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Yang Luo

Publications and source records attributed to Yang Luo.

At least 91 records · Page 5Linked to original sources
arXiv2023

Low- and High-velocity \ion{O}{6} in Milky Way-like Galaxies: the Role of Stellar Feedback

Milky Way-type galaxies are surrounded by a warm-hot gaseous halo containing a considerable amount of baryons and metals. The kinematics and spatial distribution of highly-ionized ion species such as \ion{O}{6} can be significantly affected by supernova (SN) explosions and early (pre-SN) stellar feedback (e.g., stellar winds, radiation pressure). Here, we investigate effects of stellar feedback on \ion{O}{6} absorptions in Milky Way-like galaxies by analyzing the suites of high-resolution hydrodynamical simulations under the framework of {\it SMUGGLE}, a physically motivated subgrid interstellar medium and stellar feedback model for the moving-mesh code {\sc Arepo}. We find that the fiducial run with the full suite of stellar feedback and moderate star formation activities can reasonably reproduce Galactic \ion{O}{6} absorptions observed by space telescopes such as {\it FUSE}, including the scale height of low-velocity ($|v_{\rm LSR}|< 100\, \rm km~s^{-1}$) \ion{O}{6}, the column density $-$ line width relation for high-velocity ($100 \leq |v_{\rm LSR}|< 400\, \rm km~s^{-1}$) \ion{O}{6}, and the cumulative \ion{O}{6} column densities. In contrast, model variations with more intense star formation activities deviate from observations further. Additionally, we find that the run considering only SN feedback is in broad agreement with the observations, whereas in runs without SN feedback this agreement is absent, which indicates a dominant role of SN feedback in heating and accelerating interstellar \ion{O}{6}. This is consistent with the current picture that interstellar \ion{O}{6} is predominantly produced by collisional ionization where mechanical feedback can play a central role. In contrast, photoionization is negligible for \ion{O}{6} production due to the lack of high-energy ($\gtrsim114\ {\rm eV}$) photons required.

astro-ph.GA↗
arXiv2023

Direct Collapse to Precursors of Supermassive Black Hole Seeds:Radiation-feedback-generated Outflows

We use high-resolution zoom-in cosmological simulations to model outflow triggered by radiation and thermal drivers around the central mass accumulation during direct collapse within the dark matter (DM) halo. The maximal resolution is $1.3\times 10^{-5}$\,pc, and no restrictions are put on the geometry of the inflow/outflow. The central mass is considered {\it prior} to the formation of the supermassive black hole seed at a redshift of $z\sim 15.9$, and can constitute either a supermassive star (SMS) of $\sim 10^5\,M_\odot$ surrounded by a growing accretion disk or a self-gravitating disk. The radiation transfer is modeled using the ray-tracing algorithm. Due to the high accretion rate of $\sim 1\,M_\odot\,{\rm yr^{-1}}$ determined by the DM halo, accretion is mildly supercritical, resulting in mildly super-critical luminosity which has only a limited effect on the accretion rate, with the duty cycle of $\sim 0.9$. We observe a fast development of hot cavities, which quickly extend into polar funnels and expand dense shells. Within the funnels, fast winds, $\sim 10^3\,{\rm km\,s^{-1}}$, are mass-loaded by the accreting gas. We follow the expanding shells to $\sim 1$\,pc, when the shell velocity remains substantially, $\sim 5$ times, above the escape speed. The ionization cones formed by the central UV/X-ray completely ionize the cavities. Extrapolating the outflow properties shows that the halo material outside the shell will have difficulty stopping it. We therefore conclude that the expanding wind-driven shell will break out of the central parsec and will reach the halo virial radius. Finally, the anisotropic accretion flow on sub-parsec scales will attenuate the UV/soft X-rays on the H$_2$. Hence, the formation of funnels and powerful outflows around, e.g., SMS, can have interesting observational corollaries.

astro-ph.GA↗
arXiv2023

RGB-T Tracking via Multi-Modal Mutual Prompt Learning

Object tracking based on the fusion of visible and thermal im-ages, known as RGB-T tracking, has gained increasing atten-tion from researchers in recent years. How to achieve a more comprehensive fusion of information from the two modalities with fewer computational costs has been a problem that re-searchers have been exploring. Recently, with the rise of prompt learning in computer vision, we can better transfer knowledge from visual large models to downstream tasks. Considering the strong complementarity between visible and thermal modalities, we propose a tracking architecture based on mutual prompt learning between the two modalities. We also design a lightweight prompter that incorporates attention mechanisms in two dimensions to transfer information from one modality to the other with lower computational costs, embedding it into each layer of the backbone. Extensive ex-periments have demonstrated that our proposed tracking ar-chitecture is effective and efficient, achieving state-of-the-art performance while maintaining high running speeds.

cs.CV↗
arXiv2023

CAME: Confidence-guided Adaptive Memory Efficient Optimization

Adaptive gradient methods, such as Adam and LAMB, have demonstrated excellent performance in the training of large language models. Nevertheless, the need for adaptivity requires maintaining second-moment estimates of the per-parameter gradients, which entails a high cost of extra memory overheads. To solve this problem, several memory-efficient optimizers (e.g., Adafactor) have been proposed to obtain a drastic reduction in auxiliary memory usage, but with a performance penalty. In this paper, we first study a confidence-guided strategy to reduce the instability of existing memory efficient optimizers. Based on this strategy, we propose CAME to simultaneously achieve two goals: fast convergence as in traditional adaptive methods, and low memory usage as in memory-efficient methods. Extensive experiments demonstrate the training stability and superior performance of CAME across various NLP tasks such as BERT and GPT-2 training. Notably, for BERT pre-training on the large batch size of 32,768, our proposed optimizer attains faster convergence and higher accuracy compared with the Adam optimizer. The implementation of CAME is publicly available.

cs.CL↗
arXiv2023

A unique van Hove singularity in kagome superconductor CsV$_{3-x}$Ta$_x$Sb$_5$ with enhanced superconductivity

Van Hove singularity (VHS) has been considered as a driving source for unconventional superconductivity. A VHS in two-dimensional (2D) materials consists of a saddle point connecting electron-like and hole-like bands. In a rare case, when a VHS appears at Fermi level, both electron-like and hole-like conduction can coexist, giving rise to an enhanced density of states as well as an attractive component of Coulomb interaction for unconventional electronic pairing. However, this van Hove scenario is often destroyed by an incorrect chemical potential or competing instabilities. Here, by using angle-resolved photoemission measurements, we report the observation of a VHS perfectly aligned with the Fermi level in a kagome superconductor CsV$_{3-x}$Ta$_x$Sb$_5$ (x~0.4), in which a record-high superconducting transition temperature is achieved among all the current variants of AV$_3$Sb$_5$ (A=Cs, Rb, K) at ambient pressure. Doping dependent measurements reveal the important role of van Hove scenario in boosting superconductivity, and spectroscopic-imaging scanning tunneling microscopy measurements indicate a distinct superconducting state in this system.

cond-mat.supr-con↗
arXiv2023

Self-distillation Augmented Masked Autoencoders for Histopathological Image Classification

Self-supervised learning (SSL) has drawn increasing attention in histopathological image analysis in recent years. Compared to contrastive learning which is troubled with the false negative problem, i.e., semantically similar images are selected as negative samples, masked autoencoders (MAE) building SSL from a generative paradigm is probably a more appropriate pre-training. In this paper, we introduce MAE and verify the effect of visible patches for histopathological image understanding. Moreover, a novel SD-MAE model is proposed to enable a self-distillation augmented MAE. Besides the reconstruction loss on masked image patches, SD-MAE further imposes the self-distillation loss on visible patches to enhance the representational capacity of the encoder located shallow layer. We apply SD-MAE to histopathological image classification, cell segmentation and object detection. Experiments demonstrate that SD-MAE shows highly competitive performance when compared with other SSL methods in these tasks.

cs.CV↗
arXiv2023

Response Length Perception and Sequence Scheduling: An LLM-Empowered LLM Inference Pipeline

Large language models (LLMs) have revolutionized the field of AI, demonstrating unprecedented capacity across various tasks. However, the inference process for LLMs comes with significant computational costs. In this paper, we propose an efficient LLM inference pipeline that harnesses the power of LLMs. Our approach begins by tapping into the potential of LLMs to accurately perceive and predict the response length with minimal overhead. By leveraging this information, we introduce an efficient sequence scheduling technique that groups queries with similar response lengths into micro-batches. We evaluate our approach on real-world instruction datasets using the LLaMA-based model, and our results demonstrate an impressive 86% improvement in inference throughput without compromising effectiveness. Notably, our method is orthogonal to other inference acceleration techniques, making it a valuable addition to many existing toolkits (e.g., FlashAttention, Quantization) for LLM inference.

cs.CL↗
arXiv2023

Itinerant to relocalized transition of f electrons in the Kondo insulator CeRu4Sn6

The three-dimensional electronic structure and the nature of Ce 4f electrons of the Kondo insulator CeRu4Sn6 are investigated by angle-resolved photoemission spectroscopy, utilizing tunable photon energies. Our results reveal (i) the three-dimensional k-space nature of the Fermi surface, (ii) the localized-to-itinerant transition of f electrons occurs at a much high temperature than the hybridization gap opening temperature, and (iii) the "relocalization" of itinerant f electrons below 25 K, which could be the precursor to the establishment of magnetic order.

cond-mat.str-el↗
arXiv2023

RGB-T Tracking Based on Mixed Attention

RGB-T tracking involves the use of images from both visible and thermal modalities. The primary objective is to adaptively leverage the relatively dominant modality in varying conditions to achieve more robust tracking compared to single-modality tracking. An RGB-T tracker based on mixed attention mechanism to achieve complementary fusion of modalities (referred to as MACFT) is proposed in this paper. In the feature extraction stage, we utilize different transformer backbone branches to extract specific and shared information from different modalities. By performing mixed attention operations in the backbone to enable information interaction and self-enhancement between the template and search images, it constructs a robust feature representation that better understands the high-level semantic features of the target. Then, in the feature fusion stage, a modality-adaptive fusion is achieved through a mixed attention-based modality fusion network, which suppresses the low-quality modality noise while enhancing the information of the dominant modality. Evaluation on multiple RGB-T public datasets demonstrates that our proposed tracker outperforms other RGB-T trackers on general evaluation metrics while also being able to adapt to longterm tracking scenarios.

cs.CV↗
arXiv2023

MMANet: Margin-aware Distillation and Modality-aware Regularization for Incomplete Multimodal Learning

Multimodal learning has shown great potentials in numerous scenes and attracts increasing interest recently. However, it often encounters the problem of missing modality data and thus suffers severe performance degradation in practice. To this end, we propose a general framework called MMANet to assist incomplete multimodal learning. It consists of three components: the deployment network used for inference, the teacher network transferring comprehensive multimodal information to the deployment network, and the regularization network guiding the deployment network to balance weak modality combinations. Specifically, we propose a novel margin-aware distillation (MAD) to assist the information transfer by weighing the sample contribution with the classification uncertainty. This encourages the deployment network to focus on the samples near decision boundaries and acquire the refined inter-class margin. Besides, we design a modality-aware regularization (MAR) algorithm to mine the weak modality combinations and guide the regularization network to calculate prediction loss for them. This forces the deployment network to improve its representation ability for the weak modality combinations adaptively. Finally, extensive experiments on multimodal classification and segmentation tasks demonstrate that our MMANet outperforms the state-of-the-art significantly. Code is available at: https://github.com/shicaiwei123/MMANet

cs.CV↗
arXiv2023

Real-Time Tracking of Coherent Oscillations of Electrons in a Nanodevice by Photo-assisted Tunnelling

Coherent collective oscillations of electrons excited in metallic nanostructures (localized surface plasmons) can confine incident light to atomic scales and enable strong light-matter interactions, which depend nonlinearly on the local field. Direct sampling of such collective electron oscillations in real-time is crucial to performing petahertz scale optical modulation, control, and readout in a quantum nanodevice. Here, we demonstrate real-time tracking of collective electron oscillations in an Au bowtie nanoantenna, by recording photo-assisted tunnelling currents generated by such oscillations in this quantum nanodevice. The collective electron oscillations show a noninstantaneous response to the driving laser fields with a decay time of nearly 10 femtoseconds. The temporal evolution of nonlinear electron oscillations resulting from the coherent nonlinear optical response of the nanodevice were also traced in real-time. The contributions of linear and nonlinear electron oscillations in the generated tunnelling currents in the nanodevice were precisely determined. A coherent control of electron oscillations in the nanodevice is illustrated directly in the time domain. Functioning in ambient conditions, the excitation, coherent control, and read-out of coherent electron oscillations pave the way toward on-chip light-wave electronics in quantum nanodevices.

physics.optics↗
arXiv2022

Tunable van Hove singularity without structural instability in Kagome metal CsTi$_3$Bi$_5$

In Kagome metal CsV$_3$Sb$_5$, multiple intertwined orders are accompanied by both electronic and structural instabilities. These exotic orders have attracted much recent attention, but their origins remain elusive. The newly discovered CsTi$_3$Bi$_5$ is a Ti-based Kagome metal to parallel CsV$_3$Sb$_5$. Here, we report angle-resolved photoemission experiments and first-principles calculations on pristine and Cs-doped CsTi$_3$Bi$_5$ samples. Our results reveal that the van Hove singularity (vHS) in CsTi$_3$Bi$_5$ can be tuned in a large energy range without structural instability, different from that in CsV$_3$Sb$_5$. As such, CsTi$_3$Bi$_5$ provides a complementary platform to disentangle and investigate the electronic instability with a tunable vHS in Kagome metals.

cond-mat.str-el↗
arXiv2022

Imaging and Controlling Coherent Phonon Wave Packets in Single Graphene Nanoribbons

The motion of atoms is at the heart of any chemical or structural transformation in molecules and materials. Upon activation of this motion by an external source, several (usually many) vibrational modes can be coherently coupled, thus facilitating the chemical or structural phase transformation. These coherent dynamics occur on the ultrafast time scale, as revealed, e.g., by nonlocal ultrafast vibrational spectroscopic measurements in bulk molecular ensembles and solids. Tracking and controlling vibrational coherences locally at the atomic and molecular scales is, however, much more challenging and in fact has remained elusive so far. Here, we demonstrate that the vibrational coherences induced by broadband laser pulses on a single graphene nanoribbon (GNR) can be probed by femtosecond coherent anti-Stokes Raman spectroscopy (CARS) when performed in a scanning tunnelling microscope (STM). In addition to determining dephasing (~ 440 fs) and population decay times (~1.8 ps) of the generated phonon wave packets, we are able to track and control the corresponding quantum coherences, which we show to evolve on time scales as short as ~ 70 fs. We demonstrate that a two-dimensional frequency correlation spectrum unequivocally reveals the quantum couplings between different phonon modes in the GNR.

physics.optics↗
arXiv2022

Binary Classification with Positive Labeling Sources

To create a large amount of training labels for machine learning models effectively and efficiently, researchers have turned to Weak Supervision (WS), which uses programmatic labeling sources rather than manual annotation. Existing works of WS for binary classification typically assume the presence of labeling sources that are able to assign both positive and negative labels to data in roughly balanced proportions. However, for many tasks of interest where there is a minority positive class, negative examples could be too diverse for developers to generate indicative labeling sources. Thus, in this work, we study the application of WS on binary classification tasks with positive labeling sources only. We propose WEAPO, a simple yet competitive WS method for producing training labels without negative labeling sources. On 10 benchmark datasets, we show WEAPO achieves the highest averaged performance in terms of both the quality of synthesized labels and the performance of the final classifier supervised with these labels. We incorporated the implementation of \method into WRENCH, an existing benchmarking platform.

cs.LG↗
arXiv2022

Deep Learning Based Automatic Modulation Recognition: Models, Datasets, and Challenges

Automatic modulation recognition (AMR) detects the modulation scheme of the received signals for further signal processing without needing prior information, and provides the essential function when such information is missing. Recent breakthroughs in deep learning (DL) have laid the foundation for developing high-performance DL-AMR approaches for communications systems. Comparing with traditional modulation detection methods, DL-AMR approaches have achieved promising performance including high recognition accuracy and low false alarms due to the strong feature extraction and classification abilities of deep neural networks. Despite the promising potential, DL-AMR approaches also bring concerns to complexity and explainability, which affect the practical deployment in wireless communications systems. This paper aims to present a review of the current DL-AMR research, with a focus on appropriate DL models and benchmark datasets. We further provide comprehensive experiments to compare the state of the art models for single-input-single-output (SISO) systems from both accuracy and complexity perspectives, and propose to apply DL-AMR in the new multiple-input-multiple-output (MIMO) scenario with precoding. Finally, existing challenges and possible future research directions are discussed.

eess.SP↗
arXiv2022

A new class of bilayer kagome lattice compounds with Dirac nodal lines and pressure-induced superconductivity

Kagome lattice composed of transition-metal ions provides a great opportunity to explore the intertwining between geometry, electronic orders and band topology. The discovery of multiple competing orders that connect intimately with the underlying topological band structure in nonmagnetic kagome metals $A$V$_3$Sb$_5$ ($A$ = K, Rb, Cs) further pushes this topic to the quantum frontier. Here we report the discovery and characterization of a new class of vanadium-based compounds with kagome bilayers, namely $A$V$_6$Sb$_6$ ($A$ = K, Rb, Cs) and V$_6$Sb$_4$, which, together with $A$V$_3$Sb$_5$, compose a series of kagome compounds with a generic chemical formula ($A_{m-1}$Sb$_{2m}$)(V$_3$Sb)$_n$ (m = 1, 2; n = 1, 2). Theoretical calculations combined with angle-resolved photoemission measurements reveal that these compounds feature Dirac nodal lines in close vicinity to the Fermi level. Pressure-induced superconductivity in $A$V$_6$Sb$_6$ further suggests promising emergent phenomena in these materials. The establishment of a new family of layered kagome materials paves the way for designer of fascinating kagome systems with diverse topological nontrivialities and collective ground states.

cond-mat.mtrl-sci↗
arXiv2021

Realizing Kagome Band Structure in Two-Dimensional Kagome Surface States of $RV_{6}Sn_{6}$ ($R$=Gd,Ho)

We report angle resolved photoemission experiments on a newly discovered family of kagome metals $RV_{6}Sn_{6}$ ($R$=Gd, Ho). Intrinsic bulk states and surface states of the vanadium kagome layer are differentiated from those of other atomic sublattices by the real-space resolution of the measurements with a small beam spot. Characteristic Dirac cone, saddle point and flat bands of the kagome lattice are observed. Our results establish the two-dimensional (2D) kagome surface states as a new platform to investigate the intrinsic kagome physics.

cond-mat.mtrl-sci↗
arXiv2021

Halpern-Type Accelerated and Splitting Algorithms For Monotone Inclusions

In this paper, we develop a new type of accelerated algorithms to solve some classes of maximally monotone equations as well as monotone inclusions. Instead of using Nesterov's accelerating approach, our methods rely on a so-called Halpern-type fixed-point iteration in [32], and recently exploited by a number of researchers, including [24, 70]. Firstly, we derive a new variant of the anchored extra-gradient scheme in [70] based on Popov's past extra-gradient method to solve a maximally monotone equation $G(x) = 0$. We show that our method achieves the same $\mathcal{O}(1/k)$ convergence rate (up to a constant factor) as in the anchored extra-gradient algorithm on the operator norm $\Vert G(x_k)\Vert$, , but requires only one evaluation of $G$ at each iteration, where $k$ is the iteration counter. Next, we develop two splitting algorithms to approximate a zero point of the sum of two maximally monotone operators. The first algorithm originates from the anchored extra-gradient method combining with a splitting technique, while the second one is its Popov's variant which can reduce the per-iteration complexity. Both algorithms appear to be new and can be viewed as accelerated variants of the Douglas-Rachford (DR) splitting method. They both achieve $\mathcal{O}(1/k)$ rates on the norm $\Vert G_γ(x_k)\Vert$ of the forward-backward residual operator $G_γ(\cdot)$ associated with the problem. We also propose a new accelerated Douglas-Rachford splitting scheme for solving this problem which achieves $\mathcal{O}(1/k)$ convergence rate on $\Vert G_γ(x_k)\Vert$ under only maximally monotone assumptions. Finally, we specify our first algorithm to solve convex-concave minimax problems and apply our accelerated DR scheme to derive a new variant of the alternating direction method of multipliers (ADMM).

math.OC↗