arXiv subjects

Hao Ma

Publications and source records attributed to Hao Ma.

At least 91 records · Page 5Linked to original sources

arXiv2023

Extremely thin perfect absorber by generalized multipole bianisotropic effect

Symmetry breaking plays a crucial role in understanding the fundamental physics underlying numerous physical phenomena, including the electromagnetic response in resonators, giving rise to intriguing effects such as directional light scattering, supercavity lasing, and topologically protected states. In this work, we demonstrate that adding a small fraction of lossy metal (as low as $1\times10^{-6}$ in volume), to a lossless dielectric resonator breaks inversion symmetry thereby lifting its degeneracy, leading to a strong bianisotropic response. In the case of the metasurface composed of such resonators, this effect leads to unidirectional perfect absorption while maintaining nearly perfect reflection from the opposite direction. We have developed more general Onsager-Casimir relations for the polarizabilities of particle arrays, taking into account the contributions of quadrupoles, which shows that bianisotropy is not solely due to dipoles, but also involves high-order multipoles. Our experimental validation demonstrates an extremely thin terahertz-perfect absorber with a wavelength-to-thickness ratio of up to 25,000, where the material thickness is only 2% of the theoretical minimum thickness dictated by the fundamental limit. Our findings have significant implications for a variety of applications, including energy harvesting, thermal management, single-photon detection, and low-power directional emission.

physics.optics↗

arXiv2023

On the detection of the electromagnetic counterparts from lensed gravitational wave events by binary neutron star mergers

Future ground-based gravitational wave (GW) detectors, i.e., Einstein telescope (ET) and Cosmic Explorer (CE), are expected to detect a significant number of lensed binary neutron star (BNS) mergers, which may provide a unique tool to probe cosmology. In this paper, we investigate the detectability of the optical/infrared electromagnetic (EM) counterparts (kilonovae/afterglows) from these lensed BNS mergers by future GW detectors and EM telescopes using simple kilonova, afterglow, and lens models. ET and CE are expected to detect $\sim5.32^{+26.1}_{-5.10}$ and $67.3^{+332}_{-64.7}$ lensed BNS mergers per year. We find that the EM counterparts associated with all these mergers will be detectable by an all sky-survey in the H-band with the limiting magnitude $m_{\textrm{lim}}\gtrsim27$, while the detectable fraction is $\lesssim0.4\%$ in the g-/z-band if with $m_{\textrm{lim}}\lesssim24$. Generally it is more efficient to search the lensed EM counterparts by adopting the infrared bands than the optical/UV bands with the same $m_{\textrm{lim}}$. Future telescopes like Vera C. Rubin Observatory, China Space Station Telescope, and Euclid can hardly detect the EM counterparts of even one lensed BNS merger. Roman Space Telescope (RST) and James Webb Space Telescope (JWST) have the capability to detect about a few or more such events per year. Moreover, the time delays and separations between the lensed image pairs are typically in the ranges from minutes to months and from $0.1$ to $1$\,arcsec, suggesting that both the GW and EM images of most lensed BNS mergers can be well resolved by not only CE/ET in the time domain but also RST/JWST spatially.

astro-ph.HE↗

arXiv2023

Diffractive lensing of nano-Hertz gravitational waves emitted from supermassive binary black holes by intervening galaxies

Pulsar timing array (PTA) experiments are expected to detect nano-Hertz gravitational waves (GWs) emitted from individual inspiralling supermassive binary black holes (SMBBHs). The GW signals from a small fraction of these SMBBHs may be diffractively lensed by intervening galaxies. In this paper, we investigate the diffractive lensing effects on the continuous GW signals from the lensed SMBBHs and estimate the detectable number of such signals by PTAs, such as the Chinese PTA (CPTA) and the Square Kilometer Array (SKA) PTA. We find that the amplitude of the lensed GW signals may be only amplified by a factor of $\sim 1.01-1.14$ ($16\%-84\%$ range) and the phase of the signals may shift somewhat due to the lensing, significantly different from those strongly lensed high frequency GW signals from compact binary mergers in the geometric optics. We estimate that $\sim 0.01\%$ of all detected nano-Hertz GW signals from individual SMBBHs by future PTA experiments are lensed by foreground galaxies (i.e., up to $\sim 106$ for CPTA and up to $\sim 289$ for SKA-PTA). However, the lensed nano-Hertz GW signals are difficult to be distinguished from those without lensing by the PTA observations only. We further discuss the possibility about the identification of the lensed nano-Hertz GW signals from SMBBHs via the electromagnetic detection of their host galaxies or active galactic nuclei.

astro-ph.CO↗

arXiv2023

Black-Box vs. Gray-Box: A Case Study on Learning Table Tennis Ball Trajectory Prediction with Spin and Impacts

In this paper, we present a method for table tennis ball trajectory filtering and prediction. Our gray-box approach builds on a physical model. At the same time, we use data to learn parameters of the dynamics model, of an extended Kalman filter, and of a neural model that infers the ball's initial condition. We demonstrate superior prediction performance of our approach over two black-box approaches, which are not supplied with physical prior knowledge. We demonstrate that initializing the spin from parameters of the ball launcher using a neural network drastically improves long-time prediction performance over estimating the spin purely from measured ball positions. An accurate prediction of the ball trajectory is crucial for successful returns. We therefore evaluate the return performance with a pneumatic artificial muscular robot and achieve a return rate of 29/30 (97.7%).

cs.RO↗

arXiv2023

Ab initio calculated dynamic structure factor and optical properties of beryllium along the Hugoniot

Beryllium is an ablator material in the inertial-confinement fusion and hypervelocity impact studies. The thermoelastic properties, structure factors, and optical properties of beryllium are important in these studies. In this paper, the static structure factors, ion-ion dynamic structure factors, adiabatic velocity, and optical properties of beryllium along the Hugoniot were calculated by ab initio simulations. The static structure factors show that beryllium atoms were randomly distributed. The dynamic structure factors via the scattering function have extracted the dispersion relation for the collective excitations. By collecting the peak position of dynamic structure factors, the dispersion relation and adiabatic sound velocity were derived by definitions. By the calculated equation of state, the thermoelastic properties and adiabatic sound velocity have been derived. The two calculated methods about adiabatic sound velocity were verified to be equivalent.

physics.plasm-ph↗

arXiv2023

A Dual-Cluster-Head Based Medium Access Control for Large-Scale UAV Ad-Hoc Networks

Unmanned Aerial Vehicle (UAV) ad hoc network has achieved significant growth for its flexibility, extensibility, and high deployability in recent years. The application of clustering scheme for UAV ad hoc network is imperative to enhance the performance of throughput and energy efficiency. In conventional clustering scheme, a single cluster head (CH) is always assigned in each cluster. However, this method has some weaknesses such as overload and premature death of CH when the number of UAVs increased. In order to solve this problem, we propose a dual-cluster-head based medium access control (DCHMAC) scheme for large-scale UAV networks. In DCHMAC, two CHs are elected to manage resource allocation and data forwarding cooperatively. Specifically, two CHs work on different channels. One of CH is used for intra-cluster communication and the other one is for inter-cluster communication. A Markov chain model is developed to analyse the throughput of the network. Simulation result shows that compared with FM-MAC (flying ad hoc networks multi-channel MAC,FM-MAC), DCHMAC improves the throughput by approximately 20%-50% and prolongs the network lifetime by approximately 40%.

cs.NI↗

arXiv2022

Detection of brain activations induced by naturalistic stimuli in a pseudo model-driven way

Naturalistic fMRI has been suggested to be a powerful alternative for investigations of human brain function. Stimulus-induced activation has been playing an essential role in fMRI-based brain function analyses. Due to the complexity of the stimuli, however, detection of activations induced by naturalistic stimuli (AINSs) has been a tricky problem, as AINS cannot be detected simply in a model-driven way. In this study, we proposed a method to detect AINS in a pseudo model-driven way. Inspired by the strategy of utilizing the commonalities among the brains exposed to the same stimuli for inter-subject correlation analysis, we established response models for one subject by averaging the fMRI signals across several other subjects, and then detected AINSs of the subject using general linear model. We evaluated the effectiveness of AINS with both statistical and predictive analyses on individual differences in sex and intelligence quotient (IQ), based on the four movie fMRI runs included in the Human Connectome Project dataset. The results indicate that AINS is not only sensitive to sex- and IQ-related differences, but also specific enough to decode individuals' sex and IQ. Specifically, activations in brain regions associated with visual-spatial processing were observed to be consistently stronger in the males, and individuals with higher IQ exhibited consistently stronger activations in regions within the visual and the default mode networks. Predictions of individuals' sex and IQ were significantly better than those based on random labels (P < 0.005). Taken together, AINS advanced in this study can be an effective evaluation of human brain function. The conceptual simplicity and easy application of its detection may make AINS a favorable choice for future brain function analyses and personalized medicine based on naturalistic fMRI.

q-bio.NC↗

arXiv2022

Performance of Cooperative Detection in Joint Communication-Sensing Vehicular Network: A Data Analytic and Stochastic Geometry Approach

The increasing complexity of urban environments introduces additional uncertainty to the deployment of the autonomous vehicular network. A novel road infrastructure cooperative detection model using Joint Communication and Sensing (JCS) technology is proposed in this article to simultaneously achieve high-efficient communication and obstacle detection for urban autonomous vehicles. To suppress the performance fluctuation caused by shadowing and obstruction to the JCS signals, we first derive the statistic of road obstacles from the Geographic Information System (GIS). Then, the analysis of JCS channel characteristics and shadowing factors are presented using Line-of-Sight and Non-Line-of-Sight (LoS and NLoS) channel models under the complex urban scenario. A stochastic geometry approach is applied to analyze the interference factors and the probability distribution of successful JCS detection and communication. Simulations have been made to verify the cooperative detection model by probability analysis based on LoS and NLoS channels, and the numerical results demonstrate several different optimization methods for the deployment of JCS road infrastructures. Finally, we simulated and analyzed a deployment optimization method for JCS road infrastructures that complied with the standard of urban traffic-spot structure placement.

eess.SP↗

arXiv2022

A Tightly Coupled LiDAR-IMU Odometry through Iterated Point-Level Undistortion

Scan undistortion is a key module for LiDAR odometry in high dynamic environment with high rotation and translation speed. The existing line of studies mostly focuses on one pass undistortion, which means undistortion for each point is conducted only once in the whole LiDAR-IMU odometry pipeline. In this paper, we propose an optimization based tightly coupled LiDAR-IMU odometry addressing iterated point-level undistortion. By jointly minimizing the cost derived from LiDAR and IMU measurements, our LiDAR-IMU odometry method performs more accurate and robust in high dynamic environment. Besides, the method characters good computation efficiency by limiting the quantity of parameters.

cs.RO↗

arXiv2022

Capturing the ground state of uranium dioxide from first principles: crystal distortion, magnetic structure, and phonons

Uranium dioxide (UO$_2$) remains a formidable challenge for first-principles approaches, due to the complex interplay among spin-orbit coupling, Mott physics, magnetic ordering, and crystal distortions. Here we use DFT+$U$ to explore UO$_2$ at zero temperature, incorporating all the aforementioned phenomena. The technical challenge is to navigate the many metastable electronic states produced by DFT+$U$, which is acomplished using $f$-orbital occupation matrix control to search for the ground state. We restrict our search to the high-symmetry ferromagnetic phase, including spin-orbit coupling, which produces a previously unreported occupation matrix. This newfound occupation matrix is then used as an initialization to explore the broken symmetry phases. We find the oxygen cage distortion of the 3k antiferromagnetic state to be in excellent agreement with experiments, and both the spin-orbit coupling and the Hubbard $U$ are critical ingredients. We demonstrate that only select phonon modes have a strong dependence on the Hubbard $U$, whereas magnetic ordering has only a small influence overall. We perform measurements of the phonon dispersion curves using inelastic neutron scattering, and our calculations show good agreement when using reasonable values of $U$. The quantitative success of DFT+$U$ warrants exploration of thermal transport and other observables within this level of theory.

cond-mat.mtrl-sci↗

arXiv2022

UniPELT: A Unified Framework for Parameter-Efficient Language Model Tuning

Recent parameter-efficient language model tuning (PELT) methods manage to match the performance of fine-tuning with much fewer trainable parameters and perform especially well when training data is limited. However, different PELT methods may perform rather differently on the same task, making it nontrivial to select the most appropriate method for a specific task, especially considering the fast-growing number of new PELT methods and tasks. In light of model diversity and the difficulty of model selection, we propose a unified framework, UniPELT, which incorporates different PELT methods as submodules and learns to activate the ones that best suit the current data or task setup via gating mechanism. On the GLUE benchmark, UniPELT consistently achieves 1~4% gains compared to the best individual PELT method that it incorporates and even outperforms fine-tuning under different setups. Moreover, UniPELT generally surpasses the upper bound that takes the best performance of all its submodules used individually on each task, indicating that a mixture of multiple PELT methods may be inherently more effective than single methods.

cs.CL↗

arXiv2022

IDPG: An Instance-Dependent Prompt Generation Method

Prompt tuning is a new, efficient NLP transfer learning paradigm that adds a task-specific prompt in each input instance during the model training stage. It freezes the pre-trained language model and only optimizes a few task-specific prompts. In this paper, we propose a conditional prompt generation method to generate prompts for each input instance, referred to as the Instance-Dependent Prompt Generation (IDPG). Unlike traditional prompt tuning methods that use a fixed prompt, IDPG introduces a lightweight and trainable component to generate prompts based on each input sentence. Extensive experiments on ten natural language understanding (NLU) tasks show that the proposed strategy consistently outperforms various prompt tuning baselines and is on par with other efficient transfer learning methods such as Compacter while tuning far fewer model parameters.

cs.CL↗

arXiv2022

High Phonon Scattering Rates Suppress Thermal Conductivity in Hyperstoichiometric Uranium Dioxide

Uranium dioxide (UO$_2$), one of the most important nuclear fuels, can accumulate excess oxygen atoms as interstitial defects, which significantly impacts thermal properties. In this study, thermal conductivities and inelastic neutron scattering measurements on UO$_2$ and UO$_{2+x}$ (x=0.3, 0.4, 0.8, 0.11) were performed at low temperatures (2-300 K). The thermal conductivity of UO$_{2+x}$ is significantly suppressed compared to UO$_2$ except near the Néel temperature TN= 30.8 K, where it is independent of x. Phonon measurements demonstrate that the heat capacities and phonon group velocities of UO$_2$ and UO$_{2+x}$ are similar and that the suppressed thermal conductivity in UO$_{2+x}$ results from high phonon scattering rates. These new insights advance our fundamental understanding of thermal transport properties in advanced nuclear fuels.

cond-mat.mtrl-sci↗

arXiv2022

Validating First-Principles Phonon Lifetimes via Inelastic Neutron Scattering

Phonon lifetimes are a key component of quasiparticle theories of transport, yet first-principles lifetimes are rarely directly compared to inelastic neutron scattering (INS) results. Existing comparisons show discrepancies even at temperatures where perturbation theory is expected to be reliable. In this work, we demonstrate that the reciprocal space voxel ($q$-voxel), which is the finite region in reciprocal space required in INS data analysis, must be explicitly accounted for within theory in order to draw a meaningful comparison. We demonstrate accurate predictions of peak widths of the scattering function when accounting for the $q$-voxel in CaF$_2$ and ThO$_2$. Passing this test implies high fidelity of the phonon interactions and the approximations used to compute the Green's function, serving as critical benchmark of theory, and indicating that other material properties should be accurately predicted; which we demonstrate for thermal conductivity.

cond-mat.mtrl-sci↗

arXiv2022

Heat Conduction Plate Layout Optimization using Physics-driven Convolutional Neural Networks

The layout optimization of the heat conduction is essential during design in engineering, especially for thermal sensible products. When the optimization algorithm iteratively evaluates different loading cases, the traditional numerical simulation methods used usually lead to a substantial computational cost. To effectively reduce the computational effort, data-driven approaches are used to train a surrogate model as a mapping between the prescribed external loads and various geometry. However, the existing model are trained by data-driven methods which requires intensive training samples that from numerical simulations and not really effectively solve the problem. Choosing the steady heat conduction problems as examples, this paper proposes a Physics-driven Convolutional Neural Networks (PD-CNN) method to infer the physical field solutions for random varied loading cases. After that, the Particle Swarm Optimization (PSO) algorithm is used to optimize the sizes and the positions of the hole masks in the prescribed design domain, and the average temperature value of the entire heat conduction field is minimized, and the goal of minimizing heat transfer is achieved. Compared with the existing data-driven approaches, the proposed PD-CNN optimization framework not only predict field solutions that are highly consistent with conventional simulation results, but also generate the solution space with without any pre-obtained training data.

cs.LG↗

arXiv2021

Optimal Design of Waveform Digitisers for Both Energy Resolution and Pulse Shape Discrimination

Fast digitisers and digital pulse processing have been widely used for spectral application and pulse shape discrimination (PSD) owing to their advantages in terms of compactness, higher trigger rates, offline analysis, etc. Meanwhile, the noise of readout electronics is usually trivial for organic, plastic, or liquid scintillator with PSD ability because of their poor intrinsic energy resolution. However, LaBr3(Ce) has been widely used for its excellent energy resolution and has been proven to have PSD ability for alpha/gamma particles. Therefore, designing a digital acquisition system for such scintillators as LaBr3(Ce) with both optimal energy resolution and promising PSD ability is worthwhile. Several experimental research studies about the choice of digitiser properties for liquid scintillators have already been conducted in terms of the sampling rate and vertical resolution. Quantitative analysis on the influence of waveform digitisers, that is, fast amplifier (optional), sampling rates, and vertical resolution, on both applications is still lacking. The present paper provides quantitative analysis of these factors and, hence, general rules about the optimal design of digitisers for both energy resolution and PSD application according to the noise analysis of time-variant gated charge integration.

physics.ins-det↗

arXiv2021

Reducing Target Group Bias in Hate Speech Detectors

The ubiquity of offensive and hateful content on online fora necessitates the need for automatic solutions that detect such content competently across target groups. In this paper we show that text classification models trained on large publicly available datasets despite having a high overall performance, may significantly under-perform on several protected groups. On the \citet{vidgen2020learning} dataset, we find the accuracy to be 37\% lower on an under annotated Black Women target group and 12\% lower on Immigrants, where hate speech involves a distinct style. To address this, we propose to perform token-level hate sense disambiguation, and utilize tokens' hate sense representations for detection, modeling more general signals. On two publicly available datasets, we observe that the variance in model accuracy across target groups drops by at least 30\%, improving the average target group performance by 4\% and worst case performance by 13\%.

cs.CL↗

arXiv2021

RID-Noise: Towards Robust Inverse Design under Noisy Environments

From an engineering perspective, a design should not only perform well in an ideal condition, but should also resist noises. Such a design methodology, namely robust design, has been widely implemented in the industry for product quality control. However, classic robust design requires a lot of evaluations for a single design target, while the results of these evaluations could not be reused for a new target. To achieve a data-efficient robust design, we propose Robust Inverse Design under Noise (RID-Noise), which can utilize existing noisy data to train a conditional invertible neural network (cINN). Specifically, we estimate the robustness of a design parameter by its predictability, measured by the prediction error of a forward neural network. We also define a sample-wise weight, which can be used in the maximum weighted likelihood estimation of an inverse model based on a cINN. With the visual results from experiments, we clearly justify how RID-Noise works by learning the distribution and robustness from data. Further experiments on several real-world benchmark tasks with noises confirm that our method is more effective than other state-of-the-art inverse design methods. Code and supplementary is publicly available at https://github.com/ThyrixYang/rid-noise-aaai22

cs.LG↗