34 research outputs found
Minimizing the fluctuation of resonance driving terms in dynamic aperture optimization
Dynamic aperture (DA) is an important nonlinear property of a storage ring
lattice, which has a dominant effect on beam injection efficiency and beam
lifetime. Generally, minimizing both resonance driving terms (RDTs) and
amplitude dependent tune shifts is an essential condition for enlarging the DA.
In this paper, we study the correlation between the fluctuation of RDTs along
the ring and the DA area with double- and multi-bend achromat lattices. It is
found that minimizing the RDT fluctuations is more effective than minimizing
RDTs themselves in enlarging the DA, and thus can serve as a very powerful
indicator in the DA optimization. Besides, it is found that minimizing
lower-order RDT fluctuations can also reduce higher-order RDTs, which are not
only more computationally complicated but also more numerous. The effectiveness
of controlling the RDT fluctuations in enlarging the DA confirms that the local
cancellation of nonlinear effects used in some diffraction-limited storage ring
lattices is more effective than the global cancellation
Affinity Attention Graph Neural Network for Weakly Supervised Semantic Segmentation
Weakly supervised semantic segmentation is receiving great attention due to
its low human annotation cost. In this paper, we aim to tackle bounding box
supervised semantic segmentation, i.e., training accurate semantic segmentation
models using bounding box annotations as supervision. To this end, we propose
Affinity Attention Graph Neural Network (GNN). Following previous
practices, we first generate pseudo semantic-aware seeds, which are then formed
into semantic graphs based on our newly proposed affinity Convolutional Neural
Network (CNN). Then the built graphs are input to our GNN, in which an
affinity attention layer is designed to acquire the short- and long- distance
information from soft graph edges to accurately propagate semantic labels from
the confident seeds to the unlabeled pixels. However, to guarantee the
precision of the seeds, we only adopt a limited number of confident pixel seed
labels for GNN, which may lead to insufficient supervision for training.
To alleviate this issue, we further introduce a new loss function and a
consistency-checking mechanism to leverage the bounding box constraint, so that
more reliable guidance can be included for the model optimization. Experiments
show that our approach achieves new state-of-the-art performances on Pascal VOC
2012 datasets (val: 76.5\%, test: 75.2\%). More importantly, our approach can
be readily applied to bounding box supervised instance segmentation task or
other weakly supervised semantic segmentation tasks, with state-of-the-art or
comparable performance among almot all weakly supervised tasks on PASCAL VOC or
COCO dataset. Our source code will be available at
https://github.com/zbf1991/A2GNN.Comment: Accepted by IEEE Transactions on Pattern Analysis and Machine
Intelligence (TAPMI 2021
Topological linear magnetoresistivity and thermoconductivity induced by noncentrosymmetric Berry curvature
The Berry curvature plays a key role in the magnetic transport of topological
materials. Yet, it is not clear whether the Berry curvature by itself can give
rise to universal transport phenomena with specific scaling behaviors. In this
work, based on the semiclassical Boltzmann formalism and the symmetry analysis,
we show that the noncentrosymmetric distribution of the Berry curvature
generally results in linear magnetoresistivity and thermoconductivity both
exhibiting the B-scaling behavior. We then study such kind of topological
linear magnetoresistivity in the 2D MnBi2Te4 flakes and the 3D
spin-orbit-coupled electron gas, the former showing good agreement with the
experimental observations. The difference between our mechanism and the
conventional anisotropic magnetoresistance is elucidated. Our theory proposes a
universal scenario for the topological linear magnetoresistivity and
thermoconductivity and predicts such effects to occur in various materials,
which also provides a reasonable explanation for the recent observations of
linear magnetoresistivity
Playing 20 Question Game with Policy-Based Reinforcement Learning
The 20 Questions (Q20) game is a well known game which encourages deductive
reasoning and creativity. In the game, the answerer first thinks of an object
such as a famous person or a kind of animal. Then the questioner tries to guess
the object by asking 20 questions. In a Q20 game system, the user is considered
as the answerer while the system itself acts as the questioner which requires a
good strategy of question selection to figure out the correct object and win
the game. However, the optimal policy of question selection is hard to be
derived due to the complexity and volatility of the game environment. In this
paper, we propose a novel policy-based Reinforcement Learning (RL) method,
which enables the questioner agent to learn the optimal policy of question
selection through continuous interactions with users. To facilitate training,
we also propose to use a reward network to estimate the more informative
reward. Compared to previous methods, our RL method is robust to noisy answers
and does not rely on the Knowledge Base of objects. Experimental results show
that our RL method clearly outperforms an entropy-based engineering system and
has competitive performance in a noisy-free simulation environment.Comment: Accepted by EMNLP 201
Impact of Ferrous Iron on Microbial Community of the Biofilm in Microbial Fuel Cells
The performance of microbial electrochemical cells depends upon microbial community structure and metabolic activity of the electrode biofilms. Iron as a signal affects biofilm development and enrichment of exoelectrogenic bacteria. In this study, the effect of ferrous iron on microbial communities of the electrode biofilms in microbial fuel cells (MFCs) was investigated. Voltage production showed that ferrous iron of 100 μM facilitated MFC start-up compared to 150 μM, 200 μM, and without supplement of ferrous iron. However, higher concentration of ferrous iron had an inhibitive influence on current generation after 30 days of operation. Illumina Hiseq sequencing of 16S rRNA gene amplicons indicated that ferrous iron substantially changed microbial community structures of both anode and cathode biofilms. Principal component analysis showed that the response of microbial communities of the anode biofilms to higher concentration of ferrous iron was more sensitive. The majority of predominant populations of the anode biofilms in MFCs belonged to Geobacter, which was different from the populations of the cathode biofilms. An obvious shift of community structures of the cathode biofilms occurred after ferrous iron addition. This study implied that ferrous iron influenced the power output and microbial community of MFCs