arXiv subjects

Di Huang

Publications and source records attributed to Di Huang.

At least 253 records · Page 14Linked to original sources

arXiv2019

Learning Spatial Fusion for Single-Shot Object Detection

Pyramidal feature representation is the common practice to address the challenge of scale variation in object detection. However, the inconsistency across different feature scales is a primary limitation for the single-shot detectors based on feature pyramid. In this work, we propose a novel and data driven strategy for pyramidal feature fusion, referred to as adaptively spatial feature fusion (ASFF). It learns the way to spatially filter conflictive information to suppress the inconsistency, thus improving the scale-invariance of features, and introduces nearly free inference overhead. With the ASFF strategy and a solid baseline of YOLOv3, we achieve the best speed-accuracy trade-off on the MS COCO dataset, reporting 38.1% AP at 60 FPS, 42.4% AP at 45 FPS and 43.9% AP at 29 FPS. The code is available at https://github.com/ruinmessi/ASFF

cs.CV↗

arXiv2019

Graph Representation Ensemble Learning

Representation learning on graphs has been gaining attention due to its wide applicability in predicting missing links, and classifying and recommending nodes. Most embedding methods aim to preserve certain properties of the original graph in the low dimensional space. However, real world graphs have a combination of several properties which are difficult to characterize and capture by a single approach. In this work, we introduce the problem of graph representation ensemble learning and provide a first of its kind framework to aggregate multiple graph embedding methods efficiently. We provide analysis of our framework and analyze -- theoretically and empirically -- the dependence between state-of-the-art embedding methods. We test our models on the node classification task on four real world graphs and show that proposed ensemble approaches can outperform the state-of-the-art methods by up to 8% on macro-F1. We further show that the approach is even more beneficial for underrepresented classes providing an improvement of up to 12%.

cs.SI↗

arXiv2019

Benchmarks for Graph Embedding Evaluation

Graph embedding is the task of representing nodes of a graph in a low-dimensional space and its applications for graph tasks have gained significant traction in academia and industry. The primary difference among the many recently proposed graph embedding methods is the way they preserve the inherent properties of the graphs. However, in practice, comparing these methods is very challenging. The majority of methods report performance boosts on few selected real graphs. Therefore, it is difficult to generalize these performance improvements to other types of graphs. Given a graph, it is currently impossible to quantify the advantages of one approach over another. In this work, we introduce a principled framework to compare graph embedding methods. Our goal is threefold: (i) provide a unifying framework for comparing the performance of various graph embedding methods, (ii) establish a benchmark with real-world graphs that exhibit different structural properties, and (iii) provide users with a tool to identify the best graph embedding method for their data. This paper evaluates 4 of the most influential graph embedding methods and 4 traditional link prediction methods against a corpus of 100 real-world networks with varying properties. We organize the 100 networks in terms of their properties to get a better understanding of the embedding performance of these popular methods. We use the comparisons on our 100 benchmark graphs to define GFS-score, that can be applied to any embedding method to quantify its performance. We rank the state-of-the-art embedding approaches using the GFS-score and show that it can be used to understand and evaluate novel embedding approaches. We envision that the proposed framework (https://www.github.com/palash1992/GEM-Benchmark) will serve the community as a benchmarking platform to test and compare the performance of future graph embedding techniques.

cs.SI↗

arXiv2019

Massive Multi-Agent Data-Driven Simulations of the GitHub Ecosystem

Simulating and predicting planetary-scale techno-social systems poses heavy computational and modeling challenges. The DARPA SocialSim program set the challenge to model the evolution of GitHub, a large collaborative software-development ecosystem, using massive multi-agent simulations. We describe our best performing models and our agent-based simulation framework, which we are currently extending to allow simulating other planetary-scale techno-social systems. The challenge problem measured participant's ability, given 30 months of meta-data on user activity on GitHub, to predict the next months' activity as measured by a broad range of metrics applied to ground truth, using agent-based simulation. The challenge required scaling to a simulation of roughly 3 million agents producing a combined 30 million actions, acting on 6 million repositories with commodity hardware. It was also important to use the data optimally to predict the agent's next moves. We describe the agent framework and the data analysis employed by one of the winning teams in the challenge. Six different agent models were tested based on a variety of machine learning and statistical methods. While no single method proved the most accurate on every metric, the broadly most successful sampled from a stationary probability distribution of actions and repositories for each agent. Two reasons for the success of these agents were their use of a distinct characterization of each agent, and that GitHub users change their behavior relatively slowly.

cs.MA↗

arXiv2019

An Improved Evaluation of the Neutron Background in the PandaX-II Experiment

In dark matter direct detection experiments, neutron is a serious source of background, which can mimic the dark matter-nucleus scattering signals. In this paper, we present an improved evaluation of the neutron background in the PandaX-II dark matter experiment by a novel approach. Instead of fully relying on the Monte Carlo simulation, the overall neutron background is determined from the neutron-induced high energy signals in the data. In addition, the probability of producing a dark-matter-like background per neutron is evaluated with a complete Monte Carlo generator, where the correlated emission of neutron(s) and $γ$(s) in the ($α$, n) reactions and spontaneous fissions is taken into consideration. With this method, the neutron backgrounds in the Run 9 (26-ton-day) and Run 10 (28-ton-day) data sets of PandaX-II are estimated to be 0.66$\pm$0.24 and 0.47$\pm$0.25 events, respectively.

hep-ex↗

arXiv2019

Searching for Neutrino-less Double Beta Decay of $^{136}$Xe with PandaX-II Liquid Xenon Detector

We report the Neutrino-less Double Beta Decay (NLDBD) search results from PandaX-II dual-phase liquid xenon time projection chamber. The total live time used in this analysis is 403.1 days from June 2016 to August 2018. With NLDBD-optimized event selection criteria, we obtain a fiducial mass of 219 kg of natural xenon. The accumulated xenon exposure is 242 kg$\cdot$yr, or equivalently 22.2 kg$\cdot$yr of $^{136}$Xe exposure. At the region around $^{136}$Xe decay Q-value of 2458 keV, the energy resolution of PandaX-II is 4.2%. We find no evidence of NLDBD in PandaX-II and establish a lower limit for decay half-life of 2.4 $ \times 10^{23} $ yr at the 90% confidence level, which corresponds to an effective Majorana neutrino mass $m_{ββ} < (1.3 - 3.5)$ eV. This is the first NLDBD result reported from a dual-phase xenon experiment.

hep-ex↗

arXiv2019

CompactNet: Platform-Aware Automatic Optimization for Convolutional Neural Networks

Convolutional Neural Network (CNN) based Deep Learning (DL) has achieved great progress in many real-life applications. Meanwhile, due to the complex model structures against strict latency and memory restriction, the implementation of CNN models on the resource-limited platforms is becoming more challenging. This work proposes a solution, called CompactNet\footnote{Project URL: \url{https://github.com/CompactNet/CompactNet}}, which automatically optimizes a pre-trained CNN model on a specific resource-limited platform given a specific target of inference speedup. Guided by a simulator of the target platform, CompactNet progressively trims a pre-trained network by removing certain redundant filters until the target speedup is reached and generates an optimal platform-specific model while maintaining the accuracy. We evaluate our work on two platforms of a mobile ARM CPU and a machine learning accelerator NPU (Cambricon-1A ISA) on a Huawei Mate10 smartphone. For the state-of-the-art slim CNN model made for the embedded platform, MobileNetV2, CompactNet achieves up to a 1.8x kernel computation speedup with equal or even higher accuracy for image classification tasks on the Cifar-10 dataset.

cs.LG↗

arXiv2019

Adaptive NMS: Refining Pedestrian Detection in a Crowd

Pedestrian detection in a crowd is a very challenging issue. This paper addresses this problem by a novel Non-Maximum Suppression (NMS) algorithm to better refine the bounding boxes given by detectors. The contributions are threefold: (1) we propose adaptive-NMS, which applies a dynamic suppression threshold to an instance, according to the target density; (2) we design an efficient subnetwork to learn density scores, which can be conveniently embedded into both the single-stage and two-stage detectors; and (3) we achieve state of the art results on the CityPersons and CrowdHuman benchmarks.

cs.CV↗

arXiv2019

Giant and nonreciprocal second harmonic generation from layered antiferromagnetism in bilayer CrI3

Layered antiferromagnetism is the spatial arrangement of ferromagnetic layers with antiferromagnetic interlayer coupling. Recently, the van der Waals magnet, chromium triiodide (CrI3), emerged as the first layered antiferromagnetic insulator in its few-layer form, opening up ample opportunities for novel device functionalities. Here, we discovered an emergent nonreciprocal second order nonlinear optical effect in bilayer CrI3. The observed second harmonic generation (SHG) is giant: several orders of magnitude larger than known magnetization induced SHG and comparable to SHG in the best 2D nonlinear optical materials studied so far (e.g. MoS2). We showed that while the parent lattice of bilayer CrI3 is centrosymmetric and thus does not contribute to the SHG signal, the observed nonreciprocal SHG originates purely from the layered antiferromagnetic order, which breaks both spatial inversion and time reversal symmetries. Furthermore, polarization-resolved measurements revealed the underlying C2h symmetry, and thus monoclinic stacking order in CrI3 bilayers, providing crucial structural information for the microscopic origin of layered antiferromagnetism. Our results highlight SHG as a highly sensitive probe that can reveal subtle magnetic order and open novel nonlinear and nonreciprocal optical device possibilities based on 2D magnets.

cond-mat.mes-hall↗

arXiv2019

PandaX-II Constraints on Spin-Dependent WIMP-Nucleon Effective Interactions

We present PandaX-II constraints on candidate WIMP-nucleon effective interactions involving the nucleon or WIMP spin, including, in addition to standard axial spin-dependent (SD) scattering, various couplings among vector and axial currents, magnetic and electric dipole moments, and tensor interactions. The data set corresponding to a total exposure of 54-ton-days is reanalyzed to determine constraints as a function of the WIMP mass and isospin coupling. We obtain WIMP-nucleon cross section bounds of $\rm 1.6 \times 10^{-41} cm^2$ and $\rm 9.0 \times 10^{-42} cm^2$ ($90\%$ c.l.) for neutron-only SD and tensor coupling, respectively, for a mass $M_\mathrm{WIMP} \sim {\rm 40~GeV}/c^2$. The SD limits are the best currently available for $M_\mathrm{WIMP} > {\rm 40~GeV}/c^2$. We show that PandaX-II has reached a sensitivity sufficient to probe a variety of other candidate spin-dependent interactions at the weak scale.

hep-ex↗

arXiv2019

Chiral selection rules for multi-photon processes in two-dimensional honeycomb materials

We examined the chirality dependent optical selection rules in two-dimensional monolayer materials with the honeycomb lattice, and based on symmetry argument, we generalized these rules to multi-photon transitions of arbitrary orders. We also presented the phase relations between incident and outgoing photons in such processes. The results agreed nicely with our experimental observations of second and third harmonic generations. In particular, we demonstrated that the phase relation of chiral second harmonic generation can serve as a handy tool for imaging domains and domain boundaries of these monolayers. Our results can benefit future studies on chirality related optical phenomena and opto-electronic applications of such materials.

cond-mat.mes-hall↗

arXiv2019

Learning Face Age Progression: A Pyramid Architecture of GANs

The two underlying requirements of face age progression, i.e. aging accuracy and identity permanence, are not well studied in the literature. In this paper, we present a novel generative adversarial network based approach. It separately models the constraints for the intrinsic subject-specific characteristics and the age-specific facial changes with respect to the elapsed time, ensuring that the generated faces present desired aging effects while simultaneously keeping personalized properties stable. Further, to generate more lifelike facial details, high-level age-specific features conveyed by the synthesized face are estimated by a pyramidal adversarial discriminator at multiple scales, which simulates the aging effects in a finer manner. The proposed method is applicable to diverse face samples in the presence of variations in pose, expression, makeup, etc., and remarkably vivid aging effects are achieved. Both visual fidelity and quantitative evaluations show that the approach advances the state-of-the-art.

cs.CV↗

arXiv2019

Learning Continuous Face Age Progression: A Pyramid of GANs

The two underlying requirements of face age progression, i.e. aging accuracy and identity permanence, are not well studied in the literature. This paper presents a novel generative adversarial network based approach to address the issues in a coupled manner. It separately models the constraints for the intrinsic subject-specific characteristics and the age-specific facial changes with respect to the elapsed time, ensuring that the generated faces present desired aging effects while simultaneously keeping personalized properties stable. To ensure photo-realistic facial details, high-level age-specific features conveyed by the synthesized face are estimated by a pyramidal adversarial discriminator at multiple scales, which simulates the aging effects with finer details. Further, an adversarial learning scheme is introduced to simultaneously train a single generator and multiple parallel discriminators, resulting in smooth continuous face aging sequences. The proposed method is applicable even in the presence of variations in pose, expression, makeup, etc., achieving remarkably vivid aging effects. Quantitative evaluations by a COTS face recognition system demonstrate that the target age distributions are accurately recovered, and 99.88% and 99.98% age progressed faces can be correctly verified at 0.001% FAR after age transformations of approximately 28 and 23 years elapsed time on the MORPH and CACD databases, respectively. Both visual and quantitative assessments show that the approach advances the state-of-the-art.

cs.CV↗

arXiv2018

Doping Induced Second Harmonic Generation in Centrosymmetric Graphene from Quadrupole Response

For centrosymmetric materials such as monolayer graphene, no optical second harmonic generation (SHG) is generally expected because it is forbidden under the electric-dipole approximation. Yet we observed a strong, doping induced SHG from graphene, with its highest strength comparable to the electric-dipole allowed SHG in non-centrosymmetric 2D materials. This novel SHG has the nature of an electric-quadrupole response, arising from the effective breaking of inversion symmetry by optical dressing with an in-plane photon wave vector. More remarkably, the SHG is widely tuned by carrier doping or chemical potential, being sharply enhanced at Fermi edge resonances, but vanishing at the charge neutral point that manifests the electron-hole symmetry of massless Dirac Fermions. The striking behavior in graphene, which should also arise in graphene-like Dirac materials, expands the scope of nonlinear optics, and holds the promise of novel optoelectronic and photonic applications.

cond-mat.mes-hall↗

arXiv2018

Receptive Field Block Net for Accurate and Fast Object Detection

Current top-performing object detectors depend on deep CNN backbones, such as ResNet-101 and Inception, benefiting from their powerful feature representations but suffering from high computational costs. Conversely, some lightweight model based detectors fulfil real time processing, while their accuracies are often criticized. In this paper, we explore an alternative to build a fast and accurate detector by strengthening lightweight features using a hand-crafted mechanism. Inspired by the structure of Receptive Fields (RFs) in human visual systems, we propose a novel RF Block (RFB) module, which takes the relationship between the size and eccentricity of RFs into account, to enhance the feature discriminability and robustness. We further assemble RFB to the top of SSD, constructing the RFB Net detector. To evaluate its effectiveness, experiments are conducted on two major benchmarks and the results show that RFB Net is able to reach the performance of advanced very deep detectors while keeping the real-time speed. Code is available at https://github.com/ruinmessi/RFBNet.

cs.CV↗

arXiv2018

Constraining Dark Matter Models with a Light Mediator from PandaX-II Experiment

We search for nuclear recoil signals of dark matter models with a light mediator in PandaX-II, a direct detection experiment in China Jinping underground Laboratory. Using data collected in 2016 and 2017 runs, corresponding to a total exposure of 54 ton day, we set upper limits on the zero-momentum dark matter-nucleon cross section. These limits have a strong dependence on the mediator mass when it is comparable to or below the typical momentum transfer. We apply our results to constrain self-interacting dark matter models with a light mediator mixing with standard model particles, and set strong limits on the model parameter space for the dark matter mass ranging from $5~{\rm GeV}$ to $10~{\rm TeV}$.

hep-ph↗

arXiv2018

Dark matter direct search sensitivity of the PandaX-4T experiment

The PandaX-4T experiment, a four-ton scale dark matter direct detection experiment, is being planned at the China Jinping Underground Laboratory. In this paper we present a simulation study of the expected background in this experiment. In a 2.8-ton fiducial mass and the signal region between 1 to 10 keV electron equivalent energy, the total electron recoil background is found to be 4.9x10^{-5} /(kg day keV). The nuclear recoil background in the same region is 2.8x10^{-7}/(kg day keV). With an exposure of 5.6 ton-years, the sensitivity of PandaX-4T could reach a minimum spin-independent dark matter-nucleon cross section of 6x10^{-48} cm^{2} at a dark matter mass of 40 GeV/c^{2}.

physics.ins-det↗

arXiv2018

Stacking-symmetry governed second harmonic generation in graphene trilayers

Crystal symmetry plays a central role in governing a wide range of fundamental physical phenomena. One example is the nonlinear optical second harmonic generation (SHG), which requires inversion symmetry breaking. Here we report a unique stacking-induced SHG in trilayer graphene, whose individual monolayer sheet is centrosymmetric. Depending on layer stacking sequence, we observe a strong optical SHG in Bernal (ABA) stacked non-centrosymmetric trilayer, while it vanishes in rhombohedral (ABC) stacked one which preserves inversion symmetry. This highly contrasting SHG due to the distinct stacking symmetry enables us to map out the ABA and ABC crystal domains in otherwise homogeneous graphene trilayer. The extracted second order nonlinear susceptibility of the ABA trilayer is surprisingly large, comparable to the best known 2D semiconductors enhanced by excitonic resonance. Our results reveal a novel stacking order induced nonlinear optical effect, as well as unleash the opportunity for studying intriguing physical phenomena predicted for stacking-dependent ABA and ABC graphene trilayers.

cond-mat.mes-hall↗