The Infona portal uses cookies, i.e. strings of text saved by a browser on the user's device. The portal can access those files and use them to remember the user's data, such as their chosen settings (screen view, interface language, etc.), or their login data. By using the Infona portal the user accepts automatic saving and using this information for portal operation purposes. More information on the subject can be found in the Privacy Policy and Terms of Service. By closing this window the user confirms that they have read the information on cookie usage, and they accept the privacy policy and the way cookies are used by the portal. You can change the cookie settings in your browser.
Arctic coastal morphology is increasingly affected by changes to the climate. As the season length for shorefast ice decreases and temperatures warm permafrost, coastlines are increasingly susceptible to erosion from storm waves. Such coastal erosion is significant since the majority of the population centers and infrastructure in the Arctic are located near the coasts. Stakeholders and decision makers...
The task of visual relationship recognition (VRR) is recognizing multiple objects and their relationships in an image. A fundamental difficulty of this task is class-number scalability, since the number of possible relationships we need to consider causes combinatorial explosion. Another difficulty of this task is modeling how to avoid outputting semantically redundant relationships. To overcome these...
Exponential growth in electronic health record (EHR) data has resulted in new opportunities and urgent needs to discover meaningful data-driven representations and patterns of diseases, i.e., computational phenotyping. Recent success and development of deep learning provides promising solutions to the problem of prediction and feature discovery tasks, while lots of challenges still remain and prevent...
The rapid growth of Electronic Health Records (EHRs), as well as the accompanied opportunities in Data-Driven Healthcare (DDH), has been attracting widespread interests and attentions. Recent progress in the design and applications of deep learning methods has shown promising results and is forcing massive changes in healthcare academia and industry, but most of these methods rely on massive labeled...
In multi-tier storage systems with large amounts of data, most of the data is stored on inexpensive slower tiers such as cloud or tape to achieve cost savings. This also implies that retrieving the data from the slower storage tiers incurs high latency. Therefore, it would be beneficial to proactively prefetch data from slower tiers to faster tiers by predicting future data accesses. State-of-the-art...
Genomic selection (GS) is a marker-assisted selection approach to enhance quantitative traits in breeding population in which whole genome single-nucleotide polymorphisms (SNPs) markers can be used to predict breeding values (BV). GS has been proved to increase breeding efficiency in both plant and animal breeding, such as dairy cattle, pig, rice, soybean and loblolly pine. Here, we propose a deep-learning...
We treat failure prediction in a supervised learning framework using a convolutional neural network (CNN). Due to the nature of the problem, learning a CNN model on this kind of dataset is generally associated with three primary problems: 1) negative samples (indicating a healthy system) outnumber positives (indicating system failures) by a great margin; 2) implementation design often requires chopping...
Pharmaceutical industries are interested in Cysteine-stabilized peptides because they offer an array bioactive properties while being highly stable under a range of physiological conditions. However, it is widely appreciated that only a small fraction of this type of peptides have been experimentally discovered while a large number remain unidentified. However, identification of these cysteine-stabilized...
Cyanobacteria bloom is a serious public health threat and a global challenge. Literature on the bloom prediction and forecasting has been accumulating and the emphasis appears to have been on the relation between the blooms and environmental factors, whilst the complexity of the bloom mechanism makes it difficult to reach adequate output of the models. Rapid development of next generation sequencing...
Melanoma is the fastest growing cancer worldwide, and 1 in 50 Americans will develop it in their lifetime. Sentinel lymph node (SLN) metastasis is one of the most important prognostic indicators for melanoma survival. We present several machine learning models for predicting SLN metastasis using data from a real-world dermatology electronic health record (EHR) system. The class label is the result...
Networks are models representing relationships between entities. Often these relationships are explicitly given, or we must learn a representation which generalizes and predicts observed behavior in underlying individual data (e.g. attributes or labels). Whether given or inferred, choosing the best representation affects subsequent tasks and questions on the network. This work focuses on model selection...
Forecasting models that utilize multiple predictors are gaining popularity in a variety of fields. In some cases they allow constructing more precise forecasting models, leveraging the predictive potential of many variables. Unfortunately, in practice we do not know which observed predictors have a direct impact on the target variable. Moreover, adding unrelated variables may diminish the quality...
In this paper, we study the problem of using representation learning to assist information diffusion prediction on graphs. In particular, we aim at estimating the probability of an inactive node to be activated next in a cascade. Despite the success of recent deep learning methods for diffusion, we find that they often underexplore the cascade structure. We consider a cascade as not merely a sequence...
Owning property is one of the most important investments that a person can make in their lifetime. Therefore, being able to accurately know the real-time value of any property is crucial for making wise sales and purchases. Since the online real estate database company Zillow first developed a machine learning system to predict property sale prices in real time, it has continually worked to improve...
Machine learning is currently a hot research topic and applied in intelligence transportation system to discover new valuable knowledge and patterns. In this paper, we extract trajectory information from popular traffic simulator and apply it into four different machine learning methods. In the case of the Gangnam district in Seoul, the Gradient Boosting Regression has better fit with lower values...
Low graduation rate is a significant and growing problem in U.S. higher education systems. Although previous studies have demonstrated the usefulness of building statistical models for predicting students' graduation outcomes, advanced machine learning models promise to improve the effectiveness of these models, and hone in on the “difference that makes a difference” not only on the group level, but...
In this paper, we study the influence from the sentiment of regular tweets on retweeting. We propose a method to calculate the sentiment score for each tweet and each Twitter user. This method enables us to place the tweets and retweets into the same time period to explore the sentiment factor. We adopt the correlation coefficient between the sentiment scores of regular tweets and those of retweets...
In order to solve the problem of lacking shear wave velocity information in oil and gas field, based on conventional logging data, a support vector machine(SVM) model is used to map the relationship between shear wave velocity and natural gamma, acoustic time difference and resistivity of shale, and then a machine learning method for shear wave velocity prediction is proposed. The model was trained...
Collaborative filtering is a well-known technique used for designing recommender systems when advertising services and products offered to the Internet users. In this paper, we employ the Restricted Boltzmann Machine (RBM) for collaborative filtering and propose the neighborhood-conditional RBM (N-CRBM) model based on joint distributions of similarity and popularity scores. The model is trained and...
Given the heterogeneity of the data that can be extracted from the software development process, defect prediction techniques have focused on associating different sources of data with the introduction of faulty code, usually relying on handcrafted features. While these efforts have generated considerable progress over the years, little attention has been given to the fact that the performance of...
Set the date range to filter the displayed results. You can set a starting date, ending date or both. You can enter the dates manually or choose them from the calendar.