jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Anastasiia Onyshchenko
Source: Journal of the Arkansas Academy of Science (article, 2018-01-01)
Field: Materials Science — Biomaterials

Phylogeny and Evolutionary Genomics of Non-Photosynthetic Diatoms Diatoms are prolific photosynthesizers responsible for some 20% of global primary production. In real terms, the oxygen in one of every five breaths traces back to photosynthesis by marine diatoms. Among the tens of thousands of diatom species, a small handful of colorless diatom species in the genus Nitzschia have lost photosynthesis altogether and rely exclusively on extracellular organic carbon for growth. I used DNA sequence data to reconstruct the phylogeny of this group, and found that nonphotosynthetic diatoms are monophyletic, indicating that photosynthesis was lost just one time over the course of some 200 million years of diatom evolution. Carbon metabolism in nonphotosynthetic diatoms, including the exact source of carbon used by these species, has not been fully characterized. We sequenced the nuclear genome of one species and used it to develop a comprehensive model of central carbon metabolism. Preliminary analysis of Nitzschia metabolism showed that it generally matches to the pattern of previously reported diatom metabolic networks. As well we found some hints regarding Nitzschia external carbon acquisition which possibly can help to explain its heterotrophic mode of life. Overall, this study has provided novel insights into the evolutionary origin and metabolism of non-photosynthetic diatoms, which are unique among diatoms in their ability to sustain their growth solely from extracellular carbon. Phylogenetics ; Genomics ; Evolutionary biology ; Biology ; Genome ; Genetics ; Gene Diatoms and Algae Research
Authors: Joris Roels | Jonas De Vylder | Jan Aelterman | Yvan Saeys | Wilfried Philips
Source: arXiv (Cornell University) (preprint, 2018-10-23)
Field: Materials Science — Surfaces, Coatings and Films

Convolutional Neural Network Pruning to Accelerate Membrane Segmentation\n in Electron Microscopy Biological membranes are one of the most basic structures and regions of\ninterest in cell biology. In the study of membranes, segment extraction is a\nwell-known and difficult problem because of impeding noise, directional and\nthickness variability, etc. Recent advances in electron microscopy membrane\nsegmentation are able to cope with such difficulties by training convolutional\nneural networks. However, because of the massive amount of features that have\nto be extracted while propagating forward, the practical usability diminishes,\neven with state-of-the-art GPU's. A significant part of these network features\ntypically contains redundancy through correlation and sparsity. In this work,\nwe propose a pruning method for convolutional neural networks that ensures the\ntraining loss increase is minimized. We show that the pruned networks, after\nretraining, are more efficient in terms of time and memory, without\nsignificantly affecting the network accuracy. This way, we manage to obtain\nreal-time membrane segmentation performance, for our specific electron\nmicroscopy setup.\n Convolutional neural network ; Pruning ; Computer science ; Redundancy (engineering) ; Segmentation ; Artificial intelligence ; Membrane ; Artificial neural network ; Pattern recognition (psychology) ; Machine learning ; Chemistry ; Biology Electron and X-Ray Spectroscopy Techniques ; Advanced Electron Microscopy Techniques and Applications ; Integrated Circuits and Semiconductor Failure Analysis
Authors: Joris Roels | Jonas De Vylder | Jan Aelterman | Yvan Saeys | Wilfried Philips
Source: arXiv (Cornell University) (preprint, 2018-10-23)
Field: Materials Science — Surfaces, Coatings and Films

Convolutional Neural Network Pruning to Accelerate Membrane Segmentation in Electron Microscopy Biological membranes are one of the most basic structures and regions of interest in cell biology. In the study of membranes, segment extraction is a well-known and difficult problem because of impeding noise, directional and thickness variability, etc. Recent advances in electron microscopy membrane segmentation are able to cope with such difficulties by training convolutional neural networks. However, because of the massive amount of features that have to be extracted while propagating forward, the practical usability diminishes, even with state-of-the-art GPU's. A significant part of these network features typically contains redundancy through correlation and sparsity. In this work, we propose a pruning method for convolutional neural networks that ensures the training loss increase is minimized. We show that the pruned networks, after retraining, are more efficient in terms of time and memory, without significantly affecting the network accuracy. This way, we manage to obtain real-time membrane segmentation performance, for our specific electron microscopy setup. Convolutional neural network ; Computer science ; Pruning ; Segmentation ; Redundancy (engineering) ; Artificial intelligence ; Artificial neural network ; Pattern recognition (psychology) ; Deep learning ; Machine learning ; Biology Electron and X-Ray Spectroscopy Techniques ; Advanced Electron Microscopy Techniques and Applications ; Image Processing Techniques and Applications
Authors: Mark Chilenski | Cara Murphy | Gil Raz
Source: ChemRxiv (preprint, 2018-11-02)
Field: Chemistry — Spectroscopy

A Fast Approximation for Adaptive Wavelength Selection for Infrared Chemical Sensors Active mid-infrared spectroscopy with tunable lasers is a leading technology for standoff detection and identification of trace chemicals. Information-theoretic optimal selection of the laser wavelength offers the promise of increased detection confidence at lower abundances and with fewer wavelengths. Reducing the number of wavelengths required enables faster detections and lowers sensor power consumption while keeping the optical power under eye safety limits. This paper presents an approximation to the mutual information which operates ~40000x faster than traditional techniques, thereby making near-optimal real-time sensor control computationally feasible. Application of this technique to synthetic data suggests it can reduce the number of wavelengths needed by a factor of two relative to an evenly-spaced grid, with even higher gains for chemicals with weak signatures. Wavelength ; Infrared ; Computer science ; Selection (genetic algorithm) ; TRACE (psycholinguistics) ; Laser ; Power (physics) ; Identification (biology) ; Optics ; Physics ; Artificial intelligence Spectroscopy and Laser Applications ; Analytical Chemistry and Sensors ; Water Quality Monitoring and Analysis
Authors: Mrunal K. Shirsat Vijay R. Chakote
Source: Zenodo (CERN European Organization for Nuclear Research) (article, 2018-02-16)
Field: Materials Science — Biomaterials

Design And Development Of Matrix Tablet Of Labetalol Hcl By Using Tamarind Seed Polysaccharide As Polymer The objective of present work was to design and develop sustained release matrix tablets of anti-hypertensive drug Labetalol hydrochloride. Hydroxypropyl methyl cellulose K15, Sodium CMC, Xanthan gum and Tamarind seed polysaccharide used as a rate retarding polymer. Whereas Polyvinyl Pyrrolidone and Microcrystalline cellulose are used as granulating agent and diluent. The influence of variable concentration of polymers on the release rate of drug was investigated. The results of the present work point out that the rate of Labetalol hydrochloride release from polymers like Hydroxypropyl methyl cellulose K15, Sodium CMC, Xanthan gum and Tamarind seed polysaccharide are mainly controlled by the drug–polymer ratio. The prepared sustained release matrix tablets were evaluated for various parameters like hardness, friability, uniformity of weight, uniformity of drug content and in vitro drug release studies. Keywords: Hydroxypropyl methyl cellulose K15, Sodium CMC, Xanthan gum and Tamarind seed polysaccharide, Sustained-release, Labetalol hydrochloride Formulation. Labetalol ; Polysaccharide ; Polymer ; Matrix (chemical analysis) ; Materials science ; Polymer science ; Chemical engineering ; Composite material ; Chemistry ; Engineering ; Biology ; Organic chemistry biodegradable polymer synthesis and properties ; Hydrogels: synthesis, properties, applications
Authors: Neelam Chouksey | Gunja Mishra | Sukhwinder Singh | Munneswar Singh | Rajesh Chouksey
Source: International Journal of Current Microbiology and Applied Sciences (article, 2018-07-10)
Field: Chemistry — Analytical Chemistry

Forecasting of Nitrogen Content in the Soil by Hybrid Time Series Model There are several linear time-series forecasting models available in literature. One of the important and widely used technique for analysis of univariate time-series data is Box Jenkins’ Autoregressive integrated moving average (ARIMA) methodology (Box et al., 2007). Sometimes addition of the other exogenous variables increases the prediction accuracy of ARIMA model (ARIMAX). For this aspect we applied different p and q order ARIMAX model for five nutrient combinations of nitrogen content which is further developed by including organic carbons an input (exogenous) variable. Among the linear models the ARIMAX model performed better as compare to ARIMA model. But the performance of machine intelligence techniques like Hybrid of linear and nonlinear model is better as compared to linear time series models. The variations in nitrogen content data for all treatments are large. This could be the reason that nonlinear machine learning techniques can capture the heterogeneous trend in the data set and performed well as compare to ARIMA and ARIMAX. Further the highest forecasted value by hybrid model for nitrogen content for the year 2018, was found to be 270.39 kg/ha by using 100%NPK+FYM treatment combination. On the basis of forecasted value we can say that the combination of recommended dose with farm yard manure might be useful as the best combination for establishing higher nitrogen in the soil. Autoregressive integrated moving average ; Univariate ; Autoregressive model ; Time series ; Mathematics ; Series (stratigraphy) ; Statistics ; Linear model ; Computer science ; Econometrics ; Multivariate statistics Spectroscopy and Chemometric Analyses ; Water Quality Monitoring and Analysis ; Advanced Chemical Sensor Technologies
Authors: Luke B. Godfrey | Michael S. Gashler
Source: arXiv (Cornell University) (preprint, 2018-10-19)
Field: Materials Science — Materials Chemistry

Leveraging Product as an Activation Function in Deep Networks Product unit neural networks (PUNNs) are powerful representational models with a strong theoretical basis, but have proven to be difficult to train with gradient-based optimizers. We present windowed product unit neural networks (WPUNNs), a simple method of leveraging product as a nonlinearity in a neural network. Windowing the product tames the complex gradient surface and enables WPUNNs to learn effectively, solving the problems faced by PUNNs. WPUNNs use product layers between traditional sum layers, capturing the representational power of product units and using the product itself as a nonlinearity. We find the result that this method works as well as traditional nonlinearities like ReLU on the MNIST dataset. We demonstrate that WPUNNs can also generalize gated units in recurrent neural networks, yielding results comparable to LSTM networks. MNIST database ; Artificial neural network ; Product (mathematics) ; Computer science ; Function (biology) ; Simple (philosophy) ; Artificial intelligence ; Activation function ; Nonlinear system ; Deep neural networks ; Machine learning ; Mathematics ; Physics Machine Learning in Materials Science ; Machine Learning and Data Classification ; Neural Networks and Applications
Authors: Purushottam D. Dixit
Source: arXiv (Cornell University) (preprint, 2018-06-13)
Field: Chemistry — Analytical Chemistry

Introducing user-prescribed constraints in Markov chains for nonlinear\n dimensionality reduction Stochastic kernel based dimensionality reduction approaches have become\npopular in the last decade. The central component of many of these methods is a\nsymmetric kernel that quantifies the vicinity between pairs of data points and\na kernel-induced Markov chain on the data. Typically, the Markov chain is fully\nspecified by the kernel through row normalization. However, in many cases, it\nis desirable to impose user-specified stationary-state and dynamical\nconstraints on the Markov chain. Unfortunately, no systematic framework exists\nto impose such user-defined constraints. Here, we introduce a path entropy\nmaximization based approach to derive the transition probabilities of Markov\nchains using a kernel and additional user-specified constraints. We illustrate\nthe usefulness of these Markov chains with examples.\n Markov chain ; Markov kernel ; Computer science ; Variable-order Markov model ; Markov model ; Markov chain mixing time ; Examples of Markov chains ; Kernel (algebra) ; Markov renewal process ; Mathematical optimization ; Mathematics ; Algorithm ; Machine learning ; Discrete mathematics Spectroscopy and Chemometric Analyses ; Neural Networks and Applications
Authors: Sharat Chidambaran | Amir Behjat | Souma Chowdhury
Source: arXiv (Cornell University) (preprint, 2018-07-20)
Field: Materials Science — Materials Chemistry

Multi-criteria Evolution of Neural Network Topologies: Balancing\n Experience and Performance in Autonomous Systems Majority of Artificial Neural Network (ANN) implementations in autonomous\nsystems use a fixed/user-prescribed network topology, leading to sub-optimal\nperformance and low portability. The existing neuro-evolution of augmenting\ntopology or NEAT paradigm offers a powerful alternative by allowing the network\ntopology and the connection weights to be simultaneously optimized through an\nevolutionary process. However, most NEAT implementations allow the\nconsideration of only a single objective. There also persists the question of\nhow to tractably introduce topological diversification that mitigates\noverfitting to training scenarios. To address these gaps, this paper develops a\nmulti-objective neuro-evolution algorithm. While adopting the basic elements of\nNEAT, important modifications are made to the selection, speciation, and\nmutation processes. With the backdrop of small-robot path-planning\napplications, an experience-gain criterion is derived to encapsulate the amount\nof diverse local environment encountered by the system. This criterion\nfacilitates the evolution of genes that support exploration, thereby seeking to\ngeneralize from a smaller set of mission scenarios than possible with\nperformance maximization alone. The effectiveness of the single-objective\n(optimizing performance) and the multi-objective (optimizing performance and\nexperience-gain) neuro-evolution approaches are evaluated on two different\nsmall-robot cases, with ANNs obtained by the multi-objective optimization\nobserved to provide superior performance in unseen scenarios.\n Computer science ; Overfitting ; Network topology ; Implementation ; Maximization ; Distributed computing ; Software portability ; Artificial neural network ; Evolution strategy ; Artificial intelligence ; Evolutionary algorithm ; Process (computing) ; Topology (electrical circuits) ; Mathematical optimization ; Engineering ; Mathematics Machine Learning in Materials Science ; Advanced Neural Network Applications ; Advanced Memory and Neural Computing
Authors: Ludvig Malmros
Source: Lund University Publications Student Papers (Lund University) (article, 2018-01-01)
Field: Chemistry — Analytical Chemistry

Insect Event Extraction in LIDAR Images using Image Analysis and Convolutional Neural Networks Insect monitoring has earlier been a manual, tedious and time consuming task that is impossible to do in real time. Thus there exists a need for a real time automatic insect monitoring system for counting and classifying insect for use in scientiﬁc research and pesticide spraying control. One approach to automatize this is using LIDAR to detect insects. In this thesis it has been explored how to detect, segment and do merge classiﬁcation on insect events showing up in large 2D time-range map frames created from a LIDAR optics setup as insects ﬂy through a laser beam. The suggested extraction method combines simple intensity thresholding techniques and well known edge detection techniques to do the ﬁrst selection of insect event pixels. A statistical dilation iteration stopping criteria based on background noise is suggested for region growing. Together they create a segmentation method that is used to segment insect events while avoiding accidental splitting and merging of events. Thereafter a trained convolutional neural network is suggested to classify all events that might have been merged, such that they could be discarded instead of being inputted to the species classiﬁcation system. Tests and observations indicate that the old segmentation method ﬁnds close to all the wanted insect events, but over-segments them drastically in some cases. By dividing the method into one event detection and one border ﬁnding part, the suggested extraction method are able to ﬁnd the same amount of events without increasing the number of splits and merges. At the same time it is able to ﬁnd event segmentation borders with a higher precision then previously possible. Tests on the merge classiﬁcation indicate surprisingly good results for the ability to classify event merges. Creating artiﬁcial merges to handle the imbalanced data set shows further improvement, while oversampling does not. Indicated is also that the size compression used does not seem to eﬀect the classiﬁcation negatively. Artificial intelligence ; Computer science ; Segmentation ; Convolutional neural network ; Pattern recognition (psychology) ; Thresholding ; Computer vision ; Lidar ; Pixel ; Event (particle physics) ; Image segmentation ; Remote sensing ; Geography ; Image (mathematics) Spectroscopy and Chemometric Analyses ; Smart Agriculture and AI ; Remote Sensing in Agriculture
Authors: Moritz Zaiß | A Deshmane | Mark Schuppert | Kai Herz | Philipp Ehses | Tobias Lindig | Benjamin Bender | Ulrike Ernemann | Klaus Scheffler
Source: arXiv (Cornell University) (preprint, 2018-08-30)
Field: Materials Science — Materials Chemistry

DeepCEST: 9.4 T Chemical Exchange Saturation Transfer MRI contrast\n predicted from 3 T data - a proof of concept study Purpose: Separation of different CEST signals in the Z-spectrum is a\nchallenge especially at low field strengths where amide, amine, and NOE peaks\ncoalesce with each other or with the water peak. The purpose of this work is to\ninvestigate if the information in 3T spectra can be extracted by a deep\nlearning approach trained by 9.4T human brain target data. Methods:\nHighly-spectrally-resolved Z-spectra from the same volunteer were acquired by\n3D-snapshot CEST MRI at 3 T and 9.4 T with similar saturation schemes. The\nvolume-registered 3 T Z-spectra-stack was then used as input data for a 3-layer\ndeep neural network with the volume-registered 9.4 T fitted parameter stack as\ntarget data. The neural network was optimized and applied to training data, to\nunseen data from a different volunteer, and as well to a tumor patient data\nset. Results: A useful neural net architecture could be found and verified in\nhealthy volunteers. The principle gray-/white matter contrast of the different\nCEST effects was predicted with only small deviations. The 9.4 T prediction was\nless noisy compared to the directly measured CEST maps, however at the cost of\nslightly lower tissue contrast. Application to a tumor patient measured at 3 T\nand 9.4 T revealed that tumorous tissue Z-spectra and corresponding\nhyper/hypo-intensities of different CEST effects can also be predicted.\nConclusion: Deep learning might be a powerful tool for CEST data processing and\ndeepCEST could bring the benefits and insights of the few ultra-high field\nsites to a broader clinical use. Vice versa deepCEST might help for determining\nwhich subjects are good candidates to measure additionally at UHF.\n Artificial intelligence ; Nuclear magnetic resonance ; Artificial neural network ; Computer science ; Spectral line ; Data set ; Deep learning ; Contrast (vision) ; Transfer of learning ; Pattern recognition (psychology) ; Chemistry ; Physics Lanthanide and Transition Metal Complexes ; Advanced MRI Techniques and Applications ; Electron Spin Resonance Studies
Authors: Vijay Ramalingam | A. Pandian | K. Manikandan | Neela Mega Samala Roobanvaikundaraja | . .
Source: International Journal of Engineering & Technology (article, 2018-11-27)
Field: Chemistry — Analytical Chemistry

Predicting the Blood Sugar using Machine Learning Approach Background/Objective: This study is performed for predicting the blood sugar level by machine learning classifier techniques and identifies the best techniques among the techniques. Diabetes mellitus known as Diabetes caused due to increase in the blood glucose level. This will be impacting the pancreas and will be affecting the body beta cells. The beta cells play the major role in converting the glucose into energy. If the blood glucose remain undiagnosed for longer tenure, can cause various health complications like cardiovascular diseases neuropathy, nephropathy, organ failures and Eye disease. Diagnosing the sugar level in the earlier stage can help to prevent and control the health issues and save our life. Methods/Statistical Analysis: There are many classifiers already available in the computerized system. This analysis is to compare and identify the best classification technique between the classifier techniques like Naïve Bayes (NB), Decision Tree (DT), Support Vector Machine (SVM), Classification and Regression Tree (CART), K – Nearest Neighbour (K-NN) and C5. Findings: In 2017 the International Diabetes Federation had anticipated that 50% of the people in the world with the age group of 20 to 79 are not worried about their Diabetes and not aware that they may be impacted by Diabetes in near future. It also explained that 76.5% of Diabetic patients are from very low-income countries. These are not yet diagnosed if you really speaking that they don’t know that they have Diabetes. So, there is a need to diagnose and monitor the diabetes to support and help them to cure it. Keeping that in mind there are various classification techniques used to predict the diabetes. The main objective of this study is to make a comparative study and identify the best classifier which is consistent among the various datasets. The datasets used for this comparative study is University of California, Irvine machine learning repository (UCI) and PIMA Indian Diabetes Dataset. Application/ Improvements: In this study, the R-Studio application tool is used for developing and comparing the classifiers. Computer science ; Sugar ; Artificial intelligence ; Machine learning ; Blood sugar ; Chemistry ; Medicine ; Food science ; Diabetes mellitus ; Endocrinology Spectroscopy and Chemometric Analyses ; Natural Products and Biological Research