Miracle-dz/UniFAR-FTUs huggingface dataset:
High CO2 Levels Impair Alveolar Epithelial Function Independently of pH

Background In patients with acute respiratory failure, gas exchange is impaired due to the accumulation of fluid in the lung airspaces. This life-threatening syndrome is treated with mechanical ventilation, which is adjusted to maintain gas exchange, but can be associated with the accumulation of carbon dioxide in the lung. Carbon dioxide (CO2) is a by-product of cellular energy utilization and its elimination is affected via alveolar epithelial cells. Signaling pathways sensitive to changes in CO2 levels were described in plants and neuronal mammalian cells. However, it has not been fully elucidated whether non-neuronal cells sense and respond to CO2. The Na,K-ATPase consumes ∼40% of the cellular metabolism to maintain cell homeostasis. Our study examines the effects of increased pCO2 on the epithelial Na,K-ATPase a major contributor to alveolar fluid reabsorption which is a marker of alveolar epithelial function. Principal Findings We found that short-term increases in pCO2 impaired alveolar fluid reabsorption in rats. Also, we provide evidence that non-excitable, alveolar epithelial cells sense and respond to high levels of CO2, independently of extracellular and intracellular pH, by inhibiting Na,K-ATPase function, via activation of PKCζ which phosphorylates the Na,K-ATPase, causing it to endocytose from the plasma membrane into intracellular pools. Conclusions Our data suggest that alveolar epithelial cells, through which CO2 is eliminated in mammals, are highly sensitive to hypercapnia. Elevated CO2 levels impair alveolar epithelial function, independently of pH, which is relevant in patients with lung diseases and altered alveolar gas exchange.
A Generic Local Deformation Model for Shape Registration

In this paper, we propose a new surface registration approach using a generic deformation model, which is efficient to compute and flexible to represent arbitrary local shape deformations. From Riemannian geometry, local deformation at each point of a surface can be characterized by the eigenvalues of a special transformation matrix between two canonically parameterized domains. This local transformation specifies all the deformations (i.e., diffeomorphisms) between surfaces while being independent of both intrinsic (parametrization) and extrinsic (embedding) representations. In particular, we show that existing deformation representations (e.g., isometry or conformality) can be viewed as special cases of the proposed local deformation model. Furthermore, a computationally efficient, closed-form solution is derived in the discrete setting via finite element discretization. Based on the proposed deformation model, the shape registration problem is formulated as a high-order Markov Random Field (MRF) defined on the simplicial complex (e.g., planar or tetrahedral mesh). An efficient high-order MRF optimization algorithm is designed in the paper for such a special structured MRF-MAP problem, which can be implemented in a distributed fashion and requires minimal memory. Finally, we demonstrate the speed and accuracy performance of the proposed approach in the applications of shape registration and tracking.
Thermostated Kac models

To my aunt and teacher, Vasanthi Murali. iii ACKNOWLEDGEMENTS Foremost, I thank my advisor Dr. Federico Bonetto, and Prof. Michael Loss for their invaluable guidance and mentoring. I have truly enjoyed learning from, working with, and being inspired by them. I am especially thankful for the positive, encouraging environment they fostered in the group. I thank my colleague and friend Hagop Tossounian for the stimulating mathematical discussions (and the witticisms during coffee breaks). part of the thesis committee, and for their helpful comments. I gratefully acknowledge the support from members of the School of Math throughout my studies at Georgia Tech. I am immensely thankful to my teachers, my family and my friends for their encouragement. SUMMARY We consider a model of N particles interacting through a Kac-style collision process, with m particles among them interacting, in addition, with a thermostat. When m = N , we show exponential approach to the equilibrium canonical distribution in terms of the L 2 norm, in relative entropy, and in the Gabetta-Toscani-Wennberg (GTW) metric, at a rate independent of N. When m < N , the exponential rate of approach to equilibrium in L 2 is shown to behave as m N for N large, while the relative entropy and the GTW distance from equilibrium exhibit (at least) an " eventually exponential " decay, with a rate scaling as m N 2 for large N. As an allied project, we obtain a rigorous microscopic description of the thermostat used, based on a model of a tagged particle colliding with an infinite gas in equilibrium at the thermostat temperature. These results are based on joint work with Federico Bonetto, Michael Loss and Hagop Tossounian.
Feedback-driven result ranking and query refinement for exploring semi-structured data collections

Feedback process has been used extensively in document-centric applications, such as text retrieval and multimedia retrieval. Recently, there have been efforts to apply feedback to semi-structured XML document collections as well. In this paper, we note that feedback can also be an effective tool for exploring (through result ranking and query refinement) large semi-structured data collections. In particular, in large scale data sharing and curation environments, where the user may not know the structure of the data, queries may initially be overly vague. Given a path query and a set of results identified by the system to this query over the data, we consider two types of feedback: Soft feedback captures the user's preference for some features over the others. Hard feedback, on the other hand, expresses users' assertions regarding whether certain features should be further enforced or, in contrast, are to be avoided. Both soft and hard feedback can be "positive" or "negative". For soft feedback, we develop a probabilistic feature significance measure and describe how to use this for ranking results in the presence of dependencies between the path features. To deal with the hard feedback efficiently (i.e., fast enough for interactive exploration), we present finite automata based query refinement solutions. In particular, we present a novel LazyDFA+ algorithm for managing hard feedback. We also describe optimizations that leverage the inherently iterative nature of the feedback process. We bring together these techniques in AXP, a system for adaptive and exploratory path retrieval. The experimental results show the effectiveness of the proposed techniques.
Object recognition by discriminative combinations of line segments and ellipses

We present a contour based approach to object recognition in real-world images. Contours are represented by generic shape primitives of line segments and ellipses. These primitives offer substantial flexibility to model complex shapes. We pair connected primitives as shape tokens, and learn category specific combinations of shape tokens. We do not restrict combinations to have a fixed number of tokens, but allow each combination to flexibly evolve to best represent a category. This, coupled with the generic nature of primitives, enables a variety of discriminative shape structures of a category to be learned. We compare our approach with related methods and state-of-the-art contour based approaches on two demanding datasets across 17 categories. Highly competitive results are obtained. In particular, on the challenging Weizmann horse dataset, we attain improved image classification and object detection results over the best contour based results published so far.
Ensemble Nyström method for predicting conflict level from speech

The Nyström method is an efficient technique for scaling kernel learning to very large data sets with more than millions. Instead of computing kernel matrix, it is to approximate a kernel learning problem with a linear prediction problem. We propose an ensemble Nyström method for high dimensional prediction of conflict level from speech. The experiments have been conducted over SSPNet Conflict Corpus, which contains 1430 clips of 30 seconds extracted from 45 political debates (roughly 12 hours of material). The results show that the proposed system is able to learn an optimal set of low-rank approximations and combine them to achieve higher accuracy of the prediction of conflict level. The correlation coefficient can be achieved to 84.94% between the actual and predicted labels, clearly exceeding the performance of the same regression using the original full feature set, as well as the state-of-the-art methods such as random k-nearest neighbor (RKNN) features (winning system in INTERSPEECH 2013 Computational Paralinguistics Challenge), support vector regressions, and regression techniques based on Gaussian Processes.
Multiple Deprivation in Rural China

Income is one measure of material well-being. But do the people with the lowest income also work the longest hours? Are they also malnuourished and unable to read? Do social isolation and ill-health compound the hardships of low income? On the evidence presented in this paper the answer for poor rural Chinese households generally is "no".
Calculation of a distribution free estimate of effect size and confidence intervals using VBA/Excel

Reporting effect sizes aids the transparent presentation and independent interpretation of scientific data. However, calculation and reporting of effect sizes for data obtained in basic research is rare. A standardized effect size was reported by Norman Cliff, known as Cliff's delta. It has several advantageous features, as (i) it makes no assumption on the shape of the underlying distribution, (ii) it works well for small to moderate samples (n>10), (iii) it is easy to calculate, and (iv) its basis is readily understood by non statisticians. Here, a VBA macro, implemented in Excel, is presented. The macro takes two independent samples as input and calculates Cliff's delta with 95% confidence intervals. The macro will reduce the barrier for calculating the effect size and can be a valuable tool for research and teaching.
Modeling of wheeled mobile robots as differential-algebraic systems

We formulate the problem of wheeled mobile robot (WMR) modeling under wheel slip, while moving over rough terrain, as the integral of a differential algebraic equation (DAE) of either first or second order. That is, the elements of the model include both differential equations and kinematic constraints. While such a formulation is typical of Lagrangian dynamics in augmented form, we show how identical principles apply to kinematic models of motion. In effect, we show in this paper that the very high fidelity models traditionally used in offline simulation can be readily adapted for use in real time for both control and estimation of the motion of arbitrary WMRs over arbitrary rolling terrain, while slipping. While such models are more correct and general than the naive methods that are typically used, we also show that they are orders of magnitude more efficient than such naive methods for equivalent accuracy.
Overexpression and Selectively Regulatory Roles of IL-23/IL-17 Axis in the Lesions of Oral Lichen Planus

Interleukin- (IL-) 23/IL-17 axis is a newly discovered proinflammatory signaling pathway and has been implicated in the pathogenesis of many chronic inflammatory and immune disorders. Here we investigated whether the IL-23/IL-17 axis was present and functional in the lesions of oral lichen planus (OLP), a chronic inflammatory disease affecting the oral mucosa. Using immunohistochemistry and quantitative PCR, we found that the subunits of IL-23 and IL-17 were overexpressed in OLP lesions than in normal oral mucosa tissues. In addition, the expressions of IL-23 and IL-17 are positively correlated in reticular OLP tissues. Results from in vitro studies revealed that exogenous IL-23 could increase the percentage of Th17 cells and IL-17 production in the CD4+T cells from reticular OLP patients. Furthermore, we also found that exogenous IL-17 could significantly enhance the mRNA expressions of β-defensin-2, -3, CCL-20, IL-8, and TNF-α, but not β-defensin-1, CXCL-9, -10, -11, CCL-5, and IL-6 in human oral keratinocytes. Taken together, our results revealed an overexpression pattern and selectively regulatory roles of IL-23/IL-17 axis in the OLP lesions, suggesting that it may be a pivotal regulatory pathway in the complex immune network of OLP lesions.
Face detection using PCA and skin-tone extraction for drowsy driver application

Face detection plays a huge role in many applications such as security, surveillance and human-computer interface. This paper presents a new face detection for the purpose of drowsy driver assistant system. The algorithm is based on a combination two different principles of detection, namely detection of skin color and modified PCA analysis. The algorithm has shown improved performance compared to using either of the principles alone and is performing well under different lighting conditions.
Formulation and Calibration of Fast , Accurate Vehicle Motion Models

High performance wheeled mobile robots (WMRs) require fast, accurate motion models for planning and control. This thesis addresses two challenges to producing these models: the tradeoff between fidelity and speed in model formulation, and the need for laborious calibration procedures. To address the first challenge, I propose the formulation of “enhanced” 3D velocity kinematic models that provide comparable accuracy to comprehensive physics-based models, but at a fraction of the computational cost. In a modular way for any WMR design, the motion prediction problem is formulated as the solution of a differential-algebraic equation (DAE), with wheel velocity constraints derived using a vector algebra-based approach. These models account for articulations on uneven terrain, and are enhanced to account for slip, powertrain dynamics, and extreme conditions such as rollover. To address the second challenge, I propose the convenient self-calibration of these models using an online integrated equation error (IEE) approach. The IEE approach also enables the characterization of non-systematic error, such that the planner may reason about the uncertainty of motion predictions. I will prove experimentally that models formulated and calibrated as proposed are both faster and more accurate than alternatives. This thesis unifies and extends successful prior work by Alonzo Kelly, Forrest Rogers-Marcovitz, and myself. 1 Problem Description Fast, accurate motion models are essential for planning and control on high performance wheeled mobile robots (WMRs). Motion models enable the prediction of future state, given an initial state and control inputs as a function of time. For WMRs, state typically means pose (position and orientation) of the vehicle body frame. State may also include joint positions such as steer angles if applicable. Neal Seegmiller is a Ph.D. candidate at the Robotics Institute, Carnegie Mellon University, e-mail:
Continuous tone representation of three-dimensional objects taking account of shadows and interreflection

The effect of shadows and interreflection created by room obstructions is an important factor in the continuous tone representation of interiors. For indirect illumination, in most cases a uniform ambient light has been considered, even though the interreflection gives very complex effects with the shaded images.The proposed method for indirect lighting with shadows results in the following advanced points:1) The indirect illuminance caused by the surfaces of objects such as ceilings, floors, walls, desks, bookcases etc. gives added realism to images.2) The proposed method is suitable for every type of light source such as point sources, linear sources, and area sources.
Nequivack: Assessing Mutation Score Confidence

The mutation score is defined as the number of killed mutants divided by the number of non-equivalent mutants. However, whether a mutant is equivalent to the original program is undecidable in general. Thus, even when improving a test suite, a mutant score assessing this test suite may become worse during the development of a system, because of equivalent mutants introduced during mutant creation. This is a fundamental problem. Using static analysis and symbolic execution, we show how to establish non-equivalence or "don't know" among mutants. If the number of don't knows is small, this is a good indicator that a computed mutation score actually reflects its above definition. We can therefore have an increased confidence that mutation score trends correspond to actual improvements of a test suite's quality, and are not overly polluted by equivalent mutants. Using a set of 14 representative unit size programs, we show that for some, but not all, of these programs, the above confidence can indeed be established. We also evaluate the reproducibility, efficiency and effectiveness of our Nequivack tool. Our findings are that reproducibility is completely given. A single mutant analysis can be performed within 3 seconds on average, which is efficient for practical and industrial applications.
Forward And Futures Prices With Bubbles

This paper extends and refines the Jarrow et al. (2006, 2008) arbitrage free pricing theory for bubbles to characterize forward and futures prices. Some new insights are obtained in this regard. In particular, we: (i) provide a canonical process for asset price bubbles suitable for empirical estimation, (ii) discuss new methods to test empirically for asset price bubbles using both spot prices and call/put option prices on the spot commodity, (iii) show that futures prices can have bubbles independent of the underlying asset's price bubble, (iv) relate forward and futures prices under bubbles, and (v) relate price options on futures with asset price bubbles.
Advanced Atherosclerotic Lesions in the Innominate Artery of the ApoE Knockout Mouse

Abstract—Most previous studies of atherosclerosis in hyperlipidemic mouse models have focused their investigations on lesions within the aorta or aortic sinus in young animals. None of these studies has demonstrated clinically significant advanced lesions. We previously mapped the distribution of lesions throughout the arterial tree of apolipoprotein E knockout (apoE−/−) mice between the ages of 24 and 60 weeks. We found that the innominate artery, a small vessel connecting the aortic arch to the right subclavian and right carotid artery, exhibits a highly consistent rate of lesion progression and develops a narrowed vessel characterized by atrophic media and perivascular inflammation. The present study reports the characteristics of advanced lesions in the innominate artery of apoE−/− mice aged 42 to 60 weeks. In animals aged 42 to 54 weeks, there is a very high frequency of intraplaque hemorrhage and a fibrotic conversion of necrotic zones accompanied by loss of the fibrous cap. By 60 weeks of age, the lesions are characterized by the presence of collagen-rich fibrofatty nodules often flanked by lateral xanthomas. The processes underlying these changes in the innominate artery of older apoE−/− mice could well be a model for the critical processes leading to the breakdown and healing of the human atherosclerotic plaque.
Reformulated radial basis neural networks trained by gradient descent

This paper presents an axiomatic approach for constructing radial basis function (RBF) neural networks. This approach results in a broad variety of admissible RBF models, including those employing Gaussian RBF's. The form of the RBF's is determined by a generator function. New RBF models can be developed according to the proposed approach by selecting generator functions other than exponential ones, which lead to Gaussian RBF's. This paper also proposes a supervised learning algorithm based on gradient descent for training reformulated RBF neural networks constructed using the proposed approach. A sensitivity analysis of the proposed algorithm relates the properties of RBF's with the convergence of gradient descent learning. Experiments involving a variety of reformulated RBF networks generated by linear and exponential generator functions indicate that gradient descent learning is simple, easily implementable, and produces RBF networks that perform considerably better than conventional RBF models trained by existing algorithms.
Fast Kinetics, High-Frequency Oscillations, and Subprimary Firing Range in Adult Mouse Spinal Motoneurons

The fast contraction time of mouse motor units creates a unique situation in which motoneurons have to fire at low frequencies to produce small forces but also at very high frequency (much higher than in cat or rat motoneurons) to reach the fusion frequency of their motor units. To understand how this problem is solved, we performed intracellular recordings of adult mouse spinal motoneurons and investigated systematically their subthreshold properties and their discharge pattern. We show that mouse motoneurons have a much wider range of firing frequencies than cat and rat motoneurons because of three salient features. First, they have a short membrane time constant. This results in a higher cutoff frequency and a higher resonance frequency, which allow mouse motoneurons to integrate inputs at higher frequencies. Second, their afterhyperpolarization (AHP) is faster, allowing the motoneurons to discharge at a higher rate. Third, motoneurons display high-frequency (100–150 Hz) subthreshold oscillations during the interspike intervals. The fast membrane kinetics greatly favors the appearance of these oscillations, creating a “subprimary range” of firing. In this range, which has never been reported in cat and in rat spinal motoneurons, the oscillations follow the AHP and trigger spiking after a variable delay, allowing a discharge at low frequency but at the expense of an irregular rate.
A New Image Fusion Algorithm Based on Fuzzy Logic

An image fusion algorithm is presented based on fuzzy logic and wavelet in this paper. Aiming at the visible and infrared image fusion, we analyze the pixel-level image fusion algorithms, and addresses an algorithm based on the discrete wavelet transform and fuzzy logic. We created two fuzzy relations, and estimated the importance of every wavelet coefficient with fuzzy reasoning. According to the importance of coefficients, the weighting average coefficients were calculated. Ultimately, we reconstructed the fused image through inverse wavelet transform. In the experimental section, we verified the effectiveness of the algorithm with two actual image data acquired from the field experiments.