AdaEnsemble: Learning Adaptively Sparse Structured Ensemble Network for Click-Through Rate Prediction

Learning feature interactions is crucial to success for large-scale CTR prediction in recommender systems and Ads ranking. Researchers and practitioners extensively proposed various neural network architectures for searching and modeling feature interactions. However, we observe that different datasets favor different neural network architectures and feature interaction types, suggesting that different feature interaction learning methods may have their own unique advantages. Inspired by this observation, we propose AdaEnsemble: a Sparsely-Gated Mixture-of-Experts (SparseMoE) architecture that can leverage the strengths of heterogeneous feature interaction experts and adaptively learns the routing to a sparse combination of experts for each example, allowing us to build a dynamic hierarchy of the feature interactions of different types and orders. To further improve the prediction accuracy and inference efficiency, we incorporate the dynamic early exiting mechanism for feature interaction depth selection. The AdaEnsemble can adaptively choose the feature interaction depth and find the corresponding SparseMoE stacking layer to exit and compute prediction from. Therefore, our proposed architecture inherits the advantages of the exponential combinations of sparsely gated experts within SparseMoE layers and further dynamically selects the optimal feature interaction depth without executing deeper layers. We implement the proposed AdaEnsemble and evaluate its performance on real-world datasets. Extensive experiment results demonstrate the efficiency and effectiveness of AdaEnsemble over state-of-the-art models.Comment: arXiv admin note: text overlap with arXiv:2301.01089, arXiv:2301.0813

Beyond Reward: Offline Preference-guided Policy Optimization

This study focuses on the topic of offline preference-based reinforcement learning (PbRL), a variant of conventional reinforcement learning that dispenses with the need for online interaction or specification of reward functions. Instead, the agent is provided with fixed offline trajectories and human preferences between pairs of trajectories to extract the dynamics and task information, respectively. Since the dynamics and task information are orthogonal, a naive approach would involve using preference-based reward learning followed by an off-the-shelf offline RL algorithm. However, this requires the separate learning of a scalar reward function, which is assumed to be an information bottleneck of the learning process. To address this issue, we propose the offline preference-guided policy optimization (OPPO) paradigm, which models offline trajectories and preferences in a one-step process, eliminating the need for separately learning a reward function. OPPO achieves this by introducing an offline hindsight information matching objective for optimizing a contextual policy and a preference modeling objective for finding the optimal context. OPPO further integrates a well-performing decision policy by optimizing the two objectives iteratively. Our empirical results demonstrate that OPPO effectively models offline preferences and outperforms prior competing baselines, including offline RL algorithms performed over either true or pseudo reward function specifications. Our code is available on the project website: https://sites.google.com/view/oppo-icml-2023

LIDER: cell embedding based deep neural network classifier for supervised cell type identification

Background Automatic cell type identification has been an urgent task for the rapid development of single-cell RNA-seq techniques. Generally, the current approach for cell type identification is to generate cell clusters by unsupervised clustering and later assign labels to each cell cluster with manual annotation. Methods Here, we introduce LIDER (celL embeddIng based Deep nEural netwoRk classifier), a deep supervised learning method that combines cell embedding and deep neural network classifier for automatic cell type identification. Based on a stacked denoising autoencoder with a tailored and reconstructed loss function, LIDER identifies cell embedding and predicts cell types with a deep neural network classifier. LIDER was developed upon a stacked denoising autoencoder to learn encoder-decoder structures for identifying cell embedding. Results LIDER accurately identifies cell types by using stacked denoising autoencoder. Benchmarking against state-of-the-art methods across eight types of single-cell data, LIDER achieves comparable or even superior enhancement performance. Moreover, LIDER suggests comparable robust to batch effects. Our results show a potential in deep supervised learning for automatic cell type identification of single-cell RNA-seq data. The LIDER codes are available at https://github.com/ShiMGLab/LIDER

CEIL: Generalized Contextual Imitation Learning

In this paper, we present \textbf{C}ont\textbf{E}xtual \textbf{I}mitation \textbf{L}earning~(CEIL), a general and broadly applicable algorithm for imitation learning (IL). Inspired by the formulation of hindsight information matching, we derive CEIL by explicitly learning a hindsight embedding function together with a contextual policy using the hindsight embeddings. To achieve the expert matching objective for IL, we advocate for optimizing a contextual variable such that it biases the contextual policy towards mimicking expert behaviors. Beyond the typical learning from demonstrations (LfD) setting, CEIL is a generalist that can be effectively applied to multiple settings including: 1)~learning from observations (LfO), 2)~offline IL, 3)~cross-domain IL (mismatched experts), and 4) one-shot IL settings. Empirically, we evaluate CEIL on the popular MuJoCo tasks (online) and the D4RL dataset (offline). Compared to prior state-of-the-art baselines, we show that CEIL is more sample-efficient in most online IL tasks and achieves better or competitive performances in offline tasks.Comment: NeurIPS 202

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

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

What determinants of COVID-19 vaccine hesitancy among Chinese nursing students? A cross-sectional study

BackgroundThe coronavirus disease 2019 (COVID-19) continues to threaten human health, and health professionals, including nursing students, usually work in healthcare frontiers with a high risk of infection. Vaccination is currently one of the most effective preventive measures. This study aimed to explore the determinants of COVID-19 vaccine hesitancy in nursing students.MethodsIn November 2022, a sample of undergraduate nursing students was recruited from several medical schools in Anhui Province, China, and an online cross-sectional survey was conducted using the questionnaire star platform (Wenjuanxin). A Chi-square test was used to explore vaccine hesitancy among nursing students with different social demographic characteristics and vaccine attitudes. Binary logistic regression analysis was then used to determine the influence factors of vaccine hesitancy among nursing students.ResultsA total of 1,090 valid samples were collected in this study. Of these, 27.06% (295) of nursing students reported COVID-19 vaccine hesitancy. The results showed “the need to go out of town recently” (OR = 0.670), “very confident that the outbreak could be controlled sustainably” (OR = 0.393), “feeling at risk of infection” (OR = 0.658), “not being worried/being generally worried about the vaccine's safety” (OR = 0.226 and OR = 0.686, respectively), and “not being worried about the vaccine's effectiveness” (OR = 0.411). These five factors are protective factors associated with COVID-19 vaccine hesitancy in nursing students. The factors “considering the country completely safe from an outbreak” (OR = 3.436), “considering themselves safe because others are vaccinated” (OR = 2.239), and “Agreeing that other protective measures can be relaxed after vaccination with the COVID-19 vaccine” (OR = 2.007) are risk factors associated with COVID-19 vaccine hesitancy among nursing students (P &lt; 0.05).ConclusionOverall, relatively few nursing students had COVID-19 vaccine hesitancy. Schools and relevant institutions still need to actively guide them to improve their confidence in the COVID-19 vaccine, strengthen the prevention and control measures of the epidemic, and improve their awareness of the crisis to improve the vaccination rate to reduce the COVID-19 vaccine hesitancy in nursing students