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Multimodal data has become a crucial element in the realm of big data analytics, driving advancements in data exploration, data mining, and empowering artificial intelligence applications. To support high-quality retrieval for these cutting-edge applications, a robust data retrieval platform should meet the requirements for transparent data storage, rich hybrid queries, effective feature representation, and high query efficiency. However, among the existing platforms, traditional schema-on-write systems, multi-model databases, vector databases, and data lakes, which are the primary options for multimodal data retrieval, are difficult to fulfill these requirements simultaneously. Therefore, there is an urgent need to develop a more versatile multimodal data retrieval platform to address these issues. In this paper, we introduce a Multimodal Data Retrieval Platform with Query-aware Feature Representation and Learned Index based on Data Lake (MQRLD). It leverages the transparent storage capabilities of data lakes, integrates the multimodal open API to provide a unified interface that supports rich hybrid queries, introduces a query-aware multimodal data feature representation strategy to obtain effective features, and offers high-dimensional learned indexes to optimize data query. We conduct a comparative analysis of the query performance of MQRLD against other methods for rich hybrid queries. Our results underscore the superior efficiency of MQRLD in handling multimodal data retrieval tasks, demonstrating its potential to significantly improve retrieval performance in complex environments. We also clarify some potential concerns in the discussion., Comment: 36 pages, 28 figures

In recent years, there have been significant advancements in applying attention mechanisms to point cloud analysis. However, attention module variants featured in various research papers often operate under diverse settings and tasks, incorporating potential training strategies. This heterogeneity poses challenges in establishing a fair comparison among these attention module variants. In this paper, we address this issue by rethinking and exploring attention module design within a consistent base framework and settings. Both global-based and local-based attention methods are studied, with a focus on the selection basis and scales of neighbors for local-based attention. Different combinations of aggregated local features and computation methods for attention scores are evaluated, ranging from the initial addition/concatenation-based approach to the widely adopted dot product-based method and the recently proposed vector attention technique. Various position encoding methods are also investigated. Our extensive experimental analysis reveals that there is no universally optimal design across diverse point cloud tasks. Instead, drawing from best practices, we propose tailored attention modules for specific tasks, leading to superior performance on point cloud classification and segmentation benchmarks.

The zeta ($\zeta$) potential is a pivotal metric for characterizing the electric field topology within an electric double layer - an important phenomenon on phase interface. It underpins critical processes in diverse realms such as chemistry, biomedical engineering, and micro/nanofluidics. Yet, local measurement of $\zeta$ potential at the interface has historically presented challenges, leading researchers to simplify a chemically homogenized surface with a uniform $\zeta$ potential. In the current investigation, we present evidence that, within a microchannel, the spatial distribution of $\zeta$ potential across a chemically homogeneous solid-liquid interface can become two-dimensional (2D) under an imposed flow regime, as disclosed by a state-of-art fluorescence photobleaching electrochemistry analyzer (FLEA) technique. The $\zeta$ potential' s propensity to become increasingly negative downstream, presents an approximately symmetric, V-shaped pattern in the spanwise orientation. Intriguingly, and of notable significance to chemistry and engineering, this 2D $\zeta$ potential framework was found to electrokinetically induce swirling flows in tens of nanometers, aligning with the streamwise axis, bearing a remarkable resemblance to the well-documented hairpin vortices in turbulent boundary layers. Our findings gesture towards a novel perspective on the genesis of vortex structures in nanoscale. Additionally, the FLEA technique emerges as a potent tool for discerning $\zeta$ potential at a local scale with high resolution, potentially accelerating the evolution and applications of novel surface material.

Electroosmotic flow (EOF) is a ubiquitous phenomenon at the solid-liquid interface when an external electric field is applied. Despite its prevalence, the characteristics and mechanisms of EOF driven by an alternating current (AC) electric field, particularly within complex chemical environments, have remained insufficiently understood, owing primarily to a scarcity of experimental data. In this investigation, we advance the comprehension of AC EOF by employing a high-resolution measurement technique - laser-induced fluorescent photobleaching anemometer (LIFPA). This method allows for precise empirical characterization of transient velocity of EOF along the electric double layer (EDL) far from electrode surfaces. We have discerned a distinct transition in AC EOF behavior - from linear to nonlinear - across a wide parameter space, such as the velocity of bulk flow, the AC electric field's frequency and intensity, and the pH of the bulk fluid. Moreover, the transition within the AC EOF is quantified by the transitional electric field intensity, $E_{A,C}$, paired with a correlated dimensionless parameter, $Z_{nlc}$. A power-law relationship between the linear term coefficient $Z_{l}$ and $Z_{nlc}$ has been established, with the scaling exponents determined by the pH value of the electrolyte solution. With these findings, we aspire not only to deepen the understanding of AC EOF transitions but also to establish a robust model that elucidates the interplay between the electric field and fluid flow in both linear and nonlinear regimes. This research potentially paves the way for more predictable and controllable electrokinetic processes in numerous applications, including micro-/nanofluidic systems, electrochemical reactions, and beyond.

The interference phenomenon of light is a common but most important effect in physics. In this article, we compare and analyse the interference effects of both coherent and incoherent light; in particular, the interference of spatially incoherent light sources is described. Thermal light and a two-photon entangled source may both be regarded as incoherent sources which can generate similar second-order interference effects, but their underlying physics is quite different. First-order interference can be realized with both spatially coherent and incoherent sources in the same well-designed experimental setup, but exhibit different interference patterns; their different interference mechanisms will also be clarified., Comment: 46 pages,31 figures

Multifocal laser direct writing (LDW) based on phase-only spatial light modulator (SLM) can realize flexible and parallel nanofabrication with high throughput potential. In this investigation, a novel approach of combining two-photon absorption, SLM and vector path guided by scalable vector graphics (SVG) has been developed and tested preliminarily, for fast, flexible and parallel nanofabrication. Three laser focuses are independently controlled with different paths, which are according to SVG, to optimize fabrication and promote time efficiency. The minimum structure width can be as low as 74 nm. Accompanied with a translation stage, a carp structure of 18.16 $\mu$m by 24.35 $\mu$m has been fabricated. This method shows the possibility of developing LDW techniques towards full-electrical system, and provides a potential way to efficiently engrave complex structures on nanoscales.

Flow cytometry is a widespread and powerful technique, whose resolution is determined by its capacity to accurately distinguish fluorescently positive populations from negative ones. However, most informative results are discarded while performing the measurements of conventional flow cytometry, e.g., the cell size, shape, morphology, and distribution or location of labeled exosomes within the unpurified biological samples. We, herein, propose a novel approach using an anti-diffraction light sheet with anisotroic feature to excite fluorescent tags. Constituted by an anti-diffraction Bessel-Gaussian beam array, the light sheet is 12 $\mu$m wide, 12 $\mu$m high, with a thickness of $~ 0.8 \mu$m. The intensity profile of the excited fluorescent signal can, therefore, reflect the size and allow samples in the range from O(100 nm) to 10 $\mu$m (e.g., blood cells) to be transported via hydrodynamic focusing in a microfluidic chip. The sampling rate is 500 kHz provides a capability of high throughput without sacrificing the spatial resolution. Consequently, the proposed anti-diffraction light-sheet flow cytometry (ADLSFC) can obtain more informative results than the conventional methodologies, and is able to provide multiple characteristics (e.g., the size and distribution of fluorescent signal) helping to distinguish the target samples from the complex backgrounds.

Electrokinetic flow can be generated as a highly coupled phenomenon among velocity field, electric conductivity field and electric field. It can exhibit different responses to AC electric fields in different frequency regimes, according to different instability/receptivity mechanisms. In this investigation, by both flow visualization and single-point laser-induced fluorescence (LIF) method, the response of AC electrokinetic flow and the transition routes towards chaos and turbulence have been experimentally investigated. It is found, when the AC frequency $f_f<30$ Hz, the interface responds at both the neutral frequency of the basic flow and the AC frequency. However, when $f_f>=30$ Hz, the interface responds only at the neutral frequency of the basic flow. Both periodic doubling and subcritical bifurcations have been observed in the transition of AC electrokinetic flow. We hope the current investigation can promote our current understanding on the ultrafast transition process of electrokinetic flow from laminar state to turbulence.

Turbulence, ubiquitous in nature and across various systems, exhibits chaotic and intermittent fluctuations in space and time, defying precise prediction. For nearly a century, extensive efforts have been made to uncover the underlying universality and invariant laws from the immense disorder and chaotic nature of turbulence. While the celebrated Kolmogorov -5/3 law stands as a robust cornerstone, it falls short in capturing the diverse scaling behavior exhibited in turbulence influenced by external volume forces, like thermal convection and electrokinetic flows. This study proposes a general framework that couples the fluxes of kinetic energy and scalar variance, culminating in the formulation of a universal conservation law. This framework offers a comprehensive quad-cascade depiction of turbulence, enabling predictions that beyond the limitations of existing models. We illustrate this framework with microfluidic experiments on electrokinetic turbulence, wherein power spectra of concentration and velocity fluctuations exhibit the predicted scaling behaviors, providing remarkable agreement with theory. These findings not only deepen our understanding of the complete cascade process in turbulence driven by external volume forces but also hold promise for insights into other turbulent systems.

The crucial zeroth-order light due to the pixelation effect of spatial light modulator (SLM) has been a serious issue in the field of light modulation, especially in applications with a high numerical aperture optical system. In this investigation, we report that by properly adjusting the high-level and low-level pixel voltages of an SLM, the zeroth-order light caused by the pixelation effect of SLM can be significantly eliminated. The method is further validated in an inverted fluorescence microscope. The experimental results show that the zeroth-order light can be inhibited up to 91.3%, accompanied by an improvement of the modulation efficiency from 77.5% to 92.6%., Comment: In this version, the mechanism underlined has been corrected. Accompanied, Figure 1 has been replaced

Nonlinearity of electroosmotic flows (EOFs) is ubiquitous and plays a crucial role in the mass and energy transfer in ion transport, specimen mixing, electrochemistry reaction, and electric energy storage and utilizing. When and how the transition from a linear regime to a nonlinear one is essential for understanding, prohibiting or utilizing nonlinear EOF. However, suffers the lacking of reliable experimental instruments with high spatial and temporal resolutions, the investigation of the onset of nonlinear EOF still stays in theory. Herein, we experimentally studied the velocity fluctuations of EOFs driven by AC electric field via ultra-sensitive fluorescent blinking tricks. The linear and nonlinear AC EOFs are successfully identified from both the time trace and energy spectra of velocity fluctuations. The critical electric field ($E_{A,C}$) separating the two statuses is determined and is discovered by defining a generalized scaling law with respect to the convection velocity ($U$) and AC frequency ($f_f$) as $E_{A,C}$~${f_f}^{0.48-0.027U}$. The universal control parameters are determined with surprising accuracy for governing the status of AC EOFs. We hope the current investigation could be essential in the development of both theory and applications of nonlinear EOF.

Micromixer is a key element in lab on a chip for broad applications in the analysis and measurement of chemistry and engineering. Previous investigations reported electrokinetic (EK) turbulence could be realized in a Y-type micromixer with a cross-sectional dimension of 100 ${\mu}$m order. Although the ultrafast turbulent mixing can be generated at a bulk flow Reynolds number of O(1), the micromixer has not been optimized. In this investigation, we systematically investigated the influence of electric field intensity, AC frequency, electric conductivity ratio, and channel width at the entrance on the mixing effect and transition electric Rayleigh number in the "Y" type electrokinetic micromixer. It is found the optimal mixing is realized in a 350 ${\mu}$m wide micromixer, under 100 kHz and 1.14*10^5 V/m AC electric field, with an electric conductivity ratio of 1:3000. Under the conditions, a maximum degree of mixedness of 0.93 can be achieved at 84 ${\mu}$m from the entrance and 100 ms. A further investigation of the critical electric field and the critical electric Rayleigh number indicates the most unstable condition of EK flow instability is inconsistent with that of the optimal mixing in EK turbulence. To predict the evolution of EK flow under high $Ra_{e}$, it is necessary to apply a computational turbulence model, instead of linear instability analysis., Comment: 9 pages, 7 figures

While the deep learning-based image deraining methods have made great progress in recent years, there are two major shortcomings in their application in real-world situations. Firstly, the gap between the low-level vision task represented by rain removal and the high-level vision task represented by object detection is significant, and the low-level vision task can hardly contribute to the high-level vision task. Secondly, the quality of the deraining dataset needs to be improved. In fact, the rain lines in many baselines have a large gap with the real rain lines, and the resolution of the deraining dataset images is generally not ideally. Meanwhile, there are few common datasets for both the low-level vision task and the high-level vision task. In this paper, we explore the combination of the low-level vision task with the high-level vision task. Specifically, we propose an end-to-end object detection network for reducing the impact of rainfall, which consists of two cascaded networks, an improved image deraining network and an object detection network, respectively. We also design the components of the loss function to accommodate the characteristics of the different sub-networks. We then propose a dataset based on the KITTI dataset for rainfall removal and object detection, on which our network surpasses the state-of-the-art with a significant improvement in metrics. Besides, our proposed network is measured on driving videos collected by self-driving vehicles and shows positive results for rain removal and object detection.

To date, live-cell imaging at the nanometer scale remains challenging. Even though super-resolution microscopy methods have enabled visualization of subcellular structures below the optical resolution limit, the spatial resolution is still far from enough for the structural reconstruction of biomolecules in vivo (i.e. ~24 nm thickness of microtubule fiber). In this study, we proposed an A-net network and showed that the resolution of cytoskeleton images captured by a confocal microscope can be significantly improved by combining the A-net deep learning network with the DWDC algorithm based on degradation model. Utilizing the DWDC algorithm to construct new datasets and taking advantage of A-net neural network's features (i.e., considerably fewer layers), we successfully removed the noise and flocculent structures, which originally interfere with the cellular structure in the raw image, and improved the spatial resolution by 10 times using relatively small dataset. We, therefore, conclude that the proposed algorithm that combines A-net neural network with the DWDC method is a suitable and universal approach for exacting structural details of biomolecules, cells and organs from low-resolution images., Comment: The manuscript has 17 pages, 10 figures and 58 references

Perception plays an important role in reliable decision-making for autonomous vehicles. Over the last ten years, huge advances have been made in the field of perception. However, perception in extreme weather conditions is still a difficult problem, especially in rainy weather conditions. In order to improve the detection effect of road targets in rainy environments, we analyze the physical characteristics of the rain layer and propose a deraining convolutional neural network structure. Based on this network structure, we design an ablation experiment and experiment results show that our method can effectively improve the accuracy of object detection in rainy conditions.

Computational ghost imaging or single-pixel imaging enables the image formation of an unknown scene using a lens-free photodetector. In this Letter, we present a computational panoramic ghost imaging system that can achieve the full-color panorama using a single-pixel photodetector, where a convex mirror performs the optical transformation of the engineered Hadamard-based circular illumination pattern from unidirectionally to omnidirectionally. To our best knowledge, it is the first time to propose the concept of ghost panorama and realize preliminary experimentations. It is foreseeable that ghost panorama will have more advantages in imaging and detection in many extreme conditions (e.g., scattering/turbulence, cryogenic temperatures, and unconventional spectra), as well as broad application prospects in the positioning of fast-moving targets and situation awareness for autonomous vehicles., Comment: 5 pages, 4figures

We establish a quantum theory of computational ghost imaging and propose quantum projection imaging where object information can be reconstructed by quantum statistical correlation between a certain photon number of bucket signal and DMD random patterns. The reconstructed image can be negative or positive depending on the chosen photon number. In particular, the vacuum state (zero-number) projection produces a negative image with better visibility and contrast-to-noise ratio. The experimental results of quantum projection imaging agree well with theoretical simulations and show that, under the same measurement condition, vacuum projection imaging is superior to conventional and fast first-photon ghost imaging in low-light illumination., Comment: 6 pages, 6 figures, two columns