arXiv subjects

Shuai Liu

Publications and source records attributed to Shuai Liu.

At least 163 records · Page 9Linked to original sources

arXiv2022

Electronic Origin of Half-metal to Semiconductor Transition and Colossal Magnetoresistance in Spinel HgCr2Se4

Half-metals are ferromagnets hosting spin-polarized conducting carriers and crucial for spintronics applications. The chromium spinel HgCr2Se4 represents a unique type of half-metal, which features a half-metal to semiconductor transition (HMST) and exhibits colossal magnetoresistance (CMR) across the ferromagnetic-paramagnetic (FM-PM) transition. Using angle-resolved photoemission spectroscopy (ARPES), we find that the Fermi surface of n-type HgCr2Se4 (n-HgCr2Se4) consists of a single electron pocket which moves above the Fermi level (EF) upon the FM-PM transition, leading to the HMST. Such a Lifshitz transition manifests a giant band splitting which originates from the exchange interaction unveiled with a specific chemical nonstoichiometry. The exchange band splitting and the chemical nonstoichiometry are two key ingredients to the HMST and CMR, consistent with our ab-initio calculation. Our findings provide spectroscopic evidences of the electronic origin of the anomalous properties of HgCr2Se4, which address the unique phase transition in half-metals.

cond-mat.mtrl-sci↗

arXiv2022

Learned Smartphone ISP on Mobile GPUs with Deep Learning, Mobile AI & AIM 2022 Challenge: Report

The role of mobile cameras increased dramatically over the past few years, leading to more and more research in automatic image quality enhancement and RAW photo processing. In this Mobile AI challenge, the target was to develop an efficient end-to-end AI-based image signal processing (ISP) pipeline replacing the standard mobile ISPs that can run on modern smartphone GPUs using TensorFlow Lite. The participants were provided with a large-scale Fujifilm UltraISP dataset consisting of thousands of paired photos captured with a normal mobile camera sensor and a professional 102MP medium-format FujiFilm GFX100 camera. The runtime of the resulting models was evaluated on the Snapdragon's 8 Gen 1 GPU that provides excellent acceleration results for the majority of common deep learning ops. The proposed solutions are compatible with all recent mobile GPUs, being able to process Full HD photos in less than 20-50 milliseconds while achieving high fidelity results. A detailed description of all models developed in this challenge is provided in this paper.

cs.CV↗

arXiv2022

Reversed Image Signal Processing and RAW Reconstruction. AIM 2022 Challenge Report

Cameras capture sensor RAW images and transform them into pleasant RGB images, suitable for the human eyes, using their integrated Image Signal Processor (ISP). Numerous low-level vision tasks operate in the RAW domain (e.g. image denoising, white balance) due to its linear relationship with the scene irradiance, wide-range of information at 12bits, and sensor designs. Despite this, RAW image datasets are scarce and more expensive to collect than the already large and public RGB datasets. This paper introduces the AIM 2022 Challenge on Reversed Image Signal Processing and RAW Reconstruction. We aim to recover raw sensor images from the corresponding RGBs without metadata and, by doing this, "reverse" the ISP transformation. The proposed methods and benchmark establish the state-of-the-art for this low-level vision inverse problem, and generating realistic raw sensor readings can potentially benefit other tasks such as denoising and super-resolution.

eess.IV↗

arXiv2022

AIM 2022 Challenge on Instagram Filter Removal: Methods and Results

This paper introduces the methods and the results of AIM 2022 challenge on Instagram Filter Removal. Social media filters transform the images by consecutive non-linear operations, and the feature maps of the original content may be interpolated into a different domain. This reduces the overall performance of the recent deep learning strategies. The main goal of this challenge is to produce realistic and visually plausible images where the impact of the filters applied is mitigated while preserving the content. The proposed solutions are ranked in terms of the PSNR value with respect to the original images. There are two prior studies on this task as the baseline, and a total of 9 teams have competed in the final phase of the challenge. The comparison of qualitative results of the proposed solutions and the benchmark for the challenge are presented in this report.

cs.CV↗

arXiv2022

Learning Multitask Gaussian Bayesian Networks

Major depressive disorder (MDD) requires study of brain functional connectivity alterations for patients, which can be uncovered by resting-state functional magnetic resonance imaging (rs-fMRI) data. We consider the problem of identifying alterations of brain functional connectivity for a single MDD patient. This is particularly difficult since the amount of data collected during an fMRI scan is too limited to provide sufficient information for individual analysis. Additionally, rs-fMRI data usually has the characteristics of incompleteness, sparsity, variability, high dimensionality and high noise. To address these problems, we proposed a multitask Gaussian Bayesian network (MTGBN) framework capable for identifying individual disease-induced alterations for MDD patients. We assume that such disease-induced alterations show some degrees of similarity with the tool to learn such network structures from observations to understanding of how system are structured jointly from related tasks. First, we treat each patient in a class of observation as a task and then learn the Gaussian Bayesian networks (GBNs) of this data class by learning from all tasks that share a default covariance matrix that encodes prior knowledge. This setting can help us to learn more information from limited data. Next, we derive a closed-form formula of the complete likelihood function and use the Monte-Carlo Expectation-Maximization(MCEM) algorithm to search for the approximately best Bayesian network structures efficiently. Finally, we assess the performance of our methods with simulated and real-world rs-fMRI data.

stat.ML↗

arXiv2022

Direct Visualization and Manipulation of Tunable Quantum Well State in Semiconducting Nb2SiTe4

Quantum well states (QWSs) can form at the surface or interfaces of materials with confinement potential. They have broad applications in electronic and optical devices such as high mobility electron transistor, photodetector and quantum well laser. The properties of the QWSs are usually the key factors for the performance of the devices. However, direct visualization and manipulation of such states are in general challenging. In this work, by using angle-resolved photoemission spectroscopy (ARPES) and scanning tunneling microscopy/spectroscopy (STM/STS), we directly probe the QWSs generated on the vacuum interface of a narrow band gap semiconductor Nb2SiTe4. Interestingly, the position and splitting of QWSs could be easily manipulated via potassium (K) dosage onto the sample surface. Our results suggest Nb2SiTe4 to be an intriguing semiconductor system to study and engineer the QWSs, which has great potential in device applications.

cond-mat.mtrl-sci↗

arXiv2022

Stellar Atmospheric Parameters of M-type Stars from LAMOST DR8

The Large Sky Area Multi-Object Fiber Spectroscopic Telescope (LAMOST) Low Resolution Spectroscopic Survey (LRS) provides massive spectroscopic data of M-type stars, and the derived stellar parameters could bring vital help to various studies. We adopt the ULySS package to perform $χ^2$ minimization with model spectra generated from the MILES interpolator, and determine the stellar atmospheric parameters for the M-type stars from LAMOST LRS Data Release (DR) 8. Comparison with the stellar parameters from APOGEE Stellar Parameter and Chemical Abundance Pipeline (ASPCAP) suggests that most of our results have good consistency. For M dwarfs, we achieve dispersions better than 74 K, 0.19 dex and 0.16 dex for $T_{\rm eff}$, $\log{g}$ and [Fe/H], while for M giants, the internal uncertainties are 58 K, 0.32 dex and 0.26 dex, respectively. Compared to ASPCAP we also find a systematic underestimation of $Δ{T_{\rm eff}} =$ $-$176 K for M dwarfs, and a systematic overestimation of $Δ{\log{g}} =$ 0.30 dex for M giants. However, such differences are less significant when we make comparison with common stars from other literature, which indicates that systematic biases exist in the difference of ASPCAP and other measurements. A catalog of 763,136 spectra corresponding to 616,314 M-type stars with derived stellar parameters is presented. We determine the stellar parameters for stars with $T_{\rm eff}$ higher than 2,900 K, with $\log{g}$ from -0.24 dex to 5.9 dex. The typical precisions are 45 K, 0.25 dex and 0.22 dex, for $T_{\rm eff}$, $\log{g}$ and [Fe/H], respectively, which are estimated from the duplicate observations of the same stars.

astro-ph.SR↗

arXiv2022

Lensless coherent diffraction imaging based on spatial light modulator with unknown modulation curve

Lensless imaging is a popular research field for the advantages of small size, wide field-of-view and low aberration in recent years. However, some traditional lensless imaging methods suffer from slow convergence, mechanical errors and conjugate solution interference, which limit its further application and development. In this work, we proposed a lensless imaging method based on spatial light modulator (SLM) with unknown modulation curve. In our imaging system, we use SLM to modulate the wavefront of object, and introduce the ptychographic scanning algorithm that is able to recover the complex amplitude information even the SLM modulation curve is inaccurate or unknown. In addition, we also design a split-beam interference experiment to calibrate the modulation curve of SLM, and using the calibrated modulation function as the initial value of the expended ptychography iterative engine (ePIE) algorithm can improve the convergence speed. We further analyze the effect of modulation function, algorithm parameters and the characteristics of the coherent light source on the quality of reconstructed image. The simulated and real experiments show that the proposed method is superior to traditional mechanical scanning methods in terms of recovering speed and accuracy, with the recovering resolution up to 14 um.

physics.optics↗

arXiv2022

Constraining the Hubble constant to a precision of about 1% using multi-band dark standard siren detections

Gravitational wave signal from the inspiral of stellar-mass binary black hole can be used as standard sirens to perform cosmological inference. This inspiral covers a wide range of frequency bands, from the millihertz band to the audio-band, allowing for detections by both space-borne and ground-based gravitational wave detectors. In this work, we conduct a comprehensive study on the ability to constrain the Hubble constant using the dark standard sirens, or gravitational wave events that lack electromagnetic counterparts. To acquire the redshift information, we weight the galaxies within the localization error box with photometric information from several bands and use them as a proxy for the binary black hole redshift. We discover that TianQin is expected to constrain the Hubble constant to a precision of roughly $30\%$ through detections of $10$ gravitational wave events; in the most optimistic case, the Hubble constant can be constrained to a precision of $< 10 \%$, assuming TianQin I+II. In the optimistic case, the multi-detector network of TianQin and LISA is capable of constraining the Hubble constant to within $5\%$ precision. It is worth highlighting that the multi-band network of TianQin and Einstein Telescope is capable of constraining the Hubble constant to a precision of about $1\%$. We conclude that inferring the Hubble constant without bias from photo-z galaxy catalog is achievable, and we also demonstrate self-consistency using the P$-$P plot. On the other hand, high-quality spectroscopic redshift information is crucial for improving the estimation precision of Hubble constant.

astro-ph.CO↗

arXiv2022

Capability for detection of GW190521-like binary black holes with TianQin

The detection of GW190521 gains huge attention because it is the most massive binary that LIGO and Virgo ever confidently detected until the release of GWTC-3 (GW190426_190642 is more massive), and it is the first black hole merger whose remnant is believed to be an intermediate mass black hole. Furthermore, the primary black hole mass falls in the black hole mass gap, where the pair-instability supernova prevents the formation of astrophysical black holes in this range. In this paper, we systematically explore the prospect of TianQin on detecting GW190521-like sources. For sources with small orbital eccentricities, (i) TianQin could resolve up to a dozen of sources with signal-to-noise ratio (SNR) larger than 8. Even if the signal-to-noise ratio threshold increases to 12, TianQin could still detect GW190521-like binaries. (ii) The parameters of sources merging within several years would be precisely recovered. The precision of coalescence time and sky localization closes to $1\ {\rm s}$ and $1\ {\rm deg^{2}}$ respectively. This indicates that TianQin could provide early warnings for ground-based gravitational waves detectors and electromagnetic telescopes for these sources. Furthermore, TianQin could distinguish the formation channels of these sources by measuring the orbital eccentricities with a relative precision of $10^{-4}$. (iii) TianQin could constrain the Hubble constant with a $10\%$ precision with GW190521-like sources. Finally, for very eccentric GW190521-like sources, although their gravitational wave signal might be too weak for TianQin to detect, even the null detection of TianQin could still present a significant contribution to the understanding of the underlying science.

astro-ph.HE↗

arXiv2021

Rethinking Re-Sampling in Imbalanced Semi-Supervised Learning

Semi-Supervised Learning (SSL) has shown its strong ability in utilizing unlabeled data when labeled data is scarce. However, most SSL algorithms work under the assumption that the class distributions are balanced in both training and test sets. In this work, we consider the problem of SSL on class-imbalanced data, which better reflects real-world situations. In particular, we decouple the training of the representation and the classifier, and systematically investigate the effects of different data re-sampling techniques when training the whole network including a classifier as well as fine-tuning the feature extractor only. We find that data re-sampling is of critical importance to learn a good classifier as it increases the accuracy of the pseudo-labels, in particular for the minority classes in the unlabeled data. Interestingly, we find that accurate pseudo-labels do not help when training the feature extractor, rather contrariwise, data re-sampling harms the training of the feature extractor. This finding is against the general intuition that wrong pseudo-labels always harm the model performance in SSL. Based on these findings, we suggest to re-think the current paradigm of having a single data re-sampling strategy and develop a simple yet highly effective Bi-Sampling (BiS) strategy for SSL on class-imbalanced data. BiS implements two different re-sampling strategies for training the feature extractor and the classifier and integrates this decoupled training into an end-to-end framework. In particular, BiS progressively changes the data distribution during training such that in the beginning the feature extractor is trained effectively, while towards the end of the training the data is re-balanced such that the classifier is trained reliably. We benchmark our proposed bi-sampling strategy extensively on popular datasets and achieve state-of-the-art performances.

cs.CV↗

arXiv2021

TransFG: A Transformer Architecture for Fine-grained Recognition

Fine-grained visual classification (FGVC) which aims at recognizing objects from subcategories is a very challenging task due to the inherently subtle inter-class differences. Most existing works mainly tackle this problem by reusing the backbone network to extract features of detected discriminative regions. However, this strategy inevitably complicates the pipeline and pushes the proposed regions to contain most parts of the objects thus fails to locate the really important parts. Recently, vision transformer (ViT) shows its strong performance in the traditional classification task. The self-attention mechanism of the transformer links every patch token to the classification token. In this work, we first evaluate the effectiveness of the ViT framework in the fine-grained recognition setting. Then motivated by the strength of the attention link can be intuitively considered as an indicator of the importance of tokens, we further propose a novel Part Selection Module that can be applied to most of the transformer architectures where we integrate all raw attention weights of the transformer into an attention map for guiding the network to effectively and accurately select discriminative image patches and compute their relations. A contrastive loss is applied to enlarge the distance between feature representations of confusing classes. We name the augmented transformer-based model TransFG and demonstrate the value of it by conducting experiments on five popular fine-grained benchmarks where we achieve state-of-the-art performance. Qualitative results are presented for better understanding of our model.

cs.CV↗

arXiv2021

Distributed Dual Gradient Tracking for Economic Dispatch in Power Systems with Noisy Information

Distributed algorithms can be efficiently used for solving economic dispatch problem (EDP) in power systems. To implement a distributed algorithm, a communication network is required, making the algorithm vulnerable to noise which may cause detrimental decisions or even instability. In this paper, we propose an agent-based method which enables a fully distributed solution of the EDP in power systems with noisy information exchange. Through the novel design of the gradient tracking update and introducing suppression parameters, the proposed algorithm can effectively alleviate the impact of noise and it is shown to be more robust than the existing distributed algorithms. The convergence of the algorithm is also established under standard assumptions. Moreover, a strategy are presented to accelerate our proposed algorithm. Finally, the algorithm is tested on several IEEE bus systems to demonstrate its effectiveness and scalability.

eess.SY↗

arXiv2021

3rd Place Solution to Google Landmark Recognition Competition 2021

In this paper, we show our solution to the Google Landmark Recognition 2021 Competition. Firstly, embeddings of images are extracted via various architectures (i.e. CNN-, Transformer- and hybrid-based), which are optimized by ArcFace loss. Then we apply an efficient pipeline to re-rank predictions by adjusting the retrieval score with classification logits and non-landmark distractors. Finally, the ensembled model scores 0.489 on the private leaderboard, achieving the 3rd place in the 2021 edition of the Google Landmark Recognition Competition.

cs.CV↗

arXiv2021

Lithium Evolution of Giant Stars Observed by LAMOST and Kepler

Mapping lithium evolution for evolved stars will provide restrictions and constraints on the fundamental stellar interior physical processes, which further shed light on our understanding of the theory of stellar structure and evolution. Based on a sample of 1,848 giants with known evolutionary phases and lithium abundances from the LAMOST-\kepler{} and LAMOST-\emph{K}2 fields, we construct mass-radius diagrams to characterize the evolutionary features of lithium. The stars at red giant branch (RGB) phase show natural depletion along with their stellar evolution, particularly, there is no obvious crowd stars with anomalously high Li abundances near the bump. Most of the low-mass stars reaching their zero-age sequence of core-helium-burning (ZAHeB) have Li abundances around $\sim1.0$\,dex, which show an increase of Li abundance by $\sim0.6$\,dex compared to the stars above the bump of RGB. This suggests the helium flash can be responsible for moderate Li production. While for super Li-rich stars, some special mechanisms should be considered during helium flash. Other scenarios, such as merger, could also be interpretations given the Li-rich stars can be found at anytime during the steady state phase of core He-burning. During the core He-burning (HeB) phase, there is no indication of obvious lithium depletion.

astro-ph.SR↗

arXiv2021

Viola: A Topic Agnostic Generate-and-Rank Dialogue System

We present Viola, an open-domain dialogue system for spoken conversation that uses a topic-agnostic dialogue manager based on a simple generate-and-rank approach. Leveraging recent advances of generative dialogue systems powered by large language models, Viola fetches a batch of response candidates from various neural dialogue models trained with different datasets and knowledge-grounding inputs. Additional responses originating from template-based generators are also considered, depending on the user's input and detected entities. The hand-crafted generators build on a dynamic knowledge graph injected with rich content that is crawled from the web and automatically processed on a daily basis. Viola's response ranker is a fine-tuned polyencoder that chooses the best response given the dialogue history. While dedicated annotations for the polyencoder alone can indirectly steer it away from choosing problematic responses, we add rule-based safety nets to detect neural degeneration and a dedicated classifier to filter out offensive content. We analyze conversations that Viola took part in for the Alexa Prize Socialbot Grand Challenge 4 and discuss the strengths and weaknesses of our approach. Lastly, we suggest future work with a focus on curating conversation data specifcially for socialbots that will contribute towards a more robust data-driven socialbot.

cs.CL↗

arXiv2021

Chemical abundances of three new Ba stars from the Keck/HIRES spectra

Based on high resolution, high signal-to-noise (S/N) ratio spectra from Keck/HIRES, we have determined abundances of 20 elements for 18 Ba candidates. The parameter space of these stars are in the range of 4880 $\leq$ $\rm{T_{eff}}$ $\leq$ 6050 K, 2.56 $\leq$ log $g$ $\leq$ 4.53 dex and -0.27 $\leq$ [Fe/H] $\leq$ 0.09 dex. It is found that four of them can be identified as Ba stars with [s/Fe] $>$ 0.25 dex (s: Sr, Y, Zr, Ba, La, Ce and Nd), and three of them are newly discovered, which includes two Ba giants (HD 16178 and HD 22233) and one Ba subgiant (HD 2946). Our results show that the abundances of $α$, odd and iron-peak elements (O, Na, Mg, Al, Si, Ca, Sc, Ti, Mn, Ni and Cu) for our program stars are similar to those of the thin disk, while the distribution of [hs/ls] (hs: Ba, La, Ce and Nd, ls: Sr, Y and Zr) ratios of our Ba stars is similar to those of the known Ba objects. None of the four Ba stars show clear enhancement in carbon including the known CH subgiant HD 4395. It is found that three of the Ba stars present clear evidences of hosting stellar or sub-stellar companions from the radial velocity data.

astro-ph.SR↗

arXiv2021

Optimized nonadiabatic holonomic quantum computation based on Förster resonance in Rydberg atoms

In this paper, we propose a scheme for implementing the nonadiabatic holonomic quantum computation (NHQC+) of two Rydberg atoms by using invariant-based reverse engineering (IBRE). The scheme is based on Förster resonance induced by strong dipole-dipole interaction between two Rydberg atoms, which provides a selective coupling mechanism to simply the dynamics of system. Moreover, for improving the fidelity of the scheme, the optimal control method is introduced to enhance the gate robustness against systematic errors. Numerical simulations show the scheme is robust against the random noise in control fields, the deviation of dipole-dipole interaction, the Förster defect, and the spontaneous emission of atoms. Therefore, the scheme may provide some useful perspectives for the realization of quantum computation with Rydberg atoms.

quant-ph↗