877 research outputs found
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
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
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
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
Developmental stage-specific effects of Pim-1 dysregulation on murine bone marrow B cell development
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
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