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Zhenyu Wang

Publications and source records attributed to Zhenyu Wang.

At least 127 records · Page 7Linked to original sources
arXiv2024

Non-ideal Magnetohydrodynamic Instabilities in Protoplanetary Disks: Vertical Modes and Reflection Asymmetry

Magnetized disk winds and wind-driven accretion are an essential and intensively studied dispersion mechanism of protoplanetary disks. However, the stability of these mechanisms has yet to be adequately examined. This paper employs semi-analytic linear perturbation theories under non-ideal magnetohydrodynamics, focusing on disk models whose magnetic diffusivities vary by a few orders of magnitude from the disk midplane to its surface. Linear modes are distinguished by their symmetry with respect to the midplane. These modes have qualitatively different growth rates: symmetric modes almost always decay, while at least one anti-symmetric mode always has a positive growth rate. This growth rate decreases faster than the Keplerian angular velocity with cylindrical radius $R$ in the disk and scales steeper than $R^{-5/2}$ in the fiducial disk model. The growth of anti-symmetric modes breaks the reflection symmetry across the disk equatorial plane, and may occur even in the absence of the Hall effect. In the disk regions where fully developed anti-symmetric modes occur, accretion flows appear only on one side of the disk, while disk winds occur only on the other. This may explain the asymmetry of some observed protoplanetary-disk outflows.

astro-ph.EP↗
arXiv2024

Stabilization and dynamics of magnetic antivortices in a nanodisk with anisotropic Dzyaloshinskii-Moriya interaction

We theoretically investigate the antivortex stabilized by anisotropic Dzyaloshinskii-Moriya interaction (DMI) in nanodisks. It is remarkably found that the antivortex remains stable even when the nanodisk radius is reduced to 15 nm, owing to the short-range nature of the DMI. We also investigate the antivortex dynamics under a static in-plane magnetic field, which shows that the displacement of the antivortex core depends on its vorticity and helicity, providing a fundamental basic for distinguishing different vortex types. Additionally, spin-polarized currents can trigger a self-sustained gyration of the antivortex at low current densities, while inducing polarity switching at high current densities. Our findings offer valuable insights into the DMI role in stabilizing topological solitons and their potential applications in spin-torque nano-oscillators and magnetic memories.

cond-mat.mes-hall↗
arXiv2024

The Red Supergiant Progenitor of Type II Supernova 2024ggi

We present a detailed analysis of the progenitor and its local environment for the recently discovered type II supernova (SN) 2024ggi at a distance of about 6.7~Mpc, by utilizing the pre-explosion images from the Hubble Space Telescope (HST) and \textit{Spitzer} Space Telescope. The progenitor is identified as a red, bright variable star, with absolute $F814W$-band magnitudes being $-$6.2 mag in 1995 to $-$7.2 mag in 2003, respectively, consistent with that of a normal red supergiant (RSG) star. Combining with the historical mid-infrared light curves, a pulsational period of about 379~days can be inferred for the progenitor star. Fitting its spectral energy distribution with stellar spectral models yields the stellar parameters of temperature, radius and bolometric luminosity as $T_*=3290_{-27}^{+19}$~K, $R_*=887_{-51}^{+60}$~R$_{\odot}$, and log($L$/L$_{\odot}$)$=4.92_{-0.04}^{+0.05}$, respectively. The above parameters indicate that the progenitor of SN 2024ggi is consistent with the stellar evolutionary track of a solar-metallicity massive star with an initial mass of $13_{-1}^{+1}$~M$_{\odot}$. Moreover, our analysis indicates a relatively low mass loss rate (i.e., $< 3\times10^{-6}$~M$_{\odot}$~yr$^{-1}$) for the progenitor compared to that inferred from the flashed spectra and X-ray detection (i.e., $10^{-2}$$-$$ 10$$^{-5}$~M$_{\odot}$~yr$^{-1}$), implying a significant enhancement in mass loss within a few years prior to the explosion.

astro-ph.HE↗
arXiv2024

Prospects of the multi-channel photometric survey telescope in the cosmological application of Type Ia supernovae

The Multi-channel Photometric Survey Telescope (Mephisto) is a real-time, three-color photometric system designed to capture the color evolution of stars and transients accurately. This telescope system can be crucial in cosmological distance measurements of low-redshift (low-$z$, $z$ $\lesssim 0.1$) Type Ia supernovae (SNe Ia). To optimize the capabilities of this instrument, we perform a comprehensive simulation study before its official operation is scheduled to start. By considering the impact of atmospheric extinction, weather conditions, and the lunar phase at the observing site involving the instrumental features, we simulate the light curves of SNe Ia obtained by the Mephisto. The best strategy in the case of SN Ia cosmology is to take the image at an exposure time of 130 s with a cadence of 3 days. In this condition, Mephisto can obtain hundreds of high-quality SNe Ia to achieve a distance measurement better than $4.5\%$. Given the on-time spectral classification and monitoring of the Lijiang 2.4 m Telescope at the same observatory, Mephisto, in the whole operation, can significantly enrich the well-calibrated sample of supernovae at low-$z$ and improve the calibration accuracy of high-$z$ SNe Ia.

astro-ph.IM↗
arXiv2024

SpikeNVS: Enhancing Novel View Synthesis from Blurry Images via Spike Camera

One of the most critical factors in achieving sharp Novel View Synthesis (NVS) using neural field methods like Neural Radiance Fields (NeRF) and 3D Gaussian Splatting (3DGS) is the quality of the training images. However, Conventional RGB cameras are susceptible to motion blur. In contrast, neuromorphic cameras like event and spike cameras inherently capture more comprehensive temporal information, which can provide a sharp representation of the scene as additional training data. Recent methods have explored the integration of event cameras to improve the quality of NVS. The event-RGB approaches have some limitations, such as high training costs and the inability to work effectively in the background. Instead, our study introduces a new method that uses the spike camera to overcome these limitations. By considering texture reconstruction from spike streams as ground truth, we design the Texture from Spike (TfS) loss. Since the spike camera relies on temporal integration instead of temporal differentiation used by event cameras, our proposed TfS loss maintains manageable training costs. It handles foreground objects with backgrounds simultaneously. We also provide a real-world dataset captured with our spike-RGB camera system to facilitate future research endeavors. We conduct extensive experiments using synthetic and real-world datasets to demonstrate that our design can enhance novel view synthesis across NeRF and 3DGS. The code and dataset will be made available for public access.

cs.CV↗
arXiv2024

CVD Graphene Contacts for Lateral Heterostructure MoS${_2}$ Field Effect Transistors

Intensive research is carried out on two-dimensional materials, in particular molybdenum disulfide, towards high-performance transistors for integrated circuits. Fabricating transistors with ohmic contacts is challenging due to the high Schottky barrier that severely limits the transistors' performance. Graphene-based heterostructures can be used in addition or as a substitute for unsuitable metals. We present lateral heterostructure transistors made of scalable chemical vapor-deposited molybdenum disulfide and chemical vapor-deposited graphene with low contact resistances of about 9 k$Ω$$μ$m and high on/off current ratios of 10${^8}$. We also present a theoretical model calibrated on our experiments showing further potential for scaling transistors and contact areas into the few nanometers range and the possibility of a strong performance enhancement by means of layer optimizations that would make transistors promising for use in future logic circuits.

cond-mat.mes-hall↗
arXiv2024

Superconducting stripes induced by ferromagnetic proximity in an oxide heterostructure

The intimate connection between magnetism and superconducting pairing routinely plays a central role in determining the occurrence of unconventional superconducting states. In high-transition-temperature (high-Tc) stripe-ordered cuprate superconductors and a magnetically ordered iron-based superconductor, the coupling between magnetism and superconductivity gives birth to novel phases of matter with modulation of the superconducting pairing in the real space. Further exploration of these phases can shed light on the mechanism of unconventional superconductivity. Here we report on the discovery of a peculiar spatially-varying superconducting state residing at the interface between (110)-oriented KTaO3 and ferromagnetic EuO. Electrical transport measurements reveal different Tc and upper critical fields (Hc2) with current applied along the two orthogonal in-plane directions. Such anisotropy persistently occurs in the low-carrier-density samples that are characterized by strong coupling between Ta 5d and Eu 4f electrons, whereas in the high-carrier-density samples the coupling is weakened and Tc and Hc2 becomes isotropic. Complemented by local imaging of diamagnetism and theoretical analysis, our observations imply an unprecedented emergence of superconducting stripes wherein the phase coherence is established ahead of the rest of the interface, arising from a band-filling-dependent ferromagnetic proximity. The realization of such exotic superconducting states provides impetus for the study of novel physics in heterostructures possessing both magnetism and superconductivity.

cond-mat.supr-con↗
arXiv2024

Divide and Conquer: Language Models can Plan and Self-Correct for Compositional Text-to-Image Generation

Despite significant advancements in text-to-image models for generating high-quality images, these methods still struggle to ensure the controllability of text prompts over images in the context of complex text prompts, especially when it comes to retaining object attributes and relationships. In this paper, we propose CompAgent, a training-free approach for compositional text-to-image generation, with a large language model (LLM) agent as its core. The fundamental idea underlying CompAgent is premised on a divide-and-conquer methodology. Given a complex text prompt containing multiple concepts including objects, attributes, and relationships, the LLM agent initially decomposes it, which entails the extraction of individual objects, their associated attributes, and the prediction of a coherent scene layout. These individual objects can then be independently conquered. Subsequently, the agent performs reasoning by analyzing the text, plans and employs the tools to compose these isolated objects. The verification and human feedback mechanism is finally incorporated into our agent to further correct the potential attribute errors and refine the generated images. Guided by the LLM agent, we propose a tuning-free multi-concept customization model and a layout-to-image generation model as the tools for concept composition, and a local image editing method as the tool to interact with the agent for verification. The scene layout controls the image generation process among these tools to prevent confusion among multiple objects. Extensive experiments demonstrate the superiority of our approach for compositional text-to-image generation: CompAgent achieves more than 10\% improvement on T2I-CompBench, a comprehensive benchmark for open-world compositional T2I generation. The extension to various related tasks also illustrates the flexibility of our CompAgent for potential applications.

cs.CV↗
arXiv2023

SmoothQuant+: Accurate and Efficient 4-bit Post-Training WeightQuantization for LLM

Large language models (LLMs) have shown remarkable capabilities in various tasks. However their huge model size and the consequent demand for computational and memory resources also pose challenges to model deployment. Currently, 4-bit post-training quantization (PTQ) has achieved some success in LLMs, reducing the memory footprint by approximately 75% compared to FP16 models, albeit with some accuracy loss. In this paper, we propose SmoothQuant+, an accurate and efficient 4-bit weight-only PTQ that requires no additional training, which enables lossless in accuracy for LLMs for the first time. Based on the fact that the loss of weight quantization is amplified by the activation outliers, SmoothQuant+ smoothes the activation outliers by channel before quantization, while adjusting the corresponding weights for mathematical equivalence, and then performs group-wise 4-bit weight quantization for linear layers. We have integrated SmoothQuant+ into the vLLM framework, an advanced high-throughput inference engine specially developed for LLMs, and equipped it with an efficient W4A16 CUDA kernels, so that vLLM can seamlessly support SmoothQuant+ 4-bit weight quantization. Our results show that, with SmoothQuant+, the Code Llama-34B model can be quantized and deployed on a A100 40GB GPU, achieving lossless accuracy and a throughput increase of 1.9 to 4.0 times compared to the FP16 model deployed on two A100 40GB GPUs. Moreover, the latency per token is only 68% of the FP16 model deployed on two A100 40GB GPUs. This is the state-of-the-art 4-bit weight quantization for LLMs as we know.

cs.LG↗
arXiv2023

BianQue: Balancing the Questioning and Suggestion Ability of Health LLMs with Multi-turn Health Conversations Polished by ChatGPT

Large language models (LLMs) have performed well in providing general and extensive health suggestions in single-turn conversations, exemplified by systems such as ChatGPT, ChatGLM, ChatDoctor, DoctorGLM, and etc. However, the limited information provided by users during single turn results in inadequate personalization and targeting of the generated suggestions, which requires users to independently select the useful part. It is mainly caused by the missing ability to engage in multi-turn questioning. In real-world medical consultations, doctors usually employ a series of iterative inquiries to comprehend the patient's condition thoroughly, enabling them to provide effective and personalized suggestions subsequently, which can be defined as chain of questioning (CoQ) for LLMs. To improve the CoQ of LLMs, we propose BianQue, a ChatGLM-based LLM finetuned with the self-constructed health conversation dataset BianQueCorpus that is consist of multiple turns of questioning and health suggestions polished by ChatGPT. Experimental results demonstrate that the proposed BianQue can simultaneously balance the capabilities of both questioning and health suggestions, which will help promote the research and application of LLMs in the field of proactive health.

cs.CL↗
arXiv2023

SoulChat: Improving LLMs' Empathy, Listening, and Comfort Abilities through Fine-tuning with Multi-turn Empathy Conversations

Large language models (LLMs) have been widely applied in various fields due to their excellent capability for memorizing knowledge and chain of thought (CoT). When these language models are applied in the field of psychological counseling, they often rush to provide universal advice. However, when users seek psychological support, they need to gain empathy, trust, understanding and comfort, rather than just reasonable advice. To this end, we constructed a multi-turn empathetic conversation dataset of more than 2 million samples, in which the input is the multi-turn conversation context, and the target is empathetic responses that cover expressions such as questioning, comfort, recognition, listening, trust, emotional support, etc. Experiments have shown that the empathy ability of LLMs can be significantly enhanced when finetuning by using multi-turn dialogue history and responses that are closer to the expression of a psychological consultant.

cs.CL↗
arXiv2023

A numerical tool for efficient analysis and optimization of offshore wind turbine jacket substructure considering realistic boundary and loading conditions

The jacket substructure is a critical component of the offshore wind turbine (OWT) that is the interface between the transition piece at the top and the grouted connection. This paper presents a comprehensive study on the optimization of a jacket substructure to achieve greater cost efficiency while maintain acceptable structural performance. A fast parametric finite element modelling (FEM) approach for jacket substructures was firstly proposed. The generated models took into account realistic loading conditions, including self-weight, wind load and section-dependent wave load, and soil-pile interaction. Parametric studies were conducted afterwards to investigate the trends of the mass and response of the jacket substructure with respect to the variation of geometric and sectional parameters. Optimizations of the jacket substructure were carried out using parametric optimization and numerical genetic algorithm (GA) optimization under three different optimization strategies corresponding to three groups of objective and constraint functions. The trends obtained by parametric analysis were used to guide the parameter selection in parametric optimization, while a rank-based mutation GA was established with the proposed efficient FEM embedded in as the solver to the optimization objective and constraint functions. Parametric optimization gained its advantage in computational efficiency, and the mass reduction were 6.2%,10% and 14.8% for the three strategies respectively. GA optimization was more aggressive as the mass reductions were 16.8%,22.3% and 34.3% for the three strategies, but was relatively more computational intense. The two proposed optimization methods and the three optimization strategies are both expected to be applied in practical engineering design of OWT jacket substructure with good optimization output and high computational efficiency.

cs.CE↗
arXiv2023

Uni3DETR: Unified 3D Detection Transformer

Existing point cloud based 3D detectors are designed for the particular scene, either indoor or outdoor ones. Because of the substantial differences in object distribution and point density within point clouds collected from various environments, coupled with the intricate nature of 3D metrics, there is still a lack of a unified network architecture that can accommodate diverse scenes. In this paper, we propose Uni3DETR, a unified 3D detector that addresses indoor and outdoor 3D detection within the same framework. Specifically, we employ the detection transformer with point-voxel interaction for object prediction, which leverages voxel features and points for cross-attention and behaves resistant to the discrepancies from data. We then propose the mixture of query points, which sufficiently exploits global information for dense small-range indoor scenes and local information for large-range sparse outdoor ones. Furthermore, our proposed decoupled IoU provides an easy-to-optimize training target for localization by disentangling the xy and z space. Extensive experiments validate that Uni3DETR exhibits excellent performance consistently on both indoor and outdoor 3D detection. In contrast to previous specialized detectors, which may perform well on some particular datasets but suffer a substantial degradation on different scenes, Uni3DETR demonstrates the strong generalization ability under heterogeneous conditions (Fig. 1). Codes are available at \href{https://github.com/zhenyuw16/Uni3DETR}{https://github.com/zhenyuw16/Uni3DETR}.

cs.CV↗
arXiv2023

Terahertz magnon frequency comb

Magnon frequency comb (MFC), the spin-wave spectra composing of equidistant coherent peaks, is attracting much attention in magnonics. A terahertz (THz) MFC, combining the advantages of the THz and MFC technologies, is highly desired because it would significantly advance the MFC applications in ultrafast magnonic metrology, sensing, and communications. Here, we show that the THz MFC can be generated by nonlinear interactions between spin waves and skyrmions in antiferromagnets [Z. Jin \emph{et al}., \href{https://doi.org/10.48550/arXiv.2301.03211}{arXiv:2301.03211}]. It is found that the strength of the three-wave mixing between propagating magnons and breathing skyrmions follows a linear dependence on the driving frequency and the MFC signal can be observed over a broad driving frequency range. Our results extend the working frequency of MFC to the THz regime, which would have potential applications in ultrafast spintronic devices and promote the development of nonlinear magnonics in antiferromagnets.

cond-mat.mes-hall↗
arXiv2023

Influence of microstructure on superconductivity in K$_x$Fe$_{2-y}$Se$_2$ and evidence for a new parent phase K$_2$Fe$_7$Se$_8$

The search for new superconducting materials has been spurred on by the discovery of iron-based superconductors whose structure and composition is qualitatively different from the cuprates. The study of one such material, K$_x$Fe$_{2-y}$Se$_2$ with a critical temperature of 32 K, is made more difficult by the fact that it separates into two phases: a dominant antiferromagnetic insulating phase with a K$_2$Fe$_4$Se$_5$ structure, and a minority superconducting phase whose precise structure is as yet unclear. Here we perform electrical and magnetization measurements, scanning electron microscopy and microanalysis, X-ray diffraction, and scanning tunnelling microscopy on K$_x$Fe$_{2-y}$Se$_2$ crystals prepared under different quenching processes to better understand the relationship between its microstructure and its superconducting phase. We identify a 3D network of superconducting filaments within this material and present evidence to suggest that the superconducting phase consists of a single Fe vacancy for every eight Fe-sites arranged in a $\sqrt{10}\times\sqrt{8}$ parallelogram structure.

cond-mat.supr-con↗
arXiv2023

Uncertainty-aware Consistency Learning for Cold-Start Item Recommendation

Graph Neural Network (GNN)-based models have become the mainstream approach for recommender systems. Despite the effectiveness, they are still suffering from the cold-start problem, i.e., recommend for few-interaction items. Existing GNN-based recommendation models to address the cold-start problem mainly focus on utilizing auxiliary features of users and items, leaving the user-item interactions under-utilized. However, embeddings distributions of cold and warm items are still largely different, since cold items' embeddings are learned from lower-popularity interactions, while warm items' embeddings are from higher-popularity interactions. Thus, there is a seesaw phenomenon, where the recommendation performance for the cold and warm items cannot be improved simultaneously. To this end, we proposed a Uncertainty-aware Consistency learning framework for Cold-start item recommendation (shorten as UCC) solely based on user-item interactions. Under this framework, we train the teacher model (generator) and student model (recommender) with consistency learning, to ensure the cold items with additionally generated low-uncertainty interactions can have similar distribution with the warm items. Therefore, the proposed framework improves the recommendation of cold and warm items at the same time, without hurting any one of them. Extensive experiments on benchmark datasets demonstrate that our proposed method significantly outperforms state-of-the-art methods on both warm and cold items, with an average performance improvement of 27.6%.

cs.IR↗
arXiv2023

High-precision and low-latency widefield diamond quantum sensing with neuromorphic vision sensors

During the past decade, interest has grown significantly in developing ultrasensitive widefield diamond magnetometry for various applications. Despite attempts to improve the adoption of conventional frame-based sensors, achieving high temporal resolution and sensitivity simultaneously remains a key challenge. This is largely due to the transfer and processing of massive amounts of sensor data to capture the widefield fluorescence intensity changes of spin defects in diamonds. In this study, we adopt a neuromorphic vision sensor to address this issue. This sensor pre-processes the detected signals in optically detected magnetic resonance (ODMR) measurements for quantum sensing, employing a working principle that closely resembles the operation of the human vision system. By encoding the changes of light intensity into spikes, this approach results in a vast dynamic range, high temporal resolution, and exceptional signal-to-background ratio. After a thorough evaluation of theoretical feasibility, our experiment with an off-the-shelf event camera demonstrated a 13x improvement in temporal resolution with comparable precision of detecting ODMR resonance frequencies compared with the state-of-the-art highly specialized frame-based approach. A specialized camera system with the same mechanism has the potential to enhance these benefits further. This performance improvement is primarily attributable to orders of magnitude smaller data volumes and, thus, reduced latency. We further showcase the deployment of this technology in monitoring dynamically modulated laser heating of gold nanoparticles coated on a diamond surface, a recognizably difficult task using existing approaches. The current development provides new insights for high-precision and low-latency widefield quantum sensing, with possibilities for integration with emerging memory devices for more efficient event-based data processing.

physics.ins-det↗
arXiv2023

SoundSieve: Seconds-Long Audio Event Recognition on Intermittently-Powered Systems

A fundamental problem of every intermittently-powered sensing system is that signals acquired by these systems over a longer period in time are also intermittent. As a consequence, these systems fail to capture parts of a longer-duration event that spans over multiple charge-discharge cycles of the capacitor that stores the harvested energy. From an application's perspective, this is viewed as sporadic bursts of missing values in the input data -- which may not be recoverable using statistical interpolation or imputation methods. In this paper, we study this problem in the light of an intermittent audio classification system and design an end-to-end system -- SoundSieve -- that is capable of accurately classifying audio events that span multiple on-off cycles of the intermittent system. SoundSieve employs an offline audio analyzer that learns to identify and predict important segments of an audio clip that must be sampled to ensure accurate classification of the audio. At runtime, SoundSieve employs a lightweight, energy- and content-aware audio sampler that decides when the system should wake up to capture the next chunk of audio; and a lightweight, intermittence-aware audio classifier that performs imputation and on-device inference. Through extensive evaluations using popular audio datasets as well as real systems, we demonstrate that SoundSieve yields 5%--30% more accurate inference results than the state-of-the-art.

cs.SD↗