Miracle-dz/UniFAR-FTUs huggingface dataset:
Does Simplicity Compromise Accuracy in ACS Risk Prediction? A Retrospective Analysis of the TIMI and GRACE Risk Scores

Background The Thrombolysis in Myocardial Infarction (TIMI) risk scores for Unstable Angina/Non-ST–elevation myocardial infarction (UA/NSTEMI) and ST-elevation myocardial infarction (STEMI) and the Global Registry of Acute Coronary Events (GRACE) risk scores for in-hospital and 6-month mortality are established tools for assessing risk in Acute Coronary Syndrome (ACS) patients. The objective of our study was to compare the discriminative abilities of the TIMI and GRACE risk scores in a broad-spectrum, unselected ACS population and to assess the relative contributions of model simplicity and model composition to any observed differences between the two scoring systems. Methodology/Principal Findings ACS patients admitted to the University of Michigan between 1999 and 2005 were divided into UA/NSTEMI (n = 2753) and STEMI (n = 698) subpopulations. The predictive abilities of the TIMI and GRACE scores for in-hospital and 6-month mortality were assessed by calibration and discrimination. There were 137 in-hospital deaths (4%), and among the survivors, 234 (7.4%) died by 6 months post-discharge. In the UA/NSTEMI population, the GRACE risk scores demonstrated better discrimination than the TIMI UA/NSTEMI score for in-hospital (C = 0.85, 95% CI: 0.81–0.89, versus 0.54, 95% CI: 0.48–0.60; p<0.01) and 6-month (C = 0.79, 95% CI: 0.76–0.83, versus 0.56, 95% CI: 0.52–0.60; p<0.01) mortality. Among STEMI patients, the GRACE and TIMI STEMI scores demonstrated comparably excellent discrimination for in-hospital (C = 0.84, 95% CI: 0.78–0.90 versus 0.83, 95% CI: 0.78–0.89; p = 0.83) and 6-month (C = 0.72, 95% CI: 0.63–0.81, versus 0.71, 95% CI: 0.64–0.79; p = 0.79) mortality. An analysis of refitted multivariate models demonstrated a marked improvement in the discriminative power of the TIMI UA/NSTEMI model with the incorporation of heart failure and hemodynamic variables. Study limitations included unaccounted for confounders inherent to observational, single institution studies with moderate sample sizes. Conclusions/Significance The GRACE scores provided superior discrimination as compared with the TIMI UA/NSTEMI score in predicting in-hospital and 6-month mortality in UA/NSTEMI patients, although the GRACE and TIMI STEMI scores performed equally well in STEMI patients. The observed discriminative deficit of the TIMI UA/NSTEMI score likely results from the omission of key risk factors rather than from the relative simplicity of the scoring system.
An analysis of correlation and predictability: what makes two-level branch predictors work

Pipeline flushes due to branch mispredictions is one of the most serious problems facing the designer of a deeply pipelined, superscalar processor. Many branch predictors have been proposed to help alleviate this problem, including two-level adaptive branch predictors and hybrid branch predictors. Numerous studies have shown which predictors and configurations best predict the branches in a given set of benchmarks. Some studies have also investigated effects, such as pattern history table interference, that can be detrimental to the performance of these predictors. However, little research has been done on which characteristics of branch behavior make predictors perform well. In this paper we investigate and quantify reasons why branches are predictable. We show that some of this predictability is not captured by the two-level adaptive branch predictors. An understanding of the predictability of branches may lead to insights ultimately resulting in better or less complex predictors. We also investigate and quantify what function of the branches in each benchmark is predictable using each of the methods described in this paper.
A coupled thermomechanical beam finite element for the simulation of shape memory alloy actuators

The proposed article describes the development of a new beam finite element for the coupled thermomechanical analysis of shape memory alloy actuators. The element is formulated, assuming coupled equilibrium equations for the thermoelastic stresses and thermal loads. Displacements and temperature are treated as internal degrees of freedom giving the ability to predict the coupled thermal–displacement response of a shape memory alloy beam. The constitutive shape memory alloy model of Lagoudas and coworkers is implemented in the formulation. A generalized beam theory is formulated assuming shear deformation with a cubic temperature field through the thickness. The new element is capable to simulate heat transfer phenomena, electric Joule heating as direct input, and heat convection effects. The coupling between mechanical and thermal equilibrium equations due to endothermic/exothermic martensitic transformation procedures is also included. Numerical results and evaluations of the developed beam element are presented for the thermomechanical response of shape memory alloy wire actuators and an adaptive strip subject to various types of applied thermal loading and heat convection conditions. The effect of coupling terms on the prediction of shape memory alloy actuator response is also evaluated.
Concept Formation Using Graph Grammars

Recognizing the expressive power of graph representation and the ability of certain graph grammars to generalize, we attempt to use graph grammar learning for concept formation. In this paper we describe our initial progress toward that goal, and focus on how certain graph grammars can be learned from examples. We also establish grounds for using graph grammars in machine learning tasks. Several examples are presented to highlight the validity of the approach.
Clustering and visualization of non-classified points from LiDAR data for helicopter navigation

In this paper we propose a dynamic DBSCAN-based method to cluster and visualize unclassified and potential dangerous obstacles in data sets recorded by a LiDAR sensor. The sensor delivers data sets in a short time interval, so a spatial superposition of multiple data sets is created. We use this superposition to create clusters incrementally. Knowledge about the position and size of each cluster is used to fuse clusters and the stabilization of clusters within multiple time frames. Cluster stability is a key feature to provide a smooth and un-distracting visualization for the pilot. Only a few lines are indicating the position of threatening unclassified points, where a hazardous situation for the helicopter could happen, if it comes too close. Clustering and visualization form a part of an entire synthetic vision processing chain, in which the LiDAR points support the generation of a real-time synthetic view of the environment.
Fine-Scale Genetic Population Structure of Southern Pine Beetle (Coleoptera: Curculionidae) in Mississippi Forests

Abstract The southern pine beetle, Dendroctonus frontalis Zimmerman, is the most destructive insect pest of pine forests in the southeastern United States, Mexico, and Central America. Southern pine beetle aggressively attacks pine trees, and when in epidemic stages, they are capable of killing even the most healthy pine trees in a short period of time. Despite the amount of destruction caused by the southern pine beetle and the amount of monetary loss faced by the timber industry and recreation, the population genetics of this species has been limited to comparisons among distant geographic locations. This study investigates the fine-scale genetic population structure of the southern pine beetle in Mississippi. Very little genetic differentiation was observed among samples. Bayesian assignment testing failed to detect multiple groups within all samples; estimates of genetic differentiation and genetic distance were very low in magnitude; and a Mantel test did not reveal a significant relationship between genetic distance and geographic distance. These results suggest that management of the southern pine beetle needs to consider the potential movements of individuals within and among national forests and should be focused on a large scale, at least as big as continuously forested areas and possibly even multiple forests. These results further suggest that removal of beetle-infested trees is important.
Exemplar-based colorization in RGB color space

This paper deals with the problem of image colorization. A model including total variation regularization is proposed. Our approach colorizes directly the three RGB channels, while most existing methods were only focusing on the two chrominance channels. By using the three channels, our approach is able to better preserve color consistency. Our model is non convex, but we propose an efficient primal-dual like algorithm to compute a local minimizer. Numerical examples illustrate the good behavior of our algorithm with respect to state-of-the-art methods.
H∞ controller design for switched nonlinear systems in lower triangular form under arbitrary switchings

This paper focuses on the problem of robust H∞ control for a class of non-minimum-phase switched nonlinear systems in lower triangular form under arbitrary switchings. The backstepping design technique is applied to construct a common Lyapunov function (CLF) and an H∞ feedback controller which guarantee that the closed-loop system is stability and the L2-gain from the disturbance input to the controlled output is not larger than a prescribed value under arbitrary switchings. The construction of such a CLF depends on the virtual feedback controllers which are designed at each step of the iteration.
Learning Bounds for Importance Weighting

This paper presents an analysis of importance weighting for learning from finite samples and gives a series of theoretical and algorithmic results. We point out simple cases where importance weighting can fail, which suggests the need for an analysis of the properties of this technique. We then give both upper and lower bounds for generalization with bounded importance weights and, more significantly, give learning guarantees for the more common case of unbounded importance weights under the weak assumption that the second moment is bounded, a condition related to the Renyi divergence of the traning and test distributions. These results are based on a series of novel and general bounds we derive for unbounded loss functions, which are of independent interest. We use these bounds to guide the definition of an alternative reweighting algorithm and report the results of experiments demonstrating its benefits. Finally, we analyze the properties of normalized importance weights which are also commonly used.
High Temperature - FTIR Characterization of Gadolinia Doped Ceria

Doped ceria-based (DC) materials have recently been considered as the most promising solid electrolytes for intermediate temperature solid oxide fuel cell (IT-SOFC) applications. Doped ceria is usually prepared via thermal decomposition of its water soluble salts, especially, acetates and nitrates. The properties of the obtained final product directly influenced by the starting material and the decomposition products. Therefore, it is crucial to understand the decomposition steps and intermediate products. Number of experimental work have been reported using various in-situ and ex-situ techniques such as thermogravimetry with mass spectrometry (TG/DTA-MS), X-ray diffraction with differential scanning calorimeter (XRD-DSC). However, the available literature data is limited and not reasonably in agreement with each other. High Temperature FT-IR spectroscopy, TG/DTA-MS, XRD, techniques were used and results are compared with literature. A good agreement between the thermal analyses and HT-FTIR results were obtained. Possible decomposition mechanism is discussed.
Improving In Vivo High-Resolution CT Imaging of the Tumour Vasculature in Xenograft Mouse Models through Reduction of Motion and Bone-Streak Artefacts

Introduction Preclinical in vivo CT is commonly used to visualise vessels at a macroscopic scale. However, it is prone to many artefacts which can degrade the quality of CT images significantly. Although some artefacts can be partially corrected for during image processing, they are best avoided during acquisition. Here, a novel imaging cradle and tumour holder was designed to maximise CT resolution. This approach was used to improve preclinical in vivo imaging of the tumour vasculature. Procedures A custom built cradle containing a tumour holder was developed and fix-mounted to the CT system gantry to avoid artefacts arising from scanner vibrations and out-of-field sample positioning. The tumour holder separated the tumour from bones along the axis of rotation of the CT scanner to avoid bone-streaking. It also kept the tumour stationary and insensitive to respiratory motion. System performance was evaluated in terms of tumour immobilisation and reduction of motion and bone artefacts. Pre- and post-contrast CT followed by sequential DCE-MRI of the tumour vasculature in xenograft transplanted mice was performed to confirm vessel patency and demonstrate the multimodal capacity of the new cradle. Vessel characteristics such as diameter, and branching were quantified. Results Image artefacts originating from bones and out-of-field sample positioning were avoided whilst those resulting from motions were reduced significantly, thereby maximising the resolution that can be achieved with CT imaging in vivo. Tumour vessels ≥ 77 μm could be resolved and blood flow to the tumour remained functional. The diameter of each tumour vessel was determined and plotted as histograms and vessel branching maps were created. Multimodal imaging using this cradle assembly was preserved and demonstrated. Conclusions The presented imaging workflow minimised image artefacts arising from scanner induced vibrations, respiratory motion and radiopaque structures and enabled in vivo CT imaging and quantitative analysis of the tumour vasculature at higher resolution than was possible before. Moreover, it can be applied in a multimodal setting, therefore combining anatomical and dynamic information.
Semiconductor Interfaces, Microstructures and Devices: Properties and applications

Electric field induced localization in superlattices R Tsu Intersubband transitions and quantum well infrared photodetectors K.K. Choi Real time spectroscopic ellipsometry monitoring of semiconductor growth and etching R.W. Collins et al. X-ray reflectivity from heteroepitaxial layers P. Miceli Spontaneous and stimulated emissions from optical microcavity structures H. Yokoyama et al. Radiative and nonradiative recombination in AlGaAs and GaAsP heterostructures and some features of the corresponding quantum well laser Zh. I. Alferov and D.Z. Garbuzov Far-infrared cyclotron resonance of 2-dimensional electron gas in III-V semiconductor heterostructures M.O. Manasreh Optics in lower dimensional quantum confined II-VI heterostructures A.V. Nurmikko and R.L. Gunshor Growth and doping of silicon by low temperature molecular beam epitaxy H-J Gossmann and D.J. Eaglesham Point defects and charge traps in the Si/SiO^O2 system and related structures E.H. Poindexter Growth and characterization of silicon carbide polytypes for electronic applications J.A. Powell et al.
Molecular Adaptation of rbcL in the Heterophyllous Aquatic Plant Potamogeton

Background Heterophyllous aquatic plants show marked phenotypic plasticity. They adapt to environmental changes by producing different leaf types: submerged, floating and terrestrial leaves. By contrast, homophyllous plants produce only submerged leaves and grow entirely underwater. Heterophylly and submerged homophylly evolved under selective pressure modifying the species-specific optima for photosynthesis, but little is known about the evolutionary outcome of habit. Recent evolutionary analyses suggested that rbcL, a chloroplast gene that encodes a catalytic subunit of RuBisCO, evolves under positive selection in most land plant lineages. To examine the adaptive evolutionary process linked to heterophylly or homophylly, we analyzed positive selection in the rbcL sequences of ecologically diverse aquatic plants, Japanese Potamogeton. Principal Findings Phylogenetic and maximum likelihood analyses of codon substitution models indicated that Potamogeton rbcL has evolved under positive Darwinian selection. The positive selection has operated specifically in heterophyllous lineages but not in homophyllous ones in the branch-site models. This suggests that the selective pressure on this chloroplast gene was higher for heterophyllous lineages than for homophyllous lineages. The replacement of 12 amino acids occurred at structurally important sites in the quaternary structure of RbcL, two of which (residue 225 and 281) were identified as potentially under positive selection. Conclusions/Significance Our analysis did not show an exact relationship between the amino acid replacements and heterophylly or homophylly but revealed that lineage-specific positive selection acted on the Potamogeton rbcL. The contrasting ecological conditions between heterophyllous and homophyllous plants have imposed different selective pressures on the photosynthetic system. The increased amino acid replacement in RbcL may reflect the continuous fine-tuning of RuBisCO under varying ecological conditions.
A Gestaltist approach to contour-based object recognition: Combining bottom-up and top-down cues

This paper proposes a method for detecting generic classes of objects from their representative contours that can be used by a robot with vision to find objects in cluttered environments. The approach uses a mid-level image operator to group edges into contours which likely correspond to object boundaries. This mid-level operator is used in two ways, bottom-up on simple edges and top-down incorporating object shape information, thus acting as the intermediary between low-level and high-level information. First, the mid-level operator, called the image torque, is applied to simple edges to extract likely fixation locations of objects. Using the operator’s output, a novel contour-based descriptor is created that extends the shape context descriptor to include boundary ownership information and accounts for rotation. This descriptor is then used in a multi-scale matching approach to modulate the torque operator towards the target, so it indicates its location and size. Unlike other approaches that use edges directly to guide the independent edge grouping and matching processes for recognition, both of these steps are effectively combined using the proposed method. We evaluate the performance of our approach using four diverse datasets containing a variety of object categories in clutter, occlusion and viewpoint changes. Compared with current state-of-the-art approaches, our approach is able to detect the target with fewer false alarms in most object categories. The performance is further improved when we exploit depth information available from the Kinect RGB-Depth sensor by imposing depth consistency when applying the image torque.
Oxidation-induced damage evolution in a unidirectional polymer matrix composite

Long-term degradation and failure in high-temperature polymer matrix composites are driven by chemical changes due to oxidation reactions and damage evolution. In this paper, we present a methodology for simulating oxidation-induced damage in a unidirectional composite. This approach explicitly models the time-dependent growth of oxidation layers and the evolution of discrete cracking in a homogenized representation of the composite. Long-term isothermal aging is simulated with high-resolution tracking of morphological changes and damage evolution. An element-free Galerkin method is used to simulate the oxidation layer growth, and the extended finite element method is used for computing the stress fields and predicting damage. The developed model captures both oxidation and damage growth in the unidirectional lamina through long periods of oxidative aging. The model predictions correlate well with the experimental results for a carbon/polyimide composite system.
Oral Vaccination of Baculovirus-Expressed VP28 Displays Enhanced Protection against White Spot Syndrome Virus in Penaeus monodon

White Spot Syndrome Virus (WSSV) is an infectious pathogen of shrimp and other crustaceans, and neither effective vaccines nor adequate treatments are currently available. WSSV is an enveloped dsDNA virus, and one of its major envelope proteins, VP28, plays a pivotal role in WSSV infection. In an attempt to develop a vaccine against WSSV, we inserted the VP28 gene into a baculovirus vector tailored to express VP28 on the baculovirus surface under the WSSV ie1 promoter (Bac-VP28). The Bac-VP28 incorporated abundant quantity (65.3 µg/ml) of VP28. Shrimp were treated by oral and immersion vaccination with either Bac-VP28 or wild-type baculovirus (Bac-wt). The treatment was followed by challenge with WSSV after 3 and 15 days. Bac-VP28 vaccinated shrimp showed significantly higher survival rates (oral: 81.7% and 76.7%; immersion: 75% and 68.4%) than Bac-wt or non-treated shrimp (100% mortality). To verify the protective effects of Bac-VP28, we examined in vivo expression of VP28 by immunohistochemistry and quantified the WSSV copy number by qPCR. In addition to that, we quantified the expression levels shrimp genes LGBP and STAT by real-time RT-PCR from the samples obtained from Bac-VP28 vaccinated shrimp at different duration of vaccine regime. Our findings indicate that oral vaccination of shrimp with Bac-VP28 is an attractive preventative measure against WSSV infection that can be used in the field.
An Assessment of Hermite Function Based Approximations of Mutual Information Applied to Independent Component Analysis

At the heart of many ICA techniques is a nonparametric estimate of an information measure, usually via nonparametric density estimation, for example, kernel density estimation. While not as popular as kernel density estimators, orthogonal functions can be used for nonparametric density estimation (via a truncated series expansion whose coefficients are calculated from the observed data). While such estimators do not necessarily yield a valid density, which kernel density estimators do, they are faster to calculate than kernel density estimators, in particular for a modified version of Renyi's entropy of order 2. In this paper, we compare the performance of ICA using Hermite series based estimates of Shannon's and Renyi's mutual information, to that of Gaussian kernel based estimates. The comparisons also include ICA using the RADICAL estimate of Shannon's entropy and a FastICA estimate of neg-entropy.
Learning Scripts as Hidden Markov Models

Scripts have been proposed to model the stereotypical event sequences found in narratives. They can be applied to make a variety of inferences including fillinggaps in the narratives and resolving ambiguous references. This paper proposes the first formal frameworkfor scripts based on Hidden Markov Models (HMMs). Our framework supports robust inference and learning algorithms, which are lacking in previous clustering models. We develop an algorithm for structure andparameter learning based on Expectation Maximizationand evaluate it on a number of natural datasets. The results show that our algorithm is superior to several informed baselines for predicting missing events in partialobservation sequences.