varunr14/newsummary huggingface dataset:
Deception is an inevitable component of human interaction . Researchers and practitioners are developing information systems to aid in the detection of deceptive communication . Information systems are typically adopted by end users to aid in completing a goal or objective . However end user interactions with deception detection systems are unique because the goals of the system and the user are orthogonal . Prior work investigating systems based deception detection has focused on the identification of reliable deception indicators . This research extends extant work by looking at how users of deception detection systems alter their behavior in response to the presence of guilty knowledge relevant stimuli and system knowledge . An analysis of data collected during two laboratory experiments reveals that guilty knowledge relevant stimuli and system knowledge all lead to increased use of countermeasures . The implications and limitations of this research are discussed and avenues for future research are outlined . 


Summary: We present adversarial systems as a novel growing area of IS research. Knowledge of a deception system s operations increases countermeasure use. Presenting deceivers with relevant stimuli increases countermeasure use. Truth tellers use countermeasures when aware of the system s functionality. An extensive set of novel countermeasures is identified.
A methodology based on adaptive likelihood ratios for the detection of emerging disease clusters is presented . The martingale structure of the regular likelihood ratio is preserved by the ALR . The upper limit for the false alarm rate of the proposed method depends only on the quantity of evaluated cluster candidates . Thus Monte Carlo simulations are not required to validate the procedures statistical significance allowing the construction of a fast computational algorithm to detect clusters . The number of evaluated clusters is also significantly reduced through the use of an adaptive approach to prune many unpromising clusters . This further increases the computational speed . Performance is evaluated through simulations to measure the average detection delay and the probability of correct cluster detection . We present applications for thyroid cancer in New Mexico and hanseniasis in children in the Brazilian Amazon . 


Summary: Fast computational algorithm to detect space time clusters. Methodology based on adaptive likelihood ratio. Utilizes an adaptive estimator in the configuration space of spatial clusters. The method has a low delay to detect space time clusters. Allows flexibility in the cluster shape.
Detailed and timely information on crop area production and yield is important for the assessment of environmental impacts of agriculture for the monitoring of the land use and management practices and for food security early warning systems . A machine learning approach is proposed to model crop rotations which can predict with good accuracy at the beginning of the agricultural season the crops most likely to be present in a given field using the crop sequence of the previous 3 5years . The approach is able to learn from data and to integrate expert knowledge represented as first order logic rules . Its accuracy is assessed using the French Land Parcel Information System implemented in the frame of the EU s Common Agricultural Policy . This assessment is done using different settings in terms of temporal depth and spatial generalization coverage . The obtained results show that the proposed approach is able to predict the crop type of each field before the beginning of the crop season with an accuracy as high as 60 which is better than the results obtained with current approaches based on remote sensing imagery . 


Summary: A prediction model for crop rotations is proposed. The model uses machine learning techniques applied to historic data. It allows the introduction of expert knowledge without re learning from data. The model is assessed on real data over several years and a large area.
Objective Stroke is a prominent life threatening disease in the world . The current study was performed to predict the outcome of stroke using knowledge discovery process methods artificial neural networks and support vector machine models . Materials and methods The records of 297 individuals were acquired from the databases of the department of emergency medicine . Nine predictors the levels of cholesterol and C reactive protein were used for predicting the stroke . Feature selection based on the Cramer s V test was carried out for reducing the predictors . Multilayer perceptron ANN and SVM with radial basis function kernel were used for the prediction based on the selected predictors . Results The accuracy values were 81.82 for ANN and 80.38 for SVM in the training dataset and 85.9 for ANN and 84.62 for SVM in the testing dataset respectively . ANN and SVM models yielded area under curve values of 0.905 and 0.899 in the training dataset and 0.928 and 0.91 in the testing dataset consecutively . Conclusion The findings of the current study pointed out that ANN had more predictive performance when compared with SVM in predicting stroke . The proposed ANN model would be useful when making clinical decisions regarding stroke . 


Summary: We predicted the outcome of stroke using knowledge discovery process KDP methods Artificial Neural Networks ANN and Support Vector Machine SVM models. The performance of the models assessed as accuracy and area under curve AUC for predicting stroke was 85.9 and 0.928 for ANN and 84.62 and 0.91 for SVM respectively. KDP was used for providing better understanding of the stroke data and reducing unnecessary processes to be performed. The proposed ANN model predicted successfully the stroke disease based on the selected predictors.
High dielectric gate stacks comprising HfO2 were fabricated on Ge with alumina as the barrier level . This was achieved by thermal annealing in an ultra high vacuum to remove the native oxide followed by deposition of aluminium by molecular beam epitaxy . After in situ oxidation at ambient temperature HfO2 was deposited by atomic layer deposition . The devices underwent physical and electrical characterisation and show low EOT down to 1.3nm low leakage current of less than 10 7 Acm 2 at 1V and CV hysteresis of 10mV . 


Summary: Ge gate MOS devices were investigated due to its applicability to sub 16nm node pMOST technology. GeO2 Al2O3 HfO2 gate stacks were grown by a combined MBE and ALD technique. Devices were characterised by various physical and electrical methods. Devices were found to have low EOT and leakage currents and have small hysteresis.
Testing whether two or more independent samples arise from a common distribution is a classic problem in statistics . Several multivariate two sample tests of equality are based on graphs such as the minimum spanning tree nearest neighbor and optimal nonbipartite perfect matching . Here the samples are pooled and the test statistic is the number of edges in the graph that connect points with different sample identities . These tests are typically unbiased and perform well when estimates of underlying probability densities are poor . However these tests have not been thoroughly studied when data is very high dimensional or in the multisample case . We introduce the use of orthogonal perfect matchings for testing equality in distribution . A suite of Monte Carlo simulations on artificial and real data shows that orthogonal perfect matchings and spanning trees typically have higher power than other graphs and are also more effective at discerning when samples have differences in their covariance structure compared to other nonparametric tests such as the energy and triangle tests . 


Summary: We test whether two or more samples have arisen from the same multivariate density. Test uses new graphs as well as the minimum spanning tree or nearest neighbors. Test performs well and is easy to perform for large small datasets. Power of the new tests is competitive or beats other general multivariate tests. Mean and variance of the asymptotically normal null distribution are easy to compute.
We develop an autonomous system to detect and evaluate physical therapy exercises using wearable motion sensors . We propose the multi template multi match dynamic time warping algorithm as a natural extension of DTW to detect multiple occurrences of more than one exercise type in the recording of a physical therapy session . While allowing some distortion in time the algorithm provides a quantitative measure of similarity between an exercise execution and previously recorded templates based on DTW distance . It can detect and classify the exercise types and count and evaluate the exercises as correctly incorrectly performed identifying the error type if any . To evaluate the algorithm s performance we record a data set consisting of one reference template and 10 test executions of three execution types of eight exercises performed by five subjects . We thus record a total of 120 and 1200 exercise executions in the reference and test sets respectively . The test sequences also contain idle time intervals . The accuracy of the proposed algorithm is 93.46 for exercise classification only and 88.65 for simultaneous exercise and execution type classification . The algorithm misses 8.58 of the exercise executions and demonstrates a false alarm rate of 4.91 caused by some idle time intervals being incorrectly recognized as exercise executions . To test the robustness of the system to unknown exercises we employ leave one exercise out cross validation . This results in a false alarm rate lower than 1 demonstrating the robustness of the system to unknown movements . The proposed system can be used for assessing the effectiveness of a physical therapy session and for providing feedback to the patient . 


Summary: We use five wearable motion sensor units on the arms or legs. We acquire a physical therapy data set with 1320 exercise executions. We develop the MTMM DTW algorithm for multiple exercise templates. The algorithm can detect classify and evaluate physical therapy exercises. We achieve 93.5 accuracy for exercise classification.
Site selection for companies is a complex and unstructured problem that must be analyzed carefully and properly since a localization error could drive to bankrupt . This problem has been discussed widely and effectively using multi attribute methods in a manufacturing context but it has been little studied in agribusiness . The goal of this work is a methodological approach oriented to evaluate optimal locations of new agri food warehouses . Furthermore a literature review is developed analyzing the location problem and the attributes and techniques most widely used applied to agribusiness and a case study is presented in order to exemplify the methodological proposal . The multi attribute technique called Analytic Hierarchy Process has been selected as the basis for the research and it is applied to the real case study analyzed the selection of a site for a new banana distribution warehouse . Six generic criteria have been analyzed accessibility to the area distance cost security of the region local acceptance of the company and its needs . The process includes the assignment of attributes to each one of the generic criteria as well as the assessment of their importance levels . Three different areas of Guadalajara Jalisco and Mexico DF have been evaluated for the case study and the methodological proposal has been utilized to determine the best option . 


Summary: Methodological multi criteria attribute approach for agri food warehouses location. Analysis of criteria and attributes assignment and assessment of importance levels. Development of the agribusiness location model based on Analytic Hierarchy Process. A real case study for a banana distribution warehouse exemplifies the methodology.
We present a new SAS macro pshreg that can be used to fit a proportional subdistribution hazards model for survival data subject to competing risks . Our macro first modifies the input data set appropriately and then applies SAS s standard Cox regression procedure PROC PHREG using weights and counting process style of specifying survival times to the modified data set . The modified data set can also be used to estimate cumulative incidence curves for the event of interest . The application of PROC PHREG has several advantages e.g . it directly enables the user to apply the Firth correction which has been proposed as a solution to the problem of undefined maximum likelihood estimates in Cox regression frequently encountered in small sample analyses . Deviation from proportional subdistribution hazards can be detected by both inspecting Schoenfeld type residuals and testing correlation of these residuals with time or by including interactions of covariates with functions of time . We illustrate application of these extended methods for competing risk regression using our macro which is freely available at http cemsiis.meduniwien.ac.at en kb science research software statistical software pshreg by means of analysis of a real chronic kidney disease study . We discuss differences in features and capabilities of pshreg and the recent SAS PROC PHREG implementation of proportional subdistribution hazards modelling . 


Summary: The pshreg SAS macro fits Fine Gray models for competing risks. The macro first modifies a given data set and then uses PROC PHREG for analysis. Many useful features of PROC PHREG can now be applied to a Fine Gray model. Time dependent effects can be accommodated by time by covariate interactions. For small data sets the Firth correction is available.
The paper presents a computer based assessment for facioscapulohumeral dystrophy diagnosis through characterisation of the fat and oedema percentages in the muscle region . A novel multi slice method for the muscle region segmentation in the T1 weighted magnetic resonance images is proposed using principles of the live wire technique to find the path representing the muscle region border . For this purpose an exponential cost function is used that incorporates the edge information obtained after applying the edge enhancement algorithm formerly designed for the fingerprint enhancement . The difference between the automatic segmentation and manual segmentation performed by a medical specialists is characterised using the Zijdenbos similarity index indicating a high accuracy of the proposed method . Finally the fat and oedema are quantified from the muscle region in the T1 weighted and T2 STIR magnetic resonance images respectively using the fuzzy c mean clustering approach for 10 FSHD patients . 


Summary: A novel method for lower limb muscle segmentation is proposed. The proposed method is insensitive to the muscle fat infiltration and shape variation. Segmentation is based on the shortest path detection between anatomical structures. The proposed method does not require training set.
This paper presents a new efficient and robust smooth threshold generalized estimating equations for generalized linear models with longitudinal data . The proposed method is based on a bounded exponential score function and leverage based weights to achieve robustness against outliers both in the response and the covariate domain . Our motivation for the new variable selection procedure is that it enables us to achieve better robustness and efficiency by introducing an additional tuning parameter which can be automatically selected using the observed data . Moreover its performance is near optimal and superior to some recently developed variable selection methods . Under some regularity conditions the resulting estimator possesses the consistency in variable selection and the oracle property in estimation . Finally simulation studies and a detailed real data analysis are carried out to assess and illustrate the finite sample performance which show that the proposed method works better than other existing methods in particular when many outliers are included . 


Summary: We develop a new efficient and robust variable selection approach for generalized linear models with longitudinal data. The root consistency and asymptotic normality of the proposed estimators are established. An efficient algorithm is proposed to implement the procedures. Simulation studies and a real data example have shown that our proposed estimators are superior to some recently developed variable selection methods.
The impact of subjecting a n GaN surface to an in situ argon plasma in an atomic layer deposition tool immediately before deposition of an Al2O3 dielectric film is assessed by frequency dependent evaluation of Al2O3 GaN MOSCAPs . In comparison with a control with no pre treatment the use of a 50W argon plasma for 5min reduced hysteresis from 0.25V to 0.07V frequency dispersion from 0.31V to 0.03V and minimum interface state density as determined by the conductance method from 6.8 1012 cm 2 eV 1 to 5.05 1010 cm 2 eV 1 . 


Summary: Argon in situ plasma treatment before atomic layer deposition of Al2O3 on GaN. Post forming gas annealing process in Al2O3 on GaN. Minimum interface trap density Dit of 5.05 1010 cm 2 eV 1 at 50W argon pre treatment. Approach to GaN based MOS HEMT performance for power electronics applications.
A novel negative tone molecular resist molecule featuring a tert butyloxycarbonyl protected phenol malonate group bonded to a 1 8 Diazabicycloundece 7 ene is presented. The resist shows high resolution capability in electron beam lithography at a range of beam energies. The resist demonstrated a sensitivity of 18.7 C cm2 at 20 kV. Dense features with a line width of 15 nm have been demonstrated at 30 kV whilst a feature size of 12.5 nm was achieved for dense lines at 100 kV. 


Summary: A novel molecular chemically amplified negative tone resist is presented. High resolution features have been patterned at high sensitivity. The performance of the resist at 30 50 and 100kV is evaluated.
It is often necessary to test for the presence of seasonal unit roots when working with time series data observed at intervals of less than a year . One of the most widely used methods for doing this is based on regressing the seasonal difference of the series over the transformations of the series by applying specific filters for each seasonal frequency . This provides test statistics with non standard distributions . A generalisation of this method for any periodicity is presented and a response surface regressions approach is used to calculate the values of the statistics whatever the periodicity and sample size of the data . The algorithms are prepared with the Gretl open source econometrics package and two empirical examples are presented . 


Summary: A generalisation of HEGY seasonal unit root tests was presented in Smith Taylor and del Barrio Castro 2009 . We use a response surface regressions approach to calculate values for the HEGY statistics. They can be used for any seasonal periodicity sample size and autoregressive order. A Gretl function package is provided for applying the tests and calculating their values.
The Monte Carlo method for photon transport is often used to predict the volumetric heating that an optical source will induce inside a tissue or material . This method relies on constant optical properties specifically the coefficients of scattering and absorption . In reality optical coefficients are typically temperature dependent leading to error in simulation results . The purpose of this study is to develop a method that can incorporate variable properties and accurately simulate systems where the temperature will greatly vary such as in the case of laser thawing of frozen tissues . A numerical simulation was developed that utilizes the Monte Carlo method for photon transport to simulate the thermal response of a system that allows temperature dependent optical and thermal properties . This was done by combining traditional Monte Carlo photon transport with a heat transfer simulation to provide a feedback loop that selects local properties based on current temperatures for each moment in time . Additionally photon steps are segmented to accurately obtain path lengths within a homogenous material . Validation of the simulation was done using comparisons to established Monte Carlo simulations using constant properties and a comparison to the Beer Lambert law for temperature variable properties . The simulation is able to accurately predict the thermal response of a system whose properties can vary with temperature . The difference in results between variable property and constant property methods for the representative system of laser heated silicon can become larger than 100K . This simulation will return more accurate results of optical irradiation absorption in a material which undergoes a large change in temperature . This increased accuracy in simulated results leads to better thermal predictions in living tissues and can provide enhanced planning and improved experimental and procedural outcomes . 


Summary: Couples the Monte Carlo Method of photon transport with heat transfer. Allows for both optical and thermal properties to vary with temperature. Interpolates local optical properties based on local temperatures. Enhances simulation accuracy in systems that undergo large thermal changes.
Positron emission tomography with 18fluorodeoxyglucose is increasingly used in neurology . The measurement of cerebral arterial inflow using 18F FDG complements the information provided by standard brain PET imaging . Here injections were performed after the beginning of dynamic acquisitions and the time to arrival of activity in the gantry s field of view was computed . We performed a phantom study using a branched tube and a 18F FDG solution injected at 240mL min . Data processing consisted of reconstruction of the first 3s after t0 vascular signal enhancement and clustering . This method was then applied in four subjects . We measured the volumes of the tubes or vascular trees and calculated the corresponding flows . In the phantom the flow was calculated to be 244.2mL min . In each subject our QA value was compared with that obtained by quantitative cine phase contrast magnetic resonance imaging the mean QA value of 581.4 217.5mL min calculated with 18F FDG PET was consistent with the mean value of 593.3 205.8mL min calculated with quantitative cine phase contrast magnetic resonance imaging . Our 18F FDG PET method constitutes a novel fully automatic means of measuring QA . 


Summary: We proposed the measurement of cerebral arterial inflow using 3 s bolus 18F FDG brain PET acquisition. The arrival of the bolus in the field of view was calculated directly in list mode acquisition. Tubular structure enhancement filter was used to enhance vessels and a clustering step eliminated noise outside them.