arXiv subjects

Tao Liu

Publications and source records attributed to Tao Liu.

At least 217 records · Page 12Linked to original sources

arXiv2024

High-order topological pumping on a superconducting quantum processor

High-order topological phases of matter refer to the systems of $n$-dimensional bulk with the topology of $m$-th order, exhibiting $(n-m)$-dimensional boundary modes and can be characterized by topological pumping. Here, we experimentally demonstrate two types of second-order topological pumps, forming four 0-dimensional corner localized states on a 4$\times$4 square lattice array of 16 superconducting qubits. The initial ground state of the system for half-filling, as a product of four identical entangled 4-qubit states, is prepared using an adiabatic scheme. During the pumping procedure, we adiabatically modulate the superlattice Bose-Hubbard Hamiltonian by precisely controlling both the hopping strengths and on-site potentials. At the half pumping period, the system evolves to a corner-localized state in a quadrupole configuration. The robustness of the second-order topological pump is also investigated by introducing different on-site disorder. Our work studies the topological properties of high-order topological phases from the dynamical transport picture using superconducting qubits, which would inspire further research on high-order topological phases.

quant-ph↗

arXiv2024

Chance Constrained Economic Dispatch Considering the Capability of Network Flexibility Against Renewable Uncertainties

This paper incorporates a continuous-type network flexibility into chance constrained economic dispatch (CCED). In the proposed model, both power generations and line susceptances are continuous variables to minimize the expected generation cost and guarantee a low probability of constraint violation in terms of generations and line flows under renewable uncertainties. From the analytical form of CCED, we figure out the mechanism of network flexibility against uncertainties -- while renewable uncertainties shrink the usable line capacities and aggravate transmission congestion, network flexibility mitigates congestion by re-routing the base-case line flows and reducing the line capacity shrinkage caused by uncertainties. Further, we propose an alternate iteration solver for this problem. By duality theory, we set up a master problem in the form of second-order cone programming to optimize generation dispatch scheme and a subproblem in the form of linear programming to optimize line susceptances. A satisfactory solution can be obtained efficiently by alternately solving these two problems. The proposed method applies to both Gaussian uncertainty and non-Gaussian uncertainty by means of Gaussian mixture model. The case studies on the IEEE 14-bus system and IEEE 118-bus system suggest that network flexibility can significantly improve operational economy while ensuring security under uncertainties.

eess.SY↗

arXiv2024

Post-Quantum Cryptography for Internet of Things: A Survey on Performance and Optimization

Due to recent development in quantum computing, the invention of a large quantum computer is no longer a distant future. Quantum computing severely threatens modern cryptography, as the hard mathematical problems beneath classic public-key cryptosystems can be solved easily by a sufficiently large quantum computer. As such, researchers have proposed PQC based on problems that even quantum computers cannot efficiently solve. Generally, post-quantum encryption and signatures can be hard to compute. This could potentially be a problem for IoT, which usually consist lightweight devices with limited computational power. In this paper, we survey existing literature on the performance for PQC in resource-constrained devices to understand the severeness of this problem. We also review recent proposals to optimize PQC algorithms for resource-constrained devices. Overall, we find that whilst PQC may be feasible for reasonably lightweight IoT, proposals for their optimization seem to lack standardization. As such, we suggest future research to seek coordination, in order to ensure an efficient and safe migration toward IoT for the post-quantum era.

cs.CR↗

arXiv2024

Catch the Butterfly: Peeking into the Terms and Conflicts among SPDX Licenses

The widespread adoption of third-party libraries (TPLs) in software development has accelerated the creation of modern software. However, this convenience comes with potential legal risks. Developers may inadvertently violate the licenses of TPLs, leading to legal issues. While existing studies have explored software licenses and potential incompatibilities, these studies often focus on a limited set of licenses or rely on low-quality license data, which may affect their conclusions. To address this gap, there is a need for a high-quality license dataset that encompasses a broad range of mainstream licenses to help developers navigate the complex landscape of software licenses, avoid potential legal pitfalls, and guide solutions for managing license compliance and compatibility in software development. To this end, we conduct the first work to understand the mainstream software licenses based on term granularity and obtain a high-quality dataset of 453 SPDX licenses with well-labeled terms and conflicts. Specifically, we first conduct a differential analysis of the mainstream platforms to understand the terms and attitudes of each license. Next, we propose a standardized set of license terms to capture and label existing mainstream licenses with high quality. Moreover, we include copyleft conflicts and conclude the three major types of license conflicts among the 453 SPDX licenses. Based on these, we carry out two empirical studies to reveal the concerns and threats from the perspectives of both licensors and licensees. One study provides an in-depth analysis of the similarities, differences, and conflicts among SPDX licenses, revisits the usage and conflicts of licenses in the NPM ecosystem, and draws conclusions that differ from previous work. Our studies reveal some insightful findings and disclose relevant analytical data, which set the stage for further research.

cs.SE↗

arXiv2024

Determining hyperelastic properties of the constituents of the mussel byssus system

The mussel byssus system, comprising of the adhesive plaque, distal thread, and proximal thread, plays a crucial role in the survival of marine mussels amongst ocean waves. Whilst recent research has explored the stress-strain behaviour of the distal thread and proximal thread through experimental approaches, little attention has been paid to the potential analytical or modelling methods within the current literature. In this work, analytical and finite element (FE) inverse methods were employed for the first time to identify the hyperelastic mechanical properties of both the plaque portion and the proximal thread. The results have demonstrated the feasibility of applied inverse methods in determining the mechanical properties of the constituents of the mussel byssus system, with the residual sum of squares of 0.0004 ($N^2$) and 0.01 ($mm^2$) for the proximal thread and the plaque portion, respectively. By leveraging mechanical and optical tests, this inverse methodology offers a simple and powerful means to anticipate the material properties for different portions of the mussel byssus system, thus providing insights for mimetic applications in engineering and materials design.

physics.bio-ph↗

arXiv2024

Robust Data-Driven Predictive Control for Unknown Linear Time-Invariant Systems

This paper presents a new robust data-driven predictive control scheme for unknown linear time-invariant systems by using input-state-output or input-output data based on whether the state is measurable. To remove the need for the persistently exciting (PE) condition of a sufficiently high order on pre-collected data, a set containing all systems capable of generating such data is constructed. Then, at each time step, an upper bound of a given objective function is derived for all systems in the set, and a feedback controller is designed to minimize this bound. The optimal control gain at each time step is determined by solving a set of linear matrix inequalities. We prove that if the synthesis problem is feasible at the initial time step, it remains feasible for all future time steps. Unlike current data-driven predictive control schemes based on behavioral system theory, our approach requires less stringent conditions for the pre-collected data, facilitating easier implementation. Further, the proposed predictive control scheme features an infinite prediction horizon, potentially resulting in superior overall control performance compared to existing methods with finite prediction horizons. The effectiveness of our proposed methods is demonstrated through application to an unknown and unstable batch reactor.

eess.SY↗

arXiv2024

Marine mussel plaque-inspired super soft, ductile and tough lattices

The rise of soft materials and additive manufacturing has provided the feasibility of developing elastomer lattices for various engineering applications. Although earlier attempts have been made to manufacture and test the elastomer lattices, a comprehensive understanding of their fracture behaviour has remained elusive. Inspired by the soft foams in mussel plaque core, the present study introduces the concept of soft, ductile, and tough elastomer lattices. Uniaxial and planar tension tests were conducted, and numerical models were developed to facilitate data interpretation. The applicability of planar tension for measuring the fracture energy of elastomer lattices was confirmed through tensile tests using samples with various crack lengths and double-peeling tests. In addition, the fracture energy of the elastomer lattices was sensitive to volume fraction but insensitive to initial crack length, thickness, and height. Notably, two distinct failure modes, truss failure and joint failure, were correlated with volume fraction. The specimens that failed at the transition between these two failure modes achieve a higher fracture energy. This study provides new insights to the materials science community and provides a foundation for the development of lightweight, damage-tolerant, and high-performance metamaterials.

cond-mat.mtrl-sci↗

arXiv2024

DiffDub: Person-generic Visual Dubbing Using Inpainting Renderer with Diffusion Auto-encoder

Generating high-quality and person-generic visual dubbing remains a challenge. Recent innovation has seen the advent of a two-stage paradigm, decoupling the rendering and lip synchronization process facilitated by intermediate representation as a conduit. Still, previous methodologies rely on rough landmarks or are confined to a single speaker, thus limiting their performance. In this paper, we propose DiffDub: Diffusion-based dubbing. We first craft the Diffusion auto-encoder by an inpainting renderer incorporating a mask to delineate editable zones and unaltered regions. This allows for seamless filling of the lower-face region while preserving the remaining parts. Throughout our experiments, we encountered several challenges. Primarily, the semantic encoder lacks robustness, constricting its ability to capture high-level features. Besides, the modeling ignored facial positioning, causing mouth or nose jitters across frames. To tackle these issues, we employ versatile strategies, including data augmentation and supplementary eye guidance. Moreover, we encapsulated a conformer-based reference encoder and motion generator fortified by a cross-attention mechanism. This enables our model to learn person-specific textures with varying references and reduces reliance on paired audio-visual data. Our rigorous experiments comprehensively highlight that our ground-breaking approach outpaces existing methods with considerable margins and delivers seamless, intelligible videos in person-generic and multilingual scenarios.

cs.CV↗

arXiv2024

WavMark: Watermarking for Audio Generation

Recent breakthroughs in zero-shot voice synthesis have enabled imitating a speaker's voice using just a few seconds of recording while maintaining a high level of realism. Alongside its potential benefits, this powerful technology introduces notable risks, including voice fraud and speaker impersonation. Unlike the conventional approach of solely relying on passive methods for detecting synthetic data, watermarking presents a proactive and robust defence mechanism against these looming risks. This paper introduces an innovative audio watermarking framework that encodes up to 32 bits of watermark within a mere 1-second audio snippet. The watermark is imperceptible to human senses and exhibits strong resilience against various attacks. It can serve as an effective identifier for synthesized voices and holds potential for broader applications in audio copyright protection. Moreover, this framework boasts high flexibility, allowing for the combination of multiple watermark segments to achieve heightened robustness and expanded capacity. Utilizing 10 to 20-second audio as the host, our approach demonstrates an average Bit Error Rate (BER) of 0.48\% across ten common attacks, a remarkable reduction of over 2800\% in BER compared to the state-of-the-art watermarking tool. See https://aka.ms/wavmark for demos of our work.

cs.SD↗

arXiv2023

Natural Actor-Critic for Robust Reinforcement Learning with Function Approximation

We study robust reinforcement learning (RL) with the goal of determining a well-performing policy that is robust against model mismatch between the training simulator and the testing environment. Previous policy-based robust RL algorithms mainly focus on the tabular setting under uncertainty sets that facilitate robust policy evaluation, but are no longer tractable when the number of states scales up. To this end, we propose two novel uncertainty set formulations, one based on double sampling and the other on an integral probability metric. Both make large-scale robust RL tractable even when one only has access to a simulator. We propose a robust natural actor-critic (RNAC) approach that incorporates the new uncertainty sets and employs function approximation. We provide finite-time convergence guarantees for the proposed RNAC algorithm to the optimal robust policy within the function approximation error. Finally, we demonstrate the robust performance of the policy learned by our proposed RNAC approach in multiple MuJoCo environments and a real-world TurtleBot navigation task.

cs.LG↗

arXiv2023

Interior inverse problem for global conservative multipeakon solutions of the Camassa-Holm equation

We consider the interior inverse problem associated with the global conservative {multipeakon} solution of the Camassa-Holm equation. Based on the inverse spectral theory on the half-line and the oscillation property of eigenfunctions, some {(non)}uniqueness results of {the} interior inverse problem are obtained. In addition, we give the trace formula, which connects the global conservative {multipeakon} solution with the corresponding eigenvalues and normalized eigenfunctions.

math.SP↗

arXiv2023

From Plate to Production: Artificial Intelligence in Modern Consumer-Driven Food Systems

Global food systems confront the urgent challenge of supplying sustainable, nutritious diets in the face of escalating demands. The advent of Artificial Intelligence (AI) is bringing in a personal choice revolution, wherein AI-driven individual decisions transform food systems from dinner tables, to the farms, and back to our plates. In this context, AI algorithms refine personal dietary choices, subsequently shaping agricultural outputs, and promoting an optimized feedback loop from consumption to cultivation. Initially, we delve into AI tools and techniques spanning the food supply chain, and subsequently assess how AI subfields$\unicode{x2013}$encompassing machine learning, computer vision, and speech recognition$\unicode{x2013}$are harnessed within the AI-enabled Food System (AIFS) framework, which increasingly leverages Internet of Things, multimodal sensors and real-time data exchange. We spotlight the AIFS framework, emphasizing its fusion of AI with technologies such as digitalization, big data analytics, biotechnology, and IoT extensively used in modern food systems in every component. This paradigm shifts the conventional "farm to fork" narrative to a cyclical "consumer-driven farm to fork" model for better achieving sustainable, nutritious diets. This paper explores AI's promise and the intrinsic challenges it poses within the food domain. By championing stringent AI governance, uniform data architectures, and cross-disciplinary partnerships, we argue that AI, when synergized with consumer-centric strategies, holds the potential to steer food systems toward a sustainable trajectory. We furnish a comprehensive survey for the state-of-the-art in diverse facets of food systems, subsequently pinpointing gaps and advocating for the judicious and efficacious deployment of emergent AI methodologies.

cs.CY↗

arXiv2023

Lift-up and streak waviness drive the self-sustained process in wall-bounded transition to turbulence

Flow field measurements from a Couette-Poiseuille experiment are used to examine quantitatively certain steps of the self-sustained process (SSP) of wall-bounded transition to turbulence. Although the different parts of the SSP have been discussed at large in the literature, direct measurements from experiment are scarce and, to our knowledge, the present results are the first to show, using a local analysis of the turbulent patterns, that: (1) the amplitude of streamwise rolls is related to streak waviness, bringing a quantitative picture to one of the main physical mechanisms of Waleffe's model of SSP ; and (2), at low waviness, direct measurements of the correlation between the streak and roll amplitudes, respectively probed by the streamwise and wall-normal velocity perturbations, quantify the lift-up effect.

physics.flu-dyn↗

arXiv2023

FVP: Fourier Visual Prompting for Source-Free Unsupervised Domain Adaptation of Medical Image Segmentation

Medical image segmentation methods normally perform poorly when there is a domain shift between training and testing data. Unsupervised Domain Adaptation (UDA) addresses the domain shift problem by training the model using both labeled data from the source domain and unlabeled data from the target domain. Source-Free UDA (SFUDA) was recently proposed for UDA without requiring the source data during the adaptation, due to data privacy or data transmission issues, which normally adapts the pre-trained deep model in the testing stage. However, in real clinical scenarios of medical image segmentation, the trained model is normally frozen in the testing stage. In this paper, we propose Fourier Visual Prompting (FVP) for SFUDA of medical image segmentation. Inspired by prompting learning in natural language processing, FVP steers the frozen pre-trained model to perform well in the target domain by adding a visual prompt to the input target data. In FVP, the visual prompt is parameterized using only a small amount of low-frequency learnable parameters in the input frequency space, and is learned by minimizing the segmentation loss between the predicted segmentation of the prompted target image and reliable pseudo segmentation label of the target image under the frozen model. To our knowledge, FVP is the first work to apply visual prompts to SFUDA for medical image segmentation. The proposed FVP is validated using three public datasets, and experiments demonstrate that FVP yields better segmentation results, compared with various existing methods.

cs.CV↗

arXiv2023

Provably Fast Convergence of Independent Natural Policy Gradient for Markov Potential Games

This work studies an independent natural policy gradient (NPG) algorithm for the multi-agent reinforcement learning problem in Markov potential games. It is shown that, under mild technical assumptions and the introduction of the \textit{suboptimality gap}, the independent NPG method with an oracle providing exact policy evaluation asymptotically reaches an $ε$-Nash Equilibrium (NE) within $\mathcal{O}(1/ε)$ iterations. This improves upon the previous best result of $\mathcal{O}(1/ε^2)$ iterations and is of the same order, $\mathcal{O}(1/ε)$, that is achievable for the single-agent case. Empirical results for a synthetic potential game and a congestion game are presented to verify the theoretical bounds.

cs.LG↗

arXiv2023

Quasi-static responses of marine mussel plaques attached to deformable wet substrates under directional tensions

Quantifying the response of marine mussel plaque attachment on wet surfaces remains a significant challenge to a mechanistic understanding of plaque adhesion. Here, we developed a customised microscopy system combined with two-dimensional (2D) in-situ digital image correlation (DIC) to quantify the in-plane deformation of a deformable substrate that interacts with a mussel plaque while under directional tension. By analysing the strain field in the substrate, we gained insight into how in-plane traction forces are transmitted from the mussel plaque to the underlying substrate. Finite element (FE) models were developed to assist the interpretation of the experimental measurement. Our study revealed a synergistic effect of pulling angle and substrate stiffness on plaque detachment, with mussel plaques anchoring to a 'stiff' substrate at a smaller pulling angle having mechanical advantages with higher load-bearing capacity and less plaque deformation. We identified two distinct failure modes, i.e., shear traction-governed failure (STGF) mode and normal traction-governed failure (NTGF). It was found that increasing the substrate stiffness or reducing the pulling angle resulted in a failure mode change from NTGF to STGF. Our findings offer new insights into the mechanistic understanding of plaque and substrate interaction, which provides a general plaque-inspired strategy for wet adhesion.

physics.bio-ph↗

arXiv2023

Distributed Multi-Time Slot Power Balancing Control of Power Systems with Energy Storage Devices

This paper studies a crucial problem in power system balancing control, i.e., the multi-time slot economic dispatch (MTSED) problem, for power grids with substantial renewables, synchronous generators (SGs), and energy storage devices (ESDs). The target of MTSED is to optimally coordinate active/reactive power outputs of all controllable units to meet a forecast net demand profile over multiple time slots within a receding finite time horizon. Firstly, the MTSED is formulated as an optimization problem with operational constraints, including the limits on the output of each controllable unit, ramping rates of SGss, energy levels of ESDs, and bus voltages. Then, a novel projection-based algorithm is developed to solve the problem in a distributed way. In particular, the distributed algorithm is not limited to solving the MTSED problem but also applies to more general optimization problems with both generic convex objective functions and hard feasibility constraints. Finally, case studies verify the effectiveness of the proposed method.

eess.SY↗

arXiv2023

Simulating Chern insulators on a superconducting quantum processor

The quantum Hall effect, fundamental in modern condensed matter physics, continuously inspires new theories and predicts emergent phases of matter. Here we experimentally demonstrate three types of Chern insulators with synthetic dimensions on a programable 30-qubit-ladder superconducting processor. We directly measure the band structures of the 2D Chern insulator along synthetic dimensions with various configurations of Aubry-André-Harper chains and observe dynamical localisation of edge excitations. With these two signatures of topology, our experiments implement the bulk-edge correspondence in the synthetic 2D Chern insulator. Moreover, we simulate two different bilayer Chern insulators on the ladder-type superconducting processor. With the same and opposite periodically modulated on-site potentials for two coupled chains, we simulate topologically nontrivial edge states with zero Hall conductivity and a Chern insulator with higher Chern numbers, respectively. Our work shows the potential of using superconducting qubits for investigating different intriguing topological phases of quantum matter.

quant-ph↗