RMSE-ELM: Recursive Model based Selective Ensemble of Extreme Learning Machines for Robustness Improvement

Extreme learning machine (ELM) as an emerging branch of shallow networks has shown its excellent generalization and fast learning speed. However, for blended data, the robustness of ELM is weak because its weights and biases of hidden nodes are set randomly. Moreover, the noisy data exert a negative effect. To solve this problem, a new framework called RMSE-ELM is proposed in this paper. It is a two-layer recursive model. In the first layer, the framework trains lots of ELMs in different groups concurrently, then employs selective ensemble to pick out an optimal set of ELMs in each group, which can be merged into a large group of ELMs called candidate pool. In the second layer, selective ensemble is recursively used on candidate pool to acquire the final ensemble. In the experiments, we apply UCI blended datasets to confirm the robustness of our new approach in two key aspects (mean square error and standard deviation). The space complexity of our method is increased to some degree, but the results have shown that RMSE-ELM significantly improves robustness with slightly computational time compared with representative methods (ELM, OP-ELM, GASEN-ELM, GASEN-BP and E-GASEN). It becomes a potential framework to solve robustness issue of ELM for high-dimensional blended data in the future.Comment: Accepted for publication in Mathematical Problems in Engineering, 09/22/201

The Exploration of the Application and Management of Project Cost in Smart Buildings Using BIM Technology

In recent years, the slow development of engineering construction management in China has been attributed to outdated management models and low level of informatization. To address the deficiencies in project cost management, relevant departments have proposed the application of BIM technology. BIM technology encompasses a wide range of areas, including planning, construction processes, and cost management. It enables the simulation of these processes to create dynamic real-time building models. Additionally, BIM technology facilitates the rapid transmission of various information during the construction process, thereby improving the overall efficiency of the construction project. Information technology has become ubiquitous in people’s daily lives, and this foundation has led to the emergence of smart buildings. The operation and development of smart building projects require effective project cost management. Accurate cost estimation can help construction enterprises effectively control project costs and increase economic benefits. However, many companies still rely on traditional methods such as manual measurement based on drawings, bill of quantities, or engineering rates, which often lead to calculation errors. The application of BIM technology in project cost management can help alleviate this problem

High-Throughput Screening of Transition Metal Single-Atom Catalysts for Nitrogen Reduction Reaction

The discovery of metals as catalytic centers for nitrogen reduction reactions has stimulated great enthusiasm for single-atom catalysts. However, the poor activity and low selectivity of available SACs are far away from the industrial requirement. Through the high throughout first principles calculations, the doping engineering can effectively regulate the NRR performance of b-Sb monolayer. Especially, the origin of activated N2 is revealed from the perspective of the electronic structure of the active center. Among the 24 transition metal dopants, Re@Sb and Tc@Sb showed the best NRR catalytic performance with a low limiting potential. The Re@Sb and Tc@Sb also could significantly inhibit HER and achieve a high theoretical Faradaic efficiency of 100%. Our findings not only accelerate discovery of catalysts for ammonia synthesis but also contribute to further elucidate the structure-performance correlations

Environmental taxes and the effects of partial privatization on environmental R&D, environment and welfare

This paper considers environmental R&D (ER&D) of clean technology for reducing pollutant emissions in a polluting mixed duopoly and analyzes partial privatization’s impacts on ER&D, environment and welfare. In the situation that environmental taxes are exogenously given, it finds that the impacts of privatization policy on ER&D and environment critically depend on the level of environmental damage. However, regardless of the marginal damage, an appropriate partial-privatization policy can increase social welfare. In addition, it also considers an endogenously determined optimal environmental tax and shows that if the marginal damage is high, partial privatization’s impacts on ER&D, environment and social welfare may be not the same as the exogenous environmental tax situatio

Molecular cloning of porcine Siglec-3, Siglec-5 and Siglec-10, and identification of Siglec-10 as an alternative receptor for porcine reproductive and respiratory syndrome virus (PRRSV)

In recent years, several entry mediators have been characterized for porcine reproductive and respiratory syndrome virus (PRRSV). Porcine sialoadhesin [pSn, also known as sialic acid-binding immunoglobulin-type lectin (Siglec-1)] and porcine CD163 (pCD163) have been identified as the most important host entry mediators that can fully coordinate PRRSV infection into macrophages. However, recent isolates have not only shown a tropism for sialoadhesin-positive cells, but also for sialoadhesin-negative cells. This observation might be partly explained by the existence of additional receptors that can support PRRSV binding and entry. In the search for new receptors, recently identified porcine Siglecs (Siglec-3, Siglec-5 and Siglec-10), members of the same family as sialoadhesin, were cloned and characterized. Only Siglec-10 was able to significantly improve PRRSV infection and production in a CD163-transfected cell line. Compared with sialoadhesin, Siglec-10 performed equally effectively as a receptor for PRRSV type 2 strain MN-184, but it was less capable of supporting infection with PRRSV type 1 strain LV (Lelystad virus). Siglec-10 was demonstrated to be involved in the endocytosis of PRRSV, confirming the important role of Siglec-10 in the entry process of PRRSV. In conclusion, it can be stated that PRRSV may use several Siglecs to enter macrophages, which may explain the strain differences in the pathogenesis

Split tolerance permits safe Ad5-GUCY2C-PADRE vaccine-induced T-cell responses in colon cancer patients.

Background: The colorectal cancer antigen GUCY2C exhibits unique split tolerance, evoking antigen-specific CD8+, but not CD4+, T-cell responses that deliver anti-tumor immunity without autoimmunity in mice. Here, the cancer vaccine Ad5-GUCY2C-PADRE was evaluated in a first-in-man phase I clinical study of patients with early-stage colorectal cancer to assess its safety and immunological efficacy. Methods: Ten patients with surgically-resected stage I or stage II (pN0) colon cancer received a single intramuscular injection of 1011 viral particles (vp) of Ad5-GUCY2C-PADRE. Safety assessment and immunomonitoring were carried out for 6 months following immunization. This trial employed continual monitoring of both efficacy and toxicity of subjects as joint primary outcomes. Results: All patients receiving Ad5-GUCY2C-PADRE completed the study and none developed adverse events greater than grade 1. Antibody responses to GUCY2C were detected in 10% of patients, while 40% exhibited GUCY2C-specific T-cell responses. GUCY2C-specific responses were exclusively CD8+ cytotoxic T cells, mimicking pre-clinical studies in mice in which GUCY2C-specific CD4+ T cells are eliminated by self-tolerance, while CD8+ T cells escape tolerance and mediate antitumor immunity. Moreover, pre-existing neutralizing antibodies (NAbs) to the Ad5 vector were associated with poor vaccine-induced responses, suggesting that Ad5 NAbs oppose GUCY2C immune responses to the vaccine in patients and supported by mouse studies. Conclusions: Split tolerance to GUCY2C in cancer patients can be exploited to safely generate antigen-specific cytotoxic CD8+, but not autoimmune CD4+, T cells by Ad5-GUCY2C-PADRE in the absence of pre-existing NAbs to the viral vector. TRIAL REGISTRATION: This trial (NCT01972737) was registered at ClinicalTrials.gov on October 30th, 2013. https://clinicaltrials.gov/ct2/show/NCT01972737

How to Evaluate Semantic Communications for Images with ViTScore Metric?

Semantic communications (SC) have been expected to be a new paradigm shifting to catalyze the next generation communication, whose main concerns shift from accurate bit transmission to effective semantic information exchange in communications. However, the previous and widely-used metrics for images are not applicable to evaluate the image semantic similarity in SC. Classical metrics to measure the similarity between two images usually rely on the pixel level or the structural level, such as the PSNR and the MS-SSIM. Straightforwardly using some tailored metrics based on deep-learning methods in CV community, such as the LPIPS, is infeasible for SC. To tackle this, inspired by BERTScore in NLP community, we propose a novel metric for evaluating image semantic similarity, named Vision Transformer Score (ViTScore). We prove theoretically that ViTScore has 3 important properties, including symmetry, boundedness, and normalization, which make ViTScore convenient and intuitive for image measurement. To evaluate the performance of ViTScore, we compare ViTScore with 3 typical metrics (PSNR, MS-SSIM, and LPIPS) through 5 classes of experiments. Experimental results demonstrate that ViTScore can better evaluate the image semantic similarity than the other 3 typical metrics, which indicates that ViTScore is an effective performance metric when deployed in SC scenarios