Color confinement, dark matter and the missing of anti-matter

QCD is the fundamental theory to describe the strong interaction, where quarks and gluons have the color degrees of freedom. However, a single quark or gluon can not be separated out and all observable particles are color singlet states. Color confinement or quark confinement conjecture can be proved by considering not only the strong interaction but also the electroweak interaction which is $SU(3)_c$ invariant. Any measurable state has to be color singlet is the direct consequence of the common symmetry of the standard model. Color non-singlet objects are created from the big bang when the interaction breaks $SU(3)_c$ symmetry based on the nonlocal Lagrangian. There is nearly no interaction between colored objects and color singlet universe when the momentum transfer is not large enough. Colored objects are reasonable candidates of dark matter and the missing of anti-matter in the universe can also be easily explained. Dark matter can be produced in the laboratory which can be tested by measuring the energy loss and baryon number change in the extremely high energy collisions of particles and anti-particles.

You Only Hypothesize Once: Point Cloud Registration with Rotation-equivariant Descriptors

In this paper, we propose a novel local descriptor-based framework, called You Only Hypothesize Once (YOHO), for the registration of two unaligned point clouds. In contrast to most existing local descriptors which rely on a fragile local reference frame to gain rotation invariance, the proposed descriptor achieves the rotation invariance by recent technologies of group equivariant feature learning, which brings more robustness to point density and noise. Meanwhile, the descriptor in YOHO also has a rotation equivariant part, which enables us to estimate the registration from just one correspondence hypothesis. Such property reduces the searching space for feasible transformations, thus greatly improves both the accuracy and the efficiency of YOHO. Extensive experiments show that YOHO achieves superior performances with much fewer needed RANSAC iterations on four widely-used datasets, the 3DMatch/3DLoMatch datasets, the ETH dataset and the WHU-TLS dataset. More details are shown in our project page: https://hpwang-whu.github.io/YOHO/.

Prior-Guided Deep Interference Mitigation for FMCW Radars

A prior-guided deep learning (DL) based interference mitigation approach is proposed for frequency modulated continuous wave (FMCW) radars. In this paper, the interference mitigation problem is tackled as a regression problem. Considering the complex-valued nature of radar signals, the complex-valued convolutional neural network is utilized as an architecture for implementation, which is different from the conventional real-valued counterparts. Meanwhile, as the useful beat signals of FMCW radars and interferences exhibit different distributions in the time-frequency domain, this prior feature is exploited as a regularization term to avoid overfitting of the learned representation. The effectiveness and accuracy of our proposed complex-valued fully convolutional network (CV-FCN) based interference mitigation approach are verified and analyzed through both simulated and measured radar signals. Compared to the real-valued counterparts, the CV-FCN shows a better interference mitigation performance with a potential of half memory reduction in low Signal to Interference plus Noise Ratio (SINR) scenarios. Moreover, the CV-FCN trained using only simulated data can be directly utilized for interference mitigation in various measured radar signals and shows a superior generalization capability. Furthermore, by incorporating the prior feature, the CV-FCN trained on only 1/8 of the full data achieves comparable performance as that on the full dataset in low SINR scenarios, and the training procedure converges faster.

Multi-Expert Adversarial Attack Detection in Person Re-identification Using Context Inconsistency

The success of deep neural networks (DNNs) haspromoted the widespread applications of person re-identification (ReID). However, ReID systems inherit thevulnerability of DNNs to malicious attacks of visually in-conspicuous adversarial perturbations. Detection of adver-sarial attacks is, therefore, a fundamental requirement forrobust ReID systems. In this work, we propose a Multi-Expert Adversarial Attack Detection (MEAAD) approach toachieve this goal by checking context inconsistency, whichis suitable for any DNN-based ReID systems. Specifically,three kinds of context inconsistencies caused by adversar-ial attacks are employed to learn a detector for distinguish-ing the perturbed examples, i.e., a) the embedding distancesbetween a perturbed query person image and its top-K re-trievals are generally larger than those between a benignquery image and its top-K retrievals, b) the embedding dis-tances among the top-K retrievals of a perturbed query im-age are larger than those of a benign query image, c) thetop-K retrievals of a benign query image obtained with mul-tiple expert ReID models tend to be consistent, which isnot preserved when attacks are present. Extensive exper-iments on the Market1501 and DukeMTMC-ReID datasetsshow that, as the first adversarial attack detection approachfor ReID,MEAADeffectively detects various adversarial at-tacks and achieves high ROC-AUC (over 97.5%).

Global solutions of a doubly tactic resource consumption model with logistic source

We study a doubly tactic resource consumption model bess left{begin{array}{lll} u_t=tr u- ablacd(u abla w),[1mm] v_t=tr v- ablacd(v abla u)+v(1-v^{beta-1}),[1mm] w_t=tr w-(u+v)w-w+r end{array}right. eess in a smooth bounded domain $ooinR^2$ with homogeneous Neumann boundary conditions, where $rin C^1(barOmegatimes[0,infty))cap L^infty(Omegatimes(0,infty))$ is a given nonnegative function fulfilling bess int_t^{t+1}ii| nsqrt{r}|^2<yy for all t>0. eess It is shown that, firstly, if $beta>2$, then the corresponding Neumann initial-boundary problem admits a global bounded classical solution. Secondly, when $beta=2$, the Neumann initial-boundary problem admits a global generalized solution.

Fully Hyperbolic Graph Convolution Network for Recommendation

Recently, Graph Convolution Network (GCN) based methods have achieved outstanding performance for recommendation. These methods embed users and items in Euclidean space, and perform graph convolution on user-item interaction graphs. However, real-world datasets usually exhibit tree-like hierarchical structures, which make Euclidean space less effective in capturing user-item relationship. In contrast, hyperbolic space, as a continuous analogue of a tree-graph, provides a promising alternative. In this paper, we propose a fully hyperbolic GCN model for recommendation, where all operations are performed in hyperbolic space. Utilizing the advantage of hyperbolic space, our method is able to embed users/items with less distortion and capture user-item interaction relationship more accurately. Extensive experiments on public benchmark datasets show that our method outperforms both Euclidean and hyperbolic counterparts and requires far lower embedding dimensionality to achieve comparable performance.

Label-informed Graph Structure Learning for Node Classification

Graph Neural Networks (GNNs) have achieved great success among various domains. Nevertheless, most GNN methods are sensitive to the quality of graph structures. To tackle this problem, some studies exploit different graph structure learning strategies to refine the original graph structure. However, these methods only consider feature information while ignoring available label information. In this paper, we propose a novel label-informed graph structure learning framework which incorporates label information explicitly through a class transition matrix. We conduct extensive experiments on seven node classification benchmark datasets and the results show that our method outperforms or matches the state-of-the-art baselines.

A network Poisson model for weighted directed networks with covariates

The edges in networks are not only binary, either present or absent, but also take weighted values in many scenarios (e.g., the number of emails between two users). The covariate-$p_0$ model has been proposed to model binary directed networks with the degree heterogeneity and covariates. However, it may cause information loss when it is applied in weighted networks. In this paper, we propose to use the Poisson distribution to model weighted directed networks, which admits the sparsity of networks, the degree heterogeneity and the homophily caused by covariates of nodes. We call it the emph{network Poisson model}. The model contains a density parameter $mu$, a $2n$-dimensional node parameter ${theta}$ and a fixed dimensional regression coefficient ${gamma}$ of covariates. Since the number of parameters increases with $n$, asymptotic theory is nonstandard. When the number $n$ of nodes goes to infinity, we establish the $ell_infty$-errors for the maximum likelihood estimators (MLEs), $hat{theta}$ and $hat{{gamma}}$, which are $O_p( (log n/n)^{1/2} )$ for $hat{theta}$ and $O_p( log n/n)$ for $hat{{gamma}}$, up to an additional factor. We also obtain the asymptotic normality of the MLE. Numerical studies and a data analysis demonstrate our theoretical findings. ) for b{theta} and Op(log n/n) for b{gamma}, up to an additional factor. We also obtain the asymptotic normality of the MLE. Numerical studies and a data analysis demonstrate our theoretical findings.

Quantum Monte Carlo Investigation of the H-transfer Reaction of Criegee Intermediate CH3CHOO: A Benchmark Calculation

We perform the fixed-node diffuse Monte Carlo (FN DMC) calculations to determine the barrier height and reaction energy of a critical reaction, the H-transfer reaction from syn-CH3CHOO to vinyl hydroperoxide. The FN DMC barrier height is found to be 16.60+/-0.35 kcal/mol which agrees well with the experimental measurement within a few tenths of kcal, justifying the reliability of the FN DMC method for predicting barrier height of the rapid unimolecular reaction of Criegee intermediates. By comparing the predictions from the CCSD(t), G3 (MCG3), DFT and MP2 methods with respect to the FN DMC results and available experiment measurement, we found that the CCSD(t) barrier heights agree with the FN DMC counterpart within statistical errors, and is within a closer agreement with experiment and FN DMC prediction than the G3(MCG3) models. Barrier heights predicted from the relatively more economic DFT methods are within a few tenths kcal of the FN DMC prediction. MP2 method severely underestimates the barrier height. FN DMC prediction for the reaction energy is -17.25+/-0.31 kcal/mol, setting an upper limit for the reaction energies predicted by the post Hartree-Fock methods and a lower limit for the DFT reaction energies. We provide FN DMC input for clarifying the energetic uncertainties in the critical H-transfer reaction of syn-CH3CHOO. The quantitatively close agreements between the FN DMC barrier height and experimental measurement, and between the predictions from the FN DMC and G3 model for the reaction energy provide a theoretical basis for resolving the energy uncertainty in this reaction.

A sparse $p_0$ model with covariates for directed networks

We are concerned here with unrestricted maximum likelihood estimation in a sparse $p_0$ model with covariates for directed networks. The model has a density parameter $ u$, a $2n$-dimensional node parameter $bs{eta}$ and a fixed dimensional regression coefficient $bs{gamma}$ of covariates. Previous studies focus on the restricted likelihood inference. When the number of nodes $n$ goes to infinity, we derive the $ell_infty$-error between the maximum likelihood estimator (MLE) $(widehat{bs{eta}}, widehat{bs{gamma}})$ and its true value $(bs{eta}, bs{gamma})$. They are $O_p( (log n/n)^{1/2} )$ for $widehat{bs{eta}}$ and $O_p( log n/n)$ for $widehat{bs{gamma}}$, up to an additional factor. This explains the asymptotic bias phenomenon in the asymptotic normality of $widehat{bs{gamma}}$ in cite{Yan-Jiang-Fienberg-Leng2018}. Further, we derive the asymptotic normality of the MLE. Numerical studies and a data analysis demonstrate our theoretical findings.