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We study the atomic collapse phenomenon in $K$ and $K'$ valley of gapped graphene. Bound states induced by Coulomb impurity in the gap turn into atomic collapse resonances as the charge increases beyond the supercritical charge $Z_c$. $Z_c$ increases sublinear with the band gap $\Delta$. The atomic collapse resonances result in peaks in the LDOS at the same energies in $K$ and $K'$ valley, but the strong (weak) LDOS peaks in $K$ valley are weak (strong) LDOS peaks in $K'$ valley reminiscent of pseudospin polarization phenomenon. From a spatial LDOS analysis of the atomic collapse resonance states, we assign specific atomic orbitals to the atomic collapse resonances. Remarkably, the two $p$ atomic orbital atomic collapse states are no longer degenerate and splits into two having lobes in different directions in the graphene plane.

Deep learning (DL) libraries, widely used in AI applications, often contain vulnerabilities like buffer overflows and use-after-free errors. Traditional fuzzing struggles with the complexity and API diversity of DL libraries such as TensorFlow and PyTorch, which feature over 1,000 APIs. Testing all these APIs is challenging due to complex inputs and varied usage patterns. While large language models (LLMs) show promise in code understanding and generation, existing LLM-based fuzzers lack deep knowledge of API edge cases and struggle with test input generation. To address this, we propose DFUZZ, an LLM-driven fuzzing approach for DL libraries. DFUZZ leverages two insights: (1) LLMs can reason about error-triggering edge cases from API code and apply this knowledge to untested APIs, and (2) LLMs can accurately synthesize test programs to automate API testing. By providing LLMs with a "white-box view" of APIs, DFUZZ enhances reasoning and generation for comprehensive fuzzing. Experimental results show that DFUZZ outperforms state-of-the-art fuzzers in API coverage for TensorFlow and PyTorch, uncovering 37 bugs, with 8 fixed and 19 under developer investigation.

We utilize the generalized entropy theory (GET) of glass formation to address one of the most singular and least understood properties of polymer glass-forming liquids in comparison to atomic and small molecule liquids -- the often relatively high fragility of the polymer dynamics on a segmental scale, $m_s$. We first analyze the relation between $m_s$ and the ratio, $S_c^*/ S_c(T_{\mathrm{g}})$. We find that an apparently general nonlinear relation between $m_s$ and $S_c^*/ S_c(T_{\mathrm{g}})$ holds to a good approximation for a large class of polymer models, $m_s \approx 7.9 \exp [0.6S_c^*/ S_c(T_{\mathrm{g}})]$. The predicted ranges of $m_s$ and $S_c^*/ S_c(T_{\mathrm{g}})$ are consistent with experimental estimates for high molecular-mass polymer, oligomeric, small molecule, and atomic glass-forming liquids. In particular, relatively high values of $m_s$ are found for polymers having complex monomer structures and significant chain stiffness. The variation of $m_s$ with molecular mass, chain stiffness, and intermolecular interaction strength can be traced to the variation of $S_c^*$, which is shown to provide a measure of packing frustration defined in terms of the dimensionless thermal expansion coefficient and isothermal compressibility. The often relatively high fragility and large extent of cooperative motion are found in the GET to derive from the often relatively large packing frustration in this class of polymer glass-forming liquids. Finally, we also develop a tentative model of the ``dynamical segmental relaxation time'' based on the GET, in which the polymers on a coarse-grained scale are modeled as strings of structureless ``beads'', as assumed in the Rouse and reptation models of polymer dynamics., Comment: 63 pages, 9 figures

We revisit the launch of the galactic outflow in M82 through hydrodynamic simulations. Employing a sink-particle module, we self-consistently resolve the star formation and feedback processes, avoiding the reliance on various assumed models. We probe the effects of different stellar feedback mechanisms, gas return from star-forming clouds and gas disc mass on the starburst and outflow. Our simulations can generate a starburst that lasts $\sim25$ Myr, peaking at 20-50 $\rm{M_{\odot} yr^{-1}}$. However, the total stellar mass formed in the starburst often exceeds M82's estimated value. The outflow's launch occurs in two stages. Initially, continuous SNe explosions form small bubbles, merging into a super bubble foam composed of warm/hot gas and high-density cool filaments. After $\sim10$ Myr of SN injection, the super bubble breakout the disc, marking the second stage, which takes $\sim15$ Myr to develop a kpc-scale outflow. Our simulations reveal that cool filaments within the ISM can survive from the stellar feedback, then were entrained into the outflow and stretched to hundreds pc in length. While the mass loading factor of the well-developed outflow is comparable to M82, the cool gas mass outflow rate is often lower, and its velocity is slower than the estimated value in M82 by $\sim60\%$. Warm and hot gas are $\sim25\%$ slower. SN feedback acts as the primary driver of the outflow, while gas return significantly influences the starburst and outflow. Other factors have moderate effects. To address the shortcoming in our results, enhanced SN feedback effect due to clustered SNe is likely necessary., Comment: 26 pages, 15 figures, submitted to MNRAS. Abstract has been shortened, and typos corrected

Long video understanding presents challenges due to the inherent high computational complexity and redundant temporal information. An effective representation for long videos must process such redundancy efficiently while preserving essential contents for downstream tasks. This paper introduces SEmantic Attention Learning (SEAL), a novel unified representation for long videos. To reduce computational complexity, long videos are decomposed into three distinct types of semantic entities: scenes, objects, and actions, allowing models to operate on a handful of entities rather than a large number of frames or pixels. To further address redundancy, we propose an attention learning module that balances token relevance with diversity formulated as a subset selection optimization problem. Our representation is versatile, enabling applications across various long video understanding tasks. Extensive experiments show that SEAL significantly outperforms state-of-the-art methods in video question answering and temporal grounding tasks and benchmarks including LVBench, MovieChat-1K, and Ego4D.

Generative models transform random noise into images; their inversion aims to transform images back to structured noise for recovery and editing. This paper addresses two key tasks: (i) inversion and (ii) editing of a real image using stochastic equivalents of rectified flow models (such as Flux). Although Diffusion Models (DMs) have recently dominated the field of generative modeling for images, their inversion presents faithfulness and editability challenges due to nonlinearities in drift and diffusion. Existing state-of-the-art DM inversion approaches rely on training of additional parameters or test-time optimization of latent variables; both are expensive in practice. Rectified Flows (RFs) offer a promising alternative to diffusion models, yet their inversion has been underexplored. We propose RF inversion using dynamic optimal control derived via a linear quadratic regulator. We prove that the resulting vector field is equivalent to a rectified stochastic differential equation. Additionally, we extend our framework to design a stochastic sampler for Flux. Our inversion method allows for state-of-the-art performance in zero-shot inversion and editing, outperforming prior works in stroke-to-image synthesis and semantic image editing, with large-scale human evaluations confirming user preference., Comment: Preprint

Video classification systems based on Deep Neural Networks (DNNs) have demonstrated excellent performance in accurately verifying video content. However, recent studies have shown that DNNs are highly vulnerable to adversarial examples. Therefore, a deep understanding of adversarial attacks can better respond to emergency situations. In order to improve attack performance, many style-transfer-based attacks and patch-based attacks have been proposed. However, the global perturbation of the former will bring unnatural global color, while the latter is difficult to achieve success in targeted attacks due to the limited perturbation space. Moreover, compared to a plethora of methods targeting image classifiers, video adversarial attacks are still not that popular. Therefore, to generate adversarial examples with a low budget and to provide them with a higher verisimilitude, we propose a novel black-box video attack framework, called Stylized Logo Attack (SLA). SLA is conducted through three steps. The first step involves building a style references set for logos, which can not only make the generated examples more natural, but also carry more target class features in the targeted attacks. Then, reinforcement learning (RL) is employed to determine the style reference and position parameters of the logo within the video, which ensures that the stylized logo is placed in the video with optimal attributes. Finally, perturbation optimization is designed to optimize perturbations to improve the fooling rate in a step-by-step manner. Sufficient experimental results indicate that, SLA can achieve better performance than state-of-the-art methods and still maintain good deception effects when facing various defense methods.

Face recognition pipelines have been widely deployed in various mission-critical systems in trust, equitable and responsible AI applications. However, the emergence of adversarial attacks has threatened the security of the entire recognition pipeline. Despite the sheer number of attack methods proposed for crafting adversarial examples in both digital and physical forms, it is never an easy task to assess the real threat level of different attacks and obtain useful insight into the key risks confronted by face recognition systems. Traditional attacks view imperceptibility as the most important measurement to keep perturbations stealthy, while we suspect that industry professionals may possess a different opinion. In this paper, we delve into measuring the threat brought about by adversarial attacks from the perspectives of the industry and the applications of face recognition. In contrast to widely studied sophisticated attacks in the field, we propose an effective yet easy-to-launch physical adversarial attack, named AdvColor, against black-box face recognition pipelines in the physical world. AdvColor fools models in the recognition pipeline via directly supplying printed photos of human faces to the system under adversarial illuminations. Experimental results show that physical AdvColor examples can achieve a fooling rate of more than 96% against the anti-spoofing model and an overall attack success rate of 88% against the face recognition pipeline. We also conduct a survey on the threats of prevailing adversarial attacks, including AdvColor, to understand the gap between the machine-measured and human-assessed threat levels of different forms of adversarial attacks. The survey results surprisingly indicate that, compared to deliberately launched imperceptible attacks, perceptible but accessible attacks pose more lethal threats to real-world commercial systems of face recognition., Comment: 19 pages, 12 figures

An Artificial Intelligence (AI) agent is a software entity that autonomously performs tasks or makes decisions based on pre-defined objectives and data inputs. AI agents, capable of perceiving user inputs, reasoning and planning tasks, and executing actions, have seen remarkable advancements in algorithm development and task performance. However, the security challenges they pose remain under-explored and unresolved. This survey delves into the emerging security threats faced by AI agents, categorizing them into four critical knowledge gaps: unpredictability of multi-step user inputs, complexity in internal executions, variability of operational environments, and interactions with untrusted external entities. By systematically reviewing these threats, this paper highlights both the progress made and the existing limitations in safeguarding AI agents. The insights provided aim to inspire further research into addressing the security threats associated with AI agents, thereby fostering the development of more robust and secure AI agent applications., Comment: Submitted to ACM Computing Survey

We propose Reference-Based Modulation (RB-Modulation), a new plug-and-play solution for training-free personalization of diffusion models. Existing training-free approaches exhibit difficulties in (a) style extraction from reference images in the absence of additional style or content text descriptions, (b) unwanted content leakage from reference style images, and (c) effective composition of style and content. RB-Modulation is built on a novel stochastic optimal controller where a style descriptor encodes the desired attributes through a terminal cost. The resulting drift not only overcomes the difficulties above, but also ensures high fidelity to the reference style and adheres to the given text prompt. We also introduce a cross-attention-based feature aggregation scheme that allows RB-Modulation to decouple content and style from the reference image. With theoretical justification and empirical evidence, our framework demonstrates precise extraction and control of content and style in a training-free manner. Further, our method allows a seamless composition of content and style, which marks a departure from the dependency on external adapters or ControlNets., Comment: Preprint. Under review

AI-enabled critical infrastructures (ACIs) integrate artificial intelligence (AI) technologies into various essential systems and services that are vital to the functioning of society, offering significant implications for efficiency, security and resilience. While adopting decentralized AI approaches (such as federated learning technology) in ACIs is plausible, private and sensitive data are still susceptible to data reconstruction attacks through gradient optimization. In this work, we propose Compressed Differentially Private Aggregation (CDPA), a leakage-resilient, communication-efficient, and carbon-neutral approach for ACI networks. Specifically, CDPA has introduced a novel random bit-flipping mechanism as its primary innovation. This mechanism first converts gradients into a specific binary representation and then selectively flips masked bits with a certain probability. The proposed bit-flipping introduces a larger variance to the noise while providing differentially private protection and commendable efforts in energy savings while applying vector quantization techniques within the context of federated learning. The experimental evaluation indicates that CDPA can reduce communication cost by half while preserving model utility. Moreover, we demonstrate that CDPA can effectively defend against state-of-the-art data reconstruction attacks in both computer vision and natural language processing tasks. We highlight existing benchmarks that generate 2.6x to over 100x more carbon emissions than CDPA. We hope that the CDPA developed in this paper can inform the federated AI-enabled critical infrastructure of a more balanced trade-off between utility and privacy, resilience protection, as well as a better carbon offset with less communication overhead.

Quasicrystals have been observed in a variety of materials ranging from metal alloys to block copolymers and represent an "intermediate" form of matter between crystals and amorphous materials (glasses and liquids) in that their structural and dynamical properties can not readily described in terms of conventional solid-state models of liquids and solids. In the present work, we present a comprehensive analysis of basic thermodynamic and dynamic properties of quasicrystals to better understand the nature of the atomic motion underlying diffusion and structural relaxation in these materials. As our model system, we investigate a dodecagonal quasicrystal using molecular dynamics (MD) simulations in two dimensions (2D), subject to periodic boundary conditions. We observe a two-stage relaxation dynamics in the self-intermediate scattering function $F_s(k,t)$ of our quasicrystal material involving a fast $\beta$-relaxation on a ps timescale and $\alpha$ relaxation process having a highly temperature dependent relaxation time whose activation energy varies in concert with the extent of string-like collective motion. Multi-step relaxation of the intermediate scattering function and string-like collective atomic motion have similarly been observed ubiquitously in glass-forming liquids at low temperatures and in crystalline materials at elevated temperatures where structural relaxation and diffusion are both non-Arrhenius. After examining the dynamics of our quasi-crystalline material in great detail, we conclude that its dynamics more closely resemble observations on metallic glass-forming liquids, in qualitative accord with previous neutron scattering studies., Comment: v1: comments welcome

Practical extreme ultraviolet (EUV) sources yield the desired 13.5 nm radiation but also generate debris, significantly limiting the lifespan of the collector mirror in lithography. In this study, we explore the role of buffer gas in transporting debris particles within a EUV source vessel using direct numerical simulations (DNS). Our study involves a 2m $\times$ 1m $\times$ 1m rectangular cavity with an injecting jet flow subjected to sideward outlet. Debris particles are introduced into the cavity with specified initial velocities, simulating a spherical radiating pattern with particle diameters ranging from 0.1 $\mu$m to 1 $\mu$m. Varying the inflow velocity (from $1$m/s to $50$m/s) of the buffer gas reveals a morphological transition in the flow field. At low inflow velocities, the flow remains steady, whereas higher inflow velocities induce the formation of clustered corner rolls. Upon reaching sufficiently high inflow velocities, the jet flow can penetrate the entire cavity, impacting the endwall. Interestingly, the resulting recirculation flow leads to the spontaneous formation of spiraling outflow. The distinct flow structures at various inflow velocities lead to distinct patterns of particle transport. For low-speed gas, it is efficient in expelling all particles smaller than 0.4 $\mu$m, while for high-speed gas, those fine particles accumulate near the endwall and are challenging to be extracted. Our findings highlight the significance of controlling flow conditions for effective debris particle transport and clearance in diverse applications especially in EUV source vessels.

Sampling from the posterior distribution poses a major computational challenge in solving inverse problems using latent diffusion models. Common methods rely on Tweedie's first-order moments, which are known to induce a quality-limiting bias. Existing second-order approximations are impractical due to prohibitive computational costs, making standard reverse diffusion processes intractable for posterior sampling. This paper introduces Second-order Tweedie sampler from Surrogate Loss (STSL), a novel sampler that offers efficiency comparable to first-order Tweedie with a tractable reverse process using second-order approximation. Our theoretical results reveal that the second-order approximation is lower bounded by our surrogate loss that only requires $O(1)$ compute using the trace of the Hessian, and by the lower bound we derive a new drift term to make the reverse process tractable. Our method surpasses SoTA solvers PSLD and P2L, achieving 4X and 8X reduction in neural function evaluations, respectively, while notably enhancing sampling quality on FFHQ, ImageNet, and COCO benchmarks. In addition, we show STSL extends to text-guided image editing and addresses residual distortions present from corrupted images in leading text-guided image editing methods. To our best knowledge, this is the first work to offer an efficient second-order approximation in solving inverse problems using latent diffusion and editing real-world images with corruptions., Comment: Preprint

The interplay between a magnetic field and the Coulomb potential from a charged vacancy on the electron states in graphene is investigated within the tight-binding model. The Coulomb potential removes locally Landau level degeneracy, while the vacancy introduces a satellite level next to the normal Landau level. These satellite levels are found throughout the positive energy region, but in the negative energy region they turn into atomic collapse resonances. Crossings between Landau levels with different angular quantum number $m$ are found. Unlike the point impurity system in which an anticrossing occurs between Landau levels of the same $m$, in this work anticrossing is found between the normal Landau level and the vacancy induced level. The atomic collapse resonance hybridize with the Landau levels. The charge at which the lowest Landau level $m = -1, N = 1$ crosses increases $E = 0$ with enhancing magnetic field. Landau level scaling anomaly occurs when the charge is larger than the critical charge $\beta\approx0.6$ and this critical charge is independent of the magnetic field.

The generalization ability of machine learning models degrades significantly when the test distribution shifts away from the training distribution. We investigate the problem of training models that are robust to shifts caused by changes in the distribution of class-priors or group-priors. The presence of skewed training priors can often lead to the models overfitting to spurious features. Unlike existing methods, which optimize for either the worst or the average performance over classes or groups, our work is motivated by the need for finer control over the robustness properties of the model. We present an extremely lightweight post-hoc approach that performs scaling adjustments to predictions from a pre-trained model, with the goal of minimizing a distributionally robust loss around a chosen target distribution. These adjustments are computed by solving a constrained optimization problem on a validation set and applied to the model during test time. Our constrained optimization objective is inspired by a natural notion of robustness to controlled distribution shifts. Our method comes with provable guarantees and empirically makes a strong case for distributional robust post-hoc classifiers. An empirical implementation is available at https://github.com/weijiaheng/Drops., Comment: Camera ready version, accepted at ICLR 2023

Mutation-based fuzzing is popular and effective in discovering unseen code and exposing bugs. However, only a few studies have concentrated on quantifying the importance of input bytes, which refers to the degree to which a byte contributes to the discovery of new code. They often focus on obtaining the relationship between input bytes and path constraints, ignoring the fact that not all constraint-related bytes can discover new code. In this paper, we conduct Shapely analysis to understand the effect of byte positions on fuzzing performance, and find that some byte positions contribute more than others and this property often holds across seeds. Based on this observation, we propose a novel fuzzing solution, ShapFuzz, to guide byte selection and mutation. Specifically, ShapFuzz updates Shapley values (importance) of bytes when each input is tested during fuzzing with a low overhead, and utilizes contextual multi-armed bandit to trade off between mutating high Shapley value bytes and low-frequently chosen bytes. We implement a prototype of this solution based on AFL++, i.e., ShapFuzz. We evaluate ShapFuzz against ten state-of-the-art fuzzers, including five byte schedule-reinforced fuzzers and five commonly used fuzzers. Compared with byte schedule-reinforced fuzzers, ShapFuzz discovers more edges and exposes more bugs than the best baseline on three different sets of initial seeds. Compared with commonly used fuzzers, ShapFuzz exposes 20 more bugs than the best comparison fuzzer, and discovers 6 more CVEs than the best baseline on MAGMA. Furthermore, ShapFuzz discovers 11 new bugs on the latest versions of programs, and 3 of them are confirmed by vendors.