KRF: Keypoint Refinement with Fusion Network for 6D Pose Estimation

Existing refinement methods gradually lose their ability to further improve pose estimation methods' accuracy. In this paper, we propose a new refinement pipeline, Keypoint Refinement with Fusion Network (KRF), for 6D pose estimation, especially for objects with serious occlusion. The pipeline consists of two steps. It first completes the input point clouds via a novel point completion network. The network uses both local and global features, considering the pose information during point completion. Then, it registers the completed object point cloud with corresponding target point cloud by Color supported Iterative KeyPoint (CIKP). The CIKP method introduces color information into registration and registers point cloud around each keypoint to increase stability. The KRF pipeline can be integrated with existing popular 6D pose estimation methods, e.g. the full flow bidirectional fusion network, to further improved their pose estimation accuracy. Experiments show that our method outperforms the state-of-the-art method from 93.9\% to 94.4\% on YCB-Video dataset and from 64.4\% to 66.8\% on Occlusion LineMOD dataset. Our source code is available at https://github.com/zhanhz/KRF

Distantly-Supervised Named Entity Recognition with Uncertainty-aware Teacher Learning and Student-student Collaborative Learning

Distantly-Supervised Named Entity Recognition (DS-NER) effectively alleviates the burden of annotation, but meanwhile suffers from the label noise. Recent works attempt to adopt the teacher-student framework to gradually refine the training labels and improve the overall robustness. However, we argue that these teacher-student methods achieve limited performance because poor network calibration produces incorrectly pseudo-labeled samples, leading to error propagation. Therefore, we attempt to mitigate this issue by proposing: (1) Uncertainty-aware Teacher Learning that leverages the prediction uncertainty to guide the selection of pseudo-labels, avoiding the number of incorrect pseudo-labels in the self-training stage. (2) Student-student Collaborative Learning that allows the transfer of reliable labels between two student networks instead of completely relying on all pseudo-labels from its teacher. Meanwhile, this approach allows a full exploration of mislabeled samples rather than simply filtering unreliable pseudo-labeled samples. Extensive experimental results on five DS-NER datasets demonstrate that our method is superior to state-of-the-art teacher-student methods

Ultra-Sensitive, Deformable and Transparent Triboelectric Tactile Sensor based on Micro-Pyramid Patterned Ionic Hydrogel for Interactive Human-Machine Interfaces

Rapid advances in wearable electronics and mechno-sensational human-machine interfaces impose great challenges in developing flexible and deformable tactile sensors with high efficiency, ultra-sensitivity, environment-tolerance and self-sustainability. Herein, we report a tactile hydrogel sensor (THS) based on micro-pyramid-patterned double-network (DN) ionic organohydrogels to detect subtle pressure changes by measuring the variations of triboelectric output signal without an external power supply. By the first time of pyramidal-patterned hydrogel fabrication method and laminated PDMS encapsulation process, the self-powered THS shows the advantages of remarkable flexibility, good transparency (~85), and excellent sensing performance, including extraordinary sensitivity (45.97 mV Pa-1 ), fast response (~20 ms), very low limit of detection (50 Pa) as well as high stability (36000 cycles). Moreover, with the LiBr immersion treatment method, the THS possesses excellent long-term hyper antifreezing and anti-dehydrating properties, broad environment tolerance (-20 to 60 ℃), and instantaneous peak power density of 20 μW cm-2 , providing reliable contact outputs with different materials and detecting very slight human motions. The THS shows no apparent output decline under the extreme environments of −29℃, 60℃ and even the vacuum conditions, demonstrating the excellent application potential in the field of harsh environments. By integrating the signal acquisition/process circuit, the THS with excellent self-power sensing ability is utilized as a switching button to control electric appliances and robotic hands by simulating human finger gestures, offering its great potentials for wearable and multi-functional electronic applications

An Electret/Hydrogel-Based Tactile Sensor Boosted by Micro-Patterned and Electrostatic Promoting Methods with Flexibility and Wide-Temperature Tolerance

With the rising demand for wearable, multifunctional, and flexible electronics, plenty of efforts aiming at wearable devices have been devoted to designing sensors with greater efficiency, wide environment tolerance, and good sustainability. Herein, a thin film of double-network ionic hydrogel with a solution replacement treatment method is fabricated, which not only possesses excellent stretchability (>1100%) and good transparency (>80%), but also maintains a wide application temperature range (−10~40 °C). Moreover, the hydrogel membrane further acts as both the flexible electrode and a triboelectric layer, with a larger friction area achieved through a micro-structure pattern method. Combining this with a corona-charged fluorinated ethylene propylene (FEP) film, an electret/hydrogel-based tactile sensor (EHTS) is designed and fabricated. The output performance of the EHTS is effectively boosted by 156.3% through the hybrid of triboelectric and electrostatic effects, which achieves the open-circuit peak voltage of 12.5 V, short-circuit current of 0.5 μA, and considerable power of 4.3 μW respectively, with a mentionable size of 10 mm × 10 mm × 0.9 mm. The EHTS also demonstrates a stable output characteristic within a wide range of temperature tolerance from −10 to approximately 40 °C and can be further integrated into a mask for human breath monitoring, which could provide for a reliable healthcare service during the COVID-19 pandemic. In general, the EHTS shows excellent potential in the fields of healthcare devices and wearable electronics

Flexible rechargeable Ni//Zn battery based on self-supported NiCo2O4 nanosheets with high power density and good cycling stability

The overall electrochemical performances of Ni–Zn batteries are still far from satisfactory, specifically for rate performance and cycling stability Herein, we demonstrated a high-performance flexible Ni//Zn battery with outstanding durability and high power density based on self-supported NiCo2O4 nanosheets as cathode and Zn nanosheets as anode. This Ni//Zn battery is able to deliver a remarkable capacity of 183.1 mAh g−1 and a good cycling performance (82.7% capacity retention after 3500 cycles). More importantly, this battery achieves an admirable power density of 49.0 kW kg−1 and energy density of 303.8 Wh kg−1, substantially higher than most recently reported batteries. With such excellent electrochemical performance, this battery will have great potential as an ultrafast power source in practical application

Theoretical Investigation of All Optical Switching by Intersystem Crossing

The dynamics of spin flips induced by the femtosecond laser are theoretically investigated in this article. The spin flips in this scenario are attributed to the intersystem crossing (ISC) described within the frame of the Rabi model. This new explanation is a step attempting to explain the mechanism of the all-optical magnetic orientation switching in the perspective of the conservation of the angular momentum and breaks of the selection rule, which is ignored in the Raman scattering related explanations. The final spin states discussed herein are closely related to the intensity of the incident laser and the ISC decay rate. The quantitative analysis of the relation between decay rate, temperature and the intensity of the laser is discussed

Sustainable urban development based on an adaptive cycle model: A coupled social and ecological land use development model

With the emphasis of modern societal development on the ecological environment, urban environmental issues have been lessened. However, green space occupies a significant amount of land resources, leading to more prominent human-land conflicts in some areas, thus creating serious social issues that urgently require solutions. As a result, social and ecological factors must be integrated into urban sustainability planning. Using the Chinese city of Fuzhou as an example, this study constructed an adaptive cycle model framework for sustainable urban development based on adaptive cycle theory by coordinating three core characteristic attributes of sustainable urban development: potential (urban development potential), connectedness (conflicting land use), and resilience (suitability for sustainable development). This framework was used to identify the development stages (exploitation, conservation, release, reorganization) of different areas in the city. Finally, the ecological value of the landscape created by sustainable development was verified through a multi-scenario simulation. The results were as follows: 1) land expansion under the constraints of the sustainable development model had higher ecological value and could effectively mitigate conflicts in urban land use while balancing socio-ecological contradictions during urban development; 2) the model effects varied across urban areas in different stages of development; and 3) our proposed sustainable development planning pathway based on the adaptive cycle model divided land use into eight categories and proposed targeted development measures based on zoning characteristics. This study can effectively alleviate the contradiction of sustainability between urban social and ecological development. It can be used as a policy tool for managing land assessment and zoning during future urban development and land use

Profiling of circulating serum exosomal microRNAs in elderly patients with infectious stress hyperglycaemia

Abstract Background Early diagnosis of hospitalized elderly patients with infectious stress hyperglycaemia (ISH) is clinically important, especially under the global coronavirus disease 2019 (COVID‐19) pandemic, as without timely prevention and effective treatment, it is likely to deteriorate into septic shock, thus worsening patient survival and complications. Moreover, cumulative studies have showed that patients with COVID‐19 are reported to have a greater prevalence of hyperglycaemia. However, the underlying mechanism remained unknown. Aim and method Systematic screening of specific biomarkers of serum exosome‐derived microRNAs (sE‐miRNAs) from ISH patient has not yet been reported. In this study, sE‐miRNAs were derived from 10 elderly patients with ISH and 5 control patients with disease‐match without hyperglycaemia (non‐ISH). RNA sequencing identified that a total number of 49 sE‐miRNAs with differential expression between ISH and control group. Of which, top 22 miRNAs ranked by sensitivity × specificity were chosen for further research. Moreover, 7 out of 22 miRNAs that related to glucose metabolism or immune disorder were picked up for further validation in an independent cohort consisting of 52 participants (31 ISH and 21 non‐ISH). Result A validation analysis revealed that three miRNAs (hsa‐miR‐21‐5p, hsa‐miR‐335‐5p and hsa‐miR‐28‐3p) were statistically up‐regulated in exosomes from ISH patients. In the validation cohort and discovery cohort, the AUC of three individual miRNAs ranged from 0.73 to 0.88. A logistic model combining three miRNAs achieved an AUC of 0.96. Besides, sE‐miRNAs‐based signatures effectively characterized patients' poor clinical outcome. Survival curve analysis showed that hsa‐miR‐335‐5p, hsa‐miR‐28‐3p but not hsa‐miR‐21‐5p, were significantly closely related to mortality, and the combination of these three miRNAs could also predict patients outcome (p < .05). Conclusion This study depicted the circulating exosomal miRNAs change in ISH patient, which could be used as a promising biomarker to detect ISH at an early stage and predict patients clinical outcome

The Future of Targeted Treatment of Primary Sj&ouml;gren&rsquo;s Syndrome: A Focus on Extra-Glandular Pathology

Primary Sj&ouml;gren&rsquo;s syndrome (pSS) is a chronic, systemic autoimmune disease defined by exocrine gland hypofunction resulting in dry eyes and dry mouth. Despite increasing interest in biological therapies for pSS, achieving FDA-approval has been challenging due to numerous complications in the trials. The current literature lacks insight into a molecular-target-based approach to the development of biological therapies. This review focuses on novel research in newly defined drug targets and the latest clinical trials for pSS treatment. A literature search was conducted on ClinicalTrials.gov using the search term &ldquo;Primary Sj&ouml;gren&rsquo;s syndrome&rdquo;. Articles published in English between 2000 and 2021 were included. Our findings revealed potential targets for pSS treatment in clinical trials and the most recent advances in understanding the molecular mechanisms underlying the pathogenesis of pSS. A prominent gap in current trials is in overlooking the treatment of extraglandular symptoms such as fatigue, depression, and anxiety, which are present in most patients with pSS. Based on dryness and these symptom-directed therapies, emerging biological agents targeting inflammatory cytokines, signal pathways, and immune reaction have been studied and their efficacy and safety have been proven. Novel therapies may complement existing non-pharmacological methods of alleviating symptoms of pSS. Better grading systems that add extraglandular symptoms to gauge disease activity and severity should be created. The future of pSS therapies may lie in gene, stem-cell, and tissue-engineering therapies