Regularizing Image Reconstruction for Gradient-Domain Rendering with Feature Patches

We present a novel algorithm to reconstruct high-quality images from sampled pixels and gradients in gradient-domain rendering. Our approach extends screened Poisson reconstruction by adding additional regularization constraints. Our key idea is to exploit local patches in feature images, which contain per-pixels normals, textures, position, etc., to formulate these constraints. We describe a GPU implementation of our approach that runs on the order of seconds on megapixel images. We demonstrate a significant improvement in image quality over screened Poisson reconstruction under the L1 norm. Because we adapt the regularization constraints to the noise level in the input, our algorithm is consistent and converges to the ground truth.

Computer-based summative assessment of clinical reasoning in clerkships: A mixed-method comparison of key feature problems with case-based multiple choice questions

Background: It is yet unclear if there are differences between using electronic key feature problems (KFPs) or electronic case-based multiple choice questions (cbMCQ) for the assessment of clinical decision making. Summary of Work: Fifth year medical students were exposed to clerkships which ended with a summative exam. Assessment of knowledge per exam was done by 6-9 KFPs, 9-20 cbMCQ and 9-28 MC questions. Each KFP consisted of a case vignette and three key features (KF) using “long menu” as question format. We sought students’ perceptions of the KFPs and cbMCQs in focus groups (n of students=39). Furthermore statistical data of 11 exams (n of students=377) concerning the KFPs and (cb)MCQs were compared. Summary of Results: The analysis of the focus groups resulted in four themes reflecting students’ perceptions of KFPs and their comparison with (cb)MCQ: KFPs were perceived as (i) more realistic, (ii) more difficult, (iii) more motivating for the intense study of clinical reasoning than (cb)MCQ and (iv) showed an overall good acceptance when some preconditions are taken into account. The statistical analysis revealed that there was no difference in difficulty; however KFP showed a higher discrimination and reliability (G-coefficient) even when corrected for testing times. Correlation of the different exam parts was intermediate. Conclusions: Students perceived the KFPs as more motivating for the study of clinical reasoning. Statistically KFPs showed a higher discrimination and higher reliability than cbMCQs. Take-home messages: Including KFPs with long menu questions into summative clerkship exams seems to offer positive educational effects.

Computerbasierte Prüfung von klinischem Denken in der klinischen Ausbildung: Ein Mixed-Method Vergleich von Key Feature Problemen mit fallbasierten Multiple Choice Typ A Fragen

Fragestellung/Einleitung: Es ist unklar inwiefern Unterschiede bestehen im Einsatz von Key Feature Problemen (KFP) mit Long Menu Fragen und fallbasierten Typ A Fragen (FTA) für die Überprüfung des klinischen Denkens (Clinical Reasoning) in der klinischen Ausbildung von Medizinstudierenden. Methoden: Medizinstudierende des fünften Studienjahres nahmen an ihrer klinischen Pädiatrie-Rotation teil, die mit einer summativen Prüfung endete. Die Überprüfung des Wissen wurde pro Prüfung elektronisch mit 6-9 KFP [1], [3], 9-20 FTA und 9-28 nichtfallbasierten Multiple Choice Fragen (NFTA) durchgeführt. Jedes KFP bestand aus einer Fallvignette und drei Key Features und nutzen ein sog. Long Menu [4] als Antwortformat. Wir untersuchten die Perzeption der KFP und FTA in Focus Gruppen [2] (n of students=39). Weiterhin wurden die statistischen Kennwerte der KFP und FTA von 11 Prüfungen (n of students=377) verglichen. Ergebnisse: Die Analyse der Fokusgruppen resultierte in vier Themen, die die Perzeption der KFP und deren Vergleich mit FTA darstellten: KFP wurden als 1. realistischer, 2. schwerer, und 3. motivierender für das intensive Selbststudium des klinischen Denkens als FTA aufgenommen und zeigten 4. insgesamt eine gute Akzeptanz sofern gewisse Voraussetzungen berücksichtigt werden. Die statistische Auswertung zeigte keinen Unterschied im Schwierigkeitsgrad; jedoch zeigten die KFP eine höhere Diskrimination und Reliabilität (G-coefficient) selbst wenn für die Prüfungszeit korrigiert wurde. Die Korrelation der verschiedenen Prüfungsteile war mittel. Diskussion/Schlussfolgerung: Die Studierenden erfuhren die KFP als motivierenden für das Selbststudium des klinischen Denkens. Statistisch zeigten die KFP eine grössere Diskrimination und höhere Relibilität als die FTA. Der Einbezug von KFP mit Long Menu in Prüfungen des klinischen Studienabschnitts erscheint vielversprechend und einen „educational effect“ zu haben.

Who cares about my feature request?

Previous studies on issue tracking systems for open source software (OSS) focused mainly on requests for bug fixes. However, requests to add a new feature or an improvement to an OSS project are often also made in an issue tracking system. These inquiries are particularly important because they determine the further development of the software. This study examines if there is any difference between requests of the IBM developer community and other sources in terms of the likelihood of successful implementation. Our study consists of a case study of the issue tracking system BugZilla in the Eclipse integrated development environment (IDE). Our hypothesis, which was that feature requests from outsiders have less chances of being implemented, than feature requests from IBM developers, was confirmed.

Automatic Stabilizer Feature of Fixed Exchange Rate Regimes in Emerging Markets

This paper shows that countries characterized by a financial accelerator mechanism may reverse the usual finding of the literature -- flexible exchange rate regimes do a worse job of insulating open economies from external shocks. I obtain this result with a calibrated small open economy model that endogenizes foreign interest rates by linking them to the banking sector's foreign currency leverage. This relationship renders exchange rate policy more important compared to the usual exogeneity assumption. I find empirical support for this prediction using the Local Projections method. Finally, 2nd order approximation to the model finds larger welfare losses under flexible regimes.

Assessing pediatric nasal carriage of S. aureus & MRSA

Staphylococcus aureus and methicillin-resistant Staphylococcus aureus (MRSA) has become an increasing problem in the community. Nasal carriage of these bacteria has been shown to be a predisposing factor for infection and environmental contamination. This serious public health concern prompted an investigation to assess the pediatric nasal carriage of these bacteria in an effort to better understand the populations at risk and prevention of infection.^ This prospective study surveys 30 children from the Northwest Assistance Ministries (NAM) pediatric clinic from October 2008 to the present. Two nasal swabs were taken in 2-4 week intervals to determine S. aureus carrier status. Microbiologic tests were conducted to isolate and identify S. aureus from nasal cultures. Children with 2 cultures positive for S. aureus were classified as persistent carriers, those with 1 positive and 1 negative culture were classified as intermittent carriers, and those with 2 negative cultures were classified as noncarriers. This information was related to patient records and statistical analyses (X 2 and t-tests) were performed.^ Distribution of S. aureus carriage related to patient demographics (age, sex, & race) was showed no significant differences between S. aureus positive and S. aureus negative patient populations (p = 0.8). Additionally, the distribution of carrier status related to demographics also showed no significant difference (p = 0.8). Finally, the distribution of carrier status related to relevant medical history (immunizations current, past infection, & antibiotic use at time of swabbing) showed no significant difference (p = 0.4).^ This study is a snapshot of an ongoing study to assess the pediatric nasal carriage of S. aureus and MRSA. The inability to draw any reliable conclusion from the distribution of data is likely a result of an inadequate samples size. This is one of few studies assessing pediatric nasal carriage of S. aureus and targeting an underrepresented, Hispanic population is especially unique. Continuing this study allows for a better understanding of the epidemiology of this bacterium which will hopefully lead to appropriate interventions thus preventing future S. aureus infections.^

Prevalence of methicillin-resistant Staphylococcus aureus (MRSA) nasal colonization among dental professionals

Background. MRSA (methicillin-resistant Staphylococcus aureus) is a multi-drug resistant bacterium that is quite prevalent in social environments where close person-to-person contact and crowding are an issue. In dental settings, the likelihood of transmission of MRSA may be higher than among other healthcare practitioners because of the close proximity between a patient's nose (where MRSA colonizes) and the field of procedure (the mouth) to the dental professional. Objective. To estimate the prevalence of MRSA nasal colonization among dental professionals (dentists and dental hygienists) in the Greater Houston Metropolitan Area, Texas, and analyze its associations with demographic, professional and personal protective equipment-related variables. Methods. 800 dental professionals (400 dentists and 400 dental hygienists) were randomly selected in the Greater Houston Metropolitan Area. Multiple waves of nasal swab kits and a self-administered questionnaire were mailed to increase the response rate of the study population. The swabs were cultured on chromagenic agar growth medium and bacterial growth results were evaluated after 18 hours. Positively selected bacterial colonies were confirmed as MRSA by further culturing these isolated bacteria on blood agar plates. Associations between positive nasal swabs and self-reported professional practice patterns, personal protective equipment use and demographics were analyzed using multiple logistic regression. Main Results. Completed questionnaires and nasal swabs were received from 496 study participants (68%). Fourteen cultures were positive for MRSA (4.2% among dentists and 1.6% among dental hygienists, p=0.07). After adjusting for gender, dental hygienists had a significantly lower prevalence of nasal colonization of MRSA as compared to dentists (OR: 0.20, 95% CI: 0.05–0.75). No other significant associations or interactions were found. Conclusion. The prevalence of nasal colonization with MRSA among dentists is similar to that reported for health care workers in general, whereas prevalence among dental hygienists is only slightly above that of the general population (1%). Differences in practice patterns and use of personal protective equipment did not explain this difference in this study, and was possibly due either to residual confounding or unexplored risk factors. Increased prevalence of MRSA among dentists warrants further investigation as to the reason for the increased rate and to allow implementation of measures to avoid transmission and progression to disease. ^

Radiomics of NSCLC: Quantitative CT Image Feature Characterization and Tumor Shrinkage Prediction

Radiomics is the high-throughput extraction and analysis of quantitative image features. For non-small cell lung cancer (NSCLC) patients, radiomics can be applied to standard of care computed tomography (CT) images to improve tumor diagnosis, staging, and response assessment. The first objective of this work was to show that CT image features extracted from pre-treatment NSCLC tumors could be used to predict tumor shrinkage in response to therapy. This is important since tumor shrinkage is an important cancer treatment endpoint that is correlated with probability of disease progression and overall survival. Accurate prediction of tumor shrinkage could also lead to individually customized treatment plans. To accomplish this objective, 64 stage NSCLC patients with similar treatments were all imaged using the same CT scanner and protocol. Quantitative image features were extracted and principal component regression with simulated annealing subset selection was used to predict shrinkage. Cross validation and permutation tests were used to validate the results. The optimal model gave a strong correlation between the observed and predicted shrinkages with . The second objective of this work was to identify sets of NSCLC CT image features that are reproducible, non-redundant, and informative across multiple machines. Feature sets with these qualities are needed for NSCLC radiomics models to be robust to machine variation and spurious correlation. To accomplish this objective, test-retest CT image pairs were obtained from 56 NSCLC patients imaged on three CT machines from two institutions. For each machine, quantitative image features with concordance correlation coefficient values greater than 0.90 were considered reproducible. Multi-machine reproducible feature sets were created by taking the intersection of individual machine reproducible feature sets. Redundant features were removed through hierarchical clustering. The findings showed that image feature reproducibility and redundancy depended on both the CT machine and the CT image type (average cine 4D-CT imaging vs. end-exhale cine 4D-CT imaging vs. helical inspiratory breath-hold 3D CT). For each image type, a set of cross-machine reproducible, non-redundant, and informative image features was identified. Compared to end-exhale 4D-CT and breath-hold 3D-CT, average 4D-CT derived image features showed superior multi-machine reproducibility and are the best candidates for clinical correlation.

In-loop Feature Tracking for Structure and Motion with Out-of-core Optimization

In this paper, a novel and approach for obtaining 3D models from video sequences captured with hand-held cameras is addressed. We define a pipeline that robustly deals with different types of sequences and acquiring devices. Our system follows a divide and conquer approach: after a frame decimation that pre-conditions the input sequence, the video is split into short-length clips. This allows to parallelize the reconstruction step which translates into a reduction in the amount of computational resources required. The short length of the clips allows an intensive search for the best solution at each step of reconstruction which robustifies the system. The process of feature tracking is embedded within the reconstruction loop for each clip as opposed to other approaches. A final registration step, merges all the processed clips to the same coordinate frame

Two Different Approaches of Feature Extraction for Classifying the EEG Signals

The electroencephalograph (EEG) signal is one of the most widely used signals in the biomedicine field due to its rich information about human tasks. This research study describes a new approach based on i) build reference models from a set of time series, based on the analysis of the events that they contain, is suitable for domains where the relevant information is concentrated in specific regions of the time series, known as events. In order to deal with events, each event is characterized by a set of attributes. ii) Discrete wavelet transform to the EEG data in order to extract temporal information in the form of changes in the frequency domain over time- that is they are able to extract non-stationary signals embedded in the noisy background of the human brain. The performance of the model was evaluated in terms of training performance and classification accuracies and the results confirmed that the proposed scheme has potential in classifying the EEG signals.

Two Different Approaches of Feature Extraction for Classifying the EEG Signals

The focus of this chapter is to study feature extraction and pattern classification methods from two medical areas, Stabilometry and Electroencephalography (EEG). Stabilometry is the branch of medicine responsible for examining balance in human beings. Balance and dizziness disorders are probably two of the most common illnesses that physicians have to deal with. In Stabilometry, the key nuggets of information in a time series signal are concentrated within definite time periods are known as events. In this chapter, two feature extraction schemes have been developed to identify and characterise the events in Stabilometry and EEG signals. Based on these extracted features, an Adaptive Fuzzy Inference Neural network has been applied for classification of Stabilometry and EEG signals.

Signal analysis and feature generation for pattern identification of partial discharges in high-voltage equipment

This paper proposes a method for the identification of different partial discharges (PDs) sources through the analysis of a collection of PD signals acquired with a PD measurement system. This method, robust and sensitive enough to cope with noisy data and external interferences, combines the characterization of each signal from the collection, with a clustering procedure, the CLARA algorithm. Several features are proposed for the characterization of the signals, being the wavelet variances, the frequency estimated with the Prony method, and the energy, the most relevant for the performance of the clustering procedure. The result of the unsupervised classification is a set of clusters each containing those signals which are more similar to each other than to those in other clusters. The analysis of the classification results permits both the identification of different PD sources and the discrimination between original PD signals, reflections, noise and external interferences. The methods and graphical tools detailed in this paper have been coded and published as a contributed package of the R environment under a GNU/GPL license.