arXiv subjects

Bo Gao

Publications and source records attributed to Bo Gao.

At least 73 records · Page 4Linked to original sources

arXiv2024

Building confidence in state-of-the-art ab initio calculations of the density virial coefficients of B and C of helium-4: Part 1. Indirect evaluation methods using the results of SPRIGT

In this work, we propose indirect evaluation methods to check the accuracy and the uncertainty of ab initio calculated virial coefficients. To this end, we have used single-pressure refractive-index gas thermometry (SPRIGT) to estimate the impact of the second density virial coefficient B of helium-4 on temperature measurements between 5 K to 25 K. Our results, in good agreement with values of B obtained from recent ab initio calculations by Czachorowski et al. [Phys. Rev. A 102, 042810 (2020)], suggest uncertainties u(B) estimated previously by other authors were too conservative. Concerning the value of the third density virial coefficient C of helium-4, calculated ab initio by Garberoglio et al. [J. Chem. Phys. 134, 134106 (2011)], our results suggest their uncertainty u(C) is between 1.5 and 10.2 times too high.

physics.ins-det↗

arXiv2024

Building confidence in state-of-the-art ab initio calculations of the density virial coefficients B and C of helium-4: Part 2. Direct evaluation by high accuracy experimental data using RIGT

In our previous work [1], using indirect evaluation methods we concluded that the uncertainties of the second and the third density virial coefficient, B and C, of helium-4 at 5 K calculated by various authors had been overestimated. To check the reliability of these values and appraisal of uncertainties from ab initio calculations still further, a refractive-index gas thermometry method was developed to determine simultaneously thermodynamic temperatures and density virial coefficients. Using this technique, high accuracy experimental values of B and C of helium-4 and new values of T-T90 were obtained for the range 5 K to 25 K. A direct comparison with the ab initio calculation density virial coefficients was made. Results support the conclusion of our previous work, i.e., the ab initio calculation uncertainties u(B) [J. Chem. Phys. 136, 224303 (2012)] and u(C) [J. Chem. Phys. 134, 134106 (2011)] of helium-4 were overestimated by a factor of severalfold.

physics.ins-det↗

arXiv2023

Measuring Mass and Radius of the Maximum-mass Nonrotating Neutron Star

The mass ($M_{\rm TOV}$) and radius ($R_{\rm TOV}$) of the maximum-mass nonrotating neutron star (NS) play a crucial role in constraining the elusive equation of state of cold dense matter and in predicting the fate of remnants from binary neutron star (BNS) mergers. In this study, we introduce a novel method to deduce these parameters by examining the mergers of second-generation (2G) black holes (BHs) with NSs. These 2G BHs are assumed to originate from supramassive neutron stars (SMNSs) formed in BNS mergers. Since the properties of the remnant BHs arising from the collapse of SMNSs follow a universal relation governed by $M_{\rm TOV}$ and $R_{\rm TOV}$, we anticipate that by analyzing a series ($\sim 100$ detections) of mass and spin measurements of the 2G BHs using the third-generation ground-based gravitational-wave detectors, $M_{\rm TOV}$ and $R_{\rm TOV}$ can be determined with a precision of $\sim 0.01M_\odot$ and $\sim 0.6$ km, respectively.

astro-ph.HE↗

arXiv2023

Multiwavelength observations of KS 1947+300

KS 1947+300 is a Be/X-ray binary. Despite its nearly circular orbit, it displays both giant and regular less intense X-ray outbursts. According to the viscous decretion disk model, such low eccentric binaries should not show periodic outbursts. We have studied the long-term optical variability of KS 1947+300 and its relationship with X-ray activity. Our objective is to investigate the origin of this variability. KS 1947+300 exhibits changes in brightness and H$α$ emission on time scales from months to years. The optical and IR variability shows small amplitude changes during the active X-ray state, and a long, smooth decrease during the quiescent state. The fact that the amplitude of variability increased with wavelength suggests that the long-term decrease of the optical emission is due to the weakening of the circumstellar disk. Structural changes in the disk may also be the origin of the periodic signals with periods $\sim200$ days detected in the ZTF-{\it g} and {\it r} band light curves. We speculate that these changes are related to the mechanism that ejects matter from the photosphere of the Be star into the disk. We have also studied the X-ray variability that manifested as a series of type I outbursts after the two giant outburst in 2000 and 2013 and found that the intensity and peak orbital phase differ from outburst to outburst. The type I outbursts in KS 1947+300 are not strictly periodic. This irregularity could result from precession of the interacting points between the neutron star and the disk, namely the disk apastron and the two nodes of the disk. The long-term changes in optical continuum and line emission and the X-ray variability patterns are attributed to an evolving and distorted decretion disk.

astro-ph.HE↗

arXiv2023

Federated Skewed Label Learning with Logits Fusion

Federated learning (FL) aims to collaboratively train a shared model across multiple clients without transmitting their local data. Data heterogeneity is a critical challenge in realistic FL settings, as it causes significant performance deterioration due to discrepancies in optimization among local models. In this work, we focus on label distribution skew, a common scenario in data heterogeneity, where the data label categories are imbalanced on each client. To address this issue, we propose FedBalance, which corrects the optimization bias among local models by calibrating their logits. Specifically, we introduce an extra private weak learner on the client side, which forms an ensemble model with the local model. By fusing the logits of the two models, the private weak learner can capture the variance of different data, regardless of their category. Therefore, the optimization direction of local models can be improved by increasing the penalty for misclassifying minority classes and reducing the attention to majority classes, resulting in a better global model. Extensive experiments show that our method can gain 13\% higher average accuracy compared with state-of-the-art methods.

cs.LG↗

arXiv2023

Multiwavelength observation of 1A 0535+262=HD 245770 from 2010 to 2021

Context. 1A 0535+262 is a high-mass X-ray binary that went into a giant X-ray outburst in 2020. During this event, the X-ray luminosity reached the highest value measured over the last 30 years. Aims. Our aim is to study the long-term variability of 1A 0535+262 before and after the 2020 major X-ray outburst and to uncover the mechanism that led to the X-ray outburst. Methods. We used the long-term photometric light curve and the equivalent widths of the H$α$ and He I $λ$6678 lines to monitor the state of the Be star's circumstellar disk. The H$α$ line profiles show evidence for V/R variability, which we revealed by fitting the H$α$ spectral line profiles with two Gaussian functions. In addition, we divided our data into four periods according to the intensity of the X-ray, optical, and infrared emission. Results. The H$α$ line profiles show single-peaked profiles in most cases. This is consistent with the previously reported orbital inclination of ${i}$ = $37^{\circ} \pm 2^{\circ}$. Unlike the H$α$ lines, the He I $\lambda6678$ lines show a maximal intensity in October 2020, which is one month before the giant X-ray outburst in 2020. Based on the behavior of the equivalent widths of the H$α$ and He I $\lambda6678$ lines, and the ${V}$-band magnitude, we find two mass ejection processes from the Be star to the Be disk on MJD 55820 and MJD 56600. The V/R quasi-period is about two\, years during 2011--2015, which is different from 1994 to 1995. Furthermore, the periods I$\to$II$\to$III$\to$IV in the $(B-V)$ color index versus $V$-band magnitude diagram constitute a cycle. From the behavior of the V/R ratio of H$α$ lines, and the variability of the $V$ band, we believe that the precession of the density perturbation inside the disk is retrograde.

astro-ph.HE↗

arXiv2023

Skeleton Ground Truth Extraction: Methodology, Annotation Tool and Benchmarks

Skeleton Ground Truth (GT) is critical to the success of supervised skeleton extraction methods, especially with the popularity of deep learning techniques. Furthermore, we see skeleton GTs used not only for training skeleton detectors with Convolutional Neural Networks (CNN) but also for evaluating skeleton-related pruning and matching algorithms. However, most existing shape and image datasets suffer from the lack of skeleton GT and inconsistency of GT standards. As a result, it is difficult to evaluate and reproduce CNN-based skeleton detectors and algorithms on a fair basis. In this paper, we present a heuristic strategy for object skeleton GT extraction in binary shapes and natural images. Our strategy is built on an extended theory of diagnosticity hypothesis, which enables encoding human-in-the-loop GT extraction based on clues from the target's context, simplicity, and completeness. Using this strategy, we developed a tool, SkeView, to generate skeleton GT of 17 existing shape and image datasets. The GTs are then structurally evaluated with representative methods to build viable baselines for fair comparisons. Experiments demonstrate that GTs generated by our strategy yield promising quality with respect to standard consistency, and also provide a balance between simplicity and completeness.

cs.CV↗

arXiv2023

On the suitability of rigorous coupled-wave analysis for fast optical force simulations

Optical force responses underpin nanophotonic actuator design, which requires a large number of force simulations to optimize structures. Commonly used computation methods, such as the finite-difference time-domain (FDTD) method, are resource intensive and require large amounts of calculation time when multiple structures need to be compared during optimization. This research demonstrates that performing optical force calculations on periodic structures using the rigorous coupled-wave analysis method is typically on the order of 10 times faster than FDTD with sufficient accuracy to suit optical design purposes. Moreover, this speed increase is available on consumer grade laptops with a CUDA-compatible GPU avoiding the need for a high performance computing resource.

physics.optics↗

arXiv2023

Resource-aware Probability-based Collaborative Odor Source Localization Using Multiple UAVs

Benefitting from UAVs' characteristics of flexible deployment and controllable movement in 3D space, odor source localization with multiple UAVs has been a hot research area in recent years. Considering the limited resources and insufficient battery capacities of UAVs, it is necessary to fast locate the odor source with low-complexity computation and minimal interaction under complicated environmental states. To this end, we propose a multi-UAV collaboration based odor source localization (\textit{MUC-OSL}) method, where source estimation and UAV navigation are iteratively performed, aiming to accelerate the searching process and reduce the resource consumption of UAVs. Specifically, in the source estimation phase, we present a collaborative particle filter algorithm on the basis of UAVs' cognitive difference and Gaussian fitting to improve source estimation accuracy. In the following navigation phase, an adaptive path planning algorithm is designed based on Partially Observable Markov Decision Process (POMDP) to distributedly determine the subsequent flying direction and moving steps of each UAV. The results of experiments conducted on two simulation platforms demonstrate that \textit{MUC-OSL} outperforms existing efforts in terms of mean search time and success rate, and effectively reduces the resource consumption of UAVs.

cs.MA↗

arXiv2023

Online Spatio-Temporal Correlation-Based Federated Learning for Traffic Flow Forecasting

Traffic flow forecasting (TFF) is of great importance to the construction of Intelligent Transportation Systems (ITS). To mitigate communication burden and tackle with the problem of privacy leakage aroused by centralized forecasting methods, Federated Learning (FL) has been applied to TFF. However, existing FL-based approaches employ batch learning manner, which makes the pre-trained models inapplicable to subsequent traffic data, thus exhibiting subpar prediction performance. In this paper, we perform the first study of forecasting traffic flow adopting Online Learning (OL) manner in FL framework and then propose a novel prediction method named Online Spatio-Temporal Correlation-based Federated Learning (FedOSTC), aiming to guarantee performance gains regardless of traffic fluctuation. Specifically, clients employ Gated Recurrent Unit (GRU)-based encoders to obtain the internal temporal patterns inside traffic data sequences. Then, the central server evaluates spatial correlation among clients via Graph Attention Network (GAT), catering to the dynamic changes of spatial closeness caused by traffic fluctuation. Furthermore, to improve the generalization of the global model for upcoming traffic data, a period-aware aggregation mechanism is proposed to aggregate the local models which are optimized using Online Gradient Descent (OGD) algorithm at clients. We perform comprehensive experiments on two real-world datasets to validate the efficiency and effectiveness of our proposed method and the numerical results demonstrate the superiority of FedOSTC.

cs.LG↗

arXiv2023

Pressure-Induced Superconductivity in Topological Heterostructure (PbSe)5(Bi2Se3)6

Recently, the natural heterostructure of (PbSe)5(Bi2Se3)6 has been theoretically predicted and experimentally confirmed as a topological insulator. In this work, we induce superconductivity in (PbSe)5(Bi2Se3)6 by implementing high pressure. As increasing pressure up to 10 GPa, superconductivity with Tc ~ 4.6 K suddenly appears, followed by an abrupt decrease. Remarkably, upon further compression above 30 GPa, a new superconducting state arises, where pressure raises the Tc to an unsaturated 6.0 K within the limit of our research. Combining XRD and Raman spectroscopies, we suggest that the emergence of two distinct superconducting states occurs concurrently with the pressure-induced structural transition in this topological heterostructure (PbSe)5(Bi2Se3)6.

cond-mat.supr-con↗

arXiv2022

Arrangement and chemical complexity in an $N$-body quantum system

We introduce and discuss the concept of \textit{arrangement}, traditionally found in the context of chemical reactions and few-body rearrangement collisions, in the general context of an $N$-body quantum system. We show that the ability for particles to attract and bind is a key source of complexity of an $N$-body quantum system, and \textit{arrangement} is an emergent concept necessitated by the description of this complexity. For an $N$-body system made of particles of which some or all can attract and bind, the concept of arrangement is not only necessary for a full characterization of its quantum states and processes, but it also provides a basis for the understanding and the description of the structure of its energy spectrum. We also discuss and formulate multiparticle separability in an $N$-body system, the mathematical foundation that underlies the arrangement concept in an $N$-body theory.

quant-ph↗

arXiv2022

Elliptic flow in heavy-ion collisions at intermediate energy: the role of impact parameter, mean field potential, and collision term

Within the ultrarelativistic quantum molecular dynamics (UrQMD) model, by reverse tracing nucleons that are finally emitted at mid-rapidity (|$y_0$| < 0.1) in the entire reaction process, the time evolution of elliptic flow ($v_2$) of these traced nucleons produced in Au+Au collisions at beam energy of 0.4 GeV$/$nucleon with different impact parameters ($b$) is studied. The initial value of $v_2$ is positive and increases with $b$, then it decreases as time passes and tends to saturate at a negative value. It is found that nucleon-nucleon collisions always depress the value of $v_2$ (enhance the out-of-plane emission), while the nuclear mean field potential may slightly raise the value of $v_2$ during the expansion stage in peripheral reactions. The related density mostly probed by $v_2$ of nucleons at mid-rapidity is found to be $\sim$ 60% of the maximum density reached during the collisions.

nucl-th↗

arXiv2022

Rigidity, separability, and cusp conditions of a wave function

We introduce in quantum mechanics a concept of \textit{rigidity} and a concept of a \textit{pinned point} of a wave function. The concept of a pinned point is a generalization of a familiar concept in the description of a vibrating string, while the concept of rigidity is introduced to describe the sensitivity of a wave function to changes in energy, potential, and/or external perturbation. Through these concepts and their mathematical implications, we introduce and formulate cusp conditions and cusp functions as fundamental properties of an arbitrary $N$-body quantum system with $N\ge 2$, greatly expanding their relevance beyond the Coulombic systems. The theory provides rigorous constraints on an arbitrary $N$-body quantum system, specifically on its short-range pair correlation that is essential to a better understanding of strongly correlated systems. More broadly, the theory and the derivations presented here are part of a reconstruction of the mathematical and conceptual foundation of an $N$-body quantum theory, incorporating previously hidden properties and insights revealed through the concepts of rigidity and pinned points. It includes general analytic properties of a 2-body wave function versus energy and their relations to cusp conditions and cusp functions. It includes a rigorous derivation and an understanding, in terms of an emergent length scale, of the 2-particle separability in an $(N>2)$-body quantum system and its relations to cusp conditions. It also includes a classification of quantum systems, both 2-body and $N$-body, based on the universal behaviors in their short-range correlation.

quant-ph↗

arXiv2022

Super-resolution multicolor fluorescence microscopy enabled by an apochromatic super-oscillatory lens with extended depth-of-focus

Multicolor super-resolution imaging remains an intractable challenge for both far-field and near-field based super-resolution techniques. Planar super-oscillatory lens (SOL), a far-field subwavelength-focusing diffractive lens device, holds great potential for achieving sub-diffraction-limit imaging at multiple wavelengths. However, conventional SOL devices suffer from a numerical aperture (NA) related intrinsic tradeoff among the depth of focus (DoF), chromatic dispersion and focus spot size, being an essential characteristics of common diffractive optical elements. Typically, the limited DoF and significant chromatism associated with high NA can lead to unfavorable degradation of image quality although increasing NA imporves the resolution. Here, we apply a multi-objective genetic algorithm (GA) optimization approach to design an apochromatic binary-phase SOL that generates axially jointed multifoci concurrently having prolonged DoF, customized working distance (WD) and suppressed side-lobes yet minimized main-lobe size, optimizing the aforementioned NA-dependent tradeoff. Experimental implementation of this GA-optimized SOL demonstrates simultaneous focusing of blue, green and red light beams into an optical needle half of the incident wavelength in diameter at 428 um WD, resulting in an ultimate resolution better than one third of the incident wavelength in the lateral dimension. By integrating this apochromatic SOL device with a commercial fluorescence microscope, we employ the optical needle to perform, for the first time, three-dimensional super-resolution multicolor fluorescence imaging of the unseen fine structure of neurons at one go. The present study provides not only a practical route to far-field multicolor super-resolution imaging but also a viable approach for constructing imaging systems avoiding complex sample positioning and unfavorable photobleaching.

physics.optics↗

arXiv2021

Mixed-Salt Enhanced Chemical Vapor Deposition of Two-Dimensional Transition Metal Dichalcogenides

The usage of molten salts, e.g., Na2MoO4 and Na2WO4, has shown great success in the growth of two-dimensional (2D) transition metal dichalcogenides (TMDCs) by chemical vapor deposition (CVD). In comparison with the halide salt (i.e., NaCl, NaBr, KI)-assisted growth (Salt 1.0), the molten salt-assisted vapor-liquid-solid (VLS) growth technique (Salt 2.0) has improved the reproducibility, efficiency and scalability of synthesizing 2D TMDCs. However, the growth of large-area MoSe2 and WTe2 is still quite challenging with the use Salt 2.0 technique. In this study, a renewed Salt 2.0 technique using mixed salts (e.g., Na2MoO4-Na2SeO3 and Na2WO4-Na2TeO3) is developed for the enhanced CVD growth of 2D MoSe2 and WTe2 crystals with large grain size and yield. Continuous monolayer MoSe2 film with grain size of 100-250 μm or isolated flakes up to ~ 450 μm is grown on a halved 2-inch SiO2/Si wafer. Our study further confirms the synergistic effect of Na+ and SeO32- in the enhanced CVD growth of wafer-scale monolayer MoSe2 film. And thus, the addition of Na2SeO3 and Na2TeO3 into the transition metal salts could be a general strategy for the enhanced CVD growth of many other 2D selenides and tellurides.

cond-mat.mtrl-sci↗

arXiv2021

Multi-Granularity Network with Modal Attention for Dense Affective Understanding

Video affective understanding, which aims to predict the evoked expressions by the video content, is desired for video creation and recommendation. In the recent EEV challenge, a dense affective understanding task is proposed and requires frame-level affective prediction. In this paper, we propose a multi-granularity network with modal attention (MGN-MA), which employs multi-granularity features for better description of the target frame. Specifically, the multi-granularity features could be divided into frame-level, clips-level and video-level features, which corresponds to visual-salient content, semantic-context and video theme information. Then the modal attention fusion module is designed to fuse the multi-granularity features and emphasize more affection-relevant modals. Finally, the fused feature is fed into a Mixtures Of Experts (MOE) classifier to predict the expressions. Further employing model-ensemble post-processing, the proposed method achieves the correlation score of 0.02292 in the EEV challenge.

cs.CV↗

arXiv2021

Robust Template Matching via Hierarchical Convolutional Features from a Shape Biased CNN

Finding a template in a search image is an important task underlying many computer vision applications. Recent approaches perform template matching in a deep feature-space, produced by a convolutional neural network (CNN), which is found to provide more tolerance to changes in appearance. In this article we investigate if enhancing the CNN's encoding of shape information can produce more distinguishable features that improve the performance of template matching. This investigation results in a new template matching method that produces state-of-the-art results on a standard benchmark. To confirm these results we also create a new benchmark and show that the proposed method also outperforms existing techniques on this new dataset. Our code and dataset is available at: https://github.com/iminfine/Deep-DIM.

cs.CV↗