31 research outputs found
Careful at Estimation and Bold at Exploration
Exploration strategies in continuous action space are often heuristic due to
the infinite actions, and these kinds of methods cannot derive a general
conclusion. In prior work, it has been shown that policy-based exploration is
beneficial for continuous action space in deterministic policy reinforcement
learning(DPRL). However, policy-based exploration in DPRL has two prominent
issues: aimless exploration and policy divergence, and the policy gradient for
exploration is only sometimes helpful due to inaccurate estimation. Based on
the double-Q function framework, we introduce a novel exploration strategy to
mitigate these issues, separate from the policy gradient. We first propose the
greedy Q softmax update schema for Q value update. The expected Q value is
derived by weighted summing the conservative Q value over actions, and the
weight is the corresponding greedy Q value. Greedy Q takes the maximum value of
the two Q functions, and conservative Q takes the minimum value of the two
different Q functions. For practicality, this theoretical basis is then
extended to allow us to combine action exploration with the Q value update,
except for the premise that we have a surrogate policy that behaves like this
exploration policy. In practice, we construct such an exploration policy with a
few sampled actions, and to meet the premise, we learn such a surrogate policy
by minimizing the KL divergence between the target policy and the exploration
policy constructed by the conservative Q. We evaluate our method on the Mujoco
benchmark and demonstrate superior performance compared to previous
state-of-the-art methods across various environments, particularly in the most
complex Humanoid environment.Comment: 20 page
SSBM: A Signed Stochastic Block Model for Multiple Structure Discovery in Large-Scale Exploratory Signed Networks
Signed network structure discovery has received extensive attention and has
become a research focus in the field of network science. However, most of the
existing studies are focused on the networks with a single structure, e.g.,
community or bipartite, while ignoring multiple structures, e.g., the
coexistence of community and bipartite structures. Furthermore, existing
studies were faced with challenge regarding large-scale signed networks due to
their high time complexity, especially when determining the number of clusters
in the observed network without any prior knowledge. In view of this, we
propose a mathematically principled method for signed network multiple
structure discovery named the Signed Stochastic Block Model (SSBM). The SSBM
can capture the multiple structures contained in signed networks, e.g.,
community, bipartite, and coexistence of them, by adopting a probabilistic
model. Moreover, by integrating the minimum message length (MML) criterion and
component-wise EM (CEM) algorithm, a scalable learning algorithm that has the
ability of model selection is proposed to handle large-scale signed networks.
By comparing state-of-the-art methods on synthetic and real-world signed
networks, extensive experimental results demonstrate the effectiveness and
efficiency of SSBM in discovering large-scale exploratory signed networks with
multiple structures
Learning the Network of Graphs for Graph Neural Networks
Graph neural networks (GNNs) have achieved great success in many scenarios
with graph-structured data. However, in many real applications, there are three
issues when applying GNNs: graphs are unknown, nodes have noisy features, and
graphs contain noisy connections. Aiming at solving these problems, we propose
a new graph neural network named as GL-GNN. Our model includes multiple
sub-modules, each sub-module selects important data features and learn the
corresponding key relation graph of data samples when graphs are unknown.
GL-GNN further obtains the network of graphs by learning the network of
sub-modules. The learned graphs are further fused using an aggregation method
over the network of graphs. Our model solves the first issue by simultaneously
learning multiple relation graphs of data samples as well as a relation network
of graphs, and solves the second and the third issue by selecting important
data features as well as important data sample relations. We compare our method
with 14 baseline methods on seven datasets when the graph is unknown and 11
baseline methods on two datasets when the graph is known. The results show that
our method achieves better accuracies than the baseline methods and is capable
of selecting important features and graph edges from the dataset. Our code will
be publicly available at \url{https://github.com/Looomo/GL-GNN}
Multi-Modality Multi-Scale Cardiovascular Disease Subtypes Classification Using Raman Image and Medical History
Raman spectroscopy (RS) has been widely used for disease diagnosis, e.g.,
cardiovascular disease (CVD), owing to its efficiency and component-specific
testing capabilities. A series of popular deep learning methods have recently
been introduced to learn nuance features from RS for binary classifications and
achieved outstanding performance than conventional machine learning methods.
However, these existing deep learning methods still confront some challenges in
classifying subtypes of CVD. For example, the nuance between subtypes is quite
hard to capture and represent by intelligent models due to the chillingly
similar shape of RS sequences. Moreover, medical history information is an
essential resource for distinguishing subtypes, but they are underutilized. In
light of this, we propose a multi-modality multi-scale model called M3S, which
is a novel deep learning method with two core modules to address these issues.
First, we convert RS data to various resolution images by the Gramian angular
field (GAF) to enlarge nuance, and a two-branch structure is leveraged to get
embeddings for distinction in the multi-scale feature extraction module.
Second, a probability matrix and a weight matrix are used to enhance the
classification capacity by combining the RS and medical history data in the
multi-modality data fusion module. We perform extensive evaluations of M3S and
found its outstanding performance on our in-house dataset, with accuracy,
precision, recall, specificity, and F1 score of 0.9330, 0.9379, 0.9291, 0.9752,
and 0.9334, respectively. These results demonstrate that the M3S has high
performance and robustness compared with popular methods in diagnosing CVD
subtypes
Data and Knowledge Co-driving for Cancer Subtype Classification on Multi-Scale Histopathological Slides
Artificial intelligence-enabled histopathological data analysis has become a
valuable assistant to the pathologist. However, existing models lack
representation and inference abilities compared with those of pathologists,
especially in cancer subtype diagnosis, which is unconvincing in clinical
practice. For instance, pathologists typically observe the lesions of a slide
from global to local, and then can give a diagnosis based on their knowledge
and experience. In this paper, we propose a Data and Knowledge Co-driving (D&K)
model to replicate the process of cancer subtype classification on a
histopathological slide like a pathologist. Specifically, in the data-driven
module, the bagging mechanism in ensemble learning is leveraged to integrate
the histological features from various bags extracted by the embedding
representation unit. Furthermore, a knowledge-driven module is established
based on the Gestalt principle in psychology to build the three-dimensional
(3D) expert knowledge space and map histological features into this space for
metric. Then, the diagnosis can be made according to the Euclidean distance
between them. Extensive experimental results on both public and in-house
datasets demonstrate that the D&K model has a high performance and credible
results compared with the state-of-the-art methods for diagnosing
histopathological subtypes. Code:
https://github.com/Dennis-YB/Data-and-Knowledge-Co-driving-for-Cancer-Subtypes-Classificatio
Pressure-tunable magnetic topological phases in magnetic topological insulator MnSb4Te7
Magnetic topological insulators, possessing both magnetic order and
topological electronic structure, provides an excellent platform to research
unusual physical properties. Here, we report a high-pressure study on the
anomalous Hall effect of magnetic TI MnSb4Te7 through transports measurements
combined with first-principle theoretical calculations. We discover that the
ground state of MnSb4Te7 experiences a magnetic phase transition from the
A-type antiferromagnetic state to ferromagnetic dominating state at 3.78 GPa,
although its crystal sustains a rhombohedral phase under high pressures up to 8
GPa. The anomalous Hall conductance {\sigma}xyA keeps around 10 {\Omega}-1
cm-1, dominated by the intrinsic mechanism even after the magnetic phase
transition. The results shed light on the intriguing magnetism in MnSb4Te7 and
pave the way for further studies of the relationship between topology and
magnetism in topological materials.Comment: 10 pages, 4 figure
IASM: A System for the Intelligent Active Surveillance of Malaria
Malaria, a life-threatening infectious disease, spreads rapidly via parasites. Malaria prevention is more effective and efficient than treatment. However, the existing surveillance systems used to prevent malaria are inadequate, especially in areas with limited or no access to medical resources. In this paper, in order to monitor the spreading of malaria, we develop an intelligent surveillance system based on our existing algorithms. First, a visualization function and active surveillance were implemented in order to predict and categorize areas at high risk of infection. Next, socioeconomic and climatological characteristics were applied to the proposed prediction model. Then, the redundancy of the socioeconomic attribute values was reduced using the stepwise regression method to improve the accuracy of the proposed prediction model. The experimental results indicated that the proposed IASM predicted malaria outbreaks more close to the real data and with fewer variables than other models. Furthermore, the proposed model effectively identified areas at high risk of infection
Spin excitations in metallic kagome lattice FeSn and CoSn
In two-dimensional (2D) metallic kagome lattice materials, destructive
interference of electronic hopping pathways around the kagome bracket can
produce nearly localized electrons, and thus electronic bands that are flat in
momentum space. When ferromagnetic order breaks the degeneracy of the
electronic bands and splits them into the spin-up majority and spin-down
minority electronic bands, quasiparticle excitations between the spin-up and
spin-down flat bands should form a narrow localized spin-excitation Stoner
continuum coexisting with well-defined spin waves in the long wavelengths. Here
we report inelastic neutron scattering studies of spin excitations in 2D
metallic Kagome lattice antiferromagnetic FeSn and paramagnetic CoSn, where
angle resolved photoemission spectroscopy experiments found spin-polarized and
nonpolarized flat bands, respectively, below the Fermi level. Although our
initial measurements on FeSn indeed reveal well-defined spin waves extending
well above 140 meV coexisting with a flat excitation at 170 meV, subsequent
experiments on CoSn indicate that the flat mode actually arises mostly from
hydrocarbon scattering of the CYTOP-M commonly used to glue the samples to
aluminum holder. Therefore, our results established the evolution of spin
excitations in FeSn and CoSn, and identified an anomalous flat mode that has
been overlooked by the neutron scattering community for the past 20 years