arXiv subjects

Zi-Qi Li

Publications and source records attributed to Zi-Qi Li.

8 recordsLinked to original sources

arXiv2026

A Wavelet-Integrated Search Pipeline for Narrowband Technosignatures in FAST Observations of 33 Exoplanet Systems

Building on prior FAST targeted and blind SETI campaigns toward 33 exoplanet systems, we introduce a wavelet-integrated search pipeline for narrowband technosignature candidates in radio dynamic spectra. At its core, the pipeline uses a Multi-Scale Wavelet Net (MSWNet) to produce an interpretable multi-resolution representation, followed by a lightweight parameter estimator for endpoint localization. Rather than relying solely on hard-threshold drift searches, the pipeline reframes narrowband detection as wavelet-guided feature extraction followed by endpoint regression, morphology-aware filtering, raw-data S/N validation, and multi-beam anticoincidence veto. Applied to real FAST data, the pipeline recovers representative events from prior analyses and produces a compact set of veto-ready candidates for downstream inspection. The resulting workflow preserves interpretability, low regression complexity, and auditable threshold control, making it readily transferable to other radio surveys and large-scale technosignature searches.

astro-ph.IM↗

arXiv2026

Tracing the kinematic perturbations of the Milky Way spiral arms with APOGEE DR17 and Gaia DR3

Aims. We constrain the dynamical perturbations of the spiral arms in the Milky Way disk, based on the non-axisymmetric streaming motions of RGB stars revealed by APOGEE and \textit{Gaia}. Methods. We develop a revised steady-state radial-velocity response model that incorporates both the $V_{R,\sin}$ and the dynamically important $V_{R,\cos}$ components for a two-armed logarithmic spiral potential. The model is validated using orbit integrations with \texttt{AGAMA} and Bayesian parameter recovery with \texttt{dynesty}, and is applied to the smoothed two-dimensional radial-velocity field of RGB stars while accounting for Lindblad and corotation resonances. Results. The revised model reproduces the phase and amplitude of the mock radial-velocity field to the $\sim2\%$ level, substantially improving upon earlier $V_{R,\sin}$-only formulations. Applied to the observational data, it yields a robust pitch angle of $p \simeq 10^\circ$ and a local surface density contrast of $\xi \simeq 5$--$18\%$ at the solar radius. The radial scale length is less well-constrained ($h_{R,1} \simeq 40$--$50\,\mathrm{kpc}$) due to intrinsic parameter covariance. Resonance effects strongly shape the velocity field, thus affecting the fitting: the radial velocity becomes extremely large near the Lindblad resonances, whereas it vanishes close to the corotation resonance. Conclusions. Our results demonstrate that including both the $V_{R,\sin}$ and $V_{R,\cos}$ terms is essential for a physically consistent interpretation of stellar streaming motions induced by a spiral potential. The observed kinematics constrain the spiral pattern speed to $\Omega_{p} \approx 10$--$20\,\mathrm{km\,s}^{-1}\mathrm{kpc}^{-1}$.

astro-ph.GA↗

arXiv2026

General Multimodal Protein Design Enables DNA-Encoding of Chemistry

Evolution is an extraordinary engine for enzymatic diversity, yet the chemistry it has explored remains a narrow slice of what DNA can encode. Deep generative models can design new proteins that bind ligands, but none have created enzymes without pre-specifying catalytic residues. We introduce DISCO (DIffusion for Sequence-structure CO-design), a multimodal model that co-designs protein sequence and 3D structure around arbitrary biomolecules, as well as inference-time scaling methods that optimize objectives across both modalities. Conditioned solely on reactive intermediates, DISCO designs diverse heme enzymes with novel active-site geometries. These enzymes catalyze new-to-nature carbene-transfer reactions, including alkene cyclopropanation, spirocyclopropanation, B-H, and C(sp$^3$)-H insertions, with high activities exceeding those of engineered enzymes. Random mutagenesis of a selected design further confirmed that enzyme activity can be improved through directed evolution. By providing a scalable route to evolvable enzymes, DISCO broadens the potential scope of genetically encodable transformations. Code is available at https://github.com/DISCO-design/DISCO.

cs.LG↗

arXiv2020

Learning efficient structured dictionary for image classification

Recent years have witnessed the success of dictionary learning (DL) based approaches in the domain of pattern classification. In this paper, we present an efficient structured dictionary learning (ESDL) method which takes both the diversity and label information of training samples into account. Specifically, ESDL introduces alternative training samples into the process of dictionary learning. To increase the discriminative capability of representation coefficients for classification, an ideal regularization term is incorporated into the objective function of ESDL. Moreover, in contrast with conventional DL approaches which impose computationally expensive L1-norm constraint on the coefficient matrix, ESDL employs L2-norm regularization term. Experimental results on benchmark databases (including four face databases and one scene dataset) demonstrate that ESDL outperforms previous DL approaches. More importantly, ESDL can be applied in a wide range of pattern classification tasks.

cs.CV↗

arXiv2020

Multiplication fusion of sparse and collaborative-competitive representation for image classification

Representation based classification methods have become a hot research topic during the past few years, and the two most prominent approaches are sparse representation based classification (SRC) and collaborative representation based classification (CRC). CRC reveals that it is the collaborative representation rather than the sparsity that makes SRC successful. Nevertheless, the dense representation of CRC may not be discriminative which will degrade its performance for classification tasks. To alleviate this problem to some extent, we propose a new method called sparse and collaborative-competitive representation based classification (SCCRC) for image classification. Firstly, the coefficients of the test sample are obtained by SRC and CCRC, respectively. Then the fused coefficient is derived by multiplying the coefficients of SRC and CCRC. Finally, the test sample is designated to the class that has the minimum residual. Experimental results on several benchmark databases demonstrate the efficacy of our proposed SCCRC. The source code of SCCRC is accessible at https://github.com/li-zi-qi/SCCRC.

cs.CV↗

arXiv2019

2DR1-PCA and 2DL1-PCA: two variant 2DPCA algorithms based on none L2 norm

In this paper, two novel methods: 2DR1-PCA and 2DL1-PCA are proposed for face recognition. Compared to the traditional 2DPCA algorithm, 2DR1-PCA and 2DL1-PCA are based on the R1 norm and L1 norm, respectively. The advantage of these proposed methods is they are less sensitive to outliers. These proposed methods are tested on the ORL, YALE and XM2VTS databases and the performance of the related methods is compared experimentally.

cs.CV↗

arXiv2019

Collaborative representation-based robust face recognition by discriminative low-rank representation

We consider the problem of robust face recognition in which both the training and test samples might be corrupted because of disguise and occlusion. Performance of conventional subspace learning methods and recently proposed sparse representation based classification (SRC) might be degraded when corrupted training samples are provided. In addition, sparsity based approaches are time-consuming due to the sparsity constraint. To alleviate the aforementioned problems to some extent, in this paper, we propose a discriminative low-rank representation method for collaborative representation-based (DLRR-CR) robust face recognition. DLRR-CR not only obtains a clean dictionary, it further forces the sub-dictionaries for distinct classes to be as independent as possible by introducing a structural incoherence regularization term. Simultaneously, a low-rank projection matrix can be learned to remove the possible corruptions in the testing samples. Collaborative representation based classification (CRC) method is exploited in our proposed method which has closed-form solution. Experimental results obtained on public face databases verify the effectiveness and robustness of our method.

cs.CV↗

arXiv2019

Dynamics in two-dimensional glassy systems of crowded Penrose kites

We investigate the translational and rotational relaxation dynamics of a crowded two-dimensional system of monodisperse Penrose kites, in which crystallization, quasi-crystallization, and nematic ordering are suppressed, from low to high area fractions along the metastable ergodic fluid branch. First, we demonstrate a decoupling between both the translational and the rotational diffusion coefficients and the relaxation time: the diffusivities are not inversely proportional to the relaxation time, neither in the low-density normal liquid regime nor in the high-density supercooled regime. Our simulations reveal that this inverse proportionality breaks in the normal liquid regime due to the Mermin-Wagner long-wavelength fluctuations and in the supercooled regime due to the dynamical heterogeneities. We then show that dynamical heterogeneities are mainly spatial for translational degrees of freedom and temporal for rotational ones, that there is no correlation between the particles with the largest translational and rotational displacements, and that different dynamical length scales characterize the translational and the rotational motion. Hence, despite the translational and the rotational glass-transition densities coincide, according to a mode-coupling fit, translations and rotations appear to decorrelate via different dynamical processes.

cond-mat.soft↗