arXiv subjects

Yong Xu

Publications and source records attributed to Yong Xu.

At least 163 records · Page 9Linked to original sources

arXiv2024

Universal materials model of deep-learning density functional theory Hamiltonian

Realizing large materials models has emerged as a critical endeavor for materials research in the new era of artificial intelligence, but how to achieve this fantastic and challenging objective remains elusive. Here, we propose a feasible pathway to address this paramount pursuit by developing universal materials models of deep-learning density functional theory Hamiltonian (DeepH), enabling computational modeling of the complicated structure-property relationship of materials in general. By constructing a large materials database and substantially improving the DeepH method, we obtain a universal materials model of DeepH capable of handling diverse elemental compositions and material structures, achieving remarkable accuracy in predicting material properties. We further showcase a promising application of fine-tuning universal materials models for enhancing specific materials models. This work not only demonstrates the concept of DeepH's universal materials model but also lays the groundwork for developing large materials models, opening up significant opportunities for advancing artificial intelligence-driven materials discovery.

physics.comp-ph↗

arXiv2024

QueryAgent: A Reliable and Efficient Reasoning Framework with Environmental Feedback-based Self-Correction

Employing Large Language Models (LLMs) for semantic parsing has achieved remarkable success. However, we find existing methods fall short in terms of reliability and efficiency when hallucinations are encountered. In this paper, we address these challenges with a framework called QueryAgent, which solves a question step-by-step and performs step-wise self-correction. We introduce an environmental feedback-based self-correction method called ERASER. Unlike traditional approaches, ERASER leverages rich environmental feedback in the intermediate steps to perform selective and differentiated self-correction only when necessary. Experimental results demonstrate that QueryAgent notably outperforms all previous few-shot methods using only one example on GrailQA and GraphQ by 7.0 and 15.0 F1. Moreover, our approach exhibits superiority in terms of efficiency, including runtime, query overhead, and API invocation costs. By leveraging ERASER, we further improve another baseline (i.e., AgentBench) by approximately 10 points, revealing the strong transferability of our approach.

cs.CL↗

arXiv2024

The RoboDrive Challenge: Drive Anytime Anywhere in Any Condition

In the realm of autonomous driving, robust perception under out-of-distribution conditions is paramount for the safe deployment of vehicles. Challenges such as adverse weather, sensor malfunctions, and environmental unpredictability can severely impact the performance of autonomous systems. The 2024 RoboDrive Challenge was crafted to propel the development of driving perception technologies that can withstand and adapt to these real-world variabilities. Focusing on four pivotal tasks -- BEV detection, map segmentation, semantic occupancy prediction, and multi-view depth estimation -- the competition laid down a gauntlet to innovate and enhance system resilience against typical and atypical disturbances. This year's challenge consisted of five distinct tracks and attracted 140 registered teams from 93 institutes across 11 countries, resulting in nearly one thousand submissions evaluated through our servers. The competition culminated in 15 top-performing solutions, which introduced a range of innovative approaches including advanced data augmentation, multi-sensor fusion, self-supervised learning for error correction, and new algorithmic strategies to enhance sensor robustness. These contributions significantly advanced the state of the art, particularly in handling sensor inconsistencies and environmental variability. Participants, through collaborative efforts, pushed the boundaries of current technologies, showcasing their potential in real-world scenarios. Extensive evaluations and analyses provided insights into the effectiveness of these solutions, highlighting key trends and successful strategies for improving the resilience of driving perception systems. This challenge has set a new benchmark in the field, providing a rich repository of techniques expected to guide future research in this field.

cs.CV↗

arXiv2024

FlashST: A Simple and Universal Prompt-Tuning Framework for Traffic Prediction

The objective of traffic prediction is to accurately forecast and analyze the dynamics of transportation patterns, considering both space and time. However, the presence of distribution shift poses a significant challenge in this field, as existing models struggle to generalize well when faced with test data that significantly differs from the training distribution. To tackle this issue, this paper introduces a simple and universal spatio-temporal prompt-tuning framework-FlashST, which adapts pre-trained models to the specific characteristics of diverse downstream datasets, improving generalization in diverse traffic prediction scenarios. Specifically, the FlashST framework employs a lightweight spatio-temporal prompt network for in-context learning, capturing spatio-temporal invariant knowledge and facilitating effective adaptation to diverse scenarios. Additionally, we incorporate a distribution mapping mechanism to align the data distributions of pre-training and downstream data, facilitating effective knowledge transfer in spatio-temporal forecasting. Empirical evaluations demonstrate the effectiveness of our FlashST across different spatio-temporal prediction tasks using diverse urban datasets. Code is available at https://github.com/HKUDS/FlashST.

cs.LG↗

arXiv2024

Confronting Dark Matter Freeze-In during Reheating with Constraints from Inflation

We investigate the production of particle Dark Matter (DM) in a minimal freeze-in model considering a non-instantaneous reheating phase after inflation. We demonstrate that for low reheating temperatures, bosonic or fermionic reheating from monomial potentials can lead to a different evolution in the DM production and hence to distinct predictions for the parent particle lifetime and mass, constrained by long-lived particle (LLP) searches. We highlight that such scenario predicts parent particle decay lengths larger compared to using the instantaneous reheating approximation. Moreover, we demonstrate the importance of an accurate definition of the reheating temperature and emphasize its relevance for the correct interpretation of experimental constraints. We explore different models of inflation, which can lead to the considered reheating potential. We find that the extent to which the standard DM freeze-in production can be modified crucially depends on the underlying inflationary model. Based on the latest CMB constraints, we derive lower limits on the decay length of the parent particle and confront these results with the corresponding reach of LLP searches. Our findings underscore the impact of the specific dynamics of inflation on DM freeze-in production and highlight their importance for the interpretation of collider signatures. At the same time, our results indicate the potential for LLP searches to shed light on the underlying dynamics of reheating.

hep-ph↗

arXiv2024

Midgap states induced by Zeeman field and $p$ wave superconductor pairing

The one-dimensional Su-Schrieffer-Heeger (SSH) model is central to band topology in condensed matter physics, which allows us to understand and design distinct topological states. In this work, we find another mechanism to analogize the SSH model in a spinful system, realizing an obstructed atomic insulator by introducing intrinsic spin-orbit coupling and in-plane Zeeman field. In our model, the midgap states originate from a quantized hidden polarization with invariant index $\mathbb{Z}_2$ (0; 01) due to the local inversion symmetry breaking. When the global inversion symmetry is broken, a charge pumping is designed by tuning the polarization. Moreover, by introducing the $p+ip$ superconductor pairing potential, a new topological phase dubbed obstructed superconductor (OSC) is identified. This new state is characterized by invariant index $\mathbb{Z}_2$ (0; 01) and nonchiral midgap states. More interestingly, these nonchiral edge states result in a chiral-like nonlocal conductance, which is different from the traditional chiral topological superconductor. Our findings not only find another strategy to achieve a spinful SSH model but also predict the existence of OSC, providing a promising avenue for further exploration of its transport properties.

cond-mat.mes-hall↗

arXiv2024

Stabilizing fractional Chern insulators via exchange interaction in moiré systems

Recent experimental discovery of fractional Chern insulator in moiré Chern band in twisted transition metal dichalocogenide homobilayers has sparked intensive interest in exploring the ways of engineering band topology and correlated states in moiré systems. In this letter, we demonstrate that, with an additional exchange interaction induced by proximity effect, the topology and bandwidth of the moiré minibands of twisted $\mathrm{MoTe_2}$ homobilayers can be easily tuned. Fractional Chern insulators at -2/3 filling are found to appear at enlarged twist angles over a large range of twist angles with enhanced many-body gaps. We further discover a topological phase transition between the fractional Chern insulator, quantum anomalous Hall crystal, and charge density wave. Our results shed light on the interplay between topology and correlation physics.

cond-mat.str-el↗

arXiv2024

UrbanGPT: Spatio-Temporal Large Language Models

Spatio-temporal prediction aims to forecast and gain insights into the ever-changing dynamics of urban environments across both time and space. Its purpose is to anticipate future patterns, trends, and events in diverse facets of urban life, including transportation, population movement, and crime rates. Although numerous efforts have been dedicated to developing neural network techniques for accurate predictions on spatio-temporal data, it is important to note that many of these methods heavily depend on having sufficient labeled data to generate precise spatio-temporal representations. Unfortunately, the issue of data scarcity is pervasive in practical urban sensing scenarios. Consequently, it becomes necessary to build a spatio-temporal model with strong generalization capabilities across diverse spatio-temporal learning scenarios. Taking inspiration from the remarkable achievements of large language models (LLMs), our objective is to create a spatio-temporal LLM that can exhibit exceptional generalization capabilities across a wide range of downstream urban tasks. To achieve this objective, we present the UrbanGPT, which seamlessly integrates a spatio-temporal dependency encoder with the instruction-tuning paradigm. This integration enables LLMs to comprehend the complex inter-dependencies across time and space, facilitating more comprehensive and accurate predictions under data scarcity. To validate the effectiveness of our approach, we conduct extensive experiments on various public datasets, covering different spatio-temporal prediction tasks. The results consistently demonstrate that our UrbanGPT, with its carefully designed architecture, consistently outperforms state-of-the-art baselines. These findings highlight the potential of building large language models for spatio-temporal learning, particularly in zero-shot scenarios where labeled data is scarce.

cs.CL↗

arXiv2024

FPDIoU Loss: A Loss Function for Efficient Bounding Box Regression of Rotated Object Detection

Bounding box regression is one of the important steps of object detection. However, rotation detectors often involve a more complicated loss based on SkewIoU which is unfriendly to gradient-based training. Most of the existing loss functions for rotated object detection calculate the difference between two bounding boxes only focus on the deviation of area or each points distance (e.g., $\mathcal{L}_{Smooth-\ell 1}$, $\mathcal{L}_{RotatedIoU}$ and $\mathcal{L}_{PIoU}$). The calculation process of some loss functions is extremely complex (e.g. $\mathcal{L}_{KFIoU}$). In order to improve the efficiency and accuracy of bounding box regression for rotated object detection, we proposed a novel metric for arbitrary shapes comparison based on minimum points distance, which takes most of the factors from existing loss functions for rotated object detection into account, i.e., the overlap or nonoverlapping area, the central points distance and the rotation angle. We also proposed a loss function called $\mathcal{L}_{FPDIoU}$ based on four points distance for accurate bounding box regression focusing on faster and high quality anchor boxes. In the experiments, $FPDIoU$ loss has been applied to state-of-the-art rotated object detection (e.g., RTMDET, H2RBox) models training with three popular benchmarks of rotated object detection including DOTA, DIOR, HRSC2016 and two benchmarks of arbitrary orientation scene text detection including ICDAR 2017 RRC-MLT and ICDAR 2019 RRC-MLT, which achieves better performance than existing loss functions.

cs.CV↗

arXiv2024

Probing Reheating with Gravitational Waves from Graviton Bremsstrahlung

In this talk, based on arXiv:2301.11345, arXiv:2305.16388, arXiv:2311.12694, we discuss the production of primordial gravitational waves (GW) sourced by graviton bremsstrahlung during inflationary reheating. For reheating, we consider inflaton decays and annihilations into pairs of bosons or fermions, assuming an inflaton $ϕ$ that oscillates around a generic monomial potential $V(ϕ) \propto ϕ^n$. The GW spectrum exhibits distinct features depending on the underlying reheating dynamics, which is controlled by the inflaton potential and the type of coupling between the inflaton and the matter fields. We show that the produced stochastic GW background could be probed in next-generation GW detectors, especially at high frequencies. We further highlight the potential of bremsstrahlung-induced GW to probe the underlying dynamics of reheating.

astro-ph.CO↗

arXiv2024

Van der Waals Magnetic Electrode Transfer for Two-Dimensional Spintronic Devices

Two-dimensional (2D) materials are promising candidates for spintronic applications. Maintaining their atomically smooth interfaces during integration of ferromagnetic (FM) electrodes is crucial since conventional metal deposition tends to induce defects at the interfaces. Meanwhile, the difficulties in picking up FM metals with strong adhesion and in achieving conductance match between FM electrodes and spin transport channels make it challenging to fabricate high-quality 2D spintronic devices using metal transfer techniques. Here, we report a solvent-free magnetic electrode transfer technique that employs a graphene layer to assist in the transfer of FM metals. It also serves as part of the FM electrode after transfer for optimizing spin injection, which enables the realization of spin valves with excellent performance based on various 2D materials. In addition to two-terminal devices, we demonstrate that the technique is applicable for four-terminal spin valves with nonlocal geometry. Our results provide a promising future of realizing 2D spintronic applications using the developed magnetic electrode transfer technique.

cond-mat.mes-hall↗

arXiv2024

Field-Effect Josephson Diode via Asymmetric Spin-Momentum-Locking States

Recent breakthroughs in Josephson diodes dangle the possibility of extending conventional non-reciprocal electronics into the realm of superconductivity. While a strong magnetic field is recognized for enhancing diode efficiency, it concurrently poses a risk of undermining the essential superconductivity required for non-dissipative devices. To circumvent the need for magnetic-based tuning, we propose a field-effect Josephson diode based on the electrostatic gate control of finite momentum Cooper pairs in asymmetric spin-momentum-locking states. We propose two possible implementations of our gate-controlled mechanism: (i) a topological field-effect Josephson diode in time-reversal-broken quantum spin Hall insulators; and (ii) semiconductor-based field-effect Josephson diodes attainable in current experimental setups involving a Zeeman field and spin-orbit coupling. Notably, the diode efficiency is highly enhanced in the topological field-effect Josephson diode because the current carried by the asymmetric helical edge states is topologically protected and can be tuned by local gates. In the proposed Josephson diode, the combination of gates and asymmetric spin-momentum-locking nature is equivalent to that of a magnetic field, thus providing an alternative electrical operation in designing nonreciprocal superconducting devices.

cond-mat.mes-hall↗

arXiv2024

Masked Two-channel Decoupling Framework for Incomplete Multi-view Weak Multi-label Learning

Multi-view learning has become a popular research topic in recent years, but research on the cross-application of classic multi-label classification and multi-view learning is still in its early stages. In this paper, we focus on the complex yet highly realistic task of incomplete multi-view weak multi-label learning and propose a masked two-channel decoupling framework based on deep neural networks to solve this problem. The core innovation of our method lies in decoupling the single-channel view-level representation, which is common in deep multi-view learning methods, into a shared representation and a view-proprietary representation. We also design a cross-channel contrastive loss to enhance the semantic property of the two channels. Additionally, we exploit supervised information to design a label-guided graph regularization loss, helping the extracted embedding features preserve the geometric structure among samples. Inspired by the success of masking mechanisms in image and text analysis, we develop a random fragment masking strategy for vector features to improve the learning ability of encoders. Finally, it is important to emphasize that our model is fully adaptable to arbitrary view and label absences while also performing well on the ideal full data. We have conducted sufficient and convincing experiments to confirm the effectiveness and advancement of our model.

cs.CV↗

arXiv2024

Deep-Learning Database of Density Functional Theory Hamiltonians for Twisted Materials

Moiré-twisted materials have garnered significant research interest due to their distinctive properties and intriguing physics. However, conducting first-principles studies on such materials faces challenges, notably the formidable computational cost associated with simulating ultra-large twisted structures. This obstacle impedes the construction of a twisted materials database crucial for datadriven materials discovery. Here, by using high-throughput calculations and state-of-the-art neural network methods, we construct a Deep-learning Database of density functional theory (DFT) Hamiltonians for Twisted materials named DDHT. The DDHT database comprises trained neural-network models of over a hundred homo-bilayer and hetero-bilayer moiré-twisted materials. These models enable accurate prediction of the DFT Hamiltonian for these materials across arbitrary twist angles, with an averaged mean absolute error of approximately 1.0 meV or lower. The database facilitates the exploration of flat bands and correlated materials platforms within ultra-large twisted structures.

cond-mat.mtrl-sci↗

arXiv2024

Giant and controllable nonlinear magneto-optical effects in two-dimensional magnets

The interplay of polarization and magnetism in materials with light can create rich nonlinear magneto-optical (NLMO) effects, and the recent discovery of two-dimensional (2D) van der Waals magnets provides remarkable control over NLMO effects due to their superb tunability. Here, based on first-principles calculations, we reported giant NLMO effects in CrI3-based 2D magnets, including a dramatic change of second-harmonics generation (SHG) polarization direction (90 degrees) and intensity (on/off switch) under magnetization reversal, and a 100% SHG circular dichroism effect. We further revealed that these effects could not only be used to design ultra-thin multifunctional optical devices, but also to detect subtle magnetic orderings. Remarkably, we analytically derived conditions to achieve giant NLMO effects and propose general strategies to realize them in 2D magnets. Our work not only uncovers a series of intriguing NLMO phenomena, but also paves the way for both fundamental research and device applications of ultra-thin NLMO materials.

cond-mat.mtrl-sci↗

arXiv2024

CDIMC-net: Cognitive Deep Incomplete Multi-view Clustering Network

In recent years, incomplete multi-view clustering, which studies the challenging multi-view clustering problem on missing views, has received growing research interests. Although a series of methods have been proposed to address this issue, the following problems still exist: 1) Almost all of the existing methods are based on shallow models, which is difficult to obtain discriminative common representations. 2) These methods are generally sensitive to noise or outliers since the negative samples are treated equally as the important samples. In this paper, we propose a novel incomplete multi-view clustering network, called Cognitive Deep Incomplete Multi-view Clustering Network (CDIMC-net), to address these issues. Specifically, it captures the high-level features and local structure of each view by incorporating the view-specific deep encoders and graph embedding strategy into a framework. Moreover, based on the human cognition, i.e., learning from easy to hard, it introduces a self-paced strategy to select the most confident samples for model training, which can reduce the negative influence of outliers. Experimental results on several incomplete datasets show that CDIMC-net outperforms the state-of-the-art incomplete multi-view clustering methods.

cs.CV↗

arXiv2024

Moderate deviations for rough differential equations

Small noise problems are quite important for all types of stochastic differential equations. In this paper we focus on rough differential equations driven by scaled fractional Brownian rough path with Hurst parameter H between 1/4 and 1/2. We prove a moderate deviation principle for this equation as the scale parameter tends to zero.

math.PR↗

arXiv2024

AlphaFin: Benchmarking Financial Analysis with Retrieval-Augmented Stock-Chain Framework

The task of financial analysis primarily encompasses two key areas: stock trend prediction and the corresponding financial question answering. Currently, machine learning and deep learning algorithms (ML&DL) have been widely applied for stock trend predictions, leading to significant progress. However, these methods fail to provide reasons for predictions, lacking interpretability and reasoning processes. Also, they can not integrate textual information such as financial news or reports. Meanwhile, large language models (LLMs) have remarkable textual understanding and generation ability. But due to the scarcity of financial training datasets and limited integration with real-time knowledge, LLMs still suffer from hallucinations and are unable to keep up with the latest information. To tackle these challenges, we first release AlphaFin datasets, combining traditional research datasets, real-time financial data, and handwritten chain-of-thought (CoT) data. It has a positive impact on training LLMs for completing financial analysis. We then use AlphaFin datasets to benchmark a state-of-the-art method, called Stock-Chain, for effectively tackling the financial analysis task, which integrates retrieval-augmented generation (RAG) techniques. Extensive experiments are conducted to demonstrate the effectiveness of our framework on financial analysis.

cs.CL↗