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Heterogeneous treatment effect (HTE) estimation plays a crucial role in developing personalized treatment plans across various applications. Conventional approaches assume that the observed data are independent and identically distributed (i.i.d.). In some real applications, however, the assumption does not hold: the environment may evolve, which leads to variations in HTE over time. To enable HTE estimation in evolving environments, we introduce and formulate the online HTE estimation problem. We propose an online ensemble-based HTE estimation method called ETHOS, which is capable of adapting to unknown evolving environments by ensembling the outputs of multiple base estimators that track environmental changes at different scales. Theoretical analysis reveals that ETHOS achieves an optimal expected dynamic regret O (T (1 + P T)) , where T denotes the number of observed examples and P T characterizes the intensity of environment changes. The achieved dynamic regret ensures that our method consistently approaches the optimal online estimators as long as the evolution of the environment is moderate. We conducted extensive experiments on three common benchmark datasets with various environment evolving mechanisms. The results validate the theoretical analysis and the effectiveness of our proposed method. [ABSTRACT FROM AUTHOR]

Ensemble methods that train and combine multiple learners have always been among the state-of-the-art learning methods, and ensemble pruning aims at generating a smaller-sized ensemble with even better generalization performance. Abundant ensemble pruning methods that use evaluation criteria such as diversity or margin together with validation error have been proposed. However, as these evaluation criteria are used together with the validation error, their effect on generalization performance is less clear. In this paper, we propose a margin distribution and structural diversity guided ensemble pruning framework, called Decoupled Ensemble Pruning (DEP). It decouples the optimization of margin distribution and structural diversity and the optimization of validation error into two stages. Our information-theoretic analysis reveals that the expected generalization gap is related to the combination distribution, i.e., validation error distribution of all the combinations of base learners. And show that optimizing margin mean and structural diversity benefits combination distribution. Concretely, we provide an instantiation of DEP framework in the classic tree-based ensemble pruning setting. Experimental results not only verify the effectiveness in optimizing the distribution, but also show that DEP enjoys better test accuracy than existing ensemble pruning methods. [ABSTRACT FROM AUTHOR]

The learnware paradigm was recently proposed by Zhou (2016) with the wish of developing the learnware market to help users build models more efficiently by reusing existing well-performed models rather than starting from scratch. Specifically, a learnware in the learnware market is a well-performed pre-trained model with a specification describing its specialty and utility, and the market identifies helpful learnware(s) for the user's task based on the specification. Recent studies have attempted to realize a homogeneous prototype learnware market initially through Reduced Kernel Mean Embedding (RKME) specification, which requires all models in the market to share the same feature space. However, this limits the application scope of the learnware paradigm because various pre-trained models are often obtained from different feature spaces in real-world scenarios. In this paper, we make the first attempt to enable the learnware to handle heterogeneous feature spaces. We propose a more powerful specification to manage heterogeneous learnwares by integrating subspace learning in the specification design, along with a practical approach for identifying and reusing helpful learnwares for the user's task. Empirical studies on both synthetic data and real-world tasks validate the efficacy of our approach. [ABSTRACT FROM AUTHOR]

There are complaints about current machine learning techniques such as the requirement of a huge amount of training data and proficient training skills, the difficulty of continual learning, the risk of catastrophic forgetting, and the leaking of data privacy/proprietary. Most research efforts have been focusing on one of those concerned issues separately, paying less attention to the fact that most issues are entangled in practice. The prevailing big model paradigm, which has achieved impressive results in natural language processing and computer vision applications, has not yet addressed those issues, whereas becoming a serious source of carbon emissions. This article offers an overview of the learnware paradigm, which attempts to enable users not to need to build machine learning models from scratch, with the hope of reusing small models to do things even beyond their original purposes, where the key ingredient is the specification which enables a trained model to be adequately identified to reuse according to the requirement of future users who know nothing about the model in advance. [ABSTRACT FROM AUTHOR]

Background: Aberrant activation of Wnt/β-catenin signaling by dysregulated post-translational protein modifications, especially ubiquitination is causally linked to cancer development and progression. Although Lys48-linked ubiquitination is known to regulate Wnt/β-catenin signaling, it remains largely obscure how other types of ubiquitination, such as linear ubiquitination governs its signaling activity. Methods: The expression and regulatory mechanism of linear ubiquitin chain assembly complex (LUBAC) on Wnt/β-catenin signaling was examined by immunoprecipitation, western blot and immunohistochemical staining. The ubiquitination status of β-catenin was detected by ubiquitination assay. The impacts of SHARPIN, a core component of LUBAC on malignant behaviors of gastric cancer cells were determined by various functional assays in vitro and in vivo. Results: Unlike a canonical role in promoting linear ubiquitination, SHARPIN specifically interacts with β-catenin to maintain its protein stability. Mechanistically, SHARPIN competes with the E3 ubiquitin ligase β-Trcp1 for β-catenin binding, thereby decreasing β-catenin ubiquitination levels to abolish its proteasomal degradation. Importantly, SHARPIN is required for invasiveness and malignant growth of gastric cancer cells in vitro and in vivo, a function that is largely dependent on its binding partner β-catenin. In line with these findings, elevated expression of SHARPIN in gastric cancer tissues is associated with disease malignancy and correlates with β-catenin expression levels. Conclusions: Our findings reveal a novel molecular link connecting linear ubiquitination machinery and Wnt/β-catenin signaling via SHARPIN-mediated stabilization of β-catenin. Targeting the linear ubiquitination-independent function of SHARPIN could be exploited to inhibit the hyperactive β-catenin signaling in a subset of human gastric cancers. [ABSTRACT FROM AUTHOR]

Multi-label support vector machine (Rank-SVM) is a classic and effective algorithm for multi-label classification. The pivotal idea is to maximize the minimum margin of label pairs, which is extended from SVM. However, recent studies disclosed that maximizing the minimum margin does not necessarily lead to better generalization performance, and instead, it is more crucial to optimize the margin distribution. Inspired by this idea, in this paper, we first introduce margin distribution to multi-label learning and propose multi-label Optimal margin Distribution Machine (mlODM), which optimizes the margin mean and variance of all label pairs efficiently. Extensive experiments in multiple multi-label evaluation metrics illustrate that mlODM outperforms SVM-style multi-label methods. Moreover, empirical study presents the best margin distribution and verifies the fast convergence of our method. [ABSTRACT FROM AUTHOR]

In many real-world applications, data are often collected in the form of a stream, and thus the distribution usually changes in nature, which is referred to as concept drift in the literature. We propose a novel and effective approach to handle concept drift via model reuse, that is, reusing models trained on previous data to tackle the changes. Each model is associated with a weight representing its reusability towards current data, and the weight is adaptively adjusted according to the performance of the model. We provide both generalization and regret analysis to justify the superiority of our approach. Experimental results also validate its efficacy on both synthetic and real-world datasets. [ABSTRACT FROM AUTHOR]

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In this paper, we present novel yet simple homotopy proximal mapping algorithms for reconstructing a sparse signal from (noisy) linear measurements of the signal or for learning a sparse linear model from observed data, where the former task is well-known in the field of compressive sensing and the latter task is known as model selection in statistics and machine learning. The algorithms adopt a simple proximal mapping of the ℓ 1 norm at each iteration and gradually reduces the regularization parameter for the ℓ 1 norm. We prove a global linear convergence of the proposed homotopy proximal mapping (HPM) algorithms for recovering the sparse signal under three different settings (i) sparse signal recovery under noiseless measurements, (ii) sparse signal recovery under noisy measurements, and (iii) nearly-sparse signal recovery under sub-Gaussian noisy measurements. In particular, we show that when the measurement matrix satisfies restricted isometric properties (RIP), one of the proposed algorithms with an appropriate setting of a parameter based on the RIP constants converges linearly to the optimal solution up to the noise level. In addition, in setting (iii), a practical variant of the proposed algorithms does not rely on the RIP constants and our results for sparse signal recovery are better than the previous results in the sense that our recovery error bound is smaller. Furthermore, our analysis explicitly exhibits that more observations lead to not only more accurate recovery but also faster convergence. Finally our empirical studies provide further support for the proposed homotopy proximal mapping algorithm and verify the theoretical results. [ABSTRACT FROM AUTHOR]

Inspired by successful deployments of Stackelberg Security Game in real life, researchers are working hard to optimize the game models to make them more practical. Recent security game work on wildlife protection makes a step forward by taking the possible cooperation among attackers into consideration. However, it models attackers to have complete rationality, which is not always possible in practice given they are human beings. We aim to tackle attackers' bounded rationality in the complicated, cooperation-enabled and multi-round security game for wildlife protection. Specifically, we construct a repeated Stackelberg game, and propose a novel adaptive human behavior model for attackers based on it. Despite generating defender's optimal strategy requires to solve a non-linear and non-convex optimization problem, we are able to propose an efficient algorithm that approximately solve this problem. We perform extensive real-life experiments, and results from over 25,000 game plays show our solution effectively helps the defender to deal with attackers who might cooperate. [ABSTRACT FROM AUTHOR]

The use of distance metrics such as the Euclidean or Manhattan distance for nearest neighbour algorithms allows for interpretation as a geometric model, and it has been widely assumed that the metric axioms are a necessary condition for many data mining tasks. We show that this assumption can in fact be an impediment to producing effective models. We propose to use mass-based dissimilarity, which employs estimates of the probability mass to measure dissimilarity, to replace the distance metric. This substitution effectively converts nearest neighbour (NN) algorithms into lowest probability mass neighbour (LMN) algorithms. Both types of algorithms employ exactly the same algorithmic procedures, except for the substitution of the dissimilarity measure. We show that LMN algorithms overcome key shortcomings of NN algorithms in classification and clustering tasks. Unlike existing generalised data independent metrics (e.g., quasi-metric, meta-metric, semi-metric, peri-metric) and data dependent metrics, the proposed mass-based dissimilarity is unique because its self-dissimilarity is data dependent and non-constant. [ABSTRACT FROM AUTHOR]

Researchers often summarize their work in the form of scientific posters. Posters provide a coherent and efficient way to convey core ideas expressed in scientific papers. Generating a good scientific poster, however, is a complex and time-consuming cognitive task, since such posters need to be readable, informative, and visually aesthetic. In this paper, for the first time, we study the challenging problem of learning to generate posters from scientific papers. To this end, a data-driven framework, which utilizes graphical models, is proposed. Specifically, given content to display, the key elements of a good poster, including attributes of each panel and arrangements of graphical elements, are learned and inferred from data. During the inference stage, the maximum a posterior (MAP) estimation framework is employed to incorporate some design principles. In order to bridge the gap between panel attributes and the composition within each panel, we also propose a recursive page splitting algorithm to generate the panel layout for a poster. To learn and validate our model, we collect and release a new benchmark dataset, called NJU-Fudan Paper-Poster dataset, which consists of scientific papers and corresponding posters with exhaustively labelled panels and attributes. Qualitative and quantitative results indicate the effectiveness of our approach. [ABSTRACT FROM AUTHOR]

Statistical machine learning is widely used in image classification. However, most techniques (1) require many images to achieve high accuracy and (2) do not provide support for reasoning below the level of classification, and so are unable to support secondary reasoning, such as the existence and position of light sources and other objects outside the image. This paper describes an Inductive Logic Programming approach called Logical Vision which overcomes some of these limitations. LV uses Meta-Interpretive Learning (MIL) combined with low-level extraction of high-contrast points sampled from the image to learn recursive logic programs describing the image. In published work LV was demonstrated capable of high-accuracy prediction of classes such as regular polygon from small numbers of images where Support Vector Machines and Convolutional Neural Networks gave near random predictions in some cases. LV has so far only been applied to noise-free, artificially generated images. This paper extends LV by (a) addressing classification noise using a new noise-telerant version of the MIL system Metagol, (b) addressing attribute noise using primitive-level statistical estimators to identify sub-objects in real images, (c) using a wider class of background models representing classical 2D shapes such as circles and ellipses, (d) providing richer learnable background knowledge in the form of a simple but generic recursive theory of light reflection. In our experiments we consider noisy images in both natural science settings and in a RoboCup competition setting. The natural science settings involve identification of the position of the light source in telescopic and microscopic images, while the RoboCup setting involves identification of the position of the ball. Our results indicate that with real images the new noise-robust version of LV using a single example (i.e. one-shot LV) converges to an accuracy at least comparable to a thirty-shot statistical machine learner on both prediction of hidden light sources in the scientific settings and in the RoboCup setting. Moreover, we demonstrate that a general background recursive theory of light can itself be invented using LV and used to identify ambiguities in the convexity/concavity of objects such as craters in the scientific setting and partial obscuration of the ball in the RoboCup setting. [ABSTRACT FROM AUTHOR]

Decision trees are a kind of off-the-shelf predictive models, and they have been successfully used as the base learners in ensemble learning. To construct a strong classifier ensemble, the individual classifiers should be accurate and diverse. However, diversity measure remains a mystery although there were many attempts. We conjecture that a deficiency of previous diversity measures lies in the fact that they consider only behavioral diversity, i.e., how the classifiers behave when making predictions, neglecting the fact that classifiers may be potentially different even when they make the same predictions. Based on this recognition, in this paper, we advocate to consider structural diversity in addition to behavioral diversity, and propose the TMD (tree matching diversity) measure for decision trees. To investigate the usefulness of TMD, we empirically evaluate performances of selective ensemble approaches with decision forests by incorporating different diversity measures. Our results validate that by considering structural and behavioral diversities together, stronger ensembles can be constructed. This may raise a new direction to design better diversity measures and ensemble methods. [ABSTRACT FROM AUTHOR]

One of the fundamental issues in crowdsourcing is the trade-off between the number of workers needed for high-accuracy aggregation and the budget to pay. To save cost, it is important to ensure high quality of the crowd-sourced labels, hence the total cost on label collection will be reduced. Since the confidence of the workers often has a close relationship with their abilities, a possible way for quality control is to request the workers to return the labels only when they feel confident, by means of providing them with an ‘unsure’ option. On the other hand, allowing workers to choose the unsure option can potentially waste part of the budget. In this work, we conduct an analysis towards understanding when providing the unsure option indeed leads to significant cost reduction, as well as how the confidence threshold might be set. We also propose an online mechanism, which is an alternative for threshold selection when the estimation of the crowd ability distribution is difficult. [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]

With the rapid development of smart mobile devices, phone games become an important way of entertainments. Benefitting from sophisticated payment environments of mobile platforms, e.g., Apple APP store, the In-APP purchases which sell equipments or virtual props bring in the main profits for game carriers and developers. Although virtual props from a certain type of smart phone game are monopolized by only one seller, like other commodities, products' recommendation is able to improve the profit margins as well. One main difference between virtual props recommendation and the general good recommendations lies in that the virtual props are closely related to the game contexts, and this will lead to complicated dependencies. Therefore, general recommendation systems without consideration on game contexts cannot perform very well. Besides, multiple types of props in one game may depend on different game characters of players, thus single player trends to buy only appropriate props for improving the skills of his game characters. Moreover, the purchase intensions of players are influenced by multiple factors, and will change over time. Therefore, it is desired recommendation approach to be capable of handling the role dependencies and concept variations. In this paper, we treat the game contexts as events from game log records, and model the game props recommendation into a multi-instance multi-label learning task for utilizing the complicated dependencies and capturing the rank of purchase intentions. We proposed three variants of solutions against the concept variation problem as well. Finally, we conduct comprehensive empirical investigations on real-world data sets and a series of real online smart phone games. The positive experiment results and increasing profit margins validate the remarkable effectiveness of our solutions. [ABSTRACT FROM AUTHOR]

The Internet based cyber-physical world has profoundly changed the information environment for the development of artificial intelligence (AI), bringing a new wave of AI research and promoting it into the new era of AI 2.0. As one of the most prominent characteristics of research in AI 2.0 era, crowd intelligence has attracted much attention from both industry and research communities. Specifically, crowd intelligence provides a novel problem-solving paradigm through gathering the intelligence of crowds to address challenges. In particular, due to the rapid development of the sharing economy, crowd intelligence not only becomes a new approach to solving scientific challenges, but has also been integrated into all kinds of application scenarios in daily life, e.g., online-to-offline (O2O) application, real-time traffic monitoring, and logistics management. In this paper, we survey existing studies of crowd intelligence. First, we describe the concept of crowd intelligence, and explain its relationship to the existing related concepts, e.g., crowdsourcing and human computation. Then, we introduce four categories of representative crowd intelligence platforms. We summarize three core research problems and the state-of-the-art techniques of crowd intelligence. Finally, we discuss promising future research directions of crowd intelligence. [ABSTRACT FROM AUTHOR]

Multi-instance learning (MIL) has been widely used on diverse applications involving complicated data objects such as images, where people use a bag generator to represent an original data object as a bag of instances, and then employ MIL algorithms. Many powerful MIL algorithms have been developed during the past decades, but the bag generators have rarely been studied although they affect the performance seriously. Considering that MIL has been found particularly useful in image tasks, in this paper, we empirically study the utility of nine state-of-the-art image bag generators in the literature, i.e., Row, SB, SBN, k-meansSeg, Blobworld, WavSeg, JSEG-bag, LBP and SIFT. From the 6923 (9 bag generators, 7 learning algorithms, 4 patch sizes and 43 data sets) configurations of experiments we make two significant new observations: (1) Bag generators with a dense sampling strategy perform better than those with other strategies; (2) The standard MIL assumption of learning algorithms is not suitable for image classification tasks. [ABSTRACT FROM AUTHOR]

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 (MGU), since it only contains one gate, which is a minimal design among all gated hidden units. The design of MGU benefits from evaluation results on LSTM and GRU in the literature. Experiments on various sequence data show that MGU 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. [ABSTRACT FROM AUTHOR]

Copyright of SCIENCE CHINA Information Sciences is the property of Springer Nature and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of SCIENCE CHINA Information Sciences is the property of Springer Nature and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

An introduction is presented in which the editor discusses various reports within the issue on topics including extension of Collaborative Topic Regression (CTR), low-dimensional representation of noisy data learning and clustering algorithm.

An introduction is presented in which the editor discusses papers presented at the Asian Conference on Machine Learning (ACML) that was held in Hong Kong from November 20-22, 2015 which focused on performance metrics for machines.

An introduction is presented in which the editor discusses various reports within the issue on topics including the passive-aggressive active (PAA) learning algorithms, the combination of authors, citation network and contents in a single bibliographic model, and the support vector machine (SVM).