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Inspired by the newly observed $T_{c\bar{s}}$ state in the $D_{s1}(2460)^{+} \rightarrow D_{s}^{+}\pi^{+}\pi^{-}$ reaction by the LHCb Collaboration, we investigate the amplitude in this decay to explore the origin and properties of this open-charm tetraquark state based on the final state interaction. The invariant mass distributions of $D_{s}^{+}\pi^{+}$ and $\pi^{+}\pi^{-}$ are well reproduced by the $S$-wave scattering amplitudes of the coupled channel systems $D_{s}\pi$ and $\pi\pi$. However, either only the tree-level contribution nor both the tree-level and $\pi\pi$ coupled channel scattering contributions can not to describe the experimental data well, which indicates that the $D_{s}\pi$ coupled channel scattering is required. We find the $T_{c\bar{s}}$ and $f_{0}(500)$ resonances are dynamically generated from the pseudoscalar-pseudoscalar meson interaction within the chiral unitary approach. In addition, we find the corresponding poles and also calculate the $S$-wave scattering length for the $DK$ channel, which is on the same scale as the result extracted from the experiment. We propose that the $T_{c\bar{s}}$ and $f_{0}(500)$ resonances in the $D_{s1}(2460)^{+} \rightarrow D_{s}^{+}\pi^{+}\pi^{-}$ reaction are molecular type particles.

Recent advances in foundation models, particularly Large Language Models (LLMs) and Multimodal Large Language Models (MLLMs), facilitate intelligent agents being capable of performing complex tasks. By leveraging the ability of (M)LLMs to process and interpret Graphical User Interfaces (GUIs), these agents can autonomously execute user instructions by simulating human-like interactions such as clicking and typing. This survey consolidates recent research on (M)LLM-based GUI agents, highlighting key innovations in data, frameworks, and applications. We begin by discussing representative datasets and benchmarks. Next, we summarize a unified framework that captures the essential components used in prior research, accompanied by a taxonomy. Additionally, we explore commercial applications of (M)LLM-based GUI agents. Drawing from existing work, we identify several key challenges and propose future research directions. We hope this paper will inspire further developments in the field of (M)LLM-based GUI agents.

With the rapid development of marine engineering projects such as marine resource extraction and oceanic surveys, underwater visual imaging and analysis has become a critical technology. Unfortunately, due to the inevitable non-linear attenuation of light in underwater environments, underwater images and videos often suffer from low contrast, blurriness, and color degradation, which significantly complicate the subsequent research. Existing underwater image enhancement methods often treat the haze and color cast as a unified degradation process and disregard their independence and interdependence, which limits the performance improvement. Here, we propose a Vision Transformer (ViT)-based network (referred to as WaterFormer) to improve the underwater image quality. WaterFormer contains three major components: a dehazing block (DehazeFormer Block) to capture the self-correlated haze features and extract deep-level features, a Color Restoration Block (CRB) to capture self-correlated color cast features, and a Channel Fusion Block (CFB) to capture fusion features within the network. To ensure authenticity, a soft reconstruction layer based on the underwater imaging physics model is included. To improve the quality of the enhanced images, we introduce the Chromatic Consistency Loss and Sobel Color Loss to train the network. Comprehensive experimental results demonstrate that WaterFormer outperforms other state-of-the-art methods in enhancing underwater images.

Function calling significantly extends the application boundary of large language models, where high-quality and diverse training data is critical for unlocking this capability. However, real function-calling data is quite challenging to collect and annotate, while synthetic data generated by existing pipelines tends to lack coverage and accuracy. In this paper, we present ToolACE, an automatic agentic pipeline designed to generate accurate, complex, and diverse tool-learning data. ToolACE leverages a novel self-evolution synthesis process to curate a comprehensive API pool of 26,507 diverse APIs. Dialogs are further generated through the interplay among multiple agents, guided by a formalized thinking process. To ensure data accuracy, we implement a dual-layer verification system combining rule-based and model-based checks. We demonstrate that models trained on our synthesized data, even with only 8B parameters, achieve state-of-the-art performance on the Berkeley Function-Calling Leaderboard, rivaling the latest GPT-4 models. Our model and a subset of the data are publicly available at https://huggingface.co/Team-ACE., Comment: 21 pages, 22 figures

In this work, we study the flavor-changing neutral-current process $B_{c}\to{D}_{s}^{*}(\to{D}_{s}\pi)\ell^{+}\ell^{-}$ ($\ell$= $e$, $\mu$, $\tau$). The relevant weak transition form factors are obtained by using the covariant light-front quark model, in which, the main inputs, i.e., the meson wave functions of $B_{c}$ and $D_{s}^{*}$, are adopted as the numerical wave functions from the solution of the Schr\"{o}dinger equation with the modified Godfrey-Isgur model. With the obtained form factors, we further investigate the relevant branching fractions and their ratios, and some angular observables, i.e., the forward-backward asymmetry $A_{FB}$, the polarization fractions $F_{L(T)}$, and the $CP$-averaged angular coefficients $S_{i}$ and the $CP$ asymmetry coefficients $A_{i}$. We also present our results of the clean angular observables $P_{1,2,3}$ and $P^{\prime}_{4,5,6,8}$, which can reduce the uncertainties from the form factors. Our results show that the corresponding branching fractions of the electron or muon channels can reach up to $10^{-8}$. With more data being accumulated in the LHCb experiment, our results are helpful for exploring this process, and deepen our understanding of the physics around the $b\to{s}\ell^{+}\ell^{-}$ process., Comment: 25 pages, 8 tables and 9 figures

In this work, we investigate the spectroscopy of higher $B_c$ mesons, with a special focus on the consideration of the unquenched effects. To account for such effects, we employ the modified Godfrey-Isgur model and introduce a screening potential. The resulting mass spectrum of the concerned higher $B_c$ states is then presented, showing significant deviations after considering the unquenched effects. This emphasizes the importance of considering the unquenched effects when studying of the higher $B_c$ mesons. Furthermore, we determine the corresponding spatial wave functions of these $B_c$ mesons, which have practical applications in subsequent studies of their decays. These decays include two-body Okuba-Zweig-Iizuka allowed strong decays, dipion transitions between $B_c$ mesons, radiative decays, and some typical weak decays. With the ongoing high-luminosity upgrade of the Large Hadron Collider, we expect the discovery of additional $B_c$ states in the near future. The knowledge gained from the mass spectrum and the different decay modes will undoubtedly provide valuable insights for future experimental explorations of these higher $B_c$ mesons., Comment: 30 pages, 7 figures and 17 tables

Cross-lingual transfer is important for developing high-quality chatbots in multiple languages due to the strongly imbalanced distribution of language resources. A typical approach is to leverage off-the-shelf machine translation (MT) systems to utilize either the training corpus or developed models from high-resource languages. In this work, we investigate whether it is helpful to utilize MT at all in this task. To do so, we simulate a low-resource scenario assuming access to limited Chinese dialog data in the movie domain and large amounts of English dialog data from multiple domains. Experiments show that leveraging English dialog corpora can indeed improve the naturalness, relevance and cross-domain transferability in Chinese. However, directly using English dialog corpora in its original form, surprisingly, is better than using its translated version. As the topics and wording habits in daily conversations are strongly culture-dependent, MT can reinforce the bias from high-resource languages, yielding unnatural generations in the target language. Considering the cost of translating large amounts of text and the strong effects of the translation quality, we suggest future research should rather focus on utilizing the original English data for cross-lingual transfer in dialog generation. We perform extensive human evaluations and ablation studies. The analysis results, together with the collected dataset, are presented to draw attention towards this area and benefit future research.

The space-air-ground integrated network (SAGIN), one of the key technologies for next-generation mobile communication systems, can facilitate data transmission for users all over the world, especially in some remote areas where vast amounts of informative data are collected by Internet of remote things (IoRT) devices to support various data-driven artificial intelligence (AI) services. However, training AI models centrally with the assistance of SAGIN faces the challenges of highly constrained network topology, inefficient data transmission, and privacy issues. To tackle these challenges, we first propose a novel topology-aware federated learning framework for the SAGIN, namely Olive Branch Learning (OBL). Specifically, the IoRT devices in the ground layer leverage their private data to perform model training locally, while the air nodes in the air layer and the ring-structured low earth orbit (LEO) satellite constellation in the space layer are in charge of model aggregation (synchronization) at different scales.To further enhance communication efficiency and inference performance of OBL, an efficient Communication and Non-IID-aware Air node-Satellite Assignment (CNASA) algorithm is designed by taking the data class distribution of the air nodes as well as their geographic locations into account. Furthermore, we extend our OBL framework and CNASA algorithm to adapt to more complex multi-orbit satellite networks. We analyze the convergence of our OBL framework and conclude that the CNASA algorithm contributes to the fast convergence of the global model. Extensive experiments based on realistic datasets corroborate the superior performance of our algorithm over the benchmark policies., Comment: accepted by IEEE Transactions on Wireless Communications, Dec. 2022

We calculate the form factors of the $\Lambda_b\to\Lambda_c(2625)$ and $\Xi_b\to\Xi_c(2815)$ transitions, and additionally evaluate the corresponding semileptonic decays and the color-allowed two-body nonleptonic decays. In order to obtain the concerned form factors, we use the three-body light-front quark model with the support from baryon spectroscopy. In this work, as important physical inputs, the spatial wave functions of concerned baryons are obtained by the Gaussian expansion method with a semirelativistic potential model. For the semileptonic processes, the branching ratios of the electron and muon channels can reach up to the order of magnitude of $1\%$, where our result $\mathcal{B}(\Lambda_b^0\to\Lambda_c^+(2625)\mu^-\nu_{\mu})=(1.641\pm0.113)\%$ is consistent with the current experimental data. As for the nonleptonic processes, the decays to $\pi^-$, $\rho^-$, and $D_s^{(*)-}$ final states have considerable widths. These discussed decay modes could be accessible at the LHCb experiment., Comment: 18 pages, 7 tables and 6 figures. More discussions added. Accepted by Phys. Rev. D

Edge intelligence has arisen as a promising computing paradigm for supporting miscellaneous smart applications that rely on machine learning techniques. While the community has extensively investigated multi-tier edge deployment for traditional deep learning models (e.g. CNNs, RNNs), the emerging Graph Neural Networks (GNNs) are still under exploration, presenting a stark disparity to its broad edge adoptions such as traffic flow forecasting and location-based social recommendation. To bridge this gap, this paper formally studies the cost optimization for distributed GNN processing over a multi-tier heterogeneous edge network. We build a comprehensive modeling framework that can capture a variety of different cost factors, based on which we formulate a cost-efficient graph layout optimization problem that is proved to be NP-hard. Instead of trivially applying traditional data placement wisdom, we theoretically reveal the structural property of quadratic submodularity implicated in GNN's unique computing pattern, which motivates our design of an efficient iterative solution exploiting graph cuts. Rigorous analysis shows that it provides parameterized constant approximation ratio, guaranteed convergence, and exact feasibility. To tackle potential graph topological evolution in GNN processing, we further devise an incremental update strategy and an adaptive scheduling algorithm for lightweight dynamic layout optimization. Evaluations with real-world datasets and various GNN benchmarks demonstrate that our approach achieves superior performance over de facto baselines with more than 95.8% cost eduction in a fast convergence speed., Comment: 19 pages, 20 figures, accepted by IEEE Journal of Selected Areas in Communications

We calculate the weak transition form factors of the $\Lambda_b\to\Lambda(1520)$ transition, and further calculate the angular distributions of the rare decays $\Lambda_b\to\Lambda(1520)(\to N\bar{K})\ell^{+}\ell^{-}$ ($N\bar{K}=\{pK^-,n\bar{K}^0\}$) with unpolarized $\Lambda_b$ and massive leptons. The form factors are calculated by the three-body light-front quark model with the support of numerical wave functions of $\Lambda_b$ and $\Lambda(1520)$ from solving the semirelativistic potential model associated with the Gaussian expansion method. By fitting the mass spectrum of the observed single bottom and charmed baryons, the parameters of the potential model are fixed, so this strategy can avoid the uncertainties arising from the choice of a simple harmonic oscillator wave function of the baryons. With more data accumulated in the LHCb experiment, our result can help for exploring the $\Lambda_b\to\Lambda(1520)\ell^+\ell^-$ decay and deepen our understanding on the $b\to s\ell^+\ell^-$ processes., Comment: 21 pages, 9 figures. Accepted by Phys. Rev. D

In this work, we investigate the $\Upsilon(10753)\to\Upsilon(1^3D_J)\eta$ ($J=1,2,3$) processes, where the $\Upsilon(10753)$ is assigned as a conventional bottomonium under the $4S$-$3D$ mixing scheme. Our result shows that the concerned processes have considerable branching ratios, {\it i.e.}, branching ratios $\mathcal{B}[\Upsilon(10753)\to\Upsilon(1^3D_{1})\eta]$ and $\mathcal{B}[\Upsilon(10753)\to\Upsilon(1^3D_{2})\eta]$ can reach up to the order of magnitude of $10^{-4}-10^{-3}$, while $\mathcal{B}[\Upsilon(10753)\to\Upsilon(1^3D_{3})\eta]$ is around $10^{-6}-10^{-5}$. With the running of Belle II, it is a good opportunity for finding out the concerned hidden-bottom hadronic decays., Comment: 7 pages, 2 figures and 1 table. arXiv admin note: text overlap with arXiv:2106.14123, arXiv:2201.12715

Singing melody extraction is an important problem in the field of music information retrieval. Existing methods typically rely on frequency-domain representations to estimate the sung frequencies. However, this design does not lead to human-level performance in the perception of melody information for both tone (pitch-class) and octave. In this paper, we propose TONet, a plug-and-play model that improves both tone and octave perceptions by leveraging a novel input representation and a novel network architecture. First, we present an improved input representation, the Tone-CFP, that explicitly groups harmonics via a rearrangement of frequency-bins. Second, we introduce an encoder-decoder architecture that is designed to obtain a salience feature map, a tone feature map, and an octave feature map. Third, we propose a tone-octave fusion mechanism to improve the final salience feature map. Experiments are done to verify the capability of TONet with various baseline backbone models. Our results show that tone-octave fusion with Tone-CFP can significantly improve the singing voice extraction performance across various datasets -- with substantial gains in octave and tone accuracy., Comment: Preprint Version for ICASSP 2022, Singapore

In this work, we investigate the $\Upsilon(10753)\to\Upsilon(nS)\pi^+\pi^-$ ($n=1,2,3$) processes by considering the hadronic loop mechanism, where $\Upsilon(10753)$ is assigned to a conventional bottomonium in the $4S$-$3D$ mixing scheme. Our results of the concerned processes own considerable branching ratios, which can reach up to the order of magnitude of $10^{-4}-10^{-3}$. We should indicate that the measured $\Gamma_{e^+e^-}\times\mathcal{B}[\Upsilon(10753)\to\Upsilon(nS)\pi^+\pi^-]$ values given by Belle can be reproduced well. This fact supports the former bottomonium assignment to the $\Upsilon(10753)$ in the $4S$-$3D$ mixing scheme. Obviously, it is a good opportunity for the ongoing Belle II experiment if the predicted result in this work can be tested further., Comment: 7 pages, 4 figures and 1 table. arXiv admin note: text overlap with arXiv:2106.14123

With the wide penetration of smart robots in multifarious fields, Simultaneous Localization and Mapping (SLAM) technique in robotics has attracted growing attention in the community. Yet collaborating SLAM over multiple robots still remains challenging due to performance contradiction between the intensive graphics computation of SLAM and the limited computing capability of robots. While traditional solutions resort to the powerful cloud servers acting as an external computation provider, we show by real-world measurements that the significant communication overhead in data offloading prevents its practicability to real deployment. To tackle these challenges, this paper promotes the emerging edge computing paradigm into multi-robot SLAM and proposes RecSLAM, a multi-robot laser SLAM system that focuses on accelerating map construction process under the robot-edge-cloud architecture. In contrast to conventional multi-robot SLAM that generates graphic maps on robots and completely merges them on the cloud, RecSLAM develops a hierarchical map fusion technique that directs robots' raw data to edge servers for real-time fusion and then sends to the cloud for global merging. To optimize the overall pipeline, an efficient multi-robot SLAM collaborative processing framework is introduced to adaptively optimize robot-to-edge offloading tailored to heterogeneous edge resource conditions, meanwhile ensuring the workload balancing among the edge servers. Extensive evaluations show RecSLAM can achieve up to 39% processing latency reduction over the state-of-the-art. Besides, a proof-of-concept prototype is developed and deployed in real scenes to demonstrate its effectiveness., Comment: accepted by IEEE Internet of Things Journal

In this work, we calculate the branching ratios of the color-allowed two-body nonleptonic decays of the bottom baryons, which include the $\Xi_b\to \Xi_c^{(*)}$ and $\Omega_b\to \Omega_c^{(*)}$ weak transitions by emitting a pseudoscalar meson ($\pi^{-}$, $K^{-}$, $D^{-}$, and $D_s^{-}$) or a vector meson ($\rho^{-}$, $K^{*-}$, $D^{*-}$, and $D_s^{*-}$). For achieving this aim, we adopt the three-body light-front quark model with the support of hadron spectroscopy, where the spatial wave functions of these heavy baryons involved in these weak decays are obtained by a semirelativistic potential model associated with the Gaussian expansion method. Our results show that these decays with the $\pi^-$, $\rho^-$, and $D_s^{(*)-}$-emitted mode have considerable widths, which could be accessible at the ongoing LHCb and Belle II experiments., Comment: 12 pages, 2 figures and 8 tables. Accepted by Phys. Rev. D