Learning to Generate Posters of Scientific Papers

Researchers often summarize their work in the form of posters. Posters provide a coherent and efficient way to convey core ideas from 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, that utilizes graphical models, is proposed. Specifically, given content to display, the key elements of a good poster, including panel layout and attributes of each panel, are learned and inferred from data. Then, given inferred layout and attributes, composition of graphical elements within each panel is synthesized. To learn and validate our model, we collect and make public a Poster-Paper 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.Comment: in Proceedings of the 30th AAAI Conference on Artificial Intelligence (AAAI'16), Phoenix, AZ, 201

Probabilistic Points-to Analysis for Java

Abstract. Probabilistic points-to analysis is an analysis technique for defining the probabilities on the points-to relations in programs. It provides the compiler with some optimization chances such as speculative dead store elimination, speculative redundancy elimination, and speculative code scheduling. Although several static probabilistic points-to analysis techniques have been developed for C language, they cannot be applied directly to Java because they do not handle the classes, objects, inheritances and invocations of virtual methods. In this paper, we propose a context-insensitive and flow-sensitive probabilistic points-to analysis for Java (JPPA) for statically predicting the probability of points-to relations at all program points (i.e., points before or after statements) of a Java program. JPPA first constructs an interprocedural control flow graph (ICFG) for a Java program, whose edges are labeled with the probabilities calculated by an algorithm based on a static branch prediction approach, and then calculates the probabilistic points-to relations of the program based upon the ICFG. We have also developed a tool called Lukewarm to support JPPA and conducted an experiment to compare JPPA with a traditional context-insensitive and flow-sensitive points-to analysis approach. The experimental results show that JPPA is a precise and effective probabilistic points-to analysis technique for Java

ID Embedding as Subtle Features of Content and Structure for Multimodal Recommendation

Multimodal recommendation aims to model user and item representations comprehensively with the involvement of multimedia content for effective recommendations. Existing research has shown that it is beneficial for recommendation performance to combine (user- and item-) ID embeddings with multimodal salient features, indicating the value of IDs. However, there is a lack of a thorough analysis of the ID embeddings in terms of feature semantics in the literature. In this paper, we revisit the value of ID embeddings for multimodal recommendation and conduct a thorough study regarding its semantics, which we recognize as subtle features of content and structures. Then, we propose a novel recommendation model by incorporating ID embeddings to enhance the semantic features of both content and structures. Specifically, we put forward a hierarchical attention mechanism to incorporate ID embeddings in modality fusing, coupled with contrastive learning, to enhance content representations. Meanwhile, we propose a lightweight graph convolutional network for each modality to amalgamate neighborhood and ID embeddings for improving structural representations. Finally, the content and structure representations are combined to form the ultimate item embedding for recommendation. Extensive experiments on three real-world datasets (Baby, Sports, and Clothing) demonstrate the superiority of our method over state-of-the-art multimodal recommendation methods and the effectiveness of fine-grained ID embeddings

Mesenchymal stem cells alleviate bacteria‐induced liver injury in mice by inducing regulatory dendritic cells

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/102718/1/hep26670.pd

The Cerebellum Is Related to Cognitive Dysfunction in White Matter Hyperintensities

ObjectiveWhite matter hyperintensities (WMHs) on magnetic resonance imaging (MRI) is frequently presumed to be secondary to cerebral small vessel disease (CSVD) and associated with cognitive decline. The cerebellum plays a key role in cognition and has dense connections with other brain regions. Thus, the aim of this study was to investigate if cerebellar abnormalities could occur in CSVD patients with WMHs and the possible association with cognitive performances.MethodsA total of 104 right-handed patients with WMHs were divided into the mild WMHs group (n = 39), moderate WMHs group (n = 37), and severe WMHs group (n = 28) according to the Fazekas scale, and 36 healthy controls were matched for sex ratio, age, education years, and acquired resting-state functional MRI. Analysis of voxel-based morphometry of gray matter volume (GMV) and seed-to-whole-brain functional connectivity (FC) was performed from the perspective of the cerebellum, and their correlations with neuropsychological variables were explored.ResultsThe analysis revealed a lower GMV in the bilateral cerebellum lobule VI and decreased FC between the left- and right-sided cerebellar lobule VI with the left anterior cingulate gyri in CSVD patients with WMHs. Both changes in structure and function were correlated with cognitive impairment in patients with WMHs.ConclusionOur study revealed damaged GMV and FC in the cerebellum associated with cognitive impairment. This indicates that the cerebellum may play a key role in the modulation of cognitive function in CSVD patients with WMHs

constraint-based locality analysis for x10 programs

X10 is a HPC (High Performance Computing) programming language proposed by IBMfor supporting a PGAS (Partitioned Global Address Space) programming model offering a shared address space. The address space can be further partitioned into several logical locations where objects and activities (or threads) will be dynamically created. An analysis of locations can help to check the safety of object accesses through exploring which objects and activities may reside in which locations, while in practice the objects and activities are usually designated at runtime and their locations may also vary under different environments. In this paper, we propose a constraint-based locality analysis method called Leopard for X10. Leopard calculates the points-to relations for analyzing the objects and activities in a program and uses a place constraint graph to analyze their locations. We have developed a tool to support Leopard, and conducted an experiment to evaluate its effectiveness and efficiency. The experimental results show that Leopard can calculate the locations of objects and activities precisely. Copyright © 2013 ACM.ACM SIGPLANX10 is a HPC (High Performance Computing) programming language proposed by IBMfor supporting a PGAS (Partitioned Global Address Space) programming model offering a shared address space. The address space can be further partitioned into several logical locations where objects and activities (or threads) will be dynamically created. An analysis of locations can help to check the safety of object accesses through exploring which objects and activities may reside in which locations, while in practice the objects and activities are usually designated at runtime and their locations may also vary under different environments. In this paper, we propose a constraint-based locality analysis method called Leopard for X10. Leopard calculates the points-to relations for analyzing the objects and activities in a program and uses a place constraint graph to analyze their locations. We have developed a tool to support Leopard, and conducted an experiment to evaluate its effectiveness and efficiency. The experimental results show that Leopard can calculate the locations of objects and activities precisely. Copyright © 2013 ACM