19,417 research outputs found
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
-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 -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 of the total tau mass. Signal rates of the
Higgs measurements from the LHC in the and
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 is more difficult to be measured than and
in long baseline experiments.
Some other propagation properties, such as the time evaluation of the
survival probability, the neutrino oscillation and the 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
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