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Over the past five years, significant progress has been made in understanding the magnetism and electronic properties of CaAl$_2$Si$_2$-type Eu$M_2X_2$ ($M$ = Zn, Cd; $X$ = P, As) compounds. Prior theoretical work and experimental studies suggested that EuCd$_2$As$_2$ had the potential to host rich topological phases, particularly an ideal magnetic Weyl semimetal state when the spins are polarized along the c axis. However, this perspective is challenged by recent experiments utilizing samples featuring ultra-low carrier densities, as well as meticulous calculations employing various approaches. Nonetheless, the Eu$M_2X_2$ family still exhibit numerous novel properties that remain to be satisfactorily explained, such as the giant nonlinear anomalous Hall effect and the colossal magnetoresistance effect. Moreover, Eu$M_2X_2$ compounds can be transformed from semiconducting antiferromagnets to metallic ferromagnets by introducing a small number of carriers or applying external pressure, and a further increase in the ferromagnetic transition temperature can be achieved by reducing the unit cell volume. These features make the Eu$M_2X_2$ family a fertile platform for studying the interplay between magnetism and charge transport, and an excellent candidate for applications in spintronics. This paper presents a comprehensive review of the magnetic and transport behaviors of Eu$M_2X_2$ compounds with varying carrier densities, as well as the current insights into these characteristics. An outlook for future research opportunities is also provided.

We have investigated the performance of anisotropic flows $\langle v_n^2 \rangle$, transverse momentum fluctuations $\langle \delta p_T^2 \rangle $, and their correlations $\langle v_n^2 \delta p_T \rangle$ in central collisions at relativistic energies as probes of deformation parameters $\beta_n$ of colliding nuclei, if these nuclei are light nuclei with large $\beta_n$ and different configurations of $\alpha$ clusters. The effects from higher-order $\beta_n$ terms are illustrated by derived relations based on the overlap of two nuclei with uniform density distributions and by dynamic simulations of collisions of heavy nuclei whose density distributions are of a deformed Woods-Saxon (WS) form. While the linear relations between $\beta^2_n$, $\langle v_n^2 \rangle$, and $\langle \delta p_T^2 \rangle$ and that between $\beta^3_n$ and $\langle v_n^2 \delta p_T \rangle$ can be violated for extremely large $\beta_{n}$, they are mostly valid for realistic values of $\beta_n$, as long as the density distribution of colliding nuclei can be described by a deformed WS form. However, these linear relations are generally not valid with more realistic density distributions of light nuclei with $\alpha$ clusters, and the amount of deviation depends on the detailed $\alpha$-cluster configurations. Care must be taken when one tries to extract the deformation of light nuclei, and specific probes for $\alpha$-cluster structures in these nuclei are very much needed., Comment: 15 pages, 7 figures

Human-Object Interaction (HOI) detection is a fundamental task in image understanding. While deep-learning-based HOI methods provide high performance in terms of mean Average Precision (mAP), they are computationally expensive and opaque in training and inference processes. An Efficient HOI (EHOI) detector is proposed in this work to strike a good balance between detection performance, inference complexity, and mathematical transparency. EHOI is a two-stage method. In the first stage, it leverages a frozen object detector to localize the objects and extract various features as intermediate outputs. In the second stage, the first-stage outputs predict the interaction type using the XGBoost classifier. Our contributions include the application of error correction codes (ECCs) to encode rare interaction cases, which reduces the model size and the complexity of the XGBoost classifier in the second stage. Additionally, we provide a mathematical formulation of the relabeling and decision-making process. Apart from the architecture, we present qualitative results to explain the functionalities of the feedforward modules. Experimental results demonstrate the advantages of ECC-coded interaction labels and the excellent balance of detection performance and complexity of the proposed EHOI method.

As a fundamental task in natural language processing, word embedding converts each word into a representation in a vector space. A challenge with word embedding is that as the vocabulary grows, the vector space's dimension increases and it can lead to a vast model size. Storing and processing word vectors are resource-demanding, especially for mobile edge-devices applications. This paper explores word embedding dimension reduction. To balance computational costs and performance, we propose an efficient and effective weakly-supervised feature selection method, named WordFS. It has two variants, each utilizing novel criteria for feature selection. Experiments conducted on various tasks (e.g., word and sentence similarity and binary and multi-class classification) indicate that the proposed WordFS model outperforms other dimension reduction methods at lower computational costs.

Blind Image Quality Assessment (BIQA) is an essential task that estimates the perceptual quality of images without reference. While many BIQA methods employ deep neural networks (DNNs) and incorporate saliency detectors to enhance performance, their large model sizes limit deployment on resource-constrained devices. To address this challenge, we introduce a novel and non-deep-learning BIQA method with a lightweight saliency detection module, called Green Saliency-guided Blind Image Quality Assessment (GSBIQA). It is characterized by its minimal model size, reduced computational demands, and robust performance. Experimental results show that the performance of GSBIQA is comparable with state-of-the-art DL-based methods with significantly lower resource requirements.

Materials that exhibit strongly coupled magnetic order and electronic properties are crucial for both fundamental research and technological applications. However, finding a material that not only shows remarkable magnetoresistive responses but also has an easily tunable ground state remains a challenge. Here, we report successful manipulation of the magnetic and transport properties of EuCd$_2$P$_2$, which is transformed from an A-type antiferromagnet ($T_\mathrm{N}$ = 11 K) exhibiting colossal magnetoresistance into a ferromagnet ($T_\mathrm{C}$ = 47 K) with metallic behavior. The dramatic alteration results from a low hole concentration of $10^{19}$ cm$^{-3}$ induced by changing the growth conditions. Electronic structure and total energy calculations confirm the tunability of magnetism with a small carrier concentration for EuCd$_2$P$_2$. It is feasible to switch between the magnetic states by using field-effect to control the carrier density, thereby changing the magneto-electronic response. The controllable magnetism and electrical transport of EuCd$_2$P$_2$ make it a potential candidate for spintronics.

EuZn$_2$P$_2$ was reported to be an insulating antiferromagnet with $T_\mathrm{N}$ of 23.5 K. In this study, single crystals of EuZn$_2$P$_2$ exhibiting metallic behavior and a ferromagnetic order of 72 K ($T_\mathrm{C}$) are successfully synthesized via a salt flux method. The presence of hole carriers induced by the Eu vacancies in the lattice is found to be crucial for the drastic changes in magnetism and electrical transport. The carriers mediate the interlayer ferromagnetic interaction, and the coupling strength is directly related to $T_\mathrm{C}$, as evidenced by the linear dependence of $T_\mathrm{C}$ and the fitted Curie-Weiss temperatures on the Eu-layer distances for ferromagnetic Eu$M_2X_2$ ($M$ = Zn, Cd; $X$ = P, As). The ferromagnetic EuZn$_2$P$_2$ shows conspicuous negative magnetoresistance (MR) near $T_\mathrm{C}$, owing to strong magnetic scattering. The MR behavior is consistent with the Majumdar-Littlewood model, indicating that the MR can be enhanced by decreasing the carrier density. Our findings suggest that Eu$M_2X_2$ has highly tunable magnetism and charge transport, making it a promising material family for potential applications in spintronics.

Automatic pronunciation assessment (APA) manages to evaluate the pronunciation proficiency of a second language (L2) learner in a target language. Existing efforts typically draw on regression models for proficiency score prediction, where the models are trained to estimate target values without explicitly accounting for phoneme-awareness in the feature space. In this paper, we propose a contrastive phonemic ordinal regularizer (ConPCO) tailored for regression-based APA models to generate more phoneme-discriminative features while considering the ordinal relationships among the regression targets. The proposed ConPCO first aligns the phoneme representations of an APA model and textual embeddings of phonetic transcriptions via contrastive learning. Afterward, the phoneme characteristics are retained by regulating the distances between inter- and intra-phoneme categories in the feature space while allowing for the ordinal relationships among the output targets. We further design and develop a hierarchical APA model to evaluate the effectiveness of our method. Extensive experiments conducted on the speechocean762 benchmark dataset suggest the feasibility and efficacy of our approach in relation to some cutting-edge baselines., Comment: This paper has been withdrawn because the authors aim to achieve better organization in writing and more detailed experimental analysis

We systematically investigate the scaling behavior of the disorder operator and the entanglement entropy (EE) of the easy-plane JQ (EPJQ) model at its transitions between the antiferromagnetic XY ordered phase (AFXY) and the valence bond solid (VBS) phase. We find $\mathbf{(1)}$ there exists a tiny yet finite value of the order parameters at the AFXY-VBS phase transition points of the EPJQ model, and the finite order parameter is strengthened as anisotropy $\Delta$ varies from the Heisenberg limit ($\Delta=1$) to the easy-plane limit ($\Delta=0$); $\mathbf{(2)}$ Both EE and disorder operator with smooth boundary cut exhibit anomalous scaling behavior at the transition points, resembling the scaling inside the Goldstone model phase, and the anomalous scaling becomes strengthened as the transition becomes more first order; $\mathbf{(3)}$ First put forward in Ref. [arXiv:2401.12838], with the finite-size corrections in EE for Goldstone phase is properly considered in the fitting form, the anomalous scaling behavior of EE can be adapted with emergent SO(5) symmetry breaking at the Heisenberg limit ($\Delta=1$). We extend this method in the EPJQ model and observe similar yet weaker results, which may indicate emergent SO(4) symmetry breaking in the easy-plane regime ($\Delta<1$) or emergent SO(5) symmetry breaking in the Heisenberg limit ($\Delta=1$). These observations provide evidence that the N\'eel-VBS transition in the JQ model setting evolves from weak to prominent first-order transition as the system becomes anisotropic, and the non-local probes such as EE and disorder operator, serve as the sensitive tool to detect such salient yet fundamental features.

Owing to the broad energy coverage of Insight-HXMT in the hard X-ray band, we detected the highest energy of pulsation exceeding 200 keV around the 2017-2018 outburst peak of the first Galactic pulsating ultraluminous X-ray source (PULX) Swift J0243.6+6124, which is the highest energy detected from PULXs to date. We also obtained the highest energy of pulsation of every exposure during the outburst in 2017-2018, and found the highest energy is roughly positively correlated with luminosity. Using our newly developed method, we identified the critical luminosity being $4\times 10^{38}\, \rm erg\,s^{-1}$ when the main peaks of the low and high energy pulse profiles became aligned, which separates the fan-beam dominated and pencil-beam dominated accretion regimes. Above the critical luminosity, the phase of the main peak shifted gradually from 0.5 to 0.8 until the outburst peak in all energy bands is reached, which is in agreement with the phase shift found previously at low energies. Our result is consistent with what is derived from spectral analysis., Comment: 9 pages, 9 figures, published in RAA

We design a (2+1)D quantum spin model in which spin-1/2 ladders are coupled through antiferromagnetic Ising interactions. The model hosts a quantum phase transition in the (2+1)D $Z_2$ universality class from the Haldane phase to the antiferromagnetic Ising ordered phase. We focus on studying the surface properties of three different surface configurations when the Ising couplings are tuned. Different behaviors are found on different surfaces. We find ordinary and two different extraordinary surface critical behaviors (SCBs) at the bulk critical point. The ordinary SCBs belong to the surface universality class of the classical 3D Ising bulk transition. One extraordinary SCBs is induced by the topological properties of the Haldane phase. Another extraordinary SCBs at the bulk critical point is induced by an unconventional surface phase transition where the surface develops an Ising order before the bulk. This surface transition is realized by coupling a (1+1)D $SU(2)_1$ CFT boundary to a (2+1)D bulk with $Z_2$ symmetry. We find that the transition is neither a (1+1)D $Z_2$ transition, expected based on symmetry consideration, nor a Kosterlitz-Thouless-like transition, violating the previous theoretical prediction. This new surface phase transition and related extraordinary SCBs deserve further analytical and numerical exploration., Comment: 11 pages, 12 figures

Mitigating bias in machine learning models is a critical endeavor for ensuring fairness and equity. In this paper, we propose a novel approach to address bias by leveraging pixel image attributions to identify and regularize regions of images containing significant information about bias attributes. Our method utilizes a model-agnostic approach to extract pixel attributions by employing a convolutional neural network (CNN) classifier trained on small image patches. By training the classifier to predict a property of the entire image using only a single patch, we achieve region-based attributions that provide insights into the distribution of important information across the image. We propose utilizing these attributions to introduce targeted noise into datasets with confounding attributes that bias the data, thereby constraining neural networks from learning these biases and emphasizing the primary attributes. Our approach demonstrates its efficacy in enabling the training of unbiased classifiers on heavily biased datasets., Comment: Accepted to the 2024 IEEE CVPR Workshop on Fair, Data-efficient, and Trusted Computer Vision

Image saliency detection is crucial in understanding human gaze patterns from visual stimuli. The escalating demand for research in image saliency detection is driven by the growing necessity to incorporate such techniques into various computer vision tasks and to understand human visual systems. Many existing image saliency detection methods rely on deep neural networks (DNNs) to achieve good performance. However, the high computational complexity associated with these approaches impedes their integration with other modules or deployment on resource-constrained platforms, such as mobile devices. To address this need, we propose a novel image saliency detection method named GreenSaliency, which has a small model size, minimal carbon footprint, and low computational complexity. GreenSaliency can be a competitive alternative to the existing deep-learning-based (DL-based) image saliency detection methods with limited computation resources. GreenSaliency comprises two primary steps: 1) multi-layer hybrid feature extraction and 2) multi-path saliency prediction. Experimental results demonstrate that GreenSaliency achieves comparable performance to the state-of-the-art DL-based methods while possessing a considerably smaller model size and significantly reduced computational complexity.

End-to-end automatic speech recognition (E2E ASR) systems often suffer from mistranscription of domain-specific phrases, such as named entities, sometimes leading to catastrophic failures in downstream tasks. A family of fast and lightweight named entity correction (NEC) models for ASR have recently been proposed, which normally build on phonetic-level edit distance algorithms and have shown impressive NEC performance. However, as the named entity (NE) list grows, the problems of phonetic confusion in the NE list are exacerbated; for example, homophone ambiguities increase substantially. In view of this, we proposed a novel Description Augmented Named entity CorrEctoR (dubbed DANCER), which leverages entity descriptions to provide additional information to facilitate mitigation of phonetic confusion for NEC on ASR transcription. To this end, an efficient entity description augmented masked language model (EDA-MLM) comprised of a dense retrieval model is introduced, enabling MLM to adapt swiftly to domain-specific entities for the NEC task. A series of experiments conducted on the AISHELL-1 and Homophone datasets confirm the effectiveness of our modeling approach. DANCER outperforms a strong baseline, the phonetic edit-distance-based NEC model (PED-NEC), by a character error rate (CER) reduction of about 7% relatively on AISHELL-1 for named entities. More notably, when tested on Homophone that contain named entities of high phonetic confusion, DANCER offers a more pronounced CER reduction of 46% relatively over PED-NEC for named entities., Comment: Accepted by LREC-COLING 2024

Manually designing cloze test consumes enormous time and efforts. The major challenge lies in wrong option (distractor) selection. Having carefully-design distractors improves the effectiveness of learner ability assessment. As a result, the idea of automatically generating cloze distractor is motivated. In this paper, we investigate cloze distractor generation by exploring the employment of pre-trained language models (PLMs) as an alternative for candidate distractor generation. Experiments show that the PLM-enhanced model brings a substantial performance improvement. Our best performing model advances the state-of-the-art result from 14.94 to 34.17 (NDCG@10 score). Our code and dataset is available at https://github.com/AndyChiangSH/CDGP., Comment: Findings of short paper, EMNLP 2022

With the massive developments of end-to-end (E2E) neural networks, recent years have witnessed unprecedented breakthroughs in automatic speech recognition (ASR). However, the codeswitching phenomenon remains a major obstacle that hinders ASR from perfection, as the lack of labeled data and the variations between languages often lead to degradation of ASR performance. In this paper, we focus exclusively on improving the acoustic encoder of E2E ASR to tackle the challenge caused by the codeswitching phenomenon. Our main contributions are threefold: First, we introduce a novel disentanglement loss to enable the lower-layer of the encoder to capture inter-lingual acoustic information while mitigating linguistic confusion at the higher-layer of the encoder. Second, through comprehensive experiments, we verify that our proposed method outperforms the prior-art methods using pretrained dual-encoders, meanwhile having access only to the codeswitching corpus and consuming half of the parameterization. Third, the apparent differentiation of the encoders' output features also corroborates the complementarity between the disentanglement loss and the mixture-of-experts (MoE) architecture., Comment: ICASSP 2024

The non-Hermitian one-photon and two-photon quantum Rabi models (QRMs) with imaginary couplings are respectively solved through the Bogoliubov operators approach. Transcendental functions responsible for exact solutions are derived, whose zeros produce the complete spectra. Exceptional points (EPs) can be identified in terms of the transcendental function. The EP is formed in the two nearest-neighboring excited energy levels, and shifts towards lower coupling strength at higher energy levels. Interestingly, under the resonant condition in the non-Hermitian two-photon QRM, the lowest two excited states within the same parity in the even photonic number subspace coalesce at an EP, but take always the purely real energy, in sharp contrast to the conventional EP in the non-Hermitian systems. For both non-Hermitian QRMs, the fidelity susceptibility goes to negative infinity at the EPs, consistent with the recent observations in non-Hermitian systems. All eigenstates can be labeled by the conserved energy and the QRM parity, we argue that the non-Hermitian QRMs are also integrable, similar to their Hermitian counterparts., Comment: 8 pages, 9 figures

The decay of sunspot plays a key role in magnetic flux transportation in solar active regions (ARs). To better understand the physical mechanism of the entire decay process of a sunspot, an {\alpha}-configuration sunspot in AR NOAA 12411 was studied. Based on the continuum intensity images and vector magnetic field data with stray light correction from Solar Dynamics Observatory/Helioseismic and Magnetic Imager, the area, vector magnetic field and magnetic flux in the umbra and penumbra are calculated with time, respectively. Our main results are as follows: (1) The decay curves of the sunspot area in its umbra, penumbra, and whole sunspot take the appearance of Gaussian profiles. The area decay rates of the umbra, penumbra and whole sunspot are -1.56 MSH/day, -12.61 MSH/day and -14.04 MSH/day, respectively; (2) With the decay of the sunspot, the total magnetic field strength and the vertical component of the penumbra increase, and the magnetic field of the penumbra becomes more vertical. Meanwhile, the total magnetic field strength and vertical magnetic field strength for the umbra decrease, and the inclination angle changes slightly with an average value of about 20{\deg}; (3) The magnetic flux decay curves of the sunspot in its umbra, penumbra, and whole sunspot exhibit quadratic patterns, their magnetic flux decay rates of the umbra, penumbra and whole sunspot are -9.84 * 10^19 Mx/day, -1.59 * 10^20 Mx/day and -2.60 * 10^20 Mx/day , respectively. The observation suggests that the penumbra may be transformed into the umbra, resulting in the increase of the average vertical magnetic field strength and the reduction of the inclination angle in the penumbra during the decay of the sunspot.

We extend the Single Mode Approximation (SMA) into quantum Monte Carlo (QMC) simulations to provides an efficient and fast method to obtain the dynamical dispersion of quantum many-body systems. Based on Stochastic Series Expansion (SSE) and its projector algorithms, The SMA + SSE method can simply extract the dispersion of the dynamical spectrum in the long wave-length limit and the upper bound of the dispersion elsewhere, without external calculations and high technique barriers. Meanwhile, numerical analytic continuation methods require the fine data of imaginary time correlations and complex programming. Therefore, our method can approach the excitation dispersion of large systems, e.g., we take the two-dimensional Heisenberg model on a $512 \times 512$ square lattice. We demonstrate the effectiveness and efficiency of our method with high precision via additional examples. We also demonstrate that SMA combined with SSE goes beyond spin-wave theory with numerical results.

Two dimensional (2D) semiconductors present unique opportunities to intertwine optical and magnetic functionalities and to tune these performances through defects and dopants. Here, we integrate exciton pumping into a quantum sensing protocol on nitrogen-vacancy centers in diamond to image the optically-induced transient stray fields in few-layer, antiferromagnetic CrCl$_3$. We discover that exciton recombination enhances the in-plane magnetization of the CrCl$_3$ layers, with a predominant effect in the surface monolayers. Concomitantly, time-resolved photoluminescence measurements reveal that nonradiative exciton recombination intensifies in atomically thin CrCl$_3$ with tightly localized, nearly dipole-forbidden excitons and amplified surface-to-volume ratio. Supported by experiments under controlled surface exposure and density functional theory calculations, we interpret the magnetically enhanced state to result from a defect-assisted Auger recombination that optically activates electron transfer between water vapor related surface impurities and the spin-polarized conduction band. Our work validates defect engineering as a route to enhance intrinsic magnetism in single magnetic layers and opens a novel experimental platform for studying optically-induced, transient magnetism in condensed matter systems., Comment: 11 pages, 8 figures

The disorder operator is often designed to reveal the conformal field theory information in quantum many-body systems. By using large-scale quantum Monte Carlo simulation, we study the scaling behavior of disorder operators on the boundary in the two-dimensional Heisenberg model on the square-octagon lattice with gapless topological edge state. In the Affleck-Kennedy-Lieb-Tasaki phase, the disorder operator is shown to hold the perimeter scaling with a logarithmic term associated with the Luttinger liquid parameter K. This effective Luttinger liquid parameter K reflects the low-energy physics and CFT for (1+1)D boundary. At bulk critical point, the effective K is suppressed but keeps finite value, indicating the coupling between the gapless edge state and bulk fluctuation. The logarithmic term numerically captures this coupling picture, which reveals the (1+1)D SU(2)1 CFT and (2+1)D O(3) CFT at boundary criticality. Our Letter paves a new way to study the exotic boundary state and boundary criticality., Comment: 8 Pages,7 figures

In the study of optical properties of large atomic system, a weak laser driving is often assumed to simplify the system dynamics by linearly coupled equations. Here, we investigate the light scattering properties of atomic ensembles beyond weak-field excitation through the cumulant expansion method. By progressively incorporating higher-order correlations into the steady-state equations, an enhanced accuracy can be achieved in comparison to the exact solutions from solving a full density matrix. Our analysis reveals that, in the regime of weak dipole-dipole interaction (DDI), the first-order expansion yields satisfactory predictions for optical depth, while denser atomic configurations necessitate consideration of higher-order correlations. As the intensity of incident light increases, atom saturation effects become noticeable, giving rise to significant changes in light transparency, energy shift, and decay rate. This saturation phenomenon extends to subradiant atom arrays even under weak driving conditions, leading to substantial deviations from the linear model. Our findings demonstrate the mean-field models as good extensions to linear models as it balances both accuracy and computational complexity. However, the crucial role of higher-order cumulants in large and dense atom systems remains unclear, since it is challenging theoretically owing to the exponentially increasing Hilbert space in such light-matter interacting systems., Comment: 5 figures

We investigate the role of superconducting phase fluctuations in generic 2D superconductors. For nodal d-wave superconductors, it is found that the thermal (static) phase fluctuations in the normal state significantly broaden the d-wave nodes, and lead to the pseudogap and accompanying Fermi arcs in a quite general manner. The formation of Fermi arcs can be depicted by the intertwinement between d-wave superconductivity and the scattering of Cooper pairs. To support our theoretical findings, we numerically report the observation of Fermi arcs in a concrete lattice model, proposed originally by X. Y. Xu and T. Grover in Phys. Rev. Lett. 126, 217002 (2021), using the determinant quantum Monte Carlo (DQMC) method without the infamous sign problem. As far as we noticed, it is the first time in a highly-correlated model that the Fermi arcs are identified with unbiased DQMC simulations. Despite the conciseness of our theory, our simulation results confirm the theoretical predictions qualitatively., Comment: 6 pages, 5 figures for the main text; 8 pages, 5 figures for the supplementary material

Optimization problems are the core challenge in many fields of science and engineering, yet general and effective methods are scarce for searching optimal solutions. Quantum computing has been envisioned to help solve such problems, for example, the quantum annealing (QA) method based on adiabatic evolution has been extensively explored and successfully implemented on quantum simulators such as D-wave's annealers and some Rydberg arrays. In this work, we investigate topological sector optimization (TSO) problem, which attracts particular interests in the quantum many-body physics community. We reveal that the topology induced by frustration in the spin model is an intrinsic obstruction for QA and other traditional methods to approach the ground state. We demonstrate that the optimization difficulties of TSO problem are not restricted to the gaplessness, but are also due to the topological nature which are often ignored for the analysis of optimization problems before. To solve TSO problems, we utilize quantum imaginary time evolution (QITE) with a possible realization on quantum computers, which exploits the property of quantum superposition to explore the full Hilbert space and can thus address optimization problems of topological nature. We report the performance of different quantum optimization algorithms on TSO problems and demonstrate that their capability to address optimization problems are distinct even when considering the quantum computational resources required for practical QITE implementations., Comment: 11 pages, 11 figures

Automatic pronunciation assessment (APA) manages to quantify the pronunciation proficiency of a second language (L2) learner in a language. Prevailing approaches to APA normally leverage neural models trained with a regression loss function, such as the mean-squared error (MSE) loss, for proficiency level prediction. Despite most regression models can effectively capture the ordinality of proficiency levels in the feature space, they are confronted with a primary obstacle that different phoneme categories with the same proficiency level are inevitably forced to be close to each other, retaining less phoneme-discriminative information. On account of this, we devise a phonemic contrast ordinal (PCO) loss for training regression-based APA models, which aims to preserve better phonemic distinctions between phoneme categories meanwhile considering ordinal relationships of the regression target output. Specifically, we introduce a phoneme-distinct regularizer into the MSE loss, which encourages feature representations of different phoneme categories to be far apart while simultaneously pulling closer the representations belonging to the same phoneme category by means of weighted distances. An extensive set of experiments carried out on the speechocean762 benchmark dataset suggest the feasibility and effectiveness of our model in relation to some existing state-of-the-art models., Comment: Accepted by ASRU 2023