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

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

物件導向軟體之架構(Object-Oriented Software Construction)探討

In Distributed Algorithms, Nancy Lynch provides a blueprint for designing, implementing, and analyzing distributed algorithms. She directs her book at a wide audience, including students, programmers, system designers, and researchers.



Distributed Algorithms contains the most significant algorithms and impossibility results in the area, all in a simple automata-theoretic setting. The algorithms are proved correct, and their complexity is analyzed according to precisely defined complexity measures. The problems covered include resource allocation, communication, consensus among distributed processes, data consistency, deadlock detection, leader election, global snapshots, and many others.



The material is organized according to the system model-first by the timing model and then by the interprocess communication mechanism. The material on system models is isolated in separate chapters for easy reference.



The presentation is completely rigorous, yet is intuitive enough for immediate comprehension. This book familiarizes readers with important problems, algorithms, and impossibility results in the area: readers can then recognize the problems when they arise in practice, apply the algorithms to solve them, and use the impossibility results to determine whether problems are unsolvable. The book also provides readers with the basic mathematical tools for designing new algorithms and proving new impossibility results. In addition, it teaches readers how to reason carefully about distributed algorithms-to model them formally, devise precise specifications for their required behavior, prove their correctness, and evaluate their performance with realistic measures.


Table of Contents

1 Introduction 
2 Modelling I; Synchronous Network Model 
3 Leader Election in a Synchronous Ring 
4 Algorithms in General Synchronous Networks 
5 Distributed Consensus with Link Failures 
6 Distributed Consensus with Process Failures 
7 More Consensus Problems 
8 Modelling II: Asynchronous System Model 
9 Modelling III: Asynchronous Shared Memory Model 
10 Mutual Exclusion 
11 Resource Allocation 
12 Consensus 
13 Atomic Objects 
14 Modelling IV: Asynchronous Network Model 
15 Basic Asynchronous Network Algorithms 
16 Synchronizers 
17 Shared Memory versus Networks 
18 Logical Time 
19 Global Snapshots and Stable Properties 
20 Network Resource Allocation 
21 Asynchronous Networks with Process Failures 
22 Data Link Protocols 
23 Partially Synchronous System Models 
24 Mutual Exclusion with Partial Synchrony 
25 Consensus with Partial Synchrony

Distributed algorithms

We review the field of cavity optomechanics, which explores the interaction between electromagnetic radiation and nano- or micromechanical motion This review covers the basics of optical cavities and mechanical resonators, their mutual optomechanical interaction mediated by the radiation pressure force, the large variety of experimental systems which exhibit this interaction, optical measurements of mechanical motion, dynamical backaction amplification and cooling, nonlinear dynamics, multimode optomechanics, and proposals for future cavity quantum optomechanics experiments In addition, we describe the perspectives for fundamental quantum physics and for possible applications of optomechanical devices

Cavity Optomechanics

This paper presents a novel approach for long-term software archiving which is based on preserving programs as bit blocks. A simple machine that is able to execute a single command is used to interpret these bit blocks. We suggest to compile the existing programs into the bit representation of the One-InstructionSet computer (OISC) command “SUBtract and Branch if Less than or EQual to zero”, shortly Subleq. This is in order to keep the resulting bit stream using error correcting code in a reliable storage unit. At any moment, this bit stream can be executed by a simple interpreter that possesses the functionality of a basic Random Access Machine. Furthermore, a compiler prototype based on an existing compiler and interpreter is also proposed to convert a program written by a procedural language (e.g., C) into the Subleq assembly language, and then translates it into a binary executable format. Error correcting is achieved by supplementing bit streams with Hamming codes. Our scheme nullifies the need to preserve legacy hardware in order to support/operate preserved software systems thus serving as a program “time capsule” for the

Archiving Programs for the Future

Privacy-preserving data release is an increasingly important problem in today’s ubiquitous computing. Performing data processing on edge devices saves CPU time, network traffic and improves latency. The savings are especially significant for edge devices with low-power and limited resources. In addition, processing and privacy-preserving release of the data on the edge devices prevents sensitive data from leaving the devices at all. Thus, solving a bunch of potential problems in data protection. We propose a usage of query-independent, similitude models for privacy-preserving data release on the edge devices. Those models build a compact representation of the data for any possible query, as opposite to a common method of queryspecific data release. Compared to the query-specific models, similitude models can be combined and “subtracted” when a new edge devices appear or shutdown. In addition, a data oriented approach allows further processing in a fog, near the edge devices.

Query-independent dynamic similitude data models for edge-computing

The construction of an optical computer that can explore the computation tree of a non-deterministic Turing machine in the time it takes to explore one path of the computation has been described in Dolev and Nir 2003. In this paper, we elaborate on the design considerations of Dolev and Nir 2003. The construction of such an optical computer will allow solving NP problems in polynomial time. The limitation is space, where every beam location (hitting a prism) represents a different Turing machine configuration.

By the use of writable (holographic) memory, we are able to reduce the space only by a constant factor. Tradeoffs between the use of semantics for each location in space, and the use of digital storage is discussed. In the writable model, configurations can be represented in binary (or higher base digital) representation, rather than mapping each location in space to a single configuration. We show that, the benefit of such a digital representation in the scope of concurrent exhaustive search is limited.

Optical Designs for Non-deterministic Turing Machines

. A solution of the k shortest paths problem may output paths that are identical up to a single edge. On the other hand, a solution of the k independent shortest paths problem consists of paths that share neither an edge nor an intermediate node. We investigate the case in which the number of edges that are not shared among any two paths in the output k -set is a parameter. We study two main directions: exploring near-shortest paths and exploring exactly shortest paths . We assume that the weighted graph G = ( V, E, w ) has no parallel edges and that the edge lengths (weights) are positive. Our results are also generalized to the cases of k shortest paths where there are several weights per edge, and the results should take into account the multi-criteria prioritized weight.

Shlomi Dolev

Papers

Archiving Programs for the Future

Query-independent dynamic similitude data models for edge-computing

Optical Designs for Non-deterministic Turing Machines

Partially Disjoint k Shortest Paths

Local Deal-Agreement Algorithms for Load Balancing in Dynamic General Graphs