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The design of new lightweight and dexterous configurations is a major research focus for continuum robotics. This work proposes a cruciform continuum robot. Its unique feature is that it is formed by multiple cruciform-arranged elastic sheets with a single dimension of motion connected in series, and thus it has low-coupling motion characteristics. In addition, the cruciform continuum robot has the advantages of lighter weight (65 g), better dexterity, and higher motion accuracy. In this paper, the forward and inverse kinematics models of the cruciform continuum robot are established by geometric methods based on the assumption of constant curvature, and its workspace is analysed. It is experimentally verified that the tip position errors are less than 1 mm, and the cable length errors are less than 0.4 mm. Further, the cruciform continuum robot is successfully used for the nucleic acid detection simulation experiment, which confirms its good dexterity and man–machine safety. The main contribution of this paper is to provide a new configuration for the lightweight and dexterous continuum robots, and to further provide a reference method for improving their modelling accuracy from the perspective of structure.

Temporal sentence localization in videos (TSLV) aims to retrieve the most interested segment in an untrimmed video according to a given sentence query. However, almost of existing TSLV approaches suffer from the same limitations: (1) They only focus on either frame-level or object-level visual representation learning and corresponding correlation reasoning, but fail to integrate them both; (2) They neglect to leverage the rich semantic contexts to further benefit the query reasoning. To address these issues, in this paper, we propose a novel Hierarchical Visual- and Semantic-Aware Reasoning Network (HVSARN), which enables both visual- and semantic-aware query reasoning from object-level to frame-level. Specifically, we present a new graph memory mechanism to perform visual-semantic query reasoning: For visual reasoning, we design a visual graph memory to leverage visual information of video; For semantic reasoning, a semantic graph memory is also introduced to explicitly leverage semantic knowledge contained in the classes and attributes of video objects, and perform correlation reasoning in the semantic space. Experiments on three datasets demonstrate that our HVSARN achieves a new state-of-the-art performance., Comment: Accepted by ICASSP2023

BackgroundMyositis-specific autoantibodies (MSAs) are clinically used to diagnose and define idiopathic inflammatory myopathy (IIM) subsets. However, the underlying pathogenic mechanisms of patients with different MSAs remain unclear.MethodsA total of 158 Chinese patients with IIM and 167 gender- and age-matched healthy controls (HCs) were enrolled. Transcriptome sequencing (RNA-Seq) was performed with peripheral blood mononuclear cells (PBMCs), followed by the identification of differentially expressed genes (DEGs) and analysis of gene set enrichment analysis, immune cell infiltration, and WGCNA. Monocyte subsets and related cytokines/chemokines were quantified. The expressions of interferon (IFN)-related genes were validated using qRT-PCR and Western blot in both PBMCs and monocytes. We also performed correlation analysis and ROC analysis to explore the potential clinical significance of the IFN-related genes.ResultsThere were 1,364 genes altered in patients with IIM, including 952 upregulated and 412 downregulated genes. The type I interferon (IFN-I) pathway was remarkably activated in patients with IIM. Compared with patients with other MSAs, IFN-I signatures were significantly activated in patients with anti-melanoma differentiation-associated gene 5 (MDA5) antibodies. In total, 1,288 hub genes associated with IIM onset were identified using WGCNA, including 29 key DEGs associated with IFN signaling. The patients had more CD14brightCD16- classical, CD14brightCD16+ intermediate, and fewer CD14dimCD16+ non-classical monocyte subsets. Plasma cytokines like IL-6 and TNF and chemokines including CCL3 and MCPs increased. The validation of IFN-I-related gene expressions was consistent with the findings from RNA-Seq. The IFN-related genes were correlated with laboratory parameters and helpful for IIM diagnosis.ConclusionGene expressions were remarkably altered in the PBMCs of IIM patients. Anti-MDA5+ IIM patients had a more pronounced activated IFN signature than others. Monocytes exhibited a proinflammatory feature and contributed to the IFN signature of IIM patients.

Temporal sentence grounding (TSG) is crucial and fundamental for video understanding. Although the existing methods train well-designed deep networks with a large amount of data, we find that they can easily forget the rarely appeared cases in the training stage due to the off-balance data distribution, which influences the model generalization and leads to undesirable performance. To tackle this issue, we propose a memory-augmented network, called Memory-Guided Semantic Learning Network (MGSL-Net), that learns and memorizes the rarely appeared content in TSG tasks. Specifically, MGSL-Net consists of three main parts: a cross-modal inter-action module, a memory augmentation module, and a heterogeneous attention module. We first align the given video-query pair by a cross-modal graph convolutional network, and then utilize a memory module to record the cross-modal shared semantic features in the domain-specific persistent memory. During training, the memory slots are dynamically associated with both common and rare cases, alleviating the forgetting issue. In testing, the rare cases can thus be enhanced by retrieving the stored memories, resulting in better generalization. At last, the heterogeneous attention module is utilized to integrate the enhanced multi-modal features in both video and query domains. Experimental results on three benchmarks show the superiority of our method on both effectiveness and efficiency, which substantially improves the accuracy not only on the entire dataset but also on rare cases., Accepted by AAAI2022

This paper addresses temporal sentence grounding. Previous works typically solve this task by learning frame-level video features and align them with the textual information. A major limitation of these works is that they fail to distinguish ambiguous video frames with subtle appearance differences due to frame-level feature extraction. Recently, a few methods adopt Faster R-CNN to extract detailed object features in each frame to differentiate the fine-grained appearance similarities. However, the object-level features extracted by Faster R-CNN suffer from missing motion analysis since the object detection model lacks temporal modeling. To solve this issue, we propose a novel Motion-Appearance Reasoning Network (MARN), which incorporates both motion-aware and appearance-aware object features to better reason object relations for modeling the activity among successive frames. Specifically, we first introduce two individual video encoders to embed the video into corresponding motion-oriented and appearance-aspect object representations. Then, we develop separate motion and appearance branches to learn motion-guided and appearance-guided object relations, respectively. At last, both motion and appearance information from two branches are associated to generate more representative features for final grounding. Extensive experiments on two challenging datasets (Charades-STA and TACoS) show that our proposed MARN significantly outperforms previous state-of-the-art methods by a large margin., Accepted by AAAI2022

BackgroundBy 2020, the prevalence of Obstructive Sleep Apnea Syndrome (OSAS) in the US has reached 26. 6–43.2% in men and 8.7–27.8% in women. OSAS promotes hypertension, diabetes, and tumor growth through unknown means. Chronic intermittent hypoxia (CIH), sleep fragmentation, and increased pleural pressure are central mechanisms of OSAS complications. CIH exacerbates ferroptosis, which is closely related to malignancies. The mechanism of ferroptosis in OSAS disease progression remains unknown.MethodsOSAS-related datasets (GSE135917 and GSE38792) were obtained from the GEO. Differentially expressed genes (DEGs) were screened using the R software and intersected with the ferroptosis database (FerrDb V2) to get ferroptosis-related DEGs (f-DEGs). GO, DO, KEGG, and GSEA enrichment were performed, a PPI network was constructed and hub genes were screened. The TCGA database was used to obtain the thyroid cancer (THCA) gene expression profile, and hub genes were analyzed for differential and survival analysis. The mechanism was investigated using GSEA and immune infiltration. The hub genes were validated with RT-qPCR, IHC, and other datasets. Sprague-Dawley rats were randomly separated into normoxia and CIH groups. ROS, MDA, and GSH methods were used to detect CIH-induced ferroptosis and oxidative stress.ResultsGSEA revealed a statistically significant difference in ferroptosis in OSAS (FDR < 0.05). HIF1A, ATM, HSPA5, MAPK8, MAPK14, TLR4, and CREB1 were identified as hub genes among 3,144 DEGs and 74 f-DEGs. HIF1A and ATM were the only two validated genes. F-DEGs were mainly enriched in THCA. HIF1A overexpression in THCA promotes its development. HIF1A is associated with CD8 T cells and macrophages, which may affect the immunological milieu. The result found CIH increased ROS and MDA while lowering GSH indicating that it could cause ferroptosis. In OSAS patients, non-invasive ventilation did not affect HIF1A and ATM expression. Carvedilol, hydralazine, and caffeine may be important in the treatment of OSAS since they suppress HIF1A and ATM.ConclusionsOur findings revealed that the genes HIF1A and ATM are highly expressed in OSAS, and can serve as biomarkers and targets for OSAS.

A high-altitude military UAV air-to-ground scenario model is established for the battlefield scenario without base station, the MIMO antenna array technology is used to improve the channel communication performance, and the spatial statistical channel model (SSCM) of this scenario is constructed in this paper. According to the simulation results, the dependence of the channel on T-R separation distance, frequency, rain rate and antenna HPBW parameters was investigated, the large-scale fading and small-scale fading characteristics was analyzed. By calculating and analyzing the condition number and rank of the channel transmission matrix, the spatial multiplexing under different MIMO antenna arrays was investigated. The simulation results can provide a theoretical basis for future high-altitude military UAV battlefield situation awareness frequency selection and antenna design.

While the use of graph-structured data in various fields is becoming increasingly popular, it also raises concerns about the potential unauthorized exploitation of personal data for training commercial graph neural network (GNN) models, which can compromise privacy. To address this issue, we propose a novel method for generating unlearnable graph examples. By injecting delusive but imperceptible noise into graphs using our Error-Minimizing Structural Poisoning (EMinS) module, we are able to make the graphs unexploitable. Notably, by modifying only $5\%$ at most of the potential edges in the graph data, our method successfully decreases the accuracy from ${77.33\%}$ to ${42.47\%}$ on the COLLAB dataset., Comment: This paper is accepted as a poster for NDSS 2023

End-to-end training with global optimization have popularized graph neural networks (GNNs) for node classification, yet inadvertently introduced vulnerabilities to adversarial edge-perturbing attacks. Adversaries can exploit the inherent opened interfaces of GNNs' input and output, perturbing critical edges and thus manipulating the classification results. Current defenses, due to their persistent utilization of global-optimization-based end-to-end training schemes, inherently encapsulate the vulnerabilities of GNNs. This is specifically evidenced in their inability to defend against targeted secondary attacks. In this paper, we propose the Graph Agent Network (GAgN) to address the aforementioned vulnerabilities of GNNs. GAgN is a graph-structured agent network in which each node is designed as an 1-hop-view agent. Through the decentralized interactions between agents, they can learn to infer global perceptions to perform tasks including inferring embeddings, degrees and neighbor relationships for given nodes. This empowers nodes to filtering adversarial edges while carrying out classification tasks. Furthermore, agents' limited view prevents malicious messages from propagating globally in GAgN, thereby resisting global-optimization-based secondary attacks. We prove that single-hidden-layer multilayer perceptrons (MLPs) are theoretically sufficient to achieve these functionalities. Experimental results show that GAgN effectively implements all its intended capabilities and, compared to state-of-the-art defenses, achieves optimal classification accuracy on the perturbed datasets.

Recently, ChatGPT has gained significant attention in research due to its ability to interact with humans effectively. The core idea behind this model is reinforcement learning (RL) fine-tuning, a new paradigm that allows language models to align with human preferences, i.e., InstructGPT. In this study, we propose BadGPT, the first backdoor attack against RL fine-tuning in language models. By injecting a backdoor into the reward model, the language model can be compromised during the fine-tuning stage. Our initial experiments on movie reviews, i.e., IMDB, demonstrate that an attacker can manipulate the generated text through BadGPT., Comment: This paper is accepted as a poster in NDSS2023

Existing methods of privacy-preserving action recognition (PPAR) mainly focus on frame-level (spatial) privacy removal through 2D CNNs. Unfortunately, they have two major drawbacks. First, they may compromise temporal dynamics in input videos, which are critical for accurate action recognition. Second, they are vulnerable to practical attacking scenarios where attackers probe for privacy from an entire video rather than individual frames. To address these issues, we propose a novel framework STPrivacy to perform video-level PPAR. For the first time, we introduce vision Transformers into PPAR by treating a video as a tubelet sequence, and accordingly design two complementary mechanisms, i.e., sparsification and anonymization, to remove privacy from a spatio-temporal perspective. In specific, our privacy sparsification mechanism applies adaptive token selection to abandon action-irrelevant tubelets. Then, our anonymization mechanism implicitly manipulates the remaining action-tubelets to erase privacy in the embedding space through adversarial learning. These mechanisms provide significant advantages in terms of privacy preservation for human eyes and action-privacy trade-off adjustment during deployment. We additionally contribute the first two large-scale PPAR benchmarks, VP-HMDB51 and VP-UCF101, to the community. Extensive evaluations on them, as well as two other tasks, validate the effectiveness and generalization capability of our framework.

Despite its success in image synthesis, we observe that diffusion probabilistic models (DPMs) often lack contextual reasoning ability to learn the relations among object parts in an image, leading to a slow learning process. To solve this issue, we propose a Masked Diffusion Transformer (MDT) that introduces a mask latent modeling scheme to explicitly enhance the DPMs' ability of contextual relation learning among object semantic parts in an image. During training, MDT operates on the latent space to mask certain tokens. Then, an asymmetric masking diffusion transformer is designed to predict masked tokens from unmasked ones while maintaining the diffusion generation process. Our MDT can reconstruct the full information of an image from its incomplete contextual input, thus enabling it to learn the associated relations among image tokens. Experimental results show that MDT achieves superior image synthesis performance, e.g. a new SoTA FID score on the ImageNet dataset, and has about 3x faster learning speed than the previous SoTA DiT. The source code is released at https://github.com/sail-sg/MDT.

The rise of pre-trained unified foundation models breaks down the barriers between different modalities and tasks, providing comprehensive support to users with unified architectures. However, the backdoor attack on pre-trained models poses a serious threat to their security. Previous research on backdoor attacks has been limited to uni-modal tasks or single tasks across modalities, making it inapplicable to unified foundation models. In this paper, we make proof-of-concept level research on the backdoor attack for pre-trained unified foundation models. Through preliminary experiments on NLP and CV classification tasks, we reveal the vulnerability of these models and suggest future research directions for enhancing the attack approach., Comment: This paper is accepted as a poster for NDSS 2023

This paper addresses the temporal sentence grounding (TSG). Although existing methods have made decent achievements in this task, they not only severely rely on abundant video-query paired data for training, but also easily fail into the dataset distribution bias. To alleviate these limitations, we introduce a novel Equivariant Consistency Regulation Learning (ECRL) framework to learn more discriminative query-related frame-wise representations for each video, in a self-supervised manner. Our motivation comes from that the temporal boundary of the query-guided activity should be consistently predicted under various video-level transformations. Concretely, we first design a series of spatio-temporal augmentations on both foreground and background video segments to generate a set of synthetic video samples. In particular, we devise a self-refine module to enhance the completeness and smoothness of the augmented video. Then, we present a novel self-supervised consistency loss (SSCL) applied on the original and augmented videos to capture their invariant query-related semantic by minimizing the KL-divergence between the sequence similarity of two videos and a prior Gaussian distribution of timestamp distance. At last, a shared grounding head is introduced to predict the transform-equivariant query-guided segment boundaries for both the original and augmented videos. Extensive experiments on three challenging datasets (ActivityNet, TACoS, and Charades-STA) demonstrate both effectiveness and efficiency of our proposed ECRL framework.

This paper presents a new, effective, and facile method to enhance the humidity sensitivity of lignin-based QCM sensor through fringing field effect. The biodegradable and renewable lignin was deposited on the surface of metal electrode as humidity-sensitive material. The microscopic morphology, hydrophilic groups, and water contact angle of the lignin sample were characterized. Then, the humidity-sensing performances of the lignin-based QCM sensors with symmetric and ringed electrode configurations were investigated through the oscillating circuit method. The experimental results showed that the humidity sensitivity of the ringed electrode QCM (61 Hz/%RH) is higher than that of the symmetric electrode QCM in the range of 11.3%-97.3% RH. Moreover, the ringed electrode QCM based on lignin exhibited low humidity hysteresis (~6.2% RH), good logarithmic relationship, rapid response/recovery time (28/5 s), excellent repeatability, and long-term stability. In addition, the Langmuir adsorption isotherm model and the Gibbs free energy were used to analyze the adsorption of water molecules on the surface of the lignin film. Finally, the finite element method (FEM) was used to analyze the fringing field distributions of the QCMs with symmetric and ringed electrode. The sensitivity enhancement mechanism of the lignin-based QCM humidity sensor with ringed electrode configuration was discussed through the equivalent electronic circuit analysis and simulation methods. This work demonstrates that the lignin is a potential candidate material for humidity detection, and it is an excellent method to improve the humidity sensitivity of QCM sensors through optimizing the electrode structure configuration using fringing field effect.

A transition-metal-free method for the construction of 3-substituted or 3,4-disubstituted quinolines from readily available N,N-dimethyl enaminones and o-aminobenzyl alcohols is reported. The direct oxidative cyclocondensation reaction tolerates broad functional groups, allowing the efficient synthesis of various quinolines in moderate to excellent yields. The reaction involves a C (sp3)-O bond cleavage and a C=N bind and a C=C bond formation during the oxidative cyclization process, and the mechanism was proposed.

In recent years, the problem of atmospheric pollution has been concerning in the Beijing–Tianjin–Hebei region, due to the frequent haze. It has become a significant issue to improve regional air quality through appropriate emission reduction measures. In this study, considering the regional atmospheric transmission of air pollutants, the WRF/CALPUFF model (the Weather Research and Forecasting model coupled with the California Puff air quality model) was used to describe the impact of each city’s pollutant emissions on the concentrations of every city. Then, a new optimization model was designed to calculate the maximum allowable emissions of every city. The results showed that NOx and PM2.5 emissions need to be reduced by 44% and 48%, respectively, in the traditional mitigation scenario (any city’s pollutant emissions are not allowed to increase). However, in the optimized scenario, NOx and PM2.5 emissions should be reduced by 23% and 46%, respectively, to meet the national secondary standard. The emissions of cities with low transfer coefficients, such as Zhangjiakou, Qinhuangdao, and Chengde, could even be appropriately increased. This means that the optimized scenario could reduce the pressure on emission reduction. Although the optimization results are theoretical and idealistic, this research study provides a new idea for formulating emission mitigation policies in various regions to reduce the impact on the economy.

In the factory, the steel round bar sawing machine is applied to finish the sawing mission. Due to the process of the sawing steel is operating in high speed, the grinding-wheel-saw (GWS) is quickly worn by the friction and replaced frequently. The automatic replacement system of GWSs is seldom built yet. To improve the efficiency of the replacement for GWSs, the machine-vision-based intelligent robotic replacement system (MIRRS) for GWSs is developed and reported in the study. The MIRRS is mainly consisted of the six degrees-of-freedom (DOF) manipulator and the six-axes mechanical arm system to successfully complete the task of the replacement. The finite element method (FEM) is applied to analyze and design the subsystem of the MIRRS. Besides, the machine-vision positioning system associated with the digital camera are also setup to provide the required feedback detections for the MIRRS. The hardware system architecture and the control flow chart are described in detail and the hundreds of realistic testing results are collected to verify the reliability and offer the well working efficiency of the designed MIRRS.

Graph neural networks (GNNs) have achieved state-of-the-art performance in many graph-based tasks such as node classification and graph classification. However, many recent works have demonstrated that an attacker can mislead GNN models by slightly perturbing the graph structure. Existing attacks to GNNs are either under the less practical threat model where the attacker is assumed to access the GNN model parameters, or under the practical black-box threat model but consider perturbing node features that are shown to be not enough effective. In this paper, we aim to bridge this gap and consider black-box attacks to GNNs with structure perturbation as well as with theoretical guarantees. We propose to address this challenge through bandit techniques. Specifically, we formulate our attack as an online optimization with bandit feedback. This original problem is essentially NP-hard due to the fact that perturbing the graph structure is a binary optimization problem. We then propose an online attack based on bandit optimization which is proven to be {sublinear} to the query number $T$, i.e., $\mathcal{O}(\sqrt{N}T^{3/4})$ where $N$ is the number of nodes in the graph. Finally, we evaluate our proposed attack by conducting experiments over multiple datasets and GNN models. The experimental results on various citation graphs and image graphs show that our attack is both effective and efficient. Source code is available at~\url{https://github.com/Metaoblivion/Bandit_GNN_Attack}, Comment: Accepted by CVPR 2022 Oral presentation

The transportation sector has a significant impact on the synergistic reduction of CO2 and pollutants. Using a structural path decomposition, we reveals the direct and embodied CO2 and NOx emissions from the transportation sector triggered by key supply chain pathways. Meanwhile, the emission abatement potential and economic costs of 33 vehicle specific abatement technology options are analyzed based on the input-output analysis for life cycle assessment. The results show that most of the vehicle technology options can reduce CO2 and NOx emissions while saving economic costs. Among these technologies, both the pure electric technology for passenger cars and the parallel hybrid technology for heavy-duty trucks have high abatement potential. Compared to costly pure electrification technologies for passenger cars, parallel hybrid technologies for heavy-duty trucks offer the highest economic benefits as well as a better driving mileage. Therefore, hybrid heavy-duty trucks will be a more comprehensive solution in the near future.

Sepsis-induced acute lung injury (ALI) is associated with poor survival rates. The identification of potential therapeutic targets for preventing sepsis-induced ALI has clinical importance. Here, we show that estrogen-related receptor alpha (ERRα) protects against sepsis-induced ALI through ERRα-mediated apoptosis and autophagy. ERRα was upregulated in inflammation models from lipopolysaccharides (LPS)-treated rat pulmonary microvascular endothelial cells (PMVECs) and cecal ligation and puncture (CLP)-induced septic rats. In vitro , overexpression of ERRα ameliorated LPS-induced degradation of adherens junctional molecules, upregulation of bax, cleaved caspase 3 and cleaved caspase 9 levels and downregulation of anti-apoptotic protein Bcl-2 level, and promoted the formation of autophagic flux, while the knockdown of ERRα exacerbated LPS-induced apoptosis and inhibited the activation of autophagy. Rats injected with ERRα agonist were subjected to CLP to elucidate the role of ERRα in sepsis-induced ALI in vivo . Administration of ERRα agonist alleviated the pathological damage of lung tissue, increased the levels of tight junction proteins and adherens junction proteins, and decreased the expression of apoptosis-related proteins. Promoting the expression of ERRα significantly enhanced the process of autophagy and reduced CLP-induced ALI. Mechanistically, ERRα is essential to regulate the balance between autophagy and apoptosis to maintain the adherens junctional integrity. These findings indicate that activation of ERRα provides a new therapeutic opportunity to prevent sepsis-induced ALI.

Multilabel data share important features, including label imbalance, which has a significant influence on the performance of classifiers. Because of this problem, a widely used multilabel classification algorithm, the multilabel k-nearest neighbor (ML-kNN) algorithm, has poor performance on imbalanced multilabel data. To address this problem, this study proposes an improved ML-kNN algorithm based on value and weight. In this improved algorithm, labels are divided into minority and majority, and different strategies are adopted for different labels. By considering the label of latent information carried by the nearest neighbors, a value calculation method is proposed and used to directly classify majority labels. Additionally, to address the misclassification problem caused by a lack of nearest neighbor information for minority labels, weight calculation is proposed. The proposed weight calculation converts distance information with and without label sets in the nearest neighbors into weights. The experimental results on multilabel datasets from different benchmarks demonstrate the performance of the algorithm, especially for datasets with high imbalance. Different evaluation metrics show that the results are improved by approximately 2–10%. The verified algorithm could be applied to a multilabel classification of various fields involving label imbalance, such as drug molecule identification, building identification, and text categorization.

Proliferation of smooth muscle cells, oxidative stress, and pulmonary vasoconstriction resulting from intermittent hypoxia (IH) facilitate pulmonary hypertension (PH) in patients with obstructive sleep apnea. The role of Phosphodiesterase 4 B (PDE4B) in PH has not yet been established. Herein, we investigated whether PDE4B inhibition ameliorates experimental PH by modulating cAMP signaling. We performed an integrative analysis of PDE4B expression in Gene Expression Omnibus datasets, experimental IH-induced rat PH samples, and IH-induced pulmonary arterial smooth muscle cells (PASMCs). PDE4B expression was modulated using siRNA in vitro and a specific adeno-associated virus serotype 1 in vivo. In the databases of mouse models of IH-induced and sustained hypoxia-induced PH and in a rat model of six weeks of IH, the expression of PDE4B was up-regulated. Inhibition of PDE4B attenuated IH-induced pulmonary vascular remodeling and right ventricular hypertrophy. Our results also showed that PDE4B deficiency inhibited IH-induced proliferation of PASMCs with less mitochondrial reactive oxygen species and mitochondrial damage. Meanwhile, IH induced an increase in ATF4, which positively regulated the expression of PDE4B through transcription, and inhibition of ATF4 exerted effects similar to those of PDE4B inhibition. Mechanistically, downregulating the expression of PDE4B resulted in the activation of the cAMP/PKA/p-CREB/PGC-1α pathway in PASMCs after IH. Taken together, our present study provides evidence that inhibition of PDE4B attenuates IH-induced PH by regulating cAMP signaling.

The novel method for the synthesis of gem-bisarylthio enamines and α-phenylthio ketones was developed via the coupling of α-substituted vinyl azides with thiols in the presence of tetrabutylammonium iodide (TBAI) as a redox catalyst and electrolyte at room temperature. Electronic properties were crucial in the generated products. This protocol features metal- and oxidant-free materials, broad tolerance of substrates, and mild reaction conditions.

In the period from 2011 to 2020, 4 large-scale population-based studies were conducted to identify gout in patients with obstructive sleep apnea (OSA), as well as one study to detect the frequency of sleep apnea in patients with gout.All recently published studies confirm connection between the development of OSA and an increase in the level of uric acid in blood serum, both against the background of clinically significant gout arthritis and without it. However, only one big population-based study during 2010 — 2020 assessed the development of OSA against the background of gout. According to this study, incidence of OSA was 14.3 per 1.000 person-years in patients with gout, compared with 3.9 per 1.000 person-years without gout, with HR = 2.07 (95 % CI 2.00 — 2.15 %), which is significantly higher than the incidence of gout in patients with OSA, even taking into account hypertension and hyperlipidemia.The data from these studies showed an undoubted relationship between the two diseases, but to date it is not completely known how much it is due to common risk factors and how interlinked the development mechanisms are. Older age and restricted mobility put patients with gout at a significant risk during OSA episodes. In cases where the treatment of both diseases is conducted independently, the volume of drug therapy and the likelihood of developing negative side effects increase. In this regard, it is necessary to monitor patients with gout who develop sleep apnea differently to gout only patients, as well as to prescribe adequate treatment for both diseases.In our review, we aimed to show the most recent information on the mechanisms of gout-OSA development, shared pathways, targets and biomarkers, emphasizing the likely ways that cause the progression of OSA in patients with gout. In addition, analysis of the latest publications to date showed nonuniformity in study subjects, allowing to identify different subtypes of gout-OSA patients: combined with metabolic syndrome (most common), combined with renal dysfunction without obesity, and others (dietary violations, genetic diseases, acidosis).

It is of great significance to identify the critical influential factors of pollutant emissions for emission mitigation. However, city disparity implies different priorities for regional mitigation. This study aims to estimate the consumption-based emissions of 309 prefecture-level cities in China based on the multi-region input-output table and the sectoral NOx emission inventory and investigate the emission transfer phenomenon among cities and sectors. In addition, a geographically weighted regression method is used to analyze the spatial heterogeneity in the driving factors of regional consumption-based emissions. The results reveal that the top 10 cities in consumption-based emissions account for 25.2% of emissions and contribute 22.6% to GDP. The consumption-based emissions are mainly driven by local demand (72.79%) at the regional level and by construction activities (94.43%) at the sectoral level. Besides, the results also show the spatial variances in contributions of driving forces to consumption-based emissions. Economic growth has been identified as the most important factor which promotes consumption-based emissions. However, disposable personal income, per capita road area, urbanization, and percentage of tertiary industry GDP are conducive to reduce consumption-based emissions in some cities of China. It could be concluded that policies without consideration of the emissions from a consumption perspective are difficult to achieve effective emission reduction.

Distantly-Supervised Named Entity Recognition (DS-NER) effectively alleviates the data scarcity problem in NER by automatically generating training samples. Unfortunately, the distant supervision may induce noisy labels, thus undermining the robustness of the learned models and restricting the practical application. To relieve this problem, recent works adopt self-training teacher-student frameworks to gradually refine the training labels and improve the generalization ability of NER models. However, we argue that the performance of the current self-training frameworks for DS-NER is severely underestimated by their plain designs, including both inadequate student learning and coarse-grained teacher updating. Therefore, in this paper, we make the first attempt to alleviate these issues by proposing: (1) adaptive teacher learning comprised of joint training of two teacher-student networks and considering both consistent and inconsistent predictions between two teachers, thus promoting comprehensive student learning. (2) fine-grained student ensemble that updates each fragment of the teacher model with a temporal moving average of the corresponding fragment of the student, which enhances consistent predictions on each model fragment against noise. To verify the effectiveness of our proposed method, we conduct experiments on four DS-NER datasets. The experimental results demonstrate that our method significantly surpasses previous SOTA methods., Comment: Accepted at AAAI 2023

The few-shot learning ability of vision transformers (ViTs) is rarely investigated though heavily desired. In this work, we empirically find that with the same few-shot learning frameworks, \eg~Meta-Baseline, replacing the widely used CNN feature extractor with a ViT model often severely impairs few-shot classification performance. Moreover, our empirical study shows that in the absence of inductive bias, ViTs often learn the low-qualified token dependencies under few-shot learning regime where only a few labeled training data are available, which largely contributes to the above performance degradation. To alleviate this issue, for the first time, we propose a simple yet effective few-shot training framework for ViTs, namely Self-promoted sUpervisioN (SUN). Specifically, besides the conventional global supervision for global semantic learning SUN further pretrains the ViT on the few-shot learning dataset and then uses it to generate individual location-specific supervision for guiding each patch token. This location-specific supervision tells the ViT which patch tokens are similar or dissimilar and thus accelerates token dependency learning. Moreover, it models the local semantics in each patch token to improve the object grounding and recognition capability which helps learn generalizable patterns. To improve the quality of location-specific supervision, we further propose two techniques:~1) background patch filtration to filtrate background patches out and assign them into an extra background class; and 2) spatial-consistent augmentation to introduce sufficient diversity for data augmentation while keeping the accuracy of the generated local supervisions. Experimental results show that SUN using ViTs significantly surpasses other few-shot learning frameworks with ViTs and is the first one that achieves higher performance than those CNN state-of-the-arts., Comment: Code is available at https://github.com/DongSky/few-shot-vit

Spatial-Temporal Video Grounding (STVG) is a challenging task which aims to localize the spatio-temporal tube of the interested object semantically according to a natural language query. Most previous works not only severely rely on the anchor boxes extracted by Faster R-CNN, but also simply regard the video as a series of individual frames, thus lacking their temporal modeling. Instead, in this paper, we are the first to propose an anchor-free framework for STVG, called Gaussian Kernel-based Cross Modal Network (GKCMN). Specifically, we utilize the learned Gaussian Kernel-based heatmaps of each video frame to locate the query-related object. A mixed serial and parallel connection network is further developed to leverage both spatial and temporal relations among frames for better grounding. Experimental results on VidSTG dataset demonstrate the effectiveness of our proposed GKCMN., Comment: Accepted by ICIP2022

Recent studies show that Transformer has strong capability of building long-range dependencies, yet is incompetent in capturing high frequencies that predominantly convey local information. To tackle this issue, we present a novel and general-purpose Inception Transformer, or iFormer for short, that effectively learns comprehensive features with both high- and low-frequency information in visual data. Specifically, we design an Inception mixer to explicitly graft the advantages of convolution and max-pooling for capturing the high-frequency information to Transformers. Different from recent hybrid frameworks, the Inception mixer brings greater efficiency through a channel splitting mechanism to adopt parallel convolution/max-pooling path and self-attention path as high- and low-frequency mixers, while having the flexibility to model discriminative information scattered within a wide frequency range. Considering that bottom layers play more roles in capturing high-frequency details while top layers more in modeling low-frequency global information, we further introduce a frequency ramp structure, i.e. gradually decreasing the dimensions fed to the high-frequency mixer and increasing those to the low-frequency mixer, which can effectively trade-off high- and low-frequency components across different layers. We benchmark the iFormer on a series of vision tasks, and showcase that it achieves impressive performance on image classification, COCO detection and ADE20K segmentation. For example, our iFormer-S hits the top-1 accuracy of 83.4% on ImageNet-1K, much higher than DeiT-S by 3.6%, and even slightly better than much bigger model Swin-B (83.3%) with only 1/4 parameters and 1/3 FLOPs. Code and models will be released at https://github.com/sail-sg/iFormer., Comment: Code and models will be released at https://github.com/sail-sg/iFormer

Although increasingly training-expensive, most self-supervised learning (SSL) models have repeatedly been trained from scratch but not fully utilized, since only a few SOTAs are employed for downstream tasks. In this work, we explore a sustainable SSL framework with two major challenges: i) learning a stronger new SSL model based on the existing pretrained SSL model, also called as "base" model, in a cost-friendly manner, ii) allowing the training of the new model to be compatible with various base models. We propose a Target-Enhanced Conditional (TEC) scheme which introduces two components to the existing mask-reconstruction based SSL. Firstly, we propose patch-relation enhanced targets which enhances the target given by base model and encourages the new model to learn semantic-relation knowledge from the base model by using incomplete inputs. This hardening and target-enhancing help the new model surpass the base model, since they enforce additional patch relation modeling to handle incomplete input. Secondly, we introduce a conditional adapter that adaptively adjusts new model prediction to align with the target of different base models. Extensive experimental results show that our TEC scheme can accelerate the learning speed, and also improve SOTA SSL base models, e.g., MAE and iBOT, taking an explorative step towards sustainable SSL., Comment: Extension of NeurIPS 2022 workshop

Crowd counting is a regression task that estimates the number of people in a scene image, which plays a vital role in a range of safety-critical applications, such as video surveillance, traffic monitoring and flow control. In this paper, we investigate the vulnerability of deep learning based crowd counting models to backdoor attacks, a major security threat to deep learning. A backdoor attack implants a backdoor trigger into a target model via data poisoning so as to control the model's predictions at test time. Different from image classification models on which most of existing backdoor attacks have been developed and tested, crowd counting models are regression models that output multi-dimensional density maps, thus requiring different techniques to manipulate. In this paper, we propose two novel Density Manipulation Backdoor Attacks (DMBA$^{-}$ and DMBA$^{+}$) to attack the model to produce arbitrarily large or small density estimations. Experimental results demonstrate the effectiveness of our DMBA attacks on five classic crowd counting models and four types of datasets. We also provide an in-depth analysis of the unique challenges of backdooring crowd counting models and reveal two key elements of effective attacks: 1) full and dense triggers and 2) manipulation of the ground truth counts or density maps. Our work could help evaluate the vulnerability of crowd counting models to potential backdoor attacks., Comment: To appear in ACMMM 2022. 10pages, 6 figures and 2 tables

For long-tailed classification, most works often pretrain a big model on a large-scale dataset, and then fine-tune the whole model for adapting to long-tailed data. Though promising, fine-tuning the whole pretrained model tends to suffer from high cost in computation and deployment of different models for different tasks, as well as weakened generalization ability for overfitting to certain features of long-tailed data. To alleviate these issues, we propose an effective Long-tailed Prompt Tuning method for long-tailed classification. LPT introduces several trainable prompts into a frozen pretrained model to adapt it to long-tailed data. For better effectiveness, we divide prompts into two groups: 1) a shared prompt for the whole long-tailed dataset to learn general features and to adapt a pretrained model into target domain; and 2) group-specific prompts to gather group-specific features for the samples which have similar features and also to empower the pretrained model with discrimination ability. Then we design a two-phase training paradigm to learn these prompts. In phase 1, we train the shared prompt via supervised prompt tuning to adapt a pretrained model to the desired long-tailed domain. In phase 2, we use the learnt shared prompt as query to select a small best matched set for a group of similar samples from the group-specific prompt set to dig the common features of these similar samples, then optimize these prompts with dual sampling strategy and asymmetric GCL loss. By only fine-tuning a few prompts while fixing the pretrained model, LPT can reduce training and deployment cost by storing a few prompts, and enjoys a strong generalization ability of the pretrained model. Experiments show that on various long-tailed benchmarks, with only ~1.1% extra parameters, LPT achieves comparable performance than previous whole model fine-tuning methods, and is more robust to domain-shift., Comment: ICLR 2023 (poster)

From small unicellular organisms to large mammals, swallowing is an important way for them to interact with their external environment. The majority of these animals swallow their targets for the purpose of hunting, and some fish and amphibians protect their cubs from external injury by swallowing them. Thus, swallowing can produce an efficient capture, keep the integrity of targets, and provide effective protection for swallowed objects. Inspired by this, we propose a novel soft swallowing robot (SSR) capable of swallowing various objects that have different shapes and stiffnesses, protecting objects from squeeze and collision, and withstanding high temperature, which are enabled by a compliant guiding structure consisting of a double thin-walled capsule filled with fluid and a linearly movable traction body. In this article, we study the SSR supported by air and water, respectively; furthermore, we experimentally conclude that the working medium has a great influence on the inherent characteristics of the SSR. Our study helps lay the foundation for the research of soft robotic systems with swallowing characteristics, and the SSR is expected to enter the practical application field from the laboratory.

A facile strategy to fabricate macroporous polymeric foam composites with uniformly distributed lithium ion sieves (LIS) was fabricated toward lithium ion (Li+) selective recovery. With LiMn2O4 nan...

Methanol plays a crucial role in organic synthesis as a green C1 source. Herein, the concise α-methoxymethylation and aminomethylation of propiophenones are achieved via electrochemical reactions with methanol as both the solvent and reactant. Methanol plays dual roles in this reaction: it is oxidized to formaldehyde (HCHO) and functions as a nucleophile. This protocol was performed under oxidant- and transition metal-free conditions.

While there is emerging evidence that hypoxia critically contributes to the pathobiology of obstructive sleep apnea (OSA), the diagnostic value of measuring hypoxia or its surrogates in OSA remains unclear. Here we investigated the diagnostic value of hypoxia-related genes and explored their potential molecular mechanisms of action in OSA. Expression data from OSA and control subjects were downloaded from the Gene Expression Omnibus database. Differentially-expressed genes (DEGs) between OSA and control subjects were identified using the limma R package and their biological functions investigated with the clusterProfiler R package. Hypoxia-related DEGs in OSA were obtained by overlapping DEGs with hypoxia-related genes. The diagnostic value of hypoxia-related DEGs in OSA was evaluated by receiver operating curve (ROC) analysis. Random forest (RF) and lasso machine learning algorithms were used to construct diagnostic models to distinguish OSA from control. Geneset enrichment analysis (GSEA) was performed to explore pathways related to key hypoxia-related genes in OSA. Sixty-three genes associated with hypoxia, transcriptional regulation, and inflammation were identified as differentially expressed between OSA and control samples. By intersecting these with known hypoxia-related genes, 17 hypoxia-related DEGs related to OSA were identified. Protein-protein interaction network analysis showed that 16 hypoxia-related genes interacted, and their diagnostic value was further explored. The 16 hypoxia-related genes accurately predicted OSA with AUCs >0.7. A lasso model constructed using AREG, ATF3, ZFP36, and DUSP1 had a better performance and accuracy in classifying OSA and control samples compared with an RF model as assessed by multiple metrics. Moreover, GSEA revealed that AREG, ATF3, ZFP36, and DUSP1 may regulate OSA via inflammation and contribute to OSA-related cancer risk. Here we constructed a reliable diagnostic model for OSA based on hypoxia-related genes. Furthermore, these transcriptional changes may contribute to the etiology, pathogenesis, and sequelae of OSA.

Aiming at the modulation recognition problem of mixed signal set {BPSK, QPSK, BPSK-FM, QPSK-FM} with unknown parameters, this paper proposes a modulation recognition algorithm based on two-fold PLL (phase locked loop) and goodness of fit test. The PLL is used to extract the features of the composite modulation signal, and the goodness of fit test is used to measure the distance of the phase cumulative distribution of the demodulation information. Theoretical analysis and simulation results show that the performance of the recognition method based on two-fold PLL and goodness of fit test is better than the widely used modulation recognition method based on constellation.

This study was undertaken to examine the regulatory role of multidrug resistance-associated protein 4 (MRP4) in an experimental model of sepsis-induced acute lung injury in rats. Sepsis was induced by cecal ligation and puncture in anesthetized rats. Animals were then randomly assigned to receive intravenous injection of vehicle or MRP4 inhibitor (MK571, 20 mg/kg). The pathological changes were observed by hematoxylin and eosin staining. Lung water content, lung vascular permeability and inflammatory cell count in bronchoalveolar lavage fluid (BALF) were quantified. Serum tumor necrosis factor-α (TNF-α) and interleukin-6 (IL-6) levels were measured. In addition, lung tissue cyclic adenosine monophosphate (cAMP) levels were examined by enzyme-linked immunosorbent assay. Furthermore, the effects of MRP4 knockdown on lipopolysaccharide (LPS)-induced endothelial permeability and the cytoskeleton of rat pulmonary microvascular endothelial cells (PMVECs) were detected. The protein expression levels of MRP4, Rac1, VE-cadherin, β-catenin and ZO-1 were measured by Western blot analysis. MK571 significantly reduced lung tissue damage, lung water content and lung vascular permeability. Lung tissue cAMP levels were attenuated in MK571-treated animals compared with vehicle controls. MK571 also decreased sepsis-induced inflammatory cell accumulation in BALF. In addition, the MK571 group had significantly lower serum TNF-α and IL-6 levels compared with vehicle controls. Consistently, knockdown of MRP4 protected against LPS-induced increase in the endothelial permeability and the destruction of cytoskeleton in vitro. Furthermore, silencing MRP4 gene significantly reduced MRP4 protein expression and restored the protein expression of Rac1, VE-cadherin, β-catenin and ZO-1 in rat PMVECs in response to LPS stimulation. These data suggest that inhibition of MRP4 significantly alleviates sepsis-induced acute lung injury in rats.

Objective— Pulmonary arterial hypertension is characterized by progressive pulmonary vascular remodeling and persistently elevated mean pulmonary artery pressures and pulmonary vascular resistance. We aimed to investigate whether transthoracic pulmonary artery denervation (TPADN) attenuated pulmonary artery (PA) remodeling, improved right ventricular (RV) function, and affected underlying mechanisms. We also explored the distributions of sympathetic nerves (SNs) around human PAs for clinical translation. Approach and Results— We identified numerous SNs in adipose and connective tissues around the main PA trunks and bifurcations in male Sprague Dawley rats, which were verified in samples from human heart transplant patients. Pulmonary arterial hypertensive rats were randomized into TPADN and sham groups. In the TPADN group, SNs around the PA trunk and bifurcation were completely and accurately removed under direct visualization. The sham group underwent thoracotomy. Hemodynamics, RV function, and pathological changes in PA and RV tissues were measured via right heart catheterization, cardiac magnetic resonance imaging, and pathological staining, respectively. Compared with the sham group, the TPADN group had lower mean pulmonary arterial pressures, less PA and RV remodeling, and improved RV function. Furthermore, TPADN inhibited neurohormonal overactivation of the sympathetic nervous system and renin-angiotensin-aldosterone system and regulated abnormal expressions and signaling of neurohormone receptors in local tissues. Conclusions— There are numerous SNs around the rat and human main PA trunks and bifurcations. TPADN completely and accurately removed the main SNs around PAs and attenuated pulmonary arterial hypertensive progression by inhibiting excessive activation of the sympathetic nervous system and renin-angiotensin-aldosterone system neurohormone-receptor axes.