We will review some of the major results in random graphs and some of the more challenging open problems. We will cover algorithmic and structural questions. We will touch on newer models, including those related to the WWW.

Random graphs

The 11th edition of Harrison's Principles of Internal Medicine welcomes Anthony Fauci to its editorial staff, in addition to more than 85 new contributors. While the organization of the book is similar to previous editions, major emphasis has been placed on disorders that affect multiple organ systems. Important advances in genetics, immunology, and oncology are emphasized. Many chapters of the book have been rewritten and describe major advances in internal medicine. Subjects that received only a paragraph or two of attention in previous editions are now covered in entire chapters. Among the chapters that have been extensively revised are the chapters on infections in the compromised host, on skin rashes in infections, on many of the viral infections, including cytomegalovirus and Epstein-Barr virus, on sexually transmitted diseases, on diabetes mellitus, on disorders of bone and mineral metabolism, and on lymphadenopathy and splenomegaly. The major revisions in these chapters and many

Harrison's Principles of Internal Medicine

Fractional Differential Equations

N G van Kampen 1981 Amsterdam: North-Holland xiv + 419 pp price Dfl 180 This is a book which, at a lower price, could be expected to become an essential part of the library of every physical scientist concerned with problems involving fluctuations and stochastic processes, as well as those who just enjoy a beautifully written book. It provides an extensive graduate-level introduction which is clear, cautious, interesting and readable.

https://iopscience.iop.org/article/10.1088/0031-9112/34/4/040/pdf

Stochastic Processes in Physics and Chemistry

Praise for the Third Edition: "This is one of the best books available. Its excellent organizational structure allows quick reference to specific models and its clear presentation . . . solidifies the understanding of the concepts being presented."IIE Transactions on Operations EngineeringThoroughly revised and expanded to reflect the latest developments in the field, Fundamentals of Queueing Theory, Fourth Edition continues to present the basic statistical principles that are necessary to analyze the probabilistic nature of queues. Rather than presenting a narrow focus on the subject, this update illustrates the wide-reaching, fundamental concepts in queueing theory and its applications to diverse areas such as computer science, engineering, business, and operations research.This update takes a numerical approach to understanding and making probable estimations relating to queues, with a comprehensive outline of simple and more advanced queueing models. Newly featured topics of the Fourth Edition include:Retrial queuesApproximations for queueing networksNumerical inversion of transformsDetermining the appropriate number of servers to balance quality and cost of serviceEach chapter provides a self-contained presentation of key concepts and formulae, allowing readers to work with each section independently, while a summary table at the end of the book outlines the types of queues that have been discussed and their results. In addition, two new appendices have been added, discussing transforms and generating functions as well as the fundamentals of differential and difference equations. New examples are now included along with problems that incorporate QtsPlus software, which is freely available via the book's related Web site.With its accessible style and wealth of real-world examples, Fundamentals of Queueing Theory, Fourth Edition is an ideal book for courses on queueing theory at the upper-undergraduate and graduate levels. It is also a valuable resource for researchers and practitioners who analyze congestion in the fields of telecommunications, transportation, aviation, and management science.

Fundamentals of Queueing Theory

Networks-on-chip (NoCs) have recently emerged as a scalable alternative to classical bus and point-to-point architectures. To date, performance evaluation of NoC designs is largely based on simulation which, besides being extremely slow, provides little insight on how different design parameters affect the actual network performance. Therefore, it is practically impossible to use simulation for optimization purposes. In this paper, we present a mathematical model for on-chip routers and utilize this new model for NoC performance analysis. The proposed model can be used not only to obtain fast and accurate performance estimates, but also to guide the NoC design process within an optimization loop. The accuracy of our approach and its practical use is illustrated through extensive simulation results.

An Analytical Approach for Network-on-Chip Performance Analysis

The Chip Is the Network: Towards a Science of Network-on-Chip Design reviews the major design methodologies that have had a profound effect on designing future Network-on-Chip (NoC) architectures. More precisely, it addresses the problem of NoC design in the deterministic context, where the application and the architecture are modeled as graphs with worst-case type of information about the parameters of the components influencing the network traffic. Rather than simply enumerating the proposed approaches, it takes a formal approach and also discusses the main features of each proposed solution. It then goes one step further by considering the design of NoCs with partial information available (primarily under the Markovian assumption) about the application and the architecture. Similarly to the deterministic context, it discusses various probabilistic approaches to NoC design and points out their advantages and limitations. Last, but not least, it looks at emerging approaches inspired from statistical physics and information theory. The formal approach adopted means the network concept is addressed in the most general context, pointing out the main limitations of the proposed solutions, and suggesting a few open-ended problems. The Chip Is the Network: Towards a Science of Network-on-Chip Design is an invaluable reference for the NoC research community and, indeed anyone from CAD/VLSI academe or industry with an interest in this emerging paradigm.

/pdf/the-chip-is-the-network-toward-a-science-of-network-on-chip-2q0lmj074p.pdf

The Chip Is the Network: Toward a Science of Network-on-Chip Design

As CMOS technology scales down into the deep-submicron (DSM) domain, the costs of design
and verification for Systems-on-Chip (SoCs) are rapidly increasing. Relaxing the requirement of 100 % correctness
for devices and interconnects drastically reduces the costs of design but, at the same time, requires
SoCs to be designed with some degree of system-level fault-tolerance. Towards this end, this paper introduces a
novel communication paradigm for SoCs, called stochastic communication. This scheme separates communication
from computation by allowing the on-chip interconnect to be designed as a reusable IP and also provides a
built-in tolerance to DSM failures, without a significant performance penalty. By using this communication
scheme, a large percentage of data upsets, packet losses due to buffers overflow, and severe levels of synchronization
failures can be tolerated, while providing high levels of performance.

/pdf/stochastic-communication-a-new-paradigm-for-fault-tolerant-qqpjxtob2d.pdf

Stochastic Communication: A New Paradigm for Fault-Tolerant Networks-on-Chip

The rampant spread of COVID-19, an infectious disease caused by SARS-CoV-2, all over the world has led to over millions of deaths, and devastated the social, financial and political entities around the world. Without an existing effective medical therapy, vaccines are urgently needed to avoid the spread of this disease. In this study, we propose an in silico deep learning approach for prediction and design of a multi-epitope vaccine (DeepVacPred). By combining the in silico immunoinformatics and deep neural network strategies, the DeepVacPred computational framework directly predicts 26 potential vaccine subunits from the available SARS-CoV-2 spike protein sequence. We further use in silico methods to investigate the linear B-cell epitopes, Cytotoxic T Lymphocytes (CTL) epitopes, Helper T Lymphocytes (HTL) epitopes in the 26 subunit candidates and identify the best 11 of them to construct a multi-epitope vaccine for SARS-CoV-2 virus. The human population coverage, antigenicity, allergenicity, toxicity, physicochemical properties and secondary structure of the designed vaccine are evaluated via state-of-the-art bioinformatic approaches, showing good quality of the designed vaccine. The 3D structure of the designed vaccine is predicted, refined and validated by in silico tools. Finally, we optimize and insert the codon sequence into a plasmid to ensure the cloning and expression efficiency. In conclusion, this proposed artificial intelligence (AI) based vaccine discovery framework accelerates the vaccine design process and constructs a 694aa multi-epitope vaccine containing 16 B-cell epitopes, 82 CTL epitopes and 89 HTL epitopes, which is promising to fight the SARS-CoV-2 viral infection and can be further evaluated in clinical studies. Moreover, we trace the RNA mutations of the SARS-CoV-2 and ensure that the designed vaccine can tackle the recent RNA mutations of the virus.

/pdf/an-in-silico-deep-learning-approach-to-multi-epitope-vaccine-2s31ddx2s0.pdf

An in silico deep learning approach to multi-epitope vaccine design: a SARS-CoV-2 case study.

Identification of community structures in complex network is of crucial importance for understanding the system’s function, organization, robustness and security. Here, we present a novel Ollivier-Ricci curvature (ORC) inspired approach to community identification in complex networks. We demonstrate that the intrinsic geometric underpinning of the ORC offers a natural approach to discover inherent community structures within a network based on interaction among entities. We develop an ORC-based community identification algorithm based on the idea of sequential removal of negatively curved edges symptomatic of high interactions (e.g., traffic, attraction). To illustrate and compare the performance with other community identification methods, we examine the ORC-based algorithm with stochastic block model artificial networks and real-world examples ranging from social to drug-drug interaction networks. The ORC-based algorithm is able to identify communities with either better or comparable performance accuracy and to discover finer hierarchical structures of the network. This opens new geometric avenues for analysis of complex networks dynamics.

/pdf/ollivier-ricci-curvature-based-method-to-community-detection-52amzjyttr.pdf

Paul Bogdan

Papers

An Analytical Approach for Network-on-Chip Performance Analysis

The Chip Is the Network: Toward a Science of Network-on-Chip Design

Stochastic Communication: A New Paradigm for Fault-Tolerant Networks-on-Chip

An in silico deep learning approach to multi-epitope vaccine design: a SARS-CoV-2 case study.

Ollivier-Ricci Curvature-Based Method to Community Detection in Complex Networks.