In multimodal sentiment analysis, modality missingness and quality degradation are common. Existing methods often rely on batch-level modality generation, generation but neglect sample-level missingness, hence their flexibility is limited severely in real-world scenarios. To address this, Sample-specific Modality Diagnosis and Cross-modal Enhancement for Incomplete Multimodal Representations (SMCIR) is proposed. Specifically, The Dynamic Multi-feature Fusion Detector (DMFD) is presented, which detects missingness and severity at the sample-level using indicators such as information entropy, modality similarity, and mutual information. Unlike batch-based methods, the DMFD provides fine-grained detection and adaptive responses, improving sensitivity to modality disturbances. Meanwhile, the Context-aware Modality Completion Generator (CMCG) is developed to restore missing modalities through context-guided reconstruction using multiscale feature fusion and cross-modal attention. In this way, the proposed CMCG method can avoid redundancy and inconsistency, enhancing the consistency and discriminativity of the fused representation. In CMCG, the text modality serves as a stable guide to improve context consistency. Experiments on the CMU-MOSI and CMU-MOSEI datasets show that SMCIR outperforms existing full-modal and non-recovery-based methods, well validating its efficacy and superiority in multimodal learning.

Multi-view clustering (MvC) aims to integrate information from different views to enhance the capability of the model in capturing the underlying data structures. The widely used joint training paradigm in MvC potentially does not fully leverage the multi-view information, due to the imbalanced and under-optimized view-specific features caused by the uniform learning objective for all views. For instance, particular views with more discriminative information could dominate the learning process in the joint training paradigm, leading to other views being under-optimized. To alleviate this issue, we first analyze the imbalanced phenomenon in the joint-training paradigm of multi-view clustering from the perspective of gradient descent for each view-specific feature extractor. Then, we propose a novel balanced multi-view clustering (BMvC) method, which introduces a view-specific contrastive regularization (VCR) to modulate the optimization of each view. Concretely, VCR preserves the sample similarities captured from the joint features and view-specific ones into the clustering distributions corresponding to view-specific features to enhance the learning process of view-specific feature extractors. Additionally, an analysis is provided to illustrate that VCR adaptively modulates the magnitudes of gradients for updating the parameters of view-specific feature extractors to achieve a balanced multi-view learning procedure. In such a manner, BMvC achieves a better trade-off between the exploitation of view-specific patterns and the exploration of view-invariance patterns to fully learn the multi-view information for the clustering task. Finally, a set of experiments are conducted to verify the superiority of the proposed method compared with state-of-the-art approaches both on eight benchmark MvC datasets and two spatially resolved transcriptomics datasets.

Industrial Internet applications require networks to guarantee deterministic end-to-end latency and zero packet loss at both the data link and network layers. Traditional best-effort communication models in consumer networks are insufficient to meet these stringent demands. To meet these stringent demands, the IEEE 802.1 standards introduce Time-Sensitive Networking (TSN) at the data link layer, while the IETF proposes Deterministic Networking (DetNet) for the network layer. However, enabling seamless cross-domain communication between TSN and DetNet remains a significant challenge. This paper proposes a unified cross-domain network architecture and a time-slot alignment strategy that compensates for synchronization errors between the TSN and DetNet layers. We further develop a Joint Routing and Scheduling algorithm for Deterministic Cross-Domain Transmission (JRS-DCT), which simultaneously addresses routing and scheduling under cross-domain constraints. The algorithm leverages Cycle-Specified Queuing and Forwarding (CSQF) in DetNet and Cycle Queuing and Forwarding (CQF) in TSN to ensure bounded latency and deterministic transmission. Extensive simulations demonstrate that the proposed JRS-DCT algorithm significantly improves the scheduling success rate and effectively reduces network resource utilization compared to two baseline algorithms. These results validate the effectiveness and robustness of the proposed framework in supporting time-sensitive communication across heterogeneous network environments.

In IoT environments, devices are often constrained by low power consumption and limited resources, making efficient connection establishment with minimal overhead crucial for real-time communication between edge devices and cloud servers. The QUIC protocol shows significant potential, but the encryption and decryption overhead is considerable. Reducing this overhead and improving connection establishment efficiency are critical to enhancing QUIC performance, especially for IoT edge devices that need to handle high-concurrency communication while maintaining low power consumption. This paper proposes CFcoQUIC, a CPU/FPGA co-design architecture that accelerates the handshake process and reduces the initial connection latency by parallelizing multiple encryption/decryption flows in high-concurrency environments. Experimental results demonstrate that the time for RSA encryption and decryption on FPGA is at least 19.1 times faster than on CPU, and the time for AES encryption and decryption is at least 5 times faster on FPGA. These results highlight the effectiveness of the proposed architecture in reducing QUIC handshake overhead in IoT environments with low-power edge devices.

With the increasing adoption of Retrieval-Augmented Generation (RAG) systems for knowledge-intensive tasks, ensuring the adequacy of retrieved documents has become critically important for generation quality. Traditional reranking approaches face three significant challenges: substantial computational overhead that scales with document length, dependency on plain text that limits application in sensitive scenarios, and insufficient assessment of document value beyond simple relevance metrics. We propose EAReranker, an efficient embedding-based adequacy assessment framework that evaluates document utility for RAG systems without requiring access to original text content. The framework quantifies document adequacy through a comprehensive scoring methodology considering verifiability, coverage, completeness and structural aspects, providing interpretable adequacy classifications for downstream applications. EAReranker employs a Decoder-Only Transformer architecture that introduces embedding dimension expansion method and bin-aware weighted loss, designed specifically to predict adequacy directly from embedding vectors. Our comprehensive evaluation across four public benchmarks demonstrates that EAReranker achieves competitive performance with state-of-the-art plaintext rerankers while maintaining constant memory usage ($\sim$550MB) regardless of input length and processing 2-3x faster than traditional approaches. The semantic bin adequacy prediction accuracy of 92.85\% LACC@10 and 86.12\% LACC@25 demonstrates its capability to effectively filter out inadequate documents that could potentially mislead or adversely impact RAG system performance, thereby ensuring only high-utility information serves as generation context. These results establish EAReranker as an efficient and practical solution for enhancing RAG system performance through improved context selection while addressing the computational and privacy challenges of existing methods.

The Industrial Internet of Things (IIoT) enables communication among automation systems, machinery, and sensors in an industrial setting. To optimize critical industrial operations, a substantial volume of data concerning diverse in-factory activities and automation services is generated by IoT devices and sensors. This data are subsequently transferred to distant processing systems for analysis and decision-making. Nevertheless, a substantial latency in data transmission or any abnormality in the generated data may result in delayed or erroneous decisions, consequently impacting the efficacy of essential industrial systems. To address these challenges, we established an intelligent network architecture utilizing software-defined networking that achieves tactile latencies efficiently while handling industrial data traffic in an energy-efficient manner. To address the initial challenge, the suggested architecture utilizes the self-organized maps approach to distinguish between industrial traffic requiring tactile latencies and nontactile traffic. We utilize a binary tree-based flow table mapping method to enhance flow table matching and decrease lookup times. To address the second challenge, we employ the Support Vector Machine technique to identify anomalies in real-time industrial data traffic. The Hadoop system and Mininet emulator are utilized to evaluate the proposed architecture using the UNSW dataset. The results demonstrate the effectiveness of the suggested solution in providing energy-efficient tactile assurances and identifying anomalies in traffic.

Unmanned aerial vehicles (UAV) are widely used for edge computing in poor infrastructure scenarios due to their deployment flexibility and mobility. In UAV-assisted edge computing systems, multiple UAVs can cooperate with the cloud to provide superior computing capability for diverse innovative services. However, many service-related computational tasks may fail due to the unreliability of UAVs and wireless transmission channels. Diverse solutions were proposed, but most of them employ timedriven strategies which introduce unwanted decision waiting delays. To address this problem, this paper focuses on a taskdriven reliability-aware cooperative offloading problem in UAV-assisted edge-enhanced networks. The issue is formulated as an optimization problem which jointly optimizes UAV trajectories, offloading decisions, and transmission power, aiming to maximize the long-term average task success rate. Considering the discrete-continuous hybrid action space of the problem, a dependenceaware latent-space representation algorithm is proposed to represent discrete-continuous hybrid actions. Furthermore, we design a novel deep reinforcement learning scheme by combining the representation algorithm and a twin delayed deep deterministic policy gradient algorithm. We compared our proposed algorithm with four alternative solutions via simulations and a realistic Kubernetes testbed-based setup. The test results show how our scheme outperforms the other methods, ensuring significant improvements in terms of task success rate.

RDMA over Converged Ethernet v2 (RoCEv2) is one of the most popular high-speed datacenter networking solutions. Watermark is the general term for various trigger and release thresholds of RoCEv2 flow control protocols, and its reasonable configuration is an important factor affecting RoCEv2 performance. In this paper, we propose ByteTuning, a centralized watermark tuning system for RoCEv2. First, three real cases of network performance degradation caused by non-optimal or improper watermark configuration are reported, and the network performance results of different watermark configurations in three typical scenarios are traversed, indicating the necessity of watermark tuning. Then, based on the RDMA Fluid model, the influence of watermark on the RoCEv2 performance is modeled and evaluated. Next, the design of the ByteTuning is introduced, which includes three mechanisms. They are (1) using simulated annealing algorithm to make the real-time watermark converge to the near-optimal configuration, (2) using network telemetry to optimize the feedback overhead, (3) compressing the search space to improve the tuning efficiency. Finally, We validate the performance of ByteTuning in multiple real datacenter networking environments, and the results show that ByteTuning outperforms existing solutions.

Hyperspectral images with high spectral resolution provide new insights into recognizing subtle differences in similar substances. However, object detection in hyperspectral images faces significant challenges in intra- and inter-class similarity due to the spatial differences in hyperspectral inter-bands and unavoidable interferences, e.g., sensor noises and illumination. To alleviate the hyperspectral inter-bands inconsistencies and redundancy, we propose a novel network termedSpectralDiscrepancy andCross-Modal semantic consistency learning (SDCM), which facilitates the extraction of consistent information across a wide range of hyperspectral bands while utilizing the spectral dimension to pinpoint regions of interest. Specifically, we leverage a semantic consistency learning (SCL) module that utilizes inter-band contextual cues to diminish the heterogeneity of information among bands, yielding highly coherent spectral dimension representations. On the other hand, we incorporate a spectral gated generator (SGG) into the framework that filters out the redundant data inherent in hyperspectral information based on the importance of the bands. Then, we design the spectral discrepancy aware (SDA) module to enrich the semantic representation of high-level information by extracting pixel-level spectral features. Extensive experiments on two hyperspectral datasets demonstrate that our proposed method achieves state-of-the-art performance when compared with other ones.

Computation offloading is an effective method for reducing the pressure put on networks and improving the service experience. However, most existing research on computation offloading is timeslot-driven and treats all tasks equally, resulting in decision waiting delays and failure to complete some important tasks. In this paper, we propose a novel priority-aware task-driven computation offloading model with system performance gain as the optimization objective based on a combination of task delay and energy consumption aspects. The new model is formulated as a Markov decision process (MDP). Considering the discrete-continuous hybrid action space of the optimization problem, we construct a dependence-aware latent space and propose a novel algorithm based on the Twin Delayed Deep Deterministic policy gradient algorithm (TD3). Additionally, we present the neural network structure and analyze the complexity of the algorithm. Extensive simulations show how our algorithm achieves superior performance compared to three state-of-the-art alternative approaches.

Network emulator is an equipment used in the field of computer networking to replicate and simulate real-world network conditions, especially poor-quality network conditions accompanied by various damages, in a controlled environment. It plays a crucial role in the development, testing, and validation of various new network-related technologies, protocols, and applications. Compared with simulation and test-bed methods, network emulation possesses the advantages of accuracy and cost-efficiency. However, legacy network emulation methods are implemented serially, which are typically restricted in efficiency and waste computing resources. In this paper, we propose a combined network emulator, CombNE. To implement this emulator, we consider P4 programmable switches as a desirable option. CombNE consists of three logical components. First, CombNE provides a policy specification scheme to intuitively describe operator’s intents. Secondly, the CombNE parallelizer intelligently identifies the dependencies between network damages, automatically determines parallelization and generates a combination strategy. Third, CombNE will generate optimized P4 files and flow table information based on the combination strategy and deploy them to P4 programmable switches. Finally, we evaluated the performance and resources of CombNE.

While current Time-Sensitive Network (TSN) research focuses on reducing end-to-end delay through scheduling optimization, it often overlooks routing impacts. Traditional methods like Shortest Path First (SPF) struggle with new flow schedulability and existing flow disruption. This paper presents a TSN routing algorithm based on network calculus, calculating worst-case delays to ensure deadline compliance and balance link loads. A new flow prioritization method further optimizes scheduling. Experiments show the proposed algorithm improves scheduling success, keeps average delay under 70 μs, reduces maximum link load by 18.5% compared to wt-ECMP, and achieves a tenfold runtime reduction.

With the extensive application of network traffic encryption technology, the accurate and efficient classification of encrypted traffic has become a critical need for network management. Deep learning has become the predominant method for traffic classification, primarily involving the transformation of network traffic into grayscale images and their subsequent classification using Convolutional Neural Networks (CNNs). However, traditional grayscale image generation methods are plagued with issues of redundant and lost information, and conventional channel attention mechanisms are still insufficient in capturing key traffic features, collectively hindering the enhancement of classification performance. To tackle these issues, this paper introduces a classification method based on Byte Transfer Probability and Coordinate Attention Mechanism in Residual Network (BTP-CAResNet). This method, on the foundation of the classic ResNet architecture, incorporates a new grayscale image generation method that utilizes Byte Transfer Probability, effectively overcoming the deficiencies of traditional approaches. Additionally, this paper integrates a Coordinate Attention Mechanism into the ResNet model, which effectively overcomes the limitations of traditional channel attention mechanisms and further improves the performance of traffic classification. Experimental validation on the ISCX VPN-nonVPN dataset demonstrates that, compared to previous CNN-based methods, the method proposed in this paper exhibits superior performance in key metrics such as accuracy, precision, recall, and F1 score. It provides a new perspective for traffic classification based on convolutional neural networks.

Fully supervised change detection methods have achieved significant advancements in performance, yet they depend severely on acquiring costly pixel-level labels. Considering that the patch-level annotations also contain abundant information corresponding to both changed and unchanged objects in bi-temporal images, an intuitive solution is to segment the changes with patch-level annotations. How to capture the semantic variations associated with the changed and unchanged regions from the patch-level annotations to obtain promising change results is the critical challenge for the weakly supervised change detection task. In this paper, we propose a memory-supported transformer (MS-Former), a novel framework consisting of a bi-directional attention block (BAB) and a patch-level supervision scheme (PSS) tailored for weakly supervised change detection with patch-level annotations. More specifically, the BAB captures contexts associated with the changed and unchanged regions from the temporal difference features to construct informative prototypes stored in the memory bank. On the other hand, the BAB extracts useful information from the prototypes as supplementary contexts to enhance the temporal difference features, thereby better distinguishing changed and unchanged regions. After that, the PSS guides the network learning valuable knowledge from the patch-level annotations, thus further elevating the performance. Experimental results on three benchmark datasets demonstrate the effectiveness of our proposed method in the change detection task. The demo code for our work will be publicly available at https://github.com/guanyuezhen/MS-Former.

Medication recommendation is a productive task for AI-driven healthcare systems, which can assist clinicians in prescribing judicious and effective treatments. However, existing medication recommendation methods omit two key pieces of information: Coarse-grained interaction information between distinct types of symptoms in a patient's medical history and corresponding medication representations can serve as attention for predicting the current medication combinations of the patient. Fine-grained interaction information between medication substructure representations and different types of symptoms can facilitate the construction of molecular-level disentangled medication representations. To address this dilemma, we propose a novelDualAdaptiveInteractionNetwork (DAI-Net), which encodes comprehensive interaction knowledge between patients' multifaceted health records and medication molecules to improve the performance of medication recommendation and heighten interpretability of the model. Specifically, we design a symptom-aware medication matching module to extract coordinated associations between patient symptoms and medication molecules, coarse-grained interaction learning. The medication embeddings are utilized to transform patient-medication matching properties into a symptom-substructure matching matrix for fine-grained interaction. The patient's Longitudinal representation is employed as a query to decode both symptom-medication and symptom-substructure matching information for coordinated medication representation. DAI-Net is an end-to-end recommendation model. Extensive experiments on the real-world EHR datasets, i.e., the public benchmark MIMIC-III, MIMIC-IV, and eICU, demonstrate that the proposed DAI-Net achieves competitive performance compared to other state-of-the-art ones, with an average improvement of 1.8%, 2.1% in Jaccard on MIMIC-III and -IV dataset.

Multiple Kernel Clustering (MKC) has emerged as a prominent research domain in recent decades due to its capacity to exploit diverse information from multiple views by learning an optimal kernel. Despite the successes achieved by various MKC methods, a significant challenge lies in the computational complexity associated with generating a consensus partition from the optimal kernel matrix, typically of size$n \times n$, where$n$represents the number of samples. This computational bottleneck restricts the practical applicability of these methods when confronted with large-scale datasets. Furthermore, certain existing MKC algorithms derive the consensus partition matrix by fusing all base partitions. However, this fusion process may inadvertently overlook critical information embedded in individual base kernels, potentially leading to inferior clustering performance. In light of these challenges, we introduce an innovative and efficient multiple kernel$k$-means approach, denoted as FAMKKM. Notably, FAMKKM incorporates two approximated partition matrices instead of the original individual partition matric for each base kernel. This strategic substitution significantly reduces computational complexity. Additionally, FAMKKM leverages the original kernel information to guide the fusion of all base partitions, thereby enhancing the quality of the resulting consensus partition matrix. Finally, we substantiate the efficacy and efficiency of the proposed FAMKKM through extensive experiments conducted on six benchmark datasets. Our results demonstrate its superiority over state-of-the-art methods. The demo code of this work is publicly available athttps://github.com/WangJun2023/FAMKKM

Mobile edge computing (MEC) has emerged as a solution to address the demands of computation-intensive network services by providing computational capabilities at the network edge, thus reducing service delays. Due to the flexible deployment, wide coverage and reliable wireless communication, unmanned aerial vehicles (UAVs) have been employed to assist MEC. This paper investigates the task offloading problem in a UAV-assisted MEC system with collaboration of multiple UAVs, highlighting task priorities and binary offloading mode. We defined the system gain based on energy consumption and task delay. The joint optimization of UAVs' trajectory design, binary offloading decision, computation resources allocation, and communication resources management is formulated as a mixed integer programming problem with the goal of maximizing the long-term average system gain. Considering the discrete-continuous hybrid action space of this problem, we propose a novel deep reinforcement learning (DRL) algorithm based on the latent space to solve it. The evaluation results demonstrate that our proposed algorithm outperforms three state-of-the-art alternative solutions in terms of task delay and system gain.

Large amounts of time-series data need to be continually delivered from IoT devices to the cloud for real-time data analytics. The data delivery process is intrinsically slow and costly. Therefore, lots of work proposes various data reduction methods to accelerate it. Yet, they are either designed for the simple linear time-series data or computation-intensive, which is not suitable for the IoT devices with limited resources. In this paper, we propose Xender, a system to accelerate time-series data delivery. Xender consists of two key components: data sampler and data generator. Data sampler works on IoT devices to sample time-series data with low resource footprint, and data generator works on the cloud to efficiently generate data that significantly resembles the original. Besides, Xender can adapt to the dynamic characteristics of the time-series data with the content-aware mechanism, as well as the dynamic computation resources by supporting multiple data generation quality levels and using the anytime generation mechanism. We implement Xender and evaluate it with testbed experiments using six real-world datasets. The results show that it can significantly reduce data delivery time by 45.79% on average compared against existing schemes, and adapt to computation resources with up to 1014.40Mbps data generation throughput.

In the collaborative intelligent transportation system, providing precise positioning services is costly. Reducing resource consumption and improving revenue are crucial to the development of positioning services. Therefore, a practical algorithm that combines cloud and edge network environments is necessary to improve the positioning services. Integrating network function virtualization and edge computing can provide users with more flexible and efficient services. Based on the above issues, we use the service function chain (SFC) to improve the positioning services provided in cloud-edge-vehicle collaborative networks (CEVCN). We propose a deep reinforcement learning-assisted SFC embedding algorithm and improve its performance through training. We construct a five-layer policy network to sense the environment of CEVCN and derive the optimal node selection strategy. Finally, we use the breadth-first search algorithm to solve the embedding scheme for virtual links. The simulation results show that our proposed algorithm has excellent performance. The long-term average revenue is improved by 21%, the long-term average revenue-cost ratio is improved by 13%, and the embedding rate is improved by 8%.

Multiview clustering algorithms have attracted intensive attention and achieved superior performance in various fields recently. Despite the great success of multiview clustering methods in realistic applications, we observe that most of them are difficult to apply to large-scale datasets due to their cubic complexity. Moreover, they usually use a two-stage scheme to obtain the discrete clustering labels, which inevitably causes a suboptimal solution. In light of this, an efficient and effective one-step multiview clustering (E2OMVC) method is proposed to directly obtain clustering indicators with a small-time burden. Specifically, according to the anchor graphs, the smaller similarity graph of each view is constructed, from which the low-dimensional latent features are generated to form the latent partition representation. By introducing a label discretization mechanism, the binary indicator matrix can be directly obtained from the unified partition representation which is formed by fusing all latent partition representations from different views. In addition, by coupling the fusion of all latent information and the clustering task into a joint framework, the two processes can help each other and obtain a better clustering result. Extensive experimental results demonstrate that the proposed method can achieve comparable or better performance than the state-of-the-art methods. The demo code of this work is publicly available at https://github.com/WangJun2023/EEOMVC.

With the rapid development of various data acquisition technologies, more and more multimodal data come into being. It is important to integrate different modalities which are with high-dimensional features for boosting final multimodal data classification task. However, existing multimodal classification methods mainly focus on exploiting the complementary information of different modalities, while ignoring the learning confidence during information fusion. In this paper, we propose a trustworthy multimodal classification network via multi-level confidence learning, referred to as MLCLNet. Considering that a large number of feature dimensions could not contribute to final classification performance but disturb the discriminability of different samples, we propose a feature confidence learning mechanism to suppress some redundant features, as well as enhancing the expression of discriminative feature dimensions in each modality. In order to capture the inherent sample structure information implied in each modality, we design a graph convolutional network branch to learn the corresponding structure preserved feature representation and generate modal-specific initial classification labels. Since samples from different modalities should share consistent labels, a cross-modal label fusion module is deployed to capture the label correlations of different modalities. In addition, motivated the ideally orthogonality of final fused label matrix, we design a label confidence loss to supervise the network for learning more separable data representations. To the best of our knowledge, MLCLNet is the first work which integrates both feature and label-level confidence learning for multimodal classification. Extensive experiments on four multimodal medical datasets are conducted to validate superior performance of MLCLNet when compared to other state-of-the-art methods.

Multimodal learning aims to integrate complementary information from different modalities for more reliable decisions. However, existing multimodal classification methods simply integrate the learned local features, which ignore the underlying structure of each modality and the higher-order correlation across modalities. In this paper, we propose a novel Hierarchical Attention Learning Network (HALNet) for multimodal classification. Specifically, HALNet has three merits: 1) A hierarchical feature fusion module is proposed to learn multilevel features, aggregating multi-level features for a global feature representation with the attention mechanism and progressive fusion tactics. 2) A cross-modal higher-order fusion module is introduced to capture the prospective cross-modal correlations at label space. 3) A dual prediction pattern is designed to generate credible decisions. Extensive experiments on three real-world multimodal datasets demonstrate that HALNet achieves competitive performance compared to the state-of-the-art.

Multispectral object detection has gained significant attention due to its potential in all-weather applications, particularly those involving visible (RGB) and infrared (IR) images. Despite substantial advancements in this domain, current methodologies primarily rely on rudimentary accumulation operations to combine complementary information from disparate modalities, overlooking the semantic conflicts that arise from the intrinsic heterogeneity among modalities. To address this issue, we propose a novel learning network, the Cross-modal Conflict-Aware Learning Network (CALNet), that takes into account semantic conflicts and complementary information within multi-modal input. Our network comprises two pivotal modules: the Cross-Modal Conflict Rectification Module (CCR) and the Selected Cross-modal Fusion (SCF) Module. The CCR module mitigates modal heterogeneity by examining contextual information of analogous pixels, thus alleviating multi-modal information with semantic conflicts. Subsequently, semantically coherent information is supplied to the SCF module, which fuses multi-modal features by assessing intra-modal importance to select semantically rich features and mining inter-modal complementary information. To assess the effectiveness of our proposed method, we develop a two-stream one-stage detector based on CALNet for multispectral object detection. Comprehensive experimental outcomes demonstrate that our approach considerably outperforms existing methods in resolving the cross-modal semantic conflict issue and achieving state-of-the-art accuracy in detection results.

In view of the magnetically actuated capsule robot applications in diagnoses of human stomach disease, the challenges that are associated with degrees-of-freedom (DOF), environmental adaptability, and the size of the entire system must be addressed. In this study, a new electromagnetic coil system that is based on adaptive magnetic levitation is presented; it is mainly composed of an independent orientation control electromagnetic coil and a magnetic levitation control electromagnetic coil. The system is designed to perform motion control of the capsule, including 3-DOF levitating translational motion control, and pitch and yaw motion control in levitation. In this manner, it compensates for the lack of previous magnetic levitation systems that were based on electromagnetic coils for the control of the tilt angle of the capsule. With torque-based actuation control, the tilt angle of the capsule can be controlled continuously with an angle increment of no more than 2° (within 30°) during levitation. The capsule under the magnetic levitation control, which is based on the fuzzy proportional-integral -derivative controller, can maintain the stability of levitation; the error between the actual capsule position and the required position did not exceed 0.1 mm when the capsule was out of water or completely immersed in it. Moreover, the experiment of the capsule levitating from air to water further verifies the adaptability of the system with regard to the environment; this provides a new examination method for capsule endoscopy. Note to Practitioners—This work is caused by the motion manipulation problem of a magnetically driven capsule robot. Most of the existing magnetically driven capsule robots use a rolling or sliding method that fits tightly against the stomach wall, and these forms of motion make it possible to risk missing diagnoses in the examination of gastric diseases, and the maneuverability limits its further application. This paper proposes an adaptive control strategy for a magnetically driven capsule robot based on magnetic levitation motion. It allows the magnetically driven capsule robot to levitate to any position in the stomach for photography and observation without external mechanical support, and this levitation motion is sufficiently stable. In addition, the proposed capsule robot has independent orientation and position control, which has the potential to achieve automated inspection.

Coflow scheduling is critical to data-parallel applications in data centers. While schemes like Varys can achieve optimal performance, they require a priori information about coflows which is hard to obtain in practice. Existing non-clairvoyant solutions like Aalo generalize least attained service (LAS) scheduling discipline to address this issue. However, they fail to identify the bottleneck flows in a coflow and tend to allocate excessive bandwidth to the non-bottleneck flows, leading to bandwidth wastage and inferior overall performance. To this end, we present Fai that strives to improve the overall coflow performance by accelerating the bottleneck flows without priori knowledge. Fai employs bottleneck-aware scheduling. It adopts loose coordination to update coflow priority and flow rates based on total bytes sent. In addition, Fai detects bottleneck flows based on a flow’s rate and bytes sent, and de-allocates bandwidth for other flows to match the bottleneck rate without affecting the coflow completion time (CCT). The saved bandwidth is then distributed among coflows according to their priority to improve overall performance. Testbed evaluation on a 40-node cluster shows that Fai improves average (P95) CCT by 1.73× (3.43×), compared to Aalo. Large-scale trace-driven simulations also show that Fai outperforms Aalo substantially.

Edge computing can reduce service latency and save backhaul bandwidth by completing services at network edges, providing support for diverse computation-intensive and delay-sensitive services. However, it is not practical to support all services at edge nodes due to the limited network resources. The decision that which services can be provided locally and which services should been offloaded to cloud significantly impacts the user experience. Cloud-edge computing offloading becomes an important issue in edge computing. In this paper, we take the fairness into the optimization objective of computing offloading problem, and consider both computing capacity and storage space as problem constraints. The problem is formulated as a long-term average optimization problem to maximize the α-fair utility function of saved time, and further translated as a Markov decision process. As the optimization problem with fairness guarantee and huge action space, we cannot solve it with traditional methods. Therefore, an innovative multi-update deep reinforcement learning algorithm is proposed which can optimize the objective with α-fair utility function and reduce dramatically the size of action space. We also prove the convergence of our algorithm theoretically. To our best knowledge, the long-term average optimization of computing offloading with fairness guarantee is rarely seen in literature. Extensive simulation experiments show that our algorithm can converge quickly and has better performance in terms of service delay and fairness.

Hyperspectral band selection aims to identify an optimal subset of bands for hyperspectral images (HSIs). For most existing clustering-based band selection methods, they directly stretch each band into a single feature vector and employ the pixelwise features to address band redundancy. In this way, they do not take full consideration of the spatial information and deal with the importance of different regions in HSIs, which leads to a nonoptimal selection. To address these issues, a region-aware hierarchical latent feature representation learning-guided clustering (HLFC) method is proposed. Specifically, in order to fully preserve the spatial information of HSIs, the superpixel segmentation algorithm is adopted to segment HSIs into multiple regions first. For each segmented region, the similarity graph is constructed to reflect the bands-wise similarity, and its corresponding Laplacian matrix is generated for learning low-dimensional latent features in a hierarchical way. All latent features are then fused to form a unified feature representation of HSIs. Finally, k -means clustering is utilized on the unified feature representation matrix to generate multiple clusters from which the band with maximum information entropy is selected to form the final subset of bands. Extensive experimental results demonstrate that the proposed clustering method can achieve superior performance than the state-of-the-art representative methods on the band selection. The demo code of this work is publicly available at https://github.com/WangJun2023/HLFC.

Deep learning-based hyperspectral image (HSI) classification and object detection techniques have gained significant attention due to their vital role in image content analysis, interpretation, and broader HSI applications. However, current hyperspectral object detection approaches predominantly emphasize spectral or spatial information, overlooking the valuable complementary relationship between these two aspects. In this study, we present a novel Spectral-Spatial Aggregation (S2ADet) object detector that effectively harnesses the rich spectral and spatial complementary information inherent in the hyperspectral image. S2ADet comprises a hyperspectral information decoupling (HID) module, a two-stream feature extraction network, and a one-stage detection head. The HID module processes hyperspectral data by aggregating spectral and spatial information via band selection and principal components analysis, consequently reducing redundancy. Based on the acquired spectral and spatial aggregation information, we propose a feature aggregation two-stream network for interacting spectral-spatial features. Furthermore, to address the limitations of existing databases, we annotate an extensive dataset, designated as HOD3K, containing 3,242 hyperspectral images captured across diverse real-world scenes and encompassing three object classes. These images possess a resolution of 512×256 pixels and cover 16 bands ranging from 470 nm to 620 nm. Comprehensive experiments on two datasets demonstrate that S2ADet surpasses existing state-of-the-art methods, achieving robust and reliable results. The demo code and dataset of this work are publicly available at https://github.com/hexiao-cs/S2ADet.

Remote sensing change detection (RSCD) aims to explore surface changes from co-registered pair of images. However, the high cost of memory and computation in previous convolutional neural network (CNN)-based methods prevent their successes from being applied to real-world applications. Therefore, we propose a novel lightweight network, which identifies changes based on the features extracted by mobile networks via progressive feature aggregation and supervised attention, termed as A2Net. Considering the less powerful representation capability of mobile networks, we design a neighbor aggregation module (NAM) to fuse features within nearby stages of the backbone to strengthen the representation capability of temporal features. Then, we propose a progressive change identifying module (PCIM) to extract temporal difference information from bitemporal features. Besides, we design a supervised attention module (SAM) to reweight features for effectively aggregating multilevel features from high levels to low levels. With NAM, PCIM, and SAM incorporated, A2Net can achieve favorable results compared with the state-of-the-art methods on three challenging RSCD datasets with fewer parameters (3.78 M) and lower computation costs (6.02 G). The demo code of this work is publicly available athttps://github.com/guanyuezhen/A2Net.

Multi-view spectral clustering, which exploits the complementary information among graphs of diverse views to obtain superior clustering results, has attracted intensive attention recently. However, most existing multi-view spectral clustering methods obtain the clustering partitions in a two-step scheme, i.e., spectral embedding and subsequent$k$-means. This two-step scheme inevitably seeks sub-optimal clustering results due to the information loss during the two-steps processes. Besides, existing multi-view spectral clustering methods do not jointly utilize the information of graphs and embedding matrices, which also degrades final clustering results. To solve these issues, we propose a unified one-step multi-view spectral clustering method, which integrates the spectral embedding and$k$-means into a unified framework to obtain discrete clustering labels with a one-step strategy. Under the observation that the inner product of the embedding matrix is a low-rank approximation of the graph, we combine graphs and embedding matrices of different views to obtain a unified graph. Then, we directly capture the discrete clustering indicator matrix from the unified graph. Furthermore, we design an effective optimization algorithm to solve the resultant problem. Finally, a set of experiments on various datasets are conducted to verify the effectiveness of the proposed method. The demo code of this work is publicly available atrgb]0,0,1https://github.com/guanyuezhen/UOMvSC.

Nowadays, various network telemetry technologies are proposed to monitor the network and detect failures accurately in real-time, which can be categorized into two types, including the proactive network telemetry (NT) and the passive one. The passive NT can monitor the network with low band-width overhead, yet, cannot guarantee full network coverage. The proactive one can achieve full coverage, yet, lead to high bandwidth cost. To deal with the problem, we propose a failure-aware clustered network telemetry approach, called CNT. CNT leverages the practical objective network operating experience: different network links have various failure probabilities. It is aware of the failure probabilities and assigns the network links into two clusters accordingly. Then, based on the original network topology, CNT designs an active path planning algorithm to connect the two clusters of links into two sub-topologies, respectively. Finally, CNT performs network telemetry with different cycles. We evaluate CNT with various simulation experiments. The results show that compared to existing proactive schemes, CNT can achieve comparable network coverage with less cost.

Macrophage immunotherapy is a promising clinical approach to treat cancer. However, low targeting efficiency severely limits the immunotherapeutic effect of macrophages. Here, we report a unique macrophage robot that can target and kill cancer cells using a combination of external acoustic and magnetic fields. First, the inactive macrophages (Mø) are magnetized by endocytosis of the$\gamma$-Fe2O3nanoparticles (FeNPs). Then, the magnetized M⊘can be moved towards the capillary wall under the influence of an acoustic radiation force generated from a lead zirconate titanate piezoelectric (PZT) transducer. Finally, the magnetized cells rotate forward under the action of alternating magnetic fields (AMF). During the process of magnetizing macrophages, FeNPs activate the anti-tumor immune activity of macrophages (M1) to induce cancer cell death. Overall, the present study highlights a novel cell robot that can target and kill cancer cells. Considering that the nanoparticles, macrophages, magnetic fields, and ultrasound technology have all been FDA approved for clinical settings, our targeted delivery system has tremendous clinical translational potential.

Channel estimation is important for orthogonal frequency division multiplexing (OFDM) in current wireless communication systems. Prevalent channel estimation algorithms, however, cannot be widely deployed due to some practical reasons, such as poor robustness and high computational complexity. To solve the problems for OFDM systems, we propose a new channel estimation scheme with a fine-designed deep learning model, called RRDBNet. RRDBNet can be trained easily while maintaining the advantages of residual learning and increasing the structure capacity, by combining the multi-level residual network and dense links. Our simulation results show that RRDBNet outperforms the traditional least-square algorithm and existing DL-based super-resolution schemes, which ranges from 0.5 to 1dB at low SNR and from 2 to 3dB at high SNR. Besides, in terms of the number of pilots, RRDBNet is also superior to existing schemes and approaches LMMSE.

Clustering is a fundamental task in the machine learning and data mining community. Among existing clustering methods, multiple kernel clustering (MKC) has been widely investigated due to its effectiveness to capture non-linear relationships among samples. However, most of the existing MKC methods bear intensive computational complexity in learning an optimal kernel and seeking the final clustering partition. In this paper, based on the spectral perturbation theory, we propose an efficient MKC method that reduces the computational complexity from O(n3) to O(nk2 + k3), with n and k denoting the number of data samples and the number of clusters, respectively. The proposed method recovers the optimal clustering partition from base partitions by maximizing the eigen gaps to approximate the perturbation errors. An equivalent optimization objective function is introduced to obtain base partitions. Furthermore, a kernel weighting scheme is embedded to capture the diversity among multiple kernels. Finally, the optimal partition, base partitions, and kernel weights are jointly learned in a unified framework. An efficient alternate iterative optimization algorithm is designed to solve the resultant optimization problem. Experimental results on various benchmark datasets demonstrate the superiority of the proposed method when compared to other state-of-the-art ones in terms of both clustering efficacy and efficiency.

Incomplete multi-view clustering aims to exploit the information of multiple incomplete views to partition data into their clusters. Existing methods only utilize the pair-wise sample correlation and pair-wise view correlation to improve the clustering performance but neglect the high-order correlation of samples and that of views. To address this issue, we propose a high-order correlation preserved incomplete multi-view subspace clustering (HCP-IMSC) method which effectively recovers the missing views of samples and the subspace structure of incomplete multi-view data. Specifically, multiple affinity matrices constructed from the incomplete multi-view data are treated as a third-order low rank tensor with a tensor factorization regularization which preserves the high-order view correlation and sample correlation. Then, a unified affinity matrix can be obtained by fusing the view-specific affinity matrices in a self-weighted manner. A hypergraph is further constructed from the unified affinity matrix to preserve the high-order geometrical structure of the data with incomplete views. Then, the samples with missing views are restricted to be reconstructed by their neighbor samples under the hypergraph-induced hyper-Laplacian regularization. Furthermore, the learning of view-specific affinity matrices as well as the unified one, tensor factorization, and hyper-Laplacian regularization are integrated into a unified optimization framework. An iterative algorithm is designed to solve the resultant model. Experimental results on various benchmark datasets indicate the superiority of the proposed method. The code is implemented by using MATLAB R2018a and MindSpore library: https://github.com/ChangTang/HCP-IMSC.

Although demonstrating great success, previous multi-view unsupervised feature selection (MV-UFS) methods often construct a view-specific similarity graph and characterize the local structure of data within each single view. In such a way, the cross-view information could be ignored. In addition, they usually assume that different feature views are projected from a latent feature space while the diversity of different views cannot be fully captured. In this work, we resent a MV-UFS model via cross-view local structure preserved diversity and consensus learning, referred to as CvLP-DCL briefly. In order to exploit both the shared and distinguishing information across different views, we project each view into a label space, which consists of a consensus part and a view-specific part. Therefore, we regularize the fact that different views represent same samples. Meanwhile, a cross-view similarity graph learning term with matrix-induced regularization is embedded to preserve the local structure of data in the label space. By imposing the$l_{2,1}$-norm on the feature projection matrices for constraining row sparsity, discriminative features can be selected from different views. An efficient algorithm is designed to solve the resultant optimization problem and extensive experiments on six publicly datasets are conducted to validate the effectiveness of the proposed CvLP-DCL.

Multi-view clustering, which exploits the multi-view information to partition data into their clusters, has attracted intense attention. However, most existing methods directly learn a similarity graph from original multi-view features, which inevitably contain noises and redundancy information. The learned similarity graph is inaccurate and is insufficient to depict the underlying cluster structure of multi-view data. To address this issue, we propose a novel multi-view clustering method that is able to construct an essential similarity graph in a spectral embedding space instead of the original feature space. Concretely, we first obtain multiple spectral embedding matrices from the view-specific similarity graphs, and reorganize the gram matrices constructed by the inner product of the normalized spectral embedding matrices into a tensor. Then, we impose a weighted tensor nuclear norm constraint on the tensor to capture high-order consistent information among multiple views. Furthermore, we unify the spectral embedding and low rank tensor learning into a unified optimization framework to determine the spectral embedding matrices and tensor representation jointly. Finally, we obtain the consensus similarity graph from the gram matrices via an adaptive neighbor manner. An efficient optimization algorithm is designed to solve the resultant optimization problem. Extensive experiments on six benchmark datasets are conducted to verify the efficacy of the proposed method. The code is implemented by using MATLAB R2018a and MindSpore library[1]:https://github.com/guanyuezhen/CGL.

Network function virtualization (NFV) enables elastic scaling to middlebox deployment and management. Therefore, efficient stateful scaling is an important task because operators often need to shift traffic and the associated flow states across VNF instances to deal with time-varying loads. Existing NFV scaling methods, however, typically focus on one aspect of the scaling pipeline and does not offer an end-to-end scaling framework. This article presents ScaleFlux, a complete stateful scaling system that efficiently reduces flow-level latency and achieves near-optimal resource usage. ScaleFlux (1) monitors traffic load for each VNF instance and adopts a queue-based mechanism to detect load burstiness timely, (2) deploys a flow bandwidth predictor to predict flow bandwidth time-series with the ABCNN-LSTM model, and (3) schedules the necessary flow and state migration using the simulated annealing algorithm to achieve both flow-level latency guarantee and resource usage minimization. Testbed evaluation with a five-machine cluster shows that ScaleFlux reduces flow completion time by at least 8.7× for all the workloads and achieves near-optimal CPU usage during scaling.

Rapid development of the Internet has enabled explosive growth of various network traffic. How to classify and identify different categories of network traffic among these huge network traffic for cyberspace security has always been a hot research topic. In our study, we found that the composition structure of data frames and grayscale maps in the original traffic is very similar. Combined with recent research of deep learning in image processing, this paper proposes a 2D-CNN model-based network traffic recognition algorithm, while transforming traffic to grayscale maps for recognition. To validate the effectiveness of our proposed model, we use the public network dataset ISCX-VPN-NonVPN-2016 and USTC-TF2016. Experimental results prove that the average accuracy is 98.7% in regular encrypted traffic identification and 97.6% for malicious traffic identification. Our method provides new solutions for network traffic identification.

In this paper, we propose a novel incomplete multi-view clustering method, in which a tensor nuclear norm regularizer elegantly diffuses the information of multi-view block-diagonal structure across different views. By exploring the membership between observed and missing samples and that between missing ones in each incomplete view with the guidance of the high-order view consistency, a global block-diagonal structure is well preserved in multiple spectral embedding matrices. Meanwhile, a consensus representation with strong separability is obtained for clustering. An iterative algorithm based on Augmented Lagrange Multiplier (ALM) is designed to solve the resultant model. Experimental results on six benchmark datasets indicate the superiority of the proposed method. http://github.com/ChangTang/TMBSD

Microbubble-induced acoustic microstreaming for efficient on-chip micromanipulation is widely developed in biological applications. However, it is still challenging to simultaneously transport, trap, and rotate single cells using one device in a biocompatible manner, while expensive and bulky traditional acoustic driving system also increases its limitation. This paper presents a portable acoustofluidic device for multifunctional cell manipulation and 3D reconstruction, using acoustically oscillating bottom bubble array. Based on the Arduino-based driving system, multiple bubble-induced microvortices were generated and utilized to achieve multifunctional manipulation in a noninvasive manner. Self-propelled transportation of single or multiple cells is first accomplished by bottom bubble array; Controllable trapping, 3D rotation (in the x-y or x-z plane) of DU145 cells are further performed by every single microbubble. Through experiments, rotation direction, speed and axis can be modulated by tuning the driving frequency and voltage. Finally, 3D cell reconstruction combining imaging processing algorithm with out-of-plane rotation enables a sufficient illustration of cell structures and surface morphology, providing an efficient properties measurement function. All these aspects of this device show great potentials in bioengineering, biophysics and biomedicine.

Network measurement provides rich data for network monitoring, control, and management. In-band network telemetry (INT) is a new network measurement technology that uses normal data packet to collect network information hop-by-hop. However, the design and implementation of INT protocol cannot do anything about packet loss: (1) The end-to-end telemetry mechanism makes INT unable to detect packet loss; (2) Since data packets may be lost due to various reasons, INT telemetry information will inevitably be lost. In summary, INT system by itself is unreliable. Incomplete telemetry data will seriously affect the performance of upper-layer network telemetry applications. In this paper, we present our successful experience in INT packet loss monitoring. We design, implement, and open source a powerful packet loss monitoring system for INT, called LossSight. The functions of LossSight include the detection of packet loss events, the deduction of the time and location of the losses, the diagnose of the root cause of the losses, and the recovery of the lost INT information. Experiment results show that LossSight provides excellent performance and extremely low overhead, including detection accuracy and diagnostic precision close to 100%, and detection latency of just milliseconds. In particular, LossSight uses a generative adversarial network to recover lost telemetry information, with excellent accuracy and reliability. LossSight has been running stably in the supercomputing interconnection environment of the National Supercomputing Center in Jinan. We suggest that all INT applications that require reliable telemetry information should be implemented based on LossSight.

At present, there is no unified network resource scheduling and business optimization system in cloud-edge collaboration services with supercomputing as the core, especially network transmission and data management. In this poster, we focus on how to optimize the quality of user experience while meeting huge computing power requirements, and design a novel network resource scheduling and traffic optimization platform centered on supercomputing system, which is called PANGU. PANGU is a multi-dimensional network resource scheduling solution for supercomputing that can guarantee collaborative network services. PANGU can make up for the lack of computing power of edge computing while solving the adaptability of supercomputing nodes to pervasive cloud edge computing. Through effective network resource management, PANGU can provide higher productivity.

The cloud-edge integration service model combines the advantages of computing capabilities both from cloud and edge. Therefore, the data centers with cloud-edge integrated are an irreversible trend for the evolution of future data center. Software-Defined Network (SDN), emerging as a novel network model, separates content forwarding and control, and that makes resource management across data center network more efficient. This article focuses on the network data transmission and management for future data centers. First, it reviews measurement, analysis, and monitoring methods meant for new features of global SDN network. Then it focuses on unified management of SDN resources like traffic scheduling theory for cross-domain data centers based on cloud. Specifically, we proposed a novel fault response mechanism across the network with a more precise location and less response time. With dynamic changes of cloud computing and edge computing services combined, global QoS control and QoE optimization methods are proposed correspondingly. Finally, a set of SDN control platforms supporting the functions mentioned above are formulated. We hope that our work will shed some new light and provide new theoretical support for cloud-edge-combined cross-domain data center network architecture.

Sonoporation, which typically employs acoustic cavitation microbubbles, can enhance the permeability of the cell membrane, allowing foreign matter to enter cells across the natural barriers. However, the diameter nonuniformity and random distribution of microbubbles make it difficult to achieve controllable and high-efficiency sonoporation, while complex extern acoustic driving system also limits its applicability. Herein, we demonstrate a low-cost, expandable, and portable acoustofluidic device for cell sonoporation using acoustic streaming generated by oscillating sharp edges. The streaming-induced high shear forces can (i) quickly trap target cells at the tip of sharp edges and (ii) transiently modulate the permeability of the cell membrane, which is utilized to perform cell sonoporation events. Using our device, sonoporation is successfully achieved in a microbubble-free manner, with a sonoporation efficiency of more than 90%. Furthermore, our acoustic driving system is designed around the open-source Arduino prototyping platform due to its extendibility and portability. In addition to these benefits, our acoustofluidic device is simple to fabricate and operate, and it can work at relatively low frequency (4.6 kHz). All these advantages make our novel cell sonoporation device invaluable for many biological and biomedical applications such as drug delivery and gene transfection.

QUIC provides a secure, reliable and low-latency communication foundation for HTTP. QUIC uses the connection ID to uniquely determine a connection from client to server. After the user switches the network, the server recognizes the user request according to the connection ID and continues to provide services through the connection migration technology. This paper proposes a Proactive Connection Migration (PCM) mechanism for QUIC. PCM gives QUIC the ability to select the optimal network in a heterogeneous network environment. Firstly, PCM actively perceives the different networks available to users. Then, PCM integrates the network quality exploration of different paths into the user’s multiple request actions. Finally, PCM takes response delay and jitter into account, and uses online learning to find the optimal network for current Internet service. Experimental results show that, compared with original QUIC, the average response delay of QUIC with PCM is reduced by 59.43% at most.

Curved surface mesh generation is a key step for many areas. Here, a mesh generation algorithm for closed curved surface based on Delaunay refinement is proposed. We focus on improving the shape quality of the meshes generated and making them conform to 2-manifold. The Delaunay tetrahedralization of initial sample is generated first, the initial surface mesh which is a subset of the Delaunay tetrahedralization can be achieved. A triangle is refined by inserting a new point if it is large or of bad quality. For each sample, we also check the triangles that adjoin it whether from a topological disk. If not, the largest triangle will be refined. Finally, the surface mesh is updated after a new point is inserted into the sample. The definition of mesh size function for surface mesh generation is also put in this paper. Meshing experiments of some models demonstrate that the new algorithm is advantageous in generating high quality surface mesh, the count of mesh is suitable and can well approximate the curved surface. The presented method can be used for a wide range of problems including computer graphics, computer vision and finite element method.

We propose an innovative design of microrobot, which can achieve donor cell suction, delivery and injection in a mammalian oocyte on microfluidic chip. The microrobot body contains a hollow space that produces suction and ejection forces for injection of cell nuclei using a nozzle at the tip of the robot. Specifically, a controller changes the hollow volume by balancing the magnetic and elastic forces of the membrane, and along with motion of stages in the XY plane. A glass capillary attached at the tip of the robot contains the nozzle is able to absorb and inject cell nuclei. The microrobot provides three degrees of freedom and generates micronewton forces. We demonstrate the effectiveness of the proposed microrobot through an experiment of absorption and ejection of 20 μm particles from the nozzle using magnetic control in a microfluidic chip.

Computer Supported Cooperation Work (CSCW) has been playing an increasingly important role in many areas of human social life. Cooperative design refers to the technique of product design based on CSCW and parallel engineering. With widespread use of CSCW, network bandwidth is becoming a bottleneck that affects user experience and service quality. Currently, global attention has been paid to the fifth-generation communication system (5G). In order to address the network bottleneck of the cooperative design system using the 5G network advantages, this paper focuses on optimizing QoE of the cooperative design system and proposes a distributed cache system for cooperative design in the 5G environment. The cache network is divided into different domains based on the characteristics of the 5G structure. Coupling between cache and cooperative design is implemented after taking the properties of the cooperative design system into account. Simulation results demonstrate the ability of the proposed system to considerably improve QoE of the cooperative design system and reduce bandwidth utilization.

Software-Defined Networking (SDN) is an emerging network architecture. SDN is currently attracting significant attention from both academia and industry. A large number of studies have been carried out in academic circles. However, how to build small scale experimental Software-Defined Networking is the basis of various researches. This paper builds a physical device called Xspider for building real SDN experimental environment based on NetFPGA with OpenFlow support. Xspider has the characteristics of saving space, being easy to carry, and the ability to simulate multiple topologies. This kind of physical device can facilitate the application of SDN teaching and experiment, which is beneficial to promote the technological progress of SDN.

Mesh generation is a key step for many areas. Here, an adaptive mesh generation algorithm for piecewise linear domains based on constrained Delaunay is proposed. We focus on improving the quality of the meshes generated and make them conforming to an isotropic size function which is specified by the user. The preliminary mesh with boundaries conforming is generated first. We try to add points to split the mesh either bad shape or not conforming to the size function. The new point can be added to the domain only if it is not close to the existing point in the domain. Finally, local meshes are reconstructed to make all meshes comply with the constrained Delaunay. Experimental comparisons show that the new algorithm is advantageous in generating adaptive mesh. The presented method can be used in a wide range of areas such as computer vision, finite element method and many other areas.