arXiv subjects

Yan Huang

Publications and source records attributed to Yan Huang.

At least 199 records · Page 11Linked to original sources

arXiv2023

Numerical study of synchrotron and inverse-Compton radiation from gamma-ray burst afterglows with decaying microturbulence

The multiwavelength observations of GRB afterglows, together with some high-performance particle-in-cell simulations, hint that the magnetic field may decay behind the shock front. In this work, we develop a numerical code to calculate the evolution of the accelerated electron distribution, their synchrotron and inverse-Compton (IC) spectra and accordingly the light curves (LCs) under the assumption of decaying microturbulence (DM) downstream of the shock, $ε_B(t_p')\propto t_p'^{α_t}$ with $t_p'$ the fluid proper time since injection. We find: (1) The synchrotron spectrum in the DM model is similar to that in the homogeneous turbulence (HT) model with very low magnetic field strength. However, the difference in the IC spectral component is relatively more obvious between them, due to the significant change of the postshock electron energy distribution with DM. (2) If the magnetic field decay faster, there are less electrons cool fast, and the IC spectral component becomes weaker. (3) The LCs in the DM model decay steeper than in the HT model, and the spectral evolution and the LCs in the DM model is similar to the HT model where the magnetic field energy fraction decreases with observer time, $ε_B(t) \propto t^{5α_t /8}$. (4) The DM model can naturally produce a significant IC spectral component in TeV energy range, but due to the Klein-Nishina suppression the IC power cannot be far larger than the synchrotron power. We apply the DM model to describe the afterglow data of GRB 190114C and find the magnetic field decay exponent $α_t\sim -0.4$ and the electron spectral index $p\sim2.4$. Future TeV observations of the IC emission from GRB afterglows will further help to probe the poorly known microphysics of relativistic shocks.

astro-ph.HE↗

arXiv2022

CFNet: Conditional Filter Learning with Dynamic Noise Estimation for Real Image Denoising

A mainstream type of the state of the arts (SOTAs) based on convolutional neural network (CNN) for real image denoising contains two sub-problems, i.e., noise estimation and non-blind denoising. This paper considers real noise approximated by heteroscedastic Gaussian/Poisson Gaussian distributions with in-camera signal processing pipelines. The related works always exploit the estimated noise prior via channel-wise concatenation followed by a convolutional layer with spatially sharing kernels. Due to the variable modes of noise strength and frequency details of all feature positions, this design cannot adaptively tune the corresponding denoising patterns. To address this problem, we propose a novel conditional filter in which the optimal kernels for different feature positions can be adaptively inferred by local features from the image and the noise map. Also, we bring the thought that alternatively performs noise estimation and non-blind denoising into CNN structure, which continuously updates noise prior to guide the iterative feature denoising. In addition, according to the property of heteroscedastic Gaussian distribution, a novel affine transform block is designed to predict the stationary noise component and the signal-dependent noise component. Compared with SOTAs, extensive experiments are conducted on five synthetic datasets and three real datasets, which shows the improvement of the proposed CFNet.

eess.IV↗

arXiv2022

WebCrack: Dynamic Dictionary Adjustment for Web Weak Password Detection based on Blasting Response Event Discrimination

The feature diversity of different web systems in page elements, submission contents and return information makes it difficult to detect weak password automatically. To solve this problem, multi-factor correlation detection method as integrated in the DBKER algorithm is proposed to achieve automatic detection of web weak passwords and universal passwords. It generates password dictionaries based on PCFG algorithm, proposes to judge blasting result via 4 steps with traditional static keyword features and dynamic page feature information. Then the blasting failure events are discriminated and the usernames are blasted based on response time. Thereafter the weak password dictionary is dynamically adjusted according to the hints provided by the response failure page. Based on the algorithm, this paper implements a detection system named WebCrack. Experimental results of two blasting tests on DedeCMS and Discuz! systems as well as a random backend test show that the proposed method can detect weak passwords and universal passwords of various web systems with an average accuracy rate of about 93.75%, providing security advisories for users' password settings with strong practicability.

cs.CR↗

arXiv2022

Global CD/EoR Signal Detection with a Dense Digital Beamforming Array and Beyond

The global neutral hydrogen 21 cm signal extracted from the all-sky averaged radio spectra is one of the signatures of the Cosmic Dawn and Epoch of Reionization (CD/EoR). The frequency-dependency of antenna beam patterns coupled with the strong foreground emission could introduce artificial spectral structures and cause false detection. A digital beamforming array could be potentially employed to form achromatic station beam patterns to overcome this problem. In this work, we discuss the method of forming achromatic beam patterns with a dense regular beamforming array to detect the global CD/EoR signal, covering topics including the array configuration, antenna weight optimization, and error estimation. We also show that based on the equivalence between a beamforming array and an interferometer, most antennas in the array can be removed by canceling redundant baselines. We present an example array design, optimize the antenna weights, and show the final array configuration by canceling redundant baselines. The performance of the example array is evaluated based on a simulation, which provides a positive indication towards the feasibility of detecting the CD/EoR signal using a dense digital beamforming array.

astro-ph.IM↗

arXiv2022

CNTN: Cyclic Noise-tolerant Network for Gait Recognition

Gait recognition aims to identify individuals by recognizing their walking patterns. However, an observation is made that most of the previous gait recognition methods degenerate significantly due to two memorization effects, namely appearance memorization and label noise memorization. To address the problem, for the first time noisy gait recognition is studied, and a cyclic noise-tolerant network (CNTN) is proposed with a cyclic training algorithm, which equips the two parallel networks with explicitly different abilities, namely one forgetting network and one memorizing network. The overall model will not memorize the pattern unless the two different networks both memorize it. Further, a more refined co-teaching constraint is imposed to help the model learn intrinsic patterns which are less influenced by memorization. Also, to address label noise memorization, an adaptive noise detection module is proposed to rule out the samples with high possibility to be noisy from updating the model. Experiments are conducted on the three most popular benchmarks and CNTN achieves state-of-the-art performances. We also reconstruct two noisy gait recognition datasets, and CNTN gains significant improvements (especially 6% improvements on CL setting). CNTN is also compatible with any off-the-shelf backbones and improves them consistently.

cs.CV↗

arXiv2022

Generalized Inter-class Loss for Gait Recognition

Gait recognition is a unique biometric technique that can be performed at a long distance non-cooperatively and has broad applications in public safety and intelligent traffic systems. Previous gait works focus more on minimizing the intra-class variance while ignoring the significance in constraining inter-class variance. To this end, we propose a generalized inter-class loss which resolves the inter-class variance from both sample-level feature distribution and class-level feature distribution. Instead of equal penalty strength on pair scores, the proposed loss optimizes sample-level inter-class feature distribution by dynamically adjusting the pairwise weight. Further, in class-level distribution, generalized inter-class loss adds a constraint on the uniformity of inter-class feature distribution, which forces the feature representations to approximate a hypersphere and keep maximal inter-class variance. In addition, the proposed method automatically adjusts the margin between classes which enables the inter-class feature distribution to be more flexible. The proposed method can be generalized to different gait recognition networks and achieves significant improvements. We conduct a series of experiments on CASIA-B and OUMVLP, and the experimental results show that the proposed loss can significantly improve the performance and achieves the state-of-the-art performances.

cs.CV↗

arXiv2022

Regularized Graph Structure Learning with Semantic Knowledge for Multi-variates Time-Series Forecasting

Multivariate time-series forecasting is a critical task for many applications, and graph time-series network is widely studied due to its capability to capture the spatial-temporal correlation simultaneously. However, most existing works focus more on learning with the explicit prior graph structure, while ignoring potential information from the implicit graph structure, yielding incomplete structure modeling. Some recent works attempt to learn the intrinsic or implicit graph structure directly while lacking a way to combine explicit prior structure with implicit structure together. In this paper, we propose Regularized Graph Structure Learning (RGSL) model to incorporate both explicit prior structure and implicit structure together, and learn the forecasting deep networks along with the graph structure. RGSL consists of two innovative modules. First, we derive an implicit dense similarity matrix through node embedding, and learn the sparse graph structure using the Regularized Graph Generation (RGG) based on the Gumbel Softmax trick. Second, we propose a Laplacian Matrix Mixed-up Module (LM3) to fuse the explicit graph and implicit graph together. We conduct experiments on three real-word datasets. Results show that the proposed RGSL model outperforms existing graph forecasting algorithms with a notable margin, while learning meaningful graph structure simultaneously. Our code and models are made publicly available at https://github.com/alipay/RGSL.git.

cs.LG↗

arXiv2022

Neural-FacTOR: Neural Representation Learning for Website Fingerprinting Attack over TOR Anonymity

TOR (The Onion Router) network is a widely used open source anonymous communication tool, the abuse of TOR makes it difficult to monitor the proliferation of online crimes such as to access criminal websites. Most existing approches for TOR network de-anonymization heavily rely on manually extracted features resulting in time consuming and poor performance. To tackle the shortcomings, this paper proposes a neural representation learning approach to recognize website fingerprint based on classification algorithm. We constructed a new website fingerprinting attack model based on convolutional neural network (CNN) with dilation and causal convolution, which can improve the perception field of CNN as well as capture the sequential characteristic of input data. Experiments on three mainstream public datasets show that the proposed model is robust and effective for the website fingerprint classification and improves the accuracy by 12.21% compared with the state-of-the-art methods.

cs.CR↗

arXiv2022

Actor and Action Modular Network for Text-based Video Segmentation

Text-based video segmentation aims to segment an actor in video sequences by specifying the actor and its performing action with a textual query. Previous methods fail to explicitly align the video content with the textual query in a fine-grained manner according to the actor and its action, due to the problem of \emph{semantic asymmetry}. The \emph{semantic asymmetry} implies that two modalities contain different amounts of semantic information during the multi-modal fusion process. To alleviate this problem, we propose a novel actor and action modular network that individually localizes the actor and its action in two separate modules. Specifically, we first learn the actor-/action-related content from the video and textual query, and then match them in a symmetrical manner to localize the target tube. The target tube contains the desired actor and action which is then fed into a fully convolutional network to predict segmentation masks of the actor. Our method also establishes the association of objects cross multiple frames with the proposed temporal proposal aggregation mechanism. This enables our method to segment the video effectively and keep the temporal consistency of predictions. The whole model is allowed for joint learning of the actor-action matching and segmentation, as well as achieves the state-of-the-art performance for both single-frame segmentation and full video segmentation on A2D Sentences and J-HMDB Sentences datasets.

cs.CV↗

arXiv2022

AI in Human-computer Gaming: Techniques, Challenges and Opportunities

With breakthrough of the AlphaGo, human-computer gaming AI has ushered in a big explosion, attracting more and more researchers all around the world. As a recognized standard for testing artificial intelligence, various human-computer gaming AI systems (AIs) have been developed such as the Libratus, OpenAI Five and AlphaStar, beating professional human players. The rapid development of human-computer gaming AIs indicate a big step of decision making intelligence, and it seems that current techniques can handle very complex human-computer games. So, one natural question raises: what are the possible challenges of current techniques in human-computer gaming, and what are the future trends? To answer the above question, in this paper, we survey recent successful game AIs, covering board game AIs, card game AIs, first-person shooting game AIs and real time strategy game AIs. Through this survey, we 1) compare the main difficulties among different kinds of games and the corresponding techniques utilized for achieving professional human level AIs; 2) summarize the mainstream frameworks and techniques that can be properly relied on for developing AIs for complex human-computer gaming; 3) raise the challenges or drawbacks of current techniques in the successful AIs; and 4) try to point out future trends in human-computer gaming AIs. Finally, we hope this brief review can provide an introduction for beginners, and inspire insights for researchers in the field of AI in human-computer gaming.

cs.AI↗

arXiv2022

Univoque bases of real numbers: simply normal bases, irregular bases and multiple rationals

Given a positive integer $M$ and a real number $x\in(0,1]$, we call $q\in(1,M+1]$ a univoque simply normal base of $x$ if there exists a unique simply normal sequence $(d_i)\in\{0,1,\ldots,M\}^\mathbb N$ such that $x=\sum_{i=1}^\infty d_i q^{-i}$. Similarly, a base $q\in(1,M+1]$ is called a univoque irregular base of $x$ if there exists a unique sequence $(d_i)\in\{0,1,\ldots, M\}^\mathbb N$ such that $x=\sum_{i=1}^\infty d_i q^{-i}$ and the sequence $(d_i)$ has no digit frequency. Let $\mathcal U_{SN}(x)$ and $\mathcal U_{I_r}(x)$ be the sets of univoque simply normal bases and univoque irregular bases of $x$, respectively. In this paper we show that for any $x\in(0,1]$ both $\mathcal U_{SN}(x)$ and $\mathcal U_{I_r}(x)$ have full Hausdorff dimension. Furthermore, given finitely many rationals $x_1, x_2, \ldots, x_n\in(0,1]$ so that each $x_i$ has a finite expansion in base $M+1$, we show that there exists a full Hausdorff dimensional set of $q\in(1,M+1]$ such that each $x_i$ has a unique expansion in base $q$.

math.DS↗

arXiv2022

Tackling Data Heterogeneity: A New Unified Framework for Decentralized SGD with Sample-induced Topology

We develop a general framework unifying several gradient-based stochastic optimization methods for empirical risk minimization problems both in centralized and distributed scenarios. The framework hinges on the introduction of an augmented graph consisting of nodes modeling the samples and edges modeling both the inter-device communication and intra-device stochastic gradient computation. By designing properly the topology of the augmented graph, we are able to recover as special cases the renowned Local-SGD and DSGD algorithms, and provide a unified perspective for variance-reduction (VR) and gradient-tracking (GT) methods such as SAGA, Local-SVRG and GT-SAGA. We also provide a unified convergence analysis for smooth and (strongly) convex objectives relying on a proper structured Lyapunov function, and the obtained rate can recover the best known results for many existing algorithms. The rate results further reveal that VR and GT methods can effectively eliminate data heterogeneity within and across devices, respectively, enabling the exact convergence of the algorithm to the optimal solution. Numerical experiments confirm the findings in this paper.

math.OC↗

arXiv2022

Study of background from accidental coincidence signals in the PandaX-II experiment

The PandaX-II experiment employed a 580kg liquid xenon detector to search for the interactions between dark matter particles and the target xenon atoms. The accidental coincidences of isolated signals result in a dangerous background which mimic the signature of the dark matter. We performed a detailed study on the accidental coincidence background in PandaX-II, including the possible origin of the isolated signals, the background level and corresponding background suppression method. With a boosted-decision-tree algorithm, the accidental coincidence background is reduced by 70% in the dark matter signal region, thus the sensitivity of dark matter search at PandaX-II is improved.

hep-ex↗

arXiv2022

1st Place Solutions for RxR-Habitat Vision-and-Language Navigation Competition (CVPR 2022)

This report presents the methods of the winning entry of the RxR-Habitat Competition in CVPR 2022. The competition addresses the problem of Vision-and-Language Navigation in Continuous Environments (VLN-CE), which requires an agent to follow step-by-step natural language instructions to reach a target. We present a modular plan-and-control approach for the task. Our model consists of three modules: the candidate waypoints predictor (CWP), the history enhanced planner and the tryout controller. In each decision loop, CWP first predicts a set of candidate waypoints based on depth observations from multiple views. It can reduce the complexity of the action space and facilitate planning. Then, a history-enhanced planner is adopted to select one of the candidate waypoints as the subgoal. The planner additionally encodes historical memory to track the navigation progress, which is especially effective for long-horizon navigation. Finally, we propose a non-parametric heuristic controller named tryout to execute low-level actions to reach the planned subgoal. It is based on the trial-and-error mechanism which can help the agent to avoid obstacles and escape from getting stuck. All three modules work hierarchically until the agent stops. We further take several recent advances of Vision-and-Language Navigation (VLN) to improve the performance such as pretraining based on large-scale synthetic in-domain dataset, environment-level data augmentation and snapshot model ensemble. Our model won the RxR-Habitat Competition 2022, with 48% and 90% relative improvements over existing methods on NDTW and SR metrics respectively.

cs.CV↗

arXiv2022

Intra Encoding Complexity Control with a Time-Cost Model for Versatile Video Coding

For the latest video coding standard Versatile Video Coding (VVC), the encoding complexity is much higher than previous video coding standards to achieve a better coding efficiency, especially for intra coding. The complexity becomes a major barrier of its deployment and use. Even with many fast encoding algorithms, it is still practically important to control the encoding complexity to a given level. Inspired by rate control algorithms, we propose a scheme to precisely control the intra encoding complexity of VVC. In the proposed scheme, a Time-PlanarCost (viz. Time-Cost, or T-C) model is utilized for CTU encoding time estimation. By combining a set of predefined parameters and the T-C model, CTU-level complexity can be roughly controlled. Then to achieve a precise picture-level complexity control, a framework is constructed including uneven complexity pre-allocation, preset selection and feedback. Experimental results show that, for the challenging intra coding scenario, the complexity error quickly converges to under 3.21%, while keeping a reasonable time saving and rate-distortion (RD) performance. This proves the efficiency of the proposed methods.

eess.IV↗

arXiv2022

Cyclic Differentiable Architecture Search

Differentiable ARchiTecture Search, i.e., DARTS, has drawn great attention in neural architecture search. It tries to find the optimal architecture in a shallow search network and then measures its performance in a deep evaluation network. The independent optimization of the search and evaluation networks, however, leaves room for potential improvement by allowing interaction between the two networks. To address the problematic optimization issue, we propose new joint optimization objectives and a novel Cyclic Differentiable ARchiTecture Search framework, dubbed CDARTS. Considering the structure difference, CDARTS builds a cyclic feedback mechanism between the search and evaluation networks with introspective distillation. First, the search network generates an initial architecture for evaluation, and the weights of the evaluation network are optimized. Second, the architecture weights in the search network are further optimized by the label supervision in classification, as well as the regularization from the evaluation network through feature distillation. Repeating the above cycle results in joint optimization of the search and evaluation networks and thus enables the evolution of the architecture to fit the final evaluation network. The experiments and analysis on CIFAR, ImageNet and NAS-Bench-201 demonstrate the effectiveness of the proposed approach over the state-of-the-art ones. Specifically, in the DARTS search space, we achieve 97.52% top-1 accuracy on CIFAR10 and 76.3% top-1 accuracy on ImageNet. In the chain-structured search space, we achieve 78.2% top-1 accuracy on ImageNet, which is 1.1% higher than EfficientNet-B0. Our code and models are publicly available at https://github.com/microsoft/Cream.

cs.CV↗

arXiv2022

A Closer Look at Personalization in Federated Image Classification

Federated Learning (FL) is developed to learn a single global model across the decentralized data, while is susceptible when realizing client-specific personalization in the presence of statistical heterogeneity. However, studies focus on learning a robust global model or personalized classifiers, which yield divergence due to inconsistent objectives. This paper shows that it is possible to achieve flexible personalization after the convergence of the global model by introducing representation learning. In this paper, we first analyze and determine that non-IID data harms representation learning of the global model. Existing FL methods adhere to the scheme of jointly learning representations and classifiers, where the global model is an average of classification-based local models that are consistently subject to heterogeneity from non-IID data. As a solution, we separate representation learning from classification learning in FL and propose RepPer, an independent two-stage personalized FL framework.We first learn the client-side feature representation models that are robust to non-IID data and aggregate them into a global common representation model. After that, we achieve personalization by learning a classifier head for each client, based on the common representation obtained at the former stage. Notably, the proposed two-stage learning scheme of RepPer can be potentially used for lightweight edge computing that involves devices with constrained computation power.Experiments on various datasets (CIFAR-10/100, CINIC-10) and heterogeneous data setup show that RepPer outperforms alternatives in flexibility and personalization on non-IID data.

cs.LG↗

arXiv2022

Time dependent numerical model for the very high energy emissions of distant gamma-ray busrt GRB 201216C

Recently, the MAGIC Collaboration reported a $\sim 5σ$ statistical significance of the very-high-energy (VHE) emission from a distant GRB, GRB 201216C. Such distant GRB may be effectively absorbed by the extragalactic background light (EBL). The origin of the VHE emission from such distant objects is still unknown. Here, we propose a numerical model for studying the afterglow emission of this distant GRB. The model solves the continuity equation governing the temporal evolution of electron distribution, and the broad-band observed data can be explained by the synchrotron plus synchrotron self-Compton (SSC) radiation of the forward shock. The predicted observed 0.1 TeV flux can reach $\sim 10^{-9} -10^{-10}\rm erg ~ cm^{-2} ~ s^{-1}$ at $t \sim 10^3 -10^4 \rm s$, even with strong EBL absorption, such strong Sub-TeV emissions still can be observed by MAGIC telescope. Using this numerical model, the shock parameters in the modeling are similar with two other Sub-TeV GRBs (i.e., GRB 190114C and GRB 180720B), implying that the Sub-TeV GRBs have some commonalities: they have energetic burst energy, low circum-burst medium density and low magnetic equipartition factor. We regard GRB 201216C as a typical GRB, and estimate the maximum redshift of GRB that can be detected by MAGIC telescope, i.e., $z \sim 1.6$. We also find that the VHE photon energy of such distant GRB can only reach $\sim 0.1 ~\rm TeV$. Improving the low energy sensitivity of the VHE telescope is very important to detect the Sub-TeV emissions of these distant GRBs.

astro-ph.HE↗