Local search: A guide for the information retrieval practitioner

There are a number of combinatorial optimisation problems in information retrieval in which the use of local search methods are worthwhile. The purpose of this paper is to show how local search can be used to solve some well known tasks in information retrieval (IR), how previous research in the field is piecemeal, bereft of a structure and methodologically flawed, and to suggest more rigorous ways of applying local search methods to solve IR problems. We provide a query based taxonomy for analysing the use of local search in IR tasks and an overview of issues such as fitness functions, statistical significance and test collections when conducting experiments on combinatorial optimisation problems. The paper gives a guide on the pitfalls and problems for IR practitioners who wish to use local search to solve their research issues, and gives practical advice on the use of such methods. The query based taxonomy is a novel structure which can be used by the IR practitioner in order to examine the use of local search in IR

Computational models for inferring biochemical networks

Biochemical networks are of great practical importance. The interaction of biological compounds in cells has been enforced to a proper understanding by the numerous bioinformatics projects, which contributed to a vast amount of biological information. The construction of biochemical systems (systems of chemical reactions), which include both topology and kinetic constants of the chemical reactions, is NP-hard and is a well-studied system biology problem. In this paper, we propose a hybrid architecture, which combines genetic programming and simulated annealing in order to generate and optimize both the topology (the network) and the reaction rates of a biochemical system. Simulations and analysis of an artificial model and three real models (two models and the noisy version of one of them) show promising results for the proposed method.The Romanian National Authority for Scientific Research, CNDI–UEFISCDI, Project No. PN-II-PT-PCCA-2011-3.2-0917

Computational Analysis and Design Optimization of Convective PCR Devices

Polymerase Chain Reaction (PCR) is a relatively novel technique to amplify a few copies of DNA to a detectable level. PCR has already become common in biomedical research, criminal forensics, molecular archaeology, and so on. Many have attempted to develop PCR devices in numerous types for the purpose of the lab-on-chip (LOC) or point-of-care (POC). To use PCR devices for POC lab testing, the price must be lower, and the performance should be comparable to the lab devices. For current practices with the existing methods, the price is pushed up higher partially due to too much dependence on numerous developmental experiments. Our proposition herein is that the computational methods can make it possible to design the device at lower cost and less time, and even improved performance. In the present dissertation, a convective PCR, that is the required flow circulation is driven by the buoyancy forces, is researched towards the use in POC testing. Computational Fluid Dynamics (CFD) is employed to solve the nonlinear equations for the conjugate momentum and heat transfer model and the species transport model. The first application of the models considers four reactors in contact with two separate heaters, but with different heights. Computational analyses are carried out to study the nature of buoyancy-driven flow for DNA amplification and the effect of the capillary heights on the performance. The reactor performance is quantified by the doubling time of DNA and the results are experimentally verified. The second application includes a novel design wherein a reactor is heated up by a single heater. A process is established for low-developmental cost and high-performance design. The best is searched for and found by evaluating the performance for all possible candidates. The third application focuses on the analysis of the performance of single-heater reactors affected by positions of a capillary tube: (1) horizontal, and (2) vertical. In the last application, numerous double-heater reactor designs are considered to find the one that assure the optimal performance. Artificial Neural Network (ANN) is employed to approximate the CFD results for optimization. In summary, through the four segments of our studies, the results show significant possibilities of increasing the performance and reducing the developmental cost and time. It is also demonstrated that the proposed methodology is advantageous for the development of cPCR reactors for the purpose of POC applications

Genetic algorithms for designing digital filters

This thesis presents a method of adapting IIR filters implemented as lattice structures using a Genetic Algorithm (GA), called ZGA. This method addresses some of the difficulties encountered with existing methods of adaptation, providing guaranteed filter stability and the ability to search multi-modal error surfaces. ZGA mainly focuses on convergence improvement in respects of crossover and mutation operators. Four kinds of crossover methods are used to scan as much as possible the potential solution area, only the best of them will be taken as ZGA crossover offspring. And ZGA mutation takes the best of three mutation results as final mutation offspring. Simulation results are presented, demonstrating the suitability of ZGA to the problem of IIR system identification and comparing with the results of Standard GA, Genitor and NGA

A Convergent and Dimension-Independent First-Order Algorithm for Min-Max Optimization

Motivated by the recent work of Mangoubi and Vishnoi (STOC 2021), we propose a variant of the min-max optimization framework where the max-player is constrained to update the maximization variable in a greedy manner until it reaches a *first-order* stationary point. We present an algorithm that provably converges to an approximate local equilibrium for our framework from any initialization and for nonconvex-nonconcave loss functions. Compared to the second-order algorithm of Mangoubi and Vishnoi, whose iteration bound is polynomial in the dimension, our algorithm is first-order and its iteration bound is independent of dimension. We empirically evaluate our algorithm on challenging nonconvex-nonconcave test-functions and loss functions that arise in GAN training. Our algorithm converges on these test functions and, when used to train GANs on synthetic and real-world datasets, trains stably and avoids mode collapse