arXiv subjects

Yu Tian

Publications and source records attributed to Yu Tian.

At least 217 records · Page 12Linked to original sources

arXiv2024

WorldGPT: A Sora-Inspired Video AI Agent as Rich World Models from Text and Image Inputs

Several text-to-video diffusion models have demonstrated commendable capabilities in synthesizing high-quality video content. However, it remains a formidable challenge pertaining to maintaining temporal consistency and ensuring action smoothness throughout the generated sequences. In this paper, we present an innovative video generation AI agent that harnesses the power of Sora-inspired multimodal learning to build skilled world models framework based on textual prompts and accompanying images. The framework includes two parts: prompt enhancer and full video translation. The first part employs the capabilities of ChatGPT to meticulously distill and proactively construct precise prompts for each subsequent step, thereby guaranteeing the utmost accuracy in prompt communication and accurate execution in following model operations. The second part employ compatible with existing advanced diffusion techniques to expansively generate and refine the key frame at the conclusion of a video. Then we can expertly harness the power of leading and trailing key frames to craft videos with enhanced temporal consistency and action smoothness. The experimental results confirm that our method has strong effectiveness and novelty in constructing world models from text and image inputs over the other methods.

cs.CV↗

arXiv2024

Finetuned Multimodal Language Models Are High-Quality Image-Text Data Filters

We propose a novel framework for filtering image-text data by leveraging fine-tuned Multimodal Language Models (MLMs). Our approach outperforms predominant filtering methods (e.g., CLIPScore) via integrating the recent advances in MLMs. We design four distinct yet complementary metrics to holistically measure the quality of image-text data. A new pipeline is established to construct high-quality instruction data for fine-tuning MLMs as data filters. Comparing with CLIPScore, our MLM filters produce more precise and comprehensive scores that directly improve the quality of filtered data and boost the performance of pre-trained models. We achieve significant improvements over CLIPScore on popular foundation models (i.e., CLIP and BLIP2) and various downstream tasks. Our MLM filter can generalize to different models and tasks, and be used as a drop-in replacement for CLIPScore. An additional ablation study is provided to verify our design choices for the MLM filter.

cs.CV↗

arXiv2024

Emergence of Large-Scale Structures in Holographic Superfluid Turbulence

In two-dimensional turbulence systems, the emergence of large-scale structures holds profound physical implications, particularly as it indicates the occurrence of inverse energy cascades, thereby garnering significant attention. In this paper, we report a novel vortex clusters formation in the background of near-extreme Reissner-Nordstr$\ddot{o}$m black hole holographic model. At temperatures nearing absolute zero, we observe not only the formation of vortex clusters but also the emergence of an inverse energy cascade. Distinct from typical quantum systems, the genesis of holographic vortex clusters is rooted in unique quantum dissipation properties, characterized by the near immobilization of vortex dipoles at low temperatures. Through a comparative analysis with the dynamics of the Gross-Pitaevskii equation, our investigation enhances the understanding of inverse energy cascades under these extreme conditions, thereby broadening our comprehension of quantum turbulence.

hep-th↗

arXiv2024

BSPA: Exploring Black-box Stealthy Prompt Attacks against Image Generators

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.

cs.CR↗

arXiv2024

Envisioning the Future Role of 3D Wireless Networks in Preventing and Managing Disasters and Emergency Situations

In an era marked by unprecedented climatic upheavals and evolving urban landscapes, the role of advanced communication networks in disaster prevention and management is becoming increasingly critical. This paper explores the transformative potential of 3D wireless networks, an innovative amalgamation of terrestrial, aerial, and satellite technologies, in enhancing disaster response mechanisms. We delve into a myriad of use cases, ranging from large facility evacuations to wildfire management, underscoring the versatility of these networks in ensuring timely communication, real-time situational awareness, and efficient resource allocation during crises. We also present an overview of cutting-edge prototypes, highlighting the practical feasibility and operational efficacy of 3D wireless networks in real-world scenarios. Simultaneously, we acknowledge the challenges posed by aspects such as cybersecurity, cross-border coordination, and physical layer technological hurdles, and propose future directions for research and development in this domain.

cs.NI↗

arXiv2024

A Novel Approach to WaveNet Architecture for RF Signal Separation with Learnable Dilation and Data Augmentation

In this paper, we address the intricate issue of RF signal separation by presenting a novel adaptation of the WaveNet architecture that introduces learnable dilation parameters, significantly enhancing signal separation in dense RF spectrums. Our focused architectural refinements and innovative data augmentation strategies have markedly improved the model's ability to discern complex signal sources. This paper details our comprehensive methodology, including the refined model architecture, data preparation techniques, and the strategic training strategy that have been pivotal to our success. The efficacy of our approach is evidenced by the substantial improvements recorded: a 58.82\% increase in SINR at a BER of $10^{-3}$ for OFDM-QPSK with EMI Signal 1, surpassing traditional benchmarks. Notably, our model achieved first place in the challenge \cite{datadrivenrf2024}, demonstrating its superior performance and establishing a new standard for machine learning applications within the RF communications domain.

eess.SP↗

arXiv2024

Evil Geniuses: Delving into the Safety of LLM-based Agents

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.

cs.CL↗

arXiv2024

Spreading and Structural Balance on Signed Networks

Two competing types of interactions often play an important part in shaping system behavior, such as activatory or inhibitory functions in biological systems. Hence, signed networks, where each connection can be either positive or negative, have become popular models over recent years. However, the primary focus of the literature is on the unweighted and structurally unbalanced ones, where all cycles have an even number of negative edges. Hence here, we first introduce a classification of signed networks into balanced, antibalanced or strictly unbalanced ones, and then characterize each type of signed networks in terms of the spectral properties of the signed weighted adjacency matrix. In particular, we show that the spectral radius of the matrix with signs is smaller than that without if and only if the signed network is strictly unbalanced. These properties are important to understand the dynamics on signed networks, both linear and nonlinear ones. Specifically, we find consistent patterns in a linear and a nonlinear dynamics theoretically, depending on their type of balance. We also propose two measures to further characterize strictly unbalanced networks, motivated by perturbation theory. Finally, we numerically verify these properties through experiments on both synthetic and real networks.

cs.SI↗

arXiv2024

Quench Dynamics in Holographic First-Order Phase Transition

In this work, we investigate the real-time dynamics of quenching a state from phase separation in a holographic model of first-order phase transition. In addition to the typical phase-separated and high-energy final states, we have discovered a novel dynamical process that drives the system to a low-temperature supercooled final state within a narrow range of quench parameters. The critical behavior is also revealed during the fully non-linear dynamics. Following a sudden quench with critical parameters, the phase separation can be attracted to a critical nucleus. Specifically, the critical nucleus will subsequently shrink in size and eventually disappear for super-critical parameters, where the system is actually supercooled with a temperature lower than the initial one. While for sub-critical parameters, the nucleus will grow in size and finally reform a phase separation, where the absorbed quenching energy is reflected in the increment of the latent heat.

hep-th↗

arXiv2023

Large Generative AI Models for Telecom: The Next Big Thing?

The evolution of generative artificial intelligence (GenAI) constitutes a turning point in reshaping the future of technology in different aspects. Wireless networks in particular, with the blooming of self-evolving networks, represent a rich field for exploiting GenAI and reaping several benefits that can fundamentally change the way how wireless networks are designed and operated nowadays. To be specific, large GenAI models are envisioned to open up a new era of autonomous wireless networks, in which multi-modal GenAI models trained over various Telecom data, can be fine-tuned to perform several downstream tasks, eliminating the need for building and training dedicated AI models for each specific task and paving the way for the realization of artificial general intelligence (AGI)-empowered wireless networks. In this article, we aim to unfold the opportunities that can be reaped from integrating large GenAI models into the Telecom domain. In particular, we first highlight the applications of large GenAI models in future wireless networks, defining potential use-cases and revealing insights on the associated theoretical and practical challenges. Furthermore, we unveil how 6G can open up new opportunities through connecting multiple on-device large GenAI models, and hence, paves the way to the collective intelligence paradigm. Finally, we put a forward-looking vision on how large GenAI models will be the key to realize self-evolving networks.

cs.CL↗

arXiv2023

Entanglement of defect subregions in double holography

In the framework of double holography, we investigate the entanglement behavior of a brane subregion in AdS spacetime coupled to a bath on its boundary and also extract the contribution from the quantum matter within this subregion. From the boundary perspective, the brane subregion serves as the entanglement wedge of a subsystem on the conformal defect. In the ground state, we find the subsystem undergoes an entanglement phase transition induced by the degrees of freedom on the brane. With subcritical parameters, the wedge and entanglement entropy sharply decrease to zero. In contrast, in the supercritical regime, both the wedge and entropy stabilize, enabling analysis of both entanglement and reflected entropy. In this phase, we derive formulas for entanglement measures based on defect and bath central charges in the semi-classical limit. For entanglement entropy, the classical geometry only contributes a subleading term with logarithmic divergence, but the brane-bath CFT entanglement exhibits a dominant linear divergence, even in the semi-classical limit. Regarding reflected entropy within the defect subsystem, classical geometry contributes a leading term with logarithmic divergence, while the quantum matter within the entanglement wedge only contributes a finite term.

hep-th↗

arXiv2023

Protected Transverse Electric Waves in Topological Dielectric Waveguides

Waveguides are fundamental components in communication systems. However, they suffer from reflection and scattering losses at sharp routes or defects. The breakthrough in developing topological photonic crystals (PhCs) provides promising solutions to robust signal transmission. In this work, we propose a new mechanism for protecting wave-guiding modes by decorating the boundaries of a conventional waveguide with valley-Hall PhCs. This special layout enables the robust propagation of conventional transverse electric waves against defects and bends. Moreover, the proposed waveguide is compatible with the substrate integrated waveguide (SIW). High efficient mode conversion from the SIW to the proposed waveguide is achievable. By leveraging the idea of topology to conventional waveguides, we provide a powerful and practical tool that can largely improve the performance of microwave and millimeter-wave integrated circuits while reserving the features of wave-guiding modes.

physics.app-ph↗

arXiv2023

Holographic dissipative space-time supersolids

Driving a system out of equilibrium enriches the paradigm of spontaneous symmetry breaking, which could then take place not only in space but also in time. The interplay between temporal and spatial symmetries, as well as symmetries from other internal degrees of freedom, can give rise to novel nonequilibrium phases of matter. In this study, we investigate a driven-dissipative superfluid model using holographic methods and reveal the existence of a space-time supersolid (STS) phase which concomitantly breaks the time translation, spatial translation, and the internal U(1) symmetry. The holographic methods naturally include finite temperature effects, which enables us to explore the complex phase diagram of this model and observe a cascade of out-of-equilibrium phase transitions from the STS phase to a synchronized superfluid phase, and finally to a normal fluid phase, by increasing the temperature.

hep-th↗

arXiv2023

Percolation Theories for Quantum Networks

Quantum networks have experienced rapid advancements in both theoretical and experimental domains over the last decade, making it increasingly important to understand their large-scale features from the viewpoint of statistical physics. This review paper discusses a fundamental question: how can entanglement be effectively and indirectly (e.g., through intermediate nodes) distributed between distant nodes in an imperfect quantum network, where the connections are only partially entangled and subject to quantum noise? We survey recent studies addressing this issue by drawing exact or approximate mappings to percolation theory, a branch of statistical physics centered on network connectivity. Notably, we show that the classical percolation frameworks do not uniquely define the network's indirect connectivity. This realization leads to the emergence of an alternative theory called ``concurrence percolation,'' which uncovers a previously unrecognized quantum advantage that emerges at large scales, suggesting that quantum networks are more resilient than initially assumed within classical percolation contexts, offering refreshing insights into future quantum network design.

quant-ph↗

arXiv2023

Heating up quadruply quantized vortices: Splitting patterns and dynamical transitions

Using holographic duality, we investigate the impact of finite temperature on the instability and splitting patterns of quadruply quantized vortices, providing the first-ever analysis in this context. Through linear stability analysis, we reveal the occurrence of two consecutive dynamical transitions. At a specific low temperature, the dominant unstable mode transitions from the $2$-fold rotational symmetry mode to the $3$-fold one, followed by a transition from the $3$-fold one to the $4$-fold one at a higher temperature. As the temperature is increased, we also observe the $5$ and $6$-fold rotational symmetry unstable modes get excited successively. Employing the full non-linear numerical simulations, we further demonstrate that these two novel dynamical transitions, along with the temperature-induced instabilities for the $5$ and $6$-fold rotational symmetry modes, can be identified by examining the resulting distinct splitting patterns, which offers a promising route for the experimental verification in the cold atom gases.

cond-mat.quant-gas↗

arXiv2023

UnifiedVisionGPT: Streamlining Vision-Oriented AI through Generalized Multimodal Framework

In the current landscape of artificial intelligence, foundation models serve as the bedrock for advancements in both language and vision domains. OpenAI GPT-4 has emerged as the pinnacle in large language models (LLMs), while the computer vision (CV) domain boasts a plethora of state-of-the-art (SOTA) models such as Meta's SAM and DINO, and YOLOS. However, the financial and computational burdens of training new models from scratch remain a significant barrier to progress. In response to this challenge, we introduce UnifiedVisionGPT, a novel framework designed to consolidate and automate the integration of SOTA vision models, thereby facilitating the development of vision-oriented AI. UnifiedVisionGPT distinguishes itself through four key features: (1) provides a versatile multimodal framework adaptable to a wide range of applications, building upon the strengths of multimodal foundation models; (2) seamlessly integrates various SOTA vision models to create a comprehensive multimodal platform, capitalizing on the best components of each model; (3) prioritizes vision-oriented AI, ensuring a more rapid progression in the CV domain compared to the current trajectory of LLMs; and (4) introduces automation in the selection of SOTA vision models, generating optimal results based on diverse multimodal inputs such as text prompts and images. This paper outlines the architecture and capabilities of UnifiedVisionGPT, demonstrating its potential to revolutionize the field of computer vision through enhanced efficiency, versatility, generalization, and performance. Our implementation, along with the unified multimodal framework and comprehensive dataset, is made publicly available at https://github.com/LHBuilder/SA-Segment-Anything.

cs.CV↗

arXiv2023

Fairness-Driven Optimization of RIS-Augmented 5G Networks for Seamless 3D UAV Connectivity Using DRL Algorithms

In this paper, we study the problem of joint active and passive beamforming for reconfigurable intelligent surface (RIS)-assisted massive multiple-input multiple-output systems towards the extension of the wireless cellular coverage in 3D, where multiple RISs, each equipped with an array of passive elements, are deployed to assist a base station (BS) to simultaneously serve multiple unmanned aerial vehicles (UAVs) in the same time-frequency resource of 5G wireless communications. With a focus on ensuring fairness among UAVs, our objective is to maximize the minimum signal-to-interference-plus-noise ratio (SINR) at UAVs by jointly optimizing the transmit beamforming parameters at the BS and phase shift parameters at RISs. We propose two novel algorithms to address this problem. The first algorithm aims to mitigate interference by calculating the BS beamforming matrix through matrix inverse operations once the phase shift parameters are determined. The second one is based on the principle that one RIS element only serves one UAV and the phase shift parameter of this RIS element is optimally designed to compensate the phase offset caused by the propagation and fading. To obtain the optimal parameters, we utilize one state-of-the-art reinforcement learning algorithm, deep deterministic policy gradient, to solve these two optimization problems. Simulation results are provided to illustrate the effectiveness of our proposed solution and some insightful remarks are observed.

eess.SP↗

arXiv2023

Extended black hole thermodynamics from extended Iyer-Wald formalism

In recent years, there has been significant interest in the field of extended black hole thermodynamics, where the cosmological constant and/or other coupling parameters are treated as thermodynamic variables. Drawing inspiration from the Iyer-Wald formalism, which reveals the intrinsic and universal structure of conventional black hole thermodynamics, we illustrate that a proper extension of this formalism also unveils the underlying theoretical structure of extended black hole thermodynamics. As a remarkable consequence, for any gravitational theory described by a diffeomorphism invariant action, it is always possible to construct a consistent extended thermodynamics using this extended formalism.

gr-qc↗