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Topological insulators (TIs) are emerging materials for next-generation low-power nanoelectronic and spintronic device applications. TIs possess non-trivial spin-momentum locking features in the topological surface states in addition to the spin-Hall effect (SHE), and Rashba states due to high spin-orbit coupling (SOC) properties. These phenomena are vital for observing the charge-spin conversion (CSC) processes for spin-based memory, logic and quantum technologies. Although CSC has been observed in TIs by potentiometric measurements, reliable nonlocal detection has so far been limited to cryogenic temperatures up to T = 15 K. Here, we report nonlocal detection of CSC and its inverse effect in the TI compound Bi1.5Sb0.5Te1.7Se1.3 at room temperature using a van der Waals heterostructure with a graphene spin-valve device. The lateral nonlocal device design with graphene allows observation of both spin-switch and Hanle spin precession signals for generation, injection and detection of spin currents by the TI. Detailed bias- and gate-dependent measurements in different geometries prove the robustness of the CSC effects in the TI. These findings demonstrate the possibility of using topological materials to make all-electrical room-temperature spintronic devices., Comment: 12 pages, 4 figures

In this work, we develop and release Yuan 2.0, a series of large language models with parameters ranging from 2.1 billion to 102.6 billion. The Localized Filtering-based Attention (LFA) is introduced to incorporate prior knowledge of local dependencies of natural language into Attention. A data filtering and generating system is presented to build pre-training and fine-tuning dataset in high quality. A distributed training method with non-uniform pipeline parallel, data parallel, and optimizer parallel is proposed, which greatly reduces the bandwidth requirements of intra-node communication, and achieves good performance in large-scale distributed training. Yuan 2.0 models display impressive ability in code generation, math problem-solving, and chatting compared with existing models. The latest version of YUAN 2.0, including model weights and source code, is accessible at Github.

Vortex-induced vibration (VIV) test of a tensioned flexible pipe in oscillatory sheared flow was performed in an ocean basin. The model was 28.41 mm in diameter and 3.88 m in length. The test was performed on a rotating test rig to simulate oscillatory sheared flow conditions. One end of the test pipe is fixed, and one end is forced to harmonically oscillate to simulate oscillatory sheared flows with various combinations of amplitudes and periods, Keulegan-Carpenter ($KC$) numbers from $25$ to $160$ and five kinds of reduced velocities $Vr$ from $6$ to $14$. Fiber Bragg Grating (FBG) strain sensors were arranged along the test pipe to measure bending strains, and the modal analysis approach was used to determine the VIV response. The VIV response in the cross flow (CF) direction is investigated. The results show that VIV under oscillatory sheared flow exhibit amplitude modulation and hysteresis phenomena. Compared with oscillatory uniform flow-induced VIV, the Strouhal number is smaller in oscillatory sheared flow-induced VIVs. The VIV developing process in oscillatory sheared flow is analyzed, and critical $KC$ is proposed to describe the occurrence of modulated VIV under oscillatory sheared flow., Comment: 19 pages, 18 figures

Van der Waals (vdW) magnets are promising owing to their tunable magnetic properties with doping or alloy composition, where the strength of magnetic interactions, their symmetry, and magnetic anisotropy can be tuned according to the desired application. However, most of the vdW magnet based spintronic devices are so far limited to cryogenic temperatures with magnetic anisotropies favouring out-of-plane or canted orientation of the magnetization. Here, we report room-temperature lateral spin-valve devices with strong in-plane magnetic anisotropy of the vdW ferromagnet (Co0.15Fe0.85)5GeTe2 (CFGT) in heterostructures with graphene. Magnetization measurements reveal above room-temperature ferromagnetism in CFGT with a strong in-plane magnetic anisotropy. Density functional theory calculations show that the magnitude of the anisotropy depends on the Co concentration and is caused by the substitution of Co in the outermost Fe layer. Heterostructures consisting of CFGT nanolayers and graphene were used to experimentally realize basic building blocks for spin valve devices such as efficient spin injection and detection. The spin transport and Hanle spin precession measurements prove a strong in-plane and negative spin polarization at the interface with graphene, which is supported by the calculated spin-polarized density of states of CFGT. The in-plane magnetization of CFGT at room temperature proves its usefulness in graphene lateral spin-valve devices, thus opening further opportunities for spintronic technologies.

Topological quantum materials, with novel spin textures and broken crystal symmetries are suitable candidates for spintronic memory technologies. Their unique electronic properties, such as protected surface states and exotic quasiparticles, can provide an out-of-plane spin polarized current needed for external field free magnetization switching of magnets with perpendicular magnetic anisotropy. Conventional spin-orbit torque materials, such as heavy metals and topological insulators, provide only an in-plane spin polarized current, and recently explored materials with lower crystal symmetries provide very low out-of-plane spin polarized current components, which is not suitable for energy-efficient spin-orbit torque (SOT) applications. Here, we demonstrate a large out-of-plane damping-like SOT at room temperature using a topological Weyl semimetal candidate TaIrTe4 with a lower crystal symmetry. We performed spin-orbit torque ferromagnetic resonance (STFMR) experiments in a TaIrTe4/Ni80Fe20 heterostructure and observed a large out-of-plane damping-like SOT efficiency. The out-of-plane spin Hall conductivity is estimated to be an order of magnitude higher than the reported values in other materials. These findings of high spin Hall conductivity and large out-of-plane SOT efficiency are suitable for the development of energy efficient and external field-free spintronic devices.

The discovery of van der Waals (vdW) materials exhibiting non-trivial and tunable magnetic interactions at room temperature can give rise to exotic magnetic states, which are not readily attainable with conventional materials. Such vdW magnets can provide a unique platform for studying new magnetic phenomena and realising magnetization dynamics for energy-efficient and non-volatile spintronic memory and logic technologies. Recent developments in vdW magnets have revealed their potential to enable spin-orbit torque (SOT) induced magnetization dynamics. However, the deterministic and field-free SOT switching of vdW magnets at room temperature has been lacking, prohibiting their potential applications. Here, we demonstrate magnetic field-free and deterministic SOT switching of a vdW magnet (Co0.5Fe0.5)5GeTe2 (CFGT) at room temperature, capitalizing on its non-trivial intrinsic magnetic ordering. We discover a coexistence of ferromagnetic and antiferromagnetic orders in CFGT at room temperature, inducing an intrinsic exchange bias and canted perpendicular magnetism. The resulting canted perpendicular magnetization of CFGT introduces symmetry breaking, facilitating successful magnetic field-free magnetization switching in the CFGT/Pt heterostructure devices. Furthermore, the SOT-induced magnetization dynamics and their efficiency are evaluated using 2nd harmonic Hall measurements. This advancement opens new avenues for investigating tunable magnetic phenomena in vdW material heterostructures and realizing field-free SOT-based spintronic technologies.

Quantum materials with a large charge current-induced spin polarization are promising for next-generation all-electrical spintronic science and technology. Van der Waals metals with high spin-orbit coupling and novel spin textures have attracted significant attention for an efficient charge to spin conversion process. Here, we demonstrate the electrical generation of spin polarization in NbSe2 up to room temperature. To probe the current-induced spin polarization in NbSe2, we used a graphene-based non-local spin-valve device, where the spin-polarization in NbSe2 is efficiently injected and detected using non-local spin-switch and Hanle spin precession measurements. A significantly higher charge-spin conversion in NbSe2 is observed at a lower temperature, below the superconducting transition temperature Tc ~ 7 K of NbSe2. However, the charge-spin conversion signal could only be observed with a higher bias current above the superconducting critical current, limiting the observation of the signal only to the non-superconducting state of NbSe2. Systematic measurements provide the possible origins of the spin polarization to be predominantly due to the spin Hall effect or Rashba-Edelstein effect in NbSe2, considering different symmetry allowed charge-spin conversion processes.

The prior self-supervised learning researches mainly select image-level instance discrimination as pretext task. It achieves a fantastic classification performance that is comparable to supervised learning methods. However, with degraded transfer performance on downstream tasks such as object detection. To bridge the performance gap, we propose a novel object-level self-supervised learning method, called Contrastive learning with Downstream background invariance (CoDo). The pretext task is converted to focus on instance location modeling for various backgrounds, especially for downstream datasets. The ability of background invariance is considered vital for object detection. Firstly, a data augmentation strategy is proposed to paste the instances onto background images, and then jitter the bounding box to involve background information. Secondly, we implement architecture alignment between our pretraining network and the mainstream detection pipelines. Thirdly, hierarchical and multi views contrastive learning is designed to improve performance of visual representation learning. Experiments on MSCOCO demonstrate that the proposed CoDo with common backbones, ResNet50-FPN, yields strong transfer learning results for object detection., Comment: CVPR2022 workshop

The in-memory algorithms for approximate nearest neighbor search (ANNS) have achieved great success for fast high-recall search, but are extremely expensive when handling very large scale database. Thus, there is an increasing request for the hybrid ANNS solutions with small memory and inexpensive solid-state drive (SSD). In this paper, we present a simple but efficient memory-disk hybrid indexing and search system, named SPANN, that follows the inverted index methodology. It stores the centroid points of the posting lists in the memory and the large posting lists in the disk. We guarantee both disk-access efficiency (low latency) and high recall by effectively reducing the disk-access number and retrieving high-quality posting lists. In the index-building stage, we adopt a hierarchical balanced clustering algorithm to balance the length of posting lists and augment the posting list by adding the points in the closure of the corresponding clusters. In the search stage, we use a query-aware scheme to dynamically prune the access of unnecessary posting lists. Experiment results demonstrate that SPANN is 2$\times$ faster than the state-of-the-art ANNS solution DiskANN to reach the same recall quality $90\%$ with same memory cost in three billion-scale datasets. It can reach $90\%$ recall@1 and recall@10 in just around one millisecond with only 32GB memory cost. Code is available at: {\footnotesize\color{blue}{\url{https://github.com/microsoft/SPTAG}}}., Comment: Accepted to NeurIPS 2021

All-spin-based computing combining logic and nonvolatile magnetic memory is promising for emerging information technologies. However, the realization of a universal spin logic operation representing a reconfigurable building block with all-electrical spin current communication has so far remained challenging. Here, we experimentally demonstrate a reprogrammable all-electrical multifunctional spin logic gate in a nanoelectronic device architecture utilizing graphene buses for spin communication and multiplexing and nanomagnets for writing and reading information at room temperature. This gate realizes a multistate majority spin logic operation (sMAJ), which is reconfigured to achieve XNOR, (N)AND, and (N)OR Boolean operations depending on the magnetization of inputs. Physics-based spin circuit model is developed to understand the underlying mechanisms of the multifunctional spin logic gate and its operations. These demonstrations provide a platform for scalable all-electric spin logic and neuromorphic computing in the all-spin domain logic-in-memory architecture.