Network connectivity minimization is a fundamental problem in controlling the spread of viruses in the Internet and facilitating information propagation in online social networks. The problem aims to identify a budget number of key nodes whose removal would minimize the connectivity of a network. However, the existing solutions heavily rely on the number of edges, making it challenging to handle large and densely connected social networks. In this study, we present a fast algorithm that is independent of the number of edges. To achieve this, we first introduce a surrogate matrix that approximates the residual adjacency matrix with arbitrary small predefined error. We then devise an efficient approach for inferring k influential nodes by optimizing the eigenvalues of the surrogate matrix. Remarkably, the algorithm has a small time complexity of O(knr3), with r being a small tunable number. Our algorithm thereby maintains a linear scalability in terms of the number of nodes and is unaffected by the number of edges. Hence, it has the capability to efficiently handle large and dense social networks. At last, we evaluate its performance against state-of-the-art techniques using diverse real-world datasets. The experimental results demonstrate the superiority of our proposed method in terms of both solution quality and computational efficiency.

The issue of data heterogeneity has long been a significant challenge in federated learning, impacting the speed of global model convergence and potentially lowering the overall performance of the global model, thereby impeding its development. In response to this challenge, we propose a classifier head-based method to correct the local model update direction. Unlike most existing calibration methods, our method not only ensures a global model with good performance but also guarantees the performance of a single local model. Concretely, we have introduced a new federated learning framework called FedCA. In this framework, the output of the feature extractor is imported into both a global classifier and a local classifier. The gradients are calculated separately, and then backpropagate into the model. This method ensures the performance of the local model and allows the client to maintain a consistent learning direction. Our experimental results demonstrate the feasibility of this method.

Federated learning enables collaborative AI training across organizations without compromising data privacy. Decentralized federated learning (DFL) improves this by offering enhanced reliability and security through peer-to-peer (P2P) model sharing. However, DFL faces challenges in terms of slow convergence rate due to complex P2P graphs. To address this issue, we propose an efficient algorithm to accelerate DFL by introducing a limited number of k of edges into the P2P graphs. Specifically, we establish a connection between the convergence rate and the second smallest eigenvalue of the laplacian matrix of the P2P graph. We prove that finding the optimal set of edges to maximize this eigenvalue is an NP-complete problem. Our quantitative analysis shows the positive effect of strategic edge additions on improving this eigenvalue. Based on the analysis, we then propose an efficient algorithm to compute the best set of candidate edges to maximize the second smallest eigenvalue, and consequently the convergence rate is maximized. Our algorithm has a low time complexity of O(krn^2). Experimental results on diverse datasets validate the effectiveness of our proposed algorithms in accelerating DFL convergence.

Abstract In stream processing systems, Key Grouping is a commonly employed partitioning scheme for distributing input tuples among parallel instances of stateful operators. With key grouping, tuples shared public keys in the stream are designated to the specific instance responsible for that key. Typically, the implementation of key grouping involves the use of a hash function. While it is convenient and deterministic, it is also known to cause load imbalance between parallel instances, especially in the presence of skewed data streams. Key-Splitting is an effective technique that distributes tasks associated with keys to downstream operators, facilitating load balancing at a relatively low cost. However, overly increasing parallel instances can lead to excessive aggregation costs, becoming a system bottleneck. In this paper, we show the high aggregation cost brought by the Key-Splitting partitioner at different levels of key separation. To address this challenge, we introduce an adaptive Key-Splitting method which controlling the degree of key separation. We propose a partitioner named FlexD, which aims to achieve dynamic adaptation of key separation limits for streaming data. The partitioner employs key grouping to distribute rare keys and dynamic expansion of processing instances to distribute hot keys. We implemented our method on Apache Storm and evaluated it by using real-world and synthetic datasets. Experimental results show that our method achieves a good balance between load balancing and aggregation cost. Moreover, it outperforms existing methods, achieving higher throughput.

Federated learning (FL) is an emerging paradigm using a parameter server (PS) to coordinate multiple decentralized clients for training a common model without exposing their raw data. Despite its amazing capability in preserving data privacy, FL confronts two significant challenges that have not been sufficiently addressed by existing works, which are: 1) heterogeneous data distributed on clients given that the PS cannot alter data locations. 2) limited computation resources of FL clients who may conduct model training with mobile devices. To tackle these challenges, we propose a novel federated one-shot transferring of intermediate features (FedOTF) algorithm. More specifically, FedOTF consists of two stages: feature extraction and model reconstruction. To overcome challenge 1, clients in stage 1 only collaboratively train a small model in a federated manner, with the objective to extract features. In stage 2, intermediate features (generated by the small model trained by stage 1) are transferred from clients to the PS so that a large model can be trained to overcome challenge 2. In addition, the design of FedOTF is robust, which can flexibly diminish the number of communication rounds of stage 1 when network capacity is limited, and reduce the amount of exposed features when privacy is concerned. To verify the superiority of FedOTF, we conduct comprehensive experiments with real datasets. The experiment results demonstrate that FedOTF can significantly improve the model utility of FL because a better model can be finally obtained by the PS without incurring heavy computational load on clients. Besides, we conduct robustness evaluation of FedOTF which can achieve stable performance when varying network capacity and privacy requirement.

Image-based localization is an essential component for many applications such as autonomous driving, virtual reality. Many researchers focus on developing methods for daytime via RGB images. Few research study the methods for night condition. The main reason is that the RGB-based image localization methods fail to work in dark scenes due to the low illumination. Thermal images capture temperature information instead of texture, making it well suitable for dark environments. However, thermal image-based localization methods are not well studied. To address it, we propose an attention-based localization method for night condition (DarkLoc) using thermal images. The proposed method introduce the attention mechanism in a deep learning-based framework to extract key information from low quality thermal images. The attention model can enforce the whole network focus on geometry meaningful feature in thermal images and thus improve the localization accuracy. We perform extensive experiment on the thermal image dataset. The results show that the attention model can enforce the whole network to learn geometry meaningful feature from thermal images and effectively locate the thermal image in real-time.

In wireless sensor networks, sensor nodes collect data from the surrounding environment and transfer it to the base station. If a sensor node cannot communicate with the base station directly, it needs to select one of the senor nodes that it can communicate with and transfer the data to it. The above process will go on until the data arrive at the base station. It may cause too many relays during the process in which the data are transferred to the base station. Some nodes may quickly run out of energy and cause the entire wireless network to fail. We build a breadth-first search spanning tree which is rooted in base station according to the connected relation between the sensor nodes and the base station. When sensor nodes transfer the data to the base station, the data follow the path between the node and root in the spanning tree towards the base station. This algorithm guarantees the number of relays in the process and the total energy consumption of the wireless sensor network are the least. Thus the choice of the base station position is vital to the energy consumption of the whole wireless sensor network. This paper proposes an algorithm for the base station placement that finds the optimal base station position by using computation geometry according to the relative relation between the senor nodes. In contrast to the grid computing we are familiar, this algorithm greatly reduces the computing time required by finding the optimal position of base station.

The performances of modern distributed stream processing systems are critically affected by the distribution of the load across workers. Skewed data streams in real world are very common and pose a great challenge to these systems, especially for stateful applications. Key splitting, which allows a single key to be routed to multiple workers, is a great idea to achieve good balance of load in the cluster. However, it comes with the cost of increased memory consumption and computation overhead as well as network communication. In this paper, we present a new definition of metric to model the cost of key splitting for intra-operator parallelism in stream processing systems and provide a novel perspective to reduce replication factor while keeping both overall load imbalance and processing latency low. Similar to previous work, our approach treats the head and the tail of the distribution differently in order to reduce memory requirements. For the head, it uses our proposed notion of regional load imbalance to decide dynamically whether to make one more worker responsible for the heavy hitter or not. For the tail, it simply uses hash partitioning to keep the size of the routing table for the head as small as possible. Extensive experimental evaluation demonstrates that our approach provides superior performance compared to the state-of-the-art partitioning algorithms in terms of load imbalance, replication factor and latency over different levels of skewed stream distributions.

BACKGROUND: With the reduction of gene sequencing cost and demand for emerging technologies such as precision medical treatment and deep learning in genome, it is an era of gene data outbreaks today. How to store, transmit and analyze these data has become a hotspot in the current research. Now the compression algorithm based on reference is widely used due to its high compression ratio. There exists a big problem that the data from different gene banks can't merge directly and share information efficiently, because these data are usually compressed with different references. The traditional workflow is decompression-and-recompression, which is too simple and time-consuming. We should improve it and speed it up. RESULTS: In this paper, we focus on this problem and propose a set of transformation algorithms to cope with it. We will 1) analyze some different compression algorithms to find the similarities and the differences among all of them, 2) come up with a naïve method named TDM for data transformation between difference gene banks and finally 3) optimize former method TDM and propose the method named TPI and the method named TGI. A number of experiment result proved that the three algorithms we proposed are an order of magnitude faster than traditional decompression-and-recompression workflow. CONCLUSIONS: Firstly, the three algorithms we proposed all have good performance in terms of time. Secondly, they have their own different advantages faced with different dataset or situations. TDM and TPI are more suitable for small-scale gene data transformation, while TGI is more suitable for large-scale gene data transformation.

As a competitor and alternative to mainstream general-purpose CPU (Intel/AMD/etc.), Loongson is a family of general-purpose MIPS-compatible CPUs developed at the ICT of CAS in China. The quad-core Loongson 3A is evaluated in this paper. The performance of the basic OpenMP constructs on Loongson-3A quad-core SMP is obtained by applying the EPCC Micro benchmarks. And then the performance of NAS kernel codes is obtained by applying NAS Parallel Benchmarks (NPB). These benchmarking are carried out for three different OpenMP compilers (and the runtime system), which includes GCC, OMPipth (OMPi with pthread library) and OMPi-psth (OMPi with psthread library). The results show that OMPI-pth's performance is the best and OMPi-psth's performance is the worst. Those test results might help to program the OpenMP codes as well as to select the appropriate compiler and its runtime system. And an Intel core i5 quad-core platform is used for comparison purpose, by running NPB, which implies that Loongson 3A's performance is nearly one tenth of i5's. The NPB results can help to defining a Loongson system's scale when replacing an Intel i5 system for a given problem size.

The prior studies setup the churn model by measuring the historical logs or records of a P2P network, and treat it as one whole black-box without understanding the inside of peer's population. The metrics used to characterize the churn is distributions of the node session lengths and arrival intervals. We investigate churn in a higher level point of view, and find that modeling it based on the global geographical distribution of peer nodes will result in a system which explain the fluctuation and cyclic phenomenon of network size. This model considers the user behavior pattern into account. Then we provide a Matlab tools that can provide churn events according to this model. From the output events of simulation, we do see some more future things than other models. We might expect or predict when and what nodes would return back, as well as when and what nodes would disappear at high possibility. So it is useful when designing a system optimized both to the pass and the future, which could reduce the overhead of the maintenance of underlying overlay network of DHTs and lower the redundant level of replications for P2P storage system.