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Huan Yang

Publications and source records attributed to Huan Yang.

At least 127 records · Page 7Linked to original sources
arXiv2023

Influence of microstructure on superconductivity in K$_x$Fe$_{2-y}$Se$_2$ and evidence for a new parent phase K$_2$Fe$_7$Se$_8$

The search for new superconducting materials has been spurred on by the discovery of iron-based superconductors whose structure and composition is qualitatively different from the cuprates. The study of one such material, K$_x$Fe$_{2-y}$Se$_2$ with a critical temperature of 32 K, is made more difficult by the fact that it separates into two phases: a dominant antiferromagnetic insulating phase with a K$_2$Fe$_4$Se$_5$ structure, and a minority superconducting phase whose precise structure is as yet unclear. Here we perform electrical and magnetization measurements, scanning electron microscopy and microanalysis, X-ray diffraction, and scanning tunnelling microscopy on K$_x$Fe$_{2-y}$Se$_2$ crystals prepared under different quenching processes to better understand the relationship between its microstructure and its superconducting phase. We identify a 3D network of superconducting filaments within this material and present evidence to suggest that the superconducting phase consists of a single Fe vacancy for every eight Fe-sites arranged in a $\sqrt{10}\times\sqrt{8}$ parallelogram structure.

cond-mat.supr-con↗
arXiv2023

Repeating Fast Radio Bursts from Neutron Star Binaries: Multi-band and Multi-messenger Opportunities

Recent observations indicate that magnetars may commonly reside in merging compact binaries and at least part of fast radio bursts (FRBs) are sourced by magnetar activities. It is natural to speculate that a class of merging neutron star binaries may have FRB emitters. In this work, we study the observational aspects of these binaries - particularly those with FRB repeaters, which are promising multi-band and multi-messenger observation targets of radio telescopes and ground based gravitational wave detectors as the former telescopes can probe the systems at a much earlier stage in the inspiral than the latter. We show that observations of FRB repeaters in compact binaries have a significant advantage in pinning down the binary spin dynamics, constraining neutron star equation of state, probing FRB production mechanisms, and testing beyond standard physics. As a proof of principle, we investigate several mock observations of FRB pulses originating from pre-merger neutron star binaries, and we find that using the information of FRB arriving times alone, the intrinsic parameters of this system (including the stellar masses, spins, and quadrupole moments) can be measured with high precision, and the angular dependence of the FRB emission pattern can also be well reconstructed. The measurement of stellar masses (with an error of $\mathcal{O}(10^{-6}-10^{-5})$) and quadrupole moments (with an error of $\mathcal{O}(1\%-10\%)$) may be an unprecedented discriminator of nuclear equations of state in neutron stars. In addition, we find the multi-band and multi-messenger observations of this binary will be sensitive to alternative theories of gravity and beyond standard models, e.g., dynamical Chern-Simons gravity and axion field that is coupled to matter.

astro-ph.HE↗
arXiv2023

Learning Profitable NFT Image Diffusions via Multiple Visual-Policy Guided Reinforcement Learning

We study the task of generating profitable Non-Fungible Token (NFT) images from user-input texts. Recent advances in diffusion models have shown great potential for image generation. However, existing works can fall short in generating visually-pleasing and highly-profitable NFT images, mainly due to the lack of 1) plentiful and fine-grained visual attribute prompts for an NFT image, and 2) effective optimization metrics for generating high-quality NFT images. To solve these challenges, we propose a Diffusion-based generation framework with Multiple Visual-Policies as rewards (i.e., Diffusion-MVP) for NFT images. The proposed framework consists of a large language model (LLM), a diffusion-based image generator, and a series of visual rewards by design. First, the LLM enhances a basic human input (such as "panda") by generating more comprehensive NFT-style prompts that include specific visual attributes, such as "panda with Ninja style and green background." Second, the diffusion-based image generator is fine-tuned using a large-scale NFT dataset to capture fine-grained image styles and accessory compositions of popular NFT elements. Third, we further propose to utilize multiple visual-policies as optimization goals, including visual rarity levels, visual aesthetic scores, and CLIP-based text-image relevances. This design ensures that our proposed Diffusion-MVP is capable of minting NFT images with high visual quality and market value. To facilitate this research, we have collected the largest publicly available NFT image dataset to date, consisting of 1.5 million high-quality images with corresponding texts and market values. Extensive experiments including objective evaluations and user studies demonstrate that our framework can generate NFT images showing more visually engaging elements and higher market value, compared with SOTA approaches.

cs.CV↗
arXiv2023

MobileVidFactory: Automatic Diffusion-Based Social Media Video Generation for Mobile Devices from Text

Videos for mobile devices become the most popular access to share and acquire information recently. For the convenience of users' creation, in this paper, we present a system, namely MobileVidFactory, to automatically generate vertical mobile videos where users only need to give simple texts mainly. Our system consists of two parts: basic and customized generation. In the basic generation, we take advantage of the pretrained image diffusion model, and adapt it to a high-quality open-domain vertical video generator for mobile devices. As for the audio, by retrieving from our big database, our system matches a suitable background sound for the video. Additionally to produce customized content, our system allows users to add specified screen texts to the video for enriching visual expression, and specify texts for automatic reading with optional voices as they like.

cs.CV↗
arXiv2023

Observation of photoassociation resonances in ultracold atom-molecule collisions

Photoassociation of ultracold atoms is a resonant light-assisted collision process, in which two colliding atoms absorb a photon and form an excited molecule. Since the first observation about three decades ago, the photoassociation of ultracold atoms has made a significant impact on the study of ultracold atoms and molecules. Extending the photoassociation of atoms to the photoassociation of atom-molecule pairs or molecule-molecule pairs will offer many new opportunities in the study of precision polyatomic molecular spectroscopy, formation of ultracold polyatomic molecules, and quantum control of molecular collisions and reactions. However, the high density of states and the photoexcitation of the collision complex by the trapping laser make photoassociation into well-defined quantum states of polyatomic molecules extremely difficult. Here we report on the observation of photoassociation resonances in ultracold collisions between $^{23}$Na$^{40}$K molecules and $^{40}$K atoms. We perform photoassociation in a long-wavelength optical dipole trap to form deeply bound triatomic molecules in the electronically excited states. The atom-molecule Feshbach resonance is used to enhance the free-bound Franck-Condon overlap. The photoassociation into well-defined quantum states of excited triatomic molecules is identified by observing resonantly enhanced loss features. These loss features depend on the polarization of the photoassociation lasers, allowing us to assign the rotational quantum numbers. The observation of ultracold atom-molecule photoassociation resonances paves the way toward preparing ground-state triatomic molecules, provides a new high-resolution spectroscopy technique for polyatomic molecules, and is also important to atom-molecule Feshbach resonances.

physics.atom-ph↗
arXiv2023

Online Streaming Video Super-Resolution with Convolutional Look-Up Table

Online video streaming has fundamental limitations on the transmission bandwidth and computational capacity and super-resolution is a promising potential solution. However, applying existing video super-resolution methods to online streaming is non-trivial. Existing video codecs and streaming protocols (\eg, WebRTC) dynamically change the video quality both spatially and temporally, which leads to diverse and dynamic degradations. Furthermore, online streaming has a strict requirement for latency that most existing methods are less applicable. As a result, this paper focuses on the rarely exploited problem setting of online streaming video super resolution. To facilitate the research on this problem, a new benchmark dataset named LDV-WebRTC is constructed based on a real-world online streaming system. Leveraging the new benchmark dataset, we proposed a novel method specifically for online video streaming, which contains a convolution and Look-Up Table (LUT) hybrid model to achieve better performance-latency trade-off. To tackle the changing degradations, we propose a mixture-of-expert-LUT module, where a set of LUT specialized in different degradations are built and adaptively combined to handle different degradations. Experiments show our method achieves 720P video SR around 100 FPS, while significantly outperforms existing LUT-based methods and offers competitive performance compared to efficient CNN-based methods.

eess.IV↗
arXiv2023

Resonant dynamics of extreme mass-ratio inspirals in a perturbed Kerr spacetime

Extreme mass-ratio inspirals (EMRI) are one of the most sensitive probes of black hole spacetimes with gravitational wave measurements. In this work, we systematically analyze the dynamics of an EMRI system near orbital resonances, assuming the background spacetime is weakly perturbed from Kerr. Using the action-angle formalism, we have derived an effective resonant Hamiltonian that describes the dynamics of the resonant degree of freedom, for the case that the EMRI motion across the resonance regime. This effective resonant Hamiltonian can also be used to derive the condition that the trajectory enters/exits a resonant island and the permanent change of action variables across the resonance with the gravitational wave radiation turned on. The orbital chaos, on the other hand, generally leads to transitions between different branches of rotational orbits with finite changes of the action variables. These findings are demonstrated with numerical orbital evolutions that are mapped into representations using action-angle variables. This study is one part of the program of understanding EMRI dynamics in a generic perturbed Kerr spacetime, which paves the way of using EMRIs to precisely measure the black hole spacetime.

gr-qc↗
arXiv2023

Improving the detection sensitivity to primordial stochastic gravitational waves with reduced astrophysical foregrounds

One of the primary targets of third-generation (3G) ground-based gravitational wave (GW) detectors is detecting the stochastic GW background (SGWB) from early universe processes. The astrophysical foreground from compact binary mergers will be a major contamination to the background, which must be reduced to high precision to enable the detection of primordial background. In this work, we revisit the limit of foreground reduction computed in previous studies, point out potential problems in previous foreground cleaning methods and propose a novel cleaning method subtracting the approximate signal strain and removing the average residual power. With this method, the binary black hole foreground is reduced with fractional residual energy density below $10^{-4}$ for frequency $f\in (10, 10^2)$ Hz, below $10^{-3}$ for frequency $f\in (10^2, 10^3)$ Hz and below the detector sensitivity limit for all relevant frequencies in our simulations. Similar precision is achieved to clean the foreground from binary neutron stars (BNSs) that are above the detection threshold, so that the residual foreground is dominated by sub-threshold BNSs, which will be the next critical problem to solve for detecting the primordial SGWB in the 3G era.

gr-qc↗
arXiv2023

VibHead: An Authentication Scheme for Smart Headsets through Vibration

Recent years have witnessed the fast penetration of Virtual Reality (VR) and Augmented Reality (AR) systems into our daily life, the security and privacy issues of the VR/AR applications have been attracting considerable attention. Most VR/AR systems adopt head-mounted devices (i.e., smart headsets) to interact with users and the devices usually store the users' private data. Hence, authentication schemes are desired for the head-mounted devices. Traditional knowledge-based authentication schemes for general personal devices have been proved vulnerable to shoulder-surfing attacks, especially considering the headsets may block the sight of the users. Although the robustness of the knowledge-based authentication can be improved by designing complicated secret codes in virtual space, this approach induces a compromise of usability. Another choice is to leverage the users' biometrics; however, it either relies on highly advanced equipments which may not always be available in commercial headsets or introduce heavy cognitive load to users. In this paper, we propose a vibration-based authentication scheme, VibHead, for smart headsets. Since the propagation of vibration signals through human heads presents unique patterns for different individuals, VibHead employs a CNN-based model to classify registered legitimate users based the features extracted from the vibration signals. We also design a two-step authentication scheme where the above user classifiers are utilized to distinguish the legitimate user from illegitimate ones. We implement VibHead on a Microsoft HoloLens equipped with a linear motor and an IMU sensor which are commonly used in off-the-shelf personal smart devices. According to the results of our extensive experiments, with short vibration signals ($\leq 1s$), VibHead has an outstanding authentication accuracy; both FAR and FRR are around 5%.

cs.CR↗
arXiv2023

Probing the Delay Time of Supermassive Black Hole Binary Mergers With Gravitational Waves

Merging supermassive black hole binaries is expected as a consequence of galaxy mergers, yet the detailed evolution path and underlying merging mechanisms of these binaries are still subject to large theoretical uncertainties. In this work, we propose to combine the (future) gravitational wave measurements of supermassive black hole binary merger events with the galaxy merger rate distributions from galaxy surveys/cosmological simulations, to infer the delay time of binary mergers, as a function of binary mass. The delay time encodes key information about binary evolution, which can be used to test the predictions of various evolution models. With a Mock data set of supermassive black hole binary merger events, we discuss how to infer the distribution of delay time with hierarchical Bayesian inference and test evolution models with the Bayesian model selection method. The astrophysical model uncertainties are also considered in the hierarchical Bayesian inference and Bayesian model selection.

gr-qc↗
arXiv2023

MovieFactory: Automatic Movie Creation from Text using Large Generative Models for Language and Images

In this paper, we present MovieFactory, a powerful framework to generate cinematic-picture (3072$\times$1280), film-style (multi-scene), and multi-modality (sounding) movies on the demand of natural languages. As the first fully automated movie generation model to the best of our knowledge, our approach empowers users to create captivating movies with smooth transitions using simple text inputs, surpassing existing methods that produce soundless videos limited to a single scene of modest quality. To facilitate this distinctive functionality, we leverage ChatGPT to expand user-provided text into detailed sequential scripts for movie generation. Then we bring scripts to life visually and acoustically through vision generation and audio retrieval. To generate videos, we extend the capabilities of a pretrained text-to-image diffusion model through a two-stage process. Firstly, we employ spatial finetuning to bridge the gap between the pretrained image model and the new video dataset. Subsequently, we introduce temporal learning to capture object motion. In terms of audio, we leverage sophisticated retrieval models to select and align audio elements that correspond to the plot and visual content of the movie. Extensive experiments demonstrate that our MovieFactory produces movies with realistic visuals, diverse scenes, and seamlessly fitting audio, offering users a novel and immersive experience. Generated samples can be found in YouTube or Bilibili (1080P).

cs.CV↗
arXiv2023

Absence of near-ambient superconductivity in LuH$_{2\pm\text{x}}$N$_y$

Recently near-ambient superconductivity was claimed in N-doped lutetium hydride (ref. 1). This induces a worldwide fanaticism about the dream of room temperature superconductivity under low pressures. By using a high pressure and high temperature synthesis technique, we have successfully obtained the nitrogen doped lutetium hydride (LuH$_{2\pm\text{x}}$N$_y$) with a dark-bluish color and a structure with the space group of $Fm\bar{3}m$ evidenced by x-ray diffraction. This structure is the same as that reported in ref. 1. The energy dispersive X-ray spectroscopy (EDS) confirmed the existence of nitrogen in some areas of the samples. At ambient pressure, we witness a kink of resistivity and magnetization at about 300 K, which may correspond to a rearrangement of hydrogen/nitrogen atoms, namely a structural transition. However, by applying a pressure from 1 GPa to 6 GPa, we have seen a progressively optimized metallic behavior without showing superconductivity down to 10 K. Temperature dependence of magnetization shows a roughly flat feature between 100 and 320 K, and the magnetization increases with magnetic field at 100 K, all these are not expected for superconductivity at 100 K. Thus, we conclude the absence of near-ambient superconductivity in this nitrogen-doped lutetium hydride under pressures below 6 GPa.

cond-mat.supr-con↗
arXiv2023

Minimizing CNOT-count in quantum circuit of the extended Shor's algorithm for ECDLP

Since the elliptic curve discrete logarithms problem (ECDLP) was proposed, it has been widely used in cryptosystem because of its strong security. Although the proposal of the extended Shor's algorithm offers hope for cracking ECDLP, it is debatable whether the algorithm can actually pose a threat in practice. From the perspective of the quantum circuit of the algorithm, we analyze the feasibility of cracking ECDLP with improved quantum circuits using an ion trap quantum computer. We give precise quantum circuits for extended Shor's algorithm to calculate discrete logarithms on elliptic curves over prime fields, including modulus subtraction, three different modulus multiplication, modulus inverse, and windowed arithmetic. Whereas previous studies mostly focused on minimizing the number of qubits or the depth of the circuit, we minimize the number of CNOTs, which greatly affects the time to run the algorithm on an ion trap quantum computer. First, we give the implementation of the basic arithmetic with the lowest known number of CNOTs and the construction of an improved modular inverse, point addition, and the windowing technique. Then, we precisely estimate the number of improved quantum circuits needed to perform the extended Shor's algorithm for factoring an n-bit integer. We analyze the running time and feasibility of the extended Shor's algorithm on an ion trap quantum computer according to the number of CNOTs. Finally, we discussed the lower bound of the number of CNOTs needed to implement the extended Shor's algorithm.

quant-ph↗
arXiv2023

Collaborative Learning in General Graphs with Limited Memorization: Complexity, Learnability, and Reliability

We consider a K-armed bandit problem in general graphs where agents are arbitrarily connected and each of them has limited memorizing capabilities and communication bandwidth. The goal is to let each of the agents eventually learn the best arm. It is assumed in these studies that the communication graph should be complete or well-structured, whereas such an assumption is not always valid in practice. Furthermore, limited memorization and communication bandwidth also restrict the collaborations of the agents, since the agents memorize and communicate very few experiences. Additionally, an agent may be corrupted to share falsified experiences to its peers, while the resource limit in terms of memorization and communication may considerably restrict the reliability of the learning process. To address the above issues, we propose a three-staged collaborative learning algorithm. In each step, the agents share their latest experiences with each other through light-weight random walks in a general communication graph, and then make decisions on which arms to pull according to the recommendations received from their peers. The agents finally update their adoptions (i.e., preferences to the arms) based on the reward obtained by pulling the arms. Our theoretical analysis shows that, when there are a sufficient number of agents participating in the collaborative learning process, all the agents eventually learn the best arm with high probability, even with limited memorizing capabilities and light-weight communications. We also reveal in our theoretical analysis the upper bound on the number of corrupted agents our algorithm can tolerate. The efficacy of our proposed three-staged collaborative learning algorithm is finally verified by extensive experiments on both synthetic and real datasets.

cs.LG↗
arXiv2023

A Gravitational Wave Detector for Post Merger Neutron Stars: Beyond the Quantum Loss Limit of Michelson Fabry Perot Interferometer

Advanced gravitational-wave detectors that have made groundbreaking discoveries are Michelson interferometers with resonating optical cavities as their arms. As light travels at finite speed, these cavities are optimal for enhancing signals at frequencies below their bandwidth frequency. A small amount of optical loss will, however, significantly impact the high-frequency signals which are not optimally amplified. We find an elegant interferometer configuration with an "L-resonator" as the core, significantly surpassing the loss limited sensitivity of dual recycled Fabry Perot Michelson interferometers at high frequencies. Following this concept, we provide a broadband design of a 25 km detector with outstanding sensitivity between 2-4 kHz. We have performed Monte-Carlo population studies of binary neutron star mergers, given the most recent merger rate from the GWTC-3 catalog and several representative neutron star equations of state. We find that the new interferometer configuration significantly outperforms other third-generation detectors by a factor of 3 to 7 in the signal-to-noise ratio of the post-merger signal. Assuming a detection threshold with signal-to-noise ratio >5 and for the cases we have explored, the new design is the only detector that confidently achieves a detection rate larger than one per year, with the rate being 1 to 30 events per year.

gr-qc↗
arXiv2023

Morphological tracking and tuning of silica NPs for stable levitation in vacuum optomechanical systems

Optically levitated nanomechanical resonators in vacuum perform ultrahigh sensitivity for mechanical quantities by overcoming the limitations of clamped resonators. However, the generally levitated silica nanoparticles (NPs) with low absorption and high transparence still face difficulties surviving in high vacuum with unclear reason. By monitoring the physicochemical properties like scattering, mass and density of amorphous silica NPs during pumping process. we propose that the loss of NPs may arises from the motional instability induced by laser heating lead releasing at low pressure. In this work, two types of NPs are heat treated from 100 to 1200 degree Celsius to release impurities before being loaded into an optical trap. The high vacuum levitation ratio for both NPs increase obviously after heat treatment. In particular, for NPs heated to 600 degree Celsius, the ratio strikingly improves from ~30% to 100% and ~0 to 85% for two types of NPs. The loss mechanism is further confirmed by their relatively stable physicochemical parameters during pumping process. This work paves a way for wide application of levitated nano-resonators and indicates that levitated vacuum optomechanical systems could be a promising tool for dynamics and in-situ studying of small particles like aerosols and dusts.

physics.optics↗
arXiv2023

Pressure induced color change and evolution of metallic behavior in nitrogen-doped lutetium hydride

By applying pressures up to 42 GPa on the nitrogen-doped lutetium hydride (LuH$_{2\pm\text{x}}$N$_y$), we have found a gradual change of color from dark-blue to pink-violet in the pressure region of about 12 GPa to 21 GPa. The temperature dependence of resistivity under pressures up to 50.5 GPa shows progressively optimized metallic behavior with pressure. Interestingly, in the pressure region for the color change, a clear decrease of resistivity is observed with the increase of pressure, which is accompanied by a clear increase of the residual resistivity ratio (RRR). Fitting to the low temperature resistivity gives exponents of about 2, suggesting a Fermi liquid behavior in low temperature region. The general behavior in wide temperature region suggests that the electron-phonon scattering is still the dominant one. The magnetoresistance up to 9 tesla in the state under a pressure of 50.5 GPa shows an almost negligible effect, which suggests that the electric conduction in the pink-violet state is dominated by a single band. It is highly desired to have theoretical efforts in understanding the evolution of color and resistivity in this interesting system.

cond-mat.supr-con↗
arXiv2023

MM-Diffusion: Learning Multi-Modal Diffusion Models for Joint Audio and Video Generation

We propose the first joint audio-video generation framework that brings engaging watching and listening experiences simultaneously, towards high-quality realistic videos. To generate joint audio-video pairs, we propose a novel Multi-Modal Diffusion model (i.e., MM-Diffusion), with two-coupled denoising autoencoders. In contrast to existing single-modal diffusion models, MM-Diffusion consists of a sequential multi-modal U-Net for a joint denoising process by design. Two subnets for audio and video learn to gradually generate aligned audio-video pairs from Gaussian noises. To ensure semantic consistency across modalities, we propose a novel random-shift based attention block bridging over the two subnets, which enables efficient cross-modal alignment, and thus reinforces the audio-video fidelity for each other. Extensive experiments show superior results in unconditional audio-video generation, and zero-shot conditional tasks (e.g., video-to-audio). In particular, we achieve the best FVD and FAD on Landscape and AIST++ dancing datasets. Turing tests of 10k votes further demonstrate dominant preferences for our model. The code and pre-trained models can be downloaded at https://github.com/researchmm/MM-Diffusion.

cs.CV↗