arXiv subjects

Tao Liu

Publications and source records attributed to Tao Liu.

At least 199 records · Page 11Linked to original sources

arXiv2024

Neural Slicer for Multi-Axis 3D Printing

We introduce a novel neural network-based computational pipeline as a representation-agnostic slicer for multi-axis 3D printing. This advanced slicer can work on models with diverse representations and intricate topology. The approach involves employing neural networks to establish a deformation mapping, defining a scalar field in the space surrounding an input model. Isosurfaces are subsequently extracted from this field to generate curved layers for 3D printing. Creating a differentiable pipeline enables us to optimize the mapping through loss functions directly defined on the field gradients as the local printing directions. New loss functions have been introduced to meet the manufacturing objectives of support-free and strength reinforcement. Our new computation pipeline relies less on the initial values of the field and can generate slicing results with significantly improved performance.

cs.CG↗

arXiv2024

Gluon-gluon fusion contribution to the productions of three gauge bosons at the LHC

Productions of multiple gauge bosons at the LHC are sensitive to triple or quartic gauge couplings and thus provide a sensitive test for the electroweak sector of the Standard Model and allow for a probe of new physics. In this work we calculate the gluon-gluon initiate state contribution to the productions of three gauge bosons ($Zγγ$, $ZZγ$ and $W^+W^-γ$) at the LHC, which is formally part of NNLO effects compared to the LO quark-antiquark channels corrections. For each process we present the ratio between the gluon-gluon channels contribution and the quark-antiquark channels contribution. We found that such a ratio for $Zγγ$ ($ZZγ$) is of the order of $10^{-3}$ ($10^{-4}$), much smaller than the corresponding ratio for the diboson production due to the decrease of gluon PDF when more particles appear in the final states. These small ratios imply that gluon-gluon fusion contribution is phenomenological negligible for the productions of $Zγγ$ and $ZZγ$. However, for $W^+W^-γ$ production, the ratio is about 5\%, which is of the same order of magnitude as the ratio for $W^+W^-$ production due to the big cancellation between the amplitudes of quark-antiquark channels. While such an effect can be neglected currently at the LHC, it may be accessible at the HL-LHC.

hep-ph↗

arXiv2024

Binary Supermassive Black Holes Orbiting Dark Matter Solitons: From the Dual AGN in UGC4211 to NanoHertz Gravitational Waves

We explore orbital implications of the Supermassive Black Hole (SMBH) binary in UGC4211 for the energy spectrum of stochastic gravitational wave background (SGWB), measured with pulsar timing. The SMBH binary in UGC4211 has a projected separation of $\sim 230\,$pc and relative velocity of $\sim 150\,$km/s along the line of sight. It orbits with a disk of gas and stars, with a total mass $\sim 1.7 \times 10^9 M_\odot$ that is several times larger than the combined SMBHs plus the observed gas and stars. The unseen mass can be naturally explained by a soliton of wave dark matter present within the SMBH orbit. Such a scenario is encouraging as during galaxy merger, the two precursor galactic solitons are expected to combine to generate a new soliton and hence bind the two initial SMBHs efficiently. Generalizing this scenario to the cosmological population of SMBH binaries, we show the SGWB spectrum produced by late-stage inspiraling is modified preferentially at low frequency by the presence of soliton. Finally, we demonstrate that the NANOGrav and EPTA data can be well-fit in this scenario, favoring $\{m_a, f_a\} \sim \{10^{-21.7} {\rm eV}, 10^{15.5} {\rm GeV}\}$ and $\{10^{-20.5} {\rm eV}, 10^{16.8} {\rm GeV}\}$ respectively when the UGC4211 data and the constraints from dwarf galaxies are also combined.

astro-ph.HE↗

arXiv2024

AniTalker: Animate Vivid and Diverse Talking Faces through Identity-Decoupled Facial Motion Encoding

The paper introduces AniTalker, an innovative framework designed to generate lifelike talking faces from a single portrait. Unlike existing models that primarily focus on verbal cues such as lip synchronization and fail to capture the complex dynamics of facial expressions and nonverbal cues, AniTalker employs a universal motion representation. This innovative representation effectively captures a wide range of facial dynamics, including subtle expressions and head movements. AniTalker enhances motion depiction through two self-supervised learning strategies: the first involves reconstructing target video frames from source frames within the same identity to learn subtle motion representations, and the second develops an identity encoder using metric learning while actively minimizing mutual information between the identity and motion encoders. This approach ensures that the motion representation is dynamic and devoid of identity-specific details, significantly reducing the need for labeled data. Additionally, the integration of a diffusion model with a variance adapter allows for the generation of diverse and controllable facial animations. This method not only demonstrates AniTalker's capability to create detailed and realistic facial movements but also underscores its potential in crafting dynamic avatars for real-world applications. Synthetic results can be viewed at https://github.com/X-LANCE/AniTalker.

cs.CV↗

arXiv2024

A Robust Data-Driven Iterative Control Method for Linear Systems with Bounded Disturbances

This paper proposes a new robust data-driven control method for linear systems with bounded disturbances, where the system model and disturbances are unknown. Due to disturbances, accurately determining the true system becomes challenging using the collected dataset. Therefore, instead of designing controllers directly for the unknown true system, an available approach is to design controllers for all systems compatible with the dataset. To overcome the limitations of using a single dataset and benefit from collecting more data, multiple datasets are employed in this paper. Furthermore, a new iterative method is developed to address the challenges of using multiple datasets. Based on this method, this paper develops an offline and online robust data-driven iterative control method, respectively. Compared to the existing robust data-driven controller method, both proposed control methods iteratively utilize multiple datasets in the controller design process. This allows for the incorporation of numerous datasets, potentially reducing the conservativeness of the designed controller. Particularly, the online controller is iteratively designed by continuously incorporating online collected data into the historical data to construct new datasets. Lastly, the effectiveness of the proposed methods is demonstrated using a batch reactor.

eess.SY↗

arXiv2024

Linear Convergence of Independent Natural Policy Gradient in Games with Entropy Regularization

This work focuses on the entropy-regularized independent natural policy gradient (NPG) algorithm in multi-agent reinforcement learning. In this work, agents are assumed to have access to an oracle with exact policy evaluation and seek to maximize their respective independent rewards. Each individual's reward is assumed to depend on the actions of all the agents in the multi-agent system, leading to a game between agents. We assume all agents make decisions under a policy with bounded rationality, which is enforced by the introduction of entropy regularization. In practice, a smaller regularization implies the agents are more rational and behave closer to Nash policies. On the other hand, agents with larger regularization acts more randomly, which ensures more exploration. We show that, under sufficient entropy regularization, the dynamics of this system converge at a linear rate to the quantal response equilibrium (QRE). Although regularization assumptions prevent the QRE from approximating a Nash equilibrium, our findings apply to a wide range of games, including cooperative, potential, and two-player matrix games. We also provide extensive empirical results on multiple games (including Markov games) as a verification of our theoretical analysis.

cs.LG↗

arXiv2024

Multi-Scale Heterogeneity-Aware Hypergraph Representation for Histopathology Whole Slide Images

Survival prediction is a complex ordinal regression task that aims to predict the survival coefficient ranking among a cohort of patients, typically achieved by analyzing patients' whole slide images. Existing deep learning approaches mainly adopt multiple instance learning or graph neural networks under weak supervision. Most of them are unable to uncover the diverse interactions between different types of biological entities(\textit{e.g.}, cell cluster and tissue block) across multiple scales, while such interactions are crucial for patient survival prediction. In light of this, we propose a novel multi-scale heterogeneity-aware hypergraph representation framework. Specifically, our framework first constructs a multi-scale heterogeneity-aware hypergraph and assigns each node with its biological entity type. It then mines diverse interactions between nodes on the graph structure to obtain a global representation. Experimental results demonstrate that our method outperforms state-of-the-art approaches on three benchmark datasets. Code is publicly available at \href{https://github.com/Hanminghao/H2GT}{https://github.com/Hanminghao/H2GT}.

cs.CV↗

arXiv2024

Dynamic temperature compensation for wavelength-stable entangled biphoton generation

A dynamic temperature compensation method is presented to stabilize the wavelength of the entangled biphoton source, which is generated via the spontaneous parametric down-conversion based on a MgO: PPLN waveguide. Utilizing the dispersive Fourier transformation technique combined with a digital proportional-integral-differential algorithm, the small amount of wavelength variation can be instantly identified and then compensated with active temperature correction. The long-term wavelength stability, assessed though Allan deviation, shows nearly a hundredfold enhancement, reaching 2.00*10^(-7) at the averaging time of 10000 s. It offers a simple, ready-to-use solution for precise wavelength control in quantum information processing.

quant-ph↗

arXiv2024

Beyond Traditional Threats: A Persistent Backdoor Attack on Federated Learning

Backdoors on federated learning will be diluted by subsequent benign updates. This is reflected in the significant reduction of attack success rate as iterations increase, ultimately failing. We use a new metric to quantify the degree of this weakened backdoor effect, called attack persistence. Given that research to improve this performance has not been widely noted,we propose a Full Combination Backdoor Attack (FCBA) method. It aggregates more combined trigger information for a more complete backdoor pattern in the global model. Trained backdoored global model is more resilient to benign updates, leading to a higher attack success rate on the test set. We test on three datasets and evaluate with two models across various settings. FCBA's persistence outperforms SOTA federated learning backdoor attacks. On GTSRB, postattack 120 rounds, our attack success rate rose over 50% from baseline. The core code of our method is available at https://github.com/PhD-TaoLiu/FCBA.

cs.CR↗

arXiv2024

Limits on High-Frequency Gravitational Waves in Planetary Magnetospheres

High-frequency gravitational waves (HFGWs) carry a wealth of information on the early Universe with a tiny comoving horizon and astronomical objects of small scale but with dense energy. We demonstrate that the nearby planets, such as Earth and Jupiter, can be utilized as a laboratory for detecting the HFGWs. These GWs are then expected to convert to signal photons in the planetary magnetosphere, across the frequency band of astronomical observation. As a proof of concept, we present the first limits from the existing low-Earth-orbit satellite for specific frequency bands and project the sensitivities for the future more-dedicated detections. The first limits from Juno, the latest mission orbiting Jupiter, are also presented. Attributed to the long path of effective GW-photon conversion and the wide angular distribution of signal flux, we find that these limits are highly encouraging, for a broad frequency range including a large portion unexplored before.

hep-ph↗

arXiv2024

The Share-a-Ride Problem with mixed ride-hailing and logistic vehicles

This study explores the potential of using ride-hailing vehicles (RVs) for integrated passenger and freight transport based on shared mobility. In this crowd-sourced mode, ride-hailing platforms can profit from parcel delivery services, and logistics companies can reduce operational costs by utilizing the capacities of RVs. The Share-a-Ride problem with ride-hailing and logistic vehicles (SARP-RL) determines the number of logistic vehicles (LVs) and the assignment of passenger/parcel requests to RVs and LVs, aiming at maximizing the total RV profits and minimizing logistic costs. An exact solution framework is proposed by (1) generating a feasible trip that serves a given set of requests at maximal profits; (2) generating all feasible trips for the entire set of passenger and parcel requests via an efficient enumeration method; and (3) finding all Pareto-optimal solutions of the bi-objective problem via an $\varepsilon$-constraint method. Not only is the proposed method exact, it also converts the NP-hard problem to a simple vehicle-trip matching problem. More importantly, the total computational time can be compressed to an arbitrary degree via straightforward parallelization. A case study of the Manhattan network demonstrates the solution characteristics of SARP-RL. The results indicate that: (i) Coordinating RV and LV operations to serve passenger and parcel requests (SARP-RL) can simultaneously reduce logistic costs and increase RV profits. (ii) Key factors influencing the performance of SARP-RL include the RV fleet size, spatial distribution of parcel requests, passenger/parcel request ratio, and unit price of transport service, which are quantitatively analyzed to offer managerial insights for real-world implementation.

math.OC↗

arXiv2024

OpenEval: Benchmarking Chinese LLMs across Capability, Alignment and Safety

The rapid development of Chinese large language models (LLMs) poses big challenges for efficient LLM evaluation. While current initiatives have introduced new benchmarks or evaluation platforms for assessing Chinese LLMs, many of these focus primarily on capabilities, usually overlooking potential alignment and safety issues. To address this gap, we introduce OpenEval, an evaluation testbed that benchmarks Chinese LLMs across capability, alignment and safety. For capability assessment, we include 12 benchmark datasets to evaluate Chinese LLMs from 4 sub-dimensions: NLP tasks, disciplinary knowledge, commonsense reasoning and mathematical reasoning. For alignment assessment, OpenEval contains 7 datasets that examines the bias, offensiveness and illegalness in the outputs yielded by Chinese LLMs. To evaluate safety, especially anticipated risks (e.g., power-seeking, self-awareness) of advanced LLMs, we include 6 datasets. In addition to these benchmarks, we have implemented a phased public evaluation and benchmark update strategy to ensure that OpenEval is in line with the development of Chinese LLMs or even able to provide cutting-edge benchmark datasets to guide the development of Chinese LLMs. In our first public evaluation, we have tested a range of Chinese LLMs, spanning from 7B to 72B parameters, including both open-source and proprietary models. Evaluation results indicate that while Chinese LLMs have shown impressive performance in certain tasks, more attention should be directed towards broader aspects such as commonsense reasoning, alignment, and safety.

cs.CL↗

arXiv2024

SemanticHuman-HD: High-Resolution Semantic Disentangled 3D Human Generation

With the development of neural radiance fields and generative models, numerous methods have been proposed for learning 3D human generation from 2D images. These methods allow control over the pose of the generated 3D human and enable rendering from different viewpoints. However, none of these methods explore semantic disentanglement in human image synthesis, i.e., they can not disentangle the generation of different semantic parts, such as the body, tops, and bottoms. Furthermore, existing methods are limited to synthesize images at $512^2$ resolution due to the high computational cost of neural radiance fields. To address these limitations, we introduce SemanticHuman-HD, the first method to achieve semantic disentangled human image synthesis. Notably, SemanticHuman-HD is also the first method to achieve 3D-aware image synthesis at $1024^2$ resolution, benefiting from our proposed 3D-aware super-resolution module. By leveraging the depth maps and semantic masks as guidance for the 3D-aware super-resolution, we significantly reduce the number of sampling points during volume rendering, thereby reducing the computational cost. Our comparative experiments demonstrate the superiority of our method. The effectiveness of each proposed component is also verified through ablation studies. Moreover, our method opens up exciting possibilities for various applications, including 3D garment generation, semantic-aware image synthesis, controllable image synthesis, and out-of-domain image synthesis.

cs.CV↗

arXiv2024

Stochastic Wave Dark Matter with Fermi-LAT $γ$-ray Pulsar Timing Array

Pulsar timing arrays (PTAs) can detect disturbances in the fabric of spacetime on a galactic scale by monitoring the arrival time of pulses from millisecond pulsars (MSPs). Recent advancements have enabled the use of $γ$-ray radiation emitted by MSPs, in addition to radio waves, for PTA experiments. Wave dark matter (DM), a prominent class of DM candidates, can be detected with PTAs due to its periodic perturbations of the spacetime metric. In response to this development, we perform in this Letter a first analysis of applying the $γ$-ray PTA to detect the ultralight axion-like wave DM, with the data of Fermi Large Area Telescope (Fermi-LAT). Despite its much smaller collecting area, the Fermi-LAT $γ$-ray PTA demonstrates a promising sensitivity potential. We show that the upper limits not far from those of the dedicated radio-PTA projects can be achieved. Moreover, we initiate a cross-correlation analysis using the data of two Fermi-LAT pulsars. The cross-correlation of phases, while carrying key information on the source of the spacetime perturbations, has been ignored in the existing data analyses for the wave DM detection with PTAs. Our analysis indicates that taking this information into account can improve the sensitivity to wave DM by $\gtrsim 50\%$ at masses below $10^{-23}$ eV.

astro-ph.HE↗

arXiv2024

Foot Shape-Dependent Resistive Force Model for Bipedal Walkers on Granular Terrains

Legged robots have demonstrated high efficiency and effectiveness in unstructured and dynamic environments. However, it is still challenging for legged robots to achieve rapid and efficient locomotion on deformable, yielding substrates, such as granular terrains. We present an enhanced resistive force model for bipedal walkers on soft granular terrains by introducing effective intrusion depth correction. The enhanced force model captures fundamental kinetic results considering the robot foot shape, walking gait speed variation, and energy expense. The model is validated by extensive foot intrusion experiments with a bipedal robot. The results confirm the model accuracy on the given type of granular terrains. The model can be further integrated with the motion control of bipedal robotic walkers.

cs.RO↗

arXiv2024

HMD-Poser: On-Device Real-time Human Motion Tracking from Scalable Sparse Observations

It is especially challenging to achieve real-time human motion tracking on a standalone VR Head-Mounted Display (HMD) such as Meta Quest and PICO. In this paper, we propose HMD-Poser, the first unified approach to recover full-body motions using scalable sparse observations from HMD and body-worn IMUs. In particular, it can support a variety of input scenarios, such as HMD, HMD+2IMUs, HMD+3IMUs, etc. The scalability of inputs may accommodate users' choices for both high tracking accuracy and easy-to-wear. A lightweight temporal-spatial feature learning network is proposed in HMD-Poser to guarantee that the model runs in real-time on HMDs. Furthermore, HMD-Poser presents online body shape estimation to improve the position accuracy of body joints. Extensive experimental results on the challenging AMASS dataset show that HMD-Poser achieves new state-of-the-art results in both accuracy and real-time performance. We also build a new free-dancing motion dataset to evaluate HMD-Poser's on-device performance and investigate the performance gap between synthetic data and real-captured sensor data. Finally, we demonstrate our HMD-Poser with a real-time Avatar-driving application on a commercial HMD. Our code and free-dancing motion dataset are available https://pico-ai-team.github.io/hmd-poser

cs.CV↗

arXiv2024

Longitudinal beam dynamics design fpr Super Tau-Charm Facility

The project of Super Tau-Charm Facility (STCF) proposed in China, as a new-generation high-luminosity $e^+e^-$ collider in the low-energy region with the center-of-mass energy of 2-7 GeV, is well underway. The luminosity is targeted at $1.0\times10^{35} cm^{-2}s^{-1}$ at the optimized beam energy of 2 GeV. Longitudinal beam dynamics becomes of great importance for the STCF due to the constraints from the novel beam-beam effect called coherent X-Z instability and severe beam collective effects. In this paper, we will develop an iterative optimization model for the STCF longitudinal beam dynamics design, which takes into account the influence of transverse dynamics, coherent X-Z instability, and collective effects.

physics.acc-ph↗

arXiv2024

Light quark mediated Higgs boson production in association with a jet at the next-to-next-leading order and beyond

We study the light quark effect on the Higgs boson production in association with a jet at the LHC in the intermediate transverse momentum region between the quark and the Higgs boson mass scales. Though the effect is suppressed by the small Yukawa coupling, it is enhanced by large logarithms of the quark mass ratio to the Higgs boson mass or transverse momentum. Following a remarkable success of the logarithmic expansion [39] for the prediction of the next-to-next-to-leading bottom quark contribution to the total cross section of the Higgs boson production we extend the analysis to its kinematical distributions. A new factorization formula is derived for the light quark mediated $gg\to Hg$ amplitudes and the differential cross section of the process is computed in the logarithmic approximation, which is used for an estimate of the bottom quark effect at the next-to-next-to-leading order.

hep-ph↗