, the recovery pattern of taxi demand is analyzed. The result reveals the taxi trip volume and average travel distance increased in most community areas in the city of Chicago after taking the COVID-19 vaccine. Taxi demand recovers relatively faster in the airport and area near the central downtown than in other areas in Chicago. Considering the asynchrony of data, the Pearson coefficient and Dynamic Time Warping (DTW) are both applied to investigate the correlations among different time series. It found that the recovery of taxi demand is not only related to the pandemic severity but strongly correlated with vaccination progress. Then, the leading and lagging relationship between vaccination progress and taxi demand is investigated by Time Lagging Cross-Correlation method. Taxi demand starts to recover after the first-dose vaccination period started and before the second-dose vaccination period. However, it was not until mid-April that the recovery rate of taxi demand exceeded the growth of the vaccination rate.

stat.AP↗

arXiv2022

Generalized matrix nearness problems

We show that the global minimum solution of $\lVert A - BXC \rVert$ can be found in closed-form with singular value decompositions and generalized singular value decompositions for a variety of constraints on $X$ involving rank, norm, symmetry, two-sided product, and prescribed eigenvalue. This extends the solution of Friedland--Torokhti for the generalized rank-constrained approximation problem to other constraints as well as provides an alternative solution for rank constraint in terms of singular value decompositions. For more complicated constraints on $X$ involving structures such as Toeplitz, Hankel, circulant, nonnegativity, stochasticity, positive semidefiniteness, prescribed eigenvector, etc, we prove that a simple iterative method is linearly and globally convergent to the global minimum solution.

math.NA↗

arXiv2022

Temperature effect on non-Darcian flow in low-permeability porous media

In low-permeability porous media, the velocity of a fluid flow exhibits a nonlinear dependence on the imposed pressure gradient. This non-Darcian flow behavior has important implications to geological disposal of nuclear waste, hydrocarbon extraction from shale, and flow and transport in clay-rich aquifers. Temperature has been postulated to affect the threshold pressure gradient of a non-Darcian flow; however, the supporting data is very limited. In this study we for the first time report a systematic measurement of the threshold pressure gradient under various permeabilities and temperatures. The results show that a higher temperature leads to a lower threshold pressure gradient under the same permeability and a faster reduction of the threshold pressure gradient with increasing permeability. The experimental data are fitted to a two-parameter model to determine the parameters, h0 and a, which characterize the interfacial fluid-solid interactions and the transition between the Darcy and non-Darcian regimes.

physics.geo-ph↗

arXiv2022

FD-CAM: Improving Faithfulness and Discriminability of Visual Explanation for CNNs

Class activation map (CAM) has been widely studied for visual explanation of the internal working mechanism of convolutional neural networks. The key of existing CAM-based methods is to compute effective weights to combine activation maps in the target convolution layer. Existing gradient and score based weighting schemes have shown superiority in ensuring either the discriminability or faithfulness of the CAM, but they normally cannot excel in both properties. In this paper, we propose a novel CAM weighting scheme, named FD-CAM, to improve both the faithfulness and discriminability of the CAM-based CNN visual explanation. First, we improve the faithfulness and discriminability of the score-based weights by performing a grouped channel switching operation. Specifically, for each channel, we compute its similarity group and switch the group of channels on or off simultaneously to compute changes in the class prediction score as the weights. Then, we combine the improved score-based weights with the conventional gradient-based weights so that the discriminability of the final CAM can be further improved. We perform extensive comparisons with the state-of-the-art CAM algorithms. The quantitative and qualitative results show our FD-CAM can produce more faithful and more discriminative visual explanations of the CNNs. We also conduct experiments to verify the effectiveness of the proposed grouped channel switching and weight combination scheme on improving the results. Our code is available at https://github.com/crishhh1998/FD-CAM.

cs.CV↗