Binary Classification with Positive Labeling Sources

To create a large amount of training labels for machine learning models effectively and efficiently, researchers have turned to Weak Supervision (WS), which uses programmatic labeling sources rather than manual annotation. Existing works of WS for binary classification typically assume the presence of labeling sources that are able to assign both positive and negative labels to data in roughly balanced proportions. However, for many tasks of interest where there is a minority positive class, negative examples could be too diverse for developers to generate indicative labeling sources. Thus, in this work, we study the application of WS on binary classification tasks with positive labeling sources only. We propose WEAPO, a simple yet competitive WS method for producing training labels without negative labeling sources. On 10 benchmark datasets, we show WEAPO achieves the highest averaged performance in terms of both the quality of synthesized labels and the performance of the final classifier supervised with these labels. We incorporated the implementation of \method into WRENCH, an existing benchmarking platform.Comment: CIKM 2022 (short

Gradient in microstructure and mechanical property of selective laser melted AlSi10Mg

It is known that metal parts can be made stronger, tougher and better wear resistance by introducing gradient microstructure. This work reports the cooling rate of melt pool induced discrepancy in microstructural gradient and element distribution during selective laser melting (SLM), thereby resulting in decrease in microhardness and wear resistance from surface to inside with a range of ∼100 μm of SLM- manufactured AlSi10Mg alloy. The cooling rate in the top surface of melt pool reaches ∼1.44 × 106 K/s, which is much higher than that at the bottom (≤1 × 103 K/s). Such a difference in cooling rate of melt pool is the main cause for forming gradient microstructure in terms of the distribution of Si particles, dendrite size, sub-grains and sub-boundaries. The variation in microstructure of SLM-produced AlSi10Mg alloy, as a result of gradient cooling rate, has a significant impact on its mechanical properties. Compared with core area, the surface area with a higher cooling rate is composed of finer Si particles, dendritic structure and more sub-boundaries, resulting in higher microhardness and greater wear resistance. The mechanism for formation of gradient microstructure and its influence on the mechanical properties are discussed, which provide new and deep insight into fabricating SLM-produced components with gradient microstructure

Effect of PGC-1α on Proliferation, Migration, and Transdifferentiation of Rat Vascular Smooth Muscle Cells Induced by High Glucose

We assessed the role of PGC-1α (PPARγ coactivator-1 alpha) in glucose-induced proliferation, migration, and inflammatory gene expression of vascular smooth muscle cells (VSMCs). We carried out phagocytosis studies to assess the role of PGC-1α in transdifferentiation of VSMCs by flow cytometry. We found that high glucose stimulated proliferation, migration and inflammatory gene expression of VSMCs, but overexpression of PGC-1α attenuated the effects of glucose. In addition, overexpression of PGC-1α decreased mRNA and protein level of VSMCs-related genes, and induced macrophage-related gene expression, as well as phagocytosis of VSMCs. Therefore, PGC-1α inhibited glucose-induced proliferation, migration and inflammatory gene expression of VSMCs, which are key features in the pathology of atherosclerosis. More importantly, PGC-1α transdifferentiated VSMCs to a macrophage-like state. Such transdifferentiation possibly increased the portion of VSMCs-derived foam cells in the plaque and favored plaque stability

Electrochemical Sensor for o-Nitrophenol Based on β

An electrochemical sensor for the quantification of o-nitrophenol (o-NP) has been developed based on the β-cyclodextrin functionalized graphene nanosheets modified glassy carbon electrode (CD-GNs/GCE). The results indicated that CD-GNs showed good electrochemical behavior to the redox of o-NP which is attributed to the combination of the excellent properties of graphene and cyclodextrin. The peak currents possess a linear relationship with the concentration of o-NP in the range of 5–400 μM. The detection limit of o-NP reached to 0.3 μM on the basis of the signal-to-noise characteristics (S/N=3). The peak potentials for the reversible redox waves are not affected by other nitrophenol isomers (m, p-NP), illustrating good selectivity. Furthermore, the developed electrochemical sensor exhibited good stability and reproducibility for the detection of o-NP and could be used to determine o-NP in real water sample

Efficient Deep Reinforcement Learning via Adaptive Policy Transfer

Transfer Learning (TL) has shown great potential to accelerate Reinforcement Learning (RL) by leveraging prior knowledge from past learned policies of relevant tasks. Existing transfer approaches either explicitly computes the similarity between tasks or select appropriate source policies to provide guided explorations for the target task. However, how to directly optimize the target policy by alternatively utilizing knowledge from appropriate source policies without explicitly measuring the similarity is currently missing. In this paper, we propose a novel Policy Transfer Framework (PTF) to accelerate RL by taking advantage of this idea. Our framework learns when and which source policy is the best to reuse for the target policy and when to terminate it by modeling multi-policy transfer as the option learning problem. PTF can be easily combined with existing deep RL approaches. Experimental results show it significantly accelerates the learning process and surpasses state-of-the-art policy transfer methods in terms of learning efficiency and final performance in both discrete and continuous action spaces.Comment: Accepted by IJCAI'202

Multi-Agent Game Abstraction via Graph Attention Neural Network

In large-scale multi-agent systems, the large number of agents and complex game relationship cause great difficulty for policy learning. Therefore, simplifying the learning process is an important research issue. In many multi-agent systems, the interactions between agents often happen locally, which means that agents neither need to coordinate with all other agents nor need to coordinate with others all the time. Traditional methods attempt to use pre-defined rules to capture the interaction relationship between agents. However, the methods cannot be directly used in a large-scale environment due to the difficulty of transforming the complex interactions between agents into rules. In this paper, we model the relationship between agents by a complete graph and propose a novel game abstraction mechanism based on two-stage attention network (G2ANet), which can indicate whether there is an interaction between two agents and the importance of the interaction. We integrate this detection mechanism into graph neural network-based multi-agent reinforcement learning for conducting game abstraction and propose two novel learning algorithms GA-Comm and GA-AC. We conduct experiments in Traffic Junction and Predator-Prey. The results indicate that the proposed methods can simplify the learning process and meanwhile get better asymptotic performance compared with state-of-the-art algorithms.Comment: Accepted by AAAI202

Chaotic Characteristics and Application of Cooperative Game and Evolutionary Game

According to a dynamical multiteam Cournot game in exploitation of a renewable resource, a new dynamic Cournot duopoly game model with team players in exploitation of a renewable resource is built up in this paper. Based on the theory of bifurcations of dynamical systems, the stability of the system is studied and the local stable region of Nash equilibrium point is obtained. The effect of the output adjustment speed parameters and the weight parameter of the system on the dynamic characteristics of the system are researched. The complexity of the system is described via the bifurcation diagrams, the Lyapunov exponents, the phase portrait, the time history diagram, and the fractal dimension. Furthermore, the chaos control of the system is realized by the parameter adjustment method. At last, an evolutionary game as a special dynamic system is constructed and analyzed which is more useful and helpful in application. The derived results have very important theoretical and practical values for the renewable resource market and companies

From Few to More: Large-scale Dynamic Multiagent Curriculum Learning

A lot of efforts have been devoted to investigating how agents can learn effectively and achieve coordination in multiagent systems. However, it is still challenging in large-scale multiagent settings due to the complex dynamics between the environment and agents and the explosion of state-action space. In this paper, we design a novel Dynamic Multiagent Curriculum Learning (DyMA-CL) to solve large-scale problems by starting from learning on a multiagent scenario with a small size and progressively increasing the number of agents. We propose three transfer mechanisms across curricula to accelerate the learning process. Moreover, due to the fact that the state dimension varies across curricula,, and existing network structures cannot be applied in such a transfer setting since their network input sizes are fixed. Therefore, we design a novel network structure called Dynamic Agent-number Network (DyAN) to handle the dynamic size of the network input. Experimental results show that DyMA-CL using DyAN greatly improves the performance of large-scale multiagent learning compared with state-of-the-art deep reinforcement learning approaches. We also investigate the influence of three transfer mechanisms across curricula through extensive simulations.Comment: Accepted by AAAI202