arXiv subjects

Long Liu

Publications and source records attributed to Long Liu.

27 records · Page 2Linked to original sources

arXiv2024

Strongly asymmetric magnetization switching and programmable complete Boolean logic enabled by long-range intralayer Dzyaloshinskii-Moriya interaction

Electrical switching of magnetization is central to spintronics. Despite the enormous efforts on the spin torques and the Dzyaloshinskii-Moriya interaction (DMI) effects, some fundamental physics for electrical switching of magnetization is still missing as indicated by a number of remarkable long-standing puzzles. Here, we report the discovery of the long-range intralayer DMI effect widely existing in magnetic heterostructure, which is distinct from the yet-known DMI effects as it describes the chiral coupling of two orthogonal magnetic domains within the same magnetic layer via the mediation of an adjacent heavy metal layer. The long-range intralayer DMI generates a strong perpendicular effective magnetic field (H_DMI^z) on the perpendicular magnetization. Characteristically, H_DMI^z varies with the sign/magnitude of the interfacial DMI constant, the applied in-plane magnetic fields, and the distribution of the perpendicular magnetic anisotropy. The long-range intralayer DMI results in striking consequences including the strongly asymmetric current/field switching of perpendicular magnetization, hysteresis loop shift of perpendicular magnetization in the absence of in-plane direct current, and sharp, complete switching of perpendicular magnetization purely by an in-plane magnetic field. Utilizing the long-range intralayer DMI effect, we demonstrate programable, complete Boolean logic operations (i.e., AND, NAND, NOT, OR, and NOR) within a single spin-orbit torque device. These results will stimulate the investigation of the long-range intralayer DMI effect and its impacts on a variety of spintronic devices.

cond-mat.mtrl-sci↗

arXiv2024

Active Generation Network of Human Skeleton for Action Recognition

Data generation is a data augmentation technique for enhancing the generalization ability for skeleton-based human action recognition. Most existing data generation methods face challenges to ensure the temporal consistency of the dynamic information for action. In addition, the data generated by these methods lack diversity when only a few training samples are available. To solve those problems, We propose a novel active generative network (AGN), which can adaptively learn various action categories by motion style transfer to generate new actions when the data for a particular action is only a single sample or few samples. The AGN consists of an action generation network and an uncertainty metric network. The former, with ST-GCN as the Backbone, can implicitly learn the morphological features of the target action while preserving the category features of the source action. The latter guides generating actions. Specifically, an action recognition model generates prediction vectors for each action, which is then scored using an uncertainty metric. Finally, UMN provides the uncertainty sampling basis for the generated actions.

cs.CV↗

arXiv2023

Improving Knowledge Distillation via Transferring Learning Ability

Existing knowledge distillation methods generally use a teacher-student approach, where the student network solely learns from a well-trained teacher. However, this approach overlooks the inherent differences in learning abilities between the teacher and student networks, thus causing the capacity-gap problem. To address this limitation, we propose a novel method called SLKD.

cs.CV↗

arXiv2023

Dynamic Domain Discrepancy Adjustment for Active Multi-Domain Adaptation

Multi-source unsupervised domain adaptation (MUDA) aims to transfer knowledge from related source domains to an unlabeled target domain. While recent MUDA methods have shown promising results, most focus on aligning the overall feature distributions across source domains, which can lead to negative effects due to redundant features within each domain. Moreover, there is a significant performance gap between MUDA and supervised methods. To address these challenges, we propose a novel approach called Dynamic Domain Discrepancy Adjustment for Active Multi-Domain Adaptation (D3AAMDA). Firstly, we establish a multi-source dynamic modulation mechanism during the training process based on the degree of distribution differences between source and target domains. This mechanism controls the alignment level of features between each source domain and the target domain, effectively leveraging the local advantageous feature information within the source domains. Additionally, we propose a Multi-source Active Boundary Sample Selection (MABS) strategy, which utilizes a guided dynamic boundary loss to design an efficient query function for selecting important samples. This strategy achieves improved generalization to the target domain with minimal sampling costs. We extensively evaluate our proposed method on commonly used domain adaptation datasets, comparing it against existing UDA and ADA methods. The experimental results unequivocally demonstrate the superiority of our approach.

cs.LG↗

arXiv2022

An unified material interpolation for topology optimization of multi-materials

Topology optimization is one of the engineering tools for finding efficient design. For the material interpolation scheme, it is usual to employ the SIMP (Solid Isotropic Material with Penalization) or the homogenization based interpolation function for the parameterization of the material properties with respect to the design variables assigned to each finite element. For topology optimization with single material design, i.e., solid or void, the parameterization with 1 for solid and 0 for void becomes relatively straight forward using a polynomial function. For the case of multiple materials, some issues of the equality modeling of each material and \textcolor{red}{the clear 0, 1 result of each element for the topology optimization} issues become serious because of the curse of the dimension. To relieve these issues, this research proposes a new mapping based interpolation function for multi-material topology optimization. Unlike the polynomial based interpolation, this new interpolation is formulated by the ratio of the $p$-norm of the design variables to the 1-norm of the design variable multiplied by the design variable for a specific material. With this alternative mapping based interpolation function, each material are equally modeled and \textcolor{red}{ the clear 0, 1 result of each material for the multi-material topology optimization model} can be improved. This paper solves several topology optimization problems to prove the validity of the present interpolation function.

cs.CE↗

arXiv2022

A Novel Non-Volatile Inverter-based CiM: Continuous Sign Weight Transition and Low Power on-Chip Training

In this work, we report a novel design, one-transistor-one-inverter (1T1I), to satisfy high speed and low power on-chip training requirements. By leveraging doped HfO2 with ferroelectricity, a non-volatile inverter is successfully demonstrated, enabling desired continuous weight transition between negative and positive via the programmable threshold voltage (VTH) of ferroelectric field-effect transistors (FeFETs). Compared with commonly used designs with the similar function, 1T1I uniquely achieves pure on-chip-based weight transition at an optimized working current without relying on assistance from off-chip calculation units for signed-weight comparison, facilitating high-speed training at low power consumption. Further improvements in linearity and training speed can be obtained via a two-transistor-one-inverter (2T1I) design. Overall, focusing on energy and time efficiencies, this work provides a valuable design strategy for future FeFET-based computing-in-memory (CiM).

cond-mat.mes-hall↗

arXiv2019

Room temperature 2D ferromagnetism in few-layered 1$T$-CrTe$_{2}$

Spin-related electronics using two dimensional (2D) van der Waals (vdW) materials as a platform are believed to hold great promise for revolutionizing the next generation spintronics. Although many emerging new phenomena have been unravelled in 2D electronic systems with spin long-range orderings, the scarcely reported room temperature magnetic vdW material has thus far hindered the related applications. Here, we show that intrinsic ferromagnetically aligned spin polarization can hold up to 316 K in a metallic phase of 1$T$-CrTe$_{2}$ in the few-layer limit. This room temperature 2D long range spin interaction may be beneficial from an itinerant enhancement. Spin transport measurements indicate an in-plane room temperature negative anisotropic magnetoresistance (AMR) in few-layered CrTe$_{2}$, but a sign change in the AMR at lower temperature, with -0.6$\%$ at 300 K and +5$\%$ at 10 K, respectively. This behavior may originate from the specific spin polarized band structure of CrTe$_{2}$. Our findings provide insights into magnetism in few-layered CrTe$_{2}$, suggesting potential for future room temperature spintronic applications of such 2D vdW magnets.

cond-mat.mes-hall↗

arXiv2019

BGD-based Adam algorithm for time-domain equalizer in PAM-based optical interconnects

To the best of our knowledge, for the first time, we propose adaptive moment estimation (Adam) algorithm based on batch gradient descent (BGD) to design a time-domain equalizer (TDE) for PAM-based optical interconnects. Adam algorithm has been widely applied in the fields of artificial intelligence. For TDE, BGD-based Adam algorithm can obtain globally optimal tap coefficients without being trapped in locally optimal tap coefficients. Therefore, fast and stable convergence can be achieved by BGD-based Adam algorithm with low mean square error. Meanwhile, BGD-based Adam algorithm is implemented by parallel processing, which is more efficient than conventional serial algorithms, such as least mean square and recursive least square algorithms. The experimental results demonstrate that BGD-based Adam feed-forward equalizer works well in 120-Gbit/s PAM8 optical interconnects. In conclusion, BGD-based Adam algorithm shows great potential for converging the tap coefficients of TDE in future optical interconnects.

cs.IT↗

arXiv2019

Field-free switching of a Spin-orbit torque device aided by interlayer-coupling induced domain walls

The spin-orbit torque device is promising as a candidate for next generation magnetic memory, while the static in-plane field needed to induce deterministic switching is a main obstacle for its application in highly integrated circuits. Instead of introducing effective field into the device, in this work we present an alternative way to achieve the field-free current-driven magnetization switching. By adding Tb/Co multilayers at two ends of the current channel, assisting domain wall is created by interlayer exchange coupling. The field-free deterministic switching is achieved by the movement of the domain wall driven by current. By loop shift measurement we find the driven force exerted on the domain wall is determined by the direction of the in-plane moment in the domain wall. Finally, we modify the device into a synthetic antiferromagnetic structure to solve the problem of reading out signal. The present work broadens the choice of field-free spin-orbit torque device design and clearly depicts the difference between two different switching mechanism.

physics.app-ph↗