BOCK : Bayesian Optimization with Cylindrical Kernels

A major challenge in Bayesian Optimization is the boundary issue (Swersky, 2017) where an algorithm spends too many evaluations near the boundary of its search space. In this paper, we propose BOCK, Bayesian Optimization with Cylindrical Kernels, whose basic idea is to transform the ball geometry of the search space using a cylindrical transformation. Because of the transformed geometry, the Gaussian Process-based surrogate model spends less budget searching near the boundary, while concentrating its efforts relatively more near the center of the search region, where we expect the solution to be located. We evaluate BOCK extensively, showing that it is not only more accurate and efficient, but it also scales successfully to problems with a dimensionality as high as 500. We show that the better accuracy and scalability of BOCK even allows optimizing modestly sized neural network layers, as well as neural network hyperparameters.Comment: 10 pages, 5 figures, 5 tables, 1 algorith

A linear programming based heuristic framework for min-max regret combinatorial optimization problems with interval costs

This work deals with a class of problems under interval data uncertainty, namely interval robust-hard problems, composed of interval data min-max regret generalizations of classical NP-hard combinatorial problems modeled as 0-1 integer linear programming problems. These problems are more challenging than other interval data min-max regret problems, as solely computing the cost of any feasible solution requires solving an instance of an NP-hard problem. The state-of-the-art exact algorithms in the literature are based on the generation of a possibly exponential number of cuts. As each cut separation involves the resolution of an NP-hard classical optimization problem, the size of the instances that can be solved efficiently is relatively small. To smooth this issue, we present a modeling technique for interval robust-hard problems in the context of a heuristic framework. The heuristic obtains feasible solutions by exploring dual information of a linearly relaxed model associated with the classical optimization problem counterpart. Computational experiments for interval data min-max regret versions of the restricted shortest path problem and the set covering problem show that our heuristic is able to find optimal or near-optimal solutions and also improves the primal bounds obtained by a state-of-the-art exact algorithm and a 2-approximation procedure for interval data min-max regret problems

Synchronization in complex networks

Synchronization processes in populations of locally interacting elements are in the focus of intense research in physical, biological, chemical, technological and social systems. The many efforts devoted to understand synchronization phenomena in natural systems take now advantage of the recent theory of complex networks. In this review, we report the advances in the comprehension of synchronization phenomena when oscillating elements are constrained to interact in a complex network topology. We also overview the new emergent features coming out from the interplay between the structure and the function of the underlying pattern of connections. Extensive numerical work as well as analytical approaches to the problem are presented. Finally, we review several applications of synchronization in complex networks to different disciplines: biological systems and neuroscience, engineering and computer science, and economy and social sciences.Comment: Final version published in Physics Reports. More information available at http://synchronets.googlepages.com

Synthesis Methodologies for Robust and Reconfigurable Clock Networks

In today\u27s aggressively scaled technology nodes, billions of transistors are packaged into a single integrated circuit. Electronic Design Automation (EDA) tools are needed to automatically assemble the transistors into a functioning system. One of the most important design steps in the physical synthesis is the design of the clock network. The clock network delivers a synchronizing clock signal to each sequential element. The clock signal is required to be delivered meeting timing constraints under variations and in multiple operating modes. Synthesizing such clock networks is becoming increasingly difficult with the complex power management methodologies and severe manufacturing variations. Clock network synthesis is an important problem because it has a direct impact on the functional correctness, the maximum operating frequency, and the overall power consumption of each synchronous integrated circuit. In this dissertation, we proposed synthesis methodologies for robust and reconfigurable clock networks. We have made three contributions to this topic. First, we have proposed a clock network optimization framework that can achieve better timing quality than previous frameworks. Our proposed framework improves timing quality by reducing the propagation delay on critical paths in a clock network using buffer sizing and layer assignment. Second, we have proposed a clock tree synthesis methodology that integrates the clock tree synthesis with the clock tree optimization. The methodology improves timing quality by avoiding to synthesize clock trees with topologies that are sensitive to variations. Third, we have proposed a clock network that can reconfigure the topology based on the active mode of operation. Lastly, we conclude the dissertation with future research directions

Synthesis of Clock Trees with Useful Skew based on Sparse-Graph Algorithms

Computer-aided design (CAD) for very large scale integration (VLSI) involve

Machine Learning in Wireless Sensor Networks: Algorithms, Strategies, and Applications

Wireless sensor networks monitor dynamic environments that change rapidly over time. This dynamic behavior is either caused by external factors or initiated by the system designers themselves. To adapt to such conditions, sensor networks often adopt machine learning techniques to eliminate the need for unnecessary redesign. Machine learning also inspires many practical solutions that maximize resource utilization and prolong the lifespan of the network. In this paper, we present an extensive literature review over the period 2002-2013 of machine learning methods that were used to address common issues in wireless sensor networks (WSNs). The advantages and disadvantages of each proposed algorithm are evaluated against the corresponding problem. We also provide a comparative guide to aid WSN designers in developing suitable machine learning solutions for their specific application challenges.Comment: Accepted for publication in IEEE Communications Surveys and Tutorial

Wireless sensors networks

After studying in depth look at wireless sensor networks are quite clear improvement compared to traditional wireless networks due to several factors as are the durability of the lifetime of the batteries, allowing greater portability of sensor nodes and that can record more events to power stay longer in some places, the routing protocols networks sensors allow gain than in durability also gain in efficiency the avoidance of collisions between packets, which also ensures a lower number of unnecessary network traffic. Because of the great features of such networks are currently using sensor networks in many projects related to different fields such as: environment, health, military, construction and structures, automotive, home automation, agriculture, etc. This type of network currently is leading a technological revolution similar to that had appearance of internet, because the applications appear to be infinite, also speaks global surveillance network on the planet capable of recording and tracking people specific goods and research projects have generated great interest for application in practice