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The estimation of multiple parameters is a ubiquitous requirement in many quantum metrology applications. However, achieving the ultimate precision limit, i.e. the quantum Cram\'er-Rao bound, becomes challenging in these scenarios compared to single parameter estimation. To address this issue, optimizing the parameters encoding strategies with the aid of antiunitary symmetry is a novel and comprehensive approach. For demonstration, we propose two types of quantum statistical models exhibiting antiunitary symmetry in experiments. The results showcase the simultaneous achievement of ultimate precision for multiple parameters without any trade-off and the precision is improved at least twice compared to conventional encoding strategies. Our work emphasizes the significant potential of antiunitary symmetry in addressing multi-parameter estimation problems.

A hybrid physics-machine learning modeling framework is proposed for the surface vehicles' maneuvering motions to address the modeling capability and stability in the presence of environmental disturbances. From a deep learning perspective, the framework is based on a variant version of residual networks with additional feature extraction. Initially, an imperfect physical model is derived and identified to capture the fundamental hydrodynamic characteristics of marine vehicles. This model is then integrated with a feedforward network through a residual block. Additionally, feature extraction from trigonometric transformations is employed in the machine learning component to account for the periodic influence of currents and waves. The proposed method is evaluated using real navigational data from the 'JH7500' unmanned surface vehicle. The results demonstrate the robust generalizability and accurate long-term prediction capabilities of the nonlinear dynamic model in specific environmental conditions. This approach has the potential to be extended and applied to develop a comprehensive high-fidelity simulator.

Accurate and rapid prediction of flow-fields is crucial for aerodynamic design. This work proposes a discontinuous Galerkin method (DGM) whose performance enhances with increasing data, for rapid simulation of transonic flow around airfoils under various flow conditions. A lightweight and continuously updated data-driven model is built offline to predict the roughly correct flow-field, and the DGM is then utilized to refine the detailed flow structures and provide the corrected data. During the construction of the data-driven model, a zonal proper orthogonal decomposition (POD) method is designed to reduce the dimensionality of flow-field while preserving more near-wall flow features, and a weighted-distance radial basis function (RBF) is constructed to enhance the generalization capability of flow-field prediction. Numerical results demonstrate that the lightweight data-driven model can predict the flow-field around a wide range of airfoils at Mach numbers ranging from 0.7 to 0.95 and angles of attack from -5 to 5 degrees by learning from sparse data, and maintains high accuracy of the location and essential features of flow structures (such as shock waves). In addition, the machine learning (ML) enhanced DGM is able to significantly improve the computational efficiency and simulation robustness as compared to normal DGMs in simulating transonic inviscid/viscous airfoil flow-fields on arbitrary grids, and further enables rapid aerodynamic evaluation of numerous sample points during the surrogate-based aerodynamic optimization., Comment: 32 pages, 16 figures

DarkSHINE is a newly proposed fixed-target experiment initiative to search for the invisible decay of Dark Photon via missing energy/momentum signatures, based on the high repetition rate electron beam to be deployed/delivered by the Shanghai High repetition rate XFEL and Extreme light facility (SHINE). This report elaborates the baseline design of DarkSHINE experiment by introducing the physics goals, experimental setups, details of each sub-detector system technical designs, signal and backgground modelings, expected search sensitivities and future prospects, which mark an important step towards the further prototyping and technical demonstrations.

Due to the SU(3) symmetry breaking effect, the axial-vector kaons $K_1(1270)$ and $K_1(1400)$ are established to be mixtures of two P-wave $K_{1A}\left( {^3{P_1}} \right)$ and $K_{1B}\left( {^1{P_1}} \right)$ states. In QCD sum rules, we propose a new construction of the $K_1$ current operators and calculate the two-point correlation functions by including the next-to-leading order four-quark condensates. The mixing angle is determined as $\theta = \left( {46.95_{ - 0.23}^{ + 0.25}} \right)^\circ$ by reproducing the masses of $K_1(1270)$ and $K_1(1400)$. We further compose the $K\bar K_1\left( {1270} \right)$ and $K\bar K_1\left( {1400} \right)$ interpolating currents with exotic quantum numbers $J^{PC}=1^{-+}$ to investigate the possible molecular interpretation of the recently observed ${\eta _1}(1855)$ state. We calculate the correlation functions and perform the QCD sum rule analyses for these two molecular systems. However, the spectral functions are found to be negative in physical regions so that they are not able to provide reliable investigations of the $K\bar K_1$ molecular states., Comment: 10 pages, 9 figures. More references added, some typos are corrected

In this paper, we tackle the problem of how to build and benchmark a large motion model (LMM). The ultimate goal of LMM is to serve as a foundation model for versatile motion-related tasks, e.g., human motion generation, with interpretability and generalizability. Though advanced, recent LMM-related works are still limited by small-scale motion data and costly text descriptions. Besides, previous motion benchmarks primarily focus on pure body movements, neglecting the ubiquitous motions in context, i.e., humans interacting with humans, objects, and scenes. To address these limitations, we consolidate large-scale video action datasets as knowledge banks to build MotionBank, which comprises 13 video action datasets, 1.24M motion sequences, and 132.9M frames of natural and diverse human motions. Different from laboratory-captured motions, in-the-wild human-centric videos contain abundant motions in context. To facilitate better motion text alignment, we also meticulously devise a motion caption generation algorithm to automatically produce rule-based, unbiased, and disentangled text descriptions via the kinematic characteristics for each motion. Extensive experiments show that our MotionBank is beneficial for general motion-related tasks of human motion generation, motion in-context generation, and motion understanding. Video motions together with the rule-based text annotations could serve as an efficient alternative for larger LMMs. Our dataset, codes, and benchmark will be publicly available at https://github.com/liangxuy/MotionBank.

Long text summarization, gradually being essential for efficiently processing large volumes of information, stays challenging for Large Language Models (LLMs) such as GPT and LLaMA families because of the insufficient open-sourced training datasets and the high requirement of contextual details dealing. To address the issue, we design a novel zero-shot transfer learning framework, abbreviated as T3, to iteratively training a baseline LLM on an assistant task for the target task, where the former should own richer data resources and share structural or semantic similarity with the latter. In practice, T3 is approached to deal with the long text summarization task by utilizing question answering as the assistant task, and further validated its effectiveness on the BBC summary, NarraSum, FairytaleQA, and NLQuAD datasets, with up to nearly 14% improvement in ROUGE, 35% improvement in BLEU, and 16% improvement in Factscore compared to three baseline LLMs, demonstrating its potential for more assistant-target task combinations.

The double-gluon hybrid meson configuration was recently proposed and investigated within QCD sum rules. In this talk, we discuss the color structures of the double-gluon hybrid meson and construct current operators with exotic quantum numbers $J^{PC}=1^{-+}$ and $2^{+-}$ for two of the structures. In the framework of QCD sum rules, we consider the condensates up to dimension-8 at the leading order of $\alpha_{s}$ for both charmonium and the bottomonium systems. The results indicate that the masses of the $1^{-+}$ and $2^{+-}$ charmonium double-gluon hybrid mesons are approximately $6.1-7.2$ GeV and $6.3-6.4$ GeV, respectively. As for the bottomonium systems, their masses fall within the range of $13.7-14.3$ GeV and $12.6-13.3$ GeV for the $1^{-+}$ and $2^{+-}$ channels, respectively. Additionally, the charmonium hybrids could be produced in the radiative decays of bottomonium mesons in BelleII experiment., Comment: 9 pages, 5 figures, 4 tables. Proceedings article for QCD24: 27th Hih-Energy Physics International Conference in Quantum Chromodynamis. arXiv admin note: substantial text overlap with arXiv:2403.18696

This paper studies the cooperative tracking control problem for multiple mobile robots over a directed communication network. First, it is shown that the closed-loop system is uniformly globally asymptotically stable under the proposed distributed continuous feedback control law, where an explicit strict Lyapunov function is constructed. Then, by investigating the convergence rate, it is further proven that the closed-loop system is globally $\mathcal{K}$-exponentially stable. Moreover, to make the proposed control law more practical, the distributed continuous feedback control law is generalized to a distributed sampled-data feedback control law using the emulation approach, based on the strong integral input-to-state stable Lyapunov function. Numerical simulations are presented to validate the effectiveness of the proposed control methods., Comment: 8 pages, 3 figures

Infrared ship detection (IRSD) has received increasing attention in recent years due to the robustness of infrared images to adverse weather. However, a large number of false alarms may occur in complex scenes. To address these challenges, we propose the Scene Semantic Prior-Assisted Multi-Task Perception Network (SMPISD-MTPNet), which includes three stages: scene semantic extraction, deep feature extraction, and prediction. In the scene semantic extraction stage, we employ a Scene Semantic Extractor (SSE) to guide the network by the features extracted based on expert knowledge. In the deep feature extraction stage, a backbone network is employed to extract deep features. These features are subsequently integrated by a fusion network, enhancing the detection capabilities across targets of varying sizes. In the prediction stage, we utilize the Multi-Task Perception Module, which includes the Gradient-based Module and the Scene Segmentation Module, enabling precise detection of small and dim targets within complex scenes. For the training process, we introduce the Soft Fine-tuning training strategy to suppress the distortion caused by data augmentation. Besides, due to the lack of a publicly available dataset labelled for scenes, we introduce the Infrared Ship Dataset with Scene Segmentation (IRSDSS). Finally, we evaluate the network and compare it with state-of-the-art (SOTA) methods, indicating that SMPISD-MTPNet outperforms existing approaches. The source code and dataset for this research can be accessed at https://github.com/greekinRoma/KMNDNet.

Generating human-object interactions (HOIs) is critical with the tremendous advances of digital avatars. Existing datasets are typically limited to humans interacting with a single object while neglecting the ubiquitous manipulation of multiple objects. Thus, we propose HIMO, a large-scale MoCap dataset of full-body human interacting with multiple objects, containing 3.3K 4D HOI sequences and 4.08M 3D HOI frames. We also annotate HIMO with detailed textual descriptions and temporal segments, benchmarking two novel tasks of HOI synthesis conditioned on either the whole text prompt or the segmented text prompts as fine-grained timeline control. To address these novel tasks, we propose a dual-branch conditional diffusion model with a mutual interaction module for HOI synthesis. Besides, an auto-regressive generation pipeline is also designed to obtain smooth transitions between HOI segments. Experimental results demonstrate the generalization ability to unseen object geometries and temporal compositions., Comment: Project page: https://lvxintao.github.io/himo, accepted by ECCV 2024

Weak-value amplification (WVA) is a metrological protocol that effectively amplifies ultra-small physical effects, making it highly applicable in the fields of quantum sensing and metrology. However, the amplification effect is achieved through post-selection, which leads to a significant decrease in signal intensity. Consequently, there is a heated debate regarding the trade-off between the amplification effect and the success probability of post-selection, questioning whether WVA surpasses conventional measurement (CM) in terms of measurement precision. Extensive research indicates that the specific theoretical assumptions and experimental conditions play crucial roles in determining the respective advantages of WVA and CM. WVA provides new perspectives for recognizing the important role of post-selection in precision metrology. It demonstrates significant advantages in two aspects: (i) WVA based on the phase space interaction provides feasible strategies to practically achieve the Heisenberg-scaling precision using only classical resources. (ii) WVA exhibits robustness against certain types of technical noise and imperfections of detectors. Moreover, WVA allows for various modifications to extend the applicable scope and enhance the metrological performance in corresponding situations. Despite substantial progress in recent years, the inherent connection between the advantages of WVA and its unique features remains incompletely understood. In this paper, we systematically review the recent advances in the WVA scheme, with a particular focus on the ultimate precision of WVA under diverse conditions. Our objective is to provide a comprehensive perspective on the benefits of WVA in precision measurement and facilitate the realization of its full potential., Comment: 34 pages, 15 figures

The effect of a uniform magnetic field on particle transport in low-pressure radio frequency (RF) capacitively coupled plasma (CCP) has been studied using a particle-in-cell (PIC) model. Three distinct regimes of plasma behavior can be identified as a function of the magnetic field. In the first regime at low magnetic fields, asymmetric plasma profiles are observed within the CCP chamber due to the effect of E x B drift. As the magnetic field increases, instabilities develop and form self-organized spoke-shaped structures that are distinctly seen within the bulk plasma closer to the sheath. In this second regime, the spoke-shaped coherent structures rotate inside the plasma chamber in the - E x B direction, where E and B are the DC electric and magnetic field vectors, respectively, and the DC electric field exists in the sheath and pre-sheath regions. The spoke rotation frequency is in the MHz range. As the magnetic field strength increases further, the rotating coherent spokes continue to exist near the sheath. The coherent structures are, however, accompanied by new small-scale incoherent structures originating and moving within the bulk plasma region away from the sheath. This is the third regime of plasma behavior. The threshold values of the magnetic field between these regimes were found not to vary with changing plasma reactor geometry (e.g., area ratio between ground and powered electrodes) or the use of an external capacitor between the RF-powered electrode and the RF source. The threshold values of the magnetic field between these regimes shift toward higher values with increasing gas pressure. This paper provides guidance on the upper limit of the magnetic field for instability-free operation in low-pressure CCP-based semiconductor deposition and etch systems that use the external magnetic field for plasma uniformity control., Comment: Submitted for Review to Physics of Plasmas

Quantum computational fluid dynamics (QCFD) offers a promising alternative to classical computational fluid dynamics (CFD) by leveraging quantum algorithms for higher efficiency. This paper introduces a comprehensive QCFD method, including an iterative method "Iterative-QLS" that suppresses error in quantum linear solver, and a subspace method to scale the solution to a larger size. We implement our method on a superconducting quantum computer, demonstrating successful simulations of steady Poiseuille flow and unsteady acoustic wave propagation. The Poiseuille flow simulation achieved a relative error of less than $0.2\%$, and the unsteady acoustic wave simulation solved a 5043-dimensional matrix. We emphasize the utilization of the quantum-classical hybrid approach in applications of near-term quantum computers. By adapting to quantum hardware constraints and offering scalable solutions for large-scale CFD problems, our method paves the way for practical applications of near-term quantum computers in computational science., Comment: 31 pages, 10 figures

Accurately estimating the overlap between quantum states is a fundamental task in quantum information processing. While various strategies using distinct quantum measurements have been proposed for overlap estimation, the lack of experimental benchmarks on estimation precision limits strategy selection in different situations. Here we compare the performance of four practical strategies for overlap estimation, including tomography-tomography, tomography-projection, Schur collective measurement and optical swap test. With a photonic system, the overlap-dependent estimation precision for each strategy is quantified in terms of the average estimation variance over uniformly sampled states, which highlight the different performance of different strategies. We further propose an adaptive strategy with optimized precision in full-range overlap estimation. Our results shed new light on extracting the parameter of interest from quantum systems, prompting the design of efficient quantum protocols., Comment: 30 pages, 15 figures, 1 table

We derive the discontinuities of banana integrals using the dispersion relation iteratively. We find a series of identities between the parameterized discontinuities of banana integrals (p-DOBIs). Similar to elliptic integrals, these identities enable the reduction of various p-DOBIs to be a linear combination of some fundamental ones. We present a practical application of p-DOBIs for deriving Picard-Fuchs operator. Then we establish the expression of generalized dispersion relation, which enables us to obtain the dispersion relation representation of arbitrary banana integrals. Moreover, we propose a hypothesis for generalized dispersion relation and p-DOBIs, which provides a simple way to calculate the discontinuities and transform dispersion relation representation to p-DOBIs., Comment: 7 pages, 1 figure. Published in Chinese Physics Letters

The SuperCLUE-Fin (SC-Fin) benchmark is a pioneering evaluation framework tailored for Chinese-native financial large language models (FLMs). It assesses FLMs across six financial application domains and twenty-five specialized tasks, encompassing theoretical knowledge and practical applications such as compliance, risk management, and investment analysis. Using multi-turn, open-ended conversations that mimic real-life scenarios, SC-Fin measures models on a range of criteria, including accurate financial understanding, logical reasoning, clarity, computational efficiency, business acumen, risk perception, and compliance with Chinese regulations. In a rigorous evaluation involving over a thousand questions, SC-Fin identifies a performance hierarchy where domestic models like GLM-4 and MoonShot-v1-128k outperform others with an A-grade, highlighting the potential for further development in transforming theoretical knowledge into pragmatic financial solutions. This benchmark serves as a critical tool for refining FLMs in the Chinese context, directing improvements in financial knowledge databases, standardizing financial interpretations, and promoting models that prioritize compliance, risk management, and secure practices. We create a contextually relevant and comprehensive benchmark that drives the development of AI in the Chinese financial sector. SC-Fin facilitates the advancement and responsible deployment of FLMs, offering valuable insights for enhancing model performance and usability for both individual and institutional users in the Chinese market..~\footnote{Our benchmark can be found at \url{https://www.CLUEbenchmarks.com}}., Comment: 11 pages, 19 figures, and tables

Traditional spectral imaging methods are constrained by the time-consuming scanning process, limiting the application in dynamic scenarios. One-shot spectral imaging based on reconstruction has been a hot research topic recently and the primary challenges still lie in both efficient fabrication techniques suitable for mass production and the high-speed, high-accuracy reconstruction algorithm for real-time spectral imaging. In this study, we introduce an innovative on-chip real-time hyperspectral imager that leverages nanophotonic film spectral encoders and a Massively Parallel Network (MP-Net), featuring a 4 * 4 array of compact, all-dielectric film units for the micro-spectrometers. Each curved nanophotonic film unit uniquely modulates incident light across the underlying 3 * 3 CMOS image sensor (CIS) pixels, enabling a high spatial resolution equivalent to the full CMOS resolution. The implementation of MP-Net, specially designed to address variability in transmittance and manufacturing errors such as misalignment and non-uniformities in thin film deposition, can greatly increase the structural tolerance of the device and reduce the preparation requirement, further simplifying the manufacturing process. Tested in varied environments on both static and moving objects, the real-time hyperspectral imager demonstrates the robustness and high-fidelity spatial-spectral data capabilities across diverse scenarios. This on-chip hyperspectral imager represents a significant advancement in real-time, high-resolution spectral imaging, offering a versatile solution for applications ranging from environmental monitoring, remote sensing to consumer electronics.

We revisit the masses of heavy quarkonium double-gluon hybrid mesons with exotic quantum numbers $J^{PC}=1^{-+}$ and $2^{+-}$ in the framework of the QCD sum rules. Considering the double-gluon hybrid meson operators in the octet-octet color structure, we have constructed two independent interpolating currents with $J^{PC}=1^{-+}$ and five independent currents with $J^{PC}=2^{+-}$. For the interpolating currents with antisymmetric glueball operator, there exist non-local divergences in one kind of additional Feynman diagrams of the tri-gluon condensate, which will give important contributions to the sum rule stabilities and mass predictions. We use the diagrammatic renormalization to cancel out such divergences. At the leading order of $\alpha_s$, the two-point correlation functions and spectral densities can be expressed in the analytic form of the generalized hypergeometric functions and Meijer's G-functions. After performing the numerical analysis, we predict the masses of the $1^{-+}$ and $2^{+-}$ charmonium double-gluon hybrid mesons to be around $6.1-7.2$ GeV and $6.3-6.4$ GeV, respectively. For the bottomonium systems, their masses are predicted to be $13.7-14.3$ GeV and $12.6-13.3$ GeV for the $1^{-+}$ and $2^{+-}$ channels, respectively. Besides, it is possible to hunt for these charmonium hybrids in the radiative decays of bottomonium mesons in BelleII experiment. Further investigations on these hybrid states in various theoretical and phenomenological methods are also anticipated in the future., Comment: 34 pages, 6 figures. Version to appear in JHEP

This paper proposes a novel learning-based approach for achieving exponential stabilization of nonlinear control-affine systems. We leverage the Control Contraction Metrics (CCMs) framework to co-synthesize Neural Contraction Metrics (NCMs) and Neural Network (NN) controllers. First, we transform the infinite-dimensional semi-definite program (SDP) for CCM computation into a tractable inequality feasibility problem using element-wise bounds of matrix-valued functions. The terms in the inequality can be efficiently computed by our novel algorithms. Second, we propose a free parametrization of NCMs guaranteeing positive definiteness and the satisfaction of a partial differential equation, regardless of trainable parameters. Third, this parametrization and the inequality condition enable the design of contractivity-enforcing regularizers, which can be incorporated while designing the NN controller for exponential stabilization of the underlying nonlinear systems. Furthermore, when the training loss goes to zero, we provide formal guarantees on verification of the NCM and the exponentional stabilization under the NN controller. Finally, we validate our method through benchmark experiments on set-point stabilization and increasing the region of attraction of a locally pre-stabilized closed-loop system., Comment: This paper is submitted in CDC2024 for a possible publication

Humans constantly interact with their surrounding environments. Current human-centric generative models mainly focus on synthesizing humans plausibly interacting with static scenes and objects, while the dynamic human action-reaction synthesis for ubiquitous causal human-human interactions is less explored. Human-human interactions can be regarded as asymmetric with actors and reactors in atomic interaction periods. In this paper, we comprehensively analyze the asymmetric, dynamic, synchronous, and detailed nature of human-human interactions and propose the first multi-setting human action-reaction synthesis benchmark to generate human reactions conditioned on given human actions. To begin with, we propose to annotate the actor-reactor order of the interaction sequences for the NTU120, InterHuman, and Chi3D datasets. Based on them, a diffusion-based generative model with a Transformer decoder architecture called ReGenNet together with an explicit distance-based interaction loss is proposed to predict human reactions in an online manner, where the future states of actors are unavailable to reactors. Quantitative and qualitative results show that our method can generate instant and plausible human reactions compared to the baselines, and can generalize to unseen actor motions and viewpoint changes., Comment: Accepted by CVPR 2024, Project Page: https://liangxuy.github.io/ReGenNet/

In the context of online education, designing an automatic solver for geometric problems has been considered a crucial step towards general math Artificial Intelligence (AI), empowered by natural language understanding and traditional logical inference. In most instances, problems are addressed by adding auxiliary components such as lines or points. However, adding auxiliary components automatically is challenging due to the complexity in selecting suitable auxiliary components especially when pivotal decisions have to be made. The state-of-the-art performance has been achieved by exhausting all possible strategies from the category library to identify the one with the maximum likelihood. However, an extensive strategy search have to be applied to trade accuracy for ef-ficiency. To add auxiliary components automatically and efficiently, we present deep reinforcement learning framework based on the language model, such as BERT. We firstly apply the graph attention mechanism to reduce the strategy searching space, called AttnStrategy, which only focus on the conclusion-related components. Meanwhile, a novel algorithm, named Automatically Adding Auxiliary Components using Reinforcement Learning framework (A3C-RL), is proposed by forcing an agent to select top strategies, which incorporates the AttnStrategy and BERT as the memory components. Results from extensive experiments show that the proposed A3C-RL algorithm can substantially enhance the average precision by 32.7% compared to the traditional MCTS. In addition, the A3C-RL algorithm outperforms humans on the geometric questions from the annual University Entrance Mathematical Examination of China.

Deep neural networks (DNN) are increasingly being used to learn controllers due to their excellent approximation capabilities. However, their black-box nature poses significant challenges to closed-loop stability guarantees and performance analysis. In this paper, we introduce a structured DNN-based controller for the trajectory tracking control of Lagrangian systems using backing techniques. By properly designing neural network structures, the proposed controller can ensure closed-loop stability for any compatible neural network parameters. In addition, improved control performance can be achieved by further optimizing neural network parameters. Besides, we provide explicit upper bounds on tracking errors in terms of controller parameters, which allows us to achieve the desired tracking performance by properly selecting the controller parameters. Furthermore, when system models are unknown, we propose an improved Lagrangian neural network (LNN) structure to learn the system dynamics and design the controller. We show that in the presence of model approximation errors and external disturbances, the closed-loop stability and tracking control performance can still be guaranteed. The effectiveness of the proposed approach is demonstrated through simulations.

Narrow-line Seyfert 1 galaxies (NLS1s), a subclass of active galactic nuclei (AGNs) at early stage of accretion process, are also found to host relativistic jets. However, currently known jetted NLS1s are rare. The majority of NLS1s are undetected at radio band. The radio detection rate of NLS1s raises with the LOFAR Two-metre Sky Survey (LoTSS), which gives a good opportunity to find more jetted NLS1s. The better sensitivity brings another question of whether the radio emission of NLS1s with low radio luminosity originates from jet activity. In order to clarify the origin of radio emission for NLS1s, and search for more jetted NLS1s, we explore the mid-infrared properties of LoTSS detected NLS1s by comparing them with known jetted AGNs and star forming galaxies (SFGs), which locate above and on the well studied radio/far-infrared correlation, respectively. The majority of NLS1s show mid-infrared (MIR) excess compared with SFGs. Their radio emission shows significant correlation with MIR emission. In MIR color-color diagram, NLS1s are overlapped with flat spectrum radio quasars, but well separated from SFGs and optically selected radio galaxies. The flux ratio between radio and MIR emission of these NLS1s is also similar with a radio quiet quasar with weak jet. These results imply substantial contributions from AGN activities for both radio and MIR emission of NLS1s. A small fraction of NLS1s with relatively higher radio luminosity locate in similar region with blazars in radio-MIR diagram, which suggests that the radio emission of these NLS1s is dominated by jet. We obtain a sample of jetted NLS1 candidates through their radio excess in radio-MIR diagram., Comment: Accepted for Publication in ApJ

We have studied the mass spectra of the P-wave fully charm and fully bottom tetraquark states in the framework of QCD sum rules. We construct the interpolating currents by inserting the covariant derivative operator $\overset{ \leftrightarrow } { \mathcal D }_{ \mu }$ between the S-wave diquark and antidiquark fields. The excitation structures show that the pure $\lambda$-mode excited P-wave fully heavy tetraquarks exist for the quantum numbers $J^{PC}=1^{--}, 1^{-+}, 2^{--}, 2^{-+}$ and $3^{--}$, while it is difficult to separate the $\lambda$-mode and $\rho$-mode excitations in the $0^{-+}$ channel. Within three Lorentz indices, there is no pure $\lambda$-mode excited P-wave fully charm/bottom tetraquark operators with $J^{PC}=0^{--}$ and $3^{-+}$. Our results support that the recent observed $X(6900)$ and $X(7200)$ resonances could be interpreted as the P-wave fully charm $cc \bar c \bar c$ tetraquark states with $J^{PC}=1^{-+}$ and $2^{-+}$, respectively. Some P-wave fully bottom $bb\bar b\bar b$ tetraquark states are predicted to be lower than the di-$\eta_b(1S)$ and di-$\Upsilon(1S)$ mass thresholds. Hopefully our calculations will be useful for identifying the nature of new exotic tetraquark states., Comment: 18 pages, 11 figures. Accepted by Physical Review D

We introduce SuperCLUE-Math6(SC-Math6), a new benchmark dataset to evaluate the mathematical reasoning abilities of Chinese language models. SC-Math6 is designed as an upgraded Chinese version of the GSM8K dataset with enhanced difficulty, diversity, and application scope. It consists of over 2000 mathematical word problems requiring multi-step reasoning and providing natural language solutions. We propose an innovative scheme to quantify the reasoning capability of large models based on performance over problems with different reasoning steps. Experiments on 13 representative Chinese models demonstrate a clear stratification of reasoning levels, with top models like GPT-4 showing superior performance. SC-Math6 fills the gap in Chinese mathematical reasoning benchmarks and provides a comprehensive testbed to advance the intelligence of Chinese language models., Comment: Dataset revised and finalized, results updated with new model; 8 pages, 7 figures, 4 tables

We have studied the mass spectra of doubly charmed pentaquark states in the $\Lambda _{c}^{(*)}D^{(*)}$ and $\Sigma _{c}^{(*)}D^{(*)}$ channels with $J^P=1/2^\pm$, $3/2^\pm$ and $5/2^\pm$ within the framework of QCD sum rules. We use the parity projected sum rules to separate the contributions of negative and positive parities from the two-point correlations induced by the pentaquark interpolating currents. Our results show that the bound states of $P_{cc}$ pentaquarks may exist in the $\Lambda _cD\, (\frac{1}{2}^-)$, $\Sigma _cD\, (\frac{1}{2}^-)$, $\Sigma _cD^*\, (\frac{3}{2}^-)$, $\Lambda _c^*D\, (\frac{3}{2}^-)$, $\Lambda _c^*D^*\, (\frac{5}{2}^-)$ channels with negative-parity and $\Sigma _cD\, (\frac{1}{2}^+)$, $\Sigma _cD^\ast\, (\frac{3}{2}^+)$, $\Sigma _c^\ast D\, (\frac{3}{2}^+)$ channels with positive-parity, since their masses are predicted to be lower than the corresponding meson-baryon thresholds. However, they are still allowed to decay into the $\Xi_{cc}^{(\ast)}\pi$ final states via strong interaction. The triply charged $P_{cc}^{+++}(ccuu\bar d)$ and neutral $P_{cc}^{0}(ccdd\bar u)$ in the isospin quartet would definitely be pentaquark states due to their exotic charges. We suggest searching for these characteristic doubly charmed pentaquark signals in the $P_{cc}^{+++}\to\Xi_{cc}^{(\ast) ++}\pi^+/\rho^+$, $\Sigma_c^{(\ast)++}D^{(\ast)+}$ and $P_{cc}^{0}\to\Xi_{cc}^{(\ast) +}\pi^-/\rho^-$, $\Sigma_c^{(\ast)0}D^{(\ast)0}$ decays in the near future., Comment: 25 pages, 5 figures. Accepted by Physical Review D