Recently, state space models (SSM), particularly Mamba, have attracted significant attention from scholars due to their ability to effectively balance computational efficiency and performance. However, most existing visual Mamba methods flatten images into 1D sequences using predefined scan orders, which results the model being less capable of utilizing the spatial structural information of the image during the feature extraction process. To address this issue, we proposed a novel visual foundation model called Def-Mamba. This model includes a multi-scale backbone structure and deformable mamba (DM) blocks, which dynamically adjust the scanning path to prioritize important information, thus enhancing the capture and processing of relevant input features. By combining a deformable scanning (DS) strategy, this model significantly improves its ability to learn image structures and detects changes in object details. Numerous experiments have shown that Def-Mamba achieves state-of-the-art performance in various visual tasks, including image classification, object detection, instance segmentation, and semantic segmentation. The code is open source on DefMamba .

Video Salient Object Detection (VSOD) faces significant challenges due to complex background disturbances in video sequences. Although notable progress has been made in this field, further efforts are needed to better address background interference. To address these challenges, our scene-aware background decoupling network (SBDNet) equipped with a scene-aware background decoupling strategy (SBDS) as well as collaborative fusion decoders (CFD). The SBDS includes a Dynamic Background Disentanglement Module (DBD) designed to effectively eliminate background distractions in real-world scenes. The DBD achieves this by utilizing a background mask to refine foreground elements and extracting semantic-enhanced context weights. The CFD enhances the fusion between CNN and Transformer decoders, ensuring high accuracy in saliency detection for video sequences. Extensive results demonstrate that our SBDNet significantly outperforms 14 state-of-the-art methods on four widely used benchmark datasets.

Recently, methods based on Diffusion Probability Models (DPM) have achieved notable success in the field of medical image segmentation. However, most of these methods do not perform well in segmenting ambiguous areas when dealing with prostate segmentation tasks due to the low distinguishability of prostate images and the high overlap of its boundary with adjacent organs. To address this issue, this paper introduces a diffusion-based framework named ProSegDiff, ProSegDiff employs an Adapter to dynamically adjust features from the conditional network to align with the denoising process of the denoising network. Furthermore, the denoising process is conducted in the latent space to minimize the consumption of computational resources, and a proposed selection strategy is employed to identify the better results from multiple inferences. Extensive comparative experiments on four benchmark datasets demonstrate the effectiveness of this method, which achieves superior performance across four evaluation metrics.

Co-salient object detection (CoSOD) is the task of identifying and emphasizing the common salient objects in a collection of images. The current co-salient object detection frameworks often extract features and model interimage relations separately. Although these methods achieve promising performance in many scenes, separating the feature extraction and relation modeling falls short of obtaining discriminative features for co-salient objects, resulting in subperformance, especially in some complex and cluttered real-world scenes. In this article, we introduce a novel CoSOD framework to unify feature extraction and interimage relation modeling. We design an early token interaction module (ETIM) that bridges information flow between branches to simultaneously realize feature extraction and interimage information interaction. To further enhance our network's capability to distinguish co-salient objects from other irrelevant foreground objects, we introduce a pixel-to-group contrastive (PGC) learning method. This approach aids in eliminating the need for additional interaction modules while preserving features' discriminative power for co-salient objects. Our proposed CoSOD framework only includes a backbone embedded with ETIM, a decoder without interaction modules and a project head only used during the training phase. Extensive experiments on three challenging benchmarks, that is, CoCA, CoSOD3k, and Cosal2015, demonstrate that our proposed method can outperform current leading-edge models and achieve the new state-of-the-art. The source code is available at https://github.com/zhiwang98/LDRNet.

Recently, the Diffusion Probabilistic Model (DPM)-based methods have achieved substantial success in the field of medical image segmentation. However, most of these methods are not effective in addressing the issue of blurred edges in prostate segmentation tasks. To address this issue, This paper proposes a framework based on the diffusion model, named BGDiff, which is based on Boundary Guided Injection Module(BGIM) and Adaptive Boundary Loss for prostate segmentation. The BGIM can establish connections between the denoising processes of adjacent steps, thereby providing stable guidance for boundary areas as the denoising progresses step by step, while the Adaptive Boundary Loss adjusts the loss weights for more challenging boundaries based on the model’s active feedback. Extensive experiments on four benchmark datasets demonstrate the effectiveness of the proposed method and achieve state-of-the-art performance on four evaluation metrics. The source code will be publicly available at https://github.com/zjlGO/BGDiff

To provide fast and accurate risk evaluation on network rare threats, importance sampling (IS) is widely used in the rare threat simulation; however, it becomes costly to deal withmany rare threatssimultaneously. For example, a rare threat can be the failure to provide quality-of-service (QoS) guarantees to a critical network flow. Considering network providers often need to deal with many critical flows (i.e., rare threats) simultaneously, if using IS, network providers have to simulate each rare threat with its customized importance distribution individually. To reduce such simulation cost, we propose an efficient mixture importance distribution to simulate multiple rare threats, and then formulate a mixture importance sampling optimization problem (MISO) to select the optimal mixture. We first show that it is challenging to locate the optimal mixture for the “search direction” is computationally expensive to evaluate. We then formulate an online learning (OL) framework to estimate the “search direction” and learn the optimal mixture from simulation samples of threats. And our OL framework has a “zero learning cost” as the samples generated in the learn phase can be reused to provide accurate estimation on the rare threats. We develop two multi-armed bandit OL algorithms so as to: (1) Minimize the sum of estimation variances with a regret of$(\ln T)^2 {/} T$; and (2) Minimize the simulation cost with a regret of$\sqrt{\ln T {/} T}$, where$T$denotes the number of simulation samples. We demonstrate the versatility of our method on different network applications. When compared with the uniform mixture IS, our method reduces cost measures (i.e., sum of estimation variances and simulation cost) by as high as 61.6 percent in the Internet backbone network scenario.

Multi-scale features fusion plays a critical role in salient object detection. Most of existing methods have achieved remarkable performance by exploiting various multi-scale features fusion strategies. However, an elegant fusion framework requires expert knowledge and experience, heavily relying on laborious trial and error. In this paper, we propose a multi-scale features fusion framework based on Neural Architecture Search (NAS), named Auto-MSFNet. First, we design a novel search cell, named FusionCell to automatically decide multi-scale features aggregation. Rather than searching one repeatable cell stacked, we allow different FusionCells to flexibly integrate multi-level features. Simultaneously, considering features generated from CNNs are naturally spatial and channel-wise, we propose a new search space for efficiently focusing on the most relevant information. The search space mitigates incomplete object structures or over-predicted foreground regions caused by progressive fusion. Second, we propose a progressive polishing loss to further obtain exquisite boundaries by penalizing misalignment of salient object boundaries. Extensive experiments on five benchmark datasets demonstrate the effectiveness of the proposed method and achieve state-of-the-art performance on four evaluation metrics. The code and results of our method are available at https://github.com/OIPLab-DUT/Auto-MSFNet.

Importance sampling (IS) is widely used in rare event simulation, but it is costly to deal with many rare events simultaneously. For example, a rare event can be the failure to provide the quality-of-service guarantee for a critical network flow. Since network providers often need to deal with many critical flows (i.e., rare events) simultaneously, if using IS, providers have to simulate each rare event with its customized importance distribution individually. To reduce such cost, we propose an efficient mixture importance distribution for multiple rare events, and formulate the mixture importance sampling optimization problem (MISOP) to select the optimal mixture. We first show that the "search direction" of mixture is computationally expensive to evaluate, making it challenging to locate the optimal mixture. We then formulate a " zero learning cost " online learning framework to estimate the "search direction", and learn the optimal mixture from simulation samples of events. We develop two multi-armed bandit online learning algorithms to: (1) Minimize the sum of estimation variances with a regret of (ln T)2/T; (2) Minimize the simulation cost with a regret of √ln T/T , where T denotes the number of simulation samples. We demonstrate our method on a realistic network and show that it can reduce the cost measures (i.e., sum of estimation variances and simulation cost) by as high as 61.6% compared with the uniform mixture IS.

Capacity planning and sales projection are essential tasks for network operators. This work aims to help network providers to carry out network capacity planning and sales projection by answering: Given topology and capacity, whether the network can serve current flow demands with high probabilities? We name such probability as the “flow availability”, and present the flow availability estimation (FAVE) problem with generalizing the classical network connectivity based and maximum flow based reliability estimations. To quickly estimate flow availabilities, we utilize correlations among link and flow failures to figure out the importance of roles played by different links in flow failures (i.e., flow demands could not be satisfied). And we design three sequential importance sampling (SIS) estimation methods, which are: (1) Accurate and efficient: They achieve a bounded or even vanishing relative error with linear computational complexities. Hence they can provide more accurate estimations in less simulation time. (2) Robust and scalable: They maintain such estimation efficiencies even if only a partial SEED set information is available, or when the FAVE problem is extended to the multiple flows case. When applying to a realistic backbone network, our method can reduce the flow availability estimation cost by 900 and 130 times compared with MC and baseline IS methods; and also facilitate capacity planning and sales projection by providing better flow availability guarantees, compared with traditional methods.

This paper focuses on helping network providers to carry out network capacity planning and sales projection by answering the question: For a given topology and capacity, whether the network can serve current flow demands with high probabilities? We name such probability as “ flow availability” and present the -flow availability estimation (FAVE) problem, which is a generalisation of network connectivity or maximum flow reliability estimations. Realistic networks are often large and dynamic, so flow availabilities cannot be evaluated analytically and simulation is often used. However, naive Monte Carlo (MC) or importance sampling (IS) techniques take an excessive amount of time. To quickly estimate flow availabilities, we utilize the correlations among link and flow failures to figure out the importance of roles played by different links in flow failures, and design three “sequential importance sampling” (SIS) methods which achieve “bounded or even vanishing relative error” with linear computational complexities. When applying to a realistic network, our method reduces the flow availability estimation cost by 900 and 130 times compared with MC and baseline IS methods, respectively. Our method can also facilitate capacity planning by providing better flow availability guarantees, compared with traditional methods.

The Internet is a gigantic distributed system where the end-to-end (E2E) quality-of-service (QoS) plays an important role. Yet the current inter-domain routing protocol, namely, the Border Gateway Protocol (BGP), cannot provide E2E QoS guarantees. The main reason is that an autonomous system (AS) can only receive guarantees from its first-hop ASes via service level agreements (SLAs). But beyond the first-hop, QoS along the path from a source AS to a destination AS is not within the source AS's control regime. This makes it difficult to provide high quality-of-experience services to many Internet users even when many content providers are willing to pay for such high quality E2E guarantees. In this paper, we investigate the feasibility of providing high QoS-guaranteed E2E transit services by utilizing a (small) set of ASes/IXPs to serve as “brokers” to provide supervision, control and resource negotiation. Finding an optimal set of ASes as brokers can be formulated as a Maximum Coverage with$B-$dominating path Guarantee (MCBG) problem, and we show that it is in fact NP-hard. To address this problem, we design a$(\frac{1{-}e^{-\!1}}{2}){-}$approximation algorithm and also an efficient heuristic algorithm when additional constraints (e.g., the path length) are considered. We further analyze the APX-hardness of the MCBG problem to reveal the existence of the best approximation ratio. Based on the current Internet topology, we demonstrate that it is indeed feasible to provide high QoS guarantees for most E2E connections with only a small broker set: with only 0.19, 1.9 or 6.8 percent ASes/IXPs serving as brokers, 53.13, 85.41 or 99.29 percent of all global E2E connections can receive high QoS-guaranteed services. Finally, we provide an economic model to study the behaviours of ASes when cooperating our brokerage scheme with the BGP protocol, and show that there are incentives to form and maintain such a brokerage coalition.

Recently, there is a growing emphasis on device-to-device (D2D) communication, which is the key component of the Internet-of-Things ecosystem. D2D communication can operate on the licensed spectrum of cellular networks so as to improve the spectrum utilization. In this paper, we focus on the resource allocation problem for general multihop D2D communication and introduce users' mobility into D2D communication underlaying cellular networks. Maximizing the total end-to-end data rate involves complex tasks, such as resource allocation and routing. By leveraging on the square tessellation technique, we propose an efficient square-division-based resource allocation scheme. Furthermore, we design a relay-assisted D2D communication protocol that addresses the challenges in enabling multihop D2D communications, namely, spectrum resource allocation, users' mobility, and relay incentive. Through extensive simulations, we show that our relay-assisted D2D communication protocol improves the system throughput up to 55% and the user access rate up to four times in typical scenarios, as compared with state-of-the-art schemes.

The current inter-domain routing protocol, namely, the Border Gateway Protocol (BGP), cannot provide end-to-end (E2E) quality-of-service (QoS) guarantees. The main reason is that an autonomous system (AS) can only receive guarantees from its first hop ASes via service level agreements (SLAs). But beyond the first hop, QoS along the path from source to destination AS is not within the source AS's control regime. In this paper, we investigate the feasibility of providing high QoS-guaranteed E2E transit services by utilizing a (small) set of ASes/IXPs to serve as "brokers" to provide supervision, control and resource negotiation. Finding an optimal set of ASes as brokers can be formulated as a Maximum Coverage with B-dominating path Guarantee (MCBG) problem, which we prove to be NP-hard. To address this problem, we design a (1-e-1/4)-approximation algorithm and also an efficient heuristic algorithm when considering additional constraints (e.g., path length). Based on the current Internet topology, we discover a "3540-alliance" subset (accounting only 6.8%) of 52,079 ASes/IXPs, which can provide high QoS guarantees for 99.29% E2E connections.

Device-to-device (D2D) communication has been recently proposed to mitigate the burden of base stations by leveraging the underutilized cellular spectrum resources, where high overall network throughput and D2D access rate are critical for its service performance and availability. In this paper, we study the resource allocation problem to push the limit of D2D communication underlaying cellular networks by allowing multiple D2D links to share resource with multiple cellular links. We propose FRESH, afullresourcesharing scheme where each subchannel can be shared by a cellular link and an arbitrary number of D2D links. In particular, FRESH first divides the communication links into so-called full resource sharing sets such that, within each set, all D2D link members are able to reuse the whole allocated resources. Thereafter, it allocates a sum of spectrum resources to each obtained full resource sharing set. As compared with state-of-the-art schemes, FRESH provides fine-grained resource allocation, resulting in throughput improvements of up to one order of magnitude, and D2D access rate improvements of up to 5 times with a moderate node density (e.g., on the order of 1 user per 400 square meters).