The emergence of Y6‐type nonfullerene acceptors has greatly enhanced the power conversion efficiency (PCE) of organic solar cells (OSCs). However, which structural feature is responsible for the ...excellent photovoltaic performance is still under debate. In this study, two Y6‐like acceptors BDOTP‐1 and BDOTP‐2 were designed. Different from previous Y6‐type acceptors featuring an A–D–Aʹ–D–A structure, BDOTP‐1, and BDOTP‐2 have no electron‐deficient Aʹ fragment in the core unit. Instead, there is an electron‐rich dibenzodioxine fragment in the core. Although this modification leads to a marked change in the molecular dipole moment, electrostatic potential, frontier orbitals, and energy levels, BDOTP acceptors retain similar three‐dimensional packing capability as Y6‐type acceptors due to the similar banana‐shaped molecular configuration. BDOTP acceptors show good performance in OSCs. High PCEs of up to 18.51% (certified 17.9%) are achieved. This study suggests that the banana‐shaped configuration instead of the A–D–Aʹ–D–A structure is likely to be the determining factor in realizing high photovoltaic performance.
Acceptors BDOTP‐1 and BDOTP‐2 with an electron‐rich core fragment and three‐dimensional packing capability are developed. The excellent photovoltaic performance of Y6‐type acceptors might mainly be attributed to the banana‐shaped molecular configuration instead of the A–D–Aʹ–D–A structure.
Due to lack of the kernel awareness, some popular deep image reconstruction networks are unstable. To address this problem, here we introduce the bounded relative error norm (BREN) property, which is ...a special case of the Lipschitz continuity. Then, we perform a convergence study consisting of two parts: (1) a heuristic analysis on the convergence of the analytic compressed iterative deep (ACID) scheme (with the simplification that the CS module achieves a perfect sparsification), and (2) a mathematically denser analysis (with the two approximations: 1 AT is viewed as an inverse A-1 in the perspective of an iterative reconstruction procedure and 2 a pseudo-inverse is used for a total variation operator H). Also, we present adversarial attack algorithms to perturb the selected reconstruction networks respectively and, more importantly, to attack the ACID workflow as a whole. Finally, we show the numerical convergence of the ACID iteration in terms of the Lipschitz constant and the local stability against noise.
•Heuristically designed ACID framework is analyzed to support its convergence•Some idealizations and approximations are involved in the convergence analysis•Adversarial attack algorithm is developed to test stability of the entire ACID workflow•Convergence of ACID is empirically shown in terms of the Lipschitz constant
For deep tomographic reconstruction to realize its full potential in practice, it is critically important to address the instabilities of deep reconstruction networks, which were identified in a recent PNAS paper. Our analytic compressed iterative deep (ACID) framework has provided an effective solution to address this challenge by synergizing deep learning and compressed sensing through iterative refinement. Here, we provide an initial convergence analysis, describe an algorithm to attack the entire ACID workflow, and establish not only its capability of stabilizing an unstable deep reconstruction network but also its stability against adversarial attacks dedicated to ACID as a whole. Although our theoretical results are under approximations, they shed light on the converging mechanism of ACID, serving as a basis for further investigation.
We provide an initial theoretical analysis on the convergence of the analytic compressed iterative deep (ACID) scheme and design a dedicated adversarial attacking algorithm to perturb the ACID as a whole and test its stability systematically. In our experiments, we also demonstrate the convergence of the ACID iteration in terms of the Lipschitz constant and the local stability of the ACID scheme against noise. These results help understand the mechanism and performance of ACID and serve as a basis for further research.
The similarity among samples and the discrepancy among clusters are two crucial aspects of image clustering. However, current deep clustering methods suffer from inaccurate estimation of either ...feature similarity or semantic discrepancy. In this paper, we present a Semantic Pseudo-labeling-based Image ClustEring (SPICE) framework, which divides the clustering network into a feature model for measuring the instance-level similarity and a clustering head for identifying the cluster-level discrepancy. We design two semantics-aware pseudo-labeling algorithms, prototype pseudo-labeling and reliable pseudo-labeling, which enable accurate and reliable self-supervision over clustering. Without using any ground-truth label, we optimize the clustering network in three stages: 1) train the feature model through contrastive learning to measure the instance similarity; 2) train the clustering head with the prototype pseudo-labeling algorithm to identify cluster semantics; and 3) jointly train the feature model and clustering head with the reliable pseudo-labeling algorithm to improve the clustering performance. Extensive experimental results demonstrate that SPICE achieves significant improvements (~10%) over existing methods and establishes the new state-of-the-art clustering results on six balanced benchmark datasets in terms of three popular metrics. Importantly, SPICE significantly reduces the gap between unsupervised and fully-supervised classification; e.g. there is only 2% (91.8% vs 93.8%) accuracy difference on CIFAR-10. Our code is made publicly available at https://github.com/niuchuangnn/SPICE .
With the recent advance in chemical modification of fullerenes, electrosynthesis has demonstrated increasing importance in regioselective synthesis of novel fullerene derivatives. Herein, we report ...successively regioselective synthesis of stable tetra- and hexafunctionalized 60fullerene derivatives. The cycloaddition reaction of the electrochemically generated dianions from 60fulleroindolines with phthaloyl chloride regioselectively affords 1,2,4,17-functionalized 60fullerene derivatives with two attached ketone groups and a unique addition pattern, where the heterocycle is rearranged to a 5,6-junction and the carbocycle is fused to an adjacent 6,6-junction. This addition pattern is in sharp contrast with that of the previously reported biscycloadducts, where both cycles are appended to 6,6-junctions. The obtained tetrafunctionalized compounds can be successively manipulated to 1,2,3,4,9,10-functionalized 60fullerene derivatives with an intriguing "
"-shaped configuration
a novel electrochemical protonation. Importantly, the stability of tetrafunctionalized 60fullerene products allows them to be applied in planar perovskite solar cells as efficient electron transport layers.
While micro-CT systems are instrumental in preclinical research, clinical micro-CT imaging has long been desired with cochlear implantation as a primary application. The structural details of the ...cochlear implant and the temporal bone require a significantly higher image resolution than that (about 0.2 mm ) provided by current medical CT scanners. In this paper, we propose a clinical micro-CT (CMCT) system design integrating conventional spiral cone-beam CT, contemporary interior tomography, deep learning techniques, and the technologies of a micro-focus X-ray source, a photon-counting detector (PCD), and robotic arms for ultrahigh-resolution localized tomography of a freely-selected volume of interest (VOI) at a minimized radiation dose level. The whole system consists of a standard CT scanner for a clinical CT exam and VOI specification, and a robotic micro-CT scanner for a local scan of high spatial and spectral resolution at minimized radiation dose. The prior information from the global scan is also fully utilized for background compensation of the local scan data for accurate and stable VOI reconstruction. Our results and analysis show that the proposed hybrid reconstruction algorithm delivers accurate high-resolution local reconstruction, and is insensitive to the misalignment of the isocenter position, initial view angle and scale mismatch in the data/image registration. These findings demonstrate the feasibility of our system design. We envision that deep learning techniques can be leveraged for optimized imaging performance. With high-resolution imaging, high dose efficiency and low system cost synergistically, our proposed CMCT system has great promise in temporal bone imaging as well as various other clinical applications.
Benzylation of the electrochemically generated dianion from N-p-tolyl-60fullerooxazolidinone with benzyl bromide provides three products with different addition patterns. The product distribution can ...be dramatically altered by varying the reaction conditions. Based on spectral characterizations, these products have been assigned as mono-benzylated 1,4-adduct and bis-benzylated 1,2,3,16- and 1,4,9,25-adducts, respectively. The assigned 1,2,3,16-adduct has been further established by X-ray diffraction analysis. It is believed that the 1,4-adduct is obtained by decarboxylative benzylation of the dianionic species, while bis-benzylated 1,2,3,16- and 1,4,9,25-adducts are achieved via a rearrangement process. In addition, the electrochemical properties of these products have been studied.
Deep learning has attracted rapidly increasing attention in the field of tomographic image reconstruction, especially for CT, MRI, PET/SPECT, ultrasound and optical imaging. Among various topics, ...sparse-view CT remains a challenge which targets a decent image reconstruction from very few projections. To address this challenge, in this article we propose a Dual-domain Residual-based Optimization NEtwork (DRONE). DRONE consists of three modules respectively for embedding, refinement, and awareness. In the embedding module, a sparse sinogram is first extended. Then, sparse-view artifacts are effectively suppressed in the image domain. After that, the refinement module recovers image details in the residual data and image domains synergistically. Finally, the results from the embedding and refinement modules in the data and image domains are regularized for optimized image quality in the awareness module, which ensures the consistency between measurements and images with the kernel awareness of compressed sensing. The DRONE network is trained, validated, and tested on preclinical and clinical datasets, demonstrating its merits in edge preservation, feature recovery, and reconstruction accuracy.
Neural architecture search (NAS) adopts a search strategy to explore the predefined search space to find superior architecture with the minimum searching costs. Bayesian optimization (BO) and ...evolutionary algorithms (EA) are two commonly used search strategies, but they suffer from being computationally expensive, challenging to implement, and exhibiting inefficient exploration ability. In this article, we propose a neural predictor guided EA to enhance the exploration ability of EA for NAS (NPENAS) and design two kinds of neural predictors. The first predictor is a BO acquisition function for which we design a graph-based uncertainty estimation network as the surrogate model. The second predictor is a graph-based neural network that directly predicts the performance of the input neural architecture. The NPENAS using the two neural predictors are denoted as NPENAS-BO and NPENAS-NP, respectively. In addition, we introduce a new random architecture sampling method to overcome the drawbacks of the existing sampling method. Experimental results on five NAS search spaces indicate that NPENAS-BO and NPENAS-NP outperform most existing NAS algorithms, with NPENAS-NP achieving state-of-the-art performance on four of the five search spaces.
This paper studies 3D low-dose computed tomography (CT) imaging. Although various deep learning methods were developed in this context, typically they focus on 2D images and perform denoising due to ...low-dose and deblurring for super-resolution separately. Up to date, little work was done for simultaneous in-plane denoising and through-plane deblurring, which is important to obtain high-quality 3D CT images with lower radiation and faster imaging speed. For this task, a straightforward method is to directly train an end-to-end 3D network. However, it demands much more training data and expensive computational costs. Here, we propose to link in-plane and through-plane transformers for simultaneous in-plane denoising and through-plane deblurring, termed as LIT-Former, which can efficiently synergize in-plane and through-plane sub-tasks for 3D CT imaging and enjoy the advantages of both convolution and transformer networks. LIT-Former has two novel designs: efficient multi-head self-attention modules (eMSM) and efficient convolutional feed-forward networks (eCFN). First, eMSM integrates in-plane 2D self-attention and through-plane 1D self-attention to efficiently capture global interactions of 3D self-attention, the core unit of transformer networks. Second, eCFN integrates 2D convolution and 1D convolution to extract local information of 3D convolution in the same fashion. As a result, the proposed LIT-Former synergizes these two sub-tasks, significantly reducing the computational complexity as compared to 3D counterparts and enabling rapid convergence. Extensive experimental results on simulated and clinical datasets demonstrate superior performance over state-of-the-art models. The source code is made available at https://github.com/hao1635/LIT-Former .
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