This article presents a sliding window model for defocus deblurring, named Swintormer, which achieves the best performance to date with remarkably low memory usage. This method utilizes a diffusion model to generate latent prior features, aiding in the restoration of more detailed images. Additionally, by adapting the sliding window strategy, it incorporates specialized Transformer blocks to enhance inference efficiency. The adoption of this new approach has led to a substantial reduction in Multiply-Accumulate Operations (MACs) per iteration, drastically cutting down memory requirements. In comparison to the currently leading GRL method, our Swintormer model significantly reduces the computational load that must depend on memory capacity, from 140.35 GMACs to 8.02 GMACs, while improving the Peak Signal-to-Noise Ratio (PSNR) for defocus deblurring from 27.04 dB to 27.07 dB. This innovative technique enables the processing of higher resolution images on memory-limited devices, vastly broadening potential application scenarios. The article wraps up with an ablation study, offering a comprehensive examination of how each network module contributes to the final performance. Our code and model are available at https://github.com/bnm6900030/swintormer.

Video content providers such as Douyin implement Peer-to-Peer Content Delivery Networks (PCDNs) to reduce the costs associated with Content Delivery Networks (CDNs) while still maintaining optimal user-perceived quality of experience (QoE). PCDNs rely on the remaining resources of edge devices, such as edge access devices and hosts, to store and distribute data with a Multiple-Server-to-One-Client (MS2OC) communication pattern. MS2OC parallel transmission pattern suffers from severe data out-of-order issues. However, direct applying existing schedulers designed for MPTCP to PCDN fails to meet the two goals of high aggregate bandwidth and low end-to-end delivery latency.To address this, we present the comprehensive detail of the Douyin self-developed PCDN video transmission system and propose the first QoE-enhanced packet-level scheduler for PCDN systems, called Pscheduler. Pscheduler estimates path quality using a congestion-control-decoupled algorithm and distributes data by the proposed path-pick-packet method to ensure smooth video playback. Additionally, a redundant transmission algorithm is proposed to improve the task download speed for segmented video transmission. Our large-scale online A/B tests, comprising 100,000 Douyin users that generate tens of millions of videos data, show that Pscheduler achieves an average improvement of 60% in goodput, 20% reduction in data delivery waiting time, and 30% reduction in rebuffering rate.

The Multipath TCP (MPTCP) protocol has received more attention due to the increasing number of terminals with multiple network interfaces. To meet the higher network performance demand of terminal services, many researches leverage reinforcement learning (RL) for MPTCP congestion control (CC) algorithms to improve the performance of MPTCP. However, we observe two limitations of existing RL-based mechanisms that make them impractical: 1) Fail to break the restriction of the input and output dimensions of RL, making the mechanisms unadaptable to the varying number of subflows. 2) Frequent model decisions block packet transmission, leading to under-utilization of bandwidth. This paper breaks the inertial thinking By “inertial thinking” here, we are referring to the initial reaction of others when dealing with CC in MPTCP. Given the interdependence between MPTCP subflows, scholars have traditionally opted for coupled CC. However, we have challenged this conventional thinking by independently handling the CC of different subflows in a single MPTCP flow and ensuring fairness. to overcome the above limitations and proposes Maggey, a non-blocking CC mechanism that applies the single-subflow model to multipath transmission. To this end, Maggey employs loosely coupled design principles and a unique reward function to ensure the fairness of the algorithm. Additionally, Maggey introduces iterative training to ensure the accuracy of training of the single-subflow model. Furthermore, a mode transition framework is artfully designed to avoid blocking, preserving the flexibility of RL-based CCs. These two features enhance the practicability of Maggey and the paper analyze the stability of Maggey. We implement Maggey in the Linux kernel and evaluate the performance of Maggey through extensive emulation and live experiments. The evaluation results show that Maggey boosts 26% throughput over DRL-CC at high bandwidth and improves 2%-60% throughput over traditional algorithms under different network conditions. Besides, Maggey maintains fairness in different scenarios.

Drug substructure plays a crucial role in predicting drug-drug interaction (DDI) with combination drugs for disease therapies. In order to exploit the effect of drug substructure on DDI prediction, we propose a Key Substructure-aware Graph Transformer Network for Drug-drug Interaction Prediction (KSGTN-DDI). First, the substructure-adaptive graph Transformer module adaptively explicit encoding of drug structures information. Then, the key substructure-aware module calculates the importance of different substructures in DDI prediction. Finally, the calculated important substructure aggregation features are used to reconstruct the drug-drug interactions. Relevant experiments indicate that the performance of KSGTN-DDI outperforms other DDI prediction models.

Multipath TCP is a multipath transport protocol deployed on end devices, and many learning-based multipath congestion control schemes have been proposed and verified. However, these schemes cannot adapt rapidly to new network conditions because of their convergence problems and generalization issues. To rapidly adapt to new network conditions, we propose Leopard, a learning-based multi-path congestion control frame-work that uses reinforcement learning to combine offline learning with online fine-tuning. The extensive experiments in emulated network conditions and the real world demonstrate that Leopard converges quickly and maintains consistent high performance in new network conditions, which avoids long retraining when the network environment changes. Leopard improves throughput by 13% compared with DRL-CC and reduces the convergence time by 20% compared with MPCC in new network conditions.

BACKGROUND: Automatic signal-feature extraction algorithms are crucial for profile processing in bioinformatics. Both baseline drift and noise seriously affect the position and peak area of signals. An efficient algorithm named the derivative passing accumulation (DPA) method for simultaneous baseline correction and signal extraction is presented in this article. It is an efficient method using only the first-order derivatives which are obtained through taking the simple differences. RESULTS: We developed a new signal feature extracting procedure. The vector representing the discrete first-order derivative was divided into negative and positive parts and then accumulated to build a signal descriptor. The signals and background fluctuations are easily separated according to this descriptor via thresholding. In addition, the signal peaks are simultaneously located by checking the corresponding intervals in the descriptor. Therefore, the eternal issues of parsing the 1-dimensional output of detectors in biological instruments are solved together. Thereby, the baseline is corrected, and the signal peaks are extracted. CONCLUSIONS: We have introduced a new method for signal peak picking, where baseline computation and peak identification are performed jointly. The testing results of both authentic and artificially synthesized data illustrate that the new method is powerful, and it could be a better choice for practical processing.

The idea of building query-oriented routing indices has changed the way of improving routing efficiency from the basis as it can learn the content distribution during the query routing process. It gradually improves routing efficiency with no excessive network overhead of the routing index construction and maintenance. However, the previously proposed mechanism is not practically effective due to the slow improvement of routing efficiency. In this paper, we propose a novel mechanism for query-oriented routing indices which quickly achieves high routing efficiency at low cost. The maintenance method employs reinforcement learning to utilize mass peer behaviors to construct and maintain routing indices. It explicitly uses the expected value of returned content number to depict the content distribution, which helps quickly approximate the real distribution. Meanwhile, the routing method is to retrieve as many contents as possible. It also helps speed up the learning process further. The experimental evaluation shows that the mechanism has high routing efficiency, quick learning ability and satisfactory performance under churn