arXiv subjects

Tian Tian

Publications and source records attributed to Tian Tian.

At least 55 records · Page 3Linked to original sources

arXiv2023

Physics-Guided Discovery of Highly Nonlinear Parametric Partial Differential Equations

Partial differential equations (PDEs) that fit scientific data can represent physical laws with explainable mechanisms for various mathematically-oriented subjects, such as physics and finance. The data-driven discovery of PDEs from scientific data thrives as a new attempt to model complex phenomena in nature, but the effectiveness of current practice is typically limited by the scarcity of data and the complexity of phenomena. Especially, the discovery of PDEs with highly nonlinear coefficients from low-quality data remains largely under-addressed. To deal with this challenge, we propose a novel physics-guided learning method, which can not only encode observation knowledge such as initial and boundary conditions but also incorporate the basic physical principles and laws to guide the model optimization. We theoretically show that our proposed method strictly reduces the coefficient estimation error of existing baselines, and is also robust against noise. Extensive experiments show that the proposed method is more robust against data noise, and can reduce the estimation error by a large margin. Moreover, all the PDEs in the experiments are correctly discovered, and for the first time we are able to discover three-dimensional PDEs with highly nonlinear coefficients.

cs.LG↗

arXiv2023

Metasurface-Based Free-Space Multi-port Beam Splitter with Arbitrary Power Ratio

A beam splitter (BS) is one of the most critical building blocks in optical systems. Despite various attempts of flat-type BSs to miniaturize the conventional cube BS reported, it remains a challenge to realize an ultrathin optical BS with multi-port output, non-uniform splitting ratio and steerable outgoing directions. Herein, we have demonstrated a free-space optical multi-port beam splitter (MPBS) based on a polarization-independent all-dielectric metasurface. By applying an optimized phase-pattern paradigm via a gradient-descent-based iterative algorithm to amorphous silicon (a-Si) metasurfaces, we have prepared a variety of MPBS samples with arbitrarily predetermined output port number (2~7), power ratio and spatial distribution of output beams. The experimental results reveal that the fabricated MPBSs could achieve high total splitting efficiency (TSE, above 74.7%) and beam-splitting fidelity (similarity, above 78.4%) within the bandwidth of 100 nm (1500~1600 nm). We envision that such MPBS could provide fabulous flexibility for optical integrated system and diverse applications.

physics.optics↗

arXiv2023

Beyond Independent Error Assumptions in Large GNN Atomistic Models

The practical applications of determining the relative difference in adsorption energies are extensive, such as identifying optimal catalysts, calculating reaction energies, and determining the lowest adsorption energy on a catalytic surface. Although Density Functional Theory (DFT) can effectively calculate relative values through systematic error cancellation, the accuracy of Graph Neural Networks (GNNs) in this regard remains uncertain. To investigate this issue, we analyzed approximately 483 million pairs of energy differences predicted by DFT and GNNs using the Open Catalyst 2020 - Dense dataset. Our analysis revealed that GNNs exhibit a correlated error that can be reduced through subtraction, thereby challenging the naive independent error assumption in GNN predictions and leading to more precise energy difference predictions. To assess the magnitude of error cancellation in chemically similar pairs, we introduced a new metric, the subgroup error cancellation ratio (SECR). Our findings suggest that state-of-the-art GNN models can achieve error reduction up to 77% in these subgroups, comparable to the level of error cancellation observed with DFT. This significant error cancellation allows GNNs to achieve higher accuracy than individual adsorption energy predictions, which can otherwise suffer from amplified error due to random error propagation.

physics.comp-ph↗

arXiv2022

Doubly-Asynchronous Value Iteration: Making Value Iteration Asynchronous in Actions

Value iteration (VI) is a foundational dynamic programming method, important for learning and planning in optimal control and reinforcement learning. VI proceeds in batches, where the update to the value of each state must be completed before the next batch of updates can begin. Completing a single batch is prohibitively expensive if the state space is large, rendering VI impractical for many applications. Asynchronous VI helps to address the large state space problem by updating one state at a time, in-place and in an arbitrary order. However, Asynchronous VI still requires a maximization over the entire action space, making it impractical for domains with large action space. To address this issue, we propose doubly-asynchronous value iteration (DAVI), a new algorithm that generalizes the idea of asynchrony from states to states and actions. More concretely, DAVI maximizes over a sampled subset of actions that can be of any user-defined size. This simple approach of using sampling to reduce computation maintains similarly appealing theoretical properties to VI without the need to wait for a full sweep through the entire action space in each update. In this paper, we show DAVI converges to the optimal value function with probability one, converges at a near-geometric rate with probability 1-delta, and returns a near-optimal policy in computation time that nearly matches a previously established bound for VI. We also empirically demonstrate DAVI's effectiveness in several experiments.

cs.LG↗

arXiv2022

Vibration induced transparency: Simulating an optomechanical system via the cavity QED setup with a movable atom

We simulate an optomechanical system via a cavity QED scenario with a movable atom and investigate its application in the tiny mass sensing. We find that the steady-state solution of the system exhibits a multiple stability behavior, which is similar to that in the optomechanical system. We explain this phenomenon by the opto-mechanical interaction term in the effective Hamiltonian. Due to the dressed states formed by the effective coupling between the vibration degree of the atom and the optical mode in the cavity, we observe a narrow transparent window in the output field. We utilize this vibration induced transparency phenomenon to perform the tiny mass sensing. We hope our study will broaden the application of the cavity QED system to quantum technologies.

quant-ph↗

arXiv2022

Solid-State Lifshitz-van der Waals Repulsion through Two-Dimensional Materials

In the 1960s, Lifshitz et al. predicted that quantum fluctuations can change the van der Waals (vdW) interactions from attraction to repulsion. However, the vdW repulsion, or its long-range counterpart - the Casimir repulsion, has only been demonstrated in liquid. Here we show that the atomic thickness and birefringent nature of two-dimensional materials make them a versatile medium to tailor the Lifshitz-vdW interactions. Based on our theoretical prediction, we present direct force measurement of vdW repulsion on 2D material surfaces without liquid immersion and demonstrate their substantial influence on epitaxial properties. For example, heteroepitaxy of gold on a sheet of freestanding graphene leads to the growth of ultrathin platelets, owing to the vdW repulsion-induced ultrafast diffusion of gold clusters. The creation of repulsive force in nanoscale proximity offers technological opportunities such as single-molecule actuation and atomic assembly.

cond-mat.mtrl-sci↗

arXiv2022

FINETUNA: Fine-tuning Accelerated Molecular Simulations

Machine learning approaches have the potential to approximate Density Functional Theory (DFT) for atomistic simulations in a computationally efficient manner, which could dramatically increase the impact of computational simulations on real-world problems. However, they are limited by their accuracy and the cost of generating labeled data. Here, we present an online active learning framework for accelerating the simulation of atomic systems efficiently and accurately by incorporating prior physical information learned by large-scale pre-trained graph neural network models from the Open Catalyst Project. Accelerating these simulations enables useful data to be generated more cheaply, allowing better models to be trained and more atomistic systems to be screened. We also present a method of comparing local optimization techniques on the basis of both their speed and accuracy. Experiments on 30 benchmark adsorbate-catalyst systems show that our method of transfer learning to incorporate prior information from pre-trained models accelerates simulations by reducing the number of DFT calculations by 91%, while meeting an accuracy threshold of 0.02 eV 93% of the time. Finally, we demonstrate a technique for leveraging the interactive functionality built in to VASP to efficiently compute single point calculations within our online active learning framework without the significant startup costs. This allows VASP to work in tandem with our framework while requiring 75% fewer self-consistent cycles than conventional single point calculations. The online active learning implementation, and examples using the VASP interactive code, are available in the open source FINETUNA package on Github.

physics.comp-ph↗

arXiv2022

What Makes Good Contrastive Learning on Small-Scale Wearable-based Tasks?

Self-supervised learning establishes a new paradigm of learning representations with much fewer or even no label annotations. Recently there has been remarkable progress on large-scale contrastive learning models which require substantial computing resources, yet such models are not practically optimal for small-scale tasks. To fill the gap, we aim to study contrastive learning on the wearable-based activity recognition task. Specifically, we conduct an in-depth study of contrastive learning from both algorithmic-level and task-level perspectives. For algorithmic-level analysis, we decompose contrastive models into several key components and conduct rigorous experimental evaluations to better understand the efficacy and rationale behind contrastive learning. More importantly, for task-level analysis, we show that the wearable-based signals bring unique challenges and opportunities to existing contrastive models, which cannot be readily solved by existing algorithms. Our thorough empirical studies suggest important practices and shed light on future research challenges. In the meantime, this paper presents an open-source PyTorch library \texttt{CL-HAR}, which can serve as a practical tool for researchers. The library is highly modularized and easy to use, which opens up avenues for exploring novel contrastive models quickly in the future.

cs.LG↗

arXiv2021

Membraneless organelles formed by liquid-liquid phase separation increase bacterial fitness

Liquid-liquid phase separation is emerging as a crucial phenomenon in several fundamental cell processes. A range of eukaryotic systems exhibit liquid condensates. However, their function in bacteria, which in general lack membrane-bound compartments, remains less clear. Here, we used high-resolution optical microscopy to observe single bacterial aggresomes, nanostructured intracellular assemblies of proteins, to undercover their role in cell stress. We find that proteins inside aggresomes are mobile and undergo dynamic turnover, consistent with a liquid state. Our observations are in quantitative agreement with phase-separated liquid droplet formation driven by interacting proteins under thermal equilibrium that nucleate following diffusive collisions in the cytoplasm. We have discovered aggresomes in multiple species of bacteria, and show that these emergent, metastable liquid-structured protein assemblies increase bacterial fitness by enabling cells to tolerate environmental stresses.

q-bio.BM↗

arXiv2021

Observation of heat scaling across a first-order quantum phase transition in a spinor condensate

Heat generated as a result of the breakdown of an adiabatic process is one of the central concepts of thermodynamics. In isolated systems, the heat can be defined as an energy increase due to transitions between distinct energy levels. Across a second-order quantum phase transition (QPT), the heat is predicted theoretically to exhibit a power-law scaling, but it is a significant challenge for an experimental observation. In addition, it remains elusive whether a power-law scaling of heat can exist for a first-order QPT. Here we experimentally observe a power-law scaling of heat in a spinor condensate when a system is linearly driven from a polar phase to an antiferromagnetic phase across a first-order QPT. We experimentally evaluate the heat generated during two non-equilibrium processes by probing the atom number on a hyperfine energy level. The experimentally measured scaling exponents agree well with our numerical simulation results. Our work therefore opens a new avenue to experimentally and theoretically exploring the properties of heat in non-equilibrium dynamics.

cond-mat.quant-gas↗

arXiv2020

Chiral state conversion in a levitated micromechanical oscillator with in situ control of parameter loops

Physical systems with gain and loss can be described by a non-Hermitian Hamiltonian, which is degenerated at the exceptional points (EPs). Many new and unexpected features have been explored in the non-Hermitian systems with a great deal of recent interest. One of the most fascinating features is that, chiral state conversion appears when one EP is encircled dynamically. Here, we propose an easy-controllable levitated microparticle system that carries a pair of EPs and realize slow evolution of the Hamiltonian along loops in the parameter plane. Utilizing the controllable rotation angle, gain and loss coefficients, we can control the structure, size and location of the loops in situ. We demonstrate that, under the joint action of topological structure of energy surfaces and nonadiabatic transitions (NATs), the chiral behavior emerges both along a loop encircling an EP and even along a straight path away from the EP. This work broadens the range of parameter space for the chiral state conversion, and proposes a useful platform to explore the interesting properties of exceptional points physics.

quant-ph↗

arXiv2020

QSAN: A Quantum-probability based Signed Attention Network for Explainable False Information Detection

False information detection on social media is challenging as it commonly requires tedious evidence-collecting but lacks available comparative information. Clues mined from user comments, as the wisdom of crowds, could be of considerable benefit to this task. However, it is non-trivial to capture the complex semantics from the contents and comments in consideration of their implicit correlations. Although deep neural networks have good expressive power, one major drawback is the lack of explainability. In this paper, we focus on how to learn from the post contents and related comments in social media to understand and detect the false information more effectively, with explainability. We thus propose a Quantum-probability based Signed Attention Network (QSAN) that integrates the quantum-driven text encoding and a novel signed attention mechanism in a unified framework. QSAN is not only able to distinguish important comments from the others, but also can exploit the conflicting social viewpoints in the comments to facilitate the detection. Moreover, QSAN is advantageous with its explainability in terms of transparency due to quantum physics meanings and the attention weights. Extensive experiments on real-world datasets show that our approach outperforms state-of-the-art baselines and can provide different kinds of user comments to explain why a piece of information is detected as false.

cs.SI↗

arXiv2020

Perfect coherent transfer in an on-chip reconfigurable nanoelectromechanical network

Realizing a controllable network with multiple degrees of interaction is a challenge to physics and engineering. Here, we experimentally report an on-chip reconfigurable network based on nanoelectromechanical resonators with nearest-neighbor (NN) and next-nearest-neighbor (NNN) strong couplings. By applying different parametric voltages on the same on-chip device, we carry out perfect coherent transfer in NN and NNN coupled array networks. Moreover, the low-loss resonators ensure the desired evolution to achieve perfect transfer and the demonstration of the parity-dependent phase relation at transmission cycles. The realization of NNN couplings demonstrates the capability of engineering coherent coupling beyond a simple model of a NN coupled array of doubly clamped resonators. Our reconfigurable nanoelectromechanical network provides a highly tunable physical platform and offers the possibilities of investigating various interesting phenomena, such as topological transport, synchronization of networks, as well as metamaterials.

quant-ph↗

arXiv2020

Realization of programmable nanomechanical lattice with both nearest-neighboring and next-nearest-neighboring couplings

The programmable artificial lattice, based on the controllability of coupling strengths and the scalability of multiple sites, is desperately desired in engineering metamaterials and exploring fundamental physics. In this work, we experimentally present a programmable lattice consisting of multiple paralleled nanomechanical resonators, whose internal interactions can be linearly manipulated by external voltages. Flexural modes of nearest-neighboring (NN) and next-nearest-neighboring (NNN) resonators are parametrically coupled through modulated electrostatic interactions. Particularly, in a wide range up to deep strong coupling regime, both the NN and NNN coupling strengths are precisely proportional to manipulation voltage. The realization of long-range coupling provides a promising prospect in constructing complex lattice structure, which is essential in investigating mechanical logic devices, topological physics and coherent phononic dynamics.

physics.app-ph↗

arXiv2020

VFlow: More Expressive Generative Flows with Variational Data Augmentation

Generative flows are promising tractable models for density modeling that define probabilistic distributions with invertible transformations. However, tractability imposes architectural constraints on generative flows, making them less expressive than other types of generative models. In this work, we study a previously overlooked constraint that all the intermediate representations must have the same dimensionality with the original data due to invertibility, limiting the width of the network. We tackle this constraint by augmenting the data with some extra dimensions and jointly learning a generative flow for augmented data as well as the distribution of augmented dimensions under a variational inference framework. Our approach, VFlow, is a generalization of generative flows and therefore always performs better. Combining with existing generative flows, VFlow achieves a new state-of-the-art 2.98 bits per dimension on the CIFAR-10 dataset and is more compact than previous models to reach similar modeling quality.

stat.ML↗

arXiv2020

Dynamic Window-level Granger Causality of Multi-channel Time Series

Granger causality method analyzes the time series causalities without building a complex causality graph. However, the traditional Granger causality method assumes that the causalities lie between time series channels and remain constant, which cannot model the real-world time series data with dynamic causalities along the time series channels. In this paper, we present the dynamic window-level Granger causality method (DWGC) for multi-channel time series data. We build the causality model on the window-level by doing the F-test with the forecasting errors on the sliding windows. We propose the causality indexing trick in our DWGC method to reweight the original time series data. Essentially, the causality indexing is to decrease the auto-correlation and increase the cross-correlation causal effects, which improves the DWGC method. Theoretical analysis and experimental results on two synthetic and one real-world datasets show that the improved DWGC method with causality indexing better detects the window-level causalities.

stat.ME↗

arXiv2019

Electronic polarizability as the fundamental variable in the dielectric properties of two-dimensional materials

The dielectric constant, which defines the polarization of the media, is a key quantity in condensed matter. It determines several electronic and optoelectronic properties important for a plethora of modern technologies from computer memory to field effect transistors and communication circuits. Moreover, the importance of the dielectric constant in describing electromagnetic interactions through screening plays a critical role in understanding fundamental molecular interactions. Here we show that despite its fundamental transcendence, the dielectric constant does not define unequivocally the dielectric properties of two-dimensional (2D) materials due to the locality of their electrostatic screening. Instead, the electronic polarizability correctly captures the dielectric nature of a 2D material which is united to other physical quantities in an atomically thin layer. We reveal a long-sought universal formalism where electronic, geometrical and dielectric properties are intrinsically correlated through the polarizability opening the door to probe quantities yet not directly measurable including the real covalent thickness of a layer. We unify the concept of dielectric properties in any material dimension finding a global dielectric anisotropy index defining their controllability through dimensionality.

cond-mat.mtrl-sci↗

arXiv2019

Manipulation of a Micro-object Using Topological Hydrodynamic Tweezers

Manipulating micro-scale object plays paramount roles in a wide range of fundamental researches and applications. At micro-scale, various methods have been developed in the past decades, including optical, electric, magnetic, aerodynamic and acoustic methods. However, these non-contact forces are susceptible to external disturbance, and so finding a way to make micro-scale object manipulation immune to external perturbations is challenging and remains elusive. Here we demonstrate a method based on new trapping mechanism to manipulate micro-scale object in a gas flow at ambient conditions. We first show that the micro-droplet is entrapped into a trapping ring constructed by a particular toroidal vortex. The vortex works as tweezers to control the position of the micro-droplet. We then show that the micro-droplet can be transported along the trapping ring. By virtue of the topological character of the gas flow, the transport path is able to bypass external strong perturbations automatically. We further demonstrate a topological transfer process of the micro-droplet between two hydrodynamic tweezers. Our method provides an integrated toolbox to manipulate a micro-scale object, with an intrinsic mechanism that protects the target object from external disturbances.

physics.flu-dyn↗