Review on Uric Acid Recognition by MOFs with a Future in Machine Learning

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Machine learning has provided a means to accelerate early-stage drug discovery by combining molecule generation and filtering steps in a single architecture that leverages the experience and design preferences of medicinal chemists. However, designing mach ...

This thesis focuses on understanding and improving the reasoning capabilities of neural networks. It develops theoretical results and empirical analyses to uncover reasoning potential and limitations, leveraging these insights to guide the design of improv ...

Redox homeostasis is a key factor in maintaining cellular function and health. The main determinant of the intracellular redox potential is ubiquitous glutathione (GSH) together with its oxidized dimer (GSSG). Importantly, both redox equilibrium and GSH ho ...

Multi-Object Tracking (MOT) is a fundamental computer vision task that involves detecting objects of interest in video frames and associating detections of the same object across time to form trajectories. For association, appearance cues, extracted throug ...

Machine learning (ML) enables artificial intelligent (AI) agents to learn autonomously from data obtained from their environment to perform tasks. Modern ML systems have proven to be extremely effective, reaching or even exceeding human intelligence. Altho ...

Animals exhibit remarkable locomotion skills despite significant sensorimotor delays and operating in uncertain environments. Moreover, mammals acquire these skills within minutes of birth. From the Cambrian explosion to the present day, vertebrate motor c ...

The value of operando and in situ characterization methodologies for understanding electrochemical systems under operation can be inferred from the upsurge of studies that have reported mechanistic insights into electrocatalytic processes based on such mea ...

The process of protein aggregation, central to neurodegenerative diseases like Huntington’s, is challenging to study due to its unpredictable nature and relatively rapid kinetics. Understanding its biomechanics is crucial for unraveling its role in disease ...

Aims: Understanding the correlations between underlying medical and personal characteristics of a patient with cancer and the risk of lung metastasis may improve clinical management and outcomes. We used machine learning methodologies to predict the risk o ...

Federated learning (FL) is a machine learning approach where nodes collaboratively train a global model. As more nodes participate in a round of FL, the effectiveness of individual model updates by nodes also diminishes. In this study, we increase the effe ...

The current energy crisis demands the development of new sustainable energy sources and devices with low environmental impact, including in the field of electrochemical cells. Mass transport limitations can reduce the overall performance of the device by l ...

Low-light photography produces images with low signal-to-noise ratios due to limited photons. In such conditions, common approximations like the Gaussian noise model fall short, and many denoising techniques fail to remove noise effectively. Although deep- ...

The rising atmospheric level of carbon dioxide, CO2, contributes to climate change and poses urgent need to find scalable solutions both to decrease its emissions and to be able to recycle it, if the goals of Paris Agreement to limit the temperature rise a ...

With the development of modern medical techniques, the application of orthopedic implants has been continually increasing in recent years. Passive materials, such as Ti and its alloys as well as CoCrMo alloys, are widely used in artificial joints due to th ...

Decentralized learning (DL) is an emerging approach that enables nodes to collaboratively train a machine learning model without sharing raw data. In many application domains, such as healthcare, this approach faces challenges due to the high level of hete ...

Micro- nano-electrodes have demonstrated superior performances in measuring attenuated intracellular action potentials from electrogenic cell cultures compared to traditional multi-electrode arrays. Yet, the understanding of the critical electrode features ...

Non-noble metal catalysts (NNMCs) are regarded as a promising alternative to the costly Pt-based materials required to catalyze the oxygen reduction reaction (ORR) in proton exchange membrane fuel cell (PEMFC) cathodes. However, the large diversity of NNMC ...

The evolution of airborne mapping witnesses the introduction of hybrid lidar-camera systems to enhance data collection, i.e. to obtain simultaneously high-density point-cloud and texture. Yet, the common adjustment of both optical data streams is a nontriv ...

Deriving health indicators of rotating machines is crucial for their maintenance. However, this process is challenging for the prevalent adopted intelligent methods since they may take the whole data distributions, not only introducing noise interference b ...

Heteroatom-doped polyaromatic hydrocarbons (or nanographenes) are promising molecular electrocatalysts for the oxygen reduction reaction (ORR). Here, we use density functional theory to investigate the first step of the ORR pathway (chemisorption) for a se ...

Review on Uric Acid Recognition by MOFs with a Future in Machine Learning

Chat with Graph Search

Machine learning-aided generative molecular design

Learning to Reason with Neural Networks: Principles and Methods

HaloTag-Based Tools for Live-Cell Imaging and Elucidation of Organellar Redox Biology

Person Re-Identification and its Application to Multi-Object Tracking

Few-shot Learning for Efficient and Effective Machine Learning Model Adaptation

A Bio-Inspired Learning and Control Framework for Cross-Embodiment and Cross-Task Locomotion

Insights into Electrocatalyst Transformations Studied in Real Time with Electrochemical Liquid-Phase Transmission Electron Microscopy

Self-driving microscopy detects the onset of protein aggregation and enables intelligent Brillouin imaging

Medical and Personal Characteristics Can Predict the Risk of Lung Metastasis

Harnessing Increased Client Participation with Cohort-Parallel Federated Learning

Electron imaging of gas diffusion electrodes for proton exchange membrane fuel cells

Dark Noise Diffusion: Noise Synthesis for Low-Light Image Denoising

Voltage-driven Reactivity of Liquid Ga and CuGa Nanoparticles for Electrocatalytic CO2 Reduction and Synthesis

In-vivo investigation of electrochemical behavior of Ti and CoCrMo alloy in human synovial fluids

Fair Decentralized Learning

Development of new volcano-shaped microelectrodes for single cell electrochemistry, electrophysiology and impedance studies

Catalyst Aggregate Size Effect on the Mass Transport Properties of Non-Noble Metal Catalyst Layers for PEMFC Cathodes

On the Perspectives of Image-to-Lidar Constraints in Dynamic Network Optimisation

Classifier-free diffusion-based weakly-supervised approach for health indicator derivation in rotating machines: Advancing early fault detection and condition monitoring

Metal-Free Molecular Catalysts for the Oxygen Reduction Reaction: Electron Affinity as an Activity Descriptor