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

The existence of a provable one-way function is a long-standing open problem. This short note presents an example towards the existence a provable one-way function, example in which both directions are polynomial. Namely, we prove that given a sorted array it takes Θ(n) operations to randomly permute the array values uniformly over the permutation space, while (comparison based) sorting of the permuted array (of big enough values) requires in the worst case (and in the average case) Θ(n log n) compare operations. We also present a candidate cryptosystem based on permutations of random polynomial values.

/pdf/toward-provable-one-way-functions-19nxel29e2.pdf

Toward Provable One Way Functions.

Self-stabilizing algorithms can be started in any arbitrary state to exhibit a desired behavior following a convergence period The class of self-organizing distributed algorithms is regarded here as a subclass of the self-stabilizing class of algorithms, where convergence is sub-linear in the size of the system and local perturbation of state is handled locally converging faster than the convergence from an arbitrary state The chapter starts with a short overview of several virtual infrastructures and fitting self-stabilizing and self-organizing techniques:

Self-Stabilizing and Self-Organizing Virtual Infrastructures for Mobile Networks

Given a large set of measurement sensor data, in order to identify a simple function that captures the essence of the data gathered by the sensors, we suggest representing the data by (spatial) functions, in particular by polynomials. Given a (sampled) set of values, we interpolate the datapoints to define a polynomial that would represent the data. The interpolation is challenging, since in practice the data can be noisy and even Byzantine, where the Byzantine data represents an adversarial value that is not limited to being close to the correct measured data. We present two solutions, one that extends the Welch-Berlekamp technique in the case of multidimensional data, and copes with discrete noise and Byzantine data, and the other based on Arora and Khot techniques, extending them in the case of multidimensional noisy and Byzantine data.

Big Data Interpolation an Efficient Sampling Alternative for Sensor Data Aggregation

This paper explores efficient discrete coding techniques that are motivated by the time/energy tradeoff in message transmission between mobile hosts and mobile support stations. Three algorithms are suggested two of which use guessing games in which the mobile support station guesses the message to be transmitted by the mobile host and receives approving signal for successful guess from the mobile host. The first algorithm is designed to achieve the smallest expected amount of energy while obeying a time bound for message transmissions. The second algorithm achieves the shortest expected transmission time while obeying a bound on the energy. This algorithm uses dynamic programming to construct an optimal tree for the guessing game. The third algorithm uses a different approach, approach that is based on the Lempel-Ziv (1978) compression algorithm. The time energy tradeoff is controlled by the choice of the length of the codes used to encode strings in the dictionary. The theoretical results obtained are not tied to mobile computing and are of independent interest.

The sound of silence: guessing games for saving energy in mobile environment

Despite its attractiveness, Big Data oftentimes is hard, slow and expensive to handle due to its size. Moreover, as the amount of collected data grows, individual privacy raises more and more concerns: “what do they know about me?” Different algorithms were suggested to enable privacy-preserving data release with the current de-facto standard differential privacy. However, the processing time of keeping the data private is inhibiting and currently not practical for every day use. Combined with the continuously growing data collection, the solution is not seen on a horizon.

Shlomi Dolev

Papers

Toward Provable One Way Functions.

Self-Stabilizing and Self-Organizing Virtual Infrastructures for Mobile Networks

Big Data Interpolation an Efficient Sampling Alternative for Sensor Data Aggregation

The sound of silence: guessing games for saving energy in mobile environment

Privacy via Maintaining Small Similitude Data for Big Data Statistical Representation