Diagnosis and surgical treatment of multiple endocrine neoplasia type 2A

BACKGROUND: This study aims to introduce the diagnosis and surgical treatment of the rare disease multiple endocrine neoplasia type 2A (MEN 2A). METHODS: Thirteen cases of MEN 2A were diagnosed as medullary thyroid carcinoma (MTC) and pheochromocytoma by biochemical tests and imaging examination. They were treated by bilateral adrenal tumor excision or laparoscopic surgery. RESULTS: Nine patients were treated by bilateral adrenal tumor excision and the remaining four were treated by laparoscopic surgery for pheochromocytoma. Ten patients were treated by total thyroidectomy and bilateral lymph nodes dissection and the remaining three were treated by unilateral thyroidectomy for MTC. Up to now, three patients have died of MTC distant metastasis. CONCLUSIONS: We confirmed that MEN 2A can be diagnosed by biochemical tests and imaging examination when genetic testing is not available. Surgical excision is the predominant way to treat MEN 2A; pheochromocytoma should be excised at first when pheochromocytoma and MTC occur simultaneously

Hierarchical triple mergers: testing Hawking's area theorem with the inspiral signals

Hawking's area theorem is one of the fundamental laws of black holes (BHs), which has been tested at a confidence level of $\sim 95\%$ with gravitational wave (GW) observations by analyzing the inspiral and ringdown portions of GW signals independently. In this work, we propose to carry out the test in a new way with the hierarchical triple merger (i.e., two successive BH mergers occurred sequentially within the observation window of GW detectors), for which the properties of the progenitor BHs and the remnant BH of the first coalescence can be reliably inferred from the inspiral portions of the two mergers. As revealed in our simulation, a test of the BH area law can be achieved at the significance level of $\gtrsim 3\sigma$ for the hierarchical triple merger events detected in LIGO/Virgo/KAGRA's O4/O5 runs. If the hierarchical triple mergers contribute a $\gtrsim 0.1\%$ fraction to the detected BBHs, a precision test of the BH area law with such systems is achievable in the near future. Our method also provides an additional criterion to establish the hierarchical triple merger origin of some candidate events.Comment: 5 pages, 5 figures, 1 tabl

When Prompt-based Incremental Learning Does Not Meet Strong Pretraining

Incremental learning aims to overcome catastrophic forgetting when learning deep networks from sequential tasks. With impressive learning efficiency and performance, prompt-based methods adopt a fixed backbone to sequential tasks by learning task-specific prompts. However, existing prompt-based methods heavily rely on strong pretraining (typically trained on ImageNet-21k), and we find that their models could be trapped if the potential gap between the pretraining task and unknown future tasks is large. In this work, we develop a learnable Adaptive Prompt Generator (APG). The key is to unify the prompt retrieval and prompt learning processes into a learnable prompt generator. Hence, the whole prompting process can be optimized to reduce the negative effects of the gap between tasks effectively. To make our APG avoid learning ineffective knowledge, we maintain a knowledge pool to regularize APG with the feature distribution of each class. Extensive experiments show that our method significantly outperforms advanced methods in exemplar-free incremental learning without (strong) pretraining. Besides, under strong retraining, our method also has comparable performance to existing prompt-based models, showing that our method can still benefit from pretraining. Codes can be found at https://github.com/TOM-tym/APGComment: Accepted to ICCV 202

Research on Recognition and Evaluation of Traffic Guide Sign

Traffic guide signs are effective only when they are clearly recognized by drivers. Three experiments were conducted in this study. In the first, the influence factors of guide sign recognition were studied. This study investigated 11 main factors with a convenience sample of drivers from Nanjing city in China. Weights of different influence factors were obtained through analytic hierarchy process (AHP). The results showed that the setting position, occlusion degree, and character size of guide sign had the most significant influence on the guide sign recognition, while other factors were less important. In the second stage, an evaluation model of guide sign recognition was developed based on weights of different factors. Four equations were presented to calculate the comprehensive score of guide sign, and the level of recognition was divided into five grades according to the comprehensive score. At last, a typical case in Nanjing was studied to verify the rationality and reliability of the evaluation model. Results from the real application indicate that the method had good applicability and can be used by traffic engineers

Domain Adaptation Tracker With Global and Local Searching

For the convolutional neural network (CNN)-based trackers, most of them locate the target only within a local area, which makes the trackers hard to recapture the target after drifting into the background. Besides, most state-of-the-art trackers spend a large amount of time on training the CNN-based classification networks online to adapt to the current domain. In this paper, to address the two problems, we propose a robust domain adaptation tracker based on the CNNs. The proposed tracker contains three CNNs: a local location network (LL-Net), a global location network (GL-Net), and a domain adaptation classification network (DA-Net). For the former problem, if we come to the conclusion that the tracker drifts into the background based on the output of the LL-Net, we will search for the target in a global area of the current frame based on the GL-Net. For the latter problem, we propose a CNN-based DA-Net with a domain adaptation (DA) layer. By pre-training the DA-Net offline, the DA-Net can adapt to the current domain by only updating the parameters of the DA layer in one training iteration when the online training is triggered, which makes the tracker run five times faster than MDNet with comparable tracking performance. The experimental results show that our tracker performs favorably against the state-of-the-art trackers on three popular benchmarks