72 research outputs found
Analysis of the Properties of Adjoint Equations and Accuracy Verification of Adjoint Model Based on FVM
There are two different approaches on how to formulate adjoint numerical model (ANM). Aiming at the disputes arising from the construction methods of ANM, the differences between nonlinear shallow water equation and its adjoint equation are analyzed; the hyperbolicity and homogeneity of the adjoint equation are discussed. Then, based on unstructured meshes and finite volume method, a new adjoint model was advanced by getting numerical model of the adjoint equations directly. Using a gradient check, the correctness of the adjoint model was verified. The results of twin experiments to invert the bottom friction coefficient (Manning’s roughness coefficient) indicate that the adjoint model can extract the observation information and produce good quality inversion. The reason of disputes about construction methods of ANM is also discussed in the paper
Coarse-to-Fine: Learning Compact Discriminative Representation for Single-Stage Image Retrieval
Image retrieval targets to find images from a database that are visually
similar to the query image. Two-stage methods following retrieve-and-rerank
paradigm have achieved excellent performance, but their separate local and
global modules are inefficient to real-world applications. To better trade-off
retrieval efficiency and accuracy, some approaches fuse global and local
feature into a joint representation to perform single-stage image retrieval.
However, they are still challenging due to various situations to tackle,
, background, occlusion and viewpoint. In this work, we design a
Coarse-to-Fine framework to learn Compact Discriminative representation (CFCD)
for end-to-end single-stage image retrieval-requiring only image-level labels.
Specifically, we first design a novel adaptive softmax-based loss which
dynamically tunes its scale and margin within each mini-batch and increases
them progressively to strengthen supervision during training and intra-class
compactness. Furthermore, we propose a mechanism which attentively selects
prominent local descriptors and infuse fine-grained semantic relations into the
global representation by a hard negative sampling strategy to optimize
inter-class distinctiveness at a global scale. Extensive experimental results
have demonstrated the effectiveness of our method, which achieves
state-of-the-art single-stage image retrieval performance on benchmarks such as
Revisited Oxford and Revisited Paris. Code is available at
https://github.com/bassyess/CFCD.Comment: Accepted to ICCV 202
SURE: A Visualized Failure Indexing Approach using Program Memory Spectrum
Failure indexing is a longstanding crux in software testing and debugging,
the goal of which is to automatically divide failures (e.g., failed test cases)
into distinct groups according to the culprit root causes, as such multiple
faults in a faulty program can be handled independently and simultaneously.
This community has long been plagued by two challenges: 1) The effectiveness of
division is still far from promising. Existing techniques only employ a limited
source of run-time data (e.g., code coverage) to be failure proximity, which
typically delivers unsatisfactory results. 2) The outcome can be hardly
comprehensible. A developer who receives the failure indexing result does not
know why all failures should be divided the way they are. This leads to
difficulties for developers to be convinced by the result, which in turn
affects the adoption of the results. To tackle these challenges, in this paper,
we propose SURE, a viSUalized failuRe indExing approach using the program
memory spectrum. We first collect the run-time memory information at preset
breakpoints during the execution of failed test cases, and transform it into
human-friendly images (called program memory spectrum, PMS). Then, any pair of
PMS images that serve as proxies for two failures is fed to a trained Siamese
convolutional neural network, to predict the likelihood of them being triggered
by the same fault. Results demonstrate the effectiveness of SURE: It achieves
101.20% and 41.38% improvements in faults number estimation, as well as 105.20%
and 35.53% improvements in clustering, compared with the state-of-the-art
technique in this field, in simulated and real-world environments,
respectively. Moreover, we carry out a human study to quantitatively evaluate
the comprehensibility of PMS, revealing that this novel type of representation
can help developers better comprehend failure indexing results.Comment: Due to the limitation "The abstract field cannot be longer than 1,920
characters", the abstract here is shorter than that in the PDF fil
Baicalein Inhibits Proliferation Activity of Human Colorectal Cancer Cells HCT116 Through Downregulation of Ezrin
Background/Aims: The present study was aimed at examining Ezrin expression in human colorectal cancer (CRC) tissues and elucidating the influence of baicalein on the proliferation of HCT116 cells. Methods: The expression of Ezrin was determined by qRT-PCR and immunohistochemistry. HCT116 cells were divided into four groups- baicalein groups with various concentrations, pcDNA3.1-Ezrin group, si-Ezrin group and dual inhibitory group (baicalein + si-Ezrin). CCK-8 assay and flow cytometry (FCM) were employed to assess cell proliferation and to detect the distribution of cell cycle respectively. The expression levels of Ezrin protein and cell cycle-associated proteins were detected by using western blot. The proliferation ability of CRC cells was also evaluated in vivo. Results: Ezrin expression in CRC tissues was observably higher than that in adjacent colorectal tissues. With drug concentration and action time of baicalein increasing, the cell propagation capacity of HCT116 cells was decreased and the cell cycle progression was arrested. Ezrin expression was inhibited by the administration of baicalein in a dose-dependent way. The levels of CyclinD1 and CDK4 were also significantly decreased, but the expression of P53 pathway proteins P53 and P21 was markedly upregulated. Conclusion: Baicalein repressed proliferation of human colorectal cancer cells HCT116 and blocked cell cycle through downregulating Ezrin and upregulating P53 pathway-related proteins
Online Prototype Alignment for Few-shot Policy Transfer
Domain adaptation in reinforcement learning (RL) mainly deals with the
changes of observation when transferring the policy to a new environment. Many
traditional approaches of domain adaptation in RL manage to learn a mapping
function between the source and target domain in explicit or implicit ways.
However, they typically require access to abundant data from the target domain.
Besides, they often rely on visual clues to learn the mapping function and may
fail when the source domain looks quite different from the target domain. To
address these problems, we propose a novel framework Online Prototype Alignment
(OPA) to learn the mapping function based on the functional similarity of
elements and is able to achieve the few-shot policy transfer within only
several episodes. The key insight of OPA is to introduce an exploration
mechanism that can interact with the unseen elements of the target domain in an
efficient and purposeful manner, and then connect them with the seen elements
in the source domain according to their functionalities (instead of visual
clues). Experimental results show that when the target domain looks visually
different from the source domain, OPA can achieve better transfer performance
even with much fewer samples from the target domain, outperforming prior
methods.Comment: This paper has been accepted at ICML202
Contrastive Modules with Temporal Attention for Multi-Task Reinforcement Learning
In the field of multi-task reinforcement learning, the modular principle,
which involves specializing functionalities into different modules and
combining them appropriately, has been widely adopted as a promising approach
to prevent the negative transfer problem that performance degradation due to
conflicts between tasks. However, most of the existing multi-task RL methods
only combine shared modules at the task level, ignoring that there may be
conflicts within the task. In addition, these methods do not take into account
that without constraints, some modules may learn similar functions, resulting
in restricting the model's expressiveness and generalization capability of
modular methods. In this paper, we propose the Contrastive Modules with
Temporal Attention(CMTA) method to address these limitations. CMTA constrains
the modules to be different from each other by contrastive learning and
combining shared modules at a finer granularity than the task level with
temporal attention, alleviating the negative transfer within the task and
improving the generalization ability and the performance for multi-task RL. We
conducted the experiment on Meta-World, a multi-task RL benchmark containing
various robotics manipulation tasks. Experimental results show that CMTA
outperforms learning each task individually for the first time and achieves
substantial performance improvements over the baselines.Comment: This paper has been accepted at NeurIPS 2023 as a poste
Toward Location-Enabled IoT (LE-IoT): IoT Positioning Techniques, Error Sources, and Error Mitigation
Location-Enabled IoT (LE-IoT): A Survey of Positioning Techniques, Error Sources, and Mitigation
The Internet of Things (IoT) has started to empower the future of many
industrial and mass-market applications. Localization techniques are becoming
key to add location context to IoT data without human perception and
intervention. Meanwhile, the newly-emerged Low-Power Wide-Area Network (LPWAN)
technologies have advantages such as long-range, low power consumption, low
cost, massive connections, and the capability for communication in both indoor
and outdoor areas. These features make LPWAN signals strong candidates for
mass-market localization applications. However, there are various error sources
that have limited localization performance by using such IoT signals. This
paper reviews the IoT localization system through the following sequence: IoT
localization system review -- localization data sources -- localization
algorithms -- localization error sources and mitigation -- localization
performance evaluation. Compared to the related surveys, this paper has a more
comprehensive and state-of-the-art review on IoT localization methods, an
original review on IoT localization error sources and mitigation, an original
review on IoT localization performance evaluation, and a more comprehensive
review of IoT localization applications, opportunities, and challenges. Thus,
this survey provides comprehensive guidance for peers who are interested in
enabling localization ability in the existing IoT systems, using IoT systems
for localization, or integrating IoT signals with the existing localization
sensors
Corrigendum: Genetic diversity, population structure, and selective signature of sheep in the northeastern Tarim Basin
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