arXiv subjects

Hao Sun

Publications and source records attributed to Hao Sun.

At least 487 records · Page 27Linked to original sources

arXiv2020

Existence of an equilibrium for pure exchange economy with fuzzy preferences

This paper focuses on a new model to reach the existence of equilibrium in a pure exchange economy with fuzzy preferences (PXE-FP). The proposed model integrates exchange, consumption and the agent's fuzzy preference in the consumption set. We set up a new fuzzy binary relation on the consumption set to evaluate the fuzzy preferences. Also, we prove that there exists a continuous fuzzy order-preserving function in the consumption set under certain conditions. The existence of a fuzzy competitive equilibrium for the PXE-FP is confirmed through a new result on the existence of fuzzy Nash equilibrium for fuzzy non-cooperative games. The payoffs of all strategy profiles for any agent are fuzzy numbers in fuzzy non-cooperative games. Finally, we show that the fuzzy competitive equilibrium could be characterized as a solution to an associated quasi-variational inequality, giving rise to an equilibrium solution.

cs.GT↗

arXiv2020

Topological invariants of parabolic $G$-Higgs bundles

For a semisimple real Lie group $G$, we study topological properties of moduli spaces of polystable parabolic $G$-Higgs bundles over a Riemann surface with a divisor of finitely many distinct points. For a split real form of a complex simple Lie group, we compute the dimension of apparent parabolic Teichm{ü}ller components. In the case of isometry groups of classical Hermitian symmetric spaces of tube type, we provide new topological invariants for maximal parabolic $G$-Higgs bundles arising from a correspondence to orbifold Higgs bundles. Using orbifold cohomology we count the least number of connected components of moduli spaces of such objects. We further exhibit an alternative explanation of fundamental results on counting components in the absence of a parabolic structure.

math.AG↗

arXiv2020

Single spin asymmetry $A_{UL}^{\sin(ϕ_h-ϕ_R)}$ in dihadron semi-inclusive DIS

In this paper we study the single longitudinal spin asymmetry $A_{UL}^{\sin(ϕ_h-ϕ_R)}$ of dihadron production in semi-inclusive deep inelastic scattering (SIDIS) via helicity-dependent dihadron fragmentation function (DiFF), which describes the correlation of the longitudinal polarization of a fragmenting quark with the transverse momenta of the produced hadron pair. Recently experimental searching for this azimuthal asymmetry in dihadron SIDIS by the COMPASS Collaboration yielded a very small signal. Here we calculate this unknown T-odd DiFF $G_1^\perp$ using a spectator model to access the asymmetry and clarify why the signal is very small. The transverse momentum dependent (TMD) factorization method, in which the transverse momentum of the final state hadron pair leaves unintegrated, has been applied. We estimate the $\sin(ϕ_h-ϕ_R)$ asymmetry at the kinematics of COMPASS experiments and compare with the data. What's more, the predictions on the same asymmetry are also made at the Electron Ion Collider (EIC).

hep-ph↗

arXiv2020

MixModule: Mixed CNN Kernel Module for Medical Image Segmentation

Convolutional neural networks (CNNs) have been successfully applied to medical image classification, segmentation, and related tasks. Among the many CNNs architectures, U-Net and its improved versions based are widely used and achieve state-of-the-art performance these years. These improved architectures focus on structural improvements and the size of the convolution kernel is generally fixed. In this paper, we propose a module that combines the benefits of multiple kernel sizes and we apply the proposed module to U-Net and its variants. We test our module on three segmentation benchmark datasets and experimental results show significant improvement.

cs.CV↗

arXiv2020

Physics-Informed Multi-LSTM Networks for Metamodeling of Nonlinear Structures

This paper introduces an innovative physics-informed deep learning framework for metamodeling of nonlinear structural systems with scarce data. The basic concept is to incorporate physics knowledge (e.g., laws of physics, scientific principles) into deep long short-term memory (LSTM) networks, which boosts the learning within a feasible solution space. The physics constraints are embedded in the loss function to enforce the model training which can accurately capture latent system nonlinearity even with very limited available training datasets. Specifically for dynamic structures, physical laws of equation of motion, state dependency and hysteretic constitutive relationship are considered to construct the physics loss. In particular, two physics-informed multi-LSTM network architectures are proposed for structural metamodeling. The satisfactory performance of the proposed framework is successfully demonstrated through two illustrative examples (e.g., nonlinear structures subjected to ground motion excitation). It turns out that the embedded physics can alleviate overfitting issues, reduce the need of big training datasets, and improve the robustness of the trained model for more reliable prediction. As a result, the physics-informed deep learning paradigm outperforms classical non-physics-guided data-driven neural networks.

cs.CE↗

arXiv2020

Comparison Network for One-Shot Conditional Object Detection

The current advances in object detection depend on large-scale datasets to get good performance. However, there may not always be sufficient samples in many scenarios, which leads to the research on few-shot detection as well as its extreme variation one-shot detection. In this paper, the one-shot detection has been formulated as a conditional probability problem. With this insight, a novel one-shot conditional object detection (OSCD) framework, referred as Comparison Network (ComparisonNet), has been proposed. Specifically, query and target image features are extracted through a Siamese network as mapped metrics of marginal probabilities. A two-stage detector for OSCD is introduced to compare the extracted query and target features with the learnable metric to approach the optimized non-linear conditional probability. Once trained, ComparisonNet can detect objects of both seen and unseen classes without further training, which also has the advantages including class-agnostic, training-free for unseen classes, and without catastrophic forgetting. Experiments show that the proposed approach achieves state-of-the-art performance on the proposed datasets of Fashion-MNIST and PASCAL VOC.

cs.CV↗

arXiv2019

Relic density of Dark Matter in the Inert Doublet Model beyond Leading Order. I) The Heavy Mass Case

A full renormalisation of the Inert Doublet Model (IDM) is presented and exploited for a precise calculation of the relic density of Dark Matter (DM) at one-loop. In this first paper, we study the case of a DM candidate with $m_{\rm DM} \sim 500$ GeV. In this regime, the co-annihilation channels are important. We therefore compute, for a wide range of relative velocities, the full next-to-leading order electroweak corrections to 7 annihilation/co-annihilation processes that contribute $\sim$ 70\% to the relic density of DM. These corrected cross-sections are interfaced with {\tt micrOMEGAs} to obtain the one-loop correction to the freeze-out relic density. Due to the accurate measurement of this observable, the one-loop corrections are relevant. We discuss the one-loop renormalisation scheme dependence and point out the influence, at one-loop, of a parameter that solely describes the scattering in the dark sector. A tree-level computation of the relic density is not sensitive to this parameter.

hep-ph↗

arXiv2019

Policy Continuation with Hindsight Inverse Dynamics

Solving goal-oriented tasks is an important but challenging problem in reinforcement learning (RL). For such tasks, the rewards are often sparse, making it difficult to learn a policy effectively. To tackle this difficulty, we propose a new approach called Policy Continuation with Hindsight Inverse Dynamics (PCHID). This approach learns from Hindsight Inverse Dynamics based on Hindsight Experience Replay, enabling the learning process in a self-imitated manner and thus can be trained with supervised learning. This work also extends it to multi-step settings with Policy Continuation. The proposed method is general, which can work in isolation or be combined with other on-policy and off-policy algorithms. On two multi-goal tasks GridWorld and FetchReach, PCHID significantly improves the sample efficiency as well as the final performance.

cs.LG↗

arXiv2019

Unsupervised Star Galaxy Classification with Cascade Variational Auto-Encoder

The increasing amount of data in astronomy provides great challenges for machine learning research. Previously, supervised learning methods achieved satisfactory recognition accuracy for the star-galaxy classification task, based on manually labeled data set. In this work, we propose a novel unsupervised approach for the star-galaxy recognition task, namely Cascade Variational Auto-Encoder (CasVAE). Our empirical results show our method outperforms the baseline model in both accuracy and stability.

cs.LG↗

arXiv2019

IntRepair: Informed Repairing of Integer Overflows

Integer overflows have threatened software applications for decades. Thus, in this paper, we propose a novel technique to provide automatic repairs of integer overflows in C source code. Our technique, based on static symbolic execution, fuses detection, repair generation and validation. This technique is implemented in a prototype named IntRepair. We applied IntRepair to 2,052C programs (approx. 1 million lines of code) contained in SAMATE's Juliet test suite and 50 synthesized programs that range up to 20KLOC. Our experimental results show that IntRepair is able to effectively detect integer overflows and successfully repair them, while only increasing the source code (LOC) and binary (Kb) size by around 1%, respectively. Further, we present the results of a user study with 30 participants which shows that IntRepair repairs are more than 10x efficient as compared to manually generated code repairs

cs.SE↗

arXiv2019

GetNet: Get Target Area for Image Pairing

Image pairing is an important research task in the field of computer vision. And finding image pairs containing objects of the same category is the basis of many tasks such as tracking and person re-identification, etc., and it is also the focus of our research. Existing traditional methods and deep learning-based methods have some degree of defects in speed or accuracy. In this paper, we made improvements on the Siamese network and proposed GetNet. The proposed method GetNet combines STN and Siamese network to get the target area first and then perform subsequent processing. Experiments show that our method achieves competitive results in speed and accuracy.

cs.CV↗

arXiv2019

Physics-guided Convolutional Neural Network (PhyCNN) for Data-driven Seismic Response Modeling

Seismic events, among many other natural hazards, reduce due functionality and exacerbate vulnerability of in-service buildings. Accurate modeling and prediction of building's response subjected to earthquakes makes possible to evaluate building performance. To this end, we leverage the recent advances in deep learning and develop a physics-guided convolutional neural network (PhyCNN) framework for data-driven seismic response modeling and serviceability assessment of buildings. The proposed PhyCNN approach is capable of accurately predicting building's seismic response in a data-driven fashion without the need of a physics-based analytical/numerical model. The basic concept is to train a deep PhyCNN model based on available seismic input-output datasets (e.g., from simulation or sensing) and physics constraints. The trained PhyCNN can then used as a surrogate model for structural seismic response prediction. Available physics (e.g., the law of dynamics) can provide constraints to the network outputs, alleviate overfitting issues, reduce the need of big training datasets, and thus improve the robustness of the trained model for more reliable prediction. The trained surrogate model is then utilized for fragility analysis given certain limit state criteria (e.g., the serviceability state). In addition, an unsupervised learning algorithm based on K-means clustering is also proposed to partition the limited number of datasets to training, validation and prediction categories, so as to maximize the use of limited datasets. The performance of the proposed approach is demonstrated through three case studies including both numerical and experimental examples. Convincing results illustrate that the proposed PhyCNN paradigm outperforms conventional pure data-based neural networks.

eess.SP↗

arXiv2019

Moduli Problem of Hitchin Pairs over Deligne-Mumford Stack

We define the moduli problem of Hitchin pairs over Deligne-Mumford Stack and prove this moduli problem is represented by a separated and locally finitely presented algebraic space, which is considered as the moduli space of Hitchin pairs over Deligne-Mumford stack.

math.AG↗

arXiv2019

Snowball: Iterative Model Evolution and Confident Sample Discovery for Semi-Supervised Learning on Very Small Labeled Datasets

In this work, we develop a joint sample discovery and iterative model evolution method for semi-supervised learning on very small labeled training sets. We propose a master-teacher-student model framework to provide multi-layer guidance during the model evolution process with multiple iterations and generations. The teacher model is constructed by performing an exponential moving average of the student models obtained from past training steps. The master network combines the knowledge of the student and teacher models with additional access to newly discovered samples. The master and teacher models are then used to guide the training of the student network by enforcing the consistence between their predictions of unlabeled samples and evolve all models when more and more samples are discovered. Our extensive experiments demonstrate that the discovering confident samples from the unlabeled dataset, once coupled with the above master-teacher-student network evolution, can significantly improve the overall semi-supervised learning performance. For example, on the CIFAR-10 dataset, with a very small set of 250 labeled samples, our method achieves an error rate of 11.81 %, more than 38 % lower than the state-of-the-art method Mean-Teacher (49.91 %).

cs.CV↗

arXiv2019

Adaptive Regularization of Labels

Recently, a variety of regularization techniques have been widely applied in deep neural networks, such as dropout, batch normalization, data augmentation, and so on. These methods mainly focus on the regularization of weight parameters to prevent overfitting effectively. In addition, label regularization techniques such as label smoothing and label disturbance have also been proposed with the motivation of adding a stochastic perturbation to labels. In this paper, we propose a novel adaptive label regularization method, which enables the neural network to learn from the erroneous experience and update the optimal label representation online. On the other hand, compared with knowledge distillation, which learns the correlation of categories using teacher network, our proposed method requires only a minuscule increase in parameters without cumbersome teacher network. Furthermore, we evaluate our method on CIFAR-10/CIFAR-100/ImageNet datasets for image recognition tasks and AGNews/Yahoo/Yelp-Full datasets for text classification tasks. The empirical results show significant improvement under all experimental settings.

cs.LG↗

arXiv2019

Token-Level Ensemble Distillation for Grapheme-to-Phoneme Conversion

Grapheme-to-phoneme (G2P) conversion is an important task in automatic speech recognition and text-to-speech systems. Recently, G2P conversion is viewed as a sequence to sequence task and modeled by RNN or CNN based encoder-decoder framework. However, previous works do not consider the practical issues when deploying G2P model in the production system, such as how to leverage additional unlabeled data to boost the accuracy, as well as reduce model size for online deployment. In this work, we propose token-level ensemble distillation for G2P conversion, which can (1) boost the accuracy by distilling the knowledge from additional unlabeled data, and (2) reduce the model size but maintain the high accuracy, both of which are very practical and helpful in the online production system. We use token-level knowledge distillation, which results in better accuracy than the sequence-level counterpart. What is more, we adopt the Transformer instead of RNN or CNN based models to further boost the accuracy of G2P conversion. Experiments on the publicly available CMUDict dataset and an internal English dataset demonstrate the effectiveness of our proposed method. Particularly, our method achieves 19.88% WER on CMUDict dataset, outperforming the previous works by more than 4.22% WER, and setting the new state-of-the-art results.

cs.CL↗

arXiv2019

Top FCNC interactions through dimension six four-fermion operators at the electron proton collider

We investigate the flavour changing neutral currents (FCNC) generated by dimension six four-fermion operators at the Large Hadron-Electron Collider (LHeC) proposed at CERN in an effective approach. This is performed by monte carlo analysis at the full detector level, as the background is successfully reduced by using invariant mass scheme to require the final states to reconstruct the mass (transverse mass) of the top quark (W boson). Our analysis shows that the future electron proton colliders like the LHeC can probe competitive limits for the top FCNC dimension six four fermion operators such as $C_{lq}^{(1)ee31} < 0.0647$, $C_{lu}^{ee31} < 0.109$, $C_{lequ}^{(1)ee31} < 0.217$ and $C_{lequ}^{(3)ee31} < 0.0209$ in the Warsaw basis.

hep-ph↗

arXiv2019

A Formula about W-operator and Its Application to Hurwitz Number

W-operators are differential operators on the polynomial ring. Mironov, Morosov and Natanzon construct the generalized Hurwitz numbers. They use the W-operator to prove a formula for the generating function of the generalized Hurwitz numbers. A special example of the W-operator is the cut-and-join operator. Goulden and Jackson use the cut-and-join operator to calculate the simple Hurwitz number. In this paper, we study the relation between W-operator W([d]) and the central elements in Sn. Based on the relation we find, we give another proof about a differential equation of the generating function of d-Hurwitz number.

math.CO↗