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In the past year, Multimodal Large Language Models (MLLMs) have demonstrated remarkable performance in tasks such as visual question answering, visual understanding and reasoning. However, the extensive model size and high training and inference costs have hindered the widespread application of MLLMs in academia and industry. Thus, studying efficient and lightweight MLLMs has enormous potential, especially in edge computing scenarios. In this survey, we provide a comprehensive and systematic review of the current state of efficient MLLMs. Specifically, we summarize the timeline of representative efficient MLLMs, research state of efficient structures and strategies, and the applications. Finally, we discuss the limitations of current efficient MLLM research and promising future directions. Please refer to our GitHub repository for more details: https://github.com/lijiannuist/Efficient-Multimodal-LLMs-Survey.

Relativistic spin-polarized (SP) lepton beams are important for investigating spin-dependent interaction processes. In particular, spatially structured spin-polarized (SSP) lepton beams may find new applications in material, atomic, nuclear, high-energy physics and new physics beyond the Standard Model. However, realizing ultrafast generation and spin-manipulation of relativistic SSP lepton beams pose significant challenges. Here, we put forward a novel method of ultrafast (picosecond-timescale) generation of a relativistic SSP lepton beam via employing a moderate terahertz (THz) wave in a dielectric-lined waveguide (DWL). We first find that lepton beams with customizable spin-polarization structures can be generated by utilizing different electromagnetic modes, and optimizing the lepton velocity and THz phase velocity can improve efficiency of spin-manipulation and visibility of the SP structure. These SSP beams play a profound role in studying magnetic effects in material physics, chiral-selective chemistry, generation of structured $\gamma$-rays, etc., and open a new avenue for research on relativistic SP particles.

The first source catalog of Large High Altitude Air Shower Observatory reported the detection of a very-high-energy gamma ray source, 1LHAASO J1219+2915. In this paper a further detailed study of the spectral and temporal behavior of this point-like source have been carried. The best-fit position of the TeV source ($\rm{RA}=185.05^{\circ}\pm0.04^{\circ}$, $\rm{Dec}=29.25^{\circ}\pm0.03^{\circ}$) is compatible with NGC 4278 within $\sim0.03$ degree. Variation analysis shows an indication of the variability at a few months level in the TeV band, which is consistent with low frequency observations. Based on these observations, we report the detection of TeV $\gamma$-ray emissions from this low-luminosity AGN NGC 4278. The observations by LHAASO-WCDA during active period has a significance level of 8.8\,$\sigma$ with best-fit photon spectral index $\varGamma=2.56\pm0.14$ and a flux $f_{1-10\,\rm{TeV}}=(7.0\pm1.1_{\rm{sta}}\pm0.35_{\rm{syst}})\times10^{-13}\,\rm{photons\,cm^{-2}\,s^{-1}}$, or approximately $5\%$ of the Crab Nebula. The discovery of VHE from NGC 4278 indicates that the compact, weak radio jet can efficiently accelerate particles and emit TeV photons., Comment: 11 pages, 5 figures

Polarized attosecond $\gamma-$rays may offer excitation and hyperfine tracking of reactions relevant to nuclear physics, astrophysics, high-energy physics, etc. However, unfortunately, generation of a feasible and easy-to-deploy source is still a great challenge. Here, we put forward a novel method for producing ultra-collimated high-brilliance polarized attosecond $\gamma-$rays via the interaction of an unpolarized electron beam with a solid-density plasma. As a relativistic electron beam enters a solid-density plasma, it can be modulated into high-density clusters via the self-modulation instability of itself and further into attosecond slices due to its own hosing instability. This is accompanied by the generation of similar pulse-width $\gamma-$slices via nonlinear Compton scattering. The severe hosing instability breaks the symmetry of the excited electromagnetic fields, resulting in net linear polarization of $\gamma-$slices, which challenges the conventional perception that the interaction of an axially symmetric unpolarized electron beam with a uniform plasma cannot generate polarized radiation. In addition, we also obtain high-quality electron microbunches which may serve as an alternative source for prebunched free-electron lasers.

Recent advancements in Deep Learning (DL) for Direction of Arrival (DOA) estimation have highlighted its superiority over traditional methods, offering faster inference, enhanced super-resolution, and robust performance in low Signal-to-Noise Ratio (SNR) environments. Despite these advancements, existing research predominantly focuses on multi-snapshot scenarios, a limitation in the context of automotive radar systems which demand high angular resolution and often rely on limited snapshots, sometimes as scarce as a single snapshot. Furthermore, the increasing interest in sparse arrays for automotive radar, owing to their cost-effectiveness and reduced antenna element coupling, presents additional challenges including susceptibility to random sensor failures. This paper introduces a pioneering DL framework featuring a sparse signal augmentation layer, meticulously crafted to bolster single snapshot DOA estimation across diverse sparse array setups and amidst antenna failures. To our best knowledge, this is the first work to tackle this issue. Our approach improves the adaptability of deep learning techniques to overcome the unique difficulties posed by sparse arrays with single snapshot. We conduct thorough evaluations of our network's performance using simulated and real-world data, showcasing the efficacy and real-world viability of our proposed solution. The code and real-world dataset employed in this study are available at https://github.com/ruxinzh/Deep_RSA_DOA., Comment: Invited paper for IEEE Asilomar conference 2024

In this paper, we investigate kinetic energy and streamline properties for an irrotational periodic geophysical traveling surface water waves propagating in equatorial oceanic regions. Relying on the methods from the complex analysis, we prove the logarithmic convexity and monotonicity of specific flow variables. By means of conformal mappings, we derive some qualitative results for kinetic energy and streamline, such as streamline time-period being independent of any moment and any point on the streamline in steady flow, the concavity and monotonicity of total kinetic energy within the region between two streamlines and the convexity and monotonicity of total kinetic energy over a streamline time-period. Moreover, we present several results about irrotational equatorial wind waves, such as an upper bound of the minimum of streamline time-period, an upper bound of the maximum of area within the region between two streamlines. Taking advantage of the Bernoulli's law and the Schwarz reflection principle, we show that the extremum of the kinetic energy is attained on the free surface for irrotational equatorial wind waves.

Restless multi-armed bandits (RMAB) play a central role in modeling sequential decision making problems under an instantaneous activation constraint that at most B arms can be activated at any decision epoch. Each restless arm is endowed with a state that evolves independently according to a Markov decision process regardless of being activated or not. In this paper, we consider the task of learning in episodic RMAB with unknown transition functions and adversarial rewards, which can change arbitrarily across episodes. Further, we consider a challenging but natural bandit feedback setting that only adversarial rewards of activated arms are revealed to the decision maker (DM). The goal of the DM is to maximize its total adversarial rewards during the learning process while the instantaneous activation constraint must be satisfied in each decision epoch. We develop a novel reinforcement learning algorithm with two key contributors: a novel biased adversarial reward estimator to deal with bandit feedback and unknown transitions, and a low-complexity index policy to satisfy the instantaneous activation constraint. We show $\tilde{\mathcal{O}}(H\sqrt{T})$ regret bound for our algorithm, where $T$ is the number of episodes and $H$ is the episode length. To our best knowledge, this is the first algorithm to ensure $\tilde{\mathcal{O}}(\sqrt{T})$ regret for adversarial RMAB in our considered challenging settings., Comment: Accepted by ICML 2024

We study the data packet transmission problem (mmDPT) in dense cell-free millimeter wave (mmWave) networks, i.e., users sending data packet requests to access points (APs) via uplinks and APs transmitting requested data packets to users via downlinks. Our objective is to minimize the average delay in the system due to APs' limited service capacity and unreliable wireless channels between APs and users. This problem can be formulated as a restless multi-armed bandits problem with fairness constraint (RMAB-F). Since finding the optimal policy for RMAB-F is intractable, existing learning algorithms are computationally expensive and not suitable for practical dynamic dense mmWave networks. In this paper, we propose a structured reinforcement learning (RL) solution for mmDPT by exploiting the inherent structure encoded in RMAB-F. To achieve this, we first design a low-complexity and provably asymptotically optimal index policy for RMAB-F. Then, we leverage this structure information to develop a structured RL algorithm called mmDPT-TS, which provably achieves an \tilde{O}(\sqrt{T}) Bayesian regret. More importantly, mmDPT-TS is computation-efficient and thus amenable to practical implementation, as it fully exploits the structure of index policy for making decisions. Extensive emulation based on data collected in realistic mmWave networks demonstrate significant gains of mmDPT-TS over existing approaches., Comment: IEEE Transactions on Wireless Communications

A convolutional neural network (CNN) is employed to investigate nuclear mass. By introducing the masses of neighboring nuclei and the paring effects at the input layer of the network, local features of the target nucleus are extracted to predict its mass. Then, through learning the differences between the predicted nuclear masses by the WS4 model and the experimental nuclear masses, a new global-local model (CNN-WS4) is developed, which incorporates both the global nuclear mass model and local features. This model achieves an accuracy of 0.070 MeV for the nuclei with $Z\geqslant8$ and $N\geqslant8$ in AME2016, significantly enhancing the accuracy of nuclear mass prediction. When extrapolating for newly emerged nuclei in AME2020, the CNN-WS4 also exhibits appreciable stability, thereby demonstrating its robustness., Comment: 8 pages, 4 figures

Next-generation mobile networks are expected to facilitate fast AI model downloading to end users. By caching models on edge servers, mobile networks can deliver models to end users with low latency, resulting in a paradigm called edge model caching. In this paper, we develop a novel model placement scheme, called parameter-sharing model caching (TrimCaching). TrimCaching exploits the key observation that a wide range of AI models, such as convolutional neural networks or large language models, can share a significant proportion of parameter blocks containing reusable knowledge, thereby improving storage efficiency. To this end, we formulate a parameter-sharing model placement problem to maximize the cache hit ratio in multi-edge wireless networks by balancing the fundamental tradeoff between storage efficiency and service latency. We show that the formulated problem is a submodular maximization problem with submodular constraints, for which no polynomial-time approximation algorithm exists. To overcome this challenge, we study an important special case, where a small fixed number of parameter blocks are shared across models, which often holds in practice. In such a case, a polynomial-time algorithm with $\left(1-\epsilon\right)/2$-approximation guarantee is developed. Subsequently, we address the original problem for the general case by developing a greedy algorithm. Simulation results demonstrate that the proposed TrimCaching framework significantly improves the cache hit ratio compared with state-of-the-art content caching without exploiting shared parameters in AI models., Comment: 15 pages, 11 figures. Part of this work has been accepted by ICDCS 2024

Ultrarelativistic vortex leptons with intrinsic orbital angular momenta (OAM) have important applications in high energy particle physics, nuclear physics, astrophysics, etc. However, unfortunately, their generation still poses a great challenge. Here, we put forward a novel method for generating ultrarelativistic vortex positrons and electrons through nonlinear Breit-Wheeler (NBW) scattering of vortex $\gamma$ photons. For the first time, a complete angular momentum-resolved scattering theory has been formulated, introducing the angular momentum of laser photons and vortex particles into the conventional NBW scattering framework. We find that vortex positron (electron) can be produced when the outgoing electron (positron) is generated along the collision axis. By unveiling the angular momentum transfer mechanism, we clarify that OAM of the $\gamma$ photon and angular momenta of multiple laser photons are entirely transferred to the generated pairs, leading to the production of ultrarelativistic vortex positrons or electrons with large OAM. Furthermore, we find that the cone opening angle and superposition state of the vortex $\gamma$ photon, distinct characteristics aside from its intrinsic OAM, can be determined via the angular distribution of created pairs in NBW processes. Our method paves the way for investigating strong-field quantum electrodynamics processes concerning the generation and detection of vortex particle beams in intense lasers., Comment: 5

The nuclear charge density distribution plays an important role in nuclear physics and atomic physics. As one of the most frequently used models to obtain charge density distribution, the two-parameter fermi (2pF) model has been widely applied in both nuclear physics and atomic physics. Currently, the feedforward neural network has been employed to study the available 2pF model parameters for 86 nuclei, and it is found that by introducing A^{1/3} into the input parameter of the neural network, the accuracy and precision of the parameter learning effect are improved. Furthermore, the average result of multiple predictions is more reliable than the best result of a single prediction, and there is no significant difference between the average result of density value and of parameter value for the average charge density distribution. In addition, 2pF parameters of 284 (near) stable nuclei are also predicted in this work, which provides a reference for the experiment., Comment: 11 pages, 8 figures

A deep neural network (DNN) has been developed to generate the distributions of nuclear charge density, utilizing the training data from the relativistic density functional theory and incorporating available experimental charge radii of 1014 nuclei into the loss function. The DNN achieved a root-mean-square (rms) deviation of 0.0193 fm for charge radii on its validation set. Furthermore, the DNN can improve the description in both the tail and central regions of the charge density, enhancing agreement with experimental findings. The model's predictive capability has been further validated by its agreement with recent experimental data on charge radii. Finally, this refined model is employed to predict the charge density distributions in a wider range of nuclide chart, and the parameterized charge densities, charge radii, and higher-order moments of charge density distributions are given, providing a robust reference for future experimental investigations., Comment: 11 pages, 5 figures

KM2A is one of the main sub-arrays of LHAASO, working on gamma ray astronomy and cosmic ray physics at energies above 10 TeV. Detector simulation is the important foundation for estimating detector performance and data analysis. It is a big challenge to simulate the KM2A detector in the framework of Geant4 due to the need to track numerous photons from a large number of detector units (>6000) with large altitude difference (30 m) and huge coverage (1.3 km^2). In this paper, the design of the KM2A simulation code G4KM2A based on Geant4 is introduced. The process of G4KM2A is optimized mainly in memory consumption to avoid memory overffow. Some simpliffcations are used to signiffcantly speed up the execution of G4KM2A. The running time is reduced by at least 30 times compared to full detector simulation. The particle distributions and the core/angle resolution comparison between simulation and experimental data of the full KM2A array are also presented, which show good agreement.

How are new technologies like generative AI quickly adopted and used by executive and managerial leaders to create value in organizations? A survey of INSEAD's global alumni base revealed several intriguing insights into perceptions and engagements with generative AI across a broad spectrum of demographics, industries, and geographies. Notably, there's a prevailing optimism about the role of generative AI in enhancing productivity and innovation, as evidenced by the 90% of respondents being excited about its time-saving and efficiency benefits. Analysis revealed different attitudes about adoption and use across demographic variables. Younger respondents are significantly more excited about generative AI and more likely to be using it at work and in personal life than older participants. Those in Europe have a somewhat more distant view of generative AI than those in North America in Asia, in that they see the gains more likely to be captured by organizations than individuals, and are less likely to be using it in professional and personal contexts than those in North America and Asia. This may also be related to the fact that those in Europe are more likely to be working in Financial Services and less likely to be working in Information Technology industries than those in North America and Asia. Despite this, those in Europe are more likely to see AGI happening faster than those in North America, although this may reflect less interaction with generative AI in personal and professional contexts. These findings collectively underscore the complex and multifaceted perceptions of generative AI's role in society, pointing to both its promising potential and the challenges it presents.

Considering the complementarity of scene flow estimation in the spatial domain's focusing capability and 3D object tracking in the temporal domain's coherence, this study aims to address a comprehensive new task that can simultaneously capture fine-grained and long-term 3D motion in an online manner: long-term scene flow estimation (LSFE). We introduce SceneTracker, a novel learning-based LSFE network that adopts an iterative approach to approximate the optimal trajectory. Besides, it dynamically indexes and constructs appearance and depth correlation features simultaneously and employs the Transformer to explore and utilize long-range connections within and between trajectories. With detailed experiments, SceneTracker shows superior capabilities in handling 3D spatial occlusion and depth noise interference, highly tailored to the LSFE task's needs. Finally, we build the first real-world evaluation dataset, LSFDriving, further substantiating SceneTracker's commendable generalization capacity. The code and data for SceneTracker is available at https://github.com/wwsource/SceneTracker.

We propose a method based on the Hilbert-Huang transform (HHT) to recover the high-energy waveform of low-frequency quasi-periodic oscillations (LFQPOs). Based on the method, we successfully obtain the modulation of the phase-folded light curve above 170 keV using the QPO phase reconstructed at lower energies in MAXI J1535-571 with Insight-HXMT observations. A comprehensive simulation study is conducted to demonstrate that such modulation indeed originates from the QPO. Thus the highest energies turn out to significantly exceed the upper limit of ~100 keV for QPOs reported previously using the Fourier method, marking the first opportunity to study QPO properties above 100 keV in this source. Detailed analyses of these high-energy QPO profiles reveal different QPO properties between the 30-100 keV and 100-200 keV energy ranges: the phase lag remains relatively stable, and the amplitude slightly increases below ~100 keV, whereas above this threshold, soft phase lags and a decrease in amplitude are observed. Given the reports of a hard tail detection in broad spectroscopy, we propose that the newly discovered QPO properties above 100 keV are dominated by the hard tail component, possibly stemming from a relativistic jet. Our findings also indicate a strong correlation between the QPOs originating from the jet and corona, supporting the scenario of jet-corona coupling precssion. We emphasize that our proposed HHT-based method can serve as an efficient manner in expanding the high energy band for studying QPOs, thereby enhancing our understanding of their origin., Comment: 21 pages, 15 figures, accepted for publication in ApJL

We study a generalization of chiral symmetry applicable to non-Hermitian systems and its topological consequences on one-dimensional chains. We uncover a rich family of topological phases hosting several chiral flavors characterized not by a single winding number, but a vector of of them. This, in turn, leads to a novel type of bulk-boundary correspondence, where -- in contrast with conventional chiral chains -- some flavors can have topologically stable non-zero charges on both ends. Moreover, we find that the total charge of each flavor can in some cases exceed the magnitude of the highest winding number in the vector invariant. Our work extends the topological classification of the non-Hermitian AIII class along a new axis., Comment: Main 5.5 pages with 4 figures, Appendix 2.5 pages with 1 figure

We propose an enhanced machine learning method to calculate the ground state of two-body systems. Compared to the original method [Naito, Naito, and Hashimoto, Phys. Rev. Research 5, 033189 (2023)], the present method enables one to consider the spin and isospin degrees of freedom by employing a non-fully-connected deep neural network and the unsupervised machine learning technique. The validity of this method is verified by calculating the unique bound state of deuteron., Comment: 8 pages, 4 figures, 3 tables

Object-centric learning aims to break down complex visual scenes into more manageable object representations, enhancing the understanding and reasoning abilities of machine learning systems toward the physical world. Recently, slot-based video models have demonstrated remarkable proficiency in segmenting and tracking objects, but they overlook the importance of the effective reasoning module. In the real world, reasoning and predictive abilities play a crucial role in human perception and object tracking; in particular, these abilities are closely related to human intuitive physics. Inspired by this, we designed a novel reasoning module called the Slot-based Time-Space Transformer with Memory buffer (STATM) to enhance the model's perception ability in complex scenes. The memory buffer primarily serves as storage for slot information from upstream modules, the Slot-based Time-Space Transformer makes predictions through slot-based spatiotemporal attention computations and fusion. Our experiment results on various datasets show that STATM can significantly enhance object-centric learning capabilities of slot-based video models.

We present a detailed analysis of the X-ray luminosity (Lx) dependence of the cyclotron absorption line energy (Ecyc) for the X-ray binary pulsar 1A 0535+262 during its 2020 giant outburst based on pulse-to-pulse analysis. By applying this technique to high cadence observations of Insight-HXMT, we reveal the most comprehensive Ecyc-Lx correlation across a broad luminosity range of ~(0.03-1.3)*10^38 erg/s. Apart from the positive and negative correlations between cyclotron line energy and luminosity at Lx~(1-3)*10^37 erg/s and ~(7-13)*10^37 erg/s, which are expected from the typical subcritical and supercritical accretion regimes, respectively, a plateau in the correlation is also detected at ~(3-7)*10^37 erg/s^-1. Moreover, at the lowest luminosity level (Lx<10^37 erg/s), the positive Ecyc-Lx correlation seems to be broken, and the pulse profile also occurs a significant transition. These discoveries provide the first complete view on the correlation between luminosity and the centriod energy of the cyclotron line, and therefore are relevant for understanding how accretion onto magnetized neutron stars depends on luminosity., Comment: Accepted for publication in Monthly Notices of the Royal Astronomical Society Main Journal

Depth completion aims to derive a dense depth map from sparse depth measurements with a synchronized color image. Current state-of-the-art (SOTA) methods are predominantly propagation-based, which work as an iterative refinement on the initial estimated dense depth. However, the initial depth estimations mostly result from direct applications of convolutional layers on the sparse depth map. In this paper, we present a Bilateral Propagation Network (BP-Net), that propagates depth at the earliest stage to avoid directly convolving on sparse data. Specifically, our approach propagates the target depth from nearby depth measurements via a non-linear model, whose coefficients are generated through a multi-layer perceptron conditioned on both \emph{radiometric difference} and \emph{spatial distance}. By integrating bilateral propagation with multi-modal fusion and depth refinement in a multi-scale framework, our BP-Net demonstrates outstanding performance on both indoor and outdoor scenes. It achieves SOTA on the NYUv2 dataset and ranks 1st on the KITTI depth completion benchmark at the time of submission. Experimental results not only show the effectiveness of bilateral propagation but also emphasize the significance of early-stage propagation in contrast to the refinement stage. Our code and trained models will be available on the project page., Comment: Accepted by CVPR 2024

We present the measurements of all-particle energy spectrum and mean logarithmic mass of cosmic rays in the energy range of 0.3-30 PeV using data collected from LHAASO-KM2A between September 2021 and December 2022, which is based on a nearly composition-independent energy reconstruction method, achieving unprecedented accuracy. Our analysis reveals the position of the knee at $3.67 \pm 0.05 \pm 0.15$ PeV. Below the knee, the spectral index is found to be -$2.7413 \pm 0.0004 \pm 0.0050$, while above the knee, it is -$3.128 \pm 0.005 \pm 0.027$, with the sharpness of the transition measured with a statistical error of 2%. The mean logarithmic mass of cosmic rays is almost heavier than helium in the whole measured energy range. It decreases from 1.7 at 0.3 PeV to 1.3 at 3 PeV, representing a 24% decline following a power law with an index of -$0.1200 \pm 0.0003 \pm 0.0341$. This is equivalent to an increase in abundance of light components. Above the knee, the mean logarithmic mass exhibits a power law trend towards heavier components, which is reversal to the behavior observed in the all-particle energy spectrum. Additionally, the knee position and the change in power-law index are approximately the same. These findings suggest that the knee observed in the all-particle spectrum corresponds to the knee of the light component, rather than the medium-heavy components., Comment: 8 pages, 3 figures

We study one-dimensional non-Hermitian lattices characterized by $\mathcal{PT}$-symmetric gain and loss, where the real-gap transforms into an imaginary-gap with increasing strength of gain/loss. The energy spectrum, under open boundary conditions, consists of real eigenenergies in the presence of $\mathcal{PT}$-symmetry, and the corresponding eigenstates are bulk modes. As the gain/loss is increased, $\mathcal{PT}$-symmetry breaks, leading to an increase in the proportion of imaginary eigenenergies and the appearance of bipolar Non-Hermitian skin effect (NHSE). Notably, the NHSE depending on the sign of their imaginary energy components. For Im$(E_{OBC})>(<)0$, the eigenstates localize at the right (left) boundary. These findings not only affirm the validity of our theoretical framework but also showcase the capability of engineered circuit systems to replicate intricate non-Hermitian phenomena. Our study unveils the unique characteristics of gain/loss-induced bipolar NHSE, shedding light on the exotic properties of non-Hermitian systems., Comment: After further consideration and review, we've identified significant errors in our analysis that impact the conclusions of the paper. To ensure the integrity of the scientific record, we're withdrawing this submission for comprehensive revision. We plan to resubmit once these issues have been addressed. We appreciate the understanding of the community and apologize for any inconvenience

Uniformity plays an important role in evaluating learned representations, providing insights into self-supervised learning. In our quest for effective uniformity metrics, we pinpoint four principled properties that such metrics should possess. Namely, an effective uniformity metric should remain invariant to instance permutations and sample replications while accurately capturing feature redundancy and dimensional collapse. Surprisingly, we find that the uniformity metric proposed by \citet{Wang2020UnderstandingCR} fails to satisfy the majority of these properties. Specifically, their metric is sensitive to sample replications, and can not account for feature redundancy and dimensional collapse correctly. To overcome these limitations, we introduce a new uniformity metric based on the Wasserstein distance, which satisfies all the aforementioned properties. Integrating this new metric in existing self-supervised learning methods effectively mitigates dimensional collapse and consistently improves their performance on downstream tasks involving CIFAR-10 and CIFAR-100 datasets. Code is available at \url{https://github.com/statsle/WassersteinSSL}.

Point cloud maps generated via LiDAR sensors using extensive remotely sensed data are commonly used by autonomous vehicles and robots for localization and navigation. However, dynamic objects contained in point cloud maps not only downgrade localization accuracy and navigation performance but also jeopardize the map quality. In response to this challenge, we propose in this paper a novel semantic SLAM approach for dynamic scenes based on LiDAR point clouds, referred to as SD-SLAM hereafter. The main contributions of this work are in three aspects: 1) introducing a semantic SLAM framework dedicatedly for dynamic scenes based on LiDAR point clouds, 2) Employing semantics and Kalman filtering to effectively differentiate between dynamic and semi-static landmarks, and 3) Making full use of semi-static and pure static landmarks with semantic information in the SD-SLAM process to improve localization and mapping performance. To evaluate the proposed SD-SLAM, tests were conducted using the widely adopted KITTI odometry dataset. Results demonstrate that the proposed SD-SLAM effectively mitigates the adverse effects of dynamic objects on SLAM, improving vehicle localization and mapping performance in dynamic scenes, and simultaneously constructing a static semantic map with multiple semantic classes for enhanced environment understanding.

In the rapidly evolving area of image synthesis, a serious challenge is the presence of complex artifacts that compromise perceptual realism of synthetic images. To alleviate artifacts and improve quality of synthetic images, we fine-tune Vision-Language Model (VLM) as artifact classifier to automatically identify and classify a wide range of artifacts and provide supervision for further optimizing generative models. Specifically, we develop a comprehensive artifact taxonomy and construct a dataset of synthetic images with artifact annotations for fine-tuning VLM, named SynArtifact-1K. The fine-tuned VLM exhibits superior ability of identifying artifacts and outperforms the baseline by 25.66%. To our knowledge, this is the first time such end-to-end artifact classification task and solution have been proposed. Finally, we leverage the output of VLM as feedback to refine the generative model for alleviating artifacts. Visualization results and user study demonstrate that the quality of images synthesized by the refined diffusion model has been obviously improved.

Tool-augmented large language models (LLMs) are attracting widespread attention when accessing up-to-date knowledge and alleviating hallucination issues. Nowadays, advanced closed-source LLMs (e.g., ChatGPT) have demonstrated surprising tool-usage capabilities through prompting and in-context learning techniques. To empower the capabilities of open-source LLMs (e.g., LLaMA) in manipulating tools, current efforts focus on either template-driven or token-triggered tool-usage. However, the former hampers LLMs' flexibility to address diverse user's queries due to constrained tool interactions, while the latter limits the generalizability when engaging with new tools, since tool-usage learning is based on task- and tool-specific datasets. To alleviate these concerns, in this paper, we propose a decision-aware and generalizable tool-usage framework (DEER). Specifically, we first construct the tool-usage samples with multiple decision branches via an automatic generation pipeline, thereby inspiring the decision-making awareness of LLMs under diverse scenarios. Meanwhile, we propose a novel tool sampling strategy to enhance the generalizability of LLMs over unseen tools. Extensive experiments demonstrate that our proposed DEER is effective and significantly outperforms baselines across various datasets., Comment: 20 pages, 18 figures

The mass table in the deformed relativistic Hartree-Bogoliubov theory in continuum (DRHBc) with the PC-PK1 density functional has been established for even-$Z$ nuclei with $8\le Z\le120$, extended from the previous work for even-even nuclei [Zhang $\it{et al.}$ (DRHBc Mass Table Collaboration), At. Data Nucl. Data Tables 144, 101488 (2022)]. The calculated binding energies, two-nucleon and one-neutron separation energies, root-mean-square (rms) radii of neutron, proton, matter, and charge distributions, quadrupole deformations, and neutron and proton Fermi surfaces are tabulated and compared with available experimental data. A total of 4829 even-$Z$ nuclei are predicted to be bound, with an rms deviation of 1.477 MeV from the 1244 mass data. Good agreement with the available experimental odd-even mass differences, $\alpha$ decay energies, and charge radii is also achieved. The description accuracy for nuclear masses and nucleon separation energies as well as the prediction for drip lines is compared with the results obtained from other relativistic and nonrelativistic density functional. The comparison shows that the DRHBc theory with PC-PK1 provides an excellent microscopic description for the masses of even-$Z$ nuclei. The systematics of the nucleon separation energies, odd-even mass differences, pairing energies, two-nucleon gaps, $\alpha$ decay energies, rms radii, quadrupole deformations, potential energy curves, neutron density distributions, and neutron mean-field potentials are discussed., Comment: 394 pages, 17 figures, 2 tables, accepted for publication in Atomic Data and Nuclear Data Tables, data file in the TXT form is available for download under "Ancillary files"

LLM-based Automatic Prompt Optimization, which typically utilizes LLMs as Prompt Optimizers to self-reflect and refine prompts, has shown promising performance in recent studies. Despite the success, the underlying mechanism of this approach remains unexplored, and the true effectiveness of LLMs as Prompt Optimizers requires further validation. In this work, we conducted a comprehensive study to uncover the actual mechanism of LLM-based Prompt Optimization. Our findings reveal that the LLM optimizers struggle to identify the true causes of errors during reflection, tending to be biased by their own prior knowledge rather than genuinely reflecting on the errors. Furthermore, even when the reflection is semantically valid, the LLM optimizers often fail to generate appropriate prompts for the target models with a single prompt refinement step, partly due to the unpredictable behaviors of the target models. Based on the observations, we introduce a new "Automatic Behavior Optimization" paradigm, which directly optimizes the target model's behavior in a more controllable manner. We hope our study can inspire new directions for automatic prompt optimization development.

We put forward a novel method to generate high-brilliance polarized $\gamma$-photon beams via vacuum dichroism (VD)-assisted vacuum birefringence (VB) effect. We split a linearly polarized (LP) laser pulse into two subpulses with the first one colliding with a dense unpolarized electron beam to generate LP $\gamma$ photons (via nonlinear Compton scattering), which then further collide with the second subpulse and are partially transformed into circularly polarized ones via the VB effect. We find that by manipulating the relative polarization of two subpulses, one can ``purify'' (i.e., enhance) the polarization of the $\gamma$-photon beam via the VD effect. Due to the VD assistance, the VB effect reaches optimal when the relative polarization is nearly $30^\circ$, not the widely used $45^\circ$ in the common VB detection methods. In addition, our method can be used to efficiently confirm the well-known VB effect itself, which has not been directly observed in experiments yet.