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GRB 230812B, detected by the Gamma-Ray Integrated Detectors (GRID) constellation mission, is an exceptionally bright gamma-ray burst (GRB) with a duration of only 3 seconds. Sitting near the traditional boundary ($\sim$ 2 s) between long and short GRBs, GRB 230812B is notably associated with a supernova (SN), indicating a massive star progenitor. This makes it a rare example of a short-duration GRB resulting from stellar collapse. Our analysis, using a time-evolving synchrotron model, suggests that the burst has an emission radius of approximately $10^{14.5}$~cm. We propose that the short duration of GRB 230812B is due to the combined effects of the central engine's activity time and the time required for the jet to break through the stellar envelope. Our findings provide another case that challenges the conventional view that short-duration GRBs originate exclusively from compact object mergers, demonstrating that a broader range of durations exists for GRBs arising from the collapse of massive stars., Comment: 10 pages, 3 tables, 11 figures

We introduce a relative P-wave to construct the vector doubly-charm diquark $(\widetilde{V})$ therefore the scalar and tensor tetraquark currents to investigate the decay widths of the fully-charm tetraquark states with the $J^{PC}=0^{++}$, $1^{+-}$ and $2^{++}$ via the QCD sum rules. We observe that the total width of the ground state $\widetilde{V}\overline{\widetilde{V}}$-type scalar tetraquark state is compatible with that of the $X(6552)$ within the range of uncertainties, and the branching ratios are quite different from that of the first radial excitation of the $A\bar{A}$-type scalar tetraquark state. Other predictions can be verified in the future experiments to shed light on the nature of the fully-charm tetraquark states., Comment: 12 pages, 2 figures

Although vision-language pre-training (VLP) models have achieved remarkable progress on cross-modal tasks, they remain vulnerable to adversarial attacks. Using data augmentation and cross-modal interactions to generate transferable adversarial examples on surrogate models, transfer-based black-box attacks have become the mainstream methods in attacking VLP models, as they are more practical in real-world scenarios. However, their transferability may be limited due to the differences on feature representation across different models. To this end, we propose a new attack paradigm called Feedback-based Modal Mutual Search (FMMS). FMMS introduces a novel modal mutual loss (MML), aiming to push away the matched image-text pairs while randomly drawing mismatched pairs closer in feature space, guiding the update directions of the adversarial examples. Additionally, FMMS leverages the target model feedback to iteratively refine adversarial examples, driving them into the adversarial region. To our knowledge, this is the first work to exploit target model feedback to explore multi-modality adversarial boundaries. Extensive empirical evaluations on Flickr30K and MSCOCO datasets for image-text matching tasks show that FMMS significantly outperforms the state-of-the-art baselines., Comment: 9 pages

Quantum computing holds immense potential for addressing a myriad of intricate challenges, which is significantly amplified when scaled to thousands of qubits. However, a major challenge lies in developing an efficient and scalable quantum control system. To address this, we propose a novel Hierarchical MicroArchitecture (HiMA) designed to facilitate qubit scaling and exploit quantum process-level parallelism. This microarchitecture is based on three core elements: (i) discrete qubit-level drive and readout, (ii) a process-based hierarchical trigger mechanism, and (iii) multiprocessing with a staggered triggering technique to enable efficient quantum process-level parallelism. We implement HiMA as a control system for a 72-qubit tunable superconducting quantum processing unit, serving a public quantum cloud computing platform, which is capable of expanding to 6144 qubits through three-layer cascading. In our benchmarking tests, HiMA achieves up to a 4.89x speedup under a 5-process parallel configuration. Consequently, to the best of our knowledge, we have achieved the highest CLOPS (Circuit Layer Operations Per Second), reaching up to 43,680, across all publicly available platforms.

Artificial Intelligence (AI) significantly influences many fields, largely thanks to the vast amounts of high-quality data for machine learning models. The emphasis is now on a data-centric AI strategy, prioritizing data development over model design progress. Automating this process is crucial. In this paper, we serve as the first work to introduce the automatic data-centric development (AD^2) task and outline its core challenges, which require domain-experts-like task scheduling and implementation capability, largely unexplored by previous work. By leveraging the strong complex problem-solving capabilities of large language models (LLMs), we propose an LLM-based autonomous agent, equipped with a strategy named Collaborative Knowledge-STudying-Enhanced Evolution by Retrieval (Co-STEER), to simultaneously address all the challenges. Specifically, our proposed Co-STEER agent enriches its domain knowledge through our proposed evolving strategy and develops both its scheduling and implementation skills by accumulating and retrieving domain-specific practical experience. With an improved schedule, the capability for implementation accelerates. Simultaneously, as implementation feedback becomes more thorough, the scheduling accuracy increases. These two capabilities evolve together through practical feedback, enabling a collaborative evolution process. Extensive experimental results demonstrate that our Co-STEER agent breaks new ground in AD^2 research, possesses strong evolvable schedule and implementation ability, and demonstrates the significant effectiveness of its components. Our Co-STEER paves the way for AD^2 advancements., Comment: 23 pages, 7 figures

In the NISQ era, achieving large-scale quantum computing demands compact circuits to mitigate decoherence and gate error accumulation. Quantum operations with diverse degrees of freedom hold promise for circuit compression, but conventional approaches encounter challenges in simultaneously adjusting multiple parameters. Here, we propose a transition composite gate (TCG) scheme grounded on state-selective transition path engineering, enabling more expressive conditional operations. We experimentally validate a controlled unitary (CU) gate as an example, with independent and continuous parameters. By adjusting the parameters of $\rm X^{12}$ gate, we obtain the CU family with a fidelity range of 95.2% to 99.0% leveraging quantum process tomography (QPT). To demonstrate the capability of circuit compression, we use TCG scheme to prepare 3-qubit Greenberger-Horne-Zeilinger (GHZ) and W states, with the fidelity of 96.77% and 95.72%. TCG can achieve the reduction in circuit depth of about 40% and 44% compared with the use of CZ gates only. Moreover, we show that short-path TCG (SPTCG) can further reduce the state-preparation circuit time cost. The TCG scheme exhibits advantages in certain quantum circuits and shows significant potential for large-scale quantum algorithms., Comment: 21 pages, 12 figures

Large language models (LLMs) demonstrate an impressive ability to internalize knowledge and answer natural language questions. Although previous studies validate that LLMs perform well on general knowledge while presenting poor performance on long-tail nuanced knowledge, the community is still doubtful about whether the traditional knowledge graphs should be replaced by LLMs. In this paper, we ask if the schema of knowledge graph (i.e., taxonomy) is made obsolete by LLMs. Intuitively, LLMs should perform well on common taxonomies and at taxonomy levels that are common to people. Unfortunately, there lacks a comprehensive benchmark that evaluates the LLMs over a wide range of taxonomies from common to specialized domains and at levels from root to leaf so that we can draw a confident conclusion. To narrow the research gap, we constructed a novel taxonomy hierarchical structure discovery benchmark named TaxoGlimpse to evaluate the performance of LLMs over taxonomies. TaxoGlimpse covers ten representative taxonomies from common to specialized domains with in-depth experiments of different levels of entities in this taxonomy from root to leaf. Our comprehensive experiments of eighteen state-of-the-art LLMs under three prompting settings validate that LLMs can still not well capture the knowledge of specialized taxonomies and leaf-level entities., Comment: Accepted by VLDB 2024

Graph Neural Networks (GNNs) have significantly advanced various downstream graph-relevant tasks, encompassing recommender systems, molecular structure prediction, social media analysis, etc. Despite the boosts of GNN, recent research has empirically demonstrated its potential vulnerability to backdoor attacks, wherein adversaries employ triggers to poison input samples, inducing GNN to adversary-premeditated malicious outputs. This is typically due to the controlled training process, or the deployment of untrusted models, such as delegating model training to third-party service, leveraging external training sets, and employing pre-trained models from online sources. Although there's an ongoing increase in research on GNN backdoors, comprehensive investigation into this field is lacking. To bridge this gap, we propose the first survey dedicated to GNN backdoors. We begin by outlining the fundamental definition of GNN, followed by the detailed summarization and categorization of current GNN backdoor attacks and defenses based on their technical characteristics and application scenarios. Subsequently, the analysis of the applicability and use cases of GNN backdoors is undertaken. Finally, the exploration of potential research directions of GNN backdoors is presented. This survey aims to explore the principles of graph backdoors, provide insights to defenders, and promote future security research.

Despite the superior capabilities of Multimodal Large Language Models (MLLMs) across diverse tasks, they still face significant trustworthiness challenges. Yet, current literature on the assessment of trustworthy MLLMs remains limited, lacking a holistic evaluation to offer thorough insights into future improvements. In this work, we establish MultiTrust, the first comprehensive and unified benchmark on the trustworthiness of MLLMs across five primary aspects: truthfulness, safety, robustness, fairness, and privacy. Our benchmark employs a rigorous evaluation strategy that addresses both multimodal risks and cross-modal impacts, encompassing 32 diverse tasks with self-curated datasets. Extensive experiments with 21 modern MLLMs reveal some previously unexplored trustworthiness issues and risks, highlighting the complexities introduced by the multimodality and underscoring the necessity for advanced methodologies to enhance their reliability. For instance, typical proprietary models still struggle with the perception of visually confusing images and are vulnerable to multimodal jailbreaking and adversarial attacks; MLLMs are more inclined to disclose privacy in text and reveal ideological and cultural biases even when paired with irrelevant images in inference, indicating that the multimodality amplifies the internal risks from base LLMs. Additionally, we release a scalable toolbox for standardized trustworthiness research, aiming to facilitate future advancements in this important field. Code and resources are publicly available at: https://multi-trust.github.io/., Comment: 100 pages, 84 figures, 33 tables

Quantum computational fluid dynamics (QCFD) offers a promising alternative to classical computational fluid dynamics (CFD) by leveraging quantum algorithms for higher efficiency. This paper introduces a comprehensive QCFD method, including an iterative method "Iterative-QLS" that suppresses error in quantum linear solver, and a subspace method to scale the solution to a larger size. We implement our method on a superconducting quantum computer, demonstrating successful simulations of steady Poiseuille flow and unsteady acoustic wave propagation. The Poiseuille flow simulation achieved a relative error of less than $0.2\%$, and the unsteady acoustic wave simulation solved a 5043-dimensional matrix. We emphasize the utilization of the quantum-classical hybrid approach in applications of near-term quantum computers. By adapting to quantum hardware constraints and offering scalable solutions for large-scale CFD problems, our method paves the way for practical applications of near-term quantum computers in computational science., Comment: 31 pages, 10 figures

Retrieval-Augmented Generation (RAG) has recently emerged as a promising solution to alleviate Large Language Model (LLM)'s deficiency in lack of knowledge. Existing RAG datasets, however, do not adequately represent the diverse and dynamic nature of real-world Question Answering (QA) tasks. To bridge this gap, we introduce the Comprehensive RAG Benchmark (CRAG), a factual question answering benchmark of 4,409 question-answer pairs and mock APIs to simulate web and Knowledge Graph (KG) search. CRAG is designed to encapsulate a diverse array of questions across five domains and eight question categories, reflecting varied entity popularity from popular to long-tail, and temporal dynamisms ranging from years to seconds. Our evaluation on this benchmark highlights the gap to fully trustworthy QA. Whereas most advanced LLMs achieve <=34% accuracy on CRAG, adding RAG in a straightforward manner improves the accuracy only to 44%. State-of-the-art industry RAG solutions only answer 63% questions without any hallucination. CRAG also reveals much lower accuracy in answering questions regarding facts with higher dynamism, lower popularity, or higher complexity, suggesting future research directions. The CRAG benchmark laid the groundwork for a KDD Cup 2024 challenge, attracting thousands of participants and submissions within the first 50 days of the competition. We commit to maintaining CRAG to serve research communities in advancing RAG solutions and general QA solutions.

Large language models (LLMs), including both proprietary and open-source models, have showcased remarkable capabilities in addressing a wide range of downstream tasks. Nonetheless, when it comes to practical Chinese legal tasks, these models fail to meet the actual requirements. Proprietary models do not ensure data privacy for sensitive legal cases, while open-source models demonstrate unsatisfactory performance due to their lack of legal knowledge. To address this problem, we introduce LawGPT, the first open-source model specifically designed for Chinese legal applications. LawGPT comprises two key components: legal-oriented pre-training and legal supervised fine-tuning. Specifically, we employ large-scale Chinese legal documents for legal-oriented pre-training to incorporate legal domain knowledge. To further improve the model's performance on downstream legal tasks, we create a knowledge-driven instruction dataset for legal supervised fine-tuning. Our experimental results demonstrate that LawGPT outperforms the open-source LLaMA 7B model. Our code and resources are publicly available at https://github.com/pengxiao-song/LaWGPT and have received 5.7K stars on GitHub., Comment: Technical Report

Ising model is a famous toy model in condensed matter and statistical physics. In this work we present a free-fermion formulation of the two-dimensional classical Ising models on the honeycomb, triangular and Kagom\'e lattices. Each Ising model is studied in the case of a zero field and the case of an imaginary field i({\pi}/2)k_BT. We employ the decorated lattice technique, star-triangle transformation and weak-graph expansion method to exactly map each Ising model in both cases into an eight-vertex model on the square lattice. The resulting vertex weights are shown to satisfy the free-fermion condition. In the zero field case, each Ising model is an even free-fermion model. In the case of the imaginary field, the Ising model on the honeycomb lattice is an even free-fermion model while those on the triangular and Kagom\'e lattices are odd free-fermion models. The exact solution of the Kagom\'e lattice Ising model in the imaginary field i({\pi}/2)k_BT is obtained, which has not been reported in previous works. The frustrated Ising models on the triangular and Kagom\'e lattices in the imaginary field still exhibit a non-zero residual entropy., Comment: 11 pages, 6 figures

This paper studies the challenging black-box adversarial attack that aims to generate adversarial examples against a black-box model by only using output feedback of the model to input queries. Some previous methods improve the query efficiency by incorporating the gradient of a surrogate white-box model into query-based attacks due to the adversarial transferability. However, the localized gradient is not informative enough, making these methods still query-intensive. In this paper, we propose a Prior-guided Bayesian Optimization (P-BO) algorithm that leverages the surrogate model as a global function prior in black-box adversarial attacks. As the surrogate model contains rich prior information of the black-box one, P-BO models the attack objective with a Gaussian process whose mean function is initialized as the surrogate model's loss. Our theoretical analysis on the regret bound indicates that the performance of P-BO may be affected by a bad prior. Therefore, we further propose an adaptive integration strategy to automatically adjust a coefficient on the function prior by minimizing the regret bound. Extensive experiments on image classifiers and large vision-language models demonstrate the superiority of the proposed algorithm in reducing queries and improving attack success rates compared with the state-of-the-art black-box attacks. Code is available at https://github.com/yibo-miao/PBO-Attack., Comment: ICML 2024

Despite the widespread application of large language models (LLMs) across various tasks, recent studies indicate that they are susceptible to jailbreak attacks, which can render their defense mechanisms ineffective. However, previous jailbreak research has frequently been constrained by limited universality, suboptimal efficiency, and a reliance on manual crafting. In response, we rethink the approach to jailbreaking LLMs and formally define three essential properties from the attacker' s perspective, which contributes to guiding the design of jailbreak methods. We further introduce AutoBreach, a novel method for jailbreaking LLMs that requires only black-box access. Inspired by the versatility of wordplay, AutoBreach employs a wordplay-guided mapping rule sampling strategy to generate a variety of universal mapping rules for creating adversarial prompts. This generation process leverages LLMs' automatic summarization and reasoning capabilities, thus alleviating the manual burden. To boost jailbreak success rates, we further suggest sentence compression and chain-of-thought-based mapping rules to correct errors and wordplay misinterpretations in target LLMs. Additionally, we propose a two-stage mapping rule optimization strategy that initially optimizes mapping rules before querying target LLMs to enhance the efficiency of AutoBreach. AutoBreach can efficiently identify security vulnerabilities across various LLMs, including three proprietary models: Claude-3, GPT-3.5, GPT-4 Turbo, and two LLMs' web platforms: Bingchat, GPT-4 Web, achieving an average success rate of over 80% with fewer than 10 queries, Comment: Under review

The magnetic field inside the sunspot umbra, as observed by the Full-disk MagnetoGraph (FMG) onboard the Advanced Space based Solar Observatory (ASO-S), was found to be experiencing a weakening. To address this issue, we employed a method developed by Xu et al. (2021) to correct the weakening in the data of 20 active regions observed by FMG during the period spanning December 29, 2022, to July 23, 2023. Research has revealed that the onset of magnetic field weakening occurs at a minimum magnetic field strength of 705 G, with the peak strength reaching up to 1931 G. We computed the change ratio (R1) of the unsigned magnetic flux within the sunspot umbra, considering measurements both before and after correction. The change ratio (R1) spans from 26% to 124%, indicating a significant increase in the unsigned magnetic flux within sunspot umbrae observed by FMG after correction. To illustrate this, we selected four active regions for comparison with data from the Helioseismic and Magnetic Imager (HMI). After correction, it is found that the unsigned magnetic flux in sunspot umbrae measured by FMG aligns more closely with that of HMI. This supports the effectiveness of the corrective method for FMG, despite imperfections, particularly at the umbra-penumbra boundary., Comment: 12 pages, 5 figures

Filament eruptions often result in flares and coronal mass ejections (CMEs). Most studies attribute the filament eruptions to their instabilities or magnetic reconnection. In this study, we report a unique observation of a filament eruption whose initiation process has not been reported before. This large-scale filament, with a length of about 360 Mm crossing an active region, is forced to erupted by two small-scale erupting filaments pushing out from below. This process of multi-filament eruption results in an M6.4 flare in the active region NOAA 13229 on 25th February 2023. The whole process can be divided into three stages: the eruptions of two active-region filaments F1 and F2; the interactions between the erupting F1, F2, and the large-scale filament F3; and the eruption of F3. Though this multi-filament eruption occurs near the northwest limb of the solar disk, it produces a strong halo CME that causes a significant geomagnetic disturbance. Our observations present a new filament eruption mechanism, in which the initial kinetic energy of the eruption is obtained from and transported to by other erupting structures. This event provides us a unique insight into the dynamics of multi-filament eruptions and their corresponding effects on the interplanetary space., Comment: 16 pages, 10 figures. Accepted for publication in Solar Physics

Room-temperature superconductivity represents a significant scientific milestone, with the initial report of LK-99, a copper-substituted lead apatite $\mathrm{Pb}_{10-x}\mathrm{Cu}_{x}(\mathrm{PO}_{4})_{6}\mathrm{O}$, offering a potential breakthrough. However, other researchers have encountered numerous challenges in replicating the original experimental results. In recent studies, Wang et al. successfully observed signs of a possible superconducting phase, such as smaller resistance and stronger diamagnetism, upon doping S into the samples. This indicates that the introduction of S is of significant importance for achieving an appropriate structure. To further investigate the role of S, we have considered the $\mathrm{Pb}_{10-x}\mathrm{Cu}_{x}(\mathrm{PO}_{4})_{6}\mathrm{S}$, systematically discussing its thermodynamic stability, as well as the influence of S on the distribution, concentration, and electronic properties of Cu. We find that $\mathrm{Pb}_{10-x}\mathrm{Cu}_{x}(\mathrm{PO}_{4})_{6}\mathrm{S}$ maintains thermodynamic stability, with S primarily influencing the distribution of Cu. The critical element dictating the electronic characteristics of the material post-synthesis is Cu, while the impact of S on the electronic properties is relatively minor. Our work provides valuable insights into the synthesis of potential apatite based room-temperature superconductors and the role of S in facilitating Cu doping., Comment: 8 pages, 4 figures

In the realm of autonomous driving, robust perception under out-of-distribution conditions is paramount for the safe deployment of vehicles. Challenges such as adverse weather, sensor malfunctions, and environmental unpredictability can severely impact the performance of autonomous systems. The 2024 RoboDrive Challenge was crafted to propel the development of driving perception technologies that can withstand and adapt to these real-world variabilities. Focusing on four pivotal tasks -- BEV detection, map segmentation, semantic occupancy prediction, and multi-view depth estimation -- the competition laid down a gauntlet to innovate and enhance system resilience against typical and atypical disturbances. This year's challenge consisted of five distinct tracks and attracted 140 registered teams from 93 institutes across 11 countries, resulting in nearly one thousand submissions evaluated through our servers. The competition culminated in 15 top-performing solutions, which introduced a range of innovative approaches including advanced data augmentation, multi-sensor fusion, self-supervised learning for error correction, and new algorithmic strategies to enhance sensor robustness. These contributions significantly advanced the state of the art, particularly in handling sensor inconsistencies and environmental variability. Participants, through collaborative efforts, pushed the boundaries of current technologies, showcasing their potential in real-world scenarios. Extensive evaluations and analyses provided insights into the effectiveness of these solutions, highlighting key trends and successful strategies for improving the resilience of driving perception systems. This challenge has set a new benchmark in the field, providing a rich repository of techniques expected to guide future research in this field., Comment: ICRA 2024; 32 pages, 24 figures, 5 tables; Code at https://robodrive-24.github.io/

Remote photoplethysmography (rPPG) has been widely applied to measure heart rate from face videos. To increase the generalizability of the algorithms, domain generalization (DG) attracted increasing attention in rPPG. However, when rPPG is extended to simultaneously measure more vital signs (e.g., respiration and blood oxygen saturation), achieving generalizability brings new challenges. Although partial features shared among different physiological signals can benefit multi-task learning, the sparse and imbalanced target label space brings the seesaw effect over task-specific feature learning. To resolve this problem, we designed an end-to-end Mixture of Low-rank Experts for multi-task remote Physiological measurement (PhysMLE), which is based on multiple low-rank experts with a novel router mechanism, thereby enabling the model to adeptly handle both specifications and correlations within tasks. Additionally, we introduced prior knowledge from physiology among tasks to overcome the imbalance of label space under real-world multi-task physiological measurement. For fair and comprehensive evaluations, this paper proposed a large-scale multi-task generalization benchmark, named Multi-Source Synsemantic Domain Generalization (MSSDG) protocol. Extensive experiments with MSSDG and intra-dataset have shown the effectiveness and efficiency of PhysMLE. In addition, a new dataset was collected and made publicly available to meet the needs of the MSSDG.

Residual entropy of ice systems has long been a significant and intriguing issue in condensed matter physics and statistical mechanics. The exact solutions for the residual entropy of realistic three-dimensional ice systems remain unknown. In this study, we focus on two typical realistic ice systems, namely the hexagonal ice (ice Ih) and cubic ice (ice Ic). We present a transfer matrix description of the number of ice-ruled configurations for these two systems. First, a transfer matrix M is constructed for ice Ic, where each element is the number of ice-ruled configurations of a hexagonal monolayer under certain condition. The product of M and its transpose corresponds to a bilayer unit in ice Ih lattice, therefore is exactly a transfer matrix for ice Ih. Making use of this, we simply show that the residual entropy of ice Ih is not less than that of ice Ic in the thermodynamic limit, which was first proved by Onsager in 1960s. Furthermore, we find an alternative transfer matrix M' for ice Ih, which is based on a monolayer periodic unit. Some interesting properties of M, MMT and M' are illustrated, specifically the summation of all elements, the element in the first row and first column, and the trace. Each property is equivalent with the residual entropy of a two-dimensional ice model. Our work rediscovers the relationship between the residual entropies of ice Ih and ice Ic, and provides an effective description for various two-dimensional ice models., Comment: 29 pages, 7 figures

The progress of humanity is driven by those successful discoveries accompanied by countless failed experiments. Researchers often seek the potential research directions by reading and then verifying them through experiments. The process imposes a significant burden on researchers. In the past decade, the data-driven black-box deep learning method has demonstrated its effectiveness in a wide range of real-world scenarios, which exacerbates the experimental burden of researchers and thus renders the potential successful discoveries veiled. Therefore, automating such a research and development (R&D) process is an urgent need. In this paper, we serve as the first effort to formalize the goal by proposing a Real-world Data-centric automatic R&D Benchmark, namely RD2Bench. RD2Bench benchmarks all the operations in data-centric automatic R&D (D-CARD) as a whole to navigate future work toward our goal directly. We focus on evaluating the interaction and synergistic effects of various model capabilities and aiding in selecting well-performing trustworthy models. Although RD2Bench is very challenging to the state-of-the-art (SOTA) large language model (LLM) named GPT-4, indicating ample research opportunities and more research efforts, LLMs possess promising potential to bring more significant development to D-CARD: They are able to implement some simple methods without adopting any additional techniques. We appeal to future work to take developing techniques for tackling automatic R&D into consideration, thus bringing the opportunities of the potential revolutionary upgrade to human productivity., Comment: 17 pages, 3 figures

Face anti-spoofing (FAS) and adversarial detection (FAD) have been regarded as critical technologies to ensure the safety of face recognition systems. However, due to limited practicality, complex deployment, and the additional computational overhead, it is necessary to implement both detection techniques within a unified framework. This paper aims to achieve this goal by breaking through two primary obstacles: 1) the suboptimal face feature representation and 2) the scarcity of training data. To address the limited performance caused by existing feature representations, motivated by the rich structural and detailed features of face diffusion models, we propose FaceCat, the first approach leveraging the diffusion model to simultaneously enhance the performance of FAS and FAD. Specifically, FaceCat elaborately designs a hierarchical fusion mechanism to capture rich face semantic features of the diffusion model. These features then serve as a robust foundation for a lightweight head, designed to execute FAS and FAD simultaneously. Due to the limitations in feature representation that arise from relying solely on single-modality image data, we further propose a novel text-guided multi-modal alignment strategy that utilizes text prompts to enrich feature representation, thereby enhancing performance. To combat data scarcity, we build a comprehensive dataset with a wide range of 28 attack types, offering greater potential for a unified framework in facial security. Extensive experiments validate the effectiveness of FaceCat generalizes significantly better and obtains excellent robustness against common input transformations., Comment: Under review

In this paper, we present MoMA: an open-vocabulary, training-free personalized image model that boasts flexible zero-shot capabilities. As foundational text-to-image models rapidly evolve, the demand for robust image-to-image translation grows. Addressing this need, MoMA specializes in subject-driven personalized image generation. Utilizing an open-source, Multimodal Large Language Model (MLLM), we train MoMA to serve a dual role as both a feature extractor and a generator. This approach effectively synergizes reference image and text prompt information to produce valuable image features, facilitating an image diffusion model. To better leverage the generated features, we further introduce a novel self-attention shortcut method that efficiently transfers image features to an image diffusion model, improving the resemblance of the target object in generated images. Remarkably, as a tuning-free plug-and-play module, our model requires only a single reference image and outperforms existing methods in generating images with high detail fidelity, enhanced identity-preservation and prompt faithfulness. Our work is open-source, thereby providing universal access to these advancements.

The vulnerability of deep neural networks to adversarial patches has motivated numerous defense strategies for boosting model robustness. However, the prevailing defenses depend on single observation or pre-established adversary information to counter adversarial patches, often failing to be confronted with unseen or adaptive adversarial attacks and easily exhibiting unsatisfying performance in dynamic 3D environments. Inspired by active human perception and recurrent feedback mechanisms, we develop Embodied Active Defense (EAD), a proactive defensive strategy that actively contextualizes environmental information to address misaligned adversarial patches in 3D real-world settings. To achieve this, EAD develops two central recurrent sub-modules, i.e., a perception module and a policy module, to implement two critical functions of active vision. These models recurrently process a series of beliefs and observations, facilitating progressive refinement of their comprehension of the target object and enabling the development of strategic actions to counter adversarial patches in 3D environments. To optimize learning efficiency, we incorporate a differentiable approximation of environmental dynamics and deploy patches that are agnostic to the adversary strategies. Extensive experiments demonstrate that EAD substantially enhances robustness against a variety of patches within just a few steps through its action policy in safety-critical tasks (e.g., face recognition and object detection), without compromising standard accuracy. Furthermore, due to the attack-agnostic characteristic, EAD facilitates excellent generalization to unseen attacks, diminishing the averaged attack success rate by 95 percent across a range of unseen adversarial attacks., Comment: 27pages

Characterizing non-equilibrium dynamics in quantum many-body systems is a challenging frontier of physics. In this work, we systematically construct solvable non-integrable quantum circuits that exhibit exact Markovian subsystem dynamics. This feature thus enables accurately calculating local observables for arbitrary evolution time. Utilizing the influence matrix method, we show that the effect of the time-evolved global system on a finite subsystem can be analytically described by sequential, time-local quantum channels acting on the subsystem boundary. The realization of exact Markovian property is facilitated by a solvable condition on the underlying two-site gates in the quantum circuit. We further present several concrete examples with varying local Hilbert space dimensions to demonstrate our approach., Comment: 6+9 pages, 2+1 figures

We present AnimateDiff-Lightning for lightning-fast video generation. Our model uses progressive adversarial diffusion distillation to achieve new state-of-the-art in few-step video generation. We discuss our modifications to adapt it for the video modality. Furthermore, we propose to simultaneously distill the probability flow of multiple base diffusion models, resulting in a single distilled motion module with broader style compatibility. We are pleased to release our distilled AnimateDiff-Lightning model for the community's use.

Upflows and downflows at active region (AR) boundaries have been frequently observed with spectroscopic observations at extreme ultraviolet (EUV) passbands. In this paper, we report the coexistence of upflows and downflows at the AR boundaries with imaging observations from the Solar Upper Transition Region Imager (SUTRI) and the Atmospheric Imaging Assembly (AIA). With their observations from 2022 September 21 to 2022 September 30, we find 17 persistent opposite flows occurring along the AR coronal loops. The upflows are prominent in the AIA 193 \AA images with a velocity of 50-200 km/s, while the downflows are best seen in the SUTRI 465 \AA and AIA 131 \AA images with a slower velocity of tens of kilometers per second (characteristic temperatures (log T(K)) for 193 \AA, 465 \AA and 131 \AA are 6.2, 5.7, 5.6, respectively). We also analyze the center-to-limb variation of the velocities for both upflows and downflows. The simultaneous observations of downflows and upflows can be explained by the chromosphere-corona mass-cycling process, in which the localized chromospheric plasma is impulsively heated to coronal temperature forming a upflow and then these upflows experience radiative cooling producing a downflow with the previously heated plasma returning to the lower atmosphere. In particular, the persistent downflows seen by SUTRI provide strong evidence of the cooling process in the mass cycle. For upflows associated with open loops, part of the plasma is able to escape outward and into the heliosphere as solar wind.

Recent advancements in Text-to-Image (T2I) models have raised significant safety concerns about their potential misuse for generating inappropriate or Not-Safe-For-Work (NSFW) contents, despite existing countermeasures such as NSFW classifiers or model fine-tuning for inappropriate concept removal. Addressing this challenge, our study unveils GuardT2I, a novel moderation framework that adopts a generative approach to enhance T2I models' robustness against adversarial prompts. Instead of making a binary classification, GuardT2I utilizes a Large Language Model (LLM) to conditionally transform text guidance embeddings within the T2I models into natural language for effective adversarial prompt detection, without compromising the models' inherent performance. Our extensive experiments reveal that GuardT2I outperforms leading commercial solutions like OpenAI-Moderation and Microsoft Azure Moderator by a significant margin across diverse adversarial scenarios.

Extremely large image generators offer significant transformative potential across diverse sectors. It allows users to design specific prompts to generate realistic images through some black-box APIs. However, some studies reveal that image generators are notably susceptible to attacks and generate Not Suitable For Work (NSFW) contents by manually designed toxin texts, especially imperceptible to human observers. We urgently need a multitude of universal and transferable prompts to improve the safety of image generators, especially black-box-released APIs. Nevertheless, they are constrained by labor-intensive design processes and heavily reliant on the quality of the given instructions. To achieve this, we introduce a black-box stealthy prompt attack (BSPA) that adopts a retriever to simulate attacks from API users. It can effectively harness filter scores to tune the retrieval space of sensitive words for matching the input prompts, thereby crafting stealthy prompts tailored for image generators. Significantly, this approach is model-agnostic and requires no internal access to the model's features, ensuring its applicability to a wide range of image generators. Building on BSPA, we have constructed an automated prompt tool and a comprehensive prompt attack dataset (NSFWeval). Extensive experiments demonstrate that BSPA effectively explores the security vulnerabilities in a variety of state-of-the-art available black-box models, including Stable Diffusion XL, Midjourney, and DALL-E 2/3. Furthermore, we develop a resilient text filter and offer targeted recommendations to ensure the security of image generators against prompt attacks in the future.

We propose a diffusion distillation method that achieves new state-of-the-art in one-step/few-step 1024px text-to-image generation based on SDXL. Our method combines progressive and adversarial distillation to achieve a balance between quality and mode coverage. In this paper, we discuss the theoretical analysis, discriminator design, model formulation, and training techniques. We open-source our distilled SDXL-Lightning models both as LoRA and full UNet weights.

Diffusion models are recently employed as generative classifiers for robust classification. However, a comprehensive theoretical understanding of the robustness of diffusion classifiers is still lacking, leading us to question whether they will be vulnerable to future stronger attacks. In this study, we propose a new family of diffusion classifiers, named Noised Diffusion Classifiers~(NDCs), that possess state-of-the-art certified robustness. Specifically, we generalize the diffusion classifiers to classify Gaussian-corrupted data by deriving the evidence lower bounds (ELBOs) for these distributions, approximating the likelihood using the ELBO, and calculating classification probabilities via Bayes' theorem. We integrate these generalized diffusion classifiers with randomized smoothing to construct smoothed classifiers possessing non-constant Lipschitzness. Experimental results demonstrate the superior certified robustness of our proposed NDCs. Notably, we are the first to achieve 80\%+ and 70\%+ certified robustness on CIFAR-10 under adversarial perturbations with $\ell_2$ norm less than 0.25 and 0.5, respectively, using a single off-the-shelf diffusion model without any additional data.

The early stage evolution of local atomic structures in a multicomponent metallic glass during its crystallization process has been investigated via molecular dynamics simulation. It is found that the initial thermal stability and earliest stage evolution of the local atomic clusters show no strong correlation with their initial short-range orders, and this leads to an observation of a novel symmetry convergence phenomenon, which can be understood as an atomic structure manifestation of the ergodicity. Furthermore, in our system we have quantitatively proved that the crucial factor for the thermal stability against crystallization exhibited by the metallic glass, is not the total amount of icosahedral clusters, but the degree of global connectivity among them., Comment: 13 pages, 8 figures, accepted by CPB

We present an independent catalog (FRIIRGcat) of 45,241 Fanaroff-Riley Type II (FR-II) radio galaxies compiled from the Very Large Array Faint Images of the Radio Sky at Twenty-centimeters (FIRST) survey and employed the deep learning method. Among them, optical and/or infrared counterparts are identified for 41,425 FR-IIs. This catalog spans luminosities $2.63\times10^{22}\leq L_{\rm rad}\leq6.76\times10^{29}\,{\rm W}\,{\rm Hz}^{-1}$ and redshifts up to $z=5.01$. The spectroscopic classification indicates that there are 1431 low-excitation radio galaxies and 260 high-excitation radio galaxies. Among the spectroscopically identified sources, black hole masses are estimated for 4837 FR-IIs, which are in $10^{7.5}\lesssim M_{\rm BH}\lesssim 10^{9.5}$ $M_{\odot}$. Interestingly, this catalog reveals a couple of giant radio galaxies (GRGs), which are already in the existing GRG catalog, confirming the efficiency of this FR-II catalog. Furthermore, 284 new GRGs are unveiled in this new FR-II sample; they have the largest projected sizes ranging from 701 to 1209 kpc and are located at redshifts $0.31

Diffusion models trained with mean squared error loss tend to generate unrealistic samples. Current state-of-the-art models rely on classifier-free guidance to improve sample quality, yet its surprising effectiveness is not fully understood. In this paper, we show that the effectiveness of classifier-free guidance partly originates from it being a form of implicit perceptual guidance. As a result, we can directly incorporate perceptual loss in diffusion training to improve sample quality. Since the score matching objective used in diffusion training strongly resembles the denoising autoencoder objective used in unsupervised training of perceptual networks, the diffusion model itself is a perceptual network and can be used to generate meaningful perceptual loss. We propose a novel self-perceptual objective that results in diffusion models capable of generating more realistic samples. For conditional generation, our method only improves sample quality without entanglement with the conditional input and therefore does not sacrifice sample diversity. Our method can also improve sample quality for unconditional generation, which was not possible with classifier-free guidance before.

The advent of large language models (LLMs) has heightened interest in their potential for multimodal applications that integrate language and vision. This paper explores the capabilities of GPT-4V in the realms of geography, environmental science, agriculture, and urban planning by evaluating its performance across a variety of tasks. Data sources comprise satellite imagery, aerial photos, ground-level images, field images, and public datasets. The model is evaluated on a series of tasks including geo-localization, textual data extraction from maps, remote sensing image classification, visual question answering, crop type identification, disease/pest/weed recognition, chicken behavior analysis, agricultural object counting, urban planning knowledge question answering, and plan generation. The results indicate the potential of GPT-4V in geo-localization, land cover classification, visual question answering, and basic image understanding. However, there are limitations in several tasks requiring fine-grained recognition and precise counting. While zero-shot learning shows promise, performance varies across problem domains and image complexities. The work provides novel insights into GPT-4V's capabilities and limitations for real-world geospatial, environmental, agricultural, and urban planning challenges. Further research should focus on augmenting the model's knowledge and reasoning for specialized domains through expanded training. Overall, the analysis demonstrates foundational multimodal intelligence, highlighting the potential of multimodal foundation models (FMs) to advance interdisciplinary applications at the nexus of computer vision and language., Comment: 110 Pages; 61 Figures

A reliable and cost-effective interconnect technology is required for the development of hybrid pixel detectors. The interconnect technology needs to be adapted for the pitch and die sizes of the respective applications. For small-scale applications and during the ASIC and sensor development phase, interconnect technologies must also be suitable for the assembly of single-dies typically available from Multi-Project-Wafer submissions. Within the CERN EP R&D program and the AIDAinnova collaboration, innovative and scalable hybridization concepts are under development for pixel-detector applications in future colliders. This contribution presents recent results of a newly developed in-house single-die interconnection process based on Anisotropic Conductive Adhesives (ACA). The ACA interconnect technology replaces solder bumps with conductive micro-particles embedded in an epoxy layer applied as either film or paste. The electro-mechanical connection between the sensor and ASIC is achieved via thermocompression of the ACA using a flip-chip device bonder. A specific pixel-pad topology is required to enable the connection via micro-particles and create cavities into which excess epoxy can flow. This pixel-pad topology is achieved with an in-house Electroless Nickel Immersion Gold process that is also under development within the project. The ENIG and ACA processes are qualified with a variety of different ASICs, sensors, and dedicated test structures, with pad diameters ranging from 12 {\mu}m to 140 {\mu}m and pitches between 20 {\mu}m and 1.3 mm. The produced assemblies are characterized electrically, with radioactive-source exposures, and in tests with high-momentum particle beams. A focus is placed on recent optimization of the plating and interconnect processes, resulting in an improved plating uniformity and interconnect yield., Comment: Procceding to IPRD23 conference in Siena 2023

Compared with transferable untargeted attacks, transferable targeted adversarial attacks could specify the misclassification categories of adversarial samples, posing a greater threat to security-critical tasks. In the meanwhile, 3D adversarial samples, due to their potential of multi-view robustness, can more comprehensively identify weaknesses in existing deep learning systems, possessing great application value. However, the field of transferable targeted 3D adversarial attacks remains vacant. The goal of this work is to develop a more effective technique that could generate transferable targeted 3D adversarial examples, filling the gap in this field. To achieve this goal, we design a novel framework named TT3D that could rapidly reconstruct from few multi-view images into Transferable Targeted 3D textured meshes. While existing mesh-based texture optimization methods compute gradients in the high-dimensional mesh space and easily fall into local optima, leading to unsatisfactory transferability and distinct distortions, TT3D innovatively performs dual optimization towards both feature grid and Multi-layer Perceptron (MLP) parameters in the grid-based NeRF space, which significantly enhances black-box transferability while enjoying naturalness. Experimental results show that TT3D not only exhibits superior cross-model transferability but also maintains considerable adaptability across different renders and vision tasks. More importantly, we produce 3D adversarial examples with 3D printing techniques in the real world and verify their robust performance under various scenarios., Comment: Accepted by CVPR 2024

Colon cancer is expected to become the second leading cause of cancer death in the United States in 2023. Although colonoscopy is one of the most effective methods for early prevention of colon cancer, up to 30% of polyps may be missed by endoscopists, thereby increasing patients' risk of developing colon cancer. Though deep neural networks have been proven to be an effective means of enhancing the detection rate of polyps. However, the variation of polyp size brings the following problems: (1) it is difficult to design an efficient and sufficient multi-scale feature fusion structure; (2) matching polyps of different sizes with fixed-size anchor boxes is a hard challenge. These problems reduce the performance of polyp detection and also lower the model's training and detection efficiency. To address these challenges, this paper proposes a new model called YOLO-OB. Specifically, we developed a bidirectional multiscale feature fusion structure, BiSPFPN, which could enhance the feature fusion capability across different depths of a CNN. We employed the ObjectBox detection head, which used a center-based anchor-free box regression strategy that could detect polyps of different sizes on feature maps of any scale. Experiments on the public dataset SUN and the self-collected colon polyp dataset Union demonstrated that the proposed model significantly improved various performance metrics of polyp detection, especially the recall rate. Compared to the state-of-the-art results on the public dataset SUN, the proposed method achieved a 6.73% increase on recall rate from 91.5% to 98.23%. Furthermore, our YOLO-OB was able to achieve real-time polyp detection at a speed of 39 frames per second using a RTX3090 graphics card. The implementation of this paper can be found here: https://github.com/seanyan62/YOLO-OB.

Rapid advancements in large language models (LLMs) have revitalized in LLM-based agents, exhibiting impressive human-like behaviors and cooperative capabilities in various scenarios. However, these agents also bring some exclusive risks, stemming from the complexity of interaction environments and the usability of tools. This paper delves into the safety of LLM-based agents from three perspectives: agent quantity, role definition, and attack level. Specifically, we initially propose to employ a template-based attack strategy on LLM-based agents to find the influence of agent quantity. In addition, to address interaction environment and role specificity issues, we introduce Evil Geniuses (EG), an effective attack method that autonomously generates prompts related to the original role to examine the impact across various role definitions and attack levels. EG leverages Red-Blue exercises, significantly improving the generated prompt aggressiveness and similarity to original roles. Our evaluations on CAMEL, Metagpt and ChatDev based on GPT-3.5 and GPT-4, demonstrate high success rates. Extensive evaluation and discussion reveal that these agents are less robust, prone to more harmful behaviors, and capable of generating stealthier content than LLMs, highlighting significant safety challenges and guiding future research. Our code is available at https://github.com/T1aNS1R/Evil-Geniuses., Comment: 11 pages

In this work, we propose MagicPose, a diffusion-based model for 2D human pose and facial expression retargeting. Specifically, given a reference image, we aim to generate a person's new images by controlling the poses and facial expressions while keeping the identity unchanged. To this end, we propose a two-stage training strategy to disentangle human motions and appearance (e.g., facial expressions, skin tone and dressing), consisting of (1) the pre-training of an appearance-control block and (2) learning appearance-disentangled pose control. Our novel design enables robust appearance control over generated human images, including body, facial attributes, and even background. By leveraging the prior knowledge of image diffusion models, MagicPose generalizes well to unseen human identities and complex poses without the need for additional fine-tuning. Moreover, the proposed model is easy to use and can be considered as a plug-in module/extension to Stable Diffusion. The code is available at: https://github.com/Boese0601/MagicDance, Comment: Accepted by ICML 2024. MagicPose and MagicDance are the same project. Website:https://boese0601.github.io/magicdance/ Code:https://github.com/Boese0601/MagicDance

Observations of magnetic helicity transportation through the solar photosphere reflect the interaction of turbulent plasma movements and magnetic fields in the solar dynamo process. In this chapter, we have reviewed the research process of magnetic helicity inferred from the observed solar magnetic fields in the photosphere and also the solar morphological configurations with solar cycles. After introducing some achievements in the study of magnetic helicity, some key points would like to be summarized. The magnetic (current) helicity in the solar surface layer presents a statistical distribution similar to that of the sunspot butterfly diagram, but its maximum value is delayed from the extreme value of the sunspot butterfly diagram and corresponds in the phase with the statistical eruption of solar flares. During the spatial transport of magnetic (current) helicity from the interior of the sun into the interplanetary space at the time-space scale of the solar cycle, it shows the statistical distribution and the fluctuation with the hemispheric sign rule. These show that the current helicity and magnetic helicity transport calculation methods are complementary to each other. We also notice that the study of the inherent relationship between magnetic helicity and the solar cycle still depends on the observed accuracy of the solar magnetic field., Comment: 48 page,17 figures

Two entangled qubits emerge as an essential resource for quantum control, which are normally quantum confined with atomic precision. It seems inhibitive that in the macroscopic scope collective qubit pairs manifest long coherence and quantum entanglement, especially at high temperature. Here, we report this exotic ensemble effect in solid-state sintering lead apatite samples with copper substitution, which have been repeatedly duplicated with superior stability and low cost. An extraordinarily low-field absorption signal of cw electron paramagnetic resonance (EPR) spectroscopy stems from low-mass conduction electrons implying the coherence of cuprate radicals can be long-termly protected. The pulsed EPR experiments exhibit triplet Rabi oscillation from paired cuprate diradicals with the coherence time exceeding 1 microsecond at 85K. We believe these appealing effects are sufficiently promising to be applied for scalable quantum control and computation., Comment: 16 pages, 4 figures

We study the hadronic coupling constants in the two-body strong decays of the fully-charm tetraquark states with the $J^{PC}=0^{++}$, $1^{+-}$ and $2^{++}$ via the QCD sum rules based on rigorous quark-hadron duality. Then we obtain the hadronic coupling constants and partial decay widths therefore total decay widths, which support assigning the $X(6552)$ as the first radial excitation of the scalar tetraquark state. And other predictions can be used to diagnose exotic states in the future., Comment: 17 pages, 3 figures

In the wake of relentless digital transformation, data-driven solutions are emerging as powerful tools to address multifarious industrial tasks such as forecasting, anomaly detection, planning, and even complex decision-making. Although data-centric R&D has been pivotal in harnessing these solutions, it often comes with significant costs in terms of human, computational, and time resources. This paper delves into the potential of large language models (LLMs) to expedite the evolution cycle of data-centric R&D. Assessing the foundational elements of data-centric R&D, including heterogeneous task-related data, multi-facet domain knowledge, and diverse computing-functional tools, we explore how well LLMs can understand domain-specific requirements, generate professional ideas, utilize domain-specific tools to conduct experiments, interpret results, and incorporate knowledge from past endeavors to tackle new challenges. We take quantitative investment research as a typical example of industrial data-centric R&D scenario and verified our proposed framework upon our full-stack open-sourced quantitative research platform Qlib and obtained promising results which shed light on our vision of automatic evolving of industrial data-centric R&D cycle., Comment: 29 pages, 11 figures

The present work reported a novel L12-strengthening NiCoCrAlTi high entropy alloy (HEA) with an outstanding synergy of tensile strength and ductility at both ambient and high temperatures. Transmission electron microscopy (TEM) characterization revealed a high density of rod-like and spheroidal L12 precipitates distributing in the micro/nanograins and non-recrystallized regions in the annealed specimens. The tremendously high yield stress, ultimate tensile stress (UTS), and ductility of the HEA at 600 C were ~1060 MPa, 1271 MPa, and 25%, respectively, which were significantly superior to most reported HEAs and Co- and Ni-based superalloys to date. Systematic TEM analysis unveiled that the cooperation among L12 precipitation, extensive stacking faults (SFs), deformation twins (DTs), immobile Lomer-Cottrell (L-C) locks formed from interactions between SFs and SFs/DTs, hierarchical SFs/DTs networks, as well as hetero-deformation-induced strengthening dominated the plastic deformation at 600 C. Such a unique deformation mechanism enabled extremely high tensile strength and sustained ductility of the HEA at a high temperature.

Contrastive Language-Image Pre-training (CLIP) provides a foundation model by integrating natural language into visual concepts, enabling zero-shot recognition on downstream tasks. It is usually expected that satisfactory overall accuracy can be achieved across numerous domains through well-designed textual prompts. However, we found that their performance in the worst categories is significantly inferior to the overall performance. For example, on ImageNet, there are a total of 10 categories with class-wise accuracy as low as 0\%, even though the overall performance has achieved 64.1\%. This phenomenon reveals the potential risks associated with using CLIP models, particularly in risk-sensitive applications where specific categories hold significant importance. To address this issue, we investigate the alignment between the two modalities in the CLIP model and propose the Class-wise Matching Margin (\cmm) to measure the inference confusion. \cmm\ can effectively identify the worst-performing categories and estimate the potential performance of the candidate prompts. We further query large language models to enrich descriptions of worst-performing categories and build a weighted ensemble to highlight the efficient prompts. Experimental results clearly verify the effectiveness of our proposal, where the accuracy on the worst-10 categories on ImageNet is boosted to 5.2\%, without manual prompt engineering, laborious optimization, or access to labeled validation data.

Multimodal Large Language Models (MLLMs) that integrate text and other modalities (especially vision) have achieved unprecedented performance in various multimodal tasks. However, due to the unsolved adversarial robustness problem of vision models, MLLMs can have more severe safety and security risks by introducing the vision inputs. In this work, we study the adversarial robustness of Google's Bard, a competitive chatbot to ChatGPT that released its multimodal capability recently, to better understand the vulnerabilities of commercial MLLMs. By attacking white-box surrogate vision encoders or MLLMs, the generated adversarial examples can mislead Bard to output wrong image descriptions with a 22% success rate based solely on the transferability. We show that the adversarial examples can also attack other MLLMs, e.g., a 26% attack success rate against Bing Chat and a 86% attack success rate against ERNIE bot. Moreover, we identify two defense mechanisms of Bard, including face detection and toxicity detection of images. We design corresponding attacks to evade these defenses, demonstrating that the current defenses of Bard are also vulnerable. We hope this work can deepen our understanding on the robustness of MLLMs and facilitate future research on defenses. Our code is available at https://github.com/thu-ml/Attack-Bard. Update: GPT-4V is available at October 2023. We further evaluate its robustness under the same set of adversarial examples, achieving a 45% attack success rate., Comment: Technical report

According to the standard thin disc theory, it is predicted that the radiation-pressure-dominated inner region of a thin disc is thermally unstable, while observations suggest that it is common for a thin disc of more than 0.01 Eddington luminosity to be in a thermally stable state. Previous studies have suggested that magnetically driven winds have the potential to suppress instability. In this work, we implement one-dimensional global simulations of the thin accretion disc to study the effects of magnetically driven winds on thermal instability. The winds play a role in transferring the angular momentum of the disc and cooling the disc. When the mass outflow rate of winds is low, the important role of winds is to transfer the angular momentum and then shorten the outburst period. When the winds have a high mass outflow rate, they can calm down the thermal instability. We also explore the parameter space of the magnetic field strength and the mass loading parameter., Comment: 9 pages, 6 figures, accepted for publication in MNRAS

Single-atom catalysts are considered as a promising method for efficient energy conversion, owing to their advantages of high atom utilization and low catalyst cost. However, finding a stable two-dimensional structure and high hydrogen evolution reaction (HER) performance is a current research hotspot. Herein, based on the first-principles calculations, we identify the HER properties of six catalysts (TM@MoSi2N4, TM = Sc, Ti, V, Fe, Co, and Ni) comprising transition metal atoms anchored on MoSi2N4 monolayer. The results show that the spin-polarized states appear around the Fermi level after anchoring TM atoms. Therefore, the energy level of the first available unoccupied state for accommodating hydrogen drops, regulating the bonding strength of hydrogen. Thus, the single transition metal atom activates the active site of the MoSi2N4 inert base plane, becoming a quite suitable site for the HER. Based on {\Delta}GH*, the exchange current density and volcano diagram of the corresponding catalytic system were also calculated. Among them, V@MoSi2N4 ({\Delta}GH* = -0.07 eV) and Ni@MoSi2N4 ({\Delta}GH* = 0.06 eV) systems show efficient the HER property. Our study confirms that the transition metal atom anchoring is an effective means to improve the performance of electrocatalysis, and TM@MoSi2N4 has practical application potential as a high efficiency HER electrocatalyst., Comment: 20 Pages, 6 figures