MS decreases the larger amplitude distribution in adult rats. by Jesús David Ayala-Rodríguez (17364296)

YOLO11. by Junyan Wang (4738518)

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Matrix crosslinking decreases cell migration persistence. by Michael Pamonag (12295175)

SCI-YOLO11. by Junyan Wang (4738518)

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A noxious temperature decrease without a prior increase produces a subtly larger decrease in pain intensity. by Benedict J. Alter (9745466)

The network structure of FasterNet--YOLO. by Shiwei Yu (6060308)

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Correlations of cytokines that significantly change between the pre-infusion and post-infusion blood draws pearson’s correlation between cytokine pairs is indicated by color and si... by Jordan K. Abbott (8934239)

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Detection effect diagram of YOLO11. by Jianming Meng (22209039)

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Larger example of stimuli with cue relevant for Task 3. by Marcel F. Hinss (14675457)

Long COVID prevalence decreases with vaccine uptake in the U.S. by Manlio De Domenico (626037)

“…<p>(A) Prevalence in U.S. states and the U.S. exhibits a decreasing trend with respect to vaccine uptake, both in the population vaccinated with at least one dose (top) and two doses (bottom), with the largest gap between 100% vaccinated and 100% unvaccinated scenarios observed in the reference population of adults who had COVID-19. …”

PR diagram of YOLOv8n and BGS-YOLO: (a) YOLOv8n and (b) BGS-YOLO. by Zeyu Liang (4813593)

The architecture of the proposed BGM-YOLO model. by Chenghai Yu (21440036)

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Overall network architecture of FCMI-YOLO. by Junjie Lu (160350)

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Structure of UAS-YOLO network. by Ju Liang (4277062)

“…These characteristics impose high demands on detection algorithms in terms of fine-grained feature extraction, cross-scale fusion capability, and occlusion resistance.The YOLOv11s model has significant limitations in practical applications: its feature extraction module has a single semantic representation, the traditional feature pyramid network has limited capability to detect multi-scale targets, and it lacks an effective feature compensation mechanism when targets are occluded.To address these issues, we propose a UAV aerial small target detection algorithm named UAS-YOLO (Universal Inverted Bottleneck with Adaptive BiFPN and Separated and Enhancement Attention module YOLO), which incorporates three key optimizations. …”

Points represent regions where the cluster coefficient was significantly larger in HC compared to MCI, at sparsity level of 0.15. by Therese M. Gilligan (7460906)

Comparison of YOLO-DSTD model with other models. by Jianming Meng (22209039)

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Architecture of GOLD-YOLO. by Xichun Chen (22001791)

“…The network architecture integrates the GOLD-YOLO module, an advanced object detection approach, alongside the updated deformable convolutional networks (DCNv3). …”

Decrease of injuries. by Petr Fulin (8380320)

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Example of predicted effects of choice bundling on preference for larger, later rewards (LLRs) over smaller, sooner rewards (SSRs). by Jeffrey S. Stein (11689918)

(a) YOLOv5s; (b) FasterNet-YOLO. by Shiwei Yu (6060308)

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