The volume of ads ranked impacts the performance of ad ranking systems.To enhance efficiency, multi-stage ranking systems are widely studied in academia and adopted across industry.However, as large-scale deep learning recommendation models gain prevalence, resource constraints-especially CPU and GPU limitationshave become a significant bottleneck.These constraints can hinder model iteration and lead to incomplete ranking, causing regressions in user experience and ad performance.To address these issues, we analyzed ad ranking metrics and found that ad rankings for individual users remain relatively stable over short periods.Based on this insight, we introduce Negative Exclusion Filtering, a framework that optimizes the balance between ranked ad volume and computing resources.By skipping re-ranking of consistently low-ranked ads for each user request, it reduces computing cost in large-scale social media environments.

Coverage is a fundamental issue in the Solar Insecticidal Lamps IoTs (SIL-IoTs). Compared to complete coverage, partial coverage emerges as the preferred strategy for deploying SILs within a limited budget, as this deployment solution offers the highest cost-effectiveness. In this paper, we concentrate on studying the constrained SILs deployment problem, taking into account partial coverage and energy harvesting requirements, which we refer to as the cSILDP-PCEH problem. In this context, the positions for deploying SILs are restricted to a weighted set of candidate locations on the ridges. The weight assigned to each candidate location reflects the energy harvesting potential of the SIL deployed at that position. Our objective is to deploy a group of SILs in a subset of these candidate locations, ensuring a high overall energy harvesting potential, network connectivity, and achieving partial coverage. Due to the NP-hard nature of the problem, we introduce an approximation algorithm with a provable performance ratio tailored to our problem. Finally, we conduct a theoretical analysis of our proposed algorithm and perform extensive simulations. The simulation results demonstrate that the proposed algorithm achieves a minimum improvement of 16.45% in energy harvesting potential while preserving network connectivity and maintaining a comparable coverage level.

The max–min ant system (MMAS) is a modified ant colony optimization (ACO) algorithm. Its convergence speed is effectively improved by setting the upper and lower bounds of the pheromone and updating it in the optimal path. However, MMAS still has drawbacks, such as long search time and local extremums. In this paper, the hybrid max–min ant system (HMMAS) is proposed to deal with the shortcomings of MMAS. Employing Levy flight strategy, HMMAS can dynamically adjust the parameters to increase the diversity of solutions and expand the search range. Besides, HMMAS uses the OBL strategy to generate opposite solutions in the early stage. In this way, the convergence is accelerated. When HMMAS falls into a local extremum, the path reorganization strategy is utilized. With its help, HMMAS can redistribute the pheromone in each path and achieve global optimum. To verify the effectiveness, HMMAS is first compared with the three conventional ACO algorithms of AS, ACS, and MMAS in 20 sets of experiments. The results indicate that the average results of HMMAS in the 19 sets of TSP instances are better than the other three algorithms, and the standard deviation in the 14 sets of calculation instances is the smallest. Then, HMMAS is compared with some state-of-the-art algorithms, and the results show that HMMAS is better than other comparison algorithms, either by the minimum or the average value.

By simulating the echolocation behavior of bats in nature, bat algorithm (BA) is proposed for global optimization that is a recently developed nature-inspired algorithm. Since then, it has been widely used in various fields. Bat algorithm balance the global search and local search by adjusting loudness and pulse rate. However, there is so many loudness and pulse rate combinations that it is hard to choose the most proper one for different problems. In this paper, a multi-swarm algorithm, called multi-swarm bat algorithm (MBA), is proposed for global search problem. In MBA method, immigration operator is used to exchange information between different swarms with different parameter settings. Thus, this configuration can make a good trade-off between global and local search. In addition, the best individuals of every swarm is put into the elite swarm through selection operator. The bat individuals in elite swarm pass over next generation without performing any operators, and this can ensure these best solutions cannot be damaged during optimization process. In order to evaluate the efficiency of MBA method, MBA has been benchmarked by sixteen standard test functions by comparing with basic BA. The results show that the MBA method is able to search more satisfactory function values on most benchmark problems than BA.

MOTIVATION: RNA-Seq technique has been demonstrated as a revolutionary means for exploring transcriptome because it provides deep coverage and base pair-level resolution. RNA-Seq quantification is proven to be an efficient alternative to Microarray technique in gene expression study, and it is a critical component in RNA-Seq differential expression analysis. Most existing RNA-Seq quantification tools require the alignments of fragments to either a genome or a transcriptome, entailing a time-consuming and intricate alignment step. To improve the performance of RNA-Seq quantification, an alignment-free method, Sailfish, has been recently proposed to quantify transcript abundances using all k-mers in the transcriptome, demonstrating the feasibility of designing an efficient alignment-free method for transcriptome quantification. Even though Sailfish is substantially faster than alternative alignment-dependent methods such as Cufflinks, using all k-mers in the transcriptome quantification impedes the scalability of the method. RESULTS: We propose a novel RNA-Seq quantification method, RNA-Skim, which partitions the transcriptome into disjoint transcript clusters based on sequence similarity, and introduces the notion of sig-mers, which are a special type of k-mers uniquely associated with each cluster. We demonstrate that the sig-mer counts within a cluster are sufficient for estimating transcript abundances with accuracy comparable with any state-of-the-art method. This enables RNA-Skim to perform transcript quantification on each cluster independently, reducing a complex optimization problem into smaller optimization tasks that can be run in parallel. As a result, RNA-Skim uses <4% of the k-mers and <10% of the CPU time required by Sailfish. It is able to finish transcriptome quantification in <10 min per sample by using just a single thread on a commodity computer, which represents >100 speedup over the state-of-the-art alignment-based methods, while delivering comparable or higher accuracy. AVAILABILITY AND IMPLEMENTATION: The software is available at http://www.csbio.unc.edu/rs.

MOTIVATION: RNA-seq techniques provide an unparalleled means for exploring a transcriptome with deep coverage and base pair level resolution. Various analysis tools have been developed to align and assemble RNA-seq data, such as the widely used TopHat/Cufflinks pipeline. A common observation is that a sizable fraction of the fragments/reads align to multiple locations of the genome. These multiple alignments pose substantial challenges to existing RNA-seq analysis tools. Inappropriate treatment may result in reporting spurious expressed genes (false positives) and missing the real expressed genes (false negatives). Such errors impact the subsequent analysis, such as differential expression analysis. In our study, we observe that ~3.5% of transcripts reported by TopHat/Cufflinks pipeline correspond to annotated nonfunctional pseudogenes. Moreover, ~10.0% of reported transcripts are not annotated in the Ensembl database. These genes could be either novel expressed genes or false discoveries. RESULTS: We examine the underlying genomic features that lead to multiple alignments and investigate how they generate systematic errors in RNA-seq analysis. We develop a general tool, GeneScissors, which exploits machine learning techniques guided by biological knowledge to detect and correct spurious transcriptome inference by existing RNA-seq analysis methods. In our simulated study, GeneScissors can predict spurious transcriptome calls owing to misalignment with an accuracy close to 90%. It provides substantial improvement over the widely used TopHat/Cufflinks or MapSplice/Cufflinks pipelines in both precision and F-measurement. On real data, GeneScissors reports 53.6% less pseudogenes and 0.97% more expressed and annotated transcripts, when compared with the TopHat/Cufflinks pipeline. In addition, among the 10.0% unannotated transcripts reported by TopHat/Cufflinks, GeneScissors finds that >16.3% of them are false positives. AVAILABILITY: The software can be downloaded at http://csbio.unc.edu/genescissors/. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Genome-wide association study (GWAS) aims to discover genetic factors underlying phenotypic traits. The large number of genetic factors poses both computational and statistical challenges. Various computational approaches have been developed for large scale GWAS. In this chapter, we will discuss several widely used computational approaches in GWAS. The following topics will be covered: (1) An introduction to the background of GWAS. (2) The existing computational approaches that are widely used in GWAS. This will cover single-locus, epistasis detection, and machine learning methods that have been recently developed in biology, statistic, and computer science communities. This part will be the main focus of this chapter. (3) The limitations of current approaches and future directions.

Recent studies have suggested using relative distance comparisons as constraints to represent domain knowledge. A natural extension to relative comparisons is the combination of two comparisons defined on the same set of three instances. Constraints in this form, termed Relative Constraints, provide a unified knowledge representation for both partitional and hierarchical clusterings. But many key properties of relative constraints remain unknown. In this paper, we answer the following important questions that enable the broader application of relative constraints in general clustering problems: " Feasibility: Does there exist a clustering that satisfies a given set of relative constraints? (consistency of constraints) "Completeness: Given a set of consistent relative constraints, how can one derive a complete clustering without running into dead-ends? " Informativeness: How can one extract the most informative relative constraints from given knowledge sources? We show that any hierarchical domain knowledge can be easily represented by relative constraints. We further present a hierarchical algorithm that finds a clustering satisfying all given constraints in polynomial time. Experiments showed that our algorithm achieves significantly higher accuracy than the existing metric learning approach based on relative comparisons.