MIRACLE: Multi-task Learning based Interpretable Regulation of Autoimmune Diseases through Common Latent Epigenetics

DNA methylation is a crucial regulator of gene transcription and has been linked to various diseases, including autoimmune diseases and cancers. However, diagnostics based on DNA methylation face challenges due to large feature sets and small sample sizes, resulting in overfitting and suboptimal performance. To address these issues, we propose MIRACLE, a novel interpretable neural network that leverages autoencoder-based multi-task learning to integrate multiple datasets and jointly identify common patterns in DNA methylation. MIRACLE's architecture reflects the relationships between methylation sites, genes, and pathways, ensuring biological interpretability and meaningfulness. The network comprises an encoder and a decoder, with a bottleneck layer representing pathway information as the basic unit of heredity. Customized defined MaskedLinear Layer is constrained by site-gene-pathway graph adjacency matrix information, which provides explainability and expresses the site-gene-pathway hierarchical structure explicitly. And from the embedding, there are different multi-task classifiers to predict diseases. Tested on six datasets, including rheumatoid arthritis, systemic lupus erythematosus, multiple sclerosis, inflammatory bowel disease, psoriasis, and type 1 diabetes, MIRACLE demonstrates robust performance in identifying common functions of DNA methylation across different phenotypes, with higher accuracy in prediction dieseases than baseline methods. By incorporating biological prior knowledge, MIRACLE offers a meaningful and interpretable framework for DNA methylation data analysis in the context of autoimmune diseases

Revisiting Sparse Retrieval for Few-shot Entity Linking

Entity linking aims to link ambiguous mentions to their corresponding entities in a knowledge base. One of the key challenges comes from insufficient labeled data for specific domains. Although dense retrievers have achieved excellent performance on several benchmarks, their performance decreases significantly when only a limited amount of in-domain labeled data is available. In such few-shot setting, we revisit the sparse retrieval method, and propose an ELECTRA-based keyword extractor to denoise the mention context and construct a better query expression. For training the extractor, we propose a distant supervision method to automatically generate training data based on overlapping tokens between mention contexts and entity descriptions. Experimental results on the ZESHEL dataset demonstrate that the proposed method outperforms state-of-the-art models by a significant margin across all test domains, showing the effectiveness of keyword-enhanced sparse retrieval.Comment: EMNLP 202

Study of Direct Compression Heat Pump Energy-saving Technology

AbstractAnalyzed the feasibility and necessity of the application of heat pump distillation in the gas separation unit. Through the comparison of the results of different heat exchanger, this paper verified the advantages of the heat exchanger with aluminum porous surface tube. Calculated the power consumption of the compressor by Aspen Plus steady-state process simulation, then the value of COP of the heat pump is obtained, and analyzed the economy of the heat pump distillation, the result shows that utilities and operating cost could be decreased by using heat pump distillation in gas separation unit, and the energy utilization efficiency economic benefits and energy-saving effects could be enhanced

Resilient neural network training for accelerators with computing errors

—With the advancements of neural networks, customized accelerators are increasingly adopted in massive AI applications. To gain higher energy efficiency or performance, many hardware design optimizations such as near-threshold logic or overclocking can be utilized. In these cases, computing errors may happen and the computing errors are difficult to be captured by conventional training on general purposed processors (GPPs). Applying the offline trained neural network models to the accelerators with errors directly may lead to considerable prediction accuracy loss. To address this problem, we explore the resilience of neural network models and relax the accelerator design constraints to enable aggressive design options. First of all, we propose to train the neural network models using the accelerators’ forward computing results such that the models can learn both the data and the computing errors. In addition, we observe that some of the neural network layers are more sensitive to the computing errors. With this observation, we schedule the most sensitive layer to the attached GPP to reduce the negative influence of the computing errors. According to the experiments, the neural network models obtained from the proposed training outperform the original models significantly when the CNN accelerators are affected by computing errors

A Read-and-Select Framework for Zero-shot Entity Linking

Zero-shot entity linking (EL) aims at aligning entity mentions to unseen entities to challenge the generalization ability. Previous methods largely focus on the candidate retrieval stage and ignore the essential candidate ranking stage, which disambiguates among entities and makes the final linking prediction. In this paper, we propose a read-and-select (ReS) framework by modeling the main components of entity disambiguation, i.e., mention-entity matching and cross-entity comparison. First, for each candidate, the reading module leverages mention context to output mention-aware entity representations, enabling mention-entity matching. Then, in the selecting module, we frame the choice of candidates as a sequence labeling problem, and all candidate representations are fused together to enable cross-entity comparison. Our method achieves the state-of-the-art performance on the established zero-shot EL dataset ZESHEL with a 2.55% micro-average accuracy gain, with no need for laborious multi-phase pre-training used in most of the previous work, showing the effectiveness of both mention-entity and cross-entity interaction.Comment: EMNLP 2023 Finding

High-efficiency photoelectric detector based on a p-n homojunction of monolayer black phosphorus

We numerically investigate the high-efficiency photovoltaic effect in lateral p-n homojunction based on monolayer black phosphorus (MBP) by using the non-equilibrium Green's function combined with the density functional theory. Due to the built-in electric field of the p-n junction and the wrinkle structure of MBP, the photocurrent excited by either linearly or elliptically polarized light is significantly enhanced in a wide photon energy range. Moreover, because of the electron-photon interaction, the photocurrent is related to atomic orbitals through the polarizing angle of polarized light. Therefore, we can read the orbital information of the band structure from the polarizing angular distribution of photocurrent. These findings suggest the promising application of MBP-based p-n homojunction in high-efficiency photoelectric devices and orbital-resolved photovoltaic detection

Research on the key technologies of web parts library in product configuration system

AbstractIn order to build a web-based parts library for the product configuration system, the data description norm, web browsing and application methods are researched. An ontology based data description norm is used to build the web parts library, with the help of product family and the article characteristic table. A plug-in file of Autovue is adapted for web-based browsing and interaction of geometric models of parts. The schema of Application Services Provider is used to realize the application of web parts library in the product configuration system. Empirical results show that the methods are feasible. And the library has been shown to illustrate the concept. The ontology based data description norm can solve the standardization problem. The plug-in file can show the online 3D demo of parts. And the application of ASP can help more companies to use the web parts library. These technologies help to build and use the library