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

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

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

Nanoparticle separation in cross-flow filtration by introduction of electrophoresis

Huang, Chin-PaoMembrane fouling is a long-existed problem in filtration industries. Techniques to prevent membrane fouling and improve filter performance in cross-flow filtration have been studied since early twentieth century. The research of cross-flow filtration is a milestone in filtration technique development. By applying a flow parallel to the filter medium, deposited particles on the filter can be effectively washed away. However, when particle sizes become small, like nano-sized particles, it will become much more difficult for the shear flow to remove the depositions away. Filter cakes are formed due to concentration polarization. The superimposition of an external electrical field inside the cross-flow filtration system has been proved to be able to greatly enhance the flux and particle removal efficiency. Cross-flow electro filtration (CFEF) is a hybrid separation process combining both the features of conventional cross-flow filtration and electrophoretic separation apparatus. The technique of CFEF has been applied for separation of nano-sized particles from liquids due to its high selectivity and independence of special membranes although fouling will still occur on the membrane, depending on the conditions applied in different experiments. The major goal of this research is to minimize membrane fouling and maximize nanoparticle removal efficiency. A prototype cross-flow electro filtration module has been designed and constructed for the experiment. The CFEF module is consisted of a peristaltic pump, an external tube, a tubular shaped metal net and a concentric rod as electrodes, a circular membrane placed between the two electrodes, and a D.C. power supply connected to the electrodes. Charged particles can be separated depending on their size distribution and surface charge density. Three kinds of particles with different pHzpc and mean sizes: SiO 2 , TiO2 and ??-Al2 O3 are used for this study. The influences of clogging on membrane can be ignored during each experimental running. Results demonstrate that the CFEF system can separate nanoparticles effectively. Particle removal efficiency is highly related to the electric field strength, filtration flow rate and pH of the feed solution. A mathematical model is also developed to quantify the effects of parameters including particle sizes, solution pH, filtration flow rate and electric field strength on membrane performance. The influences of Coulomb forces among particles are also evaluated in this research, which is proved to be an essential factor affecting the removal rate and has not been considered by previous researchers. The model results suit well with experimental data, which proves that the mathematical model is highly reliable. Further, based on the results of model and experiments, it is possible to separate mixture of nanoparticle solution using the CFEF module by adjusting the pH and applied electric field strength.University of Delaware, Department of Civil and Environmental EngineeringM.A.S

Measurement of chromatic dispersion of liquid in a wide spectral range based on liquid-prism surface plasmon resonance sensor

We present, in this work, a new method to measure the chromatic dispersion of liquid based on the surface plasmon resonance sensor. The liquid sample is serving as the liquid prism at the same time. Both angular and spectral interrogations are used in the experiments. The chromatic dispersions of six kinds of liquids with the wavelengths from 450nm to 1050nm are obtained, respectively. The experimental data are also compared with the data of Abbe refractometer, and they are in good agreement. The method provides a practical application for measuring the chromatic dispersion of liquid in a wide spectral range. Keywords: Surface plasmon resonance, Chromatic dispersio