Recommendation systems often rely on implicit feedback, where only positive user-item interactions can be observed. Negative sampling is therefore crucial to provide proper negative training signals. However, existing methods tend to mislabel potentially positive but unobserved items as negatives and lack precise control over negative sample selection. We aim to address these by generating controllable negative samples, rather than sampling from the existing item pool. In this context, we propose Adaptive Diffusion-based Augmentation for Recommendation (ADAR), a novel and model-agnostic module that leverages diffusion to synthesize informative negatives. Inspired by the progressive corruption process in diffusion, ADAR simulates a continuous transition from positive to negative, allowing for fine-grained control over sample hardness. To mine suitable negative samples, we theoretically identify the transition point at which a positive sample turns negative and derive a score-aware function to adaptively determine the optimal sampling timestep. By identifying this transition point, ADAR generates challenging negative samples that effectively refine the model's decision boundary. Experiments confirm that ADAR is broadly compatible and boosts the performance of existing recommendation models substantially, including collaborative filtering and sequential recommendation, without architectural modifications.

It is observed that the bottleneck in distributed training has shifted from computation to communication due to contention in concurrent transmissions and substantial redundant traffic. In the Parameter Server (PS) architecture, the server aggregates gradients from multiple workers and then distributes updated model parameters back to the workers in a one-to-many manner. Currently, model parameters are distributed via unicast, sending multiple identical copies of the data, which leads to significant bandwidth waste. Although multicast can save bandwidth, current approaches have two main drawbacks: on one hand, many protocols require maintaining excessive multicast state inside the network; on the other hand, the lack of coordination among multiple multicast trees can still lead to path conflicts. In this work, we propose path-aware multicast, which includes innetwork multicast tree reservation and per-hop control multicast. Specifically, before each round of model parameter distribution, the server queries the network for a multicast tree that satisfies the bandwidth requirement. The calculated multicast tree is then returned with bandwidth reserved at its tree nodes. Next, model parameters are forwarded with hop-by-hop control along the multicast tree. After the multicast is completed, the reserved network resources are released. Our evaluation shows that in an$8 \times 8$spine-leaf topology, path-aware multicast improves link load balancing by 32.6 % compared to random multicast and accelerates model parameter distribution by up to nearly$N \times$compared to unicast, where$N$is the number of workers.

Consensus-based time synchronization, which combines multiagent consensus and distributed network techniques, becomes an essential approach to achieve accurate time synchronization in wireless sensor networks. In actual synchronization process, random communication delays will have a negative impact on information exchange, which leads to a significant degradation in the synchronization performance. Besides, the convergence speed of synchronization errors in existing average consensus synchronization algorithms poses challenges due to the iterative update. To deal with these issues, this article proposes a maximum consensus time synchronization scheme based on the multihop virtual links. Primarily, a multihop virtual links mechanism is proposed, which shares clock information among nodes to shorten the convergence time. Meanwhile, a noniterative clock parameters compensation approach based on maximum consensus is developed to further speed up the synchronization process. In addition, a moving average estimator is designed to minimize the adverse impact of delays. The convergence performance of the proposed scheme is proved theoretically. Simulation results verify the correctness of theoretical analysis and also demonstrate that the proposed scheme outperforms existing algorithms in terms of synchronization accuracy and convergence speed under delays.

Detecting link failures is critical to ensuring the operation of data center networks (DCNs). However, some gray link failures may go undetected by switches, leading to silent packet drops. In this paper, we propose FTA-detector, a gray link failure detection and localization approach leveraging Fault Tree Analysis (FTA), a technique previously applied in the field of reliability engineering. On the data plane, we collect fine-grained hop-by-hop information through In-band Network Telemetry (INT), detect the bidirectional connectivity of end-to-end paths through a novel aging mechanism, and implement fast reroute in response to gray link failures. On the control plane, we introduce a faulty link localization algorithm based on FTA to recommend the most likely faulty links. Specifically, we use Top K and progressive failure repair to discover and repair link faults as early as possible during failure inference, significantly reducing the overall computation complexity of sequential root cause analysis. For large-scale network topology, we propose a divide and conquer optimization scheme for scalability. To verify the efficiency of our system, we build a virtual network test platform with P4 switch software and Redis database. The test results show that FTA-detector can troubleshoot multi-point failures in DCNs in a very short time with high accuracy.

In industrial wireless sensor networks, the existing event-based consensus clock synchronization schemes can achieve network-wide clock synchronization without considering the communication delays. However, communication delays are a significant factor restricting the achievement of clock synchronization in realistic scenarios. Besides, a constraint to be considered in the design of synchronization scheme is the limited communication resources of sensor nodes. Therefore, in this article, we investigate an event-based consensus clock synchronization scheme under delays. On the one hand, a new low-pass filter is used to estimate the relative skew to resist the influence of communication delays. On the other hand, a new event-triggered scheme is utilized to reduce unnecessary information transmission. Finally, we provide the convergence proof and simulation results of the proposed scheme, which further proves that it can achieve clock synchronization under communication delays while decreasing communication overhead.

Off-the-shelf x86 servers are widely deployed as middleboxes in edge and public clouds, such as cloud gateways and load balancers. They follow the “run-to-completion” model and achieve parallel traffic processing by distributing packet flows across multiple CPU cores using the RSS (receive side scaling) capability of NICs. However, RSS can cause inter-core load imbalance as it conducts stateless hashing without considering the CPU core utilization. As a result, multiple heavy-hitter flows can potentially overload a single CPU core when they are hashed onto that core. In this research, we propose P4RSS, a load-aware intra-server load balancing solution that leverages the P4 data plane. Specifically, a P4 ASIC is placed in front of the CPU to perform stateful traffic load balancing among multiple CPU cores based on real-time monitoring of core utilization. In addition, flow affinity maintenance and heavy hitter throttling are also offloaded to the P4 ASIC to free up valuable CPU computing resources. P4RSS can be implemented in the form of either hyper-converged server switches or P4-based SmartNICs. Evaluation results demonstrate that P4RSS reduces the standard deviation of CPU core utilization by 22%~53% compared to RSS. This not only improves the stability of middleboxes but also allows for higher CPU utilization without overprovisioning.

Summary The travel demand is of vital importance for transport planning. Especially in rush hours, the commuters aggregate in certain areas, resulting in traffic jams, which hinder the normal operation of the urban traffic. The key issue to solve the problem is to find out the demand of the passengers and accordingly arrange the transit resource more reasonably. This paper proposes a framework that provides a shuttle bus solution to satisfy the travel demand of the gathered passengers in rush hours according to their travel history obtained by smartcard data. Firstly, an aggregation algorithm basing on Clustering by fast search and find of density peaks (CFSFDP) is presented to highlight the area, which consists of adjacent bus stations with high passenger flow, addressed as spark region. Secondly, group travel pattern (GTP) is put forward to describe the travel trend on a city‐wide scale, which reveals the common travel demand of the commuters. Lastly, an algorithm named Variable Visibility Path Optimization Algorithm based on ant colony algorithm is proposed to make schedule solution of shuttle bus according to GTP basing on the historical running information of the bus. The experiment basing on the bus passenger flow data collected by AFCS in Aug 2014 from Beijing shows that our method helps to ease the traffic aggregation effectively and practically and offer reference to the bus scheduling issue.

As the development and building of 220kV and 500kV Unified Power Flow Controller (UPFC) in Jiangsu grid of China, a higher requirement is put forward by power grid analysis on the accuracy of modelling and simulation. UPFC has different characteristics under different control strategies, and Modular Multilevel Converters (MMC) simulation is a difficult task. In order to study its dynamic characteristics, an electromagnetic model of UPFC based on MMC was proposed, which could simulate its different control strategies and relative dynamic characteristics. The proposed model was realized in PSD-PSModel program and verified using IEEE39 case. The simulation results show that the proposed UPFC model could provide effective tool for analysing the UPFC's dynamic characteristics.

This paper presents an algorithm for blind recognition of punctured convolutional codes with application for instance in adaptive systems. Minimal basic encoding matrices of the original convolutional codes and their puncturing patterns are determined according to the accounted parity check matrices of the punctured convolutional codes.

Abstract MPI is commonly used to write parallel programs for distributed memory parallel computers. MPI‐CHECK is a tool developed to aid in the debugging of MPI programs that are written in free or fixed format Fortran 90 and Fortran 77. MPI‐CHECK provides automatic compile‐time and run‐time checking of MPI programs. MPI‐CHECK automatically detects the following problems in the use of MPI routines: (i) mismatch in argument type, kind, rank or number; (ii) messages which exceed the bounds of the source/destination array; (iii) negative message lengths; (iv) illegal MPI calls before MPI_INIT or after MPI_FINALIZE; (v) inconsistencies between the declared type of a message and its associated DATATYPE argument; and (vi) actual arguments which violate the INTENT attribute. Copyright © 2003 John Wiley & Sons, Ltd.

Abstract The Message‐Passing Interface (MPI) is commonly used to write parallel programs for distributed memory parallel computers. MPI‐CHECK is a tool developed to aid in the debugging of MPI programs that are written in free or fixed format Fortran 90 and Fortran 77. This paper presents the methods used in MPI‐CHECK 2.0 to detect many situations where actual and potential deadlocks occur when using blocking and non‐blocking point‐to‐point routines as well as when using collective routines. Copyright © 2002 John Wiley & Sons, Ltd.