scholarly journals Interactive visualization of cardiac anatomy and atrial excitation for medical diagnosis and research

2015 ◽  
Vol 1 (1) ◽  
pp. 400-404 ◽  
Author(s):  
Silvio Bauer ◽  
Tobias Oesterlein ◽  
Jochen Schmidt ◽  
Olaf Dössel
Keyword(s):  
Cardiac Arrhythmias ◽  
Biomedical Engineering ◽  
Medical Diagnosis ◽  
Software Package ◽  
State Of The Art ◽  
Cardiac Anatomy ◽  
Clinical Environment ◽  
Open Questions ◽  
Using Data ◽  
Atrial Excitation

AbstractState of the art biomedical engineering allows for acquiring enormous amounts of intracardiac data to aid diagnosis and treatment of cardiac arrhythmias. Modern catheters, which are used to acquire electrical information from within the heart, are capable of recording up to 64 channels simultaneously. The software available for data analysis, however, does not provide adequate performance to neither analyze nor visualize the acquired information in an appropriate manner. We present a software package that fascilitates interdisciplinary collaborations between engineers and physicians to adress open questions about pathophysiological mechanisms using data from everyday electrophysiogical studies. Therefore, a package has been compiled that enables algorithm development using MATLAB and subsequent visualization using the VTK C++ class libraries. The resulting application KaPAVIE, which is presented in this paper, is designed to meet the requirements from the clinical side and has been successfully applied in the clinical environment.

Revisiting the High-Pressure Properties of the Metal-Organic Frameworks ZIF-8 and ZIF-67

2020 ◽  
Author(s):  
Pia Vervoorts ◽  
Stefan Burger ◽  
Karina Hemmer ◽  
Gregor Kieslich
Keyword(s):  
High Pressure ◽  
State Of The Art ◽  
Energy Landscape ◽  
Structural Flexibility ◽  
Zeolitic Imidazolate Frameworks ◽  
Stimuli Responsive ◽  
X Ray Diffraction ◽  
X Ray ◽  
Open Questions ◽  
Metal Organic

The zeolitic imidazolate frameworks ZIF-8 and ZIF-67 harbour a series of fascinating stimuli responsive properties. Looking at their responsitivity to hydrostatic pressure as stimulus, open questions exist regarding the isotropic compression with non-penetrating pressure transmitting media. By applying a state-of-the-art high-pressure powder X-ray diffraction setup, we revisit the high-pressure behaviour of ZIF-8 and ZIF-67 up to <i>p</i> = 0.4 GPa in small pressure increments. We observe a drastic, reversible change of high-pressure powder X-ray diffraction data at <i>p</i> = 0.3 GPa, discovering large volume structural flexibility in ZIF-8 and ZIF-67. Our results imply a shallow underlying energy landscape in ZIF-8 and ZIF-67, an observation that might point at rich polymorphism of ZIF-8 and ZIF-67, similar to ZIF-4(Zn).<br>

Revisiting the High-Pressure Properties of the Metal-Organic Frameworks ZIF-8 and ZIF-67

2020 ◽  
Author(s):  
Pia Vervoorts ◽  
Stefan Burger ◽  
Karina Hemmer ◽  
Gregor Kieslich
Keyword(s):  
High Pressure ◽  
State Of The Art ◽  
Energy Landscape ◽  
Structural Flexibility ◽  
Zeolitic Imidazolate Frameworks ◽  
Stimuli Responsive ◽  
X Ray Diffraction ◽  
X Ray ◽  
Open Questions ◽  
Metal Organic

The zeolitic imidazolate frameworks ZIF-8 and ZIF-67 harbour a series of fascinating stimuli responsive properties. Looking at their responsitivity to hydrostatic pressure as stimulus, open questions exist regarding the isotropic compression with non-penetrating pressure transmitting media. By applying a state-of-the-art high-pressure powder X-ray diffraction setup, we revisit the high-pressure behaviour of ZIF-8 and ZIF-67 up to <i>p</i> = 0.4 GPa in small pressure increments. We observe a drastic, reversible change of high-pressure powder X-ray diffraction data at <i>p</i> = 0.3 GPa, discovering large volume structural flexibility in ZIF-8 and ZIF-67. Our results imply a shallow underlying energy landscape in ZIF-8 and ZIF-67, an observation that might point at rich polymorphism of ZIF-8 and ZIF-67, similar to ZIF-4(Zn).<br>

scholarly journals Five-Card AND Computations in Committed Format Using Only Uniform Cyclic Shuffles

2021 ◽  
Author(s):  
Yuta Abe ◽  
Yu-ichi Hayashi ◽  
Takaaki Mizuki ◽  
Hideaki Sone
Keyword(s):  
State Of The Art ◽  
Research Topic ◽  
The State ◽  
Major Research ◽  
Open Questions

AbstractIn card-based cryptography, designing AND protocols in committed format is a major research topic. The state-of-the-art AND protocol proposed by Koch, Walzer, and Härtel in ASIACRYPT 2015 uses only four cards, which is the minimum permissible number. The minimality of their protocol relies on somewhat complicated shuffles having non-uniform probabilities of possible outcomes. Restricting the allowed shuffles to uniform closed ones entails that, to the best of our knowledge, six cards are sufficient: the six-card AND protocol proposed by Mizuki and Sone in 2009 utilizes the random bisection cut, which is a uniform and cyclic (and hence, closed) shuffle. Thus, a question has arisen: “Can we improve upon this six-card protocol using only uniform closed shuffles?” In other words, the existence or otherwise of a five-card AND protocol in committed format using only uniform closed shuffles has been one of the most important open questions in this field. In this paper, we answer the question affirmatively by designing five-card committed-format AND protocols using only uniform cyclic shuffles. The shuffles that our protocols use are the random cut and random bisection cut, both of which are uniform cyclic shuffles and can be easily implemented by humans.

scholarly journals MLatom 2: An Integrative Platform for Atomistic Machine Learning

2021 ◽  
Vol 379 (4) ◽  
Author(s):  
Pavlo O. Dral ◽  
Fuchun Ge ◽  
Bao-Xin Xue ◽  
Yi-Fan Hou ◽  
Max Pinheiro ◽  
...  
Keyword(s):  
Machine Learning ◽  
Software Package ◽  
State Of The Art ◽  
Kernel Method ◽  
Automatic Learning ◽  
Hyperparameter Optimization ◽  
Input And Output ◽  
Spectrum Simulation ◽  
Farthest Point ◽  
Theoretical Foundations

AbstractAtomistic machine learning (AML) simulations are used in chemistry at an ever-increasing pace. A large number of AML models has been developed, but their implementations are scattered among different packages, each with its own conventions for input and output. Thus, here we give an overview of our MLatom 2 software package, which provides an integrative platform for a wide variety of AML simulations by implementing from scratch and interfacing existing software for a range of state-of-the-art models. These include kernel method-based model types such as KREG (native implementation), sGDML, and GAP-SOAP as well as neural-network-based model types such as ANI, DeepPot-SE, and PhysNet. The theoretical foundations behind these methods are overviewed too. The modular structure of MLatom allows for easy extension to more AML model types. MLatom 2 also has many other capabilities useful for AML simulations, such as the support of custom descriptors, farthest-point and structure-based sampling, hyperparameter optimization, model evaluation, and automatic learning curve generation. It can also be used for such multi-step tasks as Δ-learning, self-correction approaches, and absorption spectrum simulation within the machine-learning nuclear-ensemble approach. Several of these MLatom 2 capabilities are showcased in application examples.

Detection of Blood-Related Diseases Using Deep Neural Nets

2019 ◽  
pp. 1-15
Author(s):  
Rajithkumar B. K. ◽  
Shilpa D. R. ◽  
Uma B. V.
Keyword(s):  
Image Processing ◽  
Biomedical Engineering ◽  
Medical Diagnosis ◽  
Blood Smear ◽  
Neural Nets ◽  
Laboratory Methods ◽  
Intelligent Algorithms ◽  
Image Processing Techniques ◽  
Processing Techniques ◽  
Traditional Image

Image processing offers medical diagnosis and it overcomes the shortcomings faced by traditional laboratory methods with the help of intelligent algorithms. It is also useful for remote quality control and consultations. As machine learning is stepping into biomedical engineering, there is a huge demand for devices which are intelligent and accurate enough to target the diseases. The platelet count in a blood sample can be done by extrapolating the number of platelets counted in the blood smear. Deep neural nets use multiple layers of filtering and automated feature extraction and detection and can overcome the hurdle of devising complex algorithms to extract features for each type of disease. So, this chapter deals with the usage of deep neural networks for the image classification and platelets count. The method of using deep neural nets has increased the accuracy of detecting the disease and greater efficiency compared to traditional image processing techniques. The method can be further expanded to other forms of diseases which can be detected through blood samples.

Effective Multi-Label Classification Using Data Preprocessing

2021 ◽  
pp. 90-109
Author(s):  
Vaishali S. Tidake ◽  
Shirish S. Sane
Keyword(s):  
Hamming Distance ◽  
State Of The Art ◽  
Nearest Neighbors ◽  
Data Preprocessing ◽  
The State ◽  
Distance Metrics ◽  
Feature Similarity ◽  
Improved Performance ◽  
Using Data

Usage of feature similarity is expected when the nearest neighbors are to be explored. Examples in multi-label datasets are associated with multiple labels. Hence, the use of label dissimilarity accompanied by feature similarity may reveal better neighbors. Information extracted from such neighbors is explored by devised MLFLD and MLFLD-MAXP algorithms. Among three distance metrics used for computation of label dissimilarity, Hamming distance has shown the most improved performance and hence used for further evaluation. The performance of implemented algorithms is compared with the state-of-the-art MLkNN algorithm. They showed an improvement for some datasets only. This chapter introduces parameters MLE and skew. MLE, skew, along with outlier parameter help to analyze multi-label and imbalanced nature of datasets. Investigation of datasets for various parameters and experimentation explored the need for data preprocessing for removing outliers. It revealed an improvement in the performance of implemented algorithms for all measures, and effectiveness is empirically validated.

Radiocarbon Dating and Egyptian Chronology—From the “Curve of Knowns” to Bayesian Modeling

2016 ◽  
Author(s):  
Felix Höflmayer
Keyword(s):  
Bayesian Modeling ◽  
Radiocarbon Dating ◽  
State Of The Art ◽  
Near Eastern ◽  
Open Questions ◽  
The World ◽  
Dating Method ◽  
To Come ◽  
Almost All ◽  
Egyptian Archaeology

Radiocarbon dating has become a standard dating method in archaeology almost all over the world. However, in the field of Egyptology and Near Eastern archaeology, the method is still not fully appreciated. Recent years have seen several major radiocarbon projects addressing Egyptian archaeology and chronology that have led to an intensified discussion regarding the application of radiocarbon dating within the field of Egyptology. This chapter reviews the contribution of radiocarbon dating to the discipline of Egyptology, discusses state-of-the-art applications and their impact on archaeological as well as chronological questions, and presents open questions that will be addressed in the years to come.

scholarly journals Transfer Incremental Learning Using Data Augmentation

2018 ◽  
Vol 8 (12) ◽  
pp. 2512 ◽  
Author(s):  
Ghouthi Boukli Hacene ◽  
Vincent Gripon ◽  
Nicolas Farrugia ◽  
Matthieu Arzel ◽  
Michel Jezequel
Keyword(s):  
Incremental Learning ◽  
Deep Neural Networks ◽  
Data Augmentation ◽  
State Of The Art ◽  
Low Complexity ◽  
Computational Power ◽  
Learning Problem ◽  
Learning Techniques ◽  
Using Data ◽  
Selection Of

Deep learning-based methods have reached state of the art performances, relying on a large quantity of available data and computational power. Such methods still remain highly inappropriate when facing a major open machine learning problem, which consists of learning incrementally new classes and examples over time. Combining the outstanding performances of Deep Neural Networks (DNNs) with the flexibility of incremental learning techniques is a promising venue of research. In this contribution, we introduce Transfer Incremental Learning using Data Augmentation (TILDA). TILDA is based on pre-trained DNNs as feature extractors, robust selection of feature vectors in subspaces using a nearest-class-mean based technique, majority votes and data augmentation at both the training and the prediction stages. Experiments on challenging vision datasets demonstrate the ability of the proposed method for low complexity incremental learning, while achieving significantly better accuracy than existing incremental counterparts.

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