convergence

Small data learning problems are characterized by a significant discrepancy between the limited number of response variable observations and the large feature space dimension. In this setting, the common learning tools struggle to identify the features important for the classification task from those that bear no relevant information and cannot derive an appropriate learning rule that allows discriminating among different classes. As a potential solution to this problem, here we exploit the idea of reducing and rotating the feature space in a lower-dimensional gauge and propose the gauge-optimal approximate learning (GOAL) algorithm, which provides an analytically tractable joint solution to the dimension reduction, feature segmentation, and classification problems for small data learning problems...

We describe a highly stereoconvergent radical epoxide allylation towards diastereomerically and enantiomerically enriched α-quaternary alcohols in two steps from olefins. Our approach combines the stereospecifity and enantioselectivity of the Shi epoxidation with the unprecedented Ti(III)-promoted intramolecular radical group transfer allylation of epoxides. A directional isomerization step via configurationally labile radical intermediates enables the selective preparation of all carbon quaternary stereocenters in a unique fashion...

The apicomplexan parasite Cryptosporidium is a leading cause of childhood diarrhea in developing countries. Current treatment options are inadequate and multiple preclinical compounds are being actively pursued as potential drugs for cryptosporidiosis. Unlike most apicomplexans, Cryptosporidium spp. sequentially replicate asexually and then sexually within a single host to complete their lifecycles. Anti-cryptosporidial compounds are generally identified or tested through in vitro phenotypic assays that only assess the asexual stages...

Offline reinforcement learning (offline RL) aims to find task-solving policies from prerecorded datasets without online environment interaction. It is unfortunate that extrapolation errors can cause over-optimistic Q -value estimates when learning with a fixed dataset, limiting the performance of the learned policy. To tackle this issue, this article proposes an offline actor-critic with behavior value regularization (OAC-BVR) method. In the policy evaluation stage, the difference between the Q -function and the value of the behavior policy is considered as the regularization term, driving the learned value function to approach the value of the behavior policy...

In situ hybridization is a powerful and precise tool for revealing cell- and tissue-specific gene expression and a critical approach to validating single-cell RNA-seq (scRNA-seq). However, applying it to highly fragile animals such as ctenophores is challenging. Here, we present an in situ hybridization protocol for adult Pleurobrachia bachei (Cydippida)-a notable reference species representing the earliest-branching metazoan lineage, Ctenophora, sister to the rest of Metazoa. We provided expression patterns for several markers of cell phenotypes, as illustrated examples...

Ctenophores are the descendants of the earliest surviving lineage of ancestral metazoans, predating the branch leading to sponges (Ctenophore-first phylogeny). Emerging genomic, ultrastructural, cellular, and systemic data indicate that virtually every aspect of ctenophore biology as well as ctenophore development are remarkably different from what is described in representatives of other 32 animal phyla. The outcome of this reconstruction is that most system-level components associated with the ctenophore organization result from convergent evolution...

PURPOSE: The aim of this study was to translate and validate the "Music-Related Quality of Life Questionnaire" into Spanish (sMuRQoL) and assess its convergent validity and discriminative capacity by comparing its scores with the outcomes of the musical perception test Meludia. METHODS: The sMuRQoL was completed by 129 patients: 55 cochlear implant (CI) users and 74 normal hearing (NH) individuals. Conducted in this study were an exploratory factor analysis, an evaluation of internal consistency, an assessment of score stability through test-retest reliability, a comparison of sMuRQoL scores between CI users and NH individuals and an examination of potential evidence of convergent validity and discriminative capacity of sMuRQoL in relation to other tools...

One-body reduced density matrix functional theory provides an alternative to density functional theory, which is able to treat static correlation while keeping a relatively low computation scaling. Its disadvantageous cost comes mainly from a slow convergence of the self-consistent energy optimization. To improve on that problem, we propose in this work the use of the Hessian of the energy, including the coupling term. We show that using the exact Hessian is very effective at reducing the number of iterations...

Gas turbine engines subject materials to extreme conditions. Their high temperature materials and co-developed coatings must survive combustion gas temperatures currently approaching 1800 °C, large thermal gradients, severe thermal shock, and static and fatigue inducing applied stresses, all the while operating in highly reactive, high-pressure, high-speed combustion gas flows containing significant partial pressures of water vapor, oxygen, and other reactive species for many tens of thousands of hours...

This research focuses on the development of a state observer for performing indirect measurements of the main variables involved in the soybean oil transesterification reaction with a guishe biochar-based heterogeneous catalyst; the studied reaction takes place in a batch reactor. The mathematical model required for the observer design includes the triglycerides' conversion rate, and the reaction temperature. Since these variables are represented by nonlinear differential equations, the model is linearized around an operation point; after that, the pole placement and linear quadratic regulator (LQR) methods are considered for calculating the observer gain vector L (x)...

Training a model to recognize human actions in videos is computationally intensive. While modern strategies employ transfer learning methods to make the process more efficient, they still face challenges regarding flexibility and efficiency. Existing solutions are limited in functionality and rely heavily on pretrained architectures, which can restrict their applicability to diverse scenarios. Our work explores knowledge distillation (KD) for enhancing the training of self-supervised video models in three aspects: improving classification accuracy, accelerating model convergence, and increasing model flexibility under regular and limited-data scenarios...

The practical implementation of massive multi-user multi-input-multi-output (MU-MIMO) downlink communication systems power amplifiers that are energy efficient; otherwise, the power consumption of the base station (BS) will be prohibitive. Constant envelope (CE) precoding is gaining increasing interest for its capability to utilize low-cost, high-efficiency nonlinear radio frequency amplifiers. Our work focuses on the topic of CE precoding in massive MU-MIMO systems and presents an efficient CE precoding algorithm...

The Volunteer Functions Inventory (VFI) is an instrument widely used to assess volunteers' motivation based on the Functionalist Model of Omoto and Snyder. It assesses six factors that reflect several motivational functions. The VFI has been translated into various languages and validated in different cultural contexts, but some studies have reported different factor structures (e.g., five or four factors). In the Portuguese context, previous studies have also shown inconsistent results. The aim of this study was to adapt and validate the VFI for Portuguese volunteers, testing several alternative models (nine models) using confirmatory factor analysis...

This study introduces an improved quantum-behavior particle swarm optimization (IQPSO), tailored for the task of maximum power point tracking (MPPT) within photovoltaic generation systems (PGSs). The power stage of the MPPT system comprises a series of buck-boost converters, while the control stage contains a microprocessor executing the biomimetic algorithm. Leveraging the series buck-boost converter, the MPPT system achieves optimal operation at the maximum power point under both ideal ambient conditions and partial shade conditions (PSCs)...

One of the most important tasks in handling real-world global optimization problems is to achieve a balance between exploration and exploitation in any nature-inspired optimization method. As a result, the search agents of an algorithm constantly strive to investigate the unexplored regions of a search space. Aquila Optimizer (AO) is a recent addition to the field of metaheuristics that finds the solution to an optimization problem using the hunting behavior of Aquila. However, in some cases, AO skips the true solutions and is trapped at sub-optimal solutions...

To address the shortcomings of the recently proposed Fick's Law Algorithm, which is prone to local convergence and poor convergence efficiency, we propose a multi-strategy improved Fick's Law Algorithm (FLAS). The method combines multiple effective strategies, including differential mutation strategy, Gaussian local mutation strategy, interweaving-based comprehensive learning strategy, and seagull update strategy. First, the differential variation strategy is added in the search phase to increase the randomness and expand the search degree of space...

Deep learning technology has been widely adopted in the research of automatic arrhythmia detection. However, there are several limitations in existing diagnostic models, e.g., difficulties in extracting temporal information from long-term ECG signals, a plethora of parameters, and sluggish operation speed. Additionally, the diagnosis performance of arrhythmia is prone to mistakes from signal noise. This paper proposes a smartphone-based m-health system for arrhythmia diagnosis. First, we design a cycle-GAN-based ECG denoising model which takes real-world noise signals as input and aims to produce clean ECG signals...

The Illness Management and Recovery Scale (IMR-S) is based on the IMR program, developed to assess the recovery process for people with severe mental disorders by considering the perceptions of clients and clinicians involved in it. The aim of this study was to analyze the psychometric properties of the IMR-S so as to determine the reliability and suitability of its scores for evaluating recovery. Two coders searched five databases for studies, published between January 2004 and May 2023, that describe the psychometric assessment of the IMR-S...

The present study proves the construct validity of the German versions of the Feeling Scale (FS) and the Felt Arousal Scale (FAS) for measuring the affective responses (affective valence and arousal) for a moderate-intensity jogging (JG) exercise. In previous studies, both scales were validated for a high-intensity bicycle ergometer exercise and for relaxation techniques. In the present study, 194 participants performed the JG exercise for 45 min and completed the FS and the FAS, as well as the Self-Assessment Manikin (SAM), for a self-other comparison in a pre-test-intervention-post-test design...

Extended-spectrum β-lactamase-producing Escherichia coli ST131 has become widespread worldwide. This study aims to characterize the virulome, resistome, and population structure of E. coli ST131 isolates from clinical blood samples in Hungary. A total of 30 C2/H30Rx and 33 C1-M27 ST131 isolates were selected for Illumina MiSeq sequencing and 30 isolates for MinION sequencing, followed by hybrid de novo assembly. Five C2/H30Rx and one C1-M27 cluster were identified. C1-M27 isolates harbored the F1:A2:B20 plasmid in 93...