arXiv subjects

Jiaqi Li

Publications and source records attributed to Jiaqi Li.

At least 181 records · Page 10Linked to original sources

arXiv2024

Stochastic modelling of the instantaneous velocity profile in rough-wall turbulent boundary layers

The statistical properties of Uniform Momentum Zones (UMZs) are extracted from laboratory and field measurements in rough wall turbulent boundary layers to formulate a set of stochastic models for the simulation of instantaneous velocity profiles. A spatio-temporally resolved velocity dataset, covering a field of view of $8 \times 9$ m$^2$, was obtained in the atmospheric surface layer using super-large-scale particle image velocimetry (SLPIV), as part of the Grand-scale Atmospheric Imaging Apparatus (GAIA). Wind tunnel data from a previous study are included for comparison \citep{heisel2020mixing}. The probability density function of UMZ attributes such as their thickness, modal velocity, and averaged vertical velocity are built at varying elevations and modeled using log-normal and Gaussian distributions. Inverse transform sampling of the distributions is used to generate synthetic step-like velocity profiles that are spatially and temporally uncorrelated. Results show that in the wide range of wall-normal distances and $Re_τ$ up to $ \sim O(10^6)$ investigated here, shear velocity scaling is manifested in the velocity jump across shear interfaces between adjacent UMZs, and attached eddy behavior is observed in the linear proportionality between UMZ thickness and their wall normal location. These very same characteristics are recovered in the generated instantaneous profiles, using both a fully stochastic and a data-driven hybrid stochastic models, which address, in different ways, the coupling between modal velocities and UMZ thickness. Our method provides a stochastic approach for generating an ensemble of instantaneous velocity profiles, consistent with the structural organization of UMZs, where the ensemble reproduces the logarithmic mean velocity profile and recovers significant portions of the Reynolds stresses and thus of the streamwise and vertical velocity variability.

physics.flu-dyn↗

arXiv2024

An Initial Investigation of Neural Replay Simulator for Over-the-Air Adversarial Perturbations to Automatic Speaker Verification

Deep Learning has advanced Automatic Speaker Verification (ASV) in the past few years. Although it is known that deep learning-based ASV systems are vulnerable to adversarial examples in digital access, there are few studies on adversarial attacks in the context of physical access, where a replay process (i.e., over the air) is involved. An over-the-air attack involves a loudspeaker, a microphone, and a replaying environment that impacts the movement of the sound wave. Our initial experiment confirms that the replay process impacts the effectiveness of the over-the-air attack performance. This study performs an initial investigation towards utilizing a neural replay simulator to improve over-the-air adversarial attack robustness. This is achieved by using a neural waveform synthesizer to simulate the replay process when estimating the adversarial perturbations. Experiments conducted on the ASVspoof2019 dataset confirm that the neural replay simulator can considerably increase the success rates of over-the-air adversarial attacks. This raises the concern for adversarial attacks on speaker verification in physical access applications.

cs.SD↗

arXiv2023

Detecting DBMS Bugs with Context-Sensitive Instantiation and Multi-Plan Execution

DBMS bugs can cause serious consequences, posing severe security and privacy concerns. This paper works towards the detection of memory bugs and logic bugs in DBMSs, and aims to solve the two innate challenges, including how to generate semantically correct SQL queries in a test case, and how to propose effective oracles to capture logic bugs. To this end, our system proposes two key techniques. The first key technique is called context-sensitive instantiation, which considers all static semantic requirements (including but not limited to the identifier type used by existing systems) to generate semantically valid SQL queries. The second key technique is called multi-plan execution, which can effectively capture logic bugs. Given a test case, multi-plan execution makes the DBMS execute all query plans instead of the default optimal one, and compares the results. A logic bug is detected if a difference is found among the execution results of the executed query plans. We have implemented a prototype system called Kangaroo and applied it to three widely used and well-tested DBMSs, including SQLite, PostgreSQL, and MySQL. Our system successfully detected 50 new bugs. The comparison between our system with the state-of-the-art systems shows that our system outperforms them in terms of the number of generated semantically valid SQL queries, the explored code paths during testing, and the detected bugs.

cs.DB↗

arXiv2023

Digital Life Project: Autonomous 3D Characters with Social Intelligence

In this work, we present Digital Life Project, a framework utilizing language as the universal medium to build autonomous 3D characters, who are capable of engaging in social interactions and expressing with articulated body motions, thereby simulating life in a digital environment. Our framework comprises two primary components: 1) SocioMind: a meticulously crafted digital brain that models personalities with systematic few-shot exemplars, incorporates a reflection process based on psychology principles, and emulates autonomy by initiating dialogue topics; 2) MoMat-MoGen: a text-driven motion synthesis paradigm for controlling the character's digital body. It integrates motion matching, a proven industry technique to ensure motion quality, with cutting-edge advancements in motion generation for diversity. Extensive experiments demonstrate that each module achieves state-of-the-art performance in its respective domain. Collectively, they enable virtual characters to initiate and sustain dialogues autonomously, while evolving their socio-psychological states. Concurrently, these characters can perform contextually relevant bodily movements. Additionally, a motion captioning module further allows the virtual character to recognize and appropriately respond to human players' actions. Homepage: https://digital-life-project.com/

cs.CV↗

arXiv2023

Three-dimensional internal flow evolution of an evaporating droplet and its role in particle deposition pattern

The internal flow within an evaporating sessile droplet is one of the driving mechanisms that lead to the variety of particle deposition patterns seen in applications such as inkjet printing, surface patterning, and blood stain analysis. Despite decades of research, the causal link between droplet internal flow and particle deposition patterns has not been fully established. In this study, we employ a 3D imaging technique based on digital inline holography to quantitatively assess the evolution of internal flow fields and particle migration in three distinct types of wetting droplets: water, sucrose aqueous solution, and SDS aqueous solution droplets, throughout their entire evaporation process. Our imaging reveals the three-stage evolution of the 3D internal flow regimes driven by changes in the relative importance of capillary flow, Marangoni flow, and droplet boundary movement during evaporation, each exhibiting unique dynamics. The migration of particles from their initial locations to deposition can be divided into five categories, with particles depositing either at the contact line or inside the droplet. We observe the changing migration directions of particles due to competing Marangoni and capillary flows during droplet evaporation. We further develop an analytical model that predicts the droplet internal flow and deposition patterns and determines the dependence of the deposition mechanisms of particles on their initial locations and the evolving internal flow field. The model, validated using different types of droplets from our experiment and the literature, can be further expanded to other Newtonian and non-Newtonian droplets, which can potentially serve as a real-time assessment tool for particle deposition in various applications.

physics.flu-dyn↗

arXiv2023

Stochastic particle advection velocimetry (SPAV): theory, simulations, and proof-of-concept experiments

Particle tracking velocimetry (PTV) is widely used to measure time-resolved, three-dimensional velocity and pressure fields in fluid dynamics research. Inaccurate localization and tracking of particles is a key source of error in PTV, especially for single camera defocusing, plenoptic imaging, and digital in-line holography (DIH) sensors. To address this issue, we developed stochastic particle advection velocimetry (SPAV): a statistical data loss that improves the accuracy of PTV. SPAV is based on an explicit particle advection model that predicts particle positions over time as a function of the estimated velocity field. The model can account for non-ideal effects like drag on inertial particles. A statistical data loss that compares the tracked and advected particle positions, accounting for arbitrary localization and tracking uncertainties, is derived and approximated. We implement our approach using a physics-informed neural network, which simultaneously minimizes the SPAV data loss, a Navier-Stokes physics loss, and a wall boundary loss, where appropriate. Results are reported for simulated and experimental DIH-PTV measurements of laminar and turbulent flows. Our statistical approach significantly improves the accuracy of PTV reconstructions compared to a conventional data loss, resulting in an average reduction of error close to 50%. Furthermore, our framework can be readily adapted to work with other data assimilation techniques like state observer, Kalman filter, and adjoint-variational methods.

physics.flu-dyn↗

arXiv2023

Intell-dragonfly: A Cybersecurity Attack Surface Generation Engine Based On Artificial Intelligence-generated Content Technology

With the rapid development of the Internet, cyber security issues have become increasingly prominent. Traditional cyber security defense methods are limited in the face of ever-changing threats, so it is critical to seek innovative attack surface generation methods. This study proposes Intell-dragonfly, a cyber security attack surface generation engine based on artificial intelligence generation technology, to meet the challenges of cyber security. Based on ChatGPT technology, this paper designs an automated attack surface generation process, which can generate diversified and personalized attack scenarios, targets, elements and schemes. Through experiments in a real network environment, the effect of the engine is verified and compared with traditional methods, which improves the authenticity and applicability of the attack surface. The experimental results show that the ChatGPT-based method has significant advantages in the accuracy, diversity and operability of attack surface generation. Furthermore, we explore the strengths and limitations of the engine and discuss its potential applications in the field of cyber security. This research provides a novel approach to the field of cyber security that is expected to have a positive impact on defense and prevention of cyberthreats.

cs.CR↗

arXiv2023

Probing dynamics of elliptical vortex rings via direct vorticity measurements with digital inline holography

Investigating vorticity dynamics provides an effective way for understanding the fundamental mechanisms of fluid flows across diverse scales. However, experimental vorticity measurements often suffer from limited spatial and temporal resolution, hindering our capability to probe into small-scale dynamics in various flows, particularly turbulence. In Li et al. (EXIF, 2022, vol. 63, 161), we introduced a novel holographic vorticimetry technique for direct vorticity measurements by tracking the three-dimensional rotations of tracers with internal markers. This study further extends it to investigate the intricate vorticity dynamics during the evolution of elliptical vortex rings with different aspect ratios. Based on the shadowgraph imaging quantifying the axis-switching cycles and vortex ring deformation, holographic vorticimetry is applied to measure the vorticity distribution within the millimeter-size core of elliptical vortex rings during their evolution. Specifically, our method resolves an even vorticity spread near the core center that rapidly decays within a few hundred microns. Additionally, our results reveal the intricate vorticity fluctuations associated with the folding-unfolding behaviors during the vortex ring evolution. These subtle vorticity changes informed by simulations have not been captured by previous experiments due to limited resolution. Furthermore, we find that higher aspect ratios yield larger initial vorticity and vorticity fluctuations but also prompt earlier inception of instabilities, causing vortex core distortion. These opposing effects result in a non-monotonic vorticity evolution trend. Overall, our measurements demonstrate the efficacy of holographic vorticimetry by measuring the intricate vorticity variations in unsteady vortex flows, paving the way for capturing the vorticity dynamics of small-scale turbulence structures.

physics.flu-dyn↗

arXiv2023

Incorporating Domain Knowledge Graph into Multimodal Movie Genre Classification with Self-Supervised Attention and Contrastive Learning

Multimodal movie genre classification has always been regarded as a demanding multi-label classification task due to the diversity of multimodal data such as posters, plot summaries, trailers and metadata. Although existing works have made great progress in modeling and combining each modality, they still face three issues: 1) unutilized group relations in metadata, 2) unreliable attention allocation, and 3) indiscriminative fused features. Given that the knowledge graph has been proven to contain rich information, we present a novel framework that exploits the knowledge graph from various perspectives to address the above problems. As a preparation, the metadata is processed into a domain knowledge graph. A translate model for knowledge graph embedding is adopted to capture the relations between entities. Firstly we retrieve the relevant embedding from the knowledge graph by utilizing group relations in metadata and then integrate it with other modalities. Next, we introduce an Attention Teacher module for reliable attention allocation based on self-supervised learning. It learns the distribution of the knowledge graph and produces rational attention weights. Finally, a Genre-Centroid Anchored Contrastive Learning module is proposed to strengthen the discriminative ability of fused features. The embedding space of anchors is initialized from the genre entities in the knowledge graph. To verify the effectiveness of our framework, we collect a larger and more challenging dataset named MM-IMDb 2.0 compared with the MM-IMDb dataset. The experimental results on two datasets demonstrate that our model is superior to the state-of-the-art methods. We will release the code in the near future.

cs.AI↗

arXiv2023

Simultaneous Inference of a Partially Linear Model in Time Series

We introduce a new methodology to conduct simultaneous inference of the nonparametric component in partially linear time series regression models where the nonparametric part is a multivariate unknown function. In particular, we construct a simultaneous confidence region (SCR) for the multivariate function by extending the high-dimensional Gaussian approximation to dependent processes with continuous index sets. Our results allow for a more general dependence structure compared to previous works and are widely applicable to a variety of linear and nonlinear autoregressive processes. We demonstrate the validity of our proposed methodology by examining the finite-sample performance in the simulation study. Finally, an application in time series, the forward premium regression, is presented, where we construct the SCR for the foreign exchange risk premium from the exchange rate and macroeconomic data.

stat.ME↗

arXiv2023

Stochastic Optimization of Coupled Power Distribution-Urban Transportation Network Operations with Autonomous Mobility on Demand Systems

Autonomous mobility on demand systems (AMoDS) will significantly affect the operation of coupled power distribution-urban transportation networks (PTNs) by the optimal dispatch of electric vehicles (EVs). This paper proposes an uncertainty method to analyze the operational states of PTNs with AMoDS. First, a PTN operation framework is designed considering the controllable EVs dispatched by AMoDS as well as the uncontrollable driving behaviors of other vehicle users. Then, a bi-level power-traffic flow (PTF) model is proposed to characterize the interaction of power distribution networks (PDNs) and urban transportation networks (UTNs). In the upper level, a social optimum model is established to minimize the operating cost of PDNs and UTNs embedded with controllable EVs. In the lower level, a stochastic user equilibrium (SUE) model is established to minimize the operating cost of uncontrollable EVs and gasoline vehicles (GVs) in UTNs. Finally, a probabilistic PTF analysis method is developed to evaluate PTN operations under environmental and human uncertainties. A regional sensitivity analysis method is proposed to identify the critical uncertainties and quantify the impacts of their distribution ranges on PTN operations. The effectiveness of the proposed method is verified by the PTN consisting of a 21-bus PDN and a 20-node UTN.

eess.SY↗

arXiv2023

Diffusion-Augmented Depth Prediction with Sparse Annotations

Depth estimation aims to predict dense depth maps. In autonomous driving scenes, sparsity of annotations makes the task challenging. Supervised models produce concave objects due to insufficient structural information. They overfit to valid pixels and fail to restore spatial structures. Self-supervised methods are proposed for the problem. Their robustness is limited by pose estimation, leading to erroneous results in natural scenes. In this paper, we propose a supervised framework termed Diffusion-Augmented Depth Prediction (DADP). We leverage the structural characteristics of diffusion model to enforce depth structures of depth models in a plug-and-play manner. An object-guided integrality loss is also proposed to further enhance regional structure integrality by fetching objective information. We evaluate DADP on three driving benchmarks and achieve significant improvements in depth structures and robustness. Our work provides a new perspective on depth estimation with sparse annotations in autonomous driving scenes.

cs.CV↗

arXiv2023

ROME: Testing Image Captioning Systems via Recursive Object Melting

Image captioning (IC) systems aim to generate a text description of the salient objects in an image. In recent years, IC systems have been increasingly integrated into our daily lives, such as assistance for visually-impaired people and description generation in Microsoft Powerpoint. However, even the cutting-edge IC systems (e.g., Microsoft Azure Cognitive Services) and algorithms (e.g., OFA) could produce erroneous captions, leading to incorrect captioning of important objects, misunderstanding, and threats to personal safety. The existing testing approaches either fail to handle the complex form of IC system output (i.e., sentences in natural language) or generate unnatural images as test cases. To address these problems, we introduce Recursive Object MElting (Rome), a novel metamorphic testing approach for validating IC systems. Different from existing approaches that generate test cases by inserting objects, which easily make the generated images unnatural, Rome melts (i.e., remove and inpaint) objects. Rome assumes that the object set in the caption of an image includes the object set in the caption of a generated image after object melting. Given an image, Rome can recursively remove its objects to generate different pairs of images. We use Rome to test one widely-adopted image captioning API and four state-of-the-art (SOTA) algorithms. The results show that the test cases generated by Rome look much more natural than the SOTA IC testing approach and they achieve comparable naturalness to the original images. Meanwhile, by generating test pairs using 226 seed images, Rome reports a total of 9,121 erroneous issues with high precision (86.47%-92.17%). In addition, we further utilize the test cases generated by Rome to retrain the Oscar, which improves its performance across multiple evaluation metrics.

cs.SE↗

arXiv2023

$\ell^2$ Inference for Change Points in High-Dimensional Time Series via a Two-Way MOSUM

We propose an inference method for detecting multiple change points in high-dimensional time series, targeting dense or spatially clustered signals. Our method aggregates moving sum (MOSUM) statistics cross-sectionally by an $\ell^2$-norm and maximizes them over time. We further introduce a novel Two-Way MOSUM, which utilizes spatial-temporal moving regions to search for breaks, with the added advantage of enhancing testing power when breaks occur in only a few groups. The limiting distribution of an $\ell^2$-aggregated statistic is established for testing break existence by extending a high-dimensional Gaussian approximation theorem to spatial-temporal non-stationary processes. Simulation studies exhibit promising performance of our test in detecting non-sparse weak signals. Two applications, analyzing equity returns and COVID-19 cases in the United States, showcase the real-world relevance of our proposed algorithms.

math.ST↗

arXiv2023

High-dimensional outlier detection and variable selection via adaptive weighted mean regression

This paper proposes an adaptive penalized weighted mean regression for outlier detection of high-dimensional data. In comparison to existing approaches based on the mean shift model, the proposed estimators demonstrate robustness against outliers present in both response variables and/or covariates. By utilizing the adaptive Huber loss function, the proposed method is effective in high-dimensional linear models characterized by heavy-tailed and heteroscedastic error distributions. The proposed framework enables simultaneous and collaborative estimation of regression parameters and outlier detection. Under regularity conditions, outlier detection consistency and oracle inequalities of robust estimates in high-dimensional settings are established. Additionally, theoretical robustness properties, such as the breakdown point and a smoothed limiting influence function, are ascertained. Extensive simulation studies and a breast cancer survival data are used to evaluate the numerical performance of the proposed method, demonstrating comparable or superior variable selection and outlier detection capabilities.

math.ST↗

arXiv2023

Large Language Models are In-Context Semantic Reasoners rather than Symbolic Reasoners

The emergent few-shot reasoning capabilities of Large Language Models (LLMs) have excited the natural language and machine learning community over recent years. Despite of numerous successful applications, the underlying mechanism of such in-context capabilities still remains unclear. In this work, we hypothesize that the learned \textit{semantics} of language tokens do the most heavy lifting during the reasoning process. Different from human's symbolic reasoning process, the semantic representations of LLMs could create strong connections among tokens, thus composing a superficial logical chain. To test our hypothesis, we decouple semantics from the language reasoning process and evaluate three kinds of reasoning abilities, i.e., deduction, induction and abduction. Our findings reveal that semantics play a vital role in LLMs' in-context reasoning -- LLMs perform significantly better when semantics are consistent with commonsense but struggle to solve symbolic or counter-commonsense reasoning tasks by leveraging in-context new knowledge. The surprising observations question whether modern LLMs have mastered the inductive, deductive and abductive reasoning abilities as in human intelligence, and motivate research on unveiling the magic existing within the black-box LLMs. On the whole, our analysis provides a novel perspective on the role of semantics in developing and evaluating language models' reasoning abilities. Code is available at {\url{https://github.com/XiaojuanTang/ICSR}}.

cs.CL↗

arXiv2023

Dynamic Flow Equilibrium of Transportation and Power Distribution Networks Considering Flexible Traveling Choices and Voltage Quality Improvement

The increasing global spread of electric vehicles (EVs) has introduced significant interdependence between transportation and power networks. Most of the previous studies on coupled networks focus on the formation of equilibrium states between transportation and distribution networks but neglect diminishing the adverse influences of the interaction, especially the voltage quality problems caused by large and fluctuating charging loads. This paper constructs a dynamic interaction model of coupled networks considering both economic and safe operation. First, the dynamic traffic assignment problem is established considering both departure time and route choices, and a dynamic queuing model is designed to describe the EV queuing behaviors at fast charging stations (FCSs). Then, the charging load fluctuation toll is designed as an ancillary toll at FCSs and the bus voltage deviation penalty is considered in optimal power flow to guide the interaction of two networks and tackle the voltage deviation issue. Moreover, the equilibrium state is designed as a fixed-point problem and the solution's existence is proved under mild assumptions. Third, the linearization and convex relaxation techniques are used to improve computational efficiency and a Monte Carlo simulation technique is developed to evaluate the influence of uncertain travel demands on coupled networks. Numerical simulations of coupled networks interaction analyses in deterministic and uncertain conditions are presented.

eess.SY↗

arXiv2023

Advection-dominated accretion flow for the varied transition luminosities in black hole X-ray binaries

Observationally, two main spectral states, i.e., the low/hard state and the high/soft state, are identified in black hole X-ray binaries (BH-XRBs). Meanwhile, the transitions between the two states are often observed. In this paper, we re-investigate the transition luminosities in the framework of the self-similar solution of the advection-dominated accretion flow (ADAF). Specifically, we search for the critical mass accretion rate $\dot m_{\rm crit}$ of ADAF for different radii $r$ respectively. It is found that $\dot m_{\rm crit}$ decreases with decreasing $r$. By testing the effects of BH mass $m$, the magnetic parameter $β$ and the viscosity parameter $α$, it is found that only $α$ has significant effects on $\dot m_{\rm crit}-r$ relation. We define the minimum $\dot m_{\rm crit}$ (roughly at the innermost stable circular orbit) as the hard-to-soft transition rate $\dot m_{\rm tr: H\rightarrow S}$, above which BH will gradually transit from the low/hard state to the high/soft state, and $\dot m_{\rm crit}$ at $30$ Schwarzschild radii as the soft-to-hard transition rate $\dot m_{\rm tr: S\rightarrow H}$, below which BH will gradually transit from the high/soft state to the low/hard state. We derive fitting formulae of $\dot m_{\rm tr: H\rightarrow S}$ and $\dot m_{\rm tr: S\rightarrow H}$ as functions of $α$ respectively. By comparing with observations, it is found that the mean value of $α$ are $α\sim 0.85$ and $α\sim 0.33$ for the hard-to-soft transition and the soft-to-hard transition respectively, which indicates that two classes of $α$ are needed for explaining the hysteresis effect during the state transition. Finally, we argue that such a constrained $α$ may provide valuable clues for further exploring the accretion physics in BH-XRBs.

astro-ph.HE↗