SegRefiner: Towards Model-Agnostic Segmentation Refinement with Discrete Diffusion Process

In this paper, we explore a principal way to enhance the quality of object masks produced by different segmentation models. We propose a model-agnostic solution called SegRefiner, which offers a novel perspective on this problem by interpreting segmentation refinement as a data generation process. As a result, the refinement process can be smoothly implemented through a series of denoising diffusion steps. Specifically, SegRefiner takes coarse masks as inputs and refines them using a discrete diffusion process. By predicting the label and corresponding states-transition probabilities for each pixel, SegRefiner progressively refines the noisy masks in a conditional denoising manner. To assess the effectiveness of SegRefiner, we conduct comprehensive experiments on various segmentation tasks, including semantic segmentation, instance segmentation, and dichotomous image segmentation. The results demonstrate the superiority of our SegRefiner from multiple aspects. Firstly, it consistently improves both the segmentation metrics and boundary metrics across different types of coarse masks. Secondly, it outperforms previous model-agnostic refinement methods by a significant margin. Lastly, it exhibits a strong capability to capture extremely fine details when refining high-resolution images. The source code and trained models are available at https://github.com/MengyuWang826/SegRefiner.Comment: NeurIPS 2023, Code: https://github.com/MengyuWang826/SegRefine

Multi-Label Image Classification via Knowledge Distillation from Weakly-Supervised Detection

Multi-label image classification is a fundamental but challenging task towards general visual understanding. Existing methods found the region-level cues (e.g., features from RoIs) can facilitate multi-label classification. Nevertheless, such methods usually require laborious object-level annotations (i.e., object labels and bounding boxes) for effective learning of the object-level visual features. In this paper, we propose a novel and efficient deep framework to boost multi-label classification by distilling knowledge from weakly-supervised detection task without bounding box annotations. Specifically, given the image-level annotations, (1) we first develop a weakly-supervised detection (WSD) model, and then (2) construct an end-to-end multi-label image classification framework augmented by a knowledge distillation module that guides the classification model by the WSD model according to the class-level predictions for the whole image and the object-level visual features for object RoIs. The WSD model is the teacher model and the classification model is the student model. After this cross-task knowledge distillation, the performance of the classification model is significantly improved and the efficiency is maintained since the WSD model can be safely discarded in the test phase. Extensive experiments on two large-scale datasets (MS-COCO and NUS-WIDE) show that our framework achieves superior performances over the state-of-the-art methods on both performance and efficiency.Comment: accepted by ACM Multimedia 2018, 9 pages, 4 figures, 5 table

Safety and feasibility of toripalimab plus lenvatinib with or without radiotherapy in advanced BTC

BackgroundToripalimab shows antitumor efficacy in cholangiocarcinoma. Radiotherapy (RT) may enhance systemic responses of PD-1 inhibitors and lenvatinib. This study was designed to assess the safety and feasibility of toripalimab plus lenvatinib with or without RT in advanced BTC.MethodsThis study involved 88 patients with advanced BTC receiving toripalimab plus lenvatinib with or without RT from the clinical trials (NCT03892577). Propensity score matching (PSM) (1:1) analysis was used to balance potential bias. The overall survival (OS), progression-free survival (PFS), objective response rate (ORR), and adverse events (AEs) were evaluated.ResultsAfter PSM, the final analysis included 40 patients: 20 receiving toripalimab plus lenvatinib without RT (NRT); 20 receiving toripalimab plus lenvatinib with RT. The AEs were more frequent in the RT group than in the NRT group without treatment-associated mortality. The addition of RT did not cause specific AEs. The median PFS was significantly longer with RT (10.8 versus 4.6 months, p<0.001). The median OS was 13.7 months with RT versus 9.2 months in the NRT group (p=0.008). The ORR was 35% (95% CI: 12.1-57.9) in the RT group versus 20% (95% CI: 0.8-39.2) in the NRT group.ConclusionsThe addition of RT may enhance the efficacy of toripalimab plus lenvatinib. Toripalimab plus lenvatinib with RT have a good safety profile without an increase in specific toxicities in advanced BTC patients

Experimental investigation of mechanically laminated straight or curved-and-tapered bamboo-concrete T-beams

This study echoes the rising demand for bio-based material in concrete composite structures in the race to accelerate carbon neutrality in construction. Noticing that most previous studies are focused on straight timber or engineered bamboo-to-concrete composite beams, this study developed straight or curved-and-tapered mechanically laminated bamboo-concrete (LBC) T-beams. Six layers of 26mm thick laminated bamboo panels were glue laminated together to form the bamboo beams. The curved bamboo beams have three different rises of arch: 50mm, 100mm and 150mm. All specimen beams were tested by four-point bending tests to evaluate their structural performances of the curved and straight LBC T-beams. To monitor the flange-to-web interface shear transfer, a novel interface shear slip calibration method that captures the longitudinal after-slip strain redistribution was developed and validated by strain gauge measurements. This study also highlights the interlayer shear bonding strength of laminated bamboo as the thresholding parameter that determines the composite beams' overall flexural strength, evidenced by detailed failure mode analysis. The proposed interface shear slip calibration method can be extended to the other types of shear connectors such as screws, nails, shear plates and notched connections

Experimental exploration of five-qubit quantum error correcting code with superconducting qubits

Quantum error correction is an essential ingredient for universal quantum computing. Despite tremendous experimental efforts in the study of quantum error correction, to date, there has been no demonstration in the realisation of universal quantum error correcting code, with the subsequent verification of all key features including the identification of an arbitrary physical error, the capability for transversal manipulation of the logical state, and state decoding. To address this challenge, we experimentally realise the $[\![5,1,3]\!]$ code, the so-called smallest perfect code that permits corrections of generic single-qubit errors. In the experiment, having optimised the encoding circuit, we employ an array of superconducting qubits to realise the $[\![5,1,3]\!]$ code for several typical logical states including the magic state, an indispensable resource for realising non-Clifford gates. The encoded states are prepared with an average fidelity of $57.1(3)\%$ while with a high fidelity of $98.6(1)\%$ in the code space. Then, the arbitrary single-qubit errors introduced manually are identified by measuring the stabilizers. We further implement logical Pauli operations with a fidelity of $97.2(2)\%$ within the code space. Finally, we realise the decoding circuit and recover the input state with an overall fidelity of $74.5(6)\%$, in total with $92$ gates. Our work demonstrates each key aspect of the $[\![5,1,3]\!]$ code and verifies the viability of experimental realization of quantum error correcting codes with superconducting qubits.Comment: 6 pages, 4 figures + Supplementary Material

Seasonal variations and sources of various elements in the atmospheric aerosols in Qingdao, China

Seasonal variations and sources of various elements in the atmospheric aerosols of the North China coast were investigated by analyzing aerosol samples collected in Qingdao, China. 23 total suspended particulate (TSP) samples were collected from June 2001 to May 2002, including three samples gathered during Asian dust episodes (20 March and 7-8 April 2002). The concentrations of ten elements including iron (Fe), titanium (Ti), manganese (Mn), vanadium (V), nickel (Ni), copper (Cu), lead (Pb), zinc (Zn), cadmium (Cd) and sulfur (S) were measured by 3000 ICP-OES. All elements measured in the aerosols of Qingdao displayed a strong seasonal variation: the concentrations of Fe, Ti, Mn, V, Ni, Cu, Pb, Zn, Cd were the lowest in summer, and the highest in winter. During the Asian dust episodes, the concentrations of Fe, Ti, Mn, V, Ni, Cu increased remarkably. The concentrations of Pb, Zn, Cu, S also increased greatly during the Asian dust episodes, but they were still lower than those in winter. The enrichment factors (EFs) of all elements (with reference to crustal Fe) indicate that Ti and Mn are mainly from soil sources. V in the Qingdao aerosols is mainly derived from the soil, with a minor contribution from ship emissions. The anthropogenic sources have a relatively higher contribution to Ni and Cu compared with Fe, Ti, and Mn. The S, Pb, Zn and Cd are mainly from anthropogenic sources even during Asian dust episodes. Principal component analysis (PCA), and cluster analysis (CA) indicated that the natural sources contributed about 60\% to the sum of measured elements in all samples and anthropogenic sources contributed about 30\%, and these elements can be classified into three categories as follow: Fe, Ti, Mn, V, and Ni represent the soil source factor; Cu represents the factor of mixed sources of soil and pollution; and Pb, Zn, Cd and S represents the pollution factor. (C) 2006 Elsevier B.V. All rights reserved

Diameter- and Length-controlled Synthesis of Ultrathin ZnS Nanowires and Their Size-Dependent UV Absorption Properties, Photocatalytical Activities and Band-Edge Energy Levels

Benefiting from their ultra-small diameters and highly structural anisotropies, ultrathin semiconductor nanowires (USNWs) are well-known for their fascinating physical/chemical properties, as well as their promising applications in various fields. However, until now, it remains a challenge to synthesize high-quality USNWs with well-controlled diameters and lengths, let alone the exploration of their size-dependent properties and applications. To solve such a challenge, we report herein a ligand-induced low-temperature precursor thermolysis route for the controlled preparation of ultrathin ZnS nanowires, which is based on the oriented assembly of the in-situ formed ZnS clusters/tiny particles. Optimized synthetic conditions allowed the synthesis of ZnS nanowires with a diameter down to 1.0 nm and a length approaching 330 nm. The as-prepared ultrathin ZnS nanowires were then intensively examined by morphological, spectroscopic and electrochemical analytical means to explore their size-dependent optical absorption properties, photocatalytic activities and band-edge energy levels, as well as their underlying growth mechanism. Notably, these USNWs, especially for the thinnest nanowires, were identified to possess an excellent performance in both the selective absorption of ultraviolet (UV) light and photocatalytic degradation of dyes, thus enabling them to serve as longpass ultraviolet filters and high-efficiency photocatalysts, respectively. For the ultrathin ZnS nanowires with a diameter of 1.0 nm, it was also interesting to observe that their exciton absorption peak positions were kept almost unchanged during the continuous extension of their lengths, which has not been reported previously

Design and preliminary application of affinity peptide based on the structure of the porcine circovirus type II Capsid (PCV2 Cap)

Background Affinity peptides, as a core part of affinity chromatography, play an important role in the purification of target molecules. Methods Here we describe the use of molecular docking technology for virtual screening of affinity peptides that specifically recognize the PCV2 Cap protein for the first time. Thirteen candidate peptides with high scores were obtained and then further characterized. Experimentally, the affinity and sensitivity of the peptides studied were identified by ELISA and LSPR, respectively. In order to investigate the purification effect of a selected peptide (L11) for the recombinant PCV2 Cap protein, it was coupled to NHS agarose magnetic beads as an affinity adsorbent (NaMB-L11); and the ligand density of the affinity adsorbent and pH value in the purification of the recombinant PCV2 Cap protein were optimized. Results Our data showed that the peptide L11- DYWWQSWE has the smallest KD = 103 nM with higher specificity for PCV2 Cap protein recognition. The NaMB-L11 affinity adsorbent yielded a purified Cap sample with 98% purity at 90% recovery in a single step. Conclusion Based on the structure, we obtained a high affinity peptide L11 binding to the PCV2 Cap protein by molecular docking technology. It not only provides a theoretical basis for the design of PCV2 Cap affinity peptide, but a new method for the purification of the PCV2 Cap protein