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Music codecs are a vital aspect of audio codec research, and ultra low-bitrate compression holds significant importance for music transmission and generation. Due to the complexity of music backgrounds and the richness of vocals, solely relying on modeling semantic or acoustic information cannot effectively reconstruct music with both vocals and backgrounds. To address this issue, we propose MuCodec, specifically targeting music compression and reconstruction tasks at ultra low bitrates. MuCodec employs MuEncoder to extract both acoustic and semantic features, discretizes them with RVQ, and obtains Mel-VAE features via flow-matching. The music is then reconstructed using a pre-trained MEL-VAE decoder and HiFi-GAN. MuCodec can reconstruct high-fidelity music at ultra low (0.35kbps) or high bitrates (1.35kbps), achieving the best results to date in both subjective and objective metrics. Code and Demo: https://xuyaoxun.github.io/MuCodec_demo/.

Manipulating quantum states is at the heart of quantum information processing and quantum metrology. Landau-Zener Rabi oscillation (LZRO), which arises from a quantum two-level system swept repeatedly across the avoided crossing point in the time domain, has been suggested for widespread use in manipulating quantum states. Cold atom is one of the most prominent platforms for quantum computing and precision measurement. However, LZRO has never been observed in cold atoms due to its stringent requirements. By compensating for the linear drift of the clock laser and optimizing experimental parameters, we successfully measured LZRO on the strontium atomic optical clock platform under both fast and slow passage limits within $4$ to $6$ driving periods. Compared to previous results on other platforms, the duration of the plateau is $10^4$ times longer in the optical lattice clock. The experimental data also suggest that destructive Landau-Zener interference can effectively suppress dephasing effects in the optical lattice clock, paving the way for manipulating quantum states against various environmental effects in cold atomic systems.

Automated explanatory feedback systems play a crucial role in facilitating learning for a large cohort of learners by offering feedback that incorporates explanations, significantly enhancing the learning process. However, delivering such explanatory feedback in real-time poses challenges, particularly when high classification accuracy for domain-specific, nuanced responses is essential. Our study leverages the capabilities of large language models, specifically Generative Pre-Trained Transformers (GPT), to explore a sequence labeling approach focused on identifying components of desired and less desired praise for providing explanatory feedback within a tutor training dataset. Our aim is to equip tutors with actionable, explanatory feedback during online training lessons. To investigate the potential of GPT models for providing the explanatory feedback, we employed two commonly-used approaches: prompting and fine-tuning. To quantify the quality of highlighted praise components identified by GPT models, we introduced a Modified Intersection over Union (M-IoU) score. Our findings demonstrate that: (1) the M-IoU score effectively correlates with human judgment in evaluating sequence quality; (2) using two-shot prompting on GPT-3.5 resulted in decent performance in recognizing effort-based (M-IoU of 0.46) and outcome-based praise (M-IoU of 0.68); and (3) our optimally fine-tuned GPT-3.5 model achieved M-IoU scores of 0.64 for effort-based praise and 0.84 for outcome-based praise, aligning with the satisfaction levels evaluated by human coders. Our results show promise for using GPT models to provide feedback that focuses on specific elements in their open-ended responses that are desirable or could use improvement., Comment: 11 pages, full research paper, EDM 2024

We apply the QCD sum rule method to study the light single-gluon hybrid states with various (exotic) quantum numbers. We construct twenty-four single-gluon hybrid currents, and use eighteen of them to calculate the masses of forty-four single-gluon hybrid states with the quark-gluon contents $\bar q q g$ ($q=u/d$) and $\bar s s g$. We concentrate on the hybrid states with the exotic quantum number $J^{PC} = 1^{-+}$, whose masses and widths are calculated to be $M_{|\bar q q g;1^-1^{-+}\rangle} =1.67^{+0.15}_{-0.17}$ GeV, $\Gamma_{|\bar q q g;1^-1^{-+}\rangle} = 530^{+540}_{-330}$ MeV, $M_{|\bar q q g;0^+1^{-+}\rangle} = 1.67^{+0.15}_{-0.17}$ GeV, $\Gamma_{|\bar q q g;0^+1^{-+}\rangle} = 120^{+160}_{-110}$ MeV, $M_{|\bar s s g;0^+1^{-+}\rangle} = 1.84^{+0.14}_{-0.15}$ GeV, and $\Gamma_{|\bar s s g;0^+1^{-+}\rangle} = 100^{+110}_{-~80}$ MeV. Our results support the interpretations of the $\pi_1(1600)$ and $\eta_1(1855)$ as the hybrid states $|\bar q q g;1^-1^{-+}\rangle$ and $|\bar s s g;0^+1^{-+}\rangle$, respectively. Considering the uncertainties, our results suggest that the $\pi_1(1600)$ and $\eta_1(1855)$ may also be interpreted as the hybrid states $|\bar q q g;1^-1^{-+}\rangle$ and $|\bar q q g;0^+1^{-+}\rangle$, respectively. To differentiate these two assignments and to verify whether they are hybrid states or not, we propose to examine the $a_1(1260) \pi$ decay channel in future experiments., Comment: 18 pages 5 figures, 3 tables, revised version to be published in PRD

The atmosphere of Triton was probed directly by observing a ground-based stellar occultation on 6 October 2022. This rare event yielded 23 positive light curves collected from 13 separate observation stations contributing to our campaign. The significance of this event lies in its potential to directly validate the modest pressure fluctuation on Triton, a phenomenon not definitively verified by previous observations, including only five stellar occultations, and the Voyager 2 radio occultation in 1989. Using an approach consistent with a comparable study, we precisely determined a surface pressure of $14.07_{-0.13}^{+0.21}~\mathrm{\mu bar}$ in 2022. This new pressure rules out any significant monotonic variation in pressure between 2017 and 2022 through direct observations, as it is in alignment with the 2017 value. Additionally, both the pressures in 2017 and 2022 align with the 1989 value. This provides further support for the conclusion drawn from the previous volatile transport model simulation, which is consistent with the observed alignment between the pressures in 1989 and 2017; that is to say, the pressure fluctuation is modest. Moreover, this conclusion suggests the existence of a northern polar cap extended down to at least $45^\circ$N$-60^\circ$N and the presence of nitrogen between $30^\circ$S and $0^\circ$., Comment: Astronomy & Astrophysics, in press. 9 pages, 2 figures, 3 tables

Strangeon stars, which are proposed to describe the nature of pulsar-like compact stars, have passed various observational tests. The maximum mass of a non-rotating strangeon star could be high, which implies that the remnants of binary strangeon star mergers could even be long-lived massive strangeon stars. We study rigidly rotating strangeon stars in the slowly rotating approximation, using the Lennard-Jones model for the equation of state. Rotation can significantly increase the maximum mass of strangeon stars with unchanged baryon numbers, enlarging the mass-range of long-lived strangeon stars. During spin-down after merger, the decrease of radius of the remnant will lead to the release of gravitational energy. Taking into account the efficiency of converting the gravitational energy luminosity to the observed X-ray luminosity, we find that the gravitational energy could provide an alternative energy source for the plateau emission of X-ray afterglow. The fitting results of X-ray plateau emission of some short gamma-ray bursts suggest that the magnetic dipole field strength of the remnants can be much smaller than that of expected when the plateau emission is powered only by spin-down luminosity of magnetars., Comment: Accepted by RAA

Our article is focused on the application of Markowitz Portfolio Theory and the Single Index Model on 10-year historical monthly return data for 10 stocks included in FTSE Bursa Malaysia KLCI, which is also our market index, as well as a risk-free asset which is the monthly fixed deposit rate. We will calculate the minimum variance portfolio and maximum Sharpe portfolio for both the Markowitz model and Single Index model subject to five different constraints, with the results presented in the form of tables and graphs such that comparisons between the different models and constraints can be made. We hope this article will help provide useful information for future investors who are interested in the Malaysian stock market and would like to construct an efficient investment portfolio. Keywords: Markowitz Portfolio Theory, Single Index Model, FTSE Bursa Malaysia KLCI, Efficient Portfolio, Comment: 19 pages, 5 figures

The effectiveness of active learning largely depends on the sampling efficiency of the acquisition function. Expected Loss Reduction (ELR) focuses on a Bayesian estimate of the reduction in classification error, and more general costs fit in the same framework. We propose Bayesian Estimate of Mean Proper Scores (BEMPS) to estimate the increase in strictly proper scores such as log probability or negative mean square error within this framework. We also prove convergence results for this general class of costs. To facilitate better experimentation with the new acquisition functions, we develop a complementary batch AL algorithm that encourages diversity in the vector of expected changes in scores for unlabeled data. To allow high-performance classifiers, we combine deep ensembles, and dynamic validation set construction on pretrained models, and further speed up the ensemble process with the idea of Monte Carlo Dropout. Extensive experiments on both texts and images show that the use of mean square error and log probability with BEMPS yields robust acquisition functions and well-calibrated classifiers, and consistently outperforms the others tested. The advantages of BEMPS over the others are further supported by a set of qualitative analyses, where we visualise their sampling behaviour using data maps and t-SNE plots., Comment: 16 pages, TPAMI. arXiv admin note: text overlap with arXiv:2110.14171

The current understanding of spin-polarization phenomena in crystals relying on the crystalline symmetries is far from complete. Here, we develop a universal theory, consisting of five basic symmetry-constrained rules, to capture the diverse spin textures (STs) in non-centrosymmetric crystals, via exhaustively classifying the crystalline symmetry operations and their combinations with time-reversal operation in symmetry-preserved k-invariant subspace. Our theory is verified by extensive material examples based on the first-principles calculations, explaining their kaleidoscopic STs or even vanishing spin-polarization effect. Interestingly, based on our theory, we argue that the generally accepted radial STs in chiral crystals cannot be rigorous. More importantly, combining with topological band theory, we propose the symmetry-constrained three-dimensional persistent STs, which is long sought but never be discovered, could be abundant in two families of nonsymmorphic crystals. Our theory could broaden our understanding of spin-polarization effect and enable the dream of ab initio design of desirable STs in crystals.

The methods of QCD sum rules and light-cone sum rules within the framework of heavy quark effective theory have been widely applied to study the singly heavy baryons, and especially, we have applied these methods to predict not only the mass and width of the $\Xi_b(6087)^0$ recently discovered by LHCb, but also its observation channel and its mass difference from the $\Xi_b(6100)^-$. We apply the same approach to perform a complete study on the $P$-wave bottom baryons of the $SU(3)$ flavor $\mathbf{\bar 3}_F$ and investigate their fine structure. Besides the $\Lambda_b(5912)^0$, $\Lambda_b(5920)^0$, $\Xi_b(6087)^0$, and $\Xi_b(6100)^-$, our results suggest the existence of the other two $\Lambda_b$ and two $\Xi_b$ baryons, whose widths are limited. They are the $\Lambda_b(J^P = 3/2^-)$ with the mass and width about $(5.93^{+0.13}_{-0.13}~{\rm GeV},\,0.0^{+12.0}_{-~0.0}~{\rm MeV})$, the $\Lambda_b(5/2^-)$ with $(5.94^{+0.13}_{-0.13}~{\rm GeV},\,\approx0~{\rm MeV})$, the $\Xi_b(3/2^-)$ with $(6.10^{+0.15}_{-0.10}~{\rm GeV},\,1.4{^{+11.6}_{-~1.4}}~{\rm MeV})$, and the $\Xi_b(5/2^-)$ with $(6.11^{+0.15}_{-0.10}~{\rm GeV},\,1.0{^{+7.4}_{-1.0}}~{\rm MeV})$. Their mass splittings are calculated to be $M_{\Lambda_b(5/2^-)} - M_{\Lambda_b(3/2^-)} = 17\pm7~{\rm MeV}$ and $M_{\Xi_b(5/2^-)} - M_{\Xi_b(3/2^-)} = 14\pm7~{\rm MeV}$. All these baryons are explained as the $P$-wave bottom baryons of the $\rho$-mode, where the orbital excitation is between the two light quarks. However, the existence of this mode is still controversial, so their experimental searches can verify both our approach and the existence of the $\rho$-mode, which can significantly improve our understanding on the internal structure of hadrons., Comment: 6 pages, 2 figures, 1 table, revised version to be published in EPJC

Large Language Models (LLMs) have demonstrated exceptional capabilities across various natural language processing tasks. Yet, many of these advanced LLMs are tailored for broad, general-purpose applications. In this technical report, we introduce AcademicGPT, designed specifically to empower academic research. AcademicGPT is a continual training model derived from LLaMA2-70B. Our training corpus mainly consists of academic papers, thesis, content from some academic domain, high-quality Chinese data and others. While it may not be extensive in data scale, AcademicGPT marks our initial venture into a domain-specific GPT tailored for research area. We evaluate AcademicGPT on several established public benchmarks such as MMLU and CEval, as well as on some specialized academic benchmarks like PubMedQA, SCIEval, and our newly-created ComputerScienceQA, to demonstrate its ability from general knowledge ability, to Chinese ability, and to academic ability. Building upon AcademicGPT's foundation model, we also developed several applications catered to the academic area, including General Academic Question Answering, AI-assisted Paper Reading, Paper Review, and AI-assisted Title and Abstract Generation., Comment: Technical Report. arXiv admin note: text overlap with arXiv:2310.12081, arXiv:2310.10053 by other authors

The transformer model has gained widespread adoption in computer vision tasks in recent times. However, due to the quadratic time and memory complexity of self-attention, which is proportional to the number of input tokens, most existing Vision Transformers (ViTs) encounter challenges in achieving efficient performance in practical industrial deployment scenarios, such as TensorRT and CoreML, where traditional CNNs excel. Although some recent attempts have been made to design CNN-Transformer hybrid architectures to tackle this problem, their overall performance has not met expectations. To tackle these challenges, we propose an efficient hybrid ViT architecture named FMViT. This approach enhances the model's expressive power by blending high-frequency features and low-frequency features with varying frequencies, enabling it to capture both local and global information effectively. Additionally, we introduce deploy-friendly mechanisms such as Convolutional Multigroup Reparameterization (gMLP), Lightweight Multi-head Self-Attention (RLMHSA), and Convolutional Fusion Block (CFB) to further improve the model's performance and reduce computational overhead. Our experiments demonstrate that FMViT surpasses existing CNNs, ViTs, and CNNTransformer hybrid architectures in terms of latency/accuracy trade-offs for various vision tasks. On the TensorRT platform, FMViT outperforms Resnet101 by 2.5% (83.3% vs. 80.8%) in top-1 accuracy on the ImageNet dataset while maintaining similar inference latency. Moreover, FMViT achieves comparable performance with EfficientNet-B5, but with a 43% improvement in inference speed. On CoreML, FMViT outperforms MobileOne by 2.6% in top-1 accuracy on the ImageNet dataset, with inference latency comparable to MobileOne (78.5% vs. 75.9%). Our code can be found at https://github.com/tany0699/FMViT.

Named entity recognition (NER), a task that identifies and categorizes named entities such as persons or organizations from text, is traditionally framed as a multi-class classification problem. However, this approach often overlooks the issues of imbalanced label distributions, particularly in low-resource settings, which is common in certain NER contexts, like biomedical NER (bioNER). To address these issues, we propose an innovative reformulation of the multi-class problem as a one-vs-all (OVA) learning problem and introduce a loss function based on the area under the receiver operating characteristic curve (AUC). To enhance the efficiency of our OVA-based approach, we propose two training strategies: one groups labels with similar linguistic characteristics, and another employs meta-learning. The superiority of our approach is confirmed by its performance, which surpasses traditional NER learning in varying NER settings., Comment: 6 pages, 3 figures, ICDM 2023

Active learning, a widely adopted technique for enhancing machine learning models in text and image classification tasks with limited annotation resources, has received relatively little attention in the domain of Named Entity Recognition (NER). The challenge of data imbalance in NER has hindered the effectiveness of active learning, as sequence labellers lack sufficient learning signals. To address these challenges, this paper presents a novel reweighting-based active learning strategy that assigns dynamic smoothed weights to individual tokens. This adaptable strategy is compatible with various token-level acquisition functions and contributes to the development of robust active learners. Experimental results on multiple corpora demonstrate the substantial performance improvement achieved by incorporating our re-weighting strategy into existing acquisition functions, validating its practical efficacy.

We study strong decays of the $\phi(2170)$, along with its possible partner $X(2436)$, as two fully-strange tetraquark states of $J^{PC} = 1^{--}$. We consider seven decay channels: $\phi \eta$, $\phi \eta^\prime$, $\phi f_0(980)$, $\phi f_1(1420)$, $h_1(1415) \eta$, $h_1(1415) \eta^\prime$, and $h_1(1415) f_1(1420)$. Some of these channels are kinematically possible, and we calculate their relative branching ratios through the Fierz rearrangement. Future experimental measurements on these ratios can be useful in determining the nature of the $\phi(2170)$ and $X(2436)$. The $\phi(2170)$ has been observed in the $\phi f_0(980)$, $\phi \eta$, and $\phi \eta^\prime$ channels, and we propose to further examine it in the $h_1(1415) \eta$ channel. Evidences of the $X(2436)$ have been observed in the $\phi f_0(980)$ channel, and we propose to verify whether this structure exists or not in the $\phi \eta$, $\phi \eta^\prime$, $h_1(1415) \eta$, and $h_1(1415) \eta^\prime$ channels., Comment: 10 pages, 3 figures, 1 table, suggestions and comments are welcome

A stellar occultation by Pluto on 5 September 2019 yielded positive detections at two separate stations. Using an approach consistent with comparable studies, we derived a surface pressure of $11.478 \pm 0.55~\mathrm{\mu bar}$ for Pluto's atmosphere from the observations of this event. In addition, to avoid potential method inconsistancies highlighted by Sicardy et al. when comparing with historical pressure measurements, we reanalyzed the data by 15 August 2018 and 17 July 2019 events, respectively. All the new measurements provide a bridge between the two different perspectives on the pressure variation since 2015: a rapid pressure drop from previous studies of the 15 August 2018 and 17 July 2019 events and a plateau phase from that of the 6 June 2020 event. The pressure measurement from the 5 September 2019 event aligns with those from 2016, 2018, and 2020, supporting the latter perspective. While the measurements from the 4 June 2011 and 17 July 2019 events suggest probable V-shaped pressure variations unaccounted for by the volatile transport model (VTM) from Meza et al., the VTM remains applicable on average. And, the validity of the V-shaped variations is debatable due to the stellar faintness of the 4 June 2011 event and the grazing single-chord geometry of the 17 July 2019 event. To reveal and understand all significant pressure variations of Pluto's atmosphere, it is essential to provide constraints on both short-term and long-term evolutions of the interacting atmosphere and surface by continuous pressure monitoring through occultation observations, whenever possible, complemented by frequent spectroscopy and photometry of the surface., Comment: Astronomy & Astrophysics, in press. 10 pages, 6 figures

Weak measurement has been proven to amplify the detection of changes in meters while discarding most photons due to the low probability of post-selection. Previous power-recycling schemes enable the failed post-selection photons to be repeatedly selected, thus overcoming the inefficient post-selection and increasing the precision of detection. In this study, we focus on the polarization-based weak value angular-velocity measurement and introduce three cyclic methods to enhance the accuracy of detecting time shift in a Gaussian beam: power recycling, signal recycling, and dual recycling schemes. By incorporating one or two partially transmitting mirrors into the system, both the power and signal-to-noise ratio (SNR) of the detected light are substantially enhanced. Compared to non-polarization schemes, polarization-based approaches offer several advantages, including lower optical loss, unique cyclic directions, and a wider optimal region. These features effectively reduce crosstalk among different light paths and theoretically eliminate the walk-off effect, thus yielding improvements in both theoretical performance and application., Comment: 9 pages, 4 figures

Weak-value-amplification permits small effects to be measured as observable changes at the sacrifice of power due to post-selection. The power recycling scheme has been proven to eliminate this inefficiency of the rare post-selection, thus surpassing the limit of the shot noise and improving the precision of the measurement. However, the improvement is strictly limited by the system setup, especially the system loss. Here we introduce a dual recycling model based on the interferometric weak-value-based deflection measurement. Two mirrors, the power-recycling mirror and signal-recycling mirror, are placed at the bright and dark port of the interferometer respectively, creating a composite resonator. The results show that both the power and the signal-to-noise ratio (SNR) are greatly enhanced in a wider range of experimental parameters compared to the power-recycling scheme. This work considerably loosens the constraint of the system setup and further explores the real advantage of weak measurement over traditional schemes., Comment: 7 pages, 5 figures, reference

Having precise perception of the environment is crucial for ensuring the secure and reliable functioning of autonomous driving systems. Radar object detection networks are one fundamental part of such systems. CNN-based object detectors showed good performance in this context, but they require large compute resources. This paper investigates sparse convolutional object detection networks, which combine powerful grid-based detection with low compute resources. We investigate radar specific challenges and propose sparse kernel point pillars (SKPP) and dual voxel point convolutions (DVPC) as remedies for the grid rendering and sparse backbone architectures. We evaluate our SKPP-DPVCN architecture on nuScenes, which outperforms the baseline by 5.89% and the previous state of the art by 4.19% in Car AP4.0. Moreover, SKPP-DPVCN reduces the average scale error (ASE) by 21.41% over the baseline.

Shape-morphing structures possess the ability to change their shapes from one state to another, and therefore, offer great potential for a broad range of applications. A typical paradigm of morphing is transforming from an initial two-dimensional (2D) flat configuration into a three-dimensional (3D) target structure. One popular fabrication method for these structures involves programming cuts in specific locations of a thin sheet material (i.e.~kirigami), forming a desired 3D shape upon application of external mechanical load. In this paper, a novel inverse design strategy is proposed by modifying the bending stiffness via introducing distributed modulus in functionally graded composites (FGCs). The longitudinal modulus of each cross-sectional slice can be controlled through the rule of mixtures, hence matching the required modulus distribution along the elastic strip. Following the proposed framework, a diverse range of structures is obtained with different Gaussian curvatures in both numerical simulations and experiments. A very good agreement is achieved between the measured shapes of morphed structures and the targets. In addition, the compressive rigidity and specific energy absorption during compression of FGC-based hemi-ellipsoidal morphing structures with various aspect ratios were also examined numerically and validated against experiments. By conducting systematical numerical simulations, we also demonstrate the multifunctionality of the modulus-graded shape-morphing composites. This new inverse design framework provides an opportunity to create shape-morphing structures by utilising modulus-graded composite materials, which can be employed in a variety of applications involving multi-physical environments. Furthermore, this framework underscores the versatility of the approach, enabling precise control over material properties at a local level., Comment: Journal of the Mechanics and Physics of Solids

The quest for pragmatic room-temperature (RT) magnetic semiconductors (MSs) with a suitable bandgap constitutes one of the contemporary opportunities to be exploited. This may provide a materials platform for to bring new-generation ideal information device technologies into real-world applications where the otherwise conventionally separately utilized charge and spin are simultaneously exploited. Here we present RT ferromagnetism in an Fe-doped SnSe (Fe:SnSe) van der Waals (vdW) single crystalline ferromagnetic semiconductor (FMS) with a semiconducting bandgap of ~1.19 eV (comparable to those of Si and GaAs). The synthesized Fe:SnSe single crystals feature a dilute Fe content of less than 1.0 at%, a Curie temperature of ~310 K, a layered vdW structure identical to that of pristine SnSe, and the absence of in-gap defect states. The Fe:SnSe vdW diluted magnetic semiconductor (DMS) single crystals are grown using a simple temperature-gradient melt-growth process, in which the magnetic Fe atom doping is realized uniquely using FeI2 as the dopant precursor whose melting point is low with respect to crystal growth, and which in principle possesses industrially unlimited scalability. Our work adds a new member in the family of long-searching RT magnetic semiconductors, and may establish a generalized strategy for large-volume production of related DMSs., Comment: 25 pages, 5 figures

The nearest GRB 170817A provided an opportunity to probe the angular structure of the jet of this short gamma-ray burst (SGRB), by using its off-axis observed afterglow emission. It is investigated that whether the afterglow-constrained jet structures can be consistent with the luminosity of the prompt emission of GRB 170817A. Furthermore, by assuming that all SGRBs including GRB 170817A have the same explosive mechanism and jet structure, we apply the different jet structures into the calculation of the flux and redshfit distributions of the SGRB population, in comparison with the observational distributions of the Swift and Fermi sources. As a result, it is found that the single-Gaussian structure can be basically ruled out, whereas the power-law and two-Gaussian models can in principle survive., Comment: 9 pages,6 figures

Research demonstrates learners engaging in the process of producing explanations to support their reasoning, can have a positive impact on learning. However, providing learners real-time explanatory feedback often presents challenges related to classification accuracy, particularly in domain-specific environments, containing situationally complex and nuanced responses. We present two approaches for supplying tutors real-time feedback within an online lesson on how to give students effective praise. This work-in-progress demonstrates considerable accuracy in binary classification for corrective feedback of effective, or effort-based (F1 score = 0.811), and ineffective, or outcome-based (F1 score = 0.350), praise responses. More notably, we introduce progress towards an enhanced approach of providing explanatory feedback using large language model-facilitated named entity recognition, which can provide tutors feedback, not only while engaging in lessons, but can potentially suggest real-time tutor moves. Future work involves leveraging large language models for data augmentation to improve accuracy, while also developing an explanatory feedback interface., Comment: 12 pages Workshop paper, The 24th International Conference on Artificial Intelligence in Education, AIED 2023 Educational Dialogue Act Classification, Large Language Models, Named Entity Recognition, Tutor Training, Explanatory Feedback, Natural Language Processing

Dialogue acts (DAs) can represent conversational actions of tutors or students that take place during tutoring dialogues. Automating the identification of DAs in tutoring dialogues is significant to the design of dialogue-based intelligent tutoring systems. Many prior studies employ machine learning models to classify DAs in tutoring dialogues and invest much effort to optimize the classification accuracy by using limited amounts of training data (i.e., low-resource data scenario). However, beyond the classification accuracy, the robustness of the classifier is also important, which can reflect the capability of the classifier on learning the patterns from different class distributions. We note that many prior studies on classifying educational DAs employ cross entropy (CE) loss to optimize DA classifiers on low-resource data with imbalanced DA distribution. The DA classifiers in these studies tend to prioritize accuracy on the majority class at the expense of the minority class which might not be robust to the data with imbalanced ratios of different DA classes. To optimize the robustness of classifiers on imbalanced class distributions, we propose to optimize the performance of the DA classifier by maximizing the area under the ROC curve (AUC) score (i.e., AUC maximization). Through extensive experiments, our study provides evidence that (i) by maximizing AUC in the training process, the DA classifier achieves significant performance improvement compared to the CE approach under low-resource data, and (ii) AUC maximization approaches can improve the robustness of the DA classifier under different class imbalance ratios., Comment: 12 pages full paper, The 24th International Conference on Artificial Intelligence in Education, AIED 2023 Educational Dialogue Act Classification, Model Robustness, Low-Resource Data, Imbalanced Data, Large Language Models

Dialogue Acts (DAs) can be used to explain what expert tutors do and what students know during the tutoring process. Most empirical studies adopt the random sampling method to obtain sentence samples for manual annotation of DAs, which are then used to train DA classifiers. However, these studies have paid little attention to sample informativeness, which can reflect the information quantity of the selected samples and inform the extent to which a classifier can learn patterns. Notably, the informativeness level may vary among the samples and the classifier might only need a small amount of low informative samples to learn the patterns. Random sampling may overlook sample informativeness, which consumes human labelling costs and contributes less to training the classifiers. As an alternative, researchers suggest employing statistical sampling methods of Active Learning (AL) to identify the informative samples for training the classifiers. However, the use of AL methods in educational DA classification tasks is under-explored. In this paper, we examine the informativeness of annotated sentence samples. Then, the study investigates how the AL methods can select informative samples to support DA classifiers in the AL sampling process. The results reveal that most annotated sentences present low informativeness in the training dataset and the patterns of these sentences can be easily captured by the DA classifier. We also demonstrate how AL methods can reduce the cost of manual annotation in the AL sampling process., Comment: 12 pages full paper, The 24th International Conference on Artificial Intelligence in Education, AIED 2023

We apply the QCD sum rule method to study the double-gluon hybrid states with the quark-gluon contents $\bar q q gg$ ($q=u/d$) and $\bar s s gg$. We construct twenty-eight double-gluon hybrid currents, eleven of which are found to be zero due to some internal symmetries between the two gluons fields. We concentrate on the non-vanishing currents with the exotic quantum numbers $J^{PC} = 1^{-+}$ and $3^{-+}$. Their masses are calculated to be $M_{|\bar q q gg;1^{-+}\rangle} = 4.35^{+0.26}_{-0.30}$ GeV, $M_{|\bar s s gg;1^{-+}\rangle} = 4.49^{+0.25}_{-0.30}$ GeV, $M_{|\bar q q gg;3^{-+}\rangle} = 3.02^{+0.24}_{-0.31}$ GeV, and $M_{|\bar s s gg;3^{-+}\rangle} = 3.16^{+0.22}_{-0.28}$ GeV. The decay behaviors of the $J^{PC} = 3^{-+}$ states are studied, and we propose to search for them in the $\pi a_1(1260)/\rho \omega/\phi \phi$ channels in future particle experiments., Comment: 14 pages, 3 figures, 2 tables, slightly modified, accepted by PRD

By developing a bidirectional-pump terahertz (THz) emission spectroscopy, we reveal an anomalous Nernst effect (ANE) induced THz emission in a single ferromagnetic film. Based on the distinctive symmetry of the THz signals, ANE is unequivocally distinguished from the previously attributed ultrafast demagnetization and anomalous Hall effect mechanisms. A quantitative method is established to separate the different contributions, demonstrating a significant ANE contribution that even overwhelms other competing mechanisms. Our work not only clarifies the origin of the ferromagnetic-based THz emission, but also offers a fertile platform for investigating the ultrafast magnetism and THz spintronics.

This paper aims to experimentally and numerically probe fatigue behaviours and lifetimes of novel GLARE (glass laminate aluminium reinforced epoxy) laminates under random loading spectrum. A mixed algorithm based on fatigue damage concepts of three-phase materials was proposed for modelling progressive fatigue damage mechanisms and fatigue life of fibre metal laminates (FML) under random loading spectrum. To validate the proposed modelling algorithm, fatigue tests were conducted on the GLARE 2/1 and GLARE 3/2 laminates subjected to random loading spectrum, and fatigue mechanisms were discussed by using scanning electron microscope (SEM) analysis. It is shown that predominant fatigue failure of the GLARE laminate depends on the reference load level of random loading spectrum. Specifically, dominant fatigue failure of the GLARE laminate is dependent on fatigue strength of fibre layer at a high reference load level, but metal layer at a low reference load level. Numerical predictions agree well with experimental results, demonstrating that the proposed mixed modelling algorithm can effectively simulate fatigue behaviours and lives of the GLARE laminate under random loading spectrum.

Current work in named entity recognition (NER) uses either cross entropy (CE) or conditional random fields (CRF) as the objective/loss functions to optimize the underlying NER model. Both of these traditional objective functions for the NER problem generally produce adequate performance when the data distribution is balanced and there are sufficient annotated training examples. But since NER is inherently an imbalanced tagging problem, the model performance under the low-resource settings could suffer using these standard objective functions. Based on recent advances in area under the ROC curve (AUC) maximization, we propose to optimize the NER model by maximizing the AUC score. We give evidence that by simply combining two binary-classifiers that maximize the AUC score, significant performance improvement over traditional loss functions is achieved under low-resource NER settings. We also conduct extensive experiments to demonstrate the advantages of our method under the low-resource and highly-imbalanced data distribution settings. To the best of our knowledge, this is the first work that brings AUC maximization to the NER setting. Furthermore, we show that our method is agnostic to different types of NER embeddings, models and domains. The code to replicate this work will be provided upon request., Comment: 10 pages, 4 figures, AAAI 2023

This paper aims to numerically study the pre-fatigue, low-velocity impact (LVI) and fatigue progressive damage behaviours of a full-scale composite helicopter tail structure under multipoint coordinated loading spectrum. First, a fatigue progressive damage model (PDM) incorporating multiaxial fatigue residual strength degradation rule, fatigue failure criteria based on fatigue residual strength concept and sudden stiffness degradation rule was proposed. Then, an LVI progressive damage model for plain-weave (PW) and unidirectional (UD) composites was developed. Moreover, a full-process analysis algorithm with a reasonable damage transfer strategy for pre-fatigue, LVI and fatigue progressive damage analysis was proposed. Finally, a highly computational efficient and accurate full-scale global-local finite element (FE) model of helicopter tail structure was built to predict strain distribution under two flight working conditions, to predict LVI damage under impact loading, and to assess fatigue damage behaviours under multipoint coordinated loading spectrum. The numerical predictions agree well with test results from this work and literature data, indicating that the developed pre-fatigue, LVI, fatigue PDMs and algorithms, as well as the global-local FE modelling based on shell-to-solid coupling, can effectively analyse the impact damage tolerance of full-scale aircraft structures., Comment: 43 pages, 16 figures