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In Mobile Crowdsensing (MCS), sensing tasks have different impacts and contributions to the whole system or specific targets, so the importance of the tasks is different. Since resources for performing tasks are usually limited, prioritizing the allocation of resources to more important tasks can ensure that key data or information can be collected promptly and accurately, thus improving overall efficiency and performance. Therefore, it is very important to consider the importance of tasks in the task selection and allocation of MCS. In this paper, a task queue is established, the importance of tasks, the ability of participants to perform tasks, and the stability of the task queue are considered, and a novel task selection and allocation scheme (TSAS) in the MCS system is designed. This scheme introduces the Lyapunov optimization method, which can be used to dynamically keep the task queue stable, balance the execution ability of participants and the system load, and perform more important tasks in different system states, even when the participants are limited. In addition, the Double Deep Q-Network (DDQN) method is introduced to improve on the traditional solution of the Lyapunov optimization problem, so this scheme has a certain predictive ability and foresight on the impact of future system states. This paper also proposes action-masking and iterative training methods for the MCS system, which can accelerate the training process of the neural network in the DDQN and improve the training effect. Experiments show that the TSAS based on the Lyapunov optimization method and DDQN performs better than other algorithms, considering the long-term stability of the queue, the number and importance of tasks to be executed, and the congestion degree of tasks.

Abstract Federated learning (FL) is a new distributed learning framework that is different from traditional distributed machine learning: (1) differences in communication, computing, and storage performance among devices (device heterogeneity), (2) differences in data distribution and data volume (data heterogeneity), and (3) high communication consumption. Under heterogeneous conditions, the data distribution of clients varies greatly, which leads to the problem that the convergence speed of the training model decreases and the training model cannot converge to the global optimal solution. In this work, an FL algorithm based on stratified sampling and regularization (FedSSAR) is proposed. In FedSSAR, a density-based clustering method is used to divide the overall client into different clusters, then, some available clients are proportionally extracted from different clusters to participate in training which realizes unbiased sampling for the overall client and reduces the aggregation weight variance of the client. At the same time, when calculating the model local loss function, we limit the update direction of the model by a regular term, so that heterogeneous clients are optimized in the globally optimal direction. We prove the convergence of FedSSAR theoretically and experimentally, and demonstrate the superiority of FedSSAR by comparing it with other FL algorithms on public datasets.

The widespread conversion of synthetic receptors into luminescent sensors has been achieved via the use of fluorescent-indicator displacement assays (F-IDAs). Due to their rigid structures and efficient binding affinities, cucurbit[n]urils, combined with a variety of fluorescent guests, have gained extensive utilization in fluorescent-indicator displacement assays for sensing non-fluorescent or weakly fluorescent organic compounds (analytes) in a selective and specific manner. This mini-review summarizes recent advances in the design of cucurbit[n]uril-based fluorescent-indicator displacement assays and discusses the current challenges and future prospects in this area.

Abstract Background Lineage plasticity in prostate cancer (PCa) has emerged as an important mechanism leading to the onset of therapy‐ and castration‐resistant PCa (t‐CRPC), which is closely associated with cancer stem cell (CSC) activity. This study is to identify critical driver(s) with mechanism of action and explore new targeting strategy. Methods Various PCa cell lines with different genetic manipulations were subjected to in vitro prostasphere assay, cell viability assay and in vivo stemness potential. In addition, bioinformatic analyses such as Ingenuity pathway and Gene Set Enrichment Analysis were carried out to determine clinical relevance. The in vivo anti‐tumour activity of JAK or STAT1 inhibitors was examined in clinically relevant t‐CRPC model. Results We demonstrated the role of interferon‐related signalling pathway in promoting PCa stemness, which correlated with significant elevation of interferon related DNA damage resistance signature genes in metastatic PCa. Inhibition of JAK‐STAT1 signalling suppresses the in vitro and in vivo CSC capabilities. Mechanistically, IFIT5, a unique downstream effector of JAK‐STAT1 pathway, can facilitate the acquisition of stemness properties in PCa by accelerating the turnover of specific microRNAs (such as miR‐128 and ‐101) that can target several CSC genes (such as BMI1, NANOG, and SOX2). Consistently, knocking down IFIT5 in t‐CRPC cell can significantly reduce in vitro prostasphere formation as well as decrease in vivo tumour initiating capability. Conclusions This study provides a critical role of STAT1‐IFIT5 in the acquisition of PCSC and highlights clinical translation of JAK or STAT1 inhibitors to prevent the outgrowth of t‐CRPC.

Federated learning (FL) has emerged for solving the problem of data fragmentation and isolation in machine learning based on privacy protection. Each client node uploads the trained model parameter information to the central server based on the local training data, and the central server aggregates the parameter information to achieve the purpose of common training. In the real environment, the distribution of data among nodes is often inconsistent. By analyzing the influence of independent identically distributed data (non-IID) on the accuracy of FL, it is shown that the accuracy of the model obtained by the traditional FL method is low. Therefore, we proposed the diversified sampling strategies to simulate the non-IID data situation and came up with the OPTICS (ordering points to identify the clustering structure)-based clustering optimization federated learning method (OCFL), which solves the problem that the learning accuracy is reduced when the data of different nodes are non-IID in FL. Experiments indicate that OCFL greatly improves the model accuracy and training speed compared with the traditional FL algorithm.

Message transmission in a delay-tolerant network (DTN) closely depends on the cooperation between nodes. However, nodes in real-world may be selfish so they may not be fully cooperative, and the behavior may have a certain impact on the message transmission process. Based on the famous ordinary differential equation (ODE), this paper presents the corresponding mathematical model to analyze the influence of selfish behaviors on two famous hop-limited flooding policies (lazy L-hop limited flooding and L-hop limited flooding). Then, it runs some simulations based on both synthetic and real trace and proves the exactitude of the model. The theoretical results demonstrate that the selfish behaviors significantly decrease the performance. However, if the message has a bigger lifetime, these algorithms are more robust to the selfish behaviors. In addition, the theoretical results also show that the influence depends on the network structure (the communities' number in the network). On the other hand, the location of the source also has a certain impact on the performance.

Mobile crowdsensing is a crowdsourcing-based paradigm, where the platform executes the sensing requests with the help of many common peoples' handheld devices (typically smartphones). In this paper, we mainly address the dynamic sensing request admission and smartphone scheduling problem to maximize the long-term profit, taking into account the competitive interaction procedure between the platform and smartphones, the queue backlog, and the location of sensing requests and smartphones. First, formulate this problem as a discrete time model and the interaction procedure between the platform and smartphones as a Stackelberg game. Then, we introduce the Lyapunov optimization technique and design a Stackelberg game based dynamic Admission and Scheduling algorithm(SAS). In SAS, all control decisions are made only based on the currently available information and none of the stakeholders, including the platform and smartphones, can improve his utility by unilaterally changing its current strategy. Next, we design an online Cooperative dynamic Admission and Scheduling algorithm(CAS) for the situation where the platform and smartphones work in a cooperative way. Theoretical analysis shows that under any control parameter V > 0, both SAS and CAS algorithm can achieve O(1/V)-optimal average profit while the sensing request backlog is bounded by O(V). The extensive numerical results based on both synthetic and real trace demonstrate the Stackelberg equilibrium of the SAS. The CAS always outperforms SAS, and in some certain situations, the profit of SAS is very close to that of the CAS.

A forwarding model of opportunistic network was established based on social features of node and by introducing the Pontryagin’s maximal principle,the optimal policy was got,which obeyed the threshold form.Let h denotes the stop time,when t＆lt;h,nodes forward the messages with the maximum probability,when t＆gt;h,the node stops sending messages.Experiments show that the optimal strategy is better than optimal static policy.Further analysis show that the bigger the average number of friends of node is,the smaller the stopping time is,the better the performance is.

The heterogeneous information networks are omnipresent in real-world applications, which consist of multiple types of objects with various rich semantic meaningful links among them. Community discovery is an effective method to extract the hidden structures in networks. Usually, heterogeneous information networks are time-evolving, whose objects and links are dynamic and varying gradually. In such time-evolving heterogeneous information networks, community discovery is a challenging topic and quite more difficult than that in traditional static homogeneous information networks. In contrast to communities in traditional approaches, which only contain one type of objects and links, communities in heterogeneous information networks contain multiple types of dynamic objects and links. Recently, some studies focus on dynamic heterogeneous information networks and achieve some satisfactory results. However, they assume that heterogeneous information networks usually follow some simple schemas, such as bityped network and star network schema. In this paper, we propose a multityped community discovery method for time-evolving heterogeneous information networks with general network schemas. A tensor decomposition framework, which integrates tensor CP factorization with a temporal evolution regularization term, is designed to model the multityped communities and address their evolution. Experimental results on both synthetic and real-world datasets demonstrate the efficiency of our framework.

Heterogeneous information networks (e.g. bibliographic networks and social media networks) that consist of multiple interconnected objects are ubiquitous. Clustering analysis is an effective method to understand the semantic information and interpretable structure of the heterogeneous information networks, and it has attracted the attention of many researchers in recent years. However, most studies assume that heterogeneous information networks usually follow some simple schemas, such as bi-typed networks or star network schema, and they can only cluster one type of object in the network each time. In this paper, a novel clustering framework is proposed based on sparse tensor factorization for heterogeneous information networks, which can cluster multiple types of objects simultaneously in a single pass without any network schema information. The types of objects and the relations between them in the heterogeneous information networks are modeled as a sparse tensor. The clustering issue is modeled as an optimization problem, which is similar to the well-known Tucker decomposition. Then, an Alternating Least Squares (ALS) algorithm and a feasible initialization method are proposed to solve the optimization problem. Based on the tensor factorization, we simultaneously partition different types of objects into different clusters. The experimental results on both synthetic and real-world datasets have demonstrated that our proposed clustering framework, STFClus, can model heterogeneous information networks efficiently and can outperform state-of-the-art clustering algorithms as a generally applicable single-pass clustering method for heterogeneous network which is network schema agnostic.

The formation of multinucleated muscle cells through cell-cell fusion is a conserved process from fruit flies to humans. Numerous studies have shown the importance of Arp2/3, its regulators, and branched actin for the formation of an actin structure, the F-actin focus, at the fusion site. This F-actin focus forms the core of an invasive podosome-like structure that is required for myoblast fusion. In this study, we find that the formin Diaphanous (Dia), which nucleates and facilitates the elongation of actin filaments, is essential for Drosophila myoblast fusion. Following cell recognition and adhesion, Dia is enriched at the myoblast fusion site, concomitant with, and having the same dynamics as, the F-actin focus. Through analysis of Dia loss-of-function conditions using mutant alleles but particularly a dominant negative Dia transgene, we demonstrate that reduction in Dia activity in myoblasts leads to a fusion block. Significantly, no actin focus is detected, and neither branched actin regulators, SCAR or WASp, accumulate at the fusion site when Dia levels are reduced. Expression of constitutively active Dia also causes a fusion block that is associated with an increase in highly dynamic filopodia, altered actin turnover rates and F-actin distribution, and mislocalization of SCAR and WASp at the fusion site. Together our data indicate that Dia plays two roles during invasive podosome formation at the fusion site: it dictates the level of linear F-actin polymerization, and it is required for appropriate branched actin polymerization via localization of SCAR and WASp. These studies provide new insight to the mechanisms of cell-cell fusion, the relationship between different regulators of actin polymerization, and invasive podosome formation that occurs in normal development and in disease.

With the fast development of Internet and WWW, "information overload" has become an overwhelming problem, and collective attention of users will play a more important role nowadays. As a result, knowing how collective attention distributes and flows among different websites is the first step to understand the underlying dynamics of attention on WWW. In this paper, we propose a method to embed a large number of web sites into a high dimensional Euclidean space according to the novel concept of flow distance, which both considers connection topology between sites and collective click behaviors of users. With this geometric representation, we visualize the attention flow in the data set of Indiana university clickstream over one day. It turns out that all the websites can be embedded into a 20 dimensional ball, in which, close sites are always visited by users sequentially. The distributions of websites, attention flows, and dissipations can be divided into three spherical crowns (core, interim, and periphery). 20% popular sites (Google.com, Myspace.com, Facebook.com, etc.) attracting 75% attention flows with only 55% dissipations (log off users) locate in the central layer with the radius 4.1. While 60% sites attracting only about 22% traffics with almost 38% dissipations locate in the middle area with radius between 4.1 and 6.3. Other 20% sites are far from the central area. All the cumulative distributions of variables can be well fitted by "S"-shaped curves. And the patterns are stable across different periods. Thus, the overall distribution and the dynamics of collective attention on websites can be well exhibited by this geometric representation.

Machine-to-machine (M2M) networks could be connected by a wide range of wireless technologies (e.g., Bluetooth, WiFi, RFID). Due to some factors (e.g., mobility of machines, limited communication range), it is hard to maintain the connectivity of the network, that is, the network is disconnected and it is a specific application of delay tolerant networks (DTN). Communication in such network often needs nodes working in a cooperative way. However, due to selfishness, nodes have no incentive to help others. Therefore, if the source requests help from other nodes, it needs to pay certain fees to them. The main goal of this paper is to explore efficient ways for the source to maximize the average delivery ratio when the total fees are limited. First, we mathematically characterize the average delivery ratio under different policies. Then we get the optimal policy through Pontryagin's maximum principle, and prove that the optimal policy conforms to the threshold form when the fees that other nodes require satisfy certain conditions. Simulations based on both synthetic and real motion traces show the accuracy of our model. Through extensive numerical results, we demonstrate that the optimal policy obtained by our model is the best.