Solving the irregular strip packing problem via guided local search for overlap minimization

The irregular strip-packing problem (ISP) requires a given set of non-convex polygons to be placed without overlap within a rectangular container having a fixed width and a variable length, which is to be minimized. As a core sub-problem to solve ISP, we consider an overlap minimization problem (OMP) whose objective is to place all polygons into a container with given width and length so that the total amount of overlap between polygons is made as small as possible. We propose to use directional penetration depths to measure the amount of overlap between a pair of polygons, and present an efficient algorithm to find a position with the minimum overlap for each polygon when it is translated in a specified direction. Based on this, we develop a local search algorithm for OMP that translates a polygon in horizontal and vertical directions alternately. Then we incorporate it in our algorithm for OMP, which is a variant of the guided local search algorithm. Computational results show that our algorithm improves the best-known values of some well-known benchmark instances.     

Resistive random access memory utilizing ferritin protein with Pt nanoparticles

This study reports controlled single conductive paths found in resistive random access memory (ReRAM) formed by embedding Pt nanoparticles (Pt NPs) in NiO film. Homogeneous Pt NPs produced and placed by ferritin protein produce electric field convergence which leads to controlled conductive path formation. The ReRAM with Pt NPs shows stable switching behavior. A Pt NP density decrease results in an increase of OFF state resistance and decrease of forming voltage, whereas ON resistance was independent of the Pt NP density, which indicates that a single metal NP in a memory cell will achieve low power and stable operation.     

Finding Essential Attributes from Binary Data

We consider data sets that consist of n-dimensional binary vectors representing positive and negative examples for some (possibly unknown) phenomenon. A subset S of the attributes (or variables) of such a data set is called a support set if the positive and negative examples can be distinguished by using only the attributes in S. In this paper we study the problem of finding small support sets, a frequently arising task in various fields, including knowledge discovery, data mining, learning theory, logical analysis of data, etc. We study the distribution of support sets in randomly generated data, and discuss why finding small support sets is important. We propose several measures of separation (real valued set functions over the subsets of attributes), formulate optimization models for finding the smallest subsets maximizing these measures, and devise efficient heuristic algorithms to solve these (typically NP-hard) optimization problems. We prove that several of the proposed heuristics have a guaranteed constant approximation ratio, and we report on computational experience comparing these heuristics with some others from the literature both on randomly generated and on real world data sets.     

Compiling finite linear CSP into SAT

In this paper, we propose a new method to encode Constraint Satisfaction Problems (CSP) and Constraint Optimization Problems (COP) with integer linear constraints into Boolean Satisfiability Testing Problems (SAT). The encoding method (named order encoding) is basically the same as the one used to encode Job-Shop Scheduling Problems by Crawford and Baker. Comparison x ≤ a is encoded by a different Boolean variable for each integer variable x and integer value a. To evaluate the effectiveness of this approach, we applied the method to the Open-Shop Scheduling Problems (OSS). All 192 instances in three OSS benchmark sets are examined, and our program found and proved the optimal results for all instances including three previously undecided problems.     

Translating a Linear Logic Programming Language into Java

There have been several proposals for logic programming language based on linear logic: Lolli [8], Lygon [7], LO [3], LinLog [2], Forum [11], HACL [10]. In these languages, it is possible to create and consume resources dynamically as logical formulas. The efficient handling of resource formulas is, therefore, an important issue in the implementation of these languages. Lolli, Lygon, and Forum are implemented as interpreter systems; Lolli is on SML and λProlog, Lygon is on Prolog, Forum is on SML, λProlog and Prolog. However, none of them have been implemented in Java.In this paper, we describe the Prolog Café ¹ system which translates a linear logic programming language called LLP to Java via the LLPAM [12] [5], an extension of the standard WAM [16] [1] for LLP. LLP is a superset of Prolog and a subset of Lolli. The main difference from the first implementation [4] is resource compilation. That is to say, resource formulas are compiled into closures which consist of a reference of compiled code and a set of bindings for free variables. Calling these resources is integrated with the ordinary predicate invocation.Prolog Café is portable to any platform supporting Java and easily expandable with increasing Java's class libraries. In performance, on average, Prolog Café generate 2.2 times faster code for a set of classical Prolog benchmarks compared with jProlog.