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Abstract In recent years, recommendation explanation methods have received widespread attention due to their potentials to enhance user experience and streamline transactions. In scenarios where auxiliary information such as text and attributes are lacking, counterfactual explanation has emerged as a crucial technique for explaining recommendations. However, existing counterfactual explanation methods encounter two primary challenges. First, a substantial bias indeed exists in the calculation of the group impact function, leading to the inaccurate predictions as the counterfactual explanation group expands. In addition, the importance of collaborative filtering as a counterfactual explanation is overlooked, which results in lengthy, narrow, and inaccurate explanations. To address such issues, we propose a counterfactual explanation method based on Modified Group Influence Function for recommendation. In particular, via a rigorous formula derivation, we demonstrate that a simple summation of individual influence functions cannot reflect the group impact in recommendations. After that, building upon the improved influence function, we construct the counterfactual groups by iteratively incorporating the individuals from the training samples, which possess the greatest influence on the recommended results, and continuously adjusting the parameters to ensure accuracy. Finally, we expand the scope of searching for counterfactual groups by incorporating the collaborative filtering information from different users. To evaluate the effectiveness of our method, we employ it to explain the recommendations generated by two common recommendation models, i.e., Matrix Factorization and Neural Collaborative Filtering, on two publicly available datasets. The evaluation of the proposed counterfactual explanation method showcases its superior performance in providing counterfactual explanations. In the most significant case, our proposed method achieves a 17% lead in terms of Counterfactual precision compared to the best baseline explanation method.

Abstract Recommender system always suffers from various recommendation biases, seriously hindering its development. In this light, a series of debias methods have been proposed in the recommender system, especially for two most common biases, i.e., popularity bias and amplified subjective bias. However, existing debias methods usually concentrate on correcting a single bias. Such single-functionality debiases neglect the bias-coupling issue in which the recommended items are collectively attributed to multiple biases. Besides, previous work cannot tackle the lacking supervised signals brought by sparse data, yet which has become a commonplace in the recommender system. In this work, we introduce a disentangled debias variational auto-encoder framework (DB-VAE) to address the single-functionality issue as well as a counterfactual data enhancement method to mitigate the adverse effect due to the data sparsity. In specific, DB-VAE first extracts two types of extreme items only affected by a single bias based on the collier theory, which are, respectively, employed to learn the latent representation of corresponding biases, thereby realizing the bias decoupling. In this way, the exact unbiased user representation can be learned by these decoupled bias representations. Furthermore, the data generation module employs Pearl’s framework to produce massive counterfactual data to help fully train the model, making up the lacking supervised signals due to the sparse data. Extensive experiments on three real-world data sets demonstrate the effectiveness of our proposed model. Specifically, our model outperforms the best baseline by 19.5% in terms of Recall@20 and 9.5% in terms of NDCG@100 in the best scenario. Besides, the counterfactual data can further improve DB-VAE, especially on the data set with low sparsity.

Document-level Event Argument Extraction (DEAE) aims to extract structural event knowledge composed of arguments and roles beyond the sentence level. Existing methods mainly focus on designing prompts and using Abstract Meaning Representation (AMR) graph structure as additional features to enrich event argument representation. However, two challenges still remain: (1) the long-range dependency between event trigger and event arguments and (2) the distracting context in the document towards an event that can mislead the argument classification. To address these issues, we propose a novel document-level event argument extraction model named AMR Parser and Sparse Representation (APSR). Specifically, APSR sets inter- and intra-sentential encoders to capture the contextual information in different scopes. Especially, in the intra-sentential encoder, APSR designs three types of sparse event argument attention mechanisms to extract the long-range dependency. Then, APSR constructs AMR semantic graphs, which capture the interactions among concepts well. Finally, APSR fuses the inter- and intra-sentential representations and predicts what role a candidate span plays. Experimental results on the RAMS and WikiEvents datasets demonstrate that APSR achieves a superior performance compared with competitive baselines in terms of F1 by 1.27% and 3.12%, respectively.

Abstract Recently, segmentation-based approaches have been proposed to tackle arbitrary-shaped text detection. The trade-off between speed and accuracy is still a challenge that hinders its deployment in practical applications. Previous methods adopt complex pipelines to improve accuracy while ignoring inference speed. Moreover, the performance of most efficient scene text detectors often suffers from weak feature extraction when equipping lightweight networks. In this paper, we propose a novel distillation method for efficient and accurate arbitrary-shaped text detection, termed kernel-mask knowledge distillation. Our approach equips a low computational-cost visual transformer module (VTM) and a feature adaptation layer to make full use of feature-based and response-based knowledge in distillation. More specifically, first, the text features are obtained by aggregating the multi-level information extracted in the respective backbones of the teacher and student networks. Second, the text features are respectively sent to the VTM to enhance the feature representation ability. Then, we distill the feature-based and response-based kernel knowledge of the teacher network to obtain an efficient and accurate arbitrary-shaped text detection model. Extensive experiments on publicly available datasets demonstrate the state-of-the-art performance of our method. It is worth noting that our method can achieve a competitive F-measure of 86.92% at 34.5 FPS on Total-text. Code is available at https://github.com/giganticpower/KKDnet .

The Yangtze River Valley (YRV) experienced record-breaking extreme high temperature and weakened rainfall events in August 2022, which resulted in severe disasters and large socioeconomic losses. The importance of the intensification and westward movement of the western Pacific subtropical high (WPSH) with abnormal subsidence has been emphasized in previous studies. However, the source of the abnormal subsidence remains unknown. This study investigates the source of the abnormal subsidence over the YRV and discusses its possible causes by adopting the three-pattern decomposition of the atmospheric circulation (3P-DGAC). Meridional circulation (MC) was the main contributor to the abnormal vertical velocity (114%), while the contribution of zonal circulation (ZC) was negative (–14%). Additionally, the negative rainfall anomaly over the YRV can be explained mainly by the MC. The anomalous MC was characterized as a ‘negative-positive-negative-positive-negative’ quintuple distribution with sinking motion over the YRV. Anomalous MC is closely related to the sea surface temperature anomaly (SSTA) over the three oceans. The negative phase of the Indian Ocean dipole (IOD) and La Niña SSTA leads to an anomalous rising motion of the ZC over the Maritime Continent, favoring the existence of the rising motion of the MC by the coupling effect. The positive phase of the North Atlantic triple (NAT) SSTA results in an anomalous Rossby wave train, which further leads to a sinking motion over the YRV.

Text classification is a machine learning technique employed to assign a given text to predefined categories, facilitating the automatic analysis and processing of textual data. However, an important problem is that the number of new text categories is growing faster than that of human annotation data, which makes many new categories of text data lack a lot of annotation data. As a result, the conventional deep neural network is forced to over-fit, which damages the application in the real world. As a solution to this problem, academics recommend addressing data scarcity through few-shot learning. One of the efficient methods is prompt-tuning, which transforms the input text into a mask prediction problem featuring [MASK]. By utilizing descriptors, the model maps output words to labels, enabling accurate prediction. Nevertheless, the previous prompt-based adaption approaches often relied on manually produced verbalizers or a single label to represent the entire label vocabulary, which makes the mapping granularity low, resulting in words not being accurately mapped to their label. To address these issues, we propose to enhance the verbalizer and construct the refined external knowledge into a prompt-tuning (REKP) model. We employ the external knowledge bases to increase the mapping space of tagged terms and design three refinement methods to remove noise data. We conduct comprehensive experiments on four benchmark datasets, namely AG’s News, Yahoo, IMDB, and Amazon. The results demonstrate that REKP can outperform the state-of-the-art baselines in terms of Micro-F1 on knowledge-enhanced text classification. In addition, we conduct an ablation study to ascertain the functionality of each module in our model, revealing that the refinement module significantly contributes to enhancing classification accuracy.

Abstract For a selected train journey with multiple trains in the same traction power system, the given driving strategies of other trains can be abstracted as available external power flow. Aims at reducing the energy consumption of the multi‐train system, this paper presents an analytical solution for optimising the selected train trajectory to maximise the utilisation of the external power flow. Specifically, a spatial‐temporal area which combine the track grade along space and the external power over time is constructed and the problem is converted into the energy‐efficient control of a train travels in the spatial‐temporal area. Classical methods of constrained optimisation is used to show that the set of the optimal control modes and the optimal driving speed for the proposed problem are in a state of change. In order to obtain the optimal driving strategy, the spatial‐temporal area is divided into grids, and the evolution of a key adjoint variable when the external power changes is discussed. The authors illustrate our results by finding optimal control sequences and associated speed profile for the freight train in some elementary but realistic examples.

Aims. To evaluate the value of endoscopic screening during endoscopic submucosal dissection (ESD) in the detection of synchronous multiple early gastric cancer (SMEGC) and the risk factors for missed diagnosis of SMEGC. Methods. We conducted gastric endoscopic screening during ESD operation in 271 patients with early gastric cancer (EGC) referred for ESD, and endoscopic follow-up within 1 year after the operation. The detection and characteristics of SMEGC were analyzed in three stages: before ESD, during ESD operation, and within 1 year after ESD. Results. SMEGC was detected in 37 of 271 patients (13.6%). Among them, 21 patients with SMEGC (56.8%) were diagnosed before ESD, 9 (24.3%) were diagnosed with SMEGC by endoscopic screening during ESD operation, and 7 (18.9%) were found to have EGC lesions in the stomach during postoperative endoscopic follow-up within 1 year. The preoperative missed detection rate of SMEGC was 43.2%, and the rate of missed detection could be reduced by 24.3% (9/37) with endoscopic screening during ESD operation. Missed SMEGC lesions were more common in flat or depressed type and smaller in size than the lesions found before ESD. The presence of severe atrophic gastritis and age ≥60 years were significantly correlated with SMEGC (P

Graph Collaborative Filtering (GCF) methods have emerged as an effective recommendation approach, capturing users’ preferences over items by modeling user–item interaction graphs. However, these methods suffer from data sparsity in real scenarios, and their performance can be improved using contrastive learning. In this paper, we propose an optimized method, named LoRA-NCL, for GCF based on Neighborhood-enriched Contrastive Learning (NCL) and low-rank dimensionality reduction. We incorporate low-rank features obtained through matrix factorization into the NCL framework and employ LightGCN to extract high-dimensional representations. Extensive experiments on five public datasets demonstrate that the proposed method outperforms a competitive graph collaborative filtering base model, achieving 4.6% performance gains on the MovieLens dataset, respectively.

Taking COVID-19 as an example, we know that a pandemic can have a huge impact on normal human life and the economy. Meanwhile, the population flow between countries and regions is the main factor affecting the changes in a pandemic, which is determined by the airline network. Therefore, realizing the overall control of airports is an effective way to control a pandemic. However, this is restricted by the differences in prevention and control policies in different areas and privacy issues, such as how a patient’s personal data from a medical center cannot be effectively combined with their passenger personal data. This prevents more precise airport control decisions from being made. To address this, this paper designed a novel data-sharing framework (i.e., PPChain) based on blockchain and federated learning. The experiment uses a CPU i7-12800HX and uses Docker to simulate multiple virtual nodes. The model is deployed to run on an NVIDIA GeForce GTX 3090Ti GPU. The experiment shows that the relationship between a pandemic and aircraft transport can be effectively explored by PPChain without sharing raw data. This approach does not require centralized trust and improves the security of the sharing process. The scheme can help formulate more scientific and rational prevention and control policies for the control of airports. Additionally, it can use aerial data to predict pandemics more accurately.

Abstract Background The copper metabolism MURR1 domain (COMMD) protein family involved in tumor development and progression in several types of human cancer, but little is known about the function of COMMD proteins in hepatocellular carcinoma (HCC). Methods The ONCOMINE and the UALCAN databases were used to evaluate the expression of COMMD1–10 in HCC and the association of this family with individual cancer stage and tumor grade. Kaplan-Meier (K-M) Plotter and Cox analysis hint the prognostic value of COMMDs. A network comprising 50 most similar genes and COMMD1–10 was constructed with the STRING database. Gene set enrichment analysis (GSEA) was performed using LinkedOmics database. The correlations between COMMD expression and the presence of immune infiltrating cells were also analyzed by the tumor immune estimation resource (TIMER) database. GSE14520 dataset and 80 HCC patients were used to validated the expression and survival value of COMMD3. Human HCC cell lines were also used for validating the function of COMMD3. Results The expression of all COMMD family members showed higher expression in HCC tissues than that in normal tissues, and is associated with clinical cancer stage and pathological tumor grade. In HCC patients, the transcriptional levels of COMMD1/4 are positively correlated with overall survival (OS), while those of COMMD2/3/7/8/9 are negatively correlated with OS. Multivariate analysis indicated that a high level of COMMD3 mRNA is an independent prognostic factor for shorter OS in HCC patients. However, the subset of patients with grade 3 HCC, K-M survival curves revealed that high COMMD3/5/7/8/9 expression and low COMMD4/10 expression were associated with shorter OS. In addition, the expression of COMMD2/3/10 was associated with tumor-induced immune response activation and immune infiltration in HCC. The expression of COMMD3 from GSE14520 dataset and 80 patients are both higher in tumor than that in normal tissue, and a higher level of COMMD3 mRNA is associated with shorter OS. Knockdown of COMMD3 inhibits human HCC cell lines proliferation in vitro. Conclusions Our study indicates that COMMD3 is an independent prognostic biomarker for the survival of HCC patients. COMMD3 supports the proliferation of HCC cells and contributes to the poor OS in HCC patients.

Purpose: This is the first study to compare the pharmacokinetics, safety and, immunogenicity of QL1209, a biosimilar of Perjeta®.Methods: This study was a randomized, double-blind, parallel-controlled clinical trial evaluating the biosimilarity between QL1209 (specification: 420 mg:14 ml, single use via, manufacturer: Qilu Pharmaceutical Co., Ltd., batch number: 201808001KJL) and Perjeta® (specification: 420 mg: 14 ml, single use via, manufacturer: Roche Pharma AG, batch number: H0309H02). The trial period was 99 days (blood samples for PK were collected 99 days after infusion). Serum concentrations were determined using a validated assay. PK parameters were calculated using a non-compartmental model and analyzed statistically. Anti-drug antibody (ADA)-positive samples were further tested for the presence of neutralization antibody detection (NAb).Results: A total of 137 healthy subjects were administrated. The subjects were randomized 1:1 to receive QL1209 or Perjeta® 420 mg intravenously. The geometric mean ratio (GMRs) for QL1209 versus Perjeta® are 104.14%, 104.09%, and 110.59% for Cmax, AUC0-t, and AUC0-∞, respectively, and their 90% confidence interval (CIs) all fell within the predefined bioequivalence margin 80.00–125%. The incidence of drug-related adverse events was 95.6% and 95.5% in the QL1209 and Perjeta® groups, respectively, also comparable between the two groups.Conclusion: The results of this comparative clinical pharmacology study demonstrated the PK similarity of QL1209 (420 mg: 14 ml) and Perjeta® (420 mg: 14 ml) and there was no significant difference in safety and immunogenicity between QL1209 and Perjeta® manufactured by Roche Pharma AG.

The long non-coding RNA (lncRNA) ASAP1-IT1 has been recently shown to aberrantly increase in ovarian and bladder cancer, while its role in other malignancies remains unexplored. This study was to characterize the expression and assess the potential role of ASAP1-IT1 in hepatocellular carcinoma (HCC). Fifty-four paired HCC and histologically normal tissues were obtained from HCC patients. Human HCC cell lines (HepG2, Huh7, SMMC-7721, and BEL-7402) and a normal liver cell line (LO2) were used for in vitro studies. ASAP1-IT1-specific siRNAs were used to silence ASAP1-IT1 expression, while the pcDNA-ASAP1-IT1 vector was constructed to up-regulate its expression. In situ hybridization and qRT-PCR were performed to characterize subcellular localization and expression of ASAP1-IT1. Cell proliferation and migration assays were conducted to examine the role of ASAP1-IT1 in the progression of HCC. In silico analysis was conducted to predict putative miRNA binding sites, which were validated by luciferase reporter assays. ASAP1-IT1 levels were significantly increased in HCC tissues and cells compared with controls. Notably, higher ASAP1-IT1 levels were significantly associated with poorer prognosis of HCC patients. In situ hybridization analysis revealed that ASAP1-IT1 was mainly localized in the nucleus of hepatoma cells and differentially expressed in trabecular, compact, and pseudoglandular forms of liver cancer. Furthermore, knockdown of ASAP1-IT1 significantly suppressed cell proliferation and migration, while its overexpression significantly promoted cell proliferation and migration of HCC cells. Mechanistically, ASAP1-IT1 might exert its role in HCC progression, at least in part, by directly interacting with miR-221-3p. In conclusion, ASAP1-IT1 is abnormally elevated in HCC, and higher levels are correlated with poorer prognosis. An underlying mechanism has been proposed for ASAP1-IT1-associated promotion of proliferation and migration in HCC cells. These findings have provided evidence supporting the oncogenic role of ASAP1-IT1 in HCC.

Background: Bone–tendon junction (BTJ) is a unique structure connecting tendon and bone through a fibrocartilage zone. Owing to its unique structure, the regeneration of BTJ remains a challenge. Here, we study the fibrochondrogenic differentiation of human tendon-derived stem/progenitor cells (TSPCs) both in vitro and in vivo. Methods: TSPCs were isolated from human patellar tendon tissues and investigated for their multidifferentiation potential. TSPCs were cultured in chondrogenic medium with transforming growth factor beta 3 (TGF-β3) and BMP-2 in vitro ​and examined for the expression of fibrochondrogenic marker genes by quantitative real-time reverse transcription polymerase chain reaction, enzyme-linked immunosorbent assay, and immunofluorescence. TSPCs pretreated were also seeded in collage II sponge and then transplanted in immunocompromised nude mice to examine if the fibrochondrogenic characteristics were conserved in vivo. Results: We found that TSPCs were differentiated towards fibrochondrogenic lineage, accompanied by the expression of collagen I, collagen II, SRY-box transcription factor 9 (Sox 9), and tenascin C. Furthermore, after TSPCs were seeded in collagen II sponge and transplanted in immunocompromised nude mice, they expressed fibrochondrogenic genes, including proteoglycan, collagen I, and collagen II. Conclusion: Taken together, this study showed that TSPCs are capable of differentiating towards fibrocartilage-like cells, and the fibrochondrogenic characteristics were conserved even in vivo, and thus might have the potential application for fibrocartilage regeneration in BTJ repair. The translational potential of this article: TSPCs are able to differentiate into fibrocartilage-like cells and thus might well be one potential cell source for fibrocartilage regeneration in a damaged BTJ repair.

Session-aware recommendation is a special form of sequential recommendation, where users' previous interactions before current session are available. Recently, Recurrent Neural Network (RNN) based models are widely used in sequential recommendation tasks with great success. Previous works mainly focus on the interaction sequences of the current session without analyzing a user's long-term preferences. In this paper, we propose a joint neural network (JNN) for session-aware recommendation, which employs a Convolutional Neural Network(CNN) and a RNN to process the long-term historical interactions and the short-term sequential interactions respectively. Then, we apply a fully-connected neural network to study the complex relationship between these two types of features, which aims to generate a unified representation of the current session. Finally, a recommendation score for given items is generated by a bi-linear scheme upon the session representation. We conduct our experiments on three public datasets, showing that JNN outperforms the state-of-the-art baselines on all datasets in terms of Recall and Mean Reciprocal Rank (MRR). The outperforming results indicate that proper handling of historical interactions can improve the effectiveness of recommendation. The experimental results show that JNN is more prominent in samples with short current session or long historical interactions.

Session-aware recommendation is a special form of session-based recommendation, where users' historical interactions before the current session are available. Among the existing session-aware recommendation studies, recurrent neural network (RNN) is a popular choice to model users' current intent of the ongoing session as well as their general preference implied in previous sessions. However, these RNN-based methods present limited memory so as to have difficulty in characterizing long-term behaviour. In addition, when modeling a user's preference, existing studies mainly refer to his (her) own sessions, while ignore the collaborative information from sessions of other users that may share similar tastes. To tackle the above problems, we propose a neighbor-guided memory-based neural network (MNN) for session-aware recommendation task, which comprehensively considers users' short-term intent, long-term preference and cross-sessions information to yield the final recommendations. Specifically, we design a long-term memory generator to capture users' general preference from their historical sessions, meanwhile leverage the neighbor sessions of current session to obtain cross-session collaborative information. Furthermore, with the guidance of long-term memory and cross-session information, we employ a short-term memory generator to yield users' ongoing intent, which serves as the dominating part of recommendation. Extensive experimental results on three real world datasets show the effectiveness of our model, with improvements up to 12.0% on 30MUSIC, 26.5% on NOWPLAYING and 13.5% on TMALL in terms of Recall@20, respectively. Analysis on current session length and the number of historical interacted items comprehensively demonstrates the superiority of our proposal on processing long sessions and modeling users' long-term behaviour.

Proximal stochastic gradient plays an important role in large-scale machine learning and big data analysis. It needs to iteratively update models within a feasible set until convergence. The computational cost is usually high due to the projection over the feasible set. To reduce complexity, many projection-free methods such as Frank-Wolfe methods have been proposed. However, those projection-free methods have to solve a linear programming problem for every update of models which still leads to high computational cost for a complex feasible set, and can be unbearable in practical scenarios. Motivated by this problem, we propose a fully projection-free proximal stochastic gradient method, which has two advantages over previous methods. First, it enjoys high efficiency. The proposed method does not conduct projection directly but finds an approximately correct projection point with a very low computational cost. Second, it achieves tight and optimal convergence rates. Our theoretical analysis shows that the proposed method achieves convergence rates of O(1/√T) and O(log T/T) for convex and strongly convex functions, respectively. These convergence rates successfully match with the known lower bounds. Therefore, in this paper, we provide a valuable insight that some loss of accuracy of projection can improve the efficiency significantly, but does not impair convergence rates. Finally, empirical studies show that the proposed method achieves more than 5× speedup than previous methods.

Recently, Graph Neural Networks (GNNs) have attracted increasing attention in the field of session-based recommendation, due to its strong ability on capturing complex interactive transitions within sessions. However, existing GNN-based models either lack the use of user's long-term historical behaviors or fail to address the impact of collaborative filtering information from neighbor users on the current session, which are both important to boost recommendation. In addition, previous work only focuses on the sequential relations of interactions while neglects the time interval information which can imply the correlations between different interactions. To tackle these problems, we propose a Time-Aware Graph Neural Network (TA-GNN) for session-based recommendation. Specifically, we first construct a user behavior graph by linking the interacted items of the same user according to their corresponding time order. A time-aware generator is designed to model the correlations between different nodes of the user behavior graph by considering the time interval information. Moreover, items from the neighbor sessions of the current session are selected to build a neighborhood graph. Then the two graphs are respectively processed by two different modules to learn the representation of the current session, which is applied to produce the final recommendation list. Comprehensive experiments show that our model outperforms state-of-the-art baselines on three real world datastes. We also investigate the performance of TA-GNN on different numbers of historical interactions and on different session length, finding that our model presents consistently advantages under different conditions.

Basket-Sensitive Item Recommendation (BSIR) is a challenging task that aims to recommend an item to add to the current basket given a user’s historical behaviors. The recommended item is supposed to be relevant to the items in current basket. Previous works mainly produce a recommendation based on user’s current basket, ignoring the inherent preference released by user’s long-term behaviors and failing to accurately distinguish the item importance in the basket for detecting user intent. To tackle the above issues, we propose a hybrid model, i.e., Hybrid-Preference Neural Model (HPNM), where a user’s inherent preference is recognized by modeling the historical sequential baskets and the recent preference is identified by focusing on the current basket. In detail, we apply an attention mechanism for distinguishing the importance of items in a basket to generate an accurate basket representation. GRU is utilized for modeling the basket-level sequential information to obtain user’s long-term preference and the representation of the current session is regarded as user’s short-term preference. We evaluate the performance of our proposals against the state-of-the-art baselines in the field of BSIR on two public datasets, i.e., TaFeng and Foursquare. The experimental results show that HPNM can achieve obvious improvements against the baselines in terms of HLU and Recall. In addition, we find HPNM with an attention mechanism can lead to a larger improvement against the baseline for item recommendation in terms of HLU and Recall on testing baskets with relatively fewer items.

Relation extraction aims to predict the relation triple between the tail entity and head entity in a given text. A large body of works adopt meta-learning to address the few-shot issue faced by relation extraction, where each relation category only contains few labeled data for demonstration. Despite promising results achieved by existing meta-learning methods, these methods still struggle to distinguish the subtle differences between different relations with similar expressions. We argue this is largely owing to that these methods cannot capture unbiased and discriminative features in the very few-shot scenario. For alleviating the above problems, we propose a taxonomy-aware prototype network, which consists of a category-aware calibration module and a task-aware training strategy module. The former implicitly and explicitly calibrates the representation of prototype to become sufficiently unbiased and discriminative. The latter balances the weight between easy and hard instances, which enables our proposal to focus on data with more information during the training stage. Finally, comprehensive experiments are conducted on four typical meta tasks. Furthermore, our proposal presents superiority over the competitive baselines with an improvement of 3.30% in terms of average accuracy.

Text classification aims to assign predefined labels to unlabeled sentences, which tend to struggle in real-world applications when only a few annotated samples are available. Previous works generally focus on using the paradigm of meta-learning to overcome the classification difficulties brought by insufficient data, where a set of auxiliary tasks is given. Accordingly, prompt-based approaches are proposed to deal with the low-resource issue. However, existing prompt-based methods mainly focus on English tasks, which generally apply English pretrained language models that can not directly adapt to Chinese tasks due to structural and grammatical differences. Thus, we propose a prompt-based Chinese text classification framework that uses generated natural language sequences as hints, which can alleviate the classification bottleneck well in low-resource scenarios. In detail, we first design a prompt-based fine-tuning together with a novel pipeline for automating prompt generation in Chinese. Then, we propose a refined strategy for dynamically and selectively incorporating demonstrations into each context. We present a systematic evaluation for analyzing few-shot performance on a wide range of Chinese text classification tasks. Our approach makes few assumptions about task resources and expertise and therefore constitutes a powerful, task-independent approach for few-shot learning.

ObjectiveFibrocartilage transition zone (FC) is difficult to regenerate after surgical re-attachment of tendon to bone. Here, we investigated whether type II collagen-sponges (CII-sponges) facilitated tendon stem/progenitor cells (TSPCs) to adopt chondrogenic phenotypes and further observed if this material could increase the FC areas in bone-tendon junction (BTJ) injury model.MethodsCII-sponges were made as we previously described. The appearance and pore structure of CII-sponges were photographed by camera and microscopies. The viability, proliferation, and differentiation of TSPCs were examined by LIVE/DEAD assay, alamarBlue, and PKH67 in vitro tracking. Subsequently, TSPCs were seeded in CII-sponges, Matrigel or monolayer, and induced under chondrogenic medium for 7 or 14 days before being harvested for qPCR or being transplanted into nude mice to examine the chondrogenesis of TSPCs. Lastly, partial patellectomy (PP) was applied to establish the BTJ injury model. CII-sponges were interposed between the patellar fragment and tendon, and histological examination was used to assess the FC regeneration at BTJ after surgery at 8 weeks.ResultsCII-sponges were like sponges with interconnected pores. TSPCs could adhere, proliferate, and differentiate in this CII-sponge up to 14 days at least. Both qPCR and immunostaining data showed that compared with TSPCs cultured in monolayer or Matrigel, cells in CII-sponges group adopted more chondrogenic phenotypes with an overall increase of chondrocyte-related genes and proteins. Furthermore, in PP injured model, much more new formed cartilage-like tissues could be observed in CII-sponges group, evidenced by a large amount of positive proteoglycan expression and typical oval or round chondrocytes in this area.ConclusionOur study showed that CII-sponges facilitated the TSPCs to differentiate toward chondrocytes and increased the area of FCs, which suggests that CII-sponges are meaningful for the reconstruction of FC at bone tendon junction. However, the link between the two phenomena requires further research and validation.

In this paper, identification system of plastic solid waste (PSW) based on near-infrared (NIR) reflectance spectroscopy in combination with Support Vector Machine (SVM) was presented. A device applied to obtain NIR spectra of plastics in the detection platform was developed. After pre-processing (normalized, 1st derivative and smooth), the repeatability of spectral absorption features was improved, which would assist the identification. A “principal component analysis (PCA)SVM” identification method was proposed to identify polypropylene (PP), polystyrene (PS), polyethylene (PE), poly(methyl methacrylate) (PMMA), acrylonitrile butadiene styrene (ABS) and polyethylene terephthalate (PET) among plastics, and its identification accuracy can reach 97.5%. The type of samples could clearly be identified and the shape of samples could also be roughly discerned. It is clearly shown that this system can achieve good identification results while reducing costs considerably, which has great potential in industrial recycling. Keywords: Plastic identification, Near-infrared spectroscopy, Principal component analysis, Support vector machine

Answer selection is a crucial subtask in the question answering (QA) system. Conventional avenues for this task mainly concentrate on developing linguistic tools that are limited in both performance and practicability. Answer selection approaches based on deep learning have been well investigated with the tremendous success of deep learning in natural language processing. However, the traditional neural networks employed in existing answer selection models, i.e., recursive neural network or convolutional neural network, typically suffer from obtaining the global text information due to their operating mechanisms. The recent Transformer neural network is considered to be good at extracting the global information by employing only self-attention mechanism. Thus, in this paper, we design a Transformer-based neural network for answer selection, where we deploy a bidirectional long short-term memory (BiLSTM) behind the Transformer to acquire both global information and sequential features in the question or answer sentence. Different from the original Transformer, our Transformer-based network focuses on sentence embedding rather than the seq2seq task. In addition, we employ a BiLSTM rather than utilizing the position encoding to incorporate sequential features as the universal Transformer does. Furthermore, we apply three aggregated strategies to generate sentence embeddings for question and answer, i.e., the weighted mean pooling, the max pooling, and the attentive pooling, leading to three corresponding Transformer-based models, i.e., QA-TFWP, QA-TFMP, and QA-TFAP, respectively. Finally, we evaluate our proposals on a popular QA dataset WikiQA. The experimental results demonstrate that our proposed Transformer-based answer selection models can produce a better performance compared with several competitive baselines. In detail, our best model outperforms the state-of-the-art baseline by up to 2.37%, 2.83%, and 3.79% in terms of MAP, MRR, and accuracy, respectively.

Answer selection is an essential step in a question answering (QA) system. Traditional methods for this task mainly focus on developing linguistic features that are limited in practice. With the great success of deep learning method in distributed text representation, deep learning-based answer selection approaches have been well investigated, which mainly employ only one neural network, i.e., convolutional neural network (CNN) or long short term memory (LSTM), leading to failures in extracting some rich sentence features. Thus, in this paper, we propose a collaborative learning-based answer selection model (QA-CL), where we deploy a parallel training architecture to collaboratively learn the initial word vector matrix of the sentence by CNN and bidirectional LSTM (BiLSTM) at the same time. In addition, we extend our model by incorporating the sentence embedding generated by the QA-CL model into a joint distributed sentence representation using a strong unsupervised baseline weight removal (WR), i.e., the QA-CLWR model. We evaluate our proposals on a popular QA dataset, InsuranceQA. The experimental results indicate that our proposed answer selection methods can produce a better performance compared with several strong baselines. Finally, we investigate the models’ performance with respect to different question types and find that question types with a medium number of questions have a better and more stable performance than those types with too large or too small number of questions.

Intent recognition aims to identify users’ potential intents from their utterances, which is a key component in task-oriented dialog systems. A real challenge, however, is that the number of intent categories has grown faster than human-annotated data, resulting in only a small amount of data being available for many new intent categories. This lack of data leads to the overfitting of traditional deep neural networks on a small amount of training data, which seriously affects practical applications. Hence, some researchers have proposed few-shot learning should address the data-scarcity issue. One of the efficient methods is text augmentation, which always generates noisy or meaningless data. To address these issues, we propose leveraging the knowledge in pre-trained language models and constructed the cloze-style data augmentation (CDA) model. We employ unsupervised learning to force the augmented data to be semantically similar to the initial input sentences and contrastive learning to enhance the uniqueness of each category. Experimental results on CLINC-150 and BANKING-77 datasets show the effectiveness of our proposal by its beating of the competitive baselines. In addition, we conducted an ablation study to verify the function of each module in our models, and the results illustrate that the contrastive learning module plays the most important role in improving the recognition accuracy.

Unmanned Aerial Vehicles, commonly known as drones, have been widely used in transmission line inspection and traffic patrolling due to their flexibility and environmental adaptability. To take advantage of drones and overcome their limited endurance, the patrolling tasks are parallelized by concurrently dispatching the drones from a truck which travels on the road network to the nearby task arc. The road network considered in previous research is undirected; however, in reality, the road network usually contains unidirectional arcs, i.e., the road network is asymmetric. Hence, we propose an asymmetric coordinated vehicle-drones arc routing mode for traffic patrolling. In this mode, a truck travelling on an asymmetric road network with multiple drones needs to patrol multiple task arcs, and the drones can be launched and recovered at certain nodes on the truck route, making it possible for drones and the truck to patrol the task in parallel. The total patrol time is the objective function that needs to be minimized given the time limit constraints of drones. The whole problem can be considered as an asymmetric arc routing problem of coordinating a truck and multiple drones. To solve this problem, a large-scale neighborhood search with simulated annealing algorithm (LNS-SA) is proposed. Finally, extensive computation experiments and a real case are carried out. The experimental results show the efficiency of the proposed algorithm. Moreover, a detailed sensitivity analysis is performed on several drone-parameters of interest.

Traditional construction machinery’s full hydraulic steering system has high energy consumption. An electro-hydraulic flow matching steering system for electric wheel loaders based on closed-circuit pump control is proposed to solve the problem. The transfer function of the electro-hydraulic system is established, and the system is stable according to analysis via Routh matrix. A test platform is built to verify the effectiveness of the system and the control strategy. Taking a 1.6T wheel loader as an example, the energy consumption of the traditional steering system and the new steering system under zero-load, positive-load (shovel loaded with 600 kg gravel), and offset-load (the center of gravity of the gravel is off the center of the bucket) conditions is compared. The results show that the energy consumption of the proposed steering system is greatly reduced compared to the traditional system. Under the condition of zero-load with medium steering speed, compared to the traditional system, consumption is reduced by 22.8%.

Chinese Medical Named Entity Recognition (Chinese-MNER) aims to identify potential entities and their categories from the unstructured Chinese medical text. Existing methods for this task mainly incorporate the dictionary knowledge on the basis of traditional BiLSTM-CRF or BERT architecture. However, the construction of high-quality dictionaries is typically time consuming and labor-intensive, which may also damage the robustness of NER models. What is more, the limited amount of annotated Chinese-MNER data can easily lead to the over-fitting problem while training. With the aim of dealing with the above problems, we put forward a BERT-BiLSTM-CRF model by integrating the part-of-speech (POS) tagging features and a Regularization method (BBCPR) for Chinese-MNER. In BBCPR, we first leverage a POS fusion layer to incorporate external syntax knowledge. Next, we design a novel REgularization mothod with Adversarial training and Dropout (READ) to improve the model robustness. Specifically, READ focuses on reducing the difference between the predictions of two sub-models through minimizing the bidirectional KL divergence between the adversarial output and original output distributions for the same sample. Comprehensive evaluations on two public data sets, namely, cMedQANER and cEHRNER from the Chinese Biomedical Language Understanding Evaluation benchmark (ChineseBLUE), demonstrate the superiority of our proposal in Chinese-MNER. In addition, ablation study shows that READ can effectively improve the model performance. Our proposal does well in exploring the technical terms and identifying the word boundary.

Objective This study aimed to investigate application of the urine albumin-to-creatinine ratio (ACR), serum beta 2-microglobulin (β2-MG), and cystatin C as risk markers in a cohort of women with polycystic ovary syndrome (PCOS) for the incidence of gestational diabetes mellitus (GDM). Methods In this cross-sectional study, we analyzed 312 pregnant women with PCOS and classified them as those with and without GDM. For all participants, elbow venous blood and clean middle urine were collected in the morning after 8 hours of an empty stomach. Results Logistic regression analysis showed that the ACR, urine β2-MG levels, and serum cystatin C levels were important markers for women with PCOS concomitant with GDM. Receiver operating characteristic curve analysis showed that the area under the curve of CysC was 0.81 with the threshold based on >0.93 and that of β2-MG was 0.72 with the threshold based on >1.25. Conclusions Increased levels of β2-MG and cystatin C and a high ACR might be risk factors for Chinese women with PCOS and GDM during mid-pregnancy.

Recently, dynamic business-IT alignment (BITA) has attracted increasing attentions. However, the description and the governance of dynamic BITA are insufficient in the literature. To address this gap, a conceptual BITA evolvement model is proposed combining enterprise architecture with the BITA maturity framework, and a sustainable BITA factor on dynamic resource allocation is introduced in this evolvement model. To discuss the sustainable factor in a comprehensive way, we explored a series of possible changes and corresponding resource influences within the alignment of business functions and applications. A dynamic resource allocation algorithm is proposed, and an illustrative case study is presented to verify the algorithm's efficiency.

Because of the dynamic environments of business and IT, achieving any alignment between the two fields has become challenging. In view of its multiple viewpoints and artifacts, the discipline of enterprise architecture (EA) is often regarded as an effective methodology to deal with business-IT alignment (BITA) issues, and thus has attracted plenty of research. This article conducts a systematic literature review of BITA research using EA. Six questions are answered through 5W1H (When, Who, What, Why, Where, How) analysis; these questions aim to acquire a thorough understanding of BITA from the perspective of EA, to discover weak points in the status quo, and to identify future research directions.

Knowledge-enhanced recommendation (KER) aims to integrate the knowledge graph (KG) into collaborative filtering (CF) for alleviating the sparsity and cold start problems. The state-of-the-art graph neural network (GNN)–based methods mainly focus on exploiting the connectivity between entities in the knowledge graph, while neglecting the interaction relation between items reflected in the user-item interactions. Moreover, the widely adopted BPR loss for model optimization fails to provide sufficient supervisions for learning discriminative representation of users and items. To address these issues, we propose the collaborative knowledge-enhanced recommendation (CKER) method. Specifically, CKER proposes a collaborative graph convolution network (CGCN) to learn the user and item representations from the connection between items in the constructed interaction graph and the connectivity between entities in the knowledge graph. Moreover, we introduce the self-supervised learning to maximize the mutual information between the interaction- and knowledge-aware user preferences by deriving additional supervision signals. We conduct comprehensive experiments on two benchmark datasets, namely Amazon-Book and Last-FM, and the experimental results show that CKER can outperform the state-of-the-art baselines in terms of recall and NDCG on knowledge-enhanced recommendation.

Fact verification aims to evaluate the authenticity of a given claim based on the evidence sentences retrieved from Wikipedia articles. Existing works mainly leverage the natural language inference methods to model the semantic interaction of claim and evidence, or further employ the graph structure to capture the relation features between multiple evidences. However, previous methods have limited representation ability in encoding complicated units of claim and evidences, and thus cannot support sophisticated reasoning. In addition, a limited amount of supervisory signals lead to the graph encoder could not distinguish the distinctions of different graph structures and weaken the encoding ability. To address the above issues, we propose a Knowledge-Enhanced Graph Attention network (KEGA) for fact verification, which introduces a knowledge integration module to enhance the representation of claims and evidences by incorporating external knowledge. Moreover, KEGA leverages an auxiliary loss based on contrastive learning to fine-tune the graph attention encoder and learn the discriminative features for the evidence graph. Comprehensive experiments conducted on FEVER, a large-scale benchmark dataset for fact verification, demonstrate the superiority of our proposal in both the multi-evidences and single-evidence scenarios. In addition, our findings show that the background knowledge for words can effectively improve the model performance.

The representation learning of the knowledge graph projects the entities and relationships in the triples into a low-dimensional continuous vector space. Early representation learning mostly focused on the information contained in the triplet itself but ignored other useful information. Since entities have different types of representations in different scenarios, the rich information in the types of entity levels is helpful for obtaining a more complete knowledge representation. In this paper, a new knowledge representation frame (TRKRL) combining rule path information and entity hierarchical type information is proposed to exploit interpretability of logical rules and the advantages of entity hierarchical types. Specifically, for entity hierarchical type information, we consider that entities have multiple representations of different types, as well as treat it as the projection matrix of entities, using the type encoder to model entity hierarchical types. For rule path information, we mine Horn rules from the knowledge graph to guide the synthesis of relations in paths. Experimental results show that TRKRL outperforms baselines on the knowledge graph completion task, which indicates that our model is capable of using entity hierarchical type information, relation paths information, and logic rules information for representation learning.

Collaborative filtering (CF) aims to make recommendations for users by detecting user’s preference from the historical user–item interactions. Existing graph neural networks (GNN) based methods achieve satisfactory performance by exploiting the high-order connectivity between users and items, however they suffer from the poor training efficiency problem and easily introduce bias for information propagation. Moreover, the widely applied Bayesian personalized ranking (BPR) loss is insufficient to provide supervision signals for training due to the extremely sparse observed interactions. To deal with the above issues, we propose the Efficient Graph Collaborative Filtering (EGCF) method. Specifically, EGCF adopts merely one-layer graph convolution to model the collaborative signal for users and items from the first-order neighbors in the user–item interactions. Moreover, we introduce contrastive learning to enhance the representation learning of users and items by deriving the self-supervisions, which is jointly trained with the supervised learning. Extensive experiments are conducted on two benchmark datasets, i.e., Yelp2018 and Amazon-book, and the experimental results demonstrate that EGCF can achieve the state-of-the-art performance in terms of Recall and normalized discounted cumulative gain (NDCG), especially on ranking the target items at right positions. In addition, EGCF shows obvious advantages in the training efficiency compared with the competitive baselines, making it practicable for potential applications.

Session-based recommendation aims to model a user’s intent and predict an item that the user may interact with in the next step based on an ongoing session. Existing session-based recommender systems mainly aim to model the sequential signals based on Recurrent Neural Network (RNN) structures or the item transition relations between items with Graph Neural Network (GNN) based frameworks to identify a user’s intent for recommendation. However, in real scenarios, there may be strong sequential signals existing in users’ adjacent behaviors or multi-step transition relations among different items. Thus, either RNN- or GNN-based methods can only capture limited information for modeling complex user behavior patterns. RNNs pay attention to the sequential relations among consecutive items, while GNNs focus on structural information, i.e., how to enrich the item embedding with its adjacent items. In this paper, we propose a Collaborative Co-attention Network for Session-based Recommendation (CCN-SR) to incorporate both sequential and structural information, as well as capture the co-relations between them for obtaining an accurate session representation. To be specific, we first model the ongoing session with an RNN structure to capture the sequential information among items. Meanwhile, we also construct a session graph to learn the item representations with a GNN structure. Then, we design a co-attention network upon these two structures to capture the mutual information between them. The designed co-attention network can enrich the representation of each node in the session with both sequential and structural information, and thus generate a more comprehensive representation for each session. Extensive experiments are conducted on two public e-commerce datasets, and the results demonstrate that our proposed model outperforms state-of-the-art baseline model for session based recommendation in terms of both Recall and MRR. We also investigate different combination strategies and the experimental results verify the effectiveness of our proposed co-attention mechanism. Besides, our CCN-SR model achieves better performance than baseline models with different session lengths.

Session-based recommendation (SBRS) aims to make recommendations for users merely based on the ongoing session. Existing GNN-based methods achieve satisfactory performance by exploiting the pair-wise item transition pattern; however, they ignore the temporal evolution of the session graphs over different time-steps. Moreover, the widely applied cross-entropy loss with softmax in SBRS faces the serious overfitting problem. To deal with the above issues, we propose dynamic graph learning for session-based recommendation (DGL-SR). Specifically, we design a dynamic graph neural network (DGNN) to simultaneously take the graph structural information and the temporal dynamics into consideration for learning the dynamic item representations. Moreover, we propose a corrective margin softmax (CMS) to prevent overfitting in the model optimization by correcting the gradient of the negative samples. Comprehensive experiments are conducted on two benchmark datasets, that is, Diginetica and Gowalla, and the experimental results show the superiority of DGL-SR over the state-of-the-art baselines in terms of Recall@20 and MRR@20, especially on hitting the target item in the recommendation list.

Fact verification aims to verify the authenticity of a given claim based on the retrieved evidence from Wikipedia articles. Existing works mainly focus on enhancing the semantic representation of evidence, e.g., introducing the graph structure to model the evidence relation. However, previous methods can’t well distinguish semantic-similar claims and evidences with distinct authenticity labels. In addition, the performances of graph-based models are limited by the over-smoothing problem of graph neural networks. To this end, we propose a graph-based contrastive learning method for fact verification abbreviated as CosG, which introduces a contrastive label-supervised task to help the encoder learn the discriminative representations for different-label claim-evidence pairs, as well as an unsupervised graph-contrast task, to alleviate the unique node features loss in the graph propagation. We conduct experiments on FEVER, a large benchmark dataset for fact verification. Experimental results show the superiority of our proposal against comparable baselines, especially for the claims that need multiple-evidences to verify. In addition, CosG presents better model robustness on the low-resource scenario.

Document classification has a broad application in the field of sentiment classification, document ranking and topic labeling, etc. Previous neural network-based work has mainly focused on investigating a so-called forward implication, i.e., the preceding text segments are taken as the context of the following text segments when generating the text representation. Such a scenario typically ignores the fact that the semantics of a document are a product of the mutual implication of all text segments in a document. Thus, in this paper, we introduce a concept of interaction and propose a text representation model with Self-interaction Attention Mechanism (TextSAM) for document classification. In particular, we design three aggregated strategies to integrate the interaction into a hierarchical architecture for document classification, i.e., averaging the interaction, maximizing the interaction and adding one more attention layer on the interaction, which leads to three models, i.e., TextSAMAVE, TextSAMMAX and TextSAMATT, respectively. Our comprehensive experimental results on two public datasets, i.e., Yelp 2016 and Amazon Reviews (Electronics), show that our proposals can significantly outperform the state-of-the-art neural-based baselines for document classification, presenting a general improvement in terms of accuracy ranging from 5.97% to 14.05% against the best baseline. Furthermore, we find that our proposals with a self-interaction attention mechanism can obviously alleviate the impact brought by the increase of sentence number as the relative improvement of our proposals against the baselines are enlarged when the sentence number increases.

A Large scale of full-thickness skin defects is lack of auto-grafts and which requires the engineered skin substitutes for repair and regeneration. One major obstacle in skin tissue engineering is to expand epidermal stem cells (ESCs) and develop functional substitutes. The other one is the scaffold of the ESCs. Here, we applied type I collagen-modified chitin membrane to form collagen-chitin biomimetic membrane (C-CBM), which has been proved to have a great biocompatibility and degraded totally when it was subcutaneously transplanted into rat skin. ESCs were cultured, and the resulting biofilm was used to cover full-thickness skin defects in nude mice. The transplantation of ESCs- collagen- chitn biomimetic membrane (ESCs-C-CBM) has achieved in situ skin regeneration. In nude mice, compared to controls with collagen-chitin biomimetic membrane (C-CBM) only, the ESCs-C-CBM group had significantly more dermatoglyphs on the skin wound 10 w after surgery, and the new skin was relatively thick, red and elastic. In vivo experiments showed obvious hair follicle cell proliferation in the full-thickness skin defect. Stem cell markers examination showed active ESCs in repair and regeneration of skin. The results indicate that the collagen-modified chitin membrane carry with ESCs has successfully regenerated the whole skin with all the skin appendages and function.

Bone remodeling is intrinsically regulated by cell signaling molecules. The Protein Kinase C (PKC) family of serine/threonine kinases is involved in multiple signaling pathways including cell proliferation, differentiation, apoptosis and osteoclast biology. However, the precise involvement of individual PKC isoforms in the regulation of osteoclast formation and bone homeostasis remains unclear. Here, we identify PKC-δ as the major PKC isoform expressed among all PKCs in osteoclasts; including classical PKCs (-α, -β and -γ), novel PKCs (-δ, -ε, -η and -θ) and atypical PKCs (-ι/λ and -ζ). Interestingly, pharmacological inhibition and genetic ablation of PKC-δ impairs osteoclastic bone resorption in vitro. Moreover, disruption of PKC-δ activity protects against LPS-induced osteolysis in mice, with osteoclasts accumulating on the bone surface failing to resorb bone. Treatment with the PKC-δ inhibitor Rottlerin, blocks LPS-induced bone resorption in mice. Consistently, PKC-δ deficient mice exhibit increased trabeculae bone containing residual cartilage matrix, indicative of an osteoclast-rich osteopetrosis phenotype. Cultured ex vivo osteoclasts derived from PKC-δ null mice exhibit decreased CTX-1 levels and MARKS phosphorylation, with enhanced formation rates. This is accompanied by elevated gene expression levels of cathepsin K and PKC -α, -γ and -ε, as well as altered signaling of pERK and pcSrc416/527 upon RANKL-induction, possibly to compensate for the defects in bone resorption. Collectively, our data indicate that PKC-δ is an intrinsic regulator of osteoclast formation and bone resorption and thus is a potential therapeutic target for pathological osteolysis.

Diagnostic information for psychiatric research often depends on both clinical interviews and medical records. Although discrepancies between these two sources are well known, there have been few studies into the degree and origins of inconsistencies.We compared data from structured interviews and medical records on 1,970 Han Chinese women with recurrent DSM-IV major depression (MD). Correlations were high for age at onset of MD (0.93) and number of episodes (0.70), intermediate for family history (+0.62) and duration of longest episode (+0.43) and variable but generally more modest for individual depressive symptoms (mean kappa = 0.32). Four factors were identified for twelve symptoms from medical records and the same four factors emerged from analysis of structured interviews. Factor congruencies were high but the correlation of factors between interviews and records were modest (i.e. +0.2 to +0.4).Structured interviews and medical records are highly concordant for age of onset, and the number and length of episodes, but agree more modestly for individual symptoms and symptom factors. The modesty of these correlations probably arises from multiple factors including i) inconsistency in the definition of the worst episode, ii) inaccuracies in self-report and iii) difficulties in coding medical records where symptoms were recorded solely for clinical purposes.

Information retrieval applications have to publish their output in the form of ranked lists. Such a requirement motivates researchers to develop methods that can automatically learn effective ranking models. Many existing methods usually perform analysis on multidimensional features of query-document pairs directly and don't take users' interactive feedback information into account. They thus incur the high computation overhead and low retrieval performance due to an indefinite query expression. In this paper, we propose a Virtual Feature based Logistic Regression (VFLR) ranking method that conducts the logistic regression on a set of essential but independent variables, called virtual features (VF). They are extracted via the principal component analysis (PCA) method with the user's relevance feedback. We then predict the ranking score of each queried document to produce a ranked list. We systematically evaluate our method using the LETOR 4.0 benchmark datasets. The experimental results demonstrate that the proposal outperforms the state-of-the-art methods in terms of the Mean Average Precision (MAP), the Precision at position k (P@k), and the Normalized Discounted Cumulative Gain at position k (NDCG@k).