Communication Papers – Volume 15, Issue 30

Understanding global communications among cells requires accurate representation of cell-cell signaling links and effective systems-level analyses of those links. We construct a database of interactions among ligands, receptors and their cofactors that accurately represent known heteromeric molecular complexes. We then develop CellChat, a tool that is able to quantitatively infer and analyze intercellular communication networks from single-cell RNA-sequencing (scRNA-seq) data. CellChat predicts major signaling inputs and outputs for cells and how those cells and signals coordinate for functions using network analysis and pattern recognition approaches. Through manifold learning and quantitative contrasts, CellChat classifies signaling pathways and delineates conserved and context-specific pathways across different datasets. Applying CellChat to mouse and human skin datasets shows its ability to extract complex signaling patterns. Our versatile and easy-to-use toolkit CellChat and a web-based Explorer (http://www.cellchat.org/) will help discover novel intercellular communications and build cell-cell communication atlases in diverse tissues. Single-cell methods record molecule expressions of cells in a given tissue, but understanding interactions between cells remains challenging. Here the authors show by applying systems biology and machine learning approaches that they can infer and analyze cell-cell communication networks in an easily interpretable way.

AbstractBetween any form of communication there is an unbreakable connection thatcan lead to the improvement of the organizational competences becausecommunication remains one of the basic activities with a great influence on agencies,groups, communities and environments. The communication in successfulorganizations is clearly the integrated one. Integrated communication could only besuccessful when included and subordinated to the global organizational strategy andrequires careful planning and coordinated efforts. This is the easiest way to explain thetitle of this work as the result of a sustained effort of studying this concept. Integratedcommunication is still little known, too little recognized and used and the advantages ofthis type of communication are neglected most of the time in small and averageorganizations. This statement has been demonstrated especially by being adopted by allgreat corporations. The present work presents in its second half the results of aresearch made last year regarding the situation of the integrated communication in theorganizations of south Romania. The aim of this research is to identify the results ofapplying this concept

Most existing community-related studies focus on detection, which aim to find the community membership for each user from user friendship links. However, membership alone, without a complete profile of what a community is and how it interacts with other communities, has limited applications. This motivates us to consider systematically profiling the communities and thereby developing useful community-level applications. In this paper, we for the first time formalize the concept of community profiling. With rich user information on the network, such as user published content and user diffusion links, we characterize a community in terms of both its internal content profile and external diffusion profile. The difficulty of community profiling is often underestimated. We novelly identify three unique challenges and propose a joint Community Profiling and Detection (CPD) model to address them accordingly. We also contribute a scalable inference algorithm, which scales linearly with the data size and it is easily parallelizable. We evaluate CPD on large-scale real-world data sets, and show that it is significantly better than the state-of-the-art baselines in various tasks.

QH304.K59 2005. Lindsay, D. (2011). Scientific writing = thinking in words. Victoria Thousand Oaks, CA: Sage. The Sage handbook of qualitative research. Denzin, N.K. and Lincoln, Y.S., Eds. (2013-2017). THE SAGE HANDBOOK OF QUALITATIVE RESEARCH, 5th Edition. Thousand Oaks, CA: SAGE Publications. (2005) Poetics for a planet: discourse on some problems of being-in-place. In: Denzin NK, Lincoln YS (eds) The Sage Handbook of Qualitative Research (3rd ed.

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Discovering communities in complex networks means grouping nodes similar to each other, to uncover latent information about them. There are hundreds of different algorithms to solve the community detection task, each with its own understanding and definition of what a "community" is. Dozens of review works attempt to order such a diverse landscape -- classifying community discovery algorithms by the process they employ to detect communities, by their explicitly stated definition of community, or by their performance on a standardized task. In this paper, we classify community discovery algorithms according to a fourth criterion: the similarity of their results. We create an Algorithm Similarity Network (ASN), whose nodes are the community detection approaches, connected if they return similar groupings. We then perform community detection on this network, grouping algorithms that consistently return the same partitions or overlapping coverage over a span of more than one thousand synthetic and real world networks. This paper is an attempt to create a similarity-based classification of community detection algorithms based on empirical data. It improves over the state of the art by comparing more than seventy approaches, discovering that the ASN contains well-separated groups, making it a sensible tool for practitioners, aiding their choice of algorithms fitting their analytic needs.

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The community deception problem is about how to hide a target community C from community detection algorithms. The need for deception emerges whenever a group of entities (e.g., activists, police enforcements) want to cooperate while concealing their existence as a community. In this paper we introduce and formalize the community deception problem. To solve this problem, we describe algorithms that carefully rewire the connections of C's members. We experimentally show how several existing community detection algorithms can be deceived, and quantify the level of deception by introducing a deception score. We believe that our study is intriguing since, while showing how deception can be realized it raises awareness for the design of novel detection algorithms robust to deception techniques.

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Given a network and a partition in communities, we consider the issues "how communities influence each other" and "when two given communities do communicate". Specifically, we address these questions in the context of small-world networks, where an arbitrary quenched graph is given and long range connections are randomly added. We prove that, among the communities, a superposition principle applies and gives rise to a natural generalization of the effective field theory already presented in [Phys. Rev. E 78, 031102] (n=1), which here (n>1) consists in a sort of effective TAP (Thouless, Anderson and Palmer) equations in which each community plays the role of a microscopic spin. The relative susceptibilities derived from these equations calculated at finite or zero temperature, where the method provides an effective percolation theory, give us the answers to the above issues. Unlike the case n=1, asymmetries among the communities may lead, via the TAP-like structure of the equations, to many metastable states whose number, in the case of negative short-cuts among the communities, may grow exponentially fast with n. As examples we consider the n Viana-Bray communities model and the n one-dimensional small-world communities model. Despite being the simplest ones, the relevance of these models in network theory, as e.g. in social networks, is crucial and no analytic solution were known until now. Connections between percolation and the fractal dimension of a network are also discussed. Finally, as an inverse problem, we show how, from the relative susceptibilities, a natural and efficient method to detect the community structure of a generic network arises. For a short presentation of the main result see arXiv:0812.0608.

Modern mobile devices have access to a wealth of data suitable for learning models, which in turn can greatly improve the user experience on the device. For example, language models can improve speech recognition and text entry, and image models can automatically select good photos. However, this rich data is often privacy sensitive, large in quantity, or both, which may preclude logging to the data center and training there using conventional approaches. We advocate an alternative that leaves the training data distributed on the mobile devices, and learns a shared model by aggregating locally-computed updates. We term this decentralized approach Federated Learning. We present a practical method for the federated learning of deep networks based on iterative model averaging, and conduct an extensive empirical evaluation, considering five different model architectures and four datasets. These experiments demonstrate the approach is robust to the unbalanced and non-IID data distributions that are a defining characteristic of this setting. Communication costs are the principal constraint, and we show a reduction in required communication rounds by 10-100x as compared to synchronized stochastic gradient descent.

This book is a text to guide current and potential leaders in developing the communication capabilities needed to be transformational leaders. The content is based on research in communication and leadership and on the author's years of experience teaching professional communication and working as consultant, independently. This edition maintains the instruction to help readers achieve the objectives of the previous editions by focusing on strategy and emotional intelligence.xx, 443 hlm.; 23 c

A communication network is a graph in which each node has only local information about the graph and nodes communicate by passing messages along its edges. Here, we consider the {\it geometric communication network} where the nodes also occupy points in space and the distance between points is the Euclidean distance. Our goal is to understand the communication cost needed to solve several fundamental geometry problems, including Convex Hull, Diameter, Closest Pair, and approximations of these problems, in the asynchronous CONGEST KT1 model. This extends the 2011 result of Rajsbaum and Urrutia for finding a convex hull of a planar geometric communication network to networks of arbitrary topology.

Federated Learning is a machine learning setting where the goal is to train a high-quality centralized model while training data remains distributed over a large number of clients each with unreliable and relatively slow network connections. We consider learning algorithms for this setting where on each round, each client independently computes an update to the current model based on its local data, and communicates this update to a central server, where the client-side updates are aggregated to compute a new global model. The typical clients in this setting are mobile phones, and communication efficiency is of the utmost importance. In this paper, we propose two ways to reduce the uplink communication costs: structured updates, where we directly learn an update from a restricted space parametrized using a smaller number of variables, e.g. either low-rank or a random mask; and sketched updates, where we learn a full model update and then compress it using a combination of quantization, random rotations, and subsampling before sending it to the server. Experiments on both convolutional and recurrent networks show that the proposed methods can reduce the communication cost by two orders of magnitude.

We study the communication complexity of computing functions $F:\{0,1\}^n\times \{0,1\}^n \rightarrow \{0,1\}$ in the memoryless communication model. Here, Alice is given $x\in \{0,1\}^n$, Bob is given $y\in \{0,1\}^n$ and their goal is to compute F(x,y) subject to the following constraint: at every round, Alice receives a message from Bob and her reply to Bob solely depends on the message received and her input x; the same applies to Bob. The cost of computing F in this model is the maximum number of bits exchanged in any round between Alice and Bob (on the worst case input x,y). In this paper, we also consider variants of our memoryless model wherein one party is allowed to have memory, the parties are allowed to communicate quantum bits, only one player is allowed to send messages. We show that our memoryless communication model capture the garden-hose model of computation by Buhrman et al. (ITCS'13), space bounded communication complexity by Brody et al. (ITCS'13) and the overlay communication complexity by Papakonstantinou et al. (CCC'14). Thus the memoryless communication complexity model provides a unified framework to study space-bounded communication models. We establish the following: (1) We show that the memoryless communication complexity of F equals the logarithm of the size of the smallest bipartite branching program computing F (up to a factor 2); (2) We show that memoryless communication complexity equals garden-hose complexity; (3) We exhibit various exponential separations between these memoryless communication models. We end with an intriguing open question: can we find an explicit function F and universal constant c>1 for which the memoryless communication complexity is at least $c \log n$? Note that $c\geq 2+\varepsilon$ would imply a $Ω(n^{2+\varepsilon})$ lower bound for general formula size, improving upon the best lower bound by Nečiporuk in 1966.

Recent advances in single-cell sequencing technologies offer an opportunity to explore cell–cell communication in tissues systematically and with reduced bias. A key challenge is integrating known molecular interactions and measurements into a framework to identify and analyze complex cell–cell communication networks. Previously, we developed a computational tool, named CellChat, that infers and analyzes cell–cell communication networks from single-cell transcriptomic data within an easily interpretable framework. CellChat quantifies the signaling communication probability between two cell groups using a simplified mass-action-based model, which incorporates the core interaction between ligands and receptors with multisubunit structure along with modulation by cofactors. Importantly, CellChat performs a systematic and comparative analysis of cell–cell communication using a variety of quantitative metrics and machine-learning approaches. CellChat v2 is an updated version that includes additional comparison functionalities, an expanded database of ligand–receptor pairs along with rich functional annotations, and an Interactive CellChat Explorer. Here we provide a step-by-step protocol for using CellChat v2 on single-cell transcriptomic data, including inference and analysis of cell–cell communication from one dataset and identification of altered intercellular communication, signals and cell populations from different datasets across biological conditions. The R implementation of CellChat v2 toolkit and its tutorials together with the graphic outputs are available at https://github.com/jinworks/CellChat. This protocol typically takes ~5 min depending on dataset size and requires a basic understanding of R and single-cell data analysis but no specialized bioinformatics training for its implementation. CellChat is a software package for systematic inference, quantitative analysis and intuitive visualization of cell–cell communication in an easily interpretable way from single-cell transcriptomic data; it also enables comparative analysis of intercellular communication across different conditions. CellChat v2 is an updated version that includes additional functionalities for comparative analysis and an expanded database of ligand–receptor pairs along with rich functional annotations. CellChat is a software package for systematic inference, quantitative analysis and intuitive visualization of cell–cell communication in an easily interpretable way from single-cell transcriptomic data; it also enables comparative analysis of intercellular communication across different conditions. CellChat v2 is an updated version that includes additional functionalities for comparative analysis and an expanded database of ligand–receptor pairs along with rich functional annotations. CellChat enables systematic inference, quantitative analysis and intuitive visualization of cell–cell communication from single-cell transcriptomic data, as well as comparative analysis of intercellular communication across biological conditions.

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Massive Open Online Courses (MOOCs) bring together thousands of people from different geographies and demographic backgrounds -- but to date, little is known about how they learn or communicate. We introduce a new content-analysed MOOC dataset and use Bayesian Non-negative Matrix Factorization (BNMF) to extract communities of learners based on the nature of their online forum posts. We see that BNMF yields a superior probabilistic generative model for online discussions when compared to other models, and that the communities it learns are differentiated by their composite students' demographic and course performance indicators. These findings suggest that computationally efficient probabilistic generative modelling of MOOCs can reveal important insights for educational researchers and practitioners and help to develop more intelligent and responsive online learning environments.

Preface. 1 Introduction. 1.1 Historical Perspective. 1.2 Basic Concepts. 1.3 Optical Communication Systems. 1.4 Lightwave System Components. Problems. References. 2 Optical Fibers. 2.1 Geometrical-Optics Description. 2.2 Wave Propagation. 2.3 Dispersion in Single-Mode Fibers. 2.4 Dispersion-Induced Limitations. 2.5 Fiber Losses. 2.6 Nonlinear Optical Effects. 2.7 Fiber Design and Fabrication. Problems. References. 3 Optical Transmitters. 3.1 Semiconductor Laser Physics. 3.2 Single-Mode Semiconductor Lasers. 3.3 Laser Characteristics. 3.4 Optical Signal Generation. 3.5 Light-Emitting Diodes. 3.6 Transmitter Design. Problems. References. 4 Optical Receivers. 4.1 Basic Concepts. 4.2 Common Photodetectors. 4.3 Receiver Design. 4.4 Receiver Noise. 4.5 Coherent Detection. 4.6 Receiver Sensitivity. 4.7 Sensitivity Degradation. 4.8 Receiver Performance. Problems. References. 5 Lightwave Systems. 5.1 System Architectures. 5.2 Design Guidelines. 5.3 Long-Haul Systems. 5.4 Sources of Power Penalty. 5.5 Forward Error Correction. 5.6 Computer-Aided Design. Problems. References. 6 Multichannel Systems. 6.1 WDM Lightwave Systems. 6.2 WDM Components. 6.3 System Performance Issues. 6.4 Time-Division Multiplexing. 6.5 Subcarrier Multiplexing. 6.6 Code-Division Multiplexing. Problems. References. 7 Loss Management. 7.1 Compensation of Fiber Losses. 7.2 Erbium-Doped Fiber Amplifiers. 7.3 Raman Amplifiers. 7.4 Optical Signal-To-Noise Ratio. 7.5 Electrical Signal-To-Noise Ratio. 7.6 Receiver Sensitivity and Q Factor. 7.7 Role of Dispersive and Nonlinear Effects. 7.8 Periodically Amplified Lightwave Systems. Problems. References. 8 Dispersion Management. 8.1 Dispersion Problem and Its Solution. 8.2 Dispersion-Compensating Fibers. 8.3 Fiber Bragg Gratings. 8.4 Dispersion-Equalizing Filters. 8.5 Optical Phase Conjugation. 8.6 Channels at High Bit Rates. 8.7 Electronic Dispersion Compensation. Problems. References. 9 Control of Nonlinear Effects. 9.1 Impact of Fiber Nonlinearity. 9.2 Solitons in Optical Fibers. 9.3 Dispersion-Managed Solitons. 9.4 Pseudo-linear Lightwave Systems. 9.5 Control of Intrachannel Nonlinear Effects. Problems. References. 10 Advanced Lightwave Systems. 10.1 Advanced Modulation Formats. 10.2 Demodulation Schemes. 10.3 Shot Noise and Bit-Error Rate. 10.4 Sensitivity Degradation Mechanisms. 10.5 Impact of Nonlinear Effects. 10.6 Recent Progress. 10.7 Ultimate Channel Capacity. Problems. References. 11 Optical Signal Processing. 11.1 Nonlinear Techniques and Devices. 11.2 All-Optical Flip-Flops. 11.3 Wavelength Converters. 11.4 Ultrafast Optical Switching. 11.5 Optical Regenerators. Problems. References. A System of Units. B Acronyms. C General Formula for Pulse Broadening. D Software Package.

This chapter considers ethical issues relating to research and the communication of research. By presenting some of the ethical questions relating to communication that research students will encounter, the chapter offers a framework for understanding what is acceptable and what is unacceptable scientific conduct. It specifically outlines key ethical issues relating to data ownership and data sharing, and it explains plagiarism and strategies for avoiding plagiarism. The chapter also looks at the key ethical issues relating to the publication of primary research data including duplicate publication, conflicts of interest, authorship, and acknowledgement. Then, it provides a basic overview of copyright and how to use copyright material in your work appropriately.

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Social recommendation, which seeks to leverage social ties among users to alleviate the sparsity issue of user-item interactions, has emerged as a popular technique for elevating personalized services in recommender systems. Despite being effective, existing social recommendation models are mainly devised for recommending regular items such as blogs, images, and products, and largely fail for community recommendations due to overlooking the unique characteristics of communities. Distinctly, communities are constituted by individuals, who present high dynamicity and relate to rich structural patterns in social networks. To our knowledge, limited research has been devoted to comprehensively exploiting this information for recommending communities. To bridge this gap, this paper presents CASO, a novel and effective model specially designed for social community recommendation. Under the hood, CASO harnesses three carefully-crafted encoders for user embedding, wherein two of them extract community-related global and local structures from the social network via social modularity maximization and social closeness aggregation, while the third one captures user preferences using collaborative filtering with observed user-community affiliations. To further eliminate feature redundancy therein, we introduce a mutual exclusion between social and collaborative signals. Finally, CASO includes a community detection loss in the model optimization, thereby producing community-aware embeddings for communities. Our extensive experiments evaluating CASO against nine strong baselines on six real-world social networks demonstrate its consistent and remarkable superiority over the state of the art in terms of community recommendation performance.

À partir d’une mise en récit du développement du champ de la communication humaine à l’Université du Québec à Montréal (UQAM), nous montrons d’abord comment l’institutionnalisation des études en communication est tributaire de la rencontre de programmes de formation en communication humaine et en communication médiatique. Nous proposons ensuite une définition de l’objet d’études de la communication humaine et nous présentons des contributions de professeur.e.s à l’émergence de ce nouveau champ. Celles-ci permettent de cerner les pistes de développement de la recherche et de la formation en communication humaine.

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An alternative physical way of communication, communication by the inherent background noise, is proposed which does not need net energy transfer in the information channel. The communicator devices do dissipate energy; however, they do not emit net energy into the channel, instead of that, they modulate the parameters of inherent spontaneous fluctuations in the channel. The method can use two different mechanisms, thermal noise (Johnson-Nyquist noise) for classical communication, and vacuum fluctuations/zero point energy (quantum uncertainty noise) for quantum communication. The strongest advantage of the method that this is apparently the most hidden (stealth) way of communication, because it is using the inherent background noise for communication, therefore it is extremely difficult or impossible to discover its presence. With proper wave-based arrangements and specific conditions, the sender and the receiver can easily detect eavesdropper activities, so that the eavesdropper is detected as soon as she extracts a single bit of information, thus the security of the method is comparable to the security of quantum communication/quantum key distribution schemes. Finally, concerning practical applications, environmental noise, out of the fundamental/inherent fluctuations, can also be used for this kind of communication provided that is sufficiently stationary.

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Network communities represent mesoscopic structure for understanding the organization of real-world networks, where nodes often belong to multiple communities and form overlapping community structure in the network. Due to non-triviality in finding the exact boundary of such overlapping communities, this problem has become challenging, and therefore huge effort has been devoted to detect overlapping communities from the network. In this paper, we present PVOC (Permanence based Vertex-replication algorithm for Overlapping Community detection), a two-stage framework to detect overlapping community structure. We build on a novel observation that non-overlapping community structure detected by a standard disjoint community detection algorithm from a network has high resemblance with its actual overlapping community structure, except the overlapping part. Based on this observation, we posit that there is perhaps no need of building yet another overlapping community finding algorithm; but one can efficiently manipulate the output of any existing disjoint community finding algorithm to obtain the required overlapping structure. We propose a new post-processing technique that by combining with any existing disjoint community detection algorithm, can suitably process each vertex using a new vertex-based metric, called permanence, and thereby finds out overlapping candidates with their community memberships. Experimental results on both synthetic and large real-world networks show that PVOC significantly outperforms six state-of-the-art overlapping community detection algorithms in terms of high similarity of the output with the ground-truth structure. Thus our framework not only finds meaningful overlapping communities from the network, but also allows us to put an end to the constant effort of building yet another overlapping community detection algorithm.