Comparison of different pruning rates. by Zhongjian Xie (4633099)

“…Secondly, a lightweight convolutional module is introduced to replace the standard convolutions in the Efficient Long-range Aggregation Network (ELAN-A) module, and the channel pruning techniques are applied to further decrease the model’s complexity. Finally, the experiment significantly enhanced the efficiency of feature extraction and the detection accuracy of the model algorithm through the integration of the Dynamic Head (DyHead) module, the Content-Aware Re-Assembly of Features (CARAFE) module, and the incorporation of knowledge distillation techniques. …”

Comparison of experimental results at ablation. by Zhongjian Xie (4633099)

“…Secondly, a lightweight convolutional module is introduced to replace the standard convolutions in the Efficient Long-range Aggregation Network (ELAN-A) module, and the channel pruning techniques are applied to further decrease the model’s complexity. Finally, the experiment significantly enhanced the efficiency of feature extraction and the detection accuracy of the model algorithm through the integration of the Dynamic Head (DyHead) module, the Content-Aware Re-Assembly of Features (CARAFE) module, and the incorporation of knowledge distillation techniques. …”

Result of comparison of different lightweight. by Zhongjian Xie (4633099)

“…Secondly, a lightweight convolutional module is introduced to replace the standard convolutions in the Efficient Long-range Aggregation Network (ELAN-A) module, and the channel pruning techniques are applied to further decrease the model’s complexity. Finally, the experiment significantly enhanced the efficiency of feature extraction and the detection accuracy of the model algorithm through the integration of the Dynamic Head (DyHead) module, the Content-Aware Re-Assembly of Features (CARAFE) module, and the incorporation of knowledge distillation techniques. …”

DyHead Structure. by Zhongjian Xie (4633099)

“…Secondly, a lightweight convolutional module is introduced to replace the standard convolutions in the Efficient Long-range Aggregation Network (ELAN-A) module, and the channel pruning techniques are applied to further decrease the model’s complexity. Finally, the experiment significantly enhanced the efficiency of feature extraction and the detection accuracy of the model algorithm through the integration of the Dynamic Head (DyHead) module, the Content-Aware Re-Assembly of Features (CARAFE) module, and the incorporation of knowledge distillation techniques. …”

The parameters of the training phase. by Zhongjian Xie (4633099)

“…Secondly, a lightweight convolutional module is introduced to replace the standard convolutions in the Efficient Long-range Aggregation Network (ELAN-A) module, and the channel pruning techniques are applied to further decrease the model’s complexity. Finally, the experiment significantly enhanced the efficiency of feature extraction and the detection accuracy of the model algorithm through the integration of the Dynamic Head (DyHead) module, the Content-Aware Re-Assembly of Features (CARAFE) module, and the incorporation of knowledge distillation techniques. …”

Structure of GSConv network. by Zhongjian Xie (4633099)

“…Secondly, a lightweight convolutional module is introduced to replace the standard convolutions in the Efficient Long-range Aggregation Network (ELAN-A) module, and the channel pruning techniques are applied to further decrease the model’s complexity. Finally, the experiment significantly enhanced the efficiency of feature extraction and the detection accuracy of the model algorithm through the integration of the Dynamic Head (DyHead) module, the Content-Aware Re-Assembly of Features (CARAFE) module, and the incorporation of knowledge distillation techniques. …”

Comparison experiment of accuracy improvement. by Zhongjian Xie (4633099)

“…Secondly, a lightweight convolutional module is introduced to replace the standard convolutions in the Efficient Long-range Aggregation Network (ELAN-A) module, and the channel pruning techniques are applied to further decrease the model’s complexity. Finally, the experiment significantly enhanced the efficiency of feature extraction and the detection accuracy of the model algorithm through the integration of the Dynamic Head (DyHead) module, the Content-Aware Re-Assembly of Features (CARAFE) module, and the incorporation of knowledge distillation techniques. …”

Improved model distillation structure. by Zhongjian Xie (4633099)

“…Secondly, a lightweight convolutional module is introduced to replace the standard convolutions in the Efficient Long-range Aggregation Network (ELAN-A) module, and the channel pruning techniques are applied to further decrease the model’s complexity. Finally, the experiment significantly enhanced the efficiency of feature extraction and the detection accuracy of the model algorithm through the integration of the Dynamic Head (DyHead) module, the Content-Aware Re-Assembly of Features (CARAFE) module, and the incorporation of knowledge distillation techniques. …”

Surgical changes through ISIW implantation. by Sayaka Kimura-Uchida (22793666)

“…Pre- and postoperative data are compared by Tukey’s HSD test and asterisks mean statistical significance (p < 0.05). IOP decreases significantly from 3 to 12 months after surgery whereas GMS decreases from one day to 12 months after surgery. …”

Numbers of nuclei and centrioles per cell. by Yang-In Yim (324355)

“…Note that the values in (D) for HSET KD and combined KD of Myo10 KD and HSET, while significantly different from the control, represent decreases in centriole number, not increases (these decreases may be due to the anti-proliferative effects of HSET KD). …”

Functional and strength parameters. by Susanne S. Rauh (21192252)

“…An overall tendency to an increase in FF and a decrease in functional measures were observed over 2 years. …”

Prediction performance of each model. by Ahmed S. Alkhalifah (20721713)

“…Nonsurvivors had a significantly higher time-weighted average MP (TWA-MP) than survivors. …”

Patient inclusion flow chart. by Ahmed S. Alkhalifah (20721713)

“…Nonsurvivors had a significantly higher time-weighted average MP (TWA-MP) than survivors. …”

Characteristics of the study population. by Alexander Ritter (21982738)

“…Psychological assessment revealed that emotional distress and level of concern significantly differed across resilience levels and categories of CT findings. …”

Follow up costs for incidental findings. by Alexander Ritter (21982738)

“…Psychological assessment revealed that emotional distress and level of concern significantly differed across resilience levels and categories of CT findings. …”

Data analysis results. by Min Zhang (111999)

“…<div><p>Increasingly frequent disruptions from diseases, disasters, and human activities pose a significant challenge to the resilience of the agri-food supply chain (AFSCRE). …”

Triangular fuzzy number. by Min Zhang (111999)

“…<div><p>Increasingly frequent disruptions from diseases, disasters, and human activities pose a significant challenge to the resilience of the agri-food supply chain (AFSCRE). …”

Influencing factors system of the AFSCRE. by Min Zhang (111999)

“…<div><p>Increasingly frequent disruptions from diseases, disasters, and human activities pose a significant challenge to the resilience of the agri-food supply chain (AFSCRE). …”

Prediction plots showing (A) the increase of the rodent flea abundance was mostly likely influenced by species identity: <i>Xenopsylla brasilliensis, Pulex irritans</i>, and <i>Din... by Stella T. Kessy (18700173)

Inverse Simpson index (A) and richness (B) shown for the post-operative and perioperative groups. by Allison J. Collier (13977858)

“…<p>Significant decreases in the Inverse Simpson index (A) were noticed between Baseline and Recheck 1 (p = 0.01 perioperative group, p = 0.04 post-operative group), as well as between Baseline and Recheck 2 (p = 0.01 perioperative, p = 0.02 post-operative) in both the perioperative and post-operative groups. …”