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"Zhao, Long"
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Leveraging Prior-Knowledge for Weakly Supervised Object Detection Under a Collaborative Self-Paced Curriculum Learning Framework
Weakly supervised object detection is an interesting yet challenging research topic in computer vision community, which aims at learning object models to localize and detect the corresponding objects of interest only under the supervision of image-level annotation. For addressing this problem, this paper establishes a novel weakly supervised learning framework to leverage both the instance-level prior-knowledge and the image-level prior-knowledge based on a novel collaborative self-paced curriculum learning (C-SPCL) regime. Under the weak supervision, C-SPCL can leverage helpful prior-knowledge throughout the whole learning process and collaborate the instance-level confidence inference with the image-level confidence inference in a robust way. Comprehensive experiments on benchmark datasets demonstrate the superior capacity of the proposed C-SPCL regime and the proposed whole framework as compared with state-of-the-art methods along this research line.
Journal Article
An Algorithm for Online Stochastic Error Modeling of Inertial Sensors in Urban Cities
Regardless of whether the global navigation satellite system (GNSS)/inertial navigation system (INS) is integrated or the INS operates independently during GNSS outages, the stochastic error of the inertial sensor has an important impact on the navigation performance. The structure of stochastic error in low-cost inertial sensors is quite complex; therefore, it is difficult to identify and separate errors in the spectral domain using classical stochastic error methods such as the Allan variance (AV) method and power spectral density (PSD) analysis. However, a recently proposed estimation, based on generalized wavelet moment estimation (GMWM), is applied to the stochastic error modeling of inertial sensors, giving significant advantages. Focusing on the online implementation of GMWM and its integration within a general navigation filter, this paper proposes an algorithm for online stochastic error calibration of inertial sensors in urban cities. We further develop the autonomous stochastic error model by constructing a complete stochastic error model and determining model ranking criterion. Then, a detecting module is designed to work together with the autonomous stochastic error model as feedback for the INS/GNSS integration. Finally, two experiments are conducted to compare the positioning performance of this algorithm with other classical methods. The results validate the capability of this algorithm to improve navigation accuracy and achieve the online realization of complex stochastic models.
Journal Article
Direct observation of topological magnon polarons in a multiferroic material
Magnon polarons are novel elementary excitations possessing hybrid magnonic and phononic signatures, and are responsible for many exotic spintronic and magnonic phenomena. Despite long-term sustained experimental efforts in chasing for magnon polarons, direct spectroscopic evidence of their existence is hardly observed. Here, we report the direct observation of magnon polarons using neutron spectroscopy on a multiferroic Fe
2
Mo
3
O
8
possessing strong magnon-phonon coupling. Specifically, below the magnetic ordering temperature, a gap opens at the nominal intersection of the original magnon and phonon bands, leading to two separated magnon-polaron bands. Each of the bands undergoes mixing, interconverting and reversing between its magnonic and phononic components. We attribute the formation of magnon polarons to the strong magnon-phonon coupling induced by Dzyaloshinskii-Moriya interaction. Intriguingly, we find that the band-inverted magnon polarons are topologically nontrivial. These results uncover exotic elementary excitations arising from the magnon-phonon coupling, and offer a new route to topological states by considering hybridizations between different types of fundamental excitations.
A magnetic crystal hosts both magnons, the quanta of spin waves, and phonons, the quanta of lattice vibrations. In some materials with strong coupling between spins and lattices, a magnon-polaron can form. Here, using neutron scattering on a multiferroic, Fe
2
Mo
3
O
8
, Bao et al. observe magnon-polaron, and show that it is topologically non-trivial.
Journal Article
Efficient and simultaneous capture of iodine and methyl iodide achieved by a covalent organic framework
Radioactive molecular iodine (I
2
) and organic iodides, mainly methyl iodide (CH
3
I), coexist in the off-gas stream of nuclear power plants at low concentrations, whereas few adsorbents can effectively adsorb low-concentration I
2
and CH
3
I simultaneously. Here we demonstrate that the I
2
adsorption can occur on various adsorptive sites and be promoted through intermolecular interactions. The CH
3
I adsorption capacity is positively correlated with the content of strong binding sites but is unrelated to the textural properties of the adsorbent. These insights allow us to design a covalent organic framework to simultaneously capture I
2
and CH
3
I at low concentrations. The developed material, COF-TAPT, combines high crystallinity, a large surface area, and abundant nucleophilic groups and exhibits a record-high static CH
3
I adsorption capacity (1.53 g·g
−1
at 25 °C). In the dynamic mixed-gas adsorption with 150 ppm of I
2
and 50 ppm of CH
3
I, COF-TAPT presents an excellent total iodine capture capacity (1.51 g·g
−1
), surpassing various benchmark adsorbents. This work deepens the understanding of I
2
/CH
3
I adsorption mechanisms, providing guidance for the development of novel adsorbents for related applications.
Radioactive molecular iodine (I
2
) and methyl iodide (CH
3
I) coexist in the off-gas stream of nuclear power plants at low concentrations and only few adsorbents can effectively adsorb low-concentration I
2
and CH
3
I simultaneously. Here, the authors demonstrate simultaneous capture of I
2
and CH
3
I at low concentrations by exploiting different adsorptive sites in a covalent organic framework.
Journal Article
The nuclear factor kappa B signaling pathway is a master regulator of renal fibrosis
Renal fibrosis is increasingly recognized as a global public health problem. Acute kidney injury (AKI) and chronic kidney disease (CKD) both result in renal fibrosis. Oxidative stress and inflammation play central roles in progressive renal fibrosis. Oxidative stress and inflammation are closely linked and form a vicious cycle in which oxidative stress induces inflammation through various molecular mechanisms. Ample evidence has indicated that a hyperactive nuclear factor kappa B (NF-ƙB) signaling pathway plays a pivotal role in renal fibrosis. Hyperactive NF-ƙB causes the activation and recruitment of immune cells. Inflammation, in turn, triggers oxidative stress through the production of reactive oxygen species and nitrogen species by activating leukocytes and resident cells. These events mediate organ injury through apoptosis, necrosis, and fibrosis. Therefore, developing a strategy to target the NF-ƙB signaling pathway is important for the effective treatment of renal fibrosis. This Review summarizes the effect of the NF-ƙB signaling pathway on renal fibrosis in the context of AKI and CKD (immunoglobulin A nephropathy, membranous nephropathy, diabetic nephropathy, hypertensive nephropathy, and kidney transplantation). Therapies targeting the NF-ƙB signaling pathway, including natural products, are also discussed. In addition, NF-ƙB-dependent non-coding RNAs are involved in renal inflammation and fibrosis and are crucial targets in the development of effective treatments for kidney disease. This Review provides a clear pathophysiological rationale and specific concept-driven therapeutic strategy for the treatment of renal fibrosis by targeting the NF-ƙB signaling pathway.
Journal Article
Traditional Chinese medicine improved diabetic kidney disease through targeting gut microbiota
Diabetic kidney disease (DKD) affects nearly 40% of diabetic patients, often leading to end-stage renal disease that requires renal replacement therapies, such as dialysis and transplantation. The gut microbiota, an integral aspect of human evolution, plays a crucial role in this condition. Traditional Chinese medicine (TCM) has shown promising outcomes in ameliorating DKD by addressing the gut microbiota.
This review elucidates the modifications in gut microbiota observed in DKD and explores the impact of TCM interventions on correcting microbial dysregulation.
We searched relevant articles from databases including Web of Science, PubMed, ScienceDirect, Wiley, and Springer Nature. The following keywords were used: diabetic kidney disease, diabetic nephropathy, gut microbiota, natural product, TCM, Chinese herbal medicine, and Chinese medicinal herbs. Rigorous criteria were applied to identify high-quality studies on TCM interventions against DKD.
Dysregulation of the gut microbiota, including
,
, and
, has been observed in individuals with DKD. Key indicators of microbial dysregulation include increased uremic solutes and decreased short-chain fatty acids. Various TCM therapies, such as formulas, tablets, granules, capsules, and decoctions, exhibit unique advantages in regulating the disordered microbiota to treat DKD.
This review highlights the importance of targeting the gut-kidney axis to regulate microbial disorders, their metabolites, and associated signaling pathways in DKD. The Qing-Re-Xiao-Zheng formula, the Shenyan Kangfu tablet, the Huangkui capsule, and the Bekhogainsam decoction are potential candidates to address the gut-kidney axis. TCM interventions offer a significant therapeutic approach by targeting microbial dysregulation in patients with DKD.
Journal Article
Hypoxia and the Tumor Microenvironment
Hypoxia is an important feature of the tumor microenvironment, and is closely associated with cell proliferation, angiogenesis, metabolism and the tumor immune response. All these factors can further promote tumor progression, increase tumor aggressiveness, enhance tumor metastatic potential and lead to poor prognosis. In this review, these effects of hypoxia on tumor biology will be discussed, along with their significance for tumor detection and treatment.
Journal Article
Two-Level Integrity-Monitoring Method for Multi-Source Information Fusion Navigation
To address the issue of integrity monitoring for a multi-source information fusion navigation system, a theoretical framework of two-level integrity monitoring is proposed. Firstly, at the system level, a system-integrity-monitoring method based on the Kalman filter weighted least-squares form is established to detect and isolate faulty navigation sources. Secondly, at the sensor level, considering the redundancy of the faulty navigation sources, this paper presents the design of a multi-mode comprehensive fault-detection method for non-redundant navigation sources. Additionally, an extended-dimension matrix optimized sensor-fault detection and verification method for redundant navigation sources is proposed. Finally, integrity risk allocation criteria are established based on the effectiveness of navigation sources to calculate the system protection level and trigger integrity alarms. The two-level integrity-monitoring method was tested on a multi-source information fusion navigation system integrated with an inertial navigation system (INS), global positioning system (GPS), BeiDou satellite navigation system (BDS), Doppler velocity log (DVL), barometric altimeter (BA), and terrain-aided navigation (TAN). Test results demonstrate that the proposed method can effectively isolate the faulty navigation source within 10 s. Furthermore, it can detect the faulty sensors within the faulty navigation sources, thereby enhancing the reliability and robustness of the multi-source information fusion navigation system.
Journal Article