tions. Bootstrap has found many applications in engineering field, including artificial neural networks, biomedical engineering, environmental engineering, image processing, and radar and sonar signal processing. Basic concepts of the bootstrap are summarized in each section as a step-by-step algorithm for ease of implementation. Most of the applications are taken from the signal processing literature. The principles of the bootstrap are introduced in Chapter 2. Both the nonparametric and parametric bootstrap procedures are explained. Babu and Singh (1984) have demonstrated that in general, these two procedures behave similarly for pivotal (Studentized) statistics. The fact that the bootstrap is not the solution for all of the problems has been known to statistics community for a long time; however, this fact is rarely touched on in the manuscripts meant for practitioners. It was first observed by Babu (1984) that the bootstrap does not work in the infinite variance case. Bootstrap Techniques for Signal Processing explains the limitations of bootstrap method with an example. I especially liked the presentation style. The basic results are stated without proofs; however, the application of each result is presented as a simple step-by-step process, easy for nonstatisticians to follow. The bootstrap procedures, such as moving block bootstrap for dependent data, along with applications to autoregressive models and for estimation of power spectral density, are also presented in Chapter 2. Signal detection in the presence of noise is generally formulated as a testing of hypothesis problem. Chapter 3 introduces principles of bootstrap hypothesis testing. The topics are introduced with interesting real life examples. Flow charts, typical in engineering literature, are used to aid explanations of the bootstrap hypothesis testing procedures. The bootstrap leads to second-order correction due to pivoting; this improvement in the results due to pivoting is also explained. In the second part of Chapter 3, signal processing is treated as a regression problem. The performance of the bootstrap for matched filters as well as constant false-alarm rate matched filters is also illustrated. Chapters 2 and 3 focus on estimation problems. Chapter 4 introduces bootstrap methods used in model selection. Due to the inherent structure of the subject matter, this chapter may be difficult for nonstatisticians to follow. Chapter 5 is the most impressive chapter in the book, especially from the standpoint of statisticians. It provides real data bootstrap applications to illustrate the theory covered in the earlier chapters. These include applications to optimal sensor placement for knock detection and land-mine detection. The authors also provide a MATLAB toolbox comprising frequently used routines. Overall, this is a very useful handbook for engineers, especially those working in signal processing.

Probability and Random Processes

Transactional Memory (TM) is emerging as a promising technology to simplify parallel programming. While several TM systems have been proposed in the research literature, we are still missing the tools and workloads necessary to analyze and compare the proposals. Most TM systems have been evaluated using microbenchmarks, which may not be representative of any real-world behavior, or individual applications, which do not stress a wide range of execution scenarios. We introduce the Stanford Transactional Application for Multi-Processing (STAMP), a comprehensive benchmark suite for evaluating TM systems. STAMP includes eight applications and thirty variants of input parameters and data sets in order to represent several application domains and cover a wide range of transactional execution cases (frequent or rare use of transactions, large or small transactions, high or low contention, etc.). Moreover, STAMP is portable across many types of TM systems, including hardware, software, and hybrid systems. In this paper, we provide descriptions and a detailed characterization of the applications in STAMP. We also use the suite to evaluate six different TM systems, identify their shortcomings, and motivate further research on their performance characteristics.

/pdf/stamp-stanford-transactional-applications-for-multi-5ascbp3x5h.pdf

STAMP: Stanford Transactional Applications for Multi-Processing

Persistent, user-defined objects present an attractive abstraction for working with non-volatile program state. However, the slow speed of persistent storage (i.e., disk) has restricted their design and limited their performance. Fast, byte-addressable, non-volatile technologies, such as phase change memory, will remove this constraint and allow programmers to build high-performance, persistent data structures in non-volatile storage that is almost as fast as DRAM. Creating these data structures requires a system that is lightweight enough to expose the performance of the underlying memories but also ensures safety in the presence of application and system failures by avoiding familiar bugs such as dangling pointers, multiple free()s, and locking errors. In addition, the system must prevent new types of hard-to-find pointer safety bugs that only arise with persistent objects. These bugs are especially dangerous since any corruption they cause will be permanent.We have implemented a lightweight, high-performance persistent object system called NV-heaps that provides transactional semantics while preventing these errors and providing a model for persistence that is easy to use and reason about. We implement search trees, hash tables, sparse graphs, and arrays using NV-heaps, BerkeleyDB, and Stasis. Our results show that NV-heap performance scales with thread count and that data structures implemented using NV-heaps out-perform BerkeleyDB and Stasis implementations by 32x and 244x, respectively, by avoiding the operating system and minimizing other software overheads. We also quantify the cost of enforcing the safety guarantees that NV-heaps provide and measure the costs of NV-heap primitive operations.

/pdf/nv-heaps-making-persistent-objects-fast-and-safe-with-next-4av60ommhh.pdf

NV-Heaps: making persistent objects fast and safe with next-generation, non-volatile memories

Biological organisms use complex molecular networks to navigate their environment and regulate their internal state. The development of synthetic systems with similar capabilities could lead to applications such as smart therapeutics or fabrication methods based on self-organization. To achieve this, molecular control circuits need to be engineered to perform integrated sensing, computation and actuation. Here we report a DNA-based technology for implementing the computational core of such controllers. We use the formalism of chemical reaction networks as a 'programming language' and our DNA architecture can, in principle, implement any behaviour that can be mathematically expressed as such. Unlike logic circuits, our formulation naturally allows complex signal processing of intrinsically analogue biological and chemical inputs. Controller components can be derived from biologically synthesized (plasmid) DNA, which reduces errors associated with chemically synthesized DNA. We implement several building-block reaction types and then combine them into a network that realizes, at the molecular level, an algorithm used in distributed control systems for achieving consensus between multiple agents.

/pdf/programmable-chemical-controllers-made-from-dna-2u5cq3iiqs.pdf

Programmable chemical controllers made from DNA

Transactional memory (TM) is perceived as an appealing alternative to critical sections for general purpose concurrent programming. Despite the large amount of recent work on TM implementations, however, very little effort has been devoted to precisely defining what guarantees these implementations should provide. A formal description of such guarantees is necessary in order to check the correctness of TM systems, as well as to establish TM optimality results and inherent trade-offs.This paper presents opacity, a candidate correctness criterion for TM implementations. We define opacity as a property of concurrent transaction histories and give its graph theoretical interpretation. Opacity captures precisely the correctness requirements that have been intuitively described by many TM designers. Most TM systems we know of do ensure opacity.At a very first approximation, opacity can be viewed as an extension of the classical database serializability property with the additional requirement that even non-committed transactions are prevented from accessing inconsistent states. Capturing this requirement precisely, in the context of general objects, and without precluding pragmatic strategies that are often used by modern TM implementations, such as versioning, invisible reads, lazy updates, and open nesting, is not trivial.As a use case of opacity, we prove the first lower bound on the complexity of TM implementations. Basically, we show that every single-version TM system that uses invisible reads and does not abort non-conflicting transactions requires, in the worst case, ?(k) steps for an operation to terminate, where k is the total number of objects shared by transactions. This (tight) bound precisely captures an inherent trade-off in the design of TM systems. The bound also highlights a fundamental gap between systems in which transactions can be fully isolated from the outside environment, e.g., databases or certain specialized transactional languages, and systems that lack such isolation capabilities, e.g., general TM frameworks.

/pdf/on-the-correctness-of-transactional-memory-4niuas72dr.pdf

On the correctness of transactional memory

We describe and analyze a 3-state one-way population protocol to compute approximate majority in the model in which pairs of agents are drawn uniformly at random to interact. Given an initial configuration of x’s, y’s and blanks that contains at least one non-blank, the goal is for the agents to reach consensus on one of the values x or y. Additionally, the value chosen should be the majority non-blank initial value, provided it exceeds the minority by a sufficient margin. We prove that with high probability n agents reach consensus in O(n log n) interactions and the value chosen is the majority provided that its initial margin is at least $${\omega(\sqrt{n} \,{\rm log}\, n)}$$
 . This protocol has the additional property of tolerating Byzantine behavior in $${o(\sqrt{n})}$$
 of the agents, making it the first known population protocol that tolerates Byzantine agents.

/pdf/a-simple-population-protocol-for-fast-robust-approximate-3652eowe15.pdf

A simple population protocol for fast robust approximate majority

Fast algorithms are presented for performing computations in a probabilistic population model. This is a variant of the standard population protocol model-in which finite-state agents interact in pairs under the control of an adversary scheduler-where all pairs are equally likely to be chosen for each interaction. It is shown that when a unique leader agent is provided in the initial population, the population can simulate a virtual register machine in which standard arithmetic operations like comparison, addition, subtraction, and multiplication and division by constants can be simulated in O(n log 4 n) interactions with high probability. Applications include a reduction of the cost of computing a semilinear predicate to O(n log 4 n) interactions from the previously best-known bound of O(n 2 log n) interactions and simulation of a LOGSPACE Turing machine using the same O(n log 4 n) interactions per step. These bounds on interactions translate into O(log 4 n) time per step in a natural parallel model in which each agent participates in an expected Θ(1) interactions per time unit. The central method is the extensive use of epidemics to propagate information from and to the leader, combined with an epidemic-based phase clock used to detect when these epidemics are likely to be complete.

Fast computation by population protocols with a leader

Fast algorithms are presented for performing computations in a probabilistic population model. This is a variant of the standard population protocol model, in which finite-state agents interact in pairs under the control of an adversary scheduler, where all pairs are equally likely to be chosen for each interaction. It is shown that when a unique leader agent is provided in the initial population, the population can simulate a virtual register machine with high probability in which standard arithmetic operations like comparison, addition, subtraction, and multiplication and division by constants can be simulated in O(n log5
 n) interactions using a simple register representation or in O(n log2
 n) interactions using a more sophisticated representation that requires an extra O(n log
 O(1)
 n)-interaction initialization step. The central method is the extensive use of epidemics to propagate information from and to the leader, combined with an epidemic-based phase clock used to detect when these epidemics are likely to be complete. Applications include a reduction of the cost of computing a semilinear predicate to O(n log5
 n) interactions from the previously best-known bound of O(n
 2 log n) interactions and simulation of a LOGSPACE Turing machine using O(n log2
 n) interactions per step after an initial O(n log
 O(1)
 n)-interaction startup phase. These bounds on interactions translate into polylogarithmic time per step in a natural parallel model in which each agent participates in an expected Θ(1) interactions per time unit. Open problems are discussed, together with simulation results that suggest the possibility of removing the initial-leader assumption.

/pdf/fast-computation-by-population-protocols-with-a-leader-3bqg85790a.pdf

Recent years have seen the development of several dierent systems for software transactional memory (STM). Most either employ locks in the underlying implementation or depend on thread-safe general-purpose garbage collection to collect stale data and metadata. We consider the design of low-overhead, obstruction-free software transactional memory for non-garbage-collected languages. Our design eliminates dynamic allocation of transactional metadata and co-locates data that are separate in other systems, thereby reducing the expected number of cache misses on the common-case code path, while preserving nonblocking progress and requiring no atomic instructions other than single-word load, store, and compare-and-swap (or load-linked/store-conditional). We also employ a simple, epoch-based storage management system and introduce a novel conservative mechanism to make reader transactions visible to writers without inducing additional metadata copying or dynamic allocation. Experimental results show throughput significantly higher than that of existing nonblocking STM systems, and highlight significant, application-specific dierences among conflict detection and validation strategies.

/pdf/lowering-the-overhead-of-nonblocking-software-transactional-52b9nazxx1.pdf

Lowering the Overhead of Nonblocking Software Transactional Memory

We consider the model of population protocols introduced by Angluin et al. [2], in which anonymous finite-state agents stably compute a predicate of their inputs via two-way interactions in the all-pairs family of communication networks. We prove that all predicates stably computable in this model (and certain generalizations of it) are semilinear, answering a central open question about the power of the model.

/pdf/stably-computable-predicates-are-semilinear-3j35q5tkw5.pdf

David Eisenstat

Papers

A simple population protocol for fast robust approximate majority

Fast computation by population protocols with a leader

Fast computation by population protocols with a leader

Lowering the Overhead of Nonblocking Software Transactional Memory

Stably computable predicates are semilinear