A minimal U(1)′U(1)^\prime extension of MSSM in light of the B decay anomaly

Motivated by the $R_K$ and $R_{K^*}$ anomalies from B decays, we extend the minimal supersymmetric model with a non-universal anomaly-free $U(1)^\prime$ gauge symmetry, coupling non-universally to the lepton sector as well as the quark sector. In particular, only the third generation quarks are charged under this $U(1)^\prime$, which can easily evade the dilepton bound from the LHC searches. An extra singlet is introduced to break this $U(1)^\prime$ symmetry allowing for the $\mu$-term to be generated dynamically. The relevant constraints of $B_s-\bar{B}_s$ mixing, $D^0-\bar{D}^0$ mixing and the LHC dilepton searches are considered. We find that in the allowed parameter space this $U(1)^\prime$ gauge interaction can accommodate the $R_K$ and $R_{K^*}$ anomalies and weaken considerably the $Z^\prime$ mass limits while remaining perturbative up to the Planck scale.Comment: 12 pages,2 figure

How Important are Good Method Names in Neural Code Generation? A Model Robustness Perspective

Pre-trained code generation models (PCGMs) have been widely applied in neural code generation which can generate executable code from functional descriptions in natural languages, possibly together with signatures. Despite substantial performance improvement of PCGMs, the role of method names in neural code generation has not been thoroughly investigated. In this paper, we study and demonstrate the potential of benefiting from method names to enhance the performance of PCGMs, from a model robustness perspective. Specifically, we propose a novel approach, named RADAR (neuRAl coDe generAtor Robustifier). RADAR consists of two components: RADAR-Attack and RADAR-Defense. The former attacks a PCGM by generating adversarial method names as part of the input, which are semantic and visual similar to the original input, but may trick the PCGM to generate completely unrelated code snippets. As a countermeasure to such attacks, RADAR-Defense synthesizes a new method name from the functional description and supplies it to the PCGM. Evaluation results show that RADAR-Attack can reduce the CodeBLEU of generated code by 19.72% to 38.74% in three state-of-the-art PCGMs (i.e., CodeGPT, PLBART, and CodeT5) in the fine-tuning code generation task, and reduce the Pass@1 of generated code by 32.28% to 44.42% in three state-of-the-art PCGMs (i.e., Replit, CodeGen, and CodeT5+) in the zero-shot code generation task. Moreover, RADAR-Defense is able to reinstate the performance of PCGMs with synthesized method names. These results highlight the importance of good method names in neural code generation and implicate the benefits of studying model robustness in software engineering.Comment: UNDER REVIE

Gastric adenocarcinoma of the fundic gland: A review of clinicopathological characteristics, treatment and prognosis

Gastric adenocarcinoma of the fundic gland is a rare, well-differentiated gastric cancer entity, and very few patients transition to poorly differentiated tubular adenocarcinoma during progression. Gastric adenocarcinoma of the fundic gland originates from the mucosa of the gastric fundic gland, usually without chronic gastritis or intestinal metaplasia. Histologically, the tumor cells are closely arranged to form anastomosing tubular glands, and more than 95% of tumor cells differentiate towards chief cells. Most gastric adenocarcinoma of the fundic gland cases are characterized by submucosal involvement, but the tumor volume is usually small, with lymphatic and vascular invasion rarely observed. Therefore, endoscopic submucosal dissection can be an ideal treatment, leading to a favorable prognosis, and recurrence and metastasis of the disease are uncommon