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Video visual relation inference refers to the task of automatically detecting the relation triplets between the observed objects in videos with the form of ${ < subject, predicate, object>}$ , which requires correctly labeling each detected object and their interaction predicates. Despite the recent advances in image visual relation detection using deep learning techniques, relation inference in videos remains a challenging topic. On one hand, since the introduction of temporal information, it needs to model the rich spatio-temporal visual information for objects and videos. On the other hand, wild videos are often annotated with incomplete relation triplet tags and a few of them are semantically overlapped. However, previous methods adopt hand-crafted visual features extracted from the trajectories, describing local appearance characteristics of isolated objects. And they treat the problem as a multi-class classification task, which makes the relation tags mutually exclusive. To address the above issues, we propose a novel model, termed Visual-Semantic Relation Network (VSRN). In this network, we leverage three-dimensional convolution kernel to capture spatio-temporal features, and encode global visual features in videos through pooling operation on each time slice. Moreover, the semantic collocations between objects are also incorporated so as to obtain comprehensive representations of the relationships. For relation classification, we treat the problem as a multi-label classification task and regard each tag to be independent to predict various relationships. Additionally, we modify commonly used evaluation metric, video-wise recall, to a pair-wise metric (Roop) for testing the performance of models in predicting multiple relationships for the object pairs, Extensive experimental results on two large-scale datasets demonstrate the effectiveness of our proposed model which significantly outperforms the previous works. [ABSTRACT FROM AUTHOR]

Cross-domain word representation aims to learn high-quality semantic representations in an under-resourced domain by leveraging information in a resourceful domain. However, most existing methods mainly transfer the semantics of common words across domains, ignoring the semantic relations among domain-specific words. In this paper, we propose a domain structure-based transfer learning method to learn cross-domain representations by leveraging the relations among domain-specific words. To accomplish this, we first construct a semantic graph to capture the latent domain structure using domain-specific co-occurrence information. Then, in the domain adaptation process, beyond domain alignment, we employ Laplacian Eigenmaps to ensure the domain structure is consistently distributed in the learned embedding space. As such, the learned cross-domain word representations not only capture shared semantics across domains, but also maintain the latent domain structure. We performed extensive experiments on two tasks, namely sentiment analysis and query expansion. The experiment results show the effectiveness of our method for tasks in under-resourced domains. • A cross-domain word representation method for tasks with limited resources. • Fusing the latent semantics of the source and target domains. • Propagating common semantic knowledge from source domain to target domain. • Preserving domain structure in the domain adaptation process. • Effective and efficient for text-related and low-resource tasks. [ABSTRACT FROM AUTHOR]

Since the length of microblog texts, such as tweets, is strictly limited to 140 characters, traditional Information Retrieval techniques suffer from the vocabulary mismatch problem severely and cannot yield good performance in the context of microblogosphere. To address this critical challenge, in this paper, we focus on the use of local conceptual word embeddings for enhance microblog retrieval effectiveness. In particular, we propose a novel k-Nearest Neighbor (k NN) based Query Expansion (QE) algorithm to generate words from local word embeddings to expand the original query, which leads to better understanding of the information need. Besides, in order to further satisfy users’ real-time information need, we incorporate temporal evidences into the expansion algorithm, which can boost recent tweets in the retrieval results with respect to a given topic. Experimental results on the official TREC Twitter corpora demonstrate the significant superiority of our approach over baseline methods. [ABSTRACT FROM AUTHOR]

Yunnan plays a pivotal role in transmitting electricity from west to east within China's Southern Power Grid. During 7–13 January 2021, a large-scale continuous ice-covering event of ultra-high voltage (UHV) transmission lines occurred in the Qujing area of eastern Yunnan Province. Based on ERA5 reanalysis data and meteorological observation data of UHV transmission line icing in China's Southern Power Grid, the synoptic causes of the icing are comprehensively analyzed from various perspectives, including weather situations, vertical stratification of temperature and humidity, local meteorological elements, and atmospheric circulation indices. The results indicate a strong East Asian trough and a blocking high directing northern airflow southward ahead of the ridge. Cold air enters the Qujing area and combines with warm and moist air from the subtropical high pressure of 50–110° E. As warm and cold air masses form a quasi-stationary front over the northern mountainous area of Qujing due to topographic uplift, the mechanism of "supercooling and warm rain" caused by the "warm–cold" temperature profile structure leads to freezing rain events. Large-scale circulation indices in the Siberian High, East Asian Trough, and 50–110° E Subtropical High regions provided clear precursor signals within 0–2 days before the icing events. [ABSTRACT FROM AUTHOR]

Most short-text embedding models typically represent each short-text only using the literal meanings of the words, which makes these models indiscriminative for the ubiquitous polysemy. In order to enhance the semantic representation capability of the short-texts, we (i) propose a novel short-text conceptualization algorithm to assign the associated concepts for each short-text, and then (ii) introduce the conceptualization results into learning the conceptual short-text embeddings. Hence, this semantic representation is more expressive than some widely-used text representation models such as the latent topic model. Wherein, the short-text conceptualization algorithm used here is based on a novel co-ranking framework, enabling the signals (i.e., the words and the concepts) to fully interplay to derive the solid conceptualization for the short-texts. Afterwards, we further extend the conceptual short-text embedding models by utilizing an attention-based model that selects the relevant words within the context to make more efficient prediction. The experiments on the real-world datasets demonstrate that the proposed conceptual short-text embedding model and short-text conceptualization algorithm are more effective than the state-of-the-art methods. [ABSTRACT FROM AUTHOR]

Sentence auto-completion is an important feature that saves users many keystrokes in typing the entire sentence by providing suggestions as they type. Despite its value, the existing sentence auto-completion methods, such as query completion models, can hardly be applied to solving the object completion problem in sentences with the form of (subject, verb, object), due to the complex natural language description and the data deficiency problem. Towards this goal, we treat an SVO sentence as a three-element triple (subject, sentence pattern, object), and cast the sentence object completion problem as an element inference problem. These elements in all triples are encoded into a unified low-dimensional embedding space by our proposed TRANSFER model, which leverages the external knowledge base to strengthen the representation learning performance. With such representations, we can provide reliable candidates for the desired missing element by a linear model. Extensive experiments on a real-world dataset have well-validated our model. Meanwhile, we have successfully applied our proposed model to factoid question answering systems for answer candidate selection, which further demonstrates the applicability of the TRANSFER model. [ABSTRACT FROM PUBLISHER]

In non-autoregressive machine translation, the target tokens are generated by the decoder in one shot. Although this decoding process can significantly reduce the decoding latency, non-autoregressive machine translation still suffers from the sacrifice of translation accuracy. We argue that the reason for such decrease is the lack of the target dependencies, history and future information, between target tokens. So, in this work, we propose a novel method to address this problem. We suppose the hidden representation of a target token from the decoder should consist of three parts: history, present, and future information. And we dynamically aggregate such parts-to-whole information with capsule network for the decoder to improve the performance of non-autoregressive machine translation. In addition, to ensure the capsules learn the information as we expect, we introduce an autoregressive decoder. Several experiments on benchmark tasks demonstrate that the explicit modeling of history and future information can significantly improve performance of NAT model. Extensive analyses show that our model is able to learn history and future information as we expect. • In order to solve the problem that the NAT model cannot model target dependency, we adapt the capsule network to assist the model to learn future and history information. • To ensure the capsule network learn as we expect, we design an autoregressive model to extract information. • We conduct a series of experiments on the benchmark tasks, and the results shows that our model can improve the performance. [ABSTRACT FROM AUTHOR]

New energy sources such as solar energy have been developed and utilized due to the shortage of non-renewable energy, and photoelectric materials have attracted more attention, accordingly. Moreover, flexible optoelectronic devices are crucial in the fields of wearable artificial intelligence, portable energy, and smart medical care. At present, photoelectric devices based on traditional perovskite cobalt oxide rigid films have problems such as inability to bend and deformation, which limits their application environment. Here, natural mica with atomically flat surface, high thermal stability, high transparency and good mechanical flexibility was selected as the substrate for preparing a series of LaCoO 3 flexible photoelectric films. These films exhibit excellent photoelectric properties and flexible properties. More importantly, the flexible film still has fast response speed and reproducible light response after 500 bending cycles. It reveals that all-inorganic perovskite LaCoO 3 is a promising candidate for designing high-performance, flexible and wearable optoelectronic devices. [Display omitted] [ABSTRACT FROM AUTHOR]

Summarization systems for various applications, such as opinion mining, online news services, and answering questions, have attracted increasing attention in recent years. These tasks are complicated, and a classic representation using bag-of-words does not adequately meet the comprehensive needs of applications that rely on sentence extraction. In this paper, we focus on representing sentences as continuous vectors as a basis for measuring relevance between user needs and candidate sentences in source documents. Embedding models based on distributed vector representations are often used in the summarization community because, through cosine similarity, they simplify sentence relevance when comparing two sentences or a sentence/query and a document. However, the vector-based embedding models do not typically account for the salience of a sentence, and this is a very necessary part of document summarization. To incorporate sentence salience, we developed a model, called CCTSenEmb, that learns latent discriminative Gaussian topics in the embedding space and extended the new framework by seamlessly incorporating both topic and sentence embedding into one summarization system. To facilitate the semantic coherence between sentences in the framework of prediction-based tasks for sentence embedding, the CCTSenEmb further considers the associations between neighboring sentences. As a result, this novel sentence embedding framework combines sentence representations, word-based content, and topic assignments to predict the representation of the next sentence. A series of experiments with the DUC datasets validate CCTSenEmb's efficacy in document summarization in a query-focused extraction-based setting and an unsupervised ILP-based setting. [ABSTRACT FROM AUTHOR]

Nd:BiVO 4 nanocrystals were synthesized by an effective and simple approach. Nd was incorporated into BiVO 4 host to enhance electronic conductivity and lithium-ion diffusion kinetics, thus promoting the electrochemical performance. When employed as anode materials in lithium-ion batteries, a good cycling stability and high capacity of ~611 mAh g−1 at 100 mA g−1 were delivered. The work can be extensively employed for fabricating other electrode materials and promoting the electrochemical performance in energy storage correlative domains. [ABSTRACT FROM AUTHOR]

The modification and tuning features of nanostructured films are of great interest because of controllable and distinctive inherent properties in these materials. Here, nanocrystalline MoS 2 films were fabricated on the stainless steels by a radio frequency magnetron sputtering at ambient temperature. X-ray photoelectron spectroscopy, energy-dispersive X-ray spectroscopy, X-ray diffraction and Raman scattering spectroscopy were used to study the chemical state, chemical composition, crystal structure and vibrational properties of the fabricated MoS 2 films. The bias voltage dependent structural evolution and its influence on the optical properties of MoS 2 nanocrystalline films were systematically investigated. Besides, the residual stresses of MoS 2 nanocrystalline films were explored by employing sin2 ψ approach. X-ray diffraction demonstrates that the nanocrystalline MoS 2 films have single-phase hexagonal crystal structure. All MoS 2 films are polycrystalline in nature. The bandgap values are found to be intensively dependent on bias voltage. Our findings show that the nanocrystalline MoS 2 films with different physical properties and intense quantum confinement effect can be realized through adjusting bias voltages. This work may provide deep insight for realizing transitional metal dichalcogenide-based nanostructured film optoelectronic devices with tunable physical properties through a traditional, very cost-effective, and large-scale fabrication method. [ABSTRACT FROM AUTHOR]

Abstract Considerable attention on semiconducting nanocrystallines have been paid owing to their fascinating electrical and optical features, which can be utilized as luminophor and light absorber. Herein, we report a facile and ligand-free protocol to synthesize high-quality CdS nanocrystals on titanium plate. The crystal structural, micro-structural, local structural, surface chemistry and optical properties of CdS nanocrystals have been investigated. pH value dependent optical features of CdS nanocrystals are explored by photoluminescence and UV–vis diffuse reflectance spectroscopy. XRD results manifest that these products possess polycrystalline characteristics, cubic zinc-blende structure and nanomaterial features. The crystal defects and microstructure of these nanocrystals are researched by analysis of XRD line profiles. TEM images display nearly spherical nanocrystals with mean size about 4 nm, well crystalline feature and interplanar spacings of cubic zinc-blende CdS nanocrystals. Raman results also reveal the cubic sphalerite structure of CdS nanocrystals. The first three Raman peaks are assigned to the first, second, and third-order longitudinal optical phonon modes of cubic CdS, respectively. Interestingly, optical absorption spectra display that the absorbance shoulder shows a noticeable change with pH value of the precursor. The observations show that the nanocrystalline size is smaller at lower pH value and it changes as the pH value of the precursor alters, and CdS nanocrystals exhibit significant quantum confinement. More importantly, Photoluminescence spectra show that the first emission peaks can be attributed to band-edge luminescence, which is originated from the recombination of shallow trapped hole-electron pairs and/or excitons in cadmium sulfide nanomaterials, while the second emission peaks can be corresponded to trap-state luminescence, and PL intensity changes with the pH value of the precursor. The insights offered in this work may pave the way to devising of improved synthetic schemes for semiconductor nanocrystals with tailored microstructural properties, permitting precise adjusting of the optoelectronic performances of the prepared materials. Highlights • CdS nanocrystals were fabricated on titanium plate by a simple and ligand-free approach. • Crystal structure, local structure, surface chemistry and optical properties of the nanocrystals. • The insights may pave the way to devising of improved synthetic schemes for nanocrystals. [ABSTRACT FROM AUTHOR]

Bones play an important role in maintaining exercise and protecting organs. Bone defect, as a common orthopedic disease in clinics, can cause tremendous damage with long treatment cycles. Therefore, the treatment of bone defect remains as one of the main challenges in clinical practice. Today, with increased incidence of bone disease in the aging population, demand for bone repair material is high. At present, the method of clinical treatment for bone defects including non-invasive therapy and invasive therapy. Surgical treatment is the most effective way to treat bone defects, such as using bone grafts, Masquelet technique, Ilizarov technique etc. In recent years, the rapid development of tissue engineering technology provides a new treatment strategy for bone repair. This review paper introduces the current situation and challenges of clinical treatment of bone defect repair in detail. The advantages and disadvantages of bone tissue engineering scaffolds are comprehensively discussed from the aspect of material, preparation technology, and function of bone tissue engineering scaffolds. This paper also summarizes the 3D printing technology based on computer technology, aiming at designing personalized artificial scaffolds that can accurately fit bone defects. [ABSTRACT FROM AUTHOR]

Due to their high retrieval efficiency and low storage cost, cross-modal hashing methods have attracted considerable attention. Generally, compared with shallow cross-modal hashing methods, deep cross-modal hashing methods can achieve a more satisfactory performance by integrating feature learning and hash codes optimizing into a same framework. However, most existing deep cross-modal hashing methods either cannot learn a unified hash code for the two correlated data-points of different modalities in a database instance or cannot guide the learning of unified hash codes by the feedback of hashing function learning procedure, to enhance the retrieval accuracy. To address the issues above, in this paper, we propose a novel end-to-end Deep Cross-Modal Hashing with Hashing Functions and Unified Hash Codes Jointly Learning (DCHUC). Specifically, by an iterative optimization algorithm, DCHUC jointly learns unified hash codes for image-text pairs in a database and a pair of hash functions for unseen query image-text pairs. With the iterative optimization algorithm, the learned unified hash codes can be used to guide the hashing function learning procedure; Meanwhile, the learned hashing functions can feedback to guide the unified hash codes optimizing procedure. Extensive experiments on three public datasets demonstrate that the proposed method outperforms the state-of-the-art cross-modal hashing methods. [ABSTRACT FROM AUTHOR]

Summary: Re‐establishment of heritable latitudinal clines in growth‐related traits has been recognised as evidence for adaptive evolution in invasive plants. However, less information is known about latitudinal clines in defence and joint clinal evolution of growth and defence in invasive plants.We planted 14 native Argentinean populations and 14 introduced Chinese populations of Alternanthera philoxeroides in replicate common gardens in China. We investigated the latitudinal clines of traits related to growth and defence, and plasticity of these traits in relation to experiment site and soil nitrogen.We found that chemical defence decreased with latitude in introduced populations but increased with latitude in native populations. For growth rate, latitudinal clines were positive in introduced populations but nonexistent in native populations. There were also parallel positive latitudinal clines in total/shoot biomass and specific leaf area. Experiment site affected the occurrence or magnitude of latitudinal clines in growth rate, branch intensity and triterpenoid saponins concentration. Introduced populations were more plastic to experiment site and soil nitrogen than native populations.We provide evidence for rapid evolution of clines in growth and defence in an invasive plant. Altered herbivory gradients and trade‐off between growth and defence may explain nonparallel clines between the native and introduced ranges. [ABSTRACT FROM AUTHOR]

Different La 1-x Ba x MnO 3+δ nanopowders (with x = 0–0.20) were prepared using a traditional sol-gel method. Each sample had a size distribution between 50 and 80 nm with a single perovskite-type structure. The La 1-x Ba x MnO 3+δ based sensors showed excellent gas-sensing performance toward acetone (25–500 ppm) at 300 °C. It was also found that the La 0.80 Ba 0.20 MnO 3+δ sensor exhibited a fast response (≈25 s) and recovery time (≈15 s) in the presence of 100 ppm acetone gas, while the response value of the La 0.80 Ba 0.20 MnO 3+δ sensor to 500 ppm acetone reached 1350%. Moreover, these sensors also had good reproducibility and sensitivity to acetone. It reveals that Ba2+ as doping agent can partially convert Mn3+ to Mn4+, introducing numerous hole carriers into the p-type LaMnO 3+δ semiconductor and increasing the amount of chemisorbed oxygen ions on the surface, which can enhance the response of the sensor toward acetone. Moreover, this doping effect may also increase the hybridization of Mn 3d - O 2p orbits and double exchange interaction existing in the hybrid Mn3+/Mn4+ system, thereby promoting the carriers' conduction rate and gas sensing speed. These results suggest that the La 1-x Ba x MnO 3+δ perovskite-type structures can have large applicability in industrial acetone sensing. [ABSTRACT FROM AUTHOR]