Search

The prediction of drug-target protein interaction (DTI) is a crucial task in the development of new drugs in modern medicine. Accurately identifying DTI through computer simulations can significantly reduce development time and costs. In recent years, many sequence-based DTI prediction methods have been proposed, and introducing attention mechanisms has improved their forecasting performance. However, these methods have some shortcomings. For example, inappropriate dataset partitioning during data preprocessing can lead to overly optimistic prediction results. Additionally, only single non-covalent intermolecular interactions are considered in the DTI simulation, ignoring the complex interactions between their internal atoms and amino acids. In this paper, we propose a network model called Mutual-DTI that predicts DTI based on the interaction properties of sequences and a Transformer model. We use multi-head attention to extract the long-distance interdependent features of the sequence and introduce a module to extract the sequence's mutual interaction features in mining complex reaction processes of atoms and amino acids. We evaluate the experiments on two benchmark datasets, and the results show that Mutual-DTI outperforms the latest baseline significantly. In addition, we conduct ablation experiments on a label-inversion dataset that is split more rigorously. The results show that there is a significant improvement in the evaluation metrics after introducing the extracted sequence interaction feature module. This suggests that Mutual-DTI may contribute to modern medical drug development research. The experimental results show the effectiveness of our approach. The code for Mutual-DTI can be downloaded from https://github.com/a610lab/Mutual-DTI.

Due to their distinctive flavors, edible mushrooms have gained attention in flavor-related research, and the quality of their flavors determines their consumption. The odor is a vital element of food flavor that significantly impacts consumers' perceptions and purchase decisions. The volatile organic compounds (VOCs) of the odorant ingredient is the primary factors affecting scent characteristics. VOCs analysis and identification require technical assistance. The production and use of edible mushrooms can be aided by a broader examination of their volatile constituents. This review discusses the composition of VOCs in edible mushrooms and how they affect flavors. The principles, advantages, and disadvantages of various methods for extraction, isolation, and characterization of the VOCs of edible mushrooms are also highlighted. The numerous VOCs found in edible mushrooms such as primarily C-8 compounds, organic sulfur compounds, aldehydes, ketones, alcohols, and esters are summarized along with their effects on the various characteristics of scent. Combining multiple extraction, isolation, identification, and quantification technologies will facilitate rapid and accurate analysis of VOCs in edible mushrooms as proof of sensory attributes and quality.

Hyperlipidemia poses a serious threat to human health and evaluating the ability of natural active substances to regulate disorders of lipid metabolism is the focus of food functionality research in recent years. Bioactive peptides are distinguished by their broad range of sources, high nutritional content, ease of absorption and use by the body, and ease of determining their sequences. Bioactive peptides have a wide range of potential applications in the area of medicines and food. The regulation of lipid metabolism disorder caused by bioactive peptides from different sources provides a reference for the development and research of bioactive peptides for lipid reduction. Graphical Abstract

Enteromorpha prolifera is a nutrient-rich green alga and abound in the Yellow Sea and the Bohai Sea of China. In this study, E. prolifera was anaerobically digested for biogas production. The variations of chemical compositions and microbial community structure as well as the physical structure of E. prolifera in anaerobic digestion process were investigated. This is the first report of multiple ways to deeply analysis the process of E. prolifera anaerobic digestion. Results from the present work showed that the biogas obtained from E. prolifera anaerobic digestion could achieve 409.7 mL•g− 1 TS with an average methane concentration of 53.2%, and the VFAs content in substrate played a vital role for driving the biogas production of flora. Moreover, S1 of Thermotogaceae and Cenarchaeum, the dominant bacteria and archaea in digestion flora, respectively, played important roles in degrading E. prolifera, acidizing slurry, and providing methanogenic substrate for methanogens. Graphical Abstract

Guanosine-5'-triphosphate (GTP) not only plays a key role in a majority of cellular processes but also be proposed as a peroxidase-like mimic. Compared with nanozymes, GTP shows good tolerance under harsh conditions, which can be used to construct an easy colorimetric analysis for the detection of biomolecules. Here, on the basis of the peroxidase-like activity of GTP which can catalyze the oxidation of 3,3',5,5'-tetramethyl benzidine dihydrochloride (TMB), colorimetric sensing was established for biothiols and Hg

We have found that tris (2,2'-bipyridyl) ruthenium (II) (Ru(bpy)

In this study, Ulva lactuca polysaccharide (ULP) antihyperglycemic effect was assessed by monitoring changes in the gut microbiota of aging diabetic mice. The results showed that ULP alleviated type 2 diabetes by improving insulin tolerance, increasing SOD and CAT activities, and thus lowering blood glucose level. Moreover, ULP regulated the expressions of INSR and AMPK concurrent with inhibition the expression of JNK, JAK, STAT3, p16 and p38 to improve glucose metabolism dysfunction. Interestingly, the abundance of Alloprevotella and Pediococcus change might the key factor for ULP antihyperglycemic effectiveness in aging-related diabetes. These results suggest that ULP can exert a mechanism of blood glucose regulation by improving intestinal diversity composition asides from direct insulin mimetic actions.

Semantic segmentation of point cloud data, an efficient 3-D scattered point representation, is a fundamental task for various applications, such as autonomous driving and 3-D telepresence. In recent years, deep learning techniques have achieved significant progress in semantic segmentation, especially in the 2-D image setting. However, due to the irregularity of point clouds, most of them cannot be applied to this special data representation directly. While recent works are able to handle the irregularity problem and maintain the permutation invariance, most of them fail to capture the valuable high-dimensional local feature in fine granularity. Inspired by fuzzy mathematical methods and the analysis on the drawbacks of current state-of-the-art works, in this article, we propose a novel deep neural model, Fuzzy3DSeg, that is able to directly feed in the point clouds while maintaining invariant to the permutation of the data feeding order. We deeply integrate the learning of the fuzzy neighborhood feature of each point into our network architecture, so as to perform operations on high-dimensional features. We demonstrate the effectiveness of this network architecture level integration, compared with methods of the fuzzy data preprocessing cascading neural network. Comprehensive experiments on two challenging datasets demonstrate that the proposed Fuzzy3DSeg significantly outperforms the state-of-the-art methods.

Many existing solutions perform document-level sentiment classification based on local document only, ignoring other texts that might contribute to better classification accuracy. In this paper, we propose a novel speculative sentiment classification model named SSC. In SSC, we speculate that users with similar rating behaviours are more likely to write documents of similar sentiments toward a product. The motivation of SSC, therefore, is to exploit those speculative similar documents for improving classification accuracy. The proposed SSC model consists of three main components, namely, user-product interaction (UPI) component, document encoding (DE) component, and speculative similar document (SSD) component. The UPI component models user-product interactions, and encodes user/product ratings behaviours into user/product embeddings. The DE component utilizes learned user/product embeddings to capture the informative word vectors for comprising more accurate document representations. The SSD component aggregates documents written by similar users toward the same product for speculative sentiment classification. Because the user similarities are calculated based on user embeddings that encode user rating behaviours, the aggregated documents are more likely to have similar sentiments. The three components are seamlessly integrated into a unified model. In the unified manner, these three components are jointly optimized, and they mutually complement each other to enhance sentiment classification. We conduct extensive experiments on three public datasets, and demonstrate the advantage of the proposed SSC model over state-of-the-art baselines.

Existing research exploits the semantic information from reviews to complement user-item interactions for item recommendation. However, as these approaches either defer the user-item interactions until the prediction layer or simply concatenate all the reviews of a user/item into a single review, they fail to capture the complex correlations between each user-item pair or introduce noises. Thus, we propose a novel Hierarchical and Interactive Gate Network (HIGnet) model for rating prediction. Modeling local word informativeness and global review semantics in a hierarchical manner enable us to exploit textual features of users/items and capture complex semantic user-item correlations at different levels of granularities. Experiments on five challenging real-world datasets demonstrate the state-of-the-art performance of the proposed HIGnet model. To facilitate community research, the implementation of the proposed model is made publicly available (https://github.com/uqjwen/higan).

Some drawbacks of traditional teacher-centred teaching have gradually become apparent and have led to a growing interest in experiential teaching. Students and teachers are prominent participants in the teaching process, and their behavioural performance largely determines the learning outcomes. Likewise, a harmonious teacher-student relationship is essential to students’ learning experience. In this paper, we have analysed the impact of experiential teaching on three aspects: student engagement, teacher-teaching innovation and teacher-student collaboration, to show that experiential has clear advantages for student learning. However, some argue that the design and practice of experiential teaching are challenging for teachers and that the student-centred approach is not conducive to managing classroom order. This, therefore, suggests new elements for teacher education and schoolteacher training to avoid the gap between instructional design and practice. And further research is needed by educational researchers on how to manage classroom order in experiential teaching and to make students learn effectively.

With the evolution of society, the demands on human resources have also changed significantly. In the face of such changes, the field of education also needs to adapt. Current higher education in China, influenced by Confucianism, places more emphasis on theory than practice, leading to a lack of applied, innovative, and complex talents in the market, so the proportion of practical teaching should be increased. By discussing the benefits of practice for knowledge, talent development, and employment, this paper illustrates the important role of increasing the proportion of practical teaching in Chinese higher education today, guiding students to become applied, innovative and complex talents with solid theory and good quality and suggesting improvements in response to the problems currently faced by practical teaching.

This paper first briefly introduces the National Education Policy 2020 (NEP) document formulated by the Indian government and the five education theories (traditional academic theory, learner-centred theory, human capital theory, social efficiency theory and social reconstruction theory) in the field of education. Through relevant literature review, five educational theories or ideologies are proven that they cannot be fully applied in pedagogy. As such, this paper proposes a refutation perspective for each educational theory, showing my adequate understanding and critical awareness of education research. After that, these five significant educational theories are used to analyse the NEP contents from pages 1 to 32. Combining my direct observation and thematic coding of NEP text with the focus ideas of five education theories, the final result of this paper illustrates that social efficiency theory is most dominant in NEP; thereby, the Indian government needs to consider how to balance their education ideology. At the end of this work, I show personal consideration regarding the NEP measures that target India’s current focus social issues: Indian educators still must test and justify the rationality and effectiveness of these education policies formulated in NEP by future practices.

Money is the most common medium of exchange and plays an important role in our daily life. However, current literature has not yet specifically touched on the influence of money priming on decision-making behaviour under uncertainty and related neural mechanisms. In this study, we used event-related potentials with an adapted version of the Balloon Analogue Risk Task (BART) paradigm to examine brain activity related to the effects of money priming on outcome evaluation in decision-making under uncertainty. Reward positivity (RewP) and P300 components were analysed with respect to feedback valence (win vs. loss) and priming condition (money vs. neutral). The ERP results demonstrated that when individuals made decisions after having been primed with the monetary concept, the positive outcome feedback evoked a larger RewP component than after they had been primed with neutral stimuli. Conversely, there was no significant money priming effect when the outcome feedback was negative. In contrast, when individuals made decisions after having been primed with the monetary concept, the negative outcome feedback evoked a larger P300 than after they had been primed with neutral stimuli, whereas there was no significant money priming effect when the outcome feedback was positive. Our findings, thus, indicate that the brain response to money priming effects on the outcome evaluation in the BART occurs at both an early semi-automatic processing stage and a later cognitive appraisal stage. They further suggest that individuals prefer achieving financial gains at first and then focus on preventing financial losses in the money priming condition relative to the neutral priming condition.

A label-free, sensitive, simple and general colorimetric method was reported to monitor S1 nuclease activity based on protamine-assisted aggregation of gold nanoparticles (AuNPs). Here, protamine, a linear polycation, was used as a medium for causing the aggregation of negatively charged AuNPs by electrostatic interactions, resulting in changes in the surface plasmon resonance (SPR) absorption bands as well as the color of AuNPs. Here, the AuNPs were employed as an indicator to detect the level of S1 nuclease in the solution. Substrate DNA could be cleaved into small fragments by the specific S1 nuclease, which effectively prevents the electrostatic interaction between DNA and protamine and thus facilitates the interaction between protamine and AuNPs. The quantitative analysis of S1 nuclease activity can be performed via directly measuring the changes in the absorption spectra of the AuNPs. Using S1 nuclease as a model analyte, the limit of detection was estimated to be 1.0 × 10-4 U mL-1. Furthermore, the proposed concept has been successfully applied in S1 nuclease analysis of serum samples, offering an ultrasensitive strategy for the speedy detection of the nuclease activity and providing a new avenue for high-throughput screening of nucleases and drugs with potential inhibition properties.

In this paper, we propose solutions to advance answer selection in Community Question Answering (CQA). Automatically selecting correct answers can significantly improve intelligence for CQA, as users are not required to browse the large quantity of texts and select the right answers manually. Also, automatic answers selection can minimize the time for satisfying users seeking the correct answers and maximize user engagement with the site. Unlike previous works, we propose a hybrid attention mechanism to model question-answer pairs. Specifically, for each word, we calculate the intra-sentence attention indicating its local importance and the inter-sentence attention implying its importance to the counterpart sentence. The inter-sentence attention is based on the interactions between question-answer pairs, and the combination of these two attention mechanisms enables us to align the most informative parts in question-answer pairs for sentence matching. Additionally, we exploit user information for answer selection due to the fact that users are more likely to provide correct answers in their areas of expertise. We model users from their written answers to alleviate data sparsity problem, and then learn user representations according to the informative parts in sentences that are useful for question-answer matching task. This mean of modelling users can bridge the semantic gap between different users, as similar users may have the same way of wording their answers. The representations of users, questions and answers are learnt in an end-to-end neural network in a mean that best explains the interrelation between question-answer pairs. We validate the proposed model on a public dataset, and demonstrate its advantages over the baselines with thorough experiments.

Adenosine triphosphate (ATP) is the main energy currency for cells and an important biomolecule involved in cellular reactions, whose abnormal levels are closely related to physical disease, thus it is extremely important to establish a convenient, fast and simple ATP monitoring method. Toward this end, we developed a facile method for colorimetric detection of ATP on the basis of the inhibiting effect of ATP on the peroxidase-like activity of carbon dots (CDs). The detection principle of this method was utilizing the peroxidase-like activity of CDs, which catalyze the oxidation of 3,3’,5,5’-tetramethylbenzidine (TMB) by H2O2 to generate blue products. However, the introduction of ATP in the system can inhibit the generation of blue products, so ATP can be colorimetric detected. This method exhibited high sensitivity with a detection limit of 34 nM and a wide linear range (0.050 - 2.0 μM). The as-proposed colorimetric ATP sensor was capable of detecting ATP in real samples accurately.

Various aspects of human activity recognition have been researched so far and a variety of methods have been used to address them. Most of this research assumed that the data sources used for the recognition task are static. In real environments, however, sensors can be added or can fail and be replaced by different types of sensors. It is therefore important to create an activity recognition model that is able to leverage dynamically available sensors. To approach this problem, we propose methods for activity learning and activity recognition adaptation in environments with dynamic sensor deployments. In our previous work, we proposed sensor and activity context models to address sensor heterogeneity and also a learning-to-rank method for activity learning and its adaptation based on the proposed context models. However, most of the existing solutions, including our previous work, require labelled data for training. To tackle this problem and further improve the recognition accuracy, in this paper, we propose a knowledge-based method for activity recognition and activity model adaptation with dynamically available contexts in an unsupervised manner. We also propose a semi-supervised data selection method for activity model adaptation, so the activity model can be adapted without labelled data. We use comprehensive datasets to demonstrate effectiveness of the proposed methods, and show their advantage over the conventional machine learning algorithms in terms of recognition accuracy.

Tetraphenylethylene-naphthopyran (TPE-NP) and tetraphenylethylene-spirooxazine (TPE-SO) with conjugated photochromic groups and fluorophores were designed and synthesized. Both of them exhibit the comprehensive effects of aggregation-induced emission (AIE) and photochromism while different photochromic groups lead to a unique photochromic rate and fluorescence intensity. Among them, TPE-NP has significant photochromic and fluorescence effects with good stability. Therefore, it could be used as an anti-counterfeiting ink and is expected to be used in fluorescence switches in the future.

Pyrophosphate (P2O74−, PPi) plays a vital role in ecological environment. Its elevated levels in water bodies can lead to eutrophication. Hence, its detection is extremely significant. Whereas most of the existing methods for the actual detection of PPi may cause environmental pollution or suffer from operational complexity. In this study, we introduced a sensitive and selective method for detecting PPi based on the fact that PPi can inhibit the peroxidase-like activity of adenosine 5′-triphosphate (ATP). This strategy not only eliminated the complexity of material preparation (ATP is commercialized), but also addressed the general need for metal ions in detecting PPi. The dynamic range of PPi detection was 1.0–200 μM and the detection limit was 74 nM. In addition, this strategy had been successfully applied to the determination of PPi in tap water and lake water. This work extends the application of natural biological small molecule ATP in the analysis and provides an innovative thought for the metal-free detection of PPi.

Smart hydrogels are regarded as novel sensors due to large reversible volume change in response to external stimuli. Here we prepared a novel visual multi-triggered sensor based on inverse opal hydrogel (IOH) that responses to pH, temperature and light, respectively. The hydrogel was composed of hydrophilic pH and temperature responsive poly(dimethylaminoethyl methacrylate) (PDMAEMA), and light responsive spiropyran-containing segment. When the pH decreased, the sensor changed its structural color from blue to orange-red, and the structural color was reversibly recovered by increasing pH. In addition, the color of sensor can be controlled to change from green to violet while applying a temperature field. Furthermore, the reflection peak of IOH exhibited obvious shifted behavior by UV/visible irradiation. Repeated condition changes revealed that the sensor has a long lifetime.

Community question answering (CQA) services have emerged as a type of popular social platforms. In the social network, experts provide knowledgeable answers to the questions in their domain of expertise, while celebrities publish influential opinions toward the topics led by some questions. Given the large amount of knowledge organized in the format of question–answers, an interesting research problem is to recommend questions to users so as to maximize their engagements with the platform. However, recommending questions in CQA services is a non-trivial task. Data sources in the CQA services are of different types. It is challenging to incorporate heterogeneous information for the recommendation task. Furthermore, data sparsity is an inherent problem in such platforms. In this paper, we propose a model that is able to jointly model both implicit and explicit information for question recommendation. The model integrates multiple data sources and addresses the problem of data heterogeneity. In the proposed model, we dynamically discover latent user groups and incorporate those hierarchical information to bridge the semantic gaps among users in the shared latent space. We evaluate the proposed model on two real-world datasets, and demonstrate that our model outperforms the state-of-the-art alternatives by a large margin. We also investigate different structures of the proposed model to study the effects of different data sources. MOE (Min. of Education, S’pore) Published version

A general and efficient way for enlarging the absorption wavelength of optical materials with high molar absorptivity was explored by incorporating a hydroxyl group ortho-position to a bridging group between the electron-withdrawing and the electron-donating groups. Both theoretical deduction and experimental data indicated that the molecular design strategy was rational and efficacious. Comparing to its analogy, the target materials, 3-hydroxyl-4-(N- ethyl-N-ethoxyl phenylazo) naphthalene sulfonic acid (HPNSA), possessed the improved optical property by extending the intrinsic D-π-A conjugated structure and selective recognition to Fe2+ by forming stable 5-member rings. Importantly, the proposed material was effectively applied for colorimetric determination of Fe2+ in four real environmental water samples with an accurate deviation of 4.0% and a low detection limit of 38 nmol L−1. The action mechanism between HPNSA and Fe2+ was investigated in detail.

Common use of smartphones is a compelling reason for performing activity recognition with on-board sensors as it is more practical than other approaches, such as wearable sensors and augmented environments. Many solutions have been proposed by academia, but practical use is limited to experimental settings. Ad hoc solutions exist with different degrees in recognition accuracy and efficiency. To ease the development of activity recognition for the mobile application eco-system, Google released an activity recognition service on their Android platform. In this paper, we present a systematic evaluation of this activity recognition service and share the lesson learnt. Through our experiments, we identified scenarios in which the recognition accuracy was barely acceptable. We analyze the cause of the inaccuracy and propose four practical and light-weight solutions to significantly improve the recognition accuracy and efficiency. Our evaluation confirmed the improvement. As a contribution, we released the proposed solutions as open-source projects for developers who want to incorporate activity recognition into their applications.

We demonstrate that different people perform activities differently.Combine AdaBoost with LDA to build general activity model with minimum labelled data.Hybrid AdaBoost with HMM&CRF for temporal regulatization of human activities.Use publicly available datasets to validate the proposed methods. Activity recognition has been a hot topic for decades, from the scientific research to the development of off-the-shelf commercial products. Since people perform the activities differently, to avoid overfitting, building a general model with activity data of various users is required before the deployment for personal use. However, annotating a large amount of activity data is expensive and time-consuming. In this paper, we build a general model for activity recognition with a limited amount of labelled data. We combine Latent Dirichlet Allocation (LDA) and AdaBoost to jointly train a general activity model with partially labelled data. After that, when AdaBoost is used for online prediction, we combine it with graphical models (such as HMM and CRF) to exploit the temporal information in human activities to smooth out the accidental misclassifications. Experiments with publicly available datasets show that we are able to obtain the accuracy of more than 90% with 1% labelled data.

A large volume of knowledge has been accumulated with the prevalence of social networks. Although extra knowledge can mitigate the problem of data sparsity, it is still a challenging task to integrate data across different information spaces for recommendation. In this paper, we propose to address recommendation that involves heterogeneous information spaces, namely interactive space (i.e. user-item), structural space (user-user) and semantic space (user-attribute). Instead of modeling each information space independently, we propose to seamlessly integrate information across heterogeneous spaces. To do this, we propose an attention mechanism in which the users/items attend differently to their structural neighbors (structural space) for learning compact representations. The attentions are parameterized by the interactions between user/item attributes (semantic space), and they are collaboratively learned for the recommendation task (interactive space). In this way, information across different spaces can be complementary to each other for boosting recommendation performance. We also prove that the proposed attentive embedding method is a generalization of traditional social regularization and network embedding methods. We validate the effectiveness of the proposed model with two real world datasets, and show that the proposed model is able to outperform state-of-the-art recommender models significantly.

In recommender systems, the user-item interaction data is usually sparse and not sufficient for learning comprehensive user/item representations for recommendation. To address this problem, we propose a novel dual-bridging recommendation model (DBRec). DBRec performs latent user/item group discovery simultaneously with collaborative filtering, and interacts group information with users/items for bridging similar users/items. Therefore, a user's preference over an unobserved item, in DBRec, can be bridged by the users within the same group who have rated the item, or the user-rated items that share the same group with the unobserved item. In addition, we propose to jointly learn user-user group (item-item group) hierarchies, so that we can effectively discover latent groups and learn compact user/item representations. We jointly integrate collaborative filtering, latent group discovering and hierarchical modelling into a unified framework, so that all the model parameters can be learned toward the optimization of the objective function. We validate the effectiveness of the proposed model with two real datasets, and demonstrate its advantage over the state-of-the-art recommendation models with extensive experiments., Comment: 10 pages, 16 figures, The 28th ACM International Conference on Information and Knowledge Management (CIKM '19)

Accurate analysis of prostate cancer specific biomarkers plays an important role in the early diagnosis of prostate cancer. Traditional colorimetric lateral flow assay (LFA) has the limitations of low detection sensitivity and qualitative or semiquantitative detection. In this study, we developed a novel surface-enhanced Raman scattering (SERS)-based competitive LFA for the rapid and highly sensitive quantitative evaluation of prostate cancer antigen 3 (PCA3) mimic DNA. Herein, the competitive hybridization interaction with capture DNA between target PCA3 mimic DNA and reporter DNA-labeled SERS nanotags results in a change in the amount of SERS nanotags on the test line. The quantitative analysis of target PCA3 mimic DNA was realized by monitoring the Raman peak intensity of SERS nanotags on the test line. The limit of detection of PCA3 mimic DNA was estimated to be 3 fM, which is about three orders of magnitude more sensitive than that of a commercially available kit. By combining the outstanding characteristics of the well-established SERS-based competitive strategy and LFA platform, our design has strong potential for the early diagnosis of prostate cancer and other diseases.

Aspect Terms Extraction (ATE) and Aspect Categories Detection (ACD) are two fundamental sub-tasks for aspect-based sentiment analysis. Most of the existing works mainly focus on the ATE task or the co-extraction of aspect terms and opinion words, while few attention are paid to the ACD task. In this work, we propose a joint model to seamlessly integrate the ATE and ACD tasks into a multi-task learning framework. Each of the tasks is based on multi-layer Convolutional Neural Networks (CNNs) for computing high-level word representations, and produces a task-specific and a task-share vector. The task-share vector of one task is used to propagate information to the other, and guides the counterpart task to align the informative textual features to produce the task-specific vectors. Finally, a fully-connected layer with a softmax/sigmoid function is applied to the task-specific vectors for the specific information extraction. The rationale underlying the proposed joint model is that, aspect terms and aspect categories are semantically related, and the information propagated between the two tasks can help to capture the semantic alignments between the aspect terms and categories, and produce informative task-specific vectors. Moreover, the ATE task models local semantics at each position of a sentence, while the ACD task extracts global features of the whole sentence. The mutual interactions between local and global features, therefore, can reciprocally capture informative textual features for the information extraction tasks. We validate the effectiveness of the proposed model on two widely used datasets, and show its advantage over the state-of-the-art baselines. We also investigate the effectiveness of the multi-task framework by comparing the proposed model with its variants. Further, we study the robustness of the proposed model by presenting the model performance with respect to different hyperparameters. Finally, we provide visualization examples to gain a better understanding of the advantages the multi-task learning scheme.

A new type of electric- or thermal-responsive multilayer device composed of SiO2 bilayer inverse opal (IOP) and chiral nematic liquid crystals (N*LCs) was developed. Bilayer IOP was fabricated by layer-by-layer assembly of polystyrene (PS) spheres with two different sizes and showed a reflectance in an extended range of the near-infrared region. Furthermore, the electrically or thermally tunable reflectance of the bilayer-IOP-N*LC device was investigated. The device exhibited the photonic bandgap (PBG) of the N*LC-IOP composite structure with the application of an electric field (voltage-on), while it presented the reflectance of N*LCs without an electric field (voltage-off) and the electrically-responsive behaviour could be reversibly switched. Besides, the device exhibited a gradient redshift of reflectance as temperature increased below the clearing point (TC) while it showed the PBG of the N*LC-IOP composite structure when the temperature was above TC.

An activity recognition system essentially processes raw sensor data and maps them into latent activity classes. Most of the previous systems are built with supervised learning techniques and pre-defined data sources, and result in static models. However, in realistic and dynamic environments, original data sources may fail and new data sources become available, a robust activity recognition system should be able to perform evolution automatically with dynamic sensor availability in dynamic environments. In this paper, we propose methods that automatically incorporate dynamically available data sources to adapt and refine the recognition system at run-time. The system is built upon ensemble classifiers which can automatically choose the features with the most discriminative power. Extensive experimental results with publicly available datasets demonstrate the effectiveness of our methods. HighlightsPropose an activity recognition framework to incorporate dynamically discovered sensors automatically.Propose a method to select the most informative samples for retraining.Propose a novel way of combining AdaBoost with HMM&CRF for temporal regularization.

Neighborhood-based recommenders are a major class of Collaborative Filtering models. The intuition is to exploit neighbors with similar preferences for bridging unseen user-item pairs and alleviating data sparseness, in other words, learn the sub-graph representation of each user in a user graph. Many existing works propose neural attention networks to aggregate neighbors and place higher weights on the specific subsets of users for recommendation. However, the neighborhood information is not necessarily always informative, and the noises in the neighborhood can negatively affect the model performance. To address this issue, we propose a novel neighborhood-based recommender, where a hybrid gated network is designed to automatically separate similar neighbors from dissimilar (noisy) ones, and aggregate those similar neighbors to comprise neighborhood representations. The confidence in the neighborhood is also addressed by putting higher weights on the neighborhood representations if we are confident with the neighborhood information, and vice versa. In addition, a user-neighbor component is proposed to explicitly regularize user-neighbor proximity in latent space. These two components are combined into a unified model to complement each other for the recommendation task. Extensive experiments on three public datasets demonstrate that the proposed model consistently outperforms the state-of-the-art neighborhood-based recommenders. Furthermore, we study different variants of the proposed model to justify the underlying intuition of the proposed hybrid gated network and user-neighbor modeling components.

Traditional recommender systems encounter several challenges such as data sparsity and unexplained recommendation. To address these challenges, many works propose to exploit semantic information from review data. However, these methods have two major limitations in terms of the way to model textual features and capture textual interaction. For textual modeling, they simply concatenate all the reviews of a user/item into a single review. However, feature extraction at word/phrase level can violate the meaning of the original reviews. As for textual interaction, they defer the interactions to the prediction layer, making them fail to capture complex correlations between users and items. To address those limitations, we propose a novel Hierarchical Text Interaction model (HTI) for rating prediction. In HTI, we propose to model low-level word semantics and high-level review representations hierarchically. The hierarchy allows us to exploit textual features at different granularities. To further capture complex user–item interactions, we propose to exploit semantic correlations between each user–item pair at different hierarchies. At word level, we propose an attention mechanism specialized to each user–item pair, and capture the important words for representing each review. At review level, we mutually propagate textual features between the user and item, and capture the informative reviews. The aggregated review representations are integrated into a collaborative filtering framework for rating prediction. Experiments on five real-world datasets demonstrate that HTI outperforms state-of-the-art models by a large margin. Further case studies provide a deep insight into HTI’s ability to capture semantic correlations at different levels of granularities for rating prediction.

Fast association aims to quickly associate a large number of stations with an access point in 802.11ah networks. Existing fast association mechanisms suffer from efficiency, fairness and robustness problems. In this paper, we propose the mechanism of Fast Association based on Speculating the nUmber of Stations (FASUS). FASUS adopts new methods for retransmission, thresholding and adaptive round selection, which greatly improve the association performance. Experiments show that FASUS is able to reduce the association time by 67.1% when compared with the Linear Increase Linear Decrease (LILD) method, which is one of the best-known mechanisms for 802.11ah networks. We formulate a mathematical model to analyze the association process and to determine the optimal number of stations per round. Furthermore, we propose two methods to address the inter- and intra-network interference in congested networks. To improve the robustness and fairness of the network, we propose new solutions to address two possible attacks: 1) the Denial-of-Service (DoS) attack, which can disable the whole network, and 2) the selfish node attack, which may unfairly allow the attackers to associate in a much faster way than normal stations.

Cardiac troponin I (cTnI) was considered as the “gold standard” for acute myocardial infarction (AMI) diagnosis owing to its superior cardiac specificity for cardiac damage and showing little or no changes in patients with a skeletal muscle disease or trauma. Herein, a new signal amplification surface-enhanced Raman scattering (SERS) platform was developed for recognition and detection of cTnI by using gold nanoparticles (AuNPs), graphene oxide (GO) and magnetic beads (MB). Here, antibody/Raman reporter labeled AuNP–functionalized GO were employed as both SERS nanotags and signal amplification carriers. Monoclonal antibody modified MB were applied as the capture probe and separation agents. In the presence of cTnI, sandwich type immunocomplexes, “capture probe/target/SERS nanotags”, were formed through antibody–antigen–antibody interactions. Due to the strong SERS enhancement ability of the designed GO/AuNP complexes and a high binding chance between cTnI and the GO/AuNP complexes, the proposed SERS-based immunoassay could selectively detect cTnI with a high sensitivity (detection limit of 5 pg mL−1) and a good linearity was obtained in a range of 0.01–1000 ng mL−1. In addition, this method was also successfully applied for detecting cTnI in serum substitute media with a similar linear range. Furthermore, this strategy can be constructed with different kinds of antibodies and Raman reporters, and thus can be easily used for simultaneous detection of multiple biomarkers. Therefore, this proposed SERS-based signal amplification technique shows strong potential for the clinical diagnosis of AMI disease.

As the sheer volume of available micro-videos often undermines the users’ capability to choose the micro-videos, in this paper, we propose a multi-source multi-net micro-video recommendation model that recommends micro-videos fitting users’ best interests. Different from existing works, as micro-video inherits the characteristics of social platforms, we simultaneously incorporate multi-source content data of items and multi-networks of users to learn user and item representations for recommendation. This information can be complementary to each other in a way that multi-modality data can bridge the semantic gap among items, while multi-type user networks, such as following and reposting, are able to propagate the preferences among users. Furthermore, to discover the hidden categories of micro-videos that properly match users’ interests, we interactively learn the user-item representations. The resulted categorical representations are interacted with user representations to model user preferences at different level of hierarchies. Finally, multi-source content item data, multi-type user networks and hidden item categories are jointly modelled in a unified recommender, and the parameters of the model are collaboratively learned to boost the recommendation performance. Experiments on a real dataset demonstrate the effectiveness of the proposed model and its advantage over the state-of-the-art baselines.