92 research outputs found
Multi-Path Region Mining For Weakly Supervised 3D Semantic Segmentation on Point Clouds
Point clouds provide intrinsic geometric information and surface context for
scene understanding. Existing methods for point cloud segmentation require a
large amount of fully labeled data. Using advanced depth sensors, collection of
large scale 3D dataset is no longer a cumbersome process. However, manually
producing point-level label on the large scale dataset is time and
labor-intensive. In this paper, we propose a weakly supervised approach to
predict point-level results using weak labels on 3D point clouds. We introduce
our multi-path region mining module to generate pseudo point-level label from a
classification network trained with weak labels. It mines the localization cues
for each class from various aspects of the network feature using different
attention modules. Then, we use the point-level pseudo labels to train a point
cloud segmentation network in a fully supervised manner. To the best of our
knowledge, this is the first method that uses cloud-level weak labels on raw 3D
space to train a point cloud semantic segmentation network. In our setting, the
3D weak labels only indicate the classes that appeared in our input sample. We
discuss both scene- and subcloud-level weakly labels on raw 3D point cloud data
and perform in-depth experiments on them. On ScanNet dataset, our result
trained with subcloud-level labels is compatible with some fully supervised
methods.Comment: Accepted by CVPR202
A Cycle Deep Belief Network Model for Multivariate Time Series Classification
Multivariate time series (MTS) data is an important class of temporal data objects and it can be easily obtained. However, the MTS classification is a very difficult process because of the complexity of the data type. In this paper, we proposed a Cycle Deep Belief Network model to classify MTS and compared its performance with DBN and KNN. This model utilizes the presentation learning ability of DBN and the correlation between the time series data. The experimental results showed that this model outperforms other four algorithms: DBN, KNN_ED, KNN_DTW, and RNN
A Semi-supervised Sensing Rate Learning based CMAB Scheme to Combat COVID-19 by Trustful Data Collection in the Crowd
Mobile CrowdSensing (MCS), through employing considerable workers to sense
and collect data in a participatory manner, has been recognized as a promising
paradigm for building many large-scale applications in a cost-effective way,
such as combating COVID-19. The recruitment of trustworthy and high-quality
workers is an important research issue for MCS. Previous studies assume that
the qualities of workers are known in advance, or the platform knows the
qualities of workers once it receives their collected data. In reality, to
reduce their costs and thus maximize revenue, many strategic workers do not
perform their sensing tasks honestly and report fake data to the platform. So,
it is very hard for the platform to evaluate the authenticity of the received
data. In this paper, an incentive mechanism named Semi-supervision based
Combinatorial Multi-Armed Bandit reverse Auction (SCMABA) is proposed to solve
the recruitment problem of multiple unknown and strategic workers in MCS.
First, we model the worker recruitment as a multi-armed bandit reverse auction
problem, and design an UCB-based algorithm to separate the exploration and
exploitation, considering the Sensing Rates (SRs) of recruited workers as the
gain of the bandit. Next, a Semi-supervised Sensing Rate Learning (SSRL)
approach is proposed to quickly and accurately obtain the workers' SRs, which
consists of two phases, supervision and self-supervision. Last, SCMABA is
designed organically combining the SRs acquisition mechanism with multi-armed
bandit reverse auction, where supervised SR learning is used in the
exploration, and the self-supervised one is used in the exploitation. We prove
that our SCMABA achieves truthfulness and individual rationality. Additionally,
we exhibit outstanding performances of the SCMABA mechanism through in-depth
simulations of real-world data traces.Comment: 18 pages, 14 figure
Deep Stock Representation Learning: From Candlestick Charts to Investment Decisions
We propose a novel investment decision strategy (IDS) based on deep learning.
The performance of many IDSs is affected by stock similarity. Most existing
stock similarity measurements have the problems: (a) The linear nature of many
measurements cannot capture nonlinear stock dynamics; (b) The estimation of
many similarity metrics (e.g. covariance) needs very long period historic data
(e.g. 3K days) which cannot represent current market effectively; (c) They
cannot capture translation-invariance. To solve these problems, we apply
Convolutional AutoEncoder to learn a stock representation, based on which we
propose a novel portfolio construction strategy by: (i) using the deeply
learned representation and modularity optimisation to cluster stocks and
identify diverse sectors, (ii) picking stocks within each cluster according to
their Sharpe ratio (Sharpe 1994). Overall this strategy provides low-risk
high-return portfolios. We use the Financial Times Stock Exchange 100 Index
(FTSE 100) data for evaluation. Results show our portfolio outperforms FTSE 100
index and many well known funds in terms of total return in 2000 trading days.Comment: Accepted to International Conference on Acoustics, Speech and Signal
Processing (ICASSP) 201
Baichuan 2: Open Large-scale Language Models
Large language models (LLMs) have demonstrated remarkable performance on a
variety of natural language tasks based on just a few examples of natural
language instructions, reducing the need for extensive feature engineering.
However, most powerful LLMs are closed-source or limited in their capability
for languages other than English. In this technical report, we present Baichuan
2, a series of large-scale multilingual language models containing 7 billion
and 13 billion parameters, trained from scratch, on 2.6 trillion tokens.
Baichuan 2 matches or outperforms other open-source models of similar size on
public benchmarks like MMLU, CMMLU, GSM8K, and HumanEval. Furthermore, Baichuan
2 excels in vertical domains such as medicine and law. We will release all
pre-training model checkpoints to benefit the research community in better
understanding the training dynamics of Baichuan 2.Comment: Baichuan 2 technical report. Github:
https://github.com/baichuan-inc/Baichuan
Domestication of rice has reduced the occurrence of transposable elements within gene coding regions
A cold-induced thioredoxin h of rice, OsTrx23, negatively regulates kinase activities of OsMPK3 and OsMPK6 in vitro
AbstractCytosolic thioredoxins are small conserved proteins that are involved in cellular redox regulation. Here, we report that a major and cold-induced thioredoxin h of rice, OsTrx23, has an inhibitory activity on stress-activated mitogen-activated protein kinases (MAPKs), OsMPK3 and OsMPK6 in vitro. This inhibition effects were redox-dependent and did not involve stable physical interaction. The data suggested a novel mechanism for redox regulation of MAPKs in plants.Structured summaryMINT-7234362: MPK3 (uniprotkb:Q10N20) phosphorylates (MI:0217) MBP (uniprotkb:P02687) by protein kinase assay (MI:0424)MINT-7234435: MPK6 (uniprotkb:Q336X9) phosphorylates (MI:0217) MBP (uniprotkb:P02687) by protein kinase assay (MI:0424
Comprehensive Benefit Evaluation of Hybrid Pumped-Storage Power Stations Based on Improved Rank Correlation-Entropy Weight Method
Over the past decade, the growth of new power plants has become a trend, with new energy stations growing particularly fast. In order to solve the problem of electricity consumption, the development of hybrid pumped storage based on hydropower stations has become a focus, so it is necessary to evaluate and analyze its technical and economic characteristics. Based on the characteristics of pumped-storage power stations, this paper proposes a comprehensive benefit evaluation model for the functional, financial, and environmental benefits. The model uses the fuzzy Delphi method to improve the rank correlation analysis method and introduces the entropy weighting method, calculating the comprehensive weight of indicators by the subjective and objective combination weighting method. The fuzzy comprehensive evaluation method is used to establish a comprehensive evaluation model and calculate the comprehensive benefit evaluation grade of hybrid pumped-storage power plants. Finally, the practicality and validity of the evaluation model are verified through case studies, and the technical and economic characteristics and superiority of the hybrid pumped-storage power plants are analyzed based on the evaluation results
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