arXiv subjects

Zhao Wang

Publications and source records attributed to Zhao Wang.

At least 145 records · Page 8Linked to original sources

arXiv2023

Proton-Boron Fusion Yield Increased by Orders of Magnitude with Foam Targets

A novel intense beam-driven scheme for high yield of the tri-alpha reaction 11B(p,α)2α was investigated. We used a foam target made of cellulose triacetate (TAC, C_9H_{16}O_8) doped with boron. It was then heated volumetrically by soft X-ray radiation from a laser heated hohlraum and turned into a homogenous, and long living plasma. We employed a picosecond laser pulse to generate a high-intensity energetic proton beam via the well-known Target Normal Sheath Acceleration (TNSA) mechanism. We observed up to 10^{10}/sr α particles per laser shot. This constitutes presently the highest yield value normalized to the laser energy on target. The measured fusion yield per proton exceeds the classical expectation of beam-target reactions by up to four orders of magnitude under high proton intensities. This enhancement is attributed to the strong electric fields and nonequilibrium thermonuclear fusion reactions as a result of the new method. Our approach shows opportunities to pursue ignition of aneutronic fusion.

physics.plasm-ph↗

arXiv2023

Trapping molecules in a covalent graphene-nanotube hybrid

This study employs molecular dynamics simulations to examine the physisorption behavior of hydrocarbon molecules on a covalent graphene-nanotube hybrid nanostructure. The results indicate that the adsorbed molecules undergo self-diffusion into the nanotubes without the need for external driving forces, primarily driven by significant variations in binding energy throughout different regions. Notably, these molecules remain securely trapped within the tubes even at room temperature, thanks to a ``gate'' effect observed at the neck region, despite the presence of a concentration gradient that would typically hinder such trapping. This mechanism of passive mass transport and retention holds implications for the storage and separation of gas molecules.

cond-mat.mes-hall↗

arXiv2023

Machine-learning identified molecular fragments responsible for infrared emission features of polycyclic aromatic hydrocarbons

Machine learning feature importance calculations are used to determine the molecular substructures that are responsible for mid and far-infrared (IR) emission features of neutral polycyclic aromatic hydrocarbons (PAHs). Using the extended-connectivity fingerprint as a descriptor of chemical structure, a random forest model is trained on the spectra of 14,124 PAHs to evaluate the importance of 10,632 molecular fragments for each band within the range of 2.761 to 1172.745 microns. The accuracy of the results is confirmed by comparing them with previously studied unidentified infrared emission (UIE) bands. The results are summarized in two tables available as Supplementary Data, which can be used as a reference for assessing possible UIE carriers. We demonstrate that the tables can be used to explore the relation between the PAH structure and the spectra by discussing about the IR features of nitrogen-containing PAHs and super-hydrogenated PAHs.

astro-ph.GA↗

arXiv2023

Catalytic role of HI in the interstellar synthesis of complex organic molecule

Using quantum chemical calculations, we model the pathways for synthesizing two purine nucleobases, adenine and guanine, in the gas-phase interstellar environment, surrounded by neutral atomic hydrogen (HI). HI is found active in facilitating a series of fundamental proton transfer processes of organic synthesis, including bond formation, cyclization, dehydrogenation, and H migration. The reactive potential barriers were significantly reduced in the alternative pathways created by HI, leading to a remarkable increase in the reaction rate. The presence of HI also lowered the reactive activation temperature from 757.8 K to 131.5-147.0 K, indicating the thermodynamic feasibility of these pathways in star-forming regions where some of the reactants have been astronomically detected. Our findings suggest that HI may serve as an effective catalyst for interstellar organic synthesis.

astro-ph.GA↗

arXiv2023

Observing Quantum Synchronization of a Single Trapped-Ion Qubit

Synchronizing a few-level quantum system is of fundamental importance to understanding synchronization in deep quantum regime. Whether a two-level system, the smallest quantum system, can be synchronized has been theoretically debated for the past several years. Here, for the first time, we demonstrate that a qubit can indeed be synchronized to an external driving signal by using a trapped-ion system. By engineering fully controllable gain and damping processes, an ion qubit is synchronized to oscillate at the same frequency as the driving signal and lock in phase. We systematically investigate the parameter regions of synchronization and observe characteristic features of the Arnold tongue. Our measurements agree remarkably well with numerical simulations based on recent theory on qubit synchronization. By synchronizing the basic unit of quantum information, our research opens up the possibility of applying quantum synchronization to large-scale quantum networks.

quant-ph↗

arXiv2023

Bounds for Gallai-Ramsey functions and numbers

For two graphs $G,H$ and a positive integer $k$, the \emph{Gallai-Ramsey number} $\operatorname{gr}_k(G,H)$ is defined as the minimum number of vertices $n$ such that any $k$-edge-coloring of $K_n$ contains either a rainbow (all different colored) copy of $G$ or a monochromatic copy of $H$. If $G$ and $H$ are both complete graphs, then we call it Gallai-Ramsey function. Fox and Sudakov proved $\operatorname{gr}_k(K_s,K_t)\leq s^{4kt}$. Alon et al. showed that $\operatorname{gr}_k(K_s,K_t)\leq (2s^3+4s^2)^{kt}$. In this paper, we prove that $\operatorname{gr}_k(K_s,K_t)\leq 2^{kt}s^{3kt}$ for $t\geq 47$. We also give better upper bounds for $\operatorname{gr}_k(G,H)$ when $G,H$ are some special graphs. In this paper, we derive some lower bounds for Gallai-Ramsey functions and numbers by Lovász Local Lemma.

math.CO↗

arXiv2022

Rethinking Multi-Modal Alignment in Video Question Answering from Feature and Sample Perspectives

Reasoning about causal and temporal event relations in videos is a new destination of Video Question Answering (VideoQA).The major stumbling block to achieve this purpose is the semantic gap between language and video since they are at different levels of abstraction. Existing efforts mainly focus on designing sophisticated architectures while utilizing frame- or object-level visual representations. In this paper, we reconsider the multi-modal alignment problem in VideoQA from feature and sample perspectives to achieve better performance. From the view of feature,we break down the video into trajectories and first leverage trajectory feature in VideoQA to enhance the alignment between two modalities. Moreover, we adopt a heterogeneous graph architecture and design a hierarchical framework to align both trajectory-level and frame-level visual feature with language feature. In addition, we found that VideoQA models are largely dependent on language priors and always neglect visual-language interactions. Thus, two effective yet portable training augmentation strategies are designed to strengthen the cross-modal correspondence ability of our model from the view of sample. Extensive results show that our method outperforms all the state-of-the-art models on the challenging NExT-QA benchmark, which demonstrates the effectiveness of the proposed method.

cs.CV↗

arXiv2022

Euclidean Gallai-Ramsey Theory

In this paper, we introduce Euclidean Gallai-Ramsey theory, by combining Euclidean Ramsey theory and Gallai-Ramsey theory on graphs. More precisely, we consider the following problem: For an integer $r$ and configurations $K$ and $K'$, does there exist an integer $n_0$ such that for any $r$-coloring of the points of $n$-dimensional Euclidean space with $n \geq n_0$, there is a monochromatic configuration congruent to $K$ or a rainbow configuration congruent to $K'$? In particular, we give a bound on $n_0$ for some configurations $K$ and $K'$, such as triangles and rectangles. Those are extensions of ordinary Euclidean Ramsey theory where the purpose is to find a monochromatic configuration.

math.CO↗

arXiv2022

Shifting Perspective to See Difference: A Novel Multi-View Method for Skeleton based Action Recognition

Skeleton-based human action recognition is a longstanding challenge due to its complex dynamics. Some fine-grain details of the dynamics play a vital role in classification. The existing work largely focuses on designing incremental neural networks with more complicated adjacent matrices to capture the details of joints relationships. However, they still have difficulties distinguishing actions that have broadly similar motion patterns but belong to different categories. Interestingly, we found that the subtle differences in motion patterns can be significantly amplified and become easy for audience to distinct through specified view directions, where this property haven't been fully explored before. Drastically different from previous work, we boost the performance by proposing a conceptually simple yet effective Multi-view strategy that recognizes actions from a collection of dynamic view features. Specifically, we design a novel Skeleton-Anchor Proposal (SAP) module which contains a Multi-head structure to learn a set of views. For feature learning of different views, we introduce a novel Angle Representation to transform the actions under different views and feed the transformations into the baseline model. Our module can work seamlessly with the existing action classification model. Incorporated with baseline models, our SAP module exhibits clear performance gains on many challenging benchmarks. Moreover, comprehensive experiments show that our model consistently beats down the state-of-the-art and remains effective and robust especially when dealing with corrupted data. Related code will be available on https://github.com/ideal-idea/SAP .

cs.CV↗

arXiv2022

Target density effects on charge tansfer of laser-accelerated carbon ions in dense plasma

We report on charge state measurements of laser-accelerated carbon ions in the energy range of several MeV penetrating a dense partially ionized plasma. The plasma was generated by irradiation of a foam target with laser-induced hohlraum radiation in the soft X-ray regime. We used the tri-cellulose acetate (C$_{9}$H$_{16}$O$_{8}$) foam of 2 mg/cm$^{-3}$ density, and $1$-mm interaction length as target material. This kind of plasma is advantageous for high-precision measurements, due to good uniformity and long lifetime compared to the ion pulse length and the interaction duration. The plasma parameters were diagnosed to be T$_{e}$=17 eV and n$_{e}$=4 $\times$ 10$^{20}$ cm$^{-3}$. The average charge states passing through the plasma were observed to be higher than those predicted by the commonly-used semiempirical formula. Through solving the rate equations, we attribute the enhancement to the target density effects which will increase the ionization rates on one hand and reduce the electron capture rates on the other hand. In previsous measurement with partially ionized plasma from gas discharge and z-pinch to laser direct irradiation, no target density effects were ever demonstrated. For the first time, we were able to experimentally prove that target density effects start to play a significant role in plasma near the critical density of Nd-Glass laser radiation. The finding is important for heavy ion beam driven high energy density physics and fast ignitions.

physics.plasm-ph↗

arXiv2022

$C^$-algebra Net: A New Approach Generalizing Neural Network Parameters to $C^$-algebra

We propose a new framework that generalizes the parameters of neural network models to $C^*$-algebra-valued ones. $C^*$-algebra is a generalization of the space of complex numbers. A typical example is the space of continuous functions on a compact space. This generalization enables us to combine multiple models continuously and use tools for functions such as regression and integration. Consequently, we can learn features of data efficiently and adapt the models to problems continuously. We apply our framework to practical problems such as density estimation and few-shot learning and show that our framework enables us to learn features of data even with a limited number of samples. Our new framework highlights the potential possibility of applying the theory of $C^*$-algebra to general neural network models.

stat.ML↗

arXiv2022

Federated Learning from Only Unlabeled Data with Class-Conditional-Sharing Clients

Supervised federated learning (FL) enables multiple clients to share the trained model without sharing their labeled data. However, potential clients might even be reluctant to label their own data, which could limit the applicability of FL in practice. In this paper, we show the possibility of unsupervised FL whose model is still a classifier for predicting class labels, if the class-prior probabilities are shifted while the class-conditional distributions are shared among the unlabeled data owned by the clients. We propose federation of unsupervised learning (FedUL), where the unlabeled data are transformed into surrogate labeled data for each of the clients, a modified model is trained by supervised FL, and the wanted model is recovered from the modified model. FedUL is a very general solution to unsupervised FL: it is compatible with many supervised FL methods, and the recovery of the wanted model can be theoretically guaranteed as if the data have been labeled. Experiments on benchmark and real-world datasets demonstrate the effectiveness of FedUL. Code is available at https://github.com/lunanbit/FedUL.

cs.LG↗

arXiv2022

Scattering-assisted and logic-controllable WGM laser in liquid crystal micropillar

Whispering gallery mode (WGM) microcavities can efficiently store and manipulate light with strong light confinement and long photon lifetime, while coupling light into and from WGMs is intrinsically hindered by their unique feature of rotational symmetry. Here, a scattering-assisted liquid crystal (LC) micropillar WGM laser is proposed. WGM lasing at the surface of the micropillar is obviously enhanced by fluorescence scattering in the core of the micropillar. Besides, weak scattering of LC molecules also builds efficient coupling channels between the laser modes and the axial transmission modes of the micropillar-based waveguide, providing an all-in-one liquid WGM laser with functions of self-seeding and self-guiding. Furthermore, based on the hysteresis characteristics of the electrically anchored LC molecules under the interaction of thermal force, an erasable read-write liquid memory device is proposed, paving the way for the application of logic-controllable WGM lasers in optical storage and optical control.

physics.optics↗

arXiv2022

Long-Range Order and Quantum Criticality in Antiferromagnetic Chains with Long-Range Staggered Interactions

We study quantum phase transitions in Heisenberg antiferromagnetic chains with a staggered power-law decaying long-range interactions. Employing the density-matrix renormalization group (DMRG) algorithm and the fidelity susceptibility as the criticality measure, we establish more accurate values of quantum critical points than the results obtained from the spin-wave approximation, quantum Monte Carlo and DMRG in literatures. The deviation is especially evident for strong long-range interactions. We extend isotropic long-range interactions to the anisotropic cases and find that kaleidoscope of quantum phases emerge from the interplay of anisotropy of the long-range exchange interaction and symmetry breaking. We demonstrate nonfrustrating long-range interactions induce the true long-range order in Heisenberg antiferromagnetic chains with a continuous symmetry breaking, lifting the restrictions imposed by the Mermin-Wagner theorem.

cond-mat.str-el↗

arXiv2022

Distributed On-Sensor Compute System for AR/VR Devices: A Semi-Analytical Simulation Framework for Power Estimation

Augmented Reality/Virtual Reality (AR/VR) glasses are widely foreseen as the next generation computing platform. AR/VR glasses are a complex "system of systems" which must satisfy stringent form factor, computing-, power- and thermal- requirements. In this paper, we will show that a novel distributed on-sensor compute architecture, coupled with new semiconductor technologies (such as dense 3D-IC interconnects and Spin-Transfer Torque Magneto Random Access Memory, STT-MRAM) and, most importantly, a full hardware-software co-optimization are the solutions to achieve attractive and socially acceptable AR/VR glasses. To this end, we developed a semi-analytical simulation framework to estimate the power consumption of novel AR/VR distributed on-sensor computing architectures. The model allows the optimization of the main technological features of the system modules, as well as the computer-vision algorithm partition strategy across the distributed compute architecture. We show that, in the case of the compute-intensive machine learning based Hand Tracking algorithm, the distributed on-sensor compute architecture can reduce the system power consumption compared to a centralized system, with the additional benefits in terms of latency and privacy.

cs.AR↗

arXiv2022

Some multivariable Rado numbers

The Rado number of an equation is a Ramsey-theoretic quantity associated to the equation. Let $\mathcal{E}$ be a linear equation. Denote by $\operatorname{R}_r(\mathcal{E})$ the minimal integer, if it exists, such that any $r$-coloring of $[1,\operatorname{R}_r(\mathcal{E})]$ must admit a monochromatic solution to $\mathcal{E}$. In this paper, we give upper and lower bounds for the Rado number of $\sum_{i=1}^{m-2}x_i+kx_{m-1}=\ell x_{m}$, and some exact values are also given. Furthermore, we derive some results for the cases that $\ell=m=4$ and $m=5, \ell=k+i \ (1\leq i\leq 5)$. As a generalization, the \emph{$r$-color Rado numbers} for linear equations $\mathcal{E}_1,\mathcal{E}_2,...,\mathcal{E}_r$ is defined as the minimal integer, if it exists, such that any $r$-coloring of $[1,\operatorname{R}_r(\mathcal{E}_1,\mathcal{E}_2,...,\mathcal{E}_r)]$ must admit a monochromatic solution to some $\mathcal{E}_i$, where $1\leq i\leq r$. A lower bound for $\operatorname{R}_r(\mathcal{E}_1,\mathcal{E}_2,...,\mathcal{E}_r)$ and the exact values of $\operatorname{R}_2(x+y=z,\ell x=y)=5k$ and $\operatorname{R}_2(x+y=z, x+a=y)$ was given by Lovász Local Lemma.

math.CO↗

arXiv2022

Deep Learning for Wireless Dynamics

This paper aims to predict radio channel variations over time by deep learning from channel observations without knowledge of the underlying channel dynamics. In next-generation wideband cellular systems, multicarrier transmission for higher data rate leads to the high-resolution predicting problem. By leveraging recent advances of deep learning in high-resolution image processing, we propose a purely data-driven deep learning (DL) approach to predicting high-resolution temporal evolution of wideband radio channels. In order to investigate the effect of architectural design choices, we develop and study three deep learning prediction models, namely, baseline, image completion, and next-frame prediction models using UNet. Numerical results show that the proposed DL approach achieves a 52% lower prediction error than the traditional approach based on the Kalman filter (KF) in mean absolute errors. To quantify impact of channel aging and prediction on precoding performance, we also evaluate the performance degradation due to outdated and predicted channel state information (CSI) compared to perfect CSI. Our simulations show that the proposed DL approach can reduce the performance loss due to channel aging by 71% through adapting precoding vector to changes in radio channel while the traditional KF approach only shows a 27% reduction.

cs.IT↗

arXiv2022

Optical Properties of C$-$rich ($^{12}$C, SiC and FeC) Dust Layered Structure of Massive Stars

The composition and structure of interstellar dust are important and complex for the study of the evolution of stars and the \textbf{interstellar medium} (ISM). However, there is a lack of corresponding experimental data and model theories. By theoretical calculations based on ab-initio method, we have predicted and geometry optimized the structures of Carbon-rich (C-rich) dusts, carbon ($^{12}$C), iron carbide (FeC), silicon carbide (SiC), even silicon ($^{28}$Si), iron ($^{56}$Fe), and investigated the optical absorption coefficients and emission coefficients of these materials in 0D (zero$-$dimensional), 1D, and 2D nanostructures. Comparing the \textbf{nebular spectra} of the supernovae (SN) with the coefficient of dust, we find that the optical absorption coefficient of the 2D $^{12}$C, $^{28}$Si, $^{56}$Fe, SiC and FeC structure corresponds to the absorption peak displayed in the infrared band (5$-$8) $μ$$m$ of the spectrum at 7554 days after the SN1987A explosion. And it also corresponds to the spectrum of 535 days after the explosion of SN2018bsz, when the wavelength in the range of (0.2$-$0.8) and (3$-$10) $μ$$m$. Nevertheless, 2D SiC and FeC corresponds to the spectrum of 844 days after the explosion of SN2010jl, when the wavelength is within (0.08$-$10) $μ$$m$. Therefore, FeC and SiC may be the second type of dust in SN1987A corresponding to infrared band (5$-$8) $μ$$m$ of dust and may be in the ejecta of SN2010jl and SN2018bsz.

astro-ph.GA↗