arXiv subjects

Yanping Liu

Publications and source records attributed to Yanping Liu.

14 recordsLinked to original sources

arXiv2025

TransBench: Breaking Barriers for Transferable Graphical User Interface Agents in Dynamic Digital Environments

Graphical User Interface (GUI) agents, which autonomously operate on digital interfaces through natural language instructions, hold transformative potential for accessibility, automation, and user experience. A critical aspect of their functionality is grounding - the ability to map linguistic intents to visual and structural interface elements. However, existing GUI agents often struggle to adapt to the dynamic and interconnected nature of real-world digital environments, where tasks frequently span multiple platforms and applications while also being impacted by version updates. To address this, we introduce TransBench, the first benchmark designed to systematically evaluate and enhance the transferability of GUI agents across three key dimensions: cross-version transferability (adapting to version updates), cross-platform transferability (generalizing across platforms like iOS, Android, and Web), and cross-application transferability (handling tasks spanning functionally distinct apps). TransBench includes 15 app categories with diverse functionalities, capturing essential pages across versions and platforms to enable robust evaluation. Our experiments demonstrate significant improvements in grounding accuracy, showcasing the practical utility of GUI agents in dynamic, real-world environments. Our code and data will be publicly available at GitHub.

cs.HC↗

arXiv2025

Single-Cell Trajectory Reconstruction Reveals Migration Potential of Cell Populations

Cell migration, which is strictly regulated by intracellular and extracellular cues, is crucial for normal physiological processes and the progression of certain diseases. However, there is a lack of an efficient approach to analyze super-statistical and time-varying characteristics of cell migration based on single trajectories. Here, we propose an approach to reconstruct single-cell trajectories, which incorporates wavelet transform, power spectrum of an OU-process, and fits of the power spectrum to analyze statistical and time-varying properties of customized target-finding and migration metrics. Our results reveal diverse relationships between motility parameters and dynamic metrics, especially the existence of an optimal parameter range. Moreover, the analysis reveals that the loss of Arpin protein enhances the migration potential of D. discoideum, and a previously reported result that the rescued amoeba is distinguishable from the wild-type amoeba. Significantly, time-varying dynamic metrics emerge periodic phenomena under the influence of irregularly changing parameters, which correlates with migration potential. Our analysis suggests that the approach provides a powerful tool for estimating time-dependent migration potential and statistical features of single-cell trajectories, enabling a better understanding of the relationship between intracellular proteins and cellular behaviors. This also provides more insights on the migration dynamics of single cells and cell populations.

physics.bio-ph↗

arXiv2024

Reinforcement Learning Enhancing Entanglement for Two-Photon-Driven Rabi Model

A control scheme is proposed that leverages reinforcement learning to enhance entanglement by modulating the two-photon-driven amplitude in a Rabi model. The quantum phase diagram versus the amplitude of the two-photon process and the coupling between the cavity field and the atom in the Rabi model, is indicated by the energy spectrum of the hybrid system, the witness of entanglement, second order correlation, and negativity of Wigner function. From a dynamical perspective, the behavior of entanglement can reflect the phase transition and the reinforcement learning agent is employed to produce temporal sequences of control pulses to enhance the entanglement in the presence of dissipation. The entanglement can be enhanced in different parameter regimes and the control scheme exhibits robustness against dissipation. The replaceability of the controlled system and the reinforcement learning module demonstrates the generalization of this scheme. This research paves the way of positively enhancing quantum resources in non-equilibrium systems.

quant-ph↗

arXiv2024

Intelligent Angle Map-based Beam Alignment for RIS-aided mmWave Communication Networks

Recently, reconfigurable intelligent surface (RIS) has been widely used to enhance the performance of millimeter wave (mmWave) communication systems, making beam alignment more challenging. To ensure efficient communication, this paper proposes a novel intelligent angle map-based beam alignment scheme for both general user equipments (UEs) and RIS-aided UEs simultaneously in a fast and effective way. Specifically, we construct a beam alignment architecture that utilizes only angular information. To obtain the angle information, the currently hottest seq2seq model - the Transformer - is introduced to offline learn the relationship between UE geographic location and the corresponding optimal beam direction. Based on the powerful machine learning model, the location-angle mapping function, i.e., the angle map, can be built. As long as the location information of UEs is available, the angle map can make the acquisition of beam alignment angles effortless. In the simulation, we utilize a ray-tracing-based dataset to verify the performance of the proposed scheme. It is demonstrated that the proposed scheme can achieve high-precision beam alignment and remarkable system performance without any beam scanning.

cs.IT↗

arXiv2024

Nonparametric End-to-End Probabilistic Forecasting of Distributed Generation Outputs Considering Missing Data Imputation

In this paper, we introduce a nonparametric end-to-end method for probabilistic forecasting of distributed renewable generation outputs while including missing data imputation. Firstly, we employ a nonparametric probabilistic forecast model utilizing the long short-term memory (LSTM) network to model the probability distributions of distributed renewable generations' outputs. Secondly, we design an end-to-end training process that includes missing data imputation through iterative imputation and iterative loss-based training procedures. This two-step modeling approach effectively combines the strengths of the nonparametric method with the end-to-end approach. Consequently, our approach demonstrates exceptional capabilities in probabilistic forecasting for the outputs of distributed renewable generations while effectively handling missing values. Simulation results confirm the superior performance of our approach compared to existing alternatives.

eess.SY↗

arXiv2023

Morphological entropy encodes cellular migration strategies on multiple length scales

Cell migration is crucial to many physiological and pathological processes. During migration, a cell adapts its morphology, including the overall morphology and nucleus morphology, in response to various cues in complex microenvironments, e.g. topotaxis and chemotaxis. Thus, cellular morphology dynamics can encode migration strategies based on which various migration mechanisms can be inferred. However, how to decipher cell migration mechanisms encoded in the morphology dynamics remains a challenging problem. Here we introduce a novel universal metric, namely cell morphological entropy (CME), by combining parametric morphological analysis with Shannon entropy. The utility of CME, which accurately quantifies the complex cellular morphology on multiple length scales through the deviation from the perfect circular shape, is demonstrated using a variety of normal and tumorous cell lines in distinct in vitro microenvironments. Our results reveal that 1) the effects of geometric constraints on cell nucleus, 2) the emerging interplays of MCF-10A cells migrating on collagen gel, and 3) the critical transition of tumor spheroid from proliferation to invasion. The analysis indicates that the CME offers a physically interpretable and efficient tool to quantify morphology on multiple length scales in real-time, which provides more insights into cell migration, and further contributing to the understanding of the diverse behavioral modes as well as collective cell motility in more complex microenvironment.

physics.bio-ph↗

arXiv2021

Self-repairing high entropy oxides

All biological organisms, from plants to living creatures, can heal minor wounds and damage. The realization of a similar self-healing capacity in inorganic materials has been a design target for many decades. This would represent a breakthrough in materials engineering, enabling many novel technological applications, since such materials would be able to resist damage caused by electromagnetic irradiation and/or mechanical impact. Here we demonstrate that a high-entropy oxide is intrinsically capable of undergoing an autonomous self-repairing process. Transmission electron microscopy revealed that the spinel structure of (AlCoCrCu0.5FeNi)3O4 can regrow and repair itself at the atomic level when damaged. Density functional theory calculations reveal that the extra enthalpy stored in the high entropy material during fabrication can be released to effectively heal macroscopic defects by regrowing into a partially ordered state. This extraordinary self-repairing phenomenon makes this new material highly desirable as a coating, enabling structures used in harsh environments to better withstand damage, such as cosmic irradiation in space, nuclear irradiation in nuclear power facilities, or tribological damage. Most importantly, our results set the general design principles for the synthesis of self-repairing materials.

cond-mat.mtrl-sci↗

arXiv2020

Shannon Entropy for Time-Varying Persistence of Cell Migration

Cell migration, which can be significantly affected by intracellular signaling pathways (ICSP) and extracellular matrix (ECM), plays a crucial role in many physiological and pathological processes. The efficiency of cell migration, which is typically modeled as a persistent random walk (PRW), depends on two critical motility parameters, i.e., migration speed and persistence. It is generally very challenging to efficiently and accurately extract these key dynamics parameters from noisy experimental data. Here, we employ the normalized Shannon entropy to quantify the deviation of cell migration dynamics from that of diffusive/ballistic motion as well as to derive the persistence of cell migration based on the Fourier power spectrum of migration velocities. Moreover, we introduce the time-varying Shannon entropy based on the wavelet power spectrum of cellular dynamics and demonstrate its superior utility to characterize the time-dependent persistence of cell migration, which is typically resulted from complex and time-varying intra or extra-cellular mechanisms. We employ our approach to analyze trajectory data of in vitro cell migration regulated by distinct intracellular and extracellular mechanisms, exhibiting a rich spectrum of dynamic characteristics. Our analysis indicates that the combination of Shannon entropy and wavelet transform offers a simple and efficient tool to estimate the persistence of cell migration, which may also reflect the real-time effects of ICSP-ECM to some extent.

physics.bio-ph↗

arXiv2020

Deriving Time-varying Cellular Motility Parameters via Wavelet Analysis

Cell migration is an indispensable physiological and pathological process for normal tissue development and cancer metastasis, which is greatly regulated by intracellular signal pathways and extracellular microenvironment (ECM). However, there is a lack of adequate tools to analyze the time-varying cell migration characteristics because of the effects of some factors, i.e., the ECM including the time-dependent local stiffness due to microstructural remodeling by migrating cells. Here, we develop an approach to derive the time-dependent motility parameters from cellular trajectories, based on the time-varying persistent random walk model. In particular, we employ the wavelet denoising and wavelet transform to investigate cell migration velocities and obtain the wavelet power spectrum. The time-dependent motility parameters are subsequently derived via Lorentzian power spectrum. Our analysis shows that the combination of wavelet denoising, wavelet transform and Lorentzian power spectrum provides a powerful tool to derive accurately the time-dependent motility parameters, which reflects the time-varying microenvironment characteristics to some extent.

physics.bio-ph↗

arXiv2019

Organic Thermoelectric Textiles for Harvesting Thermal Energy and Powering Electronics

Wearable thermoelectric devices show promises to generate electricity in a ubiquitous, unintermittent and noiseless way for on-body applications. Three-dimensional thermoelectric textiles (TETs) outperform other types in smart textiles owing to their out-of-plane thermoelectric generation and good structural conformability with fabrics. Yet, there has been lack of efficient strategies in scalable manufacture of TETs for sustainably powering electronics. Here, we fabricate organic spacer fabric shaped TETs by knitting carbon nanotube yarn based segmented thermoelectric yarn in large scale. Combing finite element analysis with experimental evaluation, we elucidate that the fabric structure significantly influences the power generation. The optimally designed TET with good wearability and stability shows high output power density of 51.5 mW/m2 and high specific power of 173.3 uW/(g.K) at delta T= 47.5 K. The promising on-body applications of the TET in directly and continuously powering electronics for healthcare and environmental monitoring is fully demonstrated. This work will broaden the research vision and provide new routines for developing high-performance and large-scale TETs toward practical applications.

physics.app-ph↗

arXiv2018

IEEE 802.11ay based mmWave WLANs: Design Challenges and Solutions

Millimeter-wave (mmWave) with large spectrum available is considered as the most promising frequency band for future wireless communications. The IEEE 802.11ad and IEEE 802.11ay operating on 60 GHz mmWave are the two most expected wireless local area network (WLAN) technologies for ultra-high-speed communications. For the IEEE 802.11ay standard still under development, there are plenty of proposals from companies and researchers who are involved with the IEEE 802.11ay task group. In this survey, we conduct a comprehensive review on the medium access control layer (MAC) related issues for the IEEE 802.11ay, some cross-layer between physical layer (PHY) and MAC technologies are also included. We start with MAC related technologies in the IEEE 802.11ad and discuss design challenges on mmWave communications, leading to some MAC related technologies for the IEEE 802.11ay. We then elaborate on important design issues for IEEE 802.11ay. Specifically, we review the channel bonding and aggregation for the IEEE 802.11ay, and point out the major differences between the two technologies. Then, we describe channel access and channel allocation in the IEEE 802.11ay, including spatial sharing and interference mitigation technologies. After that, we present an in-depth survey on beamforming training (BFT), beam tracking, single-user multiple-input-multiple-output (SU-MIMO) beamforming and multi-user multiple-input-multiple-output (MU-MIMO) beamforming. Finally, we discuss some open design issues and future research directions for mmWave WLANs. We hope that this paper provides a good introduction to this exciting research area for future wireless systems.

cs.NI↗

arXiv2015

An Evolutionary Algorithm for Error-Driven Learning via Reinforcement

Although different learning systems are coordinated to afford complex behavior, little is known about how this occurs. This article describes a theoretical framework that specifies how complex behaviors that might be thought to require error-driven learning might instead be acquired through simple reinforcement. This framework includes specific assumptions about the mechanisms that contribute to the evolution of (artificial) neural networks to generate topologies that allow the networks to learn large-scale complex problems using only information about the quality of their performance. The practical and theoretical implications of the framework are discussed, as are possible biological analogs of the approach.

cs.AI↗

arXiv2015

Eye-Movement Control During the Reading of Chinese: An Analysis Using the Landolt-C Paradigm

Participants in an eye-movement experiment performed a modified version of the Landolt-C paradigm (Williams & Pollatsek, 2007) in which they searched for target squares embedded in linear arrays of spatially contiguous "words" (i.e., short sequences of squares having missing segments of variable size and orientation). Although the distributions of single- and first-of-multiple fixation locations replicated previous patterns suggesting saccade targeting (e.g., Yan, Kliegl, Richter, Nuthmann, & Shu, 2010), the distribution of all forward fixation locations was uniform, suggesting the absence of specific saccade targets. Furthermore, properties of the "words" (e.g., gap size) also influenced fixation durations and forward saccade length, suggesting that on-going processing affects decisions about when and where (i.e., how far) to move the eyes. The theoretical implications of these results for existing and future accounts of eye-movement control are discussed.

q-bio.NC↗

arXiv2015

Where are your eyes going to look during reading? A critical evaluation of saccade targeting hypothesis

The word-based account of saccades drawn by a central gravity of the PVL is supported by two pillars of evidences. The first is the finding of the initial fixation location on a word resembled a normal distribution (Rayner, 1979). The other is the finding of a moderate slope coefficient between the launch site and the landing site (b=0.49, see McConkie, Kerr, Reddix, & Zola, 1988). Four simulations on different saccade targeting strategies and one eye-movement experiment of Chinese reading have been conducted to evaluate the two findings. We demonstrated that the current understanding of the word-based account is not conclusive by showing an alternative strategy of the word-based account and identifying the problem with the calculation of the slope coefficient. Although almost all the computational models of eye-movement control during reading have built on the two findings, future efforts should be directed to understand the precise contribution of different saccade targeting strategies, and to know how their weighting might vary across desperate writing systems.

q-bio.NC↗