IEEE Transactions on Pattern Analysis and Machine Intelligence

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

IEEE transactions on computer-aided design of integrated circuits and systems : a publication of the IEEE Circuits and Systems Society

Preparing the books to read every day is enjoyable for many people. However, there are still many people who also don't like reading. This is a problem. But, when you can support others to start reading, it will be better. One of the books that can be recommended for new readers is computer aided analysis of electronic circuits algorithms and computational techniques. This book is not kind of difficult book to read. It can be read and understand by the new readers.

Computer-aided analysis of electronic circuits: Algorithms and computational techniques

The Art of Multiprocessor Programming

Most plant pentatricopeptide repeat (PPR) proteins localize and function inside plastids and mitochondria. However, the function of PPRs that only localize in the cytoplasm remains unknown. Here, we demonstrated that the rice (Oryza sativa) PPR protein CYTOPLASM-LOCALIZED PPR1 (OsCPPR1) contributes to pollen development and localizes in the cytoplasm. Knocking down OsCPPR1 transcript levels led to abnormal plastid development in tapetal cells, prolonged tapetal programmed cell death (PCD) and tapetum degradation, and significantly reduced pollen fertility. Transcriptome analysis revealed that the transcript level of OsGOLDEN-LIKE1 (OsGLK1), encoding a transcription factor that regulates plastid development and maintenance, was significantly higher in the OsCPPR1 knockdown plants compared to wild type. We further determined that OsCPPR1 downregulates OsGLK1 transcript levels by directly binding to the single-stranded regions of OsGLK1 mRNAs. Overexpression of OsGLK1 resulted in abnormal tapetum and plastid development, similar to that seen with OsCPPR1 knockdown plants, and suppression of OsGLK1 partially restored pollen fertility in the OsCPPR1 knockdown plants. Therefore, OsCPPR1 suppresses OsGLK1 in regulating plastid development and PCD in the tapetum. Our work revealed novel functions for a cytosolic PPR, demonstrating the diverse roles of PPRs in plants and identifying a new regulatory mechanism for regulating pollen development in rice.

A cytosolic pentatricopeptide repeat protein is essential for tapetal plastid development by regulating OsGLK1 transcript levels in rice.

Mixed-signal time-domain computing (TC) has recently drawn significant attention due to its high efficiency in applications such as machine learning accelerators. However, due to the nature of analog and mixed-signal design, there is a lack of a systematic flow of synthesis and place & route for time-domain circuits. This paper proposed a comprehensive design flow for TC. In the front-end, a variation-aware digital compatible synthesis flow is proposed. In the back-end, a placement technique using graph-based optimization engine is proposed to deal with the especially stringent matching requirement in TC. Simulation results show significant improvement over the prior analog placement methods. A 55nm test chip is used to demonstrate that the proposed design flow can meet the stringent timing matching target for TC with significant performance boost over conventional digital design.

Digital Compatible Synthesis, Placement and Implementation of Mixed-Signal Time-Domain Computing

model is safe, the concrete model should also be safe. However, when a counterexample is found on the abstract model, it is either feasible on the concrete model, or a spurious example due to a high abstraction level. Therefore, CEGAR further refines the abstract model by generating new predicates to rule out the current spurious example. A number of heuristics have designed to generate predicates. Unfortunately, because the specification is usually written by humans, none of the heuristics are using any knowledge in addition to the explicit model itself. As a united platform, CellScope is able to share knowledge in between its specification and verification parts. Particularly, when constructing CFAs, CellScope distinguishes protocol related variables from program control related variables. In addition, dummy adversary variables are recorded by CellScope when building message channels. An abstract model consists of these variables can largely capture the behavior of the underlying protocol yet remains small in size. Therefore, after finding the set of contradicting predicates, our model checker prioritizes more essential variables over other variables. In this way, CellScope captures the essence of the concrete model and selects new predicates more wisely. Consequently, the model checking will terminate faster. 3.2 Model Decomposition with Weakest Precondition The execution time of verification grows fast as the size of the model grows. Nevertheless, formal models are always entangled, making decomposing them into separate ones infeasible. Cellular network models, on the other hand, can be easier decomposed into protocol layers. Meanwhile, a single layer can further be divided into function modules. The interactions in between are limited to a few messages. To verify a safety property, CellScope starts from verifying the function module in which the violation to the property can occur. Then, CellScope proceed by constructing the set of weakest preconditions on the interface between modules, from which the violation can be reached. In the same way it propagates backward, until reaches the initial module. ACKNOWLEDGMENT We would like to thank the anonymous reviewers for their valuable comments.

Hai Zhou

Papers

A cytosolic pentatricopeptide repeat protein is essential for tapetal plastid development by regulating OsGLK1 transcript levels in rice.

Digital Compatible Synthesis, Placement and Implementation of Mixed-Signal Time-Domain Computing

CellScope: Automatically Specifying and Verifying Cellular Network Protocols

Lignosulfonate-Controlled Biobr/C Nano-Hollow Microsphere Photocatalyst for Efficient Removal of Tetracycline and Cr(Vi) Under Visible Light

Design and synthesis of caffeic acid derivatives and evaluation of their inhibitory activity against Pseudomonas aeruginosa