arXiv subjects

Qingqing Wang

Publications and source records attributed to Qingqing Wang.

15 recordsLinked to original sources

arXiv2025

Probing the sound speed and clustering of dark energy

Recent Dark Energy Spectroscopic Instrument (DESI) observations favor a dynamical dark energy component with a time-varying equation-of-state, potentially crossing the cosmological-constant boundary $w=-1$, challenging the standard $\Lambda$CDM paradigm. In this paper we present the first joint observational constraints on the clustering properties of such dynamical dark energy, using both the Parameterized Post-Friedmann (PPF) framework and the effective field theory (EFT) of dark energy. Combining DESI DR2 baryon acoustic oscillations with Planck 2018 cosmic microwave background data and the Union3 supernova sample, we constrain the effective sound speed $c_s^{2}$. For a time-varying equation-of-state, the degeneracy between $(1+w)$ and $c_s^{2}$ is broken, yielding the first meaningful constraint $\log_{10}c_{s}^{2}=-3.00^{+2.9}_{-0.99}$, while constant-$w$ models remain unconstrained. A complementary EFT analysis gives consistent results, favoring $c_s^{2}\sim 0.3$ or $0.4$. Our findings demonstrate that current data are now sensitive to the perturbative properties of dynamical dark energy, opening a new observational window on the nature of cosmic acceleration.

astro-ph.CO↗

arXiv2025

A learning-driven automatic planning framework for proton PBS treatments of H&N cancers

Proton pencil beam scanning (PBS) treatment planning for head & neck (H&N) cancers involves numerous conflicting objectives, requiring iterative objective parameter adjustments to balance multiple clinical goals. We propose a learning-driven inverse optimizer and integrate it into a proximal policy optimization (PPO)-based planning framework to automatically generate high-quality plans for patients with diverse treatment requirements. The inverse optimizer is a learning-to-optimize (L2O) method that predicts update steps by learning from task-specific data distributions. For the first time, long-context processing techniques developed for large language models (LLMs) are utilized to address the scalability limitations of existing L2O methods, enabling simultaneous optimization over a substantially large set of variables. The PPO framework functions as an outer-loop virtual planner, autonomously adjusting objective parameters through a policy network, and the inner-loop L2O inverse optimizer computes machine-deliverable spot monitor unit (MU) values based on the PPO-refined objectives. Moreover, a Swin UnetR dose predictor is trained with prescription- and beam-specific information to estimate the initial objective parameters. In our experiments, total 97 patients with bilateral or ipsilateral H&N cancers are collected for training and testing. Compared with the second-order gradient-based methods, our L2O optimizer improves the effectiveness and efficiency of the time-consuming inverse optimization by 22.97% and 36.41%, respectively, and in conjunction with the PPO-based virtual planner, plans are generated within clinically acceptable times, i.e. 2.55 hours in average, and shows improved or comparable organs-at-risk sparing with superior target coverage compared with human-generated plans.

cs.AI↗

arXiv2025

Modified gravity realizations of quintom dark energy after DESI DR2

We investigate the realization of quintom scenario for dynamical dark energy within modified gravity theories that can efficiently fit the recent observational datasets. Starting from a general effective field theory formulation of dark energy in metric-affine geometry, we derive the background action in unitary gauge and we demonstrate how both $f(T)$ and $f(Q)$ gravity can naturally realize quintom behavior through appropriate forms and parameter choices. Additionally, using the Gaussian process reconstruction of the latest DESI DR2 BAO data combined with SNe and CMB observations, we extract the reconstructed dark-energy equation-of-state parameter, showing that it exhibits quintom-type evolution, crossing the phantom divide from below. Moreover, through detailed parameter estimations and application of information criteria, we compare the model with the quadratic one and the $\Lambda$CDM model. Our results show that, due to its rich structure, modified gravity stands as one of the main candidates for the realization of the data-favoured dynamical dark energy.

astro-ph.CO↗

arXiv2025

Testing Cotton gravity as dark matter substitute with weak lensing

Harada proposed a modified theory of gravity called Cotton gravity, and argued that it successfully explains the rotation curves of $84$ galaxies without the need of dark matter. In this work we use galaxy-galaxy lensing technique to test whether the modification effect of Cotton gravity can indeed be a viable substitute for dark matter. Using the spherically symmetric solution of Cotton gravity, we obtain the deflection angle via Gauss-Bonnet theorem and the weak lensing shear. We use five galaxy catalogs divided in 5 stellar mass bins from the Sloan Digital Sky Survey Data Release 7 (SDSS DR7), each of which is further divided into blue star forming galaxy and red passive galaxy sub-catalogs. We find that Cotton gravity on its own has significant deviation from the measured galaxy-galaxy lensing signals, thus it cannot replace the role of dark matter. If we consider the combination of dark matter and Cotton gravity, the modification is tightly constrained. Our analysis also applies to other modified gravity theories whose an additional linear term appears in the Schwarzschild solution.

astro-ph.CO↗

arXiv2025

Gaussian-process reconstructions and model building of quintom dark energy from latest cosmological observations

In this article we use the latest cosmological observations, including SNe, BAO, CC and RSD, to reconstruct the cosmological evolution via the Gaussian process. At the background level, we find consistency with the quintom dynamics for different data combinations and divide the characteristics of dark energy into three different categories, which are negative-energy dark energy, late-dominated dark energy and oscillating dark energy, respectively. Considering the effect of modified gravity on the growth of matter perturbations, the reconstruction results at the perturbative level show that we only need minor corrections to general relativity. Furthermore, we provide theoretical interpretation for the three different types of dynamical dark-energy behavior, in the framework of modified gravity, scalar fields, and dark-energy equation-of-state parametrizations. Finally, we show that all of these models can be unified in the framework of effective field theory.

astro-ph.CO↗

arXiv2024

Static Microlensing: Concept, Method and Candidates

We propose a novel microlensing event search method that differs from either the traditional time domain method, astrometric microlensing, or the parallax microlensing method. Our method assumes that stars with nearly identical "genes" - normalized Spectral Energy Distributions (SED) bear the same luminosity within the intrinsic scatter due to stellar properties. Given a sample of stars with similar normalized SEDs, the outliers in luminosity distribution can be considered microlensing events by excluding other possible variations. In this case, we can select microlensing events from archive data rather than time domain monitoring the sky, which we describe as static microlensing. Following this concept, we collect the data from Gaia DR3 and SDSS DR16 from the northern galactic cap at high galactic latitudes. This area is not preferable for normal microlensing search due to the low stellar density and, therefore, low discovery rate. By applying a similarity search algorithm, we find 5 microlensing candidates in the Galactic halo.

astro-ph.GA↗

arXiv2024

Observational test for $f(Q)$ gravity with weak gravitational lensing

In this article we confront a class of $f(Q)$ gravity models with observational data of galaxy-galaxy lensing. Specifically, we consider the $f(Q)$ gravity models containing a small quadratic correction when compared with General Relativity (GR), and quantify this correction by a model parameter $α$. To derive the observational constraints, we start by extracting the spherically symmetric solutions which correspond to the deviations from the Schwarzschild solution that depends on the model parameter in a two-fold way, i.e., a renormalized mass and a new term proportional to $r^{-2}$. Then, we calculate the effective lensing potential, the deflection angle, the shear component, and the effective Excess Surface Density (ESD) profile. After that, we employ the group catalog and shape catalog from the SDSS DR7 for the lens and source samples respectively. Moreover, we handle the off-center radius as a free parameter and constrain it using the MCMC. Concerning the deviation parameter from GR we derive $α=1.202^{+0.277}_{-0.179}\times 10^{-6} {\rm Mpc}^{-2}$ at 1 $σ$ confidence level, and then compare the fitting efficiency with the standard $Λ$CDM paradigm by applying the AIC and BIC information criteria. Our results indicate that the $f(Q)$ corrections alongside off-center effects yield a scenario that is slightly favored.

astro-ph.CO↗

arXiv2024

Automating proton PBS treatment planning for head and neck cancers using policy gradient-based deep reinforcement learning

Proton pencil beam scanning (PBS) treatment planning for head and neck (H&N) cancers is a time-consuming and experience-demanding task where a large number of planning objectives are involved. Deep reinforcement learning (DRL) has recently been introduced to the planning processes of intensity-modulated radiation therapy and brachytherapy for prostate, lung, and cervical cancers. However, existing approaches are built upon the Q-learning framework and weighted linear combinations of clinical metrics, suffering from poor scalability and flexibility and only capable of adjusting a limited number of planning objectives in discrete action spaces. We propose an automatic treatment planning model using the proximal policy optimization (PPO) algorithm and a dose distribution-based reward function for proton PBS treatment planning of H&N cancers. Specifically, a set of empirical rules is used to create auxiliary planning structures from target volumes and organs-at-risk (OARs), along with their associated planning objectives. These planning objectives are fed into an in-house optimization engine to generate the spot monitor unit (MU) values. A decision-making policy network trained using PPO is developed to iteratively adjust the involved planning objective parameters in a continuous action space and refine the PBS treatment plans using a novel dose distribution-based reward function. Proton H&N treatment plans generated by the model show improved OAR sparing with equal or superior target coverage when compared with human-generated plans. Moreover, additional experiments on liver cancer demonstrate that the proposed method can be successfully generalized to other treatment sites. To the best of our knowledge, this is the first DRL-based automatic treatment planning model capable of achieving human-level performance for H&N cancers.

q-bio.QM↗

arXiv2024

Galaxy-galaxy lensing data: $f(T)$ gravity challenges General Relativity

We use galaxy-galaxy lensing data to test General Relativity and $f(T)$ gravity at galaxies scales. We consider an exact spherically symmetric solution of $f(T)$ theory which is obtained from an approximate quadratic correction, and thus it is expected to hold for every realistic deviation from General Relativity. Quantifying the deviation by a single parameter $Q$, and following the post-Newtonian approximation, we obtain the corresponding deviation in the gravitational potential, shear component, and effective excess surface density profile. We used five stellar mass samples and divided them into blue and red to test the model dependence on galaxy color, and we modeled the excess surface density (ESD) profiles using the Navarro-Frenk-White (NFW) profiles. Based on the group catalog from the Sloan Digital Sky Survey Data Release 7 (SDSS DR7) we finally extract $Q=-2.138^{+0.952}_{-0.516}\times 10^{-5}\,$Mpc$^{-2}$ at $1σ$ confidence. This result indicates that $f(T)$ corrections on top of General Relativity are favored. Finally, we apply information criteria, such as the AIC and BIC ones, and although the dependence of $f(T)$ gravity on the off-center effect implies that its optimality needs to be carefully studied, our analysis shows that $f(T)$ gravity is more efficient in fitting the data comparing to General Relativity and $Λ$CDM paradigm, and thus it offers a challenge to the latter.

astro-ph.CO↗

arXiv2024

Quintom cosmology and modified gravity after DESI 2024

We reconstruct the cosmological background evolution under the scenario of dynamical dark energy through the Gaussian process approach, using the latest Dark Energy Spectroscopic Instrument (DESI) baryon acoustic oscillations (BAO) combined with other observations. Our results reveal that the reconstructed dark-energy equation-of-state (EoS) parameter $w(z)$ exhibits the so-called quintom-B behavior, crossing $-1$ from phantom to quintessence regime as the universe expands. We investigate under what situation this type of evolution could be achieved from the perspectives of field theories and modified gravity. In particular, we reconstruct the corresponding actions for $f(R)$, $f(T)$, and $f(Q)$ gravity, respectively. We explicitly show that, certain modified gravity can exhibit the quintom dynamics and fit the recent DESI data efficiently, and for all cases the quadratic deviation from the $Λ$CDM scenario is mildly favored.

astro-ph.CO↗

arXiv2024

Black-Hole-to-Halo Mass Relation From UNIONS Weak Lensing

This letter presents, for the first time, direct constraints on the black-hole-to-halo-mass relation using weak gravitational lensing measurements. We construct type I and type II Active Galactic Nuclei (AGNs) samples from the Sloan Digital Sky Survey (SDSS), with a mean redshift of 0.4 0.1 for type I (type II) AGNs. This sample is cross-correlated with weak lensing shear from the Ultraviolet Near Infrared Northern Survey (UNIONS). We compute the excess surface mass density of the halos associated with $36,181$ AGNs from $94,308,561$ lensed galaxies and fit the halo mass in bins of black-hole mass. We find that more massive AGNs reside in more massive halos. We see no evidence of dependence on AGN type or redshift in the black-hole-to-halo-mass relationship when systematic errors in the measured black-hole masses are included. Our results are consistent with previous measurements for non-AGN galaxies. At a fixed black-hole mass, our weak-lensing halo masses are consistent with galaxy rotation curves, but significantly lower than galaxy clustering measurements. Finally, our results are broadly consistent with state-of-the-art hydro-dynamical cosmological simulations, providing a new constraint for black-hole masses in simulations.

astro-ph.GA↗

arXiv2022

Adaptive Graph-Based Feature Normalization for Facial Expression Recognition

Facial Expression Recognition (FER) suffers from data uncertainties caused by ambiguous facial images and annotators' subjectiveness, resulting in excursive semantic and feature covariate shifting problem. Existing works usually correct mislabeled data by estimating noise distribution, or guide network training with knowledge learned from clean data, neglecting the associative relations of expressions. In this work, we propose an Adaptive Graph-based Feature Normalization (AGFN) method to protect FER models from data uncertainties by normalizing feature distributions with the association of expressions. Specifically, we propose a Poisson graph generator to adaptively construct topological graphs for samples in each mini-batches via a sampling process, and correspondingly design a coordinate descent strategy to optimize proposed network. Our method outperforms state-of-the-art works with accuracies of 91.84% and 91.11% on the benchmark datasets FERPlus and RAF-DB, respectively, and when the percentage of mislabeled data increases (e.g., to 20%), our network surpasses existing works significantly by 3.38% and 4.52%.

cs.CV↗

arXiv2020

FACLSTM: ConvLSTM with Focused Attention for Scene Text Recognition

Scene text recognition has recently been widely treated as a sequence-to-sequence prediction problem, where traditional fully-connected-LSTM (FC-LSTM) has played a critical role. Due to the limitation of FC-LSTM, existing methods have to convert 2-D feature maps into 1-D sequential feature vectors, resulting in severe damages of the valuable spatial and structural information of text images. In this paper, we argue that scene text recognition is essentially a spatiotemporal prediction problem for its 2-D image inputs, and propose a convolution LSTM (ConvLSTM)-based scene text recognizer, namely, FACLSTM, i.e., Focused Attention ConvLSTM, where the spatial correlation of pixels is fully leveraged when performing sequential prediction with LSTM. Particularly, the attention mechanism is properly incorporated into an efficient ConvLSTM structure via the convolutional operations and additional character center masks are generated to help focus attention on right feature areas. The experimental results on benchmark datasets IIIT5K, SVT and CUTE demonstrate that our proposed FACLSTM performs competitively on the regular, low-resolution and noisy text images, and outperforms the state-of-the-art approaches on the curved text with large margins.

cs.CV↗

arXiv2019

See More Than Once -- Kernel-Sharing Atrous Convolution for Semantic Segmentation

The state-of-the-art semantic segmentation solutions usually leverage different receptive fields via multiple parallel branches to handle objects with different sizes. However, employing separate kernels for individual branches degrades the generalization and representation abilities of the network, and the number of parameters increases linearly in the number of branches. To tackle this problem, we propose a novel network structure namely Kernel-Sharing Atrous Convolution (KSAC), where branches of different receptive fields share the same kernel, i.e., let a single kernel see the input feature maps more than once with different receptive fields, to facilitate communication among branches and perform feature augmentation inside the network. Experiments conducted on the benchmark PASCAL VOC 2012 dataset show that the proposed sharing strategy can not only boost a network s generalization and representation abilities but also reduce the model complexity significantly. Specifically, on the validation set, whe compared with DeepLabV3+ equipped with MobileNetv2 backbone, 33% of parameters are reduced together with an mIOU improvement of 0.6%. When Xception is used as the backbone, the mIOU is elevated from 83.34% to 85.96% with about 10M parameters saved. In addition, different from the widely used ASPP structure, our proposed KSAC is able to further improve the mIOU by taking benefit of wider context with larger atrous rates. Finally, our KSAC achieves mIOUs of 88.1% and 45.47% on the PASCAL VOC 2012 test set and ADE20K dataset, respectively. Our full code will be released on the Github.

cs.CV↗

arXiv2017

Handwritten digit string recognition by combination of residual network and RNN-CTC

Recurrent neural network (RNN) and connectionist temporal classification (CTC) have showed successes in many sequence labeling tasks with the strong ability of dealing with the problems where the alignment between the inputs and the target labels is unknown. Residual network is a new structure of convolutional neural network and works well in various computer vision tasks. In this paper, we take advantage of the architectures mentioned above to create a new network for handwritten digit string recognition. First we design a residual network to extract features from input images, then we employ a RNN to model the contextual information within feature sequences and predict recognition results. At the top of this network, a standard CTC is applied to calculate the loss and yield the final results. These three parts compose an end-to-end trainable network. The proposed new architecture achieves the highest performances on ORAND-CAR-A and ORAND-CAR-B with recognition rates 89.75% and 91.14%, respectively. In addition, the experiments on a generated captcha dataset which has much longer string length show the potential of the proposed network to handle long strings.

cs.CV↗