arXiv subjects

Jing Li

Publications and source records attributed to Jing Li.

At least 415 records · Page 23Linked to original sources

arXiv2024

Alloyed Re$_x$Mo$_{1-x}$S$_2$ Nanoflakes with Enlarged Interlayer Distances for Hydrogen Evolution

Molybdenum sulfide (MoS$_2$) has attracted significant attention due to its great potential as a low-cost and efficient catalyst for the hydrogen evolution reaction. Developing a facile, easily upscalable, and inexpensive approach to produce catalytically active nanostructured MoS$_2$ with a high yield would significantly advance its practical application. Colloidal synthesis offers several advantages over other preparation techniques to overcome the low reaction yield of exfoliation and drawbacks of expensive equipment and processes used in chemical vapor deposition. In this work, we report an efficient synthesis of alloyed Re$_x$Mo$_{1-x}$S$_2$ nanoflakes with an enlarged interlayer distance, among which the composition Re$_{0.55}$Mo$_{0.45}$S$_2$ exhibits excellent catalytic performance with overpotentials as low as 79 mV at 10 mA/cm2 and a small Tafel slope of 42 mV/dec. Density functional theory calculations prove that enlarging the distance between layers in the Re$_x$Mo$_{1-x}$S$_2$alloy can greatly improve its catalytic performance due to a significantly reduced free energy of hydrogen adsorption. The developed approach paves the way to design advanced transition metal dichalcogenide-based catalysts for hydrogen evolution and to promote their large-scale practical application.

cond-mat.mtrl-sci↗

arXiv2024

An Adaptive Phase-Field Method for Structural Topology Optimization

In this work, we develop an adaptive algorithm for the efficient numerical solution of the minimum compliance problem in topology optimization. The algorithm employs the phase field approximation and continuous density field. The adaptive procedure is driven by two residual type a posteriori error estimators, one for the state variable and the other for the first-order optimality condition of the objective functional. The adaptive algorithm is provably convergent in the sense that the sequence of numerical approximations generated by the adaptive algorithm contains a subsequence convergent to a solution of the continuous first-order optimality system. We provide several numerical simulations to show the distinct features of the algorithm.

math.OC↗

arXiv2024

H-alpha emission and HII regions at the locations of recent supernovae in nearby galaxies

We present a statistical analysis of the local, approximately 50-100 pc scale, H-alpha emission at the locations of recent (less than 125 years) supernovae (SNe) in nearby star-forming galaxies. Our sample consists of 32 SNe in 10 galaxies that are targets of the PHANGS-MUSE survey. We find that 41% (13/32) of these SNe occur coincident with a previously identified HII region. For comparison, HII regions cover 32% of the area within 1 kpc of any recent SN. Contrasting this local covering fraction with the fraction of SNe coincident with HII regions, we find a statistical excess of 7.6% +/- 8.7% of all SNe to be associated with HII regions. This increases to an excess of 19.2% +/- 10.4% when considering only core-collapse SNe. These estimates appear to be in good agreement with qualitative results from new, higher resolution HST H-alpha imaging, which also suggest many CCSNe detonate near but not in HII regions. Our results appear consistent with the expectation that only a modest fraction of stars explode during the first 5 Myr of the life of a stellar population, when H-alpha emission is expected to be bright. Of the HII region associated SNe, 8% (11/13) also have associated detected CO(2-1) emission, indicating the presence of molecular gas. The HII region associated SNe have typical Av extinctions approximately equal to 1 mag, consistent with a significant amount of pre-clearing of gas from the region before the SNe explode.

astro-ph.GA↗

arXiv2024

Data Generation-based Operator Learning for Solving Partial Differential Equations on Unbounded Domains

Wave propagation problems are typically formulated as partial differential equations (PDEs) on unbounded domains to be solved. The classical approach to solving such problems involves truncating them to problems on bounded domains by designing the artificial boundary conditions or perfectly matched layers, which typically require significant effort, and the presence of nonlinearity in the equation makes such designs even more challenging. Emerging deep learning-based methods for solving PDEs, with the physics-informed neural networks (PINNs) method as a representative, still face significant challenges when directly used to solve PDEs on unbounded domains. Calculations performed in a bounded domain of interest without imposing boundary constraints can lead to a lack of unique solutions thus causing the failure of PINNs. In light of this, this paper proposes a novel and effective operator learning-based method for solving PDEs on unbounded domains. The key idea behind this method is to generate high-quality training data. Specifically, we construct a family of approximate analytical solutions to the target PDE based on its initial condition and source term. Then, using these constructed data comprising exact solutions, initial conditions, and source terms, we train an operator learning model called MIONet, which is capable of handling multiple inputs, to learn the mapping from the initial condition and source term to the PDE solution on a bounded domain of interest. Finally, we utilize the generalization ability of this model to predict the solution of the target PDE. The effectiveness of this method is exemplified by solving the wave equation and the Schrodinger equation defined on unbounded domains. More importantly, the proposed method can deal with nonlinear problems, which has been demonstrated by solving Burger's equation and Korteweg-de Vries (KdV) equation.

math.NA↗

arXiv2024

Masked Structural Growth for 2x Faster Language Model Pre-training

Accelerating large language model pre-training is a critical issue in present research. In this paper, we focus on speeding up pre-training by progressively growing from a small Transformer structure to a large one. There are two main research problems associated with progressive growth: determining the optimal growth schedule, and designing efficient growth operators. In terms of growth schedule, the impact of each single dimension on a schedule's efficiency is under-explored by existing work. Regarding the growth operators, existing methods rely on the initialization of new weights to inherit knowledge, and achieve only non-strict function preservation, limiting further improvements on training dynamics. To address these issues, we propose Masked Structural Growth (MSG), including (i) growth schedules involving all possible dimensions and (ii) strictly function-preserving growth operators that is independent of the initialization of new weights. Experiments show that MSG is significantly faster than related work: we achieve up to 2.2x speedup in pre-training different types of language models while maintaining comparable or better downstream performances. Code is publicly available at https://github.com/cofe-ai/MSG.

cs.CL↗

arXiv2024

High fidelity control of a many-body Tonks--Girardeau gas with an effective mean-field approach

Shortcuts to adiabaticity (STA) are powerful tools that can be used to control quantum systems with high fidelity. They work particularly well for single particle and non-interacting systems which can be described exactly and which possess invariant or self-similar dynamics. However, finding an exact STA for strongly correlated many-body systems can be difficult, as their complex dynamics may not be easily described, especially for larger systems that do not possess self-similar solutions. Here, we design STAs for one-dimensional bosonic gas in the Tonks--Girardeau limit by using a mean-field approach that succinctly captures the strong interaction effects through a quintic nonlinear term in the Schrödinger equation. We show that for the case of the harmonic oscillator with a time-dependent trap frequency the mean-field approach works exactly and recovers the well-known STA from literature. To highlight the robustness of our approach we also show that it works effectively for anharmonic potentials, achieving higher fidelities than other typical control techniques.

quant-ph↗

arXiv2024

Dual-modal Prior Semantic Guided Infrared and Visible Image Fusion for Intelligent Transportation System

Infrared and visible image fusion (IVF) plays an important role in intelligent transportation system (ITS). The early works predominantly focus on boosting the visual appeal of the fused result, and only several recent approaches have tried to combine the high-level vision task with IVF. However, they prioritize the design of cascaded structure to seek unified suitable features and fit different tasks. Thus, they tend to typically bias toward to reconstructing raw pixels without considering the significance of semantic features. Therefore, we propose a novel prior semantic guided image fusion method based on the dual-modality strategy, improving the performance of IVF in ITS. Specifically, to explore the independent significant semantic of each modality, we first design two parallel semantic segmentation branches with a refined feature adaptive-modulation (RFaM) mechanism. RFaM can perceive the features that are semantically distinct enough in each semantic segmentation branch. Then, two pilot experiments based on the two branches are conducted to capture the significant prior semantic of two images, which then is applied to guide the fusion task in the integration of semantic segmentation branches and fusion branches. In addition, to aggregate both high-level semantics and impressive visual effects, we further investigate the frequency response of the prior semantics, and propose a multi-level representation-adaptive fusion (MRaF) module to explicitly integrate the low-frequent prior semantic with the high-frequent details. Extensive experiments on two public datasets demonstrate the superiority of our method over the state-of-the-art image fusion approaches, in terms of either the visual appeal or the high-level semantics.

cs.CV↗

arXiv2024

Bilateral Unsymmetrical Graph Contrastive Learning for Recommendation

Recent methods utilize graph contrastive Learning within graph-structured user-item interaction data for collaborative filtering and have demonstrated their efficacy in recommendation tasks. However, they ignore that the difference relation density of nodes between the user- and item-side causes the adaptability of graphs on bilateral nodes to be different after multi-hop graph interaction calculation, which limits existing models to achieve ideal results. To solve this issue, we propose a novel framework for recommendation tasks called Bilateral Unsymmetrical Graph Contrastive Learning (BusGCL) that consider the bilateral unsymmetry on user-item node relation density for sliced user and item graph reasoning better with bilateral slicing contrastive training. Especially, taking into account the aggregation ability of hypergraph-based graph convolutional network (GCN) in digging implicit similarities is more suitable for user nodes, embeddings generated from three different modules: hypergraph-based GCN, GCN and perturbed GCN, are sliced into two subviews by the user- and item-side respectively, and selectively combined into subview pairs bilaterally based on the characteristics of inter-node relation structure. Furthermore, to align the distribution of user and item embeddings after aggregation, a dispersing loss is leveraged to adjust the mutual distance between all embeddings for maintaining learning ability. Comprehensive experiments on two public datasets have proved the superiority of BusGCL in comparison to various recommendation methods. Other models can simply utilize our bilateral slicing contrastive learning to enhance recommending performance without incurring extra expenses.

cs.IR↗

arXiv2024

Equivariant graph neural network interatomic potential for Green-Kubo thermal conductivity in phase change materials

Thermal conductivity is a fundamental material property that plays an essential role in technology, but its accurate evaluation presents a challenge for theory. In this work, we demonstrate the application of $E(3)$-equivariant neutral network interatomic potentials within Green-Kubo formalism to determine the lattice thermal conductivity in amorphous and crystalline materials. We apply this method to study the thermal conductivity of germanium telluride (GeTe) as a prototypical phase change material. A single deep learning interatomic potential is able to describe the phase transitions between the amorphous, rhombohedral and cubic phases, with critical temperatures in good agreement with experiments. Furthermore, this approach accurately captures the pronounced anharmonicity that is present in GeTe, enabling precise calculations of the thermal conductivity. In contrast, the Boltzmann transport equation including only three-phonon processes tends to overestimate the thermal conductivity by approximately a factor of 2 in the crystalline phases.

cond-mat.mtrl-sci↗

arXiv2024

BeLLM: Backward Dependency Enhanced Large Language Model for Sentence Embeddings

Sentence embeddings are crucial in measuring semantic similarity. Most recent studies employed large language models (LLMs) to learn sentence embeddings. Existing LLMs mainly adopted autoregressive architecture without explicit backward dependency modeling. Therefore, we examined the effects of backward dependencies in LLMs for semantic similarity measurements. Concretely, we propose a novel model: backward dependency enhanced large language model (BeLLM). It learns sentence embeddings via transforming specific attention layers from uni- to bi-directional. We extensively experiment across various semantic textual similarity (STS) tasks and downstream applications. BeLLM achieves state-of-the-art performance in varying scenarios. It shows that auto-regressive LLMs benefit from backward dependencies for sentence embeddings.

cs.CL↗

arXiv2024

Chain of Thought with Explicit Evidence Reasoning for Few-shot Relation Extraction

Few-shot relation extraction involves identifying the type of relationship between two specific entities within a text, using a limited number of annotated samples. A variety of solutions to this problem have emerged by applying meta-learning and neural graph techniques which typically necessitate a training process for adaptation. Recently, the strategy of in-context learning has been demonstrating notable results without the need of training. Few studies have already utilized in-context learning for zero-shot information extraction. Unfortunately, the evidence for inference is either not considered or implicitly modeled during the construction of chain-of-thought prompts. In this paper, we propose a novel approach for few-shot relation extraction using large language models, named CoT-ER, chain-of-thought with explicit evidence reasoning. In particular, CoT-ER first induces large language models to generate evidences using task-specific and concept-level knowledge. Then these evidences are explicitly incorporated into chain-of-thought prompting for relation extraction. Experimental results demonstrate that our CoT-ER approach (with 0% training data) achieves competitive performance compared to the fully-supervised (with 100% training data) state-of-the-art approach on the FewRel1.0 and FewRel2.0 datasets.

cs.CL↗

arXiv2024

Superconductivity in kagome metal ThRu3Si2

We report the physical properties of ThRu$_3$Si$_2$ featured with distorted Ru kagome lattice. The combined experiments of resistivity, magnetization and specific heat reveal bulk superconductivity with $T_{\rm{c}}$ = 3.8 K. The specific heat jump and calculated electron-phonon coupling indicate a moderate coupled BCS superconductor. In comparison with LaRu$_3$Si$_2$, the calculated electronic structure in ThRu$_3$Si$_2$ shows an electron-doping effect with electron filling lifted from 100 meV below flat bands to 300 meV above it. This explains the lower superconducting transition temperature and weaker electron correlations observed in ThRu$_3$Si$_2$. Our work suggests the $T_{\rm{c}}$ and electronic correlations in kagome superconductor could have intimate connection with the flat bands.

cond-mat.supr-con↗

arXiv2024

A Cluster-Based Statistical Channel Model for Integrated Sensing and Communication Channels

The emerging 6G network envisions integrated sensing and communication (ISAC) as a promising solution to meet growing demand for native perception ability. To optimize and evaluate ISAC systems and techniques, it is crucial to have an accurate and realistic wireless channel model. However, some important features of ISAC channels have not been well characterized, for example, most existing ISAC channel models consider communication channels and sensing channels independently, whereas ignoring correlation under the consistent environment. Moreover, sensing channels have not been well modeled in the existing standard-level channel models. Therefore, in order to better model ISAC channel, a cluster-based statistical channel model is proposed in this paper, which is based on measurements conducted at 28 GHz. In the proposed model, a new framework based on 3GPP standard is proposed, which includes communication clusters and sensing clusters. Clustering and tracking algorithms are used to extract and analyze ISAC channel characteristics. Furthermore, some special sensing cluster structures such as shared sensing cluster, newborn sensing cluster, etc., are defined to model correlation and difference between communication and sensing channels. Finally, accuracy of the proposed model is validated based on measurements and simulations.

eess.SP↗

arXiv2024

PopALM: Popularity-Aligned Language Models for Social Media Trendy Response Prediction

Social media platforms are daily exhibiting millions of events. To preliminarily predict the mainstream public reaction to these events, we study trendy response prediction to automatically generate top-liked user replies to social media events. While previous works focus on generating responses without factoring in popularity, we propose Popularity-Aligned Language Models (PopALM) to distinguish responses liked by a larger audience through reinforcement learning. Recognizing the noisy labels from user "likes", we tailor-make curriculum learning in proximal policy optimization (PPO) to help models capture the essential samples for easy-to-hard training. In experiments, we build a large-scale Weibo dataset for trendy response prediction, and its results show that PopALM can help boost the performance of advanced language models.

cs.CL↗

arXiv2024

Continuous Sign Language Recognition Based on Motor attention mechanism and frame-level Self-distillation

Changes in facial expression, head movement, body movement and gesture movement are remarkable cues in sign language recognition, and most of the current continuous sign language recognition(CSLR) research methods mainly focus on static images in video sequences at the frame-level feature extraction stage, while ignoring the dynamic changes in the images. In this paper, we propose a novel motor attention mechanism to capture the distorted changes in local motion regions during sign language expression, and obtain a dynamic representation of image changes. And for the first time, we apply the self-distillation method to frame-level feature extraction for continuous sign language, which improves the feature expression without increasing the computational resources by self-distilling the features of adjacent stages and using the higher-order features as teachers to guide the lower-order features. The combination of the two constitutes our proposed holistic model of CSLR Based on motor attention mechanism and frame-level Self-Distillation (MAM-FSD), which improves the inference ability and robustness of the model. We conduct experiments on three publicly available datasets, and the experimental results show that our proposed method can effectively extract the sign language motion information in videos, improve the accuracy of CSLR and reach the state-of-the-art level.

cs.CV↗

arXiv2024

Label Learning Method Based on Tensor Projection

Multi-view clustering method based on anchor graph has been widely concerned due to its high efficiency and effectiveness. In order to avoid post-processing, most of the existing anchor graph-based methods learn bipartite graphs with connected components. However, such methods have high requirements on parameters, and in some cases it may not be possible to obtain bipartite graphs with clear connected components. To end this, we propose a label learning method based on tensor projection (LLMTP). Specifically, we project anchor graph into the label space through an orthogonal projection matrix to obtain cluster labels directly. Considering that the spatial structure information of multi-view data may be ignored to a certain extent when projected in different views separately, we extend the matrix projection transformation to tensor projection, so that the spatial structure information between views can be fully utilized. In addition, we introduce the tensor Schatten $p$-norm regularization to make the clustering label matrices of different views as consistent as possible. Extensive experiments have proved the effectiveness of the proposed method.

cs.LG↗

arXiv2024

Four-fold non-Hermitian phase transitions and non-reciprocal coupled resonator optical waveguides

Non-Hermitian systems can exhibit extraordinary sensitivity to boundary conditions. Given that topological boundary modes and non-Hermitian skin effects can either coexist or individually appear in non-Hermitian systems, it is of great value to present a comprehensive non-Hermitian phase diagram, for further flexible control in realistic non-Hermitian systems. Here, we reveal four-fold non-Hermitian phase transitions at a mathematically level, where phase I exhibits only topological boundary modes, phase II displays both topological boundary modes and skin modes, phase III exhibits only skin modes, and phase IV cannot manifest any boundary modes. By deriving non-Hermitian winding numbers, the existence or non-existence condition of topological boundary modes are analytically expressed, consistent with the numerical results obtained through the iterative Green's function method. Combining with the study on non-Hermitian skin effects, we rigorously establish the four-fold phase diagram. We also design an array of coupled resonator optical waveguides. The introduction of non-Hermiticity in the photonic structure induces a phenomenon similar to band inversion in topological insulators, indicating the presence of topological boundary modes in the photonic bands.

quant-ph↗

arXiv2024

The stability analysis based on viscous theory of Faraday waves in Hele-Shaw cells

The linear instability of Faraday waves in Hele-Shaw cells is investigated with consideration of the viscosity of fluids after gap-averaging the governing equations due to the damping from two lateral walls and the dynamic behavior of contact angle. A new hydrodynamic model is thus derived and solved semi-analytically. The contribution of viscosity to critical acceleration amplitude is slight compared to other factors associated with dissipation, and the potential flow theory is sufficient to describe onset based on the present study, but the rotational component of velocity can change the timing of onset largely, which paradoxically comes from the viscosity. The model degenerates into a novel damped Mathieu equation if the viscosity is dropped with two damping terms referring to the gap-averaged damping and dissipation from dynamic contact angle, respectively. The former increases when the gap size decreases, and the latter grows as frequency rises. When it comes to the dispersion relation of Faraday waves, an unusual detuning emerges due to the imaginary part of the gap-averaged damping.

physics.flu-dyn↗