arXiv subjects

Ming Gong

Publications and source records attributed to Ming Gong.

At least 73 records · Page 4Linked to original sources

arXiv2024

Theory of vibrational Stark effect for adsorbates and diatomic molecules

Nowadays the vibrational Stark effect (VSE) of adsorbates at the electrochemical interfaces is generally investigated using the Lambert theory, in which the strong electric field across the interfaces can be treated as some kind of perturbation. Lambert found that the VSE arises mainly from the classical effect, and the quantum effect is negligible. This idea is accepted by almost all current first-principle calculations for this issue. Here we revisit this problem by addressing the fundamental question that to what extent the quantum effect is important for VSE, and if it is observable, then which physical quantity determines this effect. We use the Morse, Lennard-Jones and Dunham potentials as basic potentials to explore this problem using quantum perturbation theory. We define the relative difference between quantum and classical VSE slopes to define the quantum effect, $η$, and show that for CO, $η\sim $ 2 - 3\%, while for adsorbed hydrogen on Pt electrode, $η\sim$ 8 - 10\%, using the experimental data. We find that $η$ is determined by the anharmonic coefficient $χ_e$. Without results we present a new understanding of the VSE as a function of electric field and potential in electrochemical experiments, showing that the nonlinear slope of VSE as a function of potential should arise from the nonlinear relation between electric field and potential across the interfaces, which may resolve the long-standing controversial in experiments.

cond-mat.mtrl-sci↗

arXiv2024

Breaking Language Barriers in Multilingual Mathematical Reasoning: Insights and Observations

Existing research predominantly focuses on developing powerful language learning models (LLMs) for mathematical reasoning within monolingual languages, with few explorations in preserving efficacy in a multilingual context. To bridge this gap, this paper pioneers exploring and training powerful Multilingual Math Reasoning (xMR) LLMs. Firstly, by utilizing translation, we construct the first multilingual math reasoning instruction dataset, MGSM8KInstruct, encompassing ten distinct languages, thus addressing the issue of training data scarcity in xMR tasks. Based on the collected dataset, we propose different training strategies to build powerful xMR LLMs, named MathOctopus, notably outperform conventional open-source LLMs and exhibit superiority over ChatGPT in few-shot scenarios. Notably, MathOctopus-13B reaches 47.6% accuracy which exceeds ChatGPT 46.3% on MGSM testset. Beyond remarkable results, we unearth several pivotal observations and insights from extensive experiments: (1) When extending the rejection sampling strategy to the multilingual context, it proves effective for model performances, albeit limited. (2) Employing parallel corpora for math Supervised Fine-Tuning (SFT) across multiple languages not only significantly enhances model performance multilingually but also elevates their monolingual performance. This indicates that crafting multilingual corpora can be regarded as a vital strategy for enhancing model performance in a specific language, especially in mathematical reasoning tasks. For instance, MathOctopus-7B improves its counterparts that trained on English from 42.2% to 50.8% on GSM8K testset. Codes are available at https://github.com/microsoft/MathOctopus.

cs.CL↗

arXiv2024

$X(3872)$ Relevant $D\bar{D}^*$ Scattering in $N_f=2$ Lattice QCD

We study the $S$-wave $D\bar{D}^*(I=0)$ scattering at four different pion masses $m_π$ ranging from 250 MeV to 417 MeV from $N_f=2$ lattice QCD. Three energy levels $E_{2,3,4}$ are extracted at each $m_π$. The analysis of $E_{2,3}$ using the effective range expansion (ERE) comes out with a shallow bound state below the $D\bar{D}^*$ threshold, and the phase shifts at $E_{3,4}$ indicate the possible existence of a resonance near 4.0 GeV. We also perform a joint analysis to $E_{2,3,4}$ through the $K$-matrix parameterization of the scattering amplitude. In this way, we observe a $D\bar{D}^*$ bound state whose properties are almost the same as that from the ERE analysis. At each $m_π$, this joint analysis also results in a resonance pole with a mass slightly above 4.0 GeV and a width around 40-60 MeV, which are compatible with the properties of the newly observed $χ_{c1}(4010)$ by LHCb. More scrutinized lattice QCD calculations are desired to check the existence of this resonance.

hep-lat↗

arXiv2024

AutoDirector: Online Auto-scheduling Agents for Multi-sensory Composition

With the advancement of generative models, the synthesis of different sensory elements such as music, visuals, and speech has achieved significant realism. However, the approach to generate multi-sensory outputs has not been fully explored, limiting the application on high-value scenarios such as of directing a film. Developing a movie director agent faces two major challenges: (1) Lack of parallelism and online scheduling with production steps: In the production of multi-sensory films, there are complex dependencies between different sensory elements, and the production time for each element varies. (2) Diverse needs and clear communication demands with users: Users often cannot clearly express their needs until they see a draft, which requires human-computer interaction and iteration to continually adjust and optimize the film content based on user feedback. To address these issues, we introduce AutoDirector, an interactive multi-sensory composition framework that supports long shots, special effects, music scoring, dubbing, and lip-syncing. This framework improves the efficiency of multi-sensory film production through automatic scheduling and supports the modification and improvement of interactive tasks to meet user needs. AutoDirector not only expands the application scope of human-machine collaboration but also demonstrates the potential of AI in collaborating with humans in the role of a film director to complete multi-sensory films.

cs.CV↗

arXiv2024

Observation of electrical high-harmonic generation

High-harmonic generation (HHG), an extreme nonlinear effect, introduces an unprecedented paradigm to detect emergent quantum phases and electron dynamics inconceivable in the framework of linear and low-order nonlinear processes. As an important manifestation, the optical HHG (o-HHG) enables extraordinary opportunities to underpin attosecond physics. In addition to nonlinear optics, emerging nonlinear electric transport has been demonstrated recently and opens new paradigms to probe quantum phase transition, symmetry breaking, band geometrical and topological properties. Thus far, only electrical second-/third-harmonic generation in perturbative regime has been elucidated, while the electrical HHG (e-HHG) that can advance to extreme non-perturbative physics remains elusive. Here we report the observation of e-HHG up to 300th-order. Remarkably, the e-HHG shows a clear non-perturbative character and exhibits periodic oscillations with the reciprocal of driving current. Further, theoretical simulations corroborate the experiments, suggesting the contribution of singular distribution of Berry curvature near band edges. Our results demonstrate e-HHG in extreme nonlinear regime and may shed light on a plethora of exotic physics and applications, such as extreme non-equilibrium quantum phenomena, ultra-fast and coherent electrical signal generations and detections.

cond-mat.mes-hall↗

arXiv2024

Grounding Language Models for Visual Entity Recognition

We introduce AutoVER, an Autoregressive model for Visual Entity Recognition. Our model extends an autoregressive Multi-modal Large Language Model by employing retrieval augmented constrained generation. It mitigates low performance on out-of-domain entities while excelling in queries that require visually-situated reasoning. Our method learns to distinguish similar entities within a vast label space by contrastively training on hard negative pairs in parallel with a sequence-to-sequence objective without an external retriever. During inference, a list of retrieved candidate answers explicitly guides language generation by removing invalid decoding paths. The proposed method achieves significant improvements across different dataset splits in the recently proposed Oven-Wiki benchmark. Accuracy on the Entity seen split rises from 32.7% to 61.5%. It also demonstrates superior performance on the unseen and query splits by a substantial double-digit margin.

cs.CV↗

arXiv2024

Step-Back Profiling: Distilling User History for Personalized Scientific Writing

Large language models (LLM) excel at a variety of natural language processing tasks, yet they struggle to generate personalized content for individuals, particularly in real-world scenarios like scientific writing. Addressing this challenge, we introduce STEP-BACK PROFILING to personalize LLMs by distilling user history into concise profiles, including essential traits and preferences of users. To conduct the experiments, we construct a Personalized Scientific Writing (PSW) dataset to study multi-user personalization. PSW requires the models to write scientific papers given specialized author groups with diverse academic backgrounds. As for the results, we demonstrate the effectiveness of capturing user characteristics via STEP-BACK PROFILING for collaborative writing. Moreover, our approach outperforms the baselines by up to 3.6 points on the general personalization benchmark (LaMP), including 7 personalization LLM tasks. Our ablation studies validate the contributions of different components in our method and provide insights into our task definition. Our dataset and code are available at \url{https://github.com/gersteinlab/step-back-profiling}.

cs.CL↗

arXiv2024

Critical fluctuation and noise spectra in two-dimensional Fe$_{3}$GeTe$_{2}$ magnets

Critical fluctuations play a fundamental role in determining the spin orders for low-dimensional quantum materials, especially for recently discovered two-dimensional (2D) magnets. Here we employ the quantum decoherence imaging technique utilizing nitrogen-vacancy centers in diamond to explore the critical magnetic fluctuations and the associated temporal spin noise in van der Waals magnet $\rm{Fe_{3}GeTe_{2}}$. We show that the critical fluctuation contributes to a random magnetic field characterized by the noise spectra, which can be changed dramatically near the critical temperature $T_c$. A theoretical model to describe this phenomenon is developed, showing that the spectral density is characterized by a $1/f$ noise near the $T_c$, while away from this point it behaves like a white noise. The crossover at a certain temperature between these two situations is determined by changing of the distance between the sample and the diamond. This work provides a new way to study critical fluctuation and to extract some of the critical exponents, which may greatly deepen our understanding of criticality in a wide range of physical systems.

cond-mat.mes-hall↗

arXiv2024

Interacting Mathieu equation, synchronization dynamics and collision-induced velocity exchange in trapped ions

Recently, large-scale trapped ion systems have been realized in experiments for quantum simulation and quantum computation. They are the simplest systems for dynamical stability and parametric resonance. In this model, the Mathieu equation plays the most fundamental role for us to understand the stability and instability of a single ion. In this work, we investigate the dynamics of trapped ions with the Coulomb interaction based on the Hamiltonian equation. We show that the many-body interaction will not influence the phase diagram for instability. Then, the dynamics of this model in the large damping limit will also be analytically calculated using few trapped ions. Furthermore, we find that in the presence of modulation, synchronization dynamics can be observed, showing an exchange of velocities between distant ions on the left side and on the right side of the trap. These dynamics resemble to that of the exchange of velocities in Newton's cradle for the collision of balls at the same time. These dynamics are independent of their initial conditions and the number of ions. As a unique feature of the interacting Mathieu equation, we hope this behavior, which leads to a quasi-periodic solution, can be measured in current experimental systems. Finally, we have also discussed the effect of anharmonic trapping potential, showing the desynchronization during the collision process. It is hopped that the dynamics in this many-body Mathieu equation with damping may find applications in quantum simulations. This model may also find interesting applications in dynamics systems as a pure mathematical problem, which may be beyond the results in the Floquet theorem.

physics.class-ph↗

arXiv2024

Experimental quantum computational chemistry with optimised unitary coupled cluster ansatz

Quantum computational chemistry has emerged as an important application of quantum computing. Hybrid quantum-classical computing methods, such as variational quantum eigensolvers (VQE), have been designed as promising solutions to quantum chemistry problems, yet challenges due to theoretical complexity and experimental imperfections hinder progress in achieving reliable and accurate results. Experimental works for solving electronic structures are consequently still restricted to nonscalable (hardware efficient) or classically simulable (Hartree-Fock) ansatz, or limited to a few qubits with large errors. The experimental realisation of scalable and high-precision quantum chemistry simulation remains elusive. Here, we address the critical challenges {associated with} solving molecular electronic structures using noisy quantum processors. Our protocol presents significant improvements in the circuit depth and running time, key metrics for chemistry simulation. Through systematic hardware enhancements and the integration of error mitigation techniques, we push forward the limit of experimental quantum computational chemistry and successfully scale up the implementation of VQE with an optimised unitary coupled-cluster ansatz to 12 qubits. We produce high-precision results of the ground-state energy for molecules with error suppression by around two orders of magnitude. We achieve chemical accuracy for H$_2$ at all bond distances and LiH at small bond distances in the experiment, even beyond the two recent concurrent works. Our work demonstrates a feasible path towards a scalable solution to electronic structure calculation, validating the key technological features and identifying future challenges for this goal.

quant-ph↗

arXiv2024

Rapidly Achieving Chemical Accuracy with Quantum Computing Enforced Language Model

Finding accurate ground state energy of a many-body system has been a major challenge in quantum chemistry. The integration of classic and quantum computers has shed new light on resolving this outstanding problem. Here we propose QiankunNet-VQE, a transformer based language models enforced with quantum computing to learn and generate quantum states. It has been implemented using up to 12 qubits and attaining an accuracy level competitive with state-of-the-art classical methods. By leveraging both quantum and classical resources, this scheme overcomes the limitations of variational quantum eigensolver(VQE) without the need for cumbersome error mitigation. Moreover, QiankunNet-VQE provides a different route to achieve a practical quantum advantage for solving many-electron Schrödinger equation without requiring extremely precise preparation and measurement of the ground-state wavefunction on quantum computer.

quant-ph↗

arXiv2024

Distance between two manifolds, topological phase transitions and scaling laws

Topological phases are generally characterized by topological invariants denoted by integer numbers. However, different topological systems often require different topological invariants to measure, such as geometric phases, topological orders, winding numbers, etc. Moreover, geometric phases and its associated definitions usually fail at critical points. Therefore, it's challenging to predict what would occur during the transformation between two different topological phases. To address these issues, in this work, we propose a general definition based on fidelity and trace distance from quantum information theory: manifold distance. This definition does not rely on the berry connection of the manifolds but rather on the information of the two manifolds - their ground state wave functions. Thus, it can measure different topological systems (including traditional band topology models, non-Hermitian systems, and topological order models, etc.) and exhibit some universal laws during the transformation between two topological phases. Our research demonstrates that when the properties of two manifolds are identical, the distance and associated higher-order derivatives between them can smoothly transition to each other. However, for two different topological manifolds, the higher-order derivatives exhibit various divergent behaviors near the critical points. For subsequent studies, we expect the method to be generalized to real-space or non-lattice models, in order to facilitate the study of a wider range of physical platforms such as open systems and many-body localization.

cond-mat.mes-hall↗

arXiv2024

High spin axion insulator

Axion insulators possess a quantized axion field $θ=π$ protected by combined lattice and time-reversal symmetry, holding great potential for device applications in layertronics and quantum computing. Here, we propose a high-spin axion insulator (HSAI) defined in large spin-$s$ representation, which maintains the same inherent symmetry but possesses a notable axion field $θ=(s+1/2)^2π$. Such distinct axion field is confirmed independently by the direct calculation of the axion term using hybrid Wannier functions, layer-resolved Chern numbers, as well as the topological magneto-electric effect. We show that the guaranteed gapless quasi-particle excitation is absent at the boundary of the HSAI despite its integer surface Chern number, hinting an unusual quantum anomaly violating the conventional bulk-boundary correspondence. Furthermore, we ascertain that the axion field $θ$ can be precisely tuned through an external magnetic field, enabling the manipulation of bonded transport properties. The HSAI proposed here can be experimentally verified in ultra-cold atoms by the quantized non-reciprocal conductance or topological magnetoelectric response. Our work enriches the understanding of axion insulators in condensed matter physics, paving the way for future device applications.

cond-mat.mes-hall↗

arXiv2024

Decays of $1^{-+}$ Charmoniumlike Hybrid

By extracting the transition amplitudes, we give the first lattice QCD prediction of the two-body decay partial widths of the $1^{-+}$ charmoniumlike hybrid $η_{c1}$. Given the calculated mass value $m_{η_{c1}}=4.329(36)$ GeV, the $η_{c1}$ decay is dominated by the open charm modes $D_1\bar{D}$, $D^*\bar{D}$ and $D^*\bar{D}^*$ with partial widths of $258(133)$ MeV, $88(18)$ MeV and $150(118)$ MeV, respectively. The coupling of $η_{c1}$ to $χ_{c1}$ plus a flavor singlet pseudoscalar is not small, but $χ_{c1}η$ decay is suppressed by the small $η-η'$ mixing angle. The partial width of $η_{c1}\to η_cη'$ is estimated to be around 1 MeV. We suggest experiments to search for $η_{c1}$ in the $P$-wave $D^*\bar{D}$ and $D^*\bar{D}^*$ systems. Especially, the polarization of $D^*\bar{D}^*$ can be used to distinguish the $1^{-+}$ product (total spin $S=1$) from $1^{--}$ products ($S=0$).

hep-lat↗

arXiv2024

Form factor for Dalitz decays from $J/ψ$ to light pseudoscalars

We calculate the form factor $M(q^2)$ for the Dalitz decay $J/ψ\to γ^*(q^2)η_{(N_f=1)}$ with $η_{(N_f)}$ being the SU($N_f$) flavor singlet pseudoscalar meson. The difference among the partial widths $Γ(J/ψ\to γη_{(N_f)})$ at different $N_f$ can be attributed in part to the $\mathbf{U}_A(1)$ anomaly that induces a $N_f$ scaling. $M(q^2)$'s in $N_f=1,2$ are both well described by the single pole model $M(q^2)=M(0)/(1-q^2/Λ^2)$. Combined with the known experimental results of the Dalitz decays $J/ψ\to Pe^+e^-$, the pseudoscalar mass $m_P$ dependence of the pole parameter $Λ$ is approximated by $Λ(m_P^2)=Λ_1(1-m_P^2/Λ_2^2)$ with $Λ_1=2.64(4)~\mathrm{GeV}$ and $Λ_2=2.97(33)~\mathrm{GeV}$. These results provide inputs for future theoretical and experimental studies on the Dalitz decays $J/ψ\to Pe^+e^-$.

hep-lat↗

arXiv2024

Hypertext Entity Extraction in Webpage

Webpage entity extraction is a fundamental natural language processing task in both research and applications. Nowadays, the majority of webpage entity extraction models are trained on structured datasets which strive to retain textual content and its structure information. However, existing datasets all overlook the rich hypertext features (e.g., font color, font size) which show their effectiveness in previous works. To this end, we first collect a \textbf{H}ypertext \textbf{E}ntity \textbf{E}xtraction \textbf{D}ataset (\textit{HEED}) from the e-commerce domains, scraping both the text and the corresponding explicit hypertext features with high-quality manual entity annotations. Furthermore, we present the \textbf{Mo}E-based \textbf{E}ntity \textbf{E}xtraction \textbf{F}ramework (\textit{MoEEF}), which efficiently integrates multiple features to enhance model performance by Mixture of Experts and outperforms strong baselines, including the state-of-the-art small-scale models and GPT-3.5-turbo. Moreover, the effectiveness of hypertext features in \textit{HEED} and several model components in \textit{MoEEF} are analyzed.

cs.CL↗

arXiv2024

Ploutos: Towards interpretable stock movement prediction with financial large language model

Recent advancements in large language models (LLMs) have opened new pathways for many domains. However, the full potential of LLMs in financial investments remains largely untapped. There are two main challenges for typical deep learning-based methods for quantitative finance. First, they struggle to fuse textual and numerical information flexibly for stock movement prediction. Second, traditional methods lack clarity and interpretability, which impedes their application in scenarios where the justification for predictions is essential. To solve the above challenges, we propose Ploutos, a novel financial LLM framework that consists of PloutosGen and PloutosGPT. The PloutosGen contains multiple primary experts that can analyze different modal data, such as text and numbers, and provide quantitative strategies from different perspectives. Then PloutosGPT combines their insights and predictions and generates interpretable rationales. To generate accurate and faithful rationales, the training strategy of PloutosGPT leverage rearview-mirror prompting mechanism to guide GPT-4 to generate rationales, and a dynamic token weighting mechanism to finetune LLM by increasing key tokens weight. Extensive experiments show our framework outperforms the state-of-the-art methods on both prediction accuracy and interpretability.

q-fin.ST↗

arXiv2024

Is Bigger and Deeper Always Better? Probing LLaMA Across Scales and Layers

This paper presents an in-depth analysis of Large Language Models (LLMs), focusing on LLaMA, a prominent open-source foundational model in natural language processing. Instead of assessing LLaMA through its generative output, we design multiple-choice tasks to probe its intrinsic understanding in high-order tasks such as reasoning and computation. We examine the model horizontally, comparing different sizes, and vertically, assessing different layers. We unveil several key and uncommon findings based on the designed probing tasks: (1) Horizontally, enlarging model sizes almost could not automatically impart additional knowledge or computational prowess. Instead, it can enhance reasoning abilities, especially in math problem solving, and helps reduce hallucinations, but only beyond certain size thresholds; (2) In vertical analysis, the lower layers of LLaMA lack substantial arithmetic and factual knowledge, showcasing logical thinking, multilingual and recognitive abilities, with top layers housing most computational power and real-world knowledge.

cs.CL↗