arXiv subjects

Hao Wu

Publications and source records attributed to Hao Wu.

At least 631 records · Page 35Linked to original sources

arXiv2022

Spatial-temporal Transformers for EEG Emotion Recognition

Electroencephalography (EEG) is a popular and effective tool for emotion recognition. However, the propagation mechanisms of EEG in the human brain and its intrinsic correlation with emotions are still obscure to researchers. This work proposes four variant transformer frameworks~(spatial attention, temporal attention, sequential spatial-temporal attention and simultaneous spatial-temporal attention) for EEG emotion recognition to explore the relationship between emotion and spatial-temporal EEG features. Specifically, spatial attention and temporal attention are to learn the topological structure information and time-varying EEG characteristics for emotion recognition respectively. Sequential spatial-temporal attention does the spatial attention within a one-second segment and temporal attention within one sample sequentially to explore the influence degree of emotional stimulation on EEG signals of diverse EEG electrodes in the same temporal segment. The simultaneous spatial-temporal attention, whose spatial and temporal attention are performed simultaneously, is used to model the relationship between different spatial features in different time segments. The experimental results demonstrate that simultaneous spatial-temporal attention leads to the best emotion recognition accuracy among the design choices, indicating modeling the correlation of spatial and temporal features of EEG signals is significant to emotion recognition.

cs.RO↗

arXiv2022

A detail-enhanced sampling strategy in Hadamard single-pixel imaging

Hadamard single-pixel imaging (HSI) is an appealing imaging technique due to its features of low hardware complexity and industrial cost. To improve imaging efficiency, many studies have focused on sorting Hadamard patterns to obtain reliable reconstructed images with very few samples. In this study, we present an efficient HSI imaging method that employs an exponential probability function to sample Hadamard spectra along a direction with better energy concentration for obtaining Hadamard patterns. We also propose an XY order to further optimize the pattern-selection method with extremely fast Hadamard order generation while retaining the original performance. We used the compressed sensing algorithm for image reconstruction. The simulation and experimental results show that these pattern-selection method reliably reconstructs objects and preserves the edge and details of images.

eess.IV↗

arXiv2022

AVMiner: Expansible and Semantic-Preserving Anti-Virus Labels Mining Method

With the increase in the variety and quantity of malware, there is an urgent need to speed up the diagnosis and the analysis of malware. Extracting the malware family-related tokens from AV (Anti-Virus) labels, provided by online anti-virus engines, paves the way for pre-diagnosing the malware. Automatically extract the vital information from AV labels will greatly enhance the detection ability of security enterprises and equip the research ability of security analysts. Recent works like AVCLASS and AVCLASS2 try to extract the attributes of malware from AV labels and establish the taxonomy based on expert knowledge. However, due to the uncertain trend of complicated malicious behaviors, the system needs the following abilities to face the challenge: preserving vital semantics, being expansible, and free from expert knowledge. In this work, we present AVMiner, an expansible malware tagging system that can mine the most vital tokens from AV labels. AVMiner adopts natural language processing techniques and clustering methods to generate a sequence of tokens without expert knowledge ranked by importance. AVMiner can self-update when new samples come. Finally, we evaluate AVMiner on over 8,000 samples from well-known datasets with manually labeled ground truth, which outperforms previous works.

cs.CR↗

arXiv2022

Distinctive Image Captioning via CLIP Guided Group Optimization

Image captioning models are usually trained according to human annotated ground-truth captions, which could generate accurate but generic captions. In this paper, we focus on generating distinctive captions that can distinguish the target image from other similar images. To evaluate the distinctiveness of captions, we introduce a series of metrics that use large-scale vision-language pre-training model CLIP to quantify the distinctiveness. To further improve the distinctiveness of captioning models, we propose a simple and effective training strategy that trains the model by comparing target image with similar image group and optimizing the group embedding gap. Extensive experiments are conducted on various baseline models to demonstrate the wide applicability of our strategy and the consistency of metric results with human evaluation. By comparing the performance of our best model with existing state-of-the-art models, we claim that our model achieves new state-of-the-art towards distinctiveness objective.

cs.CV↗

arXiv2022

Production of neutron-rich actinide nuclides in isobaric collisions via multinucleon transfer reactions

We have calculated the multinucleon transfer reactions of $^{208}$Os, $^{208}$Pt, $^{208}$Hg, $^{208}$Pb,$^{208}$Po, $^{208}$Rn, $^{208}$Ra,$^{132,136}$Xe bombarding on $^{232}$Th and $^{248}$Cm at Coulomb barrier energies within the dinuclear system model, systematically. The results are in good agreement with the available experimental data. Coulomb effect and shell effect on production of actinides in these reactions have been investigated thoroughly. Potential energy surface and total kinetic energy mass distributions in the reactions $^{208}$Hg, $^{208}$Pb and$^{208}$Po colliding on $^{248}$Cm and $^{232}$Th are calculated and analyzed, respectively. It is found that PES and TKE spectra manifest the fragment formation mechanism in the multinucleon transfer reactions. The isospin effect and shell effect are shown in PES and TKE. Production cross-sections of multinucleon transfer products are highly dependent on the isobar projectiles with mass number $A=208$. The isobar projectiles with larger N/Z ratios are favorable for creating the neutron-rich target-like fragments. The isobar projectiles with larger charge number induced products tend to shift to proton-rich region. Coulomb potential coupled to shell effect is shown in production cross-sections of actinide isotopes. Based on the radioactive projectiles induced reactions, we have predicted massive new actinide isotopes around nuclear drip lines, even could access the superheavy nuclei region.

nucl-th↗

arXiv2022

Exploiting Unlabeled Data for Target-Oriented Opinion Words Extraction

Target-oriented Opinion Words Extraction (TOWE) is a fine-grained sentiment analysis task that aims to extract the corresponding opinion words of a given opinion target from the sentence. Recently, deep learning approaches have made remarkable progress on this task. Nevertheless, the TOWE task still suffers from the scarcity of training data due to the expensive data annotation process. Limited labeled data increase the risk of distribution shift between test data and training data. In this paper, we propose exploiting massive unlabeled data to reduce the risk by increasing the exposure of the model to varying distribution shifts. Specifically, we propose a novel Multi-Grained Consistency Regularization (MGCR) method to make use of unlabeled data and design two filters specifically for TOWE to filter noisy data at different granularity. Extensive experimental results on four TOWE benchmark datasets indicate the superiority of MGCR compared with current state-of-the-art methods. The in-depth analysis also demonstrates the effectiveness of the different-granularity filters. Our codes are available at https://github.com/TOWESSL/TOWESSL.

cs.CL↗

arXiv2022

A note on time hierarchies for reasonable semantic classes without advice

We show time hierarchies for reasonable semantic classes without advice by eliminating the constant bits of advice in previous results.The elimination is done by a contrapositive argument that for any reasonable computational model,let $\text{CTIME}(f(n))/{g(n)}$ denote the set of all languages decide by machines running in time $O(f(n))$ with advice of $g(n)$ bits in that model, if $\text{CTIME}(t(n))\subseteq \text{CTIME}(T(n))/{A(n)}$ then $\text{CTIME}(t(n))/a \subseteq \text{CTIME}(T(n))/{a+2^aA(n)}$ where $a$ is a constant integer.

cs.CC↗

arXiv2022

The Moment Passing Method for Wireless Channel Capacity Estimation

Wireless network capacity can be regarded as the most important performance metric for wireless communication systems. With the fast development of wireless communication technology, future wireless systems will become more and more complicated. As a result, the channel gain matrix will become a large-dimensional random matrix, leading to an extremely high computational cost to obtain the capacity. In this paper, we propose a moment passing method (MPM) to realize the fast and accurate capacity estimation for future ultra-dense wireless systems. It can determine the capacity with quadratic complexity, which is optimal considering that the cost of a single matrix operation is not less than quadratic complexity. Moreover, it has high accuracy. The simulation results show that the estimation error of this method is below 2 percent. Finally, our method is highly general, as it is independent of the distributions of BSs and users, and the shape of network areas. More importantly, it can be applied not only to the conventional multi-user multiple input and multiple output (MU-MIMO) networks, but also to the capacity-centric networks designed for B5G/6G.

cs.IT↗

arXiv2022

An Optimal Transport Approach to the Computation of the LM Rate

Mismatch capacity characterizes the highest information rate for a channel under a prescribed decoding metric, and is thus a highly relevant fundamental performance metric when dealing with many practically important communication scenarios. Compared with the frequently used generalized mutual information (GMI), the LM rate has been known as a tighter lower bound of the mismatch capacity. The computation of the LM rate, however, has been a difficult task, due to the fact that the LM rate involves a maximization over a function of the channel input, which becomes challenging as the input alphabet size grows, and direct numerical methods (e.g., interior point methods) suffer from intensive memory and computational resource requirements. Noting that the computation of the LM rate can also be formulated as an entropy-based optimization problem with constraints, in this work, we transform the task into an optimal transport (OT) problem with an extra constraint. This allows us to efficiently and accurately accomplish our task by using the well-known Sinkhorn algorithm. Indeed, only a few iterations are required for convergence, due to the fact that the formulated problem does not contain additional regularization terms. Moreover, we convert the extra constraint into a root-finding procedure for a one-dimensional monotonic function. Numerical experiments demonstrate the feasibility and efficiency of our OT approach to the computation of the LM rate.

cs.IT↗

arXiv2022

$β$-Divergence-Based Latent Factorization of Tensors model for QoS prediction

A nonnegative latent factorization of tensors (NLFT) model can well model the temporal pattern hidden in nonnegative quality-of-service (QoS) data for predicting the unobserved ones with high accuracy. However, existing NLFT models' objective function is based on Euclidean distance, which is only a special case of $β$-divergence. Hence, can we build a generalized NLFT model via adopting $β$-divergence to achieve prediction accuracy gain? To tackle this issue, this paper proposes a $β$-divergence-based NLFT model ($β$-NLFT). Its ideas are two-fold 1) building a learning objective with $β$-divergence to achieve higher prediction accuracy, and 2) implementing self-adaptation of hyper-parameters to improve practicability. Empirical studies on two dynamic QoS datasets demonstrate that compared with state-of-the-art models, the proposed $β$-NLFT model achieves the higher prediction accuracy for unobserved QoS data.

cs.LG↗

arXiv2022

Physics-embedded inverse analysis with automatic differentiation for the earth's subsurface

Inverse analysis has been utilized to understand unknown underground geological properties by matching the observational data with simulators. To overcome the underconstrained nature of inverse problems and achieve good performance, an approach is presented with embedded physics and a technique known as automatic differentiation. We use a physics-embedded generative model, which takes statistically simple parameters as input and outputs subsurface properties (e.g., permeability or P-wave velocity), that embeds physical knowledge of the subsurface properties into inverse analysis and improves its performance. We tested the application of this approach on four geologic problems: two heterogeneous hydraulic conductivity fields, a hydraulic fracture network, and a seismic inversion for P-wave velocity. This physics-embedded inverse analysis approach consistently characterizes these geological problems accurately. Furthermore, the excellent performance in matching the observational data demonstrates the reliability of the proposed method. Moreover, the application of automatic differentiation makes this an easy and fast approach to inverse analysis when dealing with complicated geological structures.

physics.comp-ph↗

arXiv2022

A novel robot calibration method with plane constraint based on dial indicator

In pace with the electronic technology development and the production technology improvement, industrial robot Give Scope to the Advantage in social services and industrial production. However, due to long-term mechanical wear and structural deformation, the absolute positioning accuracy is low, which greatly hinders the development of manufacturing industry. Calibrating the kinematic parameters of the robot is an effective way to address it. However, the main measuring equipment such as laser trackers and coordinate measuring machines are expensive and need special personnel to operate. Additionally, in the measurement process, due to the influence of many environmental factors, measurement noises are generated, which will affect the calibration accuracy of the robot. Basing on these, we have done the following work: a) developing a robot calibration method based on plane constraint to simplify measurement steps; b) employing Square-root Culture Kalman Filter (SCKF) algorithm for reducing the influence of measurement noises; c) proposing a novel algorithm for identifying kinematic parameters based on SCKF algorithm and Levenberg Marquardt (LM) algorithm to achieve the high calibration accuracy; d) adopting the dial indicator as the measuring equipment for slashing costs. The enough experiments verify the effectiveness of the proposed calibration algorithm and experimental platform.

cs.RO↗

arXiv2022

McKean-Vlasov multivalued stochastic differential equations with oblique subgradients and related stochastic control problems

In this article, we prove the existence of weak solutions as well as the existence and uniqueness of strong solutions for McKean-Vlasov multivalued stochastic differential equations with oblique subgradients (MVMSDEswOS, for short) by means of the equations of Euler type and Skorohod's representation theorem. For this type of equation, compared with the method in [19,13], since we can't use the maximal monotony property of its constituent subdifferential operator, some different specific techniques are applied to solve our problems. Afterwards, we give an example for MVMSDEswOS with time-dependent convex constraints, which can be reduced to MVMSDEswOS. Finally, we consider an optimal control problem and establish the dynamic programming principle for the value function.

math.PR↗

arXiv2022

On the Mass-Conserving Allen-Cahn Approximation for Incompressible Binary Fluids

This paper is devoted to the global well-posedness of two Diffuse Interface systems modeling the motion of an incompressible two-phase fluid mixture in presence of capillarity effects in a bounded smooth domain $Ω\subset \mathbb{R}^d$, $d=2,3$. We focus on dissipative mixing effects originating from the mass-conserving Allen-Cahn dynamics with the physically relevant Flory-Huggins potential. More precisely, we study the mass-conserving Navier-Stokes-Allen-Cahn system for nonhomogeneous fluids and the mass-conserving Euler-Allen-Cahn system for homogeneous fluids. We prove existence and uniqueness of global weak and strong solutions as well as their property of separation from the pure states. In our analysis, we combine the energy and entropy estimates, a novel end-point estimate of the product of two functions, a new estimate for the Stokes problem with non-constant viscosity, and logarithmic type Gronwall arguments.

math.AP↗

arXiv2022

Contrastive Vision-Language Pre-training with Limited Resources

Pioneering dual-encoder pre-training works (e.g., CLIP and ALIGN) have revealed the potential of aligning multi-modal representations with contrastive learning. However, these works require a tremendous amount of data and computational resources (e.g., billion-level web data and hundreds of GPUs), which prevent researchers with limited resources from reproduction and further exploration. To this end, we propose a stack of novel methods, which significantly cut down the heavy resource dependency and allow us to conduct dual-encoder multi-modal representation alignment with limited resources. Besides, we provide a reproducible baseline of competitive results, namely ZeroVL, with only 14M publicly accessible academic datasets and 8 V100 GPUs. Additionally, we collect 100M web data for pre-training, and achieve comparable or superior results than state-of-the-art methods, further proving the effectiveness of our methods on large-scale data. We hope that this work will provide useful data points and experience for future research in contrastive vision-language pre-training. Code is available at https://github.com/zerovl/ZeroVL.

cs.CV↗

arXiv2022

Improved accuracy and reproducibility of coronary artery cal-cification features using deconvolution

Our long-range goal is to improve current whole-heart CT calcium score by extracting quantitative features from individual calcifications. We performed deconvolution to improve small calcifications assessment which challenge conventional CT calcium score scanning resolution. We analyzed features of individual calcifications on repeated standard (2.5-mm) and thin (1.25-mm) slice scans from QRM-Cardio phantom, cadaver hearts, and CARDIA study participants. Pre-processing to improve resolution involved of Lucy-Richardson deconvolution with a measured PSF or 3D blind deconvolution where the PSF was iteratively optimized on high detail structures like calcifications in the images. Using QRM with inserts having known mg-calcium, we determined that both blind and conventional deconvolution improved mass measurements nearly equally well on standard images. Further, de-convolved thin images gave excellent recovery of actual mass scores, suggesting that such processing could be our gold standard. For CARDIA images, blind deconvolution greatly improved results on standard slices. Accuracy across 33 calcifications (without, with deconvolution) was (23%,9%), (18%,1%), and (-19%,-1%), for Agatston, volume, and mass scores, respectively. Reproducibility was (0.13,0.10), (0.12,0.08), and (0.11,0.06), respectively. Mass scores were more reproducible than Agatston scores or vol-ume scores. Cadaver volumes showed similar improvements in accuracy/reproducibility and slightly better results with a measured PSF. For many other calcification features in CARDIA data, blind deconvolution improved reproducibility in 21 out of 24 features. Deconvolution improves accuracy and reproducibility of multiple features extracted from individual calcifications in CT calcium score exam. Blind deconvolution improves feature assessments of coronary calcification in archived datasets.

physics.med-ph↗

arXiv2022

Dual Power Spectrum Manifold and Toeplitz HPD Manifold: Enhancement and Analysis for Matrix CFAR Detection

Recently, an innovative matrix CFAR detection scheme based on information geometry, also referred to as the geometric detector, has been developed speedily and exhibits distinct advantages in several practical applications. These advantages benefit from the geometry of the Toeplitz Hermitian positive definite (HPD) manifold $\mathcal{M}_{\mathcal{T}H_{++}}$, but the sophisticated geometry also results in some challenges for geometric detectors, such as the implementation of the enhanced detector to improve the SCR (signal-to-clutter ratio) and the analysis of the detection performance. To meet these challenges, this paper develops the dual power spectrum manifold $\mathcal{M}_{\text{P}}$ as the dual space of $\mathcal{M}_{\mathcal{T}H_{++}}$. For each affine invariant geometric measure on $\mathcal{M}_{\mathcal{T}H_{++}}$, we show that there exists an equivalent function named induced potential function on $\mathcal{M}_{\text{P}}$. By the induced potential function, the measurements of the dissimilarity between two matrices can be implemented on $\mathcal{M}_{\text{P}}$, and the geometric detectors can be reformulated as the form related to the power spectrum. The induced potential function leads to two contributions: 1) The enhancement of the geometric detector, which is formulated as an optimization problem concerning $\mathcal{M}_{\mathcal{T}H_{++}}$, is transformed to an equivalent and simpler optimization on $\mathcal{M}_{\text{P}}$. In the presented example of the enhancement, the closed-form solution, instead of the gradient descent method, is provided through the equivalent optimization. 2) The detection performance is analyzed based on $\mathcal{M}_{\text{P}}$, and the advantageous characteristics, which benefit the detection performance, can be deduced by analyzing the corresponding power spectrum to the maximal point of the induced potential function.

eess.SP↗

arXiv2022

The Effective Sample Size in Bayesian Information Criterion for Level-Specific Fixed and Random Effects Selection in a Two-Level Nested Model

Popular statistical software provides Bayesian information criterion (BIC) for multilevel models or linear mixed models. However, it has been observed that the combination of statistical literature and software documentation has led to discrepancies in the formulas of the BIC and uncertainties of the proper use of the BIC in selecting a multilevel model with respect to level-specific fixed and random effects. These discrepancies and uncertainties result from different specifications of sample size in the BIC's penalty term for multilevel models. In this study, we derive the BIC's penalty term for level-specific fixed and random effect selection in a two-level nested design. In this new version of BIC, called BIC_E, this penalty term is decomposed into two parts if the random effect variance-covariance matrix has full rank: (a) a term with the log of average sample size per cluster whose multiplier involves the overlapping number of dimensions between the column spaces of the random and fixed effect design matrices and (b) the total number of parameters times the log of the total number of clusters. Furthermore, we study the behavior of BIC_E in the presence of redundant random effects. The use of BIC_E is illustrated with a textbook example data set and a numerical demonstration shows that the derived formulae adheres to empirical values.

stat.ME↗