Search

Orbitronics is an emerging and fascinating field that explores the utilization of the orbital degree of freedom of electrons for information processing. An increasing number of orbital phenomena are being currently discovered, with spin-orbit coupling mediating the interplay between orbital and spin effects, thus providing a wealth of control mechanisms and device applications. In this context, the orbital analog of spin Dzyaloshinskii-Moriya interaction (DMI), i.e. orbital DMI, deserves to be explored in depth, since it is believed to be capable of inducing chiral orbital structures. Here, we unveil the main features and microscopic mechanisms of the orbital DMI in a two-dimensional square lattice using a tight-binding model of t2g orbitals in combination with the Berry phase theory. This approach allows us to investigate and transparently disentangle the role of inversion symmetry breaking, strength of orbital exchange interaction and spin-orbit coupling in shaping the properties of the orbital DMI. By scrutinizing the band-resolved contributions we are able to understand the microscopic mechanisms and guiding principles behind the orbital DMI and its anisotropy in two dimensional magnetic materials, and uncover a fundamental relation between the orbital DMI and its spin counterpart, which is currently explored very intensively. The insights gained from our work contribute to advancing our knowledge of orbitalrelated effects and their potential applications in spintronics, providing a path for future research in the field of chiral orbitronics.

Visual-based 3D semantic occupancy perception is a key technology for robotics, including autonomous vehicles, offering an enhanced understanding of the environment by 3D. This approach, however, typically requires more computational resources than BEV or 2D methods. We propose a novel 3D semantic occupancy perception method, OccupancyDETR, which utilizes a DETR-like object detection, a mixed dense-sparse 3D occupancy decoder. Our approach distinguishes between foreground and background within a scene. Initially, foreground objects are detected using the DETR-like object detection. Subsequently, queries for both foreground and background objects are fed into the mixed dense-sparse 3D occupancy decoder, performing upsampling in dense and sparse methods, respectively. Finally, a MaskFormer is utilized to infer the semantics of the background voxels. Our approach strikes a balance between efficiency and accuracy, achieving faster inference times, lower resource consumption, and improved performance for small object detection. We demonstrate the effectiveness of our proposed method on the SemanticKITTI dataset, showcasing an mIoU of 14 and a processing speed of 10 FPS, thereby presenting a promising solution for real-time 3D semantic occupancy perception.

We investigate the orbit-to-charge conversion in YIG/Pt/nonmagnetic material (NM) trilayer heterostructures. With the additional Ru layer on the top of YIG/Pt stacks, the charge current signal increases nearly an order of magnitude in both longitudinal spin Seebeck effect (SSE) and spin pumping (SP) measurements. Through thickness dependence studies of the Ru metal layer and theoretical model, we quantitatively clarify different contributions of the increased SSE signal that mainly comes from the inverse orbital Hall effect (IOHE) of Ru, and partially comes from the orbital sink effect in the Ru layer. A similar enhancement of SSE(SP) signals is also observed when Ru is replaced by other materials (Ta, W, and Cu), implying the universality of the IOHE in transition metals. Our findings not only suggest a more efficient generation of the charge current via the orbital angular moment channel but also provides crucial insights into the interplay among charge, spin, and orbit.

Accurate depth estimation under out-of-distribution (OoD) scenarios, such as adverse weather conditions, sensor failure, and noise contamination, is desirable for safety-critical applications. Existing depth estimation systems, however, suffer inevitably from real-world corruptions and perturbations and are struggled to provide reliable depth predictions under such cases. In this paper, we summarize the winning solutions from the RoboDepth Challenge -- an academic competition designed to facilitate and advance robust OoD depth estimation. This challenge was developed based on the newly established KITTI-C and NYUDepth2-C benchmarks. We hosted two stand-alone tracks, with an emphasis on robust self-supervised and robust fully-supervised depth estimation, respectively. Out of more than two hundred participants, nine unique and top-performing solutions have appeared, with novel designs ranging from the following aspects: spatial- and frequency-domain augmentations, masked image modeling, image restoration and super-resolution, adversarial training, diffusion-based noise suppression, vision-language pre-training, learned model ensembling, and hierarchical feature enhancement. Extensive experimental analyses along with insightful observations are drawn to better understand the rationale behind each design. We hope this challenge could lay a solid foundation for future research on robust and reliable depth estimation and beyond. The datasets, competition toolkit, workshop recordings, and source code from the winning teams are publicly available on the challenge website., Comment: Technical Report; 65 pages, 34 figures, 24 tables; Code at https://github.com/ldkong1205/RoboDepth

Magnetic skyrmions are nanoscale spin textures that their thermal stability originates from the nontrivial topology in nature. Recently, a plethora of topological spin textures have been theoretically predicted or experimentally observed, enriching the diversity of the skyrmionic family. In this work, we theoretically demonstrate the stabilities of various topological spin textures against homochiral states in chiral magnets, including chiral bobbers, dipole strings, and skyrmion tubes. They can be effectively classified by the associated topological Hall signals. Multiple transition paths are found among these textures, mediated by Bloch-point singularities, and the topological protection property here can be manifested by a finite energy barrier with the saddle point corresponding to the Bloch-point creation/destruction. By carefully modulating the local property of a surface, such as interfacial DMI induced by breaking the structural symmetry, the energy landscape of a magnetic system can be tailored decisively. Significantly, the proposed scenario also enables the manipulation of stabilities and transition barriers of these textures, even accompanied by the discovery of ground-state chiral bobbers. This study may raise great expectations on the coexistence of topological spin textures as spintronics-based information carriers for future applications.

Tailored magnetic multilayers (MMLs) provide skyrmions with enhanced thermal stability, leading to the possibility of skyrmion-based devices for room temperature applications. At the same time, the search for additional stable topological spin textures has been under intense research focus. Besides their fundamental importance, such textures may expand the information encoding capability of spintronic devices. However, fractional spin texture states within MMLs in the vertical dimension have yet to be investigated. In this work, we demonstrate numerically fractional skyrmion tubes (FSTs) in a tailored MML system. We subsequently propose to encode sequences of information signals with fractional skyrmion tubes (FSTs) as information bits in a tailored MML device. Micromagnetic simulations and theoretical calculations are used to verify the feasibility of hosting distinct FST states within a single device, and their thermal stability is investigated. A multilayer multiplexing device is proposed, where multiple sequences of the information signals can be encoded and transmitted based on the nucleation and propagation of packets of FSTs. Finally, pipelined information transmission and automatic demultiplexing is demonstrated by exploiting the skyrmion Hall effect and introducing voltage-controlled synchronizers and width-based track selectors. The findings indicate that FSTs can be potential candidates as information carriers for future spintronic applications.

Magnetic skyrmions have been in the spotlight since their observation in technologically relevant systems at room temperature. More recently, there has been increasing interest in additional quasiparticles that may exist as stable/metastable spin textures in magnets, such as the skyrmionium and the anti-skyrmionite (i.e., a double-antiskyrmion-skyrmion pair) that have distinct topological characteristics. The next challenge and opportunity, at the same time, is to investigate the use of multiple magnetic quasiparticles as information carriers in a single device for next generation nanocomputing. In this paper, we propose a spintronic interconnect device where multiple sequences of information signals are encoded and transmitted simultaneously by skyrmions, skyrmioniums, and anti-skyrmionites. The proposed spintronic interconnect device can be pipelined via voltage-controlled-magnetic-anisotropy (VCMA) gated synchronisers that behave as intermediate registers. We demonstrate theoretically that the interconnect throughput and transmission energy can be effectively tuned by the VCMA gate voltage and appropriate electric current pulses. By carefully adjusting the device structure characteristics, our spintronic interconnect device exhibits comparable energy efficiency with copper interconnects in mainstream CMOS technologies. This study provides fresh insight into the possibilities of skyrmionic devices in future spintronic applications., Comment: 28 pages, 6 figures

Inertial sensor-based human activity recognition (HAR) is the base of many human-centered mobile applications. Deep learning-based fine-grained HAR models enable accurate classification in various complex application scenarios. Nevertheless, the large storage and computational overhead of the existing fine-grained deep HAR models hinder their widespread deployment on resource-limited platforms. Inspired by the knowledge distillation's reasonable model compression and potential performance improvement capability, we design a multi-level HAR modeling pipeline called Stage-Logits-Memory Distillation (SMLDist) based on the widely-used MobileNet. By paying more attention to the frequency-related features during the distillation process, the SMLDist improves the HAR classification robustness of the students. We also propose an auto-search mechanism in the heterogeneous classifiers to improve classification performance. Extensive simulation results demonstrate that SMLDist outperforms various state-of-the-art HAR frameworks in accuracy and F1 macro score. The practical evaluation of the Jetson Xavier AGX platform shows that the SMLDist model is both energy-efficient and computation-efficient. These experiments validate the reasonable balance between the robustness and efficiency of the proposed model. The comparative experiments of knowledge distillation on six public datasets also demonstrate that the SMLDist outperforms other advanced knowledge distillation methods of students' performance, which verifies the good generalization of the SMLDist on other classification tasks, including but not limited to HAR., Comment: This work has been submitted to the IEEE for possible publication. Copyright may be transferred without notice, after which this version may no longer be accessible

Magnetic skyrmions have attracted considerable interest, especially after their recent experimental demonstration at room temperature in multilayers. The robustness, nanoscale size and non-volatility of skyrmions have triggered a substantial amount of research on skyrmion-based low-power, ultra-dense nanocomputing and neuromorphic systems such as artificial synapses. Room-temperature operation is required to integrate skyrmionic synapses in practical future devices. Here, we numerically propose a nanoscale skyrmionic synapse composed of magnetic multilayers that enables room-temperature device operation tailored for optimal synaptic resolution. We demonstrate that when embedding such multilayer skyrmionic synapses in a simple spiking neural network (SNN) with unsupervised learning via the spike-timing-dependent plasticity rule, we can achieve only a 78% classification accuracy in the MNIST handwritten data set under realistic conditions. We propose that this performance can be significantly improved to about 98.61% by using a deep SNN with supervised learning. Our results illustrate that the proposed skyrmionic synapse can be a potential candidate for future energy-efficient neuromorphic edge computing., Comment: 14 pages, 10 figures

The interfacial Dzyaloshinskii-Moriya interaction (iDMI) is attracting great interests for spintronics. An iDMI constant larger than 3 mJ/m^2 is expected to minimize the size of skyrmions and to optimize the DW dynamics. In this study, we experimentally demonstrate an enhanced iDMI in Pt/Co/X/MgO ultra-thin film structures with perpendicular magnetization. The iDMI constants were measured using a field-driven creep regime domain expansion method. The enhancement of iDMI with an atomically thin insertion of Ta and Mg is comprehensively understood with the help of ab-initio calculations. Thermal annealing has been used to crystallize the MgO thin layer for improving tunneling magneto-resistance (TMR), but interestingly it also provides a further increase of the iDMI constant. An increase of the iDMI constant up to 3.3 mJ/m^2 is shown, which could be promising for the scaling down of skyrmion electronics.

This paper proposes a novel discriminative regression method, called adaptive locality preserving regression (ALPR) for classification. In particular, ALPR aims to learn a more flexible and discriminative projection that not only preserves the intrinsic structure of data, but also possesses the properties of feature selection and interpretability. To this end, we introduce a target learning technique to adaptively learn a more discriminative and flexible target matrix rather than the pre-defined strict zero-one label matrix for regression. Then a locality preserving constraint regularized by the adaptive learned weights is further introduced to guide the projection learning, which is beneficial to learn a more discriminative projection and avoid overfitting. Moreover, we replace the conventional `Frobenius norm' with the special l21 norm to constrain the projection, which enables the method to adaptively select the most important features from the original high-dimensional data for feature extraction. In this way, the negative influence of the redundant features and noises residing in the original data can be greatly eliminated. Besides, the proposed method has good interpretability for features owing to the row-sparsity property of the l21 norm. Extensive experiments conducted on the synthetic database with manifold structure and many real-world databases prove the effectiveness of the proposed method., Comment: The paper has been accepted by IEEE Transactions on Circuits and Systems for Video Technology (TCSVT), and the code can be available at https://drive.google.com/file/d/1iNzONkRByIaUhXwdEhOkkh_0d2AAXNE8/view

The natural gas and electricity systems are now becoming more and more strongly coupled. While gas-fired units (GFUs) are replacing the retired coal plants, the power systems are also increasingly dependent on the flexibilities provided by GFUs to accommodate intermittent renewable energies. The GFUs' capability of power generation relies on the availability of gas resources, which is determined jointly by the capacity of gas suppliers and their associated pipeline networks. However, the gas and electricity markets are operated separately. As a con-sequence, the GFUs actually have to "represent" the whole power system to bid on the gas market: they need to make forecasts on future gas consumption, and bear the risk of improper contracts or being unable to meet the generation schedules due to insufficient gas supply. When facing larger shares of renewable energies and more-frequent gas network congestion, the current market framework is especially unreliable and inefficient, and also economically unfriendly to the investors of the GFU assets. In this paper, we try to develop a framework which can combine the two markets. By properly pricing the scarce resources, e.g., gas transmission capacity, the joint market can help allocate them more efficiently while satisfying the demands. And by considering the uncertainty brought by renewable energies, a more forward-looking day-ahead market clearing framework is presented. The formulation, algorithm of the pro-posed joint market model will be presented, as well as some case studies., Comment: The paper has been withdrawn by the author due to major modification

Substantial changes in the generation portfolio take place due to the fast growth of renewable energy generation, of which the major types such as wind and solar power have significant forecast uncertainty. Reducing the impacts of uncertainty requires the cooperation of system participants, which are supported by proper market rules and incentives. In this paper, we propose a bilateral reserve market for variable generation (VG) producers and capacity resource providers. In this market, VG producers purchase bilateral reserve services (BRSs) to reduce potential imbalance penalties, and BRS providers earn profits on their available capacity for re-dispatch. We show in this paper that by introducing this product, the VG producers' overall imbalance costs are linked to both their forecast quality and the available system capacity, which follows the cost-causation principle. Case studies demonstrate how the proposed BRS mechanism works and its effectiveness.

The deepening penetration of renewables in power systems has contributed to the increasing needs for generation scheduling flexibility. Specifically, for short-term operations, flexibility here indicates that sufficient ramp capacities should be reserved to respond to the expected changes in the load and intermittent generation, also covering a certain amount of their uncertainty. To address the growing requirements for flexible ramp capacity, markets for ramp products have been launched in practice such as the ones in California ISO and Midcontinent ISO. Some-times, to guarantee sufficient ramp capacity, expensive fast start units have to be committed in real-time. Moreover, with higher penetration of renewable generation, the flexibility provided by the conventional units might not be enough. Actually, wind power producers are physically capable of offering flexibility, which is sometimes also economically efficient to the entire system. In this paper, we aim to explore the mechanism and possibility of including wind power producers as ramp providers to increase the supply of flexibility. To conduct the anal-yses, a two-stage stochastic real-time unit commitment model considering ramp capacity adequacy is formulated. Case studies indicate that both the system and the wind power producers can benefit if the wind power is allowed to provide flexible ramp products., Comment: Submitted to a journal