Evaluation of Individual Contribution in Blended Collaborative Learning

With the deepening of classroom teaching reform, blended collaborative learning has become a common collaborative learning method, and its significance and value has been verified by many parties. However, there is still a lack of quantitative analysis and detailed insight into the internal interaction dynamics of the group at the individual level. There are limitations in the evaluation dimensions and methods of individual contribution in collaborative learning in previous studies, so it is difficult to obtain a comprehensive evaluation of individual contribution. The purpose of this study is to build an effective evaluation model of individual contribution in blended collaborative learning. Discussion recordings and text data in collaboration were collected in a non-invasive way to validate the model. Based on evaluation model, the characteristics and rules behind the data deeply were explored, the collaborative process of the blended collaborative learning was analyzed and mined, and the characteristics of learners\u27 contribution were summarized to support the development of blended collaborative learning

Explicit Intensity Control for Accented Text-to-speech

Accented text-to-speech (TTS) synthesis seeks to generate speech with an accent (L2) as a variant of the standard version (L1). How to control the intensity of accent in the process of TTS is a very interesting research direction, and has attracted more and more attention. Recent work design a speaker-adversarial loss to disentangle the speaker and accent information, and then adjust the loss weight to control the accent intensity. However, such a control method lacks interpretability, and there is no direct correlation between the controlling factor and natural accent intensity. To this end, this paper propose a new intuitive and explicit accent intensity control scheme for accented TTS. Specifically, we first extract the posterior probability, called as ``goodness of pronunciation (GoP)'' from the L1 speech recognition model to quantify the phoneme accent intensity for accented speech, then design a FastSpeech2 based TTS model, named Ai-TTS, to take the accent intensity expression into account during speech generation. Experiments show that the our method outperforms the baseline model in terms of accent rendering and intensity control.Comment: 5 pages, 3 figures. Submitted to ICASSP 2023. arXiv admin note: text overlap with arXiv:2209.1080

Exploiting modality-invariant feature for robust multimodal emotion recognition with missing modalities

Multimodal emotion recognition leverages complementary information across modalities to gain performance. However, we cannot guarantee that the data of all modalities are always present in practice. In the studies to predict the missing data across modalities, the inherent difference between heterogeneous modalities, namely the modality gap, presents a challenge. To address this, we propose to use invariant features for a missing modality imagination network (IF-MMIN) which includes two novel mechanisms: 1) an invariant feature learning strategy that is based on the central moment discrepancy (CMD) distance under the full-modality scenario; 2) an invariant feature based imagination module (IF-IM) to alleviate the modality gap during the missing modalities prediction, thus improving the robustness of multimodal joint representation. Comprehensive experiments on the benchmark dataset IEMOCAP demonstrate that the proposed model outperforms all baselines and invariantly improves the overall emotion recognition performance under uncertain missing-modality conditions. We release the code at: https://github.com/ZhuoYulang/IF-MMIN.Comment: 5 pages, 3 figures, 1 table. Submitted to ICASSP 2023. We release the code at: https://github.com/ZhuoYulang/IF-MMI

Temporal Interest Network for Click-Through Rate Prediction

The history of user behaviors constitutes one of the most significant characteristics in predicting the click-through rate (CTR), owing to their strong semantic and temporal correlation with the target item. While the literature has individually examined each of these correlations, research has yet to analyze them in combination, that is, the quadruple correlation of (behavior semantics, target semantics, behavior temporal, and target temporal). The effect of this correlation on performance and the extent to which existing methods learn it remain unknown. To address this gap, we empirically measure the quadruple correlation and observe intuitive yet robust quadruple patterns. We measure the learned correlation of several representative user behavior methods, but to our surprise, none of them learn such a pattern, especially the temporal one. In this paper, we propose the Temporal Interest Network (TIN) to capture the quadruple semantic and temporal correlation between behaviors and the target. We achieve this by incorporating target-aware temporal encoding, in addition to semantic embedding, to represent behaviors and the target. Furthermore, we deploy target-aware attention, along with target-aware representation, to explicitly conduct the 4-way interaction. We performed comprehensive evaluations on the Amazon and Alibaba datasets. Our proposed TIN outperforms the best-performing baselines by 0.43\% and 0.29\% on two datasets, respectively. Comprehensive analysis and visualization show that TIN is indeed capable of learning the quadruple correlation effectively, while all existing methods fail to do so. We provide our implementation of TIN in Tensorflow

Seasonal expressions of prolactin, prolactin receptor and STAT5 in the scented glands of the male muskrats (Ondatra zibethicus)

Prolactin (PRL) production in mammals has been demonstrated in extrapituitary gland, which can activate autocrine/paracrine signaling pathways to regulate physiological activity. In the current study, we characterized the gene expression profiles of PRL, prolactin receptor (PRLR) and signal transducers and activators of transcription 5 (STAT5) in the scented glandular tissues of the muskrats, to further elucidate the relationship between PRL and the scented glandular functions of the muskrats. The weight and volume of the scented glands in the breeding season were significantly higher than those of the non-breeding season. Immunohistochemical data showed that PRL, PRLR and STAT5/phospho-STAT5 (pSTAT5) were found in the glandular and epithelial cells of the scented glands in both seasons. Furthermore, we found that PRL, PRLR and STAT5 had higher immunoreactivities in the scented glands during the breeding season when compared to those of the non-breeding season. In parallel, the gene expressions of PRL, PRLR and STAT5 were significantly higher in the scented glands during the breeding season than those of the non-breeding season. The concentrations of PRL in scented glandular tissues and sera were measured by enzyme-linked immunosorbent assay (ELISA), and their levels were both notably higher in the breeding season than those of the non-breeding season. These findings suggested that the scented glands of the muskrats were capable of extrapituitary synthesis of PRL, which might attribute PRL a specific function to an endocrine or autocrine/paracrine mediator

Title2Event: Benchmarking Open Event Extraction with a Large-scale Chinese Title Dataset

Event extraction (EE) is crucial to downstream tasks such as new aggregation and event knowledge graph construction. Most existing EE datasets manually define fixed event types and design specific schema for each of them, failing to cover diverse events emerging from the online text. Moreover, news titles, an important source of event mentions, have not gained enough attention in current EE research. In this paper, We present Title2Event, a large-scale sentence-level dataset benchmarking Open Event Extraction without restricting event types. Title2Event contains more than 42,000 news titles in 34 topics collected from Chinese web pages. To the best of our knowledge, it is currently the largest manually-annotated Chinese dataset for open event extraction. We further conduct experiments on Title2Event with different models and show that the characteristics of titles make it challenging for event extraction, addressing the significance of advanced study on this problem. The dataset and baseline codes are available at https://open-event-hub.github.io/title2event.Comment: EMNLP 202

In situ-constructed LixMoS2 with highly exposed interface boosting high-loading and long-life cathode for all-solid-state Li–S batteries

As the persistent concerns regarding sluggish reaction kinetics and insufficient conductivities of sulfur cathodes in all-solid-state Li–S batteries (ASSLSBs), numerous carbon additives and solid-state electrolytes (SSEs) have been incorporated into the cathode to facilitate ion/electron pathways around sulfur. However, this has resulted in a reduced capacity and decomposition of SSEs. Therefore, it is worth exploring neotype sulfur hosts with electronic/ionic conductivity in the cathode. Herein, we present a hybrid cathode composed of few-layered S/MoS2/C nanosheets (<5 layers) that exhibits high-loading and long-life performance without the need of additional carbon additives in advanced ASSLSBs. The multifunctional MoS2/C host exposes the abundant surface for intimate contacting sites, in situ-formed LixMoS2 during discharging as mixed ion/electron conductive network improves the S/Li2S conversion, and contributes extra capacity for the part of active materials. With a high active material content (S + MoS2/C) of 60 wt% in the S/MoS2/C/Li6PS5Cl cathode composite (the carbon content is only ~3.97 wt%), the S/MoS2/C electrode delivers excellent electrochemical performance, with a high reversible discharge capacity of 980.3 mAh g−1 (588.2 mAh g−1 based on the whole cathode weight) after 100 cycles at 100 mA g−1. The stable cycling performance is observed over 3500 cycles with a Coulombic efficiency of 98.5% at 600 mA g−1, while a high areal capacity of 10.4 mAh cm−2 is achieved with active material loading of 12.8 mg cm−2

Infrared Imaging of Magnetic Octupole Domains in Non-collinear Antiferromagnets

Magnetic structure plays a pivotal role in the functionality of antiferromagnets (AFMs), which not only can be employed to encode digital data but also yields novel phenomena. Despite its growing significance, visualizing the antiferromagnetic domain structure remains a challenge, particularly for non-collinear AFMs. Currently, the observation of magnetic domains in non-collinear antiferromagnetic materials is feasible only in Mn$_{3}$Sn, underscoring the limitations of existing techniques that necessitate distinct methods for in-plane and out-of-plane magnetic domain imaging. In this study, we present a versatile method for imaging the antiferromagnetic domain structure in a series of non-collinear antiferromagnetic materials by utilizing the anomalous Ettingshausen effect (AEE), which resolves both the magnetic octupole moments parallel and perpendicular to the sample surface. Temperature modulation due to the AEE originating from different magnetic domains is measured by the lock-in thermography, revealing distinct behaviors of octupole domains in different antiferromagnets. This work delivers an efficient technique for the visualization of magnetic domains in non-collinear AFMs, which enables comprehensive study of the magnetization process at the microscopic level and paves the way for potential advancements in applications.Comment: National Science Review in pres

Global tropospheric ozone trends, attributions, and radiative impacts in 1995–2017: an integrated analysis using aircraft (IAGOS) observations, ozonesonde, and multi-decadal chemical model simulations

Quantification and attribution of long-term tropospheric ozone trends are critical for understanding the impact of human activity and climate change on atmospheric chemistry but are also challenged by the limited coverage of long-term ozone observations in the free troposphere where ozone has higher production efficiency and radiative potential compared to that at the surface. In this study, we examine observed tropospheric ozone trends, their attributions, and radiative impacts from 1995–2017 using aircraft observations from the In-service Aircraft for a Global Observing System database (IAGOS), ozonesondes, and a multi-decadal GEOS-Chem chemical model simulation. IAGOS observations above 11 regions in the Northern Hemisphere and 19 of 27 global ozonesonde sites have measured increases in tropospheric ozone (950–250 hPa) by 2.7 ± 1.7 and 1.9 ± 1.7 ppbv per decade on average, respectively, with particularly large increases in the lower troposphere (950–800 hPa) above East Asia, the Persian Gulf, India, northern South America, the Gulf of Guinea, and Malaysia/Indonesia by 2.8 to 10.6 ppbv per decade. The GEOS-Chem simulation driven by reanalysis meteorological fields and the most up-to-date year-specific anthropogenic emission inventory reproduces the overall pattern of observed tropospheric ozone trends, including the large ozone increases over the tropics of 2.1–2.9 ppbv per decade and above East Asia of 0.5–1.8 ppbv per decade and the weak tropospheric ozone trends above North America, Europe, and high latitudes in both hemispheres, but trends are underestimated compared to observations. GEOS-Chem estimates an increasing trend of 0.4 Tg yr−1 of the tropospheric ozone burden in 1995–2017. We suggest that uncertainties in the anthropogenic emission inventory in the early years of the simulation (e.g., 1995–1999) over developing regions may contribute to GEOS-Chem's underestimation of tropospheric ozone trends. GEOS-Chem sensitivity simulations show that changes in global anthropogenic emission patterns, including the equatorward redistribution of surface emissions and the rapid increases in aircraft emissions, are the dominant factors contributing to tropospheric ozone trends by 0.5 Tg yr−1. In particular, we highlight the disproportionately large, but previously underappreciated, contribution of aircraft emissions to tropospheric ozone trends by 0.3 Tg yr−1, mainly due to aircraft emitting NOx in the mid-troposphere and upper troposphere where ozone production efficiency is high. Decreases in lower-stratospheric ozone and the stratosphere–troposphere flux in 1995–2017 contribute to an ozone decrease at mid-latitudes and high latitudes. We estimate the change in tropospheric ozone radiative impacts from 1995–1999 to 2013–2017 is +18.5 mW m−2, with 43.5 mW m−2 contributed by anthropogenic emission changes (20.5 mW m−2 alone by aircraft emissions), highlighting that the equatorward redistribution of emissions to areas with strong convection and the increase in aircraft emissions are effective for increasing tropospheric ozone's greenhouse effect.</p

CRISPR/Cas9-mediated enhancement of semi-dwarf glutinous traits in elite Xiangdaowan rice (Oryza sativa L.): targeting SD1 and Wx genes for yield and quality improvement

In rice cultivation, the traits of semi-dwarfism and glutinous texture are pivotal for optimizing yield potential and grain quality, respectively. Xiangdaowan (XDW) rice, renowned for its exceptional aromatic properties, has faced challenges due to its tall stature and high amylose content, resulting in poor lodging resistance and suboptimal culinary attributes. To address these issues, we employed CRISPR/Cas9 technology to precisely edit the SD1 and Wx genes in XDW rice, leading to the development of stable genetically homozygous lines with desired semi-dwarf and glutinous characteristics. The sd1-wx mutant lines exhibited reduced gibberellin content, plant height, and amylose content, while maintaining hardly changed germination rate and other key agronomic traits. Importantly, our study demonstrated that exogenous GA3 application effectively promoted growth by compensating for the deficiency of endogenous gibberellin. Based on this, a semi-dwarf glutinous elite rice (Oryza sativa L.) Lines was developed without too much effect on most agronomic traits. Furthermore, a comparative transcriptome analysis unveiled that differentially expressed genes (DEGs) were primarily associated with the anchored component of the membrane, hydrogen peroxide catabolic process, peroxidase activity, terpene synthase activity, and apoplast. Additionally, terpene synthase genes involved in catalyzing the biosynthesis of diterpenoids to gibberellins were enriched and significantly down-regulated. This comprehensive study provides an efficient method for simultaneously enhancing rice plant height and quality, paving the way for the development of lodging-resistant and high-quality rice varieties