Attributes-Guided and Pure-Visual Attention Alignment for Few-Shot Recognition

The purpose of few-shot recognition is to recognize novel categories with a limited number of labeled examples in each class. To encourage learning from a supplementary view, recent approaches have introduced auxiliary semantic modalities into effective metric-learning frameworks that aim to learn a feature similarity between training samples (support set) and test samples (query set). However, these approaches only augment the representations of samples with available semantics while ignoring the query set, which loses the potential for the improvement and may lead to a shift between the modalities combination and the pure-visual representation. In this paper, we devise an attributes-guided attention module (AGAM) to utilize human-annotated attributes and learn more discriminative features. This plug-and-play module enables visual contents and corresponding attributes to collectively focus on important channels and regions for the support set. And the feature selection is also achieved for query set with only visual information while the attributes are not available. Therefore, representations from both sets are improved in a fine-grained manner. Moreover, an attention alignment mechanism is proposed to distill knowledge from the guidance of attributes to the pure-visual branch for samples without attributes. Extensive experiments and analysis show that our proposed module can significantly improve simple metric-based approaches to achieve state-of-the-art performance on different datasets and settings.Comment: An expanded version of the same-name paper accepted by AAAI-202

Design from Policies: Conservative Test-Time Adaptation for Offline Policy Optimization

In this work, we decouple the iterative bi-level offline RL (value estimation and policy extraction) from the offline training phase, forming a non-iterative bi-level paradigm and avoiding the iterative error propagation over two levels. Specifically, this non-iterative paradigm allows us to conduct inner-level optimization (value estimation) in training, while performing outer-level optimization (policy extraction) in testing. Naturally, such a paradigm raises three core questions that are not fully answered by prior non-iterative offline RL counterparts like reward-conditioned policy: (q1) What information should we transfer from the inner-level to the outer-level? (q2) What should we pay attention to when exploiting the transferred information for safe/confident outer-level optimization? (q3) What are the benefits of concurrently conducting outer-level optimization during testing? Motivated by model-based optimization (MBO), we propose DROP (design from policies), which fully answers the above questions. Specifically, in the inner-level, DROP decomposes offline data into multiple subsets, and learns an MBO score model (a1). To keep safe exploitation to the score model in the outer-level, we explicitly learn a behavior embedding and introduce a conservative regularization (a2). During testing, we show that DROP permits deployment adaptation, enabling an adaptive inference across states (a3). Empirically, we evaluate DROP on various tasks, showing that DROP gains comparable or better performance compared to prior methods.Comment: NeurIPS 202

Beyond OOD State Actions: Supported Cross-Domain Offline Reinforcement Learning

Offline reinforcement learning (RL) aims to learn a policy using only pre-collected and fixed data. Although avoiding the time-consuming online interactions in RL, it poses challenges for out-of-distribution (OOD) state actions and often suffers from data inefficiency for training. Despite many efforts being devoted to addressing OOD state actions, the latter (data inefficiency) receives little attention in offline RL. To address this, this paper proposes the cross-domain offline RL, which assumes offline data incorporate additional source-domain data from varying transition dynamics (environments), and expects it to contribute to the offline data efficiency. To do so, we identify a new challenge of OOD transition dynamics, beyond the common OOD state actions issue, when utilizing cross-domain offline data. Then, we propose our method BOSA, which employs two support-constrained objectives to address the above OOD issues. Through extensive experiments in the cross-domain offline RL setting, we demonstrate BOSA can greatly improve offline data efficiency: using only 10\% of the target data, BOSA could achieve {74.4\%} of the SOTA offline RL performance that uses 100\% of the target data. Additionally, we also show BOSA can be effortlessly plugged into model-based offline RL and noising data augmentation techniques (used for generating source-domain data), which naturally avoids the potential dynamics mismatch between target-domain data and newly generated source-domain data

All-polymer nanocomposites having superior strength, toughness and ultralow energy dissipation

Toughening polymers has attracted significant interest. Traditionally, polymer toughness is enhanced by constructing polymer networks or introducing sacrificial bonds into the chains between crosslink points. These strategies, though, introduce pronounced energy dissipation and associated heat, both of which are undesirable under long-term cyclic loading, for example at the interface of implants in the human body. By incorporating single-chain nanoparticles (SCNPs) into linear polymer chains to generate all-polymer nanocomposites (APNCs), we have been able to achieve high strength, high toughness with low energy dissipation. Using a combination of simulation and experimental results, we are advancing a “SCNPs effect” where tightly cross-linked SCNPs produce a modulus contrast to achieve strengthening and toughening. Benefitting from the soft interface, the penetrable and deformable SCNPs cause the surrounding polymer chains to move in concert, significantly reducing the interfacial friction to achieve low energy dissipation. The intramolecular cross-linking of the SCNPs and adhesion between the SCNPs and polymer matrix are critical for realizing such high-performance systems. Based on a Gaussian regression model and back propagation (BP) neural network, the mechanical strength can be predicted and is supported by simulations. The APNC concept described can be applied to elastomers and gels, broadening its utilization in high-cycle and low-dissipation applications, like soft robots, flexible sensors and cartilage replacements, and artificial heart valves

β2 Adrenergic receptor activation induces microglial NADPH oxidase activation and dopaminergic neurotoxicity through an ERK-dependent/protein kinase A-independent pathway

Activation of the β2 adrenergic receptor (β2AR) on immune cells has been reported to possess anti-inflammatory properties, however, the pro-inflammatory properties of β2AR activation remain unclear. In this study, using rat primary mesencephalic neuron-glia cultures, we report that salmeterol, a long-acting β2AR agonist, selectively induces dopaminergic (DA) neurotoxicity through its ability to activate microglia. Salmeterol selectively increased the production of reactive oxygen species (ROS) by NADPH oxidase (PHOX), the superoxide-producing enzyme in microglia. A key role of PHOX in mediating salmeterol-induced neurotoxicity was demonstrated by the inhibition of DA neurotoxicity in cultures pretreated with diphenylene-iodonium (DPI), an inhibitor of PHOX activity. Mechanistic studies revealed the activation of microglia by salmeterol results in the selective phosphorylation of ERK, a signaling pathway required for the translocation of the PHOX cytosolic subunit p47phox to the cell membrane. Furthermore, we found ERK inhibition, but not protein kinase A (PKA) inhibition, significantly abolished salmeterol-induced superoxide production, p47phox translocation, and its ability to mediate neurotoxicity. Together, these findings indicate that β2AR activation induces microglial PHOX activation and DA neurotoxicity through an ERK-dependent/PKA-independent pathway

Theory and technical conception of carbon-negative and high-efficient backfill mining in coal mines

Safe, high-efficient, green and low-carbon mining is an eternal theme of coal mines. Near zero rock burst, near zero ecological damage and low-carbon, zero-carbon and carbon-negative green mining will become new requirements to ensure China's energy security supply and green low-carbon development. Backfill mining is the inevitable way to achieve these requirements. However, the existing theories, technologies, and methods of backfill mining are difficult to overcome the technical bottlenecks of high yield, high efficiency, and low-carbon mining, and it is imperative to reform the filling materials and filling modes. In view of the strategic goal of low-carbon coal mining of “kilometer deep mine resource development and ten-million-ton productivity mine filling (two thousands) ” and “near zero ecological damage and near zero rock burst (two near zeros)”. The definition and concept of carbon-negative & high-efficient backfill mining in coal mines has been systematically expounded, and the theoretical development for carbon-negative & high-efficient backfill mining in coal mines has been proposed, including the topological configuration and strength theory of CGIF (CO2 Gangue Innovative Framework) for high porosity filling materials structure, the carbon sequestration theory of CGIF mixture filling body, the reaction kinetics theory of fast adhesive gel bonding material, and the prevention and control of rock burst by filling mining in mining area. The key technical systems have been proposed, such as the preparation technology of gangue fast and efficient cementation high porosity filling material, the green and efficient preparation technology of fast and efficient cementation gel binding material, the negative carbon efficient filling mining technology of CGIF backfill, the negative carbon efficient filling mining technology, the technology of multi-face mining, and the full cycle three-dimensional efficient filling mining and rock burst prevention technology. On this basis, the “three stage” development plan of “basic research, technical research, and engineering demonstration” for carbon-negative & high-efficient backfill mining in coal mines has been clarified, and a theoretical and technical system for carbon-negative & high-efficient backfill mining in coal mines has been constructed. The CO2 storage capacity with carbon-negative & high-efficient backfill mining in coal mines has been evaluated. It is expected to achieve a new pattern of carbon neutrality in the entire process of coal development and utilization through carbon-negative mining and low-carbon utilization

Object Detection for Caries or Pit and Fissure Sealing Requirement in Children's First Permanent Molars

Dental caries is one of the most common oral diseases that, if left untreated, can lead to a variety of oral problems. It mainly occurs inside the pits and fissures on the occlusal/buccal/palatal surfaces of molars and children are a high-risk group for pit and fissure caries in permanent molars. Pit and fissure sealing is one of the most effective methods that is widely used in prevention of pit and fissure caries. However, current detection of pits and fissures or caries depends primarily on the experienced dentists, which ordinary parents do not have, and children may miss the remedial treatment without timely detection. To address this issue, we present a method to autodetect caries and pit and fissure sealing requirements using oral photos taken by smartphones. We use the YOLOv5 and YOLOX models and adopt a tiling strategy to reduce information loss during image pre-processing. The best result for YOLOXs model with tiling strategy is 72.3 mAP.5, while the best result without tiling strategy is 71.2. YOLOv5s6 model with/without tiling attains 70.9/67.9 mAP.5, respectively. We deploy the pre-trained network to mobile devices as a WeChat applet, allowing in-home detection by parents or children guardian

Application of continuous nursing based on EMS management mode in preschool children with wheezing diseases

Objective·To explore the effect of continuous nursing based on EMS [environment management (E), medicine direction (M) and self monitoring (S)] management mode on the preschool children with asthmatic diseases.Methods·A total of 67 children aged 0 to 6 years with asthmatic diseases admitted to the Department of Respiratory Medicine, Shanghai Children's Hospital, Shanghai Jiao Tong University School of Medicine from December 2019 to November 2020 were selected and divided into observation group (33 cases) and control group (34 cases) according to the random number table method, with 3 cases lost, and finally 32 cases in each group. The observation group received continuous nursing care based on EMS management mode, while the control group received routine care and discharge follow-up through the telephone. The children in the two groups were followed up at 1, 3, and 6 months after discharge to evaluate the results of Test for Respiratory and Asthma Control in Kids (TRACK) and wheezing recurrence; Medication Adherence Report Scale for Asthma (MARS-A) and Nursing Job Satisfaction Questionnaire were used to evaluate medication adherence and nursing job satisfaction 6 months after discharge.Results·There was no significant difference in demographic characteristics and clinical baseline characteristics between the two groups. Repeated measures analysis of variance showed that effects of time, groups and the interaction of groups×time on the total score of TRACK were statistically significant. The total scores of TRACK in the observation group were significantly higher than those in the control group at 1, 3, and 6 months after discharge (P=0.000). The total scores of TRACK in the two groups gradually increased with time (P=0.000). The recurrence rates of wheezing in the observation group were 25.0%, 18.7%, and 9.4% at 1, 3, and 6 months after discharge, which were significantly lower than those in the control group (50.0%, 43.7%, and 31.3%, respectively, P<0.05). Generalized estimating equation analysis showed that there was a statistically significant difference between the two groups (P=0.013), and the intervention effect of the observation group was better than that of the control group (OR=0.292). The MARS-A score of the observation group was 4.519±0.395 at 6 months after discharge, which was significantly higher than that of the control group (3.994±0.739, P=0.001). The nursing job satisfaction of the observation group was significantly higher than that of the control group (P=0.000). There was a moderate positive correlation between the MARS-A score and the nursing job satisfaction (r=0.389, P=0.001).Conclusion·Continuous nursing based on EMS management mode can significantly improve the medication compliance and wheezing control level of the preschool children with asthmatic diseases, significantly reduce the recurrence rate of wheezing, and improve the nursing satisfaction