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Ya Wang

Publications and source records attributed to Ya Wang.

At least 55 records · Page 3Linked to original sources

Seed1.5-Thinking: Advancing Superb Reasoning Models with Reinforcement Learning

We introduce Seed1.5-Thinking, capable of reasoning through thinking before responding, resulting in improved performance on a wide range of benchmarks. Seed1.5-Thinking achieves 86.7 on AIME 2024, 55.0 on Codeforces and 77.3 on GPQA, demonstrating excellent reasoning abilities in STEM and coding. Beyond reasoning tasks, the method demonstrates notable generalization across diverse domains. For instance, it surpasses DeepSeek R1 by 8% in win rate on non-reasoning tasks, indicating its broader applicability. Compared to other state-of-the-art reasoning models, Seed1.5-Thinking is a Mixture-of-Experts (MoE) model with a relatively small size, featuring 20B activated and 200B total parameters. As part of our effort to assess generalized reasoning, we develop two internal benchmarks, BeyondAIME and Codeforces, both of which will be publicly released to support future research. Model trial link: https://www.volcengine.com/experience/ark.

cs.CL↗

Transverse Oscillations of Coronal Loops Induced by a Jet-Related Confined Flare on 11 July 2022

In this article, we report the multiwavelength and multiview observations of transverse oscillations of two loop strands induced by a jet-related, confined flare in active region NOAA 13056 on 11 July 2022. The jet originates close to the right footpoint of the loops and propagates in the northeast direction. The average rise time and fall time of the jet are $\approx$ 11 and $\approx$ 13.5 minutes, so that the lifetime of the jet reaches $\approx$ 24.5 minutes. The rising motion of the jet is divided into two phases with average velocities of $\approx$ 164 and $\approx$ 546\,km\,s$^{-1}$. The falling motion of the jet is coherent with an average velocity of $\approx$ 124\,km\,s$^{-1}$. The transverse oscillations of the loops, lasting for 3 $-$ 5 cycles, are of fundamental standing kink mode. The maximal initial amplitudes of the two strands are $\approx$ 5.8 and $\approx$ 4.9 Mm. The average periods are $\approx$ 405\,s and $\approx$ 407\,s. Both of the strands experience slow expansions during oscillations. The lower limits of the kink speed are 895$_{-17}^{+21}$\,km\,s$^{-1}$ for loop\_1 and 891$_{-35}^{+29}$\,km\,s$^{-1}$ for loop\_2, respectively. The corresponding lower limits of the Alfvén speed are estimated to be 664$_{-13}^{+16}$\,km\,s$^{-1}$ and 661$_{-26}^{+22}$\,km\,s$^{-1}$.

astro-ph.SR↗

Efficient Pretraining Length Scaling

Recent advances in large language models have demonstrated the effectiveness of length scaling during post-training, yet its potential in pre-training remains underexplored. We present the Parallel Hidden Decoding Transformer (\textit{PHD}-Transformer), a novel framework that enables efficient length scaling during pre-training while maintaining inference efficiency. \textit{PHD}-Transformer achieves this through an innovative KV cache management strategy that distinguishes between original tokens and hidden decoding tokens. By retaining only the KV cache of original tokens for long-range dependencies while immediately discarding hidden decoding tokens after use, our approach maintains the same KV cache size as the vanilla transformer while enabling effective length scaling. To further enhance performance, we introduce two optimized variants: \textit{PHD-SWA} employs sliding window attention to preserve local dependencies, while \textit{PHD-CSWA} implements chunk-wise sliding window attention to eliminate linear growth in pre-filling time. Extensive experiments demonstrate consistent improvements across multiple benchmarks.

cs.CL↗

Investigation of Rare-Earth Ion-Photon Interaction and Strong Coupling in Optical Microcavities

The strong coupling between an emitter and a cavity is significant for advancing quantum networks. Due to their long optical and spin coherence times, rare-earth ions (REIs) represent a compelling platform for quantum networks. However, their inherently weak intra-4f optical transitions typically result in low coupling strength, thus restricting most current achievements to the weak coupling regime. This work proposes a scheme to realize an on-chip quantum network by coupling REIs to high-quality whispering gallery mode (WGM) microcavities. Additionally, we provide numerical validation for a parametric amplification technique to enhance the emitter-cavity coupling strength. As an extension of this approach, the coupled system efficiently achieves the quantum entanglement of local and flying qubits. This study deepens the understanding of emitter-cavity interactions and contributes to realizing REIs-based photonic platforms, which are crucial to distributed quantum computing and developing robust quantum networks.

quant-ph↗

Unveiling Spatiotemporal Properties of the Quasi-periodic Pulsations in the Balmer Continuum at 3600 Å in an X-class Solar White-light Flare

Quasi-periodic pulsations (QPPs) in the Balmer continuum of solar white-light flares (WLFs) are rarely reported, and accurately pinpointing the spatial source of flaring QPPs remains a significant challenge. We present spatiotemporal characteristics of QPPs of an X2.8 two-ribbon solar WLF (SOL2023-12-14T17:02), which was well observed by the White-light Solar Telescope (WST) aboard the Advanced Space-based Solar Observatory, with high-cadence imaging (1--2 s) in the Balmer continuum at 3600 Å. Combined with additional multi-instrument data, we find that the enhancement of the WLF in both Balmer and Paschen continua shows strong spatiotemporal correlation with hard X-ray (HXR) emissions. Notably, the pulses in the WST Balmer continuum exhibited a near-zero time lag with most HXR pulses, whereas soft X-ray and extreme ultraviolet emissions showed a lag of 2--3 s. Interestingly, quasi-harmonic QPPs with periods of $\sim$11 s and $\sim$20 s were observed in multiple wavelengths in the rising phase of the white-light continuum. Furthermore, we employed Fourier transform to spatially locate the QPPs around 11 and 20 s, revealing that they primarily originated from the east flare ribbon, which exhibited the most substantial continuum enhancement. More interestingly, we find that the west ribbon contributed significantly to the 11-second QPP but had a weaker contribution to the 20-second QPP. Moreover, the occurrence of quasi-harmonic QPPs is temporally coincident with the rapid elongation and separation motions of flare ribbons. Possible mechanisms for the quasi-harmonic QPPs have been discussed. These observations provide valuable insights into QPP modeling for solar and stellar flares.

astro-ph.SR↗

Non-Hermitian non-Abelian topological transition in the S=1 electron spin system of a nitrogen vacancy centre in diamond

Topological phases and transitions are of fundamental importance in physics, which provide a deep insight into the understanding of materials. Recently, non-Abelian topological transitions have been investigated in Hermitian systems, revealing important topological features. With non-Hermiticity introduced, non-Hermitian non-Abelian topological transitions bring about more intriguing topological features, yet has not been experimentally explored. In this work, we report the observation of the non-Hermitian non-Abelian topological transition at the atomic scale utilizing a nitrogenvacancy center in diamond. While the well-established topological numbers, failed to recognize this transition, we successfully characterized such a transition with the measurement of the complex eigenvalue braids. We obtained the braid invariants from the measured relative phases between eigenvalues. The observed change in braid invariants provides a clear signature of the non-Abelian topological transition. Furthermore, we experimentally revealed an intriguing consequence of this transition, which is the creation of a third-order exceptional point through the collision of two second-order exceptional points with opposite charges. Our experimental findings shed light on the abundant non-Abelian topological phenomena involving non-Hermiticity, and provide insights into manipulating the spectral topology in atomic scale systems to achieve exotic functionalities arising from non-Abelian band braiding.

quant-ph↗

Polynomial Composition Activations: Unleashing the Dynamics of Large Language Models

Transformers have found extensive applications across various domains due to the powerful fitting capabilities. This success can be partially attributed to their inherent nonlinearity. Thus, in addition to the ReLU function employed in the original transformer architecture, researchers have explored alternative modules such as GeLU and SwishGLU to enhance nonlinearity and thereby augment representational capacity. In this paper, we propose a novel category of polynomial composition activations (PolyCom), designed to optimize the dynamics of transformers. Theoretically, we provide a comprehensive mathematical analysis of PolyCom, highlighting its enhanced expressivity and efficacy relative to other activation functions. Notably, we demonstrate that networks incorporating PolyCom achieve the $\textbf{optimal approximation rate}$, indicating that PolyCom networks require minimal parameters to approximate general smooth functions in Sobolev spaces. We conduct empirical experiments on the pre-training configurations of large language models (LLMs), including both dense and sparse architectures. By substituting conventional activation functions with PolyCom, we enable LLMs to capture higher-order interactions within the data, thus improving performance metrics in terms of accuracy and convergence rates. Extensive experimental results demonstrate the effectiveness of our method, showing substantial improvements over other activation functions. Code is available at https://github.com/BryceZhuo/PolyCom.

cs.CL↗

Coherence Properties of Rare-Earth Spins in Micrometer-Thin Films

Rare-earth ions in bulk crystals are excellent solid-state quantum systems in quantum information science, owing to the exceptional optical and spin coherence properties. However, the weak fluorescence of single rare-earth ions present a significant challenge for scalability, necessitating the integration into micro-cavities. Thin films serve as a promising material platform for the integration, yet the fabrication without compromising the properties of the materials and rare-earth ions remains challenging. In this work, we fabricate micrometer-thin yttrium aluminum garnet (YAG) films from bulk crystals using ion implantation techniques. The resulting films preserve the single-crystalline structure of the original bulk crystal. Notably, the embedded rare-earth ions are photo-stable and exhibit bulk-like spin coherence properties. Our results demonstrate the compatibility of bulk-like spin properties with the thin-film fabrication technique, facilitating the efficient integration of rare-earth ions into on-chip photonic devices and advancing the applications of rare-earth ions systems in quantum technologies.

quant-ph↗

FwNet-ECA: A Classification Model Enhancing Window Attention with Global Receptive Fields via Fourier Filtering Operations

Windowed attention mechanisms were introduced to mitigate the issue of excessive computation inherent in global attention mechanisms. In this paper, we present FwNet-ECA, a novel method that utilizes Fourier transforms paired with learnable weight matrices to enhance the spectral features of images. This method establishes a global receptive field through Filter Enhancement and avoids the use of moving window attention. Additionally, we incorporate the Efficient Channel Attention (ECA) module to improve communication between different channels. Instead of relying on physically shifted windows, our approach leverages frequency domain enhancement to implicitly bridge information across spatial regions. We validate our model on the iCartoonFace dataset and conduct downstream tasks on ImageNet, demonstrating that our model achieves lower parameter counts and computational overheads compared to shifted window approaches, while maintaining competitive accuracy. Furthermore, our visualization operations clearly demonstrated that the Filter Enhancement technique achieves greater effectiveness in the model's shallow layers, where feature maps are relatively larger. This work offers a more efficient and effective alternative for leveraging attention mechanisms in visual processing tasks, alleviating the challenges associated with windowed attention models. Code is available at https://github.com/qingxiaoli/FwNet-ECA

cs.CV↗

Compact 780 nm Rb Optical Clock

We demonstrated a compact 780 nm rubidium optical clock, which includes an optical frequency standard and an optical frequency comb, with an optical volume of 11.6 liters. Unlike the 778 nm rubidium atomic clocks based on two-photon transition, here, the laser frequency is stabilized to the Rb D2 transition, using modulation transfer spectroscopy. This approach effectively eliminates Doppler background and provides a high signal to noise ratio and high sensitivity. A nearly 300 MHz microwave signal, whose phase exactly tracks that of the optical frequency standard, is generated via the optical frequency comb, yielding a frequency instability of 1.91 E-13 @1 s and 5.29 E-14 @1000 s in the electronic domain. To the best of our knowledge, this is the most precise frequency stabilization result for the first-excited-state transition of alkali metal atoms to date and represents the first optical clock based on this transition. These results offer a promising approach for the development of portable optical clocks.

physics.optics↗

Scale-Distribution Decoupling: Enabling Stable and Effective Training of Large Language Models

Training stability is a persistent challenge in the pre-training of large language models (LLMs), particularly for architectures such as Post-Norm Transformers, which are prone to gradient explosion and dissipation. In this paper, we propose Scale-Distribution Decoupling (SDD), a novel approach that stabilizes training by explicitly decoupling the scale and distribution of the weight matrix in fully-connected layers. SDD applies a normalization mechanism to regulate activations and a learnable scaling vector to maintain well-conditioned gradients, effectively preventing $\textbf{gradient explosion and dissipation}$. This separation improves optimization efficiency, particularly in deep networks, by ensuring stable gradient propagation. Experimental results demonstrate that our method stabilizes training across various LLM architectures and outperforms existing techniques in different normalization configurations. Furthermore, the proposed method is lightweight and compatible with existing frameworks, making it a practical solution for stabilizing LLM training. Code is available at https://github.com/kaihemo/SDD.

cs.CL↗

Discovery of an anomalous non-evaporating sub-nanometre water layer in open environment

Water exhibits complex behaviors as a result of hydrogen bonding, and low-dimensional confined water plays a key role in material science, geology, and biology science. Conventional techniques like STM, TEM, and AFM enable atomic-scale observations but face limitations under ambient conditions and surface topographies. NV center magnetic resonance technology provides an opportunity to overcome these limitations, offering non-contact atomic-scale measurements with chemical resolution capability. In this study, a nanoscale layer dissection method was developed utilizing NV center technology to analyze water layers with diverse physicochemical properties. It unveiled the presence of a non-evaporating sub-nanometer water layer on a diamond surface under ambient conditions. This layer demonstrated impervious to atmospheric water vapor and exhibited unique electronic transport mediated via hydrogen bonding. These findings provide new perspectives and a platform for studying the structure and behavior of low-dimensional water, as well as the surface properties influenced by adsorbed water under native conditions.

cond-mat.mtrl-sci↗

Explainable Fuzzy Neural Network with Multi-Fidelity Reinforcement Learning for Micro-Architecture Design Space Exploration

With the continuous advancement of processors, modern micro-architecture designs have become increasingly complex. The vast design space presents significant challenges for human designers, making design space exploration (DSE) algorithms a significant tool for $μ$-arch design. In recent years, efforts have been made in the development of DSE algorithms, and promising results have been achieved. However, the existing DSE algorithms, e.g., Bayesian Optimization and ensemble learning, suffer from poor interpretability, hindering designers' understanding of the decision-making process. To address this limitation, we propose utilizing Fuzzy Neural Networks to induce and summarize knowledge and insights from the DSE process, enhancing interpretability and controllability. Furthermore, to improve efficiency, we introduce a multi-fidelity reinforcement learning approach, which primarily conducts exploration using cheap but less precise data, thereby substantially diminishing the reliance on costly data. Experimental results show that our method achieves excellent results with a very limited sample budget and successfully surpasses the current state-of-the-art. Our DSE framework is open-sourced and available at https://github.com/fanhanwei/FNN\_MFRL\_ArchDSE/\ .

cs.LG↗

Two-way Node Popularity Model for Directed and Bipartite Networks

There has been extensive research on community detection in directed and bipartite networks. However, these studies often fail to consider the popularity of nodes in different communities, which is a common phenomenon in real-world networks. To address this issue, we propose a new probabilistic framework called the Two-Way Node Popularity Model (TNPM). The TNPM also accommodates edges from different distributions within a general sub-Gaussian family. We introduce the Delete-One-Method (DOM) for model fitting and community structure identification, and provide a comprehensive theoretical analysis with novel technical skills dealing with sub-Gaussian generalization. Additionally, we propose the Two-Stage Divided Cosine Algorithm (TSDC) to handle large-scale networks more efficiently. Our proposed methods offer multi-folded advantages in terms of estimation accuracy and computational efficiency, as demonstrated through extensive numerical studies. We apply our methods to two real-world applications, uncovering interesting findings.

stat.ME↗

Simultaneous Eruption and Shrinkage of Pre-existing Flare Loops during a Subsequent Solar Eruption

We investigated two consecutive solar eruption events in the solar active region (AR) 12994 at the solar eastern limb on 2022 April 15. We found that the flare loops formed by the first eruption were involved in the second eruption. During the initial stage of the second flare, the middle part of these flare loops (E-loops) erupted outward along with the flux ropes below, while the parts of the flare loops (I-loops1 and I-loops2) on either side of the E-loops first rose and then contracted. Approximately 1 hour after the eruption, the heights of I-loops1 and I-loops2 decreased by 9 Mm and 45 Mm, respectively, compared to before the eruption. Their maximum descent velocities were 30 km/s and 130 km/s, respectively. The differential emission measure (DEM) results indicate that the plasma above I-loops1 and I-loops2 began to be heated about 23 minutes and 44 minutes after the start of the second flare, respectively. Within 20 minutes, the plasma temperature in these regions increased from ~3 MK to 6 MK. We proposed an adiabatic heating mechanism that magnetic energy would be converted into thermal and kinetic energy when the pre-stretched loops contract. Our calculations show that the magnetic energy required to heat the two high-temperature regions are 10^29-10^30 erg, which correspond to a loss of field strength of 2-3 G.

astro-ph.SR↗

Electric imaging and dynamics of photo-charged graphene edge

The one-dimensional side gate based on graphene edges shows a significant capability of reducing the channel length of field-effect transistors, further increasing the integration density of semiconductor devices. The nano-scale electric field distribution near the edge provides the physical limit of the effective channel length, however, its imaging under ambient conditions still lacks, which is a critical aspect for the practical deployment of semiconductor devices. Here, we used scanning nitrogen-vacancy microscopy to investigate the electric field distribution near edges of a single-layer-graphene. Real-space scanning maps of photo-charged floating graphene flakes were acquired with a spatial resolution of $\sim$ 10 nm, and the electric edge effect was quantitatively studied by analyzing the NV spin energy level shifts due to the electric Stark effect. Since the graphene flakes are isolated from external electric sources, we brought out a theory based on photo-thermionic effect to explain the charge transfer from graphene to oxygen-terminated diamond probe with a disordered distribution of charge traps. Real-time tracing of electric fields detected the photo-thermionic emission process and the recombination process of the emitted electrons. This study provides a new perspective for graphene-based one-dimensional gates and opto-electronics with nanoscale real-space imaging, and moreover, offers a novel method to tune the chemical environment of diamond surfaces based on optical charge transfer.

cond-mat.mes-hall↗

On unitary equivalence and reducing subspaces of analytic Toeplitz operator on vector-valued Hardy space

In this paper, we proved that $T_{z^n}$ acting on the $\mathbb{C}^m$-valued Hardy space $H_{\mathbb{C}^m}^2(\mathbb{D})$, is unitarily equivalent to $\bigoplus_1^{mn}T_z$, where $T_z$ is acting on the scalar-valued Hardy space $H_{\mathbb{C}}^2(\mathbb{D})$. And using the matrix manipulations combined with operator theory methods, we completely describe the reducing subspaces of $T_{z^n}$ on $H_{\mathbb{C}^m}^2(\mathbb{D})$.

math.FA↗

Four-order power reduction in nanoscale electron-nuclear double resonance with a nitrogen-vacancy center in diamond

Detecting nuclear spins using single Nitrogen-Vacancy (NV) centers is of particular importance in nano-scale science and engineering, but often suffers from the heating effect of microwave fields for spin manipulation, especially under high magnetic fields. Here, we realize an energy-efficient nano-scale nuclear-spin detection using a phase-modulation electron-nuclear double resonance scheme. The microwave field can be reduced to 1/250 of previous requirements and the corresponding power is over four orders lower. Meanwhile, the microwave-induced broadening to the line-width of the spectroscopy is significantly canceled and we achieve a nuclear-spin spectrum with a resolution down to 2.1 kHz under a magnetic field at 1840 Gs. The spectral resolution can be further improved by upgrading the experimental control precision. This scheme can also be used in sensing microwave fields and extended to a wide range of applications in the future.

quant-ph↗