Apo E基因multi-ARMS与PCR-RFLP分型法的比较研究

目的 :以聚合酶链反应—限制性酶切片段长度多态性(PCR -RFLP)方法作为参照 ,建立一种ApoE基因分型方法。方法 :以基因组DNA为模板 ,用PCR -RFLP法和等位基因特异性复合PCR方法(MultiplexAmplificationRefractoryMutationSystem,multi-ARMS)检测了75例个体的ApoE基因型。结果 :共检测出5种常见的ApoE基因型 :ε2/2 ,ε3/3 ,ε2/3,ε3/4 ,ε2/4。两种方法所得的结果完全一致。结论 :说明multi-ARMS方法快速简便、准确可靠 。

Distributed Weighted Data Aggregation Algorithm in End-to-Edge Communication Networks Based on Multi-armed Bandit

As a combination of edge computing and artificial intelligence,edge intelligence has become a promising technique and provided its users with a series of fast,precise,and customized services.In edge intelligence,when learning agents are deployed on the edge side,the data aggregation from the end side to the designated edge devices is an important research topic.Considering the various importance of end devices,this paper studies the weighted data aggregation problem in a single hop end-to-edge communication network.Firstly,to make sure all the end devices with various weights are fairly treated in data aggregation,a distributed end-to-edge cooperative scheme is proposed.Then,to handle the massive contention on the wireless channel caused by end devices,a multi-armed bandit(MAB)algorithm is designed to help the end devices find their most appropriate update rates.Diffe-rent from the traditional data aggregation works,combining the MAB enables our algorithm a higher efficiency in data aggregation.With a theoretical analysis,we show that the efficiency of our algorithm is asymptotically optimal.Comparative experiments with previous works are also conducted to show the strength of our algorithm.

Stochastic programming based multi-arm bandit offloading strategy for internet of things

In order to solve the high latency of traditional cloud computing and the processing capacity limitation of Internet of Things(IoT)users,Multi-access Edge Computing(MEC)migrates computing and storage capabilities from the remote data center to the edge of network,providing users with computation services quickly and directly.In this paper,we investigate the impact of the randomness caused by the movement of the IoT user on decision-making for offloading,where the connection between the IoT user and the MEC servers is uncertain.This uncertainty would be the main obstacle to assign the task accurately.Consequently,if the assigned task cannot match well with the real connection time,a migration(connection time is not enough to process)would be caused.In order to address the impact of this uncertainty,we formulate the offloading decision as an optimization problem considering the transmission,computation and migration.With the help of Stochastic Programming(SP),we use the posteriori recourse to compensate for inaccurate predictions.Meanwhile,in heterogeneous networks,considering multiple candidate MEC servers could be selected simultaneously due to overlapping,we also introduce the Multi-Arm Bandit(MAB)theory for MEC selection.The extensive simulations validate the improvement and effectiveness of the proposed SP-based Multi-arm bandit Method(SMM)for offloading in terms of reward,cost,energy consumption and delay.The results showthat SMMcan achieve about 20%improvement compared with the traditional offloading method that does not consider the randomness,and it also outperforms the existing SP/MAB based method for offloading.

Impedance control of multi-arm space robot for the capture of non-cooperative targets

Robotic systems are expected to play an increasingly important role in future space activities. The robotic on-orbital service, whose key is the capturing technology, becomes a research hot spot in recent years. This paper studies the dynamics modeling and impedance control of a multi-arm free-flying space robotic system capturing a non-cooperative target. Firstly, a control-oriented dynamics model is essential in control algorithm design and code realization. Unlike a numerical algorithm, an analytical approach is suggested. Using a general and a quasi-coordinate Lagrangian formulation, the kinematics and dynamics equations are derived.Then, an impedance control algorithm is developed which allows coordinated control of the multiple manipulators to capture a target.Through enforcing a reference impedance, end-effectors behave like a mass-damper-spring system fixed in inertial space in reaction to any contact force between the capture hands and the target. Meanwhile, the position and the attitude of the base are maintained stably by using gas jet thrusters to work against the manipulators' reaction. Finally, a simulation by using a space robot with two manipulators and a free-floating non-cooperative target is illustrated to verify the effectiveness of the proposed method.

Training a Quantum Neural Network to Solve the Contextual Multi-Armed Bandit Problem

Artificial intelligence has permeated all aspects of our lives today. However, to make AI behave like real AI, the critical bottleneck lies in the speed of computing. Quantum computers employ the peculiar and unique properties of quantum states such as superposition, entanglement, and interference to process information in ways that classical computers cannot. As a new paradigm of computation, quantum computers are capable of performing tasks intractable for classical processors, thus providing a quantum leap in AI research and making the development of real AI a possibility. In this regard, quantum machine learning not only enhances the classical machine learning approach but more importantly it provides an avenue to explore new machine learning models that have no classical counterparts. The qubit-based quantum computers cannot naturally represent the continuous variables commonly used in machine learning, since the measurement outputs of qubit-based circuits are generally discrete. Therefore, a continuous-variable (CV) quantum architecture based on a photonic quantum computing model is selected for our study. In this work, we employ machine learning and optimization to create photonic quantum circuits that can solve the contextual multi-armed bandit problem, a problem in the domain of reinforcement learning, which demonstrates that quantum reinforcement learning algorithms can be learned by a quantum device.

Strict greedy design paradigm applied to the stochastic multi-armed bandit problem

The process of making decisions is something humans do inherently and routinely,to the extent that it appears commonplace. However,in order to achieve good overall performance,decisions must take into account both the outcomes of past decisions and opportunities of future ones. Reinforcement learning,which is fundamental to sequential decision-making,consists of the following components: 1 A set of decisions epochs; 2 A set of environment states; 3 A set of available actions to transition states; 4 State-action dependent immediate rewards for each action.At each decision,the environment state provides the decision maker with a set of available actions from which to choose. As a result of selecting a particular action in the state,the environment generates an immediate reward for the decision maker and shifts to a different state and decision. The ultimate goal for the decision maker is to maximize the total reward after a sequence of time steps.This paper will focus on an archetypal example of reinforcement learning,the stochastic multi-armed bandit problem. After introducing the dilemma,I will briefly cover the most common methods used to solve it,namely the UCB and εn- greedy algorithms. I will also introduce my own greedy implementation,the strict-greedy algorithm,which more tightly follows the greedy pattern in algorithm design,and show that it runs comparably to the two accepted algorithms.

Meta-Analysis of Multi-Arm Trials Using Binomial Approach

Most meta-analysis has concentrated on combining of treatment effect measures based on comparisons of two treatments. Meta-analysis of multi-arm trials is a key component of submission to summarize evidence from all possible studies. In this paper, an exact binomial model is proposed by using logistic regression model to compare different treatment in multi-arm trials. Two approaches such as unconditional maximum likelihood and conditional maximum likelihood have been determined and compared for the logistic regression model. The proposed models are performed using the data from 27 randomized clinical trials (RCTs) which determine the efficacy of antiplatelet therapy in reduction venous thrombosis and pulmonary embolism.

Matching while Learning: Wireless Scheduling for Age of Information Optimization at the Edge

In this paper,we investigate the minimization of age of information(AoI),a metric that measures the information freshness,at the network edge with unreliable wireless communications.Particularly,we consider a set of users transmitting status updates,which are collected by the user randomly over time,to an edge server through unreliable orthogonal channels.It begs a natural question:with random status update arrivals and obscure channel conditions,can we devise an intelligent scheduling policy that matches the users and channels to stabilize the queues of all users while minimizing the average AoI?To give an adequate answer,we define a bipartite graph and formulate a dynamic edge activation problem with stability constraints.Then,we propose an online matching while learning algorithm(MatL)and discuss its implementation for wireless scheduling.Finally,simulation results demonstrate that the MatL is reliable to learn the channel states and manage the users’buffers for fresher information at the edge.

Optimal index shooting policy for layered missile defense system

In order to cope with the increasing threat of the ballistic missile(BM)in a shorter reaction time,the shooting policy of the layered defense system needs to be optimized.The main decisionmaking problem of shooting optimization is how to choose the next BM which needs to be shot according to the previous engagements and results,thus maximizing the expected return of BMs killed or minimizing the cost of BMs penetration.Motivated by this,this study aims to determine an optimal shooting policy for a two-layer missile defense(TLMD)system.This paper considers a scenario in which the TLMD system wishes to shoot at a collection of BMs one at a time,and to maximize the return obtained from BMs killed before the system demise.To provide a policy analysis tool,this paper develops a general model for shooting decision-making,the shooting engagements can be described as a discounted reward Markov decision process.The index shooting policy is a strategy that can effectively balance the shooting returns and the risk that the defense mission fails,and the goal is to maximize the return obtained from BMs killed before the system demise.The numerical results show that the index policy is better than a range of competitors,especially the mean returns and the mean killing BM number.

Hierarchical path planning for multi-arm spacecraft with general translational and rotational locomotion mode

On-orbit construction and maintenance technology will play a significant role in future space exploration.The dexterous multifunctional spacecraft equipped with multi-arm,for instance,Spider Fab Bot,has attracted a great deal of focus due to its versatility in completing these missions.In such engineering practice,point-to-point moving in a complex environment is the fundamental issue.This paper investigates the three-dimensional point-to-point path planning problem,and a hierarchical path planning architecture is developed to give the trajectory of the multi-arm spacecraft effectively and efficiently.In the proposed 3-level architecture,the high-level planner generates the global constrained centric trajectory of the spacecraft with a rigid envelop assumption;the middle-level planner contributes the action sequence,a combination of the newly developed general translational and rotational locomotion mode,to cope with the relative position and attitude of the arms about the centroid of the spacecraft;the low-level planner maps the position/attitude of the end-effector of each arm from the operational space to the joint space optimally.Finally,the simulation experiment is carried out,and the results verify the effectiveness of the proposed three-layer architecture path planning strategy.

Age of Transmission-Optimal Scheduling for State Update of Multi-Antenna Cellular Internet of Things

Timely information updates are critical for real-time monitoring and control applications in the Internet of Things(IoT). In this paper, we consider a multi-antenna cellular IoT for state update where a base station(BS) collects information from randomly distributed IoT nodes through time-varying channel.Specifically, multiple IoT nodes are allowed to transmit their state update simultaneously in a spatial multiplex manner. Inspired by age of information(AoI),we introduce a novel concept of age of transmission(AoT) for the sceneries in which BS cannot obtain the generation time of the packets waiting to be transmitted. The deadline-constrained AoT-optimal scheduling problem is formulated as a restless multi-armed bandit(RMAB) problem. Firstly, we prove the indexability of the scheduling problem and derive the closed-form of the Whittle index. Then, the interference graph and complementary graph are constructed to illustrate the interference between two nodes. The complete subgraphs are detected in the complementary graph to avoid inter-node interference. Next, an AoT-optimal scheduling strategy based on the Whittle index and complete subgraph detection is proposed.Finally, numerous simulations are conducted to verify the performance of the proposed strategy.

Channel estimation based on multi-armed approach for maritime OFDM wireless communications

With the development of maritime informatization and the increased generation of marine data,the demands of efficient and reliable maritime communication surge.However,harsh and dynamic marine communication environmentcan distort transmission signal,which significantly weaken the communication performance.Therefore,for maritime wireless communication system,the channel estimation is often required to detect the channel suffered from the impacts of changing factors.Since there is no universal maritime communication channel model and channel varies dynamically,channel estimation method needs to make decision dynamically without pre-knowledge of channel distribution.This paper studies the radio channel estimation problem of wireless communications over the sea surface.To improve the estimation accuracy,this paper utilizes multi-armed bandit(MAB)problem to deal with the uncertainty of channel state information(CSI),then proposes a dynamic channel estimation algorithm to explore the global changing channel information,and asymptotically minimize the estimation error.By the aid of MAB,the estimation is not only dynamic according to channel variation,but also does not need to know the channel distribution.Simulation results show that the proposed algorithm can achieve higher estimation accuracy compared to matching pursuit(MP)-based and fractional Fourier transform(FrFT)-based methods.

Risk-averse Contextual Multi-armed Bandit Problem with Linear Payoffs

In this paper we consider the contextual multi-armed bandit problem for linear payoffs under a risk-averse criterion.At each round,contexts are revealed for each arm,and the decision maker chooses one arm to pull and receives the corresponding reward.In particular,we consider mean-variance as the risk criterion,and the best arm is the one with the largest mean-variance reward.We apply the Thompson sampling algorithm for the disjoint model,and provide a comprehensive regret analysis for a variant of the proposed algorithm.For T rounds,K actions,and d-dimensional feature vectors,we prove a regret bound of O((1+ρ+1/ρ)d In T ln K/δ√dKT^(1+2∈)ln K/δ1/e)that holds with probability 1-δunder the mean-variance criterion with risk tolerance p,for any 0<ε<1/2,0<δ<1.The empirical performance of our proposed algorithms is demonstrated via a portfolio selection problem.

Residential HVAC Aggregation Based on Risk-averse Multi-armed Bandit Learning for Secondary Frequency Regulation

As the penetration of renewable energy continues to increase,stochastic and intermittent generation resources gradually replace the conventional generators,bringing significant challenges in stabilizing power system frequency.Thus,aggregating demand-side resources for frequency regulation attracts attentions from both academia and industry.However,in practice,conventional aggregation approaches suffer from random and uncertain behaviors of the users such as opting out control signals.The risk-averse multi-armed bandit learning approach is adopted to learn the behaviors of the users and a novel aggregation strategy is developed for residential heating,ventilation,and air conditioning(HVAC)to provide reliable secondary frequency regulation.Compared with the conventional approach,the simulation results show that the risk-averse multiarmed bandit learning approach performs better in secondary frequency regulation with fewer users being selected and opting out of the control.Besides,the proposed approach is more robust to random and changing behaviors of the users.

Simultaneously inducing synthesis of semiconductor selenium multi-armed nanorods and nanobars through bio-membrane bi-templates

Multi-armed nanorods and nanobars of semiconductor selenium were simultane- ously synthesized in the light of biomineralization process through bio-membrane bi-templates of rush at room temperature. The multi-armed nanorods are 60 nm in diameter and 1.5 μm in length; the nanobars are 150 nm in diameter and 1000—1100 nm in length. The XRD pattern indicates that these nanocrystals were crystallized in the hexagonal structure with lattice constants a = 0.437 nm, c = 0.495 nm. The possible formation mechanism was investigated.

White Electroluminescence with Simultaneous Three-Color Emission from a Four-Armed Star-Shaped Single-Polymer System

A four-armed star-shaped single-polymer system with 4,7-bis（5-（4-（9H-carbazol-9-yl）phenyl）-4-hexylthio- phen-2-yl）benzo[c][1,2,5]thiadiazole （FTBT） as a red emissive core, polyfluorene （PF） as blue emissive arms and 1,3-benzo thiadiazole （BT） as green emissive dopants was designed and synthesized, in which red, green, and blue （RGB） emission balance can be achieved by adjusting the doping concentration of FTBT and BT discreetly. A typ- ical single-emissive-layer device （ITO/PEDOT：PSS/polymer/TPBI/LiF/Al） was studied and discussed, realizing a pure and stable white emission with a luminous efficiency （LE） of 1.59 cd·A^-1 and CIE coordinates of （0.31, 0.34）. The high-color-quality white electroluminescence of the devices could be mainly attributed to the suppressed in- termolecular interactions, and partial energy transfer from the blue PF arms to the red and green dopants.

A Novel Cooperative Multi-Stage Hyper-Heuristic for Combination Optimization Problems

A hyper-heuristic algorithm is a general solution framework that adaptively selects the optimizer to address complex problems.A classical hyper-heuristic framework consists of two levels,including the high-level heuristic and a set of low-level heuristics.The low-level heuristics to be used in the optimization process are chosen by the high-level tactics in the hyper-heuristic.In this study,a Cooperative Multi-Stage Hyper-Heuristic(CMS-HH)algorithm is proposed to address certain combinatorial optimization problems.In the CMS-HH,a genetic algorithm is introduced to perturb the initial solution to increase the diversity of the solution.In the search phase,an online learning mechanism based on the multi-armed bandits and relay hybridization technology are proposed to improve the quality of the solution.In addition,a multi-point search is introduced to cooperatively search with a single-point search when the state of the solution does not change in continuous time.The performance of the CMS-HH algorithm is assessed in six specific combinatorial optimization problems,including Boolean satisfiability problems,one-dimensional packing problems,permutation flow-shop scheduling problems,personnel scheduling problems,traveling salesman problems,and vehicle routing problems.The experimental results demonstrate the efficiency and significance of the proposed CMS-HH algorithm.