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The paper is concerned with a scalar conservation law with discontinuous gradient-dependent flux. Namely, the flux is described by two different functions $f(u)$ or $g(u)$, when the gradient $u_x$ of the solution is positive or negative, respectively. We study here the stable case where $f(u)

Artificial spin ices provide a controlled platform for investigating diverse physical phenomena, such as geometric frustration, magnetic monopoles, and phase transitions, via deliberate design. Here, we introduce a novel approach by developing artificial spin ice on the surfaces of a three-dimensional cube, which leads to emergent geometric frustration mediated by topologically protected domain walls, distinct from its flat counterparts. These domain walls connect vertices at the corners of cube that acting as intrinsic topological defects. Utilizing Monte Carlo simulations, we observe robust, topologically protected correlations among the intrinsic topological defects, regardless of their spatial separation. Our findings demonstrate that three-dimensional surfaces can unveil emergent properties absent in flat architectures.

Image-based virtual try-on, widely used in online shopping, aims to generate images of a naturally dressed person conditioned on certain garments, providing significant research and commercial potential. A key challenge of try-on is to generate realistic images of the model wearing the garments while preserving the details of the garments. Previous methods focus on masking certain parts of the original model's standing image, and then inpainting on masked areas to generate realistic images of the model wearing corresponding reference garments, which treat the try-on task as an inpainting task. However, such implements require the user to provide a complete, high-quality standing image, which is user-unfriendly in practical applications. In this paper, we propose Try-On-Adapter (TOA), an outpainting paradigm that differs from the existing inpainting paradigm. Our TOA can preserve the given face and garment, naturally imagine the rest parts of the image, and provide flexible control ability with various conditions, e.g., garment properties and human pose. In the experiments, TOA shows excellent performance on the virtual try-on task even given relatively low-quality face and garment images in qualitative comparisons. Additionally, TOA achieves the state-of-the-art performance of FID scores 5.56 and 7.23 for paired and unpaired on the VITON-HD dataset in quantitative comparisons., Comment: Image virtual try-on, 7 pages, 3 figures

Existing Image Quality Assessment (IQA) methods achieve remarkable success in analyzing quality for overall image, but few works explore quality analysis for Regions of Interest (ROIs). The quality analysis of ROIs can provide fine-grained guidance for image quality improvement and is crucial for scenarios focusing on region-level quality. This paper proposes a novel network, SEAGULL, which can SEe and Assess ROIs quality with GUidance from a Large vision-Language model. SEAGULL incorporates a vision-language model (VLM), masks generated by Segment Anything Model (SAM) to specify ROIs, and a meticulously designed Mask-based Feature Extractor (MFE) to extract global and local tokens for specified ROIs, enabling accurate fine-grained IQA for ROIs. Moreover, this paper constructs two ROI-based IQA datasets, SEAGULL-100w and SEAGULL-3k, for training and evaluating ROI-based IQA. SEAGULL-100w comprises about 100w synthetic distortion images with 33 million ROIs for pre-training to improve the model's ability of regional quality perception, and SEAGULL-3k contains about 3k authentic distortion ROIs to enhance the model's ability to perceive real world distortions. After pre-training on SEAGULL-100w and fine-tuning on SEAGULL-3k, SEAGULL shows remarkable performance on fine-grained ROI quality assessment. Code and datasets are publicly available at the https://github.com/chencn2020/Seagull.

Due to power attenuation, improving transmission efficiency in the radio-frequency (RF) band remains a significant challenge, which hinders advancements in various fields of the Internet of Things (IoT), such as wireless power transfer (WPT) and wireless communication. Array design and retro-directive beamforming (RD-BF) techniques offer simple and effective ways to enhance transmission efficiency. However, when the target is an array or in the near field, the RD-BF system (RD-BFS) cannot radiate more energy to the target due to phase irregularities in the target region, resulting in challenges in achieving higher efficiency. To address this issue, we propose the RF-based resonant beam system (RF-RBS), which adaptively optimizes phase and power distribution between transmitting and receiving arrays by leveraging the resonance mechanism to achieve higher transmission efficiency. We analyze the system structure and develop an analytical model to evaluate power flow and resonance establishment. Numerical analysis demonstrates that the proposed RF-RBS achieves self-alignment without beam control and provides higher transmission efficiency compared to RD-BFS, with improvements of up to 16%. This self-alignment capability allows the system to effectively transfer power and information across varying distances and offsets. The numerical results indicate the capability to transmit watt-level power and achieve 21 bps/Hz of downlink spectral efficiency in indoor settings, highlighting the advantages of RF-RBS in information and power transfer for mobile applications.

Post-selecting output states in measurements can effectively amplify weak signals and improve precision. However, post-selection effects may also introduce unintended biases in precision measurements. Here, we investigate the influence of post-selection in the precision spectroscopy of the $2^3S - 2^3P$ transition of helium ($^4$He) using an atomic beam. We directly observe that post-selection based on atomic positions causes a shift in the measured transition frequency, amounting to approximately -55 kHz. After accounting for this post-selection shift, we obtain a corrected frequency of $276,764,094,712.45 \pm 0.86$ kHz for the $2^3S_1 - 2^3P_0$ transition. Combining this result with existing data for $^3$He, we derive a new value for the difference in squared nuclear charge radii, $\delta r^2 [r_{h}^{2} - r_{\alpha}^{2}] = 1.0733 \pm 0.0021$ fm$^2$. This value shows a $2.8\sigma$ deviation from measurements of muonic helium ions, potentially pointing to new physics that challenges lepton universality in quantum electrodynamics., Comment: 14 pages including appendix

Image editing involves a variety of complex tasks and requires efficient and precise manipulation techniques. In this paper, we present MagicQuill, an integrated image editing system that enables swift actualization of creative ideas. Our system features a streamlined yet functionally robust interface, allowing for the articulation of editing operations (e.g., inserting elements, erasing objects, altering color) with minimal input. These interactions are monitored by a multimodal large language model (MLLM) to anticipate editing intentions in real time, bypassing the need for explicit prompt entry. Finally, we apply a powerful diffusion prior, enhanced by a carefully learned two-branch plug-in module, to process editing requests with precise control. Experimental results demonstrate the effectiveness of MagicQuill in achieving high-quality image edits. Please visit https://magic-quill.github.io to try out our system., Comment: Code and demo available at https://magic-quill.github.io

In this talk, we present a new interpretation for the recently observed $\Xi(1820)$ resonance. We recall that the chiral unitary approach for the interaction of pseudoscalar mesons with the baryons of the decuplet predicts two states for the $\Xi(1820)$ resonance, one with a narrow width and the other one with a large width. We contrast this fact with the recent BESIII measurement of the $K^- \Lambda$ mass distribution in the $\psi(3686)$ decay to $K^- \Lambda \bar\Xi^+ $, which demands a width much larger than the average of the PDG, and show how the consideration of the two $\Xi(1820)$ states provides a natural explanation to this apparent contradiction., Comment: Proceeding of the QNP 2024 Conference. 6 pages, 3 figures, 1 table. arXiv admin note: substantial text overlap with arXiv:2309.03618

The discovery of advanced materials is the cornerstone of human technological development and progress. The structures of materials and their corresponding properties are essentially the result of a complex interplay of multiple degrees of freedom such as lattice, charge, spin, symmetry, and topology. This poses significant challenges for the inverse design methods of materials. Humans have long explored new materials through a large number of experiments and proposed corresponding theoretical systems to predict new material properties and structures. With the improvement of computational power, researchers have gradually developed various electronic structure calculation methods, particularly such as the one based density functional theory, as well as high-throughput computational methods. Recently, the rapid development of artificial intelligence technology in the field of computer science has enabled the effective characterization of the implicit association between material properties and structures, thus opening up an efficient paradigm for the inverse design of functional materials. A significant progress has been made in inverse design of materials based on generative and discriminative models, attracting widespread attention from researchers. Considering this rapid technological progress, in this survey, we look back on the latest advancements in AI-driven inverse design of materials by introducing the background, key findings, and mainstream technological development routes. In addition, we summarize the remaining issues for future directions. This survey provides the latest overview of AI-driven inverse design of materials, which can serve as a useful resource for researchers., Comment: 43 pages, 5 figures, 2 tables

Integrated sensing and communication (ISAC) is envisioned as a key technology for future sixth-generation (6G) networks. Classical ISAC system considering monostatic and/or bistatic settings will inevitably degrade both communication and sensing performance due to the limited service coverage and easily blocked transmission paths. Besides, existing ISAC studies usually focus on downlink (DL) or uplink (UL) communication demands and unable to achieve the systematic DL and UL communication tasks. These challenges can be overcome by networked FD ISAC framework. Moreover, ISAC generally considers the trade-off between communication and sensing, unavoidably leading to a loss in communication performance. This shortcoming can be solved by the emerging movable antenna (MA) technology. In this paper, we utilize the MA to promote communication capability with guaranteed sensing performance via jointly designing beamforming, power allocation, receiving filters and MA configuration towards maximizing sum rate. The optimization problem is highly difficult due to the unique channel model deriving from the MA. To resolve this challenge, via leveraging the cutting-the-edge majorization-minimization (MM) method, we develop an efficient solution that optimizes all variables via convex optimization techniques. Extensive simulation results verify the effectiveness of our proposed algorithms and demonstrate the substantial performance promotion by deploying MA in the networked FD ISAC system.

Affine frequency division multiplexing (AFDM) is a novel modulation technique based on chirp signals that has been recently proposed as an effective solution for highly reliable communications in high-mobility scenarios. In this paper, we focus on the design of robust index modulation (IM) schemes under the multiple-antenna AFDM transmission framework. To this end, the cyclic delay diversity (CDD) technique is employed to harvest the transmit diversity gain. As a result, we propose two novel AFDM-IM schemes with transmit diversity, termed as CDD-AFDM-IM-I and CDD-AFDM-IM-II. We analyze the full diversity conditions and parameter settings of the proposed CDD-AFDM-IM schemes for both integer and fractional Doppler cases over linear time-varying (LTV) channels. Moreover, we prove that IM enables AFDM to have stronger diversity protection when the full diversity condition is not satisfied. Asymptotically tight upper bounds on the average bit error rates (BERs) of the proposed schemes with maximum-likelihood (ML) detection are derived in closed-form. Furthermore, we propose a low-complexity double-layer message passing (DLMP) algorithm for practical large-dimensional signal detection in the proposed CDD-AFDM-IM systems. Comparison with existing detections shows that the proposed DLMP algorithm achieves a better tradeoff between the BER performance and the computational complexity. Finally, BER simulation results confirm that our proposed CDD-AFDM-IM schemes with both the ML and DLMP detections outperform the benchmark schemes over the LTV channels., Comment: accepted by IEEE Journal on Selected Areas in Communications

Kagome materials with inherent geometric frustration can produce many interesting physical properties, such as flat bands, quantum spin liquid, chiral magnetism, superconductivity and density-wave orders. Sometimes, the localized 4$f$ electrons from Ce atoms coupled with other conduction electrons would also give rise to the flat bands near the Fermi level, and results in the formation of heavy fermion. Thus, it is highly probable that kagome material incorporating Ce element will display nontrivial physical properties. In this study, we present a new Kagome material belonging to the trinary Laves phase, Ce$_{2}$Ru$_{3}$Si, in which kagome plane is formed by Ru atoms. Electrical transport and specific heat measurements reveal a density-wave like transition. A Curie-Weiss behavior is observed in low-temperature region. Meanwhile we also find a relatively large specific coefficient $\gamma_{n}(0)$. The calculated Wilson ratio $R_\mathrm{W}\propto{\chi(0)/\gamma_{n}}$ is approximately 3.1, indicating a moderate electron correlation effect. Chemical doping of Ir at the Ru site rapidly suppresses this density-wave like transition, while Mo doping leads to a gradual decrease in transition temperature. Theoretical calculation indicates both the Ce-4$f$ and Ru-4$d$ electronic bands cross the Fermi level, forming a Mexican-hat-shape Fermi surface close to the Fermi energy, potentially accounting for the observed density-wave like transition. Our findings provide an useful platform for investigating how hybridization between 4$f$ and 4$d$ electrons influences the electronic transport, and the relationship between the density-wave transition and kagome structure., Comment: 15pages, 4 figures,2 supplementary figures

The plasma edge flow, situated at the intricate boundary between plasma and neutral particles, plays a pivotal role in the design of nuclear fusion devices such as divertors and pumps. Traditional numerical simulation methods, such as the direct simulation Monte Carlo approach and the discrete velocity method, are hindered by extensive computation times when dealing with near-continuum flow conditions. This paper presents a general synthetic iterative scheme to deterministically simulate the plasma edge flows. By alternately solving the kinetic equations and macroscopic synthetic equations, our method substantially decreases the number of iterations, while maintains asymptotic-preserving properties even when the spatial cell size is much larger than the mean free path. Consequently, our approach achieves rapid convergence and high accuracy in plasma edge flow simulations, particularly in near-continuum flow regimes. This advancement provides a robust and efficient computational tool, essential for the advancement of next-generation nuclear fusion reactors.

We report on optical studies of the Ruddlesden-Popper nickelates La$_{n+1}$Ni$_{n}$O$_{3n+1}$ with $n = 2$ (La$_{3}$Ni$_{2}$O$_{7}$), $n = 3$ (La$_{4}$Ni$_{3}$O$_{10}$) and $n = \infty$ (LaNiO$_{3}$). As the number of the NiO$_{6}$ octahedra layers $n$ grows, the ratio of the kinetic energy determined from the experimental optical conductivity and that from band theory $K_{\text{exp}}/K_{\text{band}}$ increases, suggesting a reduction of electronic correlations. While the strong electronic correlations in the bilayer La$_{3}$Ni$_{2}$O$_{7}$ place it on the verge of the Mott insulating phase, the trilayer La$_{4}$Ni$_{3}$O$_{10}$ and infinite-layer LaNiO$_{3}$ exhibit moderate electronic correlations, falling into the regime of correlated metals. The evolution of the electronic correlations in La$_{n+1}$Ni$_{n}$O$_{3n+1}$ is likely to be dominated by the Ni-$d_{z^2}$ orbital. Our results provide important information for understanding the superconductivity in Ruddlesden-Popper nickelates., Comment: 7 pages, 4 figures. Comments are welcome and appreciated

Current Vehicle-to-Everything (V2X) systems have significantly enhanced 3D object detection using LiDAR and camera data. However, these methods suffer from performance degradation in adverse weather conditions. The weatherrobust 4D radar provides Doppler and additional geometric information, raising the possibility of addressing this challenge. To this end, we present V2X-R, the first simulated V2X dataset incorporating LiDAR, camera, and 4D radar. V2X-R contains 12,079 scenarios with 37,727 frames of LiDAR and 4D radar point clouds, 150,908 images, and 170,859 annotated 3D vehicle bounding boxes. Subsequently, we propose a novel cooperative LiDAR-4D radar fusion pipeline for 3D object detection and implement it with various fusion strategies. To achieve weather-robust detection, we additionally propose a Multi-modal Denoising Diffusion (MDD) module in our fusion pipeline. MDD utilizes weather-robust 4D radar feature as a condition to prompt the diffusion model to denoise noisy LiDAR features. Experiments show that our LiDAR-4D radar fusion pipeline demonstrates superior performance in the V2X-R dataset. Over and above this, our MDD module further improved the performance of basic fusion model by up to 5.73%/6.70% in foggy/snowy conditions with barely disrupting normal performance. The dataset and code will be publicly available at: https://github.com/ylwhxht/V2X-R.

Quantum mechanics gives a new breakthrough to the field of parameter estimation. In the realm of quantum metrology, the precision of parameter estimation is limited by the quantum Fisher information. We introduce the measures of partial coherence based on (quantum) Fisher information by taking into account the post-selective non-unitary parametrization process. These partial coherence measures present a clear operational interpretation by directly linking the coherence to the parameter estimation accuracy. Furthermore, we explore the distinctions between our partial coherence measure and the quantum Fisher information within the context of unitary parametrization. We provide an analytical expression for the partial coherence measure of two-qubit states. We elucidate the operational significance of the partial coherence measures by establishing the connections between the partial coherence measures and quantum state discrimination., Comment: 17 pages, 1 figure

Data augmentation is a powerful technique to mitigate data scarcity. However, owing to fundamental differences in wireless data structures, traditional data augmentation techniques may not be suitable for wireless data. Fortunately, Generative Artificial Intelligence (GenAI) can be an effective alternative to wireless data augmentation due to its excellent data generation capability. This article systemically explores the potential and effectiveness of GenAI-driven data augmentation in wireless networks. We first briefly review data augmentation techniques, discuss their limitations in wireless networks, and introduce generative data augmentation, including reviewing GenAI models and their applications in data augmentation. We then explore the application prospects of GenAI-driven data augmentation in wireless networks from the physical, network, and application layers, which provides a GenAI-driven data augmentation architecture for each application. Subsequently, we propose a general generative diffusion model-based data augmentation framework for Wi-Fi gesture recognition, which uses transformer-based diffusion models to generate high-quality channel state information data. Furthermore, we develop residual neural network models for Wi-Fi gesture recognition to evaluate the role of augmented data and conduct a case study based on a real dataset. Simulation results demonstrate the effectiveness of the proposed framework. Finally, we discuss research directions for generative data augmentation.

Recent advancements utilizing large-scale video data for learning video generation models demonstrate significant potential in understanding complex physical dynamics. It suggests the feasibility of leveraging diverse robot trajectory data to develop a unified, dynamics-aware model to enhance robot manipulation. However, given the relatively small amount of available robot data, directly fitting data without considering the relationship between visual observations and actions could lead to suboptimal data utilization. To this end, we propose VidMan (Video Diffusion for Robot Manipulation), a novel framework that employs a two-stage training mechanism inspired by dual-process theory from neuroscience to enhance stability and improve data utilization efficiency. Specifically, in the first stage, VidMan is pre-trained on the Open X-Embodiment dataset (OXE) for predicting future visual trajectories in a video denoising diffusion manner, enabling the model to develop a long horizontal awareness of the environment's dynamics. In the second stage, a flexible yet effective layer-wise self-attention adapter is introduced to transform VidMan into an efficient inverse dynamics model that predicts action modulated by the implicit dynamics knowledge via parameter sharing. Our VidMan framework outperforms state-of-the-art baseline model GR-1 on the CALVIN benchmark, achieving a 11.7% relative improvement, and demonstrates over 9% precision gains on the OXE small-scale dataset. These results provide compelling evidence that world models can significantly enhance the precision of robot action prediction. Codes and models will be public., Comment: Accepted to NeurIPS 2024

The multi-window observations, including the light curve and the evolutions of the spectral peak energy ($E_p$), the polarization degree (PD) and the polarization angle (PA), are used to infer the model parameters to predict the time-integrated PD in gamma-ray burst (GRB) prompt phase. We select 23 GRBs co-detected by Fermi/GBM and polarization detectors (i.e., GAP, POLAR and AstroSat). In our multi-window fitting, the light curve, $E_p$ curve, PD curve and PA curve are interpreted simultaneously under the synchrotron radiation model in ordered magnetic fields (i.e., the aligned-fields case and the toroidal-fields case). For the bursts with abrupt PA rotations, the predicted time-integrated PD of the aligned-fields case roughly matches the corresponding observed best fit value, while it is higher for the toroidal-fields case. For the bursts without abrupt PA rotation(s), the predicted PDs of the aligned-fields case and the toroidal-fields case are comparable and could interpret the observational data equally well. For GRB 170206A, its observed time-resolved and time-integrated PDs are comparable and both smaller than our predicted upper limits in ordered magnetic fields. So mixed magnetic fields, i.e., the magnetic fields with both ordered and random components, should be reside in the radiation regions of this burst. Except 1 out of the total 23 bursts, the predicted time-integrated PDs, which are around $\sim44\%$ for the aligned-fields case and around $49\%$ for the toroidal-fields case, are consistent with the corresponding observed values. Therefore, consistent with the former study, the models with synchrotron radiation in ordered magnetic fields could interpret most of the current polrization data within $1\sigma$ error bar., Comment: 19 pages, 26 figures, 2 tables

The observed twice abrupt ${90}^{\circ}$ rotations of the polarization angle (PA) in the prompt phase of gamma-ray bursts (GRBs) are difficult to be understandable within the current one-emitting-shell models. Here, we apply a model with multiple emitting shells to solve this new challenging problem. Two configurations of large-scale ordered magnetic fields in the shells are considered: toroidal and aligned. Together with the light curves and the spectral peak-energy evolutions, the twice ${90}^{\circ}$ PA rotations in GRB 170114A and GRB 160821A could be well interpreted with the multi-shell aligned magnetic fields configuration. Our numerical calculations also show that the multiple shells with the toroidal magnetic field configuration could not explain the observed twice ${90}^{\circ}$ PA rotations. An aligned magnetic field configuration in the GRB outflow usually indicate to prefer a magnetar central engine, while a toroidal field configuration is typically related to a central black hole. Therefore, the magnetar central engines for the two GRBs are favored., Comment: 13 pages, 6 figures, 4 tables, ApJ accepted

Rapid progress in precision nanofabrication and atomic design over the past 50 years has ushered in a succession of transformative eras for molding the generation and flow of light. The use of nanoscale and atomic features to design light sources and optical elements-encapsulated by the term nanophotonics-has led to new fundamental science and innovative technologies across the entire electromagnetic spectrum, with substantial emphasis on the microwave to visible regimes. In this review, we pay special attention to the impact and potential of nanophotonics in a relatively exotic yet technologically disruptive regime: high-energy particles such as X-ray photons and free electrons-where nanostructures and atomic design open the doors to unprecedented technologies in quantum science and versatile X-ray sources and optics. As the practical generation of X-rays is intrinsically linked to the existence of energetic free or quasi-free-electrons, our review will also capture related phenomena and technologies that combine free electrons with nanophotonics, including free-electron-driven nanophotonics at other photon energies. In particular, we delve into the demonstration and study of quantum recoil in the X-ray regime, the study of nanomaterial design and free-electron wave shaping as means to enhance and control X-ray radiation, examine the free-electron generation enabled by nanophotonics, and analyze the high-harmonic generation by quasi-free electrons. We also discuss applications of quantum nanophotonics for X-rays and free electrons, including nanostructure waveguides for X-rays, photon pair enhanced X-ray imaging, mirrors, and lenses for X-rays, among others.

Query determinacy is decidable for project-select views and a project-select-join query with no self joins, as long as the selection predicates are in a first-order theory for which satisfiability is decidable.

The concept of skyrmions was introduced as early as the 1960s by Tony Skyrme. The topologically protected configuration embedded in skyrmions has prompted some investigations into their fundamental properties and versatile applications, sparking interest and guiding ongoing development. The topological protection associated with skyrmions was initially observed in systems with interactions. It is widely believed that skyrmions are stable yet relevant confirmation and empirical research remains limited. A pertinent question is whether skyrmion configurations formed by single-particle wave functions also exhibit topological stability. In this study, we affirm this hypothesis by investigating the effects of local decoherence. We analytically and numerically demonstrate the topological resilience of skyrmions and occurrence of transition points of skyrmion numbers in local decoherence of three typical decoherence channels. On the other hand, we show that these qualities are independent of the initial state. From the numerical results, we verify that inhomogeneous but continuous decoherence channels also adhere to the same behaviors and hold topological stability of skyrmions as homogeneous decoherence channels. These properties of skyrmions contribute to further applications in various areas including communication and imaging., Comment: 19 pages

Change Detection (CD) is an essential field in remote sensing, with a primary focus on identifying areas of change in bi-temporal image pairs captured at varying intervals of the same region by a satellite. The data annotation process for the CD task is both time-consuming and labor-intensive. To make better use of the scarce labeled data and abundant unlabeled data, we present an adaptive dynamic semi-supervised learning method, AdaSemiCD, to improve the use of pseudo-labels and optimize the training process. Initially, due to the extreme class imbalance inherent in CD, the model is more inclined to focus on the background class, and it is easy to confuse the boundary of the target object. Considering these two points, we develop a measurable evaluation metric for pseudo-labels that enhances the representation of information entropy by class rebalancing and amplification of confusing areas to give a larger weight to prospects change objects. Subsequently, to enhance the reliability of sample-wise pseudo-labels, we introduce the AdaFusion module, which is capable of dynamically identifying the most uncertain region and substituting it with more trustworthy content. Lastly, to ensure better training stability, we introduce the AdaEMA module, which updates the teacher model using only batches of trusted samples. Experimental results from LEVIR-CD, WHU-CD, and CDD datasets validate the efficacy and universality of our proposed adaptive training framework.

Deep Learning has been successfully applied in diverse fields, and its impact on deepfake detection is no exception. Deepfakes are fake yet realistic synthetic content that can be used deceitfully for political impersonation, phishing, slandering, or spreading misinformation. Despite extensive research on unimodal deepfake detection, identifying complex deepfakes through joint analysis of audio and visual streams remains relatively unexplored. To fill this gap, this survey first provides an overview of audiovisual deepfake generation techniques, applications, and their consequences, and then provides a comprehensive review of state-of-the-art methods that combine audio and visual modalities to enhance detection accuracy, summarizing and critically analyzing their strengths and limitations. Furthermore, we discuss existing open source datasets for a deeper understanding, which can contribute to the research community and provide necessary information to beginners who want to analyze deep learning-based audiovisual methods for video forensics. By bridging the gap between unimodal and multimodal approaches, this paper aims to improve the effectiveness of deepfake detection strategies and guide future research in cybersecurity and media integrity.

To realize ferromagnetic semiconductors with high Curie temperature TC is still a challenge in spintronics. Recent experiments have obtained two-dimensional (2D) room temperature ferromagnetic metals, such as monolayers MnSe2 and Cr3Te6. In this paper, by the density functional theory (DFT) calculations, we proposed a way to obtain 2D high TC ferromagnetic semiconductors through element replacement in these ferromagnetic metals. High TC ferromagnetic semiconductors are predicted in the monolayers (Mn, D)Se2 and (Cr, D)3Te6, where element D is taken as vacancy, 3d, 4d and 5d transition metal elements. For the concentrations of D from 1/9 to 1/3, there are about 10 ferromagnetic semiconductors with TC above 200 K, including (Cr5/6, W1/6)3Te6 and (Cr4/6, Mo2/6)3Te6 with TC above 300 K. In addition, Mn(Se6/8, Sb2/8)2 is also predicted to be a 2D ferromagnetic semiconductor with TC above 300 K. Considering the fast developments on fabrication and manipulation of 2D materials, our theoretical results propose a way to explore the high temperature ferromagnetic semiconductors from experimentally obtained 2D high temperature ferromagnetic metals through element replacement approach.

The increasing popularity of deep learning models has created new opportunities for developing AI-based recommender systems. Designing recommender systems using deep neural networks requires careful architecture design, and further optimization demands extensive co-design efforts on jointly optimizing model architecture and hardware. Design automation, such as Automated Machine Learning (AutoML), is necessary to fully exploit the potential of recommender model design, including model choices and model-hardware co-design strategies. We introduce a novel paradigm that utilizes weight sharing to explore abundant solution spaces. Our paradigm creates a large supernet to search for optimal architectures and co-design strategies to address the challenges of data multi-modality and heterogeneity in the recommendation domain. From a model perspective, the supernet includes a variety of operators, dense connectivity, and dimension search options. From a co-design perspective, it encompasses versatile Processing-In-Memory (PIM) configurations to produce hardware-efficient models. Our solution space's scale, heterogeneity, and complexity pose several challenges, which we address by proposing various techniques for training and evaluating the supernet. Our crafted models show promising results on three Click-Through Rates (CTR) prediction benchmarks, outperforming both manually designed and AutoML-crafted models with state-of-the-art performance when focusing solely on architecture search. From a co-design perspective, we achieve 2x FLOPs efficiency, 1.8x energy efficiency, and 1.5x performance improvements in recommender models., Comment: Accepted in ACM Transactions on Recommender Systems. arXiv admin note: substantial text overlap with arXiv:2207.07187

We present JanusFlow, a powerful framework that unifies image understanding and generation in a single model. JanusFlow introduces a minimalist architecture that integrates autoregressive language models with rectified flow, a state-of-the-art method in generative modeling. Our key finding demonstrates that rectified flow can be straightforwardly trained within the large language model framework, eliminating the need for complex architectural modifications. To further improve the performance of our unified model, we adopt two key strategies: (i) decoupling the understanding and generation encoders, and (ii) aligning their representations during unified training. Extensive experiments show that JanusFlow achieves comparable or superior performance to specialized models in their respective domains, while significantly outperforming existing unified approaches across standard benchmarks. This work represents a step toward more efficient and versatile vision-language models.

The MUltiplexed Survey Telescope (MUST) is a 6.5-meter telescope under development. Dedicated to highly-multiplexed, wide-field spectroscopic surveys, MUST observes over 20,000 targets simultaneously using 6.2-mm pitch positioning robots within a ~5 deg2 field of view. MUST aims to carry out the first Stage-V spectroscopic survey in the 2030s to map the 3D Universe with over 100 million galaxies and quasars, spanning from the nearby Universe to redshift z~5.5, corresponding to around 1 billion years after the Big Bang. To cover this extensive redshift range, we present an initial conceptual target selection algorithm for different types of galaxies, from local bright galaxies, luminous red galaxies, and emission line galaxies to high-redshift (2 < z < 5.5) Lyman-break galaxies. Using Fisher forecasts, we demonstrate that MUST can address fundamental questions in cosmology, including the nature of dark energy, test of gravity theories, and investigations into primordial physics. This is the first paper in the series of science white papers for MUST, with subsequent developments focusing on additional scientific cases such as galaxy and quasar evolution, Milky Way physics, and dynamic phenomena in the time-domain Universe., Comment: To be submitted to SCPMA

Eclipsing Algol-type systems containing a $\delta$ Scuti (hereafter $\delta$ Sct) star enable precise determination of physical parameters and the investigation of stellar internal structure and evolution. We present the absolute parameters of CZ Aquarius (hereafter CZ Aqr) based on TESS data. CZ Aqr has an orbital period of 0.86275209 d, a mass ratio of 0.489 (6), and the secondary component nearly fills its Roche lobe. $O-C$ analysis reveals a downward parabolic trend and a cyclical variation with a period of 88.2 yr. The downward parabola suggests a long-term decrease in the orbital period with $\dot{P}$ = -3.09$\times$$10^{-8}$ d $\textrm{yr}^{-1}$. The mass loss rate is estimated to be 4.54$\times$$10^{-9}$ M$_{\odot}$ $\textrm{yr}^{-1}$, which possibly due to magnetic stellar wind or hot spot. The cyclical variation might be caused by the light travel time effect via the presence of a third body with a minimum mass of $M_{3min}$ = 0.312 (21) M$_{\odot}$. Additionally, there are two possible celestial bodies in a 2:7 resonance orbit around CZ Aqr. The asymmetric light curve is explained by adding a hot spot on the surface of the primary star. After removing the binary model, 26 frequencies were extracted from TESS data. Two radial modes were newly identified among three possible independent frequencies. Our results show that the eclipsing Algol-type system is composed of a $\delta$ Sct primary star and a subgiant star in a quadruple system.

We search for nuclear recoil signals of dark matter models with a light mediator using data taken from a p-type point-contact germanium detector of the CDEX-10 experiment at the China Jinping Underground Laboratory. The 90% confidence level upper limits on the DM-nucleon interaction cross section from 205.4 kg-day exposure data are derived, excluding new parameter space in 2~5 GeV DM mass when the mediator mass is comparable to or lighter than the typical momentum transfer. We further interpret our results to constrain a specific self-interacting dark matter model with a light mediator coupling to the photon through kinetic mixing, and set experimental limits on the model parameter region favored by astrophysical observations.

The online reconstruction of dynamic scenes from multi-view streaming videos faces significant challenges in training, rendering and storage efficiency. Harnessing superior learning speed and real-time rendering capabilities, 3D Gaussian Splatting (3DGS) has recently demonstrated considerable potential in this field. However, 3DGS can be inefficient in terms of storage and prone to overfitting by excessively growing Gaussians, particularly with limited views. This paper proposes an efficient framework, dubbed HiCoM, with three key components. First, we construct a compact and robust initial 3DGS representation using a perturbation smoothing strategy. Next, we introduce a Hierarchical Coherent Motion mechanism that leverages the inherent non-uniform distribution and local consistency of 3D Gaussians to swiftly and accurately learn motions across frames. Finally, we continually refine the 3DGS with additional Gaussians, which are later merged into the initial 3DGS to maintain consistency with the evolving scene. To preserve a compact representation, an equivalent number of low-opacity Gaussians that minimally impact the representation are removed before processing subsequent frames. Extensive experiments conducted on two widely used datasets show that our framework improves learning efficiency of the state-of-the-art methods by about $20\%$ and reduces the data storage by $85\%$, achieving competitive free-viewpoint video synthesis quality but with higher robustness and stability. Moreover, by parallel learning multiple frames simultaneously, our HiCoM decreases the average training wall time to $<2$ seconds per frame with negligible performance degradation, substantially boosting real-world applicability and responsiveness., Comment: Accepted to NeurIPS 2024; Code is avaliable at https://github.com/gqk/HiCoM

Nonreciprocal interaction between two spatially separated subsystems plays a crucial role in signal processing and quantum networks. Here, we propose an efficient scheme to achieve nonreciprocal interaction and entanglement between two qubits by combining coherent and dissipative couplings in a superconducting platform, where two coherently coupled transmon qubits simultaneously interact with a transmission line waveguide. The coherent interaction between the transmon qubits can be achieved via capacitive coupling or via an intermediary cavity mode, while the dissipative interaction is induced by the transmission line via reservoir engineering. With high tunability of superconducting qubits, their positions along the transmission line can be adjusted to tune the dissipative coupling, enabling to tailor reciprocal and nonreciprocal interactions between the qubits. A fully nonreciprocal interaction can be achieved when the separation between the two qubits is $(4n+3)\lambda_{0} /4$, where $n$ is an integer and $\lambda_{0}$ is the photon wavelength. This nonreciprocal interaction enables the generation of nonreciprocal entanglement between the two transmon qubits. Furthermore, applying a drive field to one of the qubit can stabilize the system into a nonreciprocal steady-state entangled state. Remarkably, the nonreciprocal interaction in this work does not rely on the presence of nonlinearity or complex configurations, which has more potential applications in designing nonreciprocal quantum devices, processing quantum information, and building quantum networks., Comment: 11 pages, 7 figures

Reusing third-party libraries increases productivity and saves time and costs for developers. However, the downside is the presence of vulnerabilities in those libraries, which can lead to catastrophic outcomes. For instance, Apache Log4J was found to be vulnerable to remote code execution attacks. A total of more than 35,000 packages were forced to update their Log4J libraries with the latest version. Although several studies have been conducted to predict software vulnerabilities, the prediction does not cover the vulnerabilities found in third-party libraries. Even if the developers are aware of the forthcoming issue, replicating a function similar to the libraries would be time-consuming and labour-intensive. Nevertheless, it is practically reasonable for software developers to update their third-party libraries (and dependencies) whenever the software vendors have released a vulnerable-free version. In this work, our manual study focuses on the real-world practices (crowd reaction) adopted by software vendors and developer communities when a vulnerability is disclosed. We manually investigated 312 CVEs and identified that the primary trend of vulnerability handling is to provide a fix before publishing an announcement. Otherwise, developers wait an average of 10 days for a fix if it is unavailable upon the announcement. Additionally, the crowd reaction is oblivious to the vulnerability severity. In particular, we identified Oracle as the most vibrant community diligent in releasing fixes. Their software developers also actively participate in the associated vulnerability announcements.

The observed jet precession period of approximately 11 years for M87* strongly suggests the presence of a supermassive rotating black hole with a tilted accretion disk at the center of the galaxy. By modeling the motion of the tilted accretion disk particle with the spherical orbits around a Kerr-Newman black hole, we study the effect of charge on the observation of the precession period, thereby exploring the potential of this strong-gravity observation in constraining multiple black hole parameters. Firstly, we study the spherical orbits around a Kerr-Newman black hole and find that their precession periods increase with the charge. Secondly, we utilize the observed M87* jet precession period to constrain the relationship between the spin, charge, and warp radius, specifically detailing the correlations between each pair of these three quantities. Moreover, to further refine constraints on the charge, we explore the negative correlation between the maximum warp radius and charge. A significant result shows that the gap between the maximum warp radii of the prograde and retrograde orbits decrease with the black hole charge. If the warp radius is provided by other observations, different constraints on the charge can be derived for the prograde and retrograde cases. These results suggest that in the era of multi-messenger astronomy, such strong-gravity observation of precessing jet nozzle presents a promising avenue for constraining black hole parameters., Comment: 15 pages, 7 figures

The nonlinear Hall effect refers to the nonlinear voltage response that is transverse to the applied electric field. Recent studies have shown that the quantum geometric quantities on Fermi surfaces serve as fundamental contributors to the nonlinear Hall effect, suggesting that the nonlinear Hall effect occurs mainly in metals. However, in this work, we demonstrate that insulators can also exhibit the nonlinear Hall effect. We find that for an insulator driven at a finite frequency, a series of frequency dependent quantum geometric quantities from the occupied bands can give rise to a nonvanishing nonlinear Hall conductivity. The nonlinear Hall conductivity is frequency dependent: at resonance, it represents the inter-band transition enabled nonlinear Hall current; near resonance, it represents the nonlinear order polarization transverse to the electric field. We further connect the nonlinear Hall conductivity to the Kleinman conjecture in nonlinear optics and point out that the nonlinear Hall effect is generally allowed in insulators given the driving frequency near resonance. For the candidate materials, we consider the biased Bernal bilayer graphene under uniaxial strain and propose polarization resolved second harmonic microscopy to detect the nonlinear Hall effect there., Comment: 7 pages, 4 figures. Comments are welcome

Existing approaches for all-in-one weather-degraded image restoration suffer from inefficiencies in leveraging degradation-aware priors, resulting in sub-optimal performance in adapting to different weather conditions. To this end, we develop an adaptive degradation-aware self-prompting model (ADSM) for all-in-one weather-degraded image restoration. Specifically, our model employs the contrastive language-image pre-training model (CLIP) to facilitate the training of our proposed latent prompt generators (LPGs), which represent three types of latent prompts to characterize the degradation type, degradation property and image caption. Moreover, we integrate the acquired degradation-aware prompts into the time embedding of diffusion model to improve degradation perception. Meanwhile, we employ the latent caption prompt to guide the reverse sampling process using the cross-attention mechanism, thereby guiding the accurate image reconstruction. Furthermore, to accelerate the reverse sampling procedure of diffusion model and address the limitations of frequency perception, we introduce a wavelet-oriented noise estimating network (WNE-Net). Extensive experiments conducted on eight publicly available datasets demonstrate the effectiveness of our proposed approach in both task-specific and all-in-one applications.

Presenting users with diverse responses from foundation models is crucial for enhancing user experience and accommodating varying preferences. However, generating multiple high-quality and diverse responses without sacrificing accuracy remains a challenge, especially when using greedy sampling. In this work, we propose a novel framework, Synthesize-Partition-Adapt (SPA), that leverages the abundant synthetic data available in many domains to elicit diverse responses from foundation models. By leveraging signal provided by data attribution methods such as influence functions, SPA partitions data into subsets, each targeting unique aspects of the data, and trains multiple model adaptations optimized for these subsets. Experimental results demonstrate the effectiveness of our approach in diversifying foundation model responses while maintaining high quality, showcased through the HumanEval and MBPP tasks in the code generation domain and several tasks in the natural language understanding domain, highlighting its potential to enrich user experience across various applications.

Utilizing various astrophysical constraints on neutron star structures, we carry out a Bayesian analysis on the density-dependent behaviors of coupling constants in RMF models as well as the nuclear matter properties at supranuclear densities. The effective nucleon interactions in the isoscalar-scalar, isoscalar-vector, and isovector-vector channels are considered, where the corresponding coupling constants ($\alpha_S, \alpha_V, \alpha_{TV}$) are fixed by dividing entire density range into three regions with six independent parameters. In this work we focus on constraining the density-dependent point-coupling constants at supranuclear densities, while the coupling constants at subsaturation densities are derived from the covariant density functional DD-ME2. For those consistent with astrophysical observations, the coupling constants generally decrease with density and approach to small positive values at large enough densities, which qualitatively agrees with various RMF models. The posterior probability density functions and their correlations of the coupling constants and various nuclear matter properties are examined as well. At $1\sigma$ level, the constrained coupling constants at density $1.5n_0$ ($2.5n_0$) are $\alpha_S = 3.1^{+0.1}_{-0.05} (1.55^{+0.85}_{-0.2}) \times 10^{-4} \mathrm{MeV}^{-2}$, $\alpha_V = 2.3^{+0.1}_{-0.0} (1.3^{+0.55}_{-0.1}) \times 10^{-4} \mathrm{MeV}^{-2}$, and $\alpha_{TV} = 2.05^{+0}_{-0.4} (2.05^{+0}_{-0.5})\times 10^{-5} \mathrm{MeV}^{-2}$. At larger densities, we find the lower limit of $\alpha_{TV}$ is not well constrained, so that more extensive calculations with larger number of free parameters are necessary.

This technical report presents our initial attempt to build a spoken large language model (LLM) for Taiwanese Mandarin, specifically tailored to enable real-time, speech-to-speech interaction in multi-turn conversations. Our end-to-end model incorporates a decoder-only transformer architecture and aims to achieve seamless interaction while preserving the conversational flow, including full-duplex capabilities allowing simultaneous speaking and listening. The paper also details the training process, including data preparation with synthesized dialogues and adjustments for real-time interaction. We also developed a platform to evaluate conversational fluency and response coherence in multi-turn dialogues. We hope the release of the report can contribute to the future development of spoken LLMs in Taiwanese Mandarin., Comment: Work in progress

Beyond-triple fact representations including hyper-relational facts with auxiliary key-value pairs, temporal facts with additional timestamps, and nested facts implying relationships between facts, are gaining significant attention. However, existing link prediction models are usually designed for one specific type of facts, making it difficult to generalize to other fact representations. To overcome this limitation, we propose a Unified Hierarchical Representation learning framework (UniHR) for unified knowledge graph link prediction. It consists of a unified Hierarchical Data Representation (HiDR) module and a unified Hierarchical Structure Learning (HiSL) module as graph encoder. The HiDR module unifies hyper-relational KGs, temporal KGs, and nested factual KGs into triple-based representations. Then HiSL incorporates intra-fact and inter-fact message passing, focusing on enhancing the semantic information within individual facts and enriching the structural information between facts. Experimental results across 7 datasets from 3 types of KGs demonstrate that our UniHR outperforms baselines designed for one specific kind of KG, indicating strong generalization capability of HiDR form and the effectiveness of HiSL module. Code and data are available at https://github.com/Lza12a/UniHR.

In recent years, Large Language Models (LLMs) have demonstrated remarkable versatility across various applications, including natural language understanding, domain-specific knowledge tasks, etc. However, applying LLMs to complex, high-stakes domains like finance requires rigorous evaluation to ensure reliability, accuracy, and compliance with industry standards. To address this need, we conduct a comprehensive and comparative study on three state-of-the-art LLMs, GLM-4, Mistral-NeMo, and LLaMA3.1, focusing on their effectiveness in generating automated financial reports. Our primary motivation is to explore how these models can be harnessed within finance, a field demanding precision, contextual relevance, and robustness against erroneous or misleading information. By examining each model's capabilities, we aim to provide an insightful assessment of their strengths and limitations. Our paper offers benchmarks for financial report analysis, encompassing proposed metrics such as ROUGE-1, BERT Score, and LLM Score. We introduce an innovative evaluation framework that integrates both quantitative metrics (e.g., precision, recall) and qualitative analyses (e.g., contextual fit, consistency) to provide a holistic view of each model's output quality. Additionally, we make our financial dataset publicly available, inviting researchers and practitioners to leverage, scrutinize, and enhance our findings through broader community engagement and collaborative improvement. Our dataset is available on huggingface.

Thermal broadening of the quasi-particle peak in the spectral function is an important physical feature in many statistical systems, but difficult to calculate. Within the projective truncation approximation (PTA) of Green's function equation of motion for classical systems, we produce the spectral function with thermal broadened quasi-particles peak using an $H$-expanded basis. We demonstrate this method on two model systems, the one-variable anharmonic oscillator model and the one-dimensional classical $\phi^4$ lattice model. Comparison with exact spectral function and the molecular dynamics simulation results shows that the method is semi-quantitatively accurate. Extension of the $H$-expanded basis to PTA for quantum system is possible., Comment: 20 pages, 14 figures

This paper studies a distributed estimation problem for time-varying/time-invariant large-scale interconnected systems (LISs). A fully distributed estimator is presented by recursively solving a distributed modified Riccati equation (DMRE) with decoupling variables. By partitioning the LIS based on the transition matrix's block structure, it turns out that the stability of the subsystem is independent of the global LIS if the decoupling variable is selected as the number of out-neighbors. Additionally, it is revealed that any LIS can be equivalently represented by a Markov system. Based on this insight, we show that the stability decoupling above can also be achieved if the decoupling variable equals the number of in-neighbors. Then, the distributed estimator is proved to be stable if the DMRE remains uniformly bounded. When the LIS is considered time-invariant, and by analyzing the spectral radius of a linear operator, it is proved that the DMRE is uniformly bounded if and only if a linear matrix inequality is feasible. Based on the boundedness result, we also show that the distributed estimator converges to a unique steady state for any initial condition. Finally, simulations verify the effectiveness of the proposed methods., Comment: 15 pages, 4 figures

This paper aims to reconstruct hundreds of people's 3D poses, shapes, and locations from a single image with unknown camera parameters. Due to the small and highly varying 2D human scales, depth ambiguity, and perspective distortion, no existing methods can achieve globally consistent reconstruction and accurate reprojection. To address these challenges, we first propose Crowd3D, which leverages a new concept, Human-scene Virtual Interaction Point (HVIP), to convert the complex 3D human localization into 2D-pixel localization with robust camera and ground estimation to achieve globally consistent reconstruction. To achieve stable generalization on different camera FoVs without test-time optimization, we propose an extended version, Crowd3D++, which eliminates the influence of camera parameters and the cropping operation by the proposed canonical upright space and ground-aware normalization transform. In the defined upright space, Crowd3D++ also designs an HVIPNet to regress 2D HVIP and infer the depths. Besides, we contribute two benchmark datasets, LargeCrowd and SyntheticCrowd, for evaluating crowd reconstruction in large scenes. The source code and data will be made publicly available after acceptance., Comment: 14 pages including reference

In reinforcement learning with image-based inputs, it is crucial to establish a robust and generalizable state representation. Recent advancements in metric learning, such as deep bisimulation metric approaches, have shown promising results in learning structured low-dimensional representation space from pixel observations, where the distance between states is measured based on task-relevant features. However, these approaches face challenges in demanding generalization tasks and scenarios with non-informative rewards. This is because they fail to capture sufficient long-term information in the learned representations. To address these challenges, we propose a novel State Chrono Representation (SCR) approach. SCR augments state metric-based representations by incorporating extensive temporal information into the update step of bisimulation metric learning. It learns state distances within a temporal framework that considers both future dynamics and cumulative rewards over current and long-term future states. Our learning strategy effectively incorporates future behavioral information into the representation space without introducing a significant number of additional parameters for modeling dynamics. Extensive experiments conducted in DeepMind Control and Meta-World environments demonstrate that SCR achieves better performance comparing to other recent metric-based methods in demanding generalization tasks. The codes of SCR are available in https://github.com/jianda-chen/SCR.

The Digital Twin Oriented Complex Networked System (DT-CNS) aims to build and extend a Complex Networked System (CNS) model with progressively increasing dynamics complexity towards an accurate reflection of reality -- a Digital Twin of reality. Our previous work proposed evolutionary DT-CNSs to model the long-term adaptive network changes in an epidemic outbreak. This study extends this framework by proposeing the temporal DT-CNS model, where reinforcement learning-driven nodes make decisions on temporal directed interactions in an epidemic outbreak. We consider cooperative nodes, as well as egocentric and ignorant "free-riders" in the cooperation. We describe this epidemic spreading process with the Susceptible-Infected-Recovered ($SIR$) model and investigate the impact of epidemic severity on the epidemic resilience for different types of nodes. Our experimental results show that (i) the full cooperation leads to a higher reward and lower infection number than a cooperation with egocentric or ignorant "free-riders"; (ii) an increasing number of "free-riders" in a cooperation leads to a smaller reward, while an increasing number of egocentric "free-riders" further escalate the infection numbers and (iii) higher infection rates and a slower recovery weakens networks' resilience to severe epidemic outbreaks. These findings also indicate that promoting cooperation and reducing "free-riders" can improve public health during epidemics.

When the period of an incommensurate charge density wave (ICDW) approaches an integer multiple of a lattice vector, the energy gain obtained from locking the period to the lattice can lead to a fascinating transition into a commensurate state. This transition actually occurs through an intermediate near-commensurate (NC) phase, with locally commensurate regions separated by an ordered array of phase slips of a complex CDW order parameter. TiSe2 is a paradigmatic CDW system where incommensuration is believed to be induced by carrier doping, yet its putative NC state has never been imaged or its nature established. Here we report the observation of a striking NC state in ultraclean, slightly doped monolayers of TiSe2, displaying an intricate network of coherent, unidirectional CDW domain walls over hundreds of nanometers. Detailed analysis reveals these are not phase slips of a complex CDW, but rather sign-changing Ising-type domain walls of two coupled real CDWs of previously known symmetry, consistent with the period doubling nature of the parent commensurate state. In addition, we observe an unexpected nematic modulation at the original lattice Bragg peaks which couples to the CDW order parameters. A Ginzburg-Landau analysis naturally explains the couplings and relative modulations of all order parameters, unveiling TiSe2 as a rare example of an NC-CDW of two intertwined real modulations and emergent nematicity.

The configuration interaction relativistic Hartree-Fock (CI-RHF) model is developed in this work. Compared to the conventional configuration interaction shell model calculations, the CI-RHF model can be applied to study the structural properties of a wide range of nuclei without readjusting any parameters, because the effective Hamiltonian for different model space can be deduced consistently from a universal density-dependent Lagrangian based on the Hartree-Fock single-particle basis. The convergence of intermediate-state excitations has been examined in evaluating the effective interactions, and the core-polarization effects are illustrated, by using $^{18}$O as an example. Employing the CI-RHF model, both the bulk properties and low-lying spectra of neon isotopes in the $sd$ shell have been well reproduced without introducing additional parameters besides those well-defined in the phenomenological Lagrangian. Moreover, the study of the isotopic evolution of charge radii and low-lying spectra highlights the shell closure at $N=14$ for neon isotopes., Comment: 5 figures

Multimodal foundation models, such as Gemini and ChatGPT, have revolutionized human-machine interactions by seamlessly integrating various forms of data. Developing a universal spoken language model that comprehends a wide range of natural language instructions is critical for bridging communication gaps and facilitating more intuitive interactions. However, the absence of a comprehensive evaluation benchmark poses a significant challenge. We present Dynamic-SUPERB Phase-2, an open and evolving benchmark for the comprehensive evaluation of instruction-based universal speech models. Building upon the first generation, this second version incorporates 125 new tasks contributed collaboratively by the global research community, expanding the benchmark to a total of 180 tasks, making it the largest benchmark for speech and audio evaluation. While the first generation of Dynamic-SUPERB was limited to classification tasks, Dynamic-SUPERB Phase-2 broadens its evaluation capabilities by introducing a wide array of novel and diverse tasks, including regression and sequence generation, across speech, music, and environmental audio. Evaluation results indicate that none of the models performed well universally. SALMONN-13B excelled in English ASR, while WavLLM demonstrated high accuracy in emotion recognition, but current models still require further innovations to handle a broader range of tasks. We will soon open-source all task data and the evaluation pipeline.