arXiv subjects
Publications and source records attributed to Zhaohui Zhang.
In this manuscript, we construct a class of projective three-weight linear codes and two classes of projective four-weight linear codes over F2 from the defining sets construction, and determine their weight distributions by using additive characters. Especially, the projective three-weight linear code and one class of projective four-weight linear codes (Theorem 4.1) can be applied in secret sharing schemes.
The error-correcting pair is a general algebraic decoding method for linear codes. The near maximal distance separable (NMDS) linear code is a subclass of linear codes and has applications in secret sharing scheme and communication systems due to the efficient performance, thus we focus on the error-correcting pair of NMDS linear codes. In 2023, He and Liao showed that for an NMDS linear code $\mathcal{C}$ with minimal distance $2\ell+1$ or $2\ell+2$, if $\mathcal{C}$ has an $\ell$-error-correcting pair $\left( \mathcal{A}, \mathcal{B} \right)$, then the parameters of $\mathcal{A}$ have 6 or 10 possibilities, respectively. In this manuscript, basing on Product Singleton Bound, we give several necessary conditions for that the NMDS linear code $\mathcal{C}$ with minimal distance $2\ell+1$ has an $\ell$-error-correcting pair $(\mathcal{A}, \mathcal{B})$, where the parameters of $\mathcal{A}$ is the 1st, 2nd, 4th or 5th case, then basing on twisted generalized Reed-Solomon codes, we give an example for that the parameters of $\mathcal{A}$ is the 1st case. Moreover, we also give several necessary conditions for that the NMDS linear code $\mathcal{C}$ with minimal distance $2\ell+2$ has an $\ell$-error-correcting pair $(\mathcal{A}, \mathcal{B})$, where the parameters of $\mathcal{A}$ is the 2nd, 4th, 7th or 8th case, then we give an example for that the parameters of $\mathcal{A}$ is the 1st or 2nd case, respectively.
Efficient and effective drug-target binding affinity (DTBA) prediction is a challenging task due to the limited computational resources in practical applications and is a crucial basis for drug screening. Inspired by the good representation ability of graph neural networks (GNNs), we propose a simple-structured GNN model named SS-GNN to accurately predict DTBA. By constructing a single undirected graph based on a distance threshold to represent protein-ligand interactions, the scale of the graph data is greatly reduced. Moreover, ignoring covalent bonds in the protein further reduces the computational cost of the model. The GNN-MLP module takes the latent feature extraction of atoms and edges in the graph as two mutually independent processes. We also develop an edge-based atom-pair feature aggregation method to represent complex interactions and a graph pooling-based method to predict the binding affinity of the complex. We achieve state-of-the-art prediction performance using a simple model (with only 0.6M parameters) without introducing complicated geometric feature descriptions. SS-GNN achieves Pearson's Rp=0.853 on the PDBbind v2016 core set, outperforming state-of-the-art GNN-based methods by 5.2%. Moreover, the simplified model structure and concise data processing procedure improve the prediction efficiency of the model. For a typical protein-ligand complex, affinity prediction takes only 0.2 ms. All codes are freely accessible at https://github.com/xianyuco/SS-GNN.
Pristine graphene is thought lack of catalytic activity up to date, although using graphene-plus-heteroatom materials as catalysts has become a subject of intensive research because it can be metal saving, eco-friendly and ultimately sustainable. Here we report observations of catalytic reactions of high-quality, clean, pristine graphene when immersed into organics containing C=O bonds, like acetone, acetic acid and acetaldehyde. The C=O bonds were found to break and form polymers including polyethers. The reaction rate is highly temperature dependent. The reaction products mainly physically adsorb on graphene and do not cause increase of defect density in graphene, hence graphene retains its intrinsic properties. This new catalysis shall not only find practical importance but also deepen our understanding on the role of graphene in all graphene based catalysis.
We study how well different types of approaches generalise in the task of 3D hand pose estimation under single hand scenarios and hand-object interaction. We show that the accuracy of state-of-the-art methods can drop, and that they fail mostly on poses absent from the training set. Unfortunately, since the space of hand poses is highly dimensional, it is inherently not feasible to cover the whole space densely, despite recent efforts in collecting large-scale training datasets. This sampling problem is even more severe when hands are interacting with objects and/or inputs are RGB rather than depth images, as RGB images also vary with lighting conditions and colors. To address these issues, we designed a public challenge (HANDS'19) to evaluate the abilities of current 3D hand pose estimators (HPEs) to interpolate and extrapolate the poses of a training set. More exactly, HANDS'19 is designed (a) to evaluate the influence of both depth and color modalities on 3D hand pose estimation, under the presence or absence of objects; (b) to assess the generalisation abilities w.r.t. four main axes: shapes, articulations, viewpoints, and objects; (c) to explore the use of a synthetic hand model to fill the gaps of current datasets. Through the challenge, the overall accuracy has dramatically improved over the baseline, especially on extrapolation tasks, from 27mm to 13mm mean joint error. Our analyses highlight the impacts of: Data pre-processing, ensemble approaches, the use of a parametric 3D hand model (MANO), and different HPE methods/backbones.
Hand pose estimation from 3D depth images, has been explored widely using various kinds of techniques in the field of computer vision. Though, deep learning based method improve the performance greatly recently, however, this problem still remains unsolved due to lack of large datasets, like ImageNet or effective data synthesis methods. In this paper, we propose HandAugment, a method to synthesize image data to augment the training process of the neural networks. Our method has two main parts: First, We propose a scheme of two-stage neural networks. This scheme can make the neural networks focus on the hand regions and thus to improve the performance. Second, we introduce a simple and effective method to synthesize data by combining real and synthetic image together in the image space. Finally, we show that our method achieves the first place in the task of depth-based 3D hand pose estimation in HANDS 2019 challenge.
In this work, we explore the possibility of enhancing a spin current under a thermal switch, i.e., connecting the central transport region to two leads in individual thermal equilibrium abruptly. Using the nonequilibrium Green's function method for the transient spin current, we obtain a closed-form solution, which is applicable in the whole nonlinear quantum transport regime with a significant reduction of computational complexity. Furthermore, we perform a model calculation on a single-level quantum dot with Lorentzian linewidth. It shows that the transient spin current may vary spatially, causing spin accumulation or depletion in the central region. Moreover, general enhancement of the spin current in the transient regime is observed. In particular, the in-plane components of the transient spin current may increase by 2-3 orders of magnitude compared to the steady-state thermoelectric spin current under a temperature difference of 30 K. Our research demonstrates that ultrafast enhancement of spin currents can be effectively achieved by thermal switches.
Using electrical detection of a strongly coupled spin-photon system comprised of a microwave cavity mode and two magnetic samples, we demonstrate the long distance manipulation of spin currents. This distant control is not limited by the spin diffusion length, instead depending on the interplay between the local and global properties of the coupled system, enabling systematic spin current control over large distance scales (several centimeters in this work). This flexibility opens the door to improved spin current generation and manipulation for cavity spintronic devices.
The phenomenon of spin transfer torque (STT) has attracted a great deal of interests due to its promising prospects in practical spintronic devices. In this paper, we report a theoretical investigation of STT in a noncollinear magnetic tunnel junction under ac modulation based on the nonequilibrium Green's function formalism, and derive a closed-formulation for predicting the time-averaged STT. Using this formulation, the ac STT of a carbon-nanotube-based magnetic tunnel junction is analyzed. Under ac modulation, the low-bias linear (quadratic) dependence of the in-plane (out-of-plane) torque on bias still holds, and the $\sinθ$ dependence on the noncollinear angle is maintained. By photon-assisted tunneling, the bias-induced components of the in-plane and out-of-plane torques can be enhanced significantly, about 12 and 75 times, respectively. Our analysis reveals the condition for achieving optimized STT enhancement and suggests that ac modulation is a very effective way for electrical manipulation of STT.