Data Sheet 1_Emotional prompting amplifies disinformation generation in AI large language models.docx by Rasita Vinay (21006911)

“…Introduction<p>The emergence of artificial intelligence (AI) large language models (LLMs), which can produce text that closely resembles human-written content, presents both opportunities and risks. …”

Presentation 1_Tracking priming-induced language recovery in aphasia with pre-trained language models.zip by Yan Cong (1491478)

Data Sheet 1_Tracking priming-induced language recovery in aphasia with pre-trained language models.zip by Yan Cong (1491478)

Feasibility of AI-powered assessment scoring: Can large language models replace human raters? by Michael Jaworski III (22156096)

Overview of the WeARTolerance program. by Ana Beato (20489933)

“…The quantitative results from Phase 1 demonstrated a decreasing trend in all primary outcomes. In phase 2, participants acknowledged the activities’ relevance, reported overall satisfaction with the program, and showed great enthusiasm and willingness to learn more. …”

Decrease in loss during the training process. by Muhammad Idrees (92773)

<b>Aversive learning decreases aversive-reinforcer sensitivity</b><b> </b><b>in honey bees</b> by Martin GIURFA (8649351)

Interaction plot with 95% confidence intervals generated from the GAM regression model 1 in Table 2 examining initial ED SBP and management group interactions. by Randeep S. Jawa (20489181)

GPT-4 results in decreased error rate for all tasks with increasing complexity when compared to GPT-3.5-turbo. by Jacqueline A. Jansen (14894276)

Machine Learning Models for High Explosive Crystal Density and Performance by Jack V. Davis (9175514)

“…Using these values, we trained machine learning models for the prediction of density, detonation velocity and detonation pressure. …”

Analysis of errors of the AI model on accuracy and decision times for different tasks during the retrospective reader study. (a) by Kerol Djoumessi (21349992)

Feature engineering. by Youli Jiang (15249070)

Data from: Colony losses of stingless bees increase in agricultural areas, but decrease in forested areas by Malena Sibaja Leyton (18400983)

“…On average, meliponiculturists lost 43.4 % of their stingless bee colonies annually, 33.3 % during the rainy season, and 22.0 % during the dry season. We found that colony losses during the rainy season decreased with higher abundance of forested areas and increased with higher abundance of agricultural area around meliponaries. …”

Effect of active learning. by Jia Li (160557)

“…This paper introduces a novel classification algorithm, ASGBC, intended to tackle related challenges in diagnosing gallbladder cancer using B-ultrasound images. Firstly, we combine active learning with self-supervised learning to decrease the reliance on labeled data. …”

Model and learning rule. by Janis Keck (21587252)

“…<b>(C), (D)</b> Invariance of learning rules with respect to temporal order. We plot synaptic weight change of a single synapse in a setup with a single pre- and postsynaptic neuron, respectively. …”

A novel RNN architecture to improve the precision of ship trajectory predictions by Martha Dais Ferreira (18704596)

“…This type of monitoring often relies on the use of navigation systems such as the Automatic Identification System (AIS). AIS data has been used to support the defense teams when identifying equipment defects, locating suspicious activity, ensuring ship collision avoidance, and detecting hazardous events. …”

Inherently interpretable deep learning framework highlights clinically relevant image regions. by Kerol Djoumessi (21349992)

Evaluation of the effectiveness of double task. by Fan Yang (1413)

“…The Spatial Attention Based Dual-Branch Information Fusion Block links these branches, enabling mutual benefit. Furthermore, we present a structured pruning method grounded in channel attention to decrease parameter count, mitigate overfitting, and uphold segmentation accuracy. …”

Evaluation of the effectiveness of pruning. by Fan Yang (1413)

“…The Spatial Attention Based Dual-Branch Information Fusion Block links these branches, enabling mutual benefit. Furthermore, we present a structured pruning method grounded in channel attention to decrease parameter count, mitigate overfitting, and uphold segmentation accuracy. …”

The summary of ablation experiment. by Fan Yang (1413)

“…The Spatial Attention Based Dual-Branch Information Fusion Block links these branches, enabling mutual benefit. Furthermore, we present a structured pruning method grounded in channel attention to decrease parameter count, mitigate overfitting, and uphold segmentation accuracy. …”