arXiv subjects

Yuan Yuan

Publications and source records attributed to Yuan Yuan.

At least 181 records · Page 10Linked to original sources

arXiv2023

Cross-modal Generative Model for Visual-Guided Binaural Stereo Generation

Binaural stereo audio is recorded by imitating the way the human ear receives sound, which provides people with an immersive listening experience. Existing approaches leverage autoencoders and directly exploit visual spatial information to synthesize binaural stereo, resulting in a limited representation of visual guidance. For the first time, we propose a visually guided generative adversarial approach for generating binaural stereo audio from mono audio. Specifically, we develop a Stereo Audio Generation Model (SAGM), which utilizes shared spatio-temporal visual information to guide the generator and the discriminator to work separately. The shared visual information is updated alternately in the generative adversarial stage, allowing the generator and discriminator to deliver their respective guided knowledge while visually sharing. The proposed method learns bidirectional complementary visual information, which facilitates the expression of visual guidance in generation. In addition, spatial perception is a crucial attribute of binaural stereo audio, and thus the evaluation of stereo spatial perception is essential. However, previous metrics failed to measure the spatial perception of audio. To this end, a metric to measure the spatial perception of audio is proposed for the first time. The proposed metric is capable of measuring the magnitude and direction of spatial perception in the temporal dimension. Further, considering its function, it is feasible to utilize it instead of demanding user studies to some extent. The proposed method achieves state-of-the-art performance on 2 datasets and 5 evaluation metrics. Qualitative experiments and user studies demonstrate that the method generates space-realistic stereo audio.

cs.SD↗

arXiv2023

Cotorsion pairs in comma categories

Let A and B be abelian categories with enough projective and injective objects, and T : A-B a left exact additive functor. Then one has a comma category (B*T). It is shown that If T : A-B is X-exact, then (*X, X) is a (hereditary) cotorsion pair in A and (*Y, Y)) is a (hereditary) cotorsion pair in B if and only if ((*X, Y ), ) is a (hereditary) cotorsion pair in (B*T) and X and Y are closed under extensions. Furthermore, we characterize when special preenveloping classes in abelian categories A and B can induce special preenveloping classes in (B*T).

math.CT↗

arXiv2023

Hardy spaces and Szegő projection on quotient domains

The Hardy spaces are defined on the quotient domain of a bounded complete Reinhardt domain by a finite subgroup of $U(n)$. The Szegő projection on the quotient domain can be studied by lifting to the covering space. This setting builds on the solution of a boundary value problem for holomorphic functions. In particular, when the covering space is either the polydisc or the unit ball in $\mathbb{C}^n$, the boundary value problem can be solved. Applying this theory in $\mathbb{C}^2$, we further obtain sharp results on the $L^p$ regularity of the Szegő projection on the symmetrized bidisc, generalized Thullen domains, and the minimal ball.

math.CV↗

arXiv2023

Stein's theorem on infinite type domains

The disc property is formulated for domains in $\mathbb{C}^n$. Holomorphic Lipschitz functions enjoy a gain in the order of Lipschitz regularity along the complex tangential direction on domains with disc property. Disc property is studied on various domains of infinite type. As applications, the local version of Stein's theorem is obtained on these domains, including the worm domains.

math.CV↗

arXiv2023

Asymptotic stability of shock profiles and rarefaction waves to the Navier-Stokes-Poisson system under space-periodic perturbations

This paper concerns with the large-time behaviors of the viscous shock profile and rarefaction wave under initial perturbations which tend to space-periodic functions at infinities for the one-dimensional compressible Navier-Stokes-Poisson equations. It is proved that: (1) for the viscous shock with small strength, if the initial perturbation is suitably small and satisfies a zero-mass type condition, then the solution tends to background viscous shock with a constant shift as time tends to the infinity, and the shift depends on both the mass of the localized perturbation, and the space-periodic perturbation; (2) for the rarefaction wave, if the initial perturbation is suitably small, then the solution tends to background rarefaction wave as time tends to infinity. The proof is based on the delicate constructions of the quadratic ansatzes, which capture the infinitely many interactions between the background waves and the periodic perturbations, and the energy method in Eulerian coordinates involving the effect of self-consistent electric field. Moreover, an abstract lemma is established to distinguish the non-decaying terms and good-decaying terms from the error terms of the equations of the quadratic ansatzes, which will be benefit to constructing the ansatzes and simplifying calculations for other non-localized perturbation problems, especially those with complicatedly coupling physical effects.

math.AP↗

arXiv2023

Gift Contagion in Online Groups: Evidence From Virtual Red Packets

Gifts are important instruments for forming bonds in interpersonal relationships. Our study analyzes the phenomenon of gift contagion in online groups. Gift contagion encourages social bonds by prompting further gifts; it may also promote group interaction and solidarity. Using data on 36 million online red packet gifts on a large social site in East Asia, we leverage a natural experimental design to identify the social contagion of gift giving in online groups. Our natural experiment is enabled by the randomization of the gift amount allocation algorithm on the platform, which addresses the common challenge of causal identifications in observational data. Our study provides evidence of gift contagion: on average, receiving one additional dollar causes a recipient to send 18 cents back to the group within the subsequent 24 hours. Decomposing this effect, we find that it is mainly driven by the extensive margin -- more recipients are triggered to send red packets. Moreover, we find that this effect is stronger for "luckiest draw" recipients, suggesting the presence of a group norm regarding the next red packet sender. Finally, we investigate the moderating effects of group- and individual-level social network characteristics on gift contagion as well as the causal impact of receiving gifts on group network structure. Our study has implications for promoting group dynamics and designing marketing strategies for product adoption.

econ.GN↗

arXiv2023

Parameter-Efficient Transfer Learning for Remote Sensing Image-Text Retrieval

Vision-and-language pre-training (VLP) models have experienced a surge in popularity recently. By fine-tuning them on specific datasets, significant performance improvements have been observed in various tasks. However, full fine-tuning of VLP models not only consumes a significant amount of computational resources but also has a significant environmental impact. Moreover, as remote sensing (RS) data is constantly being updated, full fine-tuning may not be practical for real-world applications. To address this issue, in this work, we investigate the parameter-efficient transfer learning (PETL) method to effectively and efficiently transfer visual-language knowledge from the natural domain to the RS domain on the image-text retrieval task. To this end, we make the following contributions. 1) We construct a novel and sophisticated PETL framework for the RS image-text retrieval (RSITR) task, which includes the pretrained CLIP model, a multimodal remote sensing adapter, and a hybrid multi-modal contrastive (HMMC) learning objective; 2) To deal with the problem of high intra-modal similarity in RS data, we design a simple yet effective HMMC loss; 3) We provide comprehensive empirical studies for PETL-based RS image-text retrieval. Our results demonstrate that the proposed method is promising and of great potential for practical applications. 4) We benchmark extensive state-of-the-art PETL methods on the RSITR task. Our proposed model only contains 0.16M training parameters, which can achieve a parameter reduction of 98.9% compared to full fine-tuning, resulting in substantial savings in training costs. Our retrieval performance exceeds traditional methods by 7-13% and achieves comparable or better performance than full fine-tuning. This work can provide new ideas and useful insights for RS vision-language tasks.

cs.CV↗

arXiv2023

Emergence of Bending Power Law in Higher-Order Networks

In the past two decades, a series of important results have been established in the empirical and theoretical modeling of complex networks, although considered are mainly pairwise networks. However, with the development of science and technology, an increasing number of higher-order networks with many-body interactions have gradually moved to the center stage of research when real-life systems are investigated. In the paper, the concept of higher-order degree is introduced to higher-order networks, and a bending power law (BPL) model with continuous-time growth is proposed. The evolution mechanism and topological properties of the general higher-order network are studied. The batch effect of low dimensional simplex is considered. The model is analyzed by using the mean-field method and Poisson process theory. The stationary average higher-order degree distribution of simplices is expressed analytically. The obtained analytical results agree well with those observed through simulations. In particular, this paper shows that the higher-order degree distribution of simplices in the network processes a property of bending power law, and the scale-free property of the higher-order degree is controlled by the higher-order edge, the simplex dimension and the feature parameter of the model. The BPL model of higher-order networks not only generalizes the NGF model, but also the famous scale-free model of complex networks to higher-order networks.

physics.soc-ph↗

arXiv2023

Energy-Efficient Photonic Memory Based on Electrically Programmable Embedded III-V/Si Memristors: Switches and Filters

We demonstrate non-volatile optical functionality by embedding multi-layer $HfO_2/Al_2O_3$ memristors with III-V/Si photonics. The wafer-bonded III-V/Si memristor facilitates non-volatile optical functionality for a variety of devices such as Mach-Zehnder Interferometers (MZIs), and (de-)interleaver filters. The MZI optical memristor exhibits non-volatile optical phase shifts $> π(Δn_{g} > 2.70 \times 10^{-3}$) with ~ 30 dB extinction ratio while consuming 0 electrical power consumption in a true "set-and-forget" operation. We demonstrate 6 non-volatile states with each state capable of 4 Gbps modulation. III-V/Si (de-)interleavers were also demonstrated to exhibit memristive non-volatile passband transformation with full set/reset states. Time duration tests were performed on all devices and indicated non-volatility up to 24 hours and most likely beyond. To the best of our knowledge, we have demonstrated for the first time, non-volatile III-V/Si optical memristors with the largest electric-field driven phase shifts and reconfigurable filters with the lowest power consumption.

physics.optics↗

arXiv2023

Spatio-temporal Diffusion Point Processes

Spatio-temporal point process (STPP) is a stochastic collection of events accompanied with time and space. Due to computational complexities, existing solutions for STPPs compromise with conditional independence between time and space, which consider the temporal and spatial distributions separately. The failure to model the joint distribution leads to limited capacities in characterizing the spatio-temporal entangled interactions given past events. In this work, we propose a novel parameterization framework for STPPs, which leverages diffusion models to learn complex spatio-temporal joint distributions. We decompose the learning of the target joint distribution into multiple steps, where each step can be faithfully described by a Gaussian distribution. To enhance the learning of each step, an elaborated spatio-temporal co-attention module is proposed to capture the interdependence between the event time and space adaptively. For the first time, we break the restrictions on spatio-temporal dependencies in existing solutions, and enable a flexible and accurate modeling paradigm for STPPs. Extensive experiments from a wide range of fields, such as epidemiology, seismology, crime, and urban mobility, demonstrate that our framework outperforms the state-of-the-art baselines remarkably, with an average improvement of over 50%. Further in-depth analyses validate its ability to capture spatio-temporal interactions, which can learn adaptively for different scenarios. The datasets and source code are available online: https://github.com/tsinghua-fib-lab/Spatio-temporal-Diffusion-Point-Processes.

cs.LG↗

arXiv2023

Non-volatile heterogeneous III-V/Si photonics via optical charge-trap memory

We demonstrate, for the first time, non-volatile charge-trap flash memory (CTM) co-located with heterogeneous III-V/Si photonics. The wafer-bonded III-V/Si CTM cell facilitates non-volatile optical functionality for a variety of devices such as Mach-Zehnder Interferometers (MZIs), asymmetric MZI lattice filters, and ring resonator filters. The MZI CTM exhibits full write/erase operation (100 cycles with 500 states) with wavelength shifts of $Δλ_{non-volatile} = 1.16 nm$ ($Δn_{eff,non-volatile} ~ 2.5 \times 10^{-4}$) and a dynamic power consumption $<$ 20 pW (limited by measurement). Multi-bit write operation (2 bits) is also demonstrated and verified over a time duration of 24 hours and most likely beyond. The cascaded 2nd order ring resonator CTM filter exhibited an improved ER of ~ 7.11 dB compared to the MZI and wavelength shifts of $Δλ_{non-volatile} = 0.041 nm$ ($Δn_{eff, non-volatile} = 1.5 \times 10^{-4}$) with similar pW-level dynamic power consumption as the MZI CTM. The ability to co-locate photonic computing elements and non-volatile memory provides an attractive path towards eliminating the von-Neumann bottleneck.

physics.optics↗

arXiv2023

Imbalanced Aircraft Data Anomaly Detection

Anomaly detection in temporal data from sensors under aviation scenarios is a practical but challenging task: 1) long temporal data is difficult to extract contextual information with temporal correlation; 2) the anomalous data are rare in time series, causing normal/abnormal imbalance in anomaly detection, making the detector classification degenerate or even fail. To remedy the aforementioned problems, we propose a Graphical Temporal Data Analysis (GTDA) framework. It consists three modules, named Series-to-Image (S2I), Cluster-based Resampling Approach using Euclidean Distance (CRD) and Variance-Based Loss (VBL). Specifically, for better extracts global information in temporal data from sensors, S2I converts the data to curve images to demonstrate abnormalities in data changes. CRD and VBL balance the classification to mitigate the unequal distribution of classes. CRD extracts minority samples with similar features to majority samples by clustering and over-samples them. And VBL fine-tunes the decision boundary by balancing the fitting degree of the network to each class. Ablation experiments on the Flights dataset indicate the effectiveness of CRD and VBL on precision and recall, respectively. Extensive experiments demonstrate the synergistic advantages of CRD and VBL on F1-score on Flights and three other temporal datasets.

cs.CV↗

arXiv2023

Sufficient conditions for the existence of path-factors with given properties

A spanning subgraph $H$ of a graph $G$ is called a $P_{\geq k}$-factor of $G$ if every component of $H$ is isomorphic to a path of order at least $k$, where $k\geq2$ is an integer. A graph $G$ is called a $(P_{\geq k},l)$-factor critical graph if $G-V'$ contains a $P_{\geq k}$-factor for any $V'\subseteq V(G)$ with $|V'|=l$. A graph $G$ is called a $(P_{\geq k},m)$-factor deleted graph if $G-E'$ has a $P_{\geq k}$-factor for any $E'\subseteq E(G)$ with $|E'|=m$. Intuitively, if a graph is dense enough, it will have a $P_{\geq 3}$-factor. In this paper, we give some sufficient conditions for a graph to be a $(P_{\geq 3},l)$-factor critical graph or a $(P_{\geq 3},m)$-factor deleted graph. In this paper, we demonstrate that (i) $G$ is a $(P_{\geq 3},l)$-factor critical graph if its sun toughness $s(G)>\frac{l+1}{3}$ and $κ(G)\geq l+2$. (ii) $G$ is a $(P_{\geq 3},l)$-factor critical graph if its degree sum $σ_3(G)\geq n+2l$ and $κ(G)\geq l+1$. (iii) $G$ is a $(P_{\geq 3},m)$-factor deleted graph if its sun toughness $s(G)\geq \frac{m+1}{m+2}$ and $κ(G)\geq 2m+1$. (iv) $G$ is a $(P_{\geq 3},m)$-factor deleted graph if its degree sum $σ_3(G)\geq n+2m$ and $κ(G)\geq 2m+1$.

math.CO↗

arXiv2023

Bergman projection on the symmetrized bidisk

We apply the Bekollé-Bonami estimate for the (positive) Bergman projection on the weighted $L^p$ spaces on the unit disk. As the consequences, we obtain the boundedness of the Bergman projection on the weighted Sobolev space on the symmetrized bidisk. We also improve the boundedness result of the Bergman projection on the unweighted $L^p$ space on the symmetrized bidisk in \cite{CKY}.

math.CV↗

arXiv2023

Crowd Localization from Gaussian Mixture Scoped Knowledge and Scoped Teacher

Crowd localization is to predict each instance head position in crowd scenarios. Since the distance of instances being to the camera are variant, there exists tremendous gaps among scales of instances within an image, which is called the intrinsic scale shift. The core reason of intrinsic scale shift being one of the most essential issues in crowd localization is that it is ubiquitous in crowd scenes and makes scale distribution chaotic. To this end, the paper concentrates on access to tackle the chaos of the scale distribution incurred by intrinsic scale shift. We propose Gaussian Mixture Scope (GMS) to regularize the chaotic scale distribution. Concretely, the GMS utilizes a Gaussian mixture distribution to adapt to scale distribution and decouples the mixture model into sub-normal distributions to regularize the chaos within the sub-distributions. Then, an alignment is introduced to regularize the chaos among sub-distributions. However, despite that GMS is effective in regularizing the data distribution, it amounts to dislodging the hard samples in training set, which incurs overfitting. We assert that it is blamed on the block of transferring the latent knowledge exploited by GMS from data to model. Therefore, a Scoped Teacher playing a role of bridge in knowledge transform is proposed. What' s more, the consistency regularization is also introduced to implement knowledge transform. To that effect, the further constraints are deployed on Scoped Teacher to derive feature consistence between teacher and student end. With proposed GMS and Scoped Teacher implemented on five mainstream datasets of crowd localization, the extensive experiments demonstrate the superiority of our work. Moreover, comparing with existing crowd locators, our work achieves state-of-the-art via F1-meansure comprehensively on five datasets.

cs.CV↗

arXiv2023

Near-Optimal Experimental Design Under the Budget Constraint in Online Platforms

A/B testing, or controlled experiments, is the gold standard approach to causally compare the performance of algorithms on online platforms. However, conventional Bernoulli randomization in A/B testing faces many challenges such as spillover and carryover effects. Our study focuses on another challenge, especially for A/B testing on two-sided platforms -- budget constraints. Buyers on two-sided platforms often have limited budgets, where the conventional A/B testing may be infeasible to be applied, partly because two variants of allocation algorithms may conflict and lead some buyers to exceed their budgets if they are implemented simultaneously. We develop a model to describe two-sided platforms where buyers have limited budgets. We then provide an optimal experimental design that guarantees small bias and minimum variance. Bias is lower when there is more budget and a higher supply-demand rate. We test our experimental design on both synthetic data and real-world data, which verifies the theoretical results and shows our advantage compared to Bernoulli randomization.

cs.LG↗

arXiv2023

Learning to Simulate Daily Activities via Modeling Dynamic Human Needs

Daily activity data that records individuals' various types of activities in daily life are widely used in many applications such as activity scheduling, activity recommendation, and policymaking. Though with high value, its accessibility is limited due to high collection costs and potential privacy issues. Therefore, simulating human activities to produce massive high-quality data is of great importance to benefit practical applications. However, existing solutions, including rule-based methods with simplified assumptions of human behavior and data-driven methods directly fitting real-world data, both cannot fully qualify for matching reality. In this paper, motivated by the classic psychological theory, Maslow's need theory describing human motivation, we propose a knowledge-driven simulation framework based on generative adversarial imitation learning. To enhance the fidelity and utility of the generated activity data, our core idea is to model the evolution of human needs as the underlying mechanism that drives activity generation in the simulation model. Specifically, this is achieved by a hierarchical model structure that disentangles different need levels, and the use of neural stochastic differential equations that successfully captures piecewise-continuous characteristics of need dynamics. Extensive experiments demonstrate that our framework outperforms the state-of-the-art baselines in terms of data fidelity and utility. Besides, we present the insightful interpretability of the need modeling. The code is available at https://github.com/tsinghua-fib-lab/SAND.

cs.LG↗

arXiv2022

Contactless Oxygen Monitoring with Gated Transformer

With the increasing popularity of telehealth, it becomes critical to ensure that basic physiological signals can be monitored accurately at home, with minimal patient overhead. In this paper, we propose a contactless approach for monitoring patients' blood oxygen at home, simply by analyzing the radio signals in the room, without any wearable devices. We extract the patients' respiration from the radio signals that bounce off their bodies and devise a novel neural network that infers a patient's oxygen estimates from their breathing signal. Our model, called \emph{Gated BERT-UNet}, is designed to adapt to the patient's medical indices (e.g., gender, sleep stages). It has multiple predictive heads and selects the most suitable head via a gate controlled by the person's physiological indices. Extensive empirical results show that our model achieves high accuracy on both medical and radio datasets.

cs.LG↗