209 research outputs found
Reinforcement Learning in Computing and Network Convergence Orchestration
As computing power is becoming the core productivity of the digital economy
era, the concept of Computing and Network Convergence (CNC), under which
network and computing resources can be dynamically scheduled and allocated
according to users' needs, has been proposed and attracted wide attention.
Based on the tasks' properties, the network orchestration plane needs to
flexibly deploy tasks to appropriate computing nodes and arrange paths to the
computing nodes. This is a orchestration problem that involves resource
scheduling and path arrangement. Since CNC is relatively new, in this paper, we
review some researches and applications on CNC. Then, we design a CNC
orchestration method using reinforcement learning (RL), which is the first
attempt, that can flexibly allocate and schedule computing resources and
network resources. Which aims at high profit and low latency. Meanwhile, we use
multi-factors to determine the optimization objective so that the orchestration
strategy is optimized in terms of total performance from different aspects,
such as cost, profit, latency and system overload in our experiment. The
experiments shows that the proposed RL-based method can achieve higher profit
and lower latency than the greedy method, random selection and
balanced-resource method. We demonstrate RL is suitable for CNC orchestration.
This paper enlightens the RL application on CNC orchestration
Experimental and theoretical evidence for molecular forces driving surface segregation in photonic colloidal assemblies
Surface segregation in binary colloidal mixtures offers a simple way to control both surface and bulk properties without affecting their bulk composition. Here, we combine experiments and coarse-grained molecular dynamics (CG-MD) simulations to delineate the effects of particle chemistry and size on surface segregation in photonic colloidal assemblies from binary mixtures of melanin and silica particles of size ratio (Dlarge/Dsmall) ranging from 1.0 to similar to 2.2. We find that melanin and/or smaller particles segregate at the surface of micrometer-sized colloidal assemblies (supraballs) prepared by an emulsion process. Conversely, no such surface segregation occurs in films prepared by evaporative assembly. CG-MD simulations explain the experimental observations by showing that particles with the larger contact angle (melanin) are enriched at the supraball surface regardless of the relative strength of particle-interface interactions, a result with implications for the broad understanding and design of colloidal particle assemblies
Command Filter Backstepping Sliding Model Control for Lower-Limb Exoskeleton
A command filter adaptive fuzzy backstepping control strategy is proposed for lower-limb assisting exoskeleton. Firstly, the human-robot model is established by taking the human body as a passive part, and a coupling torque is introduced to describe the interaction between the exoskeleton and human leg. Then, Vicon motion capture system is employed to obtain the reference trajectory. For the purpose of obviating the “explosion of complexity” in conventional backstepping, a second-order command filter is introduced into the sliding mode control strategy. The fuzzy logic systems (FLSs) are also applied to handle with the chattering problem by estimating the uncertainties and disturbances. Furthermore, the stability of the closed-loop system is proved based on the Lyapunov theory. Finally, simulation results are presented to illustrate the effectiveness of the control strategy
New Stabilization for Dynamical System with Two Additive Time-Varying Delays
This paper provides a new delay-dependent stabilization criterion for systems with two additive time-varying delays. The novel functional is constructed, a tighter upper bound
of the derivative of the Lyapunov functional is obtained. These results have advantages over some existing ones because the combination of the delay decomposition technique and the reciprocally convex approach. Two examples are provided to demonstrate the less conservatism and effectiveness of the results in this paper
Vertical Federated Learning
Vertical Federated Learning (VFL) is a federated learning setting where
multiple parties with different features about the same set of users jointly
train machine learning models without exposing their raw data or model
parameters. Motivated by the rapid growth in VFL research and real-world
applications, we provide a comprehensive review of the concept and algorithms
of VFL, as well as current advances and challenges in various aspects,
including effectiveness, efficiency, and privacy. We provide an exhaustive
categorization for VFL settings and privacy-preserving protocols and
comprehensively analyze the privacy attacks and defense strategies for each
protocol. In the end, we propose a unified framework, termed VFLow, which
considers the VFL problem under communication, computation, privacy, and
effectiveness constraints. Finally, we review the most recent advances in
industrial applications, highlighting open challenges and future directions for
VFL
AIGC Empowering Telecom Sector White Paper_chinese
In the global craze of GPT, people have deeply realized that AI, as a
transformative technology and key force in economic and social development,
will bring great leaps and breakthroughs to the global industry and profoundly
influence the future world competition pattern. As the builder and operator of
information and communication infrastructure, the telecom sector provides
infrastructure support for the development of AI, and even takes the lead in
the implementation of AI applications. How to enable the application of AIGC
(GPT) and implement AIGC in the telecom sector are questions that telecom
practitioners must ponder and answer. Through the study of GPT, a typical
representative of AIGC, the authors have analyzed how GPT empowers the telecom
sector in the form of scenarios, discussed the gap between the current GPT
general model and telecom services, proposed for the first time a Telco
Augmented Cognition capability system, provided answers to how to construct a
telecom service GPT in the telecom sector, and carried out various practices.
Our counterparts in the industry are expected to focus on collaborative
innovation around telecom and AI, build an open and shared innovation
ecosystem, promote the deep integration of AI and telecom sector, and
accelerate the construction of next-generation information infrastructure, in
an effort to facilitate the digital transformation of the economy and society
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