arXiv subjects

Tian Tian

Publications and source records attributed to Tian Tian.

At least 37 records · Page 2Linked to original sources

arXiv2025

High-Order Interior Penalty Finite Element Methods for Fourth-Order Phase-Field Models in Fracture Analysis

This paper presents a novel approach for solving fourth-order phase-field models in brittle fracture mechanics using the Interior Penalty Finite Element Method (IP-FEM). The fourth-order model improves numerical stability and accuracy compared to traditional second-order phase-field models, particularly when simulating complex crack paths. The IP-FEM provides an efficient framework for discretizing these models, effectively handling nonconforming trial functions and complex boundary conditions. In this study, we leverage the FEALPy framework to implement a flexible computational tool that supports high-order IP-FEM discretizations. Our results show that as the polynomial order increases, the mesh dependence of the phase-field model decreases, offering improved accuracy and faster convergence. Additionally, we explore the trade-offs between computational cost and accuracy with varying polynomial orders and mesh sizes. The findings offer valuable insights for optimizing numerical simulations of brittle fracture in practical engineering applications.

math.NA↗

arXiv2025

Polarization Decoupling Multi-Port Beam-Splitting Metasurface for Miniaturized Magneto-Optical Trap

In regular magneto-optical trap (MOT) systems, the delivery of six circularly polarized (CP) cooling beams requires complex and bulky optical arrangements including waveplates, mirrors, retroreflectors, etc. To address such technique challenges, we have proposed a beam delivery system for miniaturized MOT entirely based on meta-devices. The key component is a novel multi-port beam-splitting (PD-MPBS) metasurface that relies on both propagation phase and geometric phase. The fabricated samples exhibit high beam-splitting power uniformity (within 4.4%) and polarization purities (91.29%~93.15%). By leveraging such beam-splitting device as well as reflective beam-expanding meta-device, an integrated six-beam delivery system for miniaturized MOT application has been implemented. The experimental results indicate that six expanded beams have been successfully delivered with uniform power (within 9.5%), the desired CP configuration and large overlapping volume (76.2 mm^3). We believe that a miniaturized MOT with the proposed beam delivery system is very promising for portable application of cold atom technology in precision measurement, atomic clock, quantum simulation and computing, etc.

physics.optics↗

arXiv2025

Uncovering the Impact of Chain-of-Thought Reasoning for Direct Preference Optimization: Lessons from Text-to-SQL

Direct Preference Optimization (DPO) has proven effective in complex reasoning tasks like math word problems and code generation. However, when applied to Text-to-SQL datasets, it often fails to improve performance and can even degrade it. Our investigation reveals the root cause: unlike math and code tasks, which naturally integrate Chain-of-Thought (CoT) reasoning with DPO, Text-to-SQL datasets typically include only final answers (gold SQL queries) without detailed CoT solutions. By augmenting Text-to-SQL datasets with synthetic CoT solutions, we achieve, for the first time, consistent and significant performance improvements using DPO. Our analysis shows that CoT reasoning is crucial for unlocking DPO's potential, as it mitigates reward hacking, strengthens discriminative capabilities, and improves scalability. These findings offer valuable insights for building more robust Text-to-SQL models. To support further research, we publicly release the code and CoT-enhanced datasets.

cs.CL↗

arXiv2024

Back-Scattering Suppression for Broad-Spectral High-Absorption Silicon Extended Area Blackbody

The stability and emissivity of the online calibration blackbody used in high-precision infrared remote sensing detectors in extreme environments are the primary limiting factors for their measurement accuracy. Due to the limitations of microstructure size effects, traditional calibration extended area blackbody cannot achieve an optimal balance between emissivity and stability, thus hindering further improvement in infrared remote sensing accuracy. This work proposes a new method that utilize suppressing near-field backscattering to control far-field reflectance. Specifically, through simultaneously reducing backscattering intensity and the backscattering solid angle, the reflectance is significantly reduced to an extremely low limit, which is validated through numerical simulations. Additionally, by combining the femtosecond laser self-convergent processing technique, the spontaneous energy negative feedback mechanism during femtosecond laser processing is utilized to achieve the fabrication of a high emissivity, thermally stable, mechanically stable, and highly uniform extended area blackbody. The blackbody fabricated using this technique can be applied for online calibration in various extreme environments, significantly improving measurement accuracy and service life.

physics.optics↗

arXiv2024

Confident Natural Policy Gradient for Local Planning in $q_π$-realizable Constrained MDPs

The constrained Markov decision process (CMDP) framework emerges as an important reinforcement learning approach for imposing safety or other critical objectives while maximizing cumulative reward. However, the current understanding of how to learn efficiently in a CMDP environment with a potentially infinite number of states remains under investigation, particularly when function approximation is applied to the value functions. In this paper, we address the learning problem given linear function approximation with $q_π$-realizability, where the value functions of all policies are linearly representable with a known feature map, a setting known to be more general and challenging than other linear settings. Utilizing a local-access model, we propose a novel primal-dual algorithm that, after $\tilde{O}(\text{poly}(d) ε^{-3})$ queries, outputs with high probability a policy that strictly satisfies the constraints while nearly optimizing the value with respect to a reward function. Here, $d$ is the feature dimension and $ε> 0$ is a given error. The algorithm relies on a carefully crafted off-policy evaluation procedure to evaluate the policy using historical data, which informs policy updates through policy gradients and conserves samples. To our knowledge, this is the first result achieving polynomial sample complexity for CMDP in the $q_π$-realizable setting.

cs.LG↗

arXiv2024

SPARC-X-API: Versatile Python Interface for Real-space Density Functional Theory Calculations

Density Functional Theory (DFT) is the de facto workhorse for large-scale electronic structure calculations in chemistry and materials science. While plane-wave DFT implementations remain the most widely used, real-space DFT provides advantages in handling complex boundary conditions and scaling to very large systems by allowing for the efficient use of large-scale supercomputers and linear-scaling methods that circumvent the cubic scaling bottleneck. The SPARC-X project (https://github.com/SPARC-X) provides highly efficient and portable real-space DFT codes for a wide range of first principle applications, available in both Matlab (M-SPARC) and C/C++ (SPARC). The rapid growth of SPARC's feature set has created the need for a fully functional interface to drive SPARC in high-throughput calculations. Here we introduce SPARC-X-API, a Python package designed to bridge the SPARC-X project with broader computational frameworks. Built on the atomic simulation environment (ASE) standard, the SPARC-X-API allows users to handle SPARC file formats and run SPARC calculations through the same interface as with other ASE-compatible DFT packages. Beyond standard ASE capabilities, SPARC-X-API provides additional features including 1) support of SPARC-specific setups, including complex boundary conditions and unit conversion, 2) a JSON schema parsed from SPARC's documentation for parameter validation and compatibility checks, and 3) a comprehensive socket communication layer derived from the i-PI protocol facilitating message passing between low-level C code and the Python interface. The goal of the SPARC-X-API is to provide an easy-to-use interface for users with diverse needs and levels of expertise, allowing for minimal effort in adapting SPARC to existing computational workflows, while also supporting developers of advanced real-space methods.

physics.chem-ph↗

arXiv2024

Dissipation Driven Coherent Dynamics Observed in Bose-Einstein Condensates

We report the first experimental observation of dissipation-driven coherent quantum many-body oscillation, and this oscillation is manifested as the coherent exchange of atoms between the thermal and the condensate components in a three-dimensional partially condensed Bose gas. Firstly, we observe that the dissipation leads to two different atom loss rates between the thermal and the condensate components, such that the thermal fraction increases as dissipation time increases. Therefore, this dissipation process serves as a tool to uniformly ramp up the system's temperature without introducing extra density excitation. Subsequently, a coherent pair exchange of atoms between the thermal and the condensate components occurs, resulting in coherent oscillation of atom numbers in both components. This oscillation, permanently embedded in the atom loss process, is revealed clearly when we inset a duration of dissipation-free evolution into the entire dynamics, manifested as an oscillation of total atom number at the end. Finally, we also present a theoretical calculation to support this physical mechanism, which simultaneously includes dissipation, interaction, finite temperature, and harmonic trap effects. Our work introduces a highly controllable dissipation as a new tool to control quantum many-body dynamics.

cond-mat.quant-gas↗

arXiv2024

Direct Nanopatterning of Complex 3D Surfaces and Self-Aligned Superlattices via Molecular-Beam Holographic Lithography

Conventional lithography methods involving pattern transfer through resist templating face challenges of material compatibility with various process solvents. Other approaches of direct material writing often compromise pattern complexity and overlay accuracy. Here we explore a concept based on the moiré interference of molecular beams to directly pattern complex three-dimensional (3D) surfaces made by any evaporable materials, such as metals, oxides and organic semiconductors. Our proposed approach, termed the Molecular-Beam Holographic Lithography (MBHL), relies on precise control over angular projections of material flux passing through nanoapertures superimposed on the substrate, emulating the interference of coherent laser beams in interference lithography. Incorporating with our computational lithography (CL) algorithm, we have demonstrated self-aligned overlay of multiple material patterns to yield binary up to quinary superlattices, with a critical dimension and overlay accuracy on the order of 50 and 2 nanometers, respectively. The process is expected to substantially expand the boundary of materials combination for high-throughput fabrication of complex superstructures of translational symmetry on arbitrary substrates, enabling new nanoimaging, sensing, catalysis, and optoelectronic devices.

cond-mat.mtrl-sci↗

arXiv2024

Single-photon scattering and bound states in a one-dimensional waveguide with topological giant atom

We investigate the single photon scattering and bound states in a coupled resonator waveguide (CRW) which couples to a topological giant atom (TGA) via two distant sites. Here, the TGA is constructed by a one dimensional Su-Schrieffer-Heeger (SSH) chain with finite length. By modulating the topological phase of the TGA, the incident photon in the CRW can be completely reflected or transmitted, and is therefore beneficial to design the coherent photonic device. Meanwhile, we also achieve two pairs of bound states locating respectively above and blow the continuum. Whether the gap is open or closed depends on the boundary condition of the TGA. Therefore, the combination of the topology and the interference provides us an exciting opportunity to manipulate the photonic state in the context of waveguide quantum electrodynamics.

quant-ph↗

arXiv2024

Strong Repulsive Lifshitz-van der Waals Forces on Suspended Graphene

Understanding surface forces of two-dimensional (2D) materials is of fundamental importance as they govern molecular dynamics and atomic deposition in nanoscale proximity. Despite recent observations in wetting transparency and remote epitaxy on substrate-supported graphene, very little is known about the many-body effects on their van der Waals (vdW) interactions, such as the role of surrounding vacuum in wettability of suspended 2D monolayers. Here we report on a stark repulsive Lifshitz-van der Waals (vdW) force generated at surfaces of suspended 2D materials, arising from quantum fluctuation coupled with the atomic thickness and birefringence of 2D monolayer. In combination with our theoretical framework taking into account the many-body Lifshitz formulism, we present direct measurement of Lifshitz-vdW repulsion on suspended graphene using atomic force microscopy. We report a repulsive force of up to 1.4 kN/m$^2$ at a separation of 8.8 nm between a gold-coated AFM tip and a sheet of suspended graphene, more than two orders of magnitude greater than the Casimir-Lifshitz repulsion demonstrated in fluids. Our findings suggest that suspended 2D materials are intrinsically repulsive surfaces with substantially lowered wettability. The amplified Lifshitz-vdW repulsion could offer technological opportunities such as molecular actuation and controlled atomic assembly.

cond-mat.mes-hall↗

arXiv2024

Electrically switchable $2^N$-channel wave-front control with N cascaded polarization-dependent metasurfaces

Metasurfaces with tunable functionalities are greatly desired for modern optical system and various applications. To increase the operating channels of polarization-multiplexed metasurfaces, we proposed a structure of N cascaded dual-channel metasurfaces to achieve 2^N electrically switchable functional channels without intrinsic noise or cross-talk. As proof of principles, we have implemented a 3-layer setup to achieve 8 channels. In success, we have demonstrated two typical functionalities of vortex beam generation with switchable topological charge of l=-3 ~ +4 or l=-1~ -8, and beam steering with the deflecting direction switchable in an 8*1 line or a 4*2 grid. We believe that our proposal would provide a practical way to significantly increase the scalability and extend the functionality of polarization-multiplexed metasurfaces, which are potential for the applications of LiDAR, glasses-free 3D display, OAM (de)multiplexing, and varifocal meta-lens.

physics.optics↗

arXiv2024

Versatile Teacher: A Class-aware Teacher-student Framework for Cross-domain Adaptation

Addressing the challenge of domain shift between datasets is vital in maintaining model performance. In the context of cross-domain object detection, the teacher-student framework, a widely-used semi-supervised model, has shown significant accuracy improvements. However, existing methods often overlook class differences, treating all classes equally, resulting in suboptimal results. Furthermore, the integration of instance-level alignment with a one-stage detector, essential due to the absence of a Region Proposal Network (RPN), remains unexplored in this framework. In response to these shortcomings, we introduce a novel teacher-student model named Versatile Teacher (VT). VT differs from previous works by considering class-specific detection difficulty and employing a two-step pseudo-label selection mechanism, referred to as Class-aware Pseudo-label Adaptive Selection (CAPS), to generate more reliable pseudo labels. These labels are leveraged as saliency matrices to guide the discriminator for targeted instance-level alignment. Our method demonstrates promising results on three benchmark datasets, and extends the alignment methods for widely-used one-stage detectors, presenting significant potential for practical applications. Code is available at https://github.com/RicardooYoung/VersatileTeacher.

cs.CV↗

arXiv2024

Enhancing Organizational Performance: Harnessing AI and NLP for User Feedback Analysis in Product Development

This paper explores the application of AI and NLP techniques for user feedback analysis in the context of heavy machine crane products. By leveraging AI and NLP, organizations can gain insights into customer perceptions, improve product development, enhance satisfaction and loyalty, inform decision-making, and gain a competitive advantage. The paper highlights the impact of user feedback analysis on organizational performance and emphasizes the reasons for using AI and NLP, including scalability, objectivity, improved accuracy, increased insights, and time savings. The methodology involves data collection, cleaning, text and rating analysis, interpretation, and feedback implementation. Results include sentiment analysis, word cloud visualizations, and radar charts comparing product attributes. These findings provide valuable information for understanding customer sentiment, identifying improvement areas, and making data-driven decisions to enhance the customer experience. In conclusion, promising AI and NLP techniques in user feedback analysis offer organizations a powerful tool to understand customers, improve product development, increase satisfaction, and drive business success

econ.GN↗

arXiv2024

Transforming Investment Strategies and Strategic Decision-Making: Unveiling a Novel Methodology for Enhanced Performance and Risk Management in Financial Markets

This paper introduces a novel methodology for index return forecasting, blending highly correlated stock prices, advanced deep learning techniques, and intricate factor integration. Departing from conventional cap-weighted approaches, our innovative framework promises to reimagine traditional methodologies, offering heightened diversification, amplified performance capture, and nuanced market depiction. At its core lies the intricate identification of highly correlated company clusters, fueling predictive accuracy and robustness. By harnessing these interconnected constellations, we unlock a profound comprehension of market dynamics, bestowing both investment entities and individual enterprises with invaluable performance insights. Moreover, our methodology integrates pivotal factors such as indexes and ETFs, seamlessly woven with Hierarchical Risk Parity (HRP) portfolio optimization, to elevate performance and fortify risk management. This comprehensive amalgamation refines risk diversification, fortifying portfolio resilience against turbulent market forces. The implications reverberate resoundingly. Investment entities stand poised to calibrate against competitors with surgical precision, tactically sidestepping industry-specific pitfalls, and sculpting bespoke investment strategies to capitalize on market fluctuations. Concurrently, individual enterprises find empowerment in aligning strategic endeavors with market trajectories, discerning key competitors, and navigating volatility with steadfast resilience. In essence, this research marks a pivotal moment in economic discourse, unveiling novel methodologies poised to redefine decision-making paradigms and elevate performance benchmarks for both investment entities and individual enterprises navigating the intricate tapestry of financial realms.

q-fin.GN↗

arXiv2024

Unleashing the Power of AI: Transforming Marketing Decision-Making in Heavy Machinery with Machine Learning, Radar Chart Simulation, and Markov Chain Analysis

This pioneering research introduces a novel approach for decision-makers in the heavy machinery industry, specifically focusing on production management. The study integrates machine learning techniques like Ridge Regression, Markov chain analysis, and radar charts to optimize North American Crawler Cranes market production processes. Ridge Regression enables growth pattern identification and performance assessment, facilitating comparisons and addressing industry challenges. Markov chain analysis evaluates risk factors, aiding in informed decision-making and risk management. Radar charts simulate benchmark product designs, enabling data-driven decisions for production optimization. This interdisciplinary approach equips decision-makers with transformative insights, enhancing competitiveness in the heavy machinery industry and beyond. By leveraging these techniques, companies can revolutionize their production management strategies, driving success in diverse markets.

econ.EM↗

arXiv2023

Observation of universal dissipative dynamics in strongly correlated quantum gas

Dissipation is unavoidable in quantum systems. It usually induces decoherences and changes quantum correlations. To access the information of strongly correlated quantum matters, one has to overcome or suppress dissipation to extract out the underlying quantum phenomena. However, here we find an opposite effect that dissipation can be utilized as a powerful tool to probe the intrinsic correlations of quantum many-body systems. Applying highly-controllable dissipation in ultracold atomic systems, we observe a universal dissipative dynamics in strongly correlated one-dimensional quantum gases. The total particle number of this system follows a universal stretched-exponential decay, and the stretched exponent measures the anomalous dimension of the spectral function, a critical exponent characterizing strong quantum fluctuations of this system. This method could have broad applications in detecting strongly correlated features, including spin-charge separations and Fermi arcs in quantum materials.

cond-mat.quant-gas↗

arXiv2023

A Robust ADMM-Based Optimization Algorithm For Underwater Acoustic Channel Estimation

Accurate estimation of the Underwater acoustic (UWA) is a key part of underwater communications, especially for coherent systems. The severe multipath effects and large delay spreads make the estimation problem large-scale. The non-stationary, non-Gaussian, and impulsive nature of ocean ambient noise poses further obstacles to the design of estimation algorithms. Under the framework of compressed sensing (CS), this work addresses the issue of robust channel estimation when measurements are contaminated by impulsive noise. A first-order algorithm based on alternating direction method of multipliers (ADMM) is proposed. Numerical simulations of time-varying channel estimation are performed to show its improved performance in highly impulsive noise environments.

eess.SP↗

arXiv2023

Fair Classification via Domain Adaptation: A Dual Adversarial Learning Approach

Modern machine learning (ML) models are becoming increasingly popular and are widely used in decision-making systems. However, studies have shown critical issues of ML discrimination and unfairness, which hinder their adoption on high-stake applications. Recent research on fair classifiers has drawn significant attention to developing effective algorithms to achieve fairness and good classification performance. Despite the great success of these fairness-aware machine learning models, most of the existing models require sensitive attributes to pre-process the data, regularize the model learning or post-process the prediction to have fair predictions. However, sensitive attributes are often incomplete or even unavailable due to privacy, legal or regulation restrictions. Though we lack the sensitive attribute for training a fair model in the target domain, there might exist a similar domain that has sensitive attributes. Thus, it is important to exploit auxiliary information from a similar domain to help improve fair classification in the target domain. Therefore, in this paper, we study a novel problem of exploring domain adaptation for fair classification. We propose a new framework that can learn to adapt the sensitive attributes from a source domain for fair classification in the target domain. Extensive experiments on real-world datasets illustrate the effectiveness of the proposed model for fair classification, even when no sensitive attributes are available in the target domain.

cs.LG↗