A Free Lunch for Unsupervised Domain Adaptive Object Detection without Source Data

Unsupervised domain adaptation (UDA) assumes that source and target domain data are freely available and usually trained together to reduce the domain gap. However, considering the data privacy and the inefficiency of data transmission, it is impractical in real scenarios. Hence, it draws our eyes to optimize the network in the target domain without accessing labeled source data. To explore this direction in object detection, for the first time, we propose a source data-free domain adaptive object detection (SFOD) framework via modeling it into a problem of learning with noisy labels. Generally, a straightforward method is to leverage the pre-trained network from the source domain to generate the pseudo labels for target domain optimization. However, it is difficult to evaluate the quality of pseudo labels since no labels are available in target domain. In this paper, self-entropy descent (SED) is a metric proposed to search an appropriate confidence threshold for reliable pseudo label generation without using any handcrafted labels. Nonetheless, completely clean labels are still unattainable. After a thorough experimental analysis, false negatives are found to dominate in the generated noisy labels. Undoubtedly, false negatives mining is helpful for performance improvement, and we ease it to false negatives simulation through data augmentation like Mosaic. Extensive experiments conducted in four representative adaptation tasks have demonstrated that the proposed framework can easily achieve state-of-the-art performance. From another view, it also reminds the UDA community that the labeled source data are not fully exploited in the existing methods.Comment: accepted by AAAI202

Hydrophobic multiscale cavities for high-performance and self-cleaning surface-enhanced Raman spectroscopy (SERS) sensing

Cavity array, with excellent optical capture capability, has received increasing attention for the surface-enhanced Raman spectroscopy (SERS)-active substrates. Here, we proposed molybdenum disulfide (MoS2) nanocavities growing on pyramid Si (PSi) composed of in situ reduced Au nanoparticles (AuNPs), which can form the multiscale cavities (MSCs), and is facile for the couple of the plasmon. We demonstrated that the PSi/MoS2/Au MSCs can serve as highly sensitive, uniform, and stable SERS substrates for rhodamine 6G (R6G), crystal violet, and adenosine triphosphate detection, benefiting from the synergistic effect of the enhanced light trapping and the effective plasmonic couple. The couple of the plasmon in the MSCs is evidently proved by finite-difference time domain simulation, showing the strong electromagnetic field is located around the cavity wall. Moreover, the excellent hydrophobicity of the PSi/MoS2/AuNPs substrate endows it with the ability for the directional monitoring of organic pollutant in a mixture of oil and water. Finally, we demonstrated the MSCs with outstanding photocatalytic performance could achieve the renewable utilization by self-cleaning, which was attributed to the fast electron transfer and effective light absorption. The proposed PSi/MoS2/AuNPs MSC represents a robust mean using the plasmonic metal/semiconductor heterostructure for high-performance SERS sensors and photodegradation

SIRT1 is a regulator of autophagy: Implications in gastric cancer progression and treatment

AbstractSilent mating type information regulation 1 (SIRT1) is implicated in tumorigenesis through its effect on autophagy. In gastric cancer (GC), SIRT1 is a marker for prognosis and is involved in cell invasion, proliferation, epithelial-mesenchymal transition (EMT) and drug resistance. Autophagy can function as a cell-survival mechanism or lead to cell death during the genesis and treatment of GC. This functionality is determined by factors including the stage of the tumor, cellular context and stress levels. Interestingly, SIRT1 can regulate autophagy through the deacetylation of autophagy-related genes (ATGs) and mediators of autophagy. Taken together, these findings support the need for continued research efforts to understand the mechanisms mediating the development of gastric cancer and unveil new strategies to eradicate this disease

Attention Diversification for Domain Generalization

Convolutional neural networks (CNNs) have demonstrated gratifying results at learning discriminative features. However, when applied to unseen domains, state-of-the-art models are usually prone to errors due to domain shift. After investigating this issue from the perspective of shortcut learning, we find the devils lie in the fact that models trained on different domains merely bias to different domain-specific features yet overlook diverse task-related features. Under this guidance, a novel Attention Diversification framework is proposed, in which Intra-Model and Inter-Model Attention Diversification Regularization are collaborated to reassign appropriate attention to diverse task-related features. Briefly, Intra-Model Attention Diversification Regularization is equipped on the high-level feature maps to achieve in-channel discrimination and cross-channel diversification via forcing different channels to pay their most salient attention to different spatial locations. Besides, Inter-Model Attention Diversification Regularization is proposed to further provide task-related attention diversification and domain-related attention suppression, which is a paradigm of "simulate, divide and assemble": simulate domain shift via exploiting multiple domain-specific models, divide attention maps into task-related and domain-related groups, and assemble them within each group respectively to execute regularization. Extensive experiments and analyses are conducted on various benchmarks to demonstrate that our method achieves state-of-the-art performance over other competing methods. Code is available at https://github.com/hikvision-research/DomainGeneralization.Comment: ECCV 2022. Code available at https://github.com/hikvision-research/DomainGeneralizatio

Tribological properties and wear mechanisms of DC pulse plasma nitrided austenitic stainless steel in dry reciprocating sliding tests

Expanded austenite (Gamma-N), or S-phase, is a special phase of low-temperature nitrided austenite containing highly super-saturated nitrogen in the form of heterogeneous Cr-N nano-clusters. A nitrided layer of singe phase N is known to provide austenitic stainless steel with combined high hardness, good wear resistance and superior corrosion resistance. This paper reports recent experiments on a comparative study of the sliding wear properties and wear mechanisms of nitrided austenite stainless steel AISI 316, with a special attention paid on worn surface structural evolutions induced by frictional heating and sliding deformation. The samples were prepared by DC pulsed plasma nitriding treatments of various time at a fixed power. Knoop micro-indentation has revealed hardening behaviour of the nitrided samples. The reciprocating ball-on-disc sliding wear and friction properties were investigated at ambient environment conditions using an alumina counterpart ball. The worn surfaces have been analysed by XRD,FEG-SEM and EDX to show wear induced changes in the crystalline characteristics and the wear mechanisms of tribo-oxidation, cracking, abrasive wear and ploughing deformation. Moreover, longitudinal cross-sectional foils of the worn samples have been prepared and analysed using TEM, to investigate the wear induced structural changes, including tribofilm formation, plastic deformation and delamination in depths of nano-scale

Laryngeal Reinnervation Using Ansa Cervicalis for Thyroid Surgery-Related Unilateral Vocal Fold Paralysis: A Long-Term Outcome Analysis of 237 Cases

To evaluate the long-term efficacy of delayed laryngeal reinnervation using the main branch of the ansa cervicalis in treatment of unilateral vocal fold paralysis (UVFP) caused by thyroid surgery.UVFP remains a serious complication of thyroid surgery. Up to now, a completely satisfactory surgical treatment of UVFP has been elusive.From Jan. 1996 to Jan. 2008, a total of 237 UVFP patients who underwent ansa cervicalis main branch-to-recurrent laryngeal nerve (RLN) anastomosis were enrolled as UVFP group; another 237 age- and gender-matched normal subjects served as control group. Videostroboscopy, vocal function assessment (acoustic analysis, perceptual evaluation and maximum phonation time), and electromyography were performed preoperatively and postoperatively. The mean follow-up period was 5.2±2.7 years, ranging from 2 to 12 years.>0.05, respectively). Postoperative laryngeal electromyography confirmed successful reinnervation of laryngeal muscle.Delayed laryngeal reinnervation with the main branch of ansa cervicalis is a feasible and effective approach for treatment of thyroid surgery-related UVFP; it can restore the physiological laryngeal phonatory function to the normal or a nearly normal voice quality

The effect of precursor concentration on the particle size, crystal size, and optical energy gap of CexSn1â’xO2 nanofabrication

In the present work, a thermal treatment technique is applied for the synthesis of CexSn1−xO2 nanoparticles. Using this method has developed understanding of how lower and higher precursor values affect the morphology, structure, and optical properties of CexSn1−xO2 nanoparticles. CexSn1−xO2 nanoparticle synthesis involves a reaction between cerium and tin sources, namely, cerium nitrate hexahydrate and tin (II) chloride dihydrate, respectively, and the capping agent, polyvinylpyrrolidone (PVP). The findings indicate that lower x values yield smaller particle size with a higher energy band gap, while higher x values yield a larger particle size with a smaller energy band gap. Thus, products with lower x values may be suitable for antibacterial activity applications as smaller particles can diffuse through the cell wall faster, while products with higher x values may be suitable for solar cell energy applications as more electrons can be generated at larger particle sizes. The synthesized samples were profiled via a number of methods, such as scanning electron microscopy (SEM), transmission electron microscopy (TEM), X-ray diffraction (XRD), and Fourier transform infrared spectroscopy (FT-IR). As revealed by the XRD pattern analysis, the CexSn1−xO2 nanoparticles formed after calcination reflect the cubic fluorite structure and cassiterite-type tetragonal structure of CexSn1−xO2 nanoparticles. Meanwhile, using FT-IR analysis, Ce-O and Sn-O were confirmed as the primary bonds of ready CexSn1−xO2 nanoparticle samples, whilst TEM analysis highlighted that the average particle size was in the range 6−21 nm as the precursor concentration (Ce(NO3)3·6H2O) increased from 0.00 to 1.00. Moreover, the diffuse UV-visible reflectance spectra used to determine the optical band gap based on the Kubelka–Munk equation showed that an increase in x value has caused a decrease in the energy band gap and vice versa

Large expert-curated database for benchmarking document similarity detection in biomedical literature search

Document recommendation systems for locating relevant literature have mostly relied on methods developed a decade ago. This is largely due to the lack of a large offline gold-standard benchmark of relevant documents that cover a variety of research fields such that newly developed literature search techniques can be compared, improved and translated into practice. To overcome this bottleneck, we have established the RElevant LIterature SearcH consortium consisting of more than 1500 scientists from 84 countries, who have collectively annotated the relevance of over 180 000 PubMed-listed articles with regard to their respective seed (input) article/s. The majority of annotations were contributed by highly experienced, original authors of the seed articles. The collected data cover 76% of all unique PubMed Medical Subject Headings descriptors. No systematic biases were observed across different experience levels, research fields or time spent on annotations. More importantly, annotations of the same document pairs contributed by different scientists were highly concordant. We further show that the three representative baseline methods used to generate recommended articles for evaluation (Okapi Best Matching 25, Term Frequency-Inverse Document Frequency and PubMed Related Articles) had similar overall performances. Additionally, we found that these methods each tend to produce distinct collections of recommended articles, suggesting that a hybrid method may be required to completely capture all relevant articles. The established database server located at https://relishdb.ict.griffith.edu.au is freely available for the downloading of annotation data and the blind testing of new methods. We expect that this benchmark will be useful for stimulating the development of new powerful techniques for title and title/abstract-based search engines for relevant articles in biomedical research.Peer reviewe