ERVD: An Efficient and Robust ViT-Based Distillation Framework for Remote Sensing Image Retrieval
ERVD: An Efficient and Robust ViT-Based Distillation Framework for Remote Sensing Image Retrieval
cs.CV↗
arXiv subjects
Qixuan Cao
Publications and source records attributed to Qixuan Cao.
Dynamic Manipulation of Multiphase Fluid in Microgravity Using Photoresponsive Surfactant
Control of bubble motion is essential for improving efficiency and creating new functionalities in electrochemistry, heat transfer, and biomedical systems. Photoresponsive surfactants enable bubble manipulation by creating surface tension gradients, inducing a photo-Marangoni flow under illumination, without needing any engineered substrates, by leveraging a reversible switch in molecular conformation. Although previous studies have demonstrated bubble manipulation using photo-responsive surfactants, a comprehensive understanding of how fluid behavior is affected by critical parameters, such as bubble size, illumination, photo-switching kinetics, concentration, and adsorption desorption kinetics, remains elusive. Advances have been limited by the complex multiphysics processed involved, and by the fact that earth-bound experiments cannot study bubble photo-Marangoni dynamics without interference from bubble buoyancy and photo-thermal convection. We elucidate the factors enabling fast photo-Marangoni-driven bubble motion, by performing microgravity experiments, enabled by a bespoke photo-surfactant, complemented by a detailed modeling framework. We identify an optimal bubble size for migration, since smaller and larger bubbles incur weaker photo-Marangoni stresses and larger drag, respectively. Surfactants that switch rapidly under illumination drive fast migration, provided their reverse switch (in darkness) is much slower, yet not negligible. These foundational results enable the synthesis of next-generation photo-surfactants and photo-Marangoni manipulation across multiphase fluid systems.
physics.flu-dyn↗
Resilience patterns in higher-order meta-population networks
Meta-population networks are effective tools for capturing population movement across distinct regions, but the assumption of well-mixed regions fails to capture the reality of population higher-order interactions. As a multidimensional system capturing mobility characteristics, meta-population networks are inherently complex and difficult to interpret when subjected to resilience analysis based on N-dimensional equations. We propose a higher-order meta-population model that captures large-scale global cross-regional mobility and small-scale higher-order interactions within regions. Remarkably, we extend the dimension-reduction approach, simplifying the N-dimensional higher-order meta-population system into a one-dimensional equation by decomposing different network behaviours into a single universal resilience function, thereby allowing for convenient and accurate prediction of the system resilience. The network structure and human mobility parameters can clearly and simply express the epidemic threshold. Numerical experimental results on both real networks and star networks confirm the accuracy of the proposed dimension-reduction framework in predicting the evolution of epidemic dynamics on higher-order meta-population networks. Additionally, higher-order interactions among populations are shown to lead to explosive growth in the epidemic infection size potentially. Population mobility causes changes in the spatial distribution of infectious diseases across different regions.
physics.soc-ph↗
CMOS + stochastic nanomagnets: heterogeneous computers for probabilistic inference and learning
Extending Moore's law by augmenting complementary-metal-oxide semiconductor (CMOS) transistors with emerging nanotechnologies (X) has become increasingly important. One important class of problems involve sampling-based Monte Carlo algorithms used in probabilistic machine learning, optimization, and quantum simulation. Here, we combine stochastic magnetic tunnel junction (sMTJ)-based probabilistic bits (p-bits) with Field Programmable Gate Arrays (FPGA) to create an energy-efficient CMOS + X (X = sMTJ) prototype. This setup shows how asynchronously driven CMOS circuits controlled by sMTJs can perform probabilistic inference and learning by leveraging the algorithmic update-order-invariance of Gibbs sampling. We show how the stochasticity of sMTJs can augment low-quality random number generators (RNG). Detailed transistor-level comparisons reveal that sMTJ-based p-bits can replace up to 10,000 CMOS transistors while dissipating two orders of magnitude less energy. Integrated versions of our approach can advance probabilistic computing involving deep Boltzmann machines and other energy-based learning algorithms with extremely high throughput and energy efficiency.
cond-mat.mes-hall↗
Real-time Manipulation of Liquid Droplets using Photo-responsive Surfactant
Fast and programmable transport of liquid droplets on a solid substrate is desirable in microfluidic, thermal, biomedical, and energy devices. Past research has focused on designing substrates with asymmetric structures or gradient wettability where droplet behaviors are passively controlled, or by applying external electric, thermal, magnetic, or acoustic stimuli that either require the fabrication of electrodes or a strong applied field. In this work, we demonstrate tunable and programmable droplet motion on liquid-infused surfaces (LIS) and inside solid-surface capillary channels using low-intensity light and photo-responsive surfactants. When illuminated by the light of appropriate wavelengths, the surfactants can reversibly change their molecular conformation thereby tuning interfacial tensions in a multi-phase fluid system. This generates a Marangoni flow that drives droplet motions. With two novel surfactants that we synthesized, we demonstrate fast linear and complex 2D movements of droplets on liquid surfaces, on LIS, and inside microchannels. We also visualized the internal flow pattern using tracer particles and developed simple scaling arguments to explain droplet-size-dependent velocity. The method demonstrated in this study serves as a simple and exciting new approach for the dynamic manipulation of droplets for microfluidic, thermal, and water harvesting devices.
physics.flu-dyn↗