Model for the role of SRS2 in meiotic recombination. by Valentine Petiot (22008837)

Histogram of the extracted parameters. by Qiuyu Du (20760848)

“…Firstly, the aorta was segmented automatically by TotalSegmentator and its centerline was extracted. …”

60 Hz-induced neural entrainment. by MohammadAmin Alamalhoda (22386405)

Selective loss of macrophage subpopulations in lymphoid tissue of <i>Csf1rko</i> rats. by Dylan Carter-Cusack (20624041)

SUMO-dependent disruption of SET-26 complex formation. by Cátia A. Carvalho (20513858)

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

“…This research proposes a new RNN architecture that decreases the prediction error up to 50% for cargo vessels when compared to the OU model. …”

Weak magnetic fields disturb cytoskeleton reassembly after nocodazole exposure, and influence actin polymerization. by Abasalt Bahrami (21989867)

Gyral views of the lateral cortical surface during the wake, N2 sleep, N3 sleep and REM sleep stages. by Giridhar Kalamangalam (20968358)

HG module schematic. by Wenhao Ren (2561731)

“…Additionally, the model achieves a 6.55% reduction in size and a 0.03% decrease in computational complexity. These results highlight the practical applicability and efficiency of the proposed approach for automatic crack detection in building structures, emphasizing the novel integration of feature fusion and attention mechanisms to address challenges in real-time and high-accuracy detection of micro-cracks in complex environments.…”

Label data volume and label distribution. by Wenhao Ren (2561731)

“…Additionally, the model achieves a 6.55% reduction in size and a 0.03% decrease in computational complexity. These results highlight the practical applicability and efficiency of the proposed approach for automatic crack detection in building structures, emphasizing the novel integration of feature fusion and attention mechanisms to address challenges in real-time and high-accuracy detection of micro-cracks in complex environments.…”

The structure of the context guided block. by Wenhao Ren (2561731)

“…Additionally, the model achieves a 6.55% reduction in size and a 0.03% decrease in computational complexity. These results highlight the practical applicability and efficiency of the proposed approach for automatic crack detection in building structures, emphasizing the novel integration of feature fusion and attention mechanisms to address challenges in real-time and high-accuracy detection of micro-cracks in complex environments.…”

SEnet module. by Wenhao Ren (2561731)

“…Additionally, the model achieves a 6.55% reduction in size and a 0.03% decrease in computational complexity. These results highlight the practical applicability and efficiency of the proposed approach for automatic crack detection in building structures, emphasizing the novel integration of feature fusion and attention mechanisms to address challenges in real-time and high-accuracy detection of micro-cracks in complex environments.…”

AC-LayeringNetV2 architecture module. by Wenhao Ren (2561731)

“…Additionally, the model achieves a 6.55% reduction in size and a 0.03% decrease in computational complexity. These results highlight the practical applicability and efficiency of the proposed approach for automatic crack detection in building structures, emphasizing the novel integration of feature fusion and attention mechanisms to address challenges in real-time and high-accuracy detection of micro-cracks in complex environments.…”

Cracks included in the dataset. by Wenhao Ren (2561731)

“…Additionally, the model achieves a 6.55% reduction in size and a 0.03% decrease in computational complexity. These results highlight the practical applicability and efficiency of the proposed approach for automatic crack detection in building structures, emphasizing the novel integration of feature fusion and attention mechanisms to address challenges in real-time and high-accuracy detection of micro-cracks in complex environments.…”

Loss function comparison plot. by Wenhao Ren (2561731)

“…Additionally, the model achieves a 6.55% reduction in size and a 0.03% decrease in computational complexity. These results highlight the practical applicability and efficiency of the proposed approach for automatic crack detection in building structures, emphasizing the novel integration of feature fusion and attention mechanisms to address challenges in real-time and high-accuracy detection of micro-cracks in complex environments.…”

Edge device performance benchmarking. by Wenhao Ren (2561731)

“…Additionally, the model achieves a 6.55% reduction in size and a 0.03% decrease in computational complexity. These results highlight the practical applicability and efficiency of the proposed approach for automatic crack detection in building structures, emphasizing the novel integration of feature fusion and attention mechanisms to address challenges in real-time and high-accuracy detection of micro-cracks in complex environments.…”

Typical error cases. by Wenhao Ren (2561731)

“…Additionally, the model achieves a 6.55% reduction in size and a 0.03% decrease in computational complexity. These results highlight the practical applicability and efficiency of the proposed approach for automatic crack detection in building structures, emphasizing the novel integration of feature fusion and attention mechanisms to address challenges in real-time and high-accuracy detection of micro-cracks in complex environments.…”

Computational efficiency comparison. by Wenhao Ren (2561731)

“…Additionally, the model achieves a 6.55% reduction in size and a 0.03% decrease in computational complexity. These results highlight the practical applicability and efficiency of the proposed approach for automatic crack detection in building structures, emphasizing the novel integration of feature fusion and attention mechanisms to address challenges in real-time and high-accuracy detection of micro-cracks in complex environments.…”

Backbone comparison in crack detection. by Wenhao Ren (2561731)

“…Additionally, the model achieves a 6.55% reduction in size and a 0.03% decrease in computational complexity. These results highlight the practical applicability and efficiency of the proposed approach for automatic crack detection in building structures, emphasizing the novel integration of feature fusion and attention mechanisms to address challenges in real-time and high-accuracy detection of micro-cracks in complex environments.…”

Statistical analysis table for ablation tests. by Wenhao Ren (2561731)

“…Additionally, the model achieves a 6.55% reduction in size and a 0.03% decrease in computational complexity. These results highlight the practical applicability and efficiency of the proposed approach for automatic crack detection in building structures, emphasizing the novel integration of feature fusion and attention mechanisms to address challenges in real-time and high-accuracy detection of micro-cracks in complex environments.…”