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Article ; Online: Passively Q-switched Nd

Ren, Zihan / Yuan, Junjie / Wang, Zhengping / Zhao, Chuanrui / Du, Binjian / Zhao, Zhenyu / Liu, Qianhou / Zhang, Fang

2024 Volume 32, Issue 4, Page(s) 5922–5931

Abstract: ... passively Q-Switched all-solid-state lasers. The Hexakis-[(trimethylsilyl)ethynyl]benzene was applied ... for the first time in passively Q-Switched all-solid-state lasers, as we know. For Graphdiyne, the Q-Switched ...

Abstract	In this paper, two-dimensional Graphdiyne and Hexakis-[(trimethylsilyl)ethynyl]benzene nanosheets were prepared using the liquid-phase exfoliation method and were then successfully applied to 1.06 µm passively Q-Switched all-solid-state lasers. The Hexakis-[(trimethylsilyl)ethynyl]benzene was applied for the first time in passively Q-Switched all-solid-state lasers, as we know. For Graphdiyne, the Q-Switched pulse achieved a narrowest pulse width of 415 ns, a maximum repetition frequency of 244.2 kHz, a maximum pulse energy of 133.53 nJ, and peak power of 321.77 mW was obtained. While, the narrowest pulse width, maximum repetition frequency, maximum pulse energy, and peak power for Hexakis-[(trimethylsilyl)ethynyl]benzene are approximately 398.4 ns, 297.1 kHz, 89.61 nJ, and 220.39 mW respectively. The findings demonstrate the promising potential of both candidates as saturable absorbers for signal modulation in solid-state lasers.
Language	English
Publishing date	2024-03-04
Publishing country	United States
Document type	Journal Article
ZDB-ID	1491859-6
ISSN	1094-4087 ; 1094-4087
ISSN (online)	1094-4087
ISSN	1094-4087
DOI	10.1364/OE.510102
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Book ; Online: Q-Refine

Li, Chunyi / Wu, Haoning / Zhang, Zicheng / Hao, Hongkun / Zhang, Kaiwei / Bai, Lei / Liu, Xiaohong / Min, Xiongkuo / Lin, Weisi / Zhai, Guangtao

A Perceptual Quality Refiner for AI-Generated Image

2024

Abstract: ... negative optimization to high-quality AIGIs. To address this issue, a quality-award refiner named Q-Refine ... is proposed. Based on the preference of the Human Visual System (HVS), Q-Refine uses the Image ... of different qualities through three adaptive pipelines. Experimental shows that for mainstream T2I models, Q ...

Abstract	With the rapid evolution of the Text-to-Image (T2I) model in recent years, their unsatisfactory generation result has become a challenge. However, uniformly refining AI-Generated Images (AIGIs) of different qualities not only limited optimization capabilities for low-quality AIGIs but also brought negative optimization to high-quality AIGIs. To address this issue, a quality-award refiner named Q-Refine is proposed. Based on the preference of the Human Visual System (HVS), Q-Refine uses the Image Quality Assessment (IQA) metric to guide the refining process for the first time, and modify images of different qualities through three adaptive pipelines. Experimental shows that for mainstream T2I models, Q-Refine can perform effective optimization to AIGIs of different qualities. It can be a general refiner to optimize AIGIs from both fidelity and aesthetic quality levels, thus expanding the application of the T2I generation models. Comment: 6 pages, 5 figures
Keywords	Computer Science - Computer Vision and Pattern Recognition ; Electrical Engineering and Systems Science - Image and Video Processing
Subject code	006
Publishing date	2024-01-02
Publishing country	us
Document type	Book ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Book ; Online: Q-Net

Shen, Qianqian / Li, Yanan / Jin, Jiyong / Liu, Bin

Query-Informed Few-Shot Medical Image Segmentation

2022

Abstract: ... or calibrate her prior cognitive model accordingly. Inspired by this, we propose Q-Net, a Query ... Q-Net based on ADNet, a recently proposed anomaly detection-inspired method. Specifically, we add ... of the feature extraction module, Q-Net achieves state-of-the-art performance on widely used abdominal and ...

Abstract	Deep learning has achieved tremendous success in computer vision, while medical image segmentation (MIS) remains a challenge, due to the scarcity of data annotations. Meta-learning techniques for few-shot segmentation (Meta-FSS) have been widely used to tackle this challenge, while they neglect possible distribution shifts between the query image and the support set. In contrast, an experienced clinician can perceive and address such shifts by borrowing information from the query image, then fine-tune or calibrate her prior cognitive model accordingly. Inspired by this, we propose Q-Net, a Query-informed Meta-FSS approach, which mimics in spirit the learning mechanism of an expert clinician. We build Q-Net based on ADNet, a recently proposed anomaly detection-inspired method. Specifically, we add two query-informed computation modules into ADNet, namely a query-informed threshold adaptation module and a query-informed prototype refinement module. Combining them with a dual-path extension of the feature extraction module, Q-Net achieves state-of-the-art performance on widely used abdominal and cardiac magnetic resonance (MR) image datasets. Our work sheds light on a novel way to improve Meta-FSS techniques by leveraging query information. Comment: Accpeted by Intelligent Systems Conference (IntelliSys) 2023
Keywords	Computer Science - Computer Vision and Pattern Recognition
Subject code	006 ; 004
Publishing date	2022-08-24
Publishing country	us
Document type	Book ; Online
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Article ; Online: Q.Clear reconstruction for reducing the scanning time for

Ruan, Weiwei / Qin, Chunxia / Liu, Fang / Pi, Rundong / Gai, Yongkang / Liu, Qingyao / Lan, Xiaoli

European journal of nuclear medicine and molecular imaging

2023 Volume 50, Issue 7, Page(s) 1851–1860

Abstract: Purpose: This study aims to determine whether Q.Clear positron emission tomography (PET ... collected cases of : Results: Q.Clear reconstruction significantly increased SUV: Conclusions: Q.Clear ... reconstruction was useful for reducing PET injection dose or scanning time while maintaining the image quality. Q ...

Abstract	Purpose: This study aims to determine whether Q.Clear positron emission tomography (PET) reconstruction may reduce tracer injection dose or shorten scanning time in Methods: We retrospectively collected cases of Results: Q.Clear reconstruction significantly increased SUV Conclusions: Q.Clear reconstruction was useful for reducing PET injection dose or scanning time while maintaining the image quality. Q.Clear may affect PET quantification, and it is necessary to establish diagnostic recommendations based on Q.Clear results for Q.Clear application.
MeSH term(s)	Humans ; Retrospective Studies ; Positron-Emission Tomography/methods ; Magnetic Resonance Imaging ; Magnetic Resonance Spectroscopy ; Positron Emission Tomography Computed Tomography/methods ; Gallium Radioisotopes
Chemical Substances	68Ga-FAPI ; 1,4,7,10-tetraazacyclododecane- 1,4,7,10-tetraacetic acid (1HTE449DGZ) ; Gallium Radioisotopes
Language	English
Publishing date	2023-02-27
Publishing country	Germany
Document type	Journal Article
ZDB-ID	8236-3
ISSN	1619-7089 ; 0340-6997 ; 1619-7070
ISSN (online)	1619-7089
ISSN	0340-6997 ; 1619-7070
DOI	10.1007/s00259-023-06134-2
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Article ; Online: Cognitive diagnostic assessment: A Q-matrix constraint-based neural network method.

Tao, Jinhong / Zhao, Wei / Zhang, Yuliu / Guo, Qian / Min, Baocui / Xu, Xiaoqing / Liu, Fengjuan

Behavior research methods

2024

Abstract: ... model of a neural network constrained by a Q-matrix and named QNN. Specifically, we employ the Q-matrix to determine ...

Abstract	Cognitive diagnosis is a crucial element of intelligent education that aims to assess the proficiency of specific skills or traits in students at a refined level and provide insights into their strengths and weaknesses for personalized learning. Researchers have developed numerous cognitive diagnostic models. However, previous studies indicate that diagnostic accuracy can be significantly influenced by the appropriateness of the model and the sample size. Thus, designing a general model that can adapt to different assumptions and sample sizes remains a considerable challenge. Artificial neural networks have been proposed as a promising approach in some studies. In this paper, we propose a cognitive diagnosis model of a neural network constrained by a Q-matrix and named QNN. Specifically, we employ the Q-matrix to determine the connections between neurons and the width and depth of the neural network. Moreover, to reduce the human effort in the training algorithm, we designed a self-organizing map-based cognitive diagnosis training framework called SOM-NN, which enables the QNN to be trained unsupervised. Extensive experimental results on simulated and real datasets demonstrate that our approaches are effective in both accuracy and interpretability. Notably, under unsupervised conditions, our approach has significant advantages on small sample datasets with high levels of guessing and slipping, especially on the pattern-wise agreement rates. This work bridges the gap between psychometrics and machine learning and provides a realistic and implementable reference solution for classroom instructional assessment and the cold start of personalized and adaptive assessment systems.
Language	English
Publishing date	2024-04-30
Publishing country	United States
Document type	Journal Article
ZDB-ID	231560-9
ISSN	1554-3528 ; 0743-3808 ; 1554-351X
ISSN (online)	1554-3528
ISSN	0743-3808 ; 1554-351X
DOI	10.3758/s13428-024-02404-5
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Article ; Online: Prognostic significance of carboxypeptidase Q and its methylation in glioblastoma.

Liu, Qiaodan / Wang, Rong / Li, Zizi / Peng, Yingpeng / Qin, Wanling / Peng, Xiao-Mou / Liu, Zhi-Gang

Translational cancer research

2023 Volume 12, Issue 5, Page(s) 1073–1087

Abstract: ... of carboxypeptidase Q (CPQ) in GBM remains unknown. This study was to investigate the prognostic significance of CPQ ...

Abstract	Background: Glioblastoma (GBM) is a highly aggressive intracranial malignant tumor. The role of carboxypeptidase Q (CPQ) in GBM remains unknown. This study was to investigate the prognostic significance of CPQ and its methylation in GBM. Methods: We collected data from The Cancer Genome Atlas (TCGA)-GBM database and analyzed the different expression of CPQ in GBM tissues and normal tissues. Then we explored the correlation of CPQ mRNA expression and DNA methylation, and confirmed the prognostic significance of them based on six additional datasets from TCGA, The Chinese Glioma Genome Atlas (CGGA) and Gene Expression Omnibus (GEO) databases. Gene Ontology analysis and Kyoto Encyclopedia of Genes and Genomes analysis were utilized to investigate the biological function of CPQ in GBM. Furthermore, we determined the association of CPQ expression and immune cell infiltration, immune markers and tumor microenvironment using different bioinformatic algorithms. R (version 4.1) and GraphPad Prism (version 8.0) were used to analyze the data. Results: CPQ mRNA expression in GBM tissues was significantly higher than that in normal brain tissues. DNA methylation of CPQ was negatively correlated with its expression. Patients with low CPQ expression or higher CPQ methylation level had remarkably better overall survival (OS). The TOP20 biological processes relevant to the differentially expressed genes between high and low CPQ patients were almost all related to immunity. And the differentially expressed genes were involved in several immune-related signaling pathways. CPQ mRNA expression was outstandingly correlated with CD8 Conclusions: Low CPQ expression and high methylation are associated with longer OS. CPQ is a promising biomarker for predicting prognosis in patients with GBM.
Language	English
Publishing date	2023-05-23
Publishing country	China
Document type	Journal Article
ZDB-ID	2901601-0
ISSN	2219-6803 ; 2218-676X
ISSN (online)	2219-6803
ISSN	2218-676X
DOI	10.21037/tcr-22-2562
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Article ; Online: Engineering high coenzyme Q

Fan, Hang / Liu, Yan / Li, Chen-Yi / Jiang, Yan / Song, Jiao-Jiao / Yang, Lei / Zhao, Qing / Hu, Yong-Hong / Chen, Xiao-Ya / Xu, Jing-Jing

Metabolic engineering

2021 Volume 68, Page(s) 86–93

Abstract: Coenzyme Q (CoQ) is vital for energy metabolism in living organisms. In humans, CoQ ...

Abstract	Coenzyme Q (CoQ) is vital for energy metabolism in living organisms. In humans, CoQ
MeSH term(s)	Carotenoids/metabolism ; Fruit/metabolism ; Humans ; Lycopersicon esculentum/genetics ; Mitochondria ; Ubiquinone/genetics
Chemical Substances	Ubiquinone (1339-63-5) ; Carotenoids (36-88-4)
Language	English
Publishing date	2021-09-21
Publishing country	Belgium
Document type	Journal Article ; Research Support, Non-U.S. Gov't
ZDB-ID	1470383-x
ISSN	1096-7184 ; 1096-7176
ISSN (online)	1096-7184
ISSN	1096-7176
DOI	10.1016/j.ymben.2021.09.007
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Article ; Online: Q&A with Guang-Hui Liu.

Liu, Guang-Hui

Cell reports

2023 Volume 42, Issue 8, Page(s) 112862

Abstract: We at Cell Reports discuss with Guang-Hui Liu his work with the mechanisms and interventions ...

Abstract	We at Cell Reports discuss with Guang-Hui Liu his work with the mechanisms and interventions of aging, in particular, recent work regarding the resurrection of endogenous retroviruses in neurons in primates.
Language	English
Publishing date	2023-07-27
Publishing country	United States
Document type	Journal Article
ZDB-ID	2649101-1
ISSN	2211-1247 ; 2211-1247
ISSN (online)	2211-1247
ISSN	2211-1247
DOI	10.1016/j.celrep.2023.112862
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Book ; Online: Q-Cogni

Cunha, Cris / Liu, Wei / French, Tim / Mian, Ajmal

An Integrated Causal Reinforcement Learning Framework

2023

Abstract: We present Q-Cogni, an algorithmically integrated causal reinforcement learning framework ... that redesigns Q-Learning with an autonomous causal structure discovery method to improve the learning process ... with causal inference. Q-Cogni achieves optimal learning with a pre-learned structural causal model ...

Abstract	We present Q-Cogni, an algorithmically integrated causal reinforcement learning framework that redesigns Q-Learning with an autonomous causal structure discovery method to improve the learning process with causal inference. Q-Cogni achieves optimal learning with a pre-learned structural causal model of the environment that can be queried during the learning process to infer cause-and-effect relationships embedded in a state-action space. We leverage on the sample efficient techniques of reinforcement learning, enable reasoning about a broader set of policies and bring higher degrees of interpretability to decisions made by the reinforcement learning agent. We apply Q-Cogni on the Vehicle Routing Problem (VRP) and compare against state-of-the-art reinforcement learning algorithms. We report results that demonstrate better policies, improved learning efficiency and superior interpretability of the agent's decision making. We also compare this approach with traditional shortest-path search algorithms and demonstrate the benefits of our causal reinforcement learning framework to high dimensional problems. Finally, we apply Q-Cogni to derive optimal routing decisions for taxis in New York City using the Taxi & Limousine Commission trip record data and compare with shortest-path search, reporting results that show 85% of the cases with an equal or better policy derived from Q-Cogni in a real-world domain. Comment: 9 pages, 10 figures, 2 algorithms
Keywords	Computer Science - Machine Learning ; Computer Science - Artificial Intelligence ; I.2.8 ; I.2.9 ; I.2.1
Subject code	006 ; 629
Publishing date	2023-02-26
Publishing country	us
Document type	Book ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Book ; Online: Q-Pilot

Wang, Hanrui / Tan, Bochen / Liu, Pengyu / Liu, Yilian / Gu, Jiaqi / Cong, Jason / Han, Song

Field Programmable Quantum Array Compilation with Flying Ancillas

2023

Abstract: ... recycled during execution. We present Q-Pilot, a scalable compiler for FPQA employing flying ancillas ... to alternative technologies such as superconducting devices or fixed atom arrays, Q-Pilot effectively harnesses ...

Abstract	Neutral atom arrays have become a promising platform for quantum computing, especially the \textit{field programmable qubit array} (FPQA) endowed with the unique capability of atom movement. This feature allows dynamic alterations in qubit connectivity during runtime, which can reduce the cost of executing long-range gates and improve parallelism. However, this added flexibility introduces new challenges in circuit compilation. Inspired by the placement and routing strategies for FPGAs, we propose to map all data qubits to fixed atoms while utilizing movable atoms to route for 2-qubit gates between data qubits. Coined \textit{flying ancillas}, these mobile atoms function as ancilla qubits, dynamically generated and recycled during execution. We present Q-Pilot, a scalable compiler for FPQA employing flying ancillas to maximize circuit parallelism. For two important quantum applications, quantum simulation and the Quantum Approximate Optimization Algorithm (QAOA), we devise domain-specific routing strategies. In comparison to alternative technologies such as superconducting devices or fixed atom arrays, Q-Pilot effectively harnesses the flexibility of FPQA, achieving reductions of 1.4$\times$, 27.7$\times$, and 6.3$\times$ in circuit depth for 100-qubit random, quantum simulation, and QAOA circuits, respectively. Comment: 10 pages, 16 figures
Keywords	Quantum Physics ; Computer Science - Hardware Architecture ; Computer Science - Emerging Technologies
Publishing date	2023-11-25
Publishing country	us
Document type	Book ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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