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

다중혈관 관상동맥 환자에서 y-문합을 이용하여 양쪽 내흉동맥만을 사용한 우회술의 조기 성적

THE DESIGN AND ANALYSIS OF EXPERIMENTS. By Oscar Kempthorne. New York, John Wiley and Sons, Inc., 1952. 631 pp. $8.50. This book by a teacher of statistics (as well as a consultant for \"experimenters\") is a comprehensive study of the philosophical background for the statistical design of experiment. It is necessary to have some facility with algebraic notation and manipulation to be able to use the volume intelligently. The problems are presented from the theoretical point of view, without such practical examples as would be helpful for those not acquainted with mathematics. The mathematical justification for the techniques is given. As a somewhat advanced treatment of the design and analysis of experiments, this volume will be interesting and helpful for many who approach statistics theoretically as well as practically. With emphasis on the \"why,\" and with description given broadly, the author relates the subject matter to the general theory of statistics and to the general problem of experimental inference. MARGARET J. ROBERTSON

The Design and Analysis of Experiments

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

Data Mining - Concepts and Techniques.

The complexity of today’s ICs and shrinking process technologies have made design features more probabilistic. Thus, it is necessary to perform statistical timing analysis when evaluating path lengths and considering process variations. Statistical static timing analysis (SSTA) methods were proposed and SSTA tools were developed to deal with these issues [1, 14]. However, these methods are pattern-independent, i.e., they estimate path length using the delay of components on the path without considering the pattern-dependent parameters. Note that power supply noise and crosstalk are pattern-dependent effects and they can significantly impact the delay of the components on a path.

Process Variations- and Crosstalk-Aware Pattern Selection

Error-Tolerant Digital Microfluidic Lab-on-Chip

Graph neural networks (GNNs) are becoming popular in various real-world applications. However, hardware-level security is a concern when GNN models are mapped to emerging neuromorphic technologies such as memristor-based crossbars. These security issues can lead to malfunction of memristor-mapped GNNs. We identify a vulnerability of memristor-mapped GNNs and propose an attack mechanism based on the identified vulnerability. The proposed attack tampers memristor-mapped graph-structured data of a GNN by injecting adversarial edges to the graph and inducing slow-to-write errors in crossbars. We show that 10% adversarial edge injection induces 1.11× longer write latency, eventually leading to a 44.33% error in node classification. Experimental results for the proposed attack also show that there is a 5.72× increase in the success rate compared to a software-based baseline.

Attacking Memristor-Mapped Graph Neural Network by Inducing Slow-to-Write Errors

Electric drive using dc shunt motor or permanent magnet dc (PMDC) motor as prime mover exhibits bifurcation and chaos. The characteristics of dc shunt and PMDC motors are linear in nature. These motors are controlled by pulse width modulation (PWM) technique with the help of semiconductor switches. These switches are nonlinear element that introduces nonlinear characteristics in the drive. Any nonlinear system can exhibit bifurcation and chaos. dc shunt or PMDC drives show normal behavior with certain range of parameter values. It is also observed that these drive show chaos for significantly large ranges of parameter values. In this paper we present a method for controlling chaos applicable to dc shunt and PMDC drives. The results of numerical investigation are presented.

Stabilization of unstable periodic orbits in dc drives

Research on productivity improvement is gaining accelerated attention in enterprise industry in recent years. One notable example is the LDP solution by Xerox [1]. LDP is Xerox’s simulation-based service solution offered through Xerox Managed Services (XMS) that helps to enhance print shop productivity. There are distinctions between our and their simulation-based approaches. In our approach, we modeled the print production as a content-driven cyber-physical system [2]. Anticipating that the pervasive sensing and computing-based knowledge discovery will enable additional design space and flexibility, we have tested our optimization and knowledge discovery components on the simulation platform.

Krishnendu Chakrabarty

Papers

Process Variations- and Crosstalk-Aware Pattern Selection

Error-Tolerant Digital Microfluidic Lab-on-Chip

Attacking Memristor-Mapped Graph Neural Network by Inducing Slow-to-Write Errors

Stabilization of unstable periodic orbits in dc drives

Analysis and Prediction of Enterprise Service-Level Performance