The Freezing of Random RNA

We study secondary structures of random RNA molecules by means of a renormalized field theory based on an expansion in the sequence disorder. We show that there is a continuous phase transition from a molten phase at higher temperatures to a low-temperature glass phase. The primary freezing occurs above the critical temperature, with local islands of stable folds forming within the molten phase. The size of these islands defines the correlation length of the transition. Our results include critical exponents at the transition and in the glass phase.Comment: 4 pages, 8 figures. v2: presentation improve

Common Due-Date Problem: Exact Polynomial Algorithms for a Given Job Sequence

This paper considers the problem of scheduling jobs on single and parallel machines where all the jobs possess different processing times but a common due date. There is a penalty involved with each job if it is processed earlier or later than the due date. The objective of the problem is to find the assignment of jobs to machines, the processing sequence of jobs and the time at which they are processed, which minimizes the total penalty incurred due to tardiness or earliness of the jobs. This work presents exact polynomial algorithms for optimizing a given job sequence or single and parallel machines with the run-time complexities of $O(n \log n)$ and $O(mn^2 \log n)$ respectively, where $n$ is the number of jobs and $m$ the number of machines. The algorithms take a sequence consisting of all the jobs $(J_i, i=1,2,\dots,n)$ as input and distribute the jobs to machines (for $m>1$) along with their best completion times so as to get the least possible total penalty for this sequence. We prove the optimality for the single machine case and the runtime complexities of both. Henceforth, we present the results for the benchmark instances and compare with previous work for single and parallel machine cases, up to $200$ jobs.Comment: 15th International Symposium on Symbolic and Numeric Algorithms for Scientific Computin

A CRITICAL ASSESSMENT OF THE IMPORTANCE OF SEEDLING AGE IN THE SYSTEM OF RICE INTENSIFICATION (SRI) IN EASTERN INDIA

A survey of the system of rice intensification (SRI)-related literature indicates that different authors have drawn conflicting inferences about rice yield performances under the SRI, chiefly because the SRI methodology has been variously advocated, interpreted and implemented in the field using different rice varieties, seedling ages at transplantation, cultivation seasons and nutrient management regimes. In particular, the SRI method of single-seedling transplantation (SST) has potential economic advantage due to reduced seed costs, but it is not clear whether SST is an effective management strategy across a range of seedling ages, and whether there is any specific seedling age that is optimal for yield improvement of a given rice variety. This is an important consideration in rain-fed ecosystems where variable rainfall patterns and lack of controlled irrigation make it difficult to reliably transplant at a specific seedling age as recommended for the SRI. We conducted a five year-long experiment on a rain-fed organic farm using a short-duration upland and a medium-duration lowland landrace, following the SRI methodology. Rice seedlings of different ages (6, 10, 14, 18 and 28 days after establishment) were transplanted at 25 cm × 25 cm spacing in three replicated plots. The performance for each landrace was examined with respect to productive tillers, panicle density, total grain counts per hill and grain yield per unit area. Performances of seedlings of different ages were compared with that of control plots that employed all SRI practices with the exception that 28-day-old seedlings were transplanted with three seedlings per hill. The results indicate that (1) the SRI can improve mean panicle density if seedling age ≤ 18 days, but that responses differ between varieties; (2) the number of productive tillers per hill is significantly less in SST than that of multiple seedling transplants (MST) of 28-day-old seedlings of both upland and lowland varieties; (3) the total grain numbers per hill of the lowland variety is significantly greater for 14-day-old SST than 28-day-old MST; (4) the grain yield per unit area from young SRI transplants is significantly greater than that from 28-day-old MST for the lowland variety, although the magnitude of the improvement was small; (5) for the upland variety, grain yields declined with the oldest seedlings, but planting multiple seedlings per hill made the yield of the oldest transplants on par with that of younger seedlings planted singly. Our findings suggest that transplanting younger seedlings under the SRI management may not necessarily enhance grain yield

A critical assessment of the importance of seedling age in the system of rice intensification (sri) in Eastern India

Dieser Beitrag ist mit Zustimmung des Rechteinhabers aufgrund einer (DFG geförderten) Allianz- bzw. Nationallizenz frei zugänglich.This publication is with permission of the rights owner freely accessible due to an Alliance licence and a national licence (funded by the DFG, German Research Foundation) respectively.A survey of the system of rice intensification (SRI)-related literature indicates that different authors have drawn conflicting inferences about rice yield performances under the SRI, chiefly because the SRI methodology has been variously advocated, interpreted and implemented in the field using different rice varieties, seedling ages at transplantation, cultivation seasons and nutrient management regimes. In particular, the SRI method of single-seedling transplantation (SST) has potential economic advantage due to reduced seed costs, but it is not clear whether SST is an effective management strategy across a range of seedling ages, and whether there is any specific seedling age that is optimal for yield improvement of a given rice variety. This is an important consideration in rain-fed ecosystems where variable rainfall patterns and lack of controlled irrigation make it difficult to reliably transplant at a specific seedling age as recommended for the SRI. We conducted a five year-long experiment on a rain-fed organic farm using a short-duration upland and a medium-duration lowland landrace, following the SRI methodology. Rice seedlings of different ages (6, 10, 14, 18 and 28 days after establishment) were transplanted at 25 cm × 25 cm spacing in three replicated plots. The performance for each landrace was examined with respect to productive tillers, panicle density, total grain counts per hill and grain yield per unit area. Performances of seedlings of different ages were compared with that of control plots that employed all SRI practices with the exception that 28-day-old seedlings were transplanted with three seedlings per hill. The results indicate that (1) the SRI can improve mean panicle density if seedling age ≤ 18 days, but that responses differ between varieties; (2) the number of productive tillers per hill is significantly less in SST than that of multiple seedling transplants (MST) of 28-day-old seedlings of both upland and lowland varieties; (3) the total grain numbers per hill of the lowland variety is significantly greater for 14-day-old SST than 28-day-old MST; (4) the grain yield per unit area from young SRI transplants is significantly greater than that from 28-day-old MST for the lowland variety, although the magnitude of the improvement was small; (5) for the upland variety, grain yields declined with the oldest seedlings, but planting multiple seedlings per hill made the yield of the oldest transplants on par with that of younger seedlings planted singly. Our findings suggest that transplanting younger seedlings under the SRI management may not necessarily enhance grain yields

Frequency Fitness Assignment: Optimization without Bias for Good Solutions can be Efficient

A fitness assignment process transforms the features (such as the objective value) of a candidate solution to a scalar fitness, which then is the basis for selection. Under Frequency Fitness Assignment (FFA), the fitness corresponding to an objective value is its encounter frequency in selection steps and is subject to minimization. FFA creates algorithms that are not biased towards better solutions and are invariant under all injective transformations of the objective function value. We investigate the impact of FFA on the performance of two theory-inspired, state-of-the-art EAs, the Greedy (2+1) GA and the Self-Adjusting (1+(lambda,lambda)) GA. FFA improves their performance significantly on some problems that are hard for them. In our experiments, one FFA-based algorithm exhibited mean runtimes that appear to be polynomial on the theory-based benchmark problems in our study, including traps, jumps, and plateaus. We propose two hybrid approaches that use both direct and FFA-based optimization and find that they perform well. All FFA-based algorithms also perform better on satisfiability problems than any of the pure algorithm variants

Power prediction in smart grids with evolutionary local kernel regression

Abstract. Electric grids are moving from a centralized single supply chain towards a decentralized bidirectional grid of suppliers and consumers in an uncertain and dynamic scenario. Soon, the growing smart meter infrastructure will allow the collection of terabytes of detailed data about the grid condition, e.g., the state of renewable electric energy producers or the power consumption of millions of private customers, in very short time steps. For reliable prediction strong and fast regression methods are necessary that are able to cope with these challenges. In this paper we introduce a novel regression technique, i.e., evolutionary local kernel regression, a kernel regression variant based on local Nadaraya-Watson estimators with independent bandwidths distributed in data space. The model is regularized with the CMA-ES, a stochastic non-convex optimization method. We experimentally analyze the load forecast behavior on real power consumption data. The proposed method is easily parallelizable, and therefore well appropriate for large-scale scenarios in smart grids