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This paper investigates the utilization of simultaneously transmitting and reflecting RIS (STAR-RIS) in supporting joint physical layer security (PLS) and covert communications (CCs) in a multi-antenna millimeter wave (mmWave) system, where the base station (BS) communicates with both covert and security users while defeating eavesdropping by wardens with the help of a STAR-RIS. Specifically, analytical derivations are performed to obtain the closed-form expression of warden's minimum detection error probability (DEP). Furthermore, the asymptotic result of the minimum DEP and the lower bound of the secure rates are derived, considering the practical assumption that BS only knows the statistical channel state information (CSI) between STAR-RIS and the wardens. Subsequently, an optimization problem is formulated with the aim of maximizing the average sum of the covert rate and the minimum secure rate while ensuring the covert requirement and quality of service (QoS) for legal users by jointly optimizing the active and passive beamformers. Due to the strong coupling among variables, an iterative algorithm based on the alternating strategy and the semi-definite relaxation (SDR) method is proposed to solve the non-convex optimization problem. Simulation results indicate that the performance of the proposed STAR-RIS-assisted scheme greatly surpasses that of the conventional RIS scheme, which validates the superiority of STAR-RIS in simultaneously implementing PLS and CCs.

The new mode of Circular Synthetic Aperture Radar (CSAR) has several advantages including multi-aspect and long-time observation, which can generate high-frame-rate image sequences to detect moving targets with a single-channel system. Nonetheless, due to CSAR being sensitive to 3D structures, static high targets are observed in scene display rotational motion within CSAR subaperture image sequences. Such motion can cause false alarms rising when utilizing image sequence-based moving target detection methods like logarithm background subtraction (LBS). To address this issue, this paper first thoroughly analyzes the moving target and static high target’s difference for the trajectory in an image sequence. Two new trajectory features of the rotation angle and moving distance are proposed to differentiate them. Based on the features, a new false alarm suppression method is proposed. The method first utilizes LBS to obtain coarse binary detection results comprising both moving and static high targets, then employs morphological filtering to eliminate noise. Next, DBSCAN and target tracking steps are employed to extract the trajectory features of the target and false alarm. Finally, false alarms are suppressed with trajectory-based feature discriminators to output detection results. The W-band CSAR open dataset is used to validate the proposed method’s effectiveness.

{\bf Hypothesis:} Introducing charged terminal groups to polymers that graft nanoparticles enables Coulombic control over their assembly by tuning pH and salinity of aqueous suspensions. {\bf Experiments:} Gold nanoparticles (AuNPs) are grafted with poly(ethylene glycol) (PEG) terminated with CH3 (charge neutral), COOH (negatively charged), or NH2 (positively charged) groups. The nanoparticles are characterized using dynamic light scattering, {\zeta}-potential, and thermal gravimetric analysis. Liquid surface X-ray reflectivity (XR) and grazing incidence small-angle X-ray scattering (GISAXS) techniques are employed to determine the density profile and in-plane structure of the AuNP assembly across and on the aqueous surface. {\bf Findings:} The assembly of PEG-AuNPs at the liquid/vapor interface can be tuned by adjusting pH or salinity, particularly for COOH terminals. However, the effect is less pronounced for NH2 terminals. These distinct assembly behaviors are attributed to the overall charge of PEG-AuNPs and the conformation of PEG. The COOH-PEG corona is the most compact, resulting in smaller superlattice constants. The net charge per particle depends not only on the PEG terminal groups but also on the cation sequestration of PEG and the intrinsic negative charge of the AuNP surface. NH2-PEG, due to its closeness to overall charge neutrality and the presence of hydrogen bonding, enables the assembly of NH2-PEG-AuNPs more readily., Comment: Submitted to the Journal of Colloid and Interface Science, and it's under review currently

Image-text retrieval aims to bridge the modality gap and retrieve cross-modal content based on semantic similarities. Prior work usually focuses on the pairwise relations (i.e., whether a data sample matches another) but ignores the higher-order neighbor relations (i.e., a matching structure among multiple data samples). Re-ranking, a popular post-processing practice, has revealed the superiority of capturing neighbor relations in single-modality retrieval tasks. However, it is ineffective to directly extend existing re-ranking algorithms to image-text retrieval. In this paper, we analyze the reason from four perspectives, i.e., generalization, flexibility, sparsity, and asymmetry, and propose a novel learnable pillar-based re-ranking paradigm. Concretely, we first select top-ranked intra- and inter-modal neighbors as pillars, and then reconstruct data samples with the neighbor relations between them and the pillars. In this way, each sample can be mapped into a multimodal pillar space only using similarities, ensuring generalization. After that, we design a neighbor-aware graph reasoning module to flexibly exploit the relations and excavate the sparse positive items within a neighborhood. We also present a structure alignment constraint to promote cross-modal collaboration and align the asymmetric modalities. On top of various base backbones, we carry out extensive experiments on two benchmark datasets, i.e., Flickr30K and MS-COCO, demonstrating the effectiveness, superiority, generalization, and transferability of our proposed re-ranking paradigm., Accepted by SIGIR'2023

Recommender systems easily face the issue of user preference shifts. User representations will become out-of-date and lead to inappropriate recommendations if user preference has shifted over time. To solve the issue, existing work focuses on learning robust representations or predicting the shifting pattern. There lacks a comprehensive view to discover the underlying reasons for user preference shifts. To understand the preference shift, we abstract a causal graph to describe the generation procedure of user interaction sequences. Assuming user preference is stable within a short period, we abstract the interaction sequence as a set of chronological environments. From the causal graph, we find that the changes of some unobserved factors (e.g., becoming pregnant) cause preference shifts between environments. Besides, the fine-grained user preference over categories sparsely affects the interactions with different items. Inspired by the causal graph, our key considerations to handle preference shifts lie in modeling the interaction generation procedure by: 1) capturing the preference shifts across environments for accurate preference prediction, and 2) disentangling the sparse influence from user preference to interactions for accurate effect estimation of preference. To this end, we propose a Causal Disentangled Recommendation (CDR) framework, which captures preference shifts via a temporal variational autoencoder and learns the sparse influence from multiple environments. Specifically, an encoder is adopted to infer the unobserved factors from user interactions while a decoder is to model the interaction generation process. Besides, we introduce two learnable matrices to disentangle the sparse influence from user preference to interactions. Lastly, we devise a multi-objective loss to optimize CDR. Extensive experiments on three datasets show the superiority of CDR., This paper has been accepted for publication in Transactions on Information Systems

Adversarial examples are crafted by adding indistinguishable perturbations to normal examples in order to fool a well-trained deep learning model to misclassify. In the context of computer vision, this notion of indistinguishability is typically bounded by $L_{\infty}$ or other norms. However, these norms are not appropriate for measuring indistinguishiability for time series data. In this work, we propose adversarial examples in the Wasserstein space for time series data for the first time and utilize Wasserstein distance to bound the perturbation between normal examples and adversarial examples. We introduce Wasserstein projected gradient descent (WPGD), an adversarial attack method for perturbing univariant time series data. We leverage the closed-form solution of Wasserstein distance in the 1D space to calculate the projection step of WPGD efficiently with the gradient descent method. We further propose a two-step projection so that the search of adversarial examples in the Wasserstein space is guided and constrained by Euclidean norms to yield more effective and imperceptible perturbations. We empirically evaluate the proposed attack on several time series datasets in the healthcare domain. Extensive results demonstrate that the Wasserstein attack is powerful and can successfully attack most of the target classifiers with a high attack success rate. To better study the nature of Wasserstein adversarial example, we evaluate a strong defense mechanism named Wasserstein smoothing for potential certified robustness defense. Although the defense can achieve some accuracy gain, it still has limitations in many cases and leaves space for developing a stronger certified robustness method to Wasserstein adversarial examples on univariant time series data.

Atractylodes lancea suffers from continuous cropping obstacles that have become a major constraint in its cultivation, but there is still little information on the autotoxic allelochemicals and their interaction with soil microorganisms. In this study, we firstly identified the autotoxic allelochemicals from rhizosphere of A. lancea and determined their autotoxicity. Third-year continuous A. lancea cropping soils, i.e., rhizospheric soil and bulk soil, compared with control soil and one-year natural fallow soil were used to determine soil biochemical properties and microbial community. Eight allelochemicals from A. lancea roots were detected and exhibited significant autotoxicity effects on seed germination and seedling growth of A. lancea with the highest content of dibutyl phthalate in rhizospheric soil and lowest IC50 value of 2,4-di-tert-butylphenol inhibiting seed germination. The contents of soil nutrients and organic matter, pH value, and enzyme activity were altered between different soils, and the parameters of fallow soil were close to those of the unplanted soil. The PCoA analysis indicated that the community composition of both bacteria and fungi were differed significantly among the soil samples. Continuous cropping decreased OTUs numbers of bacterial and fungal communities, and natural fallow restored them. The relative abundance of Proteobacteria, Planctomycetes, and Actinobacteria decreased, and that of Acidobacteria and Ascomycota increased after three years cultivation. The LEfSe analysis identified 115 and 49 biomarkers for bacterial and fungal communities, respectively. The results suggested that natural fallow restored the structure of soil microbial community. Overall, our results revealed that autotoxic allelochemicals caused the variations of soil microenvironments and resulted in replantation problem of A. lancea, and natural fallow alleviated the soil deterioration by remodeling the rhizospheric microbial community and restoring soil biochemical properties. These findings provide important insights and clues for solving the continuous cropping problems and guiding the management of sustainable farmland.

In the face of disasters, organizations and groups collaborate continuously and fluidly, coordinating with one another in order to overcome extraordinary challenges. When researchers explored this phenomenon in-depth, they employed the concept of “improvisation.” However, there is a dearth of research in the area of “creative improvisation” relating to civic agencies that do not have special plans or approaches to disaster response. This study, therefor, aims at identifying some of the most eff ective improvisational approaches taken by civic agencies in the fi eld of communication where information, consciousness, and ideas converge. This paper classifi es cases of creative improvisation observed during the April 2016 Kumamoto earthquakes into two types and considers the respective conditions of improvisation. These are: 1) “Place-making”: by raising several cases representing “place to convergence” scattered throughout the region, the improvisational condition that can be summarized as, "Awareness of gathering to support the aff ected key places" is highlighted. 2) “Forming a commonality”: through participant observation at the "informal talk" held by local civil agencies, the interaction between agencies in the sense-making process is highlighted, thus clarifying the improvisational approaches of, "the process of realizing the role while being encouraged to participate initially," and "ingenuity to induce new movement by co-editing opposite opinions".

This paper aims to promote understanding of the dynamics of creative support in resource-poor environments for latent victims of disasters. The existing literature provides important insights on the practices of experts during disasters via the notion of improvisation, but also suff ers a lack of systematic discussion of practices outside expert systems. Here case studies are conducted of two local cafeterias launched by private citizens in the aftermath of disasters that took place in Japan recently. From the perspective of bricolage, the idea of “making do with what is at hand”, both the outcomes and processes related to the cafeterias are discussed. By re-imagining the value of material and non-material resources, the cafeteria organizers were able to mobilize a creative disaster response that was beyond the abilities of experts and the existing system. The analysis of the two cases shows the importance of bricolage as a tool of value creation.

Existing work on Multimodal Sentiment Analysis (MSA) utilizes multimodal information for prediction yet unavoidably suffers from fitting the spurious correlations between multimodal features and sentiment labels. For example, if most videos with a blue background have positive labels in a dataset, the model will rely on such correlations for prediction, while ``blue background'' is not a sentiment-related feature. To address this problem, we define a general debiasing MSA task, which aims to enhance the Out-Of-Distribution (OOD) generalization ability of MSA models by reducing their reliance on spurious correlations. To this end, we propose a general debiasing framework based on Inverse Probability Weighting (IPW), which adaptively assigns small weights to the samples with larger bias i.e., the severer spurious correlations). The key to this debiasing framework is to estimate the bias of each sample, which is achieved by two steps: 1) disentangling the robust features and biased features in each modality, and 2) utilizing the biased features to estimate the bias. Finally, we employ IPW to reduce the effects of large-biased samples, facilitating robust feature learning for sentiment prediction. To examine the model's generalization ability, we keep the original testing sets on two benchmarks and additionally construct multiple unimodal and multimodal OOD testing sets. The empirical results demonstrate the superior generalization ability of our proposed framework. We have released the code and data to facilitate the reproduction.

Many real-world data can be modeled as heterogeneous graphs that contain multiple types of nodes and edges. Meanwhile, due to excellent performance, heterogeneous graph neural networks (GNNs) have received more and more attention. However, the existing work mainly focuses on the design of novel GNN models, while ignoring another important issue that also has a large impact on the model performance, namely the missing attributes of some node types. The handcrafted attribute completion requires huge expert experience and domain knowledge. Also, considering the differences in semantic characteristics between nodes, the attribute completion should be fine-grained, i.e., the attribute completion operation should be node-specific. Moreover, to improve the performance of the downstream graph learning task, attribute completion and the training of the heterogeneous GNN should be jointly optimized rather than viewed as two separate processes. To address the above challenges, we propose a differentiable attribute completion framework called AutoAC for automated completion operation search in heterogeneous GNNs. We first propose an expressive completion operation search space, including topology-dependent and topology-independent completion operations. Then, we propose a continuous relaxation schema and further propose a differentiable completion algorithm where the completion operation search is formulated as a bi-level joint optimization problem. To improve the search efficiency, we leverage two optimization techniques: discrete constraints and auxiliary unsupervised graph node clustering. Extensive experimental results on real-world datasets reveal that AutoAC outperforms the SOTA handcrafted heterogeneous GNNs and the existing attribute completion method

Generative models such as Generative Adversarial Networks (GANs) and Variational Auto-Encoders (VAEs) are widely utilized to model the generative process of user interactions. However, these generative models suffer from intrinsic limitations such as the instability of GANs and the restricted representation ability of VAEs. Such limitations hinder the accurate modeling of the complex user interaction generation procedure, such as noisy interactions caused by various interference factors. In light of the impressive advantages of Diffusion Models (DMs) over traditional generative models in image synthesis, we propose a novel Diffusion Recommender Model (named DiffRec) to learn the generative process in a denoising manner. To retain personalized information in user interactions, DiffRec reduces the added noises and avoids corrupting users' interactions into pure noises like in image synthesis. In addition, we extend traditional DMs to tackle the unique challenges in practical recommender systems: high resource costs for large-scale item prediction and temporal shifts of user preference. To this end, we propose two extensions of DiffRec: L-DiffRec clusters items for dimension compression and conducts the diffusion processes in the latent space; and T-DiffRec reweights user interactions based on the interaction timestamps to encode temporal information. We conduct extensive experiments on three datasets under multiple settings (e.g. clean training, noisy training, and temporal training). The empirical results and in-depth analysis validate the superiority of DiffRec with two extensions over competitive baselines., Comment: 10 pages, 7 figures, accepted for publication in SIGIR'23

This paper investigates the multi-antenna covert communications assisted by a simultaneously transmitting and reflecting reconfigurable intelligent surface (STAR-RIS). In particular, to shelter the existence of communications between transmitter and receiver from a warden, a friendly full-duplex receiver with two antennas is leveraged to make contributions to confuse the warden. Considering the worst case, the closed-form expression of the minimum detection error probability (DEP) at the warden is derived and utilized as a covert constraint. Then, we formulate an optimization problem maximizing the covert rate of the system under the covertness constraint and quality of service (QoS) constraint with communication outage analysis. To jointly design the active and passive beamforming of the transmitter and STAR-RIS, an iterative algorithm based on globally convergent version of method of moving asymptotes (GCMMA) is proposed to effectively solve the non-convex optimization problem. Simulation results show that the proposed STAR-RIS-assisted scheme highly outperforms the case with conventional RIS.

Large Language Models (LLMs) have demonstrated remarkable performance across diverse domains, thereby prompting researchers to explore their potential for use in recommendation systems. Initial attempts have leveraged the exceptional capabilities of LLMs, such as rich knowledge and strong generalization through In-context Learning, which involves phrasing the recommendation task as prompts. Nevertheless, the performance of LLMs in recommendation tasks remains suboptimal due to a substantial disparity between the training tasks for LLMs and recommendation tasks, as well as inadequate recommendation data during pre-training. To bridge the gap, we consider building a Large Recommendation Language Model by tunning LLMs with recommendation data. To this end, we propose an efficient and effective Tuning framework for Aligning LLMs with Recommendation, namely TALLRec. We have demonstrated that the proposed TALLRec framework can significantly enhance the recommendation capabilities of LLMs in the movie and book domains, even with a limited dataset of fewer than 100 samples. Additionally, the proposed framework is highly efficient and can be executed on a single RTX 3090 with LLaMA-7B. Furthermore, the fine-tuned LLM exhibits robust cross-domain generalization. Our code and data are available at https://github.com/SAI990323/TALLRec., Comment: Accepted by RecSys 2023 (short)

Recommender systems typically retrieve items from an item corpus for personalized recommendations. However, such a retrieval-based recommender paradigm faces two limitations: 1) the human-generated items in the corpus might fail to satisfy the users' diverse information needs, and 2) users usually adjust the recommendations via passive and inefficient feedback such as clicks. Nowadays, AI-Generated Content (AIGC) has revealed significant success across various domains, offering the potential to overcome these limitations: 1) generative AI can produce personalized items to meet users' specific information needs, and 2) the newly emerged ChatGPT significantly facilitates users to express information needs more precisely via natural language instructions. In this light, the boom of AIGC points the way towards the next-generation recommender paradigm with two new objectives: 1) generating personalized content through generative AI, and 2) integrating user instructions to guide content generation. To this end, we propose a novel Generative Recommender paradigm named GeneRec, which adopts an AI generator to personalize content generation and leverages user instructions to acquire users' information needs. Specifically, we pre-process users' instructions and traditional feedback (e.g., clicks) via an instructor to output the generation guidance. Given the guidance, we instantiate the AI generator through an AI editor and an AI creator to repurpose existing items and create new items, respectively. Eventually, GeneRec can perform content retrieval, repurposing, and creation to meet users' information needs. Besides, to ensure the trustworthiness of the generated items, we emphasize various fidelity checks such as authenticity and legality checks. Lastly, we study the feasibility of implementing the AI editor and AI creator on micro-video generation, showing promising results.

Click-Through Rate (CTR) prediction plays a core role in recommender systems, serving as the final-stage filter to rank items for a user. The key to addressing the CTR task is learning feature interactions that are useful for prediction, which is typically achieved by fitting historical click data with the Empirical Risk Minimization (ERM) paradigm. Representative methods include Factorization Machines and Deep Interest Network, which have achieved wide success in industrial applications. However, such a manner inevitably learns unstable feature interactions, i.e., the ones that exhibit strong correlations in historical data but generalize poorly for future serving. In this work, we reformulate the CTR task -- instead of pursuing ERM on historical data, we split the historical data chronologically into several periods (a.k.a, environments), aiming to learn feature interactions that are stable across periods. Such feature interactions are supposed to generalize better to predict future behavior data. Nevertheless, a technical challenge is that existing invariant learning solutions like Invariant Risk Minimization are not applicable, since the click data entangles both environment-invariant and environment-specific correlations. To address this dilemma, we propose Disentangled Invariant Learning (DIL) which disentangles feature embeddings to capture the two types of correlations separately. To improve the modeling efficiency, we further design LightDIL which performs the disentanglement at the higher level of the feature field. Extensive experiments demonstrate the effectiveness of DIL in learning stable feature interactions for CTR. We release the code at https://github.com/zyang1580/DIL., Comment: 11 pages, 6 Postscript figures, to be published in SIGIR2023

Bird observation mainly relies on field surveys, which are time-consuming and laborious. In this study, we explored using street-view images in the virtual survey of urban birds and nests. Using the coastal city of Qingdao as the study area, 47 201 seamless spherical photos at 2741 sites were collected using the Baidu street-view (BSV) map. Single-rater-all photo checks and seven-rater-metapopulation checks were used to find inter–rater repeatability, the best viewing layer for BSV collection, and possible environments affecting the results. We also collected community science data for comparison. The BSV time machine was used to assess the temporal dynamics. Kappa square test, generalized linear model, redundancy ordination and ArcMap were used in the analysis. Different rater repeatability was 79.1% in nest evaluations and 46.9% in bird occurrence. A re-check of the different-rating photos can increase them to 92% and 70%. Seven-rater statistics showed that greater than 5% sampling ratio could produce a non-significant different bird and nest percentage of the whole data, and the higher sampling ratio could reduce the variation. The middle-viewing layer survey alone could produce 93% precision of the nest checks by saving 2/3 of the time used; in birds, selecting middle and upper-view photos could find 97% of bird occurrences. In the spatial distribution, the nest's hotspot areas from this method were much greater than the community science bird-watching sites. The BSV time machine made it possible to re-check nests in the same sites but challenging the re-check of bird occurrences. The nests and birds can be observed more in the leafless season, on wide, traffic-dense coastal streets with complex vertical structures of trees, and in the gaps of tall buildings dominated by road forests. Our results indicate that BSV photos could be used to virtually evaluate bird occurrence and nests from their numbers, spatial distribution and temporal dynamics. This method provides a pre-experimental and informative supplement to large-scale bird occurrence and nest abundance surveys in urban environments.

This paper investigates the multi-antenna covert communications assisted by a simultaneously transmitting and reflecting reconfigurable intelligent surface (STAR-RIS). In particular, to shelter the existence of covert communications between a multi-antenna transmitter and a single-antenna receiver from a warden, a friendly full-duplex receiver with two antennas is leveraged to make contributions where one antenna is responsible for receiving the transmitted signals and the other one transmits the jamming signals with a varying power to confuse the warden. Considering the worst case, the closed-form expression of the minimum detection error probability (DEP) at the warden is derived and utilized in a covert constraint to guarantee the system performance. Then, we formulate an optimization problem maximizing the covert rate of the system under the covertness constraint and quality of service (QoS) constraint with communication outage analysis. To jointly design the active and passive beamforming of the transmitter and STAR-RIS, an iterative algorithm based on semi-definite relaxation (SDR) method and Dinkelbachs algorithm is proposed to effectively solve the non-convex optimization problem. Simulation results show that the proposed STAR-RIS-assisted scheme highly outperforms the case with conventional RIS, which validates the effectiveness of the proposed algorithm as well as the superiority of STAR-RIS in guaranteeing the covertness of wireless communications., Comment: arXiv admin note: substantial text overlap with arXiv:2305.03991

An adaptive learning method for tool wear monitoring, taking the time of one machining process step as a monitoring period, is proposed, which aims to provide new ideas for tool life research and improve the reliability of machine tool operation. First, the motor current signal is collected as the original signal, and the RMS (root mean square) value of the current signal is extracted as the characteristic quantity. Statistical analysis of characteristic quantity RMS using SPSS (Statistical Product and Service Solutions) method shows that the RMS value of current signal obeys normal distribution approximately in the monitoring period with process step as the unit. Then, a monitoring method based on ± 3σ principle of normal distribution is proposed. Although RMS does not follow normal distribution completely, it is still possible to estimate the dispersion range of RMS by introducing distribution coefficient K through ± 3σ principle of statistical mathematics. Finally, a self-learning algorithm for the boundary mathematical model of tool wear monitoring is presented. According to the analysis of tool wear, the tool failure time could be calculated, and the tool life can be predicted. The experimental results show that the monitoring model can be formed quickly during semi-finishing and finishing machining and can get the satisfactory monitoring results.

Recent years have witnessed the great success of self-supervised learning (SSL) in recommendation systems. However, SSL recommender models are likely to suffer from spurious correlations, leading to poor generalization. To mitigate spurious correlations, existing work usually pursues ID-based SSL recommendation or utilizes feature engineering to identify spurious features. Nevertheless, ID-based SSL approaches sacrifice the positive impact of invariant features, while feature engineering methods require high-cost human labeling. To address the problems, we aim to automatically mitigate the effect of spurious correlations. This objective requires to 1) automatically mask spurious features without supervision, and 2) block the negative effect transmission from spurious features to other features during SSL. To handle the two challenges, we propose an invariant feature learning framework, which first divides user-item interactions into multiple environments with distribution shifts and then learns a feature mask mechanism to capture invariant features across environments. Based on the mask mechanism, we can remove the spurious features for robust predictions and block the negative effect transmission via mask-guided feature augmentation. Extensive experiments on two datasets demonstrate the effectiveness of the proposed framework in mitigating spurious correlations and improving the generalization abilities of SSL models.

Existing studies on multimodal sentiment analysis heavily rely on textual modality and unavoidably induce the spurious correlations between textual words and sentiment labels. This greatly hinders the model generalization ability. To address this problem, we define the task of out-of-distribution (OOD) multimodal sentiment analysis. This task aims to estimate and mitigate the bad effect of textual modality for strong OOD generalization. To this end, we embrace causal inference, which inspects the causal relationships via a causal graph. From the graph, we find that the spurious correlations are attributed to the direct effect of textual modality on the model prediction while the indirect one is more reliable by considering multimodal semantics. Inspired by this, we devise a model-agnostic counterfactual framework for multimodal sentiment analysis, which captures the direct effect of textual modality via an extra text model and estimates the indirect one by a multimodal model. During the inference, we first estimate the direct effect by the counterfactual inference, and then subtract it from the total effect of all modalities to obtain the indirect effect for reliable prediction. Extensive experiments show the superior effectiveness and generalization ability of our proposed framework.

We consider a linear combination of jackknife Anderson-Rubin (AR), jackknife Lagrangian multiplier (LM), and orthogonalized jackknife LM tests for inference in IV regressions with many weak instruments and heteroskedasticity. Following I.Andrews (2016), we choose the weights in the linear combination based on a decision-theoretic rule that is adaptive to the identification strength. Under both weak and strong identifications, the proposed test controls asymptotic size and is admissible among certain class of tests. Under strong identification, our linear combination test has optimal power against local alternatives among the class of invariant or unbiased tests which are constructed based on jackknife AR and LM tests. Simulations and an empirical application to Angrist and Krueger's (1991) dataset confirm the good power properties of our test., 94 pages

Recommender systems usually face the issue of filter bubbles: overrecommending homogeneous items based on user features and historical interactions. Filter bubbles will grow along the feedback loop and inadvertently narrow user interests. Existing work usually mitigates filter bubbles by incorporating objectives apart from accuracy such as diversity and fairness. However, they typically sacrifice accuracy, hurting model fidelity and user experience. Worse still, users have to passively accept the recommendation strategy and influence the system in an inefficient manner with high latency, e.g., keeping providing feedback (e.g., like and dislike) until the system recognizes the user intention. This work proposes a new recommender prototype called UserControllable Recommender System (UCRS), which enables users to actively control the mitigation of filter bubbles. Functionally, 1) UCRS can alert users if they are deeply stuck in filter bubbles. 2) UCRS supports four kinds of control commands for users to mitigate the bubbles at different granularities. 3) UCRS can respond to the controls and adjust the recommendations on the fly. The key to adjusting lies in blocking the effect of out-of-date user representations on recommendations, which contains historical information inconsistent with the control commands. As such, we develop a causality-enhanced User-Controllable Inference (UCI) framework, which can quickly revise the recommendations based on user controls in the inference stage and utilize counterfactual inference to mitigate the effect of out-of-date user representations. Experiments on three datasets validate that the UCI framework can effectively recommend more desired items based on user controls, showing promising performance w.r.t. both accuracy and diversity., Comment: Accepted by SIGIR 2022

In aeromagnetic surveys, poor aircraft heading and weather may frequently cause a classical optically-pumped sensor to enter into or close to its dead zone, which results in unavoidable outliers that seriously reduce aeromagnetic compensation. To address these problems, a method to identify rapidly and pre-cull magnetic outliers based on the gray system theory is proposed to reduce their negative influence during the estimation of coefficients and target detection robustness. By constructing a gray region of aeromagnetic data and then checking whether the data at the end points of the region are normal, aeromagnetic outliers can be culled. The simulation results show that even if the outlier rate is increased to 20%, the average of the correct culling rate of the proposed method can still reach 99.67%, at which the culling effect is highly robust. We constructed an experimental survey platform and conducted a flight test. The results show that the improvement ratio of the proposed method can reach 4.36, which is 10.38 times higher than the conventional method.

Deep learning models have been widely used in data-driven bridge structural damage diagnosis methods in recent years. However, these methods require training and test datasets to satisfy the same distribution, which is difficult to satisfy in practice. Domain adaptation transfer learning is an efficient method to solve this problem. Most of the current domain adaptation methods focus on close-set scenarios with the same classes in the source and target domains. However, in practical applications, new damage caused by long-term degradation often makes the target and source domains dissimilar in the class space. For such challenging open-set scenarios, existing domain adaptation methods will be powerless. To effectively solve the above problems, an adversarial auxiliary weighted subdomain adaptation algorithm is proposed for open-set scenarios. Adversarial learning is introduced to proposed an adversarial auxiliary weighting scheme to reflect the similarity of target samples with source classes. It effectively distinguishes unknown damage from known states. This paper further proposes a multi-channel multi-kernel weighted local maximum mean discrepancy metric (MCMK-WLMMD) to capture the fine-grained transferable information for conditional distribution alignment (sub-domain alignment). Extensive experiments on transfer tasks between three bridges verify the effectiveness of the algorithm in open-set scenarios.

Two-dimensional (2D) nanomaterials with unique anisotropy and electronic properties are deemed as an ideal platform for establishing clear relationships between structure and catalytic reactivity. Knowledge of their structures is essential for understanding the catalytic behavior, which further facilitates the development of high-performance catalysts. In this review, we focus on the recent progress of synchrotron radiation X-ray absorption spectrum (XAS) techniques in exploring the structure-function relationship of two-dimensional electrocatalysts. Also, we summarize the application of XAS technique in disclosing key factors that affect the catalytic activity, including identification of local atomic structure, electronic structure and defect structure. Through the characterization of the catalytic process with XAS technique, we further highlight the atomic-level correlation between structure and function in the field of oxygen evolution, oxygen reduction, hydrogen evolution and CO2 reduction. Finally, we propose the major challenges and prospects of XAS technique in advancing the development of two-dimensional electrocatalysts. We anticipate that this review provides critical insights into the application of the XAS technique in electrocatalysis, thereby promoting the development of advanced characterization techniques and the design of high-active catalysts.

At present, the ultra-fine particulate matter (UPM) with diameter less than 1 μm cannot be filtered efficiently and precisely by the current filtration materials, which leads to the UPM being the main source of air pollution. Here, we prepared Kaolin/silica/mullite fiber multilayer membranes (KSFMs) by a simple vacuum filtration. The UPM filtration efficiency has been significantly improved by the nanoporous kaolin layer coated on the support body constructed by Si-sol bonded mullite fiber. By controlling the size and amount of kaolin particles in the mixed suspension, the structure and filtration efficiency and filtration resistance of the multilayer membranes are optimized. The multilayer membranes exhibit high UPM filtration efficiency with 99% for 0.3 μm and low filtration resistance (lower than 70 Pa at 0.3 m/s).

一般論文, 近年、災害時におけるボランティアの組織化の動きが活発になり、ボランティア活動の秩序が重要視されている。一方で、ボランティアの活動を効率的に調整するだけでなく、ボランティアの被災者ニーズに対応する創造性を見出すことも求められている。これらの点を踏まえて本稿では熊本地震後、「最も小さくされた人々に偏った支援を行う」ことを行動原則に掲げる地元主導のネットワーク組織の事例を取り上げる。当該組織が潜在的ニーズに対応するという活動領域を確立するまでの2つの象徴的な活動現場の実態に迫り、被災地住民ボランティアが被災者支援の意識を形成していく過程やその変化を捉える。このような寄せ集められた災害支援の初心者が、行政や被災者同士からの自己責任論調に抑圧されている「見えにくい被災者」の声に触れることによって、彼ら彼女らがその声を代弁し、顕在化しようとする当事者意識がみられた。一方で、支援意欲が強すぎるあまり、必ずしも被災者に寄り添っていない場面が発生してしまったことを顧みて、準・公的組織と手を携えて支援体制を整える経緯を示した。, In recent times, the organization of volunteers during disaster responses has shown remarkable progress. Previous studies have indicated that the spontaneous creativity of individual volunteers in responding to the needs of disaster victims has been overlooked because of the striving for effectiveness in the organization of volunteering. This paper focuses on the initiative of volunteers belonging to organizations in responding to potential victim needs. A case study was conducted on the “Yokatai Net Kumamoto” networking organization, led by local volunteers, and launched in the aftermath of the Kumamoto earthquake. It focuses on two relief efforts this organization carried out on behalf of the most disadvantaged, and outlines how local volunteers became aware of the potential needs of victims, and acted to respond to these needs. First, the paper explores the process by which local volunteers conducted interviews of disaster victims who evacuated in their vehicles without administrative support, instead of going to shelters, and publicized their situations. Second, by depicting what took place during a party held in temporary housing, the paper reveals how local volunteers collectively collaborated with government agencies.

Shuai Yan,1,* Wenjie Wang,2,* Bifa Zhu,3 Xixi Pan,1 Xiaoyan Wu,2 Weiyang Tao1,4 1Department of Breast Surgery, Harbin Medical University Cancer Hospital, Harbin 150081, People’s Republic of China; 2Department of Nutrition and Food Hygiene, The National Key Discipline, School of Public Health, Harbin Medical University, Harbin 150081, People’s Republic of China; 3Department of Oncology, Xianning Central Hospital, The First Affiliated Hospital of Hubei University of Science and Technology, Xianning 437000, People’s Republic of China; 4Department of Thyroid and Breast Surgery, The Fifth Affiliated Hospital of Sun Yat-Sen University, Zhuhai 519000, People’s Republic of China*These authors contributed equally to this workCorrespondence: Weiyang TaoDepartment of Breast Surgery, Harbin Medical University Cancer Hospital, No. 150, Haping Road, Nangang District, Harbin 150081, People’s Republic of ChinaTel/Fax + 86 451 8629 8059Email twysci@outlook.comPurpose: Pathological complete response (pCR) is the goal of neoadjuvant chemotherapy (NAC) for the HER2-positive and triple-negative subtypes of breast cancer and is related to survival benefit; however, luminal breast cancer is not sensitive to NAC, and the size of tumor shrinkage is a more meaningful clinical indicator for the luminal breast cancer subtype. We wanted to use a nomogram or formula to develop and implement a series of prediction models for pCR or tumor shrinkage size.Patients and Methods: We developed a prediction model in a primary cohort consisting of 498 patients with invasive breast cancer, and the data were gathered from July 2016 to September 2018. The endpoint was pCR and tumor shrinkage size. In the primary cohort, the HER2-positive cohort, and the triple-negative cohort, multivariate logistic regression analysis was used to screen the significant clinical features and clinicopathological features to develop nomograms. In the luminal group, multivariate linear regression analysis was used to test the risk factors that affect tumor shrinkage size. The area under the receiver operating characteristic curve (AUC) and calibration curves were adopted to evaluate and analyze the discrimination and calibration ability of nomograms. Furthermore, we also performed internal validation and independent validation in the primary cohort.Results: ER status, KI67 status, HER2 status, number of NAC cycles, and tumor size were independent predictive factors of pCR in the primary cohort. These indicators had good discrimination and calibration in the primary and validation cohorts (AUC: 0.873, 0.820). The nomogram for HER2-positive and triple-negative breast cancer (TNBC) had an AUC of 0.820 and 0.785, respectively. Both the HER2 positive and TNBC nomogram calibration curves indicated significant agreement. Moreover, the luminal subtype prediction model was Y (tumor shrinkage size) = − 0.576 × (age at diagnosis) + 2.158 × (number of NAC cycles) + 0.233 × (pre-NAC tumor size) + 51.662.Conclusion: Utilizing this predictive model will enable us to identify patients at high probability for pCR after NAC. Clinicians can stratify these patients and make individualized and personalized recommendations for therapy.Keywords: breast cancer, neoadjuvant chemotherapy, pathologic complete response, nomogram

Interior permanent magnet synchronous motors (IPMSMs) have high power densities and speed control performance, and they are widely used in the industry. The problem of reducing the torque ripple of an IPMSM is one of the hot issues in the field of electrical machine design. In order to determine the optimal combination of the geometric parameters to reduce the torque ripple of an IPMSM, a range analysis was conducted on the data from the orthogonal experiments in this study, dividing the rotor geometric parameters into two categories (important and ordinary) based on their degree of impact on the torque ripples of the IPMSM. Thereafter, an optimization of the ordinary parameters was carried out based on the results of the range analysis, whereas the optimization of the important parameters was carried out through a method that combined a multi-island genetic algorithm (MIGA) and Radial Basis Function (RBF) neural networks. The torque ripple of the IPMSM was effectively reduced without materially affecting the output power. Finally, the results of this optimization process were verified using a finite element simulation. The optimization method used in this study divided the motor geometric parameters into two categories and applied a different method of optimization to each parameter type, so it was able to efficiently optimize multiple geometric parameters for the IPMSM.

Existing recommender systems extract the user preference based on learning the correlation in data, such as behavioral correlation in collaborative filtering, feature-feature, or feature-behavior correlation in click-through rate prediction. However, regretfully, the real world is driven by causality rather than correlation, and correlation does not imply causation. For example, the recommender systems can recommend a battery charger to a user after buying a phone, in which the latter can serve as the cause of the former, and such a causal relation cannot be reversed. Recently, to address it, researchers in recommender systems have begun to utilize causal inference to extract causality, enhancing the recommender system. In this survey, we comprehensively review the literature on causal inference-based recommendation. At first, we present the fundamental concepts of both recommendation and causal inference as the basis of later content. We raise the typical issues that the non-causality recommendation is faced. Afterward, we comprehensively review the existing work of causal inference-based recommendation, based on a taxonomy of what kind of problem causal inference addresses. Last, we discuss the open problems in this important research area, along with interesting future works., Comment: Under peer review

Additional file 3: Table S1. Model coefficients of GEE and LASSO model to predict blastocyst formation.

Nitrous acid (HONO) is an important component of the global nitrogen cycle and can regulate the atmospheric oxidative capacity. Soil is an important source of HONO. [HONO]*, the equilibrium gas-phase concentration over the aqueous solution of nitrous acid in the soil, has been suggested as a key parameter for quantifying soil fluxes of HONO. However, [HONO]* has not yet been well-validated and quantified. Here, we present a method to retrieve [HONO]* by conducting controlled dynamic chamber experiments with soil samples applied with different HONO concentrations at the chamber inlet. We show a bi-directional soil–atmosphere exchange of HONO and confirm the existence of [HONO]* over soil: when [HONO]* is higher than the atmospheric HONO concentration, HONO will be released from soil; otherwise, HONO will be deposited. We demonstrate that [HONO]* is a soil characteristic, which is independent of HONO concentrations in the chamber but varies with different soil water contents. We illustrate the robustness of using [HONO]* for quantifying soil fluxes of HONO, whereas the laboratory-determined chamber HONO fluxes can largely deviate from those in the real world for the same soil sample. This work advances the understanding of the soil–atmosphere exchange of HONO and the evaluation of its impact on the atmospheric oxidizing capacity. Version of record

Recommender systems usually learn user interests from various user behaviors, including clicks and post-click behaviors (e.g., like and favorite). However, these behaviors inevitably exhibit popularity bias, leading to some unfairness issues: 1) for items with similar quality, more popular ones get more exposure; and 2) even worse the popular items with lower popularity might receive more exposure. Existing work on mitigating popularity bias blindly eliminates the bias and usually ignores the effect of item quality. We argue that the relationships between different user behaviors (e.g., conversion rate) actually reflect the item quality. Therefore, to handle the unfairness issues, we propose to mitigate the popularity bias by considering multiple user behaviors. In this work, we examine causal relationships behind the interaction generation procedure in multi-behavior recommendation. Specifically, we find that: 1) item popularity is a confounder between the exposed items and users' post-click interactions, leading to the first unfairness; and 2) some hidden confounders (e.g., the reputation of item producers) affect both item popularity and quality, resulting in the second unfairness. To alleviate these confounding issues, we propose a causal framework to estimate the causal effect, which leverages backdoor adjustment to block the backdoor paths caused by the confounders. In the inference stage, we remove the negative effect of popularity and utilize the good effect of quality for recommendation. Experiments on two real-world datasets validate the effectiveness of our proposed framework, which enhances fairness without sacrificing recommendation accuracy.

Additional file 1: Figure S1. TCGA database-based comparison of PPT1 mRNA expression in the HCC tissues (n = 50) and its paired-normal tissues. ***P

In this article, the design and development of a 3D grain configuration representation method with small data and high accuracy are presented, by which a unified grain database is established. There are two vital components in our proposed method: the traditional signed distance field (SDF) method is used for the unified representation of the 3D grain configuration; the proper generalized decomposition is employed to decompose the original SDF function to compress the data. With the proposed method, the reconstruction of 3D grain configuration is realized with small data and high accuracy, based on which the similarity measurement of different grains is proposed and the reuse of the 3D grain configurations is realized. The efficacy and accuracy of the proposed method are validated by reconstructing 3D grain configurations and the burnback curves of two different grain models. The similarities of the eight grains are calculated, which are accurately consistent with the burning surface area calculations results. The experiment results demonstrate that the proposed methods are feasible and efficient, and are significant for design case data and knowledge reuse.

Bioactive proteins secreted by the granular glands of amphibian skin play a self-defensive role, and exhibit various bioactivities in vitro and in vivo. In light of the severity of the problem of antibiotic resistance for treating infections, many antimicrobial peptides (AMPs) have been developed and applied in clinical microbial treatments. We identified a naturally derived and potent antimicrobial peptide, temporin-FL, obtained from the skin secretion of Pelophylax nigromaculatus via “shotgun” cloning. Two truncated analogues of this peptide were chemically synthesized to explore their structural-functional relationships. The results of a functional evaluation showed that all of the tested AMPs were active against Gram-positive bacteria and fungi and demonstrated antibiofilm activity against methicillin-resistant Staphylococcus aureus (MRSA) but did not have an effect on Gram-negative bacteria. Moreover, temporin-FLa demonstrated a higher level of hydrophobicity and enhanced antimicrobial efficiency, as well as hemolytic activity and cell cytotoxicity than the parent peptide. Temporin-FLb, which evidenced significantly less α-helicity, was less potent against various microbes but exhibited lower cytotoxicity relating to mammalian cells. Both of the synthesized analogues possessed a higher therapeutic index than the original peptide. Moreover, the membrane permeability assay and the measuring membrane depolarization assay declared that temporin-FL and its analogues induced membrane fracture and depolarization; the quantitative biofilm formation assay and the observations of MRSA biofilms using scanning electron microscopy revealed that the AMPs caused biofilm disruption and blocked biofilm formation, the former experiments all confirming their antimicrobial and antibiofilm properties. Hence, the optimization of temporin-FL offers insights for the discovery of new drugs for treating MRSA infections.

We study the wild bootstrap inference for instrumental variable regressions with a small number of large clusters. We first show that the wild bootstrap Wald test controls size asymptotically up to a small error as long as the parameters of endogenous variables are strongly identified in at least one of the clusters. Second, we establish the conditions for the bootstrap tests to have power against local alternatives.We further develop a wild bootstrap Anderson-Rubin test for the full-vector inference and show that it controls size asymptotically even under weak identification in all clusters. We illustrate their good performance using simulations and provide an empirical application to a well-known dataset about US local labor markets., This version only contains results for linear IV regressions. The results for IVQR in the previous versions will be put in a separate paper

Generalized zero-shot learning (GZSL) has achieved significant progress, with many efforts dedicated to overcoming the problems of visual-semantic domain gap and seen-unseen bias. However, most existing methods directly use feature extraction models trained on ImageNet alone, ignoring the cross-dataset bias between ImageNet and GZSL benchmarks. Such a bias inevitably results in poor-quality visual features for GZSL tasks, which potentially limits the recognition performance on both seen and unseen classes. In this paper, we propose a simple yet effective GZSL method, termed feature refinement for generalized zero-shot learning (FREE), to tackle the above problem. FREE employs a feature refinement (FR) module that incorporates \textit{semantic$\rightarrow$visual} mapping into a unified generative model to refine the visual features of seen and unseen class samples. Furthermore, we propose a self-adaptive margin center loss (SAMC-loss) that cooperates with a semantic cycle-consistency loss to guide FR to learn class- and semantically-relevant representations, and concatenate the features in FR to extract the fully refined features. Extensive experiments on five benchmark datasets demonstrate the significant performance gain of FREE over its baseline and current state-of-the-art methods. Our codes are available at https://github.com/shiming-chen/FREE ., ICCV 2021

Objective: To investigate the advantages of a structural nutritional care management model (hereafter referred to as structural management) in severe acute pancreatitis (SAP) patients undergoing early enteral nutrition via nasal jejunal nutrition tubes. Methods: A total of 88 patients with SAP diagnosed and treated in our hospital were recruited as the study cohort and underwent enteral nutrition treatment. A random number table was used for the random grouping. The control group was routinely managed, and the study group was also administered structural management. In the study, we observed and compared the differences and changes in the relevant nutritional indexes (albumin (ALB), prealbumin (PA), and transferrin (TRF)) and the gastrointestinal hormone indexes (gastrin (MTL), vasoactive peptide (VIP), and 5-hydroxytryptamine (5-HT)) before and after the treatment. Between the two groups, we also compared the times required for the recovery of the relevant gastrointestinal physiological function indexes, the mechanical ventilation times, the hospitalization durations in the ICU, the complications, the satisfaction indexes and the satisfaction rates. Results: After the treatment, the relevant nutritional indicators, including ALB (35.26±3.35 g/L), PA (25.19±5.64 g/L), and TRF (2.82±0.54 g/L) in the study group were higher than the ALB (28.19±2.74 g/L), PA (21.29±4.32 g/L), and TRF (2.26±0.32 g/L) in the control group (all P

A GaN-based gate driver circuits have been successfully designed and fabricated using a commercially available 6-inch GaN-on-Si platform. The driver circuits consist of three-stage direct-coupled FET logic (DCFL) inverters featuring monolithically integrated depletion-mode (D-mode) and enhancement-mode (E-mode) high electron mobility transistors (HEMTs). At room temperature (RT), at a supply voltage of 4 V the single-stage DCFL fabricated on the same die exhibits a large gate swing (3.84 V) and large noise margins (logic-low noise margin (NML) of 1.55 V and logic-high noise margin (NMH) of 2.18 V), both of which are desirable for all-GaN IC applications. Meanwhile, the gate driver circuits were characterised up to 200°C on a PCB with a capacitance load of 1 nF in series with 4.7 Ω resistance to resemble the load condition. At 200°C, the gate driver circuits function well even at 5 MHz operation frequency. The turn on/off propagation delay and rise/fall time of the gate driver circuits are 5/40 and 22/18 ns, respectively. As far as the authors’ knowledge, this is the highest reported operation frequency for GaN-based gate driver circuits under such high temperature, making it very promising for high-temperature high-frequency all-GaN integrated circuit (IC) applications.

ハーベスト社、2019 年2 月、A5 判、384 頁、7,200 円（税別）

Shear experiments were conducted on the cement mortar–sandstone bonding interface and the materials themselves to investigate their loss of shear strength and the deterioration mechanism caused by freezing and thawing cycles. The experimental results show that the shear strength of the bonding interface is much lower than that of the original materials themselves under the same normal stress. The shear strength of this interface decreases linearly with increasing number of freeze–thaw cycles, but it linearly increases with increasing normal stress. The cohesion and internal friction angle also decrease as the number of freeze–thaw cycles increases. In addition, obvious freeze–thaw debonding of this interface is observed and it is first caused by the difference in frost-heaving deformation between the cement mortar and the red sandstone, followed by the frost-heaving pressure in the crack formed in the interface. Finally, the shear damage of this interface has been quantified by reconstitution of the interface morphology. As a result, almost all the shear breakage occurs on the red sandstone side, and a concave rough face arises. With the absence of normal stress, the shear abscission area in the red sandstone increases quickly with increasing number of freeze–thaw cycles. However, with increasing normal stress, this shear abscission area decreases, and the layered composite specimens were prone to shear failure straightly along the bonding interface, because the shear dilatancy deformation is constrained. This study provides the shear failure characteristics of cement mortar–rock interfaces under freeze–thaw cycles and contributes to a better understanding of the freeze–thaw debonding mechanism of protective cement mortar layers on rock surfaces.

一般論文, 災害時に組織／集団が継続的かつ流動的に結び目を創発し、新たなニーズに対応する動きが生まれ、それに関して「即興」という視点での研究が行われてきた。しかし災害対応の計画や専門性をもっていない、寄せ集めの市民エージェントによる創造的即興に直目した研究は少ない。そこで本稿は災害時の情報、意識、思いの交差する支援の場における、市民エージェントによる即興の内容と発生条件を提示することを目的とする。2016年4月に発生した熊本地震後の創造的即興の事例を、場の創出と共同性の形成という2つのタイプに分類し、それぞれの発生条件を考察した。複数の地域に点在する「寄合所」の事例を取り上げ、「被災キープレイスを応援する寄り合い感覚」という即興の発生条件を指摘した。また、「放談会」という対話の場での参与観察を通して、意味形成プロセスでのエージェント間の相互作用を捉え、「促された参加ながらも役割を自覚する過程」、「対立する意見を共同編集することによって新たな動向を誘発する工夫」という発生条件を明らかにした。, In the face of disasters, organizations and groups collaborate continuously and fluidly, coordinating with one another in order to overcome extraordinary challenges. When researchers explored this phenomenon in-depth, they employed the concept of “improvisation.” However, there is a dearth of research in the area of “creative improvisation” relating to civic agencies that do not have special plans or approaches to disaster response. This study, therefor, aims at identifying some of the most effective improvisational approaches taken by civic agencies in the field of communication where information, consciousness, and ideas converge. This paper classifies cases of creative improvisation observed during the April 2016 Kumamoto earthquakes into two types and considers the respective conditions of improvisation. These are: 1) “Place-making”: by raising several cases representing “place to convergence” scattered throughout the region, the improvisational condition that can be summarized as, "Awareness of gathering to support the affected key places" is highlighted. 2) “Forming a commonality”: through participant observation at the "informal talk" held by local civil agencies, the interaction between agencies in the sense-making process is highlighted, thus clarifying the improvisational approaches of, "the process of realizing the role while being encouraged to participate initially," and "ingenuity to induce new movement by co-editing opposite opinions".

Volcanic stones are a kind of natural materials, and they will occupy large amounts of land resources which brings a lot of inconvenience to local residents and traffic. Meanwhile, the annual demand for limestone in the world is about 1.2 billion tons and high-quality limestone has low natural resources and low production volume. In order to comply with the current green eco-friendly pavement concept, this paper aims to study the use of volcanic rocks in place of limestone in road pavement construction as a way of utilizing available natural mineral resource to reduce the problematic over-dependence on limestone. In this paper, asphalt mastics with different dosages of ground volcanic stone and limestone powder were produced. Combining macro and micro-methods, the applicability of volcanic stone was analyzed and evaluated from the aspects of basic performance experiments, X-ray photoelectron spectroscopy, scanning electron microscopy and infrared spectroscopy. The results clearly showed that the volcanic stone powder could get better distribution and better high-temperature performance in the asphalt mastic than limestone powder. It contained Si and much higher content of SiO2, Al2O3, Fe2O3, Na2O and K2O which promoted chemical reactions with the asphalt, making it more compatible with asphalt than limestone powder. Based on the results of this study, it can be concluded that volcanic stone could effectively replace some limestone usage in the asphalt pavement field which will in return reduce the occupation of land resources and provide a new choice for the limestone.

The outliers of the magnetic sensor that are inevitable in special aeromagnetic surveys reduce the robustness of ordinary least-squares (OLS), which are widely used for aeromagnetic compensation. To address this problem, we propose an aeromagnetic compensation algorithm based on the Huber loss method that is robust to outliers. In the proposed method, different weights are assigned to the inliers and outliers using an iteratively reweighted least-squares technique. Although the OLS performs similarly to the proposed method when only 1% of the data are outliers, it is theoretically verified that the proposed method can increase the goodness-of-fit to 0.9963, from 0.6618 in the case of OLS, in the presence of 10% outliers. An experimental platform was constructed to record real magnetic data, with special measures taken to ensure the presence of outliers in the collected data. The results of a flight test using this experimental platform demonstrate that the proposed method increases the improvement ratio to 4.14 from 2.46 when using the OLS.

Here, we developed silica/mullite fiber composite membranes with double-layer structure by a simple vacuum procedure for the removal of sub-micrometer dust. The support with three-dimensional skeleton structure exhibited high porosity (higher than 90%), low density (lower than 0.25 g/cm3) and high compressive strength (higher than 0.55 MPa) at 1000 °C. By controlling the mass ratio of silica sol to mullite fiber, we can obtain uniform and complete filtering layers with different thicknesses. The composite membranes exhibited high PM filtration efficiency with 99% for 1–10 µm, 97% for 0.5 µm and 90% for 0.3 µm. These samples had high air flow with very low pressure drop (lower than 600 Pa when airflow velocity reached 1 m/s). These results indicated that the silica/mullite fiber composite membranes were very promising for PM pollution control in the field of hot gas filtration.

Solid rocket motor design matching the thrust requirement of the overall vehicle has become an exigent task. In the present research, an efficient performance matching optimization approach is proposed and applied to the solid rocket motor design. To obtain reliable solid rocket motor designs, refined performance evaluation is accomplished successfully based on the coupling of the level set grain burnback analysis and one-dimensional ballistic simulation by the time-stepping algorithm. Due to the heavy computing burden of the coupled performance solver, a novel progressive matching optimization approach is proposed. This approach depends on partial but not full-burn thrust profile to eliminate inferior candidates, predict superior candidates, advance the simulation and drive the candidates converging to the specified performance. Validation of the proposed solid rocket motor design approach is carried out by two test cases possessing different thrust requirements, and results indicate that the proposed approach is practical and efficient.

The comparative reactivity method (CRM) was developed more than a decade to measure OH reactivity (i.e., OH loss frequency) in both laboratory and field studies. However, accurate OH reactivity quantification remains challenging under real ambient conditions, especially for OH reactivity measurements in high-NOx (e.g., > 10 ppbv) environments, as ambient NO enhances the regeneration of OH radicals in the CRM reactor. To solve this problem, we design a new and improved CRM reactor (ICRM) and add NO into the system continuously so that the HO2 radical concentration is suppressed. We confirmed the appropriate level of NO by determining the maximum decrease in the pyrrole level caused by regenerated OH radicals from NO + HO2. RO2 radicals induced by volatile organic compounds (VOCs) in the ICRM reactor were also found to react with NO, which led to the regeneration of OH radicals and thus the underestimation of OH reactivity. This effect was quantified by the calibration of representative VOC species at different NO levels, and the correction coefficients obtained were used to correct the measured OH reactivity. All these efforts resulted in reducing the uncertainty of the NO-artifact correction by at least an order of magnitude compared to the original CRM system. Additionally, these technological improvements also considerably reduced the systematic errors from pyrrole photolysis in the original system. A new operation mode was proposed for the ICRM, which is able to avoid the interference resulting from OH radicals produced by photolysis of residual humidity and save time for ambient measurement. The ICRM system was employed in a field campaign to measure OH reactivity and performed well with ambient NO levels ranging from 0 to 50 ppbv, which are typically observed in the urban and suburban atmosphere.