363 research outputs found
A Generic Distortion Free Watermarking Technique for Relational Databases
No full textIn this paper we introduce a distortion free watermarking technique for relational databases based on the Abstract Interpretation framework. The watermarking technique is partition based. The partitioning can be seen as a virtual grouping, which does not change neither the value of the table's elements nor their physical positions. Instead of inserting the watermark directly to the database partition, we treat it as an abstract representation of that concrete partition, such that any change in the concrete domain reflects in its abstract counterpart. The main idea is to generate a binary image of the partition as a watermark of that partition, that serves as ownership proof as well as tamper detection
A Generic Distortion Free Watermarking Technique for Relational Databases
No full textIn this paper we introduce a distortion free watermarking technique for relational databases based on the Abstract Interpretation framework. The watermarking technique is partition based. The partitioning can be seen as a virtual grouping, which does not change neither the value of the table's elements nor their physical positions. Instead of inserting the watermark directly to the database partition, we treat it as an abstract representation of that concrete partition, such that any change in the concrete domain reflects in its abstract counterpart. The main idea is to generate a binary image of the partition as a watermark of that partition, that serves as ownership proof as well as tamper detection
Repeat-dose sirolimus pharmacokinetics and pharmacodynamics in patients with hepatic allografts
No full textTo determine sirolimus steady-state pharmacokinetics, and to assess the relationship between time-normalized trough sirolimus concentration (C(min,TN)) and evidence of efficacy (rejection and death) and adverse reactions (stomatitis and pneumonia) in liver allograft patients
The Evolution of Animation from Pencil to Pixel
No full textABSTRACT
Background:Animation has evolved significantly as a creative medium, drawing inspiration from ancient and prehistoric depictions of motion to today\u27s advanced digital animations. This evolution has been shaped by pivotal scientific breakthroughs and the pioneering efforts of early animators like Edward Muybridge and Winsor McCay.
Purpose:This article aims to trace the developmental trajectory of animation, highlighting key milestones such as the advent of CGI, Disney\u27s Technicolour Revolution, and the Golden Age of Animation. It explores the diverse styles and techniques employed in modern animation, including 2D, 3D, stop-motion, and experimental forms.
Methods:The exploration of animation\u27s evolution is conducted through an examination of historical events, technological advancements, and the artistic innovations that have shaped the medium over time. Case studies of influential animators and studios provide insights into the methods and creative processes behind their work.
Results:The study reveals animation\u27s dynamic nature, continually propelled forward by the synergy of creative ingenuity and technological progress. It showcases how these elements have expanded the boundaries of visual storytelling, making animation a versatile and enduring art form.
Conclusions:Animation\u27s future appears promising as it continues to innovate and diversify. The medium\u27s capacity to blend tradition with cutting-edge technology ensures that it will remain a dynamic force in visual arts, continually pushing the frontiers of creativity and expression
Feature Selection under Multicollinearity & Causal Inference on Time Series
No full textIn this work, we study and extend algorithms for Sparse Regression and Causal Inference problems. Both the problems are fundamental in the area of Data Science.
The goal of regression problem is to nd out the \best" relationship between an output variable and input variables, given samples of the input and output values. We consider sparse regression under a high-dimensional linear model with strongly correlated variables, situations which cannot be handled well using many existing model selection algorithms. We study the performance of the popular feature selection algorithms such as LASSO, Elastic Net, BoLasso, Clustered Lasso as well as Projected Gradient Descent algorithms under this setting in terms of their running time, stability and consistency in recovering the true support. We also propose a new feature selection algorithm, BoPGD, which cluster the features rst based on their sample correlation and do subsequent sparse estimation using a bootstrapped variant of the projected gradient descent method with projection on the non-convex L0 ball. We attempt to characterize the efficiency and consistency of our algorithm by performing a host of experiments on both synthetic and real world datasets.
Discovering causal relationships, beyond mere correlation, is widely recognized as a fundamental problem. The Causal Inference problems use observations to infer the underlying causal structure of the data generating process. The input to these problems is either a multivariate time series or i.i.d sequences and the output is a Feature Causal Graph where the nodes correspond to the variables and edges capture the direction of causality. For high dimensional datasets, determining the causal relationships becomes a challenging task because of the curse of dimensionality. Graphical modeling of temporal data based on the concept of \Granger Causality" has gained much attention in this context. The blend of Granger methods along with model selection techniques, such as LASSO, enables efficient discovery of a \sparse" sub-set of causal variables in high dimensional settings. However, these temporal causal methods use an input parameter, L, the maximum time lag. This parameter is the maximum gap in time between the occurrence of the output phenomenon and the causal input stimulus. How-ever, in many situations of interest, the maximum time lag is not known, and indeed, finding the range of causal e ects is an important problem. In this work, we propose and evaluate a data-driven and computationally efficient method for Granger causality inference in the Vector Auto Regressive (VAR) model without foreknowledge of the maximum time lag. We present two algorithms Lasso Granger++ and Group Lasso Granger++ which not only constructs the
hypothesis feature causal graph, but also simultaneously estimates a value of maxlag (L) for each variable by balancing the trade-o between \goodness of t" and \model complexity"
Review of "South Asia in the Era of Gloablization: Trade Industrialization and Welfare" by M. Bhattacharya, R. Smyth, M. Vicziany
No full textUnsupervised Physics-Informed Health Indicator Discovery for Complex Systems
No full textDiscovering health indicators (HI) is essential for prognostics and health management of complex systems, as an HI enables timely interventions and effective maintenance strategies. However, most of the existing methodologies for HI discovery rely on labeled data which is expensive and complicated to obtain in the real world. In this paper, we propose a novel, unsupervised physics-informed model structured after expert knowledge in the form of a graphical representation of the expected relationships between sensor readings, operating conditions, and degradation. In addition, a soft constraint is used to guide the representation of the HI according to generally available expert knowledge about degradation. We evaluated the model on a turbofan engine dataset and conducted four experiments by manipulating the original data to create realistic real-world scenarios. The proposed method discovers an HI that exhibits better intrinsic qualities than the current state-of-the-art methodologies, leading to enhanced prognostic performance. Notably, in situations where the initial health state of each system varies, the proposed method achieves an average prognostic performance improvement of approximately 20% compared to existing state-of-the-art methods.Air Transport & Operation
Generic Hybrid Models for Prognostics of Complex Systems
No full textHybrid models combining physical knowledge and machine learning show promise for obtaining accurate and robust prognostic models. However, despite the increased interest in hybrid models in recent years, the proposed solutions tend to be domain-specific. As a result, there is no compelling strategy of what, where, and how physics-derived knowledge can be integrated into deep learning models depending on the available representation of physical knowledge and the quality of data for the development of prognostic models for complex systems. This Ph.D. project aims to develop a general strategy for hybridizing prognostic models by exploring multiple methods to incorporate physical knowledge at various stages of the learning algorithm. The project will prioritize general expert knowledge as the primary source of information, while domain-specific knowledge will serve as an additional feature when applicable.Air Transport & Operation
Distinct roles of estrogen receptors alpha and beta mediating acute vasodilation of epicardial coronary arteries.
No full textThis study investigated the contribution of estrogen receptors (ERs) alpha and beta for epicardial coronary artery function, vascular NO bioactivity, and superoxide (O(2)(-)) formation. Porcine coronary rings were suspended in organ chambers and precontracted with prostaglandin F(2alpha) to determine direct effects of the selective ER agonists 4,4',4''-(4-propyl-[(1)H]pyrazole-1,3,5-triyl)tris-phenol (PPT) or 2,3-bis(4-hydroxyphenyl)-propionitrile (DPN) or the nonselective ER agonist 17beta-estradiol. Indirect effects on contractility to U46619 and relaxation to bradykinin were assessed and effects on NO, nitrite, and O(2)(-) formation were measured in cultured cells. Within 5 minutes, selective ERalpha activation by PPT, but not 17beta-estradiol or the ERbeta agonist DPN, caused rapid, NO-dependent, and endothelium-dependent relaxation (49+/-5%; P<0.001 versus ethanol). PPT also caused sustained endothelium- and NO-independent vasodilation similar to 17beta-estradiol after 60 minutes (72+/-3%; P<0.001 versus ethanol). DPN induced endothelium-dependent NO-independent relaxation via endothelium-dependent hyperpolarization (40+/-4%; P<0.01 versus ethanol). 17beta-Estradiol and PPT, but not DPN, attenuated the responses to U46619 and bradykinin. All of the ER agonists increased NO and nitrite formation in vascular endothelial but not smooth muscle cells and attenuated vascular smooth muscle cell O(2)(-) formation (P<0.001). ERalpha activation had the most potent effects on both nitrite formation and inhibiting O(2)(-) (P<0.05). These data demonstrate novel and differential mechanisms by which ERalpha and ERbeta activation control coronary artery vasoreactivity in males and females and regulate vascular NO and O(2)(-) formation. The findings indicate that coronary vascular effects of sex hormones differ with regard to affinity to ERalpha and ERbeta, which will contribute to beneficial and adverse effects of hormone replacement therapy
- …
