arXiv subjects

Pengcheng Yang

Publications and source records attributed to Pengcheng Yang.

At least 19 recordsLinked to original sources

arXiv2026

Cross-spectral Analysis of the Type-C Quasi-periodic Oscillation Shoulder Component in GX 339-4

We revisit Rossi X-ray Timing Explorer (RXTE) observations of GX~339$-$4 during the rising phase of its 2006/2007 outburst and apply a joint power-density-spectrum (PDS)--cross-spectrum (CS) decomposition to the type-C quasi-periodic oscillation (QPO) region. Within this framework, the QPO region is described by a narrow QPO fundamental and a neighboring high-frequency shoulder, whose amplitudes and phase lags can be measured separately. The shoulder is first detected at MJD~54142.04, mainly through the imaginary part of the CS and a narrow local structure in the phase-lag spectrum, before becoming a resolved high-frequency shoulder in the PDS. It follows the QPO frequency evolution on the high-frequency side, with $R_{\nu}=\nu_{\rm sh}/\nu_{\rm QPO}\simeq1.04$--$1.18$. The QPO lag remains small, typically below $\sim0.17$~rad, whereas the shoulder carries a larger hard lag of $\sim0.5$--$0.8$~rad. Energy-resolved fits show the same separation: the QPO lag is close to zero or only weakly positive across most of the energy band, while the shoulder lag is systematically larger and generally increases with photon energy. The two components have broadly similar rms--energy shapes, although their relative strengths evolve during the observed sequence. Although the shoulder remains broad, with $Q\sim2$--$4$, its lag and rms--energy behavior resemble those of the type-B QPO detected shortly after our observations. This similarity raises the interesting possibility that the shoulder is related to an earlier, broader stage of the variability later seen as the type-B QPO.

astro-ph.HE↗

arXiv2026

An atypical X-ray variability component in the black hole candidate AT2019wey

Recent studies have revealed a notable timing feature in several black hole X-ray binaries (BHXBs) during the soft-to-hard transition at the outburst decay. Within a narrow frequency range, the phase lags between high- and low-energy X-ray light curves exhibit a sudden increase, accompanied by a drop in the coherence function. These narrow features have been associated with a quasi-periodic oscillation (QPO) appearing only in the imaginary part of the cross spectrum (CS). This QPO remains undetected in the power density spectrum (PDS) and is known as imaginary QPO. Motivated by these results, we analyse five years of NICER observations of the BHXB AT2019wey during its low-hard state (LHS) and hard-intermediate state (HIMS). We find an imaginary QPO in the CS of AT2019wey, with similar characteristics as those found in other BHXBs, making AT2019wey the fifth BHXB in which such QPOs have been found. As the source hardens, the frequency of the imaginary QPO drops from $\sim$ 5 Hz to $\sim$ 1 Hz, while its phase lag rises from $\sim$ 0.3 rad to $\sim$ 0.7 rad during the HIMS and from $\sim$ 0.5 rad to $\sim$ 0.6 rad during the LHS. During the HIMS, the phase-lag energy spectrum of the imaginary QPO shows a typical U-shaped profile, while the shape changes in the LHS. The rms spectrum of the imaginary QPO rises below $\sim$ 2 keV, peaks at around $\sim$ 2 keV and decreases at higher energies, which may be associated with the presence of a relatively cool corona. We compare the properties of the imaginary QPO with those of the type-B and C QPOs in BHXBs and find a tentative connection to type-C QPOs. Combining the imaginary QPOs detected in AT2019wey with those reported in other sources, we find a systematic increase of QPO phase lags with QPO frequency. However, we cannot conclude whether the phase lags of imaginary QPOs exhibit the inclination dependence previously observed in type-C QPOs.

astro-ph.HE↗

arXiv2026

LingLanMiDian: Systematic Evaluation of LLMs on TCM Knowledge and Clinical Reasoning

Large language models (LLMs) are advancing rapidly in medical NLP, yet Traditional Chinese Medicine (TCM) with its distinctive ontology, terminology, and reasoning patterns requires domain-faithful evaluation. Existing TCM benchmarks are fragmented in coverage and scale and rely on non-unified or generation-heavy scoring that hinders fair comparison. We present the LingLanMiDian (LingLan) benchmark, a large-scale, expert-curated, multi-task suite that unifies evaluation across knowledge recall, multi-hop reasoning, information extraction, and real-world clinical decision-making. LingLan introduces a consistent metric design, a synonym-tolerant protocol for clinical labels, a per-dataset 400-item Hard subset, and a reframing of diagnosis and treatment recommendation into single-choice decision recognition. We conduct comprehensive, zero-shot evaluations on 14 leading open-source and proprietary LLMs, providing a unified perspective on their strengths and limitations in TCM commonsense knowledge understanding, reasoning, and clinical decision support; critically, the evaluation on Hard subset reveals a substantial gap between current models and human experts in TCM-specialized reasoning. By bridging fundamental knowledge and applied reasoning through standardized evaluation, LingLan establishes a unified, quantitative, and extensible foundation for advancing TCM LLMs and domain-specific medical AI research. All evaluation data and code are available at https://github.com/TCMAI-BJTU/LingLan and http://tcmnlp.com.

cs.AI↗

arXiv2025

Structural Heterogeneity of the Drosophila Brain Network

Decoding the heterogeneity of biological neural systems is key to understanding the nervous system's complex dynamical behaviors. This study analyzes the comprehensive Drosophila brain connectome, which is the most recent data set, containing over 130,000 neurons and 50 million synapses. We conducted meticulous analyses of both network and spatial structure. Our findings reveal significant heterogeneity in network properties and distinct spatial clustering across functional regions. Besides, our analysis revealed a modular organizational pattern within the neural network, wherein regions with similar functions exhibited higher connection densities, forming distinct community structures. Moreover, we observed spatial clustering within functional regions but was not statistically significant. Additionally, we identify pervasive bilateral symmetry in network topology and spatial organization. Simulations based on the Kuramoto model demonstrate that the functional asymmetry between cerebral hemispheres arises from disparities in the intrinsic frequencies of neurons rather than from structural asymmetry within the neural network itself. Finally, we develop a 3D connectome visualization tool for detailed mapping of neuronal morphology. These insights advance our understanding of neural network organization and complexity in biological systems.

q-bio.NC↗

arXiv2025

Evidence for enhanced mass transfer in the disc preceding the transition to the soft state in MAXI J1820+070

We investigate the 2018-2019 main outburst and the subsequent mini-outbursts of the black hole low-mass X-ray binary MAXI J1820+070 using optical/ultraviolet data from the Las Cumbres Observatory (LCO), the American Association of Variable Star Observers (AAVSO), and $\textit{Swift}$/UVOT, as well as X-ray data from $\textit{Insight}$-HXMT and $\textit{Swift}$/XRT. Given the high-cadence observations, we identify a broad dip-like feature in both the optical and X-ray light curves preceding the transition to the soft state, with the X-ray dip lagging the optical dip by approximately 10 days. We propose that the dip is caused by a brief decrease followed by an increase in the mass accretion rate as it propagates through the disc, ultimately triggering the transition to the soft state. This might be a potential tool to predict impending hard-to-soft state transitions, although such a dip has not yet been observed in many sources. Additionally, we find that optical colour ($g^{\prime}-i^{\prime}$) becomes bluer and less variable before the transition to the intermediate state, preceding a dramatic change in the hardness ratio. This appears to be an unusual case, differing from the typical scenario where the optical colour changes usually along with the transition to the soft state. Finally, we explore the implications of the complex evolution of optical/X-ray correlation during both main outbursts and mini-outbursts. In particular, we find a loop-like evolutionary track before the transition to the soft state, which is linked to the optical/X-ray dips in the light curves.

astro-ph.HE↗

arXiv2024

Network Structure Governs Drosophila Brain Functionality

How intelligence emerges from living beings has been a fundamental question in neuroscience. However, it remains largely unanswered due to the complex neuronal dynamics and intricate connections between neurons in real neural systems. To address this challenge, we leveraged the largest available adult Drosophila connectome data set, and constructed a comprehensive computational framework based on simplified neuronal activation mechanisms to simulate the observed activation behavior within the connectome. The results revealed that even with rudimentary neuronal activation mechanisms, models grounded in real neural network structures can generate activation patterns strikingly similar to those observed in the actual brain. A significant discovery was the consistency of activation patterns across various neuronal dynamic models. This consistency, achieved with the same network structure, underscores the pivotal role of network topology in neural information processing. These results challenge the prevailing view that solely relies on neuron count or complex individual neuron dynamics. Further analysis demonstrated a near-complete separation of the visual and olfactory systems at the network level. Moreover, we found that the network distance, rather than spatial distance, is the primary determinant of activation patterns. Additionally, our experiments revealed that a reconnect rate of at least 0.1% was sufficient to disrupt the previously observed activation patterns. We also observed synergistic effects between the brain hemispheres: Even with unilateral input stimuli, visual-related neurons in both hemispheres were activated, highlighting the importance of interhemispheric communication. These findings emphasize the crucial role of network structure in neural activation and offer novel insights into the fundamental principles governing brain functionality.

q-bio.NC↗

arXiv2023

Accelerated quantum control in a three-level system by jumping along the geodesics

In a solid-state spin system, we experimentally demonstrate a protocol for quantum-state population transfer with an improved efficiency compared to traditional stimulated Raman adiabatic passage (STIRAP). Using the ground-state triplet of the nitrogen-vacancy center in diamond, we show that the required evolution time for high-fidelity state transfer can be reduced by almost one order of magnitude. Furthermore, we establish an improved robustness against frequency detuning caused by magnetic noise as compared to STIRAP. These results provide a powerful tool for coherent spin manipulation in the context of quantum sensing and quantum computation.

quant-ph↗

arXiv2022

Experimental demonstration of topological bounds in quantum metrology

Quantum metrology is deeply connected to quantum geometry, through the fundamental notion of quantum Fisher information. Inspired by advances in topological matter, it was recently suggested that the Berry curvature and Chern numbers of band structures can dictate strict lower bounds on metrological properties, hence establishing a strong connection between topology and quantum metrology. In this work, we provide a first experimental verification of such topological bounds, by performing optimal quantum multi-parameter estimation and achieving the best possible measurement precision. By emulating the band structure of a Chern insulator, we experimentally determine the metrological potential across a topological phase transition, and demonstrate strong enhancement in the topologically non-trivial regime. Our work opens the door to metrological applications empowered by topology, with potential implications for quantum many-body systems.

quant-ph↗

arXiv2022

The origin of UV/optical emission in the black hole low-mass X-ray binary Swift J1753.5-0127

The emission from the accreting black holes (BHs) in low-mass X-ray binaries (LMXBs) covers a broad energy band from radio to X-rays. Studying the correlations between emission in different energy bands during outbursts can provide valuable information about the accretion process. We analyse the simultaneous optical, ultraviolet (UV) and X-ray data of the BH-LMXB Swift J1753.5-0127 during its $\sim$ 12-year long outburst with the {\it Neil Gehrels Swift Observatory}. We find that the UV/optical and X-ray emission are strongly correlated during the hard states of the outburst. We fit the relation with a power-law function $F_{UV/optical} \propto F_{X}^β$ and find that the power-law index $β$ increases from $\sim$ 0.24 to $\sim$ 0.33 as the UV/optical wavelength decreases from $\sim$ 5400 Å (V) to $\sim$ 2030 Å (UVW2). We explore the possible reasons for this and suggest that in Swift J1753.5-0127 the UV/optical emission is dominated by a viscously heated accretion disc at large radii. We find that the data that deviate from the correlation correspond to the low-intensity peaks appeared in the X-ray band during the outburst, and suggest that these deviations are driven by the emission from the inner part of the accretion disc.

astro-ph.HE↗

arXiv2022

Quantum Fisher information measurement and verification of the quantum Cramér-Rao bound in a solid-state qubit

The quantum Cramér-Rao bound sets a fundamental limit on the accuracy of unbiased parameter estimation in quantum systems, relating the uncertainty in determining a parameter to the inverse of the quantum Fisher information. We experimentally demonstrate near saturation of the quantum Cramér-Rao bound in the phase estimation of a solid-state spin system, provided by a nitrogen-vacancy center in diamond. This is achieved by comparing the experimental uncertainty in phase estimation with an independent measurement of the related quantum Fisher information. The latter is independently extracted from coherent dynamical responses of the system under weak parametric modulations, without performing any quantum-state tomography. While optimal parameter estimation has already been observed for quantum devices involving a limited number of degrees of freedom, our method offers a versatile and powerful experimental tool to explore the Cramér-Rao bound and the quantum Fisher information in systems of higher complexity, as relevant for quantum technologies.

quant-ph↗

arXiv2021

Experimental estimation of the quantum Fisher information from randomized measurements

The quantum Fisher information (QFI) represents a fundamental concept in quantum physics. On the one hand, it quantifies the metrological potential of quantum states in quantum-parameter-estimation measurements. On the other hand, it is intrinsically related to the quantum geometry and multipartite entanglement of many-body systems. Here, we explore how the QFI can be estimated via randomized measurements, an approach which has the advantage of being applicable to both pure and mixed quantum states. In the latter case, our method gives access to the sub-quantum Fisher information, which sets a lower bound on the QFI. We experimentally validate this approach using two platforms: a nitrogen-vacancy center spin in diamond and a 4-qubit state provided by a superconducting quantum computer. We further perform a numerical study on a many-body spin system to illustrate the advantage of our randomized-measurement approach in estimating multipartite entanglement, as compared to quantum state tomography. Our results highlight the general applicability of our method to general quantum platforms, including solid-state spin systems, superconducting quantum computers and trapped ions, hence providing a versatile tool to explore the essential role of the QFI in quantum physics.

quant-ph↗

arXiv2021

Precise Spectroscopy of High-Frequency Oscillating Fields with a Single-Qubit Sensor

Precise spectroscopy of oscillating fields plays significant roles in many fields. Here, we propose an experimentally feasible scheme to measure the frequency of a fast-oscillating field using a single-qubit sensor. By invoking a stable classical clock, the signal phase correlations between successive measurements enable us to extract the target frequency with extremely high precision. In addition, we integrate dynamical decoupling technique into the framework to suppress the influence of slow environmental noise. Our framework is feasible with a variety of atomic and single solid-state-spin systems within the state-of-the-art experimental capabilities as a versatile tool for quantum spectroscopy.

quant-ph↗

arXiv2020

CAPT: Contrastive Pre-Training for Learning Denoised Sequence Representations

Pre-trained self-supervised models such as BERT have achieved striking success in learning sequence representations, especially for natural language processing. These models typically corrupt the given sequences with certain types of noise, such as masking, shuffling, or substitution, and then try to recover the original input. However, such pre-training approaches are prone to learning representations that are covariant with the noise, leading to the discrepancy between the pre-training and fine-tuning stage. To remedy this, we present ContrAstive Pre-Training (CAPT) to learn noise invariant sequence representations. The proposed CAPT encourages the consistency between representations of the original sequence and its corrupted version via unsupervised instance-wise training signals. In this way, it not only alleviates the pretrain-finetune discrepancy induced by the noise of pre-training, but also aids the pre-trained model in better capturing global semantics of the input via more effective sentence-level supervision. Different from most prior work that focuses on a particular modality, comprehensive empirical evidence on 11 natural language understanding and cross-modal tasks illustrates that CAPT is applicable for both language and vision-language tasks, and obtains surprisingly consistent improvement, including 0.6\% absolute gain on GLUE benchmarks and 0.8\% absolute increment on $\text{NLVR}^2$.

cs.CL↗

arXiv2020

Memorized Sparse Backpropagation

Neural network learning is usually time-consuming since backpropagation needs to compute full gradients and backpropagate them across multiple layers. Despite its success of existing works in accelerating propagation through sparseness, the relevant theoretical characteristics remain under-researched and empirical studies found that they suffer from the loss of information contained in unpropagated gradients. To tackle these problems, this paper presents a unified sparse backpropagation framework and provides a detailed analysis of its theoretical characteristics. Analysis reveals that when applied to a multilayer perceptron, our framework essentially performs gradient descent using an estimated gradient similar enough to the true gradient, resulting in convergence in probability under certain conditions. Furthermore, a simple yet effective algorithm named memorized sparse backpropagation (MSBP) is proposed to remedy the problem of information loss by storing unpropagated gradients in memory for learning in the next steps. Experimental results demonstrate that the proposed MSBP is effective to alleviate the information loss in traditional sparse backpropagation while achieving comparable acceleration.

cs.LG↗

arXiv2020

Inductively Representing Out-of-Knowledge-Graph Entities by Optimal Estimation Under Translational Assumptions

Conventional Knowledge Graph Completion (KGC) assumes that all test entities appear during training. However, in real-world scenarios, Knowledge Graphs (KG) evolve fast with out-of-knowledge-graph (OOKG) entities added frequently, and we need to represent these entities efficiently. Most existing Knowledge Graph Embedding (KGE) methods cannot represent OOKG entities without costly retraining on the whole KG. To enhance efficiency, we propose a simple and effective method that inductively represents OOKG entities by their optimal estimation under translational assumptions. Given pretrained embeddings of the in-knowledge-graph (IKG) entities, our method needs no additional learning. Experimental results show that our method outperforms the state-of-the-art methods with higher efficiency on two KGC tasks with OOKG entities.

cs.CL↗

arXiv2020

Quantum optimal control using phase-modulated driving fields

Quantum optimal control represents a powerful technique to enhance the performance of quantum experiments by engineering the controllable parameters of the Hamiltonian. However, the computational overhead for the necessary optimization of these control parameters drastically increases as their number grows. We devise a novel variant of a gradient-free optimal-control method by introducing the idea of phase-modulated driving fields, which allows us to find optimal control fields efficiently. We numerically evaluate its performance and demonstrate the advantages over standard Fourier-basis methods in controlling an ensemble of two-level systems showing an inhomogeneous broadening. The control fields optimized with the phase-modulated method provide an increased robustness against such ensemble inhomogeneities as well as control-field fluctuations and environmental noise, with one order of magnitude less of average search time. Robustness enhancement of single quantum gates is also achieved by the phase-modulated method. Under environmental noise, an XY-8 sequence constituted by optimized gates prolongs the coherence time by $50\%$ compared with standard rectangular pulses in our numerical simulations, showing the application potential of our phase-modulated method in improving the precision of signal detection in the field of quantum sensing.

quant-ph↗

arXiv2020

Complete Quantum-State Tomography with a Local Random Field

Single-qubit measurements are typically insufficient for inferring arbitrary quantum states of a multi-qubit system. We show that if the system can be fully controlled by driving a single qubit, then utilizing a local random pulse is almost always sufficient for complete quantum-state tomography. Experimental demonstrations of this principle are presented using a nitrogen-vacancy (NV) center in diamond coupled to a nuclear spin, which is not directly accessible. We report the reconstruction of a highly entangled state between the electron and nuclear spin with fidelity above 95%, by randomly driving and measuring the NV-center electron spin only. Beyond quantum-state tomography, we outline how this principle can be leveraged to characterize and control quantum processes in cases where the system model is not known.

quant-ph↗

arXiv2019

Visual Agreement Regularized Training for Multi-Modal Machine Translation

Multi-modal machine translation aims at translating the source sentence into a different language in the presence of the paired image. Previous work suggests that additional visual information only provides dispensable help to translation, which is needed in several very special cases such as translating ambiguous words. To make better use of visual information, this work presents visual agreement regularized training. The proposed approach jointly trains the source-to-target and target-to-source translation models and encourages them to share the same focus on the visual information when generating semantically equivalent visual words (e.g. "ball" in English and "ballon" in French). Besides, a simple yet effective multi-head co-attention model is also introduced to capture interactions between visual and textual features. The results show that our approaches can outperform competitive baselines by a large margin on the Multi30k dataset. Further analysis demonstrates that the proposed regularized training can effectively improve the agreement of attention on the image, leading to better use of visual information.

cs.CL↗