This paper addresses the Longest Filled Common Subsequence (LFCS) problem, a challenging NP-hard problem with applications in bioinformatics, including gene mutation prediction and genomic data reconstruction. Existing approaches, including exact, metaheuristic, and approximation algorithms, have primarily been evaluated on small instances, which provide limited insight into their scalability. In this work, we introduce a new benchmark dataset with significantly larger instances and demonstrate that existing datasets lack the discriminative power needed to meaningfully assess algorithm performance at scale. To solve large instances efficiently, we utilize an adaptive Construct, Merge, Solve, Adapt (CMSA) framework that iteratively generates promising subproblems via component-based construction and refines them using feedback from prior iterations. Subproblems are solved using an external black-box solver. Extensive experiments on both standard and a newly introduced set of large-sized instances prove that the designed adaptive CMSA achieves state-of-the-art performance, outperforming four known approaches. Notably, among 1510 problem instances with known optimal solutions, our approach matches the proven optimum on 1486 instances—corresponding to 98.4% of these instances. These results demonstrate substantially improved scalability on large problem instances. As an engineering contribution, we propose a novel application of LFCS to song identification from degraded audio excerpts, using real-world energy-profile instances derived from popular music. Finally, we conduct an empirical explainability analysis to identify critical feature combinations influencing algorithm performance, revealing the key problem features associated with the success or failure of the approaches across different instance types.
Effective Field Theory (EFT) is a general framework to parametrize the low-energy approximation to a UV model that is widely used in model-independent searches for new physics. The use of EFTs at the LHC can suffer from a 'validity' issue, since new physics amplitudes often grow with energy and the kinematic regions with the most sensitivity to new physics have the largest theoretical uncertainties. We propose a method to account for these uncertainties with the aim of producing robust model-independent results with a well-defined statistical interpretation. In this approach, one must specify the new operators being studied as well as the new physics cutoff M, the energy scale where the EFT approximation breaks down. At energies below M, the EFT uncertainties are accounted for by adding additional higher dimensional operators with coefficients that are treated as nuisance parameters. The size of the nuisances are governed by a prior likelihood function that incorporates information about dimensional analysis, naturalness, and the scale M. At energies above M, our method incorporates the lack of predictivity of the EFT, and we show that this is crucial to obtain consistent results. We perform a number of tests of this method in a simple toy model, illustrating its performance in analyses aimed at new physics exclusion as well as for discovery. The method is conveniently implemented by the technique of event reweighting and is easily ported to realistic LHC analyses. We find that the procedure converges quickly with the number of nuisance parameters and is conservative when compared to UV models. The paper gives a precise meaning and offers a principled and practical solution to the widely debated 'EFT validity issue'.
The resource-constrained project scheduling problem (RCPSP) is a complex optimization problem aiming to construct feasible schedules that minimize the project makespan while satisfying the precedence and renewable resource constraints. Priority rule heuristics are prevalent approaches for solving the RCPSP, particularly in practical applications. However, these rules are problem-specific, and no rule can consistently outperform others across different projects. Designing priority rules through manual methods requires substantial expertise, time, and computational effort. This has led researchers to propose automated techniques for this purpose. Most existing research in this area focuses on unsupervised learning techniques like genetic programming hyper-heuristics (GPHH), while the investigation of supervised learning algorithms remains limited. To address this gap, this research explores the potential of supervised learning algorithms, specifically regression-based methods, for the automated design of new priority rule heuristics for RCPSP. Nine widely used regression algorithms were evaluated, and the top-performing three were further enhanced using ensemble techniques to augment their effectiveness. Computational experiments show that regression-based heuristics can outperform traditional priority rules across all primary test datasets and, in some cases, even surpass priority rules designed through GPHH. To further validate the reliability of our results, we also tested the regression-based heuristics on various supplementary datasets, including project instances with more than 1,000 activities and empirical projects. Their performance highlights the robust generalization of regression-based heuristics.
We address the numerical solution of the inverse source identification problem in a particular setting: determining the location and intensity of one or more time-harmonic emission sources embedded within a known penetrable object from far-field data. This setting is therefore harder to tackle than the usual Cauchy data interior problem, since one has to deal with the severely ill-conditioned recovery of the exterior field from the given data, which then feeds the transmission conditions on the boundary of the obstacle. Also, a theoretical uniqueness result is established for the current problem.To address this problem, we divide the proposed methodology in two steps, in a hybrid approach between decomposition and iterative methods. In the first step, the exterior scattered field is recovered by solving a regularized far-field equation. Secondly, we consider two approaches based on the transmission conditions and iteratively determine approximations for the location and intensity of the unknown sources within the interior domain, considering a linearized equation. We consider a standard approach based on a Green’s representation previously published for the interior source identification problem and a second approach based on similar ideas used by the authors for the inverse obstacle scattering problem with transmission conditions. We also illustrate the feasibility of the method in two-dimensional numerical experiments, and confirm the robustness and accuracy of the proposed methodology with exact and noisy data.
IntroductionShotgun metagenomic sequencing (mNGS), an untargeted approach that sequences all nucleic acids in a sample, has emerged as a powerful tool for pathogen detection and genome characterization. However, its implementation in clinical diagnostics remains limited due to technical challenges such as contamination and reduces sensitivity, especially in low-biomass samples.MethodsWe applied mNGS to 144 clinical samples representing chronic infections, acute infections, and respiratory co-infections. To address contamination, we established a framework integrating negative controls, lab-specific contaminant watchlists, and computational filtering. Viral detection performance and genome recovery were assessed across sample types and viral loads.ResultsViral load was shown to be the primary determinant of sensitivity, with reliable recovery achieved only at higher titers. Our framework substantially improved contamination management, reducing false-positive signals and enhancing viral genome recovery. mNGS enabled the detection of clinically relevant co-infections and refined viral classification beyond targeted diagnostics, while also revealing the substantial risk of spurious detections in the absence of contamination-aware workflows.DiscussionThese findings define practical sensitivity thresholds for clinical mNGS and underscore the need for contamination-aware workflows, particularly for low-biomass samples, while providing an open-source contaminants watchlist that enhances reliability and utility of clinical metagenomics.
