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We consider a two-component scalar dark matter model in this work, where the scalars are stabilized by extra $Z_2 \times Z'_2$ symmetry. To guarantee the stability of the vacuum, we consider the copositive criteria and different choices of the signs of the couplings will contribute to different viable parameter spaces. Based on the copositive criteria, we systematically analyze the possible conditions and pick up 17 different cases with fixing some parameters to be negative. We randomly scan the parameter space under dark matter relic density constraint and direct detection constraint and focus on the quartic couplings with $\lambda_{13}$,$\lambda_{23}$,$\lambda_{14}$ and $\lambda_{24}$. The shapes of the viable parameter space for $|\lambda_{14}|-|\lambda_{24}|$ are almost similar among these cases, while for $|\lambda_{13}|$ and $|\lambda_{23}|$, the larger value are excluded as long as $\lambda_{13}\leqslant 0$ and $\lambda_{23}\leqslant 0$ due to the large interference effect of the $2 \to 2$ processes.

Attention is a vital cognitive process in the learning and memory environment, particularly in the context of online learning. Traditional methods for classifying attention states of online learners based on behavioral signals are prone to distortion, leading to increased interest in using electroencephalography (EEG) signals for authentic and accurate assessment. However, the field of attention state classification based on EEG signals in online learning faces challenges, including the scarcity of publicly available datasets, the lack of standardized data collection paradigms, and the requirement to consider the interplay between attention and other psychological states. In light of this, we present the Multi-label EEG dataset for classifying Mental Attention states (MEMA) in online learning. We meticulously designed a reliable and standard experimental paradigm with three attention states: neutral, relaxing, and concentrating, considering human physiological and psychological characteristics. This paradigm collected EEG signals from 20 subjects, each participating in 12 trials, resulting in 1,060 minutes of data. Emotional state labels, basic personal information, and personality traits were also collected to investigate the relationship between attention and other psychological states. Extensive quantitative and qualitative analysis, including a multi-label correlation study, validated the quality of the EEG attention data. The MEMA dataset and analysis provide valuable insights for advancing research on attention in online learning. The dataset is publicly available at \url{https://github.com/GuanjianLiu/MEMA}.

With the versatile manipulation capability, programmable metasurfaces are rapidly advancing in their intelligence, integration, and commercialization levels. However, as the programmable metasurfaces scale up, their control configuration becomes increasingly complicated, posing significant challenges and limitations. Here, we propose a multiple-partition cross-modulation (MPCM) programmable metasurface to enhance the wireless communication coverage with low hardware complexity. We firstly propose an innovative encoding scheme to multiply the control voltage vectors of row-column crossing, achieving high beamforming precision in free space while maintaining low control hardware complexity and reducing memory requirements for coding sequences. We then design and fabricate an MPCM programmable metasurface to confirm the effectiveness of the proposed encoding scheme. The simulated and experimental results show good agreements with the theoretically calculated outcomes in beam scanning across the E and H planes and in free-space beam pointing. The MPCM programmable metasurface offers strong flexibility and low complexity by allowing various numbers and combinations of partition items in modulation methods, catering to diverse precision demands in various scenarios. We demonstrate the performance of MPCM programmable metasurface in a realistic indoor setting, where the transmissions of videos to specific receiver positions are successfully achieved, surpassing the capabilities of traditional programmable metasurfaces. We believe that the proposed programmable metasurface has great potentials in significantly empowering the wireless communications while addressing the challenges associated with the programmable metasurface's design and implementation.

Software vulnerabilities are flaws in computer software systems that pose significant threats to the integrity, security, and reliability of modern software and its application data. These vulnerabilities can lead to substantial economic losses across various industries. Manual vulnerability repair is not only time-consuming but also prone to errors. To address the challenges of vulnerability repair, researchers have proposed various solutions, with learning-based automatic vulnerability repair techniques gaining widespread attention. However, existing methods often focus on learning more vulnerability data to improve repair outcomes, while neglecting the diverse characteristics of vulnerable code, and suffer from imprecise vulnerability localization.To address these shortcomings, this paper proposes CRepair, a CVAE-based automatic vulnerability repair technology aimed at fixing security vulnerabilities in system code. We first preprocess the vulnerability data using a prompt-based method to serve as input to the model. Then, we apply causal inference techniques to map the vulnerability feature data to probability distributions. By employing multi-sample feature fusion, we capture diverse vulnerability feature information. Finally, conditional control is used to guide the model in repairing the vulnerabilities.Experimental results demonstrate that the proposed method significantly outperforms other benchmark models, achieving a perfect repair rate of 52%. The effectiveness of the approach is validated from multiple perspectives, advancing AI-driven code vulnerability repair and showing promising applications.

In molecular systems, the ultrafast motion of electrons initiates the process of chemical change. Tracking this electronic motion across molecules requires coupling attosecond time resolution to atomic-scale spatial sensitivity. In this work, we employ a pair of attosecond x-ray pulses from an x-ray free-electron laser to follow electron motion resulting from the sudden removal of an electron from a prototypical aromatic system, para-aminophenol. X-ray absorption enables tracking this motion with atomic-site specificity. Our measurements are compared with state-of-the-art computational modeling, reproducing the observed response across multiple timescales. Sub-femtosecond dynamics are assigned to states undergoing non-radiative decay, while few-femtosecond oscillatory motion is associated with electronic wavepacket motion in stable cation states, that will eventually couple to nuclear motion. Our work provides insight on the ultrafast charge motion preceding and initiating chemical transformations in moderately complex systems, and provides a powerful benchmark for computational models of ultrafast charge motion in matter.

Twistronics, the manipulation of Moir\'e superlattices via the twisting of two layers of two-dimensional (2D) materials to control diverse and nontrivial properties, has recently revolutionized the condensed matter and materials physics. Here, we introduce the principles of twistronics to spin photonics, coining this emerging field spintwistronics. In spintwistronics, instead of 2D materials, the two layers consist of photonic topological spin lattices on a surface plasmonic polariton (SPP) platform. Each 2D SPP wave supports the construction of topological lattices formed by photonic spins with stable skyrmion topology governed by rotational symmetry. By introducing spintwistronics into plasmonics, we demonstrate theoretically and experimentally that two layers of photonic spin lattices can produce Moir\'e spin superlattices at specific magic angles. These superlattices, modulated periodically by the quantum number of total angular momentum, exhibit novel properties-including new quasiparticle topologies, multiple fractal patterns, extremely slow-light control, and more-that cannot be achieved in conventional plasmonic systems. As a result, they open up multiple degrees of freedom for practical applications in quantum information, optical data storage and chiral light-matter interactions., Comment: 4 figures

Let $n,k,s$ be three integers such that $k\geq 2$ and $n\geq s\geq 1$. Let $H$ be a $k$-partite $k$-uniform hypergraph with $n$ vertices in each class. Aharoni (2017) showed that if $e(H)>(s-1)n^{k-1}$, then $H$ has a matching of size $s$. In this paper, we give a stability result for 3-partite 3-uniform hypergraphs: if $G$ is a $3$-partite $3$-uniform hypergraph with $n\geq 162$ vertices in each class, $e(G)\geq (s-1)n^2+3n-s$ and $G$ contains no matching of size $s+1$, then $G$ has a vertex cover of size $s$. Our bound is also tight.

In this paper, we consider a class of convex programming problems with linear equality constraints, which finds broad applications in machine learning and signal processing. We propose a new adaptive balanced augmented Lagrangian (ABAL) method for solving these problems. The proposed ABAL method adaptively selects the stepsize parameter and enjoys a low per-iteration complexity, involving only the computation of a proximal mapping of the objective function and the solution of a linear equation. These features make the proposed method well-suited to large-scale problems. We then custom-apply the ABAL method to solve the ISAC beamforming design problem, which is formulated as a nonlinear semidefinite program in a previous work. This customized application requires careful exploitation of the problem's special structure such as the property that all of its signal-to-interference-and-noise-ratio (SINR) constraints hold with equality at the solution and an efficient computation of the proximal mapping of the objective function. Simulation results demonstrate the efficiency of the proposed ABAL method., Comment: 7 pages, 1 table

Neural networks have achieved remarkable performance across a wide range of tasks, yet they remain susceptible to adversarial perturbations, which pose significant risks in safety-critical applications. With the rise of multimodality, diffusion models have emerged as powerful tools not only for generative tasks but also for various applications such as image editing, inpainting, and super-resolution. However, these models still lack robustness due to limited research on attacking them to enhance their resilience. Traditional attack techniques, such as gradient-based adversarial attacks and diffusion model-based methods, are hindered by computational inefficiencies and scalability issues due to their iterative nature. To address these challenges, we introduce an innovative framework that leverages the distilled backbone of diffusion models and incorporates a precision-optimized noise predictor to enhance the effectiveness of our attack framework. This approach not only enhances the attack's potency but also significantly reduces computational costs. Our framework provides a cutting-edge solution for multi-modal adversarial attacks, ensuring reduced latency and the generation of high-fidelity adversarial examples with superior success rates. Furthermore, we demonstrate that our framework achieves outstanding transferability and robustness against purification defenses, outperforming existing gradient-based attack models in both effectiveness and efficiency.

Visual navigation is an essential skill for home-assistance robots, providing the object-searching ability to accomplish long-horizon daily tasks. Many recent approaches use Large Language Models (LLMs) for commonsense inference to improve exploration efficiency. However, the planning process of LLMs is limited within texts and it is difficult to represent the spatial occupancy and geometry layout only by texts. Both are important for making rational navigation decisions. In this work, we seek to unleash the spatial perception and planning ability of Vision-Language Models (VLMs), and explore whether the VLM, with only on-board camera captured RGB/RGB-D stream inputs, can efficiently finish the visual navigation tasks in a mapless manner. We achieve this by developing the imagination-powered navigation framework ImagineNav, which imagines the future observation images at valuable robot views and translates the complex navigation planning process into a rather simple best-view image selection problem for VLM. To generate appropriate candidate robot views for imagination, we introduce the Where2Imagine module, which is distilled to align with human navigation habits. Finally, to reach the VLM preferred views, an off-the-shelf point-goal navigation policy is utilized. Empirical experiments on the challenging open-vocabulary object navigation benchmarks demonstrates the superiority of our proposed system., Comment: 17 pages, 9 figures

Since videos record objects moving coherently, adjacent video frames have commonness (similar object appearances) and uniqueness (slightly changed postures). To prevent redundant modeling of common video signals, we propose a novel diffusion-based framework, named COMUNI, which decomposes the COMmon and UNIque video signals to enable efficient video generation. Our approach separates the decomposition of video signals from the task of video generation, thus reducing the computation complexity of generative models. In particular, we introduce CU-VAE to decompose video signals and encode them into latent features. To train CU-VAE in a self-supervised manner, we employ a cascading merge module to reconstitute video signals and a time-agnostic video decoder to reconstruct video frames. Then we propose CU-LDM to model latent features for video generation, which adopts two specific diffusion streams to simultaneously model the common and unique latent features. We further utilize additional joint modules for cross modeling of the common and unique latent features, and a novel position embedding method to ensure the content consistency and motion coherence of generated videos. The position embedding method incorporates spatial and temporal absolute position information into the joint modules. Extensive experiments demonstrate the necessity of decomposing common and unique video signals for video generation and the effectiveness and efficiency of our proposed method.

Sounding Video Generation (SVG) is an audio-video joint generation task challenged by high-dimensional signal spaces, distinct data formats, and different patterns of content information. To address these issues, we introduce a novel multi-modal latent diffusion model (MM-LDM) for the SVG task. We first unify the representation of audio and video data by converting them into a single or a couple of images. Then, we introduce a hierarchical multi-modal autoencoder that constructs a low-level perceptual latent space for each modality and a shared high-level semantic feature space. The former space is perceptually equivalent to the raw signal space of each modality but drastically reduces signal dimensions. The latter space serves to bridge the information gap between modalities and provides more insightful cross-modal guidance. Our proposed method achieves new state-of-the-art results with significant quality and efficiency gains. Specifically, our method achieves a comprehensive improvement on all evaluation metrics and a faster training and sampling speed on Landscape and AIST++ datasets. Moreover, we explore its performance on open-domain sounding video generation, long sounding video generation, audio continuation, video continuation, and conditional single-modal generation tasks for a comprehensive evaluation, where our MM-LDM demonstrates exciting adaptability and generalization ability., Comment: Accepted by ACM MM 2024

The electric field within the streamer channel is a critical parameter in the calculation model for the nonlinear breakdown voltage of SF6, motivating the research presented in this paper. By using a 2D fluid model, we investigate the microscopic characteristics of the SF6 streamer channel in highly non-uniform fields and uncover a previously unexplained coherent structure: the spatial fluctuation of the electric field (SFEF). We validate the physical validity of SFEF by modifying model parameters that could potentially introduce non-physical effects. Further comparative analysis reveals that SFEF is driven by an ion-conducting channel formed due to the strong electronegativity of SF6. This ion-conducting channel exhibits local characteristics, which fundamentally arise from the slow response of charged species to local charge relaxation. We identify that some charge separation originates from the accumulation of negative ions at the rear edge of the streamer head due to strong electric field shielding in this region. As the streamer propagates, charge separation is continuously generated and passively carried into the streamer channel, ultimately forming the SFEF. Finally, we confirm that SFEF does not occur in uniform fields, indicating that it is a phenomenon exclusive to highly non-uniform fields. These findings provide a deep insight into the electric field within the SF6 streamer channel and offer a potential avenue for further investigation into the mechanisms of SF6 nonlinear breakdown voltage.

Semiconducting single-walled carbon nanotubes (SWCNT) containing sp3-type defects are a promising class of optoelectronic materials, demonstrating single photon emission and long-lived spins. The defect introduces new optical transitions due to both symmetry breaking induced band splitting and introduction of in-gap electronic states. We investigate sulfur-oxide containing compounds as a new class of optically active dopants on (6,5) SWCNT. The SWCNT is exposed to sodium dodecyl sulfate with the resulting compound displaying a red-shifted and bright photoluminescence peak that is characteristic of sp3 doping. Density functional theory calculations are then performed on the adsorbed compounds that may arise (S, SO, SO2 and SO3). These calculations indicate that the two smallest molecules strongly bind to the SWCNT with binding energies of ~ 1.5-1.7 eV and 0.56 eV for S and SO, respectively. Moreover, these adsorbates introduce in-gap electronic states into the bandstructure of the tube consistent with the measured red-shift of (0.1-0.3) eV. Our study suggests that sulfur-based compounds are promising new dopants for (6,5) SWCNT with tunable electronic properties., Comment: 6 pages, 4 figures, 1 SI pdf

We study the precise large deviation probabilities for the sizes of intermediate level sets in branching Brownian motion (BBM). Our conclusions improve a result of A\"{i}dekon, Hu and Shi in [J. Math. Sci. \textbf{238}(2019)]. Additionally, we analyze the typical behaviors of BBM conditioned on large level sets. Our approach relies on the connections between intermediate level sets, additive martingale limits of BBM, and the global minimum of linearly transformed BBMs., Comment: 39 pages, 3 figures; Corrected some minor errors; Comments are welcome!

We solve a long-standing open problem of determining the Fourier dimension of the Mandelbrot canonical cascade measure (MCCM). This problem of significant interest was raised by Mandelbrot in 1976 and reiterated by Kahane in 1993. Specifically, we derive the exact formula for the Fourier dimension of the MCCM for random weights $W$ satisfying the condition $\mathbb{E}[W^t]<\infty$ for all $t>0$. As a corollary, we prove that the MCCM is Salem if and only if the random weight has a specific two-point distribution. In addition, we show that the MCCM is Rajchman with polynomial Fourier decay whenever the random weight satisfies $\mathbb{E}[W^{1+\delta}]<\infty$ for some $\delta>0$. As a consequence, we discover that, in the Biggins-Kyprianou's boundary case, the Fourier dimension of the MCCM exhibits a second order phase transition at the inverse temperature $\beta = 1/2$; we establish the upper Frostman regularity for MCCM; and we obtain a Fourier restriction estimate for MCCM. While the precise Fourier dimension formula seems to be a little magical in the realm of cascade theory, it turns out to be fairly natural in the context of vector-valued martingales. Indeed, the vector-valued martingales will play key roles in our new formalism of actions by multiplicative cascades on finitely additive vector measures, which is the major novelty of this paper., Comment: 78 pages

Activity cliffs, which refer to pairs of molecules that are structurally similar but show significant differences in their potency, can lead to model representation collapse and make the model challenging to distinguish them. Our research indicates that as molecular similarity increases, graph-based methods struggle to capture these nuances, whereas image-based approaches effectively retain the distinctions. Thus, we developed MaskMol, a knowledge-guided molecular image self-supervised learning framework. MaskMol accurately learns the representation of molecular images by considering multiple levels of molecular knowledge, such as atoms, bonds, and substructures. By utilizing pixel masking tasks, MaskMol extracts fine-grained information from molecular images, overcoming the limitations of existing deep learning models in identifying subtle structural changes. Experimental results demonstrate MaskMol's high accuracy and transferability in activity cliff estimation and compound potency prediction across 20 different macromolecular targets, outperforming 25 state-of-the-art deep learning and machine learning approaches. Visualization analyses reveal MaskMol's high biological interpretability in identifying activity cliff-relevant molecular substructures. Notably, through MaskMol, we identified candidate EP4 inhibitors that could be used to treat tumors. This study not only raises awareness about activity cliffs but also introduces a novel method for molecular image representation learning and virtual screening, advancing drug discovery and providing new insights into structure-activity relationships (SAR)., Comment: 33 pages, 5 figures

Because of the ease of using optical gain or loss, it's widely believed that photonics provides an ideal platform to explore various non-Hermitian (NH) paradigms. Here, without any gain or loss, the non-Bloch wave transport that is unique to NH systems is demonstrated at the junction of the two dimensional Chern insulator and the normal conductor. In the band gap of the non-trivial Chern insulator, the interface between two material types can be effectively described by a one-dimensional NH Hamiltonian--such NH character of the interface is ascribed to the conductor self-energy of a reservoir. As a consequence of asymmetric hopping terms in the interface Hamiltonian, theoretical analysis shows that the wave propagation along the interface exhibits dissipative non-reciprocity (dubed non-Bloch transport). What's more, this anomaly transport is verified in the junction formed by the electromagnetic metamaterial which is constructed by the reverse-design strategy; the strategy enables the general solution of metamaterial structures for emulating any tight-binding models. Further, implementing this strategy, we also investigate the gapless boundary modes in the Haldane-like hyperbolic metamaterial. Our work provides a conceptually rich avenue to construct NH systems for both optics and electronics.

Precise wave manipulation has undoubtedly forged the technological landscape we thrive in today. Although our understanding of wave phenomena has come a long way since the earliest observations of desert dunes or ocean waves, the unimpeded development of mathematics has enabled ever more complex and exotic physical phenomena to be comprehensively described. Here, we take wave manipulation a step further by introducing an unprecedented synthetic acoustic crystal capable of realizing simultaneous linear broadband conduction and nonlinear topological insulation, depicting a robust amplitude-dependent mode localized deep within - i.e. an amplitude-driven topological confinement of sound. The latter is achieved by means of an open acoustic waveguide lined with a chain of nonlocally and nonlinearly coupled active electroacoustic resonators. Starting from a comprehensive topological model for classical waves, we demonstrate that different topological regimes can be accessed by increasing driving amplitude and that topological robustness against coupling disorder is a direct consequence of symmetric and simultaneous response between coupled resonators. Theoretical predictions are validated by a fully programmable experimental apparatus capable of realizing the real-time manipulation of metacrystal properties. In all, our results provide a solid foundation for future research in the design and manipulation of classical waves in artificial materials involving nonlinearity, nonlocality, and non-hermiticity.

Large Language Models (LLMs) have garnered widespread attention due to their remarkable performance across various tasks. However, to mitigate the issue of hallucinations, LLMs often incorporate retrieval-augmented pipeline to provide them with rich external knowledge and context. Nevertheless, challenges stem from inaccurate and coarse-grained context retrieved from the retriever. Supplying irrelevant context to the LLMs can result in poorer responses, increased inference latency, and higher costs. This paper introduces a method called Instruction-Aware Contextual Compression, which filters out less informative content, thereby accelerating and enhancing the use of LLMs. The experimental results demonstrate that Instruction-Aware Contextual Compression notably reduces memory consumption and minimizes generation latency while maintaining performance levels comparable to those achieved with the use of the full context. Specifically, we achieved a 50% reduction in context-related costs, resulting in a 5% reduction in inference memory usage and a 2.2-fold increase in inference speed, with only a minor drop of 0.047 in Rouge-1. These findings suggest that our method strikes an effective balance between efficiency and performance., Comment: 20 pages

Symmetry control is essential for realizing unconventional properties, such as ferroelectricity, nonlinear optical responses, and complex topological order, thus it holds promise for the design of emerging quantum and photonic systems. Nevertheless, fast and reversible control of symmetry in materials remains a challenge, especially for nanoscale systems. Here, we unveil reversible symmetry changes in colloidal lead chalcogenide quantum dots on picosecond timescales. Using a combination of ultrafast electron diffraction and total X-ray scattering, in conjunction with atomic-scale structural modeling and first-principles calculations, we reveal that symmetry-broken lead sulfide quantum dots restore to a centrosymmetric phase upon photoexcitation. The symmetry restoration is driven by photoexcited electronic carriers, which suppress lead off-centering for about 100 ps. Furthermore, the change in symmetry is closely correlated with the electronic properties as shown by transient optical measurements. Overall, this study elucidates reversible symmetry changes in colloidal quantum dots, and more broadly defines a new methodology to optically control symmetry in nanoscale systems on ultrafast timescales., Comment: 19 pages, 5 figures

Deep neural networks (DNNs) are increasingly used in critical applications such as identity authentication and autonomous driving, where robustness against adversarial attacks is crucial. These attacks can exploit minor perturbations to cause significant prediction errors, making it essential to enhance the resilience of DNNs. Traditional defense methods often rely on access to detailed model information, which raises privacy concerns, as model owners may be reluctant to share such data. In contrast, existing black-box defense methods fail to offer a universal defense against various types of adversarial attacks. To address these challenges, we introduce DUCD, a universal black-box defense method that does not require access to the target model's parameters or architecture. Our approach involves distilling the target model by querying it with data, creating a white-box surrogate while preserving data privacy. We further enhance this surrogate model using a certified defense based on randomized smoothing and optimized noise selection, enabling robust defense against a broad range of adversarial attacks. Comparative evaluations between the certified defenses of the surrogate and target models demonstrate the effectiveness of our approach. Experiments on multiple image classification datasets show that DUCD not only outperforms existing black-box defenses but also matches the accuracy of white-box defenses, all while enhancing data privacy and reducing the success rate of membership inference attacks., Comment: 12 pages, 9 figures

We consider a branching random walk in the non-boundary case where the additive martingale $W_n$ converges a.s. and in mean to some non-degenerate limit $W_\infty$. We first establish the joint tail distribution of $W_\infty$ and the global minimum of this branching random walk. Next, conditioned on the event that the minimum is atypically small or conditioned on very large $W_\infty$, we study the branching random walk viewed from the minimum and obtain the convergence in law in the vague sense. As a byproduct, we also get the right tail of the limit of derivative martingale., Comment: 43 pages, 1 figure

Recent studies emphasize the crucial role of data augmentation in enhancing the performance of object detection models. However,existing methodologies often struggle to effectively harmonize dataset diversity with semantic coordination.To bridge this gap, we introduce an innovative augmentation technique leveraging pre-trained conditional diffusion models to mediate this balance. Our approach encompasses the development of a Category Affinity Matrix, meticulously designed to enhance dataset diversity, and a Surrounding Region Alignment strategy, which ensures the preservation of semantic coordination in the augmented images. Extensive experimental evaluations confirm the efficacy of our method in enriching dataset diversity while seamlessly maintaining semantic coordination. Our method yields substantial average improvements of +1.4AP, +0.9AP, and +3.4AP over existing alternatives on three distinct object detection models, respectively., Comment: 15 pages, 7 figures, ICPR2024

Assessing artistic creativity has long challenged researchers, with traditional methods proving time-consuming. Recent studies have applied machine learning to evaluate creativity in drawings, but not paintings. Our research addresses this gap by developing a CNN model to automatically assess the creativity of human paintings. Using a dataset of six hundred paintings by professionals and children, our model achieved 90% accuracy and faster evaluation times than human raters. This approach demonstrates the potential of machine learning in advancing artistic creativity assessment, offering a more efficient alternative to traditional methods.

Given a 3D mesh, we aim to synthesize 3D textures that correspond to arbitrary textual descriptions. Current methods for generating and assembling textures from sampled views often result in prominent seams or excessive smoothing. To tackle these issues, we present TexGen, a novel multi-view sampling and resampling framework for texture generation leveraging a pre-trained text-to-image diffusion model. For view consistent sampling, first of all we maintain a texture map in RGB space that is parameterized by the denoising step and updated after each sampling step of the diffusion model to progressively reduce the view discrepancy. An attention-guided multi-view sampling strategy is exploited to broadcast the appearance information across views. To preserve texture details, we develop a noise resampling technique that aids in the estimation of noise, generating inputs for subsequent denoising steps, as directed by the text prompt and current texture map. Through an extensive amount of qualitative and quantitative evaluations, we demonstrate that our proposed method produces significantly better texture quality for diverse 3D objects with a high degree of view consistency and rich appearance details, outperforming current state-of-the-art methods. Furthermore, our proposed texture generation technique can also be applied to texture editing while preserving the original identity. More experimental results are available at https://dong-huo.github.io/TexGen/, Comment: European Conference on Computer Vision (ECCV) 2024

We discuss the interplay between dark matter, leptogenesis and a new gauge boson $Z^\prime$ in the $B-L$ model. A fermion dark matter $\chi$ carrying $U(1)_{B-L}$ charge is introduced to the model but not coupling with other particles. We consider the freeze-out and freeze-in mechanisms, and obtain the correct relic density respectively. We have scanned the feasible parameter space and found that the dark matter direct detection experiments imposed the most stringent constraints on the parameter space. The constraint on the parameter space places a limit on the mass of dark matter with $m_{\chi} \approx 1/2 M_{Z'}$ within a narrow region in the case of freeze-out scenario, and we can obtain the right baryon asymmetry result in the case of $m_{\chi}\subset[654$ GeV, 664 GeV]. For the freeze-in scenario, we have a much broader parameter space for $m_{\chi}$ and $M_{Z^\prime}$ but $g_{BL}$ is restricted at $10^{-5}$ level for $Q=0.1$. In both scenarios, $g_{BL}-M_{Z^\prime}$ is limited within a narrow region by the dark matter relic density, direct detection and baryon asymmetry constraints.

We consider the Z5 two-component dark matter model within the framework of the Type-II seesaw mechanism. Due to the new annihilation processes related to triplets, the light component cannot necessarily be dominant in the dark matter relic density, which is different from the Z5 two-component dark matter model in the SM. The model is considered to explain the excess of electron-positron flux measured by the AMS-02 Collaborations in this work, which is encouraged by the decay of the triplets arising from dark matter annihilations in the Galactic halo. We discuss the cases of the light and heavy components determining dark matter density within a viable parameter space satisfying relic density and direct detection constraints, and by fitting the antiproton spectrum observed in the PAMELA and AMS experiments, we find that the parameter space is flexible and the electron-positron flux excess can be obtained in both cases with the mass of two dark matter particles being larger than that of the triplets'., Comment: arXiv admin note: text overlap with arXiv:2006.14922 by other authors

In human-computer interaction (HCI), Speech Emotion Recognition (SER) is a key technology for understanding human intentions and emotions. Traditional SER methods struggle to effectively capture the long-term temporal correla-tions and dynamic variations in complex emotional expressions. To overcome these limitations, we introduce the PCQ method, a pioneering approach for SER via \textbf{P}rogressive \textbf{C}hannel \textbf{Q}uerying. This method can drill down layer by layer in the channel dimension through the channel query technique to achieve dynamic modeling of long-term contextual information of emotions. This mul-ti-level analysis gives the PCQ method an edge in capturing the nuances of hu-man emotions. Experimental results show that our model improves the weighted average (WA) accuracy by 3.98\% and 3.45\% and the unweighted av-erage (UA) accuracy by 5.67\% and 5.83\% on the IEMOCAP and EMODB emotion recognition datasets, respectively, significantly exceeding the baseline levels., Comment: Accepted for publication by International Conference On Intelligent Computing 2024. For data and code, see this https URL

Quasicrystals,characterized by long-range order without translational symmetry,have catalyzed transformative advances in various fields,including optics in terms of field quasicrystals.Here,we present the first demonstration of photonic quasicrystals formed by spin angular momentum, unveiling novel spin-orbit coupling effects absent in traditional field quasicrystals.A de Bruijn tiling like theoretical framework was built elucidating the formation mechanism of spin quasicrystals for diverse symmetries.Moreover,the configurations of these spin textures can be manipulated through the adjustments of the wavefronts,among which phason-like discontinuous dynamics is observed and quantitatively measured. Unlike optical quasicrystals shaped by electromagnetic fields,these spin-governed quasicrystals exhibit quasi-periodic properties of kinematic parameters,extending their potential applications to other physical systems. These findings hold promise for novel advancements in optical trapping,quasicrystal fabrication,and optical encryption systems.

Open-Vocabulary Video Instance Segmentation (VIS) is attracting increasing attention due to its ability to segment and track arbitrary objects. However, the recent Open-Vocabulary VIS attempts obtained unsatisfactory results, especially in terms of generalization ability of novel categories. We discover that the domain gap between the VLM features (e.g., CLIP) and the instance queries and the underutilization of temporal consistency are two central causes. To mitigate these issues, we design and train a novel Open-Vocabulary VIS baseline called OVFormer. OVFormer utilizes a lightweight module for unified embedding alignment between query embeddings and CLIP image embeddings to remedy the domain gap. Unlike previous image-based training methods, we conduct video-based model training and deploy a semi-online inference scheme to fully mine the temporal consistency in the video. Without bells and whistles, OVFormer achieves 21.9 mAP with a ResNet-50 backbone on LV-VIS, exceeding the previous state-of-the-art performance by 7.7. Extensive experiments on some Close-Vocabulary VIS datasets also demonstrate the strong zero-shot generalization ability of OVFormer (+ 7.6 mAP on YouTube-VIS 2019, + 3.9 mAP on OVIS). Code is available at https://github.com/fanghaook/OVFormer., Comment: ECCV 2024

Advancements in lithium battery technology heavily rely on the design and engineering of electrolytes. However, current schemes for molecular design and recipe optimization of electrolytes lack an effective computational-experimental closed loop and often fall short in accurately predicting diverse electrolyte formulation properties. In this work, we introduce Uni-ELF, a novel multi-level representation learning framework to advance electrolyte design. Our approach involves two-stage pretraining: reconstructing three-dimensional molecular structures at the molecular level using the Uni-Mol model, and predicting statistical structural properties (e.g., radial distribution functions) from molecular dynamics simulations at the mixture level. Through this comprehensive pretraining, Uni-ELF is able to capture intricate molecular and mixture-level information, which significantly enhances its predictive capability. As a result, Uni-ELF substantially outperforms state-of-the-art methods in predicting both molecular properties (e.g., melting point, boiling point, synthesizability) and formulation properties (e.g., conductivity, Coulombic efficiency). Moreover, Uni-ELF can be seamlessly integrated into an automatic experimental design workflow. We believe this innovative framework will pave the way for automated AI-based electrolyte design and engineering.

ATARI is a suite of video games used by reinforcement learning (RL) researchers to test the effectiveness of the learning algorithm. Receiving only the raw pixels and the game score, the agent learns to develop sophisticated strategies, even to the comparable level of a professional human games tester. Ideally, we also want an agent requiring very few interactions with the environment. Previous competitive model-free algorithms for the task use the valued-based Rainbow algorithm without any policy head. In this paper, we change it by proposing a practical discrete variant of the soft actor-critic (SAC) algorithm. The new variant enables off-policy learning using policy heads for discrete domains. By incorporating it into the advanced Rainbow variant, i.e., the ``bigger, better, faster'' (BBF), the resulting SAC-BBF improves the previous state-of-the-art interquartile mean (IQM) from 1.045 to 1.088, and it achieves these results using only replay ratio (RR) 2. By using lower RR 2, the training time of SAC-BBF is strictly one-third of the time required for BBF to achieve an IQM of 1.045 using RR 8. As a value of IQM greater than one indicates super-human performance, SAC-BBF is also the only model-free algorithm with a super-human level using only RR 2. The code is publicly available on GitHub at https://github.com/lezhang-thu/bigger-better-faster-SAC., Comment: Chinese Conference on Pattern Recognition and Computer Vision (PRCV) 2024. GitHub Repo https://github.com/lezhang-thu/bigger-better-faster-SAC

We present GSD, a diffusion model approach based on Gaussian Splatting (GS) representation for 3D object reconstruction from a single view. Prior works suffer from inconsistent 3D geometry or mediocre rendering quality due to improper representations. We take a step towards resolving these shortcomings by utilizing the recent state-of-the-art 3D explicit representation, Gaussian Splatting, and an unconditional diffusion model. This model learns to generate 3D objects represented by sets of GS ellipsoids. With these strong generative 3D priors, though learning unconditionally, the diffusion model is ready for view-guided reconstruction without further model fine-tuning. This is achieved by propagating fine-grained 2D features through the efficient yet flexible splatting function and the guided denoising sampling process. In addition, a 2D diffusion model is further employed to enhance rendering fidelity, and improve reconstructed GS quality by polishing and re-using the rendered images. The final reconstructed objects explicitly come with high-quality 3D structure and texture, and can be efficiently rendered in arbitrary views. Experiments on the challenging real-world CO3D dataset demonstrate the superiority of our approach. Project page: https://yxmu.foo/GSD/, Comment: ECCV 2024

Increasing the quantum information processing power with limited number of hosts is vital for achieving quantum advantage. Here we propose a novel scheme that achieves a scalable n-ion-2n-qubit quantum processor utilizing four internal levels of each ion, and experimentally implement a 1-ion-2-qubit universal processor using the valence electron spin and nuclear spin of a single 171Yb+ ion. Fidelities of single-qubit and two-qubit gates are around 0.98 obtained by quantum process tomography. Additionally, the Grover's algorithm is implemented with a successful rate exceeding 0.99. We provide explicit scaling-up protocols based on standard laser-less and laser-based frameworks, and further demonstrate that the electron/nuclear-spin scheme allows less demanding two-qubit entangling gates between different ions. The replacement of some inter-atomic gates by intra-atomic gates could increase the fidelity of some quantum circuits. Our work paves the way towards achieving 2n-times increase in the size of quantum computational Hilbert space with n ions.

Decentralized Physical Infrastructure Networks (DePINS) are secured and governed by blockchains but beyond crypto-economic incentives, they lack measures to establish trust in participating devices and their services. The verification of relevant device credentials during device registration helps to overcome this problem. However, on-chain verification in decentralized applications (dApp) discloses potentially confidential device attributes whereas off-chain verification introduces undesirable trust assumptions. In this paper, we propose a credential-based device registration (CDR) mechanism that verifies device credentials on the blockchain and leverages zero-knowledge proofs (ZKP) to protect confidential device attributes from being disclosed. We characterize CDR for DePINs, present a general system model, and technically evaluate CDR using zkSNARKs with Groth16 and Marlin. Our experiments give first insights into performance impacts and reveal a tradeoff between the applied proof systems.

Direct current (DC) gas insulated transmission lines (GILs) have been widely used in power transmission, but might be threatened by partial discharge due to the presence of floating impurities (e.g., dust and metal particles) inside the sealed chamber. In this letter, by using a 2D fluid model we characterize the microscopic properties of the partial discharge induced by a floating linear metal particle in SF6 (both the discharge propagation and interaction between space charge and metal particle) under negative high voltage direct current (HVDC) conditions. Due to the strong electronegativity of SF6, the spatiotemporal distributions of the charged species (electrons, positive and negative ions), space charge, and reduced electric field are rather different from those in air. Notably, a negative ion region is observed around the top tip of the metal particle, and it plays an important role in the generation and propagation of primary and secondary streamers in SF6, which may lead to severe motion characteristics of the particle and aliasing of partial discharge signals. Additionally, we analyze the charging process and electric force reversal phenomenon, which may provide a more precise understanding of the underlying mechanisms of the firefly motion previously reported for DC GILs.

Constitutive models play a crucial role in materials science as they describe the behavior of the materials in mathematical forms. Over the last few decades, the rapid development of manufacturing technologies have led to the discovery of many advanced materials with complex and novel behaviors, which in the meantime, have also posed great challenges for constructing accurate and reliable constitutive models of these materials. In this work, we propose a data-driven approach to construct physics-consistent constitutive models for hyperelastic materials from experimentally measurable data, with the help of PDE-constrained optimization methods. Specifically, our constitutive models are based on the physically augmented neural networks~(PANNs), which has been shown to ensure that the models are both physically consistent but also mathematically well-posed by construction. Specimens with deliberately introduced inhomogeneity are used to generate the data, i.e., the full-field displacement data and the total external load, for training the model. Using such approach, a considerably diverse pairs of stress-strain states can be explored with a limited number of simple experiments, such as uniaxial tension. A loss function is defined to measure the difference between the data and the model prediction, which is obtained by numerically solving the governing PDEs under the same geometry and loading conditions. With the help of adjoint method, we can iteratively optimize the parameters of our NN-based constitutive models through gradient descent. We test our method for a wide range of hyperelastic materials and in all cases, our methods are able to capture the constitutive model efficiently and accurately. The trained models are also tested against unseen geometry and unseen loading conditions, exhibiting strong interpolation and extrapolation capabilities., Comment: 19 pages,12 figures

Reset controllers have demonstrated their effectiveness in enhancing performance in precision motion systems. To further exploiting the potential of reset controllers, this study introduces a parallel-partial reset control structure. Frequency response analysis is effective for the design and fine-tuning of controllers in industries. However, conducting frequency response analysis for reset control systems poses challenges due to their nonlinearities. We develop frequency response analysis methods for both the open-loop and closed-loop parallel-partial reset systems. Simulation results validate the accuracy of the analysis methods, showcasing precision enhancements exceeding 100% compared to the traditional describing function method. Furthermore, we design a parallel-partial reset controller within the Proportional-Integral-Derivative (PID) control structure for a mass-spring-damper system. The frequency response analysis of the designed system indicates that, while maintaining the same bandwidth and phase margin of the first-order harmonics, the new system exhibits lower magnitudes of higher-order harmonics, compared to the traditional reset system. Moreover, simulation results demonstrate that the new system achieves lower overshoot and quicker settling time compared to both the traditional reset and linear systems., Comment: Submitted to The European Control Conference 2024

Mechanical amorphization, a widely observed phenomenon, has been utilized to synthesize novel phases by inducing disorder through external loading, thereby expanding the realm of glass-forming systems. Empirically, it has been plausible that mechanical amorphization ability consistently correlates with glass-forming ability. However, through a comprehensive investigation in binary, ternary, and quaternary systems combining neutron diffraction, calorimetric experimental approaches and molecular dynamics simulation, we demonstrate that this impression is only partly true and we reveal that the mechanical amorphization ability can be inversely correlated with the glass forming ability in certain cases To provide insights into these intriguing findings, we present a stress-dependent nucleation theory that offers a coherent explanation for both experimental and simulation results. Our study identifies the intensity of mechanical work, contributed by external stress, as the key control parameter for mechanical amorphization, rendering the ability to tune this process. This discovery not only unravels the underlying correlation between mechanical amorphization and glass-forming ability but also provides a pathway for the design and discovery of new amorphous phases with tailored properties.

We describe the design and performance of a magnetic bottle electron spectrometer~(MBES) for high-energy electron spectroscopy. Our design features a ${\sim2}$~m long electron drift tube and electrostatic retardation lens, achieving sub-electronvolt (eV) electron kinetic energy resolution for high energy (several hundred eV) electrons with close to 4$\pi$ collection efficiency. A segmented anode electron detector enables the simultaneous collection of photoelectron spectra in high resolution and high collection efficiency modes. This versatile instrument is installed at the TMO endstation at the LCLS x-ray free-electron laser (XFEL). In this paper, we demonstrate its high resolution, collection efficiency and spatial selectivity in measurements where it is coupled to an XFEL source. These combined characteristics are designed to enable high-resolution time-resolved measurements using x-ray photoelectron, absorption, and Auger-Meitner spectroscopy. We also describe the pervasive artifact in MBES time-of-flight spectra that arises from a periodic modulation in electron detection efficiency, and present a robust analysis procedure for its removal.

Fine-grained urban flow inference (FUFI) is a crucial transportation service aimed at improving traffic efficiency and safety. FUFI can infer fine-grained urban traffic flows based solely on observed coarse-grained data. However, most of existing methods focus on the influence of single-scale static geographic information on FUFI, neglecting the interactions and dynamic information between different-scale regions within the city. Different-scale geographical features can capture redundant information from the same spatial areas. In order to effectively learn multi-scale information across time and space, we propose an effective fine-grained urban flow inference model called UrbanMSR, which uses self-supervised contrastive learning to obtain dynamic multi-scale representations of neighborhood-level and city-level geographic information, and fuses multi-scale representations to improve fine-grained accuracy. The fusion of multi-scale representations enhances fine-grained. We validate the performance through extensive experiments on three real-world datasets. The resutls compared with state-of-the-art methods demonstrate the superiority of the proposed model.