On Wasserstein Distributionally Robust Mean Semi-Absolute Deviation Portfolio Model: Robust Selection and Efficient Computation

This paper focuses on the Wasserstein distributionally robust mean-lower semi-absolute deviation (DR-MLSAD) model, where the ambiguity set is a Wasserstein ball centered on the empirical distribution of the training sample. This model can be equivalently transformed into a convex problem. We develop a robust Wasserstein profile inference (RWPI) approach to determine the size of the Wasserstein radius for DR-MLSAD model. We also design an efficient proximal point dual semismooth Newton (PpdSsn) algorithm for the reformulated equivalent model. In numerical experiments, we compare the DR-MLSAD model with the radius selected by the RWPI approach to the DR-MLSAD model with the radius selected by cross-validation, the sample average approximation (SAA) of the MLSAD model, and the 1/N strategy on the real market datasets. Numerical results show that our model has better out-of-sample performance in most cases. Furthermore, we compare PpdSsn algorithm with first-order algorithms and Gurobi solver on random data. Numerical results verify the effectiveness of PpdSsn in solving large-scale DR-MLSAD problems

Dual Newton Proximal Point Algorithm for Solution Paths of the L1-Regularized Logistic Regression

The l1-regularized logistic regression is a widely used statistical model in data classification. This paper proposes a dual Newton method based proximal point algorithm (PPDNA) to solve the l1-regularized logistic regression problem with bias term. The global and local convergence of PPDNA hold under mild conditions. The computational cost of a semismooth Newton (Ssn) algoithm for solving subproblems in the PPDNA can be effectively reduced by fully exploiting the second-order sparsity of the problem. We also design an adaptive sieving (AS) strategy to generate solution paths for the l1-regularized logistic regression problem, where each subproblem in the AS strategy is solved by the PPDNA. This strategy exploits active set constraints to reduce the number of variables in the problem, thereby speeding up the PPDNA for solving a series of problems. Numerical experiments demonstrate the superior performance of the PPDNA in comparison with some state-of-the-art second-order algorithms and the efficiency of the AS strategy combined with the PPDNA for generating solution paths

Correlation between periostin and SNCG and esophageal cancer invasion, infiltration and apoptosis

AbstractObjectiveTo investigate the correlation between periostin and SNCG and esophageal cancer invasion, infiltration and apoptosis.MethodsA total of 78 cases esophageal surgical resection specimens were collected, expression of periostin and SNCG in esophageal cancer were detected. Effect of periostin and SNCG in esophageal carcinoma invasion and infiltration was analyzed.ResultsThe upregulated rate of periostin had significant difference in esophageal cancer tissues (39.74%), adjacent tissues (17.86%) and normal tissues (0.00%); The positive expression rates of SNCG had significant difference in esophageal cancer tissues (61.54%), adjacent tissues (32.14%) and normal tissues (1.96%); The upregulated rate of periostin had a significant correlation with lymph node metastasis, adventitia invasion, TNM stage; The positive expression rates of SNCG had a significant correlation with differentiation degree, lymph node metastasis, adventitia invasion, TNM stage; Apoptosis index of the positive of expression of SNCG of esophageal cancer tissue (4.541±2.267) was significantly lower than that of the negative expression (7.316±2.582) (P<0.05).ConclusionsSNCG may play an important role in invasion, infiltration and apoptosis of esophageal cancer and serve as target spots in the targeted therapy of esophageal cancer

Large Language Model for Participatory Urban Planning

Participatory urban planning is the mainstream of modern urban planning that involves the active engagement of residents. However, the traditional participatory paradigm requires experienced planning experts and is often time-consuming and costly. Fortunately, the emerging Large Language Models (LLMs) have shown considerable ability to simulate human-like agents, which can be used to emulate the participatory process easily. In this work, we introduce an LLM-based multi-agent collaboration framework for participatory urban planning, which can generate land-use plans for urban regions considering the diverse needs of residents. Specifically, we construct LLM agents to simulate a planner and thousands of residents with diverse profiles and backgrounds. We first ask the planner to carry out an initial land-use plan. To deal with the different facilities needs of residents, we initiate a discussion among the residents in each community about the plan, where residents provide feedback based on their profiles. Furthermore, to improve the efficiency of discussion, we adopt a fishbowl discussion mechanism, where part of the residents discuss and the rest of them act as listeners in each round. Finally, we let the planner modify the plan based on residents' feedback. We deploy our method on two real-world regions in Beijing. Experiments show that our method achieves state-of-the-art performance in residents satisfaction and inclusion metrics, and also outperforms human experts in terms of service accessibility and ecology metrics.Comment: arXiv admin note: text overlap with arXiv:2402.0169

Genetic heterogeneity of pseudoxanthoma elasticum: the Chinese signature profile of ABCC6 and ENPP1 mutations.

Pseudoxanthoma elasticum (PXE), an autosomal recessive disorder characterized by ectopic mineralization, is caused by mutations in the ABCC6 gene. We examined clinically 29 Chinese PXE patients from unrelated families, so far the largest cohort of Asian PXE patients. In a subset of 22 patients, we sequenced ABCC6 and another candidate gene, ENPP1, and conducted pathogenicity analyses for each variant. We identified a total of 17 distinct mutations in ABCC6, 15 of them being, to our knowledge, previously unreported, including 5 frameshift and 10 missense variants. In addition, a missense mutation in combination with a recurrent nonsense mutation in ENPP1 was discovered in a pediatric PXE case. No cases with p.R1141X or del23-29 mutations, common in Caucasian patient populations, were identified. The 10 missense mutations in ABCC6 were expressed in the mouse liver via hydrodynamic tail-vein injections. One mutant protein showed cytoplasmic accumulation indicating abnormal subcellular trafficking, while the other nine mutants showed correct plasma membrane location. These nine mutations were further investigated for their pathogenicity using a recently developed zebrafish mRNA rescue assay. Minimal rescue of the morpholino-induced phenotype was achieved with eight of the nine mutant human ABCC6 mRNAs tested, implying pathogenicity. This study demonstrates that the Chinese PXE population harbors unique ABCC6 mutations. These genetic data have implications for allele-specific therapy currently being developed for PXE