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Bo Peng

Publications and source records attributed to Bo Peng.

At least 343 records · Page 19Linked to original sources
arXiv2020

Fast radio bursts: do repeaters and non-repeaters originate in statistically similar ensembles

Fast Radio Bursts (FRBs) are the short, strong radio pulses lasting several milliseconds. They are subsequently identified, for the most part, as emanating from unknown objects at cosmological distances. At present, over one hundred FRBs have been verified, classified into two groups: repeating bursts (20 samples) and apparently non-repeating bursts (91 samples). Their origins, however, are still hotly debated. Here,we investigate the statistical classifications for the two groups of samples to see if the non-repeating and repeating FRBs have different origins by employing Anderson-Darling (A-D) test and Mann-Whitney-Wilcoxon (M-W-W) test. Firstly, by taking the pulse width as a statistical variant, we found that the repeating samples do not follow the Gaussian statistics (may belong to a chi-square distribution), although the overall data and non-repeating group do follow the Gaussian. Meanwhile, to investigate the statistical differences between the two groups, we turn to M-W-W test and notice that the two distributions have different origins. Secondly, we consider the FRB radio luminosity as a statistical variant, and find that both groups of samples can be regarded as the Gaussian distributions under the A-D test, although they have different origins according to M-W-W tests. Therefore, statistically, we can conclude that our classifications of both repeaters and non-repeaters are plausible, that the two FRB classes have different origins, or each has experienced distinctive phases or been subject to its own physical processes.

astro-ph.HE↗
arXiv2020

Rank Position Forecasting in Car Racing

Forecasting is challenging since uncertainty resulted from exogenous factors exists. This work investigates the rank position forecasting problem in car racing, which predicts the rank positions at the future laps for cars. Among the many factors that bring changes to the rank positions, pit stops are critical but irregular and rare. We found existing methods, including statistical models, machine learning regression models, and state-of-the-art deep forecasting model based on encoder-decoder architecture, all have limitations in the forecasting. By elaborative analysis of pit stops events, we propose a deep model, RankNet, with the cause effects decomposition that modeling the rank position sequence and pit stop events separately. It also incorporates probabilistic forecasting to model the uncertainty inside each sub-model. Through extensive experiments, RankNet demonstrates a strong performance improvement over the baselines, e.g., MAE improves more than 10% consistently, and is also more stable when adapting to unseen new data. Details of model optimization, performance profiling are presented. It is promising to provide useful forecasting tools for the car racing analysis and shine a light on solutions to similar challenging issues in general forecasting problems.

cs.LG↗
arXiv2020

Observation of nonreciprocal magneto-optical scattering in nonencapsulated few-layered CrI3

Magneto-optical effect refers to a rotation of polarization plane, which has been widely studied in traditional ferromagnetic metal and insulator films and scarcely in two-dimensional layered materials. Here we uncover a new nonreciprocal magneto-inelastic light scattering effect in ferromagnetic few-layer CrI3. We observed a rotation of the polarization plane of inelastic light scattering between -20o and +60o that are tunable by an out-of-plane magnetic field from -2.5 to 2.5 T. It is experimentally observed that the degree of polarization can be magnetically manipulated between -20% and 85%. This work raises a new magneto-optical phenomenon and could create opportunities of applying 2D ferromagnetic materials in Raman lasing, topological photonics, and magneto-optical modulator for information transport and storage.

cond-mat.mtrl-sci↗
arXiv2020

Gradient Estimates For $Δu + au^{p+1}=0$ And Liouville Theorems

In this short note, we use a unified method to consider the gradient estimates of the positive solution to the following nonlinear elliptic equation $Δu + au^{p+1}=0$ defined on a complete noncompact Riemannian manifold $(M, g)$ where $a > 0$ and $ p <\frac{4}{n}$ or $a < 0$ and $p >0$ are two constants. For the case $a>0$, this improves considerably the previous known results except for the cases $\dim(M)=4$ and supplements the results for the case $\dim(M)\leq 2$. For the case $a<0$ and $p>0$, we also improve considerably the previous related results. When the Ricci curvature of $(M,g)$ is nonnegative, we also obtain a Liouville-type theorem for the above equation.

math.DG↗
arXiv2020

Yau Type Gradient Estimates For $Δu + au(\log u)^{p}+bu=0$ On Riemannian Manifolds

In this paper, we consider the gradient estimates of the positive solutions to the following equation defined on a complete Riemannian manifold $(M, g)$ $$Δu + au(\log u)^{p}+bu=0,$$ where $a, b\in \mathbb{R}$ and $p$ is a rational number with $p=\frac{k_1}{2k_2+1}\geq2$ where $k_1$ and $k_2$ are positive integer numbers. we obtain the gradient bound of a positive solution to the equation which does not depend on the bounds of the solution and the Laplacian of the distance function on $(M, g)$. Our results can be viewed as a natural extension of Yau's estimates on positive harmonic function.

math.DG↗
arXiv2020

The layer number of grids

The peeling process is defined as follows: starting with a finite point set $X \subset \mathbb{R}^d$, we repeatedly remove the set of vertices of the convex hull of the current set of points. The number of peeling steps needed to completely delete the set $X$ is called the layer number of $X$. In this paper, we study the layer number of the $d$-dimensional integer grid $[n]^d$. We prove that for every $d \geq 1$, the layer number of $[n]^d$ is at least $Ω\left(n^\frac{2d}{d+1}\right)$. On the other hand, we show that for every $d\geq 3$, it takes at most $O(n^{d - 9/11})$ steps to fully remove $[n]^d$. Our approach is based on an enhancement of the method used by Har-Peled and Lidický for solving the 2-dimensional case.

math.MG↗
arXiv2020

Mechanism of carrier localization in doped perovskite nanocrystals for bright emission

Nanocrystals based on metal-halide perovskites offer a promising material platform for highly efficient lighting. Using transient optical spectroscopy, we study excitation recombination dynamics in manganese-doped CsPb(Cl,Br)3 perovskite nanocrystals. We find an increase in the intrinsic excitonic radiative recombination rate upon doping, which is typically a challenging material property to tailor. Supported by ab initio calculations, we can attribute the enhanced emission rates to increased exciton localization through lattice periodicity breaking from Mn dopants, which increases exciton effective masses and overlap of electron and hole wavefunctions and thus the oscillator strength. Our report of a fundamental strategy for improving luminescence efficiencies in perovskite nanocrystals will be valuable for maximizing efficiencies in light-emitting applications.

cond-mat.mtrl-sci↗
arXiv2020

Learning Pose-invariant 3D Object Reconstruction from Single-view Images

Learning to reconstruct 3D shapes using 2D images is an active research topic, with benefits of not requiring expensive 3D data. However, most work in this direction requires multi-view images for each object instance as training supervision, which oftentimes does not apply in practice. In this paper, we relax the common multi-view assumption and explore a more challenging yet more realistic setup of learning 3D shape from only single-view images. The major difficulty lies in insufficient constraints that can be provided by single view images, which leads to the problem of pose entanglement in learned shape space. As a result, reconstructed shapes vary along input pose and have poor accuracy. We address this problem by taking a novel domain adaptation perspective, and propose an effective adversarial domain confusion method to learn pose-disentangled compact shape space. Experiments on single-view reconstruction show effectiveness in solving pose entanglement, and the proposed method achieves on-par reconstruction accuracy with state-of-the-art with higher efficiency.

cs.CV↗
arXiv2020

Hybrid Collaborative Filtering Models for Clinical Search Recommendation

With increasing and extensive use of electronic health records, clinicians are often under time pressure when they need to retrieve important information efficiently among large amounts of patients' health records in clinics. While a search function can be a useful alternative to browsing through a patient's record, it is cumbersome for clinicians to search repeatedly for the same or similar information on similar patients. Under such circumstances, there is a critical need to build effective recommender systems that can generate accurate search term recommendations for clinicians. In this manuscript, we developed a hybrid collaborative filtering model using patients' encounter and search term information to recommend the next search terms for clinicians to retrieve important information fast in clinics. For each patient, the model will recommend terms that either have high co-occurrence frequencies with his/her most recent ICD codes or are highly relevant to the most recent search terms on this patient. We have conducted comprehensive experiments to evaluate the proposed model, and the experimental results demonstrate that our model can outperform all the state-of-the-art baseline methods for top-N search term recommendation on different datasets.

cs.IR↗
arXiv2020

Least Squares Estimator for Vasicek Model Driven by Sub-fractional Brownian Processes from Discrete Observations

We study the parameter estimation problem of Vasicek Model driven by sub-fractional Brownian processes from discrete observations, and let {S_t^H,t>=0} denote a sub-fractional Brownian motion whose Hurst parameter 1/2<H<1 . The studies are as follows: firstly, two unknown parameters in the model are estimated by the least squares method. Secondly, the strong consistency and the asymptotic distribution of the estimators are studied respectively. Finally, our estimators are validated by numerical simulation.

math.ST↗
arXiv2020

Topological-darkness-assisted phase regulation for atomically thin meta-optics

Two-dimensional (2D) noble-metal dichalcogenides have emerged as a new platform for the realization of versatile flat optics with a considerable degree of miniaturization. However, light field manipulation at the atomic scale is widely considered unattainable since the vanishing thickness and intrinsic losses of 2D materials completely suppress both resonances and phase accumulation effects. Empowered by conventionally perceived adverse effects of intrinsic losses, we show that the structured PtSe2 films integrated with a uniform substrate can regulate nontrivial singular phase and realize atomic-thick meta-optics in the presence of topological darkness. We experimentally demonstrate a series of atomic-thick binary meta-optics that allows angle-robust and high unit-thickness diffraction efficiency of 0.96%/nm in visible frequencies, given its thickness of merely 4.3 nm. Our results unlock the potential of a new class of 2D flat optics for light field manipulation at an atomic thickness.

physics.optics↗
arXiv2020

Bright excitons with negative-mass electrons

Bound electron-hole excitonic states are generally not expected to form with charges of negative effective mass. We identify such excitons in a single layer of the semiconductor WSe2, where they give rise to narrow-band upconverted photoluminescence in the UV, at an energy of 1.66 eV above the first band-edge excitonic transition. Negative band curvature and strong electron-phonon coupling result in a cascaded phonon progression with equidistant peaks in the photoluminescence spectrum, resolvable to ninth order. Ab initio GW-BSE calculations with full electron-hole correlations unmask and explain the admixture of upper conduction-band states to this complex many-body excitation: an optically bright, bound exciton in resonance with the semiconductor continuum. This exciton is responsible for atomic-like quantum-interference phenomena such as electromagnetically induced transparency. Since band curvature can be tuned by pressure or strain, synthesis of exotic quasiparticles such as flat-band excitons with infinite reduced mass becomes feasible.

cond-mat.mes-hall↗
arXiv2020

High-throughput injection-acceleration of electron bunches from a linear accelerator to a laser wakefield accelerator

Plasma-based accelerators (PBAs) driven by either intense lasers (laser wakefield accelerators, LWFAs) or particle beams (plasma wakefield accelerators, PWFAs), can accelerate charged particles at extremely high gradients compared to conventional radio-frequency (RF) accelerators. In the past two decades, great strides have been made in this field, making PBA a candidate for next-generation light sources and colliders. However, these challenging applications necessarily require beams with good stability, high quality, controllable polarization and excellent reproducibility. To date, such beams are generated only by conventional RF accelerators. Therefore, it is important to demonstrate the injection and acceleration of beams first produced using a conventional RF accelerator, by a PBA. In some recent studies on LWFA staging and external injection-acceleration in PWFA only a very small fraction (from below 0.1% to few percent) of the injected charge (the coupling efficiency) was accelerated. For future colliders where beam energy will need to be boosted using multiple stages, the coupling efficiency per stage must approach 100%. Here we report the first demonstration of external injection from a photocathode-RF-gun-based conventional linear accelerator (LINAC) into a LWFA and subsequent acceleration without any significant loss of charge or degradation of quality, which is achieved by properly shaping and matching the beam into the plasma structure. This is an important step towards realizing a high-throughput, multi-stage, high-energy, hybrid conventional-plasma accelerator.

physics.plasm-ph↗
arXiv2020

O-Band Subwavelength Grating Filters in a Monolithic Photonics Technology

The data communications industry has begun transitioning from electrical to optical interconnects in datacenters in order to overcome performance bottlenecks and meet consumer needs. To mitigate the costs associated with this change and achieve performance for 5G and beyond, it is crucial to explore advanced photonic devices that can enable high-bandwidth interconnects via wavelength-division multiplexing (WDM) in photonic integrated circuits. Subwavelength grating (SWG) filters have shown great promise for WDM applications. However, the small feature sizes necessary to implement these structures have prohibited them from penetrating into industrial applications. To explore the manufacturability and performance of SWG filters in an industrial setting, we fabricate and characterize O-band subwavelength grating filters using the monolithic photonics technology at GLOBALFOUNDRIES (GF). We demonstrate a low drop channel loss of -1.2 dB with a flat-top response, a high extinction ratio of -30 dB, a 3 dB channel width of 5 nm and single-source thermal tunability without shape distortion. This filter structure was designed using elements from the product design kit provided by GF and functions in a compact footprint of 0.002 mm2 with a minimum feature size of 150 nm.

physics.app-ph↗
arXiv2020

A Fast Radio Burst discovered in FAST drift scan survey

We report the discovery of a highly dispersed fast radio burst, FRB~181123, from an analysis of $\sim$1500~hr of drift-scan survey data taken using the Five-hundred-meter Aperture Spherical radio Telescope (FAST). The pulse has three distinct emission components, which vary with frequency across our 1.0--1.5~GHz observing band. We measure the peak flux density to be $>0.065$~Jy and the corresponding fluence $>0.2$~Jy~ms. Based on the observed dispersion measure of 1812~cm$^{-3}$~pc, we infer a redshift of $\sim 1.9$. From this, we estimate the peak luminosity and isotropic energy to be $\lesssim 2\times10^{43}$~erg~s$^{-1}$ and $\lesssim 2\times10^{40}$~erg, respectively. With only one FRB from the survey detected so far, our constraints on the event rate are limited. We derive a 95\% confidence lower limit for the event rate of 900 FRBs per day for FRBs with fluences $>0.025$~Jy~ms. We performed follow-up observations of the source with FAST for four hours and have not found a repeated burst. We discuss the implications of this discovery for our understanding of the physical mechanisms of FRBs.

astro-ph.HE↗
arXiv2020

Sub-picosecond photo-induced displacive phase transition in two-dimensional MoTe$_2$

Photo-induced phase transitions (PIPTs) provide an ultrafast, energy-efficient way for precisely manipulating the topological properties of transition-metal ditellurides, and can be used to stabilize a topological phase in an otherwise semiconducting material. Using first-principles calculations, we demonstrate that the PIPT in monolayer MoTe$_2$ from the semiconducting 2H phase to the topological 1T$'$ phase can be triggered purely by electronic excitations that soften multiple lattice vibrational modes. These softenings, driven by a Peierls-like mechanism within the conduction bands, lead to structural symmetry breaking within sub-picosecond timescales, which is shorter than the timescale of a thermally driven phase transition. The transition is predicted to be triggered by photons with energies over $1.96$\,eV, with an associated excited carrier density of $3.4\times10^{14}$\,cm$^{-2}$, which enables a controllable phase transformation by varying the laser wavelength. Our results provide insight into the underlying physics of the phase transition in 2D transition-metal ditellurides, and show an ultrafast phase transition mechanism for manipulation of the topological properties of 2D systems.

cond-mat.mtrl-sci↗
arXiv2020

A deep Convolutional Neural Network for topology optimization with strong generalization ability

This paper proposes a deep Convolutional Neural Network(CNN) with strong generalization ability for structural topology optimization. The architecture of the neural network is made up of encoding and decoding parts, which provide down- and up-sampling operations. In addition, a popular technique, namely U-Net, was adopted to improve the performance of the proposed neural network. The input of the neural network is a well-designed tensor with each channel includes different information for the problem, and the output is the layout of the optimal structure. To train the neural network, a large dataset is generated by a conventional topology optimization approach, i.e. SIMP. The performance of the proposed method was evaluated by comparing its efficiency and accuracy with SIMP on a series of typical optimization problems. Results show that a significant reduction in computation cost was achieved with little sacrifice on the optimality of design solutions. Furthermore, the proposed method can intelligently solve problems under boundary conditions not being included in the training dataset.

cs.LG↗