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In the literature, the neutrino $\mu$-$\tau$ reflection symmetry (which has the interesting predictions $\theta^{}_{23} =\pi/4$ and $\delta = \pm \pi/2$ for the atmospherical neutrino mixing angle and Dirac CP phase) is an attractive and widely studied candidate for the flavor symmetries in the neutrino sector. But it is known that, when the seesaw model is furnished with this symmetry, the leptogenesis mechanism (which provides an elegant explanation for the baryon-antibaryon asymmetry of the Universe) can only work in the two-flavor regime (which only holds for the right-handed neutrino masses in the range $10^9-10^{12}$ GeV). This prohibits us to have a low scale seesaw model (which has the potential to be directly accessed by running or upcoming collider experiments) that can have the $\mu$-$\tau$ reflection symmetry and successful leptogenesis simultaneously. In this paper, for the first time, we demonstrate that the successful leptogenesis may also be achieved in low scale seesaw models furnished with the $\mu$-$\tau$ reflection symmetry, by means of the flavor non-universality of the conversion efficiencies from the flavored lepton asymmetries to the baryon asymmetry via the sphaleron process. We perform the study in both the resonant leptogenesis regime and the leptogenesis via oscillations (ARS leptogenesis) regime., Comment: arXiv admin note: text overlap with arXiv:2407.18285, arXiv:2403.18630

We calculate the low-temperature spectral function of the symmetric single impurity Anderson model using a recently proposed dynamical exchange-correlation (xc) field formalism. The xc field, coupled to the one-particle Green's function, is obtained through analytic analysis and numerical extrapolation based on finite clusters. In the Kondo regime, the xc field consists of a complex constant term and a main quasiparticle-like oscillation term. The constant term represents the Hubbard side-band contribution, containing a bath-induced broadening effect, while the quasiparticle-like term is related to the Kondo resonance peak at low-temperature. We illustrate these features in terms of analytical and numerical calculations for small and medium-size finite clusters, and in the thermodynamic limit. The results indicate that the xc field formalism provides a good trade-off between accuracy and complexity in solving impurity problems. Consequently, it can significantly reduce the complexity of the many-body problem faced by first-principles approaches to strongly correlated materials.

Traditional animation generation methods depend on training generative models with human-labelled data, entailing a sophisticated multi-stage pipeline that demands substantial human effort and incurs high training costs. Due to limited prompting plans, these methods typically produce brief, information-poor, and context-incoherent animations. To overcome these limitations and automate the animation process, we pioneer the introduction of large multimodal models (LMMs) as the core processor to build an autonomous animation-making agent, named Anim-Director. This agent mainly harnesses the advanced understanding and reasoning capabilities of LMMs and generative AI tools to create animated videos from concise narratives or simple instructions. Specifically, it operates in three main stages: Firstly, the Anim-Director generates a coherent storyline from user inputs, followed by a detailed director's script that encompasses settings of character profiles and interior/exterior descriptions, and context-coherent scene descriptions that include appearing characters, interiors or exteriors, and scene events. Secondly, we employ LMMs with the image generation tool to produce visual images of settings and scenes. These images are designed to maintain visual consistency across different scenes using a visual-language prompting method that combines scene descriptions and images of the appearing character and setting. Thirdly, scene images serve as the foundation for producing animated videos, with LMMs generating prompts to guide this process. The whole process is notably autonomous without manual intervention, as the LMMs interact seamlessly with generative tools to generate prompts, evaluate visual quality, and select the best one to optimize the final output., Comment: Accepted by SIGGRAPH Asia 2024, Project and Codes: https://github.com/HITsz-TMG/Anim-Director

Central to the layered kagome lattice superconductors AV3Sb5 (A = K, Cs, Rb) is a cascade of novel quantum states triggered by an unconventional charge density wave (CDW) order. The three-dimensional (3D) order involves a 2x2x2 phase coherent stacking of 2x2 charge density modulations in the kagome plane at low temperatures, exhibiting a CDW energy gap and evidence for time-reversal symmetry breaking. Here we report the discovery of emergent superconductivity and primary pair density wave (PDW) at the antiphase boundaries and stacking faults of bulk CDW order. We find that the {\pi}-phase shift dislocations can naturally appear on the surface as the Cs atoms form 2x2 superstructures that are out of phase with the bulk CDW. An incipient narrow band of surface states inside bulk CDW gap emerge close to the Fermi level where a particle-hole symmetric energy gap develops. We demonstrate that the energy gap originates from a novel quasi-2D kagome superconducting state (Tc ~ 5.4 K) intertwined with bulk CDW order, exhibiting an unprecedented vortex core spectrum and spatial modulations of the superconducting gap consistent with a 4x4 PDW. Intriguingly, the 2D kagome superconductivity is shown to be tunable on and off by atomically manipulating the Cs atoms on the surface. Our findings provide fresh new insights for understanding the interplay between the unconventional CDW and superconductivity in kagome metals and a pathway for atomic manipulation and topological defects engineering of quantum many-body states in correlated materials.

In the literature, motivated by the observed peculiar neutrino mixing pattern and a preliminary experimental hint for maximal Dirac CP phase (i.e., $\delta \sim 3\pi/2$), a lot of flavor and CP symmetries have been proposed to help us understand and explain these experimental results. However, for some flavor-symmetry scenarios (see section~3.1), the leptogenesis mechanism is prohibited to work as usual. To tackle this problem, in this paper we have made an exhausitive study on the possibility that the renormalization group evolution effect may induce a successful leptogenesis for these particular scenarios. Our study provides some complementarities to the previous related studies in Refs. [26, 27, 28] (see section~3.2)., Comment: 37 pages, 14 figures. arXiv admin note: text overlap with arXiv:2405.02804, arXiv:2403.18630

In this work, we present TextHarmony, a unified and versatile multimodal generative model proficient in comprehending and generating visual text. Simultaneously generating images and texts typically results in performance degradation due to the inherent inconsistency between vision and language modalities. To overcome this challenge, existing approaches resort to modality-specific data for supervised fine-tuning, necessitating distinct model instances. We propose Slide-LoRA, which dynamically aggregates modality-specific and modality-agnostic LoRA experts, partially decoupling the multimodal generation space. Slide-LoRA harmonizes the generation of vision and language within a singular model instance, thereby facilitating a more unified generative process. Additionally, we develop a high-quality image caption dataset, DetailedTextCaps-100K, synthesized with a sophisticated closed-source MLLM to enhance visual text generation capabilities further. Comprehensive experiments across various benchmarks demonstrate the effectiveness of the proposed approach. Empowered by Slide-LoRA, TextHarmony achieves comparable performance to modality-specific fine-tuning results with only a 2% increase in parameters and shows an average improvement of 2.5% in visual text comprehension tasks and 4.0% in visual text generation tasks. Our work delineates the viability of an integrated approach to multimodal generation within the visual text domain, setting a foundation for subsequent inquiries.

In traditional QCD sum rules, the simple hadron spectral density model of ``delta-function-type ground state + theta-function-type continuous spectrum" determines that there is no perfect parameter selection. In recent years, inverse problem methods, especially the inverse matrix method, have shown better handling of QCD sum rules. This work continues to develop the inverse matrix method. Considering that the narrow-width approximation may still be a good approximation, we separate the contribution of the ground state from the spectral density. Then follow the general steps of the inverse matrix method to extract physical quantities such as decay constants that we are sometimes more interested in., Comment: 13 pages, 6 figures

We investigate the purely non-factorizable $\Lambda_{c}^{+}\to\Xi^{0}K^{+}$ decay using light-cone sum rules. To extract the decay amplitudes, a three-point correlation function is defined and calculated at hadron and quark-gluon level, respectively. Both the W-exchange and the W-inward emission diagrams are considered in the quark-gluon level calculation, where the two-particle light-cone distribution amplitudes (LCDAs) of kaon are used as non-perturbative input. We obtain the decay amplitudes contributed from the twist-2 and twist-3 kaon LCDAs, respectively. The obtained P-wave amplitude is consistent with those predicted in the literature, while the S-wave one is much smaller. This significant difference between the S- and P- wave amplitudes leads to small up-down spin asymmetry, closing to the experimental measurement., Comment: 13 pages, 6 figures, 3 tables

This work presents Depth Anything V2. Without pursuing fancy techniques, we aim to reveal crucial findings to pave the way towards building a powerful monocular depth estimation model. Notably, compared with V1, this version produces much finer and more robust depth predictions through three key practices: 1) replacing all labeled real images with synthetic images, 2) scaling up the capacity of our teacher model, and 3) teaching student models via the bridge of large-scale pseudo-labeled real images. Compared with the latest models built on Stable Diffusion, our models are significantly more efficient (more than 10x faster) and more accurate. We offer models of different scales (ranging from 25M to 1.3B params) to support extensive scenarios. Benefiting from their strong generalization capability, we fine-tune them with metric depth labels to obtain our metric depth models. In addition to our models, considering the limited diversity and frequent noise in current test sets, we construct a versatile evaluation benchmark with precise annotations and diverse scenes to facilitate future research., Comment: Project page: https://depth-anything-v2.github.io

A quantum description is given of nanoskyrmions in 2D textures with localised spins and itinerant electrons, isolated or coupled to leads, in or out of equilibrium. The spin-electron exchange is treated at the mean-field level, while Tensor Networks and exact diagonalization or nonequilibrium Green's functions are used for localised spins and itinerant electrons. We motivate our scheme via exact benchmarks, then show by several examples that itinerant electrons distinctly affect the properties of quantum nanoskyrmions. Finally, we mention lines of future work and improvement of the approach.

As productivity advances, the demand of customers for multi-variety and small-batch production is increasing, thereby putting forward higher requirements for manufacturing systems. When production tasks frequent changes due to this demand, traditional manufacturing systems often cannot response promptly. The multi-agent manufacturing system is proposed to address this problem. However, because of technical limitations, the negotiation among agents in this kind of system is realized through predefined heuristic rules, which is not intelligent enough to deal with the multi-variety and small batch production. To this end, a Large Language Model-based (LLM-based) multi-agent manufacturing system for intelligent shopfloor is proposed in the present study. This system delineates the diverse agents and defines their collaborative methods. The roles of the agents encompass Machine Server Agent (MSA), Bid Inviter Agent (BIA), Bidder Agent (BA), Thinking Agent (TA), and Decision Agent (DA). Due to the support of LLMs, TA and DA acquire the ability of analyzing the shopfloor condition and choosing the most suitable machine, as opposed to executing a predefined program artificially. The negotiation between BAs and BIA is the most crucial step in connecting manufacturing resources. With the support of TA and DA, BIA will finalize the distribution of orders, relying on the information of each machine returned by BA. MSAs bears the responsibility for connecting the agents with the physical shopfloor. This system aims to distribute and transmit workpieces through the collaboration of the agents with these distinct roles, distinguishing it from other scheduling approaches. Comparative experiments were also conducted to validate the performance of this system.

Point cloud analysis has seen substantial advancements due to deep learning, although previous Transformer-based methods excel at modeling long-range dependencies on this task, their computational demands are substantial. Conversely, the Mamba offers greater efficiency but shows limited potential compared with Transformer-based methods. In this study, we introduce PoinTramba, a pioneering hybrid framework that synergies the analytical power of Transformer with the remarkable computational efficiency of Mamba for enhanced point cloud analysis. Specifically, our approach first segments point clouds into groups, where the Transformer meticulously captures intricate intra-group dependencies and produces group embeddings, whose inter-group relationships will be simultaneously and adeptly captured by efficient Mamba architecture, ensuring comprehensive analysis. Unlike previous Mamba approaches, we introduce a bi-directional importance-aware ordering (BIO) strategy to tackle the challenges of random ordering effects. This innovative strategy intelligently reorders group embeddings based on their calculated importance scores, significantly enhancing Mamba's performance and optimizing the overall analytical process. Our framework achieves a superior balance between computational efficiency and analytical performance by seamlessly integrating these advanced techniques, marking a substantial leap forward in point cloud analysis. Extensive experiments on datasets such as ScanObjectNN, ModelNet40, and ShapeNetPart demonstrate the effectiveness of our approach, establishing a new state-of-the-art analysis benchmark on point cloud recognition. For the first time, this paradigm leverages the combined strengths of both Transformer and Mamba architectures, facilitating a new standard in the field. The code is available at https://github.com/xiaoyao3302/PoinTramba., Comment: 14 pages, 4 figures, 6 tables

Text-Centric Visual Question Answering (TEC-VQA) in its proper format not only facilitates human-machine interaction in text-centric visual environments but also serves as a de facto gold proxy to evaluate AI models in the domain of text-centric scene understanding. Nonetheless, most existing TEC-VQA benchmarks have focused on high-resource languages like English and Chinese. Despite pioneering works to expand multilingual QA pairs in non-text-centric VQA datasets through translation engines, the translation-based protocol encounters a substantial "visual-textual misalignment" problem when applied to TEC-VQA. Specifically, it prioritizes the text in question-answer pairs while disregarding the visual text present in images. Moreover, it fails to address complexities related to nuanced meaning, contextual distortion, language bias, and question-type diversity. In this work, we tackle multilingual TEC-VQA by introducing MTVQA, the first benchmark featuring high-quality human expert annotations across 9 diverse languages, consisting of 6,778 question-answer pairs across 2,116 images. Further, by comprehensively evaluating numerous state-of-the-art Multimodal Large Language Models (MLLMs), including GPT-4o, GPT-4V, Claude3, and Gemini, on the MTVQA dataset, it is evident that there is still a large room for performance improvement, underscoring the value of MTVQA. Additionally, we supply multilingual training data within the MTVQA dataset, demonstrating that straightforward fine-tuning with this data can substantially enhance multilingual TEC-VQA performance. We aspire that MTVQA will offer the research community fresh insights and stimulate further exploration in multilingual visual text comprehension. The project homepage is available at https://bytedance.github.io/MTVQA/.

In this paper, we delve into a new task known as small object editing (SOE), which focuses on text-based image inpainting within a constrained, small-sized area. Despite the remarkable success have been achieved by current image inpainting approaches, their application to the SOE task generally results in failure cases such as Object Missing, Text-Image Mismatch, and Distortion. These failures stem from the limited use of small-sized objects in training datasets and the downsampling operations employed by U-Net models, which hinders accurate generation. To overcome these challenges, we introduce a novel training-based approach, SOEDiff, aimed at enhancing the capability of baseline models like StableDiffusion in editing small-sized objects while minimizing training costs. Specifically, our method involves two key components: SO-LoRA, which efficiently fine-tunes low-rank matrices, and Cross-Scale Score Distillation loss, which leverages high-resolution predictions from the pre-trained teacher diffusion model. Our method presents significant improvements on the test dataset collected from MSCOCO and OpenImage, validating the effectiveness of our proposed method in small object editing. In particular, when comparing SOEDiff with SD-I model on the OpenImage-f dataset, we observe a 0.99 improvement in CLIP-Score and a reduction of 2.87 in FID.

In this paper, we have studied the consequences of some representative modular symmetry neutrino models for tri-resonant leptogenesis (which is realized by having three nearly degenerate right-handed neutrinos). To be specific, we have considered modular ${\rm A}^{}_4$, ${\rm S}^{}_4$ and ${\rm A}^{}_5$ symmetry models that have a right-handed neutrino mass matrix $M^{}_{\rm R}$ as shown in Eq.~(\ref{5}) which gives three degenerate right-handed neutrino masses and consequently prohibits the leptogenesis mechanism to work. For these models, we have considered two minimal ways to generate the desired small mass splittings among the three right-handed neutrinos: one scenario is to modify $M^{}_{\rm R}$ to a form as shown in Eq.~(\ref{7}), while another scenario is to consider the renormalization-group corrections for the right-handed neutrino masses. In the former scenario, for the considered models, the observed value of $\eta^{}_{\rm B}$ can always be successfully reproduced for appropriate values of $\mu$. But in the latter scenario not all the considered models can successfully reproduce the observed value of $\eta^{}_{\rm B}$.

In this paper, we have studied the consequences of the $\mu$-$\tau$ reflection symmetry for leptogenesis in the type-I seesaw model with diagonal Dirac neutrino mass matrix. We have first obtained the phenomenologically allowed values of the model parameters, which show that there may exist zero or equal entries in the Majorana mass matrix for the right-handed neutrinos, and then studied their predictions for three right-handed neutrino masses, which show that there may exist two nearly degenerate right-handed neutrinos. Then, we have studied the consequences of the model for leptogenesis. Due to the $\mu$-$\tau$ reflection symmetry, leptogenesis can only work in the two-flavor regime. Furthermore, leptogenesis cannot work for the particular case of $r=1$. Accordingly, for some benchmark values of $r \neq 1$, we have given the constraints of leptogenesis for relevant parameters. Furthermore, we have investigated the possibilities of leptogenesis being induced by the renormalization group evolution effects for two particular scenarios. For the particular case of $r=1$, the renormalization group evolution effects will break the orthogonality relations among different columns of $M^{}_{\rm D}$ and consequently induce leptogenesis to work. For the low-scale resonant leptogenesis scenario which is realized for nearly degenerate right-handed neutrinos, the renormalization group evolution effects can break the $\mu$-$\tau$ reflection symmetry and consequently induce leptogenesis to work., Comment: 26 pages, 10 figures, version accepted for publication in PRD

Prompt learning has become the most effective paradigm for adapting large pre-trained vision-language models (VLMs) to downstream tasks. Recently, unsupervised prompt tuning methods, such as UPL and POUF, directly leverage pseudo-labels as supervisory information to fine-tune additional adaptation modules on unlabeled data. However, inaccurate pseudo labels easily misguide the tuning process and result in poor representation capabilities. In light of this, we propose Training-Free Unsupervised Prompts (TFUP), which maximally preserves the inherent representation capabilities and enhances them with a residual connection to similarity-based prediction probabilities in a training-free and labeling-free manner. Specifically, we integrate both instance confidence and prototype scores to select representative samples, which are used to customize a reliable Feature Cache Model (FCM) for training-free inference. Then, we design a Multi-level Similarity Measure (MSM) that considers both feature-level and semantic-level similarities to calculate the distance between each test image and the cached sample as the weight of the corresponding cached label to generate similarity-based prediction probabilities. In this way, TFUP achieves surprising performance, even surpassing the training-base method on multiple classification datasets. Based on our TFUP, we propose a training-based approach (TFUP-T) to further boost the adaptation performance. In addition to the standard cross-entropy loss, TFUP-T adopts an additional marginal distribution entropy loss to constrain the model from a global perspective. Our TFUP-T achieves new state-of-the-art classification performance compared to unsupervised and few-shot adaptation approaches on multiple benchmarks. In particular, TFUP-T improves the classification accuracy of POUF by 3.3% on the most challenging Domain-Net dataset.

Text-centric visual question answering (VQA) has made great strides with the development of Multimodal Large Language Models (MLLMs), yet open-source models still fall short of leading models like GPT4V and Gemini, partly due to a lack of extensive, high-quality instruction tuning data. To this end, we introduce a new approach for creating a massive, high-quality instruction-tuning dataset, Square-10M, which is generated using closed-source MLLMs. The data construction process, termed Square, consists of four steps: Self-Questioning, Answering, Reasoning, and Evaluation. Our experiments with Square-10M led to three key findings: 1) Our model, TextSquare, considerably surpasses open-source previous state-of-the-art Text-centric MLLMs and sets a new standard on OCRBench(62.2%). It even outperforms top-tier models like GPT4V and Gemini in 6 of 10 text-centric benchmarks. 2) Additionally, we demonstrate the critical role of VQA reasoning data in offering comprehensive contextual insights for specific questions. This not only improves accuracy but also significantly mitigates hallucinations. Specifically, TextSquare scores an average of 75.1% across four general VQA and hallucination evaluation datasets, outperforming previous state-of-the-art models. 3) Notably, the phenomenon observed in scaling text-centric VQA datasets reveals a vivid pattern: the exponential increase of instruction tuning data volume is directly proportional to the improvement in model performance, thereby validating the necessity of the dataset scale and the high quality of Square-10M.

In this paper, we would like to point out that in the scenario that the right-handed neutrinos suddenly gain some masses much larger than the temperature of the Universe at that time so that the washout effects for the lepton asymmetry generated from their decays can be neglected safely, the purely flavored leptogenesis scenario (in which the total CP asymmetries for the decays of the right-handed neutrinos are vanishing and the successful leptogenesis is realized by virtue of the flavor non-universality of the washout effects) cannot work in the usual way any more. For this problem, we put forward that the flavor non-universality of the conversion efficiencies from the flavored lepton asymmetries to the baryon asymmetry via the sphaleron processes may play a crucial role. And we will study if the requisite baryon asymmetry can be successfully reproduced from such a mechanism in the scenarios that the right-handed neutrino masses are hierarchical and nearly degenerate, respectively. A detailed study shows that this mechanism can be viable in both these two scenarios., Comment: 16 pages, 4 figures, version accepted for publication in JHEP

In this paper, we study \lambda-biharmonic hypersurfaces in the product space L^{m}\times\mathbb{R}, where L^{m} is an Einstein space and \mathbb{R} is a real line. We prove that \lambda-biharmonic hypersurfaces with constant mean curvature in L^{m}\times\mathbb{R} are either minimal or vertical cylinders, and obtain some classification results for \lambda$-biharmonic hypersurfaces under various constraints. Furthermore, we investigate \lambda-biharmonic hypersurfaces in the product space L^{m}(c)\times\mathbb{R}, where L^{m}(c) is a space form with constant sectional curvature c, and categorize hypersurfaces that are either totally umbilical or semi-parallel.

The collective modes of the superconducting order parameter fluctuation can provide key insights into the nature of the superconductor. Recently, a family of superconductors has emerged in non-magnetic kagome material AV3Sb5 (A=K, Rb, Cs), exhibiting fertile emergent phenomenology. However, the collective behaviors of Cooper pairs have not been studied. Here, we report a distinct collective mode in CsV3-xTaxSb5 using scanning tunneling microscope/spectroscopy. The spectral line-shape is well-described by one isotropic and one anisotropic superconducting gap, and a bosonic mode due to electron-mode coupling. With increasing x, the two gaps move closer in energy, merge into two isotropic gaps of equal amplitude, and then increase synchronously. The mode energy decreases monotonically to well below 2{\Delta} and survives even after the charge density wave order is suppressed. We propose the interpretation of this collective mode as Leggett mode between different superconducting components or the Bardasis-Schrieffer mode due to a subleading superconducting component., Comment: 11 pages, 4 figures

The newly discovered Kagome superconductors AV$_3$Sb$_5$ (A=K, Rb and Cs) continue to bring surprises in generating unusual phenomena and physical properties, including anomalous Hall effect, unconventional charge density wave, electronic nematicity and time-reversal symmetry breaking. Here we report an unexpected emergence of multiple flat bands in the AV$_3$Sb$_5$ superconductors. By performing high-resolution angle-resolved photoemission (ARPES) measurements, we observed four branches of flat bands that span over the entire momentum space. The appearance of the flat bands is not anticipated from the band structure calculations and cannot be accounted for by the known mechanisms of flat band generation. It is intimately related to the evolution of van Hove singularities. It is for the first time to observe such emergence of multiple flat bands in solid materials. Our findings provide new insights in revealing the underlying mechanism that governs the unusual behaviors in the Kagome superconductors. They also provide a new pathway in producing flat bands and set a platform to study the flat bands related physics., Comment: 20 pages, 4 figures

In this paper we have studied the realizations of the popular TM1 neutrino mixing and neutrino $\mu$-$\tau$ reflection symmetry (which are well motivated from the neutrino oscillation data and lead to interesting phenomenological consequences) in the most minimal seesaw model with a pseudo-Dirac pair of right-handed neutrinos, and their consequences for leptogenesis. In order to realize the low-scale resonant leptogenesis scenario, we have considered two possible ways of generating the tiny mass splitting between the two right-handed neutrinos: one way is to modify their Majorana mass matrix to a form as shown in Eq. (25); the other way is to consider the renormalization-group corrections for their masses. For the $\mu$-$\tau$ reflection symmetry, in order for leptogenesis to work, we have further considered the flavor-dependent conversion efficiencies from the lepton asymmetry to the baryon asymmetry during the sphaleron processes, and its breaking via the renormalization-group corrections., Comment: 24 pages, 10 figures, version accepted for publication in PRD

Artificial molecular rotors and motors hold great promise for functional nanomachines, but constructing diatomic rotors, crucial for these machines, is challenging due to surface constraints and limited chemical design. Here we report the construction of diatomic Cr-Cs and Fe-Cs rotors where a Cr or Fe atom revolves around a Cs atom at the Sb surface of the newly-discovered kagome superconductor CsV3Sb5. The rotation rate is controlled by bias voltage between the rotor and scanning tunneling microscope (STM) tip. The spatial distribution of rates exhibits C2 symmetry, might linked to the symmetry-breaking charge orders of CsV3Sb5. We have expanded rotor construction to include different transition metals (Cr, Fe, V) and alkali metals (Cs, K). Remarkably, designed configurations of rotors are achieved through STM manipulation. Rotor orbits and quantum states are precisely controlled by tunning inter-rotor distance. Our findings establish a novel platform for the atomically precise fabrication of atomic motors on symmetry-breaking quantum materials, paving the way for advanced nanoscale devices., Comment: 17pages, 4 figures

This paper investigates the optimal investment, consumption, and life insurance strategies for households under the impact of health shock risk. Considering the uncertainty of the future health status of family members, a non-homogeneous Markov process is used to model the health status of the breadwinner. Drawing upon the theory of habit formation, we investigate the influence of different consumption habits on households' investment, consumption, and life insurance strategies. Based on whether the breadwinner is alive or not, we formulate and solve the corresponding Hamilton-Jacobi-Bellman (HJB) equations for the two scenarios of breadwinner survival and breadwinner's demise, respectively, and obtain explicit expressions for the optimal investment, consumption, and life insurance strategies. Through sensitivity analysis, it has been shown that the presence of health shocks within households has a negative impact on investment and consumption decisions, while the formation of consumption habits increases household propensity for precautionary savings.

Vortices and bound states offer an effective means of comprehending the electronic properties of superconductors. Recently, surface dependent vortex core states have been observed in the newly discovered kagome superconductors CsV3Sb5. Although the spatial distribution of the sharp zero energy conductance peak appears similar to Majorana bound states arising from the superconducting Dirac surface states, its origin remains elusive. In this study, we present observations of tunable vortex bound states (VBSs) in two chemically doped kagome superconductors Cs(V1-xTrx)3Sb5 (Tr=Ta or Ti), using low temperature scanning tunneling microscopy/spectroscopy. The CsV3Sb5-derived kagome superconductors exhibit full gap pairing superconductivity accompanied by the absence of long range charge orders, in contrast to pristine CsV3Sb5. Zero energy conductance maps demonstrate a field-driven continuous reorientation transition of the vortex lattice, suggesting multiband superconductivity. The Ta doped CsV3Sb5 displays the conventional cross shaped spatial evolution of Caroli de Gennes Matricon bound states, while the Ti doped CsV3Sb5 exhibits a sharp, non split zero bias conductance peak (ZBCP) that persists over a long distance across the vortex. The spatial evolution of the non split ZBCP is robust against surface effects and external magnetic field but is related to the doping concentrations. Our study reveals the tunable VBSs in multiband chemically doped CsV3Sb5 system and offers fresh insights into previously reported Y shaped ZBCP in a non quantum limit condition at the surface of kagome superconductor., Comment: accepted by Science Bulletin DOI:10.1016/j.scib.2024.01.036

We report quantum oscillation measurements on the kagome compounds ATi$_3$Bi$_5$ (A=Rb, Cs) in magnetic fields up to 41.5 T and temperatures down to 350 mK. In addition to the frequencies observed in previous studies, we have observed multiple unreported frequencies above 2000 T in CsTi$_3$Bi$_5$ using a tunnel diode oscillator technique. We compare these results against density functional theory calculations and find good agreement with the calculations in the number of peaks observed, frequency, and the dimensionality of the Fermi surface. For RbTi$_3$Bi$_5$ we have obtained a different quantum oscillation spectrum, although calculated quantum oscillation frequencies for the Rb compound are remarkably similar to the Cs compound, calling for further studies.

Motivated by recent experimental data on $\Sigma^+\to p\gamma$ at BESIII, we investigate a class of hyperon weak radiative decays. To estimate these processes, in our research, we employ a new type of light-front quark model with a three-quark picture for octet baryons. In the three-quark picture, with the use of $SU(3)_f$ and spin symmetries, we present a general form of the light front wave function for each octet baryon. By including contributions from the penguin diagram and W exchange diagram, we perform a complete calculation on the branching ratios ($Br$) and the asymmetry parameter ($\alpha$) for hyperon weak radiative decay processes. Our results are helpful for discovering additional hyperon weak radiative decay processes in experimental facilities, and our research will promote the theoretical study of baryons., Comment: 15 pages, 2 figures, 5 tables

Speech emotion recognition (SER) performance deteriorates significantly in the presence of noise, making it challenging to achieve competitive performance in noisy conditions. To this end, we propose a multi-level knowledge distillation (MLKD) method, which aims to transfer the knowledge from a teacher model trained on clean speech to a simpler student model trained on noisy speech. Specifically, we use clean speech features extracted by the wav2vec-2.0 as the learning goal and train the distil wav2vec-2.0 to approximate the feature extraction ability of the original wav2vec-2.0 under noisy conditions. Furthermore, we leverage the multi-level knowledge of the original wav2vec-2.0 to supervise the single-level output of the distil wav2vec-2.0. We evaluate the effectiveness of our proposed method by conducting extensive experiments using five types of noise-contaminated speech on the IEMOCAP dataset, which show promising results compared to state-of-the-art models., Comment: Accepted by INTERSPEECH 2023

While semi-supervised learning (SSL) has yielded promising results, the more realistic SSL scenario remains to be explored, in which the unlabeled data exhibits extremely high recognition difficulty, e.g., fine-grained visual classification in the context of SSL (SS-FGVC). The increased recognition difficulty on fine-grained unlabeled data spells disaster for pseudo-labeling accuracy, resulting in poor performance of the SSL model. To tackle this challenge, we propose Soft Label Selection with Confidence-Aware Clustering based on Class Transition Tracking (SoC) by reconstructing the pseudo-label selection process by jointly optimizing Expansion Objective and Shrinkage Objective, which is based on a soft label manner. Respectively, the former objective encourages soft labels to absorb more candidate classes to ensure the attendance of ground-truth class, while the latter encourages soft labels to reject more noisy classes, which is theoretically proved to be equivalent to entropy minimization. In comparisons with various state-of-the-art methods, our approach demonstrates its superior performance in SS-FGVC. Checkpoints and source code are available at https://github.com/NJUyued/SoC4SS-FGVC., Comment: Accepted by AAAI 2024

Semi-supervised medical image segmentation studies have shown promise in training models with limited labeled data. However, current dominant teacher-student based approaches can suffer from the confirmation bias. To address this challenge, we propose AD-MT, an alternate diverse teaching approach in a teacher-student framework. It involves a single student model and two non-trainable teacher models that are momentum-updated periodically and randomly in an alternate fashion. To mitigate the confirmation bias from the diverse supervision, the core of AD-MT lies in two proposed modules: the Random Periodic Alternate (RPA) Updating Module and the Conflict-Combating Module (CCM). The RPA schedules the alternating diverse updating process with complementary data batches, distinct data augmentation, and random switching periods to encourage diverse reasoning from different teaching perspectives. The CCM employs an entropy-based ensembling strategy to encourage the model to learn from both the consistent and conflicting predictions between the teachers. Experimental results demonstrate the effectiveness and superiority of our AD-MT on the 2D and 3D medical segmentation benchmarks across various semi-supervised settings., Comment: code:https://github.com/ZhenZHAO/AD-MT, accepted by ECCV 2024

Unsupervised domain adaptation (UDA) is a critical challenge in the field of point cloud analysis. Previous works tackle the problem either by feature extractor adaptation to enable a shared classifier to distinguish domain-invariant features, or by classifier adaptation to evolve the classifier to recognize target-styled source features to increase its adaptation ability. However, by learning domain-invariant features, feature extractor adaptation methods fail to encode semantically meaningful target-specific information, while classifier adaptation methods rely heavily on the accurate estimation of the target distribution. In this work, we propose a novel framework that deeply couples the classifier and feature extractor adaption for 3D UDA, dubbed Progressive Classifier and Feature Extractor Adaptation (PCFEA). Our PCFEA conducts 3D UDA from two distinct perspectives: macro and micro levels. On the macro level, we propose a progressive target-styled feature augmentation (PTFA) that establishes a series of intermediate domains to enable the model to progressively adapt to the target domain. Throughout this process, the source classifier is evolved to recognize target-styled source features (\ie, classifier adaptation). On the micro level, we develop an intermediate domain feature extractor adaptation (IDFA) that performs a compact feature alignment to encourage the target-styled feature extraction gradually. In this way, PTFA and IDFA can mutually benefit each other: IDFA contributes to the distribution estimation of PTFA while PTFA constructs smoother intermediate domains to encourage an accurate feature alignment of IDFA. We validate our method on popular benchmark datasets, where our method achieves new state-of-the-art performance. Our code is available at https://github.com/xiaoyao3302/PCFEA., Comment: Accepted by ECCV 2024

Scene text recognition (STR) in the wild frequently encounters challenges when coping with domain variations, font diversity, shape deformations, etc. A straightforward solution is performing model fine-tuning tailored to a specific scenario, but it is computationally intensive and requires multiple model copies for various scenarios. Recent studies indicate that large language models (LLMs) can learn from a few demonstration examples in a training-free manner, termed "In-Context Learning" (ICL). Nevertheless, applying LLMs as a text recognizer is unacceptably resource-consuming. Moreover, our pilot experiments on LLMs show that ICL fails in STR, mainly attributed to the insufficient incorporation of contextual information from diverse samples in the training stage. To this end, we introduce E$^2$STR, a STR model trained with context-rich scene text sequences, where the sequences are generated via our proposed in-context training strategy. E$^2$STR demonstrates that a regular-sized model is sufficient to achieve effective ICL capabilities in STR. Extensive experiments show that E$^2$STR exhibits remarkable training-free adaptation in various scenarios and outperforms even the fine-tuned state-of-the-art approaches on public benchmarks. The code is released at https://github.com/bytedance/E2STR ., Comment: Accepted to CVPR2024

Virtual Reality (VR) can support effective and scalable training of psychomotor skills in manufacturing. However, many industry training modules offer experiences that are close-ended and do not allow for human error. We aim to address this gap in VR training tools for psychomotor skills training by exploring an open-ended approach to the system design. We designed a VR training simulation prototype to perform open-ended practice of drilling using a 3-axis milling machine. The simulation employs near "end-to-end" instruction through a safety module, a setup and drilling tutorial, open-ended practice complete with warnings of mistakes and failures, and a function to assess the geometries and locations of drilled holes against an engineering drawing. We developed and conducted a user study within an undergraduate-level introductory fabrication course to investigate the impact of open-ended VR practice on learning outcomes. Study results reveal positive trends, with the VR group successfully completing the machining task of drilling at a higher rate (75% vs 64%), with fewer mistakes (1.75 vs 2.14 score), and in less time (17.67 mins vs 21.57 mins) compared to the control group. We discuss our findings and limitations and implications for the design of open-ended VR training systems for learning psychomotor skills., Comment: 10 pages, 4 figures, 9 tables

Fast radio bursts (FRBs) are transient radio signals with millisecond-duration, large dispersion measure (DM) and extremely high brightness temperature. Among them, FRB 20180916B has been found to have a 16-day periodically modulated activity. However, the physical origin of the periodicity is still a mystery. Here, we utilize the comprehensive observational data to diagnose the periodic models. We find that the ultra-long rotation model is the most probable one for the periodic activity. However, this model cannot reproduce the observed rotation measure (RM) variations. We propose a self-consistent model, i.e., a massive star binary containing a slowly rotational neutron star and a massive star with large mass loss, which can naturally accommodate the wealth of observational features for FRB 20180916B. In this model, the RM variation is periodic, which can be tested by future observations., Comment: 17 pages, 9 figures, 3 tables, accepted for publication in ApJL

Multimodal large language models (MLLMs) have emerged as a prominent area of interest within the research community, given their proficiency in handling and reasoning with non-textual data, including images and videos. This study seeks to extend the application of MLLMs to the realm of autonomous driving by introducing DriveGPT4, a novel interpretable end-to-end autonomous driving system based on LLMs. Capable of processing multi-frame video inputs and textual queries, DriveGPT4 facilitates the interpretation of vehicle actions, offers pertinent reasoning, and effectively addresses a diverse range of questions posed by users. Furthermore, DriveGPT4 predicts low-level vehicle control signals in an end-to-end fashion. These advanced capabilities are achieved through the utilization of a bespoke visual instruction tuning dataset, specifically tailored for autonomous driving applications, in conjunction with a mix-finetuning training strategy. DriveGPT4 represents the pioneering effort to leverage LLMs for the development of an interpretable end-to-end autonomous driving solution. Evaluations conducted on the BDD-X dataset showcase the superior qualitative and quantitative performance of DriveGPT4. Additionally, the fine-tuning of domain-specific data enables DriveGPT4 to yield close or even improved results in terms of autonomous driving grounding when contrasted with GPT4-V. The code and dataset will be publicly available., Comment: The project page is available at https://tonyxuqaq.github.io/projects/DriveGPT4/