arXiv subjects

Yibo Wang

Publications and source records attributed to Yibo Wang.

At least 145 records · Page 8Linked to original sources

arXiv2024

Realization of cold atom gyroscope in space

High-precision gyroscopes in space are essential for fundamental physics research and navigation. Due to its potential high precision, the cold atom gyroscope is expected to be the next generation of gyroscopes in space. Here, we report the first realization of a cold atom gyroscope, which was demonstrated by the atom interferometer installed in the China Space Station (CSS) as a payload. By compensating for CSS's high dynamic rotation rate using a built-in piezoelectric mirror, spatial interference fringes in the interferometer are successfully obtained. Then, the optimized ratio of the Raman laser's angles is derived, the coefficients of the piezoelectric mirror are self-calibrated in orbit, and various systemic effects are corrected. We achieve a rotation measurement resolution of 50*10^-6 rad/s for a single shot and 17*10^-6 rad/s for an average number of 32. The measured rotation is (-1142+/-29)*10^-6 rad/s and is compatible with that recorded by the classical gyroscope of the CSS. This study paves the way for developing high-precision cold atom gyroscopes in space.

physics.atom-ph↗

arXiv2024

StyleCrafter: Enhancing Stylized Text-to-Video Generation with Style Adapter

Text-to-video (T2V) models have shown remarkable capabilities in generating diverse videos. However, they struggle to produce user-desired stylized videos due to (i) text's inherent clumsiness in expressing specific styles and (ii) the generally degraded style fidelity. To address these challenges, we introduce StyleCrafter, a generic method that enhances pre-trained T2V models with a style control adapter, enabling video generation in any style by providing a reference image. Considering the scarcity of stylized video datasets, we propose to first train a style control adapter using style-rich image datasets, then transfer the learned stylization ability to video generation through a tailor-made finetuning paradigm. To promote content-style disentanglement, we remove style descriptions from the text prompt and extract style information solely from the reference image using a decoupling learning strategy. Additionally, we design a scale-adaptive fusion module to balance the influences of text-based content features and image-based style features, which helps generalization across various text and style combinations. StyleCrafter efficiently generates high-quality stylized videos that align with the content of the texts and resemble the style of the reference images. Experiments demonstrate that our approach is more flexible and efficient than existing competitors.

cs.CV↗

arXiv2024

The unluckiest star: A spectroscopically confirmed repeated partial tidal disruption event AT 2022dbl

The unluckiest star orbits a supermassive black hole elliptically. Every time it reaches the pericenter, it shallowly enters the tidal radius and gets partially tidal disrupted, producing a series of flares. Confirmation of a repeated partial tidal disruption event (pTDE) requires not only evidence to rule out other types of transients, but also proof that only one star is involved, as TDEs from multiple stars can also produce similar flares. In this letter, we report the discovery of a repeated pTDE, AT 2022dbl. In a quiescent galaxy at $z=0.0284$, two separate optical/UV flares have been observed in 2022 and 2024, with no bright X-ray, radio or mid-infrared counterparts. Compared to the first flare, the second flare has a similar blackbody temperature of ~26,000 K, slightly lower peak luminosity, and slower rise and fall phases. Compared to the ZTF TDEs, their blackbody parameters and light curve shapes are all similar. The spectra taken during the second flare show a steeper continuum than the late-time spectra of the previous flare, consistent with a newly risen flare. More importantly, the possibility of two independent TDEs can be largely ruled out because the optical spectra taken around the peak of the two flares exhibit highly similar broad Balmer, N III and possible He II emission lines, especially the extreme ~4100Å emission lines. This represents the first robust spectroscopic evidence for a repeated pTDE, which can soon be verified by observing the third flare, given its short orbital period.

astro-ph.HE↗

arXiv2024

Asymmetric Mempool DoS Security: Formal Definitions and Provable Secure Designs

The mempool plays a crucial role in blockchain systems as a buffer zone for pending transactions before they are executed and included in a block. However, existing works primarily focus on mitigating defenses against already identified real-world attacks. This paper introduces secure blockchain-mempool designs capable of defending against any form of asymmetric eviction DoS attacks. We establish formal security definitions for mempools under the eviction-based attack vector. Our proposed secure transaction admission algorithm, named \textsc{saferAd-CP}, ensures eviction-security by providing a provable lower bound on the cost of executing eviction DoS attacks. Through evaluation with real transaction trace replays, \textsc{saferAd-CP} demonstrates negligible latency and significantly high lower bounds against any eviction attack, highlighting its effectiveness and robustness in securing blockchain mempools.

cs.CR↗

arXiv2024

Defining and Preventing Asymmetric Mempool DoS in Ethereum with saferAd

This paper presents secure mempool designs under asymmetric DoS attacks. We formulate safety definitions under two abstract DoSes, namely eviction- and locking-based attacks. We propose a safe transaction admission framework for securing mempools, named saferAd, that achieves both eviction- and locking-safety. The proven security stems from an upper bound of the attack damage under locking DoSes and a lower bound of the attack cost under eviction DoSes. The evaluation by replaying real transaction traces shows saferAd incurs negligible latency or insignificant change of validator revenue.

cs.CR↗

arXiv2024

Data-Driven Output Prediction and Control of Stochastic Systems: An Innovation-Based Approach

Recent years have witnessed a booming interest in data-driven control of dynamical systems. However, the implicit data-driven output predictors are vulnerable to uncertainty such as process disturbance and measurement noise, causing unreliable predictions and unexpected control actions. In this brief, we put forward a new data-driven approach to output prediction of stochastic linear time-invariant (LTI) systems. By utilizing the innovation form, the uncertainty in stochastic LTI systems is recast as innovations that can be readily estimated from input-output data without knowing system matrices. In this way, by applying the fundamental lemma to the innovation form, we propose a new innovation-based data-driven output predictor (OP) of stochastic LTI systems, which bypasses the need for identifying state-space matrices explicitly and building a state estimator. The boundedness of the second moment of prediction errors in closed-loop is established under mild conditions. The proposed data-driven OP can be integrated into optimal control design for better performance. Numerical simulations demonstrate the outperformance of the proposed innovation-based methods in output prediction and control design over existing formulations.

math.OC↗

arXiv2024

Deep Temporal Sequence Classification and Mathematical Modeling for Cell Tracking in Dense 3D Microscopy Videos of Bacterial Biofilms

Automatic cell tracking in dense environments is plagued by inaccurate correspondences and misidentification of parent-offspring relationships. In this paper, we introduce a novel cell tracking algorithm named DenseTrack, which integrates deep learning with mathematical model-based strategies to effectively establish correspondences between consecutive frames and detect cell division events in crowded scenarios. We formulate the cell tracking problem as a deep learning-based temporal sequence classification task followed by solving a constrained one-to-one matching optimization problem exploiting the classifier's confidence scores. Additionally, we present an eigendecomposition-based cell division detection strategy that leverages knowledge of cellular geometry. The performance of the proposed approach has been evaluated by tracking densely packed cells in 3D time-lapse image sequences of bacterial biofilm development. The experimental results on simulated as well as experimental fluorescence image sequences suggest that the proposed tracking method achieves superior performance in terms of both qualitative and quantitative evaluation measures compared to recent state-of-the-art cell tracking approaches.

eess.IV↗

arXiv2024

Your Mega-Constellations Can Be Slim:A Cost-Effective Approach for Constructing Survivable and Performant LEO Satellite Networks

In this paper, we investigate an important research problem facing the upcoming satellite Internet: from a network perspective, how many satellites exactly do we need to construct a survivable and performant LSN? To answer this question, we first formulate the survivable and performant LSN design (SPLD) problem, which aims to find the minimum number of needed satellites to construct an LSN that can provide sufficient amount of redundant paths, required link capacity and acceptable latency for traffic carried by the LSN. Second, to efficiently solve the tricky SPLD problem, we propose MEGAREDUCE, a requirement-driven constellation optimization mechanism, which can calculate feasible solutions for SPLD in polynomial time. Finally, we conduct extensive trace-driven simulations to verify MEGAREDUCE's cost-effectiveness in constructing survivable and performant LSNs on demand, and showcase how MEGAREDUCE can help optimize the incremental deployment and long-term maintenance of future satellite Internet.

cs.NI↗

arXiv2024

Causality-Informed Data-Driven Predictive Control

As a useful and efficient alternative to generic model-based control scheme, data-driven predictive control is subject to bias-variance trade-off and is known to not perform desirably in face of uncertainty. Through the connection between direct data-driven control and subspace predictive control, we gain insight into the reason being the lack of causality as a main cause for high variance of implicit prediction. In this article, we seek to address this deficiency by devising a novel causality-informed formulation of direct data-driven control. Built upon LQ factorization, an equivalent two-stage reformulation of regularized data-driven control is first derived, which bears clearer interpretability and a lower complexity than generic forms. This paves the way for deriving a two-stage causality-informed formulation of data-driven predictive control, as well as a regularized form that balances between control cost minimization and implicit identification of multi-step predictor. Since it only calls for block-triangularization of a submatrix in LQ factorization, the new causality-informed formulation comes at no excess cost as compared to generic ones. Its efficacy is investigated based on numerical examples and application to model-free control of a simulated industrial heating furnace. Empirical results corroborate that the proposed method yields obvious performance improvement over existing formulations in handling stochastic noise and process nonlinearity.

math.OC↗

arXiv2024

Projection-Free Variance Reduction Methods for Stochastic Constrained Multi-Level Compositional Optimization

This paper investigates projection-free algorithms for stochastic constrained multi-level optimization. In this context, the objective function is a nested composition of several smooth functions, and the decision set is closed and convex. Existing projection-free algorithms for solving this problem suffer from two limitations: 1) they solely focus on the gradient mapping criterion and fail to match the optimal sample complexities in unconstrained settings; 2) their analysis is exclusively applicable to non-convex functions, without considering convex and strongly convex objectives. To address these issues, we introduce novel projection-free variance reduction algorithms and analyze their complexities under different criteria. For gradient mapping, our complexities improve existing results and match the optimal rates for unconstrained problems. For the widely-used Frank-Wolfe gap criterion, we provide theoretical guarantees that align with those for single-level problems. Additionally, by using a stage-wise adaptation, we further obtain complexities for convex and strongly convex functions. Finally, numerical experiments on different tasks demonstrate the effectiveness of our methods.

math.OC↗

arXiv2024

Universal Online Convex Optimization with $1$ Projection per Round

To address the uncertainty in function types, recent progress in online convex optimization (OCO) has spurred the development of universal algorithms that simultaneously attain minimax rates for multiple types of convex functions. However, for a $T$-round online problem, state-of-the-art methods typically conduct $O(\log T)$ projections onto the domain in each round, a process potentially time-consuming with complicated feasible sets. In this paper, inspired by the black-box reduction of Cutkosky and Orabona (2018), we employ a surrogate loss defined over simpler domains to develop universal OCO algorithms that only require $1$ projection. Embracing the framework of prediction with expert advice, we maintain a set of experts for each type of functions and aggregate their predictions via a meta-algorithm. The crux of our approach lies in a uniquely designed expert-loss for strongly convex functions, stemming from an innovative decomposition of the regret into the meta-regret and the expert-regret. Our analysis sheds new light on the surrogate loss, facilitating a rigorous examination of the discrepancy between the regret of the original loss and that of the surrogate loss, and carefully controlling meta-regret under the strong convexity condition. In this way, with only $1$ projection per round, we establish optimal regret bounds for general convex, exponentially concave, and strongly convex functions simultaneously. Furthermore, we enhance the expert-loss to exploit the smoothness property, and demonstrate that our algorithm can attain small-loss regret for multiple types of convex and smooth functions.

cs.LG↗

arXiv2024

Learning Off-policy with Model-based Intrinsic Motivation For Active Online Exploration

Recent advancements in deep reinforcement learning (RL) have demonstrated notable progress in sample efficiency, spanning both model-based and model-free paradigms. Despite the identification and mitigation of specific bottlenecks in prior works, the agent's exploration ability remains under-emphasized in the realm of sample-efficient RL. This paper investigates how to achieve sample-efficient exploration in continuous control tasks. We introduce an RL algorithm that incorporates a predictive model and off-policy learning elements, where an online planner enhanced by a novelty-aware terminal value function is employed for sample collection. Leveraging the forward predictive error within a latent state space, we derive an intrinsic reward without incurring parameters overhead. This reward establishes a solid connection to model uncertainty, allowing the agent to effectively overcome the asymptotic performance gap. Through extensive experiments, our method shows competitive or even superior performance compared to prior works, especially the sparse reward cases.

cs.LG↗

arXiv2024

AT2023lli: A Tidal Disruption Event with Prominent Optical Early Bump and Delayed Episodic X-ray Emission

High-cadence, multiwavelength observations have continuously revealed the diversity of tidal disruption events (TDEs), thus greatly advancing our knowledge and understanding of TDEs. In this work, we conducted an intensive optical-UV and X-ray follow-up campaign of TDE AT2023lli, and found a remarkable month-long bump in its UV/optical light curve nearly two months prior to maximum brightness. The bump represents the longest separation time from the main peak among known TDEs to date. The main UV/optical outburst declines as $t^{-4.10}$, making it one of the fastest decaying optically selected TDEs. Furthermore, we detected sporadic X-ray emission 30 days after the UV/optical peak, accompanied by a reduction in the period of inactivity. It is proposed that the UV/optical bump could be caused by the self-intersection of the stream debris, whereas the primary peak is generated by the reprocessed emission of the accretion process. In addition, our results suggest that episodic X-ray radiation during the initial phase of decline may be due to the patched obscurer surrounding the accretion disk, a phenomenon associated with the inhomogeneous reprocessing process. The double TDE scenario, in which two stars are disrupted in sequence, is also a possible explanation for producing the observed early bump and main peak. We anticipate that the multicolor light curves of TDEs, especially in the very early stages, and the underlying physics can be better understood in the near future with the assistance of dedicated surveys such as the deep high-cadence survey of the 2.5-meter Wide Field Survey Telescope (WFST).

astro-ph.HE↗

arXiv2024

DetDiffusion: Synergizing Generative and Perceptive Models for Enhanced Data Generation and Perception

Current perceptive models heavily depend on resource-intensive datasets, prompting the need for innovative solutions. Leveraging recent advances in diffusion models, synthetic data, by constructing image inputs from various annotations, proves beneficial for downstream tasks. While prior methods have separately addressed generative and perceptive models, DetDiffusion, for the first time, harmonizes both, tackling the challenges in generating effective data for perceptive models. To enhance image generation with perceptive models, we introduce perception-aware loss (P.A. loss) through segmentation, improving both quality and controllability. To boost the performance of specific perceptive models, our method customizes data augmentation by extracting and utilizing perception-aware attribute (P.A. Attr) during generation. Experimental results from the object detection task highlight DetDiffusion's superior performance, establishing a new state-of-the-art in layout-guided generation. Furthermore, image syntheses from DetDiffusion can effectively augment training data, significantly enhancing downstream detection performance.

cs.CV↗

arXiv2024

ASASSN-18ap: A Dusty Tidal Disruption Event Candidate with an Early Bump in the Light Curve

We re-examined the classification of the optical transient ASASSN-18ap, which was initially identified as a supernova (SNe) upon its discovery. Based on newly emerged phenomena, such as a delayed luminous infrared outburst and the emergence of luminous coronal emission lines, we suggest that ASASSN-18ap is more likely a tidal disruption event (TDE) in a dusty environment, rather than a supernova. The total energy in the infrared outburst is $\rm 3.1\times10^{51}$ erg, which is an order of magnitude higher than the total energy in the optical-to-ultraviolet range, indicating a large dust extinction, an extra-EUV component, or anisotropic continuum emission. A bumpy feature appeared in the optical light curve at the start of brightening, which was reported in a couple of TDEs very recently. This early bump may have been overlooked in the past due to the lack of sufficient sampling of the light curves of most TDEs during their ascending phase, and it could provide insight into the origin of optical emission.

astro-ph.HE↗

arXiv2024

GeoDiffusion: Text-Prompted Geometric Control for Object Detection Data Generation

Diffusion models have attracted significant attention due to the remarkable ability to create content and generate data for tasks like image classification. However, the usage of diffusion models to generate the high-quality object detection data remains an underexplored area, where not only image-level perceptual quality but also geometric conditions such as bounding boxes and camera views are essential. Previous studies have utilized either copy-paste synthesis or layout-to-image (L2I) generation with specifically designed modules to encode the semantic layouts. In this paper, we propose the GeoDiffusion, a simple framework that can flexibly translate various geometric conditions into text prompts and empower pre-trained text-to-image (T2I) diffusion models for high-quality detection data generation. Unlike previous L2I methods, our GeoDiffusion is able to encode not only the bounding boxes but also extra geometric conditions such as camera views in self-driving scenes. Extensive experiments demonstrate GeoDiffusion outperforms previous L2I methods while maintaining 4x training time faster. To the best of our knowledge, this is the first work to adopt diffusion models for layout-to-image generation with geometric conditions and demonstrate that L2I-generated images can be beneficial for improving the performance of object detectors.

cs.CV↗

arXiv2024

Disclosure and Mitigation of Gender Bias in LLMs

Large Language Models (LLMs) can generate biased responses. Yet previous direct probing techniques contain either gender mentions or predefined gender stereotypes, which are challenging to comprehensively collect. Hence, we propose an indirect probing framework based on conditional generation. This approach aims to induce LLMs to disclose their gender bias even without explicit gender or stereotype mentions. We explore three distinct strategies to disclose explicit and implicit gender bias in LLMs. Our experiments demonstrate that all tested LLMs exhibit explicit and/or implicit gender bias, even when gender stereotypes are not present in the inputs. In addition, an increased model size or model alignment amplifies bias in most cases. Furthermore, we investigate three methods to mitigate bias in LLMs via Hyperparameter Tuning, Instruction Guiding, and Debias Tuning. Remarkably, these methods prove effective even in the absence of explicit genders or stereotypes.

cs.CL↗

arXiv2024

Understanding the Security Risks of Decentralized Exchanges by Uncovering Unfair Trades in the Wild

DEX, or decentralized exchange, is a prominent class of decentralized finance (DeFi) applications on blockchains, attracting a total locked value worth tens of billions of USD today. This paper presents the first large-scale empirical study that uncovers unfair trades on popular DEX services on Ethereum and Binance Smart Chain (BSC). By joining and analyzing 60 million transactions, we find 671,400 unfair trades on all six measured DEXes, including Uniswap, Balancer, and Curve. Out of these unfair trades, we attribute 55,000 instances, with high confidence, to token thefts that cause a value loss of more than 3.88 million USD. Furthermore, the measurement study uncovers previously unknown causes of extractable value and real-world adaptive strategies to these causes. Finally, we propose countermeasures to redesign secure DEX protocols and to harden deployed services against the discovered security risks.

cs.CR↗