arXiv subjects

Yujie Wei

Publications and source records attributed to Yujie Wei.

45 records · Page 3Linked to original sources

arXiv2023

Online Prototype Learning for Online Continual Learning

Online continual learning (CL) studies the problem of learning continuously from a single-pass data stream while adapting to new data and mitigating catastrophic forgetting. Recently, by storing a small subset of old data, replay-based methods have shown promising performance. Unlike previous methods that focus on sample storage or knowledge distillation against catastrophic forgetting, this paper aims to understand why the online learning models fail to generalize well from a new perspective of shortcut learning. We identify shortcut learning as the key limiting factor for online CL, where the learned features may be biased, not generalizable to new tasks, and may have an adverse impact on knowledge distillation. To tackle this issue, we present the online prototype learning (OnPro) framework for online CL. First, we propose online prototype equilibrium to learn representative features against shortcut learning and discriminative features to avoid class confusion, ultimately achieving an equilibrium status that separates all seen classes well while learning new classes. Second, with the feedback of online prototypes, we devise a novel adaptive prototypical feedback mechanism to sense the classes that are easily misclassified and then enhance their boundaries. Extensive experimental results on widely-used benchmark datasets demonstrate the superior performance of OnPro over the state-of-the-art baseline methods. Source code is available at https://github.com/weilllllls/OnPro.

cs.CV↗

arXiv2023

On the g$π$-Hirano invertibility in Banach algebras

In a Banach algebra, we introduce a new type of generalized inverse called g$π$-Hirano inverse. Firstly, several existence criteria and the equivalent definition of this inverse are investigated. Then, we discuss the relationship between the g$π$-Hirano invertibility of $a$, $b$ and that of the sum $a+b$ under some weaker conditions. Finally, as applications to the previous additive results, some equivalent characterizations for the g$π$-Hirano invertibility of the anti-triangular matrix over Banach algebras are obtained.In particular, some results in this paper are different from the corresponding ones of classical generalized inverses, such as Drazin inverse and generalized Drazin inverse.

math.RA↗

arXiv2020

Influence of Small Molecule Property on Antibody Response

Antibodies with high titer and affinity to small molecule are critical in the field for the development of vaccines against drugs of abuse, antidotes to toxins and immunoassays for compounds. However, little is known regarding how properties of small molecule influence and which chemical descriptor could indicate the degree of the antibody response. Based on our previous study, we designed and synthesized two groups of small molecules, called haptens, with varied hydrophobicities to investigate the relationship between properties of small molecules and antibody response in term of titer and affinity. We found that the magnitude of the antibody response is positively correlated with the degree of molecular hydrophobicity and related chemical descriptors. This study provides insight into the immunological characteristics of small molecules themselves and useful clues to produce high quality antibodies against small molecules.

q-bio.BM↗

arXiv2019

Thermal Conductivity Modeling using Machine Learning Potentials: Application to Crystalline and Amorphous Silicon

First-principles based modeling on phonon dynamics and transport using density functional theory and Boltzmann transport equation has proven powerful in predicting thermal conductivity of crystalline materials, but it remains unfeasible for modeling complex crystals and disordered solids due to the prohibitive computational cost to capture the disordered structure, especially when the quasiparticle "phonon" model breaks down. Recently, machine-learning regression algorithms show great promises for building high-accuracy potential fields for atomistic modeling with length and time scales far beyond those achievable by first-principles calculations. In this work, using both crystalline and amorphous silicon as examples, we develop machine learning based potential fields for predicting thermal conductivity. The machine learning based interatomic potential is derived from density functional theory calculations by stochastically sampling the potential energy surface in the configurational space. The thermal conductivities of both amorphous and crystalline silicon are then calculated using equilibrium molecular dynamics, which agree well with experimental measurements. This work documents the procedure for training the machine-learning based potentials for modeling thermal conductivity, and demonstrates that machine-learning based potential can be a promising tool for modeling thermal conductivity of both crystalline and amorphous materials with strong disorder.

cond-mat.mtrl-sci↗

arXiv2018

Phononic thermal properties of two-dimensional materials

Following the emergence of many novel two-dimensional (2-D) materials beyond graphene, interest has grown in exploring implications for fundamental physics and practical applications, ranging from electronics, photonics, phononics, to thermal management and energy storage. In this Colloquium, we first summarize and compare the phonon properties, such as phonon dispersion and relaxation time, of pristine 2-D materials with single layer graphene to understand the role of crystal structure and dimension on thermal conductivity. We then compare the phonon properties, contrasting idealized 2-D crystals, realistic 2-D crystals, and 3-D crystals, and synthesizing this to develop a physical picture of how the sample size of 2-D materials affects their thermal conductivity. The effects of geometry, such as number of layers, and nanoribbon width, together with the presence of defects, mechanical strain, and substrate interactions, on the thermal properties of 2-D materials are discussed. Intercalation affects both the group velocities and phonon relaxation times of layered crystals and thus tunes the thermal conductivity along both the through-plane and basal-plane directions. We conclude with a discussion of the challenges in theoretical and experimental studies of thermal transport in 2-D materials. The rich and special phonon physics in 2-D materials make them promising candidates for exploring novel phenomena such as topological phonon effects and applications such as phononic quantum devices.

cond-mat.mtrl-sci↗

arXiv2016

New Insights on Stacking Fault Behavior in Twin Induced Plasticity from Meta-Atom Molecular Dynamics Simulations

There is growing interest in promoting deformation twinning for plasticity in advanced materials, as highly organized twin boundaries are beneficial to better strength-ductility combination in contrast to disordered grain boundaries. Twinning deformation typically involves the kinetics of stacking faults, its interaction with dislocations, and dislocation - twin boundary interactions. While the latter has been intensively investigated, the dynamics of stacking faults has been less known. In this work, we report several new insights on the stacking fault behavior in twin induced plasticity from our meta-atom molecular dynamics simulation: The stacking fault interactions are dominated by dislocation reactions taking place spontaneously, different from the proposed mechanism in literatures; The competition among generating a single stacking fault, a twinning partial and a trailing partial dislocation is dependent on a unique parameter, i.e. stacking fault energy, which in turn determines deformation twinning behaviors. The complex twin-slip and twin-dislocation interactions demonstrate the dual role of deformation twins as both dislocation barrier and storage, potentially contributing to the high strength and ductility of advanced materials like TWIP steels where deformation twinning dominated plasticity accounts for the superb strength-ductility combination.

cond-mat.mtrl-sci↗

arXiv2016

Super-stretchable borophene and its stability under straining

Recent success in synthesizing two-dimensional borophene on silver substrate attracts strong interest in exploring its possible extraordinary physical properties. By using the density functional theory calculations, we show that borophene is highly stretchable along the transverse direction. The strain-to-failure in the transverse direction is nearly twice as that along the longitudinal direction. The straining induced flattening and subsequent stretch of the flat borophene are accounted for the large strain-to-failure for tension in the transverse direction. The mechanical properties in the other two directions exhibit strong anisotropy. Phonon dispersions of the strained borophene monolayers suggest that negative frequencies are presented, which indicates the instability of free-standing borophene even under high tensile stress.

cond-mat.mtrl-sci↗

arXiv2013

Tunable Band Structures of Polycrystalline Graphene by External and Mismatch Strains

Lacking a band gap largely limits the application of graphene in electronic devices. Previous study shows that grain boundaries (GBs) in polycrystalline graphene can dramatically alter the electrical properties of graphene. Here, we investigate the band structure of polycrystalline graphene tuned by externally imposed strains and intrinsic mismatch strains at the GB by density functional theory (DFT) calculations. We found that graphene with symmetrical GBs typically has zero band gap even with large uniaxial and biaxial strain. However, some particular asymmetrical GBs can open a band gap in graphene and their band structures can be substantially tuned by external strains. A maximum band gap about 0.19 eV was observed in matched-armchair GB (5, 5) | (3, 7) with a misorientation of θ=13o when the applied uniaxial strain increases to 9%. Although mismatch strain is inevitable in asymmetrical GBs, it has a small influence on the band gap of polycrystalline graphene.

cond-mat.mtrl-sci↗

arXiv2013

Mechanics and Tunable Bandgap by Straining in Single-Layer Hexagonal Boron-Nitride

Current interest in two-dimensional materials extends from graphene to others systems like single-layer hexagonal boron-nitride (h-BN), for the possibility of making heterogeneous structures to achieve exceptional properties that cannot be realized in graphene.The electrically insulating h-BN and semi-metal graphene may open good opportunities to realize a semiconductor by manipulating the morphology and composition of such heterogeneous structures.Here we report the mechanical properties of h-BN and its band structures tuned by mechanical straining by using the density functional theory calculations.The elastic properties, both the Young's modulus and bending rigidity for h-BN, are isotropic.We reveal that there is a bi-linear dependence of band gap on the applied tensile strains in h-BN. Mechanical strain can tune single-layer h-BN from an insulator to a semiconductor, with a band gap in the 4.7eV to 1.5eV range.

cond-mat.mtrl-sci↗