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Tong Li

Publications and source records attributed to Tong Li.

At least 145 records · Page 8Linked to original sources
arXiv2024

Theoretical Modeling and Bio-inspired Trajectory Optimization of A Multiple-locomotion Origami Robot

Recent research on mobile robots has focused on increasing their adaptability to unpredictable and unstructured environments using soft materials and structures. However, the determination of key design parameters and control over these compliant robots are predominantly iterated through experiments, lacking a solid theoretical foundation. To improve their efficiency, this paper aims to provide mathematics modeling over two locomotion, crawling and swimming. Specifically, a dynamic model is first devised to reveal the influence of the contact surfaces' frictional coefficients on displacements in different motion phases. Besides, a swimming kinematics model is provided using coordinate transformation, based on which, we further develop an algorithm that systematically plans human-like swimming gaits, with maximum thrust obtained. The proposed algorithm is highly generalizable and has the potential to be applied in other soft robots with multiple joints. Simulation experiments have been conducted to illustrate the effectiveness of the proposed modeling.

cs.RO↗
arXiv2024

High-precision measurement of the complex magneto-optical Kerr effect using weak measurement

The present paper introduces a quantum weak measurement (WM) scheme for the measurement of the complex magneto-optical Kerr effect (MOKE). We achieve the simultaneous measurement of the Kerr rotation angle and the ellipticity in a single WM process by utilizing two auxiliary pointers derived from the same meter state. The experimental measurement precision for both the Kerr rotation angle and the ellipticity is capable of reaching $10^{-4}$ deg. This technique is also employed for the determination of the complex magneto-optical constant $Q$. The proposed method overcomes the limitation of acquiring the complex magneto-optical Kerr parameters through a multi-step measurement process, which was previously encountered. This breakthrough holds immense significance for efficiently measuring and applying the complex MOKE with high precision and cost-effectiveness.

physics.optics↗
arXiv2024

Searching for high-frequency axion in quantum electromagnetodynamics through interface haloscopes

The so-called Witten effect implies the existence of electromagnetic interactions between axion and magnetic monopole due to the axion-photon coupling. A sound quantization in the presence of magnetic monopoles, called quantum electromagnetodynamics (QEMD), was utilized to construct a more generic axion-photon Lagrangian in the low-energy axion effective field theory. This generic axion-photon Lagrangian introduces the interactions between axion and two four-potentials, and leads to new axion-modified Maxwell equations. The interface haloscopes place an interface between two electromagnetic media with different properties and are desirable to search for high-mass axions $m_a\gtrsim \mathcal{O}(10)~μ{\rm eV}$. In this work, for the generic axion-photon couplings built under QEMD, we perform comprehensive calculations of the axion-induced propagating waves and energy flux densities in different interface setups. We also obtain the sensitivity to new axion-photon couplings for high-mass axions.

hep-ph↗
arXiv2023

MASTER: Market-Guided Stock Transformer for Stock Price Forecasting

Stock price forecasting has remained an extremely challenging problem for many decades due to the high volatility of the stock market. Recent efforts have been devoted to modeling complex stock correlations toward joint stock price forecasting. Existing works share a common neural architecture that learns temporal patterns from individual stock series and then mixes up temporal representations to establish stock correlations. However, they only consider time-aligned stock correlations stemming from all the input stock features, which suffer from two limitations. First, stock correlations often occur momentarily and in a cross-time manner. Second, the feature effectiveness is dynamic with market variation, which affects both the stock sequential patterns and their correlations. To address the limitations, this paper introduces MASTER, a MArkert-Guided Stock TransformER, which models the momentary and cross-time stock correlation and leverages market information for automatic feature selection. MASTER elegantly tackles the complex stock correlation by alternatively engaging in intra-stock and inter-stock information aggregation. Experiments show the superiority of MASTER compared with previous works and visualize the captured realistic stock correlation to provide valuable insights.

cs.CE↗
arXiv2023

Towards Fine-Grained Explainability for Heterogeneous Graph Neural Network

Heterogeneous graph neural networks (HGNs) are prominent approaches to node classification tasks on heterogeneous graphs. Despite the superior performance, insights about the predictions made from HGNs are obscure to humans. Existing explainability techniques are mainly proposed for GNNs on homogeneous graphs. They focus on highlighting salient graph objects to the predictions whereas the problem of how these objects affect the predictions remains unsolved. Given heterogeneous graphs with complex structures and rich semantics, it is imperative that salient objects can be accompanied with their influence paths to the predictions, unveiling the reasoning process of HGNs. In this paper, we develop xPath, a new framework that provides fine-grained explanations for black-box HGNs specifying a cause node with its influence path to the target node. In xPath, we differentiate the influence of a node on the prediction w.r.t. every individual influence path, and measure the influence by perturbing graph structure via a novel graph rewiring algorithm. Furthermore, we introduce a greedy search algorithm to find the most influential fine-grained explanations efficiently. Empirical results on various HGNs and heterogeneous graphs show that xPath yields faithful explanations efficiently, outperforming the adaptations of advanced GNN explanation approaches.

cs.LG↗
arXiv2023

Keyword spotting -- Detecting commands in speech using deep learning

Speech recognition has become an important task in the development of machine learning and artificial intelligence. In this study, we explore the important task of keyword spotting using speech recognition machine learning and deep learning techniques. We implement feature engineering by converting raw waveforms to Mel Frequency Cepstral Coefficients (MFCCs), which we use as inputs to our models. We experiment with several different algorithms such as Hidden Markov Model with Gaussian Mixture, Convolutional Neural Networks and variants of Recurrent Neural Networks including Long Short-Term Memory and the Attention mechanism. In our experiments, RNN with BiLSTM and Attention achieves the best performance with an accuracy of 93.9 %

cs.SD↗
arXiv2023

Oscillations of Ultralight Dark Photon into Gravitational Waves

The discovery of gravitational waves (GWs) opens a new window for exploring the physics of the early universe. Identifying the source of GWs and their spectra today turn out to be the important tasks so as to assist the experimental detection of stochastic GWs. In this paper, we investigate the oscillations of the ultralight dark photon (ULDP) into GWs in the dark halo. Assuming dark matter is composed of the ULDP and there are primordial dark magnetic fields (PDMFs) arising from the axion inflation and/or the dark phase transition, then the ULDP can oscillate into the GW when it passes through an environment of PDMFs. We derive the local energy density of GWs in the galaxy cluster induced by the instaneous oscillation of ULDP in the PDMFs. These stochastic local GWs exhibit a pulse-like spectrum, with frequency depending on the mass of the ULDP, and can be detected in Pulsar Timing Arrays (PTAs) or future space-based interferometers. We also find that the low-frequency GW signal observed by the NANOGrav collaboration and other PTA experiments can be explained by the oscillation of the ULDP in the PDMFs in the early universe.

hep-ph↗
arXiv2023

Numerical Convergence of Electromagnetic Responses with the Finite-Amplitude Method

The response of a nucleus to an electromagnetic probe is a key quantity to simulate photabsorption or photodeexcitation processes. For large calculations at the scale of the entire mass table, this response can be estimated by linear response theory. Thanks to the introduction of the finite-amplitude method (FAM), calculations are computationally efficient. In this paper, we investigate in more details the convergence of FAM calculations of the response function as a function of the parameters controlling the numerical implementation of the theory. We show that the response is much less sensitive to the details of the single-particle basis than, e.g., Hartree-Fock-Bogoliubov calculations.

nucl-th↗
arXiv2023

How AI-driven Digital Twins Can Empower Mobile Networks

The growing complexity of next-generation networks exacerbates the modeling and algorithmic flaws of conventional network optimization methodology. In this paper, we propose a mobile network digital twin (MNDT) architecture for 6G networks. To address the modeling and algorithmic shortcomings, the MNDT uses a simulation-optimization structure. The feedback from the network simulation engine, which serves as validation for the optimizer's decision outcomes, is used explicitly to train artificial intelligence (AI) empowered optimizers iteratively. In practice, we develop a network digital twin prototype system leveraging data-driven technology to accurately model the behaviors of mobile network elements (e.g., mobile users and base stations), wireless environments, and network performance. An AI-powered network optimizer has been developed based on the deployed MNDT prototype system for providing reliable and optimized network configurations. The results of the experiments demonstrate that the proposed MNDT infrastructure can provide practical network optimization solutions while adapting to the more complex environment.

cs.NI↗
arXiv2023

Quantum calculation of axion-photon transition in electromagnetodynamics for cavity haloscope

The Witten effect implies the presence of electric charge of magnetic monople and possible relationship between axion and dyon. The axion-dyon dynamics can be reliably built based on the quantum electromagnetodynamics (QEMD) which was developed by Schwinger and Zwanziger in 1960's. A generic low-energy axion-photon effective field theory can also be realized in the language of ``generalized symmetries'' with higher-form symmetries and background gauge fields. In this work, we implement the quantum calculation of axion-single photon transition rate inside a homogeneous electromagnetic field in terms of the new axion interaction Hamiltonian in QEMD. This quantum calculation can clearly imply the enhancement of conversion rate through resonant cavity in axion haloscope experiments. We also show the promising potentials on the cavity search of new axion-photon couplings in QEMD.

hep-ph↗
arXiv2023

Tri-photon at muon collider: a new process to probe the anomalous quartic gauge couplings

The muon collider has recently received a great deal of attention because of its ability to achieve both high energy and high luminosity. It plays as a gauge boson collider because the vector boson scattering (VBS) becomes the dominant production topology for Standard Model processes starting from a few TeV of collision energy. In this paper, we propose that the process of $μ^+μ^-$ annihilation into tri-photon is also very sensitive to the search of anomalous quartic gauge couplings (aQGCs). We investigate the projected constraints on the transverse operators contributing to aQGCs through $μ^+μ^-\to Z^\ast/γ^\ast\to γγγ$ at muon colliders. For the muon collider with $\sqrt{s}=3$ TeV and $\mathcal{L}=1\;{\rm ab}^{-1}$, the expected constraints are about two orders of magnitude stronger than those at the 13 TeV LHC.

hep-ph↗
arXiv2023

Measuring the anomalous quartic gauge couplings in the $W^+W^-\to W^+W^-$ process at muon collider using artificial neural networks

The muon collider provides a unique opportunity to study the vector boson scattering processes and dimension-8 operators contributing to anomalous quartic gauge couplings~(aQGCs). Because of the cleaner final state, it is easier to decode subprocess and certain operator couplings at a muon collider. We attempt to identify the anomalous $WWWW$ coupling in the exclusive $WW\to WW$ scattering in this paper. Since one aQGC can be induced by multiple dimension-8 operators, the study of one coupling can help to confine different operators. Meanwhile, singling out the $WW\to WW$ process can help to study the unitarity bounds. The vector boson scattering process corresponding to the anomalous $WWWW$ coupling is $μ^+μ^-\to νν\barν\barν\ell^+\ell^-$, with four (anti-)neutrinos in the final state, which brings troubles in phenomenological studies. In this paper, the machine learning method is used to tackle this problem. We find that, using the artificial neural network can extract the $W^+W^-\to W^+W^-$ contribution, and is helpful to reconstruct the center of mass energy of the subprocess which is important in the study of the Standard Model effective field theory. The sensitivities and the expected constraints on the dimension-8 operators at the muon collider with $\sqrt{s}=30$ TeV are presented. We demonstrate that the artificial neural networks exhibit great potential in the phenomenological study of processes with multiple neutrinos in the final state.

hep-ph↗
arXiv2023

Shining light on magnetic monopoles through high-energy muon colliders

The search for magnetic monopoles has been a longstanding concern of the physics community for nearly a century. However, up to now, the existence of elementary magnetic monopoles remains an open question. The electroweak 't Hooft-Polyakov monopoles have been predicted with mass at the order of 10 TeV. This mass scale is unreachable at current colliders. Recently, the muon colliders have gained much attention in the community due to technological developments. The advantages of the muon beam encourage us to raise the high-energy option and consider the high-energy muon collider as a unique opportunity to search for magnetic monopoles. This letter discusses the production of magnetic monopoles via the annihilation process and proposes the search for magnetic monopoles at future high-energy muon colliders.

hep-ph↗
arXiv2023

Using Adaptive Bandit Experiments to Increase and Investigate Engagement in Mental Health

Digital mental health (DMH) interventions, such as text-message-based lessons and activities, offer immense potential for accessible mental health support. While these interventions can be effective, real-world experimental testing can further enhance their design and impact. Adaptive experimentation, utilizing algorithms like Thompson Sampling for (contextual) multi-armed bandit (MAB) problems, can lead to continuous improvement and personalization. However, it remains unclear when these algorithms can simultaneously increase user experience rewards and facilitate appropriate data collection for social-behavioral scientists to analyze with sufficient statistical confidence. Although a growing body of research addresses the practical and statistical aspects of MAB and other adaptive algorithms, further exploration is needed to assess their impact across diverse real-world contexts. This paper presents a software system developed over two years that allows text-messaging intervention components to be adapted using bandit and other algorithms while collecting data for side-by-side comparison with traditional uniform random non-adaptive experiments. We evaluate the system by deploying a text-message-based DMH intervention to 1100 users, recruited through a large mental health non-profit organization, and share the path forward for deploying this system at scale. This system not only enables applications in mental health but could also serve as a model testbed for adaptive experimentation algorithms in other domains.

cs.AI↗
arXiv2023

Improving Knowledge Distillation via Transferring Learning Ability

Existing knowledge distillation methods generally use a teacher-student approach, where the student network solely learns from a well-trained teacher. However, this approach overlooks the inherent differences in learning abilities between the teacher and student networks, thus causing the capacity-gap problem. To address this limitation, we propose a novel method called SLKD.

cs.CV↗
arXiv2023

Probing general $U(1)'$ models with non-universal lepton charges at FASER/FASER2, COHERENT and long-baseline oscillation experiments

The general anomaly-free $U(1)'$ models allow non-universal lepton charges. We explore the sensitivities of FASER/FASER2, COHERENT and DUNE/T2HK precision experiments to the new gauge boson $Z'$ and the new CP-even scalar $ϕ$. With non-universal lepton charges, distinctive reaches at FASER/FASER2 emerge in the regime of low $m_{Z'}$ and small gauge coupling $g_{BL}$ for different $U(1)'$ charge setups. The COHERENT experiment and the future long-baseline experiments DUNE/T2HK also provide complementary probes to the available parameter space. For $m_ϕ< 2m_{Z'}$, the search for the scalar $ϕ$ at FASER/FASER2 is sensitive to the mixing angle between the scalar singlet and the SM Higgs. In the case of $m_ϕ> 2m_{Z'}$, the kinematically allowed decay $ϕ\to Z' Z'$ changes the lifetime and decay rates of the scalar $ϕ$. The sensitivity reach highly depends on the $Z'$ mass and the gauge coupling $g_{BL}$.

hep-ph↗
arXiv2023

MARC: Multipolicy and Risk-aware Contingency Planning for Autonomous Driving

Generating safe and non-conservative behaviors in dense, dynamic environments remains challenging for automated vehicles due to the stochastic nature of traffic participants' behaviors and their implicit interaction with the ego vehicle. This paper presents a novel planning framework, Multipolicy And Risk-aware Contingency planning (MARC), that systematically addresses these challenges by enhancing the multipolicy-based pipelines from both behavior and motion planning aspects. Specifically, MARC realizes a critical scenario set that reflects multiple possible futures conditioned on each semantic-level ego policy. Then, the generated policy-conditioned scenarios are further formulated into a tree-structured representation with a dynamic branchpoint based on the scene-level divergence. Moreover, to generate diverse driving maneuvers, we introduce risk-aware contingency planning, a bi-level optimization algorithm that simultaneously considers multiple future scenarios and user-defined risk tolerance levels. Owing to the more unified combination of behavior and motion planning layers, our framework achieves efficient decision-making and human-like driving maneuvers. Comprehensive experimental results demonstrate superior performance to other strong baselines in various environments.

cs.RO↗
arXiv2023

Searching for heavy neutral lepton and lepton number violation through VBS at high-energy muon colliders

High-energy muon collider can play as an emitter of electroweak gauge bosons and thus leads to substantial vector boson scattering (VBS) processes. In this work, we investigate the production of heavy neutral lepton (HNL) $N$ and lepton number violation (LNV) signature through VBS at high-energy muon colliders. VBS induces LNV processes $W^\pm Z/γ\to \ell^\pm N \to \ell^\pm \ell^\pm W^\mp\to \ell^\pm \ell^\pm q\bar{q}'$ with an on-shell HNL $N$ at $μ^+μ^-$ colliders. In analogy to neutrinoless double-beta decay with the HNL in t-channel, the LNV signature $W^+W^+\to \ell^+\ell^+$ can also happen via VBS at same-sign muon collider. They provide clean and robust LNV signatures to tell the nature of Majorana HNLs and thus have more advantageous benefits than direct $μμ$ annihilation. We analyze the potential of searching for Majorana HNL and obtain the exclusion limits on mixing $V_{\ell N}$. Based on this same-sign lepton signature, we also obtain the sensitivity of muon collider to the Weinberg operator.

hep-ph↗