arXiv subjects

Feng Gao

Publications and source records attributed to Feng Gao.

At least 181 records · Page 10Linked to original sources

arXiv2023

Human Motion Generation: A Survey

Human motion generation aims to generate natural human pose sequences and shows immense potential for real-world applications. Substantial progress has been made recently in motion data collection technologies and generation methods, laying the foundation for increasing interest in human motion generation. Most research within this field focuses on generating human motions based on conditional signals, such as text, audio, and scene contexts. While significant advancements have been made in recent years, the task continues to pose challenges due to the intricate nature of human motion and its implicit relationship with conditional signals. In this survey, we present a comprehensive literature review of human motion generation, which, to the best of our knowledge, is the first of its kind in this field. We begin by introducing the background of human motion and generative models, followed by an examination of representative methods for three mainstream sub-tasks: text-conditioned, audio-conditioned, and scene-conditioned human motion generation. Additionally, we provide an overview of common datasets and evaluation metrics. Lastly, we discuss open problems and outline potential future research directions. We hope that this survey could provide the community with a comprehensive glimpse of this rapidly evolving field and inspire novel ideas that address the outstanding challenges.

cs.CV↗

arXiv2023

High-efficiency edge couplers enabled by vertically tapering on lithium-niobate photonic chips

In the past decade, photonic integrated circuits (PICs) based on thin-film lithium niobate (TFLN) have advanced in various fields, including optical communication, nonlinear photonics, and quantum optics. A critical component is an efficient edge coupler connecting PICs to light sources or detectors. Here, we propose an innovative edge coupler design with a wedge-shaped TFLN waveguide and a silicon oxynitride (SiON) cladding. Experimental results show that the coupling loss between the TFLN PIC and a 3-μm mode field diameter (MFD) lensed fiber is low at 1.52 dB/facet, with the potential for improvement to 0.43 dB/facet theoretically. The coupling loss between the edge coupler and a UHNA7 fiber with an MFD of 3.2 μm is reduced to 0.92 dB/facet. This design maintains robust fabrication and alignment tolerance. Importantly, the minimum linewidth of the TFLN waveguide of the coupler (600 nm) can be easily achieved using foundry-available i-line stepper lithography. This work benefits the development of TFLN integrated platforms, such as on-chip electro-optic modulators, frequency comb generation, and quantum sensors.

physics.optics↗

arXiv2023

SS-MAE: Spatial-Spectral Masked Auto-Encoder for Multi-Source Remote Sensing Image Classification

Masked image modeling (MIM) is a highly popular and effective self-supervised learning method for image understanding. Existing MIM-based methods mostly focus on spatial feature modeling, neglecting spectral feature modeling. Meanwhile, existing MIM-based methods use Transformer for feature extraction, some local or high-frequency information may get lost. To this end, we propose a spatial-spectral masked auto-encoder (SS-MAE) for HSI and LiDAR/SAR data joint classification. Specifically, SS-MAE consists of a spatial-wise branch and a spectral-wise branch. The spatial-wise branch masks random patches and reconstructs missing pixels, while the spectral-wise branch masks random spectral channels and reconstructs missing channels. Our SS-MAE fully exploits the spatial and spectral representations of the input data. Furthermore, to complement local features in the training stage, we add two lightweight CNNs for feature extraction. Both global and local features are taken into account for feature modeling. To demonstrate the effectiveness of the proposed SS-MAE, we conduct extensive experiments on three publicly available datasets. Extensive experiments on three multi-source datasets verify the superiority of our SS-MAE compared with several state-of-the-art baselines. The source codes are available at \url{https://github.com/summitgao/SS-MAE}.

eess.IV↗

arXiv2023

Sulphur isotopes toward Sagittarius B2 extended envelope in the Galactic Center

The isotopic ratios are good tools for probing the stellar nucleosynthesis and chemical evolution. We performed high-sensitivity mapping observations of the J=7-6 rotational transitions of OCS, OC34S, O13CS, and OC33S toward the Galactic Center giant molecular cloud, Sagittarius B2 (Sgr B2) with IRAM 30m telescope. Positions with optically thin and uncontaminated lines are chosen to determine the sulfur isotope ratios. A 32S/34S ratio of 17.1\pm0.9 was derived with OCS and OC34S lines, while 34S/33S ratio of 6.8\pm1.9 was derived directly from integrated intensity ratio of OC34S and OC33S. With independent and accurate measurements of 32S/34S ratio, our results confirm the termination of the decreasing trend of 32S/34S ratios toward the Galactic Center, suggesting a drop in the production of massive stars at the Galactic centre.

astro-ph.GA↗

arXiv2023

Learning non-Markovian Decision-Making from State-only Sequences

Conventional imitation learning assumes access to the actions of demonstrators, but these motor signals are often non-observable in naturalistic settings. Additionally, sequential decision-making behaviors in these settings can deviate from the assumptions of a standard Markov Decision Process (MDP). To address these challenges, we explore deep generative modeling of state-only sequences with non-Markov Decision Process (nMDP), where the policy is an energy-based prior in the latent space of the state transition generator. We develop maximum likelihood estimation to achieve model-based imitation, which involves short-run MCMC sampling from the prior and importance sampling for the posterior. The learned model enables \textit{decision-making as inference}: model-free policy execution is equivalent to prior sampling, model-based planning is posterior sampling initialized from the policy. We demonstrate the efficacy of the proposed method in a prototypical path planning task with non-Markovian constraints and show that the learned model exhibits strong performances in challenging domains from the MuJoCo suite.

cs.LG↗

arXiv2023

Mapping Observations of Peptide-like molecules around Sagittarius B2

Peptide-like molecule, which has a close connection with the origin of life, has been detected in universe. Mapping observations of HCONH$_2$ and CH$_3$CONH$_2$, two simplest peptide-like molecules, are performed towards Sagittarius B2 (Sgr B2) complex with the IRAM 30m telescope. Seven transitions of HCONH$_2$ and five transitions of CH$_3$CONH$_2$ are used in analysis. The spatial distribution of excitation temperature and column density of HCONH$_2$ in the molecular envelope of Sgr B2 are obtained by the rotation diagrams. Assuming the same excitation temperature as HCONH$_2$, the column densities of CH$_3$CONH$_2$ are also calculated. The results show that excitation temperature ranges from 6 K to 46 K in the molecular envelope of Sgr B2. The abundance ratio between HCONH$_2$ and CH$_3$CONH$_2$ are calculated to explore the relationship among them, as well as HNCO mentioned in our pervious research. The abundance ratio of CH$_3$CONH$_2$/HCONH$_2$ varies from 10% to 20%, while that of HCONH$_2$/HNCO ranges from 1.5% to 10%. CH$_3$CONH$_2$ is enhanced with respect to HCONH$_2$ in the northwest region of Sgr B2. One transition of H$^{13}$CONH$_2$ is detected toward 12 positions of Sgr B2, from which a $^{12}$C/$^{13}$C ratio of 28.7 is obtained. A time-dependent chemical model with a short duration of X-ray burst is used to explain the observed abundances of HCONH$_2$ and CH$_3$CONH$_2$, with the best fitting result at T$\rm_{dust}$ = 53-56 K. More chemical reactions are required to be included into the model since the modeled abundance is lower than the observed one at the observed T$\rm_{dust}$.

astro-ph.GA↗

arXiv2023

A Critical Insight into Pretransitional Behavior and Dielectric Tunability of Relaxor Ceramics

The model discussion focused on links between the unique properties of relaxor ceramics and the basics of critical phenomena physics and glass transition physics. It indicates the significance of uniaxiality for appearing mean-field features near paraelectric_ferroelectric transition. Pretransitional fluctuations, increasing up to grain size and leading to inter-grain, random, local electric fields, are indicated to be responsible for relaxor ceramics characteristics. Their impacts yield the pseudo spinodal behavior associated with weakly discontinuous local phase transitions. The emerging model redefines the meaning of the Burns temperature and polar nanoregions PNRs. It explains coherently dielectric constant changes with the diffused maximum near paraelectric_ferroelectric transition, the sensitivity even to moderate electric fields, tunability, and the glassy dynamics. These considerations are confronted with experimental results for the complex dielectric permittivity studies in relaxor ceramic, covering the 200K range, from the paraelectric to the deep ferroelectric phase. The distortions-sensitive and derivative-based analysis revealed the preference for the exponential scaling pattern in the paraelectric phase and the surroundings of the paraelectric-ferroelectric transition. It may suggest the Griffith phase behavior associated with the mean-field criticality disturbed by random local impacts. The discussion of experimental results is supplemented by relaxation times changes and the coupled energy losses analysis. The studies also led to the description of tunability temperature changes with scaling relations.

physics.app-ph↗

arXiv2023

OmniDrones: An Efficient and Flexible Platform for Reinforcement Learning in Drone Control

In this work, we introduce OmniDrones, an efficient and flexible platform tailored for reinforcement learning in drone control, built on Nvidia's Omniverse Isaac Sim. It employs a bottom-up design approach that allows users to easily design and experiment with various application scenarios on top of GPU-parallelized simulations. It also offers a range of benchmark tasks, presenting challenges ranging from single-drone hovering to over-actuated system tracking. In summary, we propose an open-sourced drone simulation platform, equipped with an extensive suite of tools for drone learning. It includes 4 drone models, 5 sensor modalities, 4 control modes, over 10 benchmark tasks, and a selection of widely used RL baselines. To showcase the capabilities of OmniDrones and to support future research, we also provide preliminary results on these benchmark tasks. We hope this platform will encourage further studies on applying RL to practical drone systems.

cs.RO↗

arXiv2023

Convolution and Attention Mixer for Synthetic Aperture Radar Image Change Detection

Synthetic aperture radar (SAR) image change detection is a critical task and has received increasing attentions in the remote sensing community. However, existing SAR change detection methods are mainly based on convolutional neural networks (CNNs), with limited consideration of global attention mechanism. In this letter, we explore Transformer-like architecture for SAR change detection to incorporate global attention. To this end, we propose a convolution and attention mixer (CAMixer). First, to compensate the inductive bias for Transformer, we combine self-attention with shift convolution in a parallel way. The parallel design effectively captures the global semantic information via the self-attention and performs local feature extraction through shift convolution simultaneously. Second, we adopt a gating mechanism in the feed-forward network to enhance the non-linear feature transformation. The gating mechanism is formulated as the element-wise multiplication of two parallel linear layers. Important features can be highlighted, leading to high-quality representations against speckle noise. Extensive experiments conducted on three SAR datasets verify the superior performance of the proposed CAMixer. The source codes will be publicly available at https://github.com/summitgao/CAMixer .

eess.IV↗

arXiv2023

TSI-Net: A Timing Sequence Image Segmentation Network for Intracranial Artery Segmentation in Digital Subtraction Angiography

Cerebrovascular disease is one of the major diseases facing the world today. Automatic segmentation of intracranial artery (IA) in digital subtraction angiography (DSA) sequences is an important step in the diagnosis of vascular related diseases and in guiding neurointerventional procedures. While, a single image can only show part of the IA within the contrast medium according to the imaging principle of DSA technology. Therefore, 2D DSA segmentation methods are unable to capture the complete IA information and treatment of cerebrovascular diseases. We propose A timing sequence image segmentation network with U-shape, called TSI-Net, which incorporates a bi-directional ConvGRU module (BCM) in the encoder. The network incorporates a bi-directional ConvGRU module (BCM) in the encoder, which can input variable-length DSA sequences, retain past and future information, segment them into 2D images. In addition, we introduce a sensitive detail branch (SDB) at the end for supervising fine vessels. Experimented on the DSA sequence dataset DIAS, the method performs significantly better than state-of-the-art networks in recent years. In particular, it achieves a Sen evaluation metric of 0.797, which is a 3% improvement compared to other methods.

eess.IV↗

arXiv2023

Review of photoacoustic imaging plus X

Photoacoustic imaging (PAI) is a novel modality in biomedical imaging technology that combines the rich optical contrast with the deep penetration of ultrasound. To date, PAI technology has found applications in various biomedical fields. In this review, we present an overview of the emerging research frontiers on PAI plus other advanced technologies, named as PAI plus X, which includes but not limited to PAI plus treatment, PAI plus new circuits design, PAI plus accurate positioning system, PAI plus fast scanning systems, PAI plus novel ultrasound sensors, PAI plus advanced laser sources, PAI plus deep learning, and PAI plus other imaging modalities. We will discuss each technology's current state, technical advantages, and prospects for application, reported mostly in recent three years. Lastly, we discuss and summarize the challenges and potential future work in PAI plus X area.

physics.med-ph↗

arXiv2023

Chemical reaction mechanism of pre-curing process of two-component adhesive based on deformation behavior for automobile hood

Shearing test is carried out on the joint which bonded under different pre curing processes with two component adhesives of acrylic and epoxy resin respectively. The pre curing strength is obtained, which used to analyze the relationship between the pre curing strength and time. The hoods with different pre curing strength are baking with high temperature. The deformation of different areas of the hood is measured with gauges, and the deformation characteristics of the hood after baking are acquired with comparative analysis. Combining the components of two component adhesives and DSC test, the pre curing mechanism of different adhesive systems are studied. Therefore, the key factors and regular pattern of the deformation for the hood are obtained. The results indicate that, finally deformation of the hood after high temperature baking varies with the pre curing time. The key of the pre curing time of acrylic adhesives lies in the chain initiation stage of the free radical polymerization reaction. Due to the influence of the chemical properties of methyl acrylate and its initiator, the pre curing reaction induced by free radical polymerization is very fast. The shear strength of this joint can reach to 3.67 MPa with a pre curing time of 1 h, which quickly achieving the pre cure strength required for deformation control. For epoxy adhesives, the rate determining step in pre curing process is the esterification reaction. Due to the influence of the structure of carboxylic acid, the esterification process is relatively long. Shear strength of this joint can only reach 0.87 MPa after pre curing for 4 h without external heating.

physics.chem-ph↗

arXiv2023

Efficient option pricing with unary-based photonic computing chip and generative adversarial learning

In the modern financial industry system, the structure of products has become more and more complex, and the bottleneck constraint of classical computing power has already restricted the development of the financial industry. Here, we present a photonic chip that implements the unary approach to European option pricing, in combination with the quantum amplitude estimation algorithm, to achieve a quadratic speedup compared to classical Monte Carlo methods. The circuit consists of three modules: a module loading the distribution of asset prices, a module computing the expected payoff, and a module performing the quantum amplitude estimation algorithm to introduce speed-ups. In the distribution module, a generative adversarial network is embedded for efficient learning and loading of asset distributions, which precisely capture the market trends. This work is a step forward in the development of specialized photonic processors for applications in finance, with the potential to improve the efficiency and quality of financial services.

quant-ph↗

arXiv2023

Enhancing Cell Proliferation and Migration by MIR-Carbonyl Vibrational Coupling: Insights from Transcriptome Profiling

Cell proliferation and migration highly relate to normal tissue self-healing, therefore it is highly significant for artificial controlling. Recently, vibrational strong coupling between biomolecules and Mid-infrared (MIR) light photons has been successfully used to modify in vitro bioreactions, neuronal signaling and even animal behavior. However, the synergistic effects from molecules to cells remains unclear, and the regulation of MIR on cells needs to be explained from the molecular level. Herein, the proliferation rate and migration capacity of fibroblasts were increased by 156% and 162.5%, respectively, by vibratory coupling of 5.6 micrometers photons with carbonyl groups in biomolecules. Through transcriptome sequencing analysis, the regulatory mechanism of infrared light in 5.6 micrometers was explained from the level of signal pathway and cell components. 5.6 micrometers optical high power lasers can regulate cell function through vibrational strong coupling while minimizing photothermal damage. This work not only sheds light on the non-thermal effect on MIR light-based on wound healing, but also provides new evidence to future frequency medicine.

q-bio.GN↗

arXiv2023

The intelligent Photomultiplier Tubes for OSIRIS

The dimensions of liquid scintillator neutrino detectors have grown over the years. Thus, the analog cable length required for the readout increases. In consequence, the signal loss increases as well. With the intelligent Photomultiplier Tube this problem can be solved. The digitiser is moved as close as possible to the Photomultiplier Tube. This concept allows nearly lossless digitisation of the analog signal. Furthermore, a computing unit next to the analog-to-digital converter enables implementation of on-the-fly data processing and control algorithms. This paper presents the detailed concept and the performance of the intelligent Photomultiplier Tube.

physics.ins-det↗

arXiv2023

Geometric Wavelet Scattering Networks on Compact Riemannian Manifolds

The Euclidean scattering transform was introduced nearly a decade ago to improve the mathematical understanding of convolutional neural networks. Inspired by recent interest in geometric deep learning, which aims to generalize convolutional neural networks to manifold and graph-structured domains, we define a geometric scattering transform on manifolds. Similar to the Euclidean scattering transform, the geometric scattering transform is based on a cascade of wavelet filters and pointwise nonlinearities. It is invariant to local isometries and stable to certain types of diffeomorphisms. Empirical results demonstrate its utility on several geometric learning tasks. Our results generalize the deformation stability and local translation invariance of Euclidean scattering, and demonstrate the importance of linking the used filter structures to the underlying geometry of the data.

stat.ML↗

arXiv2023

Gradient-based adaptive wavelet de-noising method for photoacoustic imaging in vivo

Photoacoustic imaging (PAI) has been applied to many biomedical applications over the past decades. However, the received PA signal usually suffers from poor signal-to-noise ratio (SNR). Conventional solution of employing higher-power laser, or doing long-time signal averaging, may raise the system cost, time consumption, and tissue damage. Another strategy is de-noising algorithm design. In this paper, we propose a new de-noising method, termed gradient-based adaptive wavelet de-noising, which sets the energy gradient mutation point of low-frequency wavelet components as the threshold. We conducted simulation, ex vivo and in vivo experiments to validate the performance of the algorithm. The quality of de-noised PA image/signal by our proposed algorithm has improved by 20%-40%, in comparison to the traditional signal denoising algorithms, which produces better contrast and clearer details. The proposed de-noising method provides potential to improve the SNR of PA signal under single-shot low-power laser illumination for biomedical applications in vivo.

eess.SP↗

arXiv2023

ClueReader: Heterogeneous Graph Attention Network for Multi-hop Machine Reading Comprehension

Multi-hop machine reading comprehension is a challenging task in natural language processing as it requires more reasoning ability across multiple documents. Spectral models based on graph convolutional networks have shown good inferring abilities and lead to competitive results. However, the analysis and reasoning of some are inconsistent with those of humans. Inspired by the concept of grandmother cells in cognitive neuroscience, we propose a heterogeneous graph attention network model named ClueReader to imitate the grandmother cell concept. The model is designed to assemble the semantic features in multi-level representations and automatically concentrate or alleviate information for reasoning through the attention mechanism. The name ClueReader is a metaphor for the pattern of the model: it regards the subjects of queries as the starting points of clues, takes the reasoning entities as bridge points, considers the latent candidate entities as grandmother cells, and the clues end up in candidate entities. The proposed model enables the visualization of the reasoning graph, making it possible to analyze the importance of edges connecting entities and the selectivity in the mention and candidate nodes, which is easier to comprehend empirically. Evaluations on the open-domain multi-hop reading dataset WikiHop and drug-drug interaction dataset MedHop proved the validity of ClueReader and showed the feasibility of its application of the model in the molecular biology domain.

cs.CL↗