Search

A transition-metal-free strategy regarding an iodine–sodium percarbonate catalysis to achieve the ortho-aminomethylation of phenols in aqueous media has been developed. This method can effectively broaden a wide range of phenols, tolerate sensitive functional groups, and achieve the late-stage functionalization of ten functional molecules that contain phenolic structures. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Current neural networks are mostly built on the MP model, which usually formulates the neuron as executing an activation function on the real-valued weighted aggregation of signals received from other neurons. This letter proposes the flexible transmitter (FT) model, a novel bio-plausible neuron model with flexible synaptic plasticity. The FT model employs a pair of parameters to model the neurotransmitters between neurons and puts up a neuron-exclusive variable to record the regulated neurotrophin density. Thus, the FT model can be formulated as a two-variable, two-valued function, taking the commonly used MP neuron model as its particular case. This modeling manner makes the FT model biologically more realistic and capable of handling complicated data, even spatiotemporal data. To exhibit its power and potential, we present the flexible transmitter network (FTNet), which is built on the most common fully connected feedforward architecture taking the FT model as the basic building block. FTNet allows gradient calculation and can be implemented by an improved backpropagation algorithm in the complex-valued domain. Experiments on a broad range of tasks show that FTNet has power and potential in processing spatiotemporal data. This study provides an alternative basic building block in neural networks and exhibits the feasibility of developing artificial neural networks with neuronal plasticity. [ABSTRACT FROM AUTHOR]

Background: Wilson's disease (WD) is one of the few hereditary diseases that can be successfully treated with medicines. We conduct this survey research to assess treatment persistence among patients with WD and try to identify what factors affect the treatment persistence. Methods: We employed WeChat which is the most popular social software in China to carry out this anonymous questionnaire research. The questionnaire included medication adherence scale. We also collected available medical records related to demographic and clinical characteristics. All the patients were divided into group of persistence with drug treatment (PDT) and nonpersistence with drug treatment (n‐PDT). Results: We collected 242 qualified questionnaires. Only 66.5% of patients were PDT during the mean 12.6 years of follow‐up. In PDT group, better outcomes were observed: improvement (78.3%) and no change (16.1%) versus those in n‐PDT (55.6%; and 28.4%, respectively). In PDT group, only nine patients deteriorated (6.8%) in comparison with 13 patients in n‐PDT (16.0%). The adverse events (AEs) in PDT group were significantly less than those in n‐PDT group. There were no significant differences in clinical type, gender, age, education level, and family knowledge about WD between the two groups. There were significant differences in AEs and family position toward treatment. Conclusion: Medication Adherence of Chinese WD patients was low. One third of the patients (33.5%) were unable to PDT, and it had an important negative effect on clinical outcome. AEs and family support had an important impact on treatment persistence. [ABSTRACT FROM AUTHOR]

Support Vector Machine (SVM) has always been one of the most successful learning algorithms, with the central idea of maximizing the minimum margin, i.e., the smallest distance from the instances to the classification boundary. However, recent theoretical results disclosed that maximizing the minimum margin does not necessarily lead to better generalization performance, and instead, the margin distribution has been proven to be more crucial. Based on this idea, we propose the Optimal margin Distribution Machine (ODM), which can achieve a better generalization performance by optimizing the margin distribution explicitly. We characterize the margin distribution by the first- and second-order statistics, i.e., the margin mean and variance. The proposed method is a general learning approach which can be applied in any place where SVMs are used, and its superiority is verified both theoretically and empirically in this paper. [ABSTRACT FROM AUTHOR]

Background: Bilateral medial medullary infarction (MMI) is uncommon and bilateral medial pons infarction (MPI) is even rarer. "Heart appearance" on magnetic resonance imaging (MRI) is a characteristic presentation of bilateral medial medullary infarction (MMI).Case Presentation: We present 67-year-old Chinese diabetic and hypertensive female patient affected with "heart appearance-like" infarction in bilateral ponto-medullary junction on MRI. Abnormal signal was observed in the bilateral ponto-medullary junction on T1, T2, fluid-attenuated inversion recovery and apparent diffusion coefficient (ADC). The whole brain digital subtraction angiography (DSA) showed the basilar artery and vertebral artery remained intact. Therefore, we speculated that the bilateral ponto-medullary junction infarction might be caused by the deep perforating branch of the basilar artery.Conclusions: As far as we know, the "heart appearance-like" infraction in bilateral ponto-medullary junction was not reported. Our case also suggests that bilateral ischemic infraction involvement of the medulla and pon is possible even in the context of an intact basilar artery. [ABSTRACT FROM AUTHOR]

Summary of main observation and conclusion: Converting CO2 into value‐added chemicals represents a promising way to alleviate the CO2 derived environmental issues, for which the development of catalysts with high efficiency and recyclability is very desirable. Herein, the catalytic system by combining cobalt source and ionic liquid (IL) has been developed as the efficacious and recyclable catalyst for the carboxylative cyclization of propargylic amine and CO2 to prepare 2‐oxazolinones. In this protocol, various propargylic amines were successfully transformed into the corresponding 2‐oxazolinones with CoBr2 and diethylimidazolium acetate ([EEIM][OAc]) as the catalyst under atmospheric CO2 pressure. It is worth noting that the turnover number (TON) of this transformation can be up to 1740, presumably being attributed to the cooperative effect of the cobalt and IL. Furthermore, the existence of IL enables the catalytic system to be easily recycled to 10 times without losing its activity. [ABSTRACT FROM AUTHOR]

Current deep-learning models are mostly built upon neural networks, i.e. multiple layers of parameterized differentiable non-linear modules that can be trained by backpropagation. In this paper, we explore the possibility of building deep models based on non-differentiable modules such as decision trees. After a discussion about the mystery behind deep neural networks, particularly by contrasting them with shallow neural networks and traditional machine-learning techniques such as decision trees and boosting machines, we conjecture that the success of deep neural networks owes much to three characteristics, i.e. layer-by-layer processing, in-model feature transformation and sufficient model complexity. On one hand, our conjecture may offer inspiration for theoretical understanding of deep learning; on the other hand, to verify the conjecture, we propose an approach that generates deep forest holding these characteristics. This is a decision-tree ensemble approach, with fewer hyper-parameters than deep neural networks, and its model complexity can be automatically determined in a data-dependent way. Experiments show that its performance is quite robust to hyper-parameter settings, such that in most cases, even across different data from different domains, it is able to achieve excellent performance by using the same default setting. This study opens the door to deep learning based on non-differentiable modules without gradient-based adjustment, and exhibits the possibility of constructing deep models without backpropagation. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Supervised learning techniques construct predictive models by learning from a large number of training examples, where each training example has a label indicating its ground-truth output. Though current techniques have achieved great success, it is noteworthy that in many tasks it is difficult to get strong supervision information like fully ground-truth labels due to the high cost of the data-labeling process. Thus, it is desirable for machine-learning techniques to work with weak supervision. This article reviews some research progress of weakly supervised learning, focusing on three typical types of weak supervision: incomplete supervision, where only a subset of training data is given with labels; inexact supervision, where the training data are given with only coarse-grained labels; and inaccurate supervision, where the given labels are not always ground-truth. [ABSTRACT FROM AUTHOR]

Machine learning is the driving force of the hot artificial intelligence (AI) wave. In an interview with NSR, Prof. Thomas Dietterich, the distinguished professor emeritus of computer science at Oregon State University in the USA, the former president of Association of Advancement of Artificial Intelligence (AAAI, the most prestigious association in the field of artificial intelligence) and the founding president of the International Machine Learning Society, talked about exciting recent advances and technical challenges of machine learning, as well as its big impact on the world. [ABSTRACT FROM AUTHOR]

Performing CO2 conversion in a cost-effective and environmentally benign manner would be promising and remains challenging due to its thermodynamic stability and kinetic inertness. Herein, we would like to summarise significant advances in organic synthesis using CO2 with high catalytic efficiency and excellent selectivity towards the target product mainly during the last five years (2012–2016). Achieving an efficient and selective CO2 conversion depends on the development of metal catalysts (especially functional metal complex catalysis) including main-group metal, typical transition metal and lanthanide series metal as well as organocatalysts e.g. N-heterocyclic carbenes, N-heterocyclic olefins, task-specific ionic liquids, superbases and frustrated Lewis pairs that are able to effectively activate CO2 and/or the substrate on the basis of the mechanistic understanding at the molecular level. This review just covers typical catalytic transformation of CO2, for instance, carboxylation, amidation, hydrogenation, and representative green processes like solvent-less, halogen-free that use CO2 as an ideal carbon-neutral source to prepare valuable compounds with improved atom economy and enhanced sustainability of chemical processes through green catalysis. In particular, in situ catalytic CO2 conversion, i.e. the combination of carbon capture and subsequent conversion, a recent breakthrough in the CO2 chemistry field, is also discussed. [ABSTRACT FROM AUTHOR]

Purpose: To evaluate the prognostic value and pathobiological significance of Glasgow microenvironment score (GMS), a parameter based on tumor stroma percentage and inflammatory cell infiltration, in gastric cancer. Methods: A total of 225 cases of gastric cancer were histologically reviewed, and GMS was evaluated for each case. The association between GMS and patients' survival was investigated. Then the relationship between GMS and mismatch repair (MMR) status, Epstein-Barr virus (EBV) infection were determined using immunohistochemistry (IHC) and in situ hybridization, and the expression of PD1/PD-L1 was examined. Furthermore, the amount of cancer-associated fibroblasts (CAFs), the content and maturity of collagen components were detected using IHC, Picrosirius Red staining and second harmonic generation imaging. Results: GMS was significantly associated with clinical outcomes of gastric cancer, and multivariate analysis indicated that GMS was an independent factor (HR 1.725, P = 0.002). Low GMS was a manifestation of better prognosis and inflammatory tumor microenvironment, which was related to MMR deficiency ( P = 0.042) and EBV infection ( P = 0.032), and within this microenvironment, expression of PD-L1 in carcinoma cells ( P = 0.030) or in inflammatory cells ( P = 0.029) was significantly higher. In contrast, high GMS linked to a poorer survival and desmoplastic stroma, in which there existed markedly increased CAFs and collagen deposition. Conclusion: GMS can serve as a useful prognostic factor for gastric cancer, and according to GMS, the tumor microenvironment in this cancer type may be partially classified as inflammatory or desmoplastic microenvironment that possesses different pathobiological features. [ABSTRACT FROM AUTHOR]

Parkinson's disease (PD) is an insidious onset neurodegenerative disease affecting approximately 1% of the population over the age of 65. So far available therapies for PD have only aimed at improving or alleviating symptoms, but not at slowing, preventing, and reversing the course of PD. Recently, some studies have indicated that the levels and activation of Abelson non-receptor tyrosine kinase (c-Abl, Abl1) were up-regulated in the brain tissue of patients with PD and demonstrated that c-Abl inhibitors could improve motor behavior, prevent the loss of dopamine neurons, inhibit phosphorylation of Cdk5, regulate α-synuclein phosphorylation and clearance, inhibit the tyrosine phosphorylation of parkin and decrease parkin substrate, for example, PARIS (zinc finger protein 746), AIMP2 (aminoacyl-tRNA synthetase-interacting multifunctional protein type2), FBP1 (fuse-binding protein 1), and synphilin-1. Therefore, we review the mechanism of the c-Abl inhibitor in PD and conclude that c-Abl inhibitors may be a potential treatment in PD and other neurodegenerative disease. [ABSTRACT FROM AUTHOR]

A silver(I)-catalyzed three-component reaction of propargylic alcohols, CO2, and monohydric alcohols was successfully developed for the synthesis of β-oxopropyl carbonates. As such, a series of β-oxopropyl carbonates were exclusively produced in excellent yields (up to 98 %), even under atmospheric pressure of CO2. The silver catalyst works efficiently for both the carboxylative cyclization of propargylic alcohols with CO2 and subsequent transesterification of α-alkylidene cyclic carbonates with monohydric alcohols; thus this tandem process performs smoothly under mild conditions. This work provides a versatile and thermodynamically favorable approach to dissymmetric dialkyl carbonates. [ABSTRACT FROM AUTHOR]

In many real-world applications, different types of misclassification usually suffer from different costs, but the accurate cost is often hard to be determined and usually one can only get an interval-estimation like that one type of mistake is about 5 to 10 times more serious than the other type. On the other hand, there are usually abundant unlabeled data available, leading to great research effort about semi-supervised learning. It is noticeable that cost interval and unlabeled data usually appear simultaneously in practice tasks; however, there is rare study tackling them together. In this paper, we propose the cisLDM approach which is able to handle cost interval and exploit unlabeled data in a principled way. Rather than maximizing the minimum margin like traditional large margin classifiers, cisLDM tries to optimize the margin distribution on both labeled and unlabeled data when minimizing the worst-case total-cost and the mean total-cost simultaneously according to the cost interval. Experiments on a broad range of datasets and cost settings exhibit the impressive performance of cisLDM. In particular, cisLDM is able to reduce 47 percent more total-cost than standard SVM and 27 percent more total-cost than cost-sensitive semi-supervised SVM which assumes the true cost value is known in advance. [ABSTRACT FROM PUBLISHER]

A new nonlinear beam-column element capable of considering the shear deformation is proposed under the concept of consistent deformation. For the traditional displacement interpolation function, the beam-column element produces membrane locking under large deformation and shear locking when the element becomes slender. To eliminate the membrane and shear locking, force equilibrium equations are employed to derive the displacement function. Numerical examples herein show that membrane locking in the traditional nonlinear beam-column element could cause a considerable error. Comparison of the present improved formulae based on the Timoshenko beam theory with that based on the Euler-Bernoulli beam theory indicates that the present approach requires several additional parameters to consider shear deformation and it is suitable for computer analysis readily for incorporation into the frames analysis software using the co-rotational approach for large translations and rotations. The examples confirm that the proposed element has higher accuracy and numerical efficiency. [ABSTRACT FROM AUTHOR]

To reevaluate the association between the costimulatory molecule cytotoxic T lymphocyte-associated antigen4 ( CTLA4) single nucleotide polymorphism (SNP) +49A/G and acute rejection (AR) in renal transplantation, nine studies published before June 2013 were analyzed. Meta-analysis and cumulative meta-analysis (metacum) were performed for each genotype in a random/fixed effect model. The combined odds ratios (OR) with 95% confidence intervals (CI) were calculated to estimate the strength of the association. In the sensitivity analysis, a single study involved in the meta-analysis was deleted each time to investigate the influence of the individual data sets on the pooled ORs. Meta-analysis regression was used for some influence factors, such as year of publication, total number in each group (AR group and control group), ethnicity, the ratio of GG to GA + AA, the ratio of G to A in CTLA4 +49A/G. Overall, a significant correlation was noted between the CTLA4 SNP (+49A/G) and the risk of AR (for GG vs. AG + AA: OR = 1.35, 95% CI = 1.05-1.73, p = 0.02; for G vs. A: OR = 1.21, 95% CI = 1.03-1.42, p = 0.02), especially in the Asian subgroup (for GG vs. AG + AA: OR = 1.79, 95% CI = 1.15-2.78, p = 0.009; for G vs. A: OR = 1.47, 95% CI = 1.04-2.07, p = 0.03). Of the influence factors, the ratio of GG to GA+AA ( p = 0.046) and the ratio of G to A ( p = 0.017) were significant factors. In conclusion, our results suggest that CTLA4 +49A/G contribute to the risk of AR following renal transplantation. [ABSTRACT FROM AUTHOR]

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 FROM AUTHOR]

Multi-label learning studies the problem where each example is represented by a single instance while associated with a set of labels simultaneously. During the past decade, significant amount of progresses have been made toward this emerging machine learning paradigm. This paper aims to provide a timely review on this area with emphasis on state-of-the-art multi-label learning algorithms. Firstly, fundamentals on multi-label learning including formal definition and evaluation metrics are given. Secondly and primarily, eight representative multi-label learning algorithms are scrutinized under common notations with relevant analyses and discussions. Thirdly, several related learning settings are briefly summarized. As a conclusion, online resources and open research problems on multi-label learning are outlined for reference purposes. [ABSTRACT FROM PUBLISHER]

Complex-valued neural networks have attracted increasing attention in recent years, while it remains open on the advantages of complex-valued neural networks in comparison with real-valued networks. This work takes one step on this direction by introducing the complex-reaction network with fully-connected feed-forward architecture. We prove the universal approximation property for complex-reaction networks, and show that a class of radial functions can be approximated by a complex-reaction network using the polynomial number of parameters, whereas real-valued networks need at least exponential parameters to reach the same approximation level. For empirical risk minimization, we study the landscape and convergence of complex gradient descents. Our theoretical result shows that the critical point set of complex-reaction networks is a proper subset of that of real-valued networks, which may show some insights on finding the optimal solutions more easily for complex-reaction networks. [ABSTRACT FROM AUTHOR]

Recent research has used margin theory to analyze the generalization performance for deep neural networks (DNNs). The existed results are almost based on the spectrally-normalized minimum margin. However, optimizing the minimum margin ignores a mass of information about the entire margin distribution, which is crucial to generalization performance. In this paper, we prove a generalization upper bound dominated by the statistics of the entire margin distribution. Compared with the minimum margin bounds, our bound highlights an important measure for controlling the complexity, which is the ratio of the margin standard deviation to the expected margin. We utilize a convex margin distribution loss function on the deep neural networks to validate our theoretical results by optimizing the margin ratio. Experiments and visualizations confirm the effectiveness of our approach and the correlation between generalization gap and margin ratio. [ABSTRACT FROM AUTHOR]

Learning with streaming data has attracted much attention during the past few years. Though most studies consider data stream with fixed features, in real practice the features may be evolvable. For example, features of data gathered by limited-lifespan sensors will change when these sensors are substituted by new ones. In this article, we propose a novel learning paradigm: Feature Evolvable Streaming Learning where old features would vanish and new features would occur. Rather than relying on only the current features, we attempt to recover the vanished features and exploit it to improve performance. Specifically, we learn a mapping from the overlapping period to recover old features and then we learn two models from the recovered features and the current features, respectively. To benefit from the recovered features, we develop two ensemble methods. In the first method, we combine the predictions from two models and theoretically show that with the assistance of old features, the performance on new features can be improved and we provide a tighter bound when the loss function is exponentially concave. In the second approach, we dynamically select the best single prediction and establish a better performance guarantee when the best model switches. Experiments on both synthetic and real data validate the effectiveness of our proposal. [ABSTRACT FROM AUTHOR]

Background: Wilson's disease (WD) is an autosomal recessive disease of impaired copper metabolism. Previous study demonstrated that WD with corpus callosum abnormalities (WD-CCA) was limited to the posterior part (splenium). This study aimed to compare clinical features between WD-CCA and WD without corpus callosum abnormalities (WD-no-CCA).Methods: Forty-one WD patients who had markedly neurological dysfunctions were included in this study. We retrospectively reviewed clinical, biochemical characteristics and MRI findings in the 41 WD patients. All patients were assessed using the Unified Wilson's Disease Rating Scale.Results: Nine patients had corpus callosum abnormalities, 4 of 9 patients had abnormal signal in the genu and splenium, 5 of 9 patients had abnormal signal only in the splenium. WD-CCA had longer course (9.9 ± 4.0 years vs. 3.4 ± 3.6 years, p<0.01), more severe neurological dysfunctions (37.6 vs. 65.9, p<0.01) and higher psychiatric symptoms scores (11.2 vs. 22.5, p<0.01) than WD-no-CCA. The MRI findings indicated that WD-CCA had higher ratio than WD-no-CCA in globus pallidus (88.9% vs. 43.8%, p = 0.024) and thalamus (100% vs. 59.4%, p = 0.038). The index of liver function and copper metabolism had no significant in WD-CCA and WD-no-CCA patients.Conclusion: Our findings indicate Wilson's disease can involve the posterior as well as the anterior part of CC and patients with CC involvement had more extensive brain lesions, more severe neurological dysfunctions and psychiatric symptoms. [ABSTRACT FROM AUTHOR]

In this paper, we study weakly supervised learning where a large amount of data supervision is not accessible. This includes i) incomplete supervision, where only a small subset of labels is given, such as semi-supervised learning and domain adaptation; ii) inexact supervision, where only coarse-grained labels are given, such as multi-instance learning and iii) inaccurate supervision, where the given labels are not always ground-truth, such as label noise learning. Unlike supervised learning which typically achieves performance improvement with more labeled examples, weakly supervised learning may sometimes even degenerate performance with more weakly supervised data. Such deficiency seriously hinders the deployment of weakly supervised learning to real tasks. It is thus highly desired to study safe weakly supervised learning, which never seriously hurts performance. To this end, we present a generic ensemble learning scheme to derive a safe prediction by integrating multiple weakly supervised learners. We optimize the worst-case performance gain and lead to a maximin optimization. This brings multiple advantages to safe weakly supervised learning. First, for many commonly used convex loss functions in classification and regression, it is guaranteed to derive a safe prediction under a mild condition. Second, prior knowledge related to the weight of the base weakly supervised learners can be flexibly embedded. Third, it can be globally and efficiently addressed by simple convex quadratic or linear program. Finally, it is in an intuitive geometric interpretation with the least square loss. Extensive experiments on various weakly supervised learning tasks, including semi-supervised learning, domain adaptation, multi-instance learning and label noise learning demonstrate our effectiveness. [ABSTRACT FROM AUTHOR]

A highly efficient carboxylative cyclization of propargylic alcohols with CO2 under atmospheric pressure catalyzed by silver (0) nanoparticles decorated reduced graphene oxide (Ag‐rGO) is reported. Ag‐rGO was fully characterized by scanning electron microscope spectra (SEM), transmission electron microscopy (TEM), Fourier transform infrared (FT‐IR) spectra, Raman spectra and X‐ray photoelectron spectroscopy (XPS). Notably, Ag‐rGO can be also applied to the construction of other value‐added chemicals (β‐oxopropylcarbamates and 2‐oxazolidinones) from CO2 at ambient conditions. In addition, Ag‐rGO is stable and reusable, which shows the potential for the practical application for CO2 capture and utilization (CCU). [ABSTRACT FROM AUTHOR]

Multi-label learning methods assign multiple labels to one object. In practice, in addition to differentiating relevant labels from irrelevant ones, it is often desired to rank relevant labels for an object, whereas the ranking of irrelevant labels is not important. Thus, we require an algorithm to do classification and ranking of relevant labels simultaneously. Such a requirement, however, cannot be met because most existing methods were designed to optimize existing criteria, yet there is no criterion which encodes the aforementioned requirement. In this paper, we present a new criterion, PRO Loss, concerning the prediction of all labels as well as the ranking of only relevant labels. We then propose ProSVM which optimizes PRO Loss efficiently using alternating direction method of multipliers. We further improve its efficiency with an upper approximation that reduces the number of constraints from $O(T^2)$ O (T 2) to $O(T)$ O (T) , where $T$ T is the number of labels. We then notice that in real applications, it is difficult to get full supervised information for multi-label data. To make the proposed algorithm more robust to supervised information, we adapt ProSVM to deal with the multi-label learning with partial labels problem. Experiments show that our proposal is not only superior on PRO Loss, but also highly competitive on existing evaluation criteria. [ABSTRACT FROM AUTHOR]

Positive unlabeled (PU) learning is useful in various practical situations, where there is a need to learn a classifier for a class of interest from an unlabeled data set, which may contain anomalies as well as samples from unknown classes. The learning task can be formulated as an optimization problem under the framework of statistical learning theory. Recent studies have theoretically analyzed its properties and generalization performance, nevertheless, little effort has been made to consider the problem of scalability, especially when large sets of unlabeled data are available. In this work we propose a novel scalable PU learning algorithm that is theoretically proven to provide the optimal solution, while showing superior computational and memory performance. Experimental evaluation confirms the theoretical evidence and shows that the proposed method can be successfully applied to a large variety of real-world problems involving PU learning. [ABSTRACT FROM AUTHOR]

In many real-world tasks, particularly those involving data objects with complicated semantics such as images and texts, one object can be represented by multiple instances and simultaneously be associated with multiple labels. Such tasks can be formulated as multi-instance multi-label learning (MIML) problems, and have been extensively studied during the past few years. Existing MIML approaches have been found useful in many applications; however, most of them can only handle moderate-sized data. To efficiently handle large data sets, in this paper we propose the MIMLfast approach, which first constructs a low-dimensional subspace shared by all labels, and then trains label specific linear models to optimize approximated ranking loss via stochastic gradient descent. Although the MIML problem is complicated, MIMLfast is able to achieve excellent performance by exploiting label relations with shared space and discovering sub-concepts for complicated labels. Experiments show that the performance of MIMLfast is highly competitive to state-of-the-art techniques, whereas its time cost is much less. Moreover, our approach is able to identify the most representative instance for each label, and thus providing a chance to understand the relation between input patterns and output label semantics. [ABSTRACT FROM AUTHOR]

An introduction is presented in which the editors discuss various reports published within the issue on topics including the contour regression framework, the single-tree algorithm for exact max-kernel, and the multiple source of transfer learning.

Great efforts have been devoted to causal discovery from observational data, and it is well known that introducing some background knowledge attained from experiments or human expertise can be very helpful. However, it remains unknown that what causal relations are identifiable given background knowledge in the presence of latent confounders. In this paper, we solve the problem with sound and complete orientation rules when the background knowledge is given in a local form. Furthermore, based on the solution to the problem, this paper proposes two applications that are of independent interests. One is that we give a maximal ancestral graph (MAG) listing algorithm, to output all the MAGs consistent to the observational data in the presence of latent variables. The other application is that we present a general active learning framework for causal discovery in the presence of latent confounders, where we propose a baseline criterion to select the intervention variable with a Metropolis-Hastings MAG-sampling method. Experiments validate the efficiency of the proposed MAG listing method and the effectiveness of the active learning framework. [ABSTRACT FROM AUTHOR]

The procedure affords various title compounds in mostly high yields, if terminal propargylic alcohols are used. [ABSTRACT FROM AUTHOR]

In a multi-label learning task, an object possesses multiple concepts where each concept is represented by a class label. Previous studies on multi-label learning have focused on a fixed set of class labels, i.e., the class label set of test data is the same as that in the training set. In many applications, however, the environment is dynamic and new concepts may emerge in a data stream. In order to maintain a good predictive performance in this environment, a multi-label learning method must have the ability to detect and classify instances with emerging new labels. To this end, we propose a new approach called Multi-label learning with Emerging New Labels (MuENL). It has three functions: classify instances on currently known labels, detect the emergence of a new label, and construct a new classifier for each new label that works collaboratively with the classifier for known labels. In addition, we show that MuENL can be easily extended to handle sparse high dimensional data streams by simply reducing the original dimensionality, and then applying MuENL on the reduced dimensional space. Our empirical evaluation shows the effectiveness of MuENL on several benchmark datasets and MuENLHD on the sparse high dimensional Weibo dataset. [ABSTRACT FROM AUTHOR]

Background: Morphological changes of retina in patients with Wilson's disease (WD) can be found by optical coherence tomography (OCT), and such changes had significant differences between neurological forms (NWD) and hepatic forms (HWD) of WD. The aim of this study was to evaluate the relationship between morphological parameters of retina and brain magnetic resonance imaging (MRI) lesions, course of disease, type of disease, and sexuality in WD. Methods: A total of 46 WD patients and 40 health controls (HC) were recruited in this study. A total of 42 WD patients were divided into different groups according to clinical manifestations, course of disease, sexuality, and brain MRI lesions. We employed the Global Assessment Scale to assess neurological severity of WD patients. All WD patients and HC underwent retinal OCT to assess the thickness of inner limiting membrane (ILM) layer to retinal pigment epithelium layer and inner retina layer (ILM to inner plexiform layer, ILM–IPL). Results: Compared to HWD, NWD had thinner superior parafovea zone (108.07 ± 6.89 vs. 114.40 ± 5.54 μm, p <.01), temporal parafovea zone (97.17 ± 6.65 vs. 103.60 ± 4.53 μm, p <.01), inferior parafovea zone (108.114 ± 7.65 vs. 114.93 ± 5.84 μm, p <.01), and nasal parafovea zone (105.53 ± 8.01 vs. 112.10 ± 5.44 μm, p <.01) in inner retina layer. Course of disease influenced the retina thickness. Male patients had thinner inner retina layer compared to female patients. Conclusion: Our results demonstrated that WD had thinner inner retina layer compared to HC, and NWD had thinner inner retina layer compared to HWD. We speculated the thickness of inner retina layer may be a potential useful biomarker for NWD. [ABSTRACT FROM AUTHOR]

Catalytic conversion of CO2 to value ‐ added chemicals has gained increasing attention. In the study featured on the inside cover, a thermodynamically favorable approach to β ‐ oxopropyl carbonates through the silver(I) ‐ catalyzed three ‐ component reaction of propargylic alcohols, CO2, and monohydric alcohols was successfully developed. The silver compound in this study acts as a versatile and efficient catalyst for both the carboxylative cyclization of propargylic alcohols with CO2 and subsequent transesterification of α ‐ alkylidene cyclic carbonates with monohydric alcohols. Accordingly, this tandem process proceeds smoothly under mild conditions. More information can be found in the Full Paper by Liang ‐ Nian He et   al. on page   2065 in Issue 14, 2016 (DOI: 10.1002/asia.201600600). [ABSTRACT FROM AUTHOR]

Using ammonium carbamates (II), propargylic alcohols are converted into the title compounds (III) and (V) in good to high yields. [ABSTRACT FROM AUTHOR]

It is well-known that exploiting label correlations is important to multi-label learning. Existing approaches either assume that the label correlations are global and shared by all instances; or that the label correlations are local and shared only by a data subset. In fact, in the real-world applications, both cases may occur that some label correlations are globally applicable and some are shared only in a local group of instances. Moreover, it is also a usual case that only partial labels are observed, which makes the exploitation of the label correlations much more difficult. That is, it is hard to estimate the label correlations when many labels are absent. In this paper, we propose a new multi-label approach GLOCAL dealing with both the full-label and the missing-label cases, exploiting global and local label correlations simultaneously, through learning a latent label representation and optimizing label manifolds. The extensive experimental studies validate the effectiveness of our approach on both full-label and missing-label data. [ABSTRACT FROM PUBLISHER]

Many optimization tasks must be handled in noisy environments, where the exact evaluation of a solution cannot be obtained, only a noisy one. For optimization of noisy tasks, evolutionary algorithms (EAs), a type of stochastic metaheuristic search algorithm, have been widely and successfully applied. Previous work mainly focuses on the empirical study and design of EAs for optimization under noisy conditions, while the theoretical understandings are largely insufficient. In this study, we first investigate how noisy fitness can affect the running time of EAs. Two kinds of noise-helpful problems are identified, on which the EAs will run faster with the presence of noise, and thus the noise should not be handled. Second, on a representative noise-harmful problem in which the noise has a strong negative effect, we examine two commonly employed mechanisms dealing with noise in EAs: reevaluation and threshold selection. The analysis discloses that using these two strategies simultaneously is effective for the one-bit noise but ineffective for the asymmetric one-bit noise. Smooth threshold selection is then proposed, which can be proved to be an effective strategy to further improve the noise tolerance ability in the problem. We then complement the theoretical analysis by experiments on both synthetic problems as well as two combinatorial problems, the minimum spanning tree and the maximum matching. The experimental results agree with the theoretical findings and also show that the proposed smooth threshold selection can deal with the noise better. [ABSTRACT FROM AUTHOR]

Objective: To analyze and explore the risk factors for neurological symptoms in patients with purely hepatic Wilson's disease (WD) at diagnosis. Methods: This retrospective study was conducted at the First Affiliated Hospital of the Guangdong Pharmaceutical University on 68 patients with purely hepatic WD aged 20.6 ± 7.2 years. The physical examinations, laboratory tests, color Doppler ultrasound of the liver and spleen, and magnetic resonance imaging (MRI) of the brain were performed. Results: The elevated alanine transaminase (ALT) and aspartate transaminase (AST) levels and 24-h urinary copper level were higher in the purely hepatic WD who developed neurological symptoms (NH-WD) group than those in the purely hepatic WD (H-WD) group. Adherence to low-copper diet, and daily oral doses of penicillamine (PCA) and zinc gluconate (ZG) were lower in the NH-WD group than those in the H-WD group. Logistic regression analysis showed that insufficient doses of PCA and ZG were associated with the development of neurological symptoms in patients with purely hepatic WD at diagnosis. Conclusion: The development of neurological symptoms in patients with purely hepatic WD was closely associated with insufficient doses of PCA and ZG, and the inferior efficacy of copper-chelating agents. During the course of anti-copper treatment, the patient's medical status and the efficacy of copper excretion should be closely monitored. [ABSTRACT FROM AUTHOR]

This paper investigates an important problem in stream mining, i.e., classification under streaming emerging new classes or SENC. The SENC problem can be decomposed into three subproblems: detecting emerging new classes, classifying known classes, and updating models to integrate each new class as part of known classes. The common approach is to treat it as a classification problem and solve it using either a supervised learner or a semi-supervised learner. We propose an alternative approach by using unsupervised learning as the basis to solve this problem. The proposed method employs completely-random trees which have been shown to work well in unsupervised learning and supervised learning independently in the literature. The completely-random trees are used as a single common core to solve all three subproblems: unsupervised learning, supervised learning, and model update on data streams. We show that the proposed unsupervised-learning-focused method often achieves significantly better outcomes than existing classification-focused methods. [ABSTRACT FROM PUBLISHER]

In plenty of real-life tasks, strongly supervised information is hard to obtain, and thus weakly supervised learning has drawn considerable attention recently. This paper investigates the problem of learning from incomplete and inaccurate supervision, where only a limited subset of training data is labeled but potentially with noise. This setting is challenging and of great importance but rarely studied in the literature. We notice that in many applications, the limited labeled data are with certain structures, which paves us a way to design effective methods. Specifically, we observe that labeled data are usually with one-sided noise such as the bug detection task, where the identified buggy codes are indeed with defects, while codes checked many times or newly fixed may still have other flaws. Furthermore, when there occurs two-sided noise in the labeled data, we exploit the class-prior information of unlabeled data, which is typically available in practical tasks. We propose novel approaches for the incomplete and inaccurate supervision learning tasks and effectively alleviate the negative influence of label noise with the help of a vast number of unlabeled data. Both theoretical analysis and extensive experiments justify and validate the effectiveness of the proposed approaches. [ABSTRACT FROM AUTHOR]

Random forests have been one of the successful ensemble algorithms in machine learning, and the basic idea is to construct a large number of random trees individually and make predictions based on an average of their predictions. The great successes have attracted much attention on theoretical understandings of random forests, mostly focusing on regression problems. This work takes one step towards the convergence rates of random forests for classification. We present the first finite-sample rate O (n − 1 / (8 d + 2)) on the convergence of purely random forests for binary classification, which can be improved to be of O (n − 1 / (3.87 d + 2)) by considering the midpoint splitting mechanism. We introduce another variant of random forests, which follows Breiman's original random forests but with different mechanisms on splitting dimensions and positions. We present the convergence rate O (n − 1 / (d + 2) (ln ⁡ n) 1 / (d + 2)) for the variant of random forests, which reaches the minimax rate, except for a factor (ln ⁡ n) 1 / (d + 2) , of the optimal plug-in classifier under the L -Lipschitz assumption. We achieve the tighter convergence rate O (ln ⁡ n / n) under some assumptions over structural data. This work also takes one step towards the convergence rate of random forests for multi-class learning, and presents the same convergence rates of random forests for multi-class learning as that of binary classification, yet with different constants. We finally provide empirical studies to support the theoretical analysis. [ABSTRACT FROM AUTHOR]

Most studies about deep learning are based on neural network models, where many layers of parameterized nonlinear differentiable modules are trained by backpropagation. Recently, it has been shown that deep learning can also be realized by non-differentiable modules without backpropagation training called deep forest. We identify that deep forest has high time costs and memory requirements—this has inhibited its use on large-scale datasets. In this paper, we propose a simple and effective approach with three main strategies for efficient learning of deep forest. First, it substantially reduces the number of instances that needs to be processed through redirecting instances having high predictive confidence straight to the final level for prediction, by-passing all the intermediate levels. Second, many non-informative features are screened out, and only the informative ones are used for learning at each level. Third, an unsupervised feature transformation procedure is proposed to replace the supervised multi-grained scanning procedure. Our theoretical analysis supports the proposed approach in varying the model complexity from low to high as the number of levels increases in deep forest. Experiments show that our approach achieves highly competitive predictive performance with reduced time cost and memory requirement by one to two orders of magnitude. [ABSTRACT FROM AUTHOR]

Given a ground set of items, the result diversification problem aims to select a subset with high "quality" and "diversity" while satisfying some constraints. It arises in various real-world artificial intelligence applications, such as web-based search, document summarization and feature selection, and also has applications in other areas, e.g., computational geometry, databases, finance and operations research. Previous algorithms are mainly based on greedy or local search. In this paper, we propose to reformulate the result diversification problem as a bi-objective maximization problem, and solve it by a multi-objective evolutionary algorithm (EA), i.e., the GSEMO. We theoretically prove that the GSEMO can achieve the (asymptotically) optimal theoretical guarantees under both static and dynamic environments. For cardinality constraints, the GSEMO can achieve the optimal polynomial-time approximation ratio, 1/2. For more general matroid constraints, the GSEMO can achieve an asymptotically optimal polynomial-time approximation ratio, 1 / 2 − ϵ / (4 n) , where ϵ > 0 and n is the size of the ground set of items. Furthermore, when the objective function (i.e., a linear combination of quality and diversity) changes dynamically, the GSEMO can maintain this approximation ratio in polynomial running time, addressing the open question proposed by Borodin et al. [7]. This also theoretically shows the superiority of EAs over local search for solving dynamic optimization problems for the first time, and discloses the robustness of the mutation operator of EAs against dynamic changes. Experiments on the applications of web-based search, multi-label feature selection and document summarization show the superior performance of the GSEMO over the state-of-the-art algorithms (i.e., the greedy algorithm and local search) under both static and dynamic environments. [ABSTRACT FROM AUTHOR]

An ionic liquid containing copper(I) proved to be an effective homogeneous catalyst for the carboxylation of terminal alkynes with ambient CO 2 . This developed procedure needs no external ligands and terminal alkynes with various groups proceeded smoothly at atmospheric CO 2 pressure and room temperature. Interestingly, the ILs containing copper(I) in both the anion and the cation showed much higher activity in comparison with the counterparts incorporating copper(I) solely in the form of halocuprate, that is, copper(I) in the anion. Especially, activated effect of the terminal alkyne by the ionic liquid was also validated by the NMR technique. [ABSTRACT FROM AUTHOR]