24 papers · ranked by Valyu relevance
WENBIAO LI, ANISA HALIMI, JAIDEEP VAIDYA, XIAOQIAN JIANG + 1 more
We present a privacy-preserving framework to verify whether a declared data preprocessing pipeline was correctly applied before training a machine learning model on sensitive data. The verifier has only black-box query access to the model and combines three behavior indicators: shift in prediction accuracy…
Craig Michoski, Matthew M. Waller, B. Sammuli, Zeyu Li + 14 more
Craig Michoskia,d, ∗ , Matthew Waller a , Brian Sammuli b , Zeyu Li b , Tapan Ganatma Nakkina a , Raffi Nazikian b , Sterling Smith b , David Orozco b , Dongyang Kuang a , Martin Foltin c , Erik Olofsson b , Mike Fredrickson a , Jerry Louis-Jeune a , David R. Hatcha,d, Todd A. Olivera,d, Mitchell Clark b , Steph-Yves…
Yanfan Zhu, Marilyn M. Lionts, Ezekiel J. Haugen, Alec B. Walter + 10 more
Raman spectroscopy offers a uniquely rich window into molecular structure and composition, making it a powerful tool across fields ranging from materials science to biology. However, the reproducibility of Raman data analysis remains a fundamental bottleneck. In practice, transforming raw spectra into meaningful…
Zuzanna Stępnicka, Natalia Piórkowska, Malwina Brożyna, Tomasz Matys + 1 more
Invertebrate and larval model organisms such as Drosophila melanogaster, Caenorhabditis elegans, Danio rerio larvae, and Galleria mellonella are increasingly employed in biomedical, toxicological, and ecological research. Their behavioral responses serve as sensitive indicators of functional changes, yet traditional…
Saranzaya Magsarjav, Melissa Humphries, Jonathan Tuke, Lewis Mitchell
Sentiment analysis in Twitter datasets is important because it enables monitoring public opinion on products and analysis of political and social movements. One critical step is preprocessing: the automated processing of text for machine learning algorithms. Preprocessing plays a critical role in reducing noise and…
Amélie Gruel, Pierre Lewden, Adrien F. Vincent, Sylvain Saïghi
Neuromorphic vision made significant progress in recent years, thanks to the natural match between spiking neural networks and event data in terms of biological inspiration, energy savings, latency and memory use for dynamic visual data processing. However, optimising its energy requirements still remains a challenge…
Daniele Scanzi, Dylan A. Taylor, Katie A. McNair, Rohan O. C. King + 2 more
Electroencephalography (EEG) data are inherently contaminated by non-neuronal noise, including eye movements, muscle activity, cardiac signals, electrical interference, and technical issues such as poorly connected electrodes. Preprocessing to remove these artefacts is essential, yet the optimal method remains unclear…
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The discovery of chemically novel or structurally anomalous metal-organic frameworks (MOFs) is essential for expanding reticular design space and enhancing dataset reliability. We present CHEM-AD (Chemically Unusual Metal–organic Frameworks via Autoencoder-based Detection), a label-free, CPU-efficient pipeline that…
Javad Hassannataj Joloudari, Mohammad Maftoun, Bahareh Nakisa, Roohallah Alizadehsani + 2 more
The Complex Emotion Recognition System (CERS) deciphers complex emotional states by examining combinations of basic emotions expressed, their interconnections, and their dynamic variations. Through the utilization of advanced algorithms, the system provides profound insights into emotional dynamics, facilitating a…
Xingyu Liu, Yijun Zhang, Zi Yin, Zonglei Zhen + 1 more
Macaque MRI bridges non-invasive systems neuroscience with cellular and circuit-level mechanisms, but preprocessing tools remain difficult to integrate and deploy reproducibly. We present Brainana, an automated, BIDS-compatible preprocessing and visualization framework for macaque neuroimaging. Brainana integrates…
Roman Kessler, Johanna J. Finnemann, Michael A. Skeide
Electrophysiological responses to visual objects carry information about stimulus identity and semantic category, but it remains difficult to know whether such information represents semantic knowledge or merely regularities in physical image features. Here, we presented two-tone images while recording EEG to…
Garrett Mathesen, Kyle Mumm, Carter Taylor, Jian Cao
In laser powder bed fusion (L-PBF) processes, the rapid evolution of gas and fluid interactions complicates our ability to properly monitor or control the process, with unstable keyholes leading to porosity and spatter formation. High-speed operando x-ray imaging of the keyhole has been used to better understand the…
Sai Prakash Challa, Melvin Alexis Lara de Leon, Jiri Koziorek, Ibrahim A. Hameed + 1 more
Machine vision and AI-based defect detection systems are increasingly deployed in manufacturing to support consistent product quality and high production efficiency. However, these automated inspection systems often suffer from sensitivity to imaging variability, dependence on large labeled datasets, and the need for…
Phanindra Reddy Madduru, Bijo Thomas
This paper proposes a preprocessing framework for optimizing large-scale graph database ingestion through intelligent edge filtering based on value ranking. We combine adapted PageRank algorithms with business-specific metrics and edge type importance to evaluate and rank edges, enabling selective retention of…
Nicolò Formento Moletta, Ana Fló, Ghislaine Dehaene-Lambertz, Jhunlyn Lorenzo
Electroencephalography (EEG) is fundamental to cognitive neuroscience as it provides a direct measure of human neural activities with millisecond precision. Its noninvasive nature allows for the study of brain function across diverse age groups and experimental contexts—from newborns to adults, and from tightly…
Mohammad, Noor Islam S.
—This study introduces a modular framework for spatial image processing, integrating grayscale quantization, color and brightness enhancement, image sharpening, bidirectional transformation pipelines, and geometric feature extraction. A stepwise intensity transformation quantizes grayscale images into eight discrete…
A.A. Poyda, V.A. Orlov, A.D. Zhemchuzhnikov, S.O. Kozlov + 4 more
The aim of the study was to analyze the influence of various pipelines for preprocessing raw functional magnetic resonance imaging (fMRI) data on the accuracy of classification of subjects into schizophrenia patients and healthy controls using machine learning methods, and to give recommendations for optimizing the…
Li Cunzhi, Louis Kang, Hideaki Shimazaki
Diffusion models are a class of generative models that have demonstrated remarkable success in tasks such as image generation. However, one of the bottlenecks of these models is slow sampling due to the delay before the onset of trajectory bifurcation, at which point substantial reconstruction begins. This issue…
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This comprehensive review examines the evolution of autonomous materials synthesis laboratories that integrate artificial intelligence with advanced robotics to accelerate discovery. Traditional materials development pipelines typically require 10-20 years, but self-driving laboratories (SDLs) and Materials…
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Photocatalytic overall water splitting is a promising pathway to green hydrogen but also presents unique research challenges due to the need to detect both gaseous products (H2 and O2). While gas chromatography (GC) is the most commonly employed method in this context, it faces multiple shortcomings: low time…
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Artificial intelligence (AI) is reshaping chemical engineering. Still, its role in safety-critical operations is limited because we rarely see tools that link physical models with data-driven methods. This study brings together three elements: physics-constrained neural networks, uncertainty quantification, and a…
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This study outlines a process simulation for producing biodiesel from waste cooking oil (WCO) using Aspen HYSYS V11. A two-stage Continuous Stirred-Tank Reactor (CSTR) system was designed to operate at 65 °C and 1 atm, with a methanol-to-oil molar ratio used is 6:1 along with 2 wt% KOH as a catalyst. The simulation…
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The integration of machine learning methods is transforming many areas of research by, for instance, accelerating molecular dynamics simulations and enabling improved prediction and optimization of chemical reactions. However, despite this progress, the adoption of data-driven approaches in atomic layer deposition…
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Machine olfaction—the artificial replication of the sense of smell—faces significant challenges due to the absence of large, standardized training datasets. Unlike vision, language, and audio models, which benefit from extensive corpora such as ImageNet, GLUE, and AudioSet, olfaction lacks scaled equivalents and…