arXiv subjects

Gang Xiao

Publications and source records attributed to Gang Xiao.

At least 19 recordsLinked to original sources

arXiv2026

Intrinsic topological spin probes for electrical imaging of nanoscale energy landscapes

Disorder in magnetic materials prevents reliable control of spin textures and constrains their integration into spintronic devices. Existing methods access disorder only indirectly through external imaging probes or bulk transport measurements, leaving the internal energy landscape inaccessible. We introduce an intrinsic magnetic microscopy method in which a topological spin texture serves as a mobile probe of disorder, directly mapping energy landscapes inside multilayer devices without probe-sample separation. Using a ~10-nm magnetic vortex core confined within a magnetic tunnel junction, we track its displacement with nanometer-scale sensitivity to resolve intrinsic and engineered defect-induced potentials and directly quantify local pinning forces. This framework establishes spin textures as internal spectroscopic probes of disorder and enables quantitative engineering of pinning structures in functional magnetic systems.

cond-mat.mes-hall↗

arXiv2023

A Survey of Knowledge Enhanced Pre-trained Models

Pre-trained language models learn informative word representations on a large-scale text corpus through self-supervised learning, which has achieved promising performance in fields of natural language processing (NLP) after fine-tuning. These models, however, suffer from poor robustness and lack of interpretability. We refer to pre-trained language models with knowledge injection as knowledge-enhanced pre-trained language models (KEPLMs). These models demonstrate deep understanding and logical reasoning and introduce interpretability. In this survey, we provide a comprehensive overview of KEPLMs in NLP. We first discuss the advancements in pre-trained language models and knowledge representation learning. Then we systematically categorize existing KEPLMs from three different perspectives. Finally, we outline some potential directions of KEPLMs for future research.

cs.CL↗

arXiv2023

Point Contrastive Prediction with Semantic Clustering for Self-Supervised Learning on Point Cloud Videos

We propose a unified point cloud video self-supervised learning framework for object-centric and scene-centric data. Previous methods commonly conduct representation learning at the clip or frame level and cannot well capture fine-grained semantics. Instead of contrasting the representations of clips or frames, in this paper, we propose a unified self-supervised framework by conducting contrastive learning at the point level. Moreover, we introduce a new pretext task by achieving semantic alignment of superpoints, which further facilitates the representations to capture semantic cues at multiple scales. In addition, due to the high redundancy in the temporal dimension of dynamic point clouds, directly conducting contrastive learning at the point level usually leads to massive undesired negatives and insufficient modeling of positive representations. To remedy this, we propose a selection strategy to retain proper negatives and make use of high-similarity samples from other instances as positive supplements. Extensive experiments show that our method outperforms supervised counterparts on a wide range of downstream tasks and demonstrates the superior transferability of the learned representations.

cs.CV↗

arXiv2023

Electronic Noise of a Single Skyrmion

To enable the practical use of skyrmion-based devices, it is essential to achieve a balance between energy efficiency and thermal stability, while also ensuring reliable electrical detection against noise. Understanding how a skyrmion interacts with material disorder and external perturbations is thus essential. Here we investigate the electronic noise of a single skyrmion under the influence of thermal fluctuations and spin currents in a magnetic thin film. We detect the thermally induced noise with a 1/f signature in the strong pinning regime but a random telegraph noise in the intermediate pinning regime. Both the thermally dominated and current-induced telegraph-like signals are detected in the weak pinning regime. Our results provide a comprehensive electronic noise picture of a single skyrmion, demonstrating the potential of noise fluctuation as a valuable tool for characterizing the pinning condition of a skyrmion. These insights could also aid in the development of low-noise and reliable skyrmion-based devices.

cond-mat.mes-hall↗

arXiv2023

Exploring the Physical World Adversarial Robustness of Vehicle Detection

Adversarial attacks can compromise the robustness of real-world detection models. However, evaluating these models under real-world conditions poses challenges due to resource-intensive experiments. Virtual simulations offer an alternative, but the absence of standardized benchmarks hampers progress. Addressing this, we propose an innovative instant-level data generation pipeline using the CARLA simulator. Through this pipeline, we establish the Discrete and Continuous Instant-level (DCI) dataset, enabling comprehensive experiments involving three detection models and three physical adversarial attacks. Our findings highlight diverse model performances under adversarial conditions. Yolo v6 demonstrates remarkable resilience, experiencing just a marginal 6.59% average drop in average precision (AP). In contrast, the ASA attack yields a substantial 14.51% average AP reduction, twice the effect of other algorithms. We also note that static scenes yield higher recognition AP values, and outcomes remain relatively consistent across varying weather conditions. Intriguingly, our study suggests that advancements in adversarial attack algorithms may be approaching its ``limitation''.In summary, our work underscores the significance of adversarial attacks in real-world contexts and introduces the DCI dataset as a versatile benchmark. Our findings provide valuable insights for enhancing the robustness of detection models and offer guidance for future research endeavors in the realm of adversarial attacks.

cs.CV↗

arXiv2023

Spin Textures in Synthetic Antiferromagnets: Challenges, Opportunities, and Future

Spin textures such as magnetic domain walls and skyrmions have the potential to revolutionize electronic devices by encoding information bits. Although recent advancements in ferromagnetic films have led to promising device prototypes, their widespread implementation has been hindered by the material-related drawbacks. Antiferromagnetic spin textures, however, offer a solution to many of these limitations, paving the way for faster, smaller, more energy-efficient, and more robust electronics. The functionality of synthetic antiferromagnets, comprised of two or more magnetic layers separated by spacers, may be easily manipulated by making use of different materials as well as interface engineering. In this Perspective article, we examine the challenges and opportunities presented by spin textures in synthetic antiferromagnets and propose possible directions and prospects for future research in this burgeoning field.

cond-mat.mtrl-sci↗

arXiv2023

Contrastive Predictive Autoencoders for Dynamic Point Cloud Self-Supervised Learning

We present a new self-supervised paradigm on point cloud sequence understanding. Inspired by the discriminative and generative self-supervised methods, we design two tasks, namely point cloud sequence based Contrastive Prediction and Reconstruction (CPR), to collaboratively learn more comprehensive spatiotemporal representations. Specifically, dense point cloud segments are first input into an encoder to extract embeddings. All but the last ones are then aggregated by a context-aware autoregressor to make predictions for the last target segment. Towards the goal of modeling multi-granularity structures, local and global contrastive learning are performed between predictions and targets. To further improve the generalization of representations, the predictions are also utilized to reconstruct raw point cloud sequences by a decoder, where point cloud colorization is employed to discriminate against different frames. By combining classic contrast and reconstruction paradigms, it makes the learned representations with both global discrimination and local perception. We conduct experiments on four point cloud sequence benchmarks, and report the results on action recognition and gesture recognition under multiple experimental settings. The performances are comparable with supervised methods and show powerful transferability.

cs.CV↗

arXiv2022

Perspectives on Antiferromagnetic Spintronics

Although the development of spintronic devices has advanced significantly over the past decade with the use of ferromagnetic materials, the extensive implementation of such devices has been limited by the notable drawbacks of these materials. Antiferromagnets claim to resolve many of these shortcomings leading to faster, smaller, more energy-efficient, and more robust electronics. Antiferromagnets exhibit many desirable properties including zero net magnetization, imperviousness to external magnetic fields, intrinsic high-frequency dynamics with a characteristic precession frequency on the order of terahertz (THz), and the ability to serve as passive exchange-bias materials in multiple magnetoresistance (MR)- based devices. In this Perspective article, we will discuss the fundamental physics of magnetic structures in antiferromagnets and their interactions with external stimuli such as spin current, voltage, and magnons. A discussion on the challenges lying ahead is also provided along with an outlook of future research directions of these systems.

cond-mat.mtrl-sci↗

arXiv2022

A Comprehensive Evaluation Framework for Deep Model Robustness

Deep neural networks (DNNs) have achieved remarkable performance across a wide range of applications, while they are vulnerable to adversarial examples, which motivates the evaluation and benchmark of model robustness. However, current evaluations usually use simple metrics to study the performance of defenses, which are far from understanding the limitation and weaknesses of these defense methods. Thus, most proposed defenses are quickly shown to be attacked successfully, which results in the ``arm race'' phenomenon between attack and defense. To mitigate this problem, we establish a model robustness evaluation framework containing 23 comprehensive and rigorous metrics, which consider two key perspectives of adversarial learning (i.e., data and model). Through neuron coverage and data imperceptibility, we use data-oriented metrics to measure the integrity of test examples; by delving into model structure and behavior, we exploit model-oriented metrics to further evaluate robustness in the adversarial setting. To fully demonstrate the effectiveness of our framework, we conduct large-scale experiments on multiple datasets including CIFAR-10, SVHN, and ImageNet using different models and defenses with our open-source platform. Overall, our paper provides a comprehensive evaluation framework, where researchers could conduct comprehensive and fast evaluations using the open-source toolkit, and the analytical results could inspire deeper understanding and further improvement to the model robustness.

cs.CV↗

arXiv2022

A Route Network Planning Method for Urban Air Delivery

High-tech giants and start-ups are investing in drone technologies to provide urban air delivery service, which is expected to solve the last-mile problem and mitigate road traffic congestion. However, air delivery service will not scale up without proper traffic management for drones in dense urban environment. Currently, a range of Concepts of Operations (ConOps) for unmanned aircraft system traffic management (UTM) are being proposed and evaluated by researchers, operators, and regulators. Among these, the tube-based (or corridor-based) ConOps has emerged in operations in some regions of the world for drone deliveries and is expected to continue serving certain scenarios that with dense and complex airspace and requires centralized control in the future. Towards the tube-based ConOps, we develop a route network planning method to design routes (tubes) in a complex urban environment in this paper. In this method, we propose a priority structure to decouple the network planning problem, which is NP-hard, into single-path planning problems. We also introduce a novel space cost function to enable the design of dense and aligned routes in a network. The proposed method is tested on various scenarios and compared with other state-of-the-art methods. Results show that our method can generate near-optimal route networks with significant computational time-savings.

cs.MA↗

arXiv2022

The Large High Altitude Air Shower Observatory (LHAASO) Science Book (2021 Edition)

Since the science white paper of the Large High Altitude Air Shower Observatory (LHAASO) published on arXiv in 2019 [e-Print: 1905.02773 (astro-ph.HE)], LHAASO has completed the transition from a project to an operational gamma-ray astronomical observatory LHAASO is a new generation multi-component facility located in Daocheng, Sichuan province of China, at an altitude of 4410 meters. It aims at measuring with unprecedented sensitivity the spectrum, composition, and anisotropy of cosmic rays in the energy range between 10$^{12}$ and 10$^{18}$~eV, and acting simultaneously as a wide aperture (one stereoradiant) continuously operating gamma-ray telescope in the energy range between 10$^{11}$ and $10^{15}$~eV with the designed sensitivity of 1.3\% of the Crab Unit (CU) above 100 TeV. LHAASO's capability of measuring simultaneously different shower components (electrons, muons, and Cherenkov/fluorescence light), will allow it to investigate the origin, acceleration, and propagation of CR through measurement of the energy spectrum, elemental composition, and anisotropy with unprecedented resolution. The remarkable sensitivity of LHAASO will play a key role in CR physics and gamma-ray astronomy for a general and comprehensive exploration of the high energy universe and will allow important studies of fundamental physics (such as indirect dark matter search, Lorentz invariance violation, quantum gravity) and solar and heliospheric physics. The LHAASO Collaboration organized an editorial working group and finished all editorial work of this science book, to summarize the instrumental features and outline the prospects of scientific researches with the LHAASO experiment.

astro-ph.HE↗

arXiv2021

NAS-TC: Neural Architecture Search on Temporal Convolutions for Complex Action Recognition

In the field of complex action recognition in videos, the quality of the designed model plays a crucial role in the final performance. However, artificially designed network structures often rely heavily on the researchers' knowledge and experience. Accordingly, because of the automated design of its network structure, Neural architecture search (NAS) has achieved great success in the image processing field and attracted substantial research attention in recent years. Although some NAS methods have reduced the number of GPU search days required to single digits in the image field, directly using 3D convolution to extend NAS to the video field is still likely to produce a surge in computing volume. To address this challenge, we propose a new processing framework called Neural Architecture Search- Temporal Convolutional (NAS-TC). Our proposed framework is divided into two phases. In the first phase, the classical CNN network is used as the backbone network to complete the computationally intensive feature extraction task. In the second stage, a simple stitching search to the cell is used to complete the relatively lightweight long-range temporal-dependent information extraction. This ensures our method will have more reasonable parameter assignments and can handle minute-level videos. Finally, we conduct sufficient experiments on multiple benchmark datasets and obtain competitive recognition accuracy.

cs.CV↗

arXiv2021

Magnetic field-induced thermal emission tuning of InSb-based metamaterials in the terahertz frequency regime

This work theoretically and analytically demonstrates the magnetic field-induced spectral radiative properties of photonic metamaterials incorporating both Indium Antimonide (InSb) and Tungsten (W) in the terahertz (THz) frequency regime. We have varied multiple factors of the nanostructures, including composite materials, layer thicknesses and surface grating fill factors, which impact the light-matter interactions and in turn modify the thermal emission of the metamaterials. We have proposed and validated a method for determining the spectral properties of InSb under an applied direct current (DC) magnetic field, and have employed this method to analyze how these properties can be dynamically tuned by modulating the magnitude of the field. For the first time, we have designed an InSb-W metamaterial exhibiting unity narrowband emission which can serve as an emitter for wavelengths around 55 $\mu$m (approximately 5.5 THz). Additionally, the narrowband emission of this metamaterial can be magnetically tuned in both bandwidth and peak wavelength with a normal emissivity close to unity.

physics.optics↗

arXiv2020

Robust Facial Landmark Detection by Cross-order Cross-semantic Deep Network

Recently, convolutional neural networks (CNNs)-based facial landmark detection methods have achieved great success. However, most of existing CNN-based facial landmark detection methods have not attempted to activate multiple correlated facial parts and learn different semantic features from them that they can not accurately model the relationships among the local details and can not fully explore more discriminative and fine semantic features, thus they suffer from partial occlusions and large pose variations. To address these problems, we propose a cross-order cross-semantic deep network (CCDN) to boost the semantic features learning for robust facial landmark detection. Specifically, a cross-order two-squeeze multi-excitation (CTM) module is proposed to introduce the cross-order channel correlations for more discriminative representations learning and multiple attention-specific part activation. Moreover, a novel cross-order cross-semantic (COCS) regularizer is designed to drive the network to learn cross-order cross-semantic features from different activation for facial landmark detection. It is interesting to show that by integrating the CTM module and COCS regularizer, the proposed CCDN can effectively activate and learn more fine and complementary cross-order cross-semantic features to improve the accuracy of facial landmark detection under extremely challenging scenarios. Experimental results on challenging benchmark datasets demonstrate the superiority of our CCDN over state-of-the-art facial landmark detection methods.

cs.CV↗

arXiv2020

VIFB: A Visible and Infrared Image Fusion Benchmark

Visible and infrared image fusion is one of the most important areas in image processing due to its numerous applications. While much progress has been made in recent years with efforts on developing fusion algorithms, there is a lack of code library and benchmark which can gauge the state-of-the-art. In this paper, after briefly reviewing recent advances of visible and infrared image fusion, we present a visible and infrared image fusion benchmark (VIFB) which consists of 21 image pairs, a code library of 20 fusion algorithms and 13 evaluation metrics. We also carry out large scale experiments within the benchmark to understand the performance of these algorithms. By analyzing qualitative and quantitative results, we identify effective algorithms for robust image fusion and give some observations on the status and future prospects of this field.

cs.CV↗

arXiv2020

Efficient Solar-driven Steam Generation Enabled by An Ultra-black Paint

Solar-driven interfacial steam generation for desalination has attracted broad attention. However, a significant challenge still remains for achieving a higher evaporation rate and high water quality, together with a cost-effective and easy-to-manufacture device to provide a feasible solar-driven steam generation system. In this study, a novel ultra-black paint, Black 3.0, serving as a perfect solar absorber is introduced into the hot-pressed melamine foam networks, allowing us to construct an ultra-black (99% absorptance in the solar region) and self-floating evaporation device. The high performing features of effective solar absorptance and salt-rejection capability contribute to a high-to-date evaporation rate of freshwater at 2.48 kg m-2 h-1 under one sun (1 kW m-2). This interfacial solar evaporator has a daily drinkable water yield of 2.8 kg m-2 even in cloudy winter weather and maintains stability in water with a wide range of acidity and alkalinity (pH 1~14). These features will enable the construction of a facilely fabricated, robust, highly-efficient, and cost-effective solar steam generation system for freshwater production.

physics.app-ph↗

arXiv2020

High-temperature and Abrasion Resistant Selective Solar Absorber under Ambient Environment

Selective solar absorbers (SSAs) with high performance are the key to concentrated solar power systems. Optical metamaterials are emerging as a promising strategy to enhance selective photon absorption, however, the high-temperature resistance (>500C) remains as one of the main challenges for their practical applications. Here, a multilayered metamaterial system (Al2O3/W/SiO2/W) based on metal-insulator-metal (MIM) resonance effect has been demonstrated with high solar absorptance over 92%, low thermal emittance loss below 6%, and significant high-temperature resistance: it has been proved that the optical performance remains 93.6% after 1-hour thermal annealing under ambient environment up to 500C, and 94.1% after 96-hour thermal cycle test at 400C, which is also confirmed by the microscopic morphology characterization. The spectral selectivity of fabricated SSAs is angular independent and polarization insensitive. Outdoor tests demonstrate that a peak temperature rise (193.5C) can be achieved with unconcentrated solar irradiance and surface abrasion resistance test yields that SSAs have a robust resistance to abrasion attack for engineering applications.

physics.app-ph↗

arXiv2018

Novel methods for measuring the optical parameters of the water Cherenkov detector

In this paper, novel methods are presented to measure the optical properties of muon detectors (MDs) in the Large High-Altitude Air Shower Observatory (LHAASO), which can also be used by other experiments. Each MD consists of a cylindrical water Cherenkov detector, with a Tyvek liner containing pure water, and a photomultiplier tube (PMT) mounted on the top. The time distribution of the photons collected by the PMT in the water Cherenkov detector have an approximately exponential distribution, and their decay factor is determined by the photon absorption length in the water, the reflectivity of the inner Tyvek surface, the mean reflecting step length of photons reflected by the inner surface, and the ratio of reflectional Tyvek area to the total inner surface area. By considering the principles of photon propagation in the water Cherenkov detector, we have developed novel methods to measure the water absorption length, the Tyvek reflectivity, and the mean step length of photons. The step length of photons can be determined by measuring the time distribution of the reflected photons hitting the PMT after multiple photons with single wavelength are generated in the tank, with slightly different ratios of the inner Tyvek to the total inner area. The water absorption length and Tyvek reflectivity can be measured simultaneously by the PMT by changing the height of the water, while the step length and Tyvek reflectivity in air can also be measured simultaneously. The proposed novel methods are supported by deduction of formulae and verified by GEANT4 simulations and the prototype experiment.

physics.ins-det↗