arXiv subjects

Bo Peng

Publications and source records attributed to Bo Peng.

At least 289 records · Page 16Linked to original sources

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↗

arXiv2022

Order Selection Prophet Inequality: From Threshold Optimization to Arrival Time Design

In the classical prophet inequality, a gambler faces a sequence of items, whose values are drawn independently from known distributions. Upon the arrival of each item, its value is realized and the gambler either accepts it and the game ends, or irrevocably rejects it and continues to the next item. The goal is to maximize the value of the selected item and compete against the expected maximum value of all items. A tight competitive ratio of $\frac{1}{2}$ is established in the classical setting and various relaxations have been proposed to surpass the barrier, including the i.i.d. model, the order selection model, and the random order model. In this paper, we advance the study of the order selection prophet inequality, in which the gambler is given the extra power for selecting the arrival order of the items. Our main result is a $0.725$-competitive algorithm, that substantially improves the state-of-the-art $0.669$ ratio by Correa, Saona and Ziliotto~(Math. Program. 2021), achieved in the harder random order model. Recently, Agrawal, Sethuraman and Zhang~(EC 2021) proved that the task of selecting the optimal order is NP-hard. Despite this fact, we introduce a novel algorithm design framework that translates the discrete order selection problem into a continuous arrival time design problem. From this perspective, we can focus on the arrival time design without worrying about the threshold optimization afterwards. As a side result, we achieve the optimal $0.745$ competitive ratio by applying our algorithm to the i.i.d. model.

cs.DS↗

arXiv2022

Hybrid quantum-classical approach for coupled-cluster Green's function theory

The three key elements of a quantum simulation are state preparation, time evolution, and measurement. While the complexity scaling of time evolution and measurements are well known, many state preparation methods are strongly system-dependent and require prior knowledge of the system's eigenvalue spectrum. Here, we report on a quantum-classical implementation of the coupled-cluster Green's function (CCGF) method, which replaces explicit ground state preparation with the task of applying unitary operators to a simple product state. While our approach is broadly applicable to many models, we demonstrate it here for the Anderson impurity model (AIM). The method requires a number of $T$ gates that grows as $ \mathcal{O} \left(N^5 \right)$ per time step to calculate the impurity Green's function in the time domain, where $N$ is the total number of energy levels in the AIM. Since the number of $T$ gates is analogous to the computational time complexity of a classical simulation, we achieve an order of magnitude improvement over a classical CCGF calculation of the same order, which requires $ \mathcal{O} \left(N^6 \right)$ computational resources per time step.

quant-ph↗

arXiv2022

The unusual emission from PSR B1859+07 with FAST

We present simultaneous broad-band radio observations on the abnormal emission mode from PSR B1859$+$07 using the Five-hundred-meter Aperture Spherical radio Telescope (FAST). This pulsar shows peculiar emission phenomena, which are occasional shifts of emission to an early rotational phase and mode change of emission at the normal phase. We confirm all these three emission modes with our datasets, including the B (burst) and Q (quiet) modes of the non-shift pulses and the emission shift mode with a quasi-periodicity of 155 pulses. We also identify a new type of emission shift event, which has emission at the normal phase during the event. We studied polarisation properties of these emission modes in details, and found that they all have similar polarisation angle (PA) curve, indicating the emission of all these three modes are from the same emission height.

astro-ph.HE↗

arXiv2022

M2: Mixed Models with Preferences, Popularities and Transitions for Next-Basket Recommendation

Next-basket recommendation considers the problem of recommending a set of items into the next basket that users will purchase as a whole. In this paper, we develop a novel mixed model with preferences, popularities and transitions (M2) for the next-basket recommendation. This method models three important factors in next-basket generation process: 1) users' general preferences, 2) items' global popularities and 3) transition patterns among items. Unlike existing recurrent neural network-based approaches, M2 does not use the complicated networks to model the transitions among items, or generate embeddings for users. Instead, it has a simple encoder-decoder based approach (ed-Trans) to better model the transition patterns among items. We compared M2 with different combinations of the factors with 5 state-of-the-art next-basket recommendation methods on 4 public benchmark datasets in recommending the first, second and third next basket. Our experimental results demonstrate that M2 significantly outperforms the state-of-the-art methods on all the datasets in all the tasks, with an improvement of up to 22.1%. In addition, our ablation study demonstrates that the ed-Trans is more effective than recurrent neural networks in terms of the recommendation performance. We also have a thorough discussion on various experimental protocols and evaluation metrics for next-basket recommendation evaluation.

cs.LG↗