Abstract
Regulating nonlinear optical (NLO) property of metal−organic frameworks (MOFs) is of pronounced significance for their scientific research and practical application, but the regulation ...through external stimuli is still a challenging task. Here we prepare and electrically control the nonlinear optical regulation of conductive MOFs Cu-HHTP films with 001- (Cu-HHTP
001
) and 100-orientations (Cu-HHTP
100
). Z-scan results show that the nonlinear absorption coefficient (
β
) of Cu-HHTP
001
film (7.60 × 10
−6
m/W) is much higher than that of Cu-HHTP
100
film (0.84 × 10
−6
m/W) at 0 V and the
β
of Cu-HHTP
001
and Cu-HHTP
100
films gradually increase to 3.84 × 10
−5
and 1.71 × 10
−6
m/W at 10 V by increasing the applied voltage, respectively. Due to 2D Cu-HHTP having anisotropy of charge transfer in different orientations, the NLO of MOFs film can be dependent on their growth orientations and improved by tuning the electrical field. This study provides more avenues for the regulation and NLO applications of MOFs.
Localized surface plasmon resonance (LSPR) excitation of noble metal nanoparticles has been shown to accelerate and drive photochemical reactions. Here, LSPR excitation is shown to enhance the ...electrocatalysis of a fuel‐cell‐relevant reaction. The electrocatalyst consists of PdxAg alloy nanotubes (NTs), which combine the catalytic activity of Pd toward the methanol oxidation reaction (MOR) and the visible‐light plasmonic response of Ag. The alloy electrocatalyst exhibits enhanced MOR activity under LSPR excitation with significantly higher current densities and a shift to more positive potentials. The modulation of MOR activity is ascribed primarily to hot holes generated by LSPR excitation of the PdxAg NTs.
Plasmonic excitation of a palladium‐silver alloy nanotube electrocatalyst results in the enhancement of methanol oxidation reaction. Although photothermal heating of the electrochemical interface contributes to the enhancement, the primary mechanism involves hot holes generated by plasmonic excitation. Hot holes drive a methanol oxidation pathway that is complementary to electro‐oxidation.
Active learning reduces the labeling cost by iteratively selecting the most valuable data to query their labels. It has attracted a lot of interests given the abundance of unlabeled data and the high ...cost of labeling. Most active learning approaches select either informative or representative unlabeled instances to query their labels, which could significantly limit their performance. Although several active learning algorithms were proposed to combine the two query selection criteria, they are usually ad hoc in finding unlabeled instances that are both informative and representative. We address this limitation by developing a principled approach, termed QUIRE, based on the min-max view of active learning. The proposed approach provides a systematic way for measuring and combining the informativeness and representativeness of an unlabeled instance. Further, by incorporating the correlation among labels, we extend the QUIRE approach to multi-label learning by actively querying instance-label pairs. Extensive experimental results show that the proposed QUIRE approach outperforms several state-of-the-art active learning approaches in both single-label and multi-label learning.
Towards Making Unlabeled Data Never Hurt Li, Yu-Feng; Zhou, Zhi-Hua
IEEE transactions on pattern analysis and machine intelligence,
2015-Jan., 2015-Jan, 2015-1-00, 20150101, Volume:
37, Issue:
1
Journal Article
Peer reviewed
Open access
It is usually expected that learning performance can be improved by exploiting unlabeled data, particularly when the number of labeled data is limited. However, it has been reported that, in some ...cases existing semi-supervised learning approaches perform even worse than supervised ones which only use labeled data. For this reason, it is desirable to develop safe semi-supervised learning approaches that will not significantly reduce learning performance when unlabeled data are used. This paper focuses on improving the safeness of semi-supervised support vector machines (S3VMs). First, the S3VM-us approach is proposed. It employs a conservative strategy and uses only the unlabeled instances that are very likely to be helpful, while avoiding the use of highly risky ones. This approach improves safeness but its performance improvement using unlabeled data is often much smaller than S3VMs. In order to develop a safe and well-performing approach, we examine the fundamental assumption of S3VMs, i.e., low-density separation. Based on the observation that multiple good candidate low-density separators may be identified from training data, safe semi-supervised support vector machines (S4VMs) are here proposed. This approach uses multiple low-density separators to approximate the ground-truth decision boundary and maximizes the improvement in performance of inductive SVMs for any candidate separator. Under the assumption employed by S3VMs, it is here shown that S4VMs are provably safe and that the performance improvement using unlabeled data can be maximized. An out-of-sample extension of S4VMs is also presented. This extension allows S4VMs to make predictions on unseen instances. Our empirical study on a broad range of data shows that the overall performance of S4VMs is highly competitive with S3VMs, whereas in contrast to S3VMs which hurt performance significantly in many cases, S4VMs rarely perform worse than inductive SVMs.
Abstract
Conventional machine learning studies generally assume close-environment scenarios where important factors of the learning process hold invariant. With the great success of machine learning, ...nowadays, more and more practical tasks, particularly those involving open-environment scenarios where important factors are subject to change, called open-environment machine learning in this article, are present to the community. Evidently, it is a grand challenge for machine learning turning from close environment to open environment. It becomes even more challenging since, in various big data tasks, data are usually accumulated with time, like streams, while it is hard to train the machine learning model after collecting all data as in conventional studies. This article briefly introduces some advances in this line of research, focusing on techniques concerning emerging new classes, decremental/incremental features, changing data distributions and varied learning objectives, and discusses some theoretical issues.
This article briefly introduces Open Environment Machine Learning, where important factors of the machine learning process are subject to change, as occurring in many practical tasks.
Developing coordination complexes (such as metal–organic frameworks, MOFs) with circularly polarized luminescence (CPL) is currently attracting tremendous attention and remains a significant ...challenge in achieving MOF with circularly polarized afterglow. Herein, MOFs‐based circularly polarized afterglow is first reported by combining the chiral induction approach and tuning the afterglow times by using the auxiliary ligands regulation strategy. The obtained chiral R/S‐ZnIDC, R/S‐ZnIDC(bpy), and R/S‐ZnIDC(bpe)(IDC = 1H‐Imidazole‐4,5‐dicarboxylate, bpy = 4,4′‐Bipyridine, bpe = trans‐1,2‐Bis(4‐pyridyl) ethylene) containing a similar structure unit display different afterglow times with 3, 1, and <0.1 s respectively which attribute to that the longer auxiliary ligand hinders the energy transfer through the hydrogen bonding. The obtained chiral complexes reveal a strong chiral signal, obvious photoluminescence afterglow feature, and strong CPL performance (glum up to 3.7 × 10−2). Furthermore, the photo‐curing 3D printing method is first proposed to prepare various chiral MOFs based monoliths from 2D patterns to 3D scaffolds for anti‐counterfeiting and information encryption applications. This work not only develops chiral complexes monoliths by photo‐curing 3D printing technique but opens a new strategy to achieve tunable CPL afterglow in optical applications.
Chiral metal‐organic frameworks (MOFs) nanoparticles with different afterglow times are prepared by combining chiral induction approach and auxiliary ligands regulation strategy, displaying tunable circularly polarized luminescence afterglow performance. Furthermore, the corresponding chiral MOFs nanoparticles are first prepared to various chiral monoliths by photo‐curing 3D printing technology for anti‐counterfeiting and information encryption applications.
In many real tasks the features are evolving, with some features vanished and some other features being augmented. For example, in environment monitoring some sensors expired whereas some new ones ...were deployed; in mobile game recommendation some games dropped whereas some new ones were added. Learning with such incremental and decremental features is crucial but rarely studied, particularly when the data comes like a stream and thus it is infeasible to keep the whole data for optimization. In this paper, we study this challenging problem and present the OPID approach. Our approach attempts to compress important information of vanished features into functions of survived features, and then expand to include the augmented features. It is an one-pass learning approach, which only needs to scan each instance once and does not need to store the whole data, and thus satisfies the evolving streaming data nature. After tackling this problem in one-shot scenario, we then extend it to multi-shot case. Empirical study on a broad range of data sets shows that our approach can address this problem effectively.
Deep convolutional neural network models pre-trained for the ImageNet classification task have been successfully adopted to tasks in other domains, such as texture description and object proposal ...generation, but these tasks require annotations for images in the new domain. In this paper, we focus on a novel and challenging task in the pure unsupervised setting: fine-grained image retrieval. Even with image labels, fine-grained images are difficult to classify, letting alone the unsupervised retrieval task. We propose the selective convolutional descriptor aggregation (SCDA) method. The SCDA first localizes the main object in fine-grained images, a step that discards the noisy background and keeps useful deep descriptors. The selected descriptors are then aggregated and the dimensionality is reduced into a short feature vector using the best practices we found. The SCDA is unsupervised, using no image label or bounding box annotation. Experiments on six fine-grained data sets confirm the effectiveness of the SCDA for fine-grained image retrieval. Besides, visualization of the SCDA features shows that they correspond to visual attributes (even subtle ones), which might explain SCDA's high-mean average precision in fine-grained retrieval. Moreover, on general image retrieval data sets, the SCDA achieves comparable retrieval results with the state-of-the-art general image retrieval approaches.
Minimal Gated Unit for Recurrent Neural Networks Zhou, Guo-Bing; Wu, Jianxin; Zhang, Chen-Lin ...
International journal of automation and computing,
06/2016, Volume:
13, Issue:
3
Journal Article
Peer reviewed
Open access
Recurrent neural networks （RNN） have been very successful in handling sequence data. However, understanding RNN and finding the best practices for RNN learning is a difficult task, partly because ...there are many competing and complex hidden units, such as the long short-term memory （LSTM） and the gated recurrent unit （GRU）. We propose a gated unit for RNN, named as minimal gated unit （MCU）, since it only contains one gate, which is a minimal design among all gated hidden units. The design of MCU benefits from evaluation results on LSTM and GRU in the literature. Experiments on various sequence data show that MCU has comparable accuracy with GRU, but has a simpler structure, fewer parameters, and faster training. Hence, MGU is suitable in RNN＇s applications. Its simple architecture also means that it is easier to evaluate and tune, and in principle it is easier to study MGU＇s properties theoretically and empirically.
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