Ultrahigh Enhancement of Electromagnetic Fields by Exciting Localized with Extended Surface Plasmons

Excitation of localized surface plasmons (LSPs) of metal nanoparticles (NPs) residing on a flat metal film has attracted great attentions recently due to the enhanced electromagnetic (EM) fields found to be higher than the case of NPs on a dielectric substrate. In the present work, it is shown that even much higher enhancement of EM fields is obtained by exciting the LSPs through extended surface plasmons (ESPs) generated at the metallic film surface using the Kretschmann-Raether configuration. We show that the largest EM field enhancement and the highest surface-enhanced fluorescence intensity are obtained when the incidence angle is the ESP resonance angle of the underlying metal film. The finite-difference time-domain simulations indicate that excitation of LSPs using ESPs can generate 1-3 orders higher EM field intensity than direct excitation of the LSPs using incidence from free space. The ultrahigh enhancement is attributed to the strong confinement of the ESP waves in the vertical direction. The drastically intensified EM fields are significant for highly-sensitive refractive index sensing, surface-enhanced spectroscopies, and enhancing the efficiency of optoelectronic devices.Comment: 25 pages, 5 figures and supplimentary informatio

Donor–Acceptor Fluorophores for Energy-Transfer-Mediated Photocatalysis

Triplet–triplet energy transfer (EnT) is a fundamental activation pathway in photocatalysis. In this work, we report the mechanistic origins of the triplet excited state of carbazole-cyanobenzene donor–acceptor (D–A) fluorophores in EnT-based photocatalytic reactions and demonstrate the key factors that control the accessibility of the 3LE (locally excited triplet state) and 3CT (charge-transfer triplet state) via a combined photochemical and transient absorption spectroscopic study. We found that the energy order between 1CT (charge transfer singlet state) and 3LE dictates the accessibility of 3LE/3CT for EnT, which can be effectively engineered by varying solvent polarity and D–A character to depopulate 3LE and facilitate EnT from the chemically more tunable 3CT state for photosensitization. Following the above design principle, a new D–A fluorophore with strong D–A character and weak redox potential is identified, which exhibits high efficiency for Ni(II)-catalyzed cross-coupling of carboxylic acids and aryl halides with a wide substrate scope and high selectivity. Our results not only provide key fundamental insight on the EnT mechanism of D–A fluorophores but also establish its wide utility in EnT-mediated photocatalytic reactions

Unbiased Scene Graph Generation via Two-stage Causal Modeling

Despite the impressive performance of recent unbiased Scene Graph Generation (SGG) methods, the current debiasing literature mainly focuses on the long-tailed distribution problem, whereas it overlooks another source of bias, i.e., semantic confusion, which makes the SGG model prone to yield false predictions for similar relationships. In this paper, we explore a debiasing procedure for the SGG task leveraging causal inference. Our central insight is that the Sparse Mechanism Shift (SMS) in causality allows independent intervention on multiple biases, thereby potentially preserving head category performance while pursuing the prediction of high-informative tail relationships. However, the noisy datasets lead to unobserved confounders for the SGG task, and thus the constructed causal models are always causal-insufficient to benefit from SMS. To remedy this, we propose Two-stage Causal Modeling (TsCM) for the SGG task, which takes the long-tailed distribution and semantic confusion as confounders to the Structural Causal Model (SCM) and then decouples the causal intervention into two stages. The first stage is causal representation learning, where we use a novel Population Loss (P-Loss) to intervene in the semantic confusion confounder. The second stage introduces the Adaptive Logit Adjustment (AL-Adjustment) to eliminate the long-tailed distribution confounder to complete causal calibration learning. These two stages are model agnostic and thus can be used in any SGG model that seeks unbiased predictions. Comprehensive experiments conducted on the popular SGG backbones and benchmarks show that our TsCM can achieve state-of-the-art performance in terms of mean recall rate. Furthermore, TsCM can maintain a higher recall rate than other debiasing methods, which indicates that our method can achieve a better tradeoff between head and tail relationships.Comment: 17 pages, 9 figures. Accepted by IEEE Transactions on Pattern Analysis and Machine Intelligenc

Implicit Neural Representation for Cooperative Low-light Image Enhancement

The following three factors restrict the application of existing low-light image enhancement methods: unpredictable brightness degradation and noise, inherent gap between metric-favorable and visual-friendly versions, and the limited paired training data. To address these limitations, we propose an implicit Neural Representation method for Cooperative low-light image enhancement, dubbed NeRCo. It robustly recovers perceptual-friendly results in an unsupervised manner. Concretely, NeRCo unifies the diverse degradation factors of real-world scenes with a controllable fitting function, leading to better robustness. In addition, for the output results, we introduce semantic-orientated supervision with priors from the pre-trained vision-language model. Instead of merely following reference images, it encourages results to meet subjective expectations, finding more visual-friendly solutions. Further, to ease the reliance on paired data and reduce solution space, we develop a dual-closed-loop constrained enhancement module. It is trained cooperatively with other affiliated modules in a self-supervised manner. Finally, extensive experiments demonstrate the robustness and superior effectiveness of our proposed NeRCo. Our code is available at https://github.com/Ysz2022/NeRCo

ScribFormer: Transformer Makes CNN Work Better for Scribble-based Medical Image Segmentation

Most recent scribble-supervised segmentation methods commonly adopt a CNN framework with an encoder-decoder architecture. Despite its multiple benefits, this framework generally can only capture small-range feature dependency for the convolutional layer with the local receptive field, which makes it difficult to learn global shape information from the limited information provided by scribble annotations. To address this issue, this paper proposes a new CNN-Transformer hybrid solution for scribble-supervised medical image segmentation called ScribFormer. The proposed ScribFormer model has a triple-branch structure, i.e., the hybrid of a CNN branch, a Transformer branch, and an attention-guided class activation map (ACAM) branch. Specifically, the CNN branch collaborates with the Transformer branch to fuse the local features learned from CNN with the global representations obtained from Transformer, which can effectively overcome limitations of existing scribble-supervised segmentation methods. Furthermore, the ACAM branch assists in unifying the shallow convolution features and the deep convolution features to improve model's performance further. Extensive experiments on two public datasets and one private dataset show that our ScribFormer has superior performance over the state-of-the-art scribble-supervised segmentation methods, and achieves even better results than the fully-supervised segmentation methods. The code is released at https://github.com/HUANGLIZI/ScribFormer.Comment: Accepted by IEEE Transactions on Medical Imaging (TMI

ScribFormer: Transformer Makes CNN Work Better for Scribble-based Medical Image Segmentation

Most recent scribble-supervised segmentation methods commonly adopt a CNN framework with an encoder-decoder architecture. Despite its multiple benefits, this framework generally can only capture small-range feature dependency for the convolutional layer with the local receptive field, which makes it difficult to learn global shape information from the limited information provided by scribble annotations. To address this issue, this paper proposes a new CNN-Transformer hybrid solution for scribble-supervised medical image segmentation called ScribFormer. The proposed ScribFormer model has a triple-branch structure, i.e., the hybrid of a CNN branch, a Transformer branch, and an attention-guided class activation map (ACAM) branch. Specifically, the CNN branch collaborates with the Transformer branch to fuse the local features learned from CNN with the global representations obtained from Transformer, which can effectively overcome limitations of existing scribble-supervised segmentation methods. Furthermore, the ACAM branch assists in unifying the shallow convolution features and the deep convolution features to improve model’s performance further. Extensive experiments on two public datasets and one private dataset show that our ScribFormer has superior performance over the state-of-the-art scribble-supervised segmentation methods, and achieves even better results than the fully-supervised segmentation methods. The code is released at https://github.com/HUANGLIZI/ScribFormer

Investigation of mycotoxins in grain and its products in Henan Province

Objective To understand the types and extent of mycotoxins in grains and its products sold in Henan Province. Methods During 2018-2019, 16 kinds of mycotoxins were detected by isotope dilution ultra high performance liquid chromatography tandem mass spectrometry (UPLC-MS/MS). The results were evaluated and analyzed according to GB 2761-2017 national food safety standard mycotoxin limit in food. Results Fumonisin, zearalenone, aflatoxin and deoxynivalenol were the main mycotoxins in 100 corn flour, corn dregs and corn kernels, the detection rate ranged from 0.0% to 95.7%. The detection rates of deoxynivalenol in 220 wheat flour, noodles and steamed bread were 78.0% (124/159), 64.3% (18/28) and 87.9% (29/33) respectively, and the content of other mycotoxins was very low or not detected. Conclusion There were different levels of mycotoxin pollution in grain and its products in Henan Province, especially fumonisin in corn and its products. It is necessary to carry out traceability investigation in time and take corresponding control measures as soon as possible

CFD Analysis of the Primary Cooling System for the Small Modular Natural Circulation Lead Cooled Fast Reactor SNRLFR-100

Small modular reactor (SMR) has drawn wide attention in the past decades, and Lead cooled fast reactor (LFR) is one of the most promising advanced reactors which are able to meet the safety economic goals of Gen-IV nuclear energy systems. A small modular natural circulation lead cooled fast reactor-100 MWth (SNRLFR-100) is being developed by University of Science and Technology of China (USTC). In the present work, a 3D CFD model, primary heat exchanger model, fuel pin model, and point kinetic model were established based on some reasonable simplifications and assumptions, the steady-state natural circulation characteristics of SNCLFR-100 primary cooling system were discussed and illustrated, and some reasonable suggestions were proposed for the reactor’s thermal-hydraulic and structural design. Moreover, in order to have a first evaluation of the system behavior in accident conditions, an unprotected loss of heat sink (ULOHS) transient simulation at beginning of the reactor cycle (BOC) has been analyzed and discussed based on the steady-state simulation results. The key temperatures of the reactor core are all under the safety limits at transient state; the reactor has excellent thermal-hydraulic performance