arXiv subjects

Bo Peng

Publications and source records attributed to Bo Peng.

At least 289 records · Page 16Linked to original sources

arXiv2022

Luminosity distribution of fast radio bursts from CHIME/FRB Catalog 1 by means of the updated Macquart relation

Fast radio bursts (FRBs) are extremely strong radio flares lasting several micro- to milliseconds and come from unidentified objects at cosmological distances, most of which are only seen once. Based on recently published data in the CHIME/FRB Catalog 1 in the frequency bands 400-800 MHz, we analyze 125 apparently singular FRBs with low dispersion measure (DM) and find that the distribution of their luminosity follows a lognormal form according to statistical tests. In our luminosity measurement, the FRB distance is estimated by using the Macquart relation which was obtained for 8 localized FRBs, and we find it still applicable for 18 sources after adding the latest 10 new localized FRBs. In addition, we test the validity of the luminosity distribution up to the Macquart relation and find that the lognormal form feature decreases as the uncertainty increases. Moreover, we compare the luminosity of these apparent non-repeaters with that of the previously observed 10 repeating FRBs also at low DM, noting that they belong to different lognormal distributions with the mean luminosity of non-repeaters being two times greater than that of repeaters. Therefore, from the two different lognormal distributions, different mechanisms for FRBs can be implied.

astro-ph.HE↗

arXiv2022

DFGC 2022: The Second DeepFake Game Competition

This paper presents the summary report on our DFGC 2022 competition. The DeepFake is rapidly evolving, and realistic face-swaps are becoming more deceptive and difficult to detect. On the contrary, methods for detecting DeepFakes are also improving. There is a two-party game between DeepFake creators and defenders. This competition provides a common platform for benchmarking the game between the current state-of-the-arts in DeepFake creation and detection methods. The main research question to be answered by this competition is the current state of the two adversaries when competed with each other. This is the second edition after the last year's DFGC 2021, with a new, more diverse video dataset, a more realistic game setting, and more reasonable evaluation metrics. With this competition, we aim to stimulate research ideas for building better defenses against the DeepFake threats. We also release our DFGC 2022 dataset contributed by both our participants and ourselves to enrich the DeepFake data resources for the research community (https://github.com/NiCE-X/DFGC-2022).

cs.CV↗

arXiv2022

SelfNeRF: Fast Training NeRF for Human from Monocular Self-rotating Video

In this paper, we propose SelfNeRF, an efficient neural radiance field based novel view synthesis method for human performance. Given monocular self-rotating videos of human performers, SelfNeRF can train from scratch and achieve high-fidelity results in about twenty minutes. Some recent works have utilized the neural radiance field for dynamic human reconstruction. However, most of these methods need multi-view inputs and require hours of training, making it still difficult for practical use. To address this challenging problem, we introduce a surface-relative representation based on multi-resolution hash encoding that can greatly improve the training speed and aggregate inter-frame information. Extensive experimental results on several different datasets demonstrate the effectiveness and efficiency of SelfNeRF to challenging monocular videos.

cs.CV↗

arXiv2022

Monolayer fullerene networks as photocatalysts for overall water splitting

Photocatalytic water splitting can produce hydrogen in an environmentally friendly way and provide alternative energy sources to reduce global carbon emissions. Recently, monolayer fullerene networks have been successfully synthesized [Hou $\textit{et al., Nature}$ $\textbf{2022}$, 606, 507], offering new material candidates for photocatalysis because of their large surface area with abundant active sites, feasibility to be combined with other 2D materials to form heterojunctions, and the C$_{60}$ cages for potential hydrogen storage. However, efficient photocatalysts need a combination of a suitable band gap and appropriate positions of the band edges with sufficient driving force for water splitting. In this study, I employ semilocal density functional theory and hybrid functional calculations to investigate the electronic structures of monolayer fullerene networks. I find that only the weakly screened hybrid functional, in combine with time-dependent Hartree-Fock calculations to include the exciton binding energy, can reproduce the experimentally obtained optical band gap of monolayer C$_{60}$. All the phases of monolayer fullerene networks have suitable band gaps with high carrier mobility and appropriate band edges to thermodynamically drive overall water splitting. In addition, the optical properties of monolayer C$_{60}$ are studied, and different phases of fullerene networks exhibit distinct absorption and recombination behavior, providing unique advantages either as an electron acceptor or as an electron donor in photocatalysis.

cond-mat.mtrl-sci↗

arXiv2022

Baseline correction for FAST radio recombination lines: a modified penalized least squares smoothing technique

A pilot project has been proceeded to map 1 deg$^2$ on the Galactic plane for radio recombination lines (RRLs) using the Five hundred meter Aperture Spherical Telescope (FAST). The motivation is to verify the techniques and reliabilities for a large-scale Galactic plane RRL survey with FAST aiming to investigate the ionized environment in the Galaxy. The data shows that the bandpass of the FAST 19 beam L-band is severely affected by radio frequency interferences (RFIs) and standing wave ripples, which can hardly be corrected by traditional low order polynomials. In this paper, we investigate a series of penalized least square (PLS) based baseline correction methods for radio astronomical spectra that usually contain weak signals with high level of noise. Three promising penalized least squares based methods, AsLS, arPLS, and asPLS are evaluated. Adopting their advantages, a modified method named rrlPLS is developed to optimize the baseline fitting to our RRL spectra. To check their effectiveness, the four methods are tested by simulations and further verified using observed data sets. It turns out that the rrlPLS method, with optimized parameter $λ= 2 \times 10^8$ , reveals the most sensitive and reliable emission features in the RRL map. By injecting artificial line profiles into the real data cube, a further evaluation of profile distortion is conducted for rrlPLS. Comparing to simulated signals, the processed lines with low signal-to-noise ratio are less affected, of which the uncertainties are mainly caused by the rms noise. The rrlPLS method will be applied for baseline correction in future data processing pipeline of FAST RRL survey. Configured with proper parameters, the rrlPLS technique verified in this work may also be used for other spectroscopy projects.

astro-ph.GA↗

arXiv2022

Recursive Attentive Methods with Reused Item Representations for Sequential Recommendation

Sequential recommendation aims to recommend the next item of users' interest based on their historical interactions. Recently, the self-attention mechanism has been adapted for sequential recommendation, and demonstrated state-of-the-art performance. However, in this manuscript, we show that the self-attention-based sequential recommendation methods could suffer from the localization-deficit issue. As a consequence, in these methods, over the first few blocks, the item representations may quickly diverge from their original representations, and thus, impairs the learning in the following blocks. To mitigate this issue, in this manuscript, we develop a recursive attentive method with reused item representations (RAM) for sequential recommendation. We compare RAM with five state-of-the-art baseline methods on six public benchmark datasets. Our experimental results demonstrate that RAM significantly outperforms the baseline methods on benchmark datasets, with an improvement of as much as 11.3%. Our stability analysis shows that RAM could enable deeper and wider models for better performance. Our run-time performance comparison signifies that RAM could also be more efficient on benchmark datasets.

cs.IR↗

arXiv2022

Scattering Model Guided Adversarial Examples for SAR Target Recognition: Attack and Defense

Deep Neural Networks (DNNs) based Synthetic Aperture Radar (SAR) Automatic Target Recognition (ATR) systems have shown to be highly vulnerable to adversarial perturbations that are deliberately designed yet almost imperceptible but can bias DNN inference when added to targeted objects. This leads to serious safety concerns when applying DNNs to high-stake SAR ATR applications. Therefore, enhancing the adversarial robustness of DNNs is essential for implementing DNNs to modern real-world SAR ATR systems. Toward building more robust DNN-based SAR ATR models, this article explores the domain knowledge of SAR imaging process and proposes a novel Scattering Model Guided Adversarial Attack (SMGAA) algorithm which can generate adversarial perturbations in the form of electromagnetic scattering response (called adversarial scatterers). The proposed SMGAA consists of two parts: 1) a parametric scattering model and corresponding imaging method and 2) a customized gradient-based optimization algorithm. First, we introduce the effective Attributed Scattering Center Model (ASCM) and a general imaging method to describe the scattering behavior of typical geometric structures in the SAR imaging process. By further devising several strategies to take the domain knowledge of SAR target images into account and relax the greedy search procedure, the proposed method does not need to be prudentially finetuned, but can efficiently to find the effective ASCM parameters to fool the SAR classifiers and facilitate the robust model training. Comprehensive evaluations on the MSTAR dataset show that the adversarial scatterers generated by SMGAA are more robust to perturbations and transformations in the SAR processing chain than the currently studied attacks, and are effective to construct a defensive model against the malicious scatterers.

cs.CV↗

arXiv2022

Flat electronic band structure and anisotropic optical, mechanical, and thermoelectric properties of two-dimensional fullerene networks

Nanoclusters like fullerenes as the unit to build intriguing two-dimensional topological structures is of great challenge. Here we propose three bridged fullerene monolayers and comprehensively investigate the novel fullerene monolayer as synthesized experimentally Zheng et al.,[Nature 606, 507-510 (2022)] by state of the art first principles calculations. Our results show that alpha-C60-2D has a direct bandgap of 1.49 eV owing to a flat conduction band bottom close to the experimental value, the optical linear dichroism with strong absorption in long-wave ultraviolet region, a small anisotropic Youngs modulus, the large hole mobility, and the ultrahigh Seebeck coefficient at middle low temperatures. Moreover, Li ions are found to migrate easily along the X path in alpha-C60-2D. It is unveiled that the anisotropic optical, mechanical, electrical, and thermoelectric properties of alpha-C60-2D originate from the asymmetric bridging arrangements between C60 clusters. Our study promises potential applications of monolayer fullerene networks in diverse fields.

cond-mat.mtrl-sci↗

arXiv2022

Linear change and minutes variability of solar wind velocity revealed by FAST

Observation of Interplanetary Scintillation (IPS) provides an important and effective way to study the solar wind and the space weather. A series of IPS observations were conducted by the Five-hundred-meter Aperture Spherical radio Telescope (FAST). The extraordinary sensitivity and the wide frequency coverage make FAST an ideal platform for IPS studies. In this paper we present some first scientific results from FAST observations of IPS with the L-band receiver. Based on the solar wind velocity fitting values of FAST observations on September 26-28, 2020, we found that the velocity decreases with increasing frequency linearly, which has not yet been reported in literature. And we have also detected a variation of solar wind velocity on a timescale of 3-5 minutes, which imply the slow change of the background solar wind, a co-existence of high- and low-speed streams, or a reflect of the quasi-periodic electron-density fluctuations.

astro-ph.SR↗

arXiv2022

MultiEarth 2022 -- The Champion Solution for the Matrix Completion Challenge via Multimodal Regression and Generation

Earth observation satellites have been continuously monitoring the earth environment for years at different locations and spectral bands with different modalities. Due to complex satellite sensing conditions (e.g., weather, cloud, atmosphere, orbit), some observations for certain modalities, bands, locations, and times may not be available. The MultiEarth Matrix Completion Challenge in CVPR 2022 [1] provides the multimodal satellite data for addressing such data sparsity challenges with the Amazon Rainforest as the region of interest. This work proposes an adaptive real-time multimodal regression and generation framework and achieves superior performance on unseen test queries in this challenge with an LPIPS of 0.2226, a PSNR of 123.0372, and an SSIM of 0.6347.

cs.CV↗

arXiv2022

MultiEarth 2022 -- The Champion Solution for Image-to-Image Translation Challenge via Generation Models

The MultiEarth 2022 Image-to-Image Translation challenge provides a well-constrained test bed for generating the corresponding RGB Sentinel-2 imagery with the given Sentinel-1 VV & VH imagery. In this challenge, we designed various generation models and found the SPADE [1] and pix2pixHD [2] models could perform our best results. In our self-evaluation, the SPADE-2 model with L1-loss can achieve 0.02194 MAE score and 31.092 PSNR dB. In our final submission, the best model can achieve 0.02795 MAE score ranked No.1 on the leader board.

cs.CV↗

arXiv2022

Prospective Preference Enhanced Mixed Attentive Model for Session-based Recommendation

Session-based recommendation aims to generate recommendations for the next item of users' interest based on a given session. In this manuscript, we develop prospective preference enhanced mixed attentive model (P2MAM) to generate session-based recommendations using two important factors: temporal patterns and estimates of users' prospective preferences. Unlike existing methods, P2MAM models the temporal patterns using a light-weight while effective position-sensitive attention mechanism. In P2MAM, we also leverage the estimate of users' prospective preferences to signify important items, and generate better recommendations. Our experimental results demonstrate that P2MAM models significantly outperform the state-of-the-art methods in six benchmark datasets, with an improvement as much as 19.2%. In addition, our run-time performance comparison demonstrates that during testing, P2MAM models are much more efficient than the best baseline method, with a significant average speedup of 47.7 folds.

cs.IR↗

arXiv2022

Tunable photostriction of halide perovskites through energy dependent photoexcitation

Halide perovskites exhibit giant photostriction, that is, volume or shape changes upon illumination. However, the microscopic origin of this phenomenon remains unclear and there are experimental reports of both light-induced lattice expansion and contraction. In this paper we establish a general method, based on first-principles calculations and molecular orbital theory, which provides a microscopic picture of photostriction in insulators based on the orbital characters of their electronic bands near the Fermi level. For lead-halide perovskites, we find that different valence states have different bonding characters, leading to opposing strengthening or weakening of bonds depending on the photoexcitation energy. The overall trend is that light induces lattice contraction at low excitation energies, while giant lattice expansion occurs at high excitation energies, rationalizing experimental reports.

cond-mat.mtrl-sci↗

arXiv2022

Real-time equation-of-motion CC cumulant and CC Green's function simulations of photoemission spectra of water and water dimer

Newly developed coupled-cluster (CC) methods enable simulations of ionization potentials and spectral functions of molecular systems in a wide range of energy scales ranging from core-binding to valence. This paper discusses results obtained with the real-time equation-of-motion CC cumulant approach (RT-EOM-CC), and CC Green's function (CCGF) approaches in applications to the water and water dimer molecules. We compare the ionization potentials obtained with these methods for the valence region with the results obtained with the CCSD(T) formulation as a difference of energies for N and N-1 electron systems. All methods show good agreement with each other. They also agree well with experiment, with errors usually below 0.1 eV for the ionization potentials. We also analyze unique features of the spectral functions, associated with the position of satellite peaks, obtained with the RT-EOM-CC and CCGF methods employing single and double excitations, as a function of the monomer OH bond length and the proton transfer coordinate in the dimer. Finally, we analyze the impact of the basis set effects on the quality of calculated ionization potentials and find that the basis set effects are less pronounced for the augmented-type sets.

physics.chem-ph↗

arXiv2022

HeadNeRF: A Real-time NeRF-based Parametric Head Model

In this paper, we propose HeadNeRF, a novel NeRF-based parametric head model that integrates the neural radiance field to the parametric representation of the human head. It can render high fidelity head images in real-time on modern GPUs, and supports directly controlling the generated images' rendering pose and various semantic attributes. Different from existing related parametric models, we use the neural radiance fields as a novel 3D proxy instead of the traditional 3D textured mesh, which makes that HeadNeRF is able to generate high fidelity images. However, the computationally expensive rendering process of the original NeRF hinders the construction of the parametric NeRF model. To address this issue, we adopt the strategy of integrating 2D neural rendering to the rendering process of NeRF and design novel loss terms. As a result, the rendering speed of HeadNeRF can be significantly accelerated, and the rendering time of one frame is reduced from 5s to 25ms. The well designed loss terms also improve the rendering accuracy, and the fine-level details of the human head, such as the gaps between teeth, wrinkles, and beards, can be represented and synthesized by HeadNeRF. Extensive experimental results and several applications demonstrate its effectiveness. The trained parametric model is available at https://github.com/CrisHY1995/headnerf.

cs.CV↗

arXiv2022

Multi-wavelength magnetic coding of helical luminescence in ferromagnetic 2D layered CrI3

Two-dimensional (2D) van der Waals (vdW) ferromagnets have opened new avenues for manipulating spin at the limits of single or few atomic layers, and for creating unique magneto-exciton devices through the coupling of long-range ferromagnetic (FM) orders and excitons. However, 2D vdW ferromagnets explored so far have rarely possessed exciton behaviors; to date, FM CrI3 have been recently revealed to show ligand-field photoluminescence correlated with FM ordering, but typically with a broad emission peak. Alternatively, many-body excitons have been observed in antiferromagnetic (AFM) NiPS3, but the coupling of excitons with AFM orders is exponentially more difficult, owing to extremely high coercivity. Here, we report a straightforward approach to realize strong coupling of narrow helical emission and FM orders at a low magnetic field in CrI3 through a relatively simple microsphere cavity. We show that the resonant whispering-gallery-modes (WGM) of SiO2 microspheres give rising to a series of strong oscillation helical emissions with a full width at half-maximum (FWHM) of ~5 nm under continuous wave excitation. Reversible magnetic control and coding of helical luminescence with multiwavelength is realized in the range of 950-1100 nm. This work enables plenty of opportunities for creating magnetic encoding lasing for photonic integrated chips.

cond-mat.mtrl-sci↗

arXiv2022

Observation of intrinsic crystal phase in bare CrI3 ferromagnetism

Intrinsic structural phase is a crucial foundation for the fundamental physical properties, and for creating innovative devices with unprecedented performances and unique functionalities. Long-range ferromagnetic orders of van der Waals CrI3 are strongly tied with interlayer stacking orders. However, the intrinsic structure of few-layer CrI3 still remains elusive; the predicted monoclinic phase has not yet been experimentally detected in bare few-layer CrI3. Here we uncover the intrinsic structure of few-layer CrI3 with interlayer antiferromagnetic coupling, which unambiguously show monoclinic stacking in both bare and hBN-encapsulated bilayer and tri-five-layer CrI3 throughout an entire temperature range from 300 to 10 K. An exotic spring damping effect from hBN encapsulation layers is experimentally observed in hBN/CrI3/hBN heterostructures, which partly hinders interlayer sliding of CrI3. This work demonstrates the intrinsic monoclinic crystal phase of few-layer CrI3 and associated correlation with magnetic orders, opening up numerous opportunities for creating magnetic texture by stacking design.

cond-mat.mtrl-sci↗

arXiv2022

The FAST Ultra-Deep Survey (FUDS): observational strategy, calibration and data reduction

The FAST Ultra-Deep Survey (FUDS) is a blind survey that aims for the direct detection of HI in galaxies at redshifts $z<0.42$. The survey uses the multibeam receiver on the Five Hundred Meter Aperture Spherical Telescope (FAST) to map six regions, each of size 0.72 deg$^2$ at high sensitivity ($\sim 50 μ$Jy) and high frequency resolution (23 kHz). The survey will enable studies of the evolution of galaxies and their HI content with an eventual sample size of $\sim 1000$. We present the science goals, observing strategy, the effects of radio frequency interference (RFI) at the FAST site, our mitigation strategies and the methods for calibration, data reduction and imaging as applied to initial data. The observations and reductions for the first field, FUDS0, are completed, with around 128 HI galaxies detected in a preliminary analysis. Example spectra are given in this paper, including a comparison with data from the overlapping GAL2577 field of Arecibo Ultra-Deep Survey (AUDS).

astro-ph.GA↗