In the proposed algorithm, the access prices of BSs are adjusted according to incomes and load situations in the leaderlevel game, and each UE independently chooses the BS that maximizes its payoff (or minimizes its payment) in the follower-level game. Thus, the algorithm achieves optimization in a distributed manner. When a small BS is overloaded, the price will increase to ensure that the offloaded UE has a higher access payment. For the design of the price, a particle swarm optimization-based pricing strategy (PSO-PS) is proposed to maximize the BS revenue. Finally, we use the resident-oriented Gale-Shapley (GS) algorithm to obtain a stable single-BS association. The main contributions of this paper can be summarized as follows:翻译

EWA.zip_The One And Only_cauchy mutation_earthworm_metaheuristic

Inspired by the earthworm contribution in nature, a new kind of bio-inspired metaheuristic algorithm, called earthworm optimization algorithm (EWA), is proposed in this paper. The EWA method is ...

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referencing methods using the reflected signal from the bottom of the imaging window material to further compensate for time-dependent system fluctuations and position-dependent variation in the ...

翻译：As shown in Fig. 2, to solve problems (7) and (11), a multileader multi-follower Stackelberg game SL, in which BSs are regarded as leaders and UE are followers, is adopted. Although players make their decisions independently, a hierarchy of players at different levels exists in this game, and the strategies chosen by the followers depend on the strategies selected by the leaders. Moreover, the choice of the optimal strategy of leaders can vary with the follower equilibrium that occurs. Here, the Stackelberg equilibrium (SE) of the proposed Stackelberg game is defined as follows.

如图2所示，为了解决问题（7）和（11），采用了一个多领导者多追随者斯塔克伯格博弈SL，其中基站被视为领导者，用户设备（UE）则是追随者。尽管玩家们独立地做出决策，但在这个博弈中存在不同层级的玩家，并且追随者...

翻译：With the given constraints, the problem is two-fold. How should a BS determine the optimal access price? How should each UE choose the BS to associate with in response to the block-varying price? First, UE interest must be discussed. Offloading to a certain BS requires an access payment of the offlfloaded UE based on the announced access price vector p. Second, on the BS side, a larger load will increase the access revenue, but spectrum acquisition for the BS will also increase. Therefore, in the user association procedure, all BSs determine their optimal access prices to maximize their benefifits in terms of cell load, congestion and UE payments. Then, using the announced price vector, each UE device maximizes the profifit of its utility function minus the access payment. In this section, the mathematical representation of the proposed user association and resource allocation procedure will be discussed.

在给定的约束条件下，问题是双重的。一个基站应该如何确定最优的接入价格？每个用户设备应该如何选择要关联的基站，以应对块变化的价格？首先，必须讨论用户设备的兴趣。将数据卸载到某个基站需要根据宣布的接入...

4 Experiments This section examines the effectiveness of the proposed IFCS-MOEA framework. First, Section 4.1 presents the experimental settings. Second, Section 4.2 examines the effect of IFCS on MOEA/D-DE. Then, Section 4.3 compares the performance of IFCS-MOEA/D-DE with five state-of-the-art MOEAs on 19 test problems. Finally, Section 4.4 compares the performance of IFCS-MOEA/D-DE with five state-of-the-art MOEAs on four real-world application problems. 4.1 Experimental Settings MOEA/D-DE [23] is integrated with the proposed framework for experiments, and the resulting algorithm is named IFCS-MOEA/D-DE. Five surrogate-based MOEAs, i.e., FCS-MOEA/D-DE [39], CPS-MOEA [41], CSEA [29], MOEA/DEGO [43] and EDN-ARM-OEA [12] are used for comparison. UF1–10, LZ1–9 test problems [44, 23] with complicated PSs are used for experiments. Among them, UF1–7, LZ1–5, and LZ7–9 have 2 objectives, UF8–10, and LZ6 have 3 objectives. UF1–10, LZ1–5, and LZ9 are with 30 decision variables, and LZ6–8 are with 10 decision variables. The population size N is set to 45 for all compared algorithms. The maximum number of FEs is set as 500 since the problems are viewed as expensive MOPs [39]. For each test problem, each algorithm is executed 21 times independently. For IFCS-MOEA/D-DE, wmax is set to 30 and η is set to 5. For the other algorithms, we use the settings suggested in their papers. The IGD [6] metric is used to evaluate the performance of each algorithm. All algorithms are examined on PlatEMO [34] platform.

4 实验本节将研究所提出的IFCS-MOEA框架的有效性。首先，在第4.1节中介绍实验设置。其次，在第4.2节中研究IFCS对MOEA/D-DE的影响。然后，在第4.3节中，将IFCS-MOEA/D-DE与19个测试问题上的五种最先进的MOEA进行比较...

翻译：where ri = j∈B xij cij is the receiving rate of UE i, and Ui(ri) reflects the degree of satisfaction for UE i. Different degrees of satisfaction are obtained for different receiving rates. Therefore, the utility function is a monotonically increasing function of the receiving rate. This function can be modeled as either a logarithmic function or a sigmoid function. Notably, different types of UE devices may have different levels of satisfaction, even for the same receiving rate, and one can reasonably assume that the utility functions are different for different types of UE devices. In the simulation section, a specific utility function, such as a logarithmic function, is used. In addition, the proposed algorithm can be easily implemented when UEs have different utility functions.

其中ri = j∈B xij cij 是用户设备 i 的接收速率，而Ui(ri) 反映了用户设备 i 的满意程度。不同的接收速率会获得不同程度的满意度。因此，效用函数是接收速率的单调递增函数。这个函数可以建模为对数函数或S型函数。...

Abstract— Image nonlocal self-similarity (NSS) property has been widely exploited via various sparsity models such as joint sparsity (JS) and group sparse coding (GSC). However, the existing NSS-based sparsity models are either too restrictive, e.g., JS enforces the sparse codes to share the same support, or too general, e.g., GSC imposes only plain sparsity on the group coefficients, which limit their effectiveness for modeling real images. In this paper, we propose a novel NSS-based sparsity model, namely, low-rank regularized group sparse coding (LR-GSC), to bridge the gap between the popular GSC and JS. The proposed LR-GSC model simultaneously exploits the sparsity and low-rankness of the dictionary-domain coefficients for each group of similar patches. An alternating minimization with an adaptive adjusted parameter strategy is developed to solve the proposed optimization problem for different image restoration tasks, including image denoising, image deblocking, image inpainting, and image compressive sensing. Extensive experimental results demonstrate that the proposed LR-GSC algorithm outperforms many popular or state-of-the-art methods in terms of objective and perceptual metrics.翻译

摘要—图像的非局部自相似性（NSS）属性已经被广泛应用于各种稀疏模型中，例如联合稀疏（JS）和群组稀疏编码（GSC）。然而，现有的基于NSS的稀疏模型要么太过严格，例如JS强制使稀疏编码共享相同的支持，要么太过...

1.Discuss how and why the SecurityManager class for JVM check methods can be defined differently based on context.. 2.The library class Collections contains a method sort with the following type signature. public static <T> void sort (T[] a, Comparator <? super T> c); The Comparator interface contains the following method which returns a negative integer, zero or a positive integer according to whether o1 is considered to be before, equal to or after o2 in a proposed ordering. int compare (T o1, T o2) ; What does the identifier T mean in the declaration of the method sort? Illustrate your answer with an example. 3.What does the notation <? super T> mean in the declaration of sort? Again, illustrate your answer with an example. 4.Explain the concept of an anonymous inner class in Java using a method call example. 5.Convert the method call example using an anonymous inner class to a lambda expression. Give the code required for this. 6.In what types of situations might you prefer to use an anonymous class instead of a lambda expression, and vice versa? 7.Explain (auto)boxing and (auto)unboxing in Java? 8.What does the annotation @Override mean in Java? What are the benefits of adding it? 9.What are wildcards in Java and how are they used in generic programming? 10.Can you explain the bounded wildcards, and provide an example

For example, a web application may need to restrict access to certain resources, while a desktop application may not have the same restrictions. 2. The identifier T in the declaration of the method ...

检查下列语句的语法和拼写问题。Traditional network security situation prediction methods depend on the accuracy of historical situation value. Moreover, there are differences in correlation and importance among various network security factors. In order to solve these problems, a combined prediction model based on the temporal convolution attention network (TCAN) and bi-directional gate recurrent unit (BiGRU) network optimized by singular spectrum analysis (SSA) and improved quantum particle swarm optimization algorithm (IQPSO) was proposed. This model was first decomposed and reconstructed into a series of subsequences through the SSA of network security situation data. Next, a prediction model of TCAN-BiGRU was established for each subsequence, respectively. The TCN with relatively simple structure was used in the TCAN to extract features from the data. Besides, the improved channel attention mechanism (CAM) was used to extract important feature information from TCN. Afterwards, the before-after status of the learning situation value of the BiGRU neural network was used to extract more feature information from sequences for prediction. Meanwhile, an improved IQPSO was proposed to optimize the hyper-parameter of the BiGRU neural network. Finally, the prediction results of subsequence were superimposed to obtain the final predicted value. In the experiment, on the one hand, the IQPSO was compared with other optimization algorithms; and the results showed that the IQPSO has better optimization performance; on the other hand, the comparison with traditional prediction methods was performed through the simulation experiment and the established prediction model; and the results showed that the combined prediction model established has higher prediction accuracy.

我们可以看到，本文提出的综合预测模型基于时间卷积注意网络（TCAN）和双向门递归单元（BiGRU）网络，并通过奇异谱分析（SSA）和改进的量子粒子群优化算法（IQPSO）进行优化。实验表明，该模型具有更高的预测精度。

精简下面表达：Existing protein function prediction methods integrate PPI networks and multivariate bioinformatics data to improve the performance of function prediction. By combining multivariate information, the interactions between proteins become diverse. Different interactions’ functions in functional prediction are various. Combining multiple interactions simply between two proteins can effectively reduce the effect of false negatives and increase the number of predicted functions, but it can also increase the number of false positive functions, which contribute to nonobvious enhancement for the overall functional prediction performance. In this article, we have presented a framework for protein function prediction algorithms based on PPI network and semantic similarity with the addition of protein hierarchical functions to them. The framework relies on diverse clustering algorithms and the calculation of protein semantic similarity for protein function prediction. Classification and similarity calculations for protein pairs clustered by the functional feature are more accurate and reliable, allowing for the prediction of protein function at different functional levels from different proteomes, and giving biological applications greater flexibility.The method proposed in this paper performs well on protein data from wine yeast cells, but how well it matches other data remains to be verified. Yet until now, most unknown proteins have only been able to predict protein function by calculating similarities to their homologues. The predictions result of those unknown proteins without homologues are unstable because they are relatively isolated in the protein interaction network. It is difficult to find one protein with high similarity. In the framework proposed in this article, the number of features selected after clustering and the number of protein features selected for each functional layer has a significant impact on the accuracy of subsequent functional predictions. Therefore, when making feature selection, it is necessary to select as many functional features as possible that are important for the whole interaction network. When an incorrect feature was selected, the prediction results will be somewhat different from the actual function. Thus as a whole, the method proposed in this article has improved the accuracy of protein function prediction based on the PPI network method to a certain extent and reduces the probability of false positive prediction results.

本文提出了一种基于PPI网络和语义相似性，加上蛋白质分层功能的蛋白质功能预测算法框架，对酒葡萄酵母细胞的蛋白质数据表现出良好的效果，但其他数据的效果如何仍有待验证。此外，该框架中的功能特征选择的数量以及...

As shown in Figure 1, the proposed network contains four strided convolutional layers and four strided deconvo- lutional layers. The Leaky Rectified Linear Unit (LReLU) with a negative slope of 0.2 is used after each convolutional and deconvolutional layer. The residual group [38] consists of three residual blocks, and 18 residual blocks are used in GRes. The filter size is set as 11×11 pixels in the first convo- lutional layer in the encoder module and 3 × 3 in all the other convolutional and deconvolutional layers. We jointly train the MSBDN and DFF module and use the Mean Squared Error (MSE) as the loss function to constrain the network output and ground truth. The entire training process con- tains 100 epochs optimized by the ADAM solver [28 ] with β1 = 0.9 and β2 = 0.999 with a batch size of 16. The initial learning rate is set as 10−4 with a decay rate of 0.75 after every 10 epochs. All the experiments are conducted on an NVIDIA 2080Ti GPU. The source code and trained models are availabe at https://github.com/BookerDeWitt/MSBDN- DFF 翻译

如图1所示，所提出的网络包含四个步幅卷积层和四个步幅反卷积层。在每个卷积和反卷积层之后使用LReLU（带有负斜率0.2的泄露整流线性单元）。残差组[38]包含三个残差块，GRes中使用18个残差块。在编码器模块的第一个...

In conclusion, we have proposed a six-deep-feature radiomics signature that have the potential to be an imag- ing biomarker for prediction of the OS in patients with GBM. It was demonstrated that the deep learning method can be incorporated into the state-of-the-art radiomics model to achieve a better performance. The proposed signature predicted the OS in GBM patients with better performance compared with conventional factors such as age and KPS. A nomogram was proposed for prediction of the probability of survival. Despite the limitations, the proposed radiomics model has the potential to facilitate the preoperative care of patients with GBM 解释

这段话总结了该研究的主要发现和贡献。研究提出了一个由六个深度特征组成的放射组学标记，具有成为GBM患者OS预测的成像生物标志物的潜力。研究表明，深度学习方法可以融入最新的放射组学模型，以实现更好的性能。...

In our study the proposed radiomics signature performed better than traditional risk factors such as age and KPS. None of these clinical factors successfully stratified patients into groups with different prognostic risks. After combining the radiomics signature with clinical factors into a Cox regression model, the predictive power improved with C-index of 0.739 in validation data set. According to the radiomics signature and the two clinical risk factors, we drew a nomogram that can visually predict the probability of survival. According to the calibra- tion curve we can see that our nomogram had good predictive performance. 解释

这段话是在讲述一项医学研究，研究人员使用了一种叫做“放射组学签名”的方法来预测患者的预后风险，这种方法比传统的风险因素如年龄和身体状况评分（KPS）更有效。经过将放射组学签名与临床因素结合起来，研究人员...

润色下面英文：The controlled drug delivery systems, due to their precise control of drug release in spatiotemporal level triggered by specific stimulating factors and advantages such as higher utilization ratio of drug, less side-effects to normal tissues and so forth, provide a new strategy for the precise treatment of many serious diseases, especially tumors. The materials that constitute the controlled drug delivery systems are called “smart materials” and they can respond to the stimuli of some internal (pH, redox, enzymes, etc.) or external (temperature, electrical/magnetic, ultrasonic and optical, etc.) environments. Before and after the response to the specific stimulus, the composition or conformational of smart materials will be changed, damaging the original balance of the delivery systems and releasing the drug from the delivery systems. Amongst them, the photo-controlled drug delivery systems, which display drug release controlled by light, demonstrated extensive potential applications, and received wide attention from researchers. In recent years, photo-controlled drug delivery systems based on different photo-responsive groups have been designed and developed for precise photo-controlled release of drugs. Herein, in this review, we introduced four photo-responsive groups including photocleavage groups, photoisomerization groups, photo-induced rearrangement groups and photocrosslinking groups, and their different photo-responsive mechanisms. Firstly, the photocleavage groups represented by O-nitrobenzyl are able to absorb the energy of the photons, inducing the cleavage of some specific covalent bonds. Secondly, azobenzenes, as a kind of photoisomerization groups, are able to convert reversibly between the apolar trans form and the polar cis form upon different light irradiation. Thirdly, 2-diazo-1,2-naphthoquinone as the representative of the photo-induced rearrangement groups will absorb specific photon energy, carrying out Wolff rearrangement reaction. Finally, coumarin is a promising category photocrosslinking groups that can undergo [2＋2] cycloaddition reactions under light irradiation. The research progress of photo-controlled drug delivery systems based on different photo-responsive mechanisms were mainly reviewed. Additionally, the existing problems and the future research perspectives of photo-controlled drug delivery systems were proposed.

控制药物释放系统是指可以控制药物空间和时间释放的系统，可以通过一些特定的刺激因素激活，具有药物利用率更高、对正常组织的副作用更小等优势，为精确治疗许多严重疾病，尤其是肿瘤提供了新的策略。...

Algorithm 1: The online LyDROO algorithm for solving (P1). input : Parameters V , {γi, ci}Ni=1, K, training interval δT , Mt update interval δM ; output: Control actions 􏰕xt,yt􏰖Kt=1; 1 Initialize the DNN with random parameters θ1 and empty replay memory, M1 ← 2N; 2 Empty initial data queue Qi(1) = 0 and energy queue Yi(1) = 0, for i = 1,··· ,N; 3 fort=1,2,...,Kdo 4 Observe the input ξt = 􏰕ht, Qi(t), Yi(t)􏰖Ni=1 and update Mt using (8) if mod (t, δM ) = 0; 5 Generate a relaxed offloading action xˆt = Πθt 􏰅ξt􏰆 with the DNN; 6 Quantize xˆt into Mt binary actions 􏰕xti|i = 1, · · · , Mt􏰖 using the NOP method; 7 Compute G􏰅xti,ξt􏰆 by optimizing resource allocation yit in (P2) for each xti; 8 Select the best solution xt = arg max G 􏰅xti , ξt 􏰆 and execute the joint action 􏰅xt , yt 􏰆; { x ti } 9 Update the replay memory by adding (ξt,xt); 10 if mod (t, δT ) = 0 then 11 Uniformly sample a batch of data set {(ξτ , xτ ) | τ ∈ St } from the memory; 12 Train the DNN with {(ξτ , xτ ) | τ ∈ St} and update θt using the Adam algorithm; 13 end 14 t ← t + 1; 15 Update {Qi(t),Yi(t)}N based on 􏰅xt−1,yt−1􏰆 and data arrival observation 􏰙At−1􏰚N using (5) and (7). i=1 i i=1 16 end With the above actor-critic-update loop, the DNN consistently learns from the best and most recent state-action pairs, leading to a better policy πθt that gradually approximates the optimal mapping to solve (P3). We summarize the pseudo-code of LyDROO in Algorithm 1, where the major computational complexity is in line 7 that computes G􏰅xti,ξt􏰆 by solving the optimal resource allocation problems. This in fact indicates that the proposed LyDROO algorithm can be extended to solve (P1) when considering a general non-decreasing concave utility U (rit) in the objective, because the per-frame resource allocation problem to compute G􏰅xti,ξt􏰆 is a convex problem that can be efficiently solved, where the detailed analysis is omitted. In the next subsection, we propose a low-complexity algorithm to obtain G 􏰅xti, ξt􏰆. B. Low-complexity Algorithm for Optimal Resource Allocation Given the value of xt in (P2), we denote the index set of users with xti = 1 as Mt1, and the complementary user set as Mt0. For simplicity of exposition, we drop the superscript t and express the optimal resource allocation problem that computes G 􏰅xt, ξt􏰆 as following (P4) : maximize 􏰀j∈M0 􏰕ajfj/φ − Yj(t)κfj3􏰖 + 􏰀i∈M1 {airi,O − Yi(t)ei,O} (28a) τ,f,eO,rO 17 ，建立了什么模型

这段文字并没有建立具体的模型，而是介绍了LyDROO算法的伪代码以及解决问题(P1)和(P4)的方法。该算法是一种在线学习算法，利用深度神经网络来近似最优策略，并通过解决最优资源分配问题来求解目标函数。...

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