Star-Structured High-Order Heterogeneous Data Co-clustering Based on Consistent Information Theory

Currently, many of the processing techniques for the location-based queries provide information of a single type of spatial objects, based on their spatial closeness to the query object. However, in real-life applications the user may be interested in obtaining information about different types of objects, in terms of their quality, cost, and neighboring relationship. We term the different types of objects with better quality and closer to each other the Neighboring skyline set (or NS set). Three new types of location-based queries, the Distance-based neighboring skyline query (Dist-NS query), the Cost-based neighboring skyline query (Cost-NS query), and the Budget-based neighboring skyline query (BGT-NS query), are presented to determine the NS sets according to user’s specific requirement. A R-tree-based index, the $$R^{a,c}$$-tree, is first designed to manage each type of objects with their locations, attributes, and costs. Then, a simultaneous traversal of the $$R^{a,c}$$-trees built on different types of objects is employed with several pruning criteria to prune the non-qualifying object sets as early as possible, so as to improve the query performance. Extensive experiments using the synthetic dataset demonstrate the efficiency and the effectiveness of the proposed algorithms.

Heterogeneous information networks have drawn much attention in recent years due to their significant applications, such as text mining, e-commerce, social networks, and bioinformatics. Clustering different types of objects simultaneously based upon not only their relations of the same type, but also the relations between different types of objects can improve the clustering quality mutually. In this paper, we propose a general model, in which both the homogeneous and heterogeneous relations are considered simultaneously, to describe the structure of the heterogeneous information networks and devise a novel parametric free multi-type overlapped clustering approach. In this model, different types of relations between different types of objects are represented by a group of matrices. In this way, we transfer the multi-type clustering problem into the information compression problem. Subsequently, greedy search approaches, which aim at describing the group of relational matrices with least bits, are proposed. Moreover, by discovering the discriminative clusters among different types of objects, we devise effective parameter-free strategies to discover either overlapping or non-overlapping structure among different types of clusters. Extensive experiments on real-world and synthetic data sets demonstrate our methods are effective and efficient.

Predicting response to treatment and disease-specific deaths are key tasks in cancer research yet there is a lack of methodologies to achieve these. Large-scale ’omics and digital pathology technologies have led to the need for effective statistical methods for data fusion to extract the most useful patterns from these diverse data types. We present FusionGP, a method for combining heterogeneous data types designed specifically for predicting outcome of treatment and disease. FusionGP is a Gaussian process model that includes a generalization of feature selection for biomarker discovery, allowing for simultaneous, sparse feature selection across multiple data types. Importantly, it can accommodate highly nonlinear structure in the data, and automatically infers the optimal contribution from each input data type. FusionGP compares favourably to several popular classification methods, including the Random Forest classifier, a stepwise logistic regression model and the Support Vector Machine on single data types. By combining gene expression, copy number alteration and digital pathology image data in 119 estrogen receptor (ER)-negative and 345 ER-positive breast tumours, we aim to predict two important clinical outcomes: death and chemoinsensitivity. While gene expression data give the best predictive performance in the majority of cases, the digital pathology data are much better for predicting death in ER cases. Thus, FusionGP is a new tool for selecting informative features from heterogeneous data types and predicting treatment response and prognosis.

Recently, many computational models have been proposed to simulate visual cognition process. For example, the hierarchical Max-Pooling (HMAX) model was proposed according to the hierarchical and bottom-up structure of V1 to V4 in the ventral pathway of primate visual cortex, which could achieve position- and scale-tolerant recognition. In our previous work, we have introduced memory and association into the HMAX model to simulate visual cognition process. In this paper, we improve our theoretical framework by mimicking a more elaborate structure and function of the primate visual cortex. We will mainly focus on the new formation of memory and association in visual processing under different circumstances as well as preliminary cognition and active adjustment in the inferior temporal cortex, which are absent in the HMAX model. The main contributions of this paper are: 1) in the memory and association part, we apply deep convolutional neural networks to extract various episodic features of the objects since people use different features for object recognition. Moreover, to achieve a fast and robust recognition in the retrieval and association process, different types of features are stored in separated clusters and the feature binding of the same object is stimulated in a loop discharge manner and 2) in the preliminary cognition and active adjustment part, we introduce preliminary cognition to classify different types of objects since distinct neural circuits in a human brain are used for identification of various types of objects. Furthermore, active cognition adjustment of occlusion and orientation is implemented to the model to mimic the top-down effect in human cognition process. Finally, our model is evaluated on two face databases CAS-PEAL-R1 and AR. The results demonstrate that our model exhibits its efficiency on visual recognition process with much lower memory storage requirement and a better performance compared with the traditional purely computational methods.

This paper investigates the effects of encumbrance (holding different types of objects while using mobile devices) to understand the interaction difficulties that it causes. An experiment was conducted where participants performed a target acquisition task on a touchscreen mobile phone while carrying different types of bags and boxes. Mobility was also evaluated since people carry items from one place to another. Motion capture hardware was used to track hand and arm postures to examine how holding the different types of objects caused excessive movement and instability therefore resulting in performance to decline. The results showed encumbrance and mobility caused target accuracy to decrease although input while holding the box under the non-dominant arm was more accurate and exerted quicker targeting times than holding no objects. Encumbrance affected the dominant hand more than the non-dominant hand as targeting error significantly increased and caused greater hand instability. The issues caused by encumbrance suggest the topic requires more attention from researchers and users would benefit greatly if better interaction techniques and applications are developed to counteract the problems.

In an object oriented system, different types of objects may have one or more attributes in common. For example different types of objects in a library system might be loanable or catalogued. The first part of the paper introduces a distinction between object types and attribute types, showing that there are advantages in treating them as separate constructs of an object oriented programming language. Then generalized attribute types, (e.g. synchronized, journalized, protected) are discussed. It is shown how implementations involving a technique called bracket routines can lead to clearly modeled and cleanly implemented programs. It is then shown how the bracket technique can be effectively used to introduce protection and security concepts into a system. Among the clear advantages of this technique is that it results in better reusability of code.

There are various kinds of social media information, including different types of objects and relations among these objects, in music social communities such as Last.fm and Pandora. This information is valuable for music recommendation. However, there are two main challenges to exploit this rich social media information: (a) There are many different types of objects and relations in music social communities, which makes it difficult to develop a unified framework taking into account all objects and relations. (b) In these communities, some relations are much more sophisticated than pairwise relation, and thus cannot be simply modeled by a graph. We propose a novel music recommendation algorithm by using both multiple kinds of social media information and music acoustic-based content. Instead of graph, we use hypergraph to model the various objects and relations, and consider music recommendation as a ranking problem on this hypergraph. While an edge of an ordinary graph connects only two objects, a hyperedge represents a set of objects. In this way, hypergraph can be naturally used to model high-order relations.

Aggregating multiple types of complex data in stock market prediction: A model-independent framework

“Imaging genetics” studies the genetic contributions to brain structure and function by finding correspondence between genetic data—such as single nucleotide polymorphisms (SNPs)—and neuroimaging data—such as diffusion tensor imaging (DTI). However, genetic and neuroimaging data are heterogenous data types, where neuroimaging data are quantitative and genetic data are (usually) categorical. So far, methods used in imaging genetics treat all data as quantitative, and this sometimes requires unrealistic assumptions about the nature of genetic data. In this article we present a new formulation of Partial Least Squares Correlation (PLSC)—called Mixed-modality Partial Least Squares (MiMoPLS)—specifically tailored for heterogeneous (mixed-) data types. MiMoPLS integrates features of PLSC and Correspondence Analysis (CA) by using special properties of quantitative data and Multiple Correspondence Analysis (MCA). We illustrate MiMoPLS with an example data set from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) with DTI and SNPs.

In order to construct algorithmic solutions to the problem of geolocalization of user-generated photographs and videos, one must first populate a geographic database with the different types of objects and markings that are likely to be seen in the photographs and videos. Toward that end, this paper presents a framework for detecting and labeling objects in satellite imagery. The objects that we are interested in are characterized by low-level features that exist mostly at or beyond the limits of spatial resolution in the satellite images. To deal with the challenges posed by the extraction of such features from satellite imagery, we confine our search to the vicinity of the OpenStreetMap (OSM) delineated roads in a geographic area. The OSM roads are projected into the satellite images through inverse orthorectification. As an illustration of the performance of the object detection framework presented in this paper, our system can detect pedestrian crosswalks in a 200000 sq. km. region of Australia (that is covered by 222 satellite images) with a recall rate of 63% and a precision of 89% (evaluated on a 100 sq. km. subregion). All of the computer processing for this result takes a total of 6 h on our in-house cloud computing framework that consists of five nodes, each an off-the-shelf high-end PC class workstation.

A method for recording and playing back spatial sound data associated with an object in a scene of a virtual environment from the perspective of a character controlled by a user. Different types of spatial sound data can be encoded for different types of objects, e.g., fast moving, directional, slow moving and stationary objects. Based on at least the position, distance, and direction of the object in regard to the character, at least two channels of an audio file can be recorded with spatial sound data for subsequent playback in the virtual environment.

High-Order Co-Clustering (HOCC) methods have attracted high attention in recent years because of their ability to cluster multiple types of objects simultaneously using all available information. During the clustering process, HOCC methods exploit object co-occurrence information, i.e., inter-type relationships amongst different types of objects as well as object affinity information, i.e., intra-type relationships amongst the same types of objects. However, it is difficult to learn accurate intra-type relationships in the presence of noise and outliers. Existing HOCC methods consider the p nearest neighbours based on Euclidean distance for the intra-type relationships, which leads to incomplete and inaccurate intra-type relationships. In this paper, we propose a novel HOCC method that incorporates multiple subspace learning with a heterogeneous manifold ensemble to learn complete and accurate intra-type relationships. Multiple subspace learning reconstructs the similarity between any pair of objects that belong to the same subspace. The heterogeneous manifold ensemble is created based on two-types of intra-type relationships learnt using p-nearest-neighbour graph and multiple subspaces learning. Moreover, in order to make sure the robustness of clustering process, we introduce a sparse error matrix into matrix decomposition and develop a novel iterative algorithm. Empirical experiments show that the proposed method achieves improved results over the state-of-art HOCC methods for FScore and NMI.

The problem of routing between two types of objects is considered. Two variants of the problem statement are considered: with a single point of collection and without it. An important aspect of the routes is the mandatory alternation of different types of objects. A mathematical model of the problem with boolean variables is proposed for each statement. A meaningful statement can be represented as the task of collecting haystacks in trucks afield. Two algorithms are developed to solve this problem. The first one is based on the “greedy” strategies, and the second one is based on the approach of Ant Colony Optimization (ACO) algorithms. To configure the parameters of the second algorithm it is carried out the computational experiment. The results of the experiment are provided. In conclusion, the main results of the work are presented.

The SiamFC target tracking algorithm has attracted extensive attention because of its good balance between speed and performance, but the tracking effect of the SiamFC algorithm is not satisfactory in complex background scenes. When SiamFC algorithm uses deep semantic features for tracking, it has good recognition ability for different types of objects, but it has insufficient discrimination for the same types of objects. Therefore, we propose an effective anti-interference module to improve the discrimination ability of the algorithm. The anti-interference module uses another feature extraction network to extract the features of the candidate target images generated by the SiamFC main network. In addition, we set up the feature vector set to save the feature vectors of the tracking target and the template image. Finally, the tracking target is selected by calculating the minimum cosine distance between the feature vector of the candidate target and the vector in the feature vector set. A large number of experiments show that our anti-interference module can effectively improve the performance of SiamFC algorithm, and the performance of this algorithm can be comparable to the popular algorithms.

The influence of architectural design on well-being is increasingly recognised, underscoring the need to understand how humans evaluate their surroundings. This study examines the impact of low-level visual features and high-level architectural elements in images of house facades generated by a generative adversarial network (GAN). We introduce a novel method to create controlled image datasets using the style mixing component of StyleGAN2-ADA, enabling the combination of specific architectural features with styles representing low-level image properties, such as brightness or colour. Our dataset consists of 900 images encompassing five high-level features (house size, garage door visibility, number of windows, entrance door visibility, and roof shape) and 16 low-level visual features. These GAN-generated images were rated in an online experiment by 303 participants on six dimensions: facelikeness, hominess, relaxation, invitingness, safety, and price. Linear mixed-effects models identified significant predictors, including the number of floors as a high-level feature and green pixel percentage, saturation (SD), brightness (SD), and contrast as low-level features. This indicated that houses with two floors and images with an overall higher amount of green pixel, as well as diversity of saturation and brightness and a lower contrast were rated as, e.g., more inviting and safer. The analysis showed that high- and low-level features explained up to 54% of the variance in house price ratings, with high-level features accounting for the larger share and highlighting their relevance for house facade evaluations. This work advances the use of GANs and offers a method to modify visual elements that shape architectural evaluations.