arXiv subjects

Qi Feng

Publications and source records attributed to Qi Feng.

At least 73 records · Page 4Linked to original sources

arXiv2023

Sub-Riemannian Ricci curvature via generalized Gamma $z$ calculus

We derive sub-Riemannian Ricci curvature tensor for sub-Riemannian manifolds. We provide examples including the Heisenberg group, displacement group, and Martinet sub-Riemannian structure with arbitrary weighted volumes, in which we establish analytical bounds for sub-Riemannian curvature dimension bounds and log-Sobolev inequalities. {These bounds can be used to establish the entropy dissipation results for sub-Riemannian drift diffusion processes on a compact spatial domain, in term of $L_1$ distance.} Our derivation of Ricci curvature is based on generalized Gamma $z$ calculus and $z$--Bochner's formula, where $z$ stands for extra directions introduced into the sub-Riemannian degenerate structure.

math.DG↗

arXiv2023

Effective manipulation and realization of a colossal nonlinear Hall effect in an electric-field tunable moiré system

The second-order nonlinear Hall effect illuminates a frequency-doubling transverse current emerging in quantum materials with broken inversion symmetry even when time-reversal symmetry is preserved. This nonlinear response originates from both the Berry curvature dipole and the chiral Bloch electron skew scatterings, reflecting various information of the lattice symmetries, band dispersions, and topology of the electron wavefunctions. Even though many efforts have been put in detecting the nonlinear Hall effect in diverse condensed matter systems, effective manipulation of the two principal mechanisms in a single system has been lacking, and the reported response is relatively weak. Here, we report effective manipulation of the nonlinear Hall effect and realization of a colossal second-order Hall conductivity, $\sim500 μmSV^{-1}$, orders of magnitudes higher than the reported values, in AB-BA stacked twisted double bilayer graphene. A Berry-curvature-dipole-dominated nonlinear Hall effect, as well as its controllable transition to skew-scattering-dominated response, is identified near the band edge. The colossal response, on the other hand, is detected near the van Hove singularities, mainly determined by the skew scattering of the chiral Bloch electrons. Our findings establish electrically tunable moiré systems promising for nonlinear Hall effect manipulations and applications.

cond-mat.mes-hall↗

arXiv2023

Rethinking the Data Annotation Process for Multi-view 3D Pose Estimation with Active Learning and Self-Training

Pose estimation of the human body and hands is a fundamental problem in computer vision, and learning-based solutions require a large amount of annotated data. In this work, we improve the efficiency of the data annotation process for 3D pose estimation problems with Active Learning (AL) in a multi-view setting. AL selects examples with the highest value to annotate under limited annotation budgets (time and cost), but choosing the selection strategy is often nontrivial. We present a framework to efficiently extend existing single-view AL strategies. We then propose two novel AL strategies that make full use of multi-view geometry. Moreover, we demonstrate additional performance gains by incorporating pseudo-labels computed during the AL process, which is a form of self-training. Our system significantly outperforms simulated annotation baselines in 3D body and hand pose estimation on two large-scale benchmarks: CMU Panoptic Studio and InterHand2.6M. Notably, on CMU Panoptic Studio, we are able to reduce the turn-around time by 60% and annotation cost by 80% when compared to the conventional annotation process.

cs.CV↗

arXiv2022

Non-reversible Parallel Tempering for Deep Posterior Approximation

Parallel tempering (PT), also known as replica exchange, is the go-to workhorse for simulations of multi-modal distributions. The key to the success of PT is to adopt efficient swap schemes. The popular deterministic even-odd (DEO) scheme exploits the non-reversibility property and has successfully reduced the communication cost from $O(P^2)$ to $O(P)$ given sufficiently many $P$ chains. However, such an innovation largely disappears in big data due to the limited chains and few bias-corrected swaps. To handle this issue, we generalize the DEO scheme to promote non-reversibility and propose a few solutions to tackle the underlying bias caused by the geometric stopping time. Notably, in big data scenarios, we obtain an appealing communication cost $O(P\log P)$ based on the optimal window size. In addition, we also adopt stochastic gradient descent (SGD) with large and constant learning rates as exploration kernels. Such a user-friendly nature enables us to conduct approximation tasks for complex posteriors without much tuning costs.

cs.LG↗

arXiv2022

Deep Signature FBSDE Algorithm

We propose a deep signature/log-signature FBSDE algorithm to solve forward-backward stochastic differential equations (FBSDEs) with state and path dependent features. By incorporating the deep signature/log-signature transformation into the recurrent neural network (RNN) model, our algorithm shortens the training time, improves the accuracy, and extends the time horizon comparing to methods in the existing literature. Moreover, our algorithms can be applied to a wide range of applications such as state and path dependent option pricing involving high-frequency data, model ambiguity, and stochastic games, which are linked to parabolic partial differential equations (PDEs), and path-dependent PDEs (PPDEs). Lastly, we also derive the convergence analysis of the deep signature/log-signature FBSDE algorithm.

cs.LG↗

arXiv2022

Efficient CNN Architecture Design Guided by Visualization

Modern efficient Convolutional Neural Networks(CNNs) always use Depthwise Separable Convolutions(DSCs) and Neural Architecture Search(NAS) to reduce the number of parameters and the computational complexity. But some inherent characteristics of networks are overlooked. Inspired by visualizing feature maps and N$\times$N(N$>$1) convolution kernels, several guidelines are introduced in this paper to further improve parameter efficiency and inference speed. Based on these guidelines, our parameter-efficient CNN architecture, called \textit{VGNetG}, achieves better accuracy and lower latency than previous networks with about 30%$\thicksim$50% parameters reduction. Our VGNetG-1.0MP achieves 67.7% top-1 accuracy with 0.99M parameters and 69.2% top-1 accuracy with 1.14M parameters on ImageNet classification dataset. Furthermore, we demonstrate that edge detectors can replace learnable depthwise convolution layers to mix features by replacing the N$\times$N kernels with fixed edge detection kernels. And our VGNetF-1.5MP archives 64.4%(-3.2%) top-1 accuracy and 66.2%(-1.4%) top-1 accuracy with additional Gaussian kernels.

cs.CV↗

arXiv2022

Snowmass2021 Cosmic Frontier White Paper:Primordial Black Hole Dark Matter

Primordial Black Holes (PBHs) are a viable candidate to comprise some or all of the dark matter and provide a unique window into the high-energy physics of the early universe. This white paper discusses the scientific motivation, current status, and future reach of observational searches for PBHs. Future observational facilities supported by DOE, NSF, and NASA will provide unprecedented sensitivity to PBHs. However, devoted analysis pipelines and theoretical modeling are required to fully leverage these novel data. The search for PBHs constitutes a low-cost, high-reward science case with significant impact on the high energy physics community.

hep-ph↗

arXiv2022

The 6th AI City Challenge

The 6th edition of the AI City Challenge specifically focuses on problems in two domains where there is tremendous unlocked potential at the intersection of computer vision and artificial intelligence: Intelligent Traffic Systems (ITS), and brick and mortar retail businesses. The four challenge tracks of the 2022 AI City Challenge received participation requests from 254 teams across 27 countries. Track 1 addressed city-scale multi-target multi-camera (MTMC) vehicle tracking. Track 2 addressed natural-language-based vehicle track retrieval. Track 3 was a brand new track for naturalistic driving analysis, where the data were captured by several cameras mounted inside the vehicle focusing on driver safety, and the task was to classify driver actions. Track 4 was another new track aiming to achieve retail store automated checkout using only a single view camera. We released two leader boards for submissions based on different methods, including a public leader board for the contest, where no use of external data is allowed, and a general leader board for all submitted results. The top performance of participating teams established strong baselines and even outperformed the state-of-the-art in the proposed challenge tracks.

cs.CV↗

arXiv2022

Giant second-order nonlinearity in twisted bilayer graphene

In the second-order response regime, the Hall voltage can be nonzero without breaking the time-reversal symmetry, as long as the system is noncentrosymmetric. There are multiple mechanisms with different scaling rules that contribute to the nonlinear Hall effect (NLHE). The intrinsic contribution is closely related to the Berry curvature dipole and has been extensively investigated recently. The study of the extrinsic contribution, however, is scarce, although it can enter the NLHE even in the leading order. Here, we report a giant nonlinear transport response in TBG, in which the intrinsic mechanism is forbidden. The magnitude and direction of the second-order nonlinearity can be effectively tuned by the gate voltage. The peak value of the second-order Hall conductivity close to the full filling of the moiré band reaches 8.76 $μmSV^{-1}$, four-order larger than those detected in $WTe_2$. The observed giant second-order nonlinearity can be understood from the collaboration of the asymmetric scattering of electrons off the static (Coulomb impurities) and dynamic disorders (phonons) in noncentrosymmetric crystals. It is mainly determined by the skew-scattering contribution from impurities at 1.7 K. The skew-scattering from phonons has a much larger coupling coefficient as suggested by the scaling results, and becomes as important as the impurity contribution as the temperature rises. Our observations demonstrate the potential of TBG in studying nonlinear response and possible rectification applications.

cond-mat.mes-hall↗

arXiv2022

Industrial Scene Text Detection with Refined Feature-attentive Network

Detecting the marking characters of industrial metal parts remains challenging due to low visual contrast, uneven illumination, corroded character structures, and cluttered background of metal part images. Affected by these factors, bounding boxes generated by most existing methods locate low-contrast text areas inaccurately. In this paper, we propose a refined feature-attentive network (RFN) to solve the inaccurate localization problem. Specifically, we design a parallel feature integration mechanism to construct an adaptive feature representation from multi-resolution features, which enhances the perception of multi-scale texts at each scale-specific level to generate a high-quality attention map. Then, an attentive refinement network is developed by the attention map to rectify the location deviation of candidate boxes. In addition, a re-scoring mechanism is designed to select text boxes with the best rectified location. Moreover, we construct two industrial scene text datasets, including a total of 102156 images and 1948809 text instances with various character structures and metal parts. Extensive experiments on our dataset and four public datasets demonstrate that our proposed method achieves the state-of-the-art performance.

cs.CV↗

arXiv2022

Design and Performance of the Prototype Schwarzschild-Couder Telescope Camera

The prototype Schwarzschild-Couder Telescope (pSCT) is a candidate for a medium-sized telescope in the Cherenkov Telescope Array. The pSCT is based on a novel dual mirror optics design which reduces the plate scale and allows for the use of silicon photomultipliers as photodetectors. The prototype pSCT camera currently has only the central sector instrumented with 25 camera modules (1600 pixels), providing a 2.68$^{\circ}$ field of view (FoV). The camera electronics are based on custom TARGET (TeV array readout with GSa/s sampling and event trigger) application specific integrated circuits. Field programmable gate arrays sample incoming signals at a gigasample per second. A single backplane provides camera-wide triggers. An upgrade of the pSCT camera is in progress, which will fully populate the focal plane. This will increase the number of pixels to 11,328, the number of backplanes to 9, and the FoV to 8.04$^{\circ}$. Here we give a detailed description of the pSCT camera, including the basic concept, mechanical design, detectors, electronics, current status and first light.

astro-ph.IM↗

arXiv2022

Tuning the competition between superconductivity and charge order in kagome superconductor Cs(V1-xNbx)3Sb5

The recently discovered coexistence of superconductivity and charge density wave order in the kagome systems AV3Sb5 (A = K, Rb, Cs) has stimulated enormous interest. According to theory, a vanadium-based kagome system may host a flat band, nontrivial linear dispersive Dirac surface states and electronic correlation. Despite intensive investigations, it remains controversial about the origin of the charge density wave (CDW) order, how does the superconductivity relate to the CDW, and whether the anomalous Hall effect (AHE) arises primarily from the kagome lattice or the CDW order. We report an extensive investigation on Cs(V1-xNbx)3Sb5 samples with systematic Nb doping. Our results show that the Nb doping induces apparent suppression of CDW order and promotes superconductivity; meanwhile, the AHE and magnetoresistance (MR) will be significantly weakened together with the CDW order. Combining with our density functional calculations, we interpret these effects by an antiphase shift of the Fermi energy with respect to the saddle points near M and the Fermi surface centered around Γ. It is found that the former depletes the filled states for the CDW instability and worsens the nesting condition for CDW order; while the latter lifts the Fermi level upward and enlarges the Fermi surface surrounding the Γ point, and thus promotes superconductivity. Our results uncover a delicate but unusual competition between the CDW order and superconductivity.

cond-mat.supr-con↗

arXiv2022

360 Depth Estimation in the Wild -- The Depth360 Dataset and the SegFuse Network

Single-view depth estimation from omnidirectional images has gained popularity with its wide range of applications such as autonomous driving and scene reconstruction. Although data-driven learning-based methods demonstrate significant potential in this field, scarce training data and ineffective 360 estimation algorithms are still two key limitations hindering accurate estimation across diverse domains. In this work, we first establish a large-scale dataset with varied settings called Depth360 to tackle the training data problem. This is achieved by exploring the use of a plenteous source of data, 360 videos from the internet, using a test-time training method that leverages unique information in each omnidirectional sequence. With novel geometric and temporal constraints, our method generates consistent and convincing depth samples to facilitate single-view estimation. We then propose an end-to-end two-branch multi-task learning network, SegFuse, that mimics the human eye to effectively learn from the dataset and estimate high-quality depth maps from diverse monocular RGB images. With a peripheral branch that uses equirectangular projection for depth estimation and a foveal branch that uses cubemap projection for semantic segmentation, our method predicts consistent global depth while maintaining sharp details at local regions. Experimental results show favorable performance against the state-of-the-art methods.

cs.CV↗

arXiv2021

Abnormal Occupancy Grid Map Recognition using Attention Network

The occupancy grid map is a critical component of autonomous positioning and navigation in the mobile robotic system, as many other systems' performance depends heavily on it. To guarantee the quality of the occupancy grid maps, researchers previously had to perform tedious manual recognition for a long time. This work focuses on automatic abnormal occupancy grid map recognition using the residual neural networks and a novel attention mechanism module. We propose an effective channel and spatial Residual SE(csRSE) attention module, which contains a residual block for producing hierarchical features, followed by both channel SE (cSE) block and spatial SE (sSE) block for the sufficient information extraction along the channel and spatial pathways. To further summarize the occupancy grid map characteristics and experiment with our csRSE attention modules, we constructed a dataset called occupancy grid map dataset (OGMD) for our experiments. On this OGMD test dataset, we tested few variants of our proposed structure and compared them with other attention mechanisms. Our experimental results show that the proposed attention network can infer the abnormal map with state-of-the-art (SOTA) accuracy of 96.23% for abnormal occupancy grid map recognition.

cs.CV↗

arXiv2021

Exploring the High-Energy Gamma-Ray Spectra of TeV Blazars

The highest-energy blazars exhibit non-thermal radiation extending beyond 1 TeV with high luminosities and strong variabilities, indicating extreme particle acceleration in their relativistic jets. The gamma-ray spectra of blazars contain information about the distribution and cooling processes of high-energy particles in jets, the extragalactic background light between the source and the observer, and potentially, the environment of the gamma-ray emitting region and exotic physics that may modify the opacity of the universe to gamma rays. We use data from Fermi-LAT and VERITAS to study the variability and spectra of a sample of TeV blazars across a wide range of gamma-ray energies, taking advantage of more than ten years of data from both instruments. The variability in both the GeV and TeV gamma-ray bands is investigated using a Bayesian blocks method to identify periods with a steady flux, during which the average gamma-ray spectra, after correcting for the pair absorption effect from propagation, can be parameterized without the risk of mixing different flux states. We report on the search for intrinsic spectral curvature and spectral variability in these blazars, in an effort to understand the physical mechanisms behind the high-energy gamma-ray spectra of TeV blazars.

astro-ph.HE↗

arXiv2021

Dynamic Multi-Scale Loss Optimization for Object Detection

With the continuous improvement of the performance of object detectors via advanced model architectures, imbalance problems in the training process have received more attention. It is a common paradigm in object detection frameworks to perform multi-scale detection. However, each scale is treated equally during training. In this paper, we carefully study the objective imbalance of multi-scale detector training. We argue that the loss in each scale level is neither equally important nor independent. Different from the existing solutions of setting multi-task weights, we dynamically optimize the loss weight of each scale level in the training process. Specifically, we propose an Adaptive Variance Weighting (AVW) to balance multi-scale loss according to the statistical variance. Then we develop a novel Reinforcement Learning Optimization (RLO) to decide the weighting scheme probabilistically during training. The proposed dynamic methods make better utilization of multi-scale training loss without extra computational complexity and learnable parameters for backpropagation. Experiments show that our approaches can consistently boost the performance over various baseline detectors on Pascal VOC and MS COCO benchmark.

cs.CV↗

arXiv2021

SiamSNN: Siamese Spiking Neural Networks for Energy-Efficient Object Tracking

Recently spiking neural networks (SNNs), the third-generation of neural networks has shown remarkable capabilities of energy-efficient computing, which is a promising alternative for deep neural networks (DNNs) with high energy consumption. SNNs have reached competitive results compared to DNNs in relatively simple tasks and small datasets such as image classification and MNIST/CIFAR, while few studies on more challenging vision tasks on complex datasets. In this paper, we focus on extending deep SNNs to object tracking, a more advanced vision task with embedded applications and energy-saving requirements, and present a spike-based Siamese network called SiamSNN. Specifically, we propose an optimized hybrid similarity estimation method to exploit temporal information in the SNNs, and introduce a novel two-status coding scheme to optimize the temporal distribution of output spike trains for further improvements. SiamSNN is the first deep SNN tracker that achieves short latency and low precision loss on the visual object tracking benchmarks OTB2013/2015, VOT2016/2018, and GOT-10k. Moreover, SiamSNN achieves notably low energy consumption and real-time on Neuromorphic chip TrueNorth.

cs.CV↗

arXiv2021

A Search for TeV Gamma-ray Emission from Pulsar Tails by VERITAS

We report on the search for very-high-energy gamma-ray emission from the regions around three nearby supersonic pulsars (PSR B0355+54, PSR J0357+3205 and PSR J1740+1000) that exhibit long X-ray tails. To date there is no clear detection of TeV emission from any pulsar tail that is prominent in X-ray or radio. We provide upper limits on the TeV flux, and luminosity, and also compare these limits with other pulsar wind nebulae detected in X-rays and the tail emission model predictions. We find that at least one of the three tails is likely to be detected in observations that are a factor of 2-3 more sensitive. The analysis presented here also has implications for deriving the properties of pulsar tails, for those pulsars whose tails could be detected in TeV.

astro-ph.HE↗