25 papers · ranked by Valyu relevance
Hunmin Lee, Jaya Krishna Mandivarapu, Nahom Ogbazghi, Yingshu Li
Capacitive sensing is a prominent technology that is cost-effective and low power consuming with fast recognition speed compared to existing sensing systems. On account of these advantages, Capacitive sensing has been widely studied and commercialized in the domains of touch sensing, localization, existence detection…
Zechen Liang, Yuan‐Gen Wang, Wei Lü, Xiaochun Cao
Semi-Supervised Learning (SSL) has advanced classification tasks by inputting both labeled and unlabeled data to train a model jointly. However, existing SSL methods only consider the unlabeled data whose predictions are beyond a fixed threshold (e.g., 0.95), ignoring the valuable information from those less than 0.95.…
Yukai Lin, Qiangfu Zhao, Giovanni Collodi, Marco Passafiume + 1 more
'Stefano Maddio'] In the current era of advanced IoT technology, human occupancy monitoring and positioning technology is widely used in various scenarios. For example, it can optimize passenger flow in public transportation systems, enhance safety in large shopping malls, and adjust smart home devices based on the…
Hongchuan Huang, Yang Xu, Tingyu Zhao, Yitzhak Yitzhaky
Conventional High Dynamic Range (HDR) image fusion algorithms generally require two or more original images with different exposure times for synthesis, making them unsuitable for real-time processing scenarios such as video streams. Additionally, the synthesized HDR images have the same bit depth as the original…
Fenglin Ding, Yilin Zhao, Zongliang Li, Haibin Tang + 3 more
Anomaly detection and degradation trend prediction are two pivotal tasks in system health management. However, most existing approaches treat them as independent problems and fail to exploit their intrinsic interdependence. In addition, the scarcity of labeled data in real-world scenarios limits the applicability of…
Handuo Zhang, Jun Na, Bin Zhang, Claudia Campolo
With the development of intelligent IoT applications, vast amounts of data are generated by various volume sensors. These sensor data need to be reduced at the sensor and then reconstructed later to save bandwidth and energy. As the reduced data increase, the reconstructed data become less accurate. Usually, the…
Ananias Pereira Neto, Fabrício J. B. Barros
Introduction Wavelet thresholding techniques are crucial in mitigating noise in data communication and storage systems. In image processing, particularly in medical imaging like MRI, noise reduction is vital for improving visual quality and accurate analysis. While existing methods offer noise reduction, they often…
Zahra Farzadpour, Masoumeh Azghani
Fingerprint liveness detection systems have been affected by spoofing, which is a severe threat for fingerprint-based biometric systems. Therefore, it is crucial to develop some techniques to distinguish the fake fingerprints from the real ones. As fingerprint liveness detection systems play an important role in many…
Erik Alan Petersen, Yi Shen
The Auditory Brainstem Response (ABR) can be used to evaluate hearing sensitivity of animals who are unable to respond to behavioral tasks. However, typical data collection methods are time consuming; decreasing the measurement time may save resources or allow researchers to spend more time on other tasks. Here, an…
Martin Jung, Cynthia Fuertes Panizo, Liam Dugan, May Fung + 2 more
Detection Authors: ['Martin Jung' 'Cynthia Fuertes Panizo' 'Liam Dugan' 'May Fung' 'Pin‐Yu Chen' 'Paul Pu Liang'] The advancement of large language models (LLMs) has made it difficult to differentiate human-written text from AI-generated text. Several AI-text detectors have been developed in response, which typically…
Lanfei Sun, Haifeng Zhang, Kai Kang, Xiaoxin Wang + 4 more
Adaptive therapy (AT) improves cancer treatment by controlling the competition between sensitive and resistant cells through treatment holidays. This study highlights the critical role of treatment-holiday thresholds in AT for tumors composed of drug-sensitive and resistant cells. Using a Lotka-Volterra model, the…
Ebenezer R. H. P. Isaac, Akshat Sharma
—A plethora of outlier detectors have been explored in the time series domain, however, in a business sense, not all outliers are anomalies of interest. Existing anomaly detection solutions are confined to certain outlier detectors limiting their applicability to broader anomaly detection use cases. Network KPIs (Key…
Fangfang Hong, Ruby Bouhassira, Jason Chow, Craig Sanders + 4 more
Color discrimination thresholds—the smallest detectable color differences—provide a benchmark for models of color vision, enable quantitative evaluation of eye diseases, and inform the design of display technologies. Despite their importance, a comprehensive characterization of these thresholds has long been considered…
Omar Al-Ghattas, Daniel Sanz-Alonso
This paper studies sparse covariance operator estimation for nonstationary Gaussian processes with sharply varying marginal variance and small correlation lengthscale. We introduce a covariance operator estimator that adaptively thresholds the sample covariance function using an estimate of the variance components.…
Fangfang Hong, Ruby Bouhassira, Jason Chow, Craig Sanders + 4 more
Discrimination thresholds reveal the limits of human perception; scientists have studied them since the time of Fechner in the 1800s. Forced-choice psychophysical methods combined with the method of constant stimuli or parametric adaptive trial-placement procedures are well-suited for measuring one-dimensional…
Xue Yang, Enda Howley, Micheal Schukat
The complexity and scale of IT systems are increasing dramatically, posing many challenges to real-world anomaly detection. Deep learning anomaly detection has emerged, aiming at feature learning and anomaly scoring, which has gained tremendous success. However, little work has been done on the thresholding problem…
Justin M. Aronoff, Jordan Deutsch, Josephine R. LaPapa, Karla Rodriguez + 1 more
The detection and discrimination of interaural time differences (ITDs) underpin many binaural abilities. Obtaining precise “threshold”-ITDs is, however, often quite time consuming. This study investigated the use of a rapid, descending series procedure to estimate threshold-ITDs. The procedure involves beginning with a…
Thaweesak Trongtirakul, Karen Panetta, Artyom M. Grigoryan, Sos S. Agaian + 2 more
'Sos S. Agaian' 'Andrea Murari' 'Jun Chen'] Image segmentation is a fundamental challenge in computer vision, transforming complex image representations into meaningful, analyzable components. While entropy-based multilevel thresholding techniques, including Otsu, Shannon, fuzzy, Tsallis, Renyi, and Kapur approaches…
Daniel Shepherd, Michael J. Hautus
Two adaptations of the Single-Interval Adjust-Matrix Yes-No (SIAM-YN) task, designed to increase the efficiency of absolute threshold estimation, are described. The first, the SIAM Twin Track (SIAM-TT) task, consists of two interleaved tracks of the standard SIAM-YN that are run in the same trial with a single…
Jiyu Tian, Mingchu Li, Liming Chen, Zumin Wang
—Anomaly detection for cyber-physical systems (AD-CPS) is crucial in identifying faults and potential attacks by analyzing the time series of sensor measurements and actuator states. However, current methods lack adaptation to data distribution shifts in both temporal and spatial dimensions as cyber-physical systems…
Shiyue Yang, Graeme Day
We describe the implementation of the Monte Carlo threshold algorithm for molecular crystals as a method to provide an estimate of the energy barriers separating crystal structures. By sampling the local energy minima accessible from multiple starting structures, the simulations yield a global picture of the crystal…
Authors not listed
Graph Neural Networks (GNNs) are powerful tools for molecular property prediction, but they are not magic. When applied to molecules unlike their training data, they produce unreliable predictions that are difficult to detect. The Applicability Domain (AD) concept addresses this by defining regions of chemical space…
Finlay Clark, Graeme Robb, Daniel Cole, Julien Michel
Alchemical absolute binding free energy (ABFE) calculations have substantial potential in drug discovery, but are often prohibitively computationally expensive. To unlock their potential, efficient automated ABFE workflows are required to reduce both computational cost and human intervention. We present a…
Authors not listed
For applications in gas sensing, purification, and capture, we often wish to search a large set of metal-organic frameworks (MOFs) for the top-K in terms of their Henry coefficient of an adsorbate. A molecular simulation to predict the Henry coefficient of a MOF constitutes a Monte Carlo integration where each sample…
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.…