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 this paper, we propose a novel analysis method for large-scale networks with consideration of the behavior of the congestion control mechanism of TCP. In the analysis, we model the behavior of TCP at end-host and network link as independent systems, and combine them into a single system in order to analyze the entire network. Using this analysis, we can analyze a large-scale network, i.e. with over 100/1,000/10,000 routers/hosts/links and 100,000 TCP connections very rapidly. Especially, a calculation time of our analysis, it is different from that of ns-2, is independent of a network bandwidth and/or propagation delay. Specifically, we can derive the utilization of the network links, the packet loss ratio of the link buffer, the round-trip time (RTT) and the throughput of TCP connections, and the location and degree of the network congestion. We validate our approximate analysis by comparing analytic results with simulation ones. We also show that our analysis method treats the behavior of TCP connection in a large-scale network appropriately.

Performance Analysis of Large-Scale IP Networks Considering TCP Traffic

We propose a new packet scheduling algorithm for 2×2 photonic switches with FDL buffers to avoid contention. The performances of the switch and the multi-stage switch based on our element switch are evaluated by applying our algorithm.

http://ieeexplore.ieee.org/iel5/10668/33653/01601149.pdf

A Packet Scheduling Algorithm for the 2&#215;2 Photonic Packet Switch with FDL Buffers

あらまし 現在,ISP (インターネットサービスプロバイダ)で広く用いられている料金制度は,ISPへの接続時間を基準にし たものである.しかしながら,接続時間は実際のネットワーク資源の使用量を表すものではない.また,近年,イ ンターネットにおける音声や動画像などの実時間アプリケーションの開発も盛んであるが,実時間通信が広く利用 されるようになると,現在の料金制度のもとでは,ユーザ間の公平性が保たれない可能性がある.実時間通信のサ ポートのために,ネットワーク資源を予約することによって通信品質を保証する資源予約型サービスも検討される ようになっているが,帯域予約に必要なコストを含めた評価は十分になされていない.そこで,本論文ではユーザ 効用を通信品質とコストによって定義し,現状の料金制度におけるユーザ効用を明らかにし,また,実時間通信ア プリケーションと従来のデータ通信アプリケーションがネットワーク資源を共有している場合におけるユーザ効用 を調べることによって,料金制度を含めたネットワークサービスの比較評価を行っている. キーワード 料金制度,通信品質,ベストエフォート型サービス,資源予約型サービス,ユーザ効用

Comparison of Network Services with User Utilities

To provide distributed multimedia applications with end-to-end QoS (quality of service) guarantees, resource reservation-based control mechanisms should be employed in both networks and end systems. In this paper, we propose a resource allocation scheme for real-time video multicasting described as a utility maximization problem. In this scheme, clients are first divided into multicast groups by means of a clustering technique. Then system resources are allocated to each group so that the total utility is maximized. We have confirmed that our proposed scheme can achieve effective use of resources while providing high-quality video to users.

Masayuki Murata

Papers

Performance Analysis of Large-Scale IP Networks Considering TCP Traffic

A Packet Scheduling Algorithm for the 2×2 Photonic Packet Switch with FDL Buffers

Comparison of Network Services with User Utilities

Integrated resource allocation scheme for real-time video multicast

Delete and Update Methods for Neighbor Cache in IPv6

Masayuki Murata

Papers

Performance Analysis of Large-Scale IP Networks Considering TCP Traffic

A Packet Scheduling Algorithm for the 2&#215;2 Photonic Packet Switch with FDL Buffers

Comparison of Network Services with User Utilities

Integrated resource allocation scheme for real-time video multicast

Delete and Update Methods for Neighbor Cache in IPv6

A Packet Scheduling Algorithm for the 2×2 Photonic Packet Switch with FDL Buffers