arXiv subjects

Gang Li

Publications and source records attributed to Gang Li.

At least 433 records · Page 24Linked to original sources

arXiv2022

Neutron-diffraction and linear {Grüneisen} parameter studies of magnetism in NdFe$_2$Ga$_8$

We study the magnetism in NdFe$_2$Ga$_8$ by the neutron-diffraction and temperature-modulated linear {Grüneisen} parameter measurements. Previous thermodynamical measurements have demonstrated that there are two magnetic transitions at 10 and 14.5 K, respectively. Neutron-diffraction measurements confirm that the lower one is an antiferromagnetic (AFM) transition with a commensurate magnetic structure. Both the commensurate and the incommensurate (IC) magnetic peaks are found below the higher transition but their intensities only gradually increase with decreasing temperature. Below 10 K, the commensurate peak intensity increases quickly with decreasing temperature, signaling the AFM transition, while the IC peak intensity disappears below 5 K. The linear {Grüneisen} parameter along the $c$ axis, $Γ_c$, shows a hysteresis behavior that is different from the hysteresis behavior for the magnetization $M$. We give a discussion of the origin of the magnetism in NdFe$_2$Ga$_8$.

cond-mat.str-el↗

arXiv2022

Efficient Algorithms and Implementation of a Semiparametric Joint Model for Longitudinal and Competing Risks Data: With Applications to Massive Biobank Data

Semiparametric joint models of longitudinal and competing risks data are computationally costly and their current implementations do not scale well to massive biobank data. This paper identifies and addresses some key computational barriers in a semiparametric joint model for longitudinal and competing risks survival data. By developing and implementing customized linear scan algorithms, we reduce the computational complexities from $O(n^2)$ or $O(n^3)$ to $O(n)$ in various components including numerical integration, risk set calculation, and standard error estimation, where $n$ is the number of subjects. Using both simulated and real world biobank data, we demonstrate that these linear scan algorithms generate drastic speed-up of up to hundreds of thousands fold when $n>10^4$, sometimes reducing the run-time from days to minutes. We have developed an R-package, FastJM, based on the proposed algorithms for joint modeling of longitudinal and time-to-event data with and without competing risks, and made it publicly available on the Comprehensive R Archive Network (CRAN).

stat.ME↗

arXiv2022

Distributed Differentially Private Ranking Aggregation

Ranking aggregation is commonly adopted in cooperative decision-making to assist in combining multiple rankings into a single representative. To protect the actual ranking of each individual, some privacy-preserving strategies, such as differential privacy, are often used. This, however, does not consider the scenario where the curator, who collects all rankings from individuals, is untrustworthy. This paper proposed a mechanism to solve the above situation using the distribute differential privacy framework. The proposed mechanism collects locally differential private rankings from individuals, then randomly permutes pairwise rankings using a shuffle model to further amplify the privacy protection. The final representative is produced by hierarchical rank aggregation. The mechanism was theoretically analysed and experimentally compared against existing methods, and demonstrated competitive results in both the output accuracy and privacy protection.

cs.CR↗

arXiv2022

$J/ψ$ associated production with a bottom quark pair from the Higgs boson decay in next-to-leading order QCD

In this work, we investigate the next-to-leading order (NLO) QCD correction to $J/ψ$ associated production with a bottom quark pair from the Higgs boson decay within the nonrelativistic QCD framework. From numerical results, {we find that the decay width of process $H \rightarrow b+ J/ψ+\bar{b}$ at leading order (LO) mainly comes from the contribution of the Fock state $^3S^{(8)}_1$, and the NLO QCD corrections significantly enhance the decay width at LO accuracy by about 2 times. At NLO accuracy, the Fock states $^3S^{(8)}_1$ and $^3P^{(8)}_J$ channels give the main contribution, accounting for about $68\%$ and $29\%$ of the total decay width of $J/ψ$ associated production with a bottom quark pair at NLO accuracy from the Higgs boson decay, respectively. Considering the dominant contribution of color octet (CO) channels at NLO accuracy, the inclusive decay process $H\to b+J/ψ+\bar b + X$ has the potential to be found in future colliders with high energy/luminosity.} The study of $J/ψ$ associated production with a bottom quark pair from the Higgs boson decay is not only useful to study the mechanism of color-octet, but also to assist in the investigation of the coupling for the Higgs boson with the bottom quark.

hep-ph↗

arXiv2022

Learning to Denoise Raw Mobile UI Layouts for Improving Datasets at Scale

The layout of a mobile screen is a critical data source for UI design research and semantic understanding of the screen. However, UI layouts in existing datasets are often noisy, have mismatches with their visual representation, or consists of generic or app-specific types that are difficult to analyze and model. In this paper, we propose the CLAY pipeline that uses a deep learning approach for denoising UI layouts, allowing us to automatically improve existing mobile UI layout datasets at scale. Our pipeline takes both the screenshot and the raw UI layout, and annotates the raw layout by removing incorrect nodes and assigning a semantically meaningful type to each node. To experiment with our data-cleaning pipeline, we create the CLAY dataset of 59,555 human-annotated screen layouts, based on screenshots and raw layouts from Rico, a public mobile UI corpus. Our deep models achieve high accuracy with F1 scores of 82.7% for detecting layout objects that do not have a valid visual representation and 85.9% for recognizing object types, which significantly outperforms a heuristic baseline. Our work lays a foundation for creating large-scale high quality UI layout datasets for data-driven mobile UI research and reduces the need of manual labeling efforts that are prohibitively expensive.

cs.HC↗

arXiv2021

High-temperature quantum spin Hall states in buckled III-V-monolayer/SiO$_{2}

After establishing the fundamental understanding and the high throughput topological characterization of nearly all inorganic three-dimensional materials, the general interest and the demand of functional applications drive the research of topological insulators to the exploration of systems with a more robust topological nature and fewer fabrication challenges. The successful demonstration of the room-temperature quantum spin Hall (QSH) states in bismuthene/SiC(0001), thus, triggers the search of two-dimensional topological systems that are experimentally easy to access and of even larger topological gaps. In this work, we propose a family of III-V honeycomb monolayers on SiO$_{2}$ to be the next generation of large gap QSH systems, based on which a spintronic device may potentially operate at room temperature due to its enlarged topological gap ($\sim$ 900 meV) as compared to bismuthene/SiC(0001). Fundamentally, this also realizes a band-inversion type QSH insulator that is distinct to the Kane-Mele type bismuthene/SiC(0001).

cond-mat.mes-hall↗

arXiv2021

Deep Treatment-Adaptive Network for Causal Inference

Causal inference is capable of estimating the treatment effect (i.e., the causal effect of treatment on the outcome) to benefit the decision making in various domains. One fundamental challenge in this research is that the treatment assignment bias in observational data. To increase the validity of observational studies on causal inference, representation based methods as the state-of-the-art have demonstrated the superior performance of treatment effect estimation. Most representation based methods assume all observed covariates are pre-treatment (i.e., not affected by the treatment), and learn a balanced representation from these observed covariates for estimating treatment effect. Unfortunately, this assumption is often too strict a requirement in practice, as some covariates are changed by doing an intervention on treatment (i.e., post-treatment). By contrast, the balanced representation learned from unchanged covariates thus biases the treatment effect estimation.

cs.LG↗

arXiv2021

Reentrance of metal-insulator transition and magnetic competitions on a triangular lattice with second nearest-neighbor hopping

The $120^{\circ}$ antiferromagnetism (AFM) is widely believed as the magnetic ground state of the triangular systems because of the geometrical frustration. The emergence of novel magnetism, such as the row-wise AFM in Mn/Cu(111) and Sn/Si(111), reveals the importance of the longer-range hopping on magnetic competitions in realistic material systems. By utilizing advanced many-body techniques, we systematically studied the isotropic triangular Hubbard model with second nearest-neighbor hopping $t^{\prime}$, including both the single- and the two-particle responses. We found that both electronic and magnetic phase transitions show a clear dependence on $t^{\prime}/t$. Consequently, we observed a remarkable reentrance of the metal-insulator transition and a crossover between the $120^{\circ}$- and the row-wise AFM. The Fermi surface (FS) shows two distinct structures with the nesting vectors consistent with the magnetic correlations. When $t^{\prime}$ evolves from 0 to 1, the correlated Fermi surface demonstrates a Lifschitz transition between the two nesting structures, and exotic phases like the featureless insulating state can be realized. Our work sheds light on the engineering of electronic and magnetic correlations of correlated triangular surfaces via longer-range hopping. The rich phase diagram and the high degree of tunability make the triangular lattice with longer-range hopping a more realistic platform to study the emergent magnetic competitions.

cond-mat.str-el↗

arXiv2021

SimViT: Exploring a Simple Vision Transformer with sliding windows

Although vision Transformers have achieved excellent performance as backbone models in many vision tasks, most of them intend to capture global relations of all tokens in an image or a window, which disrupts the inherent spatial and local correlations between patches in 2D structure. In this paper, we introduce a simple vision Transformer named SimViT, to incorporate spatial structure and local information into the vision Transformers. Specifically, we introduce Multi-head Central Self-Attention(MCSA) instead of conventional Multi-head Self-Attention to capture highly local relations. The introduction of sliding windows facilitates the capture of spatial structure. Meanwhile, SimViT extracts multi-scale hierarchical features from different layers for dense prediction tasks. Extensive experiments show the SimViT is effective and efficient as a general-purpose backbone model for various image processing tasks. Especially, our SimViT-Micro only needs 3.3M parameters to achieve 71.1% top-1 accuracy on ImageNet-1k dataset, which is the smallest size vision Transformer model by now. Our code will be available in https://github.com/ucasligang/SimViT.

cs.CV↗

arXiv2021

VUT: Versatile UI Transformer for Multi-Modal Multi-Task User Interface Modeling

User interface modeling is inherently multimodal, which involves several distinct types of data: images, structures and language. The tasks are also diverse, including object detection, language generation and grounding. In this paper, we present VUT, a Versatile UI Transformer that takes multimodal input and simultaneously accomplishes 5 distinct tasks with the same model. Our model consists of a multimodal Transformer encoder that jointly encodes UI images and structures, and performs UI object detection when the UI structures are absent in the input. Our model also consists of an auto-regressive Transformer model that encodes the language input and decodes output, for both question-answering and command grounding with respect to the UI. Our experiments show that for most of the tasks, when trained jointly for multi-tasks, VUT substantially reduces the number of models and footprints needed for performing multiple tasks, while achieving accuracy exceeding or on par with baseline models trained for each individual task.

cs.CV↗

arXiv2021

Knowledge Distillation for Object Detection via Rank Mimicking and Prediction-guided Feature Imitation

Knowledge Distillation (KD) is a widely-used technology to inherit information from cumbersome teacher models to compact student models, consequently realizing model compression and acceleration. Compared with image classification, object detection is a more complex task, and designing specific KD methods for object detection is non-trivial. In this work, we elaborately study the behaviour difference between the teacher and student detection models, and obtain two intriguing observations: First, the teacher and student rank their detected candidate boxes quite differently, which results in their precision discrepancy. Second, there is a considerable gap between the feature response differences and prediction differences between teacher and student, indicating that equally imitating all the feature maps of the teacher is the sub-optimal choice for improving the student's accuracy. Based on the two observations, we propose Rank Mimicking (RM) and Prediction-guided Feature Imitation (PFI) for distilling one-stage detectors, respectively. RM takes the rank of candidate boxes from teachers as a new form of knowledge to distill, which consistently outperforms the traditional soft label distillation. PFI attempts to correlate feature differences with prediction differences, making feature imitation directly help to improve the student's accuracy. On MS COCO and PASCAL VOC benchmarks, extensive experiments are conducted on various detectors with different backbones to validate the effectiveness of our method. Specifically, RetinaNet with ResNet50 achieves 40.4% mAP in MS COCO, which is 3.5% higher than its baseline, and also outperforms previous KD methods.

cs.CV↗

arXiv2021

The In-Flight Realtime Trigger and Localization Software of GECAM

Realtime trigger and localization of bursts are the key functions of GECAM, which is an all-sky gamma-ray monitor launched in Dec 10, 2020. We developed a multifunctional trigger and localization software operating on the CPU of the GECAM electronic box (EBOX). This onboard software has the following features: high trigger efficiency for real celestial bursts with a suppression of false triggers caused by charged particle bursts and background fluctuation, dedicated localization algorithm optimized for short and long bursts respetively, short time latency of the trigger information which is downlinked throught the BeiDou satellite navigation System (BDS). This paper presents the detailed design and deveopment of this trigger and localization software system of GECAM, including the main functions, general design, workflow and algorithms, as well as the verification and demonstration of this software, including the on-ground trigger tests with simulated gamma-ray bursts made by a dedicated X-ray tube and the in-flight performance to real gamma-ray bursts and magnetar bursts.

astro-ph.IM↗

arXiv2021

A Fast and Scalable Implementation Method for Competing Risks Data with the R Package fastcmprsk

Advancements in medical informatics tools and high-throughput biological experimentation make large-scale biomedical data routinely accessible to researchers. Competing risks data are typical in biomedical studies where individuals are at risk to more than one cause (type of event) which can preclude the others from happening. The Fine-Gray model is a popular and well-appreciated model for competing risks data and is currently implemented in a number of statistical software packages. However, current implementations are not computationally scalable for large-scale competing risks data. We have developed an R package, fastcmprsk, that uses a novel forward-backward scan algorithm to significantly reduce the computational complexity for parameter estimation by exploiting the structure of the subject-specific risk sets. Numerical studies compare the speed and scalability of our implementation to current methods for unpenalized and penalized Fine-Gray regression and show impressive gains in computational efficiency.

stat.CO↗

arXiv2021

Production of $Z_{cs}$ in $B$ and $B_s$ decay

In the present work, we investigate the production of $Z_{cs}^+$ in $B^+$ and $B_s^0$ decay, where $Z_{cs}^+$ is assigned as a $D_s^{+} \bar{D}^{\ast0} + D_s^{\ast +}\bar{D}^0$ molecular state. By using an effective Lagrangian approach, we evaluate the branching ratio of $B^0_s\rightarrow K^- Z^+_{cs}$ and $B^+\rightarrow ϕZ^{+}_{cs}$ via the triangle loop mechanism. The estimated branching fractions of $B^0_s\rightarrow K^- Z^+_{cs}$ and $B^+\rightarrow ϕZ^{+}_{cs}$ are an order of $10^{-4} $ and $10^{-5}$, respectively. The ratio of these two branching fraction is estimated to be about 5, which indicate that the $B_s^0 \to K^\pm Z^\mp_{cs} \to K^+ K^- J/ψ$ may be a better process of searching $Z_{cs}$ and accessible for further experimental measurement of the Belle II and LHCb collaborations.

hep-ph↗

arXiv2021

Estimate of the Background and Sensitivity of theFollow-up X-ray Telescope onboard Einstein Probe

As a space X-ray imaging mission dedicated to time-domain astrophysics, the Einstein Probe (EP) carries two kinds of scientific payloads, the wide-field X-ray telescope (WXT) and the follow-up X-ray telescope (FXT). FXT utilizes Wolter-I type mirrors and the pn-CCD detectors. In this work, we investigate the in-orbit background of FXT based on Geant4 simulation. The impact of various space components present in the EP orbital environment are considered, such as the cosmic photon background, cosmic ray primary and secondary particles (e.g. protons, electrons and positrons), albedo gamma rays, and the low-energy protons near the geomagnetic equator. The obtained instrumental background at 0.5-10 keV, which is mainly induced by cosmic ray protons and cosmic photon background, corresponds to a level of $\sim$3.1$\times$10$^{-2}$ counts s$^{-1}$ keV$^{-1}$ in the imaging area of the focal plane detector (FPD), i.e. 3.7$\times$10$^{-3}$ counts s$^{-1}$ keV$^{-1}$ cm$^{-2}$ after normalization. Compared with the instrumental background, the field of view (FOV) background, which is induced by cosmic photons reflected by the optical mirror, dominates below 2 keV. Based on the simulated background level within the focal spot (a 30$^{\prime\prime}$-radius circle), the sensitivity of FXT is calculated, which could theoretically achieve several $μ$crab (in the order of 10$^{-14}$ erg cm$^{-2}$ s$^{-1}$) in 0.5-2 keV and several tens of $μ$crab (in the order of 10$^{-13}$ erg cm$^{-2}$ s$^{-1}$) in 2-10 keV for a pointed observation with an exposure of 25 minutes. This sensitivity becomes worse by a factor of $\sim2$ if additional 10% systematic uncertainty of the background subtraction is included.

astro-ph.IM↗

arXiv2021

Discovery of two families of VSb-based compounds with V-kagome lattice

We report the structure and physical properties of two newly-discovered compounds AV8Sb12 and AV6Sb6 (A = Cs, Rb), which have C2 (space group: Cmmm) and C3 (space group: R-3m) symmetry, respectively. The basic V-kagome unit is present in both compounds, but stacking differently. A V2Sb2 layer is sandwiched between two V3Sb5 layers in AV8Sb12, altering the V-kagome lattice and lowering the symmetry of kagome layer from hexagonal to orthorhombic. In AV6Sb6, the building block is a more complex slab made up of two half-V3Sb5 layers that are intercalated by Cs cations along the c-axis. Transport property measurements demonstrate that both compounds are nonmagnetic metals, with carrier concentrations at around 1021cm-3. No superconductivity has been observed in CsV8Sb12 above 0.3 K under in-situ pressure up to 46 GPa. In contrast to CsV3Sb5, theoretical calculations and angle-resolved photoemission spectroscopy (ARPES) reveal a quasi-two-dimensional electronic structure in CsV8Sb12 with C2 symmetry and no van Hove singularities near the Fermi level. Our findings will stimulate more research into V-based kagome quantum materials.

cond-mat.supr-con↗

arXiv2021

Relating the Solar Wind Turbulence Spectral Break at the Dissipation Range with an Upstream Spectral Bump at Planetary Bow Shocks

At scales much larger than the ion inertial scale and the gyro-radius of thermal protons, magnetohydrodynamic (MHD) theory is well equipped to describe the nature of solar wind turbulence. The turbulent spectrum itself is defined by a power-law manifesting the energy cascading process. A break in the turbulence spectrum develops near ion scales, signaling the onset of energy dissipation. The exact mechanism for the spectral break is still a matter of debate. In this work, we use the 20 Hz \textit{MESSENGER} magnetic field data during four planetary flybys at different heliocentric distances to examine the nature of the spectral break in the solar wind. %By carefully selecting the spacecraft trajectory, We relate the spectral break frequencies of the solar wind MHD turbulence, found in the range of $0.3$ to $0.7$ Hz, with the well-known characteristic spectral bump at frequencies $\sim 1$ Hz upstream of planetary bow shocks. Spectral breaks and spectral bumps during three planetary flybys are identified from the \textit{MESSENGER} observations, with heliocentric distances in the range of $0.3$ to $0.7$ au. The \textit{MESSENGER} observations are complemented by one \textit{MMS} observation made at 1 au. We find that the ratio of the spectral bump frequency to the spectral break frequency appears to be $r$- and $B$- independent. From this, we postulate that the wavenumber of the spectral break and the frequency of the spectral bump have the same dependence on the magnetic field strength $|B|$. The implication of our work on the nature of the break scale is discussed.

physics.space-ph↗

arXiv2021

Learnable Fourier Features for Multi-Dimensional Spatial Positional Encoding

Attentional mechanisms are order-invariant. Positional encoding is a crucial component to allow attention-based deep model architectures such as Transformer to address sequences or images where the position of information matters. In this paper, we propose a novel positional encoding method based on learnable Fourier features. Instead of hard-coding each position as a token or a vector, we represent each position, which can be multi-dimensional, as a trainable encoding based on learnable Fourier feature mapping, modulated with a multi-layer perceptron. The representation is particularly advantageous for a spatial multi-dimensional position, e.g., pixel positions on an image, where $L_2$ distances or more complex positional relationships need to be captured. Our experiments based on several public benchmark tasks show that our learnable Fourier feature representation for multi-dimensional positional encoding outperforms existing methods by both improving the accuracy and allowing faster convergence.

cs.LG↗