There is, I think, something ethereal about i —the square root of minus one. I remember first hearing about it at school. It seemed an odd beast at that time—an intruder hovering on the edge of reality.

Usually familiarity dulls this sense of the bizarre, but in the case of i it was the reverse: over the years the sense of its surreal nature intensified. It seemed that it was impossible to write mathematics that described the real world in …

I and i

Machine Learning is the study of methods for programming computers to learn. Computers are applied to a wide range of tasks, and for most of these it is relatively easy for programmers to design and implement the necessary software. However, there are many tasks for which this is difficult or impossible. These can be divided into four general categories. First, there are problems for which there exist no human experts. For example, in modern automated manufacturing facilities, there is a need to predict machine failures before they occur by analyzing sensor readings. Because the machines are new, there are no human experts who can be interviewed by a programmer to provide the knowledge necessary to build a computer system. A machine learning system can study recorded data and subsequent machine failures and learn prediction rules. Second, there are problems where human experts exist, but where they are unable to explain their expertise. This is the case in many perceptual tasks, such as speech recognition, hand-writing recognition, and natural language understanding. Virtually all humans exhibit expert-level abilities on these tasks, but none of them can describe the detailed steps that they follow as they perform them. Fortunately, humans can provide machines with examples of the inputs and correct outputs for these tasks, so machine learning algorithms can learn to map the inputs to the outputs. Third, there are problems where phenomena are changing rapidly. In finance, for example, people would like to predict the future behavior of the stock market, of consumer purchases, or of exchange rates. These behaviors change frequently, so that even if a programmer could construct a good predictive computer program, it would need to be rewritten frequently. A learning program can relieve the programmer of this burden by constantly modifying and tuning a set of learned prediction rules. Fourth, there are applications that need to be customized for each computer user separately. Consider, for example, a program to filter unwanted electronic mail messages. Different users will need different filters. It is unreasonable to expect each user to program his or her own rules, and it is infeasible to provide every user with a software engineer to keep the rules up-to-date. A machine learning system can learn which mail messages the user rejects and maintain the filtering rules automatically. Machine learning addresses many of the same research questions as the fields of statistics, data mining, and psychology, but with differences of emphasis. Statistics focuses on understanding the phenomena that have generated the data, often with the goal of testing different hypotheses about those phenomena. Data mining seeks to find patterns in the data that are understandable by people. Psychological studies of human learning aspire to understand the mechanisms underlying the various learning behaviors exhibited by people (concept learning, skill acquisition, strategy change, etc.).

Machine learning

Probability Distributions.- Linear Models for Regression.- Linear Models for Classification.- Neural Networks.- Kernel Methods.- Sparse Kernel Machines.- Graphical Models.- Mixture Models and EM.- Approximate Inference.- Sampling Methods.- Continuous Latent Variables.- Sequential Data.- Combining Models.

Pattern Recognition and Machine Learning

A new aspect of cell membrane structure is presented, based on the dynamic clustering of sphingolipids and cholesterol to form rafts that move within the fluid bilayer. It is proposed that these rafts function as platforms for the attachment of proteins when membranes are moved around inside the cell and during signal transduction.

Functional rafts in cell membranes

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

In realizing the network environment assumed by the Internet-of-Things, network slicing has drawn considerable attention as a way to enhance the utilization of physical networks (PNs). Meanwhile, slicing has been shown to cause interdependence among sliced virtual networks (VNs) by propagating traffic fluctuations from one network to others. However, for interconnected networks with mutual dependencies, known as a network of networks (NoN), finding a reliable design method that can cope with environmental changes is an important issue that is yet to be addressed. Some NoN models exist that describe the behavior of interdependent networks in complex systems, and previous studies have shown that an NoN model based on the functional networks of the brain can achieve high robustness, but its application to dynamic and practical systems is yet to be considered. Consequently, this paper proposes the Physical–Virtual NoN (PV-NoN) model assuming a network-slicing environment. This model defines an NoN availability state to deal with traffic fluctuations and interdependence among a PN and VNs. Further, we assume three basic types of interdependence among VNs for this model. Simulation experiments confirm that the one applying complementary interdependence inspired by brain functional networks achieves high availability and communication performance while preventing interference among the VNs. Also investigated is a method for designing a reliable network structure for the PV-NoN model. To this end, the deployment of network influencers (i.e., the most influential elements over the entire network) is configured from the perspective of intra/internetwork assortativity. Simulation experiments confirm that availability or communication performance is improved when each VN is formed assortatively or disassortatively, respectively. Regarding internetwork assortativity, both the availability and communication performance are improved when the influencers are deployed disassortatively among the VNs.

Reliable Design for a Network of Networks with Inspiration from Brain Functional Networks

Sophisticated routing protocols, such as content-based routing, have been studied and proposed to replace Internet Protocol (IP) in the future Internet. Routing based on content will make finding, querying, and retrieving the desired information possible within the routers rather than having the routers forward the packets to data servers or domain name servers. However, one of the main reasons that has prevented achieving content-based routing within the routers in the past has been their storage constraints. In our previous work, we showed that name-based routing, which is a good example of content-based routing, is feasible in practice utilizing Ternary Content Addressable Memory (TCAM). Using our proposed algorithms based on longest alphabet match and Distributed Hash Table (DHT), the Fully Qualified Domain Name (FQDN) database was distributed among less than the number of routers that are currently existing in the Internet. In this paper, we evaluate the feasibility of robustly updating the routing information database entries and show that there are no drastic changes in the routers' forwarding tables. We propose an algorithm for mapping a logical network topology to the physical topology to achieve robustness against dynamically changing databases that also considers the characteristic of TCAMs.

The impact of FQDN database updates on name-based routing architecture

Famous rule-of-thumb states that a buffer sized at B = RTT × BW, where RTT is the average round trip time and BW is the bandwidth of output link is necessary in order to achieve high utilization with TCP flows. However, as the link speeds continue increasing with technological advances, this buffer requirement starts becoming an important cost factor on routers of electronic networks. On the other hand, bursty nature of TCP limits further decreasing the buffer requirements, because it brings a high packet drop rate in small buffered networks. In this paper, we evaluate several transmission control algorithms in small buffered networks. The algorithms include TCP Reno, TCP NewReno, Highspeed TCP with SACK, and XCP. Simulation results show that all non-paced TCP and XCP variants perform poorly. Furthermore, the results show that even rule-of-thumb buffers are not enough for XCP in some cases because of high burstiness of XCP. We evaluate the effectiveness of pacing method at sender side for making transmission control algorithms suitable for very small buffered networks. We show that pacing alone is not enough for XCP to make suitable for small buffered networks, so we introduce a suitable parameter set. Simulation results show that buffer requirements on routers are greatly reduced by paced XCP with suitable parameter settings, while keeping the high fairness, fast convergence, and high utilization.

Performance of Paced and Non-Paced Transmission Control Algorithms in Small Buffered Networks

Fifth-generation mobile and wireless communication systems are actively being studied as a next-generation mobile communication network. People and devices will connect to the Internet via wireless networks in future communication networks, but many challenges must be solved to realize 5G networks. For example, it is difficult to manage connected user devices using centralized control in the control plane. We previously proposed an architecture for autonomous and distributed mobility management and a biology-inspired mobility management scheme that adaptively selects the location of management entities. However, this scheme has some problems from a network-wide perspective, due to entities' autonomous decision-making. This paper introduces a control node for monitoring and managing the network to resolve the otherwise unsolvable problem of autonomous distributed control. We show that this control node can improve network stability without much loss of performance in the entire network.

A control method for autonomous mobility management systems toward 5G mobile networks

In an ad hoc wireless network system, wireless terminals can autonomously construct a network and directly communicate with each other without using a wired network A flexible radio network is one of the ad hoc wireless network systems which has been operational in recent years In the flexible radio network, an original communication protocol is implemented to obtain reliability of communication We evaluate the performance by means of simulations for the data-link protocol and routing protocol of the flexible radio network We first show the performance when system parameters are changed to examine how these parameters affect the system performance Furthermore, we investigate the performance improvement by changing the network configuration

Masayuki Murata

Papers

Reliable Design for a Network of Networks with Inspiration from Brain Functional Networks

The impact of FQDN database updates on name-based routing architecture

Performance of Paced and Non-Paced Transmission Control Algorithms in Small Buffered Networks

A control method for autonomous mobility management systems toward 5G mobile networks

Performance evaluation of a wireless ad hoc network: flexible radio network (FRN)