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Search · four archives
25 papers · ranked by Valyu relevance
Aarti Sardhara, Vipul Vekariya, Ajeet Ram Pathak, Sital Dash
Introduction The increasingly lifelike nature of digitally manipulated images, as well as those generated by AI, presents significant problems for both media authenticity and digital trust. Various detection methods mostly depend on the visual content and thus, might miss the subtle forensic traces. This paper focuses…
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…
Zhuoran Li, Xianqing Liu, Joshua Tatz, Umair Hassan + 5 more
Transcranial magnetic stimulation combined with intracranial EEG (TMS-iEEG) has emerged as a powerful approach for probing the causal organization and dynamics of the human brain. Despite its promise, the presence of TMS-induced artifacts poses significant challenges for accurately characterizing and interpreting…
Vincenzo Levi, Stefania Coelli, Chiara Gorlini, Federica Forzanini + 6 more
Microelectrode recording (MER) is commonly used to validate preoperative targeting during subthalamic nucleus (STN) deep brain stimulation (DBS) surgery for Parkinson’s Disease (PD). Although machine learning (ML) has been used to improve STN localization using MER data, the impact of preprocessing steps on the…
Authors not listed
Atomic Force Microscopy (AFM) enables high-resolution surface imaging at the nanoscale, yet the output is often degraded by artifacts introduced by environmental noise, scanning imperfections, and tip-sample interactions. To address this challenge, a lightweight and fully automated framework for artifact detection and…
Albert Vong, Howard Yanxon, Eric Roberts, Hannah Parraga + 7 more
The capabilities of a U-Net convolutional neural network for masking artifacts in time-resolved X-ray diffraction measurements of battery materials are demonstrated.
Lingzhi Zhang, Zhengjie Xu, Connelly Barnes, Yuqian Zhou + 6 more
Generative models have made significant progress in a myriad of image synthesis tasks, including unconditional generation , image inpainting , image-to-image translation , and text-to-image synthesis , among others. However, even cutting-edge models occasionally generate implausible content or display unpleasant…
Jack-Michael Wesierski, Nicholas Rodriguez
Physiological time-series data, like electroencephalography (EEG), are vulnerable to motion, ocular, and muscle artifacts that hinder real-time inference and bias offline analyses. We present the Minds AI Filter: a lightweight, physics-based, sensor-fusion method that exploits multichannel spatial structure and…
Aonan He, Xi Wang, Jiangwei Yu, Xiaojia Wang + 6 more
Electroencephalography (EEG) serves as a fundamental tool in modern neurology, cognitive neuroscience, and brain-computer interfaces, but its practical application is often compromised by artifacts. Physiological artifacts are particularly intractable due to overlapping spectral features with neural signals, hindering…
Dennis Menn, Feng Liang, Diana Marculescu
Artifact detectors have been shown to enhance the performance of image-generative models by serving as reward models during fine-tuning. These detectors enable the generative model to improve overall output fidelity and aesthetics. However, training the artifact detector requires expensive pixel-level human annotations…
Sari Saba-Sadiya, Eric Chantland, Tuka Alhanai, Taosheng Liu + 1 more
Electroencephalography (EEG) is used in the diagnosis, monitoring, and prognostication of many neurological ailments including seizure, coma, sleep disorders, brain injury, and behavioral abnormalities. One of the primary challenges of EEG data is its sensitivity to a breadth of non-stationary noises caused by…
Fabian Rehn, Marlene Pils, Tuyen Bujnicki, Oliver Bannach + 1 more
'Dieter Willbold'] To ensure analytical accuracy in fluorescence microscopy image analysis, robust artifact detection is essential. For large datasets or time-sensitive analyses, automation is advisable, as it not only reduces time and costs but also eliminates human bias and enhances reproducibility. Although…
Morteza Zangeneh Soroush
Artifact elimination has become an inseparable part while processing electroencephalogram (EEG) in most brain computer interface (BCI) applications. Scientists have tried to introduce effective and efficient methods which can remove artifacts and also reserve desire information pertaining to brain activity. Blind…
Ouxiang Li, Jiayin Cai, Yanbin Hao, Xiaolong Jiang + 2 more
Transformation Perspective Authors: ['Ouxiang Li' 'Jiayin Cai' 'Yanbin Hao' 'Xiaolong Jiang' 'Yao Hu' 'Fuli Feng'] With recent generative models facilitating photo-realistic image synthesis, the proliferation of synthetic images has also engendered certain negative impacts on social platforms, thereby raising an urgent…
Parmida Davarmanesh, Kuanhao Jiang, Tingting Ou, Artem Vysogorets + 4 more
'Stanislav Ivashkevich' 'Max Kiehn' 'Shantanu H. Joshi' 'Nicholas Malaya'] In spite of advances in gaming hardware and software, gameplay is often tainted with graphics errors, glitches, and screen artifacts. This proof of concept study presents a machine learning approach for automated detection of graphics…
Gail McConnell
Microscopy datasets are often spatially sparse, wherein relevant structures occupy only a small fraction of the total field of view (FOV), leaving large regions of background devoid of signal. This inherent inefficiency creates file sizes that are larger than needed, which increases the time needed for computational…
Authors not listed
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…
Akshay Gadi Patil, Shanmuganathan Raman
—Non-photorealistic rendering techniques work on image features and often manipulate a set of characteristics such as edges and texture to achieve a desired depiction of the scene. Most computational photography methods decompose an image using edge preserving filters and work on the resulting base and detail layers…
Heeseung Lee, Daeho Kim, Heyin Lee, Namyoung Gwak + 6 more
- 1. Computational Science Research Center, Korea Institute of Science and Technology, Seoul 02792, Republic of Korea - 2. Department of Materials Science and Engineering, Korea University, 145 Anam-ro, Seoul 02841, Republic of Korea - 3. Department of Chemical and Biological Engineering, Korea University, Seoul 02841…
Mallikarjuna Reddy Ayaluri, Sudheer Reddy K., Srinivasa Reddy Konda, Sudharshan Reddy Chidirala + 1 more
'Sudharshan Reddy Chidirala' 'Rajanikanth Aluvalu'] Steganalysis is the process of analyzing and predicting the presence of hidden information in images. Steganalysis would be most useful to predict whether the received images contain useful information. However, it is more difficult to predict the hidden information…
Wei Chen, Tian Lin, Cheng Yang
Influence maximization is the task of finding a set of seed nodes in a social network such that the influence spread of these seed nodes based on certain influence diffusion model is maximized. Topic-aware influence diffusion models have been recently proposed to address the issue that influence between a pair of users…
Authors not listed
This study presents a validation and refinement of the “yellow cards” error detection workflow that can be applied to any property connected to molecular structure. In our implementation the workflow employed 5 predictive models with each assigning a “yellow card” to 5% of the entries with worst prediction accuracy.…
Authors not listed
High-quality data preprocessing is essential for untargeted metabolomics experiments, where increasing dataset scale and complexity demand adaptable, robust, and reproducible software solutions. Modern preprocessing tools must evolve to integrate seamlessly with downstream analysis platforms, ensuring efficient and…
Authors not listed
Identifying synthesis routes from knowledge graphs poses challenges beyond retrosynthesis, including path–finding artifacts and data issues. We introduce “SynGPS”, a novel algorithm that overcomes these limitations by identifying viable routes even with common artifacts. SynGPS can resolve nonsensical cycles…
Keisuke Ozawa
Statistically weighted principal component analysis (wPCA) is widely used to reduce the noise of scanning transmission electron microscopy-energy-dispersive X-ray (STEM-EDX) spectroscopy data. It is beneficial to retain the spatial resolution of observation in each step of the analysis, but the direct application of…