varunr14/newsummary huggingface dataset:
Numerous facets of scientific research implicitly or explicitly call for the estimation of probability densities . Histograms and kernel density estimates are two commonly used techniques for estimating such information with the KDE generally providing a higher fidelity representation of the probability density function . Both methods require specification of either a bin width or a kernel bandwidth . While techniques exist for choosing the kernel bandwidth optimally and objectively they are computationally intensive since they require repeated calculation of the KDE . A solution for objectively and optimally choosing both the kernel shape and width has recently been developed by Bernacchia and Pigolotti . While this solution theoretically applies to multidimensional KDEs it has not been clear how to practically do so . A method for practically extending the Bernacchia Pigolotti KDE to multidimensions is introduced . This multidimensional extension is combined with a recently developed computational improvement to their method that makes it computationally efficient a 2D KDE on 10 samples only takes 1 s on a modern workstation . This fast and objective KDE method called the fastKDE method retains the excellent statistical convergence properties that have been demonstrated for univariate samples . The fastKDE method exhibits statistical accuracy that is comparable to state of the science KDE methods publicly available in and it produces kernel density estimates several orders of magnitude faster . The fastKDE method does an excellent job of encoding covariance information for bivariate samples . This property allows for direct calculation of conditional PDFs with fastKDE . It is demonstrated how this capability might be leveraged for detecting non trivial relationships between quantities in physical systems such as transitional behavior . 


Summary: A multidimensional fast and robust kernel density estimation is proposed fastKDE. fastKDE has statistical performance comparable to state of the science kernel density estimate packages in R. fastKDE is demonstrably orders of magnitude faster than comparable state of the science density estimate packages in R. A Python based implementation of fastKDE is available at https bitbucket.org lbl cascade fastkde.
Insulin pharmacokinetics is not well understood during continuous subcutaneous insulin infusion in type 2 diabetes . We analyzed data collected in 11 subjects with T2D years BMI 30.1kg m2 fasting C peptide 1002.2pmol l fasting plasma glucose 9.6mmol l diabetes duration 8.0 years and HbA1c 8.3 mean who underwent a 24 h study investigating closed loop insulin delivery at the Wellcome Trust Clinical Research Facility Cambridge UK . Subcutaneous delivery of insulin lispro was modulated every 15min according to a model predictive control algorithm . Two complementary insulin assays facilitated discrimination between exogenous and endogenous plasma insulin concentrations measured every 15 60min . Lispro pharmacokinetics was represented by a linear two compartment model whilst parameters were estimated using a Bayesian approach applying a closed form model solution . The time to peak of lispro absorption was 109.6 min and the metabolic clearance rate 1.26 10 2 l kg min . MCR was negatively correlated with fasting C peptide and with fasting plasma insulin concentration . In conclusion compartmental modelling adequately represents lispro kinetics during continuous subcutaneous insulin infusion in T2D . Fasting plasma C peptide or fasting insulin may be predictive of lispro metabolic clearance rate in T2D but further investigations are warranted . 


Summary: A model described lispro PK during closed loop insulin delivery in T2D. The linear two compartment model fitted well the data. Time to peak of lispro and its metabolic clearance rate MCR were estimated. Relationships between PK and metabolic demographic data were examined. Negative correlation found between lispro MCR and fasting insulin C peptide.
Corn height measured manually has shown promising results in improving the relationship between active optical sensor readings and crop yield . Manual measurement of corn height is not practical in US commercial corn production so an alternative automatic method must be found in order to capture the benefit of including canopy height into in season yield estimates and from there into in season nitrogen fertilizer applications . One existing alternative to measure canopy height is an acoustic height sensor . A commercial acoustic height sensor was utilized in these experiments at two corn growth stages along with AO sensors . Eight corn N rate sites in North Dakota USA were used to compare the acoustic height sensor as a practical alternative to manual height measurements as an additional parameter to increase the relationship between AO sensor readings and corn yield . Six N treatments 0 45 90 134 179 and 224kgha 1 were applied before planting in a randomized complete block experimental design with four replications . Height measurement using the acoustic sensor provided an improved yield relationship compared to manual height at all locations . The level of improvement of the relationship between AO readings multiplied by acoustic sensor readings and yield was greater at V6 growth stage compared to the V12 growth stage . At V12 corn height measured manually and with the acoustic sensor multiplied by AO readings provided similar improvement to the relationship with yield compared to relating AO readings alone with yield at most locations . The acoustic height sensor may be useful in increasing the usefulness of AO sensor corn yield prediction algorithms for use in on the go in season N application to corn particularly if the sensor height is normalized within site before combining multiple locations . 


Summary: An acoustic height sensor could be used with the INSEY value to improve corn in season N management. The height sensor was especially useful at earlier and later growth stages depending on the site. Normalizing height aided the relationships with active optical sensor INSEY when combining sites.
Background Overall survival and progression free survival are key outcome measures for head and neck cancer as they reflect treatment efficacy and have implications for patients and health services . The UK has recently developed a series of national cancer audits which aim to estimate survival and recurrence by relying on institutions manually submitting interval data on patient status a labour intensive method . However nationally data are routinely collected on hospital admissions surgery radiotherapy and chemotherapy . We have developed a technique to automate the interpretation of these routine datasets allowing us to derive patterns of treatment in head and neck cancer patients from routinely acquired data . Methods We identified 122 patients with head and neck cancer and extracted treatment histories from hospital notes to provide a gold standard dataset . We obtained routinely collected local data on inpatient admission and procedures chemotherapy and radiotherapy for these patients and analysed them with a computer algorithm which identified relevant time points and then calculated OS and PFS . We validated these by comparison with the gold standard dataset . The algorithm was then optimised to maximise correct identification of each timepoint and minimise false identification of recurrence events . Results Of the 122 patients 82 had locally advanced disease . OS was 88 at 1 year and 77 at 2 years and PFS was 75 and 66 at 1 and 2 years . 40 patients developed recurrent disease . Our automated method provided an estimated OS of 87 and 77 and PFS of 87 and 78 at 1 and 2 years 98 and 82 of patients showed good agreement between the automated technique and Gold standard dataset of OS and PFS respectively . The automated technique correctly assigned recurrence in 101 out of 122 of the patients 21 of the 40 patients with recurrent disease were correctly identified 19 were too unwell to receive further treatment and were missed . Of the 82 patients who did not develop a recurrence 77 were correctly identified and 2 were incorrectly identified as having recurrent disease when they did not . Conclusions We have demonstrated that our algorithm can be used to automate the interpretation of routine datasets to extract survival information for this sample of patients . It currently underestimates recurrence rates due to many patients not being well enough to be treated for recurrent disease . With some further optimisation this technique could be extended to a national level providing a new approach to measuring outcomes on a larger scale than is currently possible . This could have implications for healthcare provision and policy for a range of different disease types . 


Summary: Routinely collected data can estimate rates of disease progression progression free and overall survival in patients with head neck cancer. Estimates of overall survival are accurate. Estimates of recurrence and progression free survival are less accurate. Patients who do not have treatment for recurrent disease may be missed by this approach.
Quantile inference with adjustment for covariates has not been widely investigated on competing risks data . We propose covariate adjusted quantile inferences based on the cause specific proportional hazards regression of the cumulative incidence function . We develop the construction of confidence intervals for quantiles of the cumulative incidence function given a value of covariates and for the difference of quantiles based on the cumulative incidence functions between two treatment groups with common covariates . Simulation studies show that the procedures perform well . We illustrate the proposed methods using early stage breast cancer data . 


Summary: Quantile inferences with adjustment for covariates are proposed. Quantiles are defined based on regression of the cumulative incidence function. The construction of confidence intervals for quantiles is developed. A confidence interval for the difference of two quantiles is derived.
In this paper the interpolation of two data points and two tangent directions with spatial cubic rational PH curves is considered . It is shown that interpolants exist for any true spatial data configuration . The equations that determine the interpolants are derived by combining a closed form representation of a ten parametric family of rational PH cubics given in Kozak et al . and the Gram matrix approach . The existence of a solution is proven by using a homotopy analysis and numerical method to compute solutions is proposed . In contrast to polynomial PH cubics for which the range of data admitting the existence of interpolants is limited a switch to rationals provides an interpolation scheme with no restrictions . 


Summary: interpolation scheme with spatial rational PH cubics is presented. The existence of the interpolant is proven for any true spatial data configurations. Homotopy analysis and a Gram matrix approach are used. Numerical method to compute the solutions is proposed.
This paper proposes a fast weighted horizontal visibility graph constructing algorithm to identify seizure from EEG signals . The performance of the FWHVA is evaluated by comparing with Fast Fourier Transform and sample entropy method . Two noise robustness graph features based on the FWHVA mean degree and mean strength are investigated using two chaos signals and five groups of EEG signals . Experimental results show that feature extraction using the FWHVA is faster than that of SampEn and FFT . And mean strength feature associated with ictal EEG is significant higher than that of healthy and inter ictal EEGs . In addition an 100 classification accuracy for identifying seizure from healthy shows that the features based on the FWHVA are more promising than the frequency features based on FFT and entropy indices based on SampEn for time series classification . 


Summary: Developing a fast algorithm for constructing a network from a time series in linear time. Discriminating between healthy and seizure EEG signals with 100 accuracy with only two features. Extracted features from a time series is faster and more robust to against noise than those based on FFT.
Smart farming is a management style that includes smart monitoring planning and control of agricultural processes . This management style requires the use of a wide variety of software and hardware systems from multiple vendors . Adoption of smart farming is hampered because of a poor interoperability and data exchange between ICT components hindering integration . Software Ecosystems is a recent emerging concept in software engineering that addresses these integration challenges . Currently several Software Ecosystems for farming are emerging . To guide and accelerate these developments this paper provides a reference architecture for Farm Software Ecosystems . This reference architecture should be used to map assess design and implement Farm Software Ecosystems . A key feature of this architecture is a particular configuration approach to connect ICT components developed by multiple vendors in a meaningful feasible and coherent way . The reference architecture is evaluated by verification of the design with the requirements and by mapping two existing Farm Software Ecosystems using the Farm Software Ecosystem Reference Architecture . This mapping showed that the reference architecture provides insight into Farm Software Ecosystems as it can describe similarities and differences . A main conclusion is that the two existing Farm Software Ecosystems can improve configuration of different ICT components . Future research is needed to enhance configuration in Farm Software Ecosystems . 


Summary: We mould the concept Software Ecosystems to the agricultural domain. We propose a reference architecture for Farm Software Ecosystems. Our reference architecture describes an organizational and technical infrastructure. We motivate that our reference architecture can improve farm enterprise integration. Our reference architecture is used to review some existing initiatives.
Since falls are a major public health problem in an aging society there is considerable demand for low cost fall detection systems . One of the main reasons for non acceptance of the currently available solutions by seniors is that the fall detectors using only inertial sensors generate too much false alarms . This means that some daily activities are erroneously signaled as fall which in turn leads to frustration of the users . In this paper we present how to design and implement a low cost system for reliable fall detection with very low false alarm ratio . The detection of the fall is done on the basis of accelerometric data and depth maps . A tri axial accelerometer is used to indicate the potential fall as well as to indicate whether the person is in motion . If the measured acceleration is higher than an assumed threshold value the algorithm extracts the person calculates the features and then executes the SVM based classifier to authenticate the fall alarm . It is a 365 7 24 embedded system permitting unobtrusive fall detection as well as preserving privacy of the user . 


Summary: An embedded system for fully automatic fall detection. A method to achieve reliable fall detection with low false alarm ratio. An algorithm with low computational demands for person extraction in depth images. A freely available database for evaluation of fall detection consisting of both accelerometric and depth data.
This paper presents a novel method for QRS detection in electrocardiograms . It is based on the S Transform a new time frequency representation . The S Transform provides frequency dependent resolution while maintaining a direct relationship with the Fourier spectrum . We exploit the advantages of the S Transform to isolate the QRS complexes in the time frequency domain . Shannon energy of each obtained local spectrum is then computed in order to localize the R waves in the time domain . Significant performance enhancement is confirmed when the proposed approach is tested with the MIT BIH arrhythmia database . The obtained results show a sensitivity of 99.84 a positive predictivity of 99.91 and an error rate of 0.25 . Furthermore to be more convincing the authors illustrated the detection parameters in the case of certain ECG segments with complicated patterns . 


Summary: This work makes a QRS detection based on S Transform TS . The TS allows us access to the frequency content of the QRS complexes without recourse to any filtering. Shannon energy is computed for each obtained local spectrum. Shannon energy highlights all QRSs even those having a small amplitude.
Electrocardiography has been recently proposed as biometric trait for identification purposes . Intra individual variations of ECG might affect identification performance . These variations are mainly due to Heart Rate Variability . In particular HRV causes changes in the QT intervals along the ECG waveforms . This work is aimed at analysing the influence of seven QT interval correction methods on the performance of ECG fiducial based identification systems . In addition we have also considered the influence of training set size classifier classifier ensemble as well as the number of consecutive heartbeats in a majority voting scheme . The ECG signals used in this study were collected from thirty nine subjects within the Physionet open access database . Public domain software was used for fiducial points detection . Results suggested that QT correction is indeed required to improve the performance . However there is no clear choice among the seven explored approaches for QT correction . MultiLayer Perceptron and Support Vector Machine seemed to have better generalization capabilities in terms of classification performance with respect to Decision Tree based classifiers . No such strong influence of the training set size and the number of consecutive heartbeats has been observed on the majority voting scheme . 


Summary: Electrocardiography ECG has been recently proposed as biometric trait for identification purposes. The influence of the QT interval correction method on the performance of ECG based identification systems is analyzed. ECG signals were collected from the Physionet open access database. A public domain software was used for fiducial points detection. Results suggested that QT correction is always required to improve the performance.
A normal cardiac activation starts in the sinoatrial node and then spreads throughout the atrial myocardium thus defining the P wave of the electrocardiogram . However when the onset of paroxysmal atrial fibrillation approximates a highly disturbed electrical activity occurs within the atria thus provoking fragmented and eventually longer P waves . Although this altered atrial conduction has been successfully quantified just before PAF onset from the signal averaged P wave spectral analysis its evolution during the hours preceding the arrhythmia has not been assessed yet . This work focuses on quantifying the P wave spectral content variability over the 2h preceding PAF onset with the aim of anticipating as much as possible the arrhythmic episode envision . For that purpose the time course of several metrics estimating absolute energy and ratios of high to low frequency power in different bands between 20 and 200Hz has been computed from the P wave autoregressive spectral estimation . All the analyzed metrics showed an increasing variability trend as PAF onset approximated providing the P wave high frequency energy a diagnostic accuracy around 80 to discern between healthy subjects patients far from PAF and patients less than 1h close to a PAF episode . This discriminant power was similar to that provided by the most classical time domain approach i.e . the P wave duration . Furthermore the linear combination of both metrics improved the diagnostic accuracy up to 88.07 thus constituting a reliable noninvasive harbinger of PAF onset with a reasonable anticipation . The information provided by this methodology could be very useful in clinical practice either to optimize the antiarrhythmic treatment in patients at high risk of PAF onset and to limit drug administration in low risk patients . 


Summary: The P wave spectral content variability over the 2h preceding PAF onset is assessed. The P wave high frequency energy and duration contain complementary information. The onset of PAF is predicted at least with a time in advance of 2h.
A mixture of skew factor analyzers is introduced as well as a family of mixture models based thereon . The particular formulation of the skew distribution used arises as a special case of the generalized hyperbolic distribution . Like their Gaussian and distribution analogues mixtures of skew factor analyzers are very well suited for model based clustering of high dimensional data . The alternating expectation conditional maximization algorithm is used for model parameter estimation and the Bayesian information criterion is used for model selection . The models are applied to both real and simulated data giving superior clustering results when compared to a well established family of Gaussian mixture models . 


Summary: A mixture of skew factor analyzers is introduced. A family of parsimonious mixtures of skew factor analyzers is developed. The models are well suited for clustering high dimensional data. Mixture components can be asymmetric and or heavy tailed as required. The models can also be applied for classification or discriminant analysis.
The prediction of substantially short survivability in patients is extremely risky . In this study we proposed a probabilistic model using Bayesian network to predict the short survivability of patients with brain metastasis from lung cancer . A nationwide cancer patient database from 1996 to 2010 in Taiwan was used . The cohort consisted of 438 patients with brain metastasis from lung cancer . We utilized synthetic minority over sampling technique to solve the imbalanced property embedded in the problem . The proposed BN was compared with three competitive models namely naive Bayes logistic regression and support vector machine . Statistical analysis showed that performances of BN LR NB and SVM were statistically the same in terms of all indices with low sensitivity when these models were applied on an imbalanced data set . Results also showed that SMOTE can improve the performance of the four models in terms of sensitivity while keeping high accuracy and specificity . Further the proposed BN is more effective as compared with NB LR and SVM from two perspectives the transparency and ability to show the relation of factors affecting brain metastasis from lung cancer it allows decision makers to find the probability despite incomplete evidence and information and the sensitivity of the proposed BN is the highest among all standard machine learning methods . 


Summary: The use of Bayesian network to predict the short survivability of patients. The use of synthetic minority over sampling technique to improve the sensitivity. The proposed model has the highest sensitivity among all benchmark methods.
Current progress in tissue engineering is focused on the creation of environments in which cultures of relevant cells can adhere grow and form functional tissue . We propose a method for controlled chemical and topographical cues through surface patterning of self folding hydrogel films . This provides a conversion of 2D patterning techniques into a viable method of manufacturing a 3D scaffold . While similar bilayers have previously been demonstrated here we present a faster and high throughput process for fabricating self folding hydrogel devices incorporating controllable surface nanotopographies by serial hot embossing of sacrificial layers and photolithography . 


Summary: A novel and fast sacrificial layer embossing method for nanopatterning gel films. Method creates nanopatterned environmentally responsive hydrogel films. Double sided micro and nanopatterned gel films are produced in one stage. Films will roll and unroll in response to changes in aqueous pH. Produces sub millimetre injectable and patterned cylindrical tissue scaffolds.