arXiv subjects

Xin Yan

Publications and source records attributed to Xin Yan.

35 records · Page 2Linked to original sources

arXiv2024

Quantum-classical correspondence of non-Hermitian spin-orbit coupled bosonic junction

We investigate the classical-quantum correspondence of non-Hermitian Spin-orbit (SO)-coupled bosonic junctions, where an effective decay term is introduced in one of the two wells. Starting from the normalized two-point functions, we analytically demonstrate that the mean-field system has a classical Hamiltonian structure, and we successfully derive a non-Hermitian discrete nonlinear Schrödinger (Gross-Pitaevskii) equation. We discover that near the symmetry-breaking phase transition point, the correspondence between classical (mean-field) and quantum dynamics is more likely to break down. When the effective spin-orbit coupling (SOC) strength assumes half-integer values, atomic self-trapping in the non-lossy well definitely occurs, regardless of the system parameters, and the quantum dynamics is insensitive to the number of particles. Additionally, we reveal that in both the mean-field and many-particle models, the SOC effects can greatly promote the synchronous periodic oscillations between the spin-up and spin-down components, and this synchronization dynamics is protected by a symmetry mechanism.

quant-ph↗

arXiv2024

Centroid-centered Modeling for Efficient Vision Transformer Pre-training

Masked Image Modeling (MIM) is a new self-supervised vision pre-training paradigm using a Vision Transformer (ViT). Previous works can be pixel-based or token-based, using original pixels or discrete visual tokens from parametric tokenizer models, respectively. Our proposed centroid-based approach, CCViT, leverages k-means clustering to obtain centroids for image modeling without supervised training of the tokenizer model, which only takes seconds to create. This non-parametric centroid tokenizer only takes seconds to create and is faster for token inference. The centroids can represent both patch pixels and index tokens with the property of local invariance. Specifically, we adopt patch masking and centroid replacing strategies to construct corrupted inputs, and two stacked encoder blocks to predict corrupted patch tokens and reconstruct original patch pixels. Experiments show that our CCViT achieves 84.4% top-1 accuracy on ImageNet-1K classification with ViT-B and 86.0% with ViT-L. We also transfer our pre-trained model to other downstream tasks. Our approach achieves competitive results with recent baselines without external supervision and distillation training from other models.

cs.CV↗

arXiv2024

ContPhy: Continuum Physical Concept Learning and Reasoning from Videos

We introduce the Continuum Physical Dataset (ContPhy), a novel benchmark for assessing machine physical commonsense. ContPhy complements existing physical reasoning benchmarks by encompassing the inference of diverse physical properties, such as mass and density, across various scenarios and predicting corresponding dynamics. We evaluated a range of AI models and found that they still struggle to achieve satisfactory performance on ContPhy, which shows that the current AI models still lack physical commonsense for the continuum, especially soft-bodies, and illustrates the value of the proposed dataset. We also introduce an oracle model (ContPRO) that marries the particle-based physical dynamic models with the recent large language models, which enjoy the advantages of both models, precise dynamic predictions, and interpretable reasoning. ContPhy aims to spur progress in perception and reasoning within diverse physical settings, narrowing the divide between human and machine intelligence in understanding the physical world. Project page: https://physical-reasoning-project.github.io

cs.CV↗

arXiv2024

Spin-orbit coupling mediated photon-like resonance for a single atom trapped in a symmetric double well

We employ a method involving coherent periodic modulation of Raman laser intensity to induce resonance transitions between energy levels of a spin-orbit coupled atom in a symmetric double-well trap. By integrating photon-assisted tunneling (PAT) technique with spin-orbit coupling (SOC), we achieve resonance transitions between the predefined energy levels of the atom, thereby enabling further precise control of the atom's dynamics. We observe that such photon-like resonance can induce a transition from a localized state to atomic Rabi oscillation between two wells, or effectively reduce tunneling as manifested by a quantum beating phenomenon. Moreover, such resonance transitions have the potential to induce spin flipping in a spin-orbit coupled atom. Additionally, the SOC-mediated transition from multiphoton resonance to fundamental resonance and the SOC-induced resonance suppression are also discovered. In these cases, the analytical results of the effective coupling coefficients of the resonance transition derived from a four-level model can account for the entire dynamics, demonstrating surprisingly good agreement with the numerically exact results based on the realistic continuous model.

quant-ph↗

arXiv2024

Estimate the building height at a 10-meter resolution based on Sentinel data

Building height is an important indicator for scientific research and practical application. However, building height products with a high spatial resolution (10m) are still very scarce. To meet the needs of high-resolution building height estimation models, this study established a set of spatial-spectral-temporal feature databases, combining SAR data provided by Sentinel-1, optical data provided by Sentinel-2, and shape data provided by building footprints. The statistical indicators on the time scale are extracted to form a rich database of 160 features. This study combined with permutation feature importance, Shapley Additive Explanations, and Random Forest variable importance, and the final stable features are obtained through an expert scoring system. This study took 12 large, medium, and small cities in the United States as the training data. It used moving windows to aggregate the pixels to solve the impact of SAR image displacement and building shadows. This study built a building height model based on a random forest model and compared three model ensemble methods of bagging, boosting, and stacking. To evaluate the accuracy of the prediction results, this study collected Lidar data in the test area, and the evaluation results showed that its R-Square reached 0.78, which can prove that the building height can be obtained effectively. The fast production of high-resolution building height data can support large-scale scientific research and application in many fields.

cs.CV↗

arXiv2024

3D-VLA: A 3D Vision-Language-Action Generative World Model

Recent vision-language-action (VLA) models rely on 2D inputs, lacking integration with the broader realm of the 3D physical world. Furthermore, they perform action prediction by learning a direct mapping from perception to action, neglecting the vast dynamics of the world and the relations between actions and dynamics. In contrast, human beings are endowed with world models that depict imagination about future scenarios to plan actions accordingly. To this end, we propose 3D-VLA by introducing a new family of embodied foundation models that seamlessly link 3D perception, reasoning, and action through a generative world model. Specifically, 3D-VLA is built on top of a 3D-based large language model (LLM), and a set of interaction tokens is introduced to engage with the embodied environment. Furthermore, to inject generation abilities into the model, we train a series of embodied diffusion models and align them into the LLM for predicting the goal images and point clouds. To train our 3D-VLA, we curate a large-scale 3D embodied instruction dataset by extracting vast 3D-related information from existing robotics datasets. Our experiments on held-in datasets demonstrate that 3D-VLA significantly improves the reasoning, multimodal generation, and planning capabilities in embodied environments, showcasing its potential in real-world applications.

cs.CV↗

arXiv2023

Out of the Box Thinking: Improving Customer Lifetime Value Modelling via Expert Routing and Game Whale Detection

Customer lifetime value (LTV) prediction is essential for mobile game publishers trying to optimize the advertising investment for each user acquisition based on the estimated worth. In mobile games, deploying microtransactions is a simple yet effective monetization strategy, which attracts a tiny group of game whales who splurge on in-game purchases. The presence of such game whales may impede the practicality of existing LTV prediction models, since game whales' purchase behaviours always exhibit varied distribution from general users. Consequently, identifying game whales can open up new opportunities to improve the accuracy of LTV prediction models. However, little attention has been paid to applying game whale detection in LTV prediction, and existing works are mainly specialized for the long-term LTV prediction with the assumption that the high-quality user features are available, which is not applicable in the UA stage. In this paper, we propose ExpLTV, a novel multi-task framework to perform LTV prediction and game whale detection in a unified way. In ExpLTV, we first innovatively design a deep neural network-based game whale detector that can not only infer the intrinsic order in accordance with monetary value, but also precisely identify high spenders (i.e., game whales) and low spenders. Then, by treating the game whale detector as a gating network to decide the different mixture patterns of LTV experts assembling, we can thoroughly leverage the shared information and scenario-specific information (i.e., game whales modelling and low spenders modelling). Finally, instead of separately designing a purchase rate estimator for two tasks, we design a shared estimator that can preserve the inner task relationships. The superiority of ExpLTV is further validated via extensive experiments on three industrial datasets.

cs.IR↗

arXiv2023

The nanoscale imaging of the bulk polycrystalline material with the effects of depth of field and field of view based on x-ray free electron laser

Microscale imaging of mesoscale bulk materials under dynamic compression is important for understanding their properties. In this work, we study the effects of the depth of field (DoF) and field of view (FoV) of the optical lens and extract the scattered light of the region to be imaged within the bulk polycrystalline material based on the objective Bragg coherent diffraction imaging. We describe how the DoF and FoV quantitatively limit the diffraction volume, where the DoF and FoV limit the scattering region parallel and perpendicular to the direction of the light source respectively. We demonstrate this scheme by simulating the separate imaging of a submicron-sized crack region within a few μm-sized Si bulk material, and obtain a high imaging quality. This scheme enables imaging of selected regions within bulk polycrystalline materials with the resolution up to the order of 10 nm.

physics.optics↗

arXiv2022

Theoretical and experimental study on Noise Equivalent Power of X-ray semiconductor ultra-fast response material based on the rad-optic effect

Semiconductor material based on the rad-optic effect enables ultra-fast detection of X-rays and plays an important role in fusion diagnostics. Obtaining the accurate noise equivalent power (NEP) of the semiconductor ultrafast response material is the key to detecting X-rays. In this paper, the refractive index change mechanism of the semiconductor under X-ray irradiation was analyzed, and the quantitative relationship between the diffraction efficiency and the X-ray photon energy was established through the LT-AlGaAs diffraction imaging experiments. The impulse responses of LT-AlGaAs under 1 KeV-10 KeV X-ray radiation were calculated, revealing the variation of NEP density with radiated photon energy. In the case of bombarding the Al target to generate 1.5 KeV X-rays, the imaging experiments of LT-AlGaAs were performed. The diffraction image of LT-AlGaAs has a linear relationship with the radiation intensity, and the NEP density of LT-AlGaAs reaches 4.80*105W/cm2. This study has reference significance for the development of ultra-fast X-ray imaging systems based on the rad-optic effect.

physics.ins-det↗

arXiv2022

Operational detection of entanglement via quantum designs

From an operational point of view, we propose several new entanglement detection criteria using quantum designs. These criteria are constructed by considering the correlations defined with quantum designs. Counter-intuitively, the criteria with more settings are exactly equivalent to the corresponding ones with the minimal number of settings, namely the symmetric informationally complete positive operator-valued measures (SIC POVMs). Fundamentally, this observation highlights the potentially unique role played by SIC POVMs in quantum information processing. Experimentally, this provides the minimal number of settings that one should choose for detecting entanglement. Furthermore, we find that nonlinear criteria are not always better than linear ones for the task of entanglement detection.

quant-ph↗

arXiv2021

Clustering Effect of (Linearized) Adversarial Robust Models

Adversarial robustness has received increasing attention along with the study of adversarial examples. So far, existing works show that robust models not only obtain robustness against various adversarial attacks but also boost the performance in some downstream tasks. However, the underlying mechanism of adversarial robustness is still not clear. In this paper, we interpret adversarial robustness from the perspective of linear components, and find that there exist some statistical properties for comprehensively robust models. Specifically, robust models show obvious hierarchical clustering effect on their linearized sub-networks, when removing or replacing all non-linear components (e.g., batch normalization, maximum pooling, or activation layers). Based on these observations, we propose a novel understanding of adversarial robustness and apply it on more tasks including domain adaption and robustness boosting. Experimental evaluations demonstrate the rationality and superiority of our proposed clustering strategy.

cs.LG↗

arXiv2020

Counterfactual Samples Synthesizing for Robust Visual Question Answering

Despite Visual Question Answering (VQA) has realized impressive progress over the last few years, today's VQA models tend to capture superficial linguistic correlations in the train set and fail to generalize to the test set with different QA distributions. To reduce the language biases, several recent works introduce an auxiliary question-only model to regularize the training of targeted VQA model, and achieve dominating performance on VQA-CP. However, since the complexity of design, current methods are unable to equip the ensemble-based models with two indispensable characteristics of an ideal VQA model: 1) visual-explainable: the model should rely on the right visual regions when making decisions. 2) question-sensitive: the model should be sensitive to the linguistic variations in question. To this end, we propose a model-agnostic Counterfactual Samples Synthesizing (CSS) training scheme. The CSS generates numerous counterfactual training samples by masking critical objects in images or words in questions, and assigning different ground-truth answers. After training with the complementary samples (ie, the original and generated samples), the VQA models are forced to focus on all critical objects and words, which significantly improves both visual-explainable and question-sensitive abilities. In return, the performance of these models is further boosted. Extensive ablations have shown the effectiveness of CSS. Particularly, by building on top of the model LMH, we achieve a record-breaking performance of 58.95% on VQA-CP v2, with 6.5% gains.

cs.CV↗

arXiv2018

Interfacial bound exciton state in a hybrid structure of monolayer WS2 and InGaN quantum dots

Van der Waals heterostructures usually formed using atomic thin transition metal dichalcogenides (TMDCs) with a direct bandgap in the near-infrared to the visible range are promising candidates for low-dimensional optoelectronic applications. The interlayer interaction or coupling between two-dimensional (2D) layer and the substrate or between adjacent 2D layers plays an important role in modifying properties of the individual 2D material or device performances through Coulomb interaction or forming interlayer excitons. Here, we report the realization of quasi zero-dimensional (0D) photon emission of WS2 in a coupled hybrid structure of monolayer WS2 and InGaN quantum dots (QDs). An interfacial bound exciton, i.e., coupling between excitons in WS2 and the electrons in QDs has been identified. The emission of this interfacial bound exciton inherits the 0-D confinement of QDs as well as the spin-valley physics of excitons in monolayer WS2. The effective coupling between 2D materials and conventional semiconductors observed in this work provides an effective way to realize 0D emission of 2D materials and opens the potential of compact on-chip integration of valleytronics and conventional electronics/optoelectronics.

cond-mat.mes-hall↗

arXiv2014

Featureless quantum spin liquid, 1/3-magnetization plateau state and exotic thermodynamic properties of spin-1/2 frustrated Heisenberg antiferromagnet on an infinite Husimi lattice

By utilizing tensor-network-based methods, we investigate the zero- and finite-temperature properties of the spin-1/2 Heisenberg antiferromagnetic (HAF) model on an infinite Husimi lattice that contains 3/2 sites per triangle. The ground state of this model is found to possess vanishing local magnetization and is featureless; the spin-spin and dimer-dimer correlation functions are verified to decay exponentially; and its ground-state energy per site is determined to be $e_0=-0.4343(1)$, which is very close to that [$e_0=-0.4386(5)$] of the intriguing kagome HAF model. The magnetization curve shows the absence of a zero-magnetization plateau, implying a gapless excitation. A $1/3$-magnetization plateau with spin up-up-down state is observed, which is selected and stabilized by quantum fluctuations. A ground state phase diagram under magnetic fields is presented. Moreover, both magnetic susceptibility and the specific heat are studied, whose low-temperature behaviors reinforce the conclusion that the HAF model on the infinite Husimi lattice owns a gapless and featureless spin liquid ground state.

cond-mat.str-el↗

arXiv2013

Kosterlitz-Thouless phase transition and reentrance in an anisotropic 3-state Potts model on the generalized Kagome lattice

The unusual reentrant phenomenon is observed in the anisotropic 3-state Potts model on a gen- eralized Kagome lattice. By employing the linearized tensor renormalization group method, we find that the reentrance can appear in the region not only under a partial ordered phase as commonly known but also a phase without a local order parameter, which is uncovered to fall into the uni- versality of the Kosterlitz-Thouless (KT) type. The region of the reentrance depends strongly on the ratios of the next nearest couplings α = J2 /|J1 | and β = J3 /|J1 |. The phase diagrams in the plane of temperature versus β for different α are obtained. Through massive calculations, it is also revealed that the quasi-entanglement entropy can be used to accurately detect the KT transition temperature.

cond-mat.str-el↗

arXiv2012

Phase Diagrams, Distinct Conformal Anomalies and Thermodynamics of Spin-1 Bond-Alternating Heisenberg Antiferromagnetic Chain in Magnetic Fields

The ground state phase diagrams and thermodynamic properties of a spin-1 bond-alternating Heisenberg antiferromagnetic chain with a single-ion anisotropy in longitudinal and transverse magnetic fields are investigated jointly by means of the infinite time evolving block decimation, the linearized tensor renormalization group and the density matrix renormalization group methods. It is found that in the magnetic field-bond alternating ratio plane six phases such as singlet dimer, Haldane, two Tomonaga-Luttinger liquid, 1/2 magnetization plateau and spin polarized phases are identified in a longitudinal field, while in a transverse field there are fives phases including singlet dimer, Haldane, Z$_2$ symmetry breaking, quasi-1/2 magnetization plateau and quasi spin polarized phases. A reentrant behavior of the staggered magnetization in a transverse field is observed. The quantum critical behaviors in longitudinal and transverse magnetic fields are disclosed to fall into different universality classes with corresponding conformal field central charge $c=1$ and 1/2, respectively. The experimental data of the compound NTENP under both longitudinal and transverse magnetic fields are nicely fitted, and the Luttinger liquid behavior of low temperature specific heat experimentally observed is also confirmed.

cond-mat.str-el↗

arXiv2007

On the Achievable Rate Regions for Interference Channels with Degraded Message Sets

The interference channel with degraded message sets (IC-DMS) refers to a communication model in which two senders attempt to communicate with their respective receivers simultaneously through a common medium, and one of the senders has complete and a priori (non-causal) knowledge about the message being transmitted by the other. A coding scheme that collectively has advantages of cooperative coding, collaborative coding, and dirty paper coding, is developed for such a channel. With resorting to this coding scheme, achievable rate regions of the IC-DMS in both discrete memoryless and Gaussian cases are derived, which, in general, include several previously known rate regions. Numerical examples for the Gaussian case demonstrate that in the high-interference-gain regime, the derived achievable rate regions offer considerable improvements over these existing results.

cs.IT↗