Consistent Correspondences for Shape and Image Problems

2020 ◽  
Author(s):  
◽  
Taiwei Wang
Keyword(s):  
Similarity Measures ◽  
Research Problem ◽  
Classic Problem ◽  
Puzzle Solving ◽  
Deformable Shapes ◽  
Loop Constraint ◽  
Matching Techniques ◽  
Loop Constraints ◽  
Image Problems ◽  
Important Research Problem

Establish consistent correspondences between different objects is a classic problem in computer science/vision. It helps to match highly similar objects in both 3D and 2D domain. Inthe 3D domain, finding consistent correspondences has been studying for more than 20 yearsand it is still a hot topic. In 2D domain, consistent correspondences can also help in puzzlesolving. However, only a few works are focused on this approach. In this thesis, we focuson finding consistent correspondences and extend to develop robust matching techniques inboth 3D shape segments and 2D puzzle solving. In the 3D domain, segment-wise matching isan important research problem that supports higher-level understanding of shapes in geometryprocessing. Many existing segment-wise matching techniques assume perfect input segmentation and would suffer from imperfect or over-segmented input. To handle this shortcoming,we propose multi-layer graphs (MLGs) to represent possible arrangements of partially mergedsegments of input shapes. We then adapt the diffusion pruning technique on the MLGs to findconsistent segment-wise matching. To obtain high-quality matching, we develop our own voting step which is able to remove inconsistent results, for finding hierarchically consistent correspondences as final output. We evaluate our technique with both quantitative and qualitativeexperiments on both man-made and deformable shapes. Experimental results demonstrate theeffectiveness of our technique when compared to two state-of-art methods. In the 2D domain,solving jigsaw puzzles is also a classic problem in computer vision with various applications.Over the past decades, many useful approaches have been introduced. Most existing worksuse edge-wise similarity measures for assembling puzzles with square pieces of the same size, and recent work innovates to use the loop constraint to improve efficiency and accuracy. Weobserve that most existing techniques cannot be easily extended to puzzles with rectangularpieces of arbitrary sizes, and no existing loop constraints can be used to model such challenging scenarios. We propose new matching approaches based on sub-edges/corners, modelledusing the MatchLift or diffusion framework to solve square puzzles with cycle consistency.We demonstrate the robustness of our approaches by comparing our methods with state-of-artmethods. We also show how puzzles with rectangular pieces of arbitrary sizes, or puzzles withtriangular and square pieces can be solved by our techniques.

scholarly journals Performance Comparison of Deep Learning Autoencoders for Cancer Subtype Detection Using Multi-Omics Data

Cancers ◽  
2021 ◽  
Vol 13 (9) ◽  
pp. 2013
Author(s):  
Edian F. Franco ◽  
Pratip Rana ◽  
Aline Cruz ◽  
Víctor V. Calderón ◽  
Vasco Azevedo ◽  
...  
Keyword(s):  
Deep Learning ◽  
Data Fusion ◽  
Similarity Measures ◽  
Research Problem ◽  
Optimal Number ◽  
Performance Comparison ◽  
The Cancer Genome Atlas ◽  
Cancer Type ◽  
Omics Data ◽  
Cancer Subtype

A heterogeneous disease such as cancer is activated through multiple pathways and different perturbations. Depending upon the activated pathway(s), the survival of the patients varies significantly and shows different efficacy to various drugs. Therefore, cancer subtype detection using genomics level data is a significant research problem. Subtype detection is often a complex problem, and in most cases, needs multi-omics data fusion to achieve accurate subtyping. Different data fusion and subtyping approaches have been proposed over the years, such as kernel-based fusion, matrix factorization, and deep learning autoencoders. In this paper, we compared the performance of different deep learning autoencoders for cancer subtype detection. We performed cancer subtype detection on four different cancer types from The Cancer Genome Atlas (TCGA) datasets using four autoencoder implementations. We also predicted the optimal number of subtypes in a cancer type using the silhouette score and found that the detected subtypes exhibit significant differences in survival profiles. Furthermore, we compared the effect of feature selection and similarity measures for subtype detection. For further evaluation, we used the Glioblastoma multiforme (GBM) dataset and identified the differentially expressed genes in each of the subtypes. The results obtained are consistent with other genomic studies and can be corroborated with the involved pathways and biological functions. Thus, it shows that the results from the autoencoders, obtained through the interaction of different datatypes of cancer, can be used for the prediction and characterization of patient subgroups and survival profiles.

Invariant Probabilities with Geometric Tail

1996 ◽  
Vol 10 (2) ◽  
pp. 213-221 ◽  
Author(s):  
Jean B. Lasserre ◽  
Henk Tijms
Keyword(s):  
Markov Chain ◽  
Linear Algebra ◽  
Research Problem ◽  
Important Research ◽  
Countable State ◽  
Important Research Problem

We present necessary and suffi2ient Foster-type conditions for a countable state Markov chain to have an invariant probability with at least a geometric tail. These conditions are obtained by using a generalized Farkas Theorem in Linear Algebra. The purpose of this note is also to pose an interesting and important research problem that is still largely open.

scholarly journals Advanced exact synthesis of Clifford+T circuits

2020 ◽  
Vol 19 (9) ◽  
Author(s):  
Philipp Niemann ◽  
Robert Wille ◽  
Rolf Drechsler
Keyword(s):  
Large Scale ◽  
Fault Tolerant ◽  
Logic Synthesis ◽  
Research Problem ◽  
Quantum Systems ◽  
Quantum Circuits ◽  
Research Attention ◽  
Small Quantum ◽  
Significant Research ◽  
Important Research Problem

Abstract Quantum systems provide a new way of conducting computations based on the so-called qubits. Due to the potential for significant speed-ups, this field received significant research attention in recent years. The Clifford+T library is a very promising and popular gate library for these kinds of computations. Unlike other libraries considered so far, it consists of only a small number of gates for all of which robust, fault-tolerant realizations are known for many technologies that seem to be promising for large-scale quantum computing. As a consequence, (logic) synthesis of Clifford+T quantum circuits became an important research problem. However, previous work in this area has several drawbacks: Corresponding approaches are either only applicable to very small quantum systems or lead to circuits that are far from being optimal. The latter is mainly caused by the fact that current synthesis realizes the desired circuit by a local, i.e., column-wise, consideration of the underlying unitary transformation matrix to be synthesized. In this paper, we analyze the conceptual drawbacks of this approach and propose to overcome them by taking a global view of the matrices and perform a separation of concerns regarding individual synthesis steps. We precisely describe a corresponding algorithm as well as its efficient implementation on top of decision diagrams. Experimental results confirm the resulting benefits and show improvements of up to several orders of magnitudes in costs compared to previous work.

scholarly journals A Reinforcement Learning Based Dirt-Exploration for Cleaning-Auditing Robot

Sensors ◽  
2021 ◽  
Vol 21 (24) ◽  
pp. 8331
Author(s):  
Thejus Pathmakumar ◽  
Mohan Rajesh Elara ◽  
Braulio Félix Gómez ◽  
Balakrishnan Ramalingam
Keyword(s):  
Reinforcement Learning ◽  
Research Work ◽  
Leading Edge ◽  
Research Problem ◽  
Fundamental Question ◽  
Policy Learning ◽  
Policy Network ◽  
Important Research ◽  
Important Research Problem

Cleaning is one of the fundamental tasks with prime importance given in our day-to-day life. Moreover, the importance of cleaning drives the research efforts towards bringing leading edge technologies, including robotics, into the cleaning domain. However, an effective method to assess the quality of cleaning is an equally important research problem to be addressed. The primary footstep towards addressing the fundamental question of “How clean is clean” is addressed using an autonomous cleaning-auditing robot that audits the cleanliness of a given area. This research work focuses on a novel reinforcement learning-based experience-driven dirt exploration strategy for a cleaning-auditing robot. The proposed approach uses proximal policy approximation (PPO) based on-policy learning method to generate waypoints and sampling decisions to explore the probable dirt accumulation regions in a given area. The policy network is trained in multiple environments with simulated dirt patterns. Experiment trials have been conducted to validate the trained policy in both simulated and real-world environments using an in-house developed cleaning audit robot called BELUGA.

scholarly journals The CV as an Initiative for Sustainable Development of Small-Scale Capture Fisheries in Central Java, Indonesia

2020 ◽  
Vol 10 ◽  
pp. 249-253
Author(s):  
Siti Mahmudah ◽  
◽  
Etty Susilowati ◽  
Yunanto, Amiek Soemarmi ◽  
Siti Malikhatun Badriyah ◽  
...  
Keyword(s):  
Secondary Data ◽  
Research Problem ◽  
Alternative Form ◽  
Small Scale ◽  
Literature Study ◽  
Conceptual Approach ◽  
Limited Partnership ◽  
Central Java ◽  
Classic Problem ◽  
The Law

The problem of this study is that small-scale capture fisheries business in Indonesia still faces a classic problem, namely limited sources of capital to develop the business, so a strategy is needed to overcome this problem, including working with other parties as partners in running the business. This study aims to investigate the legality of the capture fisheries business in Indonesia, and the limited partnership (Commanditaire Vennootschap/CV) as an alternative form of capture fisheries business in Indonesia. This study uses a normative juridical research method with a statutory approach and a conceptual approach, the legal data used is secondary data in the form of primary legal materials, namely the Law on Capture Fisheries and the Law on CV, and secondary legal material in the form of literature related to the research problem. The data and legal materials were collected through a literature study and analyzed descriptively and analytically. The results of the study concluded that in carrying out fishery business activities, the entrepreneurs can use CV as an alternative form of small-scale capture fisheries business that allows overcoming the problem of limited capital faced by small-scale capture fisheries entrepreneurs in Indonesia. The study concludes that with the formation of CV, there will be two partners in small-scale capture fisheries business, namely complementary allies as the party who manages what is done by fishermen and limited allies are parties who include capital in small-scale capture fisheries businesses.

scholarly journals A System for Outlier Detection of High Dimensional Data

2012 ◽  
pp. 197-201
Author(s):  
Bharat Gupta ◽  
Durga Toshniwal
Keyword(s):  
Outlier Detection ◽  
High Dimensional Data ◽  
Research Problem ◽  
High Dimensional ◽  
Full Data ◽  
Data Set ◽  
Detection Techniques ◽  
New Concepts ◽  
Low Dimensional ◽  
Important Research Problem

In high dimensional data large no of outliers are embedded in low dimensional subspaces known as projected outliers, but most of existing outlier detection techniques are unable to find these projected outliers, because these methods perform detection of abnormal patterns in full data space. So, outlier detection in high dimensional data becomes an important research problem. In this paper we are proposing an approach for outlier detection of high dimensional data. Here we are modifying the existing SPOT approach by adding three new concepts namely Adaption of Sparse Sub-Space Template (SST), Different combination of PCS parameters and set of non outlying cells for testing data set.

scholarly journals Topic Jerk Detector: Detection of Tweet Bursts Related to the Fukushima Daiichi Nuclear Disaster

Information ◽  
2020 ◽  
Vol 11 (7) ◽  
pp. 368 ◽  
Author(s):  
Hiroshi Nagaya ◽  
Teruaki Hayashi ◽  
Hiroyuki A. Torii ◽  
Yukio Ohsawa
Keyword(s):  
Social Media ◽  
Information Sharing ◽  
Social Issues ◽  
Research Problem ◽  
Nuclear Disaster ◽  
Social Media Platforms ◽  
Efficient Information ◽  
Fukushima Disaster ◽  
Important Research Problem

In recent disaster situations, social media platforms, such as Twitter, played a major role in information sharing and widespread communication. These situations require efficient information sharing; therefore, it is important to understand the trends in popular topics and the underlying dynamics of information flow on social media better. Developing new methods to help us in these situations, and testing their effectiveness so that they can be used in future disasters is an important research problem. In this study, we proposed a new model, “topic jerk detector.” This model is ideal for identifying topic bursts. The main advantage of this method is that it is better fitted to sudden bursts, and accurately detects the timing of the bursts of topics compared to the existing method, topic dynamics. Our model helps capture important topics that have rapidly risen to the top of the agenda in respect of time in the study of specific social issues. It is also useful to track the transition of topics more effectively and to monitor tweets related to specific events, such as disasters. We attempted three experiments that verified its effectiveness. First, we presented a case study applied to the tweet dataset related to the Fukushima disaster to show the outcomes of the proposed method. Next, we performed a comparison experiment with the existing method. We showed that the proposed method is better fitted to sudden burst accurately detects the timing of the bursts of the topic. Finally, we received expert feedback on the validity of the results and the practicality of the methodology.

Variants of the organization in transport in the distribution system

2018 ◽  
Vol 120 ◽  
pp. 319-330
Author(s):  
Dariusz Pyza ◽  
Monika Miętus
Keyword(s):  
Distribution System ◽  
Distribution Systems ◽  
Direct Consequence ◽  
Research Problem ◽  
Economic Effects ◽  
Main Task ◽  
Specific Group ◽  
Common Criterion ◽  
Important Research Problem ◽  
Made In

Distribution occupies a significant place in the elements of the logistics chain, because its main task is to meet the expectations set by the customer. The decisions regarding the method of selling goods made in enterprises can be classified as strategic. Their direct consequence is the company's economic effects. The article analyzed the popularity of transport in the distribution system and developed variant ways of delivering goods for a specific group of goods. An important research problem is the identification of distribution channels, which gather dependent and interacting organizations involved in the process of meeting the requirements of the buyer. An unambiguous assessment of the choice of distribution system depends on many criteria, which depending on the demand may be different. The most common criterion used to select a carriage is the time and cost of the task. The specificity of distribution systems shows that full-truck and groupage systems are the most often chosen.

scholarly journals Sensor Ontology Metamatching with Heterogeneity Measures

2020 ◽  
Vol 2020 ◽  
pp. 1-10
Author(s):  
Xingsi Xue ◽  
Jiawei Lu ◽  
Chengcai Jiang ◽  
Yikun Huang
Keyword(s):  
State Of The Art ◽  
Similarity Measures ◽  
Ontology Matching ◽  
Learning Approach ◽  
Ontology Alignment ◽  
Matching Process ◽  
Ontology Alignment Evaluation Initiative ◽  
Matching Techniques ◽  
First Time ◽  
Concept Pairs

The heterogeneity problem among different sensor ontologies hinders the interaction of information. Ontology matching is an effective method to address this problem by determining the heterogeneous concept pairs. In the matching process, the similarity measure serves as the kernel technique, which calculates the similarity value of two concepts. Since none of the similarity measures can ensure its effectiveness in any context, usually, several measures are combined together to enhance the result’s confidence. How to find suitable aggregating weights for various similarity measures, i.e., ontology metamatching problem, is an open challenge. This paper proposes a novel ontology metamatching approach to improve the sensor ontology alignment’s quality, which utilizes the heterogeneity features on two ontologies to tune the aggregating weight set. In particular, three ontology heterogeneity measures are firstly proposed to, respectively, evaluate the heterogeneity values in terms of syntax, linguistics, and structure, and then, a semiautomatically learning approach is presented to construct the conversion functions that map any two ontologies’ heterogeneity values to the weights for aggregating the similarity measures. To the best of our knowledge, this is the first time that heterogeneity features are proposed and used to solve the sensor ontology metamatching problem. The effectiveness of the proposal is verified by comparing with using state-of-the-art ontology matching techniques on Ontology Alignment Evaluation Initiative (OAEI)’s testing cases and two pairs of real sensor ontologies.

scholarly journals Notes from the Editor

2017 ◽  
Vol 111 (2) ◽  
pp. iii-x
Keyword(s):  
Political Science ◽  
American Political Science Review ◽  
Research Problem ◽  
General Interest ◽  
Original Research ◽  
Political Issues ◽  
Theoretical Perspectives ◽  
Research Problems ◽  
American Political Science ◽  
Important Research Problem

In this issue's Notes from the Editors, we are excited to be able to present not only our first big innovation for theAmerican Political Science Review, ourletterformat, but also articles that are concurrent with present political affairs, a difficult task due to the intricacies of peer reviewed science. We would first like to draw attention to our new publication format,letters. We hope to further the idea of publishing important insights to research problems in political science and encourage scholarly debate in the discipline. Some of these insights, however, might not fit in the traditional, longerarticleformat, which is tailored to original work advancing the understanding of political issues that are of general interest to the field of political science. Instead, letters provide an opportunity to report about original research that moves the subfields of political science forward as they develop alongside their counterparts in related disciplines, such as new theoretical perspectives, methodological progress, alternative empirical findings, as well as comments on and extensions of existing work. Moreover, our letter format attempts to increase inter-disciplinary recognition by broadening readership and eventually authorship from scholars of other disciplines that address an important research problem in political science.

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