arXiv subjects
Publications and source records attributed to Yong Hu.
We prove that for all nonsingular projective 3-folds of general type with third plurigenus $P_3 \geq 2$, the pluricanonical map $φ_m$ is birational onto its image for all $m \geq 14$, which is optimal.
The Hidden Quantum Markov Model (HQMM) has significant potential for analyzing time-series data and studying stochastic processes in the quantum domain as an upgrading option with potential advantages over classical Markov models. In this paper, we introduced the split HQMM (SHQMM) for implementing the hidden quantum Markov process, utilizing the conditional master equation with a fine balance condition to demonstrate the interconnections among the internal states of the quantum system. The experimental results suggest that our model outperforms previous models in terms of scope of applications and robustness. Additionally, we establish a new learning algorithm to solve parameters in HQMM by relating the quantum conditional master equation to the HQMM. Finally, our study provides clear evidence that the quantum transport system can be considered a physical representation of HQMM. The SHQMM with accompanying algorithms present a novel method to analyze quantum systems and time series grounded in physical implementation.
The newly discovered family of titanium-based kagome metals, ATi$_{3}$Bi$_{5}$ (where A can be Rb or Cs), has been found to exhibit non-trivial band topology and fascinating electronic instabilities, including electronic nematicity and potential bulk superconductivity. Distinct from their vanadium-based counterparts (AV$_{3}$Sb$_{5}$), which display a charge density wave (CDW) phase that already breaks rotational symmetry, ATi$_{3}$Bi$_{5}$ shows no evidence of CDW, providing a unique platform to study nematicity in its pure form and its interplay with other correlated quantum phenomena, such as superconductivity. In this review, we highlight recent progress in both experimental and theoretical research on ATi$_{3}$Bi$_{5}$ and discuss the unresolved questions and challenges in this burgeoning field.
Chinese Spell Checking (CSC) aims to detect and correct spelling errors in sentences. Despite Large Language Models (LLMs) exhibit robust capabilities and are widely applied in various tasks, their performance on CSC is often unsatisfactory. We find that LLMs fail to meet the Chinese character-level constraints of the CSC task, namely equal length and phonetic similarity, leading to a performance bottleneck. Further analysis reveal that this issue stems from the granularity of tokenization, as current mixed character-word tokenization struggles to satisfy these character-level constraints. To address this issue, we propose C-LLM, a Large Language Model-based Chinese Spell Checking method that learns to check errors Character by Character. Character-level tokenization enables the model to learn character-level alignment, effectively mitigating issues related to character-level constraints. Furthermore, CSC is simplified to replication-dominated and substitution-supplemented tasks. Experiments on two CSC benchmarks demonstrate that C-LLM achieves an average improvement of 10% over existing methods. Specifically, it shows a 2.1% improvement in general scenarios and a significant 12% improvement in vertical domain scenarios, establishing state-of-the-art performance. The source code can be accessed at https://github.com/ktlKTL/C-LLM.
In non-Hermitian crystal systems under open boundary condition (OBC), it is generally believed that the OBC modes with frequencies containing positive imaginary parts, when excited by external driving, will experience exponential growth in population, thereby leading to instability. However, our work challenges this conventional understanding. In such a system, we find an anomalous response that grows exponentially with the frequency aligned with those of saddle points. The frequencies of these saddle points on the complex plane are below the maximum imaginary part of OBC spectrum, but they can lie within or beyond the OBC spectrum. We derive general formulas of excitation-response relationships and find that this anomalous response can occur because the excitation of OBC modes eventually evolve toward these saddle points at long times. Only when the frequencies of all these saddle points are below the real axis do the non-Hermitian crystal systems remain stable under periodic excitation. Thus our results also provide new insights on the stability criterion of non-Hermitian crystal systems.
Large language models (LLMs) have demonstrated impressive generalization capabilities on specific tasks with human-written instruction data. However, the limited quantity, diversity, and professional expertise of such instruction data raise concerns about the performance of LLMs in psychotherapy tasks when provided with domain-specific instructions. To address this, we firstly propose Domain-Specific Assistant Instructions based on AlexanderStreet therapy, and secondly, we use an adaption fine-tuning method and retrieval augmented generation method to improve pre-trained LLMs. Through quantitative evaluation of linguistic quality using automatic and human evaluation, we observe that pre-trained LLMs on Psychotherapy Assistant Instructions outperform state-of-the-art LLMs response baselines. Our Assistant-Instruction approach offers a half-annotation method to align pre-trained LLMs with instructions and provide pre-trained LLMs with more psychotherapy knowledge.
A positive integer $m$ is called a Hall number if any finite group of order precisely divisible by $m$ has a Hall subgroup of order $m$. We prove that, except for the obvious examples, the three integers $12$, $24$ and $60$ are the only Hall numbers, solving a problem proposed by Jiping Zhang.
We extend some parts of the representation theory for integral quadratic forms over the ring of integers of a number field to the case over the coordinate ring $k[C]$ of an affine curve $C$ over a general base field $k$. By using the genus theory, we link the strong approximation property of certain spin groups to the Hasse principle for representations of integral quadratic forms over $k[C]$ and derive several applications. In particular, we give an example where a spin group does not satisfy strong approximation.
The locally noncentrosymmetric heavy fermion superconductor CeRh$_2$As$_2$ has attracted considerable interests due to its rich superconducting phases, accompanied by a quadrupole density wave and pronounced antiferromagnetic excitations. To understand the underlying physics, we here report measurements from high-resolution angle-resolved photoemission. Our results reveal fine splittings of the conduction bands related to the locally noncentrosymmetric structure, as well as a quasi-two-dimensional Fermi surface (FS) with strong $4f$ contributions. The FS exhibits nesting with an in-plane vector $(π/a, π/a)$, which is facilitated by the van Hove singularity near $\bar X$ that arises from the characteristic conduction-$f$ hybridization. The FS nesting provides a natural explanation for the observed antiferromagnetic excitations at $(π/a, π/a)$, which could be intimately connected to its unconventional superconductivity. Our experimental results are well supported by density functional theory plus dynamical mean field theory calculations, which can capture the strong correlation effects. Our study not only provides spectroscopic proof of the key factors underlying the field-induced superconducting transition, but also uncovers the critical role of FS nesting and lattice Kondo effect in the intertwined spin and charge fluctuations.
In this paper, we present CSCD-NS, the first Chinese spelling check (CSC) dataset designed for native speakers, containing 40,000 samples from a Chinese social platform. Compared with existing CSC datasets aimed at Chinese learners, CSCD-NS is ten times larger in scale and exhibits a distinct error distribution, with a significantly higher proportion of word-level errors. To further enhance the data resource, we propose a novel method that simulates the input process through an input method, generating large-scale and high-quality pseudo data that closely resembles the actual error distribution and outperforms existing methods. Moreover, we investigate the performance of various models in this scenario, including large language models (LLMs), such as ChatGPT. The result indicates that generative models underperform BERT-like classification models due to strict length and pronunciation constraints. The high prevalence of word-level errors also makes CSC for native speakers challenging enough, leaving substantial room for improvement.
Let $X$ be a smooth irregular $3$-fold of general type over $\mathbb{C}$. We prove that the optimal Noether inequality $$ \mathrm{vol}(X) \ge \frac{4}{3}p_g(X) $$ holds if $p_g(X) \ge 16$ or if $X$ has a Gorenstein minimal model. Moreover, when $X$ attains the equality and $p_g(X) \ge 16$, its canonical model can be explicitly described.
Complete surgical resection of the tumor for Head and neck squamous cell carcinoma (HNSCC) remains challenging, given the devastating side effects of aggressive surgery and the anatomic proximity to vital structures. To address the clinical challenges, we introduce a wide-field, label-free imaging tool that can assist surgeons delineate tumor margins real-time. We assume that autofluorescence lifetime is a natural indicator of the health level of tissues, and ratio-metric measurement of the emission-decay state to the emission-peak state of excited fluorophores will enable rapid lifetime mapping of tissues. Here, we describe the principle, instrumentation, characterization of the imager and the intraoperative imaging of resected tissues from 13 patients undergoing head and neck cancer resection. 20 x 20 mm2 imaging takes 2 second/frame with a working distance of 50 mm, and characterization shows that the spatial resolution reached 70 μm and the least distinguishable fluorescence lifetime difference is 0.14 ns. Tissue imaging and Hematoxylin-Eosin stain slides comparison reveals its capability of delineating cancerous boundaries with submillimeter accuracy and a sensitivity of 91.86% and specificity of 84.38%.
Improving the controllability, portability, and inference speed of diffusion language models (DLMs) is a key challenge in natural language generation. While recent research has shown significant success in complex text generation with language models, the memory and computational power are still very demanding and fall short of expectations, which naturally results in low portability and instability for the models. To mitigate these issues, numerous well-established methods were proposed for neural network quantization. To further enhance their portability of independent deployment as well as improve their stability evaluated by language perplexity, we propose a novel approach called the Quantized Embedding Controllable Diffusion Language Model (QE-CDLM). QE-CDLM builds upon the recent successful controllable DLMs by remodeling the task-specific embedding space via quantization. This leads to a gradient-based controller for the generation tasks, and more stable intermediate latent variables are obtained, which naturally brings in an accelerated convergence as well as better controllability. Additionally, the adaption fine-tuning method is employed to reduce tunable weights. Experimental results on five challenging fine-grained control tasks demonstrate that QE-CDLM compares favorably to existing methods in terms of quality and feasibility, achieving better perplexity and lightweight fine-tuning.
We prove some triviality results for reduced Whitehead groups and reduced unitary Whitehead groups for division algebras over a henselian discrete valuation field whose residue field has virtual cohomological dimension or separable dimension $\le 2$. These results are applied to show strong approximation for isotropic absolutely almost simple simply connected groups of type A. In particular, such a group defined over the function field of a nonreal curve $C/k$ satisfies strong approximation if the base field $k$ is a number field, a $p$-adic field, $\mathbb{C}(\!(t)\!)$ or a two-variable function field over $\mathbb{R}$.
Color centers in diamonds have emerged as a leading solid-state platform for advancing quantum technologies, satisfying the DiVincenzo criteria and recently achieving a quantum advantage in secret key distribution. Recent theoretical works estimate that general-purpose quantum computing using local quantum communication networks will require millions of physical qubits to encode thousands of logical qubits, which presents a substantial challenge to the hardware architecture at this scale. To address the unanswered scaling problem, in this work, we first introduce a scalable hardware modular architecture "Quantum System-on-Chip" (QSoC) that features compact two-dimensional arrays "quantum microchiplets" (QMCs) containing tin-vacancy (SnV-) spin qubits integrated on a cryogenic application-specific integrated circuit (ASIC). We demonstrate crucial architectural subcomponents, including (1) QSoC fabrication via a lock-and-release method for large-scale heterogeneous integration; (2) a high-throughput calibration of the QSoC for spin qubit spectral inhomogenous registration; (3) spin qubit spectral tuning functionality for inhomogenous compensation; (4) efficient spin-state preparation and measurement for improved spin and optical properties. QSoC architecture supports full connectivity for quantum memory arrays in a set of different resonant frequencies and offers the possibility for further scaling the number of solid-state physical qubits via larger and denser QMC arrays and optical frequency multiplexing networking.
The Mott insulator provides an excellent foundation for exploring a wide range of strongly correlated physical phenomena, such as high-temperature superconductivity, quantum spin liquid, and colossal magnetoresistance. A Mott insulator with the simplest degree of freedom is an ideal and highly desirable system for studying the fundamental physics of Mottness. In this study, we have unambiguously identified such an anticipated Mott insulator in a van der Waals layered compound Nb3Cl8. In the high-temperature phase, where interlayer coupling is negligible, density functional theory calculations for the monolayer of Nb3Cl8 suggest a half-filled flat band at the Fermi level, whereas angle-resolved photoemission spectroscopy experiments observe a large gap. This observation is perfectly reproduced by dynamical mean-field theory calculations considering strong electron correlations, indicating a correlation-driven Mott insulator state. Since this half-filled band derived from a single 2a1 orbital is isolated from all other bands, the monolayer of Nb3Cl8 is an ideal realization of the celebrated single-band Hubbard model. Upon decreasing the temperature, the bulk system undergoes a phase transition, where structural changes significantly enhance the interlayer coupling. This results in a bonding-antibonding splitting in the Hubbard bands, while the Mott gap remains dominant. Our discovery provides a simple and seminal model system for investigating Mott physics and other emerging correlated states.
ChatGPT has demonstrated impressive performance in various downstream tasks. However, in the Chinese Spelling Correction (CSC) task, we observe a discrepancy: while ChatGPT performs well under human evaluation, it scores poorly according to traditional metrics. We believe this inconsistency arises because the traditional metrics are not well-suited for evaluating generative models. Their overly strict length and phonics constraints may lead to underestimating ChatGPT's correction capabilities. To better evaluate generative models in the CSC task, this paper proposes a new evaluation metric: Eval-GCSC. By incorporating word-level and semantic similarity judgments, it relaxes the stringent length and phonics constraints. Experimental results show that Eval-GCSC closely aligns with human evaluations. Under this metric, ChatGPT's performance is comparable to traditional token-level classification models (TCM), demonstrating its potential as a CSC tool. The source code and scripts can be accessed at https://github.com/ktlKTL/Eval-GCSC.
The recently discovered layered kagome superconductors $\textit{A}$V$_{3}$Sb$_{5}$ ($\textit{A}$ = K, Rb, Cs) have garnered significant attention, as they exhibit an intriguing combination of superconductivity, charge density wave (CDW) order, and nontrivial band topology. As such, these kagome systems serve as an exceptional quantum platform for investigating the intricate interplay between electron correlation effects, geometric frustration, and topological electronic structure. A comprehensive understanding of the underlying electronic structure is crucial for unveiling the nature and origin of the CDW order, as well as determining the electron pairing symmetry in the kagome superconductors. In this review, we present a concise survey of the electronic properties of $\textit{A}$V$_{3}$Sb$_{5}$, with a particular focus on the insights derived from angle-resolved photoemission spectroscopy (ARPES). Through the lens of ARPES, we shed light on the electronic characteristics of the kagome superconductors $\textit{A}$V$_{3}$Sb$_{5}$, which will pave the way for exciting new research frontiers in kagome-related physics.