arXiv subjects

Ju Wang

Publications and source records attributed to Ju Wang.

13 recordsLinked to original sources

arXiv2026

Fueling Dynamics towards Tunable Liquid Metal Machine

Self-propelled liquid metal-aluminum hybrid machines represent a promising class of autonomous motion systems capable of sustained movement without external power sources. While interactions between machines and their environment inevitably occur, the fundamental question of how spatial confinement affects the motion dynamics and the controllability of speed, direction, and lifetime of such liquid metal machines (LMMs) remains underexplored. Understanding these confined dynamics is essential for practical applications. Here, we present a comprehensive investigation of the non-symmetrical fueling principle governing the direction-tuning effect in LMMs. By confining LMMs within one-dimensional semi-open channels, we thoroughly disclose their impact and turning dynamics with different end obstacles throughout their lifecycle, with particular focus on fuel region morphological evolution, overall motion, and local flow characteristics after reaction times exceeding one hour. Utilizing ultra-high-speed imaging techniques, we systematically clarify how fuel region evolution and end-obstacle interactions influence symmetry-breaking mechanisms and reciprocating dynamics. Our findings reveal complex interactions between material properties, charge transfer processes, and fluid dynamics during end-turning processes, establishing a theoretical foundation for LMM driving dynamics. Beyond the theoretical mechanisms, we further demonstrate that LMM exhibits efficient heat and mass transfer capabilities, paving the way for applications in controlled transport systems and autonomous robotics.

physics.app-ph↗

arXiv2025

Biomimetic Liquid Metal Cell

Gallium-based liquid metals, as a broad category of emerging functional materials with unique physical, chemical, and biological properties, offer numerous possibilities for advancing intelligent systems. However, a basic query persistently remains for the complex liquid metal system: Is there a minimal functional unit that can fully capture its diversity of morphology and function? Cells, as the most basic structural and functional units of life, are small in scale but have complex structures, functions, and life activities. Analogous to nature, this article proposes the concept of liquid metal cells, and systematically explores their construction routes, sensing capabilities, motion behaviors, and potential applications. We first construct a multi-phase composite structure with liquid metal as the nucleus, ionic solution as the cytoplasm, and polymer as the cell membrane by developing a layered cryogenic molding method. Furthermore, we reveal that liquid metal cells exhibit inherently versatile responsive characteristics and self-adaptive behaviors to thermal, pressure, chemical, electrical, and magnetic fields, indicating "small world, vast potential". Based on these fundamental findings, we finally demonstrate the feasibility of utilizing liquid metal cells as sensors, fluidic valves, and material transport carriers in flow channels through dynamic control.

physics.app-ph↗

arXiv2024

Toward a Better Understanding of Fourier Neural Operators from a Spectral Perspective

In solving partial differential equations (PDEs), Fourier Neural Operators (FNOs) have exhibited notable effectiveness. However, FNO is observed to be ineffective with large Fourier kernels that parameterize more frequencies. Current solutions rely on setting small kernels, restricting FNO's ability to capture complex PDE data in real-world applications. This paper offers empirical insights into FNO's difficulty with large kernels through spectral analysis: FNO exhibits a unique Fourier parameterization bias, excelling at learning dominant frequencies in target data while struggling with non-dominant frequencies. To mitigate such a bias, we propose SpecB-FNO to enhance the capture of non-dominant frequencies by adopting additional residual modules to learn from the previous ones' prediction residuals iteratively. By effectively utilizing large Fourier kernels, SpecB-FNO achieves better prediction accuracy on diverse PDE applications, with an average improvement of 50%.

cs.LG↗

arXiv2023

Teacher-Student Architecture for Knowledge Distillation: A Survey

Although Deep neural networks (DNNs) have shown a strong capacity to solve large-scale problems in many areas, such DNNs are hard to be deployed in real-world systems due to their voluminous parameters. To tackle this issue, Teacher-Student architectures were proposed, where simple student networks with a few parameters can achieve comparable performance to deep teacher networks with many parameters. Recently, Teacher-Student architectures have been effectively and widely embraced on various knowledge distillation (KD) objectives, including knowledge compression, knowledge expansion, knowledge adaptation, and knowledge enhancement. With the help of Teacher-Student architectures, current studies are able to achieve multiple distillation objectives through lightweight and generalized student networks. Different from existing KD surveys that primarily focus on knowledge compression, this survey first explores Teacher-Student architectures across multiple distillation objectives. This survey presents an introduction to various knowledge representations and their corresponding optimization objectives. Additionally, we provide a systematic overview of Teacher-Student architectures with representative learning algorithms and effective distillation schemes. This survey also summarizes recent applications of Teacher-Student architectures across multiple purposes, including classification, recognition, generation, ranking, and regression. Lastly, potential research directions in KD are investigated, focusing on architecture design, knowledge quality, and theoretical studies of regression-based learning, respectively. Through this comprehensive survey, industry practitioners and the academic community can gain valuable insights and guidelines for effectively designing, learning, and applying Teacher-Student architectures on various distillation objectives.

cs.LG↗

arXiv2023

Poisson Pseudoalgebras

For any cocommutative Hopf algebra $H$ and a left $H$-module $V$, we construct an operad $\mathcal{P}^{cl}_H(V)$, which in the special case when $H$ is the algebra of polynomials in one variable reduces to the classical operad $\mathcal{P}^{cl}(V)$. Morphisms from the Lie operad to $\mathcal{P}^{cl}(V)$ correspond to Poisson vertex algebra structures on $V$. Likewise, our operad $\mathcal{P}^{cl}_H(V)$ gives rise to the notion of a Poisson pseudoalgebra; thus extending the notion of a Lie pseudoalgebra. As a byproduct of our construction, we introduce two cohomology theories for Poisson pseudoalgebras, generalizing the variational and classical cohomology of Poisson vertex algebras.

math.QA↗

arXiv2023

Multi-agent Attention Actor-Critic Algorithm for Load Balancing in Cellular Networks

In cellular networks, User Equipment (UE) handoff from one Base Station (BS) to another, giving rise to the load balancing problem among the BSs. To address this problem, BSs can work collaboratively to deliver a smooth migration (or handoff) and satisfy the UEs' service requirements. This paper formulates the load balancing problem as a Markov game and proposes a Robust Multi-agent Attention Actor-Critic (Robust-MA3C) algorithm that can facilitate collaboration among the BSs (i.e., agents). In particular, to solve the Markov game and find a Nash equilibrium policy, we embrace the idea of adopting a nature agent to model the system uncertainty. Moreover, we utilize the self-attention mechanism, which encourages high-performance BSs to assist low-performance BSs. In addition, we consider two types of schemes, which can facilitate load balancing for both active UEs and idle UEs. We carry out extensive evaluations by simulations, and simulation results illustrate that, compared to the state-of-the-art MARL methods, Robust-\ours~scheme can improve the overall performance by up to 45%.

cs.AI↗

arXiv2022

Teacher-Student Architecture for Knowledge Learning: A Survey

Although Deep Neural Networks (DNNs) have shown a strong capacity to solve large-scale problems in many areas, such DNNs with voluminous parameters are hard to be deployed in a real-time system. To tackle this issue, Teacher-Student architectures were first utilized in knowledge distillation, where simple student networks can achieve comparable performance to deep teacher networks. Recently, Teacher-Student architectures have been effectively and widely embraced on various knowledge learning objectives, including knowledge distillation, knowledge expansion, knowledge adaption, and multi-task learning. With the help of Teacher-Student architectures, current studies are able to achieve multiple knowledge-learning objectives through lightweight and effective student networks. Different from the existing knowledge distillation surveys, this survey detailedly discusses Teacher-Student architectures with multiple knowledge learning objectives. In addition, we systematically introduce the knowledge construction and optimization process during the knowledge learning and then analyze various Teacher-Student architectures and effective learning schemes that have been leveraged to learn representative and robust knowledge. This paper also summarizes the latest applications of Teacher-Student architectures based on different purposes (i.e., classification, recognition, and generation). Finally, the potential research directions of knowledge learning are investigated on the Teacher-Student architecture design, the quality of knowledge, and the theoretical studies of regression-based learning, respectively. With this comprehensive survey, both industry practitioners and the academic community can learn insightful guidelines about Teacher-Student architectures on multiple knowledge learning objectives.

cs.LG↗

arXiv2022

Detecting Soil Moisture Levels Using Battery-Free Wi-Fi Tag

Soil sensing plays an important role in increasing agricultural output and protecting soil sites. Existing soil sensing methods failed to achieve both high accuracy and low cost. In this paper, we design and implement a high-accuracy and low cost chipless soil moisture sensing system called SoilTAG. We propose a general chipless sensor design methodology which can allow us to customize the signal feature for sensing soil moisture, instead of blindly capturing the disturbance law of the soil. Based on this principle, we design a battery-free passive tag which can respond to different soil-moisture. Further, we optimize hardware and algorithm design of SoilTAG to locate the passive tag and extract its reflection signal feature to identify soil-moisture using WiFi signals. Extensive experimental results reveal that it can identify 2% absolute soil water content with a sensing distance up to 3m in open field. When the sensing distance is up to 13 m, it can also achieve 5% absolute soil-moisture sensing resolution.

eess.SP↗

arXiv2022

AlphaFold Accelerates Artificial Intelligence Powered Drug Discovery: Efficient Discovery of a Novel Cyclin-dependent Kinase 20 (CDK20) Small Molecule Inhibitor

The AlphaFold computer program predicted protein structures for the whole human genome, which has been considered as a remarkable breakthrough both in artificial intelligence (AI) application and structural biology. Despite the varying confidence level, these predicted structures still could significantly contribute to structure-based drug design of novel targets, especially the ones with no or limited structural information. In this work, we successfully applied AlphaFold in our end-to-end AI-powered drug discovery engines constituted of a biocomputational platform PandaOmics and a generative chemistry platform Chemistry42, to identify a first-in-class hit molecule of a novel target without an experimental structure starting from target selection towards hit identification in a cost- and time-efficient manner. PandaOmics provided the targets of interest and Chemistry42 generated the molecules based on the AlphaFold predicted structure, and the selected molecules were synthesized and tested in biological assays. Through this approach, we identified a small molecule hit compound for CDK20 with a Kd value of 8.9 +/- 1.6 uM (n = 4) within 30 days from target selection and after only synthesizing 7 compounds. Based on the available data, the second round of AI-powered compound generation was conducted and through which, a more potent hit molecule, ISM042-2 048, was discovered with a Kd value of 210.0 +/- 42.4 nM (n = 2), within 30 days and after synthesizing 6 compounds from the discovery of the first hit ISM042-2-001. To the best of our knowledge, this is the first reported small molecule targeting CDK20 and more importantly, this work is the first demonstration of AlphaFold application in the hit identification process in early drug discovery.

q-bio.BM↗

arXiv2019

Temperature dependent threshold for amplified emission from hybrid lead perovskite films

The optical amplification emission of hybrid lead perovskite attracted great research interests. We systematically examined and compared temperature dependent optical amplification behavior of a series of organic-inorganic hybrid perovskite films of (MA/FA)Pb(Br/I)3. The optical amplification threshold of the films showed considerable exponential increase towards the temperature increasing. We figured out that the critical temperature for the four films presented a sequence of FA+I < MA+I < FA+Br < MA+Br. Our systematical study is crucial for in depth understanding the fundamental mechanism of amplified emission of hybrid perovskite materials.

physics.app-ph↗

arXiv2013

Joint DOA and Array Manifold Estimation for a MIMO Array Using Two Calibrated Antennas

A simple scheme for joint direction of arrival (DOA) and array manifold estimation for a MIMO array system is proposed, where only two transmit antennas are calibrated initially. It first obtains a set of initial DOA results by employing a rotational invariance property between two sets of received data, and then more accurate DOA and array manifold estimation is obtained through a local searching algorithm with several iterations. No strict half wavelength spacing is required for the uncalibrated antennas to avoid the spatial aliasing problem.

cs.IT↗

arXiv2012

Ultrafast modulation of near-field heat transfer with tunable metamaterials

We propose a mechanism of active near-field heat transfer modulation relying on externally tunable metamaterials. A large modulation effect is observed and can be explained by the coupling of surface modes, which is dramatically varied in the presence of controllable magnetoelectric coupling in metamaterials. We finally discuss how a practical picosecond-scale thermal modulator can be made. This modulator allows manipulating nanoscale heat flux in an ultrafast and noncontact (by optical means) manner.

cond-mat.mes-hall↗

arXiv2012

High-precision Absolute Distance Measurements over a Long Range Based on Two Optoelectronic Oscillators

Absolute distance measurement (ADM) over a long range has been studied intensely over the last several decades, due to its important applications in large-scale manufacturing and outer space explorations [1-5]. Traditional absolute distance measurements utilize detection of time-of-flight information, detection of phase shift, or a combination of the two [6-17]. In this paper, we present a novel scheme for high-precision ADM over a long range based on frequency detection by using two optoelectronic oscillators (OEO) to convert distance information to frequency information. By taking advantage of accumulative magnification theory, the absolute error of the measured distance is magnified by about 2*10E5 times, which makes the precision of the measured distance significantly improved. In our experiments, the maximum error is 1.5 um at the emulated ~6 km distance, including the drift error of about 1 um in the air path due to the change in environmental conditions. In addition, the measurable distance using this scheme could be further extended. The highest relative measurement precision is 2*10E10 in our current system while the actual relative measurement precision of our experimental system is limited by the variation of atmospheric conditions and is about 4*10E9.

physics.ins-det↗