varunr14/newsummary huggingface dataset:
Background and objective The evaluation of the clinical status of a patient is frequently based on the temporal evolution of some parameters making the detection of temporal patterns a priority in data analysis . Temporal abstraction is a methodology widely used in medical reasoning for summarizing and abstracting longitudinal data . Methods This paper describes JTSA a framework including a library of algorithms for time series preprocessing and abstraction and an engine to execute a workflow for temporal data processing . The JTSA framework is grounded on a comprehensive ontology that models temporal data processing both from the data storage and the abstraction computation perspective . The JTSA framework is designed to allow users to build their own analysis workflows by combining different algorithms . Thanks to the modular structure of a workflow simple to highly complex patterns can be detected . The JTSA framework has been developed in Java 1.7 and is distributed under GPL as a jar file . Results JTSA provides a collection of algorithms to perform temporal abstraction and preprocessing of time series a framework for defining and executing data analysis workflows based on these algorithms and a GUI for workflow prototyping and testing . The whole JTSA project relies on a formal model of the data types and of the algorithms included in the library . This model is the basis for the design and implementation of the software application . Taking into account this formalized structure the user can easily extend the JTSA framework by adding new algorithms . Results are shown in the context of the EU project MOSAIC to extract relevant patterns from data coming related to the long term monitoring of diabetic patients . Conclusions The proof that JTSA is a versatile tool to be adapted to different needs is given by its possible uses both as a standalone tool for data summarization and as a module to be embedded into other architectures to select specific phenotypes based on TAs in a large dataset . 


Summary: JTSA provides a collection of algorithms to perform temporal abstraction and preprocessing of time series. JTSA provides a framework for defining and executing data analysis workflows based on time series processing techniques. JTSA provides a GUI for workflow prototyping and testing.
Intestinal abnormalities and ischemia are medical conditions in which inflammation and injury of the intestine are caused by inadequate blood supply . Acute ischemia of the small bowel can be life threatening . Computed tomography is currently a gold standard for the diagnosis of acute intestinal ischemia in the emergency department . However the assessment of the diagnostic performance of CT findings in the detection of intestinal abnormalities and ischemia has been a difficult task for both radiologists and surgeons . Little effort has been found in developing computerized systems for the automated identification of these types of complex gastrointestinal disorders . In this paper a geostatistical mapping of spatial uncertainty in CT scans is introduced for medical image feature extraction which can be effectively applied for diagnostic detection of intestinal abnormalities and ischemia from control patterns . Experimental results obtained from the analysis of clinical data suggest the usefulness of the proposed uncertainty mapping model . 


Summary: An original work on automated identification of bowel ischemia is reported. The computerized detection can greatly assist surgeons in emergency medicine. Testing on clinical data shows the potential application of the proposed approach.
Background and objective Meaningful targeting of brain structures is required in a number of experimental designs in neuroscience . Current technological developments as high density electrode arrays for parallel electrophysiological recordings and optogenetic tools that allow fine control of activity in specific cell populations provide powerful tools to investigate brain physio pathology . However to extract the maximum yield from these fine developments increased precision reproducibility and cost efficiency in experimental procedures is also required . Methods We introduce here a framework based on magnetic resonance imaging and digitized brain atlases to produce customizable 3D environments for brain navigation . It allows the use of individualized anatomical and or functional information from multiple MRI modalities to assist experimental neurosurgery planning and in vivo tissue processing . Results As a proof of concept we show three examples of experimental designs facilitated by the presented framework with extraordinary applicability in neuroscience . Conclusions The obtained results illustrate its feasibility for identifying and selecting functionally and or anatomically connected neuronal population in vivo and directing electrode implantations to targeted nodes in the intricate system of brain networks . 


Summary: Framework based on magnetic resonance imaging and digitized 3D brain atlases for brain navigation. It allows the use of individualized information from multiple MRI modalities to assist experimental neurosurgery planning and in vivo tissue processing. Three examples of experimental designs in neuroscience are presented. The results illustrate its feasibility for identifying and selecting connected neuronal population in vivo and directing electrode implantations to targeted nodes in the intricate system of brain networks.
Cell counting is one of the basic needs of most biological experiments . Numerous methods and systems have been studied to improve the reliability of counting . However at present manual cell counting performed with a hemocytometer still represents the gold standard despite several problems limiting reproducibility and repeatability of the counts and at the end jeopardizing their reliability in general . We present our own approach based on image processing techniques to improve counting reliability . It works in two stages first building a high resolution image of the hemocytometer s grid then counting the live and dead cells by tagging the image with flags of different colours . In particular we introduce GridMos a fully automated mosaicing method to obtain a mosaic representing the whole hemocytometer s grid . In addition to offering more significant statistics the mosaic freezes the culture status thus permitting analysis by more than one operator . Finally the mosaic achieved can thus be tagged by using an image editor thus markedly improving counting reliability . The experiments performed confirm the improvements brought about by the proposed counting approach in terms of both reproducibility and repeatability also suggesting the use of a mosaic of an entire hemocytometer s grid then labelled trough an image editor as the best likely candidate for the new gold standard method in cell counting . 


Summary: We present a new two stage image based approach to perform cell counting. Counting is performed on big high resolution images of the whole hemocytometer s grid. Counting the cells by also tagging the big image improves counting reliability. Big images are built using GridMos our fully automated mosaicing software. Both GridMos source code and a standalone executable version are freely available.
Hilbert Huang Transform has proven to be extremely powerful for signal processing and analysis in 1D time series and its generalization to regular tensor product domains has also demonstrated its widespread utilities in image processing and analysis . Compared with popular Fourier transform and wavelet transform the most prominent advantage of Hilbert Huang Transform is that it is a fully data driven adaptive method especially valuable for handling non stationary and nonlinear signals . Two key technical elements of Hilbert Huang transform are Empirical Mode Decomposition and Hilbert spectra computation . HHT s uniqueness results from its capability to reveal both global information enabled by EMD and local information and phase information enabled by Hilbert spectra computation from input signals . Despite HHT s rapid advancement in the past decade its theory and applications on surfaces remain severely under explored due to the current technical challenge in conducting Hilbert spectra computation on surfaces . To ameliorate this paper takes a new initiative to compute the Riesz transform on 3D surfaces a natural generalization of Hilbert transform in higher dimensional cases with a goal to make the theoretic breakthrough . The core of our theoretic and computational framework is to fully exploit the relationship between Riesz transform and fractional Laplacian operator which can enable the computation of Riesz transform on surfaces via eigenvalue decomposition of Laplacian matrix . Moreover we integrate the techniques of EMD and our newly proposed Riesz transform on 3D surfaces by monogenic signals to compute Hilbert spectra which include the space frequency energy distribution of signals defined over 3D surfaces and characterize key local feature information . Experiments and applications in spectral geometry processing and prominent feature detection illustrate the effectiveness of the current computational framework of HHT on 3D surfaces which could serve as a solid foundation for upcoming more serious applications in graphics and geometry computing fields . 


Summary: A method to compute Riesz transform RT on surfaces is proposed. The computation of RT depends on the eigenvalue decomposition of Laplacian matrix. EMD and RT on surfaces are integrated by monogenic signals to get Hilbert spectra. The graphics applications based on Hilbert spectra illustrate HHT s potential.
An algorithm for the evaluation of the exact Gaussian likelihood of an dimensional vector autoregressive moving average process of order with time dependent coefficients including a time dependent innovation covariance matrix is proposed . The elements of the matrices of coefficients and those of the innovation covariance matrix are deterministic functions of time and assumed to depend on a finite number of parameters . These parameters are estimated by maximizing the Gaussian likelihood function . The advantages of that approach is that the Gaussian likelihood function can be computed exactly and efficiently . The algorithm is based on the Cholesky decomposition method for block band matrices . It is shown that the number of operations as a function of and the size of the series is barely doubled with respect to a VARMA model with constant coefficients . A detailed description of the algorithm followed by a data example is provided . 


Summary: We consider VARMA models with time dependent coefficients. These coefficients and the error variance can be deterministic functions of time. We present an efficient algorithm for computing their exact Gaussian likelihood. It is based on an algorithm for traditional VARMA models. An illustration on financial data is provided.
Because of the increased volume of information available to physicians from advanced medical technology the obtained information of each symptom with respect to a disease may contain truth falsity and indeterminacy information . Since a single valued neutrosophic set consists of the three terms like the truth membership indeterminacy membership and falsity membership functions it is very suitable for representing indeterminate and inconsistent information . Then similarity measure plays an important role in pattern recognition and medical diagnosis . However existing medical diagnosis methods can only handle the single period medical diagnosis problem but can not deal with the multi period medical diagnosis problems with neutrosophic information . Hence the purpose of this paper was to propose similarity measures between SVNSs based on tangent function and a multi period medical diagnosis method based on the similarity measure and the weighted aggregation of multi period information to solve multi period medical diagnosis problems with single valued neutrosophic information . Then we compared the tangent similarity measures of SVNSs with existing similarity measures of SVNSs by a numerical example about pattern recognitions to indicate the effectiveness and rationality of the proposed similarity measures . In the multi period medical diagnosis method we can find a proper diagnosis for a patient by the proposed similarity measure between the symptoms and the considered diseases represented by SVNSs and the weighted aggregation of multi period information . Then a multi period medical diagnosis example was presented to demonstrate the application of the proposed diagnosis method and to indicate the effectiveness of the proposed diagnosis method by the comparative analysis . The diagnosis results showed that the developed multi period medical diagnosis method can help doctors make a proper diagnosis by the comprehensive information of multi periods . 


Summary: We proposed similarity measures of SVNSs based on tangent function. We developed a multi period medical diagnosis method using the similarity measure. A diagnosis example showed the application of the proposed diagnosis method.
A resolution of the Fisher effect puzzle in terms of statistical inference is attempted . Motivation stems from empirical evidence of time varying coefficients in the data generating process of both the interest rates and inflation rates for 19 OECD countries . These time varying dynamics crucially affect the behaviour of all the co integration estimators considered especially in small samples . When employing simulated critical values instead of asymptotic ones the results provide ample evidence supporting the existence of a long run Fisher effect in which interest rates move one to one with inflation rates in all countries under scrutiny except for Ireland and Switzerland . 


Summary: We introduce a time varying Data Generating Process for the Fisher Effect. We examine the effect of time variation on the behaviour of cointegration estimators. We argue that inference should be based on simulated critical values. We provide evidence that support the validity of the Fisher hypothesis.
We propose a frequency domain generalized likelihood ratio test for testing nonstationarity in time series . The test is constructed in the frequency domain by comparing the goodness of fit in the log periodogram regression under the varying coefficient fractionally exponential models . Under such a locally stationary specification the proposed test is capable of detecting dynamic changes of short range and long range dependences in a regression framework . The asymptotic distribution of the proposed test statistic is known under the null stationarity hypothesis and its finite sample distribution can be approximated by bootstrap . Numerical results show that the proposed test has good power against a wide range of locally stationary alternatives . 


Summary: We propose a frequency domain test against nonstationarity in time series. The test is based on comparing the goodness of fit in log periodogram regression. The regression model is the varying coefficient fractionally exponential model. The test is applicable for dynamic changes of both short and long range dependences. The finite sample test distribution is approximated by bootstrap.
Retrospective clinical datasets are often characterized by a relatively small sample size and many missing data . In this case a common way for handling the missingness consists in discarding from the analysis patients with missing covariates further reducing the sample size . Alternatively if the mechanism that generated the missing allows incomplete data can be imputed on the basis of the observed data avoiding the reduction of the sample size and allowing methods to deal with complete data later on . Moreover methodologies for data imputation might depend on the particular purpose and might achieve better results by considering specific characteristics of the domain . The problem of missing data treatment is studied in the context of survival tree analysis for the estimation of a prognostic patient stratification . Survival tree methods usually address this problem by using surrogate splits that is splitting rules that use other variables yielding similar results to the original ones . Instead our methodology consists in modeling the dependencies among the clinical variables with a Bayesian network which is then used to perform data imputation thus allowing the survival tree to be applied on the completed dataset . The Bayesian network is directly learned from the incomplete data using a structural expectation maximization procedure in which the maximization step is performed with an exact anytime method so that the only source of approximation is due to the EM formulation itself . On both simulated and real data our proposed methodology usually outperformed several existing methods for data imputation and the imputation so obtained improved the stratification estimated by the survival tree . 


Summary: Retrospective clinical datasets have often small sample size and many missing data. We use Bayesian networks to impute missing data enhancing survival tree analysis. The Bayesian network is learned from incomplete data and used for the imputation. Our method generally achieved more accurate predictions than widely used approaches.
The goal of our study is to develop a fast parallel implementation of group independent component analysis for functional magnetic resonance imaging data using graphics processing units . Though ICA has become a standard method to identify brain functional connectivity of the fMRI data it is computationally intensive especially has a huge cost for the group data analysis . GPU with higher parallel computation power and lower cost are used for general purpose computing which could contribute to fMRI data analysis significantly . In this study a parallel group ICA on GPU mainly consisting of GPU based PCA using SVD and Infomax ICA is presented . In comparison to the serial group ICA the proposed method demonstrated both significant speedup with 6 11 times and comparable accuracy of functional networks in our experiments . This proposed method is expected to perform the real time post processing for fMRI data analysis . 


Summary: A parallel group ICA PGICA on GPU was proposed for fMRI data analysis. PGICA demonstrated significant speedup in comparison with serial group ICA. PGICA demonstrated comparable accuracy of functional networks detection.
We present a new chlorine free dry etching process which was used to successfully etch indium antimonide grown on gallium arsenide substrates while keeping the substrate temperature below 150 C. By use of a reflowed photoresist mask a sidewall with 60 degree positive slope was achieved whereas a nearly vertical one was obtained when hard masks were used. Long etch tests demonstrated the non selectivity of the process by etching through the entire multi layer epitaxial structure. Electrical and optical measurements on devices fabricated both by wet and dry etch techniques provided similar results proving that the dry etch process does not cause damage to the material. This technique has a great potential to replace the standard wet etching techniques used for fabrication of indium antimonide devices with a non damaging low temperature plasma process. 


Summary: Chlorine free low temperature dry etch of MBE grown InSb on GaAs 60 positive sloped or nearly vertical etch depending on mask Non selective process etching through Insb GaSb and GaAs Low damage process with potential to replace standard InSb wet etch
In this paper a passive planar micromixer with ellipse like micropillars is proposed to operate in the laminar flow regime for high mixing efficiency . With a splitting and recombination concept the diffusion distance of the fluids in a micromixer with ellipse like micropillars was decreased . Thus space usage for micromixer of an automatic sample collection system is also minimized . Numerical simulation was conducted to evaluate the performance of proposed micromixer by solving the governing Navier Stokes equation and convection diffusion equation . With software for computational fluid dynamics we simulated the mixing of fluids in a micromixer with ellipse like micropillars and basic T type mixer in a laminar flow regime . The efficiency of the proposed micromixer is shown in numerical results and is verified by measurement results . 


Summary: The SAR structures result in the reduction of the diffusion distance of two fluids. The mixing strength is increased when one stream is injected into the other. Fluids flow faster in SAR mixer for rapid mixing and mixing efficiency.
This work addresses the problem of detecting human behavioural anomalies in crowded surveillance environments . We focus in particular on the problem of detecting subtle anomalies in a behaviourally heterogeneous surveillance scene . To reach this goal we implement a novel unsupervised context aware process . We propose and evaluate a method of utilising social context and scene context to improve behaviour analysis . We find that in a crowded scene the application of Mutual Information based social context permits the ability to prevent self justifying groups and propagate anomalies in a social network granting a greater anomaly detection capability . Scene context uniformly improves the detection of anomalies in both datasets . The strength of our contextual features is demonstrated by the detection of subtly abnormal behaviours which otherwise remain indistinguishable from normal behaviour . 


Summary: We propose a novel context aware anomaly detection process applied to human behaviour. We evaluate our method upon 2 different datasets. We utilise a novel social connections metric and a scene model as contextual information. The use of scene contextual information improves the detection of subtle anomalies. The use of social contextual information improves the detection of abnormal behaviour.
The aims of this study are summarized in the following items first to investigate the class discrimination power of long term heart rate variability features for risk assessment in patients suffering from congestive heart failure second to introduce the most discriminative features of HRV to discriminate low risk patients and high risk patients and third to examine the influence of feature dimension reduction in order to achieve desired accuracy of the classification . We analyzed two public Holter databases 12 data of patients suffering from mild CHF labeled as LRPs and 32 data of patients suffering from severe CHF labeled as HRPs . A K nearest neighbor classifier was used to evaluate the performance of feature set in the classification . Moreover to reduce the number of features as well as the overlap of the samples of two classes in feature space we used generalized discriminant analysis as a feature extraction method . By applying GDA to the discriminative nonlinear features we achieved sensitivity and specificity of 100 having the least number of features . Finally the results were compared with other similar conducted studies regarding the performance of feature selection procedure and classifier besides the number of features used in training . 


Summary: Investigating the discrimination power of long term HRV for risk assessment in CHF patients. Introducing the significant features of HRV for risk assessment of CHF patients. To examine the influence of GDA in order to achieve desired accuracy of the classification. We achieved sensitivity and specificity of 100 having the least number of features. The results are far better than any other previously reported ones.