824 research outputs found
Entity Linking for Queries by Searching Wikipedia Sentences
We present a simple yet effective approach for linking entities in queries.
The key idea is to search sentences similar to a query from Wikipedia articles
and directly use the human-annotated entities in the similar sentences as
candidate entities for the query. Then, we employ a rich set of features, such
as link-probability, context-matching, word embeddings, and relatedness among
candidate entities as well as their related entities, to rank the candidates
under a regression based framework. The advantages of our approach lie in two
aspects, which contribute to the ranking process and final linking result.
First, it can greatly reduce the number of candidate entities by filtering out
irrelevant entities with the words in the query. Second, we can obtain the
query sensitive prior probability in addition to the static link-probability
derived from all Wikipedia articles. We conduct experiments on two benchmark
datasets on entity linking for queries, namely the ERD14 dataset and the GERDAQ
dataset. Experimental results show that our method outperforms state-of-the-art
systems and yields 75.0% in F1 on the ERD14 dataset and 56.9% on the GERDAQ
dataset
DiffusionGPT: LLM-Driven Text-to-Image Generation System
Diffusion models have opened up new avenues for the field of image
generation, resulting in the proliferation of high-quality models shared on
open-source platforms. However, a major challenge persists in current
text-to-image systems are often unable to handle diverse inputs, or are limited
to single model results. Current unified attempts often fall into two
orthogonal aspects: i) parse Diverse Prompts in input stage; ii) activate
expert model to output. To combine the best of both worlds, we propose
DiffusionGPT, which leverages Large Language Models (LLM) to offer a unified
generation system capable of seamlessly accommodating various types of prompts
and integrating domain-expert models. DiffusionGPT constructs domain-specific
Trees for various generative models based on prior knowledge. When provided
with an input, the LLM parses the prompt and employs the Trees-of-Thought to
guide the selection of an appropriate model, thereby relaxing input constraints
and ensuring exceptional performance across diverse domains. Moreover, we
introduce Advantage Databases, where the Tree-of-Thought is enriched with human
feedback, aligning the model selection process with human preferences. Through
extensive experiments and comparisons, we demonstrate the effectiveness of
DiffusionGPT, showcasing its potential for pushing the boundaries of image
synthesis in diverse domains
Development of 15kA/cm Fabrication Process for Superconducting Integrated Digital Circuits
A new fabrication process for superconducting integrated digital circuits is
reported. We have developed the "SIMIT Nb04" fabrication technique for
superconducting integrated circuits with Nb-based Josephson junctions based on
the validated "SIMIT Nb03" process and Chemical Mechanical Planarization (CMP)
technology. Seven Nb superconducting layers and one Mo resistor layer are
included in the "SIMIT Nb04" process with 19 mask levels. The device structure
is composed of active layers including junctions at the bottom, two passive
transmission line (PTL) layers in the middle and a DC power layer at the top.
The circuit fabrication started with the fabrication of Mo resistors with a
target sheet resistance Rsh of 3 , followed by the deposition of
Nb/Al-AlO/Nb trilayer Josephson-junction with a target critical current
density Jc at 15 kA/cm. To increase the Al-AlO barrier layer etching's
repeatability, an additional barrier protection layer was applied. To
accomplish high-quality planarization, we created a planarization procedure
coupled with dummy filling. To assess the process dependability and
controllability, a set of process control monitors (PCMs) for monitoring
fabrication and design parameters was designed and monitored. The successful
manufacturing and testing of a few small-scale circuits, like our standard
library cells, further attests to the viability of our fabrication process for
superconducting integrated circuits
A novel unambiguous strategy of molecular feature extraction in machine learning assisted predictive models for environmental properties
Environmental properties of compounds provide significant information in treating organic pollutants, which drives the chemical process and environmental science toward eco-friendly technology. Traditional group contribution methods play an important role in property estimations, whereas various disadvantages emerge in their applications, such as scattered predicted values for certain groups of compounds. In order to address such issues, an extraction strategy for molecular features is proposed in this research, which is characterized by interpretability and discriminating power with regard to isomers. Based on the Henry's law constant data of organic compounds in water, we developed a hybrid predictive model that integrates the proposed strategy in conjunction with a neural network framework. The structure of the predictive model is optimized using cross-validation and grid search to improve its robustness. Moreover, the predictive model is improved by introducing the plane of best fit descriptor as input and adopting k-means clustering in sampling. In contrast with reported models in the literature, the developed predictive model demonstrates improved generality, higher accuracy, and fewer molecular features used in its development
Geometric Scaling of the Current-Phase Relation of Niobium Nano-Bridge Junctions
The nano-bridge junction (NBJ) is a type of Josephson junction that is
advantageous for the miniaturization of superconducting circuits. However, the
current-phase relation (CPR) of the NBJ usually deviates from a sinusoidal
function which has been explained by a simplified model with correlation only
to its effective length. Here, we investigated both measured and calculated
CPRs of niobium NBJs of a cuboidal shape with a three-dimensional bank
structure. From a sine-wave to a saw-tooth-like form, we showed that deviated
CPRs of NBJs can be described quantitatively by its skewness {\Delta}{\theta}.
Furthermore, the measured dependency of {\Delta}{\theta} on the critical
current {I_0} from 108 NBJs turned out to be consistent with the calculated
ones derived from the change in geometric dimensions. It suggested that the
CPRs of NBJs can be tuned by their geometric dimensions. In addition, the
calculated scaling behavior of {\Delta}{\theta} versus {I_0} in
three-dimensional space was provided for the future design of superconducting
circuits of a high integration level by using niobium NBJs.Comment: 20 pages, 10 figure
Label-free LC-MS/MS proteomics analyses reveal CLIC1 as a predictive biomarker for bladder cancer staging and prognosis
IntroductionBladder cancer (BC) is a significant carcinoma of the urinary system that has a high incidence of morbidity and death owing to the challenges in accurately identifying people with early-stage BC and the lack of effective treatment options for those with advanced BC. Thus, there is a need to define new markers of prognosis and prediction.MethodsIn this study, we have performed a comprehensive proteomics experiment by label-free quantitative proteomics to compare the proteome changes in the serum of normal people and bladder cancer patients—the successful quantification of 2064 Quantifiable proteins in total. A quantitative analysis was conducted to determine the extent of changes in protein species' relative intensity and reproducibility. There were 43 upregulated proteins and 36 downregulated proteins discovered in non-muscle invasive bladder cancer and normal individuals. Sixty-four of these proteins were elevated, and 51 were downregulated in muscle-invasive and non-muscle-invasive bladder cancer, respectively. Functional roles of differentially expressed proteins were annotated using Gene Ontology (GO) and Clusters of Orthologous Groups of Proteins (COG). To analyze the functions and pathways enriched by differentially expressed proteins, GO enrichment analysis, protein domain analysis, and KEGG pathway analysis were performed. The proteome differences were examined and visualized using radar plots, heat maps, bubble plots, and Venn diagrams.ResultsAs a result of combining the Venn diagram with protein-protein interactions (PPIs), Chloride intracellular channel 1 (CLIC1) was identified as the primary protein. Using the Gene Set Cancer Analysis (GSCA) website, the influence of CLIC1 on immune infiltration was analyzed. A negative correlation between CD8 naive and CLIC1 levels was found. For validation, immunohistochemical (IHC), qPCR, and western blotting (WB) were performed.Further, we found that CLIC1 was associated with a poor prognosis of bladder cancer in survival analysis.DiscussionOur research screened CLIC1 as a tumor-promoting protein in bladder cancer for the first time using serum mass spectrometry. And CLIC1 associated with tumor stage, and immune infiltrate. The prognostic biomarker and therapeutic target CLIC1 may be new for bladder cancer patients
PCA-based lung motion model
Organ motion induced by respiration may cause clinically significant
targeting errors and greatly degrade the effectiveness of conformal
radiotherapy. It is therefore crucial to be able to model respiratory motion
accurately. A recently proposed lung motion model based on principal component
analysis (PCA) has been shown to be promising on a few patients. However, there
is still a need to understand the underlying reason why it works. In this
paper, we present a much deeper and detailed analysis of the PCA-based lung
motion model. We provide the theoretical justification of the effectiveness of
PCA in modeling lung motion. We also prove that under certain conditions, the
PCA motion model is equivalent to 5D motion model, which is based on physiology
and anatomy of the lung. The modeling power of PCA model was tested on clinical
data and the average 3D error was found to be below 1 mm.Comment: 4 pages, 1 figure. submitted to International Conference on the use
of Computers in Radiation Therapy 201
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