Spatial relations in text refer to how a geographical entity is located in space in relation to a reference *** spatial relations from text is a fundamental task in natural language *** studies have only focused on ge...
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Spatial relations in text refer to how a geographical entity is located in space in relation to a reference *** spatial relations from text is a fundamental task in natural language *** studies have only focused on generic fine-tuning methods with additional classifiers,ignoring the importance of the semantic correlation between different spatial elements and the large offset between the relation extraction task and the pre-trained *** address the above two issues,we propose a spatial relation extraction model based on Dual-view Prompt and Element Correlation(DPEC).Specifically,we first reformulate spatial relation extraction as a mask language model with a Dual-view Prompt(i.e.,Link Prompt and Confidence Prompt).Link Prompt can not only guide the model to incorporate more contextual information related to the spatial relation extraction task,but also better adapt to the original pre-training task of the language ***,Confidence Prompt can measure the confidence of candidate triplets in Link Prompt and work as a supplement to identify those easily confused examples in Link ***,we incorporate the element correlation to measure the consistency between different spatial elements,which is an effective cue for identifying the rationality of spatial *** results on the popular SpaceEval show that our DPEC significantly outperforms the SOTA baselines.
Multiarmed bandit(MAB) models are widely used for sequential decision-making in uncertain environments, such as resource allocation in computer communication systems.A critical challenge in interactive multiagent syst...
Multiarmed bandit(MAB) models are widely used for sequential decision-making in uncertain environments, such as resource allocation in computer communication systems.A critical challenge in interactive multiagent systems with bandit feedback is to explore and understand the equilibrium state to ensure stable and tractable system performance.
The slow development of traditional computing has prompted the search for new materials to replace silicon-based computers. Bio-computers, which use molecules as the basis of computation, are highly parallel and infor...
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The slow development of traditional computing has prompted the search for new materials to replace silicon-based computers. Bio-computers, which use molecules as the basis of computation, are highly parallel and information capable, attracting a lot of attention. In this study, we designed a NAND logic gate based on the DNA strand displacement mechanism. We assembled a molecular calculation model, a 4-wire-2-wire priority encoder logic circuit, by cascading the proposed NAND gates. Different concentrations of input DNA chains were added into the system, resulting in corresponding output, through DNA hybridization and strand displacement. Therefore, it achieved the function of a priority encoder. Simulation results verify the effectiveness and accuracy of the molecular NAND logic gate and the priority coding system presented in this study. The unique point of this proposed circuit is that we cascaded only one kind of logic gate, which provides a beneficial exploration for the subsequent development of complex DNA cascade circuits and the realization of the logical coding function of information.
The pixel-wise dense prediction tasks based on weakly supervisions currently use Class Attention Maps(CAMs)to generate pseudo masks as ***,existing methods often incorporate trainable modules to expand the immature cl...
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The pixel-wise dense prediction tasks based on weakly supervisions currently use Class Attention Maps(CAMs)to generate pseudo masks as ***,existing methods often incorporate trainable modules to expand the immature class activation maps,which can result in significant computational overhead and complicate the training *** this work,we investigate the semantic structure information concealed within the CNN network,and propose a semantic structure aware inference(SSA)method that utilizes this information to obtain high-quality CAM without any additional training ***,the semantic structure modeling module(SSM)is first proposed to generate the classagnostic semantic correlation representation,where each item denotes the affinity degree between one category of objects and all the ***,the immature CAM are refined through a dot product operation that utilizes semantic structure ***,the polished CAMs from different backbone stages are fused as the *** advantage of SSA lies in its parameter-free nature and the absence of additional training costs,which makes it suitable for various weakly supervised pixel-dense prediction *** conducted extensive experiments on weakly supervised object localization and weakly supervised semantic segmentation,and the results confirm the effectiveness of SSA.
Implementing quantum wireless multi-hop network communication is essential to improve the global quantum network system. In this paper, we employ eight-level GHZ states as quantum channels to realize multi-hop quantum...
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Implementing quantum wireless multi-hop network communication is essential to improve the global quantum network system. In this paper, we employ eight-level GHZ states as quantum channels to realize multi-hop quantum communication, and utilize the logical relationship between the measurements of each node to derive the unitary operation performed by the end node. The hierarchical simultaneous entanglement switching(HSES) method is adopted, resulting in a significant reduction in the consumption of classical information compared to multi-hop quantum teleportation(QT)based on general simultaneous entanglement switching(SES). In addition, the proposed protocol is simulated on the IBM Quantum Experiment platform(IBM QE). Then, the data obtained from the experiment are analyzed using quantum state tomography, which verifies the protocol's good fidelity and accuracy. Finally, by calculating fidelity, we analyze the impact of four different types of noise(phase-damping, amplitude-damping, phase-flip and bit-flip) in this protocol.
Software-defined networking decouples the control plane from the data plane to enable centralized flow-level network management, while requiring periodically collecting traffic statistics from the data plane to enforc...
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Software-defined networking decouples the control plane from the data plane to enable centralized flow-level network management, while requiring periodically collecting traffic statistics from the data plane to enforce optimal management. As one of the most important traffic measurement tasks, heavy flow detection has received wide attention for its providing fundamental statistics in various practical applications. Existing studies have proposed sketch-based detection solutions to address the mismatch problem between massive traffic and limited high-speed memory resources for measurement in the data plane. However,they overlook the potential of integrating the flow table, where each entry simultaneously enforces forwarding rules for specific flows and records flow statistics into the sketch design, leading to redundant measurement between the flow table and sketch and being unable to utilize their statistics to jointly enhance estimation accuracy. We propose flow entries assisted sketch(FEA-Sketch) in this work, which employs a differentiated flow recording strategy to record flow statistics jointly using the flow table and sketch for memory-efficient and computationally efficient heavy flow detection. We also propose an optimization-based estimation algorithm to accurately recover per-flow sizes for the flows that only have aggregated statistics due to the sharing of entries in the table(or counters in the sketch). We extend the FEA-Sketch to the distributed measurement setting with a hop-based collaborative measurement strategy, which reduces the measurement workload on switches across the network by avoiding redundant measurements. The experimental results on real Internet traces show that the accuracy of heavy flow detection is improved up to 1.95 times, and the bias of flow size estimation is improved up to 2.99 times, demonstrating that integrating flow entries can significantly improve the performance of heavy flow detection.
Diffusive molecular communication (DMC) utilizes the emission, diffusion and reception of molecules to transmit information. It has promising prospects in the field of drug delivery. The estimation of emission time an...
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Low earth orbit(LEO) satellite edge computing can overcome communication difficulties in harsh environments, which lack the support of terrestrial communication infrastructure. It is an indispensable option for achiev...
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Low earth orbit(LEO) satellite edge computing can overcome communication difficulties in harsh environments, which lack the support of terrestrial communication infrastructure. It is an indispensable option for achieving worldwide wireless communication coverage in the future. To improve the quality-of-service(QoS) for Internet-of-things(IoT) devices, we combine LEO satellite edge computing and ground communication systems to provide network services for IoT devices in harsh environments. We study the QoS-aware computation offloading(QCO) problem for IoT devices in LEO satellite edge computing. Then we investigate the computation offloading strategy for IoT devices that can minimize the total QoS cost of all devices while satisfying multiple constraints, such as the computing resource constraint, delay constraint, and energy consumption constraint. We formulate the QoSaware computation offloading problem as a game model named QCO game based on the non-cooperative competition game among IoT devices. We analyze the finite improvement property of the QCO game and prove that there is a Nash equilibrium for the QCO game. We propose a distributed QoS-aware computation offloading(DQCO) algorithm for the QCO game. Experimental results show that the DQCO algorithm can effectively reduce the total QoS cost of IoT devices.
Dear Editor,This letter presents a distributed adaptive second-order latent factor(DAS) model for addressing the issue of high-dimensional and incomplete data representation. Compared with first-order optimizers, a se...
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Dear Editor,This letter presents a distributed adaptive second-order latent factor(DAS) model for addressing the issue of high-dimensional and incomplete data representation. Compared with first-order optimizers, a second-order optimizer has stronger ability in approaching a better solution when dealing with the non-convex optimization problems, thus obtaining better performance in extracting the latent factors(LFs) well representing the known information from high-dimensional and incomplete data.
Nowadays, voice input on earphones has become one of the most paramount human-computer interaction approaches. Traditional voice interaction is built on the air channel, which is highly noise-susceptible and suffers f...
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