Experimental setup and trajectory estimation methodology. by Nouman Javed (16624785)

The illustration depicts our CQ-CNN architecture for binary image classification. by Mominul Islam (3513758)

MARGO workflow, tracking algorithm, and sample behavioral box. by Zach Werkhoven (6473165)

Surround Suppression mechanism. by Elnaz Nemati (21402730)

Table_1_Identification of sources of DIF using covariates in patient-reported outcome measures: a simulation study comparing two approaches based on Rasch family models.DOCX by Yseulys Dubuy (16809567)

“…<p>When analyzing patient-reported outcome (PRO) data, sources of differential item functioning (DIF) can be multiple and there may be more than one covariate of interest. …”

Fig 12 - by Nisha Yadav (366131)

Data_Sheet_1_Multiclass Classification Based on Combined Motor Imageries.pdf by Cecilia Lindig-León (7889777)

“…Here, we propose a solution to address the limitation of identifiable motor activities by using combined MIs (i.e., MIs involving 2 or more body parts at the same time). And we propose two new multilabel uses of the Common Spatial Pattern (CSP) algorithm to optimize the signal-to-noise ratio, namely MC2CMI and MC2SMI approaches. …”

fdata-02-00029_Reflections on Gender Analyses of Bibliographic Corpora.pdf by Helena Mihaljević (8541108)

“…An author's gender is typically inferred from their name, further reduced to a binary feature by an algorithmic procedure. This and subsequent data processing steps introduce biases whose effects are hard to estimate. …”

fdata-02-00029_Reflections on Gender Analyses of Bibliographic Corpora.xml by Helena Mihaljević (8541108)

“…An author's gender is typically inferred from their name, further reduced to a binary feature by an algorithmic procedure. This and subsequent data processing steps introduce biases whose effects are hard to estimate. …”

3D Microvascular Image Data and Labels for Machine Learning by Natalie Holroyd (7099391)

“…The image data has been processed using an asymmetric deconvolution algorithm described by ​Walsh et al., 2020​. …”

Development of a Battery of <i>In Silico</i> Prediction Tools for Drug-Induced Liver Injury from the Vantage Point of Translational Safety Assessment by James Rathman (1632322)

“…A human DILI training set of 305 oral marketed drugs was prepared and a binary classification scheme applied. The second knowledge base consists of mammalian repeated dose toxicity with liver toxicity data from various regulatory sources. …”

DataSheet_1_Automated detection and detection range of primate duets: a case study of the red titi monkey (Plecturocebus discolor) using passive acoustic monitoring.pdf by Silvy M. van Kuijk (16881867)

“…We studied the duetting behavior of pair-living red titi monkeys (Plecturocebus discolor) using PAM coupled with an open-source automated detection tool. Using data on spontaneous duetting by one titi pair, combined with recordings from two Song Meter SM2 ARUs placed within their home range, we estimated that the average source level of titi duets was ~105 dB re 20 μPa at 1 m with an attenuation rate of 8 dB per doubling of distance, and we determined that the detection radius for manual annotation of duets in audio recordings was at least 125 to 200 m, depending on the approach used. …”

Table_2_Automated detection and detection range of primate duets: a case study of the red titi monkey (Plecturocebus discolor) using passive acoustic monitoring.pdf by Silvy M. van Kuijk (16881867)

“…We studied the duetting behavior of pair-living red titi monkeys (Plecturocebus discolor) using PAM coupled with an open-source automated detection tool. Using data on spontaneous duetting by one titi pair, combined with recordings from two Song Meter SM2 ARUs placed within their home range, we estimated that the average source level of titi duets was ~105 dB re 20 μPa at 1 m with an attenuation rate of 8 dB per doubling of distance, and we determined that the detection radius for manual annotation of duets in audio recordings was at least 125 to 200 m, depending on the approach used. …”

Table_3_Automated detection and detection range of primate duets: a case study of the red titi monkey (Plecturocebus discolor) using passive acoustic monitoring.pdf by Silvy M. van Kuijk (16881867)

“…We studied the duetting behavior of pair-living red titi monkeys (Plecturocebus discolor) using PAM coupled with an open-source automated detection tool. Using data on spontaneous duetting by one titi pair, combined with recordings from two Song Meter SM2 ARUs placed within their home range, we estimated that the average source level of titi duets was ~105 dB re 20 μPa at 1 m with an attenuation rate of 8 dB per doubling of distance, and we determined that the detection radius for manual annotation of duets in audio recordings was at least 125 to 200 m, depending on the approach used. …”

Table_1_Automated detection and detection range of primate duets: a case study of the red titi monkey (Plecturocebus discolor) using passive acoustic monitoring.pdf by Silvy M. van Kuijk (16881867)

“…We studied the duetting behavior of pair-living red titi monkeys (Plecturocebus discolor) using PAM coupled with an open-source automated detection tool. Using data on spontaneous duetting by one titi pair, combined with recordings from two Song Meter SM2 ARUs placed within their home range, we estimated that the average source level of titi duets was ~105 dB re 20 μPa at 1 m with an attenuation rate of 8 dB per doubling of distance, and we determined that the detection radius for manual annotation of duets in audio recordings was at least 125 to 200 m, depending on the approach used. …”

An AI-based Ecosystem for Real-time Gravitational Wave Analyses by Erik Katsavounidis (19369348)

“…These searches include A-frame, a low-latency machine learning pipeline for compact binary sources of gravitational waves, DeepClean, a deep learning-based denoising scheme for astrophysical gravitational waves, GWAK, a semi-supervised AI strategy to identify unmodeled gravitational wave transients, and AMPLFI, a pipeline for deep learning-based parameter estimation, leading to latency improvements for multi-messenger targets. …”