Indirect speech acts (ISAs) are a natural pragmatic feature of human communication, allowing requests to be conveyed implicitly while maintaining subtlety and flexibility. Although advancements in speech recognition h...
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The recently proposed concept of Age of Information (AoI) measures the freshness of the sensor data sampled by remote Internet of Things (IoT) devices, which is an important indicator for the smart city. Unmanned Aeri...
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Amorphous indium gallium zinc oxide (a-IGZO)-based thin film transistors (TFTs) are increasingly becoming popular because of their potential in futuristic applications, including CMOS technology. Given the demand for ...
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We consider the power allocation problem over shared spectrum for millimeter-Wave (mmWave) cellular down-link. Existing approaches usually find sub-optimal solutions by solving a non-convex optimization which leads to...
We consider the power allocation problem over shared spectrum for millimeter-Wave (mmWave) cellular down-link. Existing approaches usually find sub-optimal solutions by solving a non-convex optimization which leads to scalability issues due to centralized control. Therefore, distributed and adaptive approaches are desirable. Recently, model-free Deep Reinforcement Learning (DRL) has achieved success in such wireless resource management tasks. By modeling the radio environment as a Markov Decision Process (MDP) with the base stations (BSs) being the agents, power allocation can be automated at the agent level with comparable throughput performance to conventional centralized schemes. The multi-agent setting presents new challenges as the radio environment is impacted by the joint actions of the agents and is no longer stationary from any individual agent's perspective. Existing literature bypasses this non-stationarity violation by ignoring it which may cause performance degradation. To tackle this issue, we propose a distributed continuous power allocation scheme based on a modified version of multi-agent Deep Deterministic Policy Gradient (MADDPG) that is tailored for the distributed multiple-agent setting. The proposed scheme employs a centralized-training-distributed-execution framework where Q-functions are trained over subsets of BSs while each BS determines its transmit power based only on its own local observation. It admits constant per-BS communication and computation complexity and is thus scalable to large networks. Numerical evaluation shows that the proposed scheme adapts well to a wide range of interference conditions and can achieve comparable or better performance than several state-of-the-art non-learning approaches.
Despite the extensive research and various existing systems for detecting and classifying anomalies in operational networks, Internet Service Providers (ISPs) continue to seek efficient methods to handle the increasin...
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It is known that two-dimensional two-component fundamental solitons of the semivortex (SV) type, with vorticities (s+,s−)=(0,1) in their components, are stable ground states (GSs) in the spin-orbit-coupled (SOC) binar...
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It is known that two-dimensional two-component fundamental solitons of the semivortex (SV) type, with vorticities (s+,s−)=(0,1) in their components, are stable ground states (GSs) in the spin-orbit-coupled (SOC) binary Bose-Einstein condensate with the contact self-attraction acting in both components, in spite of the possibility of the critical collapse in the system. However, excited states (ESs) of the SV solitons, with the vorticity set (s+,s−)=(S+,S++1) and S+=1,2,3,..., are unstable in the same system. We construct ESs of SV solitons in the SOC system with opposite signs of the self-interaction in the two components. The main finding is stability of the ES-SV solitons, with the extra vorticity (at least) up to S+=6. The threshold value of the norm for the onset of the critical collapse, Nthr, in these excited states is higher than the commonly known critical value, Nc≈5.85, associated with the single-component Townes solitons, Nthr increasing with the growth of S+. A velocity interval for stable motion of the GS-SV solitons is found too. The results suggest a solution for the challenging problem of the creation of stable vortex solitons with high topological charges.
We use tridiagonal models to study the limiting behavior of β-Laguerre and β-Jacobi ensembles,focusing on the limiting behavior of the extremal eigenvalues and the central limit theorem for the two *** the central l...
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We use tridiagonal models to study the limiting behavior of β-Laguerre and β-Jacobi ensembles,focusing on the limiting behavior of the extremal eigenvalues and the central limit theorem for the two *** the central limit theorem of β-Laguerre ensembles,we follow the idea in[1]while giving a modified version for the generalized *** we use the total variation distance between the two sorts of ensembles to obtain the limiting behavior of β-Jacobi ensembles.
This paper presents Visual Evaluative AI, a decision aid that provides positive and negative evidence from image data for a given hypothesis. This tool finds high-level human concepts in an image and generates the Wei...
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Load balancing is vital for the efficient and long-term operation of cloud data *** virtualization,post(reactive)migration of virtual machines(VMs)after allocation is the traditional way for load balancing and consoli...
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Load balancing is vital for the efficient and long-term operation of cloud data *** virtualization,post(reactive)migration of virtual machines(VMs)after allocation is the traditional way for load balancing and consoli***,it is not easy for reactive migration to obtain predefined load balance objectives and it may interrupt services and bring ***,we provide a new approach,called Prepartition,for load *** partitions a VM request into a few sub-requests sequentially with start time,end time and capacity demands,and treats each sub-request as a regular VM *** this way,it can proactively set a bound for each VM request on each physical machine and makes the scheduler get ready before VM migration to obtain the predefined load balancing goal,which supports the resource allocation in a fine-grained *** with real-world trace and synthetic data show that our proposed approach with offline version(PrepartitionOff)scheduling has 10%–20%better performance than the existing load balancing baselines under several metrics,including average utilization,imbalance degree,makespan and Capacity_*** also extend Prepartition to online load *** results show that our proposed approach also outperforms state-of-the-art online algorithms.
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