benchmark

Absolute (ALW) and relative (RLW) liver weight changes are sensitive endpoints in repeat-dose rodent toxicity studies, and their changes are often used for quantitative assessment of health effects induced by hepatotoxic chemicals using the benchmark dose-response modeling (BMD) approach. To find biologically relevant liver weight changes to chemical exposures, we evaluated all data available for liver weight changes and associated liver histopathologic findings from the Toxicity Reference Database (ToxRefDB)...

Speech-driven gesture generation is an emerging field within virtual human creation. However, a significant challenge lies in accurately determining and processing the multitude of input features (such as acoustic, semantic, emotional, personality, and even subtle unknown features). Traditional approaches, reliant on various explicit feature inputs and complex multimodal processing, constrain the expressiveness of resulting gestures and limit their applicability. To address these challenges, we present Persona-Gestor, a novel end-to-end generative model designed to generate highly personalized 3D full-body gestures solely relying on raw speech audio...

Graph Attention (GA) which aims to learn the attention coefficients for graph edges has achieved impressive performance in GNNs on many graph learning tasks. However, existing GAs are usually learned based on edges' (or connected nodes') features which fail to fully capture the rich structural information of edges. Some recent research attempts to incorporate the structural information into GA learning but how to fully exploit them in GA learning is still a challenging problem. To address this challenge, in this work, we propose to leverage a new Replicator Dynamics model for graph attention learning, termed Graph Replicator Attention (GRA)...

Vision Transformer (ViT) architectures are becoming increasingly popular and widely employed to tackle computer vision applications. Their main feature is the capacity to extract global information through the self-attention mechanism, outperforming earlier convolutional neural networks. However, ViT deployment and performance have grown steadily with their size, number of trainable parameters, and operations. Furthermore, self-attention's computational and memory cost quadratically increases with the image resolution...

This article aims to solve the video object segmentation (VOS) task in a scribble-supervised manner, in which VOS models are not only initialized with sparse target scribbles for inference but also trained by sparse scribble annotations. Thus, the annotation burdens for both initialization and training can be substantially lightened. The difficulties of scribble-supervised VOS lie in two aspects: 1) it demands a strong reasoning ability to carefully segment the target given only a sparse initial target scribble and 2) it necessitates learning dense prediction from sparse scribble annotations during training, requiring powerful learning capability...

In recent years, fusing high spatial resolution multispectral images (HR-MSIs) and low spatial resolution hyperspectral images (LR-HSIs) has become a widely used approach for hyperspectral image super-resolution (HSI-SR). Various unsupervised HSI-SR methods based on deep image prior (DIP) have gained wide popularity thanks to no pre-training requirement. However, DIP-based methods often demonstrate mediocre performance in extracting latent information from the data. To resolve this performance deficiency, we propose a coupled spatial and spectral deep image priors (CS2DIPs) method for the fusion of an HR-MSI and an LR-HSI into an HR-HSI...

Detecting infrared small targets under cluttered background is mainly challenged by dim textures, low contrast and varying shapes. This paper proposes an approach to facilitate infrared small target detection by learning contrast-enhanced shape-biased representations. The approach cascades a contrast-shape encoder and a shape-reconstructable decoder to learn discriminative representations that can effectively identify target objects. The contrast-shape encoder applies a stem of central difference convolutions and a few large-kernel convolutions to extract shape-preserving features from input infrared images...

Neural Architecture Search (NAS) has emerged as a promising tool in the field of AutoML for designing more accurate and efficient architectures. The majority of NAS works employ a weight-sharing technique to reduce the search cost by sharing the weights of a supernet, which is a composite of all architectures produced from the search space. Nonetheless, this method has a significant drawback in that negative interference may arise when candidate architectures share the same weights. This issue becomes even more severe in multi-task searches, where a supernet is shared across tasks...

In this work we develop a new scheme to construct a diabatic potential energy matrix (DPEM). We propose a diabatization method which is based on integrating the diabatic potential gradient difference to diabatize adiabatic ab initio energies. This method is capable of performing high-precision adiabatic-to-diabatic transformations, with a unique advantage in effectively handling the significant fluctuations in derivative-couplings caused by conical intersection (CI) seams. The above scheme is applied to the DPEM construction of the Na(3p) + H2 → NaH + H reaction...

The vibrational and electronic spectroscopy of the radical cations of two nucleobases (NB) (uracil and thymine) was studied by cryogenic ion photodissociation spectroscopy. The radical cations have been generated from the photodissociation of NB-Ag+ complexes. A charge transfer process from the NB to Ag+ governs the deactivation mechanism, leading to the formation of the radical cation without further tautomerization. Single- and double-resonance spectroscopy allows for structural assignments of both the silver complexes and the radical cations by comparison with DFT-based calculations...

Warfarin dosing remains challenging due to substantial inter-individual variability, which can lead to unsafe or ineffective therapy with standard dosing. Model-informed precision dosing (MIPD) can help individualize warfarin dosing, requiring the selection of a suitable model. For models developed from clinical data, the dependence on the study design and population raises questions about generalizability. Quantitative system pharmacology (QSP) models promise better extrapolation abilities; however, their complexity and lack of validation on clinical data raise questions about applicability in MIPD...

These results highlight the transformative potential of neural network algorithms in providing consistency and transparency while reducing the inherent subjectivity in human evaluations, revolutionizing translation quality assessment in academia. The findings have significant implications for academia, as reliable translation quality evaluations are crucial for fostering cross-cultural knowledge exchange. However, challenges such as domain-specific adaptation require further investigation to improve and maximize the effectiveness of this novel approach, ultimately enhancing the accessibility of academic content and promoting global academic discourse...

INTRODUCTION: Our study undertakes a detailed exploration of gene expression dynamics within human lung organ tissue equivalents (OTEs) in response to Influenza A virus (IAV), Human metapneumovirus (MPV), and Parainfluenza virus type 3 (PIV3) infections. Through the analysis of RNA-Seq data from 19,671 genes, we aim to identify differentially expressed genes under various infection conditions, elucidating the complexities of virus-host interactions. METHODS: We employ Generalized Linear Models (GLMs) with Quasi-Likelihood (QL) F-tests (GLMQL) and introduce the novel Magnitude-Altitude Score (MAS) and Relaxed Magnitude-Altitude Score (RMAS) algorithms to navigate the intricate landscape of RNA-Seq data...

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BACKGROUND: The collection of data on 'infant feeding at hospital discharge' is used to monitor breastfeeding outcomes, health service benchmarking, and research. While some Australian states have clear definitions of this data collection point, there is no operational definition of 'infant feeding at hospital discharge' in the Australian state of New South Wales. Little is known about how midwives interpret the term 'infant feeding at hospital discharge', in particular, the timeframe used to calculate these important indicators...

BACKGROUND: Severity of illness scoring systems are used in intensive care units to enable the calculation of adjusted outcomes for audit and benchmarking purposes. Similar tools are lacking for pre-hospital emergency medicine. Therefore, using a national helicopter emergency medical services database, we developed and internally validated a mortality prediction algorithm. METHODS: We conducted a multicentre retrospective observational register-based cohort study based on the patients treated by five physician-staffed Finnish helicopter emergency medical service units between 2012 and 2019...

Biomedical image segmentation is essential in clinical practices, offering critical insights for accurate diagnosis and strategic treatment approaches. Nowadays, self-attention-based networks have achieved competitive performance in both natural language processing and computer vision, but the computational cost has reduced their popularity in practical applications. The recent study of Convolutional Neural Network (CNN) explores linear functions within modified CNN layer demonstrating pure CNN-based networks can still achieve competitive results against Vision Transformer (ViT) in biomedical image segmentation, with fewer parameters...

In recent years, generative protein sequence models have been developed to sample novel sequences. However, predicting whether generated proteins will fold and function remains challenging. We evaluate a set of 20 diverse computational metrics to assess the quality of enzyme sequences produced by three contrasting generative models: ancestral sequence reconstruction, a generative adversarial network and a protein language model. Focusing on two enzyme families, we expressed and purified over 500 natural and generated sequences with 70-90% identity to the most similar natural sequences to benchmark computational metrics for predicting in vitro enzyme activity...

The emergence of microgrids arises from the growing integration of Renewable Energy Resources (RES) and Energy Storage Systems (ESSs) into Distribution Networks (DNs). Effective integration, coordination, and control of Multiple Microgrids (MMGs) whereas navigating the complexities of energy transition within this context poses a significant challenge. The dynamic operation of MMGs is a challenge faced by the traditional distributed hierarchical control techniques. The application of Artificial Intelligence (AI) techniques is a promising way to improve the control and dynamic operation of MMGs in future smart DNs...

The effect of the lead (Pb), cadmium (Cd), mercury (Hg) and arsenic (As) mixture (MIX) on hematotoxicity development was investigated trough combined approach. In vivo subacute study (28 days) was performed on rats (5 per group): a control group and five groups orally exposed to increasing metal(loid) mixture doses, MIX 1- MIX 5 (mg/kg bw./day) (Pb: 0.003, 0.01, 0.1, 0.3, 1; Cd: 0.01, 0.03, 0.3, 0.9, 3; Hg: 0.0002, 0.0006, 0.006, 0.018, 0.06; As: 0.002, 0.006, 0.06, 0.18, 0.6). Blood was taken for analysis of hematological parameters and serum iron (Fe) analysis...