Patterns, within the context of computational imaging, can be defined as "a more or less repeatable, discernible regularity in the spatial arrangement of a type of theme of more or less recurring objects, with possibly both radiometrical and geometrical features, sometimes referred to as elements of a collection of objects."[i] The term "radiometrical features" refers to what can be called "tone." Machines discern this portion of machine "sight" by measuring how an object responds to electromagnetic radiation. A machine blasts an object with either ultraviolet, visible, or infrared light, or all three and uses the resulting radiation to recognize patterns and compare them against known patterns.

This paper presents a review of deep learning (DL) based medical image registration methods. We summarized the latest developments and applications of DL-based registration methods in the medical field. These methods were classified into seven categories according to their methods, functions and popularity. A detailed review of each category was presented, highlighting important contributions and identifying specific challenges. A short assessment was presented following the detailed review of each category to summarize its achievements and future potentials. We provided a comprehensive comparison among DL-based methods for lung and brain deformable registration using benchmark datasets. Lastly, we analyzed the statistics of all the cited works from various aspects, revealing the popularity and future trend of development in medical image registration using deep learning.

In this paper, we study a new graph learning problem: learning to count subgraph isomorphisms. Although the learning based approach is inexact, we are able to generalize to count large patterns and data graphs in polynomial time compared to the exponential time of the original NP-complete problem. Different from other traditional graph learning problems such as node classification and link prediction, subgraph isomorphism counting requires more global inference to oversee the whole graph. To tackle this problem, we propose a dynamic intermedium attention memory network (DIAMNet) which augments different representation learning architectures and iteratively attends pattern and target data graphs to memorize different subgraph isomorphisms for the global counting. We develop both small graphs (<= 1,024 subgraph isomorphisms in each) and large graphs (<= 4,096 subgraph isomorphisms in each) sets to evaluate different models. Experimental results show that learning based subgraph isomorphism counting can help reduce the time complexity with acceptable accuracy. Our DIAMNet can further improve existing representation learning models for this more global problem.

Sequential pattern mining is an interesting research area with broad range of applications. Most prior research on sequential pattern mining has considered point-based data where events occur instantaneously. However, in many application domains, events persist over intervals of time of varying lengths. Furthermore, traditional frameworks of sequential pattern mining assume all events to have the same weight or utility. This simplifying assumption neglects the opportunity to find informative patterns in terms of utilities such as costs. To address these issues, we incorporate the concept of utility into interval-based sequences and define a framework to mine high utility patterns in interval-based sequences i.e., patterns whose utility meets or exceeds a minimum threshold. In the proposed framework, the utility of events is considered while assuming multiple events can occur coincidentally and persist over varying periods of time. An Apriori-based algorithm name High Utility Interval-based Pattern Miner (HUIPMiner) is proposed and applied to real datasets. To achieve an efficient solution, HUIPMiner is augmented with a pruning strategy. Experimental results show that HUIPMiner is an effective solution to the problem of mining high utility interval-based sequences.

We study the problem of classification of interval-based temporal sequences (IBTSs). Since common classification algorithms cannot be directly applied to IBTSs, the main challenge is to define a set of features that effectively represents the data such that learning classifiers are able to perform. Most prior work utilizes frequent pattern mining to define a feature set based on discovered patterns. However, frequent pattern mining is computationally expensive and often discovers many irrelevant patterns. To address this shortcoming, we propose the FIBS framework for classifying IBTSs. FIBS extracts features relevant to classification from IBTSs based on relative frequency and temporal relations. To avoid selecting irrelevant features, a filter-based selection strategy is incorporated into FIBS. Our empirical evaluation on five real-world datasets demonstrate the effectiveness of our methods in practice. The results provide evidence that FIBS framework effectively represents IBTSs for classification algorithms and it can even achieve better performance when the selection strategy is applied.

Graph clustering groups entities -- the vertices of a graph -- based on their similarity, typically using a complex distance function over a large number of features. Successful integration of clustering approaches in automated decision-support systems hinges on the interpretability of the resulting clusters. This paper addresses the problem of generating interpretable clusters, given features of interest that signify interpretability to an end-user, by optimizing interpretability in addition to common clustering objectives. We propose a $\beta$-interpretable clustering algorithm that ensures that at least $\beta$ fraction of nodes in each cluster share the same feature value. The tunable parameter $\beta$ is user-specified. We also present a more efficient algorithm for scenarios with $\beta\!=\!1$ and analyze the theoretical guarantees of the two algorithms. Finally, we empirically demonstrate the benefits of our approaches in generating interpretable clusters using four real-world datasets. The interpretability of the clusters is complemented by generating simple explanations denoting the feature values of the nodes in the clusters, using frequent pattern mining.

Machine Learning (ML) is widely used for several tasks with time-series and biosensor data such as for human activity recognition, electronic health records data-based predictions (Ismail Fawaz et al., 2019), and real-time bionsensor-based decisions. V arious classification goals are addressed related to electrocardiography (ECG) (Jambukia et al., 2015), elec-troencephalography (EEG) (Craik et al., 2019; Dose et al., 2018), and electromyograpy (EMG) (Ketyk et al., 2019; Hu et al., 2018; Patricia et al., 2014; Du et al., 2017). Sensing hand gestures can be done by means of wearables or by means of image or video analysis of hand or finger motion. A wearable-based detection can physically rely on measuring the acceleration and rotations of our body parts (arms, hands or fingers) with Inertial Measurement Unit (IMU) sensors or by measuring the myo-electric signals generated by the various muscles of our arms or fingers with EMG sensors. Surface EMG (sEMG) records muscle activity from the surface of the skin which is above the muscle being evaluated. The signal is collected via surface electrodes. We are interested in sEMG-sensor placement to the forearm and performing hand gesture recognition with ML.

Graph learning methods, especially graph convolutional networks, have been investigated for their potential applicability in many fields of study based on topological data. Their topological data processing capabilities have proven to be powerful. However, the relationships among separate entities include not only topological adjacency, but also correlation in vision, for example, the spatial vector data of buildings. In this study, we propose a spatial adaptive algorithm framework with a data-driven design to accomplish building group division and building group pattern recognition tasks, which is not sensitive to the difference in the spatial distribution of the buildings in various geographical regions. In addition, the algorithm framework has a multi-stage design, and processes the building group data from whole to parts, since the objective is closely related to multi-object detection on topological data.

App classification is useful in a number of applications such as adding apps to an app store or building a user model based on the installed apps. Presently there are a number of existing methods to classify apps based on a given taxonomy on the basis of their text metadata. However, text based methods for app classification may not work in all cases, such as when the text descriptions are in a different language, or missing, or inadequate to classify the app. One solution in such cases is to utilize the app images to supplement the text description. In this paper, we evaluate a number of approaches in which app images can be used to classify the apps. In one approach, we use Optical character recognition (OCR) to extract text from images, which is then used to supplement the text description of the app. In another, we use pic2vec to convert the app images into vectors, then train an SVM to classify the vectors to the correct app label. In another, we use the captionbot.ai tool to generate natural language descriptions from the app images. Finally, we use a method to detect and label objects in the app images and use a voting technique to determine the category of the app based on all the images. We compare the performance of our image-based techniques to classify a number of apps in our dataset. We use a text based SVM app classifier as our base and obtained an improved classification accuracy of 96% for some classes when app images are added.

You're going to need a free video content marketing strategy template. It can be as simple as scanning a barcode, or as complex as PiP. Yep… one of the most advanced pet identification systems out there... PiP is a smartphone app created for pet owners who've lost their cat, dog, fish. Should you misplace your pet, its photo will be analyzed and matched with photos of pets that have been found wandering the streets. Image analysis is used to beat lost tags, outdated microchips, and fading tattoos.