arXiv subjects

Yangyang Chen

Publications and source records attributed to Yangyang Chen.

At least 19 recordsLinked to original sources

arXiv2026

Signless Laplacian spectral conditions for rainbow matchings in a collection of bipartite graphs

Let ${\cal G}=\{G_1,\ldots,G_k\}$ be a collection of (not necessarily distinct) bipartite graphs on the same vertex bipartition $(X,Y)$, where $|X|=a$, $|Y|=b$ and $2\le k\le a\le b$. A \emph{rainbow matching} of ${\cal G}$ is a set of pairwise disjoint edges that can be chosen from distinct members of ${\cal G}$. Denote by $q(G)$ the signless Laplacian spectral radius of a graph $G$. In this paper, we prove that if $q(G_i)\ge b+k-1$ for each $i\in\{1,2,\ldots,k\}$, then ${\cal G}$ admits a rainbow matching of size $k$ unless $G_1=\cdots=G_k\cong K_{k-1,b}\cup\overline{K_{a-k+1}}$, and show that the threshold is sharp and attained by the exceptional collection. The condition is also extended to larger collections for a prescribed level $t$. In addition, we obtain a lower bound for the rainbow matching number in terms of the ordered signless Laplacian spectral radii of the members, and provide a stability version of the extremal characterization. In the proofs, we use the shifting technique and a quotient matrix arising from an equitable partition of a signless Laplacian matrix.

math.CO↗

arXiv2026

Empowering Chemical Structures with Biological Insights for Scalable Phenotypic Virtual Screening

Motivation: The scalable identification of bioactive compounds is essential for contemporary drug discovery. This process faces a key trade-off: structural screening offers scalability but lacks biological context, whereas high-content phenotypic profiling provides deep biological insights but is resource-intensive. The primary challenge is to extract robust biological signals from noisy data and encode them into representations that do not require biological data at inference. Results: This study presents DECODE (DEcomposing Cellular Observations of Drug Effects), a framework that bridges this gap by empowering chemical representations with intrinsic biological semantics to enable structure-based in silico biological profiling. DECODE leverages limited paired transcriptomic and morphological data as supervisory signals during training, enabling the extraction of a measurement-invariant biological fingerprint from chemical structures and explicit filtering of experimental noise. Our evaluations demonstrate that DECODE retrieves functionally similar drugs in zero-shot settings with over 20% relative improvement over chemical baselines in mechanism-of-action (MOA) prediction. Furthermore, the framework achieves a 6-fold increase in hit rates for novel anti-cancer agents during external validation. Availability and implementation: The codes and datasets of DECODE are available at https://github.com/lian-xiao/DECODE.

q-bio.QM↗

arXiv2025

Probing Hidden Symmetry and Altermagnetism with Sub-Picometer Sensitivity via Nonlinear Transport

X-ray and neutron diffraction are foundational tools for determining crystal structures, but their resolution limits can lead to misassignments, especially in materials with subtle distortions or competing phases. Here, we demonstrate the use of nonlinear transport as a complementary approach to uncover hidden crystal symmetries, using the strongly correlated Ca$_3$Ru$_2$O$_7$ as a case study. Below 48 K (T$_S$), where the magnetic moments of the antiferromagnetic phase reorient from the a- to the b-axis, leading to a pseudogap opening, our measurements, with support of DFT, reveal a previously overlooked lower-symmetry phase. This is manifested by the emergence of longitudinal nonlinear resistance (NLR) along the b-axis below T$_S$, providing direct evidence of combined translational and time-reversal symmetry breaking. This response also suggests a transformation from a conventional antiferromagnet into an altermagnet. The lower-symmetry phase arises from a subtle lattice distortion (~0.1 pm) associated with the magnetic transition at T$_S$, below the detection limit of conventional diffraction. Moreover, this NLR below T$_S$ is accompanied by a nonlinear Hall effect, both of which are enhanced by the large quantum metric associated with Weyl chains near the Fermi surface. Our findings demonstrate nonlinear transport as a sensitive probe of hidden symmetry breaking and altermagnetism, complementing and extending beyond the reach of traditional diffraction and spectroscopic techniques.

cond-mat.str-el↗

arXiv2025

Gear-tuning meta-shaft for low-frequency torsional vibration suppression

Metastructures with band gaps provide a new solution for the torsional vibration attenuation in shaft systems, while tunable band gaps remain challenging, typically relying on additional physical fields or complex assembly processes. In this study, a gear-shifting tunable meta-shaft with self-locking gear (SLG) resonators is proposed, where a simple gear-shifting mechanism replaces complex tuning methods to achieve low-frequency torsional vibration suppression in tunable frequency ranges. First, as the key components of the SLG resonator, six inner curved beams are designed to provide precisely tunable torsional stiffness of the resonator through their deformed shapes controlled by shifting the gear teeth on the edge of the resonator. Also, based on the gear-shifting mechanism, the SLG resonator achieves resonant frequency modulation and opens tunable low-frequency torsional band gaps consistent with theoretical predictions. Then, the SLG resonators are periodically attached to a uniform shaft to construct a gear-shifting tunable meta-shaft, whose dynamic response is obtained using numerical simulations to evaluate its torsional wave attenuation performance. Finally, a gear-shifting tunable meta-shaft prototype is fabricated and experiments are carried out to study the propagation characteristics of torsional waves therein. Through the consistency observed among theoretical analysis, numerical simulations, and experimental results, the gear-shifting tunable meta-shaft is found to exhibit excellent attenuation performance in the tunable low-frequency band gaps. Therefore, the proposed gear-shifting tunable meta-shaft paves a new way for low-frequency torsional vibration suppression.

physics.app-ph↗

arXiv2025

Investigation on high-order planar Hall effect in trigonal PtBi$_2$

The trigonal PtBi$_2$ (t-PtBi$_2$) as a Weyl semimetal possessing triply degenerate points in its electronic bands near the Fermi level endows it with rich electronic properties. Previous studies have already measured the planar Hall effect (PHE) and in-plane anisotropic magnetoresistance (AMR) of t-PtBi$_2$. We noticed that their experimental results exhibited high-order features in both the PHE and AMR, yet these features were not systematically investigated. In our work, we conducted more systematic measurements and analyses of the PHE and AMR in t-PtBi$_2$. Both PHE and AMR show high-order features under low temperatures and strong magnetic fields, and these features share a similar temperature and magnetic field dependence with the turn-on behavior of resistance and temperature curves, indicating a common physical origin for them. We further summarize the critical conditions for the emergence of high-order PHE in t-PtBi$_2$, which will help to understand the origin of high-order features. In addition, we performed computational simulations on the AMR of t-PtBi$_2$, and the results were consistent with the experiments, indicating the high-order features are the result of the combined contribution of the Fermi surface anisotropy and the scaling behavior of magnetoresistance. Our findings will contribute to a deeper understanding of the origins of high-order features in non-magnetic topological materials.

cond-mat.mtrl-sci↗

arXiv2024

Property Enhanced Instruction Tuning for Multi-task Molecule Generation with Large Language Models

Large language models (LLMs) are widely applied in various natural language processing tasks such as question answering and machine translation. However, due to the lack of labeled data and the difficulty of manual annotation for biochemical properties, the performance for molecule generation tasks is still limited, especially for tasks involving multi-properties constraints. In this work, we present a two-step framework PEIT (\textbf{P}roperty \textbf{E}nhanced \textbf{I}nstruction \textbf{T}uning) to improve LLMs for molecular-related tasks. In the first step, we use textual descriptions, SMILES, and biochemical properties as multimodal inputs to pre-train a model called PEIT-GEN, by aligning multi-modal representations to synthesize instruction data. In the second step, we fine-tune existing open-source LLMs with the synthesized data, the resulting PEIT-LLM can handle molecule captioning, text-based molecule generation, molecular property prediction, and our newly proposed multi-constraint molecule generation tasks. Experimental results show that our pre-trained PEIT-GEN outperforms MolT5, BioT5, MolCA and Text+Chem-T5 in molecule captioning, demonstrating modalities align well between textual descriptions, structures, and biochemical properties. Furthermore, PEIT-LLM shows promising improvements in multi-task molecule generation, demonstrating the effectiveness of the PEIT framework for molecular tasks. The code and appendix are available at https://github.com/chenlong164/PEIT.

cs.AI↗

arXiv2024

TransMA: an explainable multi-modal deep learning model for predicting properties of ionizable lipid nanoparticles in mRNA delivery

As the primary mRNA delivery vehicles, ionizable lipid nanoparticles (LNPs) exhibit excellent safety, high transfection efficiency, and strong immune response induction. However, the screening process for LNPs is time-consuming and costly. To expedite the identification of high-transfection-efficiency mRNA drug delivery systems, we propose an explainable LNPs transfection efficiency prediction model, called TransMA. TransMA employs a multi-modal molecular structure fusion architecture, wherein the fine-grained atomic spatial relationship extractor named molecule 3D Transformer captures three-dimensional spatial features of the molecule, and the coarse-grained atomic sequence extractor named molecule Mamba captures one-dimensional molecular features. We design the mol-attention mechanism block, enabling it to align coarse and fine-grained atomic features and captures relationships between atomic spatial and sequential structures. TransMA achieves state-of-the-art performance in predicting transfection efficiency using the scaffold and cliff data splitting methods on the current largest LNPs dataset, including Hela and RAW cell lines. Moreover, we find that TransMA captures the relationship between subtle structural changes and significant transfection efficiency variations, providing valuable insights for LNPs design. Additionally, TransMA's predictions on external transfection efficiency data maintain a consistent order with actual transfection efficiencies, demonstrating its robust generalization capability. The code, model and data are made publicly available at https://github.com/wklix/TransMA/tree/master. We hope that high-accuracy transfection prediction models in the future can aid in LNPs design and initial screening, thereby assisting in accelerating the mRNA design process.

cs.AI↗

arXiv2023

Irreducible representations of $\textrm{GL}_n(\mathbb{C})$ of minimal Gelfand-Kirillov dimension

In this article, by studying the Bernstein degrees and Goldie rank polynomials, we establish a comparison between the irreducible representations of $G=\textrm{GL}_n(\mathbb{C})$ possessing the minimal Gelfand-Kirillov dimension and those induced from finite-dimensional representations of the maximal parabolic subgroup of $G$ of type $(n-1,1)$. We give the transition matrix between the two bases for the corresponding coherent families.

math.RT↗

arXiv2023

Wavelet estimation of nonstationary spatial covariance function

This work proposes a new procedure for estimating the non-stationary spatial covariance function for Spatial-Temporal Deformation. The proposed procedure is based on a monotonic function approach. The deformation functions are expanded as a linear combination of the wavelet basis. The estimate of the deformation guarantees an injective transformation. Such that two distinct locations in the geographic plane are not mapped into the same point in the deformation plane. Simulation studies have shown the effectiveness of this procedure. An application to historical daily maximum temperature records exemplifies the flexibility of the proposed methodology when dealing with real datasets.

stat.ME↗

arXiv2023

Time-varying STARMA models by wavelets

The spatio-temporal autoregressive moving average (STARMA) model is frequently used in several studies of multivariate time series data, where the assumption of stationarity is important, but it is not always guaranteed in practice. One way to proceed is to consider locally stationary processes. In this paper we propose a time-varying spatio-temporal autoregressive and moving average (tvSTARMA) modelling based on the locally stationarity assumption. The time-varying parameters are expanded as linear combinations of wavelet bases and procedures are proposed to estimate the coefficients. Some simulations and an application to historical daily precipitation records of Midwestern states of the USA are illustrated.

stat.ME↗

arXiv2023

LRBmat: A Novel Gut Microbial Interaction and Individual Heterogeneity Inference Method for Colorectal Cancer

Many diseases are considered to be closely related to the changes in the gut microbial community, including colorectal cancer (CRC), which is one of the most common cancers in the world. The diagnostic classification and etiological analysis of CRC are two critical issues worthy of attention. Many methods adopt gut microbiota to solve it, but few of them simultaneously take into account the complex interactions and individual heterogeneity of gut microbiota, which are two common and important issues in genetics and intestinal microbiology, especially in high-dimensional cases. In this paper, a novel method with a Binary matrix based on Logistic Regression (LRBmat) is proposed to deal with the above problem. The binary matrix can directly weakened or avoided the influence of heterogeneity, and also contain the information about gut microbial interactions with any order. Moreover, LRBmat has a powerful generalization, it can combine with any machine learning method and enhance them. The real data analysis on CRC validates the proposed method, which has the best classification performance compared with the state-of-the-art. Furthermore, the association rules extracted from the binary matrix of the real data align well with the biological properties and existing literatures, which are helpful for the etiological analysis of CRC. The source codes for LRBmat are available at https://github.com/tsnm1/LRBmat.

q-bio.QM↗

arXiv2021

Discrete Transformation Elasticity: An Approach to Design Lattice-based Polar Metamaterials

The transformation method is a powerful tool for providing the constitutive parameters of the transformed material in the new coordinates. In transformation elasticity, a general curvilinear change of coordinates transforms conventional Hooke's law into a different constitutive law in which the transformed material is not only anisotropic but also polar and chiral and no known elastic solid satisfies. However, this state-of-the-art description provides no insight as to what the underlying microstructure of this transformed material could be, the design of which is a major challenge in this field. The study aims to theoretically justify the fundamental need for the polar material by critically revisiting the discrete transformation method. The key idea is to let transformation gradient operate not only on the elastic properties but on the underlying architectures of the mechanical lattice. As an outstanding application, we leverage the proposed design paradigm to physically construct a polar lattice metamaterial for the observation of elastic carpet cloaking. Numerical simulations are then implemented to show excellent cloaking performance under different static and dynamic mechanical loads. The approach presented herein could promote and accelerate new designs of lattice topologies for transformation elasticity in particular and is able to be extended for realizing other emerging elastic properties and unlocking peculiar functions including statics and dynamics in general.

physics.app-ph↗

arXiv2020

Homological Finiteness of Representations of Almost Linear Nash Groups

Let $G$ be an almost linear Nash group, namely, a Nash group that admits a Nash homomorphism with finite kernel to some $\GL_k(\mathbb R)$. A smooth \Fre representation $V$ with moderate growth of $G$ is called homologically finite if the Schwartz homology $\oH_{i}^{\CS}(G;V)$ is finite dimensional for every $i\in\BZ$. We show that the space of Schwartz sections $Γ^ς(X,\SE)$ of a tempered $G$-vector bundle $(X,\SE)$ is homologically finite as a representation of $G$, under some mild assumptions.

math.RT↗

arXiv2020

Realization of active metamaterials with odd micropolar elasticity

Materials made from active, living, or robotic components can display emergent properties arising from local sensing and computation. Here, we realize a freestanding active metabeam with piezoelectric elements and electronic feed-forward control that gives rise to an odd micropolar elasticity absent in energy-conserving media. The non-reciprocal odd modulus enables bending and shearing cycles that convert electrical energy into mechanical work, and vice versa. The sign of this elastic modulus is linked to a non-Hermitian topological index that determines the localization of vibrational modes to sample boundaries. At finite frequency, we can also tune the phase angle of the active modulus to produce a direction-dependent bending modulus and control non-Hermitian vibrational properties. Our continuum approach, built on symmetries and conservation laws, could be exploited to design others systems such as synthetic biofilaments and membranes with feed-forward control loops.

physics.app-ph↗

arXiv2019

Ferromagnetic van der Waals compound MnSb$_{1.8}$Bi$_{0.2}$Te$_4$

The intersection of topology and magnetism represents a new playground to discover novel quantum phenomena and device concepts. In this work, we show that a van der Waals compound MnSb$_{1.8}$Bi$_{0.2}$Te$_4$ exhibits a ferromagnetic ground state with a Curie temperature of 26 K, in contrast to the antiferromagnetic order previously found for other members of the Mn(Sb, Bi)$_2$Te$_4$ family. We employ magneto-transport, bulk magnetization and neutron scattering studies to illustrate the magnetic and electrical properties of MnSb$_{1.8}$Bi$_{0.2}$Te$_4$ and report on the observation of an unusual anomalous Hall effect. Our results are an important step in the synthesis and understanding of ferromagnetic topological insulators.

cond-mat.mes-hall↗

arXiv2019

Classification of abelian Nash manifolds

By the algebraization of affine Nash groups, a connected affine Nash group is an abelian Nash manifold if and only if its algebraization is a real abelian variety. We first classify real abelian varieties up to isomorphisms. Then with a bit more efforts, we classify abelian Nash manifolds up to Nash equivalences.

math.RT↗

arXiv2019

The normalized Laplacian spectrum and eigentime identities of hype-cubes

Many popular graph metrics encode average properties of individual network elements. Complementing these conventional graph metrics, the eigenvalue spectrum of the normalized Laplacian describes a network's structure directly at a systems level, without referring to individual nodes or connections. In this paper, we study the spectrum and their applications of normalized Laplacian matrices of hype-cubes, a special kind of Cayley graphs. We determine explicitly all the eigenvalues and their corresponding multiplicities by a recursive method. By using the relation between normalized Laplacian spectrum and eigentime identity, we derive the explicit formula to the eigentime identity for random walks on the hype-cubes and show that it grows linearly with the network size. Moreover, we compute the number of spanning trees of the hype-cubes.

math.SP↗

arXiv2019

Anomalous Hall effect mechanisms in quasi-2D van der Waals ferromagnet Fe0.29TaS2

The recent emergence of two-dimensional (2D) van der Waals ferromagnets has provided a new platform for exploring magnetism in the flatland and for designing 2D ferromagnet-based spintronics devices. Despite intensive studies, the anomalous Hall effect (AHE) mechanisms in 2D van der Waals ferromagnets have not been investigated yet. In this paper, we report the AHE mechanisms in quasi-2D van der Waals ferromagnet Fe0.29TaS2 via systematically measuring Fe0.29TaS2 devices with thickness from 14 nm to bulk single crystal. The AHE mechanisms are investigated via the scaling relationship between the anomalous Hall and channel conductivities. As the Fe0.29TaS2 thickness decreases, the major AHE mechanism changes from extrinsic scattering to intrinsic contribution. The crossover of the AHE mechanisms is found to be highly associated with the channel conductivities as the Fe0.29TaS2 thickness varies.

cond-mat.mtrl-sci↗