On boundedness, gradient estimate, blow-up and convergence in a two-species and two-stimuli chemotaxis system with/without loop

In this work, we study dynamic properties of classical solutions to a homogenous Neumann initial-boundary value problem (IBVP) for a two-species and two-stimuli chemotaxis model with/without chemical signalling loop in a 2D bounded and smooth domain. We successfully detect the product of two species masses as a feature to determine boundedness, gradient estimates, blow-up and $W^{j,\infty}(1\leq j\leq 3)$-exponential convergence of classical solutions for the corresponding IBVP. More specifically, we first show generally a smallness on the product of both species masses, thus allowing one species mass to be suitably large, is sufficient to guarantee global boundedness, higher order gradient estimates and $W^{j,\infty}$-convergence with rates of convergence to constant equilibria; and then, in a special case, we detect a straight line of masses on which blow-up occurs for large product of masses. Our findings provide new understandings about the underlying model, and thus, improve and extend greatly the existing knowledge relevant to this model.Comment: 34 pages,To appear in Calc. Var. Partial Differential Equation

Multi-Stage Self-Supervised Learning for Graph Convolutional Networks on Graphs with Few Labels

Graph Convolutional Networks(GCNs) play a crucial role in graph learning tasks, however, learning graph embedding with few supervised signals is still a difficult problem. In this paper, we propose a novel training algorithm for Graph Convolutional Network, called Multi-Stage Self-Supervised(M3S) Training Algorithm, combined with self-supervised learning approach, focusing on improving the generalization performance of GCNs on graphs with few labeled nodes. Firstly, a Multi-Stage Training Framework is provided as the basis of M3S training method. Then we leverage DeepCluster technique, a popular form of self-supervised learning, and design corresponding aligning mechanism on the embedding space to refine the Multi-Stage Training Framework, resulting in M3S Training Algorithm. Finally, extensive experimental results verify the superior performance of our algorithm on graphs with few labeled nodes under different label rates compared with other state-of-the-art approaches.Comment: AAAI Conference on Artificial Intelligence (AAAI 2020

Towards Understanding Adversarial Examples Systematically: Exploring Data Size, Task and Model Factors

Most previous works usually explained adversarial examples from several specific perspectives, lacking relatively integral comprehension about this problem. In this paper, we present a systematic study on adversarial examples from three aspects: the amount of training data, task-dependent and model-specific factors. Particularly, we show that adversarial generalization (i.e. test accuracy on adversarial examples) for standard training requires more data than standard generalization (i.e. test accuracy on clean examples); and uncover the global relationship between generalization and robustness with respect to the data size especially when data is augmented by generative models. This reveals the trade-off correlation between standard generalization and robustness in limited training data regime and their consistency when data size is large enough. Furthermore, we explore how different task-dependent and model-specific factors influence the vulnerability of deep neural networks by extensive empirical analysis. Relevant recommendations on defense against adversarial attacks are provided as well. Our results outline a potential path towards the luminous and systematic understanding of adversarial examples

LIRS: Enabling efficient machine learning on NVM-based storage via a lightweight implementation of random shuffling

Machine learning algorithms, such as Support Vector Machine (SVM) and Deep Neural Network (DNN), have gained a lot of interests recently. When training a machine learning algorithm, randomly shuffle all the training data can improve the testing accuracy and boost the convergence rate. Nevertheless, realizing training data random shuffling in a real system is not a straightforward process due to the slow random accesses in hard disk drive (HDD). To avoid frequent random disk access, the effect of random shuffling is often limited in existing approaches. With the emerging non-volatile memory-based storage device, such as Intel Optane SSD, which provides fast random accesses, we propose a lightweight implementation of random shuffling (LIRS) to randomly shuffle the indexes of the entire training dataset, and the selected training instances are directly accessed from the storage and packed into batches. Experimental results show that LIRS can reduce the total training time of SVM and DNN by 49.9% and 43.5% on average, and improve the final testing accuracy on DNN by 1.01%

End-to-End Knowledge-Routed Relational Dialogue System for Automatic Diagnosis

Beyond current conversational chatbots or task-oriented dialogue systems that have attracted increasing attention, we move forward to develop a dialogue system for automatic medical diagnosis that converses with patients to collect additional symptoms beyond their self-reports and automatically makes a diagnosis. Besides the challenges for conversational dialogue systems (e.g. topic transition coherency and question understanding), automatic medical diagnosis further poses more critical requirements for the dialogue rationality in the context of medical knowledge and symptom-disease relations. Existing dialogue systems (Madotto, Wu, and Fung 2018; Wei et al. 2018; Li et al. 2017) mostly rely on data-driven learning and cannot be able to encode extra expert knowledge graph. In this work, we propose an End-to-End Knowledge-routed Relational Dialogue System (KR-DS) that seamlessly incorporates rich medical knowledge graph into the topic transition in dialogue management, and makes it cooperative with natural language understanding and natural language generation. A novel Knowledge-routed Deep Q-network (KR-DQN) is introduced to manage topic transitions, which integrates a relational refinement branch for encoding relations among different symptoms and symptom-disease pairs, and a knowledge-routed graph branch for topic decision-making. Extensive experiments on a public medical dialogue dataset show our KR-DS significantly beats state-of-the-art methods (by more than 8% in diagnosis accuracy). We further show the superiority of our KR-DS on a newly collected medical dialogue system dataset, which is more challenging retaining original self-reports and conversational data between patients and doctors.Comment: 8 pages, 5 figues, AAA

Tau flavored dark matter and its impact on tau Yukawa coupling

In this paper we preform a systematic study of the tau flavored dark matter model by introducing two kinds of mediators (a scalar doublet and a charged scalar singlet). The electromagnetic properties of the dark matter, as well as their implications in dark matter direct detections, are analyzed in detail. The model turns out contributing a significant radiative correction to the tau lepton mass, in addition to loosing the tension between the measured dark matter relic density and constraints of dark matter direct detections. The loop corrections can be ${\cal O}(10\%)$ of the total tau mass. Signal rates of the Higgs measurements from the LHC in the $h\to\tau \tau$ and $h\to \gamma \gamma$ channels, relative to the Standard Model expectations, can be explained in this model.Comment: 18 pages, 7 figure

Look into Person: Joint Body Parsing & Pose Estimation Network and A New Benchmark

Human parsing and pose estimation have recently received considerable interest due to their substantial application potentials. However, the existing datasets have limited numbers of images and annotations and lack a variety of human appearances and coverage of challenging cases in unconstrained environments. In this paper, we introduce a new benchmark named "Look into Person (LIP)" that provides a significant advancement in terms of scalability, diversity, and difficulty, which are crucial for future developments in human-centric analysis. This comprehensive dataset contains over 50,000 elaborately annotated images with 19 semantic part labels and 16 body joints, which are captured from a broad range of viewpoints, occlusions, and background complexities. Using these rich annotations, we perform detailed analyses of the leading human parsing and pose estimation approaches, thereby obtaining insights into the successes and failures of these methods. To further explore and take advantage of the semantic correlation of these two tasks, we propose a novel joint human parsing and pose estimation network to explore efficient context modeling, which can simultaneously predict parsing and pose with extremely high quality. Furthermore, we simplify the network to solve human parsing by exploring a novel self-supervised structure-sensitive learning approach, which imposes human pose structures into the parsing results without resorting to extra supervision. The dataset, code and models are available at http://www.sysu-hcp.net/lip/.Comment: We proposed the most comprehensive dataset around the world for human-centric analysis! (Accepted By T-PAMI 2018) The dataset, code and models are available at http://www.sysu-hcp.net/lip/ . arXiv admin note: substantial text overlap with arXiv:1703.0544

Neutrino oscillation from the beam with Gaussian-like energy distribution

A recent neutrino experiment at Daya Bay gives superior data of the distribution of the prompt energy. In this paper, the energy distribution presented in the experiment is simulated by applying a Gaussian-like packet to the neutrino wave function received by the detector. We find that the wave packet of neutrinos is expanded during the propagation. As a result, the mixing angle $\theta_{13}$ is more difficult to be measured than $\theta_{12}$ and $\theta_{23}$ in long baseline experiments. Some other propagation properties, such as the time evaluation of the survival probability, the neutrino oscillation and the $CP$ violation, are also studied with the employment of the coherent state method. When the Gaussian packet width increases, the amplitude of the neutrino oscillation decreases, whereas the oscillation period increases gradually.Comment: 7 pages, 8 figure

Three-dimensional array foci of generalized Fibonacci photon sieves

We present a new kind of photon sieves on the basis of the generalized Fibonacci sequences. The required numbers and locations of axial foci can be designed by generalized Fibonacci photon sieves (GFiPS). Furthermore, the three-dimensional array foci can be controllable and adjustable by the optical path difference scaling factor (OPDSF) when the amplitude modulation is replaced with the phase modulation. Multi-focal technologies can be applied to nano-imaging, THZ, laser communications, direct laser writing, optical tweezers or atom trapping, etc.Comment: 8 pages, 8 figures, 3082 character

Supercurrent and its quantum statistical properties in mesoscopic Josephson junction in presence of non-classical light fields

In this paper, we study the supercurrent in a mesoscopic Josephson junction (MJJ) and its quantum statistical properties in the presence of nonclassical light fields. We investigate in detail the influence of external nonclassical light fields on current-voltage step structures of the MJJ. We also study in detail quantum statistical properties of the supercurrent when the external quantum electromagnetic fields are even and odd coherent-state light fields. It is shown that the supercurrent in the MJJ exhibits both squeezing effect and quantum coherences. It is demonstrated that the MJJ can feel the difference not only between classical light fields and nonclassical light fields but also between different nonclassical light fields.Comment: 21 papes, Preprint ( reprint ) of Nankai Institute of Mathematics. For hard copy, write to Prof. Mo-lin Ge, Director of Nankai Institute of Mathematics. Do not send emails to this computer accoun