varunr14/newsummary huggingface dataset:
Background and Objective The HIV AIDS related issue has given rise to a priority concern in which potential new therapies are increasingly highlighted to lessen the negative impact of highly active anti retroviral therapy in the healthcare industry . With the motivation of medical applications this study focuses on the main advanced feature selection techniques and classification approaches that reflect a new architecture and a trial to build a hybrid model for interested parties . Methods This study first uses an integrated linear nonlinear feature selection technique to identify the determinants influencing HAART medication and utilizes organizations of different condition attributes to generate a hybrid model based on a rough set classifier to study evolving HIV AIDS research in order to improve classification performance . Results The proposed model makes use of a real data set from Taiwan s specialist medical center . The experimental results show that the proposed model yields a satisfactory result that is superior to the listed methods and the core condition attributes PVL CD4 Code Age Year PLT and Sex were identified in the HIV AIDS data set . In addition the decision rule set created can be referenced as a knowledge based healthcare service system as the best of evidence based practices in the workflow of current clinical diagnosis . Conclusions This study highlights the importance of these key factors and provides the rationale that the proposed model is an effective alternative to analyzing sustained HAART medication in follow up studies of HIV AIDS treatment in practice . 


Summary: To develop an integrated linear nonlinear feature selection technique to identify the determinants of sustained HAART. To generate a hybrid model based on rough set classifiers to verify its performance. To create a relevant decision rule set of an LEM2 algorithm for specialist physicians as a diagnosis reference. To effectively offer the study findings and results from the given data set to relevant medical institutions and patients.
Facial expression recognition systems must ultimately work on real data in uncontrolled environments although most research studies have been conducted on lab based data with posed or evoked facial expressions obtained in pre set laboratory environments . It is very difficult to obtain data in real world situations because privacy laws prevent unauthorized capture and use of video from events such as funerals birthday parties marriages etc . It is a challenge to acquire such data on a scale large enough for benchmarking algorithms . Although video obtained from TV or movies or postings on the World Wide Web may also contain acted emotions and facial expressions they may be more realistic than lab based data currently used by most researchers . Or is it One way of testing this is to compare feature distributions and FER performance . This paper describes a database that has been collected from television broadcasts and the World Wide Web containing a range of environmental and facial variations expected in real conditions and uses it to answer this question . A fully automatic system that uses a fusion based approach for FER on such data is introduced for performance evaluation . Performance improvements arising from the fusion of point based texture and geometry features and the robustness to image scale variations are experimentally evaluated on this image and video dataset . Differences in FER performance between lab based and realistic data between different feature sets and between different train test data splits are investigated . 


Summary: A new realistic facial expression recognition FER database Compared feature and performance differences between lab based and realistic data Examined factors that affect FER performance regarding data feature and training
In this paper we tackle the problem of gait recognition based on the model free approach. Numerous methods exist they all lead to high dimensional feature spaces. To address the problem of high dimensional feature space we propose the use of the Random Forest algorithm to rank features importance. In order to efficiently search throughout subspaces we apply a backward feature elimination search strategy. Our first experiments are carried out on unknown covariate conditions. Our first results suggest that the selected features contribute to increase the CCR of different existing classification methods. Secondary experiments are performed on unknown covariate conditions and viewpoints. Inspired by the location of our first experiments features we proposed a simple mask. Experimental results demonstrate that the proposed mask gives satisfactory results for all angles of the probe and consequently is not view specific. We also show that our mask performs well when an uncooperative experimental setup is considered as compared to the state of the art methods. As a consequence we propose a panoramic gait recognition framework on unknown covariate conditions. Our results suggest that panoramic gait recognition can be performed under unknown covariate conditions. Our approach can greatly reduce the complexity of the classification problem while achieving fair correct classification rates when gait is captured with unknown conditions. 


Summary: A feature selection framework is proposed to achieve high performance model free gait recognition. The feature selection mechanism relies on the Random Forest algorithm. Regions selected are more robust to covariates while reducing the computational cost. Panoramic gait recognition is achieved under covariate conditions.
A large number of methods have been published that aim to evaluate various components of multi view geometry systems. Most of these have focused on the feature extraction description and matching stages the visual front end since geometry computation can be evaluated through simulation. Many data sets are constrained to small scale scenes or planar scenes that are not challenging to new algorithms or require special equipment. This paper presents a method for automatically generating geometry ground truth and challenging test cases from high spatio temporal resolution video. The objective of the system is to enable data collection at any physical scale in any location and in various parts of the electromagnetic spectrum. The data generation process consists of collecting high resolution video computing accurate sparse 3D reconstruction video frame culling and down sampling and test case selection. The evaluation process consists of applying a test 2 view geometry method to every test case and comparing the results to the ground truth. This system facilitates the evaluation of the whole geometry computation process or any part thereof against data compatible with a realistic application. A collection of example data sets and evaluations is included to demonstrate the range of applications of the proposed system. 


Summary: Easy data collection in any 3D feature rich scene with any calibrated camera Ground truth acquired automatically by high resolution structure from motion Rigorous 2 view geometry evaluation with many reduced resolution test samples More elaborate rigid geometry tests are trivial to devise given 3D ground truth.
In this paper we propose a visual tracking algorithm by incorporating the appearance information gathered from two collaborative feature sets and exploiting its geometric structures. A structured visual dictionary SVD can be learned from both appearance and geometric structure thereby enhancing its discriminative strength between the foreground object and the background. Experimental results show that the proposed tracking algorithm using SVD SVDTrack performs favorably against the state of the art methods. 


Summary: A tracking algorithm which exploits both appearance and geometric information Two complementary features are adopted in building the appearance dictionary. A shape context approach to capture the stable geometric patterns of keypoints The proposed tracking algorithm performs well in challenging conditions.
We present a novel method for on line joint object tracking and segmentation in a monocular video captured by a possibly moving camera . Our goal is to integrate tracking and fine segmentation of a single previously unseen potentially non rigid object of unconstrained appearance given its segmentation in the first frame of an image sequence as the only prior information . To this end we tightly couple an existing kernel based object tracking method with Random Walker based image segmentation . Bayesian inference mediates between tracking and segmentation enabling effective data fusion of pixel wise spatial and color visual cues . The fine segmentation of an object at a certain frame provides tracking with reliable initialization for the next frame closing the loop between the two building blocks of the proposed framework . The effectiveness of the proposed methodology is evaluated experimentally by comparing it to a large collection of state of the art tracking and video based object segmentation methods on the basis of a data set consisting of several challenging image sequences for which ground truth data is available . 


Summary: A novel method for joint tracking and fine object segmentation in videos Efficient integration of EM based tracking and Random Walker based image segmentation No strong constraints are imposed on the target appearance or the camera motion. Experimental evaluation against a large collection of state of the art methods Explicit and efficient fine object segmentation facilitates drift free tracking.
Background and objectives Gene splicing is a vital source of protein diversity . Perfectly eradication of introns and joining exons is the prominent task in eukaryotic gene expression as exons are usually interrupted by introns . Identification of splicing sites through experimental techniques is complicated and time consuming task . With the avalanche of genome sequences generated in the post genomic age it remains a complicated and challenging task to develop an automatic robust and reliable computational method for fast and effective identification of splicing sites . Methods In this study a hybrid model iSS Hyb mRMR is proposed for quickly and accurately identification of splicing sites . Two sample representation methods namely pseudo trinucleotide composition and pseudo tetranucleotide composition were used to extract numerical descriptors from DNA sequences . Hybrid model was developed by concatenating PseTNC and PseTetraNC . In order to select high discriminative features minimum redundancy maximum relevance algorithm was applied on the hybrid feature space . The performance of these feature representation methods was tested using various classification algorithms including K nearest neighbor probabilistic neural network general regression neural network and fitting network . Jackknife test was used for evaluation of its performance on two benchmark datasets S 1 and S 2 respectively . Results The predictor proposed in the current study achieved an accuracy of 93.26 sensitivity of 88.77 and specificity of 97.78 for S 1 and the accuracy of 94.12 sensitivity of 87.14 and specificity of 98.64 for S 2 respectively . Conclusion It is observed that the performance of proposed model is higher than the existing methods in the literature so for and will be fruitful in the mechanism of RNA splicing and other research academia . 


Summary: iSS Hyb mRMR model is proposed for identification of splicing sites. Trinucleotide and tetranucleotide composition are used as feature extraction schemes. Hybrid space is formed by using TNC and TetraNC spaces. Various classification algorithms are analyzed. mRMR is utilized to reduce feature space.
Facial expression is central to human experience . Its efficiency and valid measurement are challenges that automated facial image analysis seeks to address . Most publically available databases are limited to 2D static images or video of posed facial behavior . Because posed and un posed facial expressions differ along several dimensions including complexity and timing well annotated video of un posed facial behavior is needed . Moreover because the face is a three dimensional deformable object 2D video may be insufficient and therefore 3D video archives are required . We present a newly developed 3D video database of spontaneous facial expressions in a diverse group of young adults . Well validated emotion inductions were used to elicit expressions of emotion and paralinguistic communication . Frame level ground truth for facial actions was obtained using the Facial Action Coding System . Facial features were tracked in both 2D and 3D domains . To the best of our knowledge this new database is the first of its kind for the public . The work promotes the exploration of 3D spatiotemporal features in subtle facial expression better understanding of the relation between pose and motion dynamics in facial action units and deeper understanding of naturally occurring facial action . 


Summary: We present the first 3D dynamic spontaneous facial expression database. Meta data include FACS coding head pose data and 2D 3D landmarks. We present an effective emotion elicitation protocol using eight tasks. The database is analyzed by self report observers rating and AU annotation. The database is validated through expression AU recognition and pain analysis.
Background and Objectives Light sharing PET detector configuration coupled with thick light guide and Geiger mode avalanche photodiode with large area microcells was proposed to overcome the energy non linearity problem and to obtain high light collection efficiency . Methods A Monte Carlo simulation was conducted for the three types of LSO block 4 4 array of 3 3 20 mm3 discrete crystals 6 6 array of 2 2 20 mm3 discrete crystals and 12 12 array of 1 1 20 mm3 discrete crystals to investigate the scintillation light distribution after conversion of the rays in LSO . The incident photons were read out by three types of 4 4 array photosensors which were PSPMT of 25 quantum efficiency GAPD1 with 50 50 m2 microcells of 30 photon detection efficiency and GAPD2 with 100 100 m2 of 45 PDE . The number of counted photons in each photosensor was analytically calculated . The LCE linearity and flood histogram were examined for each PET detector module having 99 different configurations as a function of light guide thickness ranging from 0 to 10 mm . Results The performance of PET detector modules based on GAPDs was considerably improved by using the thick light guide . The LCE was increased from 24 to 30 and from 14 to 41 and the linearity was also improved from 0.97 to 0.99 and from 0.75 to 0.99 for GAPD1 and GAPD2 respectively . As expected the performance of PSPMT based detector did not change . The flood histogram of 12 12 array PET detector modules using 3 mm light guide coupled with GAPDs was obtained by simulation and all crystals of 1 1 20 mm3 size were clearly identified . PET detector module coupled with thick light guide and GAPD array with large area microcells was proposed to obtain high QE and high spatial resolution and its feasibility was verified . Conclusions This study demonstrated that the overall PET performance of the proposed design was considerably improved and this approach will provide opportunities to develop GAPD based PET detector with a high LCE . 


Summary: This study proposed and examined a new approach employing the light sharing PET detector configuration with thick light guide and GAPD array having large area microcells for high effective quantum efficiency. The number of counted photons was considerably increased and the LCE of 40 could be obtained with the detector configuration. The flood histogram showed good separation and wide margins between the spots of the scintillators. Moreover a considerable improvement in the energy linearity performance was observed and severe saturation effects could be clearly avoided. This study demonstrated that GAPDs with large area microcells which have not been actively studied as PET photosensors because of their non linearity properties could be utilized for PET applications and even high photon flux regime.
Range imaging sensors such as Kinect and time of flight cameras can produce aligned depth and color images in real time. However the depth maps captured by such sensors contain numerous invalid regions and suffer from heavy noise. These defects more or less influence the use of depth information in practical applications. In order to enhance the depth maps this paper proposes a new inpainting approach based on the fast marching method FMM . We extend the inpainting model and the propagation strategy of FMM to incorporate color information for depth inpainting. An edge preserving guided filter is further applied for noise reduction. To validate our algorithm we perform experiments on both Kinect data and Middlebury dataset which respectively provide qualitative and quantitative results. Meanwhile we also compare it to the original FMM and other two state of the art depth enhancement methods. Experimental results show that our method performs better than the local methods in terms of both visual and metric qualities and it achieves visually comparable results to the time consuming global method. 


Summary: We propose a new depth enhancement method for RGB D sensors. It extends the fast marching method to incorporate color and depth information. It outperforms state of the art local methods in terms of visual and metric qualities. It achieves visually comparable results to time consuming global methods. It provides better inputs to the applications based on RGB D sensors.
Motor unit action potential which consists of individual muscle fiber action potentials represents the electrical activity of the motor unit . The values of the MUAP features are changed by denervation and reinnervation in neurogenic involvement as well as muscle fiber loss with increased diameter variability in myopathic diseases . The present study is designed to investigate how increased muscle fiber diameter variability affects MUAP parameters in simulated motor units . In order to detect this variation simulated MUAPs were calculated both at the innervation zone where the MFAPs are more synchronized and near the tendon where they show increased temporal dispersion . Reinnervation in neurogenic state increases MUAP amplitude for the recordings at both the innervation zone and near the tendon . However MUAP duration and the number of peaks significantly increased in a case of myopathy for recordings near the tendon . Furthermore of the new features number of peaks spike duration was found as the strongest indicator of MFAP dispersion in myopathy . MUAPs were also recorded from healthy participants in order to investigate the biological counterpart of the simulation data . MUAPs which were recorded near to tendon revealed significantly prolonged duration and decreased amplitude . Although the number of peaks was increased by moving the needle near to tendon this was not significant . 


Summary: We simulated five motor units for normal neurogenic and myopathic cases. We investigated the effect of recording sites on electromyography signals. Myopathic cases demonstrated the most prolonged phase duration near the tendon. Highest number of peaks are observed near the tendon in myopathic conditions. A new feature referred as number of peaks spike duration was defined.
Current telehealth services are dominated by conventional 2D video conferencing systems which are limited in their capabilities in providing a satisfactory communication experience due to the lack of realism . The immersiveness provided by 3D technologies has the potential to promote telehealth services to a wider range of applications . However conventional stereoscopic 3D technologies are deficient in many aspects including low resolution and the requirement for complicated multi camera setup and calibration and special glasses . The advent of light field photography enables us to record light rays in a single shot and provide glasses free 3D display with continuous motion parallax in a wide viewing zone which is ideally suited for 3D telehealth applications . As far as our literature review suggests there have been no reports of 3D telemedicine systems using LF technology . In this paper we propose a cross platform solution for a LF based 3D telemedicine system . Firstly a novel system architecture based on LF technology is established which is able to capture the LF of a patient and provide an immersive 3D display at the doctor site . For 3D modeling we further propose an algorithm which is able to convert the captured LF to a 3D model with a high level of detail . For the software implementation on different platforms a cross platform solution is proposed . Demo applications have been developed for 2D 3D video conferencing 3D model display and edit blood pressure and heart rate monitoring and patient data viewing functions . The demo software can be extended to multi discipline telehealth applications such as tele dentistry tele wound and tele psychiatry . The proposed 3D telemedicine solution has the potential to revolutionize next generation telemedicine technologies by providing a high quality immersive tele consultation experience . 


Summary: We propose a novel framework for a world s first LF based 3D telemedicine system. We develop an algorithm to convert the LF into 3D models with high levels of details. We design a cross platform solution for different systems and platforms. We develop a demo platform with multidiscipline 3D telemedicine applications.
An acetabular cup with larger abduction angles is able to affect the normal function of the cup seriously that may cause early failure of the total hip replacement . Complexity of the finite element simulation in the wear analysis of the THR is usually concerned with the contact status the computational effort and the possible divergence of results which become more difficult on THRs with larger cup abduction angles . In the study we propose a FE approach with contact transformation that offers less computational effort . Related procedures such as Lagrangian Multiplier partitioned matrix inversion detection of contact forces continuity of contact surface nodal area estimation etc . are explained in this report . Through the transformed methodology the computer round off error is tremendously reduced and the embedded repetitive procedure can be processed precisely and quickly . Here wear behaviors of THR with various abduction angles are investigated . The most commonly used combination i.e . metal on polyethylene is adopted in the current study where a cobalt chromium femoral head is paired with an Ultra High Molecular Weight Polyethylene cup . In all illustrations wear coefficients are estimated by self averaging strategy with available experimental datum reported elsewhere . The results reveal that the THR with larger abduction angles may produce deeper depth of wear but the volume of wear presents an opposite tendency these results are comparable with clinical and experimental reports . The current approach can be widely applied easily to fields such as the study of the wear behaviors on ante version impingement and time dependent behaviors of prostheses etc . 


Summary: Wear behaviors of the total hip replacement THR with various abduction angles are investigated. The current finite element approach can improve the computational efforts without significant loss of data precision. The THR with larger abduction angles may produce deeper depth of wear but the volume of wear presents an opposite tendency.