On the Sweet Spot of Contrastive Views for Knowledge-enhanced Recommendation

In recommender systems, knowledge graph (KG) can offer critical information that is lacking in the original user-item interaction graph (IG). Recent process has explored this direction and shows that contrastive learning is a promising way to integrate both. However, we observe that existing KG-enhanced recommenders struggle in balancing between the two contrastive views of IG and KG, making them sometimes even less effective than simply applying contrastive learning on IG without using KG. In this paper, we propose a new contrastive learning framework for KG-enhanced recommendation. Specifically, to make full use of the knowledge, we construct two separate contrastive views for KG and IG, and maximize their mutual information; to ease the contrastive learning on the two views, we further fuse KG information into IG in a one-direction manner.Extensive experimental results on three real-world datasets demonstrate the effectiveness and efficiency of our method, compared to the state-of-the-art. Our code is available through the anonymous link:https://figshare.com/articles/conference_contribution/SimKGCL/2278338

Resilient Wide-Area Damping Control Using GrHDP to Tolerate Communication Failures

This paper proposes a goal representation heuristic dynamic programming (GrHDP)-based resilient wide-area damping controller (WADC) for voltage source converter high voltage direct current (VSC-HVDC) employing redundant wide-area signals as input signals to tolerate communication failure. A supervisory fuzzy logic module is proposed and added in the resilient WADC to adjust the learning rate of GrHDP online when encountering communication failure. Moreover, the resilient WADC does not need the accurate model of the power system and has the adaptability to the variation of operation conditions and communication failures. Case studies are conducted in a 10-machine 39-bus system with one VSC-HVDC transmission line. Simulation results show that the resilient WADC can counteract the negative impact of communication failures on control performance under a wide range of system operating conditions

Characteristics of multiple‐year nitrous oxide emissions from conventional vegetable fields in southeastern China

The annual and interannual characteristics of nitrous oxide (N2O) emissions from conventional vegetable fields are poorly understood. We carried out 4 year measurements of N2O fluxes from a conventional vegetable cultivation area in the Yangtze River delta. Under fertilized conditions subject to farming practices, approximately 86% of the annual total N2O release occurred following fertilization events. The direct emission factors (EFd) of the 12 individual vegetable seasons investigated ranged from 0.06 to 14.20%, with a mean of 3.09% and a coefficient of variation (CV) of 142%. The annual EFd varied from 0.59 to 4.98%, with a mean of 2.88% and an interannual CV of 74%. The mean value is much larger than the latest default value (1.00%) of the Intergovernmental Panel on Climate Change. Occasional application of lagoon‐stored manure slurry coupled with other nitrogen fertilizers, or basal nitrogen addition immediately followed by heavy rainfall, accounted for a substantial portion of the large EFds observed in warm seasons. The large CVs suggest that the emission factors obtained from short‐term observations that poorly represent seasonality and/or interannual variability will inevitably yield large uncertainties in inventory estimation. The results of this study indicate that conventional vegetable fields associated with intensive nitrogen addition, as well as occasional applications of manure slurry, may substantially account for regional N2O emissions. However, this conclusion needs to be further confirmed through studies at multiple field sites. Moreover, further experimental studies are needed to test the mitigation options suggested by this study for N2O emissions from open vegetable fields

Suppressing electron disorder-induced heating of ultracold neutral plasma via optical lattice

Disorder-induced heating (DIH) prevents ultracold neutral plasma into electron strong coupling regime. Here we propose a scheme to suppress electronic DIH via optical lattice. We simulate the evolution dynamics of ultracold neutral plasma constrained by three-dimensional optical lattice using classical molecular dynamics method. The results show that for experimentally achievable condition, electronic DIH is suppressed by a factor of 1.3, and the Coulomb coupling strength can reach to 0.8 which is approaching the strong coupling regime. Suppressing electronic DIH via optical lattice may pave a way for the research of electronic strongly coupled plasma

Analysis of Dynamic Characteristics of Pilots Under Different Intentions in Complex Flight Environment

Intention is the main embodiment of human cerebral conscious activities, which has an important influence on guiding the realization of human behaviour. It is a vital prerequisite for analysing the dynamic characteristics of pilots with different intentions. Considering the intention law of the generation, transfer and reduction, this paper analyses dynamic characteristics of pilots with different intentions, starting from the factors of effect on the intention. Taking airfield traffic pattern as an example for simulating flight experiments, the pilot’s multi-source dynamic data of human – aircraft – environment system under different intentions and their psycho-physiological-physical characteristics were recorded. Based on Matlab, one-way analysis of variance was used to extract variables with significant changes, and the variables under different intentions were compared and analysed. The results show that the conventional pilots are more conducive to control the aircraft to keep a stable flight attitude. This study is of great significance for perfecting the warning system of flight safety and improving the pilot’s micro-behaviour assessment system.</p

Energy-Efficient Joint Resource Allocation Algorithms for MEC-Enabled Emotional Computing in Urban Communities

This paper considers a mobile edge computing (MEC) system, where the MEC server first collects data from emotion sensors and then computes the emotion of each user. We give the formula of the emotional prediction accuracy. In order to improve the energy efficiency of the system, we propose resources allocation algorithms. We aim to minimize the total energy consumption of the MEC server and sensors by jointly optimizing the computing resources allocation and the data transmitting time. The formulated problem is a non-convex problem, which is very difficult to solve in general. However, we transform it into convex problems and apply convex optimization techniques to address it. The optimal solution is given in closed form. Simulation results show that the total energy consumption of our system can be effectively reduced by the proposed scheme compared with the benchmark