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The optical manipulation of magnetism enabled by rare earth-transition metal ferrimagnets holds the promise of ultrafast, energy efficient spintronic technologies. This work investigates laser-induced picosecond spin currents generated by ferrimagnetic GdCo via terahertz emission spectroscopy. A suppression of the THz emission and spin current is observed at magnetization compensation when varying the temperature or alloy composition in the presence of a magnetic field. It is demonstrated that this is due to the formation of domains in the GdCo equilibrium magnetic configuration. Without an applied magnetic field, the picosecond spin current persists at the compensation point. The experimental findings support the model for THz spin current generation based on transport of hot spin-polarized electrons, which is dominated by the Co sublattice at room temperature. Only at low temperature a comparable contribution from Gd is detected but with slower dynamics. Finally, spectral analysis reveals a blueshift of the THz emission related to the formation of magnetic domains close to magnetization compensation.

Two-dimensional materials with Janus structure host novel physical properties due to their inversional symmetry breaking. However, it remains elusive to synthesize Janus monolayer crystals with tailored long-range magnetic orders. Here, we have developed a general method to fabricate uniform Janus CrTeSe monolayers by selective selenization of preformed CrTe2 monolayers with molecular beam epitaxy. The uniform Janus structure of CrTeSe with high crystal quality is confirmed by high-resolution scanning transmission electron microscopy. Spin-polarized scanning tunneling microscopy/spectroscopy measurements unveil that the Janus CrTeSe undergoes a charge density wave (CDW) transition and a robust antiferromagnetic order. The magnetic anisotropy of CrTeSe is drastically altered compared to monolayer CrTe2 by the breaking symmetries induced from the Janus structure and the CDW transition, as is substantiated with first principles calculations. Our research achieves the construction of large-area Janus structures, and artificially tailors the electronic and magnetic properties of Janus systems at the two-dimensional limit., Comment: 19 pages, 4 figures

Plasmoid instability is usually accounted for the onset of fast reconnection events observed in astrophysical plasmas. However, the measured reconnection rate from observations can be one order of magnitude higher than that derived from MHD simulations. In this study, we present the results of magnetic reconnection in the partially ionized low solar atmosphere based on 2.5D magnetohydrodynamics (MHD) simulations. The whole reconnection process covers two different fast reconnection phases. In the first phase, the slow Sweet-Parker reconnection transits to the plasmoid-mediated reconnection, and the reconnection rate reaches about 0.02. In the second phase, a faster explosive reconnection appears, with the reconnection rate reaching above 0.06. At the same time, a sharp decrease in plasma temperature and density at the principle X-point is observed which is associated with the strong radiative cooling, the ejection of hot plasma from the local reconnection region or the motion of principle X-point from hot and denser region to cool and less dense one along the narrow current sheet. This causes gas pressure depletion and the increasing of magnetic diffusion at the main X-point, resulting in the local Petschek-like reconnection and a violent and rapid increase in the reconnection rate. This study for the first time reveals a common phenomenon that the plasmoid dominated reconnection transits to an explosive faster reconnection with the rate approaching the order of 0.1 in partially ionized plasma in the MHD scale.

Magnetic reconnection plays a crucial role in the energy release process for different kinds of solar eruptions and activities. The rapid solar eruption requires a fast reconnection model. Plasmoid instability in the reconnecting current sheets is one of the most acceptable fast reconnection mechanisms for explaining the explosive events in the magnetohydrodynamics (MHD) scale, which is also a potential bridge between the macroscopic MHD reconnection process and microscale dissipations. Plenty of high resolution observations indicate that the plasmoid-like structures exist in the high temperature solar corona, but such evidences are very rare in the lower solar atmosphere with partially ionized plasmas. Utilizing joint observations from the Goode Solar Telescope (GST) and the Solar Dynamics Observatory (SDO), we discovered a small-scale eruptive phenomenon in NOAA AR 13085, characterized by clear reconnection cusp structures, supported by Nonlinear Force-Free Field (NLFFF) extrapolation results. The plasmoid-like structures with a size about 150 km were observed to be ejected downward from the current sheet at a maximum velocity of 24 km$\cdot$s$^{-1}$ in the H$\alpha$ line wing images, followed by enhanced emissions at around the post flare loop region in multiple wave lengths. Our 2.5D high-resolution MHD simulations further reproduced such a phenomenon and revealed reconnection fine structures. These results provide comprehensive evidences for the plasmoid mediated reconnection in partially ionized plasmas, and suggest an unified reconnection model for solar flares with different length scales from the lower chromosphere to corona.

Previous studies on music style transfer have mainly focused on one-to-one style conversion, which is relatively limited. When considering the conversion between multiple styles, previous methods required designing multiple modes to disentangle the complex style of the music, resulting in large computational costs and slow audio generation. The existing music style transfer methods generate spectrograms with artifacts, leading to significant noise in the generated audio. To address these issues, this study proposes a music style transfer framework based on diffusion models (DM) and uses spectrogram-based methods to achieve multi-to-multi music style transfer. The GuideDiff method is used to restore spectrograms to high-fidelity audio, accelerating audio generation speed and reducing noise in the generated audio. Experimental results show that our model has good performance in multi-mode music style transfer compared to the baseline and can generate high-quality audio in real-time on consumer-grade GPUs., Comment: 8 pages, 6 figures, ICMC 2023

Distributed optical fiber acoustic sensing (DAS) is a rapidly-developed seismic acquisition technology with advantages of low cost, high resolution, high sensitivity, and small interval, etc. Nonetheless, consecutively missing cases often appear in real seismic data acquired by DAS system due to some factors, including optical fiber damage and inferior coupling between cable and well. Recently, some deep-learning seismic interpolation methods based on convolutional neural network (CNN) have shown impressive performance in regular and random missing cases but still remain the consecutively missing case as a challenging task. The main reason is that the weight sharing makes it difficult for CNN to capture enough comprehensive features. In this paper, we propose a transformer-based interpolation method, called seismic interpolation transformer (SIT), to deal with the consecutively missing case. This proposed SIT is an encoder-decoder structure connected by some U-shaped swin-transformer blocks. In encoder and decoder part, the multi-head self-attention (MSA) mechanism is used to capture global features which is essential for the reconstruction of consecutively missing traces. The U-shaped swin-transformer blocks are utilized to perform feature extraction operations on feature maps with different resolutions. Moreover, we combine the loss based on structural similarity index (SSIM) and L1 norm to propose a novel loss function for SIT. In experiments, this proposed SIT outperforms U-Net and swin-transformer. Moreover, ablation studies also demonstrate the advantages of new network architecture and loss function.

Convolutional neural networks (CNNs) with large kernels, drawing inspiration from the key operations of vision transformers (ViTs), have demonstrated impressive performance in various vision-based applications. To address the issue of computational efficiency degradation in existing designs for supporting large-kernel convolutions, an FPGA-based inference accelerator is proposed for the efficient deployment of CNNs with arbitrary kernel sizes. Firstly, a Z-flow method is presented to optimize the computing data flow by maximizing data reuse opportunity. Besides, the proposed design, incorporating the kernel-segmentation (Kseg) scheme, enables extended support for large-kernel convolutions, significantly reducing the storage requirements for overlapped data. Moreover, based on the analysis of typical block structures in emerging CNNs, vertical-fused (VF) and horizontal-fused (HF) methods are developed to optimize CNN deployments from both computation and transmission perspectives. The proposed hardware accelerator, evaluated on Intel Arria 10 FPGA, achieves up to 3.91 times better DSP efficiency than prior art on the same network. Particularly, it demonstrates efficient support for large-kernel CNNs, achieving throughputs of 169.68 GOPS and 244.55 GOPS for RepLKNet-31 and PyConvResNet-50, respectively, both of which are implemented on hardware for the first time., Comment: 5 pages,4 figures. This work has been accepted by 2024 lEEE International Symposium on Circuits and Systems (lSCAS 2024) as a regular paper

Ellerman bombs (EBs) with significant H$\alpha$ wing emissions and ultraviolet bursts (UV bursts) with strong Si IV emissions are two kinds of small transient brightening events that occur in the low solar atmosphere.We numerically investigated the magnetic reconnection process between the emerging arch magnetic field and the lower atmospheric background magnetic field. We aim to find out if the hot UV emissions and much colder H$\alpha$ wing emissions can both appear in the same reconnection process and how they are located in the reconnection region. The open-source code NIRVANA was applied to perform the 2.5D magnetohydrodynamic (MHD) simulation. We developed the related sub-codes to include the more realistic radiative cooling process for the photosphere and chromosphere and the time-dependent ionization degree of hydrogen. The initial background magnetic field is 600 G, and the emerged magnetic field in the solar atmosphere is of the same magnitude, meaning that it results in a low- $\beta$ magnetic reconnection environment. We also used the radiative transfer code RH1.5D to synthesize the Si IV and H$\alpha$ spectral line profiles based on the MHD simulation results. Magnetic reconnection between emerged and background magnetic fields creates a thin, curved current sheet, which then leads to the formation of plasmoid instability and the nonuniform density distributions. The mix of hot tenuous and much cooler dense plasmas in the turbulent reconnection region can appear at about the same height, or even in the same plasmoid. The turbulent current sheet is always in a dense plasma environment with an optical depth larger than 6.5$\times$10$^{-5}$ due to the emerged magnetic field pushing high-density plasmas upward.

RISC-V processors encounter substantial challenges in deploying multi-precision deep neural networks (DNNs) due to their restricted precision support, constrained throughput, and suboptimal dataflow design. To tackle these challenges, a scalable RISC-V vector (RVV) processor, namely SPEED, is proposed to enable efficient multi-precision DNN inference by innovations from customized instructions, hardware architecture, and dataflow mapping. Firstly, dedicated customized RISC-V instructions are proposed based on RVV extensions, providing SPEED with fine-grained control over processing precision ranging from 4 to 16 bits. Secondly, a parameterized multi-precision systolic array unit is incorporated within the scalable module to enhance parallel processing capability and data reuse opportunities. Finally, a mixed multi-precision dataflow strategy, compatible with different convolution kernels and data precision, is proposed to effectively improve data utilization and computational efficiency. We perform synthesis of SPEED in TSMC 28nm technology. The experimental results demonstrate that SPEED achieves a peak throughput of 287.41 GOPS and an energy efficiency of 1335.79 GOPS/W at 4-bit precision condition, respectively. Moreover, when compared to the pioneer open-source vector processor Ara, SPEED provides an area efficiency improvement of 2.04$\times$ and 1.63$\times$ under 16-bit and 8-bit precision conditions, respectively, which shows SPEED's significant potential for efficient multi-precision DNN inference., Comment: The work is accepted by 2024 IEEE International Symposium on Circuits and Systems (ISCAS 2024)

We select 1,052,469 (754,635) thin disk stars from {\it Gaia} eDR3 and LAMOST DR7 in the range of Galactocentric radius $R$ (guiding center radius $R_\mathrm{g}$) from 8 to 11\,kpc to investigate the asymmetries between the North and South of the disk midplane. More specifically we analyze the vertical velocity dispersion profiles ($\sigma_{v_{z}}(z$)) in different bins of $R$ ($R_\mathrm{g}$) and $[\mathrm{Fe/H}]$. We find troughs in the profiles of $\sigma_{v_{z}}(z)$ located in both the North ($z \sim 0.7$\,kpc) and South ($z \sim -0.5$\,kpc) of the disk at all radial and chemical bins studied. The difference between the Northern and Southern vertical velocity dispersion profiles ($\Delta\sigma_{v_{z}}(|z|)$) shows a shift between curves of different $R$ and $R_\mathrm{g}$. A similar shift exists in these NS asymmetry profiles further divided into different $[\mathrm{Fe/H}]$ ranges. The sample binned with $R_\mathrm{g}$ more clearly displays the features in the velocity dispersion profiles. The shift in the peaks of the $\Delta\sigma_{v_{z}}$ profiles and the variation in the phase spiral shape binned by metallicity indicate the variation of the vertical potential profiles and the radial metallicity gradient. The wave-like signal in NS asymmetry of $\sigma_{v_{z}}(z)$ largely originates from phase spiral; while the NS asymmetry profiles of [Fe/H] only display a weak wave-like feature near solar radius. We perform a test particle simulation to qualitatively reproduce the observed results. A quantitative explanation of the NS asymmetry in the metallicity profile needs careful consideration of the spiral shape and the perturbation model, and we leave this for future work., Comment: 13 pages, 12 figures, accepted for publication in MNRAS

We propose a mechanism for the excitation of large-scale quasi-periodic fast-propagating magnetoacoustic (QFP) waves observed on both sides of the coronal mass ejection (CME). Through a series of numerical experiments, we successfully simulated the quasi-static evolution of the equilibrium locations of the magnetic flux rope in response to the change of the background magnetic field, as well as the consequent loss of the equilibrium that eventually gives rise to the eruption. During the eruption, we identified QFP waves propagating radially outwards the flux rope, and tracing their origin reveals that they result from the disturbance within the flux rope. Acting as an imperfect waveguide, the flux rope allows the internal disturbance to escape to the outside successively via its surface, invoking the observed QFP waves. Furthermore, we synthesized the images of QFP waves on the basis of the data given by our simulations, and found the consistence with observations. This indicates that the leakage of the disturbance outside the flux rope could be a reasonable mechanism of QFP waves., Comment: 8 figures, accepted by the Astrophysical Journal

We study a discrete non-autonomous system whose autonomous counterpart (with the frozen bifurcation parameter) admits a saddle-node bifurcation, and in which the bifurcation parameter slowly changes in time and is characterized by a sweep rate constant $\epsilon$. The discrete system is more appropriate for modeling realistic systems since only time series data is available. We show that in contrast to its autonomous counterpart, when the time mesh size $\Delta t$ is less than the order $O(\epsilon)$, there is a bifurcation delay as the bifurcation time-varying parameter is varied through the bifurcation point, and the delay is proportional to the two-thirds power of the sweep rate constant $\epsilon$. This bifurcation delay is significant in various realistic systems since it allows one to take necessary action promptly before a sudden collapse or shift to different states. On the other hand, when the time mesh size $\Delta t$ is larger than the order $o(\epsilon)$, the dynamical behavior of the solution is dramatically changed before the bifurcation point. This behavior is not observed in the autonomous counterpart. Therefore, the dynamical behavior of the system strongly depends on the time mesh size. Finally. due to the very discrete feature of the system, there are no efficient tools for the analytical study of the system. Our approach is elementary and analytical.

Extreme edge platforms, such as in-vehicle smart devices, require efficient deployment of quantized deep neural networks (DNNs) to enable intelligent applications with limited amounts of energy, memory, and computing resources. However, many edge devices struggle to boost inference throughput of various quantized DNNs due to the varying quantization levels, and these devices lack floating-point (FP) support for on-device learning, which prevents them from improving model accuracy while ensuring data privacy. To tackle the challenges above, we propose a precision-scalable RISC-V DNN processor with on-device learning capability. It facilitates diverse precision levels of fixed-point DNN inference, spanning from 2-bit to 16-bit, and enhances on-device learning through improved support with FP16 operations. Moreover, we employ multiple methods such as FP16 multiplier reuse and multi-precision integer multiplier reuse, along with balanced mapping of FPGA resources, to significantly improve hardware resource utilization. Experimental results on the Xilinx ZCU102 FPGA show that our processor significantly improves inference throughput by 1.6$\sim$14.6$\times$ and energy efficiency by 1.1$\sim$14.6$\times$ across various DNNs, compared to the prior art, XpulpNN. Additionally, our processor achieves a 16.5$\times$ higher FP throughput for on-device learning., Comment: To appear in the 29th Asia and South Pacific Design Automation Conference (ASP-DAC 2024)

A current sheet (CS) is the central structure in the disrupting magnetic configuration during solar eruptions. More than 90\% of the free magnetic energy (the difference between the energy in the non-potential magnetic field and that in the potential one) stored in the coronal magnetic field beforehand is converted into heating and kinetic energy of the plasma, as well as accelerating charged particles, by magnetic reconnection occurring in the CS. However, the detailed physical properties and fine structures of the CS are still unknown since there is no relevant information obtained via in situ detections. The Parker Solar Probe (PSP) may provide us such information should it traverse a CS in the eruption. The perihelion of PSP's final orbit is located at about 10 solar radii from the center of the Sun, so it can observe the CS at a very close distance, or even traverses the CS, which provides us a unique opportunity to look into fine properties and structures of the CS, helping reveal the detailed physics of large-scale reconnection that was impossible before. We evaluate the probability that PSP can traverse a CS, and examine the orbit of a PSP-like spacecraft that has the highest probability to traverse a CS., Comment: 28 pages, 12 figure, accepted by ApJS

The discovery of all-optical ultra-fast deterministic magnetization switching has opened up new possibilities for manipulating magnetization in devices using femtosecond laser pulses. Previous studies on single pulse all-optical helicity-independent switching (AO-HIS) have mainly focused on perpendicularly magnetized thin films. This work presents a comprehensive study on AO-HIS for in-plane magnetized GdxCo100-x thin films. Deterministic single femtosecond laser pulse toggle magnetization switching is demonstrated in a wider concentration range (x=10% to 25%) compared to the perpendicularly magnetized counterparts with GdCo thicknesses up to 30 nm. The switching time strongly depends on the GdxCo100-x concentration, with lower Gd concentration exhibiting shorter switching times (less than 500 fs). Our findings in this geometry provide insights into the underlying mechanisms governing single pulse AO-HIS, which challenge existing theoretical predictions. Moreover, in-plane magnetized GdxCo100-x thin films offer extended potential for opto-spintronic applications compared to their perpendicular magnetized counterparts., Comment: 19 pages, 4 figures

Recent studies indicate that dense retrieval models struggle to perform well on a wide variety of retrieval tasks that lack dedicated training data, as different retrieval tasks often entail distinct search intents. To address this challenge, in this work we leverage instructions to flexibly describe retrieval intents and introduce I3, a unified retrieval system that performs Intent-Introspective retrieval across various tasks, conditioned on Instructions without any task-specific training. I3 innovatively incorporates a pluggable introspector in a parameter-isolated manner to comprehend specific retrieval intents by jointly reasoning over the input query and instruction, and seamlessly integrates the introspected intent into the original retrieval model for intent-aware retrieval. Furthermore, we propose progressively-pruned intent learning. It utilizes extensive LLM-generated data to train I3 phase-by-phase, embodying two key designs: progressive structure pruning and drawback extrapolation-based data refinement. Extensive experiments show that in the BEIR benchmark, I3 significantly outperforms baseline methods designed with task-specific retrievers, achieving state-of-the-art zero-shot performance without any task-specific tuning., Comment: Accepted by SIGIR 2024

Turbulence plays a key role for forming the complex geometry of the large-scale current sheet (CS) and fast energy release in a solar eruption. In this paper, we present full 3D high-resolution simulations for the process of a moderate Coronal Mass Ejection (CME) and the thermodynamical evolution of the highly confined CS. Copious elongated blobs are generated due to tearing and plasmoid instabilities giving rise to a higher reconnection rate and undergo the splitting, merging and kinking processes in a more complex way in 3D. A detailed thermodynamical analysis shows that the CS is mainly heated by adiabatic and numerical viscous terms, and thermal conduction is the dominant factor that balances the energy inside the CS. Accordingly, the temperature of the CS reaches to a maximum of about 20 MK and the range of temperatures is relatively narrow. From the face-on view in the synthetic Atmospheric Imaging Assembly 131 $\mathring{A}$, the downflowing structures with similar morphology to supra-arcade downflows are mainly located between the post-flare loops and loop-top, while moving blobs can extend spikes higher above the loop-top. The downward-moving plasmoids can keep the twisted magnetic field configuration until the annihilation at the flare loop-top, indicating that plasmoid reconnection dominates in the lower CS. Meanwhile, the upward-moving ones turn into turbulent structures before arriving at the bottom of the CME, implying that turbulent reconnection dominates in the upper CS. The spatial distributions of the turbulent energy and anisotropy are addressed, which show a significant variation in the spectra with height.

We numerically studied magnetic reconnection in a high $\beta$ hydrogen-helium plasma at different altitudes from the photosphere to the upper chromosphere. The time dependent ionization degrees were included to get more realistic diffusivities and viscosity, and appropriate radiative cooling models were applied. Our numerical results indicate that the plasmoid instability always plays a vital role in speeding up magnetic reconnection at different atmospheric layers. In addition, both the strong radiative cooling and the magnetic diffusion caused by the electron-neutral collision ($\eta_{en}$) can significantly accelerate magnetic reconnection below the middle chromosphere. On the other hand, both the ambipolar diffusion and the viscosity result in higher temperature and plasma pressure in the reconnection region in the upper chromosphere, which then hinder the fast reconnection process from developing. The local compression heating triggered by turbulent reconnection mediated with plasmoids is the dominant heating mechanism in the unstable reconnection stage at different atmospheric layers, but the viscous heating and the ambipolar diffusion heating are equally important in the upper chromosphere. The Joule heating contributed by $\eta_{en}$ dominates during the early quasi-steady reconnection stage below the middle chromosphere, the strong radiative cooling also leads to much stronger compression heating and more generation of thermal energy in this region. Though the plasma $\beta$ is the same in all the simulation cases at different altitudes, the temperature increase is more significant in the upper chromosphere with much lower density and weaker radiative cooling.

Visual Relation Extraction (VRE) is a powerful means of discovering relationships between entities within visually-rich documents. Existing methods often focus on manipulating entity features to find pairwise relations, yet neglect the more fundamental structural information that links disparate entity pairs together. The absence of global structure information may make the model struggle to learn long-range relations and easily predict conflicted results. To alleviate such limitations, we propose a GlObal Structure knowledge-guided relation Extraction (GOSE) framework. GOSE initiates by generating preliminary relation predictions on entity pairs extracted from a scanned image of the document. Subsequently, global structural knowledge is captured from the preceding iterative predictions, which are then incorporated into the representations of the entities. This "generate-capture-incorporate" cycle is repeated multiple times, allowing entity representations and global structure knowledge to be mutually reinforced. Extensive experiments validate that GOSE not only outperforms existing methods in the standard fine-tuning setting but also reveals superior cross-lingual learning capabilities; indeed, even yields stronger data-efficient performance in the low-resource setting. The code for GOSE will be available at https://github.com/chenxn2020/GOSE., Comment: Accepted by EMNLP 2023 (Findings)

We present exotic quantum phases in a quantum Rabi hexagonal ring, which is derived by an analytical solution. We find that an artificial magnetic field applied in the ring induces an effect magnetic flux in the even and odd subring. It gives rise to two chiral quantum phases besides a ferro-superradiant and an antiferro-superradiant phases. With analogy to the magnetic system, two chiral phases are distinguished by the magnetization orientation in the $xy$ plane in two subrings, which correspond to skyrmion structures with different vorticity. In such chiral phases, photons in the subrings triangle flow in the same or opposite directions by comparing to the current in the hexagonal ring, which depend on the signs of the induced magnetic flux in the subrings. Interestingly, the critical exponents of the excitation energy in two chiral phases are the same as that of the subring triangle, exhibiting subring-size dependent critical exponents. Our analysis can be straightforwardly extended to a larger lattice size with subrings of a triangular or hexagonal structure, predicting a novel universality class of superradiant phase transitions. An implementation of the system considered is an exciting prospect in quantum many-body simulations of light-matter interactions in future., Comment: 7 pages,4 figures