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Yao Yao

Publications and source records attributed to Yao Yao.

At least 145 records · Page 8Linked to original sources
arXiv2023

AnimateAnything: Fine-Grained Open Domain Image Animation with Motion Guidance

Image animation is a key task in computer vision which aims to generate dynamic visual content from static image. Recent image animation methods employ neural based rendering technique to generate realistic animations. Despite these advancements, achieving fine-grained and controllable image animation guided by text remains challenging, particularly for open-domain images captured in diverse real environments. In this paper, we introduce an open domain image animation method that leverages the motion prior of video diffusion model. Our approach introduces targeted motion area guidance and motion strength guidance, enabling precise control the movable area and its motion speed. This results in enhanced alignment between the animated visual elements and the prompting text, thereby facilitating a fine-grained and interactive animation generation process for intricate motion sequences. We validate the effectiveness of our method through rigorous experiments on an open-domain dataset, with the results showcasing its superior performance. Project page can be found at https://animationai.github.io/AnimateAnything.

cs.CV↗
arXiv2023

Directing entanglement spreading by means of a quantum East/West heterojunction structure

We extend the translationally invariant quantum East model to an inhomogeneous chain with East/West heterojunction structure. In analogy to the quantum diffusion of substantial particles, we observe a cyclic entanglement entropy spreading in the heterojunction during time evolution, which can be regarded as continuous cycles in a quantum heat engine. In order to figure out the possibility of manipulating the entanglement entropy as a quantum resource, the entropy growth is shown to be determined by the initial occupation and the site-dependent chemical potential, and the former is equivalent to an effective temperature. Through fine adjustment of these parameters, we discover the entanglement flow is simply superposed with those from two sources of the chain. An intriguing relation between our model and the traditional heat engines is subsequently established.

quant-ph↗
arXiv2023

Igniting Language Intelligence: The Hitchhiker's Guide From Chain-of-Thought Reasoning to Language Agents

Large language models (LLMs) have dramatically enhanced the field of language intelligence, as demonstrably evidenced by their formidable empirical performance across a spectrum of complex reasoning tasks. Additionally, theoretical proofs have illuminated their emergent reasoning capabilities, providing a compelling showcase of their advanced cognitive abilities in linguistic contexts. Critical to their remarkable efficacy in handling complex reasoning tasks, LLMs leverage the intriguing chain-of-thought (CoT) reasoning techniques, obliging them to formulate intermediate steps en route to deriving an answer. The CoT reasoning approach has not only exhibited proficiency in amplifying reasoning performance but also in enhancing interpretability, controllability, and flexibility. In light of these merits, recent research endeavors have extended CoT reasoning methodologies to nurture the development of autonomous language agents, which adeptly adhere to language instructions and execute actions within varied environments. This survey paper orchestrates a thorough discourse, penetrating vital research dimensions, encompassing: (i) the foundational mechanics of CoT techniques, with a focus on elucidating the circumstances and justification behind its efficacy; (ii) the paradigm shift in CoT; and (iii) the burgeoning of language agents fortified by CoT approaches. Prospective research avenues envelop explorations into generalization, efficiency, customization, scaling, and safety. This paper caters to a wide audience, including beginners seeking comprehensive knowledge of CoT reasoning and language agents, as well as experienced researchers interested in foundational mechanics and engaging in cutting-edge discussions on these topics. A repository for the related papers is available at https://github.com/Zoeyyao27/CoT-Igniting-Agent.

cs.CL↗
arXiv2023

i-Razor: A Differentiable Neural Input Razor for Feature Selection and Dimension Search in DNN-Based Recommender Systems

Input features play a crucial role in DNN-based recommender systems with thousands of categorical and continuous fields from users, items, contexts, and interactions. Noisy features and inappropriate embedding dimension assignments can deteriorate the performance of recommender systems and introduce unnecessary complexity in model training and online serving. Optimizing the input configuration of DNN models, including feature selection and embedding dimension assignment, has become one of the essential topics in feature engineering. However, in existing industrial practices, feature selection and dimension search are optimized sequentially, i.e., feature selection is performed first, followed by dimension search to determine the optimal dimension size for each selected feature. Such a sequential optimization mechanism increases training costs and risks generating suboptimal input configurations. To address this problem, we propose a differentiable neural input razor (i-Razor) that enables joint optimization of feature selection and dimension search. Concretely, we introduce an end-to-end differentiable model to learn the relative importance of different embedding regions of each feature. Furthermore, a flexible pruning algorithm is proposed to achieve feature filtering and dimension derivation simultaneously. Extensive experiments on two large-scale public datasets in the Click-Through-Rate (CTR) prediction task demonstrate the efficacy and superiority of i-Razor in balancing model complexity and performance.

cs.IR↗
arXiv2023

Consistent4D: Consistent 360° Dynamic Object Generation from Monocular Video

In this paper, we present Consistent4D, a novel approach for generating 4D dynamic objects from uncalibrated monocular videos. Uniquely, we cast the 360-degree dynamic object reconstruction as a 4D generation problem, eliminating the need for tedious multi-view data collection and camera calibration. This is achieved by leveraging the object-level 3D-aware image diffusion model as the primary supervision signal for training Dynamic Neural Radiance Fields (DyNeRF). Specifically, we propose a Cascade DyNeRF to facilitate stable convergence and temporal continuity under the supervision signal which is discrete along the time axis. To achieve spatial and temporal consistency, we further introduce an Interpolation-driven Consistency Loss. It is optimized by minimizing the discrepancy between rendered frames from DyNeRF and interpolated frames from a pre-trained video interpolation model. Extensive experiments show that our Consistent4D can perform competitively to prior art alternatives, opening up new possibilities for 4D dynamic object generation from monocular videos, whilst also demonstrating advantage for conventional text-to-3D generation tasks. Our project page is https://consistent4d.github.io/.

cs.CV↗
arXiv2023

CDR-Adapter: Learning Adapters to Dig Out More Transferring Ability for Cross-Domain Recommendation Models

Data sparsity and cold-start problems are persistent challenges in recommendation systems. Cross-domain recommendation (CDR) is a promising solution that utilizes knowledge from the source domain to improve the recommendation performance in the target domain. Previous CDR approaches have mainly followed the Embedding and Mapping (EMCDR) framework, which involves learning a mapping function to facilitate knowledge transfer. However, these approaches necessitate re-engineering and re-training the network structure to incorporate transferrable knowledge, which can be computationally expensive and may result in catastrophic forgetting of the original knowledge. In this paper, we present a scalable and efficient paradigm to address data sparsity and cold-start issues in CDR, named CDR-Adapter, by decoupling the original recommendation model from the mapping function, without requiring re-engineering the network structure. Specifically, CDR-Adapter is a novel plug-and-play module that employs adapter modules to align feature representations, allowing for flexible knowledge transfer across different domains and efficient fine-tuning with minimal training costs. We conducted extensive experiments on the benchmark dataset, which demonstrated the effectiveness of our approach over several state-of-the-art CDR approaches.

cs.IR↗
arXiv2023

Static Virus Spread Algorithm for DNA Sequence Design

DNA is not only the genetic material of life, but also a favorable material for a new computing model. Various research works based on DNA computing have been carried out in recent years. DNA sequence design is the foundation of such research. The sequence quality directly affects the universality, robustness, and stability of DNA computing. How to design DNA sequences depends on the biological properties and target requirements, which is a typical combinatorial optimization problem. In this paper, in order to design DNA sequences with high-quality, we propose a novel meta-heuristic evolutionary algorithm, termed the static virus spread algorithm (SVS). Through this algorithm, we focus on the constraints of universal DNA sequence design and produce a large number of DNA sequences with non-complementarity and small difference in melting temperature as the objectives, and fully considering the balanced proportion of the four bases. The computer simulation and polyacrylamide gel electrophoresis experiments show that the high-quality DNA sequences designed by this algorithm are effective, which is expected to provide a convenient tool for sequence preparation before DNA biochemical operations.

cs.ET↗
arXiv2023

Long-coherence pairing of low-mass conduction electrons in copper-substituted lead apatite

Two entangled qubits emerge as an essential resource for quantum control, which are normally quantum confined with atomic precision. It seems inhibitive that in the macroscopic scope collective qubit pairs manifest long coherence and quantum entanglement, especially at high temperature. Here, we report this exotic ensemble effect in solid-state sintering lead apatite samples with copper substitution, which have been repeatedly duplicated with superior stability and low cost. An extraordinarily low-field absorption signal of cw electron paramagnetic resonance (EPR) spectroscopy stems from low-mass conduction electrons implying the coherence of cuprate radicals can be long-termly protected. The pulsed EPR experiments exhibit triplet Rabi oscillation from paired cuprate diradicals with the coherence time exceeding 1 microsecond at 85K. We believe these appealing effects are sufficiently promising to be applied for scalable quantum control and computation.

quant-ph↗
arXiv2023

JointNet: Extending Text-to-Image Diffusion for Dense Distribution Modeling

We introduce JointNet, a novel neural network architecture for modeling the joint distribution of images and an additional dense modality (e.g., depth maps). JointNet is extended from a pre-trained text-to-image diffusion model, where a copy of the original network is created for the new dense modality branch and is densely connected with the RGB branch. The RGB branch is locked during network fine-tuning, which enables efficient learning of the new modality distribution while maintaining the strong generalization ability of the large-scale pre-trained diffusion model. We demonstrate the effectiveness of JointNet by using RGBD diffusion as an example and through extensive experiments, showcasing its applicability in a variety of applications, including joint RGBD generation, dense depth prediction, depth-conditioned image generation, and coherent tile-based 3D panorama generation.

cs.CV↗
arXiv2023

Anti-Aliased Neural Implicit Surfaces with Encoding Level of Detail

We present LoD-NeuS, an efficient neural representation for high-frequency geometry detail recovery and anti-aliased novel view rendering. Drawing inspiration from voxel-based representations with the level of detail (LoD), we introduce a multi-scale tri-plane-based scene representation that is capable of capturing the LoD of the signed distance function (SDF) and the space radiance. Our representation aggregates space features from a multi-convolved featurization within a conical frustum along a ray and optimizes the LoD feature volume through differentiable rendering. Additionally, we propose an error-guided sampling strategy to guide the growth of the SDF during the optimization. Both qualitative and quantitative evaluations demonstrate that our method achieves superior surface reconstruction and photorealistic view synthesis compared to state-of-the-art approaches.

cs.CV↗
arXiv2023

IRS-Enabled Covert and Reliable Communications: How Many Reflection Elements are Required?

Short-packet communications are applied to various scenarios where transmission covertness and reliability are crucial due to the open wireless medium and finite blocklength. Although intelligent reflection surface (IRS) has been widely utilized to enhance transmission covertness and reliability, the question of how many reflection elements at IRS are required remains unanswered, which is vital to system design and practical deployment. The inherent strong coupling exists between the transmission covertness and reliability by IRS, leading to the question of intractability. To address this issue, the detection error probability at the warder and its approximation are derived first to reveal the relation between covertness performance and the number of reflection elements. Besides, to evaluate the reliability performance of the system, the decoding error probability at the receiver is also derived. Subsequently, the asymptotic reliability performance in high covertness regimes is investigated, which provides theoretical predictions about the number of reflection elements at IRS required to achieve a decoding error probability close to 0 with given covertness requirements. Furthermore, Monte-Carlo simulations verify the accuracy of the derived results for detection (decoding) error probabilities and the validity of the theoretical predictions for reflection elements. Moreover, results show that more reflection elements are required to achieve high reliability with tighter covertness requirements, longer blocklength and higher transmission rates.

cs.IT↗
arXiv2023

Strong [O III] λ5007 Compact Galaxies Identified from SDSS DR16 and Their Scaling Relations

Green pea galaxies are a special class of star-forming compact galaxies with strong [O III]λ5007 and considered as analogs of high-redshift Lyα-emitting galaxies and potential sources for cosmic reionization. In this paper, we identify 76 strong [O III]λ5007 compact galaxies at z < 0.35 from DR1613 of the Sloan Digital Sky Survey. These galaxies present relatively low stellar mass, high star formation rate, and low metallicity. Both star-forming main sequence relation (SFMS) and mass-metallicity relation (MZR) are investigated and compared with green pea and blueberry galaxies collected from literature. It is found that our strong [O III] λ5007 compact galaxies share common properties with those compact galaxies with extreme star formation and show distinct scaling relations in respect to those of normal star-forming galaxies at the same redshift. The slope of SFMS is higher, indicates that strong [O III]λ5007 compact galaxies might grow faster in stellar mass. The lower MZR implies that they may be less chemically evolved and hence on the early stage of star formation. A further environmental investigation confirms that they inhabit relatively low-density regions. Future largescale spectroscopic surveys will provide more details on their physical origin and evolution.

astro-ph.GA↗
arXiv2023

SPADA: A Toolbox of Designing Soft Pneumatic Actuators for Shape Matching based on Surrogate Modeling

Soft pneumatic actuators (SPAs) produce motions for soft robots with simple pressure input, however they require to be appropriately designed to fit the target application. Available design methods employ kinematic models and optimization to estimate the actuator response and the optimal design parameters, to achieve a target actuator's shape. Within SPAs, Bellow-SPAs excel in rapid prototyping and large deformation, yet their kinematic models often lack accuracy due to the geometry complexity and the material nonlinearity. Furthermore, existing shape-matching algorithms are not providing an end-to-end solution from the desired shape to the actuator. In addition, despite the availability of computational design pipelines, an accessible and user-friendly toolbox for direct application remains elusive. This paper addresses these challenges, offering an end-to-end shape-matching design framework for bellow-SPAs to streamline the design process, and the open-source toolbox SPADA (Soft Pneumatic Actuator Design frAmework) implementing the framework with a GUI for easy access. It provides a kinematic model grounded on a modular design to improve accuracy, Finite Element Method (FEM) simulations, and piecewise constant curvature (PCC) approximation. An Artificial Neural Network-trained surrogate model, based on FEM simulation data, is trained for fast computation in optimization. A shape-matching algorithm, merging 3D PCC segmentation and a surrogate model-based genetic algorithm, identifies optimal actuator design parameters for desired shapes. The toolbox, implementing the proposed design framework, has proven its end-to-end capability in designing actuators to precisely match 2D shapes with root-mean-square errors of 4.16, 2.70, and 2.51mm, and demonstrating its potential by designing a 3D deformable actuator.

cs.RO↗
arXiv2023

Dose rate effects in radiation-induced changes to phenyl-based polymeric scintillators

Results on the effects of ionizing radiation on the signal produced by plastic scintillating rods manufactured by Eljen Technology company are presented for various matrix materials, dopant concentrations, fluors (EJ-200 and EJ-260), anti-oxidant concentrations, scintillator thickness, doses, and dose rates. The light output before and after irradiation is measured using an alpha source and a photomultiplier tube, and the light transmission by a spectrophotometer. Assuming an exponential decrease in the light output with dose, the change in light output is quantified using the exponential dose constant $D$. The $D$ values are similar for primary and secondary doping concentrations of 1 and 2 times, and for antioxidant concentrations of 0, 1, and 2 times, the default manufacturer's concentration. The $D$ value depends approximately linearly on the logarithm of the dose rate for dose rates between 2.2 Gy/hr and 70 Gy/hr for all materials. For EJ-200 polyvinyltoluene-based (PVT) scintillator, the dose constant is approximately linear in the logarithm of the dose rate up to 3400 Gy/hr, while for polystyrene-based (PS) scintillator or for both materials with EJ-260 fluors, it remains constant or decreases (depending on doping concentration) above about 100 Gy/hr. The results from rods of varying thickness and from the different fluors suggest damage to the initial light output is a larger effect than color center formation for scintillator thickness $\leq1$ cm. For the blue scintillator (EJ-200), the transmission measurements indicate damage to the fluors. We also find that while PVT is more resistant to radiation damage than PS at dose rates higher than about 100 Gy/hr for EJ-200 fluors, they show similar damage at lower dose rates and for EJ-260 fluors.

physics.ins-det↗
arXiv2023

Covert and Reliable Short-Packet Communications against A Proactive Warder

Wireless short-packet communications pose challenges to the security and reliability of the transmission. Besides, the proactive warder compounds these challenges, who detects and interferes with the potential transmission. An extra jamming channel is introduced by the proactive warder compared with the passive one, resulting in the inapplicability of analytical methods and results in exsiting works. Thus, effective system design schemes are required for short-packet communications against the proactive warder. To address this issue, we consider the analysis and design of covert and reliable transmissions for above systems. Specifically, to investigate the reliable and covert performance of the system, detection error probability at the warder and decoding error probability at the receiver are derived, which is affected by both the transmit power and the jamming power. Furthermore, to maximize the effective throughput, an optimization framework is proposed under reliability and covertness constraints. Numerical results verify the accuracy of analytical results and the feasibility of the optimization framework. It is shown that the tradeoff between transmission reliability and covertness is changed by the proactive warder compared with the passive one. Besides, it is shown that longer blocklength is always beneficial to improve the throughput for systems with optimized transmission rates. But when transmission rates are fixed, the blocklength should be carefully designed since the maximum one is not optimal in this case.

cs.IT↗
arXiv2023

Spatial inversion symmetry breaking of vortex current in biased-ladder superfluid

We investigate the quench dynamics of interacting bosons on a two-leg ladder in presence of a uniform Abelian gauge field. The model hosts a variety of emergent quantum phases, and we focus on the superfluid biased-ladder phase breaking the $Z_{2}$ symmetry of two legs. We observe an asymmetric spreading of vortex current and particle density, i.e., the current behaves particle-like on the right and wave-like on the left, indicating spontaneous breaking of the spatial inversion symmetry. By decreasing the repulsion strength, it is found the particle-like current is more robust than the wave-like one. The evolution of entanglement entropy manifests logarithmic growth with time suggesting many-body localization matters.

cond-mat.quant-gas↗
arXiv2023

Evolution of Non-parametric Morphology of Galaxies in the JWST CEERS Field at $z\simeq$0.8-3.0

Galaxy morphology is one of the most fundamental ways to describe galaxy properties, but the morphology we observe may be affected by wavelength and spatial resolution, which may introduce systematic bias when comparing galaxies at different redshift. Taking advantage of the broad wavelength coverage from optical to near-IR and high resolution NIRCam instrument of JWST, we measure the non-parametric morphological parameters of a total of 1376 galaxies at $z\simeq$0.8-3.0 in the CEERS field through an optimized code called {\tt\string statmorph\_csst}. We divide our sample into three redshift intervals and investigate the wavelength- and redshift-dependence of the morphological parameters. We also explore how the widely-used galaxy type classification methods based on the morphological parameters depend on wavelength and spatial resolution. We find that there are variations in all morphological parameters with rest-frame wavelength ($λ_{\rm rf}$), especially at the short wavelength end, and the $λ_{\rm rf}$ mainly affects the classification between late-type and early-type galaxy. As the $λ_{\rm rf}$ increases, the galaxies on the $G-M_{20}$ diagram move to the upper left with a slope of -0.23$\pm$0.03 on average. We find that spatial resolution mainly affects the merger identification. The merger fraction in F200W resolution can be $\ga$2 times larger than that in F444W resolution. Furthermore, We compare the morphological parameter evolution of galaxies with different stellar masses. We find that there are differences in the morphological evolution of high- and low-mass (log$M_*\geqslant$10 and 9$<$log$M_*<$10) galaxies in the studied redshift range, which may be caused by their different evolution paths.

astro-ph.GA↗
arXiv2023

AvatarBooth: High-Quality and Customizable 3D Human Avatar Generation

We introduce AvatarBooth, a novel method for generating high-quality 3D avatars using text prompts or specific images. Unlike previous approaches that can only synthesize avatars based on simple text descriptions, our method enables the creation of personalized avatars from casually captured face or body images, while still supporting text-based model generation and editing. Our key contribution is the precise avatar generation control by using dual fine-tuned diffusion models separately for the human face and body. This enables us to capture intricate details of facial appearance, clothing, and accessories, resulting in highly realistic avatar generations. Furthermore, we introduce pose-consistent constraint to the optimization process to enhance the multi-view consistency of synthesized head images from the diffusion model and thus eliminate interference from uncontrolled human poses. In addition, we present a multi-resolution rendering strategy that facilitates coarse-to-fine supervision of 3D avatar generation, thereby enhancing the performance of the proposed system. The resulting avatar model can be further edited using additional text descriptions and driven by motion sequences. Experiments show that AvatarBooth outperforms previous text-to-3D methods in terms of rendering and geometric quality from either text prompts or specific images. Please check our project website at https://zeng-yifei.github.io/avatarbooth_page/.

cs.CV↗