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Ming Gong

Publications and source records attributed to Ming Gong.

At least 91 records · Page 5Linked to original sources
arXiv2024

Quark masses and low energy constants in the continuum from the tadpole improved clover ensembles

We present the light-flavor quark masses and low energy constants using the 2+1 flavor full-QCD ensembles with stout smeared clover fermion action and Symanzik gauge actions. Both the fermion and gauge actions are tadpole improved self-consistently. The simulations are performed on 11 ensembles at 3 lattice spacings $a\in[0.05,0.11]$ fm, 4 spatial sizes $L\in[2.5, 5.1]$ fm, 7 pion masses $m_π\in[135,350]$ MeV, and several values of the strange quark mass. The quark mass is defined through the partially conserved axial current (PCAC) relation and renormalized to $\overline{\mathrm{MS}}$ 2 GeV through the intermediate regularization independent momentum subtraction (RI/MOM) scheme. The systematic uncertainty of using the symmetric momentum subtraction (SMOM) scheme is also included. Eventually, we predict $m_u=2.45(22)(20)$ MeV, $m_d=4.74(11)(09)$ MeV, and $m_s=98.8(2.9)(4.7)$ MeV with the systematic uncertainties from lattice spacing determination, continuum extrapolation and renormalization constant included. We also obtain the chiral condensate $Σ^{1/3}=268.6(3.6)(0.7)$ MeV and the pion decay constant $F=86.6(7)(1.4) $ MeV in the $N_f=2$ chiral limit, and the next-to-leading order low energy constants $\ell_3=2.43(54)(05)$ and $\ell_4=4.322(75)(96)$.

hep-lat↗
arXiv2024

Siamese Residual Neural Network for Musical Shape Evaluation in Piano Performance Assessment

Understanding and identifying musical shape plays an important role in music education and performance assessment. To simplify the otherwise time- and cost-intensive musical shape evaluation, in this paper we explore how artificial intelligence (AI) driven models can be applied. Considering musical shape evaluation as a classification problem, a light-weight Siamese residual neural network (S-ResNN) is proposed to automatically identify musical shapes. To assess the proposed approach in the context of piano musical shape evaluation, we have generated a new dataset, containing 4116 music pieces derived by 147 piano preparatory exercises and performed in 28 categories of musical shapes. The experimental results show that the S-ResNN significantly outperforms a number of benchmark methods in terms of the precision, recall and F1 score.

cs.SD↗
arXiv2023

On the different Floquet Hamiltonians in a periodic-driven Bose-Josephson junction

The bosonic Josephson junction, one of the maximally simple models for periodic-driven many-body systems, has been intensively studied in the past two decades. Here, we revisit this problem with five different methods, all of which have solid theoretical reasoning. We find that to the order of $ω^{-2}$ ($ω$ is the modulating frequency), these approaches will yield slightly different Floquet Hamiltonians. In particular, the parameters in the Floquet Hamiltonians may be unchanged, increased, or decreased, depending on the approximations used. Especially, some of the methods generate new interactions, which still preserve the total number of particles; and the others do not. The validity of these five effective models is verified using dynamics of population imbalance and self-trapping phase transition. In all results, we find the method by first performing a unitary rotation to the Hamiltonian will have the highest accuracy. The difference between them will become significate when the modulating frequency is comparable with the driving amplitude. The results presented in this work indicate that the analysis of the Floquet Hamiltonian has some kind of subjectivity, which will become an important issue in future experiments with the increasing of precision. We demonstrate this physics using a Bose-Josephson junction, and it is to be hoped that the validity of these methods and their tiny differences put forward in this work can be verified in realistic experiments in future using quantum simulating platforms, including but not limited to ultracold atoms.

cond-mat.quant-gas↗
arXiv2023

Typos-aware Bottlenecked Pre-Training for Robust Dense Retrieval

Current dense retrievers (DRs) are limited in their ability to effectively process misspelled queries, which constitute a significant portion of query traffic in commercial search engines. The main issue is that the pre-trained language model-based encoders used by DRs are typically trained and fine-tuned using clean, well-curated text data. Misspelled queries are typically not found in the data used for training these models, and thus misspelled queries observed at inference time are out-of-distribution compared to the data used for training and fine-tuning. Previous efforts to address this issue have focused on \textit{fine-tuning} strategies, but their effectiveness on misspelled queries remains lower than that of pipelines that employ separate state-of-the-art spell-checking components. To address this challenge, we propose ToRoDer (TypOs-aware bottlenecked pre-training for RObust DEnse Retrieval), a novel re-training strategy for DRs that increases their robustness to misspelled queries while preserving their effectiveness in downstream retrieval tasks. ToRoDer utilizes an encoder-decoder architecture where the encoder takes misspelled text with masked tokens as input and outputs bottlenecked information to the decoder. The decoder then takes as input the bottlenecked embeddings, along with token embeddings of the original text with the misspelled tokens masked out. The pre-training task is to recover the masked tokens for both the encoder and decoder. Our extensive experimental results and detailed ablation studies show that DRs pre-trained with ToRoDer exhibit significantly higher effectiveness on misspelled queries, sensibly closing the gap with pipelines that use a separate, complex spell-checker component, while retaining their effectiveness on correctly spelled queries.

cs.IR↗
arXiv2023

Theory of mobility edge and non-ergodic extended phase in coupled random matrices

The mobility edge, as a central concept in disordered models for localization-delocalization transitions, has rarely been discussed in the context of random matrix theory (RMT). Here we report a new class of random matrix model by direct coupling between two random matrices, showing that their overlapped spectra and un-overlapped spectra exhibit totally different scaling behaviors, which can be used to construct tunable mobility edges. This model is a direct generalization of the Rosenzweig-Porter model, which hosts ergodic, localized, and non-ergodic extended (NEE) phases. A generic theory for these phase transitions is presented, which applies equally well to dense, sparse, and even corrected random matrices in different ensembles. We show that the phase diagram is fully characterized by two scaling exponents, and they are mapped out in various conditions. Our model provides a general framework to realize the mobility edges and non-ergodic phases in a controllable way in RMT, which pave avenue for many intriguing applications both from the pure mathematics of RMT and the possible implementations of ME in many-body models, chiral symmetry breaking in QCD and the stability of the large ecosystems.

cond-mat.dis-nn↗
arXiv2023

Quantumness and quantum to classical transition in the generalized Rabi model

The quantum to classical transition (QCT) is one of the central mysteries in quantum physics. This process is generally interpreted as state collapse from measurement or decoherence from interacting with the environment. Here we define the quantumness of a Hamiltonian by the free energy difference between its quantum and classical descriptions, which vanishes during QCT. We apply this criterion to the many-body Rabi model and study its scaling law across the phase transition, finding that not only the temperature and Planck constant, but also all the model parameters are important for this transition. We show that the Jaynes-Cummings and anti Jaynes-Cummings models exhibit greater quantumness than the Rabi model. Moreover, we show that the rotating wave and anti-rotating wave terms in this model have opposite quantumness in QCT. We demonstrate that the quantumness may be enhanced or suppressed at the critical point. Finally, we estimate the quantumness of the Rabi model in current trapped ion experiments. The quantumness provides an important tool to characterize the QCT in a vast number of many-body models.

quant-ph↗
arXiv2023

Instructed Language Models with Retrievers Are Powerful Entity Linkers

Generative approaches powered by large language models (LLMs) have demonstrated emergent abilities in tasks that require complex reasoning abilities. Yet the generative nature still makes the generated content suffer from hallucinations, thus unsuitable for entity-centric tasks like entity linking (EL) requiring precise entity predictions over a large knowledge base. We present Instructed Generative Entity Linker (INSGENEL), the first approach that enables casual language models to perform entity linking over knowledge bases. Several methods to equip language models with EL capability were proposed in this work, including (i) a sequence-to-sequence training EL objective with instruction-tuning, (ii) a novel generative EL framework based on a light-weight potential mention retriever that frees the model from heavy and non-parallelizable decoding, achieving 4$\times$ speedup without compromise on linking metrics. INSGENEL outperforms previous generative alternatives with +6.8 F1 points gain on average, also with a huge advantage in training data efficiency and training compute consumption. In addition, our skillfully engineered in-context learning (ICL) framework for EL still lags behind INSGENEL significantly, reaffirming that the EL task remains a persistent hurdle for general LLMs.

cs.CL↗
arXiv2023

Coherent Entity Disambiguation via Modeling Topic and Categorical Dependency

Previous entity disambiguation (ED) methods adopt a discriminative paradigm, where prediction is made based on matching scores between mention context and candidate entities using length-limited encoders. However, these methods often struggle to capture explicit discourse-level dependencies, resulting in incoherent predictions at the abstract level (e.g. topic or category). We propose CoherentED, an ED system equipped with novel designs aimed at enhancing the coherence of entity predictions. Our method first introduces an unsupervised variational autoencoder (VAE) to extract latent topic vectors of context sentences. This approach not only allows the encoder to handle longer documents more effectively, conserves valuable input space, but also keeps a topic-level coherence. Additionally, we incorporate an external category memory, enabling the system to retrieve relevant categories for undecided mentions. By employing step-by-step entity decisions, this design facilitates the modeling of entity-entity interactions, thereby maintaining maximum coherence at the category level. We achieve new state-of-the-art results on popular ED benchmarks, with an average improvement of 1.3 F1 points. Our model demonstrates particularly outstanding performance on challenging long-text scenarios.

cs.CL↗
arXiv2023

RI/(S)MOM renormalizations of overlap quark bilinears with different levels of hypercubic smearing

On configurations with 2+1-flavor dynamical domain-wall fermions, we calculate the RI/(S)MOM renormalization constants (RC) of overlap quark bilinears. Hypercubic (HYP) smearing is used to construct the overlap Dirac operator. We investigate the possible effects of the smearing on discretization errors in the RCs by varying the level of smearing from 0 to 1 and 2. The lattice is of size $32^3\times64$ and with lattice spacing $1/a=2.383(9)$ GeV. The RCs in the $\overline{\rm MS}$ scheme at 2 GeV are given at the end, with the uncertainty of $Z_T$ reaching $\le1$% for the tensor current. Results of the renormalized quark masses and hadron matrix elements show that the renormalization procedure suppresses the $\sim$ 30% difference of the bare quantities with or without HYP smearing into the 3%-5% level.

hep-lat↗
arXiv2023

Large Language Models are Diverse Role-Players for Summarization Evaluation

Text summarization has a wide range of applications in many scenarios. The evaluation of the quality of the generated text is a complex problem. A big challenge to language evaluation is that there is a clear divergence between existing metrics and human evaluation. A document summary's quality can be assessed by human annotators on various criteria, both objective ones like grammar and correctness, and subjective ones like informativeness, succinctness, and appeal. Most of the automatic evaluation methods like BLUE/ROUGE may be not able to adequately capture the above dimensions. In this paper, we propose a new evaluation framework based on LLMs, which provides a comprehensive evaluation framework by comparing generated text and reference text from both objective and subjective aspects. First, we propose to model objective and subjective dimensions of generated text based on roleplayers prompting mechanism. Furthermore, we introduce a context-based prompting mechanism that is able to generate dynamic roleplayer profiles based on input context. Finally, we design a multi-roleplayer prompting technology based on batch prompting and integrate multiple outputs into the final evaluation results. Experimental results on three real datasets for summarization show that our model is highly competitive and has a very high consistency with human annotators.

cs.CL↗
arXiv2023

RUEL: Retrieval-Augmented User Representation with Edge Browser Logs for Sequential Recommendation

Online recommender systems (RS) aim to match user needs with the vast amount of resources available on various platforms. A key challenge is to model user preferences accurately under the condition of data sparsity. To address this challenge, some methods have leveraged external user behavior data from multiple platforms to enrich user representation. However, all of these methods require a consistent user ID across platforms and ignore the information from similar users. In this study, we propose RUEL, a novel retrieval-based sequential recommender that can effectively incorporate external anonymous user behavior data from Edge browser logs to enhance recommendation. We first collect and preprocess a large volume of Edge browser logs over a one-year period and link them to target entities that correspond to candidate items in recommendation datasets. We then design a contrastive learning framework with a momentum encoder and a memory bank to retrieve the most relevant and diverse browsing sequences from the full browsing log based on the semantic similarity between user representations. After retrieval, we apply an item-level attentive selector to filter out noisy items and generate refined sequence embeddings for the final predictor. RUEL is the first method that connects user browsing data with typical recommendation datasets and can be generalized to various recommendation scenarios and datasets. We conduct extensive experiments on four real datasets for sequential recommendation tasks and demonstrate that RUEL significantly outperforms state-of-the-art baselines. We also conduct ablation studies and qualitative analysis to validate the effectiveness of each component of RUEL and provide additional insights into our method.

cs.IR↗
arXiv2023

Fate of localization in coupled free chain and disordered chain

It has been widely believed that almost all states in one-dimensional (1d) disordered systems with short-range hopping and uncorrelated random potential are localized. Here, we consider the fate of these localized states by coupling between a disordered chain (with localized states) and a free chain (with extended states), showing that states in the overlapped and un-overlapped regimes exhibit totally different localization behaviors, which is not a phase transition process. In particular, while states in the overlapped regime are localized by resonant coupling, in the un-overlapped regime of the free chain, significant suppression of the localization with a prefactor of $ξ^{-1} \propto t_v^4/Δ^4$ appeared, where $t_v$ is the inter-chain coupling strength and $Δ$ is the energy shift between them. This system may exhibit localization lengths that are comparable with the system size even when the potential in the disordered chain is strong. We confirm these results using the transfer matrix method and sparse matrix method for systems $L \sim 10^6 - 10^9$. These findings extend our understanding of localization in low-dimensional disordered systems and provide a concrete example, which may call for much more advanced numerical methods in high-dimensional models.

cond-mat.dis-nn↗
arXiv2023

Bridging the Gap Between Indexing and Retrieval for Differentiable Search Index with Query Generation

The Differentiable Search Index (DSI) is an emerging paradigm for information retrieval. Unlike traditional retrieval architectures where index and retrieval are two different and separate components, DSI uses a single transformer model to perform both indexing and retrieval. In this paper, we identify and tackle an important issue of current DSI models: the data distribution mismatch that occurs between the DSI indexing and retrieval processes. Specifically, we argue that, at indexing, current DSI methods learn to build connections between the text of long documents and the identifier of the documents, but then retrieval of document identifiers is based on queries that are commonly much shorter than the indexed documents. This problem is further exacerbated when using DSI for cross-lingual retrieval, where document text and query text are in different languages. To address this fundamental problem of current DSI models, we propose a simple yet effective indexing framework for DSI, called DSI-QG. When indexing, DSI-QG represents documents with a number of potentially relevant queries generated by a query generation model and re-ranked and filtered by a cross-encoder ranker. The presence of these queries at indexing allows the DSI models to connect a document identifier to a set of queries, hence mitigating data distribution mismatches present between the indexing and the retrieval phases. Empirical results on popular mono-lingual and cross-lingual passage retrieval datasets show that DSI-QG significantly outperforms the original DSI model.

cs.IR↗
arXiv2023

From single-particle to many-body mobility edges and the fate of overlapped spectra in coupled disorder models

Mobility edge (ME) has played an essential role in disordered models. However, while this concept has been well established in disordered single-particle models, its existence in disordered many-body models is still under controversy. Here, a general approach based on coupling between extended and localized states in their overlapped spectra for ME is presented. We show that in the one-dimensional (1d) disordered single-particle models, all states are localized by direct coupling between them. However, in $d \ge 2$ disordered single-particle and 1d disordered many-body models, the resonant hybridization between these states in their overlapped spectra makes all states be extended, while these in the un-overlapped spectra are unchanged, leading to tunable MEs. We propose several models, including two disordered many-body spin models, to verify this mechanism. Our results establish a unified mechanism for MEs and demonstrate its universality in single-particle and many-body models, which opens an intriguing avenue for the realization and verification of MEs in many-body localization.

cond-mat.dis-nn↗
arXiv2023

The general approach to the critical phase with coupled quasiperiodic chains

In disordered systems, wave functions in the Schrödinger equation may exhibit a transition from the extended phase to the localized phase, in which the states at the boundaries or mobility edges may exhibit multifractality. Meanwhile, the Critical Phase (CP), where all states exhibit multifractal structures, has also attracted much attention in the past decades. However, a generic way to construct the CP on demand still remains elusive. Here, a general approach for this phase is presented using two coupled quasiperiodic chains, where the chains are chosen so that before coupling one of them has extended states while the other one has localized states. We demonstrate the existence of CP in the overlapped spectra in the presence of inter-chain coupling using fractal dimension and minimal scaling index based on multifractal analysis. Then we examine the generality of this physics by changing the forms of inter-chain coupling and quasiperiodic potential, where the CP also emerges in the overlapped spectra. We account for the emergence of this phase as a result of effective unbounded potential, which yields singular continuous spectra and excludes the extended states in the overlapped regimes. Finally, the realization of this CP in the continuous model using ultracold atoms with bichromatic incommensurate optical lattice is also discussed. Due to the tunability of the two chains, this work provides a general approach to realizing the CP in a tunable way. This approach may have wide applications in the experimental detection of CP and can be generalized to much more intriguing physics in the presence of interaction for the many-body CP.

quant-ph↗
arXiv2023

Logical Magic State Preparation with Fidelity Beyond the Distillation Threshold on a Superconducting Quantum Processor

Fault-tolerant quantum computing based on surface code has emerged as an attractive candidate for practical large-scale quantum computers to achieve robust noise resistance. To achieve universality, magic states preparation is a commonly approach for introducing non-Clifford gates. Here, we present a hardware-efficient and scalable protocol for arbitrary logical state preparation for the rotated surface code, and further experimentally implement it on the \textit{Zuchongzhi} 2.1 superconducting quantum processor. An average of \hhl{$0.8983 \pm 0.0002$} logical fidelity at different logical states with distance-three is achieved, \hhl{taking into account both state preparation and measurement errors.} In particular, \hhl{the magic states $|A^{π/4}\rangle_L$, $|H\rangle_L$, and $|T\rangle_L$ are prepared non-destructively with logical fidelities of $0.8771 \pm 0.0009 $, $0.9090 \pm 0.0009 $, and $0.8890 \pm 0.0010$, respectively, which are higher than the state distillation protocol threshold, 0.859 (for H-type magic state) and 0.827 (for T -type magic state).} Our work provides a viable and efficient avenue for generating high-fidelity raw logical magic states, which is essential for realizing non-Clifford logical gates in the surface code.

quant-ph↗
arXiv2023

$η$-glueball mixing from $N_f=2$ lattice QCD

We perform the first lattice study on the mixing of the isoscalar pseudoscalar meson $η$ and the pseudoscalar glueball $G$ in the $N_f=2$ QCD at the pion mass $m_π\approx 350$ MeV. The $η$ mass is determined to be $m_η=714(6)(16)$ MeV. Through the Witten-Veneziano relation, this value can be matched to a mass value of $\sim 981$ MeV for the $\mathrm{SU(3)}$ counterpart of $η$. Based on a large gauge ensemble, the $η-G$ mixing energy and the mixing angle are determined to be $|x|=107(15)(2)$ MeV and $|θ|=3.46(46)^\circ$ from the $η-G$ correlators that are calculated using the distillation method. We conclude that the $η-G$ mixing is tiny and the topology induced interaction contributes most of $η$ mass owing to the QCD $\mathrm{U_A(1)}$ anomaly.

hep-lat↗
arXiv2023

Alleviating Over-smoothing for Unsupervised Sentence Representation

Currently, learning better unsupervised sentence representations is the pursuit of many natural language processing communities. Lots of approaches based on pre-trained language models (PLMs) and contrastive learning have achieved promising results on this task. Experimentally, we observe that the over-smoothing problem reduces the capacity of these powerful PLMs, leading to sub-optimal sentence representations. In this paper, we present a Simple method named Self-Contrastive Learning (SSCL) to alleviate this issue, which samples negatives from PLMs intermediate layers, improving the quality of the sentence representation. Our proposed method is quite simple and can be easily extended to various state-of-the-art models for performance boosting, which can be seen as a plug-and-play contrastive framework for learning unsupervised sentence representation. Extensive results prove that SSCL brings the superior performance improvements of different strong baselines (e.g., BERT and SimCSE) on Semantic Textual Similarity and Transfer datasets. Our codes are available at https://github.com/nuochenpku/SSCL.

cs.CL↗