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https://www.readbyqxmd.com/read/28821785/machine-learning-quantum-phases-of-matter-beyond-the-fermion-sign-problem
#1
Peter Broecker, Juan Carrasquilla, Roger G Melko, Simon Trebst
State-of-the-art machine learning techniques promise to become a powerful tool in statistical mechanics via their capacity to distinguish different phases of matter in an automated way. Here we demonstrate that convolutional neural networks (CNN) can be optimized for quantum many-fermion systems such that they correctly identify and locate quantum phase transitions in such systems. Using auxiliary-field quantum Monte Carlo (QMC) simulations to sample the many-fermion system, we show that the Green's function holds sufficient information to allow for the distinction of different fermionic phases via a CNN...
August 18, 2017: Scientific Reports
https://www.readbyqxmd.com/read/28820674/restoration-of-fmri-decodability-does-not-imply-latent-working-memory-states
#2
Sebastian Schneegans, Paul M Bays
Recent imaging studies have challenged the prevailing view that working memory is mediated by sustained neural activity. Using machine learning methods to reconstruct memory content, these studies found that previously diminished representations can be restored by retrospective cueing or other forms of stimulation. These findings have been interpreted as evidence for an activity-silent working memory state that can be reactivated dependent on task demands. Here, we test the validity of this conclusion by formulating a neural process model of working memory based on sustained activity and using this model to emulate a spatial recall task with retro-cueing...
August 18, 2017: Journal of Cognitive Neuroscience
https://www.readbyqxmd.com/read/28820010/diagnosing-asthma-and-chronic-obstructive-pulmonary-disease-with-machine-learning
#3
Dimitris Spathis, Panayiotis Vlamos
This study examines the clinical decision support systems in healthcare, in particular about the prevention, diagnosis and treatment of respiratory diseases, such as Asthma and chronic obstructive pulmonary disease. The empirical pulmonology study of a representative sample (n = 132) attempts to identify the major factors that contribute to the diagnosis of these diseases. Machine learning results show that in chronic obstructive pulmonary disease's case, Random Forest classifier outperforms other techniques with 97...
August 1, 2017: Health Informatics Journal
https://www.readbyqxmd.com/read/28819683/an-iphone-application-using-a-novel-stool-color-detection-algorithm-for-biliary-atresia-screening
#4
Eri Hoshino, Kuniyoshi Hayashi, Mitsuyoshi Suzuki, Masayuki Obatake, Kevin Y Urayama, Satoshi Nakano, Yasuyuki Taura, Masaki Nio, Osamu Takahashi
BACKGROUND: The stool color card has been the primary tool for identifying acholic stools in infants with biliary atresia (BA), in several countries. However, BA stools are not always acholic, as obliteration of the bile duct occurs gradually. This study aims to introduce Baby Poop (Baby unchi in Japanese), a free iPhone application, employing a detection algorithm to capture subtle differences in colors, even with non-acholic BA stools. METHODS: The application is designed for use by caregivers of infants aged approximately 2 weeks-1 month...
August 17, 2017: Pediatric Surgery International
https://www.readbyqxmd.com/read/28819355/box-office-forecasting-considering-competitive-environment-and-word-of-mouth-in-social-networks-a-case-study-of-korean-film-market
#5
Taegu Kim, Jungsik Hong, Pilsung Kang
Accurate box office forecasting models are developed by considering competition and word-of-mouth (WOM) effects in addition to screening-related information. Nationality, genre, ratings, and distributors of motion pictures running concurrently with the target motion picture are used to describe the competition, whereas the numbers of informative, positive, and negative mentions posted on social network services (SNS) are used to gauge the atmosphere spread by WOM. Among these candidate variables, only significant variables are selected by genetic algorithm (GA), based on which machine learning algorithms are trained to build forecasting models...
2017: Computational Intelligence and Neuroscience
https://www.readbyqxmd.com/read/28819297/energy-free-machine-learning-force-field-for-aluminum
#6
Ivan Kruglov, Oleg Sergeev, Alexey Yanilkin, Artem R Oganov
We used the machine learning technique of Li et al. (PRL 114, 2015) for molecular dynamics simulations. Atomic configurations were described by feature matrix based on internal vectors, and linear regression was used as a learning technique. We implemented this approach in the LAMMPS code. The method was applied to crystalline and liquid aluminum and uranium at different temperatures and densities, and showed the highest accuracy among different published potentials. Phonon density of states, entropy and melting temperature of aluminum were calculated using this machine learning potential...
August 17, 2017: Scientific Reports
https://www.readbyqxmd.com/read/28819110/development-and-assessment-of-a-lysophospholipid-based-deep-learning-model-to-discriminate-geographical-origins-of-white-rice
#7
Nguyen Phuoc Long, Dong Kyu Lim, Changyeun Mo, Giyoung Kim, Sung Won Kwon
Geographical origin determination of white rice has become the major issue of food industry. However, there is still lack of a high-throughput method for rapidly and reproducibly differentiating the geographical origins of commercial white rice. In this study, we developed a method that employed lipidomics and deep learning to discriminate white rice from Korea to China. A total of 126 white rice of 30 cultivars from different regions were utilized for the method development and validation. By using direct infusion-mass spectrometry-based targeted lipidomics, 17 lysoglycerophospholipids were simultaneously characterized within minutes per sample...
August 17, 2017: Scientific Reports
https://www.readbyqxmd.com/read/28817571/automated-segmentation-of-mouse-oct-volumes-asimov-validation-clinical-study-of-a-light-damage-model
#8
Bhavna Josephine Antony, Byung-Jin Kim, Andrew Lang, Aaron Carass, Jerry L Prince, Donald J Zack
The use of spectral-domain optical coherence tomography (SD-OCT) is becoming commonplace for the in vivo longitudinal study of murine models of ophthalmic disease. Longitudinal studies, however, generate large quantities of data, the manual analysis of which is very challenging due to the time-consuming nature of generating delineations. Thus, it is of importance that automated algorithms be developed to facilitate accurate and timely analysis of these large datasets. Furthermore, as the models target a variety of diseases, the associated structural changes can also be extremely disparate...
2017: PloS One
https://www.readbyqxmd.com/read/28816702/as-above-so-below-towards-understanding-inverse-models-in-bci
#9
Jussi T Lindgren
In Brain-Computer Interfaces (BCI), measurements of the users brain activity are classified into commands for the computer. With EEG-based BCIs, the origins of the classified phenomena are often considered to be spatially localized in the cortical volume and mixed in the EEG. Does the reconstruction of the source activities in the volume help in building more accurate BCIs? The answer remains inconclusive despite previous work. In this paper, we study the question by contrasting the physiology-driven source reconstruction with data-driven representations obtained by statistical machine learning...
August 17, 2017: Journal of Neural Engineering
https://www.readbyqxmd.com/read/28816675/adaboost-llp-a-boosting-method-for-learning-with-label-proportions
#10
Zhiquan Qi, Fan Meng, Yingjie Tian, Lingfeng Niu, Yong Shi, Peng Zhang
How to solve the classification problem with only label proportions has recently drawn increasing attention in the machine learning field. In this paper, we propose an ensemble learning strategy to deal with the learning problem with label proportions (LLP). In detail, we first give a loss function based on different weights for LLP, and then construct the corresponding weak classifier, at the same time, estimate its conditional probabilities by a standard logistic function. At last, by introducing the maximum likelihood estimation, we propose a new anyboost learning system for LLP (called Adaboost-LLP)...
August 15, 2017: IEEE Transactions on Neural Networks and Learning Systems
https://www.readbyqxmd.com/read/28815994/imrt-qa-using-machine-learning-a-multi-institutional-validation
#11
Gilmer Valdes, Maria F Chan, Seng Boh Lim, Ryan Scheuermann, Joseph O Deasy, Timothy D Solberg
PURPOSE: To validate a machine learning approach to Virtual intensity-modulated radiation therapy (IMRT) quality assurance (QA) for accurately predicting gamma passing rates using different measurement approaches at different institutions. METHODS: A Virtual IMRT QA framework was previously developed using a machine learning algorithm based on 498 IMRT plans, in which QA measurements were performed using diode-array detectors and a 3%local/3 mm with 10% threshold at Institution 1...
August 17, 2017: Journal of Applied Clinical Medical Physics
https://www.readbyqxmd.com/read/28815301/keeping-brains-young-with-making-music
#12
Lars Rogenmoser, Julius Kernbach, Gottfried Schlaug, Christian Gaser
Music-making is a widespread leisure and professional activity that has garnered interest over the years due to its effect on brain and cognitive development and its potential as a rehabilitative and restorative therapy of brain dysfunctions. We investigated whether music-making has a potential age-protecting effect on the brain. For this, we studied anatomical magnetic resonance images obtained from three matched groups of subjects who differed in their lifetime dose of music-making activities (i.e., professional musicians, amateur musicians, and non-musicians)...
August 16, 2017: Brain Structure & Function
https://www.readbyqxmd.com/read/28815134/interrogating-patient-level-genomics-and-mouse-phenomics-towards-understanding-cytokines-in-colorectal-cancer-metastasis
#13
Xiaoshu Cai, Yang Chen, Chunlei Zheng, Rong Xu
Background: Colorectal cancer is the second leading cancer-related death worldwide and a majority of patients die from metastasis. Chronic intestinal inflammation plays an important role in tumor progression of colorectal cancer. However, few study works on systematically predicting colorectal cancer metastasis using inflammatory cytokine genes. Results: We developed a supervised machine learning approach to predict colorectal cancer tumor progression using patient level genomic features. To better understand the role of cytokines, we integrated the metastatic-related genes from mouse phenotypic data...
2017: AMIA Summits on Translational Science Proceedings
https://www.readbyqxmd.com/read/28815128/a-comparative-study-of-different-methods-for-automatic-identification-of-clopidogrel-induced-bleedings-in-electronic-health-records
#14
Hee-Jin Lee, Min Jiang, Yonghui Wu, Christian M Shaffer, John H Cleator, Eitan A Friedman, Joshua P Lewis, Dan M Roden, Josh Denny, Hua Xu
Electronic health records (EHRs) linked with biobanks have been recognized as valuable data sources for pharmacogenomic studies, which require identification of patients with certain adverse drug reactions (ADRs) from a large population. Since manual chart review is costly and time-consuming, automatic methods to accurately identify patients with ADRs have been called for. In this study, we developed and compared different informatics approaches to identify ADRs from EHRs, using clopidogrel-induced bleeding as our case study...
2017: AMIA Summits on Translational Science Proceedings
https://www.readbyqxmd.com/read/28815118/deep-learning-from-eeg-reports-for-inferring-underspecified-information
#15
Travis R Goodwin, Sanda M Harabagiu
Secondary use(1)of electronic health records (EHRs) often relies on the ability to automatically identify and extract information from EHRs. Unfortunately, EHRs are known to suffer from a variety of idiosyncrasies - most prevalently, they have been shown to often omit or underspecify information. Adapting traditional machine learning methods for inferring underspecified information relies on manually specifying features characterizing the specific information to recover (e.g. particular findings, test results, or physician's impressions)...
2017: AMIA Summits on Translational Science Proceedings
https://www.readbyqxmd.com/read/28814034/automated-assessment-of-symptom-severity-changes-during-deep-brain-stimulation-dbs-therapy-for-parkinson-s-disease
#16
Paolo Angeles, Yen Tai, Nicola Pavese, Samuel Wilson, Ravi Vaidyanathan
Deep brain stimulation (DBS) is currently being used as a treatment for symptoms of Parkinson's disease (PD). Tracking symptom severity progression and deciding the optimal stimulation parameters for people with PD is extremely difficult. This study presents a sensor system that can quantify the three cardinal motor symptoms of PD - rigidity, bradykinesia and tremor. The first phase of this study assesses whether data recorded from the system during physical examinations can be used to correlate to clinician's severity score using supervised machine learning (ML) models...
July 2017: IEEE ... International Conference on Rehabilitation Robotics: [proceedings]
https://www.readbyqxmd.com/read/28814032/studying-the-implementation-of-iterative-impedance-control-for-assistive-hand-rehabilitation-using-an-exoskeleton
#17
T Martineau, R Vaidyanathan
A positive training synergy can be obtained when two individuals attempt to learn the same motor task while mechanically coupled to one another. In this paper, we have studied how mimicking this interaction through impedance control can be exploited to improve assistance delivered by hand exoskeleton devices during rehabilitation. In this context, the machine and user take complementary roles akin to two coupled individuals. We present the derivation of a dynamic model of the human hand for the purpose of controller development for new hand exoskeleton platforms...
July 2017: IEEE ... International Conference on Rehabilitation Robotics: [proceedings]
https://www.readbyqxmd.com/read/28814023/representing-high-dimensional-data-to-intelligent-prostheses-and-other-wearable-assistive-robots-a-first-comparison-of-tile-coding-and-selective-kanerva-coding
#18
Jaden B Travnik, Patrick M Pilarski
Prosthetic devices have advanced in their capabilities and in the number and type of sensors included in their design. As the space of sensorimotor data available to a conventional or machine learning prosthetic control system increases in dimensionality and complexity, it becomes increasingly important that this data be represented in a useful and computationally efficient way. Well structured sensory data allows prosthetic control systems to make informed, appropriate control decisions. In this study, we explore the impact that increased sensorimotor information has on current machine learning prosthetic control approaches...
July 2017: IEEE ... International Conference on Rehabilitation Robotics: [proceedings]
https://www.readbyqxmd.com/read/28813990/a-learning-based-agent-for-home-neurorehabilitation
#19
Andreas Lydakis, Yuanliang Meng, Christopher Munroe, Yi-Ning Wu, Momotaz Begum
This paper presents the iterative development of an artificially intelligent system to promote home-based neurorehabilitation. Although proper, structured practice of rehabilitation exercises at home is the key to successful recovery of motor functions, there is no home-program out there which can monitor a patient's exercise-related activities and provide corrective feedback in real time. To this end, we designed a Learning from Demonstration (LfD) based home-rehabilitation framework that combines advanced robot learning algorithms with commercially available wearable technologies...
July 2017: IEEE ... International Conference on Rehabilitation Robotics: [proceedings]
https://www.readbyqxmd.com/read/28813969/prediction-of-user-preference-over-shared-control-paradigms-for-a-robotic-wheelchair
#20
Ahmetcan Erdogan, Brenna D Argall
The design of intelligent powered wheelchairs has traditionally focused heavily on providing effective and efficient navigation assistance. Significantly less attention has been given to the end-user's preference between different assistance paradigms. It is possible to include these subjective evaluations in the design process, for example by soliciting feedback in post-experiment questionnaires. However, constantly querying the user for feedback during real-world operation is not practical. In this paper, we present a model that correlates objective performance metrics and subjective evaluations of autonomous wheelchair control paradigms...
July 2017: IEEE ... International Conference on Rehabilitation Robotics: [proceedings]
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