Split learning, as a distributed learning framework, effectively addresses the issue of limited computing resources. However, despite achieving a separation of data and computation, recent studies have pointed out that this framework still faces two major security challenges: privacy leakage and model security. Most current research focuses on the problem of privacy leakage, emphasizing how to prevent malicious servers from recovering or inferring the client's private data. However, the issue of model security in split learning has not received sufficient attention. This paper reveals the vulnerability of split learning to backdoor attacks. Since split learning cannot access client data directly, it can only guide the client model to incorporate backdoors through gradients. To address this issue, we design an attack framework that modifies intermediate activations to influence the gradients. We designed a parrot model that learns the client’s feature space, enabling the server to obtain the intermediate activations of poisoned data. During the forward pass, some of the intermediate activations and labels transmitted from the client to the server are replaced with poisoned activations and target labels. This replacement method effectively integrates the backdoor task into the model while partially retaining the main task. This approach ensures that the main task is preserved while seamlessly embedding the backdoor task. Our attack framework minimizes reliance on client knowledge and ensures that the attack process remains undetectable by the client. Through extensive experiments, we demonstrated high attack success rates using triggers such as BadNet, SIG, Blended, and WaNet, while minimizing the impact on the main task.

Due to the sensitive and confidential nature of healthcare data, it cannot be freely transmitted, resulting in the challenge of data silos. While federated learning offers a solution to this data island dilemma, it also faces potential threats from malicious client attacks. The system becomes more vulnerable when dealing with non-independent and identically distributed (non-IID) data. Hence, a robust federated learning framework is indispensable to defend against malicious attacks. In this study, we employ a dynamic feature extractor based on the sparse representation, and the parameters of each iteration construct the feature extractor for the subsequent parameters. Then, we use optimized k-means clustering to get benign clients and aggregate them. The experimental results show that the system robustness is lower for non-IID datasets compared to IID healthcare datasets. And our RobustHealthFL can significantly enhance the robustness of the system when facing non-IID data. Our code is available at https://github.com/xipengp/RobustHealthFL.

Conventional video captioning methods are either stage-wise or simple end-to-end. While the former might introduce additional noise when exploiting off-the-shelf models to provide extra information, the latter suffers from lacking high-level cues. Therefore, a more desired framework should be able to capture multi-aspects of videos consistently. To this end, we present a concept-aware and task-specific model named CAT that accounts for both low-level visual and high-level concept cues, and incorporates them effectively in an end-to-end manner. Specifically, low-level visual and high-level concept features are obtained from the video transformer and concept parser of CAT. And a concept loss is further introduced to regularize the learning process of concept parser w.r.t. generated pseudo ground truth. To combine multi-level features, a caption transformer is later introduced in CAT, where visual and concept features are the inputs and caption is its output. In particular, we make critical design choices in the caption transformer to learn to exploit these cues with a multi-modal graph. This is achieved by a graph loss that enforces effective learning of intra and inter correlations between multi-level cues. Extensive experiments on three benchmark datasets demonstrate that CAT achieves 2.3 and 0.7 improvements in the CIDEr metric on MSVD and MSR-VTT compared to the state-of-the-art method SwinBERT and also achieves a competitive result on VATEX.

The past decades have seen a proliferation of middlebox deployment in various scenarios, including backbone networks and cloud networks. Since flows have to traverse specific service function chains (SFCs) for security and performance enhancement, it becomes much complex for SFC routing due to routing loops, traffic dynamics and scalability requirement. The existing SFC routing solutions may consume many resources (e.g., TCAM) on the data plane and lead to massive overhead on the control plane, which decrease the scalability of middlebox networks. Due to SFC requirement and potential routing loops, solutions like traditional default paths (e.g., using ECMP) that are widely used in non-middlebox networks will no longer be feasible. In this paper, we present and implement a scalable and flexible middlebox policy enforcement (SAFE-ME) system to minimize the TCAM usage and control overhead. To this end, we design the smart tag operations for construction of default SFC paths with less TCAM rules in the data plane, and present lightweight SFC routing update with less control overhead for dealing with traffic dynamics in the control plane. We implement our solution and evaluate its performance with experiments on both physical platform (Pica8) and Programming Protocol-independent Packet Processors (P4) based data plane, as well as large-scale simulations. Both experimental and simulation results show that SAFE-ME can greatly improve scalability (e.g., TCAM cost, update delay, and control overhead) in middlebox networks, especially for large-scale clouds. For example, our system can reduce the control traffic overhead by about 85% while achieving almost the similar middlebox load, compared with state-of-the-art solutions.

The dipole orientation of fluorophores could be resolved by fluorescence polarization microscopy (FPM), which in turn reveals structural specificity for the labeled organelles. Conventional FPM can detect only the averaged fluorescence anisotropy collected from dipoles within the diffraction-limited volume. Super-resolution dipole orientation mapping (SDOM) method, which applies sparse deconvolution and least square estimation to fluorescence polarization modulation data, achieves the dipole orientation measurement within a sub-diffraction focal area. However, during SDOM analysis, some pixels with fluorescence signal are not resolved with orientation for relatively small adjusted R2. Here we report group-sparsity-based SDOM (GS-SDOM), which utilizes the relevance of modulation sequences to effectively improve the SDOM reconstruction model. More credible resolved dipole orientations with higher adjusted R2can be mapped and false positive estimation for local dipole orientation is vitally corrected. In addition to achieving the same spatial super-resolution as SDOM does, GS-SDOM accesses more morphological information with more credible orientations and more accurate local dipole distribution estimation. During the GS-SDOM analysis of actin filaments in mammalian kidney cells, the dipole orientation of fluorescence is detected always parallel to the direction of the actin filaments. Also with dipole orientation information extracted by GS-SDOM, the reconstructed visual circle from intensity dimension is discerned as jointed by double close filaments and 3-dimensional co-localization is accomplished in the intersection of actin filaments.

There is a fundamental and critical problem in modern mobile applications, in which the battery life of mobile devices is usually limited. Recently, some researchers prolong the life of batteries by offloading computation tasks to edge-servers which are deployed near the mobile devices. However, computing offloading causes extra delay, which may severely downgrade the user experience especially for the delay-sensitive applications. Moreover, the dynamic nature of mobile devices and the limited computation capacity of edge-servers also bring another challenges for tradeoff optimization between energy consumption and task completion latency. In this paper, we propose a dynamic computing offloading (DCL) problem, which aims to minimize the maximum energy consumption of the mobile devices with constraints on computation tasks latency in a Mobile Edge-Computing (MEC) network. To solve the problem, we consider two complementary cases: offline case (we sacrifice response time to achieve better service results) and online case (where we have to make immediate offloading decision for each computation task arrived online). For the offline case, we propose an efficient RMCL algorithm, and prove that our RMCL method achieves at least O((log m)/α + 1) of the optimum with high probability, where m is the number of computation tasks in a time slot, and α is a value depending on the minimum edge-server capacity and the maximum computation task demand, with α ≥ 1 under most practical situations. For the online case, we propose an algorithm, named OMCL, which considers a trade off between the latency and energy consumption. The performance of our proposed algorithms is evaluated by formal analysis and simulation on a small-scale system. The simulation results show that the algorithm can reduce the maximum energy consumption in a set of mobile devices by 40% compared with executing computation tasks locally.

To better serve a diversity of flows, load balancing is crucial to ensure operational efficiency. However, previous works for load balancing have several disadvantages: 1) limited applicability with sub-flow scheduling (e.g., LetFlow); 2) hash collision (e.g., ECMP); or 3) transient network congestion due to reactive scheduling for traffic dynamics (e.g., Hedera and DevoFlow). An important reason for the above disadvantages is that it is difficult to provide fully fine-grained flow control for load balancing in an SDN as the flow table size of each SDN switch is usually limited. Inspired by the fact that a virtual switch (vswitch) has more powerful processing capacity and more flow entries compared with a physical switch, the previous work (e.g., Presto) deploys one vswitch for each ingress switch, and achieves the load balancing through efficient flow routing. However, this mechanism may lead to high cost and not well deal with topology asymmetry. Thus, this paper proposes to achieve the load balancing by incrementally deploying a certain number of vswitches in an SDN. We formulate the joint optimization of vswitch deployment and routing (JVR) problem as an integer linear program, and prove its NP-hardness. A rounding-based algorithm with bounded approximation factors is proposed to solve the JVR problem. We implement the proposed algorithm on an SDN testbed for experimental studies and use simulations for large-scale investigation. The experimental results and simulation results show high efficiency of our algorithm. For example, our proposed algorithm can reduce the link load ratio by about 41.5% compared with ECMP by deploying a small number of virtual switches.

Conventional two-dimensional structured illumination microscopy (SIM) requires 9 raw images to reconstruct a super-resolved image. In order to increase the frame rate of 2DSIM, attempts are being made to reduce the number of raw SIM images. However, all the proposed SIM reconstruction algorithms (SIM-RA) capable of reconstructing super-resolution (SR) image with a reduced number of raw SIM images operate in the spatial domain. Here, we present a frequency domain SIM-RA based on ordinary least squares technique, which enables reconstruction of SR image using 4 raw SIM images. Unlike the spatial domain RA, which produces the SR image through iterative convergence, the presented RA provides a single step solution. It also reveals the fundamental limitation of least number of raw images required for resolution doubling in SIM.

The broad applicability of super-resolution microscopy has been widely demonstrated in various areas and disciplines. The optimization and improvement of algorithms used in super-resolution microscopy are of great importance for achieving optimal quality of super-resolution imaging. In this review, we comprehensively discuss the computational methods in different types of super-resolution microscopy, including deconvolution microscopy, polarization-based super-resolution microscopy, structured illumination microscopy, image scanning microscopy, super-resolution optical fluctuation imaging microscopy, single-molecule localization microscopy, Bayesian super-resolution microscopy, stimulated emission depletion microscopy, and translation microscopy. The development of novel computational methods would greatly benefit super-resolution microscopy and lead to better resolution, improved accuracy, and faster image processing.

While internationally there is an awareness that we need new, innovative approaches to engineering education, it is unclear whether a pioneering pedagogy within one cultural context will readily transfer and find acceptance in a very different cultural context. In this paper, we report on a three-site, collaborative experiment in using problem-driven learning in an introductory engineering class. Our three sites are universities in the United States, Abu Dhabi and China. In each location, first year students in biomedical engineering begin the major by joining a team of students who tackle real-world, open-ended problems. Our paper will report on 1) the development and exchange of problems across the three sites, 2) the different constraints, realizations and outcomes of the problem-driven approach at each of using problem-driven learning as a transnational pedagogy for 21stcentury engineering education.