arXiv subjects

Xiang Hu

Publications and source records attributed to Xiang Hu.

At least 37 records · Page 2Linked to original sources

arXiv2025

Analysis of Radiation Level and Estimation of Protection Distance of γ Mobile Flaw Detection Source

Objective To analyze the radiation dose associated with gamma-ray mobile flaw detection, estimate the extent of the supervision and control areas, and assess the associated radiation risks. Methods A combination of theoretical calculations and actual measurements was used to compare and analyze the ambient equivalent dose rates of 192 Ir and 75 Se at their nominal source strengths. Measurements were conducted at distances of 1 m, 2 m, and 5 m from the radiation source. The extents of the control and supervision areas were estimated under three working scenarios: 1 without considering air attenuation, 2 considering air attenuation, and 3 after shielding by the flaw detection workpiece, using source activities of 3.7 * 10^10 Bq and 3.7 * 10^12Bq. Results Actual measurement of radiation dose of 192 Ir and 75 Se were measured under three different nominal activities. Theoretical calculation of radiation dose estimates at various distances were obtained for both nuclides, and the results showed that the theoretical values were basically consistent with the measured values. Conclusion The estimated scope of the supervision and control areas provided in this study can serve as a reference for flaw detection companies. Technicians can use these estimates to calculate appropriate distances for safety zones based on different nuclide activities. This enables flaw detection personnel to reduce the measurement scope on-site and to quickly and accurately define area boundaries.

physics.ins-det↗

arXiv2024

A new pathway to impact ionization in a photo-excited one-dimensional ionic Hubbard model

Using the time-dependent Lanczos method, we study the non-equilibrium dynamics of the half-filled one-dimensional ionic Hubbard model, deep within the Mott insulating regime, under the influence of a transient laser pulse. In equilibrium, increasing the staggered potential in the Mott regime reduces the Mott gap and broadens the Hubbard bands, creating favorable conditions for impact ionization. After laser excitation, impact ionization is observed, with its occurrence depending on both the staggered potential and the laser pump frequency. By analyzing the time evolution of the kinetic, ionic, and Coulomb interaction energies, we identify a novel mechanism for impact ionization, in which excess ionic potential energy is converted into additional double occupancy-distinct from the conventional mechanism where excess kinetic energy drives this process. We further show that impact ionization arises from interference between excited states driven by photon excitation of the same order. These results present a new pathway for realizing impact ionization in strongly correlated electron systems.

cond-mat.str-el↗

arXiv2024

Nova: An Iterative Planning and Search Approach to Enhance Novelty and Diversity of LLM Generated Ideas

Scientific innovation is pivotal for humanity, and harnessing large language models (LLMs) to generate research ideas could transform discovery. However, existing LLMs often produce simplistic and repetitive suggestions due to their limited ability in acquiring external knowledge for innovation. To address this problem, we introduce an enhanced planning and search methodology designed to boost the creative potential of LLM-based systems. Our approach involves an iterative process to purposely plan the retrieval of external knowledge, progressively enriching the idea generation with broader and deeper insights. Validation through automated and human assessments indicates that our framework substantially elevates the quality of generated ideas, particularly in novelty and diversity. The number of unique novel ideas produced by our framework is 3.4 times higher than without it. Moreover, our method outperforms the current state-of-the-art, generating at least 2.5 times more top-rated ideas based on 170 seed papers in a Swiss Tournament evaluation.

cs.AI↗

arXiv2024

Generative Pretrained Structured Transformers: Unsupervised Syntactic Language Models at Scale

A syntactic language model (SLM) incrementally generates a sentence with its syntactic tree in a left-to-right manner. We present Generative Pretrained Structured Transformers (GPST), an unsupervised SLM at scale capable of being pre-trained from scratch on raw texts with high parallelism. GPST circumvents the limitations of previous SLMs such as relying on gold trees and sequential training. It consists of two components, a usual SLM supervised by a uni-directional language modeling loss, and an additional composition model, which induces syntactic parse trees and computes constituent representations, supervised by a bi-directional language modeling loss. We propose a representation surrogate to enable joint parallel training of the two models in a hard-EM fashion. We pre-train GPST on OpenWebText, a corpus with $9$ billion tokens, and demonstrate the superiority of GPST over GPT-2 with a comparable size in numerous tasks covering both language understanding and language generation. Meanwhile, GPST also significantly outperforms existing unsupervised SLMs on left-to-right grammar induction, while holding a substantial acceleration on training.

cs.CL↗

arXiv2024

Augmenting Transformers with Recursively Composed Multi-grained Representations

We present ReCAT, a recursive composition augmented Transformer that is able to explicitly model hierarchical syntactic structures of raw texts without relying on gold trees during both learning and inference. Existing research along this line restricts data to follow a hierarchical tree structure and thus lacks inter-span communications. To overcome the problem, we propose a novel contextual inside-outside (CIO) layer that learns contextualized representations of spans through bottom-up and top-down passes, where a bottom-up pass forms representations of high-level spans by composing low-level spans, while a top-down pass combines information inside and outside a span. By stacking several CIO layers between the embedding layer and the attention layers in Transformer, the ReCAT model can perform both deep intra-span and deep inter-span interactions, and thus generate multi-grained representations fully contextualized with other spans. Moreover, the CIO layers can be jointly pre-trained with Transformers, making ReCAT enjoy scaling ability, strong performance, and interpretability at the same time. We conduct experiments on various sentence-level and span-level tasks. Evaluation results indicate that ReCAT can significantly outperform vanilla Transformer models on all span-level tasks and baselines that combine recursive networks with Transformers on natural language inference tasks. More interestingly, the hierarchical structures induced by ReCAT exhibit strong consistency with human-annotated syntactic trees, indicating good interpretability brought by the CIO layers.

cs.CL↗

arXiv2023

Site-selective doublon-holon dynamics in a pumped one-dimensional Hubbard superlattice with staggered Coulomb interactions

Doublon-holon dynamics is investigated in a pumped one-dimensional Hubbard model with a staggered on?site Coulomb interaction at half-filling. When the system parameters are set to be in the Mott insulating regime the equilibrium sublattice density of states exhibits several characteristic peaks, corresponding to the lower and upper Hubbard bands as well as hybridization bands. We study the linear absorption spectrum and find two main peaks characterizing the photon frequencies which excite the ground state to an excited state. For a system driven by a laser pulse with general intensity and frequency, both the energy absorption and the doublon-holon dynamics exhibit distinct behaviors as a function of laser amplitude and frequency. Single-photon processes are observed at low laser intensity where the energy is absorbed for resonance laser frequencies. For strong laser intensity multi-photon induced dynamics are observed in the system, which are confirmed by an evaluation of the Loschmidt amplitude. The contribution of multi-photon processes to site-specific double occupancy is also characterized by the generalized Loschmidt amplitude. The site-selective doublon-holon dynamics are observed in both the one and multi-photon processes and the site-selective behavior is explained within a quasiparticle picture. Our study suggests strategies to optically engineer the doublon-holon dynamics in one dimensional strongly correlated many-body systems.

cond-mat.str-el↗

arXiv2023

A Multi-Grained Self-Interpretable Symbolic-Neural Model For Single/Multi-Labeled Text Classification

Deep neural networks based on layer-stacking architectures have historically suffered from poor inherent interpretability. Meanwhile, symbolic probabilistic models function with clear interpretability, but how to combine them with neural networks to enhance their performance remains to be explored. In this paper, we try to marry these two systems for text classification via a structured language model. We propose a Symbolic-Neural model that can learn to explicitly predict class labels of text spans from a constituency tree without requiring any access to span-level gold labels. As the structured language model learns to predict constituency trees in a self-supervised manner, only raw texts and sentence-level labels are required as training data, which makes it essentially a general constituent-level self-interpretable classification model. Our experiments demonstrate that our approach could achieve good prediction accuracy in downstream tasks. Meanwhile, the predicted span labels are consistent with human rationales to a certain degree.

cs.CL↗

arXiv2022

Quench dynamics in the one-dimensional mass-imbalanced ionic Hubbard model

Using the time-dependent Lanczos method, we study the non-equilibrium dynamics of the one-dimensional ionic-mass imbalanced Hubbard chain driven by a quantum quench of the on-site Coulomb interaction, where the system is prepared in the ground state of the Hamiltonian with a different Hubbard interaction. A full exact diagonalization is adopted to study the zero temperature phase diagram in equilibrium, which is shown to be in good agreement with previous studies using density matrix renormalization group (DMRG). We then study the non-equilibrium quench dynamics of the spin and charge order parameters by fixing the initial and final Coulomb interaction while changing the quenching time protocols. The Lanczos method allows us to reach longer times following the quench than DMRG. Our study shows that the time evolution of the charge and spin order parameters strongly depend on the quenching time protocols. In particular, the effective temperature of the system will decrease monotonically as the quenching time is increased. Finally, by taking the final Coulomb interaction strength to be in the strong coupling regime, we find that the oscillation frequency of the charge order parameter increases monotonically with the Coulomb interaction. By contrast, the frequency of the spin order parameter decreases monotonically with increasing Coulomb interaction. We explain this result using an effective spin model in the strong coupling limit. Our study suggests strategies to engineer the relaxation behavior of interacting quantum many-particle systems.

cond-mat.str-el↗

arXiv2022

Fast-R2D2: A Pretrained Recursive Neural Network based on Pruned CKY for Grammar Induction and Text Representation

Recently CKY-based models show great potential in unsupervised grammar induction thanks to their human-like encoding paradigm, which runs recursively and hierarchically, but requires $O(n^3)$ time-complexity. Recursive Transformer based on Differentiable Trees (R2D2) makes it possible to scale to large language model pre-training even with complex tree encoder by introducing a heuristic pruning method. However, the rule-based pruning approach suffers from local optimum and slow inference issues. In this paper, we fix those issues in a unified method. We propose to use a top-down parser as a model-based pruning method, which also enables parallel encoding during inference. Typically, our parser casts parsing as a split point scoring task, which first scores all split points for a given sentence, and then recursively splits a span into two by picking a split point with the highest score in the current span. The reverse order of the splits is considered as the order of pruning in R2D2 encoder. Beside the bi-directional language model loss, we also optimize the parser by minimizing the KL distance between tree probabilities from parser and R2D2. Our experiments show that our Fast-R2D2 improves performance significantly in grammar induction and achieves competitive results in downstream classification tasks.

cs.CL↗

arXiv2022

Quantum-metric-enabled exciton condensate in double twisted bilayer graphene

Flat-band systems are a promising platform for realizing exotic collective ground states with spontaneously broken symmetry because the electron-electron interactions dominate over the kinetic energy. A collective ground state of particular interest is the chased after exciton condensate (EC). However, in flat band systems other collective ground states can compete with an EC phase, and the conventional treatment of the effect of thermal and quantum fluctuations predicts the EC phase should be unstable. Here, using double twisted bilayer graphene (TBLG) heterostructures as an example, we show that for realistic interaction strengths the EC phase is favored with respect to other TBLG's phases -- orbital magnetism and superconductivity -- when the TBLGs have opposite doping, and that the quantum metric of the Bloch wave functions stabilizes the EC, reversing the conclusion that would be drawn from the conventional approach in which quantum metric contributions are neglected. Our results suggest that the quantum metric plays a critical role in determining the stability of exciton condensates in double layers formed by systems with flat-bands.

cond-mat.mes-hall↗

arXiv2022

R2D2: Recursive Transformer based on Differentiable Tree for Interpretable Hierarchical Language Modeling

Human language understanding operates at multiple levels of granularity (e.g., words, phrases, and sentences) with increasing levels of abstraction that can be hierarchically combined. However, existing deep models with stacked layers do not explicitly model any sort of hierarchical process. This paper proposes a recursive Transformer model based on differentiable CKY style binary trees to emulate the composition process. We extend the bidirectional language model pre-training objective to this architecture, attempting to predict each word given its left and right abstraction nodes. To scale up our approach, we also introduce an efficient pruned tree induction algorithm to enable encoding in just a linear number of composition steps. Experimental results on language modeling and unsupervised parsing show the effectiveness of our approach.

cs.CL↗

arXiv2021

Anisotropic Andreev reflection in semi-Dirac materials

In the framework of Bogoliubov-de Gennes equation, we theoretically study the Andreev reflection in normal-superconducting junctions based on semi-Dirac materials. Owing to the intrinsic anisotropy of semi-Dirac materials, the configuration of Andreev reflection and differential conductance are strongly orientation-dependent. For the transport along the linear dispersion direction, the differential conductance exhibits a clear crossover from retro Andreev reflection to specular Andreev reflection with an increasing bias-voltage, and the differential conductance oscillates without a decaying profile when the interfacial barrier strength increases. However, for the transport along the quadratic dispersion direction, the boundary between the retro Andreev reflection and specular Andreev reflection is ambiguous, and the differential conductance decays with increasing the momentum mismatch or the interfacial barrier strength. We illustrate the pseudo-spin textures to reveal the underling physics behind the anisotropic coherent transport properties. These results enrich the understanding of the superconducting coherent transport in semi-Dirac materials.

cond-mat.mes-hall↗

arXiv2020

Interactive Question Clarification in Dialogue via Reinforcement Learning

Coping with ambiguous questions has been a perennial problem in real-world dialogue systems. Although clarification by asking questions is a common form of human interaction, it is hard to define appropriate questions to elicit more specific intents from a user. In this work, we propose a reinforcement model to clarify ambiguous questions by suggesting refinements of the original query. We first formulate a collection partitioning problem to select a set of labels enabling us to distinguish potential unambiguous intents. We list the chosen labels as intent phrases to the user for further confirmation. The selected label along with the original user query then serves as a refined query, for which a suitable response can more easily be identified. The model is trained using reinforcement learning with a deep policy network. We evaluate our model based on real-world user clicks and demonstrate significant improvements across several different experiments.

cs.CL↗

arXiv2019

Geometric and conventional contribution to superfluid weight in twisted bilayer graphene

By tuning the angle between graphene layers to specific "magic angles" the lowest energy bands of twisted bilayer graphene (TBLG) can be made flat. The flat nature of the bands favors the formation of collective ground states and, in particular, TBLG has been shown to support superconductivity. When the energy bands participating in the superconductivity are well-isolated, the superfluid weight scales inversely with the effective mass of such bands. For flat-band systems one would therefore conclude that even if superconducting pairing is present most of the signatures of the superconducting state should be absent. This conclusion is at odds with the experimental observations for TBLG. We calculate the superfluid weight for TBLG taking into account both the conventional contribution and the contribution arising from the quantum geometry of the bands. We find that both contributions are larger than one would expect treating the bands as well-isolated, that at the magic angle the geometric contribution is larger than the conventional one, and that for small deviations away from the magic angle the conventional contribution is larger than the geometric one. Our results show that, despite the flatness of the bands the superfluid weight in TBLG is finite and consistent with experimental observations. We also show how the superfluid weight can be tuned by varying the chemical potential and the twist angle opening the possibility to tune the nature of the superconducting transition between the standard BCS transition and the Berezinskii-Kosterlitz-Thouless transition.

cond-mat.supr-con↗

arXiv2019

Second-order Dirac superconductors and magnetic field induced Majorana hinge modes

We identify three-dimensional higher-order superconductors characterized by the coexistence of one-dimensional Majorana hinge states and gapless surface sates. We show how such superconductors can be obtained starting from the model of a spinful quadrupolar semimetal with two orbitals and adding an s-wave superconducting pairing term. By considering all the possible s-wave pairings satisfying Fermi-Dirac statistics we obtain six different superconducting models. We find that for two of these models a flat-band of hinge Majorana states coexist with surface states, and that these models have a non-vanishing quadrupole-like topological invariant. Two of the other models, in the presence of a Zeeman term, exhibit helical and dispersive hinge states localized only at two of the four hinges. We find that these states are protected by combinations of rotation and mirror symmetries, and that the pair of corners exhibiting hinge states switches upon changing the sign of the Zeeman term. Furthermore, we show that these states can be localized to a single hinge with suitable perturbations. The remaining two models retain gapless bulk and surface states that spectroscopically obscure any possible hinge states.

cond-mat.mes-hall↗

arXiv2015

Correlation effects in pyrochlore iridate thin films grown along the $[111]$ direction

Over the past few years bulk pyrochlore iridates of the form $A_2$Ir$_2$O$_7$ (where $A$ is a rare earth element, Ir is iridium, and O is oxygen) have been studied as model systems for investigating the interplay of electronic correlations and strong spin-orbit coupling, particularly with the aim of finding correlation-driven topological phases. In this work, we use cellular dynamical mean field theory (CDMFT) to study effects of electronic correlations beyond Hartree-Fock theory in thin films of pyrochlore irradiates grown along the $[111]$ direction. We focus on the bilayer and trilayer systems, and compute the phase diagrams of these systems as a function of electron-electron interaction strength, which is modeled by an on-site Hubbard interaction. By evaluating the $Z_2$ invariant and Chern number using formulas based on the single-particle Green's function and the quasi-particle effective Hamiltonian, we show that on-site correlations can drive an interaction-induced topological phase transition, turning a time-reversal invariant topological insulator and a nearly flat band metal to a correlated Chern insulator (CI) in bilayer and trilayer systems, respectively. By comparing with the Hartree-Fock results, the CDMFT results show that quantum fluctuations enhance the robustness of the interaction-driven CI phase in the thin films. Furthermore, our numerical analysis of the quasiparticle spectrum reveals that the topological phases we find in our many-body calculations are adiabatically connected to those in the single-particle picture.

cond-mat.str-el↗

arXiv2014

First Principles Prediction of Topological Phases in Thin Films of Pyrochlore Iridates

Topological phases have attracted much interest in recent years. While there are a number of three-dimensional materials exhibiting topological properties, there are relatively few two-dimensional examples aside from the well-known quantum Hall systems. Here we make materials-specific predictions for topological phases using density functional theory combined with Hartree-Fock theory that includes the full orbital structure of the relevant iridum $d$-orbitals and the strong but finite spin-orbit coupling strength. We find Y$_2$Ir$_2$O$_7$ bilayer and trilayer films grown along the [111] direction can support topological metallic phases with a direct gap of up to 0.02 eV, which could potentially bring transition metal oxides to the fore as a new class of topological materials with potential applications in oxide electronics.

cond-mat.str-el↗

arXiv2013

Topological phases in layered pyrochlore oxide thin films along the [111] direction

We theoretically study a multi-band Hubbard model of pyrochlore oxides of the form A$_2$B$_2$O$_7$, where B is a heavy transition metal ion with strong spin-orbit coupling, in a thin film geometry orientated along the [111] direction. Along this direction, the pyrochlore lattice consists of alternating kagome and triangular lattice planes of B ions. We consider a single kagome layer, a bilayer, and the two different trilayers. As a function of the strength of the spin-orbit coupling, the direct and indirect $d$-orbital hopping, and the band filling, we identify a number of scenarios where a non-interacting time-reversal invariant Z$_2$ topological phase is expected and we suggest some candidate materials. We study the interactions in the half-filled $d$-shell within Hatree-Fock theory and identify parameter regimes where a zero magnetic field Chern insulator with Chern number $\pm1$ can be found. The most promising geometries for topological phases appear to be the bilayer which supports both a Z$_2$ topological insulator and a Chern insulator, and the triangular-kagome-triangular trilayer which supports a relatively robust Chern insulator phase.

cond-mat.str-el↗