The efficacy of targeted cancer drug therapy is significantly compromised by imprecise drug delivery mechanisms. Micro/nano robots (MNRs), characterized by their controllable motion, present a promising solution to this challenge. However, the non-Newtonian nature of blood, with its high viscosity and blood cells’ interference, poses substantial limitations on the upstream efficiency of MNRs. This paper innovatively discusses for the first time the effects of blood viscosity and blood cell interference on the motion of MNRs, investigating their upstream motion capabilities in blood through comprehensive theoretical modeling, simulation, and experimental validation. A dynamic model of MNR motion was developed, and the velocity formula for MNRs in non-Newtonian fluid was derived. Experiments were conducted using different magnetic fields in pure water, high-viscosity simulated blood, and diluted blood. Results indicated that under a gradient magnetic field, the upstream velocities of MNRs in pure water, simulated blood, and diluted blood were 45.0, 14.4, and 11.1 mm/s, respectively. Under a rotating magnetic field, the velocities of vortex swarms were 825, 240, and 145 µm/s, respectively. Increased fluid viscosity reduced MNR velocity by 70%, while blood cells caused an additional 10% reduction. This research establishes a theoretical and experimental framework for the upstream motion of MNRs against blood flow, enhancing their potential in targeted drug delivery and broader biomedical applications.

Magnetically controlled micro-nano robots hold revolutionary significance in the clinical targeted treatment of brain tumors. Imaging and tracking miniature robots can provide feedback for precise magnetic field control. The cooperation among micro-nano robots, magnetic field control system, and imaging system is a significant challenge for transitioning micro-nano robots from laboratory research to clinical applications. This study explores the control and spatial localization of magnetic nanorobot swarms in a highly realistic, human-sized vascular phantom which is manufactured using the raw CT scan images. The cerebral arterial vessels are the key focus area with four main inlets and twenty-six branch outlets. The simulation results show that, under the influence of a magnetic field, the nanorobots can accumulate at the target tumor site. The Kernelized Correlation Filter (KCF) algorithm was employed to achieve single-plane tracking of nanorobots. Furthermore, based on a biplanar imaging system, three-dimensional spatial trajectory tracking of nanorobots was realized. This study provides a reference for in vivo spatial localization and imaging of magnetic nanorobot swarms (MNRS) transported through vascular system.

Recent advancements in 3D Gaussian Splatting (3D-GS) have demonstrated the potential of using 3D Gaussian primitives for high-speed, high-fidelity, and cost-efficient novel view synthesis from continuously calibrated input views. However, conventional methods require high-frame-rate dense and high-quality sharp images, which are time-consuming and inefficient to capture, especially in dynamic environments. Event cameras, with their high temporal resolution and ability to capture asynchronous brightness changes, offer a promising alternative for more reliable scene reconstruction without motion blur. In this paper, we propose SweepEvGS, a novel hardware-integrated method that leverages event cameras for robust and accurate novel view synthesis across various imaging settings from a single sweep. SweepEvGS utilizes the initial static frame with dense event streams captured during a single camera sweep to effectively reconstruct detailed scene views. We also introduce different real-world hardware imaging systems for real-world data collection and evaluation for future research. We validate the robustness and efficiency of SweepEvGS through experiments in three different imaging settings: synthetic objects, real-world macro-level, and real-world micro-level view synthesis. Our results demonstrate that SweepEvGS surpasses existing methods in visual rendering quality, rendering speed, and computational efficiency, highlighting its potential for dynamic practical applications.

Neuromorphic imaging is an emerging technique that imitates the human retina to sense variations in dynamic scenes. It responds to pixel-level brightness changes by asynchronous streaming events and boasts microsecond temporal precision over a high dynamic range, yielding blur-free recordings under extreme illumination. Nevertheless, this modality falls short in spatial resolution and leads to a low level of visual richness and clarity. Pursuing hardware upgrades is expensive and might cause compromised performance due to more burdens on computational requirements. Another option is to harness offline, plug-in-play super-resolution solutions. However, existing ones, which demand substantial sample volumes for lengthy training on massive computing resources, are largely restricted by real data availability owing to the current imperfect high-resolution devices, as well as the randomness and variability of motion. To tackle these challenges, we introduce the first self-supervised neuromorphic super-resolution prototype. It can be self-adaptive to per input source from any low-resolution camera to estimate an optimal, high-resolution counterpart of any scale, without the need of side knowledge and prior training. Evaluated on downstream tasks, such a simple yet effective method can obtain competitive results against the state-of-the-arts, significantly promoting flexibility but not sacrificing accuracy. It also delivers enhancements for inferior natural images and optical micrographs acquired under non-ideal imaging conditions, breaking through the limitations that are challenging to overcome with frame-based techniques. In the current landscape where the use of high-resolution cameras for event-based sensing remains an open debate, our solution is a cost-efficient and practical alternative, paving the way for more intelligent imaging systems.

The advent of event cameras, with their unique asynchronous sensing capabilities to capture the edge details of moving objects, has sparked new directions in video generation. So far, the challenge of integrating event-based data for controllable video generation remains largely unexplored. Addressing this gap, we introduce a framework that leverages the edge information from events and combines it with textual descriptions to synthesize videos without the requirement of extensive training. The framework marks a pioneering venture into event-based video generation using diffusion models. Comprehensive evaluations demonstrate the superior performance of our framework compared to existing methods. Code is available at: https://github.com/IndigoPurple/CUBE.

Optoelectronic tweezers (OET) has shown great promise in various applications, especially in the precise manipulation of microparticles and microorganisms on a micron and nanometer scale. This technology significantly enhances the efficiency of single-cell sorting and the development of antibody-based drugs. However, conventional OET platforms are limited by issues such as low autofocusing accuracy, restricted imaging field of view, and uneven illumination. To overcome these limitations, we have innovatively developed a dynamic adaptive imaging system. By incorporating peak-finding and in situ Gaussian blur compensation algorithms, we achieved rapid automatic focusing and illumination shadow compensation across an expanded field of view. At the same time, the system can also dynamically adjust compensation parameters under different lighting conditions. Our system has successfully completed comprehensive scanning of the optoelectronic tweezers chip, achieving a 60% reduction in autofocus time and a 15.8% improvement in lighting uniformity. Moreover, this imaging system demonstrates robust versatility and can serve as a reference for other optical systems.

Neuromorphic imaging reacts to per-pixel brightness changes of a dynamic scene with high temporal precision and responds with asynchronous streaming events as a result. It also often supports a simultaneous output of an intensity image. Nevertheless, the raw events typically involve a large amount of noise due to the high sensitivity of the sensor, while capturing fast-moving objects at low frame rates results in blurry images. These deficiencies significantly degrade human observation and machine processing. Fortunately, the two information sources are inherently complementary - events with microsecond-level temporal resolution, which are triggered by the edges of objects recorded in a latent sharp image, can supply rich motion details missing from the blurry one. In this work, we bring the two types of data together and introduce a simple yet effective unifying algorithm to jointly reconstruct blur-free images and noise-robust events in an iterative coarse-to-fine fashion. Specifically, an event-regularized prior offers precise high-frequency structures and dynamic features for blind deblurring, while image gradients serve as a kind of faithful supervision in regulating neuromorphic noise removal. Comprehensively evaluated on real and synthetic samples, such a synergy delivers superior reconstruction quality for both images with severe motion blur and raw event streams with a storm of noise, and also exhibits greater robustness to challenging realistic scenarios such as varying levels of illumination, contrast and motion magnitude. Meanwhile, it can be driven by much fewer events and holds a competitive edge at computational time overhead, rendering itself preferable as available computing resources are limited. Our solution gives impetus to the improvement of both sensing data and paves the way for highly accurate neuromorphic reasoning and analysis.

The robustness of networks against malicious agents is a critical issue for their reliability in distributed learning. While a significant number of works in recent years have investigated the development of robust algorithms for distributed learning, few have examined the influence and design of the underlying network topology on robustness. Robust schemes for distributed learning typically require certain conditions on the arrangement of malicious agents in the network. In particular, the majority of neighbors of any benign agent must be benign, and the subgraph of benign agents must be connected. In this work, we propose a scheme for the design of such topologies based on prior information of the risk profile of participating agents. We show that the resulting topology is asymptotically almost surely connected and benign agents have majority benign neighborhoods. At the same time, the proposed design asymptotically tolerates a fraction of malicious agents arbitrarily close to one, while risk agnostic designs, such as complete graphs, break down as soon as the majority of agents is malicious.

This work presents a novel electrical method, implemented in the form of a microfluidic device, for cell arraying and target cell lysis. The microfluidic device contains a micro-well array on the photoconductive layer based on the optoelectronic tweezers (OET) method, where parallel cell manipulation is performed. As cell suspension flows over the micro-wells, cells can be actively captured in the micro-wells by light-induced dielectrophoresis (DEP) forces, form the designed pattern array in less than 120 s. The single-cell capture rate is over 83 % in the patterned cell array, and about 94% of micro-wells are occupied by cells. Then, the target cell in the specific micro-well is illuminated and lysed by electroporation in 5 seconds. The micro-well barriers and DEP forces block the influence of the flow, and a relatively closed space is critical to preserve the cell lysates. Through experiments, light-induced DEP force cell capture and target cell electroporation can be modulated by changing the light patterns and the applied signal. This device, based on the OET and dynamic electroporation, allows the rapidity in the cell capture and target lysis at the single-cell level and can enable single-cell-based studies, such as molecular diagnostics and disease detection.

Magnetic microrobots exhibit enormous potential in targeted drug delivery owing to the remote wireless manipulation and minimum invasion for medical treatment. High degree of freedom offers the magnetic propelled robots extraordinary application prospect since they can be controlled precisely when different magnetic fields sources working cooperatively. However, the biocompatibility of microrobots have attracted sustained and general concern. Therefore, it is highly necessary to develop a promising carrier with high biocompatibility and investigate the mechanism of drug loading-release triggered by special microenvironment in the targeted region. In this paper, we proposed a magnetically controlled cell robots (MCRs) based on macrophages propelled by a rotating magnetic field. The innovative MCRs exhibit good biocompatibility and low toxicity by optimizing the concentration of polylysine-coated Fe nanoparticles (PLL@FeNPs) to 40 µg/mL. These MCRs loaded with murine interleukin-12 (IL-12), murine chemokine (C-C motif) ligand 5 (CCL-5), and murine C-X-C motif chemokine ligand 10 (CXCL-10) which can stimulate T cell differentiation and recruitment of monocytes, respectively. The macrophages showed an obvious M1-polarization tendency of macrophages to phagocytose intracellular pathogens and resist the growth of tumor cells. Under the control of a magnetic propelling system composed of 3 pairs of Helmholtz coil, the cell robot can be propelled wirelessly and moved along a predefined path with high accuracy. Moreover, the MCRs could approach to cancer cells and stop at places of interest in vitro. In conclusion, we have accomplished the preliminary construction of a targeted drug delivery system which displays great immune-enhancing potential for targeted drug delivery.

The untethered microrobots driven by multiple external physics fields have promising ability in minimally invasive disease treatments. One common type of the driving fields is gradient magnetic field, which can provide microrobots with adequate driving force in complicated environment. In this study, a control method of microrobot through gradient magnetic field system is presented, which is realized by moving the field free point (FFP) to produce an alterable magnetic driving force. A confirmatory experiment of the robot reciprocating motion control is undertaken in a 1D gradient magnetic robot system. The control method could be applied to further studies on in vivo applications of targeted microrobot drug delivery system.

With the rapid development of Deepfake synthesis technology, our information security and personal privacy have been severely threatened in recent years. To achieve a robust Deepfake detection, researchers attempt to exploit the joint spatial-temporal information in the videos, like using recurrent networks and 3D convolutional networks. However, these spatial-temporal models remain room to improve. Another general challenge for spatial-temporal models is that people do not clearly understand what these spatial-temporal models really learn. To address these two challenges, in this paper, we propose an Interpretable Spatial-Temporal Video Transformer (ISTVT), which consists of a novel decomposed spatial-temporal self-attention and a self-subtract mechanism to capture spatial artifacts and temporal inconsistency for robust Deepfake detection. Thanks to this decomposition, we propose to interpret ISTVT by visualizing the discriminative regions for both spatial and temporal dimensions via the relevance (the pixel-wise importance on the input) propagation algorithm. We conduct extensive experiments on large-scale datasets, including FaceForensics++, FaceShifter, DeeperForensics, Celeb-DF, and DFDC datasets. Our strong performance of intra-dataset and cross-dataset Deepfake detection demonstrates the effectiveness and robustness of our method, and our visualization-based interpretability offers people insights into our model.

The recent development of Deepfake videos directly threatens our information security and personal privacy. Although lots of previous works have made much progress on the Deepfake detection, we empirically find that the existing approaches do not perform well on the low definition (LD) and crossdefinition (high and low) videos. To address this problem, in this paper, we follow two motivations: (1) high-level semantics reduction and (2) cross-domain training. For (1), we propose the Facial Structure Destruction and Adversarial Jigsaw Loss to reduce our model to learn high-level semantics and focus on learning low-level discriminative information; For (2), we propose a domain generalization method based on adversarial learning. We conduct extensive experiments on the FaceForensics++ dataset. Results show the great effectiveness of our method and we also achieve very competitive performance against state-of-the-art methods.