185 research outputs found
Effects of intraseasonal variations of the Arctic Oscillation on the Barents Sea
This paper investigates possible connections among the wintertime Arctic Oscillation(AO), North Atlantic water inflow into the Barents Sea, and sea ice and sea water temperature in the Barents Sea on monthly to seasonal time scales using a coupled sea-ice-ocean model. The forcing is from winters with large anomalies of the AO. The inflow of the North Atlantic water into the Barents Sea forced by significantly different wind stresses over the area south of the Barents Sea shows a close relation to the AO only during the AO high-phase periods rather than during the low-phase periods. The responses to forcing by the opposite phases of the AO differ substantially in surface and subsurface water temperature of the Barents Sea. The positive phase of the AO raises subsurface water temperature in the Barents Sea, with concurrent surface cooling in the western and central Barents Sea. One exception is in the eastern Barents Sea where the surface water temperature is higher during the positive phase than during the negative phase. The enhanced net inflow of warmer Atlantic water into the Barents Sea causes decrease of sea ice
Soft BPR Loss for Dynamic Hard Negative Sampling in Recommender Systems
In recommender systems, leveraging Graph Neural Networks (GNNs) to formulate
the bipartite relation between users and items is a promising way. However,
powerful negative sampling methods that is adapted to GNN-based recommenders
still requires a lot of efforts. One critical gap is that it is rather tough to
distinguish real negatives from massive unobserved items during hard negative
sampling. Towards this problem, this paper develops a novel hard negative
sampling method for GNN-based recommendation systems by simply reformulating
the loss function. We conduct various experiments on three datasets,
demonstrating that the method proposed outperforms a set of state-of-the-art
benchmarks.Comment: 9 pages, 16 figure
A Novel Cross-layer Communication Protocol for Vehicular Sensor Networks
Communication protocols in Vehicular Sensor Networks (VSNs) in urban areas play an important role in intelligent transport systems applications. Many cross layer communication protocols studies are originated from topology-based algorithms, which is not suitable for the frequently-changing computational scenario. In addition, the influence factors that have been considered for VSNs routing are not enough. With these aspects in mind, this paper proposes a multi-factor cross layer position-based routing (MCLPR) protocol for VSNs to improve reliability and efficiency in message delivery. Considering the complex intersection environment, the algorithm for vehicles selection at intersections (called AVSI) is further proposed, in which comprehensive factors are taken into account including the position and direction of vehicle, the vehicle density, the signal-to-noise-plus-interference ratio (SNIR), as well as the frame error rate (FER) in MAC layer. Meanwhile, the dynamic HELLO STREAM broadcasting system with the various vehicle speeds is proposed to increase the decisions accuracy. Experimental results in Network Simulator 3 (NS-3) show the advantage of MCLPR protocol over traditional state-of the-art algorithms in terms of packet delivery ratio (PDR), overhead and the mean end-to-end delay
Data Upcycling Knowledge Distillation for Image Super-Resolution
Knowledge distillation (KD) emerges as a challenging yet promising technique
for compressing deep learning models, characterized by the transmission of
extensive learning representations from proficient and computationally
intensive teacher models to compact student models. However, only a handful of
studies have endeavored to compress the models for single image
super-resolution (SISR) through KD, with their effects on student model
enhancement remaining marginal. In this paper, we put forth an approach from
the perspective of efficient data utilization, namely, the Data Upcycling
Knowledge Distillation (DUKD) which facilitates the student model by the prior
knowledge teacher provided via upcycled in-domain data derived from their
inputs. This upcycling process is realized through two efficient image zooming
operations and invertible data augmentations which introduce the label
consistency regularization to the field of KD for SISR and substantially boosts
student model's generalization. The DUKD, due to its versatility, can be
applied across a broad spectrum of teacher-student architectures. Comprehensive
experiments across diverse benchmarks demonstrate that our proposed DUKD method
significantly outperforms previous art, exemplified by an increase of up to
0.5dB in PSNR over baselines methods, and a 67% parameters reduced RCAN model's
performance remaining on par with that of the RCAN teacher model
Nonlinear dielectric geometric-phase metasurface with simultaneous structure and lattice symmetry design
In this work, we utilize thin dielectric meta-atoms placed on a silver
substrate to efficiently enhance and manipulate the third harmonic generation.
We theoretically and experimentally reveal that when the structural symmetry of
the meta-atom is incompatible with the lattice symmetry of an array, some
generalized nonlinear geometric phases appear, which offers new possibilities
for harmonic generation control beyond the accessible symmetries governed by
the selection rule. The underlying mechanism is attributed to the modified
rotation of the effective principal axis of a dense meta-atom array, where the
strong coupling among the units gives rise to a generalized linear geometric
phase modulation on the pump light. Therefore, nonlinear geometric phases
carried by the third-harmonic emissions are the natural result of the
wave-mixing process among the modes excited at the fundamental frequency. This
mechanism further points out a new strategy to predict the nonlinear geometric
phases delivered by the nanostructures according to their linear responses. Our
design is simple and efficient, and offers alternatives for the nonlinear
meta-devices that are capable of flexible photon generation and manipulation
Severe Ice Cover on Great Lakes During Winter 2008–2009
Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/94591/1/eost17090.pd
Cloud-Assisted Safety Message Dissemination in VANET-Cellular Heterogeneous Wireless Network
Abstract-In vehicular ad-hoc networks (VANETs), efficient message dissemination is critical to road safety and traffic efficiency. Since many VANET-based schemes suffer from high transmission delay and data redundancy, integrated VANETcellular heterogeneous network has been proposed recently and attracted significant attention. However, most existing studies focus on selecting suitable gateways to deliver safety message from the source vehicle to a remote server, while rapid safety message dissemination from the remote server to a targeted area has not been well studied. In this paper, we propose a framework for rapid message dissemination that combines the advantages of diverse communication and cloud computing technologies
Disturbance Rejection Control for Autonomous Trolley Collection Robots with Prescribed Performance
Trajectory tracking control of autonomous trolley collection robots (ATCR) is
an ambitious work due to the complex environment, serious noise and external
disturbances. This work investigates a control scheme for ATCR subjecting to
severe environmental interference. A kinematics model based adaptive sliding
mode disturbance observer with fast convergence is first proposed to estimate
the lumped disturbances. On this basis, a robust controller with prescribed
performance is proposed using a backstepping technique, which improves the
transient performance and guarantees fast convergence. Simulation outcomes have
been provided to illustrate the effectiveness of the proposed control scheme
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